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Demonstration of
Two Long-Term Groundwater Monitoring
Optimization Approaches
Report with Appendices
Compliance boundary
Groundwater flow direction
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Office of Solid Waste and EPA 542-R-04-001 b
Emergency Response September 2004
(5102G) wwww.clu-in.org
www.epa.gov/tio
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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://yyww.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)
MS 5102G
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
j ohn. 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
jazizgaj.gsi-net.com
in
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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 Access® 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
Range 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"7
Fort Lewis
180
107- 113
37-40
$33,500 - $36,500
Long Prairie
51
22-36
29-51
$4,200 -$8,100
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.
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.
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.
vn
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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 GROUND WATER MONITORING 2
1.4 LONG-TERM GROUND WATER MONITORING PROGRAM OPTIMIZATION 4
1.5 REPORT ORGANIZATION 6
2.0 EVALUATION AND OPTIMIZATION OF LONG-TERM
MONITORING PROGRAMS 7
2.1 CONCEPTS IN GROUND WATER 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 OUD 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 MARO S 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
IX
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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
XI
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LIST OF ACRONYMS AND ABBREVIATIONS
a
AFB
AFCEE/ERT
AFRPA
AR
ASCE
P
bgs
BRAC
CAH
CERCLA
CES
CFR
COC
COV
CR
CSM
CT
DCA
DCE
DNAPL
DQO
EGDY
ERPIMS
ESRI
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
xn
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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
USACE
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
xin
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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'3'
Total number of wells
included in program
Total number of samples
collected (per year)
Total cost0' of monitoring
(per year)
Example Site"7
Fort Lewis
2 (Upper Vashon and
Lower Vashon)
cis-l,2-DCE, PCE,
1,1,1-TCA,TCE,VC
21 extraction wells
40 upper Vashon
monitoring wells
11 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
13 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.
DCA = dichloroethane; DCE = dichloroethene; PCE = tetrachloroethene;
TCA = trichloroethane; TCE = trichloroethene; VC = vinyl chloride.
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 §9604[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 §9601 [23] and 42 USC §9601 [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 a/., 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 a/., 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 a/., 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 a/.,
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.afccc.brooks.af.mil/cr/
rgohtm . 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 Access® 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
Access® files, or Microsoft EXCEL® 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™ Access® 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, 2003 c, 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; England and Sparks, 1992], GSLIB [Deutsch and Journel, 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., ArcView® GIS)
(Environmental Systems Research Institute, Inc. [ESRI], 2001) with spatial-statistical capabilities
(e.g., Geostatistical Analyst™, an extension to the ArcView 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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13
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Ol
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nt
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y
7
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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.
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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 costb/ of LTM program
Monitorin
Original
(prior to
December
2001)
59
--
—
--
--
59
236
$118,000
Current
(LOGRAM,
after December
2001)
35
o
5
34
--
—
72
180
$90,000
2, 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.
b/ 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, c/s-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).
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4.0 SUMMARY OF DEMONSTRATIONS AT LONG PRAIRIE
GROUND WATER 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 trench drain.
The subsequent migration of contaminants through the vadose zone to groundwater produced a
dissolved CAH 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 c/s-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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SI
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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, 2003 c). 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
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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, cis-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.
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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"1 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, 2003 c) 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 overtime (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 Program"7
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.
b/ 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 Areaa/
Well ID
Current13
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite07
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
_>
-
Quarterly
Quarterly
Not Considered0
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-137b
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- lib (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
i h/
approach
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
Example Composite
Recommendations
Monitoring Wells Completed in Lower Vashon Subunit
FL4a (new)
LC-41b (new)
LC-64b
LC-lllb
LC-116b
LC-122b
LC-128
LC-137c
MAMC1
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
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
Information from GSI (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 Sitea/
Well ID
Current13'
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite0
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
Remove6
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
-------
Table 6.2: Summary of Optimization of Groundwater Monitoring Program at
Long Prairie Groundwater Contamination Superfund Site
Well ID
Current13
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).
b/
"Current" monitoring program was in effect in 2002.
c/ "Composite" recommendations generated considering the current monitoring program, and recommendations
generated by MAROS tool and three-tiered approach.
Al
A dash (--) indicates that the well is not included in the current monitoring program.
el
"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 Da/
Well ID
Current157
Sampling
Frequency
Recommendations
Generated Using
MAROS Tool
Recommendations
Generated Using
Three-Tiered
Approach
Example Composite0
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
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Remove
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Remove
Biennial
Biennial
Biennial
Remove
Remove
Annual
Remove
Biennial
Biennial
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Annual
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Remove
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Remove
Biennial
Biennial
Biennial
Zone B Monitoring Wells
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Remove
Biennial
Biennial
Remove
Biennial
Biennial
Biennial
Annual
Remove
Biennial
Biennial
Remove
Remove
Remove
Remove
Biennial
Remove
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Remove
Biennial
Biennial
Remove
Biennial
39
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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
Groundwater Extraction Wells
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.
c/ "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 McClellan 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 McClellan AFB OU D) to more than 50 percent (using MAROS at the Long Prairie site
and the three-tiered approach at McClellan 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 McClellan 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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Long Prairie Site, Long Prairie, Minnesota. Final. May.
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Unit D, McClellan Air Force Base, California. Final. April.
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Loaiciga, H.A., R.J. Charbeneau, L.G. Everett, G.E. Fogg, B.F. Hobbs, and S. Rouhani. 1992.
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Parsons Corporation (Parsons). 2000. Remedial Process Optimization Report for Operable Unit 1,
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Fort Lewis Logistics Center, Washington. Draft Final. May.
Parsons. 2003c. Three-Tiered Groundwater Monitoring Network Optimization Evaluation for Long
Prairie Groundwater Contamination Superfund Site, Minnesota. Draft Final. May.
Parsons. 2003d. Three-Tiered Groundwater Monitoring Network Optimization Evaluation for
Operable Unit D, McClellan Air Force Base, California. Draft Final. May.
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a)
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APPENDIX A
CONCEPTS AND PRACTICES IN
MONITORING OPTIMIZATION
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APPENDIX A
CONCEPTS AND PRACTICES IN MONITORING OPTIMIZATION
Al.O CONCEPTS IN GROUND WATER MONITORING
The U.S. Environmental Protection Agency (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. "
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).
Designing an effective groundwater-quality monitoring program involves selecting a set of sampling
sites, suite of analytes, and sampling schedule based upon one or more monitoring-program
objectives (Hudak et a/., 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.
A-l
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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 spatial and temporal 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.
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.
Hydrogeologic units are part of the basic framework of a CSM; thus, it is convenient to identify the
volume of earth material targeted for groundwater sampling in terms of hydrogeologic units. Target
parameters and analytes typically will include those constituents that are known or suspected to be
potential contaminants, or contaminants of concern (COCs) at a particular site. Target analytes also
may include constituents or parameters that are not necessarily related to the occurrence of
contaminants, but which provide information regarding hydrogeologic or geochemical conditions
affecting the fate of identified COCs (e.g., oxidation/reduction potential as an indicator of in-situ
degradation of organic chemicals) or the performance of a selected remedy (Makeig, 1991). The
number of wells sampled depends primarily on the known or anticipated spatial variability in
groundwater conditions and quality, because if spatial variability is great, a greater number of wells
must be sampled to capture that variability (Franke, 1997). Sampling frequency also is an extremely
important consideration in the design of a monitoring program - if samples are not collected
frequently enough, some of the temporal variability in groundwater quality and conditions may be
missed, and potentially important information will be lost. On the other hand, if samples are
collected more frequently than necessary, some of the information obtained is redundant (Zhou,
1996).
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Criteria used to identify wells that are suitable for sampling are program-specific (Franke, 1997).
The most fundamental criterion is that a well must produce water from, and only from, the particular
hydrogeologic unit that is targeted for sampling. A second criterion, which considers the primary
purpose for which the well was constructed, relates to existing wells and is an important
consideration in judging the suitability of a particular well in meeting program objectives (Lapham et
al, 1996). In particular, large-capacity wells (groundwater extraction or production wells) may not
be suitable for particular sampling purposes; these must be distinguished from small-capacity wells
(groundwater monitoring wells). A third criterion involves the construction features of the well,
including the length and placement of the completion interval (well screen), types of materials used in
well construction, and methods used during well installation. Wells meeting the criteria established
for a particular groundwater monitoring program can be considered for inclusion in the program,
depending upon the suitability of their locations with respect to achieving the spatial objectives of the
program.
In the past, most monitoring programs have been designed and evaluated based on qualitative insight
into the characteristics of the hydrologic system, and using professional judgment (Loaiciga et al.,
1992; 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 of the report), 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.
Al. 1 CONSIDERATIONS AND APPROACHES IN QUALITATIVE EVALUATION OF MONITORING
PROGRAMS
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. Sampling locations are determined
by hydrogeologic and other conditions within, and at locations distal from the source(s) of
contaminants (e.g., Schock et al., 1989). The ultimate configuration of the monitoring program,
including the location of wells, analytes included in the evaluation, and frequency of monitoring, is
subject to the investigator's understanding of:
• The properties and configuration of the groundwater system;
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• The ways in which these properties (and configuration) influence the movement and fate of
contaminants, and the resultant contaminant distributions, and
• What constitutes an "optimal" monitoring program, given probable contaminant migration
pathways and travel times.
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
long-term monitoring (LTM) objectives may not be readily evaluated by qualitative methods.
Al .2 CONSIDERATIONS AND APPROACHES IN EVALUATION OF TEMPORAL DATA
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
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 contaminants to groundwater, 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 LTM (evaluate contaminant concentrations in groundwater through time;
Section 1.4 of the report) can be addressed by identifying trends in contaminant concentrations, by
identifying periodic fluctuations in concentrations, or by estimating long-term average ("mean")
values of concentrations (Zhou, 1996). Decisions regarding the frequency of sampling necessary to
achieve the temporal objective of monitoring then can be based on trend detection, accuracy of
estimation of periodic fluctuations, or accuracy of estimation of long-term mean concentrations.
Trends in contaminant concentrations can be identified by plotting temporal concentration data
(Wiedemeier and Haas, 1999); however, visual identification of trends in plotted data may be a
subjective process, particularly if the concentration data do not have a uniform trend, but are variable
through time. It is preferable to examine temporal trends in chemical concentrations using various
statistical procedures, including the Student's t-test (Zhou, 1996), regression analyses, Sen's (1968)
non-parametric test for the slope of a trend, and the Mann-Kendall test for trends. The Mann-Kendall
non-parametric test (Gibbons, 1994) is well-suited for evaluation of environmental data because the
sample size can be small (as few as four data points), no assumptions are made regarding the
underlying statistical distribution of the data, and the test can be adapted to account for seasonal
variations in the data (Hirsch et al, 1991). The Mann-Kendall test statistic can be calculated at a
specified level of confidence to evaluate whether a temporal trend is present in contaminant
concentrations detected through time in samples from an individual well. Sampling should be
conducted at a frequency sufficient to detect temporal trends in concentrations at a specified level of
statistical power (Zhou, 1996). If a trend is determined to be present, a non-parametric slope of the
trend line (change in concentrations per unit time) also can be estimated using the test procedure, or
using Sen's (1968) test. A negative slope (indicating decreasing contaminant concentrations through
time) or a positive slope (increasing concentrations through time) provides statistical confirmation of
temporal trends that may have been identified visually (Figure A.I).
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MIC
Trent!
Trend
Factor
(CF)
Trend
CF < ill
Trend
Of
Trend
Fluctuating
Trend
Figure A.1: Interpretation of Mann-Kendall Test for Trends (after AFCEE, 2000)
Periodic fluctuations in temporal concentration data can be evaluated using harmonic decomposition,
Fourier-series analysis, by evaluating the correlation structure of the time series, or using other time-
series statistical techniques (Davis, 1986). The half-width of the confidence interval of the mean can
be used to estimate the mean value of a time series. The characteristics of any identified periodicity,
or the confidence interval of the mean, then can be used to adjust the frequency of sampling (Zhou,
1996).
Al .3 CONSIDERATIONS AND APPROACHES IN EVALUATION OF SPATIAL DATA
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. (Note that the "optimal" configurations identified using this approach
generally are not unique [Reed et al., 2000].) In addition to a stochastic simulator for generating
multiple realizations of the spatial distribution of hydraulic properties, it is necessary to apply a mass-
transport model to derive numerous realizations of dissolved contaminant plumes. Because transport
modeling in two dimensions can fail to identify optimal vertical locations of sampling horizons, and
also can result in monitoring points being placed too far from a contaminant source and at non-
optimal spacings, three-dimensional transport modeling is preferable (Hudak, 2000). Numerical
modeling of contaminant transport in moving groundwater, especially in three dimensions, is
considerably more difficult than is simulation of groundwater movement alone. In addition, transport
modeling is vulnerable to numerical errors (numerical dispersion and oscillation), and can require
considerable computational resources and execution time, making simulation approaches impractical
for many applications.
Ranking approaches utilize weighting schemes that express the relative values to the monitoring
program of candidate sampling sites distributed throughout a sampling domain (Hudak et al, 1993).
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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.
Alternative weighting schemes also can be used to express the relative value of candidate monitoring
locations. For example, 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). Approaches using geostatistical methods can be classified further as
local or global in nature. The local approach to monitoring network evaluation uses geostatistics to
assess monitoring networks by iteratively analyzing the effectiveness of adding sampling points to
the network, or removing sampling points from the network (Reed et al, 2000). Additional sampling
points are added to the network based on an analysis of which locations will generate the maximum
decrease in the estimation variance attained in geostatistical interpolation. Sampling points are
removed from the network based on an analysis of which locations will generate the minimum
increase in the estimation variance during geostatistical interpolation. The global approach to
monitoring network design uses geostatistical methods to evaluate the likely performance of potential
monitoring networks still in the planning stage (ASCE, 1990a and 1990b). In the global approach,
several spatial configurations and densities of sampling points are considered, each of which is
evaluated using the global estimation variance for each potential monitoring network. The global
estimation variance then is minimized to optimize the performance of potential network designs.
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A2.0 EXAMPLE APPLICATIONS OF 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 for
screening 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.
Loaiciga et al. (1992) examined several methods of designing and optimizing monitoring networks,
including qualitative techniques based primarily on hydrogeologic interpretations, and statistical
methods, including simulation methods, variance-reduction methods, and probabilistic methods.
They found that most of the existing methods used in designing groundwater monitoring networks
make several important simplifications:
• In the majority of the methods, monitoring design decisions are made only once, at the
beginning of program development, with no opportunity to modify the program as additional
information is compiled and evaluated;
• Most monitoring design methods use surrogate objectives for cost and risk-based criteria; and
• In many instances, the methods oversimplify the hydrogeologic environment, and the
applicability in more complex and realistic settings remains unproven.
If not recognized, these shortcomings can lead to the development and implementation of a flawed
monitoring program.
Storck et al. (1995) used a simulation approach (Section A1.3) to examine ways to design and
evaluate groundwater monitoring networks for leaking disposal facilities. A Monte Carlo simulator
was used to generate a large number of equally likely realizations of a random hydraulic conductivity
field and a contaminant source location. A numerical model simulating groundwater flow and
dissolved contaminant transport was used to generate a contaminant plume for each realization of the
hydraulic-conductivity field. The results of the transport simulations then were used as input to an
optimization model, which generated optimal trade-off curves among three conflicting objectives:
1. Maximize probability of contaminant detection,
2. Minimize cost of monitoring network (i.e., minimum number of monitoring wells), and
3. Minimize volume of contaminated groundwater.
The model was applied to a hypothetical scenario in order to examine the sensitivity of the trade-off
curves to various model parameters.
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Kelly (1996) applied a numerical model of groundwater flow and dissolved contaminant migration,
together with knowledge of locations of potential contaminant sources, to determine screened-interval
elevations and locations for 75 monitoring wells in 35 clusters, for a network designed to assure
protection of the municipal well field for Independence, Missouri.
Dresel and Murray (1998) used a ranking approach (Section A1.3) to assist in the design of a
groundwater monitoring network at the US Department of Energy's Hanford site in Washington. A
geostatistical model of existing plumes was used to generate a large number of realizations of
contaminant distribution in groundwater at the facility. Analysis of the realizations provided a
quantitative measure of the uncertainties in contaminant concentrations, and a measure of the
probability that a cutoff value (e.g., a target remedial concentration) would be exceeded at any point.
A metric based on uncertainty measures and declustering weights was developed to rank the relative
value of each monitoring well in the network design. The metric was used, together with
hydrogeologic and regulatory considerations, in identifying candidate locations for inclusion in or
removal from the network.
Hudak et al. (1993) applied a ranking methodology to the design of a detection-monitoring network
for the Butler County Municipal Landfill in southwest Ohio. A geographic information system (GIS)
was used to assign relative weights to candidate monitoring locations on the basis of distance from
possible contaminant sources, location relative to probable contaminant migration pathways, and
distance to a potential receptor exposure point. The GIS application was found to be relatively
straightforward to implement, was capable of addressing established regulatory policy, and could be
used to address several monitoring objectives.
Chieniawski et al. (1995) used a simulation approach combined with a ranking approach to examine
the problem of optimizing detection monitoring at a waste facility under conditions of uncertainty. A
numerical model was used, together with stochastic realizations of contaminant transport, to generate
numerous realizations of contaminant movement for use as input into a multi-objective optimization
model. The optimization model was solved using a genetic algorithm and generated trade-off curves
comparing the relative cost of a particular monitoring network design with the probability that the
network could detect a leak.
The studies described above dealt primarily with detection (i.e., sentinel-well) monitoring and global
approaches to the design of new monitoring networks. By 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 that the existing monitoring network is sufficient to characterize the concentrations and
spatial distribution of contaminants being monitored. It also is not intended for use in optimizing
detection monitoring.
Ridley et al. (1995) developed a method (the "Cost-Effective Sampling [CES] Method") for
estimating the lowest-frequency (and, as a result, lowest- cost) sampling schedule for a particular
sampling location which will still provide information at the level needed for making regulatory and
remedial decisions. The determination of optimal sampling frequency is based on the magnitude and
variability of concentrations, and on concentration trends at the sampling location. The underlying
principle is that the sampling schedule at a particular location should be determined primarily by the
rate of change in contaminant concentrations that have been detected at that location in the recent past
— the faster the rate of change, the more frequently sampling should be conducted.
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Reed et al. (2000) developed and applied a simulation approach for optimizing existing monitoring
programs using a numerical model of groundwater flow and contaminant transport, several
statistically-based plume-interpolation techniques, and a formal mathematical optimization model
based on a genetic algorithm. The optimization approach was used to identify cost-effective
sampling plans that were based on the assumption that the total mass of dissolved contaminant in
groundwater could be accurately quantified. Application of the approach to the monitoring program
at Hill AFB indicated that monitoring costs could be reduced by as much as 60 percent without
significant changes in the resulting estimates of dissolved contaminant mass. Reed and Minsker
(2004) and Reed et al. (2001 and 2003) extended this work using several different mathematical
optimization algorithms to address multi-objective monitoring optimization problems.
Tuckfield et al. (2001) reviewed the operational efficiency of groundwater monitoring networks at
the U.S. Department of Energy's Savannah River Site. The purpose of the evaluation was to
optimize the number of groundwater wells requisite for monitoring the plumes of the principal
constituent of concern, trichloroethene (TCE). A multidisciplinary approach, combining
geochemistry, geohydrology, geostatistics, and regulatory knowledge was used to evaluate whether or
not a well should remain on the current sampling schedule. The wells within each of three aquifer
zones were evaluated with respect to relevancy, reliability, and regulatory importance. These
evaluations identified sets of wells that were considered to be candidates for deletion from the
sampling schedule. The effects of a reduced amount of data due to well deletion were then evaluated
using geostatistical redundancy analysis. In addition, historical trends in the contaminant
concentration data were examined to determine those analytes that should remain on the sampling
schedule for each well. At the conclusion of the evaluation, approximately 20 percent of the
currently-sampled wells were recommended for removal from the monitoring program; and the list of
analytes to be sampled and analyzed was reduced considerably.
Cameron and Hunter (2002) applied a spatial and temporal optimization algorithm known as the
Geostatistical Temporal/Spatial (GTS) Optimization Algorithm to the evaluation and optimization of
two existing monitoring programs at the Massachusetts Military Reservation (MMR), Cape Cod,
Massachusetts. The GTS algorithm is intended for use in optimizing LTM networks using
geostatistical methods, and was developed to ensure that only those monitoring data sufficient and
necessary to support decisions crucial to addressing monitoring program objectives are collected and
analyzed. The algorithm uses geostatistical methods to optimize sampling frequency and to define a
network of essential sampling locations. The algorithm incorporates a decision-pathway analysis that
is separated into temporal and spatial (i.e., frequency and location) components, which are used to
identify temporal and spatial redundancies in existing monitoring networks. The results of the
temporal analysis applied to the monitoring programs at MMR indicated that sampling frequency
could be reduced at most locations by 40 to 70 percent. The results of the spatial analysis indicated
that 109 of the 536 wells included in the two monitoring programs at MMR were spatially redundant,
and could be removed from the programs. More recently, Cameron and Hunter (2004) applied the
GTS algorithm to monitoring programs at three other sites, and confirmed that use of this
optimization approach could generate savings ranging from 30 percent to 63 percent of monitoring
costs.
Ling et al. (2003) developed an innovative methodology for improving existing groundwater
monitoring plans at small-scale sites. The methodology consists of three stand-alone procedures: a
procedure for reducing spatial redundancy, a well-siting procedure for adding new sampling
locations, and a procedure for determining optimal sampling frequency. The spatial redundancy
reduction procedure was used to eliminate redundant wells through an optimization process that
minimizes the errors in plume delineation and the estimation of average plume concentration. The
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well-siting procedure was used to locate possible new sampling points for an inadequately delineated
plume via regression analysis of plume centerline concentrations and estimation of plume dispersivity
values. The sampling frequency determination procedure was used to generate recommendations
regarding the future frequency of sampling for each sampling location based on the direction,
magnitude, and uncertainty of the concentration trend derived from representative historical
concentration data. Although the methodology was designed for small-scale sites, it is adaptable for
large-scale site applications. The methodology was applied to a small petroleum hydrocarbon-
contaminated site with a network of 12 monitoring wells to demonstrate its effectiveness and validity.
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APPENDIX B
DESCRIPTION OF MAROS TOOL AND
THREE-TIERED OPTIMIZATION APPROACH
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APPENDIX B
DESCRIPTION OF MAROS TOOL AND
THREE-TIERED OPTIMIZATION APPROACH
Bl.O OPTIMIZATION OF LONG-TERM MONITORING PROGRAMS
"Optimization" is defined (American Heritage Dictionary of the English Language, 2000) to be
"The procedure or procedures used to make a system or design as effective or functional as
possible ..."
and when a particular system has been "optimized", its operation occurs under "optimal" conditions,
defined (WordNet 2.0 at ilttl!l//wwwj;ogsa^^ ) to be those conditions
"... most desirable possible under a restriction expressed or implied".
Long-term monitoring (LTM) programs typically are implemented 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 concentrations and distribution of contaminants being monitored. Two
approaches to evaluating monitoring networks - the MAROS tool and the three-tiered evaluation
approach - were developed specifically for use in optimizing existing LTM 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 keeping with the definitions
provided above, "optimization" in subsequent discussion 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 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 the following subsections.
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B2.0 DESCRIPTION OF MAROS TOOL
B2.1 GENERAL CHARACTERISTICS OF MAROS SOFTWARE TOOL
The Monitoring and Remediation Optimization System (MAROS) software originally was developed
primarily for use as a tool to assist non-technical personnel (e.g., facility environmental managers)
with the organization, preliminary evaluation, and presentation of monitoring data (Air Force Center
for Environmental Excellence [AFCEE], 2000). 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, Groundwater Services, Inc. (GSI) has
refined MAROS and expanded its capabilities; the new version of MAROS was issued by AFCEE
(2002) for additional testing in 2002. The public-domain software, and accompanying
documentation, are available for free download on the AFCEE website at ht^//www.afcee.brQQks.
af.mil/er/rpo.htm . All subsequent discussion refers to features of the most-current version of
MAROS (Version 2); and all case-study example monitoring programs examined in the current
demonstration project were evaluated and optimized using this version of MAROS (Appendices C
and D of this report).
The MAROS tool consists of a software package that operates in conjunction with an electronic
database environment (Microsoft Access 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, 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 sampling frequency at each sampling
point in a monitoring program by applying a modified CES algorithm, based on the CES method
developed at Lawrence Livermore National Laboratory (Ridley et al, 1995). 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 COCs (Table B.I).
Although the MAROS tool primarily is used to evaluate temporal data, it also incorporates a spatial
statistical algorithm, based on a ranking system (Appendix A) 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 (Table B.I).
Formal decision logic structures 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. 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.)
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Table B.I: Features of MAROS
Feature
Maximum Number of Wells/Points Examined
Maximum Number of COCs Examined
COC Identification
Temporal Trend Analysis
Sampling Frequency Optimization
Well Significance Spatial Analysis
Plume Moment Analysis
Power Analysis at Individual Wells
Risk-Based Power Analysis of Site
MAROS
200
5
S
S
s
s
s
s
s
MAROS is intended to assist users in establishing practical and cost-effective long-term monitoring
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.
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 conceptual site model (CSM),
describing site-specific conditions (e.g., direction and rate of groundwater movement, locations of
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contaminant sources and potential receptor exposure points) prior to applying the MAROS tool.
Furthermore, the extent of contaminants in the subsurface at the site must be adequately delineated
before the monitoring program can be optimized.
MAROS is designed to accept data in any of three formats: text files in US Air Force Environmental
Restoration Program Information System (ERPIMS) format, Microsoft Access® files, or Microsoft
EXCEL 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.
MAROS can be used to identify site-specific COCs by comparing COC concentrations in the
chemical database with applicable regulatory standards (e.g., maximum contaminant levels [MCLs]).
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 use MAROS to analyze those components of the monitoring
network completed in individual layers, during separate evaluations. The primary features and
capabilities of the MAROS software are briefly described in the following subsections; additional
details are available in the user's manuals (AFCEE, 2000 and 2002).
B2.2 PRELIMINARY EVALUATION OF PLUME STABILITY
In the preliminary MAROS evaluation, the entire historical groundwater COC database for the
monitoring program is examined to assess overall plume stability; and the results of the plume-
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stability evaluation then are used to estimate the frequency and duration of sampling, and the density
of the monitoring-well network that would be appropriate to address plume conditions. The
preliminary evaluation incorporates several of the elements of a qualitative evaluation of the
monitoring program (Appendix A). As a database to be used with the MAROS tool is constructed,
each monitoring point is designated as occupying some relative location within, or downgradient
from, the plume. Designations for the locations of monitoring points allowed by MAROS include
"source," "tail," and "not used." Each monitoring point is assigned to one of these categories on the
basis of the direction of groundwater movement, location of the monitoring point relative to the
plume(s), and COC concentrations measured at the monitoring point. MAROS then uses these
designations in the preliminary evaluation, together with the local velocity of groundwater movement
(supplied by the user), a description of plume characteristics and other local conditions (Table B.2),
and the results of concentration trend analyses, to assess overall plume stability, and to generate
recommendations regarding sampling frequency and duration, and the spatial density of sampling
points in the network for the monitoring program. A schematic of the procedures followed in the
preliminary evaluation is presented in Figure B.I.
Table B.2: Simulation Parameters Used in Basic MAROS Evaluations
Simulation Parameter
Current Plume Width
Current Plume Length
Groundwater Seepage Velocity
Distance from Source to Nearest Downgradient Receptor
Distance from Source to Facility Boundary or Point of Compliance
Distance from "Tail" of Plume to Nearest Downgradient Receptor
Distance from "Tail" of Plume to Facility Boundary or Point of Compliance
Non-Aqueous-Phase Liquids (NAPLs) Present? (Y/N)
Temporal Fluctuations in Groundwater Elevations? (Y/N)
Remediation System Currently Active? (Y/N)
B2.2.1 System Design Category
In the preliminary evaluation, MAROS assigns the COC plume to one of three system design
categories ^Moderate" [M], "Extensive" [E], or "Limited" [L]), based on the degree of stability in
COC concentrations through time at monitoring locations near the source and tail of the plume. The
assigned system design category then is used in conjunction with the results of analyses of temporal
trends in COC concentrations in groundwater to develop preliminary recommendations regarding the
duration of monitoring, sampling frequency, and the density of sampling points in the monitoring
network.
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Figure B.I: Overview of Preliminary Evaluation Methodology (after GSI, 2003a)
B-6
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B2.2.2 Sampling Frequency
Recommendations regarding the sampling frequency at a site are generated by MAROS during the
preliminary evaluation based on the monitoring system design category to which the site is assigned
(i.e., M, E, or L), and the required length of time calculated for a "conservative" COC (i.e., a
constituent that moves advectively with groundwater at the groundwater seepage velocity, and is not
slowed by sorption reactions) to move in groundwater to the designated receptor exposure point.
B2.2.3 Duration of Monitoring
Recommendations regarding the duration of continued monitoring at a site are generated by MAROS
during the preliminary evaluation based on the length of the historical monitoring record (sites having
longer historical records require continued future sampling through periods of shorter duration) and
on temporal trends that are identified in the historical monitoring records of monitoring points in the
source and tail areas of the plume (monitoring at points having decreasing trends in COC
concentrations is continued through periods of shorter duration than is monitoring at points having
"no trend").
B2.2.4 Density of Monitoring Network
Recommendations regarding the relative density of sampling points in a monitoring network are
derived during the preliminary evaluation using a simple "rule of thumb," as expressed in the
following equation (AFCEE, 2000):
Sampling density (number of wells/acre) = 1,5 x (plume length)0'4 Equation B-l
B2.2.5 "Spurious" Trends
Recommendations generated by MAROS regarding the duration and frequency of sampling are based
in large part on the system design category selected by MAROS using the results from evaluation of
temporal trends in COC concentrations at monitoring locations in the source area and tail areas of a
plume. However, the presence or absence of concentration trends identified by MAROS may be
misleading, because of the way in which MAROS deals with concentration values below the
analytical detection limit (or reporting limit) for a particular COC. MAROS assigns a surrogate value
(selected by the user to be the reporting limit, or some fraction of the reporting limit) to sample
results having a constituent concentration below the reporting limit. This practice can lead to
identification of spurious temporal trends in concentrations, or to incorrectly concluding that reported
concentrations are unstable through time, as a consequence of misinterpreting temporal changes in
COC reporting limits as representing actual changes in COC concentrations. This possibility
suggests that the results of temporal-trend analyses completed by MAROS should be examined
critically before conclusions are made regarding temporal trends in COC concentrations.
B2.3 TEMPORAL EVALUATION
After the preliminary evaluation of a monitoring program has been completed (Section B1.2), the
MAROS analysis can be extended to provide detailed results for individual monitoring points, using
temporal and spatial techniques. The MAROS tool can be used to examine the concentration history
of the specified COCs at each sampling point in the monitoring network for the presence of temporal
trends in concentrations, using parametric linear regression techniques and the Mann-Kendall test.
MAROS uses the results of the temporal-trend analyses to classify trends in COC concentrations at
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each monitoring point into one of six categories: "Increasing" (I), "Probably Increasing" (PI),
"Stable" (S), "Probably Decreasing" (PD), "Decreasing" (D), or "No Trend" (NT), based on the
decision logic presented in Tables B.3 and B.4. Identified trends in COC concentrations then are
applied in conjunction with the results of the preliminary evaluation of plume stability to generate
preliminary recommendations regarding appropriate sampling frequencies for each COC on a
location-specific basis. Note that the same considerations regarding the possible identification of
spurious trends that can occur during the preliminary evaluation of plume stability (Section
B 1.2.5) also apply to the evaluation of temporal trends in COC concentrations at individual
monitoring wells.
Table B.3: Decision Matrix Used in Linear Regression Analysis
as Implemented in MAROS
Confidence in Trend
< 90%
90% - 95%
> 95%
Logarithmic Slope
Positive
No Trend
Probably Increasing
Increasing
Negative
COV37 < 1 Stable
COV > 1 No Trend
Probably Decreasing
Decreasing
COV = coefficient of variation.
Table B.4: Decision Matrix Used In Mann-Kendall Trend Analysis
as Implemented in MAROS
Mann-Kendal
Test Statistic37
S>0
S>0
S>0
S<0
S<0
S<0
S<0
Confidence in Trend
> 95%
90% - 95 %
< 90%
< 90% and COVb/ > 1
< 90% and COV < 1
90% - 95%
95%
Concentration Trend
Increasing
Probably Increasing
No Trend
No Trend
Stable
Probably Decreasing
Decreasing
Mann-Kendall test statistic (S) is used to evaluate whether a trend is present in temporal data, and the degree of statistical
confidence regarding the presence of a trend. The numerical sign of the test statistic indicates whether the trend is
increasing or decreasing.
COV = coefficient of variation.
MAROS 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 CES
algorithm, which uses recent and historical COC measurements to determine optimal sampling
frequency, based on the six categories of concentration trends (CT) used in the Mann-Kendall
analysis, the rate-of-change (ROC) parameter (derived from the slope of the line fitted to COC
concentration data in the linear-regression analysis), and the MCL for each constituent.
Recommendations regarding sampling frequencies at individual wells are developed in three stages:
-------
1. Determine Sampling Frequency using Recent Concentration Trends. Sampling
frequency initially is determined using the ROC and CT, applied using the decision matrix
presented in Table B.5.
2. Adjust Sampling Frequency Based on Recent/Overall Ratio. Next, the frequency of
recent sampling events is compared with the overall frequency of sampling through the
entire history of monitoring at a particular location. If recent sampling events have been
completed at greater frequency than the overall frequency (e.g., recent events have been
completed quarterly, while the frequency of sampling events through much of the prior
history of monitoring at that location has been semi-annual), continuation of the frequency
of recent monitoring events is recommended. If recent sampling events have been
completed at lesser frequency than the overall frequency (e.g., recent events have been
completed annually, while the frequency of sampling events through much of the prior
history of monitoring at that location has been semi-annual) and the concentrations of one
or more COCs are "Increasing", "Probably Increasing", or display "No Trend", then
MAROS generates a recommendation that the more conservative overall frequency of
sampling (more frequent sampling) be adopted for future monitoring events.
3. Adjust Sampling Frequency Based on MCL. If the maximum concentration of a
particular COC detected at a monitoring point historically has been less than one-half the
MCL concentration for that constituent, and constituent concentrations have not increased
through time, then the sampling frequency can be reduced (e.g., from semi-annual
monitoring to annual monitoring).
Table B.5: Decision Matrix Used to Develop Recommendations for Monitoring Frequency
as Implemented in MAROS
Rate of Change (ROC)
Mann-Kendall
Trend Results
Increasing
Probably Increasing
No Trend
Stable
Probably Decreasing
Decreasing
High
Moderately
High
Medium
Moderately
Low
Low
Recommended Sampling Frequency^
Q
Q
Q
Q
Q
Q
Q
Q
Q
s
s
s
S
S
S
A
A
A
S
S
S
A
A
A
A
A
A
A
A
A
Sampling frequencies are as follow: Q = quarterly, S = semi-annual, A = annual.
The documentation for MAROS (AFCEE, 2000 and 2002) also recommends that sampling can be
terminated at monitoring points that are not critical to the monitoring program, and at which cleanup
standards have been attained; however, monitoring points meeting these criteria are not identified in
the program output.
B2.4
SPATIAL EVALUATION
A spatial evaluation also is completed for each sampling location in the monitoring program during
the second stage of the MAROS assessment. In the spatial evaluation of a monitoring network,
MAROS applies an algorithm based on Delaunay triangulation to assign a relative importance to each
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sampling point in the network, for use in identifying redundant sampling locations that could
potentially be removed from the monitoring program, with relatively little impact on the statistical
characterization of the contaminant plume (AFCEE, 2000 and 2002). (Although Delaunay
triangulation is not, strictly speaking, a "spatial-statistical" procedure, triangulated irregular
networking, of which Delaunay triangulation is a subset, is regarded by many investigators [e.g.,
Griffith, 1996] as forming the basis of spatial statistics. Consequently, the algorithms used to conduct
the spatial evaluations in MAROS may be referred to as "spatial-statistical procedures".) In
conducting the spatial evaluation, MAROS uses an inverse-distance weighting algorithm to estimate
a COC concentration at each sampling location, based on the measured concentrations at the "natural
neighbor" locations defined by the Delaunay triangles surrounding the location for which the
estimated concentration is generated (Figure B.2). MAROS then calculates a slope factor (SF) based
on the standardized difference between the measured and estimated concentrations at the location for
which the concentration is being estimated. The value of the SF can range from 0.0 to 1.0, with a SF
of 0.0 indicating that the concentration at a particular location can be estimated exactly using the
concentration values at surrounding monitoring points. Values of the SF greater than 0.0 indicate that
some degree of error is present in the estimate, with increasing values of SF indicating progressively
greater differences between estimated and measured values. Significant differences between COC
concentrations measured at a particular monitoring point, and the COC concentrations estimated for
that monitoring point using the inverse-distance weighting algorithm (as indicated by values of SF
near 1.0), suggest that actual sampling results from the monitoring point provide a significant amount
of information, which might not be obtainable by other means.
Delaunay
Voronoi
diaaram
Figure B.2: Natural Neighbors of a Monitoring Point (No) as Defined Using Delaunay Triangles
(after AFCEE, 2000)
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If the SF for an individual monitoring point is below a specified threshold value (currently
established at 0.01 for sampling points on the periphery of the monitoring network, and at 0.10 for
sampling points in the interior of the network), MAROS computes two other parameters for that
monitoring point. The average concentration ratio (CR) is the ratio of the plume-wide average COC
concentration calculated based on the plume-wide average concentration, which is calculated using an
area-weighted averaging method and excluding the actual COC concentration result from the
monitoring point in question, to the plume-wide average COC concentration calculated using the
area-weighted averaging method, including the actual COC concentration result from that point. The
area ratio (AR) is the ratio of the total area covered by all the Delaunay triangles within the
simulation domain with the monitoring point in question excluded from the network, to the area
covered by all the Delaunay triangles within the simulation domain with the monitoring point in
question included in the network. If the CR and AR are above a specified threshold value (currently
established at 0.95 for both parameters), the monitoring location is classified as "redundant" for that
COC. Monitoring points (wells) are removed iteratively from the network — the well having the
smallest value of SF is removed, and the CR and AR then are calculated. If the values of the CR and
AR are below the specified threshold, the well having the next lowest SF value is removed, the CR
and AR values are checked, and so on. This process is repeated for all COCs. If removal of any
monitoring point from the network does not result in significant loss of information (as indicated by a
SF having a value below the specified threshold, with corresponding values of CR and AR above the
specified threshold) for all COCs, that monitoring point is considered "redundant", and can be
removed from the monitoring program.
The results of the spatial evaluation also can be used in a "well sufficiency analysis", to evaluate
whether new sampling locations are needed in areas within the existing monitoring network where
there is a high degree of uncertainty in COC concentrations. The MAROS software identifies
potential new sampling locations in unsampled (or undersampled) regions by examining the SF
values derived for those regions using SF values obtained using the Delaunay triangulation algorithm
applied to existing sampling locations. Areas having large values of SF (near 1.0) are candidate
regions for new sampling locations.
B2.5 MOMENT ANALYSES
Other features of the MAROS software also enable the user to evaluate whether:
• Temporal trends in the overall concentrations and movement of COCs throughout a plume
(rather than at individual monitoring points) are statistically significant,
• Cleanup criteria have been met for each COC at each well, to some pre-determined
statistically-significant level, and
• Non-exceedance criteria for each COC were met at defined compliance boundaries during
each sampling event.
These features are implemented by means of two additional statistical analyses: a moment analysis
of COC conditions throughout the plume, and a data-sufficiency analysis, consisting of an analysis of
statistical power of the COC data available for individual wells, and a risk-based power analysis of
the entire plume (Table B.I).
The Moment Analysis consists of an assessment of the characteristics and overall stability of a plume,
based on spatial and temporal COC concentration data. MAROS uses these data, together with
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additional site-specific information (supplied by the user) (Table B.6), to generate three statistical
moments:
• The zero"1 moment, which represents the total mass of a COC dissolved in the plume;
• The first moment, representing the coordinates of the center of mass of the plume and the
distance from the center of mass to the contaminant source; and
• The second moment, which is a measure of the overall spread of the plume about the center of
mass in the longitudinal and transverse directions of the horizontal plane.
Table B.6: Simulation Parameters Used in MAROS Evaluations of Moments
and Risk-Based Power
Simulation Parameter
Effective Porosity of Saturated Earth Material (used in Moment Analysis)
Saturated Thickness of Water-Bearing Unit (used in Moment Analysis)
General Direction of Groundwater Movement (used in Moment Analysis)
Identification of Wells Along Plume Centerline (used in Risk-Based Power Analysis)
Distance from "Tail" of Plume to Nearest Downgradient Receptor (used in Risk-Based Power
Analysis)
The effective porosity of the saturated earth material ("soil") at the site is used, together with the
saturated thickness of the water-bearing unit and site-specific COC concentration data, to estimate the
total mass of a particular COC within a plume (used in calculating the zero"1 moment). The general
direction of groundwater movement is necessary to establish the overall configuration of the plume
(defined in MAROS by the directions of the longitudinal and transverse plume axes), which then is
used to calculate the first and second moments. Each moment is calculated by numerically
integrating contaminant concentration data over spatial regions defined during the spatial evaluation.
Because the value of each of the moments is COC-dependent, MAROS calculates moments for each
COC during each monitoring event. (In order to conduct the moment analysis for COC
concentrations detected during a particular monitoring event, the spatial configuration of the plume
must be relatively well defined for that event. Therefore, the moment analysis cannot be completed
by MAROS for monitoring events having fewer than six locations sampled for a particular COC.)
MAROS then applies a non-parametric temporal analysis (using the Mann-Kendall test for trends) to
identify overall trends in each moment for each COC. These trends can provide the user an
indication of the overall magnitude and stability of a plume, and also can be used to evaluate the
relative importance of information generated at each monitoring point during a particular sampling
event.
B2.6 DATA-SUFFICIENCY ANALYSES
During implementation of groundwater remedies involving LTM, cleanup levels specified for
particular COCs in groundwater may be achieved only gradually at certain monitoring locations.
Therefore, MAROS also incorporates a two-part Data-Sufficiency Analysis (Table B.I). The results
of the power-analysis component can be used as an indication of whether remedial action objectives
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(RAOs) for each COC have been, or are being, achieved at individual monitoring locations. If the
long-term average concentration of a particular COC at some monitoring location can be
demonstrated to be below some specified target-level concentration (e.g., the RAO concentration)
with a specified degree of statistical confidence, that monitoring point can be removed from the
monitoring program, or the frequency of sampling at that location may be reduced. If the long-term
average concentration at some monitoring location appears to be below the specified target-level
concentration, but this cannot be demonstrated at the specified level of statistical confidence, then the
power analysis can be used to establish the number of additional samples that must be collected from
the monitoring point in order to confirm that target-level concentrations have been achieved at that
location. The algorithm for power analysis for individual monitoring points uses the number of
samples collected at a monitoring point through its complete sampling history, and the temporal
variability among COC concentration data at that monitoring point, to evaluate whether there is
statistically-significant evidence that the concentrations of a particular COC at that location have
decreased to levels below a specified threshold concentration (currently established at 80 percent of
the RAO concentration of a particular COC) (AFCEE, 2002). MAROS then uses the results of the
power analyses to classify each monitoring point into one of three categories: "RAOs Attained",
"RAOs Not Attained", or "Continue Sampling", based on the decision logic presented in Table B.7.
Table B.7: Decision Matrix Used in Power Analysis as Implemented in MAROS
Decision Criteria"7
for COC
LR < p/(l - a)
P/(l - a) < LR < (1 - p)/a
(1 - p)/a < LR
Inference
Mean concentration is above
RAOb/
Mean concentration may be
below RAO (but the
difference is not statistically
significant)
Mean concentration is below
RAO (at a high level of
confidence)
Cleanup Status
Not Attained
Continue Sampling
Attained
Decision criteria are as follow:
LR = likelihood ratio estimator.
a = pre-specified statistical confidence level.
P = pre-specified statistical power.
b/ RAO = remedial action objective (COC cleanup concentration).
If the specified threshold concentration for a particular COC has been "Attained' at a particular
monitoring point, MAROS then uses the results of the power analysis to calculate the minimum
number of samples that would have been required to obtain the degree of statistical power specified
for the analysis. This information may be used to estimate the numbers of samples required to be
collected at adjacent monitoring points to achieve a similar level of confidence in the monitoring
results.
The algorithm used in the power analysis is parametric, in that the underlying statistical distribution
of the concentration data for a particular COC is assumed to follow some known form (Rock, 1988).
MAROS conducts the power analysis in accordance with the assumption that the concentration data
for a particular COC are normally distributed, and repeats the analysis using the same data, in
accordance with the assumption that the concentration data are lognormally distributed. The
assumption of lognormally distributed data is recommended for concentration data having a high
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degree of temporal variability, or for data sets containing fewer than about 20 results (AFCEE, 2002).
If the concentration data are neither normally nor lognormally distributed, the results of the power
analysis should be regarded with skepticism. Furthermore, the assumption that the concentrations of
a particular COC are "Stable" at the monitoring point under consideration is implicit in the algorithm
used to implement the power analysis. If COC concentrations display some temporal trend, rather
than being "Stable", the results of the power analysis will be misleading. This possibility should be
assessed during the evaluation using the results of the Mann-Kendall analysis of temporal trends
(Section B 1.3).
The data-quality objectives (DQO) process of the U.S. Environmental Protection Agency (U.S. EPA)
(1994a and 1996) requires that the number of samples collected during monitoring activities be
sufficient to support remediation decisions, with a pre-specified probability of making Type I (a) and
Type II (P) errors. A Type I error occurs if the hypothesis under consideration (e.g., "the target-level
concentration of trichloroethene [TCE] in groundwater at monitoring well MW-XX has not been
achieved") is rejected when it actually is true. A statistical "confidence level" (a) is selected for a
test of the hypothesis to reduce the likelihood of a Type I error to some acceptable degree. A Type II
error occurs if the hypothesis under consideration is accepted when it actually is false. Statistical
"power" (P) is established for a test of the hypothesis at a level sufficient to reduce the likelihood of a
Type II error to some acceptable degree. The confidence of a compliance-monitoring test is given by
1-ot, and the power of the test is given by 1-p. Any statistical confidence level may be selected by the
individual conducting a particular statistical test; however, the power of a test depends not only on
the intrinsic characteristics of the test itself, but also on the characteristics of the data the test is used
to evaluate (in particular, the variability of the data, and the number of individual measurements)
(Rock, 1988). The power of any statistical test increases as the data conform more closely to any
assumptions (e.g., normality of the statistical distribution) on which the test depends, and also as the
number of individual measurements increases.
It is not possible to completely eliminate Type I or Type II errors from any statistical test (Sheskin,
2000); and in most circumstances it is only possible to increase statistical confidence (small a) by
reducing statistical power (larger P). In the context of LTM, the consequences of committing a Type
I error (e.g., cessation of monitoring at a location where target-level concentrations of COCs have
not, in fact, been achieved) are regarded as much greater than are the consequences of committing a
Type II error (e.g., continued monitoring at a location where target-level concentrations of COCs
actually have been achieved). Accordingly, standard environmental statistical practice seeks to
minimize the likelihood of committing a Type I error, at the expense of possibly committing a Type
II error (Gibbons, 1994). Following the U.S. EPA's (1994a) convention, 95 percent confidence (1-a)
and 80 percent power (1-P) currently are established as DQO decision criteria in MAROS (AFCEE,
2002), for tests used to assess whether the concentrations of COCs in groundwater at particular
locations are below specified target-level concentrations.
The other component of the Data-Sufficiency Analysis is a risk-based power analysis of the plume,
which uses the historic concentrations of COCs at a minimum of three user-specified monitoring
points along the "centerline" of a plume, together with historic concentrations of COCs measured at
all other site-related wells, to predict COC concentrations at user-defined "compliance points"
downgradient from the plume. The algorithm for the risk-based power analysis examines COC
concentrations at specified monitoring points, fits a first-order exponential decay model to observed
concentrations detected at those locations, and then uses the fitted exponential model to calculate the
corresponding COC concentrations that might occur as a result of COC migration to a compliance
point located a specified distance downgradient from the monitoring location nearest the compliance
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point. MAROS uses this algorithm to predict COC concentrations arriving at the compliance point
(at a distance specified by the user downgradient from the tail of the plume) from each of the
specified centerline locations (Table B.6) and from all other sampling locations for which a result is
available for a particular monitoring event, computes the mean and variance of predicted COC
concentrations, and compares these values with RAO concentrations to determine whether RAOs
were achieved at the specified compliance point for particular COCs during each historical
monitoring event.
Results generated by this component of the data-sufficiency analysis fall into one of three categories:
1. The predicted mean concentration at the compliance boundary is significantly higher than
the RAO, indicating that RAOs at the specified boundary have been exceeded. In this
case, no risk-based power analysis is performed.
2. The predicted mean concentration at the compliance boundary is significantly lower than
the RAO, indicating that RAOs have been attained at the specified boundary. This type of
result usually is produced only when a sufficiently large number of sample results is
available (resulting in high statistical power).
3. The predicted mean concentration at the compliance boundary apparently is below the
RAO, but the statistical significance associated with this result is low (low statistical
power). In this case, more samples must be collected (to increase the level of statistical
power) or additional time must elapse for the effects of a remedy to become apparent
(resulting in lower COC concentrations and lower associated statistical variance).
MAROS then estimates the number of additional samples that must be collected to achieve
the necessary statistical power.
Additional details regarding site-specific applications of the MAROS software tool are presented in
Appendix D.
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B3.0 DESCRIPTION OF THREE-TIERED APPROACH
B3.1 GENERAL CHARACTERISTICS OF THREE-TIERED APPROACH
As described by Parsons (2003b, 2003c, and 2003d), a three-tiered long-term monitoring
optimization (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 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.
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;
and the spatial-statistical evaluation can be completed by a user familiar with geostatistical concepts,
with access to a standard geostatistical software package (e.g., Geostatistical Environmental Exposure
Software [GeoEAS; Englund and Sparks, 1992], GSLIB [Deutsch and Journel, 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
geographical information system (GIS) software package (e.g., ArcView® GIS) (Environmental
Systems Research Institute, Inc. [ESRI], 2001) with spatial-statistical capabilities (e.g., Geostatistical
Analyst™, an extension to the ArcView® GIS software package) is utilized in the LTMO evaluation.
As with the MAROS tool, the site-specific evaluation of a monitoring program is completely
dependent upon the amount and quality of the available data. Typical data requirements for
completing a three-tiered LTMO evaluation are presented in Table B.8.
Table B.8: Typical Information Required to Complete Three-Tiered LTMO Evaluation
General Types of Information Needed
Site features (roads, buildings, surface-water bodies, property boundaries)
Hydrogeologic conditions
Well locations (coordinates)
Well completion information
Configuration of groundwater potentiometric surface (used to derive directions of groundwater
movement and horizontal hydraulic gradients)
Groundwater levels through time
Identification of COCs
All historical COC analyses/results
Cleanup goals and monitoring objectives
Locations of potential exposure points and receptors
Description of current monitoring program
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B3.2 QUALITATIVE EVALUATION
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 contaminant migration and changes in chemical concentrations
through time, so that decision-makers can verify that contaminants are not endangering potential
receptors, and that remediation is occurring at rates sufficient to achieve RAOs within a reasonable
timeframe. The evaluation of the monitoring program therefore must consider existing receptor
exposure pathways, as well as exposure pathways arising from potential future use of the
groundwater. Potential redundancies in sampling location, and inappropriate sampling frequencies,
also are examined in the qualitative evaluation. Typical factors that are considered in the qualitative
evaluation include (Parsons, 2003b, 2003 c, and 2003d):
• Heterogeneity of water-bearing unit(s),
• Type(s) of contaminant(s),
• Distance and direction to potential receptor exposure point(s),
• Direction of groundwater movement and groundwater seepage velocity,
• Potential impacts to surface water, and
• Effects associated with implemented remedy(ies).
These factors will influence the locations and spacing of monitoring points, and the sampling
frequency. Typically, the greater the seepage velocity and the lesser the distance to receptor exposure
points, the more frequently groundwater sampling should be conducted. Examples of application of
qualitative considerations are described in detail in Appendix D.
All monitoring points that are sampled periodically in conjunction with the LTM program under
consideration are included in the qualitative evaluation. Multiple factors are considered in
developing recommendations for continued monitoring or cessation of monitoring at each monitoring
point or well. In some cases, a recommendation is made to continue monitoring a particular well, but
at less frequent intervals than at present. Factors considered in developing recommendations to retain
a well in, or to remove a well from the monitoring program, are summarized in Table B.9. Typical
factors considered in developing recommendations for monitoring frequency are summarized in
Table B. 10.
The analytes and methods used for chemical analyses also are examined in the qualitative evaluation.
Typically, LTM programs are initiated only after site characterization has been completed (Reed et
a/., 2000), and site-related COCs have been identified. Because the COCs have been identified, it
may be possible in some cases to conduct the required chemical analyses using a different analytical
method than was used during site characterization activities. If the alternate method has a shorter list
of analytes or if the analyte list is restricted only to the identified site-related COCs, it may be
possible to reduce the unit cost of chemical analysis of samples. For example, analyses for volatile
organic compounds (VOCs) often are conducted during the site- characterization phase of
investigations using U.S. EPA Method SW8260B (a gas-chromatographic/mass-spectrometric
[GC/MS] method). If the analytes to be determined in samples are known, Method SW8260B can be
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replaced by U.S. EPA Method SW8021B (a GC method), with potentially-significant cost savings
realized on a unit-cost basis.
The qualitative stage of the three-tiered evaluation is complete when recommendations regarding
retention in, or removal from the program, the frequency of sample collection, and the analytes and
analytical methods to be used, have been generated for every sampling location (well) in the
monitoring program.
Table B.9: Qualitative Monitoring Network Optimization Decision Logic
Reasons for Retaining a Well in a
Monitoring Network
Reasons for Removing a Well From a
Monitoring Network
Well is needed to further characterize the site
or monitor changes in contaminant
concentrations through time
Well provides spatially redundant information
with a neighboring well (e.g., same constituents,
and/or short distance between wells
Well is important for defining the lateral or
vertical extent of contaminants
Well has been dry for more than two years
Well is needed to monitor water quality at
compliance point or receptor exposure point
(e.g., municipal wells)
Contaminant concentrations are consistently
below laboratory detection limits or cleanup goals
Well is important for defining background
water quality
Well is completed in same water-bearing zone as
nearby well(s)
Table B.10: Qualitative Monitoring Frequency Decision Logic
Reasons for
Increasing Sampling Frequency
Reasons for
Decreasing Sampling Frequency
Groundwater velocity is high
Groundwater velocity is low
Change in concentration would significantly
alter a decision or course of action
Change in concentration would not significantly
alter a decision or course of action
Well is close to source area or operating
remedy
Well is farther from source area or operating
remedy
Cannot predict if concentrations will change
significantly over time
Concentrations are not expected to change
significantly over time, or contaminant levels
have been below cleanup objectives for some
period of time
B3.3
TEMPORAL STATISTICAL EVALUATION
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
(Appendix A). The Mann-Kendall test statistic is calculated at a specified level of confidence to
evaluate whether a temporal trend is present in contaminant concentrations detected through time in
samples from an individual well. As implemented, the algorithm used to evaluate trends assigns a
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value of "Not Detected" to those wells with sampling results that are consistently below analytical
detection limits (or reporting limits) through time, rather than assigning a surrogate value
corresponding to the detection limit - a procedure that could generate potentially-misleading and
spurious "trends" in concentration (e.g., the procedure used by MAROS [Section Bl.2.5]). In
addition, a value of "Below PQL" is assigned to those constituents for which no values are measured
at levels above the practical quantitation limit (PQL). In the absence of the "Below PQL"
classification category, the results of the trend analysis applied to a sampling point having consistent
detections of trace concentrations of a particular COC could indicate an increasing or decreasing
trend in concentrations, which would be primarily an artifact of the analytical methods, rather than
representing actual increases or decreases in COC concentrations in groundwater.
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 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. The degree to which the amount and quality of
information that can be obtained at a particular monitoring point serves the two primary objectives of
monitoring (temporal and spatial objectives) is considered in this evaluation, in accordance with the
decision logic structure presented in Figure B.3. The temporal evaluation stage of the three-tiered
evaluation is complete when recommendations regarding retention in, or removal from the program
have been generated for every sampling location (well) in the monitoring program, using the
temporal-trend decision logic (Figure B.3).
B3.4 SPATIAL STATISTICAL EVALUATION
In the third stage of the three-tiered evaluation, spatial statistical techniques are used to assess the
relative value of information generated by sampling at each monitoring point in the network, by using
COC concentrations to identify those areas having the greatest uncertainty associated with the
estimated extent and concentrations of COCs in groundwater. In order to ensure that the spatial
evaluation is as representative of actual conditions in the groundwater system as possible, the
sampling event during which the greatest number of discrete points were sampled is identified, and
the results of that event are used in the spatial statistical evaluation. As with the MAROS tool
(Section Bl.l), geostatistical methods generally are used in evaluating groundwater systems having
only a single layer. However, for a multi-groundwater system, the user could complete a sequential
layer-by-layer examination of the groundwater system during separate evaluations. A further
limitation is that geostatistical methods can be used to examine the spatial characteristics of only a
single COC during an evaluation. One approach is to identify the most widespread COC for use as
an "indicator contaminant", and complete the spatial statistical evaluation using monitoring results
only for that COC. If this is judged to be unsatisfactory, the spatial statistical evaluation should be
completed for several or all of the COCs.
After the COC of interest has been identified, and the monitoring event for which COC concentration
results are to be used has been selected, the COC concentrations are used to generate a
semivariogram, which depicts the range of distances over which, and the degree to which, sample
values at a given point are related to sample values at adjacent, or nearby, points (Clark, 1987), and
which also indicates how close together sample points must be for a value determined at one point to
be useful in predicting unknown values at other points. When a semivariogram is calculated for a
variable over an area (e.g., concentrations of TCE in groundwater within a water-bearing unit), an
B-19
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irregular spread of points across the semivariogram plot is the usual result (Rock, 1988). One of the
most subjective tasks of geostatistical analysis is to identify a continuous, theoretical semivariogram
model that closely honors the actual data. Fitting a theoretical model to calculated semivariance
points usually is accomplished by trial-and-error, rather than by a formal statistical procedure (Davis,
1986; Clark, 1987; Rock, 1988), and requires the expertise of an experienced geostatistical analyst.
. O Yes 1( Remove J
{ Retain J
Renove
Figure B.3: Temporal COC Concentration Trend Decision Logic Structure
(after Parsons, 2003b)
B-20
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After a semivariogram model has been developed to describe the spatial distribution of a particular
COC, it can be used to estimate the concentrations of that COC at every point in the spatial domain
(the area of covered by the monitoring network), and simultaneously to calculate prediction standard
errors for the COC concentrations that have been estimated, using the spatial-statistical procedure
known as kriging (Clark, 1987). First, the median kriging standard deviation is obtained from the
kriging standard errors calculated using the complete monitoring network sampled in the current
program. Next, each of the monitoring wells is removed sequentially from the network; and for each
resulting network configuration having one well less than the current program, a kriging realization is
completed using the concentrations of the COC of interest detected in samples from the remaining
wells. The "missing well" monitoring network realizations are used to calculate prediction standard
errors; and the median kriging standard deviations are obtained for each "missing well" realization
and compared with the median kriging standard deviation for the "base-case" realization obtained
using the current complete monitoring network, as a means of evaluating the amount of information
loss (as represented by increases in kriging error) resulting from the use of fewer monitoring points.
If removal of a particular well from the monitoring network causes very little change in the resulting
median kriging standard deviation (currently established at less than about 1 percent), that well is
regarded as contributing only a limited amount of information to the monitoring program. Likewise,
if removal of a well from the monitoring network produces larger increases in kriging standard
deviation, this is regarded as an indication that the well contributes a relatively greater amount of
information, and is relatively more important to the monitoring network. At the conclusion of the
kriging realizations, each well is ranked, from those providing the least information to those
providing the most information, based on the amount of information (as measured by changes in
median kriging standard deviation) 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. In general, no conclusions regarding removal from or retention in the
monitoring network can be made about the wells providing information intermediate between the
greatest and least relative amounts of spatial information.
B3.5 SUMMARY OF THREE-TIERED EVALUATION
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 evaluations 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 results of the three tiers of the evaluation are
combined and summarized in accordance with the following algorithm:
1. Wells designated as point-of-compliance or remedy-performance monitoring points in
decision documents are retained in the monitoring program under all circumstances,
regardless of possible rationale for removing such wells from the program.
2. Each well retained in the monitoring program on the basis of the qualitative hydrogeologic
evaluation is recommended to be retained in the refined monitoring program.
3. Each well retained in the monitoring program on the basis of the temporal hydrogeologic
evaluation is recommended to be retained in the refined monitoring program.
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4. Those wells identified during the spatial evaluation of the monitoring network as
contributing the most information regarding the occurrence and distribution of COCs in
groundwater are recommended to be retained in any subset of the network that will be used
for monitoring.
5. Any well recommended for removal from the monitoring program on the basis of one
evaluation (e.g., qualitative hydrogeologic) and for retention on the basis of another
evaluation (e.g., temporal statistical) is recommended for retention in the refined
monitoring program, and is further examined to determine if a less-frequent monitoring
schedule is appropriate.
6. Only those wells recommended for removal on the basis of all three evaluations, or on the
basis of the qualitative and temporal evaluations (with no recommendation resulting from
the spatial evaluation) should be removed from the monitoring program.
Additional details regarding site-specific applications of the three-tiered approach are presented in
Appendix D.
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APPENDIX C
SYNOPSES OF CASE-STUDY EXAMPLES
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APPENDIX C
SYNOPSES OF CASE-STUDY EXAMPLES
The Monitoring and Remediation Optimization System (MAROS) tool and the three-tiered approach
each were applied to the evaluation and optimization of existing groundwater monitoring networks at
three different sites - the Logistics Center area at Fort Lewis, Washington, the Long Prairie
Groundwater Contamination Superfund Site in Minnesota, and Operable Unit (OU) D at McClellan
AFB, California. Features of each site, and a summary of the results of the MAROS evaluation and
the three-tiered evaluation of the groundwater monitoring program at each site, are described in the
following subsections. 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.
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Cl.O LOGISTICS CENTER AREA, FORT LEWIS, WASHINGTON
The following summary of information regarding the location, operational history, geology, and
hydrogeology of Fort Lewis, Washington, the current monitoring program at the Logistics Center
area, available hydrologic and chemical data that were used in the monitoring-program evaluations,
and the results of the long-term monitoring optimization (LTMO) evaluations, has been excerpted
from Parsons (2003b) and Groundwater Services, Inc. (GSI) (2003a). Copies of both documents are
included in Appendix D-l; the reader is referred to the Appendix for additional details.
Cl.l SITE DESCRIPTION AND OPERATIONAL HISTORY
The Fort Lewis Military Reservation is located near the southern end of Puget Sound in Pierce
County, approximately 11 miles south of Tacoma and 17 miles northeast of Olympia, Washington.
The installation is bounded on the northwest by Interstate 5 and on the south and southwest by
Murray Creek. Murray Creek discharges into American Lake, approximately 2 miles northwest of
the East Gate Disposal Yard (EGDY). 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 EGDY),
located southeast of the Logistics Center (Figure C.I). 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. 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 (Section C1.3), was the subject of the MAROS and three-tiered evaluations.
C1.2 GEOLOGY AND HYDROGEOLOGY
Fort Lewis is underlain by a complex sequence of glacial and non-glacial Quaternary sediments,
ranging up to 2,000 feet in thickness. Most of the dissolved contaminants originating at the EGDY
source area occur within the uppermost water-bearing zone (the "Vashon Aquifer") at the Fort Lewis
Logistics Center, and the groundwater monitoring wells within the Logistics Center monitoring
network all are completed in the Vashon Aquifer. The stratigraphic units that comprise the Vashon
Aquifer include (from uppermost to lowermost) the Vashon Drift, Olympia beds, and Pre-Olympia
Drift.
Vashon Drift deposits typically extend from ground surface to depths of approximately 60 to 95 feet
below ground surface (bgs), but may extend to approximately 230 feet bgs in some areas. The
Vashon Drift consists primarily of sands and gravels, which occasionally are silty. The Olympia
beds, which underlie the Vashon Drift in some areas beneath the northern part of the EGDY, consist
of alluvial sands and gravels with silt, silty gravel, scattered wood, and peat, and may be up to 40 feet
thick. The Pre-Olympia Drift ranges from 10 to 70 feet in thickness, and consists of very fine to
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05 t
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I
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coarse sand with lenses of gravelly sand and sandy silt, sandy gravel with cobbles, and silty gravel
with sand and clay seams.
Groundwater within the Vashon Aquifer (also termed the "Upper Aquifer") is unconfined. The
aquifer occurs within Vashon Drift deposits and Pre-Olympia Drift deposits, and is subdivided into
Upper and Lower Vashon subunits, although regionally these subunits are considered to comprise a
single unconfined aquifer. Silty or clayey units within the Vashon deposits and Olympia beds may
act locally as discontinuous confining layers, hydraulically separating the Upper and Lower Vashon
subunits within the Vashon Aquifer. The stratigraphic units comprising the Lower Vashon Aquifer
are laterally discontinuous, and are present beneath the EGDY and in the area north and east of well
LC-41 (Figure C.I), but are absent between the EGDY and well LC-41.
The depth to groundwater beneath Fort Lewis is spatially variable, but generally ranges from 5 to 25
feet bgs throughout most of the Logistics Center area. The elevation of the water table fluctuates
approximately 5 to 6 feet seasonally, and can change by nearly 15 feet over periods of several years.
Regionally, the direction of groundwater movement within the Vashon Aquifer is to the northwest;
however, flow directions are locally and seasonally variable. Murray Creek, a northwesterly-flowing
stream that discharges into American Lake (Figure C.I), probably affects local groundwater gradients
in the upper part of the Vashon Aquifer. The calculated horizontal velocity of groundwater
movement in the more-permeable strata within the Vashon Aquifer, which are the primary pathways
for groundwater movement and contaminant migration at the Logistics Center area, ranges up to
about 15 feet per day (ft/day), or more than 5,000 feet per year (ft/year).
C1.3 NATURE AND EXTENT OF CONTAMINANTS IN GROUNDWATER
TCE has been identified as the primary contaminant in groundwater beneath the Logistics Center,
based on its widespread detection in wells across the site. Other contaminants of concern (COCs) in
groundwater include c/s-l,2-dichloroethene (c/s-l,2-DCE), tetrachloroethene (PCE), 1,1,1-
trichloroethane (1,1,1-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.
Within the Vashon Aquifer, TCE is present in groundwater at concentrations exceeding the federal
drinking-water maximum contaminant level (MCL) of 5 micrograms per liter (fig/L) (U.S.
Environmental Protection Agency [U.S. EPA], 2000), at distances extending more than 2 miles
downgradient from the EGDY to American Lake, where contaminants originating at the EGDY are
presumed to discharge. CAH constituents have migrated in groundwater to the west-southwest from
the EDGY source area toward Murray Creek, probably as a consequence of a local westerly hydraulic
gradient; and CAHs also apparently have migrated in groundwater to a gaining reach of Murray
Creek, where contaminated groundwater discharges to the stream (Figure C.I).
In most locations at the Logistics Center area, the extent of TCE in groundwater, as defined by the 5-
(ig/L isopleth for TCE, has remained relatively stable since it was assessed during the remedial
investigation (completed in 1990). The westernmost extent of COCs in groundwater was poorly
defined until recently; therefore, as a consequence of the lack of historic contaminant-concentration
data in this area, it is not known whether the western edge of the plume is stable, expanding, or
contracting. The concentrations of TCE and czs-l,2-DCE in groundwater samples from most wells in
C-4
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the Logistics Center area have remained relatively constant since the late 1980s. COC concentrations
at some wells (primarily extraction wells and monitoring wells near extraction wells) have exhibited
slight decreasing trends, while other wells within the interior of the plume have exhibited slight
increasing trends over time.
Two groundwater extraction and treatment systems have been in operation at the Fort Lewis Logistics
Center since 1995, to address contaminated groundwater in the Vashon Aquifer. The "1-5 system",
which consists of 15 extraction wells and 4 infiltration galleries installed near the northwest
installation boundary (Figure C.I), is operated to prevent the continued migration of contaminated
groundwater in the Vashon Aquifer across the installation boundary. The "East Gate system",
consisting of a 4-well primary extraction system and a 2-well secondary system, was installed to
remove and treat contaminated groundwater from the Vashon Aquifer directly downgradient from the
source area in the former EGDY.
C1.4 CURRENT GROUNDWATER MONITORING PROGRAM IN LOGISTICS CENTER AREA
Beginning in December 1995, groundwater monitoring was conducted at the Logistics Center on a
quarterly basis. In conjunction with 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) (Table C. 1). (Note that Table C. 1 is based upon information provided in
Parsons [2003b].) 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 within the Vashon Aquifer, and to assess the rate of removal of
contaminant mass from the subsurface.
The Upper and Lower Vashon subunits are regarded as two distinct monitoring zones 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). As part of an LTMO
evaluation of the groundwater extraction system and associated monitoring network at the Logistics
Center, completed in May 2001 by the Fort Lewis project team using MAROS Version 1, all
available TCE concentration data were examined to determine whether sampling frequencies could
be reduced, and concurrently to identify those wells that were most suited for continued monitoring
of the performance of the groundwater-extraction remedy. No extraction wells were considered for
removal from the network. Based on the results of the May 2001 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), and sampling
frequencies generally were reduced (Table C.I). The revised Logistics Center monitoring program
(LOGRAM), which was initiated in December 2001, includes 72 wells — 51 Vashon Aquifer
monitoring wells (29 wells sampled quarterly, 3 wells sampled semi-annually, and 19 wells sampled
annually), and all 21 extraction wells (6 wells sampled quarterly and 15 wells sampled annually).
The reduction in sampling frequency at a number of wells (Table C.I) 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.
C-5
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Table C.I: Groundwater Monitoring Program at Fort Lewis Logistics Center Area"
Well ID
Sampling Frequency
(prior to December 2001)
(after December 2001)
Monitoring Wells Completed in Upper Vashon Subunit
FL2 (newc/)
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)
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-137b
LC-149c
LC-149d
LC-165
LC-167 (new)
NEW-1 (new)
NA*
NA
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
NA
Quarterly
Quarterly
Quarterly
NA
NA
Quarterly
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Semi-Annual
Annual
Quarterly
Quarterly
e/
--
Quarterly
Quarterly
Annual
Quarterly
Annual
--
Annual
--
Annual
Quarterly
Quarterly
Quarterly
--
Annual
--
--
--
Quarterly
Annual
--
Quarterly
Annual
--
--
Quarterly
Quarterly
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Table C.I: Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Well ID
Sampling Frequency
(prior to December 2001)
(after December 2001)
Monitoring Wells Completed in Upper Vashon Subunit (continued)
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- lib (new)
T-12b
T-13b
NA
NA
NA
NA
NA
Quarterly
Quarterly
Quarterly
NA
Quarterly
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Quarterly
Semi- Annual
Quarterly
Quarterly
Semi- Annual
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 (new)
MAMC 6 (new)
T-10 (new)
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
NA
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
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
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
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Table C.I: Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Well ID
Sampling Frequency
(prior to December 2001)
(after December 2001)
Groundwater Extraction Wells (continued)
LX-17
LX-18
LX-19
LX-21
RW-1
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Information from Parsons (2003b).
b/ Sampling frequencies were adjusted in conjunction with other revisions to the groundwater monitoring
program in December 2001.
c/
"new" indicates that the well was not included in the monitoring program prior to December 2001.
Al
NA = well was not sampled prior to December 2001.
e/
A dash (—) indicates that the well is not included in the current monitoring program.
C1.5 SUMMARY OF LTMO EVALUATION COMPLETED USING MAROS TOOL
Cl.5.1 Summary of Groundwater Analytical Data for Logistics Center Area Used in
MAROS Evaluation
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 monitoring wells included in the original monitoring program (the program that was
in effect prior to December 2001), and was not used to evaluate the LOGRAM program. Rather, the
groundwater monitoring program at the Logistics Center area was evaluated using the MAROS tool,
applied to the results of quarterly sampling events completed during the period November 1995
through September 2001, prior to development and implementation of the LOGRAM program (GSI,
2003a). By September 2001, 24 separate monitoring events had been completed at the Logistics
Center area. The historic sampling results for the 59 wells that remained in the monitoring program
in September 2001 (21 extraction wells and 38 monitoring wells; Column 2 of Table C.I) were
examined in the MAROS evaluation. The locations of these wells, and their status in the current
monitoring program, are presented on Figure C.2.
Prior to the evaluation, wells that potentially would provide "redundant" information were identified
on the basis of qualitative considerations; the following monitoring wells were identified as
redundant with other, existing wells:
• Wells LC-19b and LC-19c were redundant with existing well LC-19a;
• Well LC-66a was redundant with well LC-66b;
• Well LC-137a was redundant with well LC-137b; and
• Well LC-149d was redundant with well LC-149c.
-------
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Wells considered to be "redundant" with other wells were not included in the moment analysis or in
the spatial evaluation (using the Delaunay method; Appendix B). Historic monitoring results from all
monitoring and extraction wells were included in the temporal evaluation (using the modified cost-
effective sampling [CES] approach; Appendix B). 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 (wells LC-64b and LC-137c) 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).
At the beginning of the MAROS evaluation, the sampling-results database provided by the US Army
Corps of Engineers (USAGE) was processed to remove duplicate data measurements, by averaging
the primary and duplicate analytical results and using this average to represent a single value detected
at that sampling point, during that sampling event. Concentration values that were below reporting
limits were replaced with surrogate values, selected to be the minimum reporting limit for that
particular constituent, a procedure that assumes that reporting limits remained uniform through time.
Trace-level results were represented by their actual values. The processed database contained
analytical data for the 38 monitoring wells and 21 extraction wells in service in September 2001.
Although five COCs (PCE, TCE, cis-l,2-DCE, VC, and 1,1,1-TCA) historically have been detected
in groundwater at the site (Section C1.3), TCE was used as an indicator compound, based on its
widespread detection at relatively elevated concentrations in wells across the site; and the MAROS
evaluation of the monitoring program at the Fort Lewis Logistics Center area used only the results of
analyses for TCE in groundwater samples.
Cl.5.2 Results of Evaluation Completed Using MAROS Tool
Application of the Mann-Kendall and linear regression temporal trend evaluation methods
(Appendices A and B) indicated that the trends in TCE concentrations at about 60 percent of the
monitoring wells designated as "source area" wells were "Probably Decreasing", "Decreasing", or
"Stable", while TCE concentrations at extraction wells in the source area all were "Probably
Decreasing", or "Decreasing". This 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 were "Probably Decreasing", "Decreasing", or "Stable"; and about one-half of the wells
in the "tail" and off-axis parts of the plume displayed similar TCE concentration trends. The results
of the moment analysis (Appendix B) indicated that the location of the center of mass of the plume
has remained essentially unchanged, and the extent of TCE in groundwater has decreased over time,
providing further evidence that the plume is stable. The evaluation of overall plume stability
(Appendix B) indicated that the extent of TCE in groundwater of the upper Vashon Aquifer is stable
or decreasing, resulting in the recommendation that a monitoring strategy appropriate for a
"Moderate " design category (Appendix B) be adopted.
The results of detailed spatial analyses using the Delaunay method (Appendix B) indicated that 8
monitoring wells could be removed from the original monitoring program (which included 38
monitoring wells) without significant loss of information (Table C.2; compare the results of the
MAROS evaluation with the original and LOGRAM monitoring programs). 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 historic sampling
information may be inadequate; new monitoring wells were recommended for installation in these six
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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
(wells FL3, LC-16, LC-20, LC-167, NEW-3, and NEW-5; Table C.2). All groundwater extraction
wells were recommended for retention in the refined monitoring program.
Table C.2: Refined Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Generated Using the MAROS Toola/
Well ID
Historic Sampling Frequency13'
(prior to
December 2001)
(after
December 2001)
Results of MAROS Evaluation
Remove/Retain0
Recommended
Sampling Frequency
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)
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-137b
LC-149c
LC-149d
LC-165
NAe/
NA
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
NA
Quarterly
Quarterly
Quarterly
NA
NA
Quarterly
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Semi- Annual
Annual
Quarterly
Quarterly
-
-
Quarterly
Quarterly
Annual
Quarterly
Annual
-
Annual
-
Annual
Quarterly
Quarterly
Quarterly
-
Annual
-
-
-
Quarterly
Annual
-
Quarterly
Annual
-
-
Not Considered*7
Addh/
Not Considered
Not Considered
Retain
Retain
Retain
Retain
Add
Retain
Remove
Remove
Add
Not Considered
Retain
Not Considered
Retain
Remove
Retain
Remove
Retain
Not Considered
Not Considered
Retain
Remove
Retain
Retain
Retain
Retain
Retain
Remove
Remove
Retain
Retain
Remove
Retain
-et
Quarterly
-
-
Annual
Quarterly
Quarterly
Annual
Quarterly
Annual
-
-
Quarterly
-
Annual
-
Quarterly
-
Semi- Annual
-
Quarterly
-
-
Quarterly
-
Annual
Biennial
Annual
Quarterly
Quarterly
-
-
Quarterly
Biennial
-
Biennial
C-ll
-------
Table C.2: Refined Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Generated Using the MAROS Tool
Well ID
Historic Sampling Frequency
(prior to
December 2001)
(after
December 2001)
Results of MAROS Evaluation
Remove/Retain
Recommended
Sampling Frequency
Monitoring Wells Completed in Upper Vashon Subunit (continued)
LC-167 (new)*
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- lib (new)
T-12b
T-13b
NA
NA
NA
NA
NA
NA
NA
Quarterly
Quarterly
Quarterly
NA
Quarterly
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Quarterly
Semi- Annual
Quarterly
Quarterly
Semi- Annual
Add
Not Considered
Not Considered
Add
Not Considered
Add
Not Considered
Retain
Retain
Retain
Not Considered
Retain
Not Considered
Retain
Retain
Quarterly
-
-
Quarterly
-
Quarterly
-
Annual
Biennial
Annual
-
Annual
-
Annual
Annual
Monitoring Wells Completed in Lower Vashon Subunit
FL4a (new)
LC-41b (new)
LC-64b
LC-lllb
LC-116b
LC-122b
LC-128
LC-137c
MAMC1
MAMC 6 (new)
T-10 (new)
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
NA
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Not Considered
Not Considered
Retain
Retain
Retain
Retain
Retain
Retain
Not Considered
Not Considered
Not Considered
—
-
Annual
Biennial
Semi- Annual
Biennial
Annual
Annual
-
-
-
Groundwater Extraction Wells
LX-1 Quarterly Annual Retain Annual
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
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
C-12
-------
Table C.2: Refined Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Generated Using the MAROS Tool
Well ID
Historic Sampling Frequency
(prior to
December 2001)
(after
December 2001)
Results of MAROS Evaluation
Remove/Retain
Recommended
Sampling Frequency
Groundwater Extraction Wells (continued)
LX-18
LX-19
LX-21
RW-1
aJ
b/
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Retain
Retain
Retain
Retain
Quarterly
Quarterly
Annual
Quarterly
Information from GSI (2003a).
December 2001.
c/ "Remove" = MAROS recommended that the well be removed from the monitoring program.
"Retain" = MAROS recommended that the well continue to be sampled at the indicated frequency.
d/
"new" = the well was not included in the monitoring program prior to December 2001.
el
NA = well was not sampled prior to December 2001.
s
"Not Considered" = the well was not included in the MAROS evaluation.
A dash (--) indicates that the well is not included in the current or refined monitoring program.
h/
Add = current LOGRAM well identified by MAROS for inclusion in the refined monitoring program.
Using a modified CES method, MAROS applies the results of the temporal-trend analysis to develop
recommendations regarding sampling frequency for each well in a monitoring program (Appendix
B). However, because MAROS substitutes a surrogate value (typically, the laboratory reporting
limit) for measurements that are below the reporting limit (Appendix B), the algorithm cannot
distinguish between a well at which detectable concentrations of COCs never have occurred (i.e.,
"Not Detected" classification in the three-tiered approach; Appendix B) and a well which historically
has contained very low (but detectable) concentrations of COCs in samples. Logically, a well having
no detectable concentrations of COCs throughout its monitoring history should be assigned a
"Stable" classification by MAROS, based on the criteria presented in Table B.4 (i.e., a Mann-Kendall
test statistic of zero and a covariance less than 1). However, because reporting limits can vary
through time or among samples, it is possible for MAROS to identify spurious trends in COC
concentrations for such wells. To partially rectify this shortcoming, the minimum reporting limit for
TCE was assigned to all sampling results for TCE which were below reporting limits (Section
C 1.5.1). Although this substitutional procedure assumes that reporting limits remained uniform
through time, and potentially introduces bias into the result, its application resulted in assignment of a
"Stable" classification by MAROS to TCE concentrations in the only well at the Fort Lewis Logistics
Center area having no detectable concentrations of TCE in groundwater samples collected throughout
its monitoring history (well LC-149c) (GSI, 2003a, Appendix B, "Statistical Trend Analysis
Summary").
MAROS also may identify spurious temporal trends in COC concentrations at wells where COCs
historically have been detected, particularly if measured concentrations have been below practical
quantitation limits (this situation corresponds to the "below PQL" classification in the three-tiered
LTMO approach; Appendix B). Wells at which TCE was been detected historically at low
frequencies and low concentrations, but for which MAROS identified a trend that differed from a
"Stable" trend, using either linear regression or the Mann-Kendall test, are shaded in Table C.2,
together with the sampling frequencies developed using TCE concentration trends, even though the
"trends" identified for those wells by MAROS may be spurious. For example, even though TCE has
C-13
-------
been measured at concentrations greater than the reporting limit in only one of eight samples
collected and analyzed through the entire period of monitoring at well T-12b, MAROS identified "No
Trend" in concentrations of TCE in samples from this well using the Mann-Kendall test (GSI, 2003 a,
Appendix B, "Statistical Trend Analysis Summary"). In this instance, assigning a classification of a
"Stable" trend probably would be more appropriate. Such a classification should be inserted by the
practitioner, following examination and evaluation of output generated by MAROS.
The results of the sampling frequency optimization analysis completed by MAROS indicated that
most wells in the monitoring network could be sampled less frequently than once per quarter. The
results of the data sufficiency evaluation, completed using power analysis methods (Appendix B),
indicated that remedial action objective (RAO) concentrations of TCE in groundwater have nearly
been achieved at the compliance boundary 2,000 feet downgradient from well LC-19a (the well
furthest downgradient from the EGDY source area). This suggests that the monitoring program is
adequate to evaluate the extent of TCE in groundwater relative to the compliance boundary through
time (GSI, 2003a).
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 (Table
C.2). 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 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 (pre-December 2001)
program. Assuming a cost per sample of $500 for collection and chemical analyses, 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. (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).
C1.6 SUMMARY OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH
Cl.6.1 Summary of Groundwater Analytical Data for Logistics Center Area Used in
Three-Tiered Approach
The groundwater monitoring program at the Logistics Center area also was evaluated using the three-
tiered approach, applied to the results of quarterly sampling events completed during the period
February 1995 through December 2001 (Parsons, 2003b). During that period, a total of 83 wells (21
extraction wells and 62 monitoring wells) have been sampled, in conjunction with the original
monitoring program, the LOGRAM monitoring program, or both (Table C.I). Prior to the
evaluation, the sampling-results database provided by the USAGE was processed to remove duplicate
data measurements by retaining the greater of the primary and duplicate analytical results, and
discarding the lower value. The database that was utilized in the three-tiered evaluation of the
groundwater monitoring program for the Logistics Center area differed slightly from the database that
was utilized in the corresponding MAROS evaluation in the following respects:
C-14
-------
• The three-tiered approach was applied using a database having a slightly longer historical
period of record, extending from February 1995 through December 2001, versus a historical
period of record extending from November 1995 through September 2001 that was utilized in
the MAROS evaluation (Section Cl.5.1).
• The method used in the three-tiered approach to deal with analytical results from duplicate
samples (retaining the greater of the primary and duplicate analytical results, and discarding
the lower value) differed from the method used in the MAROS evaluation (averaging the
primary and duplicate analytical results, and using this average to represent a single value;
Section Cl.5.1).
• The method used in the three-tiered approach to deal with concentration values that were
below reporting limits (value reported as "Not Detected"; Appendix B) differed from the
method adopted in the MAROS evaluation (assigning a surrogate value corresponding to the
minimum reporting limit for a particular constituent; Appendix B).
The processed database used in the three-tiered evaluation contained analytical data for 74 of the 83
wells included in the original and/or the LOGRAM monitoring program, and contained the results of
more than 20 sampling events for each of the 21 extraction wells and the 38 monitoring wells
included in the original monitoring program (1995 to December 2001). However, the results of fewer
than four sampling events were available for 18 of the wells that were added to the monitoring
program in December 2001; and no results were available for 9 of the wells (the six NEW wells, and
wells MAMC 1, MAMC 6, and T-l Ib), which were added to the program in 2001.
TCE is the COC that historically has been detected most frequently (in 90 percent of samples) and at
the highest concentrations in groundwater at the Logistics Center area, with TCE concentrations
exceeding the MCL for TCE (5 (J-g/L) in approximately 74 percent of samples (Table C.3). (Note that
Table C.3 is based upon information provided in Parsons [2003b].) TCE has been detected in
groundwater samples from 71 of the 74 wells for which sampling results are available, and has
exceeded its MCL in samples from 56 of these wells. The other primary COCs (c/s-l,2-DCE, PCE,
and VC) have been detected less frequently, at lower concentrations, and in samples from fewer wells
than has TCE (Table C.3). Accordingly, TCE was selected as an indicator compound, based on its
widespread detection at relatively elevated concentrations in wells across the site. Although the other
primary COCs (PCE, c/s-l,2-DCE, and VC) were considered, together with TCE, in the qualitative
and temporal stages of the three-tiered evaluation, the spatial-statistical stage of the three-tiered
evaluation of the monitoring program at the Fort Lewis Logistics Center area 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 C1.4), 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.
C-15
-------
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Cl.6.2 Results of 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). In the three-tiered evaluation, sampling results for 74 of the 83 wells
included in the original and/or the LOGRAM groundwater monitoring programs at the Fort Lewis
Logistics Center were evaluated using qualitative hydrogeologic knowledge, temporal statistical
techniques, and spatial statistics. (Because extensive historical data were not available for the new
wells included in the LOGRAM program, temporal analyses were not used in evaluating the
LOGRAM - 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.) At
each tier of the evaluation, monitoring points that provide relatively greater amounts of information
regarding the occurrence and distribution of COCs in groundwater were identified, and were
distinguished from those monitoring points that provide relatively lesser amounts of information.
The results of the tiered evaluations were combined and summarized to provide recommendations
regarding optimization of the monitoring network, and the frequency of sample collection (Parsons,
2003b).
The results of the three-tiered evaluation indicated that 15 of the 83 existing wells (including 6 of the
wells currently monitored in the LOGRAM program) could be removed from the groundwater long-
term monitoring (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 (Table C.4; compare the
results of the three-tiered evaluation with the original and LOGRAM monitoring programs),
consisting of 69 wells, with 16 wells sampled quarterly, 7 wells sampled semi-annually, 17 wells
sampled annually, 14 wells sampled biennially, and the 15 1-5 extraction wells sampled every 3 years,
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 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. 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, 2003b).
C-17
-------
Table C.4: Refined Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Generated Using the Three-Tiered Approach"7
Well ID
Historic Sampling Frequency13'
(prior to
December 2001)
(after
December 2001)
Results of Three-Tiered Evaluation
Remove/Retain0
Recommended
Sampling Frequency
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)
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-137b
LC-149c
LC-149d
LC-165
LC-167 (new)
LC-180
NEW-1 (new)
NEW-2 (new)
NAe/
NA
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
NA
Quarterly
Quarterly
Quarterly
NA
NA
Quarterly
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Semi- Annual
Annual
Quarterly
Quarterly
--
--
Quarterly
Quarterly
Annual
Quarterly
Annual
-
Annual
--
Annual
Quarterly
Quarterly
Quarterly
--
Annual
--
--
--
Quarterly
Annual
--
Quarterly
Annual
--
--
Quarterly
Retain
Remove
Retain
Retain
Retain
Remove
Retain
Retain
Remove
Retain
Remove
Remove
Retain
Retain
Remove
Retain
Retain
Remove
Retain
Remove
Retain
Retain
Retain
Retain
Remove
Retain
Remove
Remove
Retain
Retain
Retain
Remove
Remove
Retain
Retain
Remove
Retain
Proposed for installation8'
NA
NA
Quarterly
Quarterly
Retain
Retain
Annual
f/
Biennial
Biennial
Biennial
--
Annual
Annual
--
Annual
--
--
Biennial
Biennial
--
Biennial
Annual
--
Annual
--
Annual
Biennial
Semi- Annual
Quarterly
-
Annual
-
-
Annual
Quarterly
Annual
-
-
Biennial
Biennial
-
Semi- Annual
Annual
Quarterly
Quarterly
C-18
-------
Table C.4: Refined Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Generated Using the Three-Tiered Approach
Well ID
Historic Sampling Frequency
(prior to
December 2001)
(after
December 2001)
Results of Three-Tiered Evaluation
Remove/Retain
Recommended
Sampling Frequency
Monitoring Wells Completed in Upper Vashon Subunit (continued)
NEW-3 (new)
NEW-4 (new)
NEW-5 (new)
NEW-6 (new)
PA-381
PA-383
T-04
T-06 (new)
T-08
T- lib (new)
T-12b
T-13b
NA
NA
NA
NA
Quarterly
Quarterly
Quarterly
NA
Quarterly
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Quarterly
Semi-Annual
Quarterly
Quarterly
Semi-Annual
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Quarterly
Quarterly
Quarterly
Quarterly
Biennial
Biennial
Annual
Quarterly
Semi-Annual
Quarterly
Biennial
Semi-Annual
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 (new)
MAMC 6 (new)
T-10 (new)
NA
NA
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
NA
NA
NA
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Retain
Retain
Retain
Retain
Retain
Remove
Retain
Retain
Retain
Retain
Retain
Biennial
Annual
Annual
Biennial
Annual
-
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
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
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
C-19
-------
Table C.4: Refined Groundwater Monitoring Program at Fort Lewis Logistics Center Area
Generated Using the Three-Tiered Approach
Well ID
Historic Sampling Frequency
(prior to
December 2001)
(after
December 2001)
Results of Three-Tiered Evaluation
Remove/Retain
Recommended
Sampling Frequency
Groundwater Extraction Wells (continued)
LX-16
LX-17
LX-18
LX-19
LX-21
RW-1
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Retain
Retain
Retain
Retain
Retain
Retain
Semi- Annual
Quarterly
Quarterly
Quarterly
Quarterly
Semi- Annual
Information from Parsons (2003b).
Sampling frequencies were adjusted in conjunction with other revisions to the groundwater monitoring program in
December 2001.
"Remove" = Three-tiered evaluation recommended that the well be removed from the monitoring program.
"Retain" = Three-tiered evaluation recommended that the well continue to be sampled at the indicated frequency.
"new" = the well was not included in the monitoring program prior to December 2001.
NA = well was not sampled prior to December 2001.
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.
C-20
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C2.0 LONG PRAIRIE GROUNDWATER CONTAMINATION
SUPERFUND SITE, MINNESOTA
The following summary of information regarding the location, operational history, geology, and
hydrogeology of the Groundwater Contamination Superfund Site at Long Prairie, Minnesota (Long
Prairie site), the current monitoring program, available chemical data that were used in the
monitoring-program evaluations, and the results of the LTMO evaluations, has been excerpted from
Parsons (2003c) and GSI (2003b). Copies of both documents are included in Appendix D-2; the
reader is referred to the Appendix for additional details.
C2.1 SITE DESCRIPTION AND HISTORY
The town of Long Prairie, Minnesota is a small farming community located on the east bank of the
Long Prairie River, in Todd County, central Minnesota, about 120 miles northwest of the
Minneapolis/St. Paul metroplex. The Long Prairie site comprises a 0.16-acre source area of
contaminated soil that has generated a plume of dissolved CAH contaminants 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 trench
drain. The subsequent migration of contaminants through the vadose zone to groundwater produced
a dissolved CAH 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.
Contaminants first were identified in groundwater in 1983, during a survey of municipal drinking-
water-supply wells for synthetic organic contaminants. PCE and other CAHs, including TCE and cis-
1,2-DCE, were detected in samples from two wells (wells CW4 and CW5) of the five Long Prairie
municipal water-supply wells, which are completed in the lower unit of the Long Prairie Sand Plain
aquifer. CAH contaminants also were detected in samples from eight of 21 residential wells that
were sampled. Subsequently, a remedial investigation and feasibility study (RI/FS) was completed in
accordance with the terms of a Multi-Site Cooperative Agreement signed in 1984 between the
Minnesota Pollution Control Agency (MPCA) and the U.S. EPA. Based on the results of the RI/FS,
the Long Prairie Groundwater Contamination Site was promulgated to the National Priorities List
(NPL) in 1985, and a Record of Decision (ROD) was signed in 1988.
The ROD established three OUs at the Long Prairie site. The plume of contaminated groundwater
was identified as OU1; the response action at OU1 consists of extraction of CAH-contaminated
groundwater via nine extraction wells, treatment of the extracted water, and discharge of treated
water to the Long Prairie River. Operation of the groundwater extraction, treatment, and discharge
(ETD) system is intended to restore aquifer quality to MCLs, and to prevent further migration and
discharge of the CAH plume to the Long Prairie River. The source-area soils were designated as
OU2, and were addressed by means of a soil-vapor extraction (SVE) system. OU3 comprises an
alternative water supply system, which provided municipal water hookups to local residents with
private wells affected by CAH contaminants.
The performance of the OU1 groundwater extraction and treatment 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
C-21
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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 ETD system (Section C2.3), was the subject of the MAROS and three-tiered
evaluations.
C2.2 GEOLOGY AND HYDROGEOLOGY
The earth materials underlying the town of Long Prairie consist of a series of glacial till and outwash
deposits nearly 700 feet thick, that were deposited in a large valley along the Long Prairie River.
Outwash sediments within the valley comprise coarse sands and gravels deposited during two
separate periods of glaciation; the outwash deposits are separated by finer-grained tills. The
uppermost distinct geologic unit is called the surficial, upper outwash unit, and is present only within
the glacial valley. The upper outwash unit is underlain by a till deposit (the upper Wadena till),
which is not present everywhere in the vicinity. Beneath the upper Wadena till is the lower outwash
unit, which in turn is underlain by a lower till deposit. The upper Wadena till is absent immediately
east of the Long Prairie River, and in this area the upper and lower outwash deposits are in physical
and hydraulic contact, and form a single hydrogeologic unit. However, the upper Wadena till is
intact along the eastern side of the outwash valley, and where present, functions as a confining unit
lying between the upper and lower outwash units. In these areas, groundwater within the lower
outwash unit is present under confined to semi-confined conditions. Where the upper Wadena till is
absent, groundwater in the outwash aquifer occurs under water-table (unconfined) conditions.
Groundwater at all locations within the surficial, upper outwash unit is under water-table
(unconfined) conditions. The vertical hydraulic gradients between the upper and lower outwash
deposits generally are negligible, but may be slightly downward near the northern end of the CAH
plume.
The solvent release at the Long Prairie site occurred in an area where the upper Wadena till is present
between the upper and lower outwash units. However, the till is not present immediately north of the
source area, and CAH contaminants are present in groundwater in both the upper and lower parts of
the outwash deposits west of the western edge of the upper Wadena till. Because the upper and lower
outwash units are in direct hydraulic communication where the confining till is absent, it is possible
for contaminants originating at the solvent-release source area to move from the upper outwash unit
into the lower outwash unit, and then to be drawn into the city wells (wells CW3 and CW6) which are
completed in the lower outwash unit to the east of the source area (Figure C.3). The directions of
groundwater movement in the upper and lower outwash deposits generally are parallel to the channel
of the Long Prairie River, suggesting that the river is not in direct hydraulic communication with the
groundwater system, and that the influence of the river on the configuration of the groundwater
potentiometric surface (and on the directions of groundwater movement) in the area is limited.
Groundwater moves to the northeast beneath the PCE source area, to the vicinity of extraction wells
RW5 and RW7, and from there moves west-northwest toward the Long Prairie River (Figure C.3).
The directions of groundwater movement also are influenced locally by pumping of the city water-
supply wells, and by operation of the OU1 extraction wells. The calculated horizontal velocity of
groundwater movement in the upper outwash unit ranges up to about 1.7 ft/day, or more than 600
ft/year. The hydraulic properties of the lower outwash unit are inferred to be comparable to those of
the upper outwash unit, and the corresponding rates of groundwater movement probably also are
comparable.
C-22
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C2.3 NATURE AND EXTENT OF CONTAMINANTS IN GROUNDWATER
Contaminants were introduced to the subsurface at the Long Prairie site by discharge of dry-cleaning
solvents directly into glacial outwash deposits at the site of the former dry-cleaning establishment.
The waste solvents then percolated through the coarse outwash soils at the source area to the water
table in the Long Prairie Sand Plain aquifer, and subsequently migrated as dissolved constituents in
groundwater. PCE and its daughter products TCE and cis-l,2-DCE have been detected through a
volume of groundwater about 1,000 feet wide, which extended (in October 2002) from the source
area, near the inactive RW1A/1B/1C extraction well cluster, approximately 3,200 feet downgradient
to the northwest, to the vicinity of nested monitoring well pair MW18A/B (Figure C.3). VC also has
been detected in groundwater samples, although at few locations and at lower concentrations than
other CAHs. CAH contaminants have been detected in groundwater through the full saturated
thickness of the upper glacial outwash deposits, and also historically have been detected in the lower
outwash deposits beneath the upper till at city well CW3.
The maximum concentrations of PCE historically detected in groundwater have been as high as
150,000 (ig/L. Recently, the maximum detected concentrations of PCE have decreased to
approximately 100 (ig/L, and PCE no longer is present at detectable concentrations in the lower
outwash deposits east of the glacial channel. However, CAH contaminants persist throughout the
saturated upper outwash deposits within the glacial channel (along the centerline of the plume), and
the overall extent of CAHs in groundwater, as defined by the 5-(ig/L isopleth for PCE, has not
changed significantly since operation of the groundwater ETD system was initiated, in 1996. In
October 2002, PCE concentrations in the plume ranged from 2.4 (ig/L at the northern end of the
plume (well MW18B) to 110 (ig/L near the center of the plume, at well MW14B (Figure C.3).
The OU1 groundwater ETD system was installed to prevent continued migration of CAH
contaminants to Long Prairie River, and to remove sufficient contaminant mass that contaminant
concentrations in groundwater at the site would be reduced to levels below their respective MCLs.
The groundwater extraction system includes 10 groundwater extraction wells located along the axis
of the plume, four of which (wells RW1A, RW1B, RW1C, and RW4) have been removed
permanently from service. The system is designed to extract and treat up to 250 gallons per minute
(gpm) of groundwater; treated groundwater is discharged to the Long Prairie River.
C2.4 CURRENT GROUNDWATER MONITORING PROGRAM AT LONG PRAIRIE SITE
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. 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 monitoring event (October 2002) for
which sampling results are available.
Several of the monitoring locations include wells installed in clusters, with each well in a cluster
completed at a different depth. The screens of monitoring wells having an "A" designation (e.g.,
MW6A) extend across the water table; wells having a "B" designation (e.g., MW6B) are completed
at the base of the upper glacial outwash unit; and wells having a "C" designation (e.g., MW6C) are
completed within the lower outwash unit. Approximately one-half of the wells sampled during
C-24
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October 2002 are sampled routinely in conjunction with the groundwater monitoring program. For
example, in 2000 and 2001, 26 of the 44 wells were sampled (Table C.5), with the six active
groundwater extraction wells (wells RW3, RW5, RW6, RW7, RW8, and RW9) and municipal water-
supply well CW3 sampled quarterly, and 18 monitoring wells sampled annually. Inactive extraction
well RW4 also was included in the monitoring program, and was sampled annually. In 2002,
municipal water-supply well CW6 was added to the monitoring program, and was sampled quarterly.
In the second quarter of 2000, the suite of VOCs for which groundwater samples were analyzed was
reduced to the identified COCs (PCE, TCE, cis-l,2-DCE, and VC). In addition, a gas-
chromatographic (GC) analytical method (assumed to be U.S. EPA Method SW8021B) now is used
instead of the gas-chromatographic/mass spectrometric (GC/MS) method (assumed to be U.S. EPA
Method SW8260B) that formerly was required.
The "current" (2002) 27-well monitoring program at the Long Prairie site includes the 18 monitoring
wells, 6 active and one inactive groundwater extraction wells sampled during scheduled monitoring
events in 2000 and 2001, together with municipal-supply wells CW3 and CW6. The locations of
these wells, and their status in the current monitoring program, are presented on Figure C.3.
Table C.5: Groundwater Monitoring Program at Long Prairie
Groundwater Contamination Superfund Sitea/
Well ID
Sampling Frequency
2000
2001
2002
October 2002
Monitoring Wells
BAL2B
BAL2C
MW1A
MW1B
MW2A
MW2B
MW2C
MW3A
MW3B
MW4A
MW4B
MW4C
MW5A
MW5B
MW6A
MW6B
MW6C
MW10A
MW11A
MW11B
MW11C
MW13C
MW14B
MW14C
b/
--
--
--
Annual
Annual
Annual
--
--
--
Annual
Annual
--
-_
Annual
Annual
Annual
Annual
-_
Annual
Annual
--
Annual
Annual
—
-_
-_
-_
Annual
Annual
Annual
--
--
--
Annual
Annual
--
-_
Annual
Annual
Annual
Annual
-_
Annual
Annual
--
Annual
Annual
—
-_
-_
-_
Annual
Annual
Annual
--
--
--
Annual
Annual
--
-_
Annual
Annual
Annual
Annual
-_
Annual
Annual
--
Annual
Annual
^c/
•/
^
^
^
^
^
^
v'
v'
•/
v'
v'
•/
^
^
^
^
^
^
v'
v'
•/
v'
C-25
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Table C.5: Groundwater Monitoring Program at Long Prairie
Groundwater Contamination Superfund Site
Well ID
Sampling Frequency
2000
2001
Well ID
2000
Monitoring Wells (continued)
MW15A
MW15B
MW16A
MW16B
MW17B
MW18A
MW18B
MW19B
Annual
Annual
-_
Annual
Annual
-_
-_
Annual
Annual
Annual
-_
Annual
Annual
-_
-_
Annual
Annual
Annual
-_
Annual
Annual
-_
-_
Annual
S
•/
•/
S
s
s
s
s
Groundwater Extraction Wells
RW1A
RW1B
RW1C
RW3
RW4
RW5
RW6
RW7
RW8
RW9
—
-_
-_
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
—
-_
-_
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
—
-_
-_
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
S
•/
•/
S
s
s
s
s
s
s
Municipal Water-Supply Wells
CW3
CW6
Quarterly
Quarterly
Quarterly
Quarterly
•/
S
Information from Parsons (2003c).
b/
A dash (--) indicates that the well was not included in the monitoring program for that year.
A check mark (v ) indicates that the well was sampled during the October 2002 monitoring event.
C2.5 SUMMARY OF LTMO EVALUATION COMPLETED USING MAROS TOOL
C2.5.1 Summary of Groundwater Analytical Data for Long Prairie Site Used in MAROS
Evaluation
The groundwater monitoring program at the Long Prairie site was evaluated using the MAROS tool,
applied to the results of sampling events completed during the period May 1996 through October
2002 (GSI, 2003b). The available monitoring network consists of 44 wells (31 monitoring wells, 3
municipal-supply wells, and 10 extraction wells) (Table C.5). The frequency of sampling the wells in
the network has varied through time — extraction wells generally have been sampled quarterly, while
monitoring wells generally have been sampled on a semi-annual or annual basis since the LTM plan
was adopted in 1996. Sampling at some wells was terminated for a period of several years before
they were sampled again in October 2002. As a consequence of the irregular sampling schedule,
some monitoring wells have been sampled on as few as five occasions during the seven-year period
C-26
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from 1996 to 2002. Sampling data from 1996 to 2002 were used for the detailed optimization
analysis, with a subset of these data used in some of the analyses.
Prior to beginning the MAROS evaluation, the sampling-results database provided by the MPCA's
environmental contractor was processed to remove duplicate data measurements by averaging the
primary and duplicate analytical results, and using this average to represent a single value detected at
that sampling point, during that sampling event. Concentration values that were below reporting
limits were replaced with surrogate values, selected to be the minimum reporting limit for that
particular constituent. Trace-level results were represented by their actual values. The processed
database contained results for each constituent measured in groundwater samples from each of the 44
wells in the vicinity of the Long Prairie site.
Although four COCs (PCE, TCE, cis-l,2-DCE, and VC; Section 3.2.3) historically have been
detected in groundwater at the site, PCE was used as an indicator compound, based on its widespread
detection at relatively elevated concentrations in wells across the site; and the MAROS evaluation of
the monitoring program at the Long Prairie site used only the results of analyses for PCE in
groundwater samples.
C2.5.2 Results of Evaluation Completed Using MAROS Tool
Sufficient data (the results of at least six sampling events) were available for 31 monitoring wells and
9 groundwater extraction wells within the time period 1996 to 2002 to assess temporal trends in PCE
concentrations. Application of the Mann-Kendall and linear regression temporal trend evaluation
methods (Appendix B) indicated that the trends in PCE concentrations at two of four of the
monitoring wells designated as "source area" wells were "Probably Decreasing", "Decreasing", or
"Stable", while PCE concentrations at seven of 10 extraction wells in the source area were "Probably
Decreasing", "Decreasing" or "Stable". This 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 in the "tail" part of the plume also were "Probably
Decreasing", "Decreasing" or "Stable". The results of the moment analysis (Appendix B) 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 (Appendix B)
indicated that the extent of PCE in groundwater of the upper outwash unit is stable or decreasing,
resulting in a recommendation that a monitoring strategy appropriate to a "Moderate" design
category (Appendix B) be adopted.
The sampling results available for 17 of the wells in the 44-well monitoring network were sufficient
to conduct a detailed spatial analysis using the Delaunay method (Appendix B). The results of the
spatial analysis indicated that none of the 17 wells was redundant. Other wells in the 44-well
monitoring network were examined qualitatively; and the results of evaluation using qualitative
considerations (GSI, 2003b) indicated that nine monitoring wells could be removed from the
monitoring network without significant loss of information (Table C.6; compare with the 2001 and
2002 monitoring programs). 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
existing 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
C-27
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concentrations throughout the network, so that no new monitoring wells were recommended for
installation (GSI, 2003b).
In some instances, the results of the sampling frequency optimization analysis, completed using the
modified CES method (Appendix B), were affected by the lack of consistent monitoring. The
sampling frequency analysis requires sampling results from a minimum of six separate monitoring
events at a particular sampling location. In instances when fewer than six separate results were
available for a particular monitoring well, the algorithm implemented in MAROS selected a
"conservative" sampling frequency (i.e., MAROS specified that samples should be collected from
that well more frequently than would otherwise have been the case). In some instances, the
recommendations generated by MAROS were examined qualitatively, by inspecting the historic and
recent PCE concentrations in samples from those wells, and occasionally the MAROS
recommendations were not adopted (GSI, 2003b). For example, PCE has not been measured at
concentrations above reporting limits in any of 14 samples historically collected from well CW6.
However, MAROS identified a spurious "Increasing" trend in PCE concentrations at well CW6 using
linear regression, which would have resulted in assignment of quarterly or semi-annual sampling
frequency for this well (Appendix B). The MAROS-assigned frequency was changed to biennial
sampling (Table C.6). Wells at which PCE has been detected at low frequencies (or not detected) and
low concentrations, but for which MAROS identified a trend that differed from a "Stable" trend,
using either linear regression or the Mann-Kendall test, are shaded in Table C.6, together with the
final recommended sampling frequencies.
Table C.6: Refined Groundwater Monitoring Program at Long Prairie
Groundwater Contamination Superfund Site Generated Using the MAROS Tool"
Well ID
Historic Sampling Frequency
2001
2002
Results of MAROS Evaluation
Remove/Retain13
Recommended
Sampling Frequency
Monitoring Wells
BAL2B
BAL2C
MW1A
MW1B
MW2A
MW2B
MW2C
MW3A
MW3B
MW4A
MW4B
MW4C
MW5A
MW5B
MW6A
MW6B
MW6C
MW10A
c/
—
—
—
Annual
Annual
Annual
—
—
—
Annual
Annual
—
—
Annual
Annual
Annual
Annual
—
—
—
—
Annual
Annual
Annual
—
—
—
Annual
Annual
—
—
Annual
Annual
Annual
Annual
Retain
Retain
Remove
Retain
Remove
Retain
Retain
Remove
Retain
Remove
Retain
Retain
Remove
Retain
Remove
Retain
Retain
Retain
Biennial
Biennial
—
Biennial
—
Annual
Annual
—
Biennial
—
Annual
Annual
—
Biennial
—
Annual
Annual
Annual
C-28
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Table C.6: Refined Groundwater Monitoring Program at Long Prairie
Groundwater Contamination Superfund Site Generated Using the MAROS Tool
Well ID
Historic Sampling Frequency
2001
2002
Results of MAROS Evaluation
Remove/Retain
Recommended
Sampling Frequency
Monitoring Wells (continued)
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
—
Annual
Annual
—
—
Annual
Remove
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Remove
Retain
Retain
Remove
Retain
Retain
—
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
—
Annual
Annual
—
Biennial
Biennial
Groundwater Extraction Wells
RW1A
RW1B
RW1C
RW3
RW4
RW5
RW6
RW7
RW8
RW9
—
—
--
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
—
—
--
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Remove
Remove
Remove
Retain
Retain
Retain
Retain
Retain
Retain
Retain
—
—
--
Annual
Biennial
Annual
Annual
Annual
Annual
Biennial
Municipal Water-Supply Wells
CW3
CW6
Quarterly
Quarterly
Quarterly
Retain
Retain
Biennial
Biennial
Information from GSI (2003b).
b/ "Remove" = MAROS recommended that the well be removed from the monitoring program.
"Retain" = MAROS recommended that the well continue to be sampled at the indicated frequency.
c/
A dash (--) indicates that the well is not included in the current or refined monitoring program.
The results of the data sufficiency evaluation, completed using power analysis methods (Appendix B)
suggest that the monitoring program is adequate to evaluate the extent of PCE in groundwater relative
to the compliance boundary 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 (Table C.6). Adoption of the optimized program
C-29
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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. Implementing 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 materials generated during sample
collection [e.g., purge water] as IDW.) The optimized program remains adequate to delineate the
extent of COCs in groundwater, and to monitor changes in the plume over time (GSI, 2003b).
C2.6 SUMMARY OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH
C2.6.1 Summary of Groundwater Analytical Data for Long Prairie Site Used in Three-
Tiered Approach
The groundwater monitoring program at the Long Prairie site also was evaluated using the three-
tiered approach, applied to the results of sampling events completed during the period May 1996
through October 2002 (Parsons, 2003c). Prior to the evaluation, the sampling-results database
provided by MPCA's environmental contractor was processed to remove duplicate data
measurements by retaining the greater of the primary and duplicate analytical results, and discarding
the lower value. The database that was utilized in the three-tiered evaluation of the groundwater
monitoring program for the Long Prairie site differed slightly from the database that was utilized in
the corresponding MAROS evaluation in the following respects:
• The method used in the three-tiered approach to deal with analytical results from duplicate
samples (retaining the greater of the primary and duplicate analytical results, and discarding
the lower value) differed from the method used in the MAROS evaluation (averaging the
primary and duplicate analytical results, and using this average to represent a single value;
Section C2.5.1).
• The method used in the three-tiered approach for dealing with concentration values that were
below reporting limits (value reported as "Not Detected"; Appendix B) differed from the
method used in the MAROS evaluation (assigning a surrogate value corresponding to the
reporting limit; Appendix B).
The processed database contained results for each constituent measured in groundwater samples from
each of the 44 wells in the vicinity of the Long Prairie site. Depending upon the number of times a
particular well was sampled, from 1 (well sampled once) to 29 (well sampled 29 times) records were
available for each constituent at a particular well.
The primary COCs in groundwater at the Long Prairie site are PCE, TCE, and c/s-l,2-DCE (Section
C2.3). The occurrence of these three COCs in groundwater at the Long Prairie site, based on data
collected from 33 monitoring wells during the period May 1996 through October 2002, is
summarized in Table C.7. The data summarized in Table C.7 exclude results for the extraction wells
(with the exception of inactive extraction well RW4, which is sampled annually as a monitoring well)
and municipal water-supply wells CW3 and CW6.
C-30
-------
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PCE is the COC that historically has been detected at the highest concentrations in groundwater at the
Long Prairie site, with PCE concentrations exceeding the MCL for PCE (5 ng/L) (USEPA, 2000) in
approximately 33 percent of samples. PCE has been detected frequently (in 39 percent of samples),
has been measured in groundwater samples from 20 of the 33 wells included in this summary, and
has exceeded its MCL in samples from 14 of these wells. cz's-l,2-DCE (a product of the reductive
dechlorination of PCE) also is widespread in groundwater at the site, and has been detected in 44
percent of samples. However, detected concentrations of c/s-l,2-DCE have exceeded its MCL (70
(ig/L) in only about 1 percent of samples. The other primary COC (TCE) has been detected less
frequently, at lower concentrations, and in samples from fewer wells than have PCE and c/s-l,2-DCE
(Table C.7). As a consequence of the widespread detection of PCE, at relatively elevated
concentrations in groundwater across the site, PCE was selected to be an indicator compound.
Although the other primary COCs (TCE and czs-l,2-DCE) were considered, together with PCE, in
the qualitative and temporal stages of the three-tiered evaluation, the spatial-statistical stage of the
three-tiered evaluation of the monitoring program at the Long Prairie site used only the results of
analyses for PCE in groundwater samples.
Sixteen of the 44 wells sampled in October 2002 were included in the spatial-statistical evaluation.
Although samples from the OU1 extraction wells have been used historically to define the extent of
contaminants in groundwater, data from extraction wells are not appropriate for use in a kriging
analysis because they represent COC concentrations averaged over the volume within the well's
capture zone, and thus are not point-specific, nor temporally discrete; the recovery wells also
typically are screened across a longer interval than are the site monitoring wells. Similarly, city wells
CW3 and CW6 were excluded from the spatial analysis because they also are active extraction wells.
Kriging was used to predict concentrations over a two-dimensional surface, and thus including data
from multiple co-located wells screened at different depths is not appropriate. In this application, the
well within each cluster of well having the highest concentration of PCE was retained for use in the
geostatistical evaluation. Of the clustered wells, the "B" zone wells usually displayed the highest
PCE concentrations in October 2002 and were included in the spatial analysis; however, the "C" zone
well MW6C from the MW6 cluster also was included in the spatial analysis.
C2.6.2 Results of Evaluation Completed Using Three-Tiered Approach
The results of the three-tiered evaluation (Parsons, 2003 c) 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 suggest that the current monitoring program (18 monitoring wells, 6
active extraction wells, one inactive extraction well, and municipal water-supply wells CW3 and
CW6 included in the 2002 sampling schedule) could be further refined by removing four of the 27
wells now in the LTM program, and adding three existing wells that currently are not included in the
program (Table C.8; compare with the 2001 and 2002 monitoring programs). 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. This would
represent a 29-percent reduction in the number of samples collected and analyzed annually, as
compared with the current program. 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 approach is projected to result in savings ranging from about $1,500 per year to about $4,200
per year, as compared with the current program (Parsons, 2003c).
C-32
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Table C.8: Refined Groundwater Monitoring Program at Long Prairie Groundwater
Contamination Superfund Site Generated Using the Three-Tiered Approach"7
Well ID
Historic Sampling Frequency
2001
2002
Results of Three-Tiered Evaluation
Remove/Retain13
Recommended
Sampling Frequency
Monitoring Wells
BAL2B
BAL2C
MW1A
MW1B
MW2A
MW2B
MW2C
MW3A
MW3B
MW4A
MW4B
MW4C
MW5A
MW5B
MW6A
MW6B
MW6C
MW10A
MW11A
MW11B
MW11C
MW13C
MW14B
MW14C
MW15A
MW15B
MW16A
MW16B
MW17B
MW18A
MW18B
MW19B
c/
--
—
—
Annual
Annual
Annual
—
—
—
Annual
Annual
—
—
Annual
Annual
Annual
Annual
—
Annual
Annual
—
Annual
Annual
Annual
Annual
—
Annual
Annual
—
—
Annual
—
--
—
—
Annual
Annual
Annual
—
—
—
Annual
Annual
—
—
Annual
Annual
Annual
Annual
—
Annual
Annual
—
Annual
Annual
Annual
Annual
—
Annual
Annual
—
—
Annual
Remove
Remove
Remove
Remove
Remove
Retain
Remove
Remove
Remove
Remove
Retain
Retain
Remove
Retain
Remove
Retain
Retain
Retain
Remove
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Remove
Retain
Retain
Remove
Retain
Retain
—
--
—
—
—
Annual
—
—
—
—
Annual
Annual
—
Annual
—
Annual
Annual
Annual
—
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
—
Annual
Annual
—
Biennial
Biennial
Groundwater Extraction Wells
RW1A
RW1B
RW1C
RW3
RW4
—
—
—
Quarterly
Annual
—
—
—
Quarterly
Annual
Remove
Remove
Remove
Retain
Retain
—
—
—
Annual
Biennial
C-33
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Table C.8: Refined Groundwater Monitoring Program at Long Prairie Groundwater
Contamination Superfund Site Generated Using the Three-Tiered Approach
Well ID
Historic Sampling Frequency
2001
2002
Results of Three-Tiered Evaluation
Remove/Retain
Recommended
Sampling Frequency
Groundwater Extraction Wells (continued)
RW5
RW6
RW7
RW8
RW9
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Retain
Retain
Retain
Retain
Retain
Annual
Annual
Annual
Annual
Biennial
Municipal Water-Supply Wells
CW3
CW6
Quarterly
Quarterly
Quarterly
Retain
Retain
Biennial
Biennial
Information from Parsons (2003c).
"Remove" = Three-tiered evaluation recommended that the well be removed from the monitoring program.
"Retain" = Three-tiered evaluation recommended that the well continue to be sampled at the indicated frequency.
A dash (--) indicates that the well is not included in the current or refined monitoring program.
C-34
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C3.0 MCCLELLAN AFB OU D, CALIFORNIA
The following summary of information regarding the location, operational history, geology, and
hydrogeology of OU D at McClellan AFB, the current monitoring program at OU D, available
chemical data that were used in the monitoring-program evaluations, and the results of the LTMO
evaluations, has been excerpted from Parsons (2003d) and GSI (2003c). Copies of both documents
are included in Appendix D-3; the reader is referred to the Appendix for additional details.
C3.1 SITE DESCRIPTION AND OPERATIONAL HISTORY
McClellan AFB is located approximately 7 miles northeast of downtown Sacramento, California.
The installation covers approximately 3,000 acres and is bounded by the city of Sacramento on the
west and southwest, the unincorporated areas of Antelope on the north, Rio Linda on the northwest,
and North Highlands on the east. OU D is located 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.
Historic waste-disposal practices included the use of burial pits for the disposal and/or burning of
these materials. Fifteen sites that were used as waste pits from the mid-1950s through the 1970s are
located at OU D. In 1985, the "Area D" cap was constructed over several waste pits, to reduce the
infiltration of precipitation through the waste pits, thereby also reducing the migration of
contaminants from the vadose zone to groundwater at the site. Prior to 1985, three waste pits were
excavated to remove the sludge waste.
McClellan AFB was included on the Superfund NPL in 1987. A single OU was designated for
groundwater at the Base, and an Interim Record of Decision (IROD), which specifies groundwater
extraction and treatment as the interim remedy for groundwater, was signed for the Base-wide
Groundwater OU (GWOU) in 1995. In 1995, McClellan AFB was recommended for closure under
the Base Realignment and Closure Act (BRAC); and the installation was closed in July 2001.
Ongoing environmental restoration activities are being directed by the Air Force Real Property
Agency (AFRPA) (formerly the Air Force Base Conversion Agency).
C3.2 GEOLOGY AND HYDROGEOLOGY
The sediments in the upper few hundred feet of the subsurface beneath the Base consist of coalescing
deposits laid down by fluvial systems of various sizes and competence that flowed generally from
northeast to southwest or west. Geologic materials are primarily sand, silt, and clay, generally poorly
sorted, with localized occurrences of gravel in the southern part of the Base. The sediments were
deposited by streams, producing morphologically irregular lenses and strata that are laterally and
vertically discontinuous. Distinguishing among units, or correlating stratigraphy over distances
greater than a few tens of feet, is difficult, as a consequence of the coalescing and intercalating nature
of the sediments.
Although the stratigraphy of the sediments beneath McClellan AFB is complex, the juxtaposed and
intercalated strata of sand, silt, clay, and gravel comprise a single water-bearing unit (the "upper"
water-bearing unit). The geologic and hydraulic properties of the upper water-bearing unit vary over
C-35
-------
short distances, and the more permeable intervals are hydraulically-interconnected laterally and
vertically, so that in general, groundwater movement (and associated advective migration of
contaminants) may occur throughout the water-bearing unit. The upper unit beneath McClellan AFB
has been divided into the vadose (unsaturated) zone and five monitoring zones (Zones A through E,
from shallowest to deepest) below the water table, distinguished on the basis of general hydraulic
characteristics. Generally, the strata associated with the various zones dip to the west, and increase in
thickness from east to west. As a consequence of the heterogeneity of the sediments beneath the
Base, and the relative capacities of different deposits to transmit water, it is entirely possible for two
adjacent wells screened at different depth intervals to be completed within the same monitoring zone,
or for two wells screened at similar depths to be completed in different monitoring zones.
The thickness of monitoring zone A ranges from 9 to 50 feet, and groundwater occurs in the A zone
under unconfined conditions. The thickness of monitoring zone B ranges from 40 to 75 feet, and
groundwater in this zone appears to occur under partially confined conditions. Monitoring wells at
OU D have been constructed only in the A and B monitoring zones; therefore, no information is
available regarding the deeper monitoring zones at OU D.
The depth to the water table beneath McClellan AFB ranges between about 90 and 110 feet bgs. At
OU D, the depth to groundwater within the upper unit varies from approximately 99 to 102 feet bgs.
As a consequence of the relatively great depth to the water table, surface streams are not in direct
hydraulic communication with the groundwater system beneath the Base. Water-table elevations
have declined at rates ranging from 1 to 2 feet per year during the past 50 years, and are expected to
continue to decline at a rate of about 2 feet per year as a consequence of large-scale groundwater
production for industrial, irrigation, and municipal uses in the Sacramento area.
Under natural conditions, prior to installation and operation of the OU D groundwater extraction
system, groundwater typically moved from northeast to south or southwest in the A monitoring zone,
and from north to south in the B monitoring zone. The local directions of groundwater movement
beneath OU D currently are strongly influenced by the groundwater extraction system operating at
the site. Groundwater movement generally is directed radially inward toward the extraction wells
(EWs). The largest horizontal hydraulic gradients in the groundwater system at OU D occur near
active EWs. Vertical gradients within that part of the groundwater system influenced by active
groundwater extraction at OU D generally are downward, similar to vertical gradients that exist
between the A and B monitoring zones in other parts of the Base. At distances greater than about
1,000 feet from the extraction system, vertical gradients may be directed upward or downward,
depending on local potentiometric conditions. The calculated horizontal advective velocity of
groundwater movement in the A and B monitoring zones at OU D ranges between about 14 and 30
ft/year; and the bulk value of horizontal hydraulic conductivity of the saturated materials within the
upper water-bearing unit is about 5 to 15 times greater than the vertical hydraulic conductivity,
indicating that advective groundwater movement beneath OU D occurs primarily in the horizontal
plane.
C3.3 NATURE AND EXTENT OF CONTAMINANTS IN GROUNDWATER
The COCs in groundwater targeted by the current LTM program at OU D are exclusively CAHs,
including PCE, TCE, cis-l,2-DCE, and 1,2-dichloroethane (1,2-DCA), with 1,1-DCA, 1,1-DCE,
1,1,1-TCA, and vinyl chloride also detected, but at lower concentrations and/or lower frequencies.
Some evidence suggests that one or more of these CAHs may remain in vadose-zone soils near the
former waste pits at OU D as dense, non-aqueous-phase liquids (DNAPLs); and that a free or residual
DNAPL remains in the subsurface near or below the water table in some locations at OU D. Residual
C-36
-------
DNAPL near or below the water table at OU D may persist as a continuing source of dissolved
contaminants for an extended period of time. Dissolved CAHs originating from sources near the OU
D waste pits 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 C.4).
The remediation systems currently operating at OU D include an SVE system, a groundwater
extraction and treatment system, and associated monitoring networks. The current groundwater
extraction system in OU D consists of six EWs (EW-73, and EW-83 through EW-87), five of which
are operational. (Well EW-84 was removed from service in August 1997.) All EWs were installed to
a depth of about 160 feet bgs, and are fully screened across both the A and B monitoring zones (and
consequently extract groundwater from both zones). Although low concentrations of VOCs were
detected historically in groundwater samples collected from off-Base wells located northwest of OU
D, no contaminants have been detected in groundwater samples from off-Base monitoring wells to
the west or northwest of OU D since 1995, possibly because dissolved contaminants have been
hydraulically captured by the OU D groundwater extraction system. In general, the concentrations of
CAHs dissolved in groundwater have declined during the period of system operation. However, low
concentrations of VOCs continue to be detected sporadically at locations distal from potential source
areas, in the west and southwestern parts of OU D.
C3.4 CURRENT GROUNDWATER MONITORING PROGRAM AT MCCLELLAN AFB OU D
In 1996, the Groundwater Monitoring Plan (GWMP) for all on- and off-Base wells was established
under the Long-Term Monitoring Program (LTMP) to update the GSAP and to support GWOU
IROD activities. In accordance with the requirements of the GWMP, 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 MCLs in monitoring
zones A and B. Groundwater monitoring data also are used to evaluate contaminant mass-removal
rates. The field sampling plan identifies the wells to be sampled in OU D based on the rationale and
decision logic presented in the GWMP; the monitoring frequency and sampling rationale for each
well are continually evaluated, and can change as new sampling data are obtained. Based on
groundwater-quality data collected through the first quarter of 2002, 6 EWs and 45 monitoring wells
(Figure C.4) have been identified as sampling points for OU D groundwater.
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). The six EWs are sampled annually (Table
C.9). Currently, 22 of the 32 wells that monitor Zone A groundwater at OU D are sampled
biennially, and 10 are sampled annually. Twelve of the 13 Zone B wells are sampled biennially, and
the remaining well is sampled annually. (Note that Table C.9 is based upon information provided in
Parsons [2003d].) 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 period. All samples from the monitoring and extraction wells are analyzed for VOCs by
U.S. EPA Method SW8260B.
C-37
-------
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5
en o
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-
'
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-*•"". - > ?'-is * •":•':- * •
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Table C.9: Current Groundwater Monitoring Program at McClellan AFB OU Da
Well ID
Completion Zone
Assumed for Evaluation
Sampling
Frequency
Zone A Monitoring Wells
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
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
A
A
A
A
A
A*b/
A*
A*
A*
A*
A
A*
A*
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A*
A
A
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Zone B Monitoring Wells
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-1001
MW-1003
MW-1010
B
B
B*
B*
B
B
B
B
B*
B*
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
C-39
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Table C.9: Current Groundwater Monitoring Program at McClellan AFB OU D
Well ID
Completion Zone
Assumed for Evaluation
Sampling
Frequency
Zone B Monitoring Wells (continued)
MW-1027
MW-1028
MW-1043
B
B
B
Biennial
Biennial
Biennial
Groundwater Extraction Wells
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
A/B
A/B
A/B
A/B
A/B
A/B
Annual
Annual
Annual
Annual
Annual
Annual
Information from Parsons (2003d).
b/
* = Assumed monitoring zone assigned based on criteria presented by Parsons (2003d).
C3.5 SUMMARY OF LTMO EVALUATION COMPLETED USING MAROS TOOL
C3.5.1 Summary of Groundwater Analytical Data for McClellan AFB OU D Used in
MAROS Evaluation
The groundwater database for McClellan AFB OU D contains the results of sampling events
completed during the period April 1990 through August 2001 (GSI, 2003c). Sampling results for
2001 were excluded from the database used for the MAROS evaluation, because a different sampling
technique (passive diffusion sampling) was being tested during that period, and the comparability of
the 2001 analytical data with historic data (collected using other techniques) was regarded as
uncertain. The available monitoring network consists of 32 monitoring wells completed in Zone A,
13 monitoring wells completed in Zone B, and six extraction wells completed in both Zone A and
Zone B.
Prior to beginning the MAROS evaluation, the sampling-results database provided by the Base was
processed to remove analytical data collected during 2001, and to remove duplicate data
measurements by averaging the primary and duplicate analytical results, and using this average to
represent a single value detected at that sampling point, during that sampling event. Concentration
values that were below reporting limits were replaced with surrogate values, selected to be the
minimum reporting limit for that particular constituent. Trace-level results were represented by their
actual values. The processed database contained results for each constituent measured in
groundwater samples from each of the 51 wells at OU D.
Although four primary COCs (PCE, TCE, c/s-l,2-DCE, and 1,2-DCA; Section C3.3) are present in
groundwater at the site, with other CAHs occasionally present at low concentrations, TCE was used
as an indicator compound, based on its widespread detection at relatively elevated concentrations in
wells across the site; and the MAROS evaluation of the monitoring program at McClellan AFB OU D
used only the results of analyses for TCE in groundwater samples.
C-40
-------
C3.5.2 Results of Evaluation Completed Using MAROS Tool
Sufficient data (the results of at least six sampling events) were available for all 32 monitoring wells
completed in Zone A, all 13 monitoring wells completed in Zone B, and all 6 groundwater extraction
wells, for the time period April 1990 through 2000, to assess temporal trends in TCE concentrations.
Application of the Mann-Kendall and linear regression temporal trend evaluation methods (Appendix
B) indicated that the trends in TCE concentrations at nine of ten of the A-zone monitoring wells
designated as "source area" wells were "Probably Decreasing", "Decreasing", or "Stable", while TCE
concentrations at five of six extraction wells in the source area were "Probably Decreasing",
"Decreasing" or "Stable". This indicated that the extent and concentrations of TCE in groundwater at
the OU D source area probably are decreasing (GSI, 2003c).
The trends in TCE concentrations at nine of 22 A-zone monitoring wells and at six of 12 B-zone
monitoring wells in the "tail" part of the plume also were "Probably Decreasing", "Decreasing", or
"Stable", although there appear to be no trends in TCE concentrations at most B-zone monitoring
wells. The absence of identifiable trends in TCE concentrations at many locations in the "tail" and
off-axis parts 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 (Appendix B) 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 is 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"1 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 sampling locations, resulting from an inconsistent
monitoring program through time (GSI, 2003c). The evaluation of overall plume stability (Section
2.3.2) 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 (Appendix B).
The sampling results available for 31 A-zone monitoring wells and for 12 B-zone monitoring wells
were used to conduct a detailed spatial analysis based on the Delaunay method (Appendix B). The
results of the spatial analysis indicated that 3 of the 31 A-zone wells were candidates for removal
from the monitoring network, and that 2 of the B-zone wells were candidates for removal. These
recommendations were examined qualitatively, considering historic detections of COCs in the wells,
and the possible need for continued characterization of the extent of COCs in groundwater at OU D;
and a total of 5 monitoring wells (3 A-zone wells and 2 B-zone wells) were recommended for
removal from the monitoring program (Table C. 10; compare the current monitoring program with the
MAROS recommendations). Removal of the recommended 5 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 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).
C-41
-------
Table C.10: Refined Groundwater Monitoring Program at McClellan AFB OU D
Generated Using the MAROS Toola/
Well ID
Current
Sampling Frequency
Results of MAROS Evaluation
Remove/Retain15'
Recommended
Sampling Frequency
Zone A Monitoring Wells
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
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
Annual
Annual
Biennial
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Retain
Retain
Retain
Remove
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Remove
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Remove
Retain
Retain
Retain
Annual
Annual
Annual
c/
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
—
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
—
Biennial
Biennial
Biennial
Zone B Monitoring Wells
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-1001
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Retain
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
C-42
-------
Table C.10: Refined Groundwater Monitoring Program at McClellan AFB OU D
Generated Using the MAROS Tool
Well ID
Current
Sampling Frequency
Results of MAROS Evaluation
Remove/Retain
Recommended
Sampling Frequency
Zone B Monitoring Wells (continued)
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
Biennial
Biennial
Biennial
Biennial
Biennial
Remove
Retain
Retain
Remove
Retain
—
Biennial
Biennial
—
Biennial
Groundwater Extraction Wells
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
Annual
Annual
Annual
Annual
Annual
Annual
Retain
Retain
Retain
Retain
Retain
Retain
Annual
Annual
Annual
Annual
Annual
Annual
Information from GSI (2003c).
b/ "Remove" = MAROS recommended that the well be removed from the monitoring program.
"Retain" = MAROS recommended that the well continue to be sampled at the indicated frequency.
A dash (--) indicates that the well is not included in the refined monitoring program.
Not all of the wells identified by MAROS as candidates for removal were eliminated from the refined
monitoring program. The results of application of the MAROS algorithm indicated that well MW-72
was a candidate for removal; however, qualitative considerations suggested that MW-72 should be
retained in the monitoring program. The concentrations of TCE in samples from well MW-72 have
been greater than the MCL concentration for TCE (as of the 2000 sampling event), and the well is
located on the centerline of the CAH plume and was used as the basis for the risk-based power
analysis for containment at the compliance boundary. Well MW-1041 was not recommended by
MAROS as a candidate for removal; however, well MW-1041 is located near the maximum
upgradient extent of CAHs in groundwater at OU D, together with wells MW-1042, MW-1064, MW-
1043 and MW-1010, far cross-gradient wells MW-237, MW-1026, MW-1027, and MW-1028, and
far down-gradient well MW-350 (Figure C.4). Well MW-1041 was judged to be redundant with well
MW-1042 on qualitative grounds, and was recommended for removal from the monitoring program
(Table C.10). The possibility of removing other periphery monitoring wells also was examined, and
it was concluded (GSI, 2003c) that although the MAROS analysis indicated that new wells could be
used to replace the periphery wells, 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.
In nearly all instances, the results of the sampling frequency optimization analysis at McClellan AFB
OU D, completed using the modified CES method (Appendix B), were adversely affected by the lack
of consistent monitoring. The sampling frequency analysis requires sampling results from a
minimum of six separate monitoring events at a particular sampling point. Historically, sampling
frequencies for all wells at OU D have been irregular, so that no more than 5 to 7 records are
C-43
-------
available for numerous monitoring wells throughout the entire period from 1990 to 2000. In
instances when fewer than six separate results were available for a particular monitoring well, or
when a temporal trend in TCE concentrations could not be identified, the algorithm implemented in
MAROS selected a "conservative" sampling frequency (i.e., MAROS specified that samples should
be collected from that well more frequently than would otherwise have been the case). Accordingly,
all recommendations generated by MAROS were examined qualitatively, by inspecting the historic
and recent TCE concentrations in samples from those wells, and as a result, very few of the MAROS
recommendations regarding sampling frequency were adopted. Rather, the subsequent qualitative
evaluation that was conducted using the COC concentrations detected historically in samples from
OU D monitoring wells was felt to generate more reasonable recommendations regarding sampling
frequency (GSI, 2003c).
The results of the data-sufficiency evaluation, completed using power analysis methods (Appendix B)
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
sampled annually, and 29 monitoring wells sampled biennially (Table C.10). 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. 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, based on information provided by facility
personnel (GSI, 2003c), to result in savings of approximately $300 per year. (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.) Although projected
annual cost savings are small, optimization of the monitoring program in accordance with the
recommendations generated by MAROS could result in moderate cost savings over the life of the
LTM program. 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, 2003 c).
C3.6 SUMMARY OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH
C3.6.1 Summary of Groundwater Analytical Data for McClellan AFB OU D Used in
Three-Tiered Approach
The OU D groundwater monitoring program also was evaluated using the three-tiered approach,
applied to the results of sampling events completed during the period April 1990 through August
2001 (Parsons, 2003d), including the period of time during which passive diffusion sampling was
conducted. Prior to the evaluation, the sampling-results database provided by the Base was processed
to remove duplicate data measurements by retaining the greater of the primary and duplicate
analytical results, and discarding the lower value. The processed analytical database contained from
5 to 18 sampling results for each constituent, at each of the 51 wells in the current OU D monitoring
program. The database that was utilized in the three-tiered evaluation of the groundwater monitoring
program for McClellan AFB OU D differed slightly from the database that was utilized in the
corresponding MAROS evaluation in the following respects:
C-44
-------
• The three-tiered approach was applied to a database having a slightly longer historical period
of record, extending from April 1990 through August 2001, versus a historical period of
record extending from April 1990 through the end of 2000 utilized in the MAROS evaluation
(Section C3.5.1). The database utilized in the three-tiered evaluation included the analytical
results for samples that were collected using passive diffusion sampling methods.
• The method used in the three-tiered approach to deal with analytical results from duplicate
samples (retaining the greater of the primary and duplicate analytical results, and discarding
the lower value) differed from the method used in the MAROS evaluation (averaging the
primary and duplicate analytical results, and using this average to represent a single value;
Section C3.5.1).
• The method used in the three-tiered approach for dealing with concentration values that were
below reporting limits (reporting the value as "Not Detected"; Appendix B) differed from the
method used in the MAROS evaluation (assigning a surrogate value corresponding to the
reporting limit; Appendix B).
The occurrence of the four primary COCs identified in the GWOU IROD for groundwater at OU D
(TCE, 1,2-DCA, cis-l,2-DCE, and PCE) is summarized in Table C.ll. TCE historically has been
detected most frequently (in 63 percent of samples) and at the highest concentrations of any COC in
groundwater at McClellan AFB OU D, with TCE concentrations exceeding the MCL for TCE (5
(ig/L) in approximately 38 percent of samples. TCE has been detected in groundwater samples from
46 of the 51 wells in the monitoring program, and has exceeded its MCL in samples from 26 of these
wells. The other primary COCs (1,2-DCA, czs-l,2-DCE, and PCE) have been detected less
frequently, at lower concentrations, and in samples from fewer wells than has TCE (Table C.ll);
therefore, TCE was selected as an indicator compound, based on its widespread detection at relatively
elevated concentrations in wells across the site. Although the other primary COCs (1,2-DCA, cis-
1,2-DCE, and PCE) were considered, together with TCE, in the qualitative and temporal stages of the
three-tiered evaluation, the spatial-statistical evaluation of the monitoring program at McClellan AFB
OU D used only the results of analyses for TCE in groundwater samples.
The A-zone wells were considered separately from the B-zone wells in the spatial analysis because
even though the A and B zones are hydraulically connected, the A- and B-zone wells generally are
completed in shallower and deeper zones, respectively, of the water-bearing unit. The number of
wells completed in the B zone (13) was considered to be too few for use in a spatial-statistical
analysis; and active extraction wells also were excluded from the spatial analysis.
C3.6.2 Results of Evaluation Completed Using Three-Tiered Approach
The spatial-statistical stage of the three-tiered evaluation was limited to monitoring wells completed
in the A zone, because the number of wells completed in the B zone was not sufficient to complete a
separate spatial evaluation for that zone (Parsons, 2003d). The most recent validated analytical data
available (sampling results from the February 2000 or March 2001 monitoring events) were used in
spatial-statistical evaluation, because an "instantaneous" representation of the spatial distribution of
the variable of interest (TCE in groundwater) is required for the geostatistical analysis. As
semivariogram models were calculated for TCE (a pre-requisite for the spatial evaluation),
considerable scatter of the data was apparent during fitting of the models. Several data
transformations (including a log transformation) were applied in attempts to obtain a reasonable
semivariogram model. Ultimately, the concentration data were transformed to rank statistics, and
nonparametric techniques were utilized to develop a semivariogram model. The inability to fit a
C-45
-------
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parametric semivariogram model is a further illustration of the high degree of spatial variability in
TCE concentrations, which also was noted during the MAROS evaluation of the monitoring program
at McClellan AFB OU D (Section C3.5.2).
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 (Table C.I2; compare the current monitoring program with the recommendations
generated during the three-tiered evaluation). 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 (Section C3.5.2). However, the conclusion of
the MAROS evaluation was 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" (GSI, 2003c).
If this refined monitoring program, 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 (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).
Table C.12: Refined Groundwater Monitoring Program at McClellan AFB OU D
Generated Using the Three-Tiered Approach"7
Well ID
Current
Sampling Frequency
Results of Three-Tiered Evaluation
Remove/Retain13'
Recommended
Sampling Frequency
Zone A Monitoring Wells
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Retain
Retain
Retain
Retain
Retain
Retain
Remove
Remove
Retain
Remove
Remove
Remove
Annual
Annual
Annual
Biennial
Annual
Annual
c/
—
Biennial
—
-
-
C-47
-------
Table C.12: Refined Groundwater Monitoring Program at McClellan AFB OU D
Generated Using the Three-Tiered Approach
Well ID
Current
Sampling Frequency
Results of Three-Tiered Evaluation
Remove/Retain
Recommended
Sampling Frequency
Zone A Monitoring Wells (continued)
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
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Retain
Remove
Retain
Retain
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Remove
Annual
—
Biennial
Biennial
—
—
--
—
—
—
—
—
—
—
—
—
—
—
—
--
Zone B Monitoring Wells
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Retain
Retain
Retain
Remove
Retain
Retain
Remove
Remove
Remove
Remove
Retain
Remove
Retain
Biennial
Biennial
Annual
—
Biennial
Biennial
—
—
—
—
Biennial
—
Biennial
Groundwater Extraction Wells
EW-73
EW-83
Annual
Annual
Retain
Retain
Annual
Annual
C-48
-------
Table C.12: Refined Groundwater Monitoring Program at McClellan AFB OU D
Generated Using the Three-Tiered Approach
Well ID
Current
Sampling Frequency
Results of Three-Tiered Evaluation
Remove/Retain
Recommended
Sampling Frequency
Groundwater Extraction Wells (continued)
EW-84
EW-85
EW-86
EW-87
Annual
Annual
Annual
Annual
Retain
Retain
Retain
Retain
Annual
Annual
Annual
Annual
Information from Parsons (2003d).
b/ "Remove" = Three-tiered evaluation recommended that the well be removed from the monitoring program.
"Retain" = Three-tiered evaluation recommended that the well continue to be sampled at the indicated frequency.
A dash (--) indicates that the well is not included in the refined monitoring program.
C-49
-------
APPENDIX D
ORIGINAL MONITORING PROGRAM
OPTIMIZATION REPORTS
BY
GROUNDWATER SERVICES, INC. AND PARSONS
-------
APPENDIX D-l
OPTIMIZATION OF MONITORING PROGRAM
AT
FORT LEWIS LOGISTICS CENTER, WASHINGTON
-------
G-2236-15
2.0
Pierce County, Washington
to
Air
April 7, 2003
Groundwater Services, Inc.
2211 Norfolk, Suite 1000, Houston, Texas 77098
-------
V
GROUNDWATER
SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER
Pierce County, Washington
Prepared
by
Groundwater Services, Inc.
2211 Norfolk, Suite 1000
Houston, Texas 77098
(713)522-6300
GSI Job No. G-2236
Revision No. 2
Date: 4/07/03
-------
GSI Job No. G-2236-15 GROUNDWATER
January 15, 2003 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK
OPTIMIZATION, FORT LEWIS LOGISTICS CENTER
Pierce County, Washington
Table of Contents
Executive Summary 1
Project Objectives 1
Results 2
1.0 Introduction 4
1.1 Geology/Hydrogeology 4
1.2 Remedial Action 4
2.0 MAROS Methodology 6
2.1 MAROS Conceptual Model 6
2.2 Data Management 7
2.3 Site Details 7
2.4 Data Consolidation 8
2.5 Overview Statistics: Plume Trend Analysis 8
2.5.1 Mann-Kendall Analysis 9
2.5.2 Linear Regression Analysis 9
2.5.3 Overall Plume Analysis 10
2.5.4 Moment Analysis 11
2.6 Detailed Statistics: Optimization Analysis 12
2.6.1 Well Redundancy Analysis- Delaunay Method 13
2.6.2 Well Sufficiency Analysis - Delaunay Method 14
2.6.3 Sampling Frequency- Modified CES Method 14
2.6.4 Data Sufficiency - Power Analysis 15
3.0 Site Results 17
3.1 Data Consolidation 17
3.2 Overview Statistics: Plume Trend Analysis 18
3.2.1 Mann-Kendall/Linear Regression Analysis 18
3.2.2 Moment Analysis 19
3.2.3 Overall Plume Analysis 19
3.3 Detailed Statistics: Optimization Analysis 20
3.3.1 Well Redundancy Analysis 21
3.3.2 Well Sufficiency Analysis 22
3.3.3 Sampling Frequency Analysis 23
3.3.4 Data Sufficiency - Power Analysis 24
4.0 Summary and Recommendations 26
Fort Lewis Logistics Center ^^1 MAROS 2.0 Application
Pierce County, Washington Monitoring Network Optimization
-------
GSIJobNo. G-2236-15
January 15, 2003
GROUNDWATER
SERVICES, INC.
Tables
Table 1 The Mann-Kendall Analysis Decision Matrix
Table 2 The Linear Regression Analysis Decision Matrix
Table 3 Sampling Locations Used in the MAROS Analysis
Table 4 Upper Aquifer Site-Specific Parameters
Table 5 Results of Upper Aquifer Trend Analysis
Table 6 Sampling Location Determination Results - Delaunay Method
Table 7 Sampling Frequency Determination Results - Modified CES
Table 8 Selected Plume Centerline Wells for Risk-Based Site Cleanup Evaluation -
Power Analysis
Table 9 Plume Centerline Concentration Regression Results - Power Analysis
Table 10 Risk-Based Site Cleanup Evaluation Results - Power Analysis
Table 11 Summary of MAROS Sampling Optimization Results
Figures
Figure 1 Upper Aquifer Groundwater Monitoring Network
Figure 2 MAROS Decision Support Tool Flow Chart
Figure 3 MAROS Overview Statistics Trend Analysis Methodology
Figure 4 Decision Matrix for Determining Provisional Frequency
Figure 5 Upper Aquifer TCE Mann-Kendall Trend Results
Figure 6 Upper Aquifer TCE Linear Regression Trend Results
Figure 7 Upper Aquifer TCE Mann-Kendall Trend Results, Extraction Wells
Figure 8 Upper Aquifer TCE Linear Regression Trend Results, Extraction Wells
Figure 9 Upper Aquifer TCE First Moment (Center of Mass) Over Time
Figure 10 The TCE plume drawn with September 2001 data: (A) before optimization
and (B) after optimization
Figure 11 Well Sufficiency Results: Recommendation for New Sampling Locations
Appendices
Appendix A: Upper Aquifer Fort Lewis Historical TCE Maps
Appendix B: Upper Aquifer Fort Lewis MAROS 2.0 Reports
Fort Lewis Logistics Center
Pierce County, Washington
MAROS 2.0 Application
Monitoring Network Optimization
-------
GROUNDWATER
January 75, 2003 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER
EXECUTIVE SUMMARY
Long-term monitoring programs, whether applied for process control, performance
measurement, or compliance purposes, require large scale data collection effort and
time commitment, making their cumulative costs very high. With the increasing use of
risk-based goals and natural attenuation in recent years as well as the move toward
long-term closure upon completion of cleanup activities, the need for better-designed
long-term monitoring plans that are cost-effective, efficient, and protective of human and
ecological health has greatly increased. The Monitoring and Remediation Optimization
System (MAROS) methodology provides an optimal monitoring network solution, given
the parameters within a complicated groundwater system which will increase its
effectiveness. By applying statistical techniques to existing historical and current site
analytical data, as well as considering hydrogeologic factors and the location of potential
receptors, the software suggests an optimal plan along with an analysis of individual
monitoring wells for the current monitoring system. This report summarizes the findings
of an application of the MAROS 2.0 software to the Upper Aquifer long-term monitoring
well network at the Fort Lewis Logistics Center in Pierce County, Washington.
The primary constituent of concern at the site is trichloroethylene (TCE) which is
analyzed at 43 monitoring wells in the Upper Aquifer original well network, as of 2001
(Figure 1). All monitoring wells, unless abandoned, have been sampled quarterly in the
Upper Aquifer for TCE since the implementation of the original long-term monitoring
plan. By September 2001, 24 sampling events had been carried out at the site. The
historical TCE data for all or in some cases a subset of wells were analyzed using the
MAROS 2.0 software in order to : 1) gain an overall understanding of the plume stability,
and 2) recommend changes in sampling frequency and sampling locations without
compromising the effectiveness of the long-term monitoring network.
Project Objectives
The general objective of the project was to optimize the original Fort Lewis Upper
Aquifer long-term monitoring network and sampling plan applying the MAROS 2.0
statistical and decision support methodology. The key objectives of the project included:
Determining the overall plume stability through trend analysis and moment
analysis
Evaluating individual well TCE concentration trends over time
• Addressing adequate and effective sampling through reduction of redundant
wells without information loss and addition of new wells for future sampling
Assessing future sampling frequency recommendations while maintaining
sufficient plume stability information
Fort Lewis Logistics Center 1 MAROS 2.0 Application
Pierce County, Washington Monitoring Network Optimization
-------
GROUNDWATER
January 75, 2003 SERVICES, INC.
Evaluating risk-based site cleanup status using data sufficiency analysis
Comparing the MAROS 2.0 original (2001) monitoring plan optimization with the
2002 LOGRAM plan implemented in 2002
Results
The MAROS 2.0 sampling optimization software/methodology has been applied to the
Fort Lewis Upper Aquifer's original RAM program as of September, 2001. Results from
the temporal trend analysis, moment analysis, sampling location determination, sampling
frequency determination, and data sufficiency analysis indicate that:
• Site monitoring wells were divided into source wells and tail wells where source
wells are in the vicinity of NAPL or have historically elevated concentrations of
TCE.
• 6 out of 10 source wells and 15 out of 33 tail wells have a Probably Decreasing,
Decreasing, or Stable trend. Both of the statistical methods used to evaluate
trends (Mann-Kendall and Linear Regression) gave similar trend estimates for
each well.
• 6 out of 6 source area extraction wells have a Probably Decreasing, or
Decreasing trend. Both the Mann-Kendall and Linear Regression methods gave
similar trend estimates for each well.
• 12 out of 15 plume containment extraction wells have Probably Decreasing,
Decreasing, or Stable trend. Both the Mann-Kendall and Linear Regression
methods gave similar trend estimates for each well.
• The dissolved mass shows an increase over time, whereas the center of mass
has stayed stable and the plume spread shows a decrease over time. The
increase in dissolved mass maybe due to either 1) the extraction system moving
high concentration groundwaterfrom source zones to nearby monitoring wells; or
2) the change in the wells sampled over the sampling period analyzed.
• Overall plume stability results indicate that a monitoring system of "Moderate"
intensity is appropriate for this plume compared to "Limited" or "Extensive"
systems due to a stable Upper Aquifer plume.
• The well redundancy optimization tool, using the Delaunay method, indicates that
8 existing monitoring wells may not be needed for plume monitoring and can be
eliminated from the original monitoring network of 38 wells without compromising
the accuracy of the monitoring network.
• The well sufficiency optimization tool, using the Delaunay method, indicates that
6 new monitoring wells may help reduce uncertainty in selected areas within the
original monitoring network.
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• The well sampling frequency tool, the Modified CES method, indicates the
number of samples collected over time sampling can potentially be reduced by
56% by sampling at a less-than-quarterly frequency for most of the monitoring
wells. A 57% reduction in sampling can potentially be achieved for the
monitoring extraction wells using the sampling frequency recommended by the
MAROS analysis.
• The MAROS Data Sufficiency (Power Analysis) application indicates that the
monitoring record has sufficient statistical power at this time to say that the plume
will not cross a "hypothetical statistical compliance boundary" located 2000 feet
downgradient of the most downgradient well at the site. As more sampling
records accumulate, this hypothetical statistical compliance boundary will get
closer and closer to the downgradient wells of the monitoring system.
• Comparison of the original Upper Aquifer monitoring plan with the 2002
LOGRAM sampling plan, indicates that similar sampling frequency and well
redundancy results were obtained. However, well adequacy results differed due
to the constraints of assessing only the existing well network within the MAROS
software.
The MAROS optimized plan consists of 56 wells: 19 sampled quarterly, 2 sampled
semiannually, 30 sampled annually, and 5 sampled biennially. The MAROS optimized
plan would result in 113 (112.5) samples per year, compared to 180 samples per year in
the current LOGRAM monitoring program and 236 samples per year in the original
sampling program. Implementing these recommendations could lead to a 37% reduction
from the LOGRAM plan and 52% reduction from the original plan in terms of the
samples to be collected per year. Based on a per sample cost of $500, the MAROS
optimized plan could reduce the site monitoring cost by $33,500 and $61,500 from the
LOGRAM plan and the original plan, respectively.
The recommended long-term monitoring strategy, based on the analysis of the original
monitoring plan, results in considerable reduction in sampling costs and allows site
personnel to develop a better understanding of plume behavior over time. A reduction in
the number of redundant wells, an increase in the number of wells in areas with
inadequate information, as well as reduction in sampling frequency is expected to results
in a significant cost savings over the long-term at Fort Lewis. An approximate cost
savings estimate of $33,500 per year is projected while still maintaining adequate
delineation of the plume as well as knowledge of the plume state over time.
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1.0 INTRODUCTION
Long-term monitoring programs, whether applied for process control, performance
measurement, or compliance purposes, require large scale data collection effort and
time commitment, making their cumulative costs very high. With the increasing use of
risk-based goals and natural attenuation in recent years as well as the move toward
long-term closure upon completion of cleanup activities, the need for better-designed
long-term monitoring plans that are cost-effective, efficient, and protective of human and
ecological health has greatly increased. AFCEE's Monitoring and Remediation
Optimization System (MAROS) methodology provides an optimal monitoring network
solution, given the parameters within a complicated groundwater system which will
increase its effectiveness. By applying statistical techniques to existing historical and
current site analytical data, as well as considering hydrogeologic factors and the location
of potential receptors, the software suggests an optimal plan along with an analysis of
individual monitoring wells for the current monitoring system. This report summarizes the
findings of an application of the MAROS 2.0 software to the original Upper Aquifer long-
term monitoring well network at the Fort Lewis Logistics Center in Pierce County,
Washington.
1.1 Geology/Hydrogeology
The Fort Lewis Logistics Center is located in Pierce County, Washington. The shallow
geologic units under the Logistics Center (known as the Upper Aquifer) consists
primarily of outwash sand and outwash gravel. The geologic units that comprise the
Upper Aquifer are the Upper Vashon Recessional Outwash and the Lower Vashon
Advance Outwash. The depth to groundwater is typically 10 to 30 feet below ground
surface (bgs), with the Upper Aquifer having an approximate saturated thickness of 60
feet. The groundwater flow direction is predominantly toward the west-northwest and the
groundwater seepage velocity is approximately 550 ft/yr. For a detailed description of
site geology and hydrogeology refer to USAGE (2002).
1.2 Remedial Action and Long-Term Monitoring
The Fort Lewis Logistics Center was activated in April 1942. Trichloroethene (TCE) was
used as a degreasing agent at this facility until the mid-1970s when it was replaced with
1,1,1-trichloroethane. Waste TCE was disposed of in several locations. In 1985, the
Army identified traces of TCE in several monitoring wells installed in the Upper Aquifer.
A limited site investigation was performed in 1986 and a CERCLA remedial investigation
(Rl) began in 1987. The results of the Rl showed that the ground water plume in the
shallow Upper Aquifer principally contains TCE and is over 2 miles long, between 3,000
to 4,000 feet wide and 60 to 80 feet thick (USAGE 2000). The plume also contains 1,2-
dichloroethene (1,2-DCE) at concentrations of approximately 10 percent of the TCE
level. According to the results of the Rl, the East Gate Disposal Yard (EGDY) is the most
significant source of TCE. Nonaqueous-phase liquid (NAPL) was found in the "source
area" consisting primarily of TPH (diesel-, gasoline-, motor-oil- and total) and TCE. The
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Logistics Center area that extends from the west boundary of EGDY toward Interstate 5
is designated as the "down-gradient area".
A pump-and-treat system installed at the site began operation in August 1995. The
remedial action applied at the site includes groundwater extraction and treatment and
recharge of treated groundwater back into the Upper Aquifer (USAGE 2000). The
objective of the remediation is to restore the Upper Aquifer to drinking water standards
by reducing the TCE concentration to less than 5 ppb within 30-40 years at down-
gradient compliance points.
The original groundwater long-term monitoring plan was completed in August 1995
(USAGE, 2000). It consisted of performance monitoring and compliance monitoring with
the following goals: 1) plume containment monitoring to confirm that the plume remains
hydraulically controlled; and 2) plume reduction monitoring to verify progress toward
achieving cleanup goals. The number of monitoring wells that were sampled in the
original Upper Aquifer monitoring network is 43 (Figure 1). There are also 21 extraction
wells in the monitoring plan. All monitoring wells, unless abandoned, have been
sampled quarterly in the Upper Aquifer for TCE since the implementation of the original
long-term monitoring plan. Between November 1995 and September 2001, 24 sampling
events had been carried out at the site.
In 2001, USAGE used the MAROS 1.0 software to optimize the sampling frequency at
the Fort Lewis site and used a qualitative evaluation to assess the well redundancy and
well adequacy in the well network (USAGE 2001). The resulting LOGRAM revised
monitoring plan was approved by the EPA and implemented in 2002. The revised
monitoring network consists of 51 monitoring wells and 21 extraction wells, with a
reduction of some of the wells to be sampled semiannually and annually that had
previously been sampled quarterly. The well redundancy analysis resulted in 11
monitoring wells being removed from the network, while the well adequacy analysis
resulted in 24 monitoring wells added to the monitoring plan. The LOGRAM plan was
implemented in December, 2001. However, at the time of this study there were
inadequate sampling results (less than 4 quarters of data) to include the new well's data
in the LOGRAM monitoring plan in the current MAROS 2.0 analysis.
The MAROS 2.0 analysis performed for this study utilizes the data from the original Fort
Lewis long-term monitoring plan (November 1995 to September 2001). The monitoring
data from the optimized LOGRAM plan was not utilized in this study, but a comparison of
the MAROS 2.0 results with the USAGE will be provided.
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2.0 MAROS METHODOLOGY
The MAROS 2.0 software used to optimize the LTM network at the Fort Lewis Logistics
Center is explained in general terms in this section. MAROS is a collection of tools in
one software package that is used in an explanatory, non-linear fashion. The tool
includes models, statistics, heuristic rules, and empirical relationships to assist the user
in optimizing a groundwater monitoring network system while maintaining adequate
delineation of the plume as well as knowledge of the plume state over time. Different
users utilize the tool in different ways and interpret the results from a different viewpoint.
For a detailed description of the structure of the software and further utilities, refer to the
MAROS 2.0 Manual (Aziz et al. 2002).
2.1 MAROS Conceptual Model
In MAROS 2.0, two levels of analysis are used for optimizing long-term monitoring plans:
1) an overview statistical evaluation with interpretive trend analysis based on temporal
trend analysis and plume stability information; and 2) a more detailed statistical
optimization based on spatial and temporal redundancy reduction methods (see Figure 2
for further details). In general, the MAROS method applies to 2-D aquifers that have
relatively simple site hydrogeology. However, for a multi-aquifer (3-D) system, the user
could apply the statistical analysis layer-by-layer.
The overview statistics or interpretive trend analysis assesses the general monitoring
system category by considering individual well concentration trends, overall plume
stability, hydrogeologic factors (e.g., seepage velocity, and current plume length), and
the location of potential receptors (e.g., property boundaries or drinking water wells). The
analysis relies on temporal trend analysis to assess plume stability, which is then used
to determine the general monitoring system category. Since the temporal trend analysis
focuses on where the monitoring well is located, the site wells are divided into two
different zones: the source zone or the tail zone. The source zone includes areas with
non-aqueous phase liquids (NAPLs), contaminated vadose zone soils, and areas where
aqueous-phase releases have been introduced into ground water. The source zone
generally contains locations with historical high ground water concentrations of the
COCs. The tail zone is usually the area downgradient of the contaminant source zone.
Although this classification is a simplification of the well location, this broadness makes
the user aware on an individual well basis that the concentration trend results can have
a different interpretation depending on the well location in and around the plume. The
location and type of the individual wells allows further interpretation of the trend results,
depending on what type of well is being analyzed (e.g., remediation well, leading plume
edge well, or monitoring well). General recommendations for the monitoring network
frequency and density are suggested based on heuristic rules applied to the source and
tail trend results.
The detailed statistics level of analysis or sampling optimization, on the other hand,
consists of a well redundancy analysis and well sufficiency analysis using the Delaunay
method, a sampling frequency analysis using the Modified Cost Effective Sampling
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(CES) method and a data sufficiency analysis using power analysis. The well
redundancy analysis is designed to minimize monitoring locations and the Modified CES
method is designed to minimize the frequency of sampling. The data sufficiency
analysis uses power analysis to assess the sampling record to determine if the current
monitoring network and record is sufficient in terms of evaluating risk-based site target
level status.
2.2 Data Management
In MAROS, ground water monitoring data can be imported from simple database-format
Microsoft® Excel spreadsheets, Microsoft Access tables, previously created MAROS
database archive files, or entered manually. Compliance monitoring data interpretation in
MAROS is based on historical ground water monitoring data from a consistent set of
wells over a series of sampling events. Statistical validity of the concentration trend
analysis requires constraints on the minimum data input of at least four wells (ASTM
1998) in which COCs have been detected. Individual sampling locations need to include
data from at least six most-recent sampling events. To ensure a meaningful comparison
of COC concentrations over time and space, both data quality and data quantity need to
be considered. Prior to statistical analysis, the user can consolidate irregularly sampled
data or smooth data that might result from seasonal fluctuations or a change in site
conditions.
Imported ground water monitoring data and the site-specific information entered in Site
Details can be archived and exported as MAROS archive files. These archive files can
be appended as new monitoring data becomes available, resulting in a dynamic long-
term monitoring database that reflects the changing conditions at the site (i.e.
biodegradation, compliance attainment, completion of remediation phase, etc.).
2.3 Site Details
Information needed for the MAROS analysis includes site-specific parameters such as
seepage velocity and current plume length. Part of the trend analysis methodology
applied in MAROS focuses on where the monitoring well is located, therefore the user
needs to divide site wells into two different zones: the source zone or the tail zone. The
source zone includes areas with non-aqueous phase liquids (NAPLs), contaminated
vadose zone soils, and areas where aqueous-phase releases have been introduced into
ground water. The source zone generally contains locations with historical high ground
water concentrations of the COCs. The tail zone is usually the area downgradient of the
contaminant source zone. Although this classification is a simplification of the well
location, this broadness makes the user aware on an individual well basis that the
concentration trend results can have a different interpretation depending on the well
location in and around the plume. It is up to the user to make further interpretation of the
trend results, depending on what type of well is being analyzed (e.g., remediation well,
leading plume edge well, or monitoring well).
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MAROS allows the analysis of up to 5 COCs concurrently and users can pick COCs
from a list of compounds existing in the monitoring data, or select COCs based on
recommendations provided in MAROS based on toxicity, prevalence, and mobility of
compounds.
2.4 Data Consolidation
Typically long-term monitoring raw data have been measured irregularly in time or
contain many non-detects, trace level results, and duplicates. Therefore, before the data
can be further analyzed, raw data are filtered, consolidated, transformed, and possibly
smoothed to allow for a consistent dataset meeting the minimum data requirements for
statistical analysis mentioned previously.
MAROS allows users to specify the period of interest in which data will be consolidated
(i.e., monthly, bi-monthly, quarterly, semi-annual, yearly, or a biennial basis). In
computing the representative value when consolidating, one of four statistics can be
used: median, geometric mean, mean, and maximum. Non-detects can be transformed
to one half the reporting or method detection limit (DL), the DL, or a fraction of the DL.
Trace level results can be represented by their actual values, one half of the DL, the DL,
or a fraction of their actual values. Duplicates are reduced in MAROS by one of three
ways: assigning the average, maximum, or first value. The reduced data for each COC
and each well can be viewed as a time series in a graphical form on a linear or semi-log
plot generated by the software.
2.5 Overview Statistics: Plume Trend Analysis
Within the MAROS software there are historical data analyses that support a conclusion
about plume stability (e.g., increasing plume, etc.) through statistical trend analysis of
historical monitoring data. Plume stability results are assessed from time-series
concentration data with the application of three statistical tools: Mann-Kendall Trend
analysis, linear regression trend analysis and moment analysis. The two trend methods
are used to estimate the concentration trend for each well and each COC based on a
statistical trend analysis of concentrations versus time at each well. These trend
analyses are then consolidated to give the user a general plume stability and general
monitoring frequency and density recommendations (see Figure 3 for further step-by-
step details). Both qualitative and quantitative plume information can be gained by these
evaluations of monitoring network historical data trends both spatially and temporally.
The MAROS Overview Statistics are the foundation the user needs to make informed
optimization decisions at the site. The Overview Statistics are designed to allow site
personnel to develop a better understanding of the plume behavior over time and
understand how the individual well concentration trends are spatially distributed within
the plume. This step allows the user to gain information that will support a more
informed decision to be made in the next level or detailed statistics optimization analysis
(Figure 2).
2.5.1 Mann-Kendall Analysis
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The Mann-Kendall test is a non-parametric statistical procedure that is well suited for
analyzing trends in data over time. The Mann-Kendall test can be viewed as a
nonparametric test for zero slope of the first-order regression of time-ordered
concentration data versus time. The Mann-Kendall test does not require any
assumptions as to the statistical distribution of the data (e.g. normal, lognormal, etc.)
and can be used with data sets which include irregular sampling intervals and missing
data. The Mann-Kendall test is designed for analyzing a single groundwater constituent,
multiple constituents are analyzed separately. The Mann-Kendall S statistic measures
the trend in the data: positive values indicate an increase in concentrations over time
and negative values indicate a decrease in concentrations over time. The strength of the
trend is proportional to the magnitude of the Mann-Kendall statistic (i.e., a large value
indicates a strong trend). The confidence in the trend is determined by consulting the S
statistic and the sample size n in a Kendall probability table such as the one reported in
Hollander and Wolfe (1973).
The concentration trend is determined for each well and each COC based on results of
the S statistic, the confidence in the trend, and the Coefficient of Variation (COV). The
decision matrix for this evaluation is shown in Table 1. A Mann-Kendall S statistic that is
greater than 0 combined with a confidence of greater than 95% is categorized as an
Increasing trend while a Mann-Kendall S statistic of greater than 0 with a confidence
between 90% and 95% is defined as a Probably Increasing trend, and so on.
Depending on statistical indicators, the concentration trend is classified into six
categories:
• Decreasing (D),
• Probably Decreasing (PD),
• Stable (S),
• No Trend (NT),
• Probably Increasing (PI)
• Increasing (I).
These trend estimates are then analyzed to identify the source and tail region overall
stability category (see Figure 3 for further details).
2.5.2 Linear Regression Analysis
Linear Regression is a parametric statistical procedure that is typically used for
analyzing trends in data over time. Using this type of analysis, a higher degree of
scatter simply corresponds to a wider confidence interval about the average log-slope.
Assuming the sign (i.e., positive or negative) of the estimated log-slope is correct, a level
of confidence that the slope is not zero can be easily determined. Thus, despite a poor
goodness of fit, the overall trend in the data may still be ascertained, where low levels of
confidence correspond to "Stable" or "No Trend" conditions (depending on the degree of
scatter) and higher levels of confidence indicate the stronger likelihood of a trend. The
linear regression analysis is based on the first-order linear regression of the log-
transformed concentration data versus time. The slope obtained from this log-
transformed regression, the confidence level for this log-slope, and the COV of the
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untransformed data are used to determine the concentration trend. The decision matrix
for this evaluation is shown in Table 2. To estimate the confidence in the log-slope, the
standard error of the log-slope is calculated. The coefficient of variation, defined as the
standard deviation divided by the average, is used as a secondary measure of scatter to
distinguish between "Stable" or "No Trend" conditions for negative slopes. The Linear
Regression Analysis is designed for analyzing a single groundwater constituent; multiple
constituents are analyzed separately, (up to five COCs simultaneously). For this
evaluation, a decision matrix developed by Groundwater Services, Inc. is also used to
determine the "Concentration Trend" category (plume stability) for each well.
Depending on statistical indicators, the concentration trend is classified into six
categories:
Decreasing (D),
Probably Decreasing (PD),
Stable (S),
No Trend (NT),
Probably Increasing (PI)
Increasing (I).
The resulting confidence in the trend, together with the log-slope and the COV of the
untransformed data, are used in the linear regression analysis decision matrix to
determine the concentration trend. For example, a positive log-slope with a confidence
of less than 90% is categorized as having No Trend whereas a negative log-slope is
considered Stable if the COV is less than 1 and categorized as No Trend if the COV is
greater than 1.
2.5.3 Overall Plume Analysis
General recommendations for the monitoring network frequency and density are
suggested based on heuristic rules applied to the source and tail trend results.
Individual well trend results are consolidated and weighted by the MAROS according to
user input, and the direction and strength of contaminant concentration trends in the
source zone and tail zone for each COC are determined. Based on
i) the consolidated trend analysis,
ii) hydrogeologic factors (e.g., seepage velocity), and
iii) location of potential receptors (e.g., wells, discharge points, or property
boundaries),
the software suggests an general optimization plan for the current monitoring system in
order to efficiently effectively monitor in the future. A flow chart of the utilizing the trend
analysis results and other site-specific parameters to form a general sampling frequency
and well density recommendation is outlined in Figure 2. For example, a generic plan for
a shrinking petroleum hydrocarbon plume (BTEX) in a slow hydrogeologic environment
(silt) with no nearby receptors would entail minimal, low frequency sampling of just a few
indicators. On the other hand, the generic plan for a chlorinated solvent plume in a fast
hydrogeologic environment that is expanding but has very erratic concentrations over
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time would entail more extensive, higher frequency sampling. The generic plan is based
on a heuristically derived algorithm for assessing future sampling duration, location and
density that takes into consideration plume stability. For a detailed description of the
heuristic rules used in the MAROS software, refer to the MAROS 2.0 Manual (Aziz et al.
2002).
2.5.3 Moment Analysis
An analysis of moments can help resolve plume trends, where the zeroth moment shows
change in dissolved mass vs. time, the first moment shows the center of mass location
vs. time, and the second moment shows the spread of the plume vs. time. Moment
calculations can predict how the plume will change in the future if further statistical
analysis is applied to the moments to identify a trend (in this case, Mann Kendall Trend
Analysis is applied). The trend analysis of moments can be summarized as:
• Zeroth Moment: Change in dissolved mass over time
• First Moment: Change in the center of mass location over time
• Second Moment: Spread of the plume over time
The role of moment analysis in MAROS is to provide a relative measure of plume
stability and condition. Plume stability may vary by constituent, therefore the MAROS
Moment analysis can be used to evaluate multiple COCs simultaneously which can be
used to provide a quick way of comparing individual plume parameters to determine the
size and movement of constituents relative to one another. Moment analysis in the
MAROS software can also be used to assist the user in evaluating the impact on plume
delineation in future sampling events by removing identified "redundant" wells from a
long-term monitoring program (this analysis was not performed as part of this study, for
more details on this application of moment analysis refer to the MAROS 2.0 Manual
(Aziz et al. 2002)).
The zeroth moment is a dissolved mass estimate. The zeroth moment calculation can
show high variability over time, largely due to the fluctuating concentrations at the most
contaminated wells as well as varying monitoring well network. Plume analysis and
delineation based exclusively on concentration can exhibit a fluctuating degree of
temporal and spatial variability. The mass estimate is also sensitive to the extent of the
site monitoring well network over time. The zeroth moment trend over time is determined
by using the Mann-Kendall Trend Methodology. The zeroth Moment trend test allows
the user to understand how the plume mass has changed over time. Results for the
trend include: Increasing, Probably Increasing, No Trend, Stable, Probably Decreasing,
Decreasing or Not Applicable (Insufficient Data). When considering the results of the
Zeroth moment trend, the following factors should be considered which could effect the
calculation and interpretation of the plume mass over time: 1) Change in the spatial
distribution of the wells sampled historically 2) Different wells sampled within the well
network over time (addition and subtraction of well within the network). 3) Adequate
versus inadequate delineation of the plume over time
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The first moment estimates the center of mass, coordinates (Xc and Yc) for each
sample event and COC. The changing center of mass locations indicate the movement
of the center of mass over time. Whereas, the distance from the original source location
to the center of mass locations indicate the movement of the center of mass over time
relative to the original source. Calculation of the first moment normalizes the spread by
the concentration indicating the center of mass. The first moment trend of the distance to
the center of mass over time shows movement of the plume in relation to the original
source location over time. Analysis of the movement of mass should be viewed as it
relates to 1) the original source location of contamination 2) the direction of groundwater
flow and/or 3) source removal or remediation. Spatial and temporal trends in the center
of mass can indicate spreading or shrinking or transient movement based on season
variation in rainfall or other hydraulic considerations. No appreciable movement or a
neutral trend in the center of mass would indicate plume stability. However, changes in
the first moment over time do not necessarily completely characterize the changes in the
concentration distribution (and the mass) over time. Therefore, in order to fully
characterize the plume the First Moment trend should be compared to the Zeroth
moment trend (mass change over time).
The second moment indicates the spread of the contaminant about the center of mass
(Sxx and Syy), or the distance of contamination from the center of mass for a particular
COC and sample event. The Second Moment represents the spread of the plume over
time in both the x and y directions. The Second Moment trend indicates the spread of
the plume about the center of mass. Analysis of the spread of the plume should be
viewed as it relates to the direction of groundwater flow. An increasing trend in the
second moment indicates an expanding plume, whereas a declining trend in the plume
indicates a shrinking plume. No appreciable movement or a neutral trend in the center of
mass would indicate plume stability. The second moment provides a measure of the
spread of the concentration distribution about the plume's center of mass. However,
changes in the second moment over time do not necessarily completely characterize the
changes in the concentration distribution (and the mass) over time. Therefore, in order to
fully characterize the plume the Second Moment trend should be compared to the zeroth
moment trend (mass change over time).
2.6 Detailed Statistics: Optimization Analysis
Although the overall plume analysis shows a general recommendation regarding
sampling frequency reduction and a general sampling density, a more detailed analysis
is also available with the MAROS 2.0 software in order to allow for further reductions on
a well-by-well basis for frequency, well redundancy, well sufficiency and sampling
sufficiency. The MAROS Detailed Statistics allows for a quantitative analysis for spatial
and temporal optimization of the well network on a well-by-well basis. The results from
the Overview Statistics should be considered along with the MAROS optimization
recommendations gained from the Detailed Statistical Analysis described previously.
The MAROS Detailed Statistics results should be reassessed in view of site knowledge
and regulatory requirements as well as in consideration of the Overview Statistics
(Figure 2).
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The Detailed Statistics or Sampling Optimization MAROS module can be used to
determine the minimal number of sampling locations and the lowest frequency of
sampling that can still meet the requirements of sampling spatially and temporally for an
existing monitoring program. It also provides an analysis of the sufficiency of data for
the monitoring program.
Sampling optimization in MAROS consists of four parts:
• Well redundancy analysis using the Delaunay method
• Well sufficiency analysis using the Delaunay method
• Sampling frequency determination using the Modified CES method
• Data sufficiency analysis using statistical power analysis.
The well redundancy analysis using the Delaunay method identifies and eliminates
redundant locations from the monitoring network. The well sufficiency analysis can
determine the areas where new sampling locations might be needed. The Modified CES
method determines the optimal sampling frequency for a sampling location based on the
direction, magnitude, and uncertainty in its concentration trend. The data sufficiency
analysis examines the risk-based site cleanup status and power and expected sample
size associated with the cleanup status evaluation.
2.6.1 Well Redundancy Analysis - Delaunay Method
The well redundancy analysis using the Delaunay method is designed to select the
minimum number of sampling locations based on the spatial analysis of the relative
importance of each sampling location in the monitoring network. The approach allows
elimination of sampling locations that have little impact on the historical characterization
of a contaminant plume. An extended method or wells sufficiency analysis, based on
the Delaunay method, can also be used for recommending new sampling locations.
Details about the Delaunay method can be found in Appendix A.2 of the MAROS Manual
(AFCEE 2002).
Well redundancy analysis uses the Delaunay triangulation method to determine the
significance of the current sampling locations relative to the overall monitoring network.
The Delaunay method calculates the network Area and Average concentration of the
plume using data from multiple monitoring wells. A slope factor (SF) is calculated for
each well to indicate the significance of this well in the system (i.e. how removing a well
changes the average concentration.)
The well redundancy optimization process is performed in a stepwise fashion. Step one
involves assessing the significance of the well in the system, if a well has a small SF
(little significance to the network), the well may be removed from the monitoring network.
Step two involves evaluating the information loss of removing a well from the network. If
one well has a small SF, it may or may not be eliminated depending on whether the
information loss is significant. If the information loss is not significant, the well can be
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eliminated from the monitoring network and the process of optimization continues with
fewer wells. However if the well information loss is significant then the optimization
terminates. This sampling optimization process allows the user to assess "redundant"
wells that will not incur significant information loss on a constituent-by-constituent basis
for individual sampling events.
2.6.2 Well Sufficiency Analysis - Delaunay Method
The well sufficiency analysis, using the Delaunay method, is designed to recommend
new sampling locations in areas within the existing monitoring network where there is a
high level uncertainty in plume concentration. Details about the well sufficiency analysis
can be found in Appendix A.2 of the MAROS Manual (AFCEE 2002).
In many cases, new sampling locations need to be added to the existing network to
enhance the spatial plume characterization. In MAROS, the method for determining new
sampling locations recommends the area for a possible new sampling location where
there is a high level of uncertainty in concentration estimation. The Slope Factor (SF)
values obtained from the redundancy reduction described above are used to calculate
the concentration estimation error at each triangle area formed in the Delaunay
triangulation. The estimated SF value at each triangle area is then classified into four
levels: Small, Moderate, Large, or Extremely large because the larger the estimated SF
value, the higher the estimation error at this area. Therefore, the triangle areas with the
estimated SF value at the Extremely large or Large level are candidate regions for new
sampling locations.
The results from the Delaunay method and the method for determining new sampling
locations are derived solely from the spatial configuration of the monitoring network and
the spatial pattern of the contaminant plume. No parameters such as the hydrogeologic
conditions are considered in the analysis. Therefore, professional judgement and
regulatory considerations must be used to make final decisions.
2.6.3 Sampling Frequency Determination - Modified CES Method
The Modified CES method optimizes sampling frequency for each sampling location
based on the magnitude, direction, and uncertainty of its concentration trend derived
from its recent and historical monitoring records. The Modified Cost Effective Sampling
estimates the lowest-frequency sampling schedule for a given groundwater monitoring
location yet still provide needed information for regulatory and remedial decision-making.
The Modified CES method was developed on the basis of the Cost Effective Sampling
(Ridley et al. 1995). Details about the Modified CES method can be found in Appendix
A.3 of the MAROS Manual (AFCEE 2002).
In order to estimate the least frequent sampling schedule for a monitoring location that
still provides enough information for regulatory and remedial decision-making, MCES
employs three steps to determine the sampling frequency. The first step involves
analyzing frequency based on recent trends. A preliminary location sampling frequency
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(PLSF) is determined based on the trends determined by rates of change from linear
regression and Mann-Kendall analysis of the most recent monitoring data (Figure 4).
The variability of the sequential sampling data is accounted for by the Mann-Kendall
analysis. The PLSF is then adjusted based on overall trends. If the long-term history of
change is significantly greater than the recent trend, the frequency may be reduced by
one level. Otherwise, no change could be made. The final step in the analysis involves
reducing frequency based on risk. Since not all compounds in the target being
assessed are equally harmful, frequency is reduced by one level if recent maximum
concentration for compound of high risk is less than 1/2 of the Maximum Concentration
Limit (MCL). The result of applying this method is a suggested sampling frequency
based on recent sampling data trends and overall sampling data trends.
The finally determined sampling frequency from the Modified CES method can be
Quarterly, Semiannual, Annual, and Biennial. Users can further reduce the sampling
frequency to, for example, once every three years, if the trend estimated from Biennial
data (i.e., data drawn once every two years from the original data) is the same as that
estimated from the original data.
2.6.4 Data Sufficiency Analysis - Power Analysis
Statistical power analysis is a technique for interpreting the results of statistical tests. It
provides additional information about a statistical test: 1) the power of the statistical test,
i.e., the probability of finding a difference in the variable of interest when a difference
truly exists; and 2) the expected sample size of a future sampling plan given the
minimum detectable difference it is supposed to detect. For example, if the mean
concentration is lower than the cleanup goal but a statistical test cannot prove this, the
power and expected sample size can tell the reason and how many more samples are
needed to result in a significant test. The additional samples can be obtained by a
longer period of sampling or an increased sampling frequency. Details about the data
sufficiency analysis can be found in Appendix A.6 of the MAROS Manual (AFCEE 2002).
When applying the MAROS power analysis method, a hypothetical statistical compliance
boundary (HSCB) is assigned to be a line perpendicular to the groundwater flow
direction (see figure below). Monitoring well concentrations are projected onto the
HSCB using the distance from each well to the compliance boundary along with a decay
coefficient. The projected concentrations from each well and each sampling event are
then used in the risk-based power analysis. Since there may be more than one sampling
event selected by the user, the risk-based power analysis results are given on an event-
by-event basis. This power analysis can then indicate if target are statistically achieved
at the HSCB. For instance, at a site where the historical monitoring record is short with
few wells, the HSCB would be distant; whereas, at a site with longer duration of
sampling with many wells, the HSCB would be close. Ultimately, at a site the goal would
be to have the HSCB coincide with or be within the actual compliance boundary
(typically the site property line).
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®
L
o
Groundwater flow direction
Concentrations
projected to this
® 4-
The nearest
downgradient
receptor
In order to perform a risk-based cleanup status evaluation for the whole site, a strategy
was developed as follows.
• Estimate concentration versus distance decay coefficient from plume centerline
wells.
• Extrapolate concentration versus distance for each well using this decay
coefficient.
• Comparing the extrapolated concentrations with the compliance concentration
using power analysis.
Results from this analysis can be Attained or Not Attained, providing a statistical
interpretation of whether the cleanup goal has been met on the site-scale from the risk-
based point of view. The results as a function of time can be used to evaluate if the
monitoring system has enough power at each step in the sampling record to indicate
certainty of compliance by the plume location and condition relative to the compliance
boundary. For example, if results are Not Attained at early sampling events but are
Attained in recent sampling events, it indicates that the recent sampling record provides
a powerful enough result to indicate compliance of the plume relative to the location of
the receptor or compliance boundary.
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3.0 SITE RESULTS
The original groundwater long-term monitoring plan for the Fort Lewis Logistics Center
was completed in August 1995 (USAGE 2000). The monitoring plan consisted of
performance monitoring and compliance monitoring with the following goals:
1) plume containment monitoring to confirm that the TCE plume remains
hydraulically controlled; and
2) plume reduction monitoring to verify progress toward achieving cleanup goals.
43 monitoring wells and 21 extraction wells in the Upper Aquifer were included in the
long-term monitoring network as of 2001 (Figure 1). All monitoring wells, unless
abandoned, have been sampled quarterly in the Upper Aquifer for TCE since the
implementation of the long-term monitoring plan. By September 2001, 24 sampling
events had been carried out at the site.
In applying the MAROS methodology to develop a revised monitoring strategy for the
Fort Lewis Upper Aquifer, many site and dataset parameters were applied. General site
assumptions include:
• Only wells included in the long-term groundwater monitoring plan as of
September, 2001 were considered in the temporal concentration trend analysis
(Table 3).
• Five chemicals of concern (COCs) that have been historically present at the site:
tetrachloroethene (PCE), trichloroethene (TCE), cis-1,2-dichloroethene (cis-1,2-
DCE), and vinyl chloride (VC), and 1,1,1-trichloroethane (1,1,1-TCA), however,
TCE was used as a indicator compound due to its presence throughout the site
at elevated concentrations.
• All source/tail assignments were made based on the TCE Plume. Source wells
were selected based on historically elevated concentrations of TCE and proximity
to the East Gate Disposal Yard.
• Site-specific hydrogeologic parameters related to the Upper Aquifer including
groundwater flow direction, seepage velocity, saturated thickness, porosity,
receptor locations, and can be found in the Table 4.
• Monitoring Data from November, 1995 to September, 2001 were used in the
MAROS analysis. Monitoring data obtained from the LOGRAM monitoring plan
implementation was insufficient (less than 4 quarters of monitoring data) to be
used in the MAROS optimization analysis.
3.1 Data Consolidation
In MAROS, ground water monitoring data can be imported from simple database-format
Microsoft® Excel spreadsheets, Microsoft® Access tables, previously created MAROS
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database archive files, or entered manually. The historical monitoring data from Fort
Lewis were received in Excel format. The constituent name for TCE was then changed
to the MAROS input parameter nomenclature, the columns in the file where formatted to
the MAROS Access file import format and then imported into the MAROS software using
the import tool. The long-term monitoring raw data contained many non-detects, trace
level results, and duplicates. Therefore, in the MAROS software the raw data are filtered,
consolidated, and the period of interest was specified (i.e. monitoring data from
November 1995 to September 2001). For statistical evaluation of the data, a
representative value for each sample point in time is needed. MAROS has many
automated options to choose how these values are assigned. For the Fort Lewis data,
non-detects values were chosen to be set to the minimum detection limit, allowing for
uniform detection limits over time. Trace level results were chosen to be represented by
their actual values and duplicates samples were chosen to be assigned the average of
the two samples. The reduced data for each well were viewed as a time series in a
graphical form on a linear or semi-log plot generated by the software.
3.2 Overview Statistics: Plume Trend Analysis
3.2.1 Mann-Kendall/Linear Regression Analysis
All 43 monitoring wells and 21 extraction wells had sufficient data within the time period
of November 1995 to September 2001 (greater than 2 years of quarterly data) to assess
the trends in the wells. Trend results from the Mann-Kendall and Linear Regression
temporal trend analysis for both Upper Aquifer monitoring wells and extraction wells are
given in Table 5. The monitoring well trend results show that 6 out of 10 source wells
and 15 out of 33 tail wells have a Probably Decreasing, Decreasing, or Stable trend.
Both methods gave similar trend estimates for each well. The extraction well trend
results show that 18 out of 21 wells have a Probably Decreasing, Decreasing, or Stable
trend. Both methods gave similar trend estimates for each well. When considering the
spatial distribution of the trend results (Figures 5 and 6 - maps created in ArcGIS from
MAROS results), the majority of the decreasing or stable trend results are located near
the East Gate Storage Yard, indicating a decreasing source. Another area of decreasing
trends is in the vicinity of the line of extraction or plume containment wells. However,
there are some extraction wells within the line of plume containment that show
increasing trends over time (Figures 7 and 8 - maps created in ArcGIS from MAROS
results). Whereas, the extraction wells in the source all show decreasing or probably
decreasing trends.
Well Type
Source
Tail
Extraction
MAROS Trend Analysis
PD, D, S
6 of 10 (60%)
15 of 33 (45%)
18 of 21 (85%)
I, PI
4 of 10(40%)
1 1 of 33 (33%)
2 of 21 (9%)
Note: Decreasing (D), Probably Decreasing (PD), Stable (S), Probably Increasing (PI), and Increasing (I)
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Although monitoring wells and extraction wells are present in the well network, these
well trend results need to be treated differently for the purpose of individual trend
analysis interpretation primarily due to the different course of action possible for the two
types of wells. For monitoring wells, strongly decreasing concentration trends may lead
the site manager to decrease their monitoring frequency, as well look at the well as
possibly attaining its remediation goal. Conversely, strongly decreasing concentration
trends in extraction wells may indicate ineffective or near-asymptotic contamination
extraction, which may in turn lead to either the shutting down of the well or a drastic
change in the extraction scheme. Other reasons favoring the separation of these two
types of wells in the trend analysis interpretation is the fact that they produce very
different types of samples. On average, the extraction wells at Fort Lewis possess
screens that are twice as large and extraction wells pull water from a much wider area
than the average monitoring well. Therefore, the potential for the dilution of extraction
well samples is far greater than monitoring well samples.
3.2.2 Moment Analysis
Moment Trend results from the Zeroth, First, and Second Moment analyses for the
Upper Aquifer monitoring well network were varied. Moment Trend results from a
selected Upper Aquifer monitoring well dataset are given in the Moment Analysis Report,
Appendix B. Approximately 35 wells were used in the moment analysis. Wells with
redundant spatial concentration information were not utilized in the moment analysis (i.e.
LC-137b, LC149d, LC-19c, etc.).
Moment
Type
Zeroth
First
Second
Mann-Kendall Trend Analysis
Trend
Increasing
Stable
Decreasing
Comment
The increase in dissolved mass maybe due to the extraction system moving high
concentration ground water from source zones to nearby monitoring wells or the
change in monitoring wells sampled over the sampling period analyzed.
The center of mass remained in relatively the same location through time, with
slight movement forward or backward along the direction of groundwater flow.
Shrinking to stable plume, indicating that wells representing very large areas
both on the tip and the sides of the plume show decreasing concentrations.
The zeroth moment analysis showed an increasing trend (increase in dissolved mass)
over time (Appendix B). The zeroth moment or mass estimate can show high variability
over time, largely due to the fluctuating concentrations at the most contaminated wells
as well as a varying monitoring well network. In order to consider the fluctuating factors
that could influence a mass increase, the data were consolidated to annual sampling
and the zeroth moment trend re-evaulated, however the trend in the mass remained the
same. Another factor to consider when interpreting the mass increase over time is the
change in the spatial distribution of the wells sampled historically. At the Fort Lewis site
there were changes in the well distribution over time, due to addition and subtraction of
wells from the well network. The observed mass increase could also stem from more
mass being dissolved from the NAPL while the remediation system is operating.
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The first moment, or center of mass, for each sample event remained relatively stable to
slightly decreasing in distance relative to the approximate source location (LC-108), see
Figure 9, as well as the MAROS First Moment Report in Appendix B. The center of
mass remained in relatively the same location through time, with slight movement
forward or backward along the direction of groundwater flow. These spatial and
temporal trends in the center of mass distance from the source location can indicate
transient movement based on season variation in rainfall or other hydraulic
considerations. With no appreciable movement or a neutral trend in center of mass as is
the case at Fort Lewis, there is additional confirmation separate from the individual well
trend analysis, that the plume is relatively stable to decreasing. This stable center of the
mass indicates that although the mass has been increasing over time, the plume itself is
stable.
The second moment, or spread of the plume over time in both the x and y directions for
each sample event, showed a decreasing trend over time, Appendix B. The second
moment provides a measure of the spread of the concentration distribution about the
plume's center of mass. Analysis of the spread of the plume indicates a shrinking to
stable plume, indicating that wells representing very large areas both on the tip and the
sides of the plume show decreasing concentrations. This decreasing trend in the spread
of the plume strengthens the individual well Mann-Kendall and Linear Regression trend
analysis spatially, where most of the tail wells showed a decreasing or probably
decreasing TCE concentration trend.
3.2.3 Overview Statistics: Plume Analysis
In evaluating overall plume stability, the trend analysis results and all monitoring wells
were assigned "Medium" weights within the MAROS software (as described in Figure 3),
assuming equal importance for each well and each trend result in the overall analysis.
Overview Statistics Results:
• Overall trend for Source region: Stable,
• Overall trend for Tail region: near Stable (No Trend),
• Overall results from moment analysis indicate a stable to decreasing plume,
• Overall monitoring intensity needed: Moderate.
These results matched with the judgment based on the visual comparison of TCE
plumes over time, as well as the Moment Analysis. The TCE concentrations observed in
2001 are plotted in Figure B.10. The TCE plume concentrations observed in 1998 was
very similar to that of 2001, indicating that the TCE plume is relatively stable over time.
For a generic plume, the MAROS software indicates to:
• Change from quarterly sampling frequency to biannual sampling
• May need > 50 wells
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These MAROS results are for a generic site, and are based on knowledge gained from
applying the MAROS Overview Statistics. The frequency reduction is a only site-wide
reduction recommendation for whole monitoring network and the number of wells seems
high. Therefore, it was decided to do a detailed analysis for both the well redundancy
and sampling frequency utilizing the detailed statistics analysis in the MAROS 2.0
software in order to allow for reductions and recommendations on a well-by-well basis.
These overview statistics were also used when evaluating a final recommendation for
each well after the detailed statistical analysis was applied.
3.3 Detailed Statistics: Optimization Analysis
From December 1995 to September 2001, a total of 24 rounds of quarterly sampling
have been performed. More than 40 Upper Aquifer monitoring wells were sampled
throughout this time period, but in the interim a number of wells were abandoned. The
number of Upper Aquifer monitoring wells that are sampled as of September 2001 is 38.
These 38 wells, as well as the Upper Aquifer's 21 extraction wells, were used in the
MAROS sampling optimization analysis (see Table 3 for a list of the wells used in the
analysis). In both the well redundancy analysis and well sufficiency analysis, only the
monitoring wells were used. In the sampling frequency analysis and data sufficiency
analysis, both the monitoring wells and the extraction wells were analyzed. Results for
well redundancy, well sufficiency, sampling frequency, and data sufficiency analyses are
detailed in the following sub-sections.
3.3.1 Well Redundancy Analysis - Delaunav Method
The goal of the well redundancy analysis is to identify wells that are redundant within
monitoring network as candidates for removal from the sampling plan. The approach
allows elimination of sampling locations that have little impact on the historical
characterization of a contaminant plume.
A monitoring network of 30 monitoring wells was used for the Delaunay Analysis.
Clustered wells that had equivalent duplicates were excluded from the analysis (Table 3
lists the wells excluded and the 30 wells used in the analysis). The Delaunay analysis
was conducted with the 8 latest sampling events (December 1999 to September 2001).
The results show that 8 monitoring wells are candidates for elimination from the original
2001 long-term monitoring network. These wells are overall most redundant in the past
two years (December 1999 to September 2001), from the standpoint of their contribution
to the spatial definition of the plume.
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After consideration of the MAROS recommendations and the need for plume and site
characterization, 8 wells can be eliminated from the existing 38-well network, resulting in
a reduction of 21%. Well removal candidates include:
LC-136b • LC-19c
LC-137a • LC-44a
LC-149d • LC-51
LC-19b • LC-66a
There were some wells that the MAROS software suggested eliminating from the
monitoring network, however, there were site-specific reasons to keep the wells within
the monitoring network (Table 6 and Table 11). For example, well LC-06 was suggested
for elimination, but the well shows an increasing trend in the TCE concentration data
over time and the well defines the middle-lateral boundary of the plume. Also, well LC-
19a was suggested for elimination, however, the well is located on the plume centerline
and is the basis for risk-based power analysis for attainment at the compliance
boundary. Similarly, there were wells that were not used in the Delaunay analysis that
were clustered wells with equivalent duplicates very close to a well that had similar
concentrations and concentration trends over time (see Table 3 lists the wells excluded
and the 30 wells used in the analysis, see Table 11 for results). These clustered wells
with similar screen intervals, concentration trends, and concentration ranges to a nearby
well were suggested for elimination without having used them in the MAROS well
redundancy analysis. However, information gained from the MAROS trend analysis and
a qualitative assessment of the concentration history of the well, indicated these wells
could be eliminated without significant loss of information.
The TCE plumes shown in Figure 10 generated based on the existing and optimized
networks using September 2001 data agree with each other quite well, indicating that
eliminating these wells from the monitoring network does not show any significant loss of
information.
3.3.2 Well Sufficiency Analysis - Delaunay Method
The well sufficiency analysis, an extended application of analysis of the monitoring
network, also based on the Delaunay method, can be used for recommending new
sampling locations in areas where additional plume information is needed. It is designed
to recommend new sampling locations in areas within the existing monitoring network
where there is a high level uncertainty in plume concentration. In many cases, new
sampling locations need to be added to the existing network to enhance the spatial
plume characterization. The results for determining new sampling locations are derived
solely from the spatial configuration of the monitoring network and the spatial pattern of
the contaminant plume. Therefore, new well locations needed outside the existing
monitoring well network (i.e. a new sentinel well outside the existing plume network) are
not able to be assessed.
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The well sufficiency analysis for recommending new sampling locations was performed
using the same wells as in the well redundancy analysis. With this analysis, areas within
the monitoring well network where there is high uncertainty for predicting concentrations
are recommended for additional sample locations within the existing well network. The
SF values obtained from the analysis shown in Table 6 were used to generate Figure 11,
which recommends the triangular regions for placing new sampling locations. The
estimated SF value at each triangle area is classified into four levels: Small, Moderate,
Large, or Extremely large because the larger the estimated SF value, the higher the
estimation error at this area. Therefore, the triangle areas with the estimated SF value
at the Extremely large or Large level are candidate regions for new sampling locations.
5 new sampling locations (New #2 to #6) are recommended at regions with large
estimation errors and 1 new location (New #1) in the center of the three regions with
moderate estimation errors. Proposed well New #1 and well New #5 correspond to
New-3 and New-5 in the new series wells, respectively. Well New #2 corresponds to FL-
3 proposed in the LOGRAM plan, New #2 corresponds to LC-167, New #4 corresponds
to LC-16, and New #6 corresponds to LC-20. Because these 6 wells are new proposed
wells, their sampling frequencies are all set to quarterly, at least until 6 samples are
available for trend estimation. Because the MAROS analysis for well sufficiency (new
sampling locations) can only calculate SF values inside the triangulation domain, new
wells that are used to better characterize the plume extent outside the triangulation
domain need to be decided based on the plume and site hydrogeologic conditions and
were not considered at this site.
3.3.3 Sampling Frequency Analysis- Modified CES Method
The sampling frequency analysis, using the Modified CES method, was applied to
optimize the sampling frequency for each sampling location based on the magnitude,
direction, and uncertainty of its concentration trend of its recent and historical TCE data.
The Modified Cost Effective Sampling method estimates the lowest-frequency sampling
schedule for a given groundwater monitoring location yet still provide needed information
for regulatory and remedial decision-making. In sampling frequency analysis with the
Modified CES method, both the 38 monitoring wells and the 21 extraction wells were
analyzed (Table 7). Results from the analysis show that the sampling frequency can be
significantly reduced in the future, for both monitoring wells and extraction wells (Table
7).
For the original monitoring well plan as of 2001, considering all the wells prior to the well
redundancy analysis, 7 wells can be sampled biennially, 18 annually, 3 semiannually,
and 12 quarterly. The reduction in sampling for the monitoring wells is about 52% (152
quarterly samples with the original plan versus 79 samples with the MAROS optimized
plan). For the extraction well system, 16 wells are recommended to be sampled
annually and 5 wells still need to be sampled quarterly, resulting in a reduction of
approximately 57% (84 quarterly samples with the original plan versus 36 samples with
the optimized plan).
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Monitoring
Weils
Group 1
Group 2
Group 3
Group 4
Frequency Analysis
Current Sampling
Frequency
Quarterly
Quarterly
Quarterly
Quarterly
Recommended
Sampling Frequency
Annual
Semiannual
Quarterly
Biennial
Number of Wells
(See Table 7 for List)
16
3
12 (No Change)
7
In all cases, the MAROS suggested sampling frequency seemed reasonable when
considering the location of the wells, sampling history, site conditions, well type, and
concentration levels. The Fort Lewis site data used for the MAROS analysis was
uniform and sufficient (24 quarterly monitoring records available for each well),
Therefore, concentration trend estimation and the resulting frequency results, either
based on recent data or overall data, accurately reflect the site conditions in recent and
long-term data trends. Furthermore, all wells with a MAROS annual or biennial sampling
recommendation have the same recent and overall frequency (Table 7). This indicates
that concentration trends in these wells are consistent over the time period being
analyzed (1995 to 2001), therefore, a reduced sampling frequency will continue to reflect
changes in the concentration over time. For the biennial recommendations, recent
maximum concentrations in those wells are below half of the MCL and show no signs of
increasing/probably increasing trend, making the biennial recommendation a
conservative estimate of future sampling frequency. Third, because the TCE plume is
quite stable spatially over past several years and is contained by the extraction wells, it
is unlikely that the plume will show rapid changes over the long-term. Therefore,
reducing the frequency of most wells to annual, and in a few cases biennially, will allow
the monitoring network to continue adequately delineating the plume over time.
If the cost of each sample, including collection and analysis, is $500 then the total cost
savings each year with the sample frequency reduction alone would be $39,500 and
$24,000 for the monitoring well system and extraction well system, respectively.
3.3.4 Data Sufficiency - Power Analysis
In the MAROS data sufficiency analysis, statistical power analysis was used to assess
the sufficiency of monitoring plans for detecting difference between the mean
concentration and cleanup goal. Results from the analysis indicate remediation
progress from the risk-based standpoint at a hypothetical statistical compliance
boundary (HSCB). The power and expected sample size associated with the target level
evaluation may indicate the need for expansion or redundancy reduction of future
sampling plans.
In the risk-based site cleanup evaluation, two analyses were performed (see Appendix B
for all related MAROS reports). In the first analysis, the distance from the most
downgradient well to the nearest downgradient receptor (HSCB) was assumed to be
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1000 ft. The general groundwater flow angle is 140 degrees counterclockwise from
East. Selected plume centerline wells are LC-137b, LC-19a, LC-49, LC-66b, LC-14a,
and T-13b (Table 8). A total of 14 sampling events (June 1998 to September 2001)
were used in the analysis. The second analysis used the same parameters except that
the distance from the HSCB was assumed to be 2000 ft. Table 9 shows plume
centerline concentration regression results for each selected sampling event, which
range from 6 x 10"5 to 3 x 10"4 per ft (see Appendix B for individual well projected
concentration values).
Table 10 shows the risk-based site target level status at selected sampling events for
both analyses (i.e., HSCB at 1000 ft and HSCB at 2000 ft downgradient of the
monitoring system). A general trend observed from the results is that the site changed
from fully "not attained" (i.e., "S/E", the mean concentration significantly exceeds the
target level) to partly "not attained" (i.e., the mean concentration is lower than the target
level but statistically significant, as indicated by the lower power) or "attained." This is
especially true for the second analysis where the HSCB distance is assumed 2000 ft.
Therefore, at this site the HSCB is now at 2000 feet (3 straight "attains") but will likely
move to 1000 feet as soon as monitoring record becomes larger. For example, the 1000
feet HSBC has one "attain" in the second to last sampling event. The HSCB is getting
tighter and tighter as the monitoring record increases. In general, monitoring networks
become more powerful over time. This analysis indicates that the monitoring system is
working because it is powerful enough to accurately reflect the location of the plume
relative to the compliance point. Therefore, the current monitoring network is sufficient
in terms of evaluating risk-based site target level status, if the pump-and-treat remedial
system contains the plume and keeps reducing the contaminant concentration in the
Upper Aquifer.
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4.0 SUMMARY AND RECOMMENDATIONS
In recent years, the high cost of long-term monitoring as part of active or passive
remediation of affected ground water has made the design of efficient and effective
ground water monitoring plans a pressing concern. Periodically updating and revising
long-term monitoring programs with changing conditions at the site can mean
considerable savings in site monitoring costs. The MAROS decision-support software
presented in this report assists in revising existing long-term monitoring plans based on
the historical and current monitoring data and plume behavior over time.
The MAROS 2.0 sampling optimization software/methodology has been applied to the
Fort Lewis Upper Aquifer's original RAM program as of September 2001. The
optimization results and subsequent recommendations allow for optimization of the
spatial and temporal groundwater monitoring system at the Fort Lewis Logistics Center.
The original long-term monitoring network could be optimized through reduction in
sampling frequency and sampling locations, as well as improving the understanding of
the plume through additional optimally placed sampling locations (Results are
summarized in Table 11 and all MAROS Reports are in Appendix B).
Overview Statistics
Both the Mann-Kendall and Linear Regression temporal trend methods gave similar
trend estimates for each well. Results from the temporal trend analysis indicate that
60% of the plume source area monitoring wells indicate a Probably Decreasing,
Decreasing, or Stable TCE concentration trend, whereas only about half of the wells in
the tail and edges of the plume have similar trends. The trend results for the extraction
wells in the source area indicate all wells have Probably Decreasing, or Decreasing
concentrations over time, indicating a decreasing source area. The majority of the
extraction/plume containment wells located northwest of the source area show Probably
Decreasing, Decreasing, or Stable trends.
Results from the moment trend analysis give further evidence of a stable plume, with the
center of mass location has stayed stable and the plume spread shows a decrease over
time. Overall plume stability results recommend a moderate monitoring strategy due
to a stable to decreasing Upper Aquifer plume. The overview results are relatively
generic and not well specific, therefore, a detailed statistical analysis with a well-by-well
analysis was assessed.
Detailed Statistics
Further analysis from the well redundancy analysis using the Delaunay method
optimization indicate that 8 monitoring wells could be eliminated from the original
monitoring network of 38 wells without any significant loss of plume information (Table
11). However, the well sufficiency analysis indicates there are 6 areas within the
network where there are high uncertainties in the predicted TCE concentration that seem
to lack adequate sampling and are recommended for new monitoring well placement.
Fort Lewis Logistics Center 26 MAROS 2.0 Application
Pierce County, Washington Monitoring Network Optimization
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GROUNDWATER
January 75, 2003 SERVICES, INC.
The sampling frequency optimization analysis using the modified CES method, indicated
that most of the wells in the monitoring network could be sampled at a less-than-
quarterly frequency.
Data sufficiency analysis using power analysis methods, shows that the site is close to
target levels at the compliance boundary 2000 ft downgradient from the most
downgradient well. This analysis indicates that the monitoring system is working
because it is powerful enough to accurately reflect the location of the plume relative to
the compliance boundary. This shows the sufficiency of the monitoring system in terms
of evaluating risk-based site target level status if the pump-and-treat remedial system
continues to contain the plume and keeps reducing the contaminant concentration in the
Upper Aquifer.
Comparison of the original Upper Aquifer monitoring plan as of 2001 with the 2002
LOGRAM sampling plan (Table 11), indicates that similar sampling frequency and well
redundancy results were recommended at Fort Lewis. However, well adequacy results
(analysis of addition of wells to the monitoring network) differed due to the constraints of
assessing only the existing well network within the MAROS software.
The MAROS optimized plan consists of 56 wells: 19 sampled quarterly, 2 sampled
semiannually, 30 sampled annually, and 5 sampled biennially. The MAROS optimized
plan would result in 113 (112.5) samples per year, compared to 180 samples per year in
the current LOGRAM monitoring program and 236 samples per year in the original
sampling program. Implementing these recommendations could lead to a 37% reduction
from the LOGRAM plan and 52% reduction from the original plan in terms of the
samples to be collected per year. Based on a per sample cost of $500, the MAROS
optimized plan could reduce the site monitoring cost by $33,500 and $61,500 from the
LOGRAM plan and the original plan, respectively.
The recommended long-term monitoring strategy results in considerable reduction in
sampling costs and allows site personnel to develop a better understanding of plume
behavior over time. A reduction in the number of redundant wells, an increase in the
number of wells in areas with inadequate information, as well as reduction in sampling
frequency is expected to results in a significant cost savings over the long-term at Fort
Lewis. An approximate cost savings estimate of $33,500 per year is projected while still
maintaining adequate delineation of the plume as well as knowledge of the plume state
over time.
Fort Lewis Logistics Center 27 MAROS 2.0 Application
Pierce County, Washington Monitoring Network Optimization
-------
GROUNDWATER
January 75, 2003 SERVICES, INC.
CITED REFERENCES
Air Force Center for Environmental Excellence (AFCEE), 2002. Monitoring and
Remediation Optimization System (MAROS) 2.0 Software Users Guide.
Ridley, M.N. et al., 1995. Cost-Effective Sampling of Groundwater Monitoring Wells, the
Regents of UC/LLNL, Lawrence Livermore National Laboratory.
Air Force Center for Environmental Excellence (AFCEE), 1997, AFCEE Long-Term
Monitoring Optimization Guide, http://www.afcee.brooks.af.mil.
Aziz, J, Ling, M., Newell, C., Rifai, H., and Gonzales, J., 2002, MAROS: a Decision
Support System for Optimizing Monitoring Plans, Ground Water, In Press.
Gilbert, R. O., 1987, Statistical Methods for Environmental Pollution Monitoring, Van
Nostrand Reinhold, New York, NY, ISBN 0-442-23050-8.
U.S. Army Corps of Engineers - Seattle District, 1993. Draft Technical Memorandum of
Fort Lewis Logistics Center Lower Aquifer Groundwater Study. Prepared by Ebasco
Environmental, Inc.
U.S. Army Corps of Engineers - Seattle District, 1994. Fort Lewis Logistics Center
Compliance Monitoring Plan.
U.S. Army Corps of Engineers - Seattle District, 1999. Final Phase I Technical
Memorandum, East Gate Disposal Yard Expanded Site Investigation. Prepared by URS
Greiner Woodward Clyde.
U.S. Army Corps of Engineers - Seattle District, 2000. Draft Fort Lewis Logistics Center
Remedial Action Monitoring Revised Addendum Management Plan. Prepared by URS
Greiner Woodward Clyde.
U.S. Army Corps of Engineers - Seattle District, 2001. Draft Fort Lewis Logistics Center
Remedial Action Monitoring, Fifth annual Monitoring Report. Prepared by URS, Inc.
U.S. Army Corps of Engineers - Seattle District, 2001. Draft Fort Lewis Logistics Center
Remedial Action Monitoring Network Optimization Report. Prepared by U.S. Army
Engineer District.
U.S. Army Corps of Engineers - Seattle District, 2002. Final Field Investigation Report
Phase II Remedial Investigation East Gate Disposal Yard. Prepared by U.S. Army
Engineer District and URS
U.S. Department of the Army, 1990. Record of Decision for the Department of the Army
Logistics Center, Fort Lewis, Washington.
Fort Lewis Logistics Center 28 MAROS 2.0 Application
Pierce County, Washington Monitoring Network Optimization
-------
GROUNDWATER
January 75, 2003 SERVICES, INC.
U.S. Environmental Protection Agency, 1992. Methods for Evaluating the Attainment of
Cleanup Standards Volume 2: Ground Water.
Fort Lewis Logistics Center 29 MAROS 2.0 Application
Pierce County, Washington Monitoring Network Optimization
-------
January 15, 2003
GSI Job No. G-2236-15
V
GROUNDWATER
SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
Fort Lewis Logistics Center
Pierce County, Washington
TABLES
Table 1 The Mann-Kendall Analysis Decision Matrix
Table 2 The Linear Regression Analysis Decision Matrix
Table 3 Sampling Locations Used in the MAROS Analysis
Table 4 Upper Aquifer Site-Specific Parameters
Table 5 Results of Upper Aquifer Trend Analysis
Table 6 Sampling Location Determination Results - Delaunay Method
Table 7 Sampling Frequency Determination Results - Modified CES
Table 8 Selected Plume Centerline Wells for Risk-Based Site Cleanup Evaluation
Power Analysis
Table 9 Plume Centerline Concentration Regression Results - Power Analysis
Table 10 Risk-Based Site Cleanup Evaluation Results - Power Analysis
Table 11 Summary of MAROS Sampling Optimization Results
-------
GSI Job No. G-2236-15
Issued 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Mann-Kendall
Mann-Kendall
Statistic
S>0
S>0
S>0
S<0
S<0
S<0
S<0
Table 1
Analysis Decision Matrix
Confidence in the
Trend
> 95%
90 - 95%
< 90%
< 90% and COV > 1
< 90% and COV < 1
90 - 95%
> 95%
(Aziz, et. al., 2002)
Concentration Trend
Increasing
Probably Increasing
No Trend
No Trend
Stable
Probably Decreasing
Decreasing
Table 2
Linear Regression Analysis Decision Matrix (Aziz, et. al., 2002)
Confidence in the
Trend
Log-slope
Positive
Negative
< 90%
90 - 95%
> 95%
No Trend
Probably Increasing
Increasing
COV < 1 Stable
COV > 1 No Trend
Probably Decreasing
Decreasing
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 3
If
GROUNDWATER
SERVICES, INC.
Table 3
Sampling Locations Used in the MAROS Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Welt
Name
LC-03
LC-05
LC-06
LC-14a
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-66a
LC-66b
LC-73a
LC-108
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-149C
LC-149d
LC-165
PA-381
PA-383
T-04
T-08
Hydrologic
Unit
UV
uv
UV
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
Used in Delaunay
Analysis?
Yes
Yes
Yes
Yes
Yes
No: duplicates LC-19a
No: duplicates LC-19a
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No: duplicates LC-66b
Yes
Yes
Yes
Yes
No
Yes
No: duplicates LC-137b
Yes
Yes
No: duplicates LC-149c
Yes
Yes
Yes
Yes
Yes
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since June 98
Sampled quarterly since June 98
Sampled quarterly since June 98
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Notes: 1) UV=Upper Vashon (the upper layer of the Upper Aquifer), LV=Lower Vashon (the lower layer of the Upper
Aquifer), EW=Upper Aquifer extraction well
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 2 of 3
If
GROUNDWATER
SERVICES, INC.
Table 3
Sampling Locations Used in the MAROS Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Well
Name
T-12b
T-13b
LC-64b
LC-111b
LC-116b
LC-122b
LC-128
LC-13/c
LX-1
LX-2
LX-3
LX-4
LX-5
LX-6
LX-7
Hydrologic
Unit
UV
uv
LV
LV
LV
LV
LV
LV
EW
EW
EW
EW
EW
EW
EW
Used in Delaunay
Analysis?
Yes
Yes
No: screened in the lower
part of the Upper Aquifer
Yes
Yes
Yes
Yes
No: screened in the lower
part of the Upper Aquifer
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History
Sampled quarterly since
December 99
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled quarterly since 95
Sampled for the last two quarters
of 95, then quarterly since
December 96
Sampled for the last two quarters
of 95, then quarterly since
December 96
Sampled for the last two quarters
of 95, then quarterly since
December 96
Sampled for the last two quarters
of 95, then quarterly since
December 96
Sampled for the last two quarters
of 95, then quarterly since
December 96
Sampled for the last two quarters
of 95, then quarterly since
December 96
Sampled for the last two quarters
of 95, then quarterly since
December 96
Notes: 1) UV=Upper Vashon (the upper layer of the Upper Aquifer), LV=Lower Vashon (the lower layer of the Upper
Aquifer), EW=Upper Aquifer extraction well
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 3 of 3
If
GROUNDWATER
SERVICES, INC.
Table 3
Sampling Locations Used in the MAROS Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Well
Name
LX-8
LX-9
LX-10
LX-11
LX-12
LX-13
LX-14
LX-15
LX-16
LX-17
LX-18
LX-19
LX-21
RW-1
Hydrologic
Unit
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
Used in Delaunay
Analysis?
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
No, extraction well
Used in Modified CES
Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for the last two quarters of
95, then quarterly since December 96
Sampled for four quarters between 97
and 98, then quarterly since March 01
Notes: 1) UV=Upper Vashon (the upper layer of the Upper Aquifer), LV=Lower Vashon (the lower layer of the Upper
Aquifer), EW=Upper Aquifer extraction well
-------
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-------
GSI Job No. G-2236-15
Issued 11/27/02
Page 1 of 2
CICMMDWOT!
INC
TABLE 5
Results of Upper Aquifer Trend Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Well
LC-03
LC-05
LC-06
LC-14a
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-49a
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-144a
LC-149C
LC-149d
LC-162
LC-165
PA-381
PA-383
T-01
Well
Type3
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
Well
Category 5
T
T
T
T
T
T
T
T
T
T
T
T
T
T
S
S
T
T
T
S
T
T
T
T
T
S
S
S
S
S
S
T
T
T
S
T
T
T
T
Mann-Kendall
Trend 4
I
I
I
PD
S
S
S
NT
NT
NT
PI
NT
I
I
NT
D
S
S
I
PD
PD
I
S
PI
I
D
I
NT
I
PI
D
S
S
S
D
S
PI
S
S
Linear
Regression
Trend 4
I
I
I
S
S
S
S
PI
NT
NT
PI
NT
PI
I
PI
D
NT
NT
I
D
PD
I
S
NT
I
D
I
S
I
PI
D
S
S
D
D
PD
NT
S
PD
Overall
Trend 8
I
I
I
S
S
S
S
PI
NT
NT
PI
NT
PI
I
PI
D
S
S
I
D
PD
I
S
PI
I
D
I
S
I
PI
D
S
S
PD
D
S
PI
S
S
Number
of
Samples
23
24
24
24
14
14
14
23
24
24
24
12
24
24
24
24
24
24
23
24
23
24
23
24
24
20
24
23
24
24
24
11
24
24
20
23
24
24
16
Number
of
Detects
22
24
24
24
14
14
14
11
24
24
24
12
24
24
24
24
24
24
23
24
8
20
2
24
24
20
24
23
24
24
19
11
0
2
20
4
24
24
16
-------
GSI Job No. G-2236-15
Issued 11/27/02
Page 2 of 2
CICMMDWOT!
INC
TABLE 5
Results of Upper Aquifer Trend Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Well
LX-5
LX-6
LX-7
LX-8
LX-9
LX-10
LX-11
LX-12
LX-13
LX-14
LX-15
LX-16
LX-17
LX-18
LX-19
LX-21
RW-1
Well
Type3
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
Well
Category 5
T
T
T
T
T
T
T
T
T
T
T
T
S
S
S
S
T
Mann-Kendall
Trend 4
D
D
S
NT
D
S
D
D
PI
S
NT
D
S
D
S
D
S
Linear
Regression
Trend 4
D
D
S
NT
PD
D
D
D
I
S
NT
D
S
D
S
S
S
Overall
Trend 8
D
D
S
NT
D
PD
D
D
PI
S
NT
D
S
D
S
PD
S
Number
of
Samples
19
20
20
18
19
20
20
20
15
20
20
8
19
20
18
20
8
Number
of
Detects
19
20
20
18
19
20
20
20
15
20
20
8
19
20
18
20
8
Notes:
1. Consolidation of data included non-detect values set to the minium detection limit (0.001 mg/L)
and duplicate data for the quarter were averaged.
2. Only wells that were part of the network in 2001 were analyzed.
3. EW = Extraction Well; MW = Monitoring Well
4. Decreasing (D), Probably Decreasing (PD), Stable (S), No Trend (NT), Probably Increasing (PI), and Increasing (I)
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 2
If
GROUNDWATER
SERVICES, INC.
Table 6
Well Redundancy Analysis Results - Delaunay Method
Fort Lewis Logistics Center
Pierce County, Washington
Welt Name
LC-03
LC-05
LC-06
LC-14a
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LC-108
LC-111b
Well Used in Analysis?
Yes
Yes
Yes
Yes
Yes
No: duplicates LC-19a
No: duplicates LC-19a
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No: screened in the lower
part of the Upper Aquifer
No: duplicates LC-66b
Yes
Yes
Yes
Yes
MAROS Well
Redundancy
Analysis Result
Keep
Keep
Eliminate
Keep
Eliminate
-
-
Keep
Eliminate
Eliminate
Eliminate
Eliminate
Keep
Keep
-
-
Keep
Keep
Keep
Keep
MAROS
Interpreted
Well
Redundancy
Keep
Keep
Keep
Keep
Keep
Eliminate
Eliminate
Keep
Keep
Eliminate
Keep
Eliminate
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Comments
Defines the middle-lateral
boundary of plume
On plume centerline and used in
MAROS data sufficiency analysis
Duplicates LC-19a
Duplicates LC-19a
Monitors leak to the Lower Aquifer
Spatially redundant
On plume centerline and used in
MAROS data sufficiency analysis
Spatially Redundant
Monitors source area in the lower
part of the Upper Aquifer
Duplicates LC-66b
Notes: 1) The latest 8 sampling events (December 1999 to September 2001) were used in the above analysis
2) InsideSF = 0.15, HullSF = 0.01, AR = CR = 0.95
3)"-" = Not Applicable.
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 2 of 2
If
GROUNDWATER
SERVICES, INC.
Table 6
Well Redundancy Analysis Results - Delaunay Method
Fort Lewis Logistics Center
Pierce County, Washington
Well Name
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-13/c
LC-149C
LC-149d
LC-165
PA-381
PA-383
T-04
T-08
T-12b
T-13b
Well Used in Analysis?
Yes
Yes
Yes
Yes
No
Yes
No: duplicates LC-137b
Yes
No: screened in the lower
part of the Upper Aquifer
Yes
No: duplicates LC-149c
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Analysis
Result
Keep
Keep
Keep
Keep
-
Eliminate
-
Keep
-
Keep
-
Keep
Keep
Keep
Keep
Keep
Keep
Eliminate
Keep or
Eliminate?
Keep
Keep
Keep
Keep
Keep
Eliminate
Eliminate
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Comments
Monitors the hot spot in the
source area
Spatially redundant
Duplicates LC-137b
Monitors the lower part of the
Upper Aquifer
Duplicates LC-149c
On plume centerline and used in
MAROS data sufficiency analysis
Notes: 1) The latest 8 sampling events (December 1999 to September 2001) were used in the above analysis
2) InsideSF = 0.15, HullSF = 0.01, AR = CR = 0.95
3)"-" = Not Applicable.
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 2
If
GROUNDWATER
SERVICES, INC.
Table 7
Sampling Frequency Analysis Results - Modified CES
Fort Lewis Logistics Center
Pierce County, Washington
Well Name
LC-03
LC-05
LC-06
LC-14a
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-13/c
LC-149C
MAROS Frequency
Based on Recent
Trend'11
Annual
Quarterly
Annual
Annual
Annual
Quarterly
Semiannual
Annual
Quarterly
Annual
Semiannual
Quarterly
Semiannual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Semiannual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
MAROS Frequency
Based on Overall
Trend121
Annual
Semiannual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Semiannual
Semiannual
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Annual
Quarterly
Quarterly
Annual
Annual
MAROS
Recommended
Frequency'31
Annual
Quarterly
Quarterly
Annual
Annual
Quarterly
Semiannual
Annual
Quarterly
Annual
Semiannual
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Biennial
Annual
Biennial
Semiannual
Biennial
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Biennial
Notes: (1) The frequency determined by MAROS based on the analysis of the latest 8 sampling events, i.e.,
December 1999 to September 2001
(2) The frequency determined by MAROS based on the analysis of all sampling events, i.e., December 1995
to September 2001
(3) The frequency finally recommended by MAROS after considering recent and overall frequency results as
well as the rates of change in these trends. Rate parameters used are 1MCL/year, 2MCL/year, and
4MCL/year for Low, Medium, and High rates, respectively; the MCL of TCE is 0.005 mg/L
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 2 of 2
If
GROUNDWATER
SERVICES, INC.
Table 7
Sampling Frequency Determination Results - Modified CES
Fort Lewis Logistics Center
Pierce County, Washington
Well Name
LC-149d
LC-165
PA-381
PA-383
T-04
T-08
T-12b
T-13b
MAROS Frequency
Based on Recent
Trend"1
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS Frequency
Based on Overall
Trend'21
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Recommended
Frequency131
Biennial
Biennial
Annual
Biennial
Annual
Annual
Annual
Annual
Extraction Wells
LX-1
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-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
RW-1
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Notes: (1) The frequency determined by MAROS based on the analysis of the latest 8 sampling events, i.e.,
December 1999 to September 2001
(2) The frequency determined by MAROS based on the analysis of all sampling events, i.e., December 1995
to September 2001
(3) The frequency finally recommended by MAROS after considering recent and overall frequency results.
Rate parameters used are 1MCL/year, 2MCL/year, and 4MCL/year for Low, Medium, and High rates,
respectively; the MCL of TCE is 0.005 mg/L
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 8
Selected Plume Centerline Wells
Risk-Based Site Cleanup Evaluation - Power Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Well Name
T-13b
LC-14a
LC-66b
LC-49
LC-19a
LC-137b
Distance from Well to Receptor (feet)
1931.0
3382.6
6318.1
9390.6
11025.9
12082.4
Note: Groundwater flow angle is 140 degrees counterclockwise from East; the
distance from the most downgradient well to the nearest downgradient receptor is
assumed 1000 feet.
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 9
Plume Centerline Concentration
Regression Results - Power Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Sampling Event
2nd Quarter 1998
3rd Quarter 1 998
4th Quarter 1 998
1st Quarter 1999
2nd Quarter 1999
3rd Quarter 1 999
4th Quarter 1 999
1st Quarter 2000
2nd Quarter 2000
3rd Quarter 2000
4th Quarter 2000
1st Quarter 2001
2nd Quarter 2001
3rd Quarter 2001
Number of
Centerline Wells
6
6
5
6
6
6
6
6
6
6
6
6
6
6
Regression
Coefficient (1/ft)
-2.88E-04
-2.59E-04
-5.62E-05
-2.44E-04
-1.95E-04
-2.80E-04
-2.71 E-04
-2.73E-04
-2.55E-04
-3.21 E-04
-3.03E-04
-3.13E-04
-3.54E-04
-3.43E-04
Confidence in
Coefficient
97.4%
93.3%
64.3%
93.7%
95.2%
98.1%
97.1%
97.0%
96.0%
98.4%
97.6%
98.3%
99.3%
98.8%
Note: Regression is on natural log concentration of TCE versus distance from source centerline wells
shown in Table 8.
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 10
Risk-Based Site Cleanup Evaluation Results - Power Analysis
Fort Lewis Logistics Center
Pierce County, Washington
Sampling Event
2nd Quarter 1998
3rd Quarter 1998
4th Quarter 1998
1st Quarter 1999
2nd Quarter 1999
3rd Quarter 1999
4th Quarter 1 999
1st Quarter 2000
2nd Quarter 2000
3rd Quarter 2000
4th Quarter 2000
1st Quarter 2001
2nd Quarter 2001
3rd Quarter 2001
Sample
Size
35
35
28
35
35
35
36
36
36
36
36
36
36
36
Distance to HSCB = 1000 ft
Cleanup Status
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Attained
Not Attained
Power
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
0.075
S/E
0.059
0.739
0.304
Distance to HSCB = 2000 ft
Cleanup Status
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Attained
Not Attained
Attained
Attained
Attained
Power
0.436
S/E
S/E
S/E
S/E
0.102
0.091
0.211
S/E
0.690
0.475
0.594
1.000
0.972
Note: The power analysis used for this application assumes normality of data. Distance to the Hypothetical
Statistical Compliance Boundary (HSCB) is the distance from the most downgradient well to the HSCB; S/E
= extrapolated result significantly exceeds the target level (0.005 mg/L).
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-------
January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
FIGURES
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
Fort Lewis Logistics Center
Pierce County, Washington
Figure 1 Upper Aquifer Groundwater Monitoring Network
Figure 2 MAROS Decision Support Tool Flow Chart
Figure 3 MAROS Overview Statistics Trend Analysis Methodology
Figure 4 Decision Matrix for Determining Provisional Frequency
Figure 5 Upper Aquifer TCE Mann-Kendall Trend Results
Figure 6 Upper Aquifer TCE Linear Regression Trend Results
Figure 7 Upper Aquifer TCE Mann-Kendall Trend Results, Extraction Wells
Figure 8 Upper Aquifer TCE Linear Regression Trend Results, Extraction Wells
Figure 9 Upper Aquifer TCE First Moment (Center of Mass) Over Time
Figure 10 The TCE plume drawn with September 2001 data: (A) before optimization
and (B) after optimization
Figure 11 Well Sufficiency Results: Recommendation for New Sampling Locations
-------
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GSI Job No. G-2236-15
Issued: 1/15/03 GROUNDWATER
Page 1 of 1 SERVICES, INC.
MAROS: Decision Support Tool
MAROS is a collection of tools in one software package that is used in an explanatory, non-linear fashion. The tool
includes models, geostatistics, heuristic rules, and empirical relationships to assist the user in optimizing a
groundwater monitoring network system while maintaining adequate delineation of the plume as well as knowledge
of the plume state over time. Different users utilize the tool in different ways and interpret the results from a different
viewpoint.
Overview Statistics
What it is: Simple, qualitative and quantitative plume information can be gained through evaluation of monitoring
network historical data trends both spatially and temporally. The MAROS Overview Statistics are the foundation the
user needs to make informed optimization decisions at the site.
What it does: The Overview Statistics are designed to allow site personnel to develop a better understanding of the
plume behavior over time and understand how the individual well concentration trends are spatially distributed within
the plume. This step allows the user to gain information that will support a more informed decision to be made in the
next level of optimization analysis.
What are the tools: Overview Statistics includes two analytical tools:
1) Trend Analysis: includes Mann-Kendall and Linear Regression statistics for individual wells and results in
general heuristically-derived monitoring categories with a suggested sampling density and monitoring
frequency.
2) Moment Analysis: includes dissolved mass estimation (0th Moment), center of mass (1st Moment), and
plume spread (2nd Moment) over time. Trends of these moments show the user another piece of
information about the plume stability over time.
What is the product: A first-cut blueprint for a future long-term monitoring program that is intended to be a
foundation for more detailed statistical analysis.
T
Detailed Statistics
What it is: The MAROS Detailed Statistics allows for a quantitative analysis for spatial and temporal optimization of
the well network on a well-by-well basis.
What it does: The results from the Overview Statistics should be considered along side the MAROS optimization
recommendations gained from the Detailed Statistical Analysis. The MAROS Detailed Statistics results should be
reassessed in view of site knowledge and regulatory requirements as well as the Overview Statistics.
What are the tools: Detailed Statistics includes four analytical tools:
1) Sampling Frequency Optimization: uses the Modified CES method to establish a recommended future
sampling frequency.
2) Well Redundancy Analysis: uses the Delaunay Method to evaluate if any wells within the monitoring
network are redundant and can be eliminated without any significant loss of plume information.
3) Well Sufficiency Analysis: uses the Delaunay Method to evaluate areas where new wells are
recommended within the monitoring network due to high levels of concentration uncertainty.
4) Data Sufficiency Analysis: uses Power Analysis to assess if the historical monitoring data record has
sufficient power to accurately reflect the location of the plume relative to the nearest receptor or
compliance point.
What is the product: List of wells to remove from the monitoring program, locations where monitoring wells may
need to be added, recommended frequency of sampling for each well, analysis if the overall system is statistically
powerful to monitor the plume.
Figure 2. MAROS Decision Support Tool Flow Chart
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
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MAROS Overview Statistics Trend Analysis Methodology
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
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Figure 4. Decision Matrix for Determining Provisional Frequency (Figure A.3.1 of the
MAROS Manual (AFCEE 2001))
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-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Figure 10. The TCE plume drawn with September 2001 data: (A) before optimization
and (B) after optimization
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
New#l
Potential areas for
new locations are
indicated toy triangles
with a high SF level.
Estimated SF Level:
S - Small
M - Moderate
L - Large
E -Extremely
New #5
New #6
(LC-
LC-149C
Figure 11. Well Sufficiency Results: Recommendation for New Sampling Locations.
Notes: L: SF values > 0.4; M: SF values between 0.3 ~ 0.4; S: SF values < 0.3. Areas with L or M symbols are candidate
regions for placing new wells. Six new wells are recommended (existing or proposed wells around these locations
according to 2002 LOGRAM plan are shown in parentheses).
-------
January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
Fort Lewis Logistics Center
Pierce County, Washington
APPENDICES
Appendix A: Upper Aquifer Fort Lewis Historical TCE Maps
Appendix B: Upper Aquifer Fort Lewis MAROS 2.0 Reports
-------
January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
Fort Lewis Logistics Center
Pierce County, Washington
APPENDIX A: Upper Aquifer Fort Lewis Historical TCE Maps
Upper Aquifer Fort Lewis Historical TCE Maps (1985-2001)
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January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER AQUIFER MONITORING NETWORK OPTIMIZATION
Fort Lewis Logistics Center
Pierce County, Washington
APPENDIX B: Upper Aquifer Fort Lewis MAROS 2.0 Reports
Linear Regression Statistics Summary
Mann-Kendall Statistics Summary
Spatial Moment Analysis Summary
Zeroth, First, and Second Moment Reports
Plume Analysis Summary
Site Results Summary
Sampling Location Optimization Results
Sampling Frequency Optimization Results
Risk-Based Power Analysis - Plume Centerline Concentrations
Risk-Based Power Analysis - Site Cleanup Status
-------
MAROS Mann-Kendall Statistics Summary
Fort Lewis Upper Aquifer
Location: Pierce County
Julia Aziz
Washington
Time Period: 11/1/1995 to 10/1/2001
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Source/
Tail
Number of
Samples
Number of
Detects
Coefficient
of Variation
Mann-Kendall
Statistic
Confidence
in Trend
All
Samples Concentration
"ND" ? Trend
TRICHLOROETHYLENE (TCE)
LC-64b
LC-108
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-162
LC-64a
LX-17
LX-18
LX-19
LX-21
T-01
LC-53
LC-19b
LC-19a
LC-165
LX-9
LC-14a
LC-149d
LC-149C
LC-144a
PA-381
LC-26
RW-1
LC^Ha
T-04
T-08
LC-132
LC-128
LC-122b
LC-116b
LC-1 1 1 b
T-12b
LC-06
LC-05
s
s
s
s
s
s
s
s
s
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
24
24
20
24
23
24
24
24
20
24
19
20
18
20
16
24
14
14
23
19
24
24
24
11
24
23
8
24
24
24
24
24
23
24
23
8
24
24
24
24
20
24
23
24
24
19
20
24
19
20
18
20
16
24
14
14
4
19
24
2
0
11
24
11
8
24
24
24
24
24
2
20
8
1
24
24
0.51
1.81
0.74
0.57
0.28
0.92
0.56
1.12
0.62
2.24
0.37
0.46
0.26
0.32
0.31
0.25
0.62
0.21
0.82
0.19
0.30
0.60
0.00
0.49
0.31
4.26
0.14
0.21
0.47
0.21
0.34
0.45
0.70
1.99
1.36
2.38
0.69
0.67
-159
-61
-57
198
21
92
67
-189
-122
50
-34
-65
-13
-53
-16
86
-12
-21
-27
-57
-55
-1
0
-13
59
23
-7
40
48
50
188
64
-11
118
-59
-7
103
106
100.0%
93.1%
96.6%
100.0%
69.9%
98.9%
94.9%
100.0%
100.0%
88.7%
87.4%
98.2%
67.3%
95.4%
74.7%
98.3%
72.3%
86.0%
75.2%
97.5%
90.9%
50.0%
49.0%
82.1%
92.5%
71 .7%
76.4%
83.1%
87.7%
88.7%
100.0%
94.1%
60.3%
99.9%
93.7%
76.4%
99.5%
99.6%
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
D
PD
D
I
NT
I
PI
D
D
NT
S
D
S
D
S
I
S
S
s
D
PD
S
S
S
PI
NT
S
NT
NT
NT
I
PI
S
I
PD
NT
I
'
MAROS Version 2, 2002, AFCEE
Wednesday, November 20, 2002
Page 1 of 2
-------
Project: Fort Lewis Upper Aquifer
Location: Pierce County
Julia Aziz
Washington
Well
TRICHLOROETHYLENE
PA-383
LX-1
LX^
LX-2
LX-5
LX-6
LX-7
LX-1 6
LX-1 5
LX-1 4
LX-1 3
LX-1 2
LC-19c
LX-10
LX-3
LC-73a
LC-66b
LC-66a
T-13b
LX-8
LC-03
LC-51
LC-49a
LC-49
LC-44a
LX-11
Source/
Tail
(TCE)
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
Number of
Samples
24
20
18
20
19
20
20
8
20
20
15
20
14
20
20
23
24
24
23
18
23
24
12
24
24
20
Number of
Detects
24
20
18
20
19
20
20
8
20
20
15
20
14
20
20
23
24
24
23
18
22
24
12
24
24
20
Coefficient
of Variation
0.38
0.27
0.23
0.28
0.22
0.21
0.21
0.15
0.29
0.25
0.27
0.29
0.22
0.27
0.24
0.42
0.59
0.29
0.14
0.12
2.39
0.20
0.33
0.25
0.41
0.34
Mann-Kendall
Statistic
-33
-44
-75
-106
-79
-108
-39
-17
2
-3
33
-73
-8
-37
-112
67
-8
-35
15
21
140
96
7
67
41
-92
Confidence
in Trend
78.4%
91 .8%
99.8%
100.0%
99.7%
100.0%
89.0%
97.7%
51 .3%
52.6%
94.3%
99.1%
64.6%
87.7%
100.0%
95.9%
56.8%
79.8%
64.3%
77.3%
100.0%
99.1%
65.6%
94.9%
83.8%
99.9%
All
Samples
"ND" ?
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Concentration
Trend
s
PD
D
D
D
D
S
D
NT
S
PI
D
S
S
D
I
S
S
NT
NT
I
I
NT
PI
NT
D
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-
Due to insufficient Data (< 4 sampling events); Source/Tail (S/T)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Wednesday, November 20, 2002
Page 2 of 2
-------
MAROS Linear Regression Statistics Summary
Project: Fort Lewis Upper Aquifer
Location: Pierce County
Julia Aziz
Washington
Time Period: 11/1/1995 to 10/1/2001
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Average
Source/ Cone
Tail (mg/L)
Median
Cone
(mg/L)
Standard
Deviation
All
Samples
"ND" ?
Coefficient
Ln Slope of Variation
Confidence Concentration
in Trend Trend
TRICHLOROETHYLENE (TCE)
LX-17
LC-108
LX-21
LX-18
LC-64b
LC-64a
LC-162
LC-137C
LC-137b
LC-137a
LC-136b
LC-136a
LC-134
LX-19
LC-05
LC-149C
LC-19c
LC-19b
LC-19a
LC-165
LC-14a
LC-149d
LC-144a
LC-132
LC-128
LC-122b
LC-116b
LC-41a
LC-06
LC-44a
LC-03
LC-111b
PA-381
LC-26
LX^
LX-5
LX-6
LX-7
LX-2
s
s
s
s
s
s
s
s
s
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
6.0E-01
3.2E-02
1.1E-01
9.4E-01
4.3E-02
2.1E+00
4.7E-01
1 .2E-02
1 .6E-01
1 .6E-01
9.0E-02
1.1E+02
2.3E+00
1 .2E-01
3.2E-02
1 .OE-04
5.1E-02
1.1E-01
1 .7E-01
1 .3E-04
5.7E-02
1 .2E-04
9.4E-02
7.3E-02
2.2E-02
1 .2E-04
2.2E-03
1 .7E-01
5.9E-02
2.3E-02
1 .5E-03
2.2E-04
3.8E-02
2.3E-03
6.2E-02
9.6E-02
1 .OE-01
8.0E-02
1 .4E-02
5.5E-01
1 .4E-02
1.1E-01
8.4E-01
4.5E-02
4.2E-01
4.3E-01
9.0E-03
1 .3E-01
8.6E-02
8.8E-02
8.3E+01
2.0E+00
1.1E-01
3.0E-02
1 .OE-04
4.9E-02
9.4E-02
1 .7E-01
1 .OE-04
5.8E-02
1 .OE-04
9.4E-02
7.8E-02
2.1E-02
1 .OE-04
2.7E-04
1 .7E-01
4.9E-02
2.0E-02
8.0E-04
1 .OE-04
3.6E-02
1 .OE-04
5.8E-02
9.8E-02
9.5E-02
8.3E-02
1 .4E-02
2.2E-01
5.8E-02
3.4E-02
4.3E-01
2.2E-02
4.8E+00
2.9E-01
1 .4E-02
9.0E-02
1 .5E-01
2.5E-02
6.1E+01
1.7E+00
3.1E-02
2.2E-02
2.1E-12
1 .1 E-02
6.8E-02
3.4E-02
1.1E-04
1 .7E-02
7.2E-05
4.6E-02
2.5E-02
9.6E-03
8.5E-05
4.4E-03
3.5E-02
4.1 E-02
9.5E-03
3.6E-03
2.9E-04
1 .2E-02
9.8E-03
1 .4E-02
2.1 E-02
2.1 E-02
1 .7E-02
3.9E-03
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
-1.1E-04
-6.7E-04
-1 .4E-04
-3.8E-04
-7.5E-04
7.6E-04
-8.3E-04
-3.2E-03
3.3E-04
7.3E-04
-3.5E-05
8.2E-04
-5.1E-04
-6.7E-05
6.6E-04
O.OE+00
-5.8E-05
-3.3E-04
-1 .3E-04
-2.1E-04
-4.2E-05
-1 .3E-06
-4.6E-04
5.7E-04
1 .3E-04
-2.4E-05
1 .8E-03
5.6E-05
7.0E-04
1 .6E-04
1 .3E-03
-3.7E-04
1 .2E-04
6.7E-04
-2.1E-04
-2.4E-04
-2.3E-04
-9.3E-05
-2.9E-04
0.37
1.81
0.32
0.46
0.51
2.24
0.62
1.12
0.56
0.92
0.28
0.57
0.74
0.26
0.67
0.00
0.22
0.62
0.21
0.82
0.30
0.60
0.49
0.34
0.45
0.70
1.99
0.21
0.69
0.41
2.39
1.36
0.31
4.26
0.23
0.22
0.21
0.21
0.28
79.3%
95.3%
87.1%
99.6%
100.0%
94.1%
100.0%
100.0%
94.5%
99.5%
64.6%
100.0%
97.8%
70.9%
99.7%
100.0%
64.4%
83.9%
75.8%
94.7%
66.3%
100.0%
77.7%
100.0%
87.7%
58.1%
100.0%
74.2%
99.7%
89.7%
100.0%
94.2%
88.5%
93.0%
99.6%
99.9%
99.9%
85.4%
99.8%
S
D
S
D
D
PI
D
D
PI
I
S
I
D
S
I
S
S
s
s
PD
S
D
S
I
NT
S
I
NT
I
NT
I
PD
NT
PI
D
D
D
S
D
MAROS Version 2, 2002, AFCEE
Wednesday, November 20, 2002
Page 1 of 2
-------
Project; Fort Lewis Upper Aquifer
User Julia Aziz
Pierce County
Washington
Well
Average
Source/ Cone
Tail (mg/L)
Median
Cone
(mg/L)
All
Standard Samples
Deviation "ND" ?
Coefficient
Ln Slope of Variation
Confidence Concentration
in Trend Trend
TRICHLOROETHYLENE (TCE)
LX-9
LX-16
PA-383
RW-1
T-01
T-04
T-08
T-12b
LX-8
LX-1
LC-49
LC-49a
LC-51
LC-53
LC-66a
LX-3
LC-73a
T-13b
LX-10
LX-11
LX-1 2
LX-1 3
LX-1 4
LX-1 5
LC-66b
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
6.6E-02
1 .6E-01
1 .2E-03
1 .7E-01
1 .9E-03
8.2E-03
2.5E-03
6.4E-04
7.5E-02
1 .OE-02
2.2E-01
8.5E-02
1 .4E-01
1.7E-01
9.1E-02
2.8E-02
7.0E-04
4.6E-03
6.3E-02
3.8E-02
2.4E-02
5.0E-03
5.8E-03
3.1E-03
1.3E-01
Note: Increasing (I); Probably Increasing (PI);
Due to insufficient Data (< 4 sampling events)
6.8E-02
1 .6E-01
1 .3E-03
1 .6E-01
1 .8E-03
8.3E-03
2.4E-03
1 .OE-04
7.4E-02
1 .OE-02
2.4E-01
8.6E-02
1.5E-01
1.7E-01
9.6E-02
2.8E-02
7.0E-04
4.5E-03
6.3E-02
4.0E-02
2.4E-02
5.3E-03
5.8E-03
3.0E-03
1.2E-01
1 .2E-02
2.4E-02
4.8E-04
2.3E-02
6.1E-04
3.8E-03
5.4E-04
1 .5E-03
9.2E-03
2.7E-03
5.7E-02
2.8E-02
2.9E-02
4.2E-02
2.6E-02
6.8E-03
3.0E-04
6.6E-04
1 .7E-02
1 .3E-02
7.0E-03
1 .3E-03
1 .5E-03
8.8E-04
7.8E-02
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Stable (S); Probably Decreasing (PD);
; COV = Coefficient of Variation
-1 .2E-04
-1 .5E-04
-9.0E-05
-6.8E-05
-1 .9E-04
2.2E-04
9.1E-05
-3.5E-03
4.5E-05
-1 .4E-04
1 .6E-04
3.1E-04
1.1E-04
1 .5E-04
2.3E-05
-3.2E-04
3.0E-04
1 .8E-05
-1 .7E-04
-3.8E-04
-2.8E-04
2.1E-04
-2.9E-05
9.3E-05
6.0E-05
Decreasing (D);
0.19
0.15
0.38
0.14
0.31
0.47
0.21
2.38
0.12
0.27
0.25
0.33
0.20
0.25
0.29
0.24
0.42
0.14
0.27
0.34
0.29
0.27
0.25
0.29
0.59
No Trend
90.6%
98.7%
72.3%
83.1%
90.3%
87.1%
91 .6%
93.3%
80.8%
89.5%
93.6%
80.7%
93.7%
96.7%
57.6%
100.0%
98.4%
64.9%
95.4%
99.6%
99.1%
95.3%
60.3%
77.8%
68.4%
(NT); Not Applicable
PD
D
S
S
PD
NT
PI
PD
NT
S
PI
NT
PI
I
NT
D
I
NT
D
D
D
I
S
NT
NT
(N/A) -
MAROS Version 2, 2002, AFCEE
Wednesday, November 20, 2002
Page 2 of 2
-------
MAROS Statistical Trend Analysis Summary
Fort Lewis Upper Aquifer
Location: Pierce County
Julia Aziz
Washington
Time Period: 11/1/1995 to 10/1/2001
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
TRICHLOROETHYLENE
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-144a
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC^4a
LC-49
LC-49a
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
Source/
Tail
(TCE)
T
T
T
S
T
T
T
T
T
S
S
S
S
S
S
T
T
T
T
S
T
T
T
T
T
T
T
T
T
T
T
S
S
T
T
Number Number Average Median
of of Cone. Cone.
Samples Detects (mg/L) (mg/L)
23
24
24
24
23
24
23
24
24
20
24
23
24
24
24
11
24
24
24
20
23
14
14
14
23
24
24
24
12
24
24
24
24
24
24
22
24
24
24
8
20
2
24
24
20
24
23
24
24
19
11
0
2
24
20
4
14
14
14
11
24
24
24
12
24
24
24
24
24
24
1 .5E-03
3.2E-02
5.9E-02
3.2E-02
2.2E-04
2.2E-03
1 .2E-04
2.2E-02
7.3E-02
2.3E+00
1.1E+02
9.0E-02
1 .6E-01
1 .6E-01
1 .2E-02
9.4E-02
1 .OE-04
1 .2E-04
5.7E-02
4.7E-01
1 .3E-04
1 .7E-01
1.1E-01
5.1E-02
2.3E-03
1 .7E-01
2.3E-02
2.2E-01
8.5E-02
1 .4E-01
1 .7E-01
2.1E+00
4.3E-02
9.1E-02
1 .3E-01
8.0E-04
3.0E-02
4.9E-02
1 .4E-02
1 .OE-04
2.7E-04
1 .OE-04
2.1E-02
7.8E-02
2.0E+00
8.3E+01
8.8E-02
8.6E-02
1.3E-01
9.0E-03
9.4E-02
1 .OE-04
1 .OE-04
5.8E-02
4.3E-01
1 .OE-04
1 .7E-01
9.4E-02
4.9E-02
1 .OE-04
1 .7E-01
2.0E-02
2.4E-01
8.6E-02
1.5E-01
1.7E-01
4.2E-01
4.5E-02
9.6E-02
1.2E-01
All
Samples
"ND" ?
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Mann-
Kendall
Trend
I
I
I
PD
PD
I
S
PI
I
D
I
NT
I
PI
D
S
S
S
PD
D
S
S
S
S
NT
NT
NT
PI
NT
I
I
NT
D
S
S
Linear
Regression
Trend
I
I
I
D
PD
I
S
NT
I
D
I
S
I
PI
D
S
S
D
S
D
PD
S
S
S
PI
NT
NT
PI
NT
PI
I
PI
D
NT
NT
MAROS Version 2, 2002, AFCEE
Wednesday, November 20, 2002
Page 1 of 2
-------
MAROS Statistical Trend Analysis Summary
Well
Source/
Tail
Number Number Average Median
of of Cone. Cone.
Samples Detects (mg/L) (mg/L)
All
Samples
"ND" ?
Mann-
Kendall
Trend
Linear
Regression
Trend
TRICHLOROETHYLENE (TCE)
LC-73a
LX-1
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-2
LX-21
LX-3
LX^
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-01
T-04
T-08
T-12b
T-13b
T
T
T
T
T
T
T
T
T
S
S
S
T
S
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
23
20
20
20
20
15
20
20
8
19
20
18
20
20
20
18
19
20
20
18
19
24
24
8
16
24
24
8
23
23
20
20
20
20
15
20
20
8
19
20
18
20
20
20
18
19
20
20
18
19
24
24
8
16
24
24
1
23
7.0E-04
1 .OE-02
6.3E-02
3.8E-02
2.4E-02
5.0E-03
5.8E-03
3.1E-03
1 .6E-01
6.0E-01
9.4E-01
1 .2E-01
1 .4E-02
1.1E-01
2.8E-02
6.2E-02
9.6E-02
1 .OE-01
8. OE-02
7.5E-02
6.6E-02
3.8E-02
1 .2E-03
1 .7E-01
1 .9E-03
8.2E-03
2.5E-03
6.4E-04
4.6E-03
7.0E-04
1 .OE-02
6.3E-02
4.0E-02
2.4E-02
5.3E-03
5.8E-03
3.0E-03
1.6E-01
5.5E-01
8.4E-01
1.1E-01
1 .4E-02
1.1E-01
2.8E-02
5.8E-02
9.8E-02
9.5E-02
8.3E-02
7.4E-02
6.8E-02
3.6E-02
1 .3E-03
1.6E-01
1 .8E-03
8.3E-03
2.4E-03
1 .OE-04
4.5E-03
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
I
PD
S
D
D
PI
S
NT
D
S
D
S
D
D
D
D
D
D
S
NT
D
PI
S
S
S
NT
NT
NT
NT
I
S
D
D
D
I
S
NT
D
S
D
S
D
S
D
D
D
D
S
NT
PD
NT
S
S
PD
NT
PI
PD
NT
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable
(N/A); Not Applicable (N/A) - Due to insufficient Data (< 4 sampling events); No Detectable Concentration (NDC)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Wednesday, November 20, 2002
Page 2 of 2
-------
MAROS Zeroth Moment Analysis
Project: Fort Lewis Upper Aquifer
Location: Pierce County
COC: TRICHLOROETHYLENE (TCE)
Julia Aziz
Washington
Change in Dissolved Mass Over Time
8.0E+02 -
7.0E+02 •
6.0E+02 •
3 5.0E+02 •
^
7 4.0E+02 •
U)
m
5 3.0E+02 -
2.0E+02 -
1.0E+02 •
n riF+nn
u.uc^uu
Data Table:
Effective Date
12/1/1995
3/1/1996
6/1/1996
9/1/1996
12/1/1996
3/1/1997
6/1/1997
9/1/1997
12/1/1997
3/1/1998
6/1/1998
9/1/1998
12/1/1998
3/1/1999
6/1/1999
9/1/1999
12/1/1999
3/1/2000
6/1/2000
9/1/2000
12/1/2000
3/1/2001
Date
^\ o* G\ .A A. _o% _o% _o» _Oi jc\ c\
TRICHLOROETHYLENE (TCE) 3.2E+02
TRICHLOROETHYLENE (TCE) 4.4E+02
TRICHLOROETHYLENE (TCE) 4.3E+02
TRICHLOROETHYLENE (TCE) 5.7E+02
TRICHLOROETHYLENE (TCE) 4.1E+02
TRICHLOROETHYLENE (TCE) 4.4E+02
TRICHLOROETHYLENE (TCE) 4.6E+02
TRICHLOROETHYLENE (TCE) 4.8E+02
TRICHLOROETHYLENE (TCE) 5.1E+02
TRICHLOROETHYLENE (TCE) 4.0E+02
TRICHLOROETHYLENE (TCE) 4.8E+02
TRICHLOROETHYLENE (TCE) 6.8E+02
TRICHLOROETHYLENE (TCE) 5.4E+02
TRICHLOROETHYLENE (TCE) 5.4E+02
TRICHLOROETHYLENE (TCE) 4.0E+02
TRICHLOROETHYLENE (TCE) 6.5E+02
TRICHLOROETHYLENE (TCE) 6.3E+02
TRICHLOROETHYLENE (TCE) 6.0E+02
TRICHLOROETHYLENE (TCE) 6.1E+02
TRICHLOROETHYLENE (TCE) 6.9E+02
TRICHLOROETHYLENE (TCE) 5.1E+02
TRICHLOROETHYLENE (TCE) 5.5E+02
Porosity: 0.25
rt
&
' Saturated Thickness:
Uniform: 60 ft
Mann Kendall S Statistic:
il 128
I
Confidence in
Trend:
I 99.9%
Coefficient of Variation:
| 0.19
Zeroth Moment
Trend:
I '
Number of Wells
35
35
35
35
34
35
35
35
35
35
36
35
27
34
34
34
34
34
34
34
32
32
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 1 of 2
-------
MAROS Zeroth Moment Analysis
Effective Date
Constituent
Estimated
Mass (Kg)
Number of Wells
6/1/2001
9/1/2001
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
5.6E+02
5.8E+02
32
32
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events); ND = Non-detect. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 2 of 2
-------
MAROS First Moment Analysis
Project: Fort Lewis Upper Aquifer
Location: Pierce County
COC: TRICHLOROETHYLENE (TCE)
Julia Aziz
Washington
Distance from Source to Center of Mass
Date
3.5E+03 -
.-. 3.0E+03 -
g 2.5E+03 -
D
« 2.0E+03 -
E
£ 1.5E+03 -
o
| 1.0E+03 -
to
5 5.0E+02 -
O.OE+00 -
Data Table:
Effective Date
J? & Jr J> J>
-jy ^(N jp ^(N jp
*> j5» $>
s»
-------
MAROS First Moment Analysis
Effective Date Constituent
Xc (ft)
Yc (ft) Distance from Source (ft)
Number of Wells
9/1/2001 TRICHLOROETHYLENE (TCE) 1,494,069 653,844 2,703 32
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 2 of 2
-------
MAROS First Moment Analysis
Project: Fort Lewis Upper Aquifer
Location: Pierce County
Julia Aziz
Washington
COC: TRICHLOROETHYLENE (TCE)
Change in Location of Center of Mass Over Time
o OH e. u u •
654100 •
654000 •
653900 •
653800 •
653700 •
653600 •
653500 •
653400 •
fisiinn .
• 09/98
• 06/96
0 03/99 ^ -ft/lg/01
* 06/01
* °»6/0997/00 * 03/00
4- 09/99
* 03/97 * 06/98
• 06/99
• 09/96
* 12/96 » 06/0
4 12/97
• 03/96
«P 09/93> 12/99
• 12/98
Groundwater
Flow Direction:
Source
Coordinate:
X:
Y:
1,496,486
652,634
Effective
12/1/1995
3/1/1996
6/1/1996
9/1/1996
12/1/1996
3/1/1997
6/1/1997
9/1/1997
12/1/1997
3/1/1998
6/1/1998
9/1/1998
12/1/1998
3/1/1999
6/1/1999
9/1/1999
12/1/1999
3/1/2000
6/1/2000
9/1/2000
12/1/2000
3/1/2001
6/1/2001
9/1/2001
1493800 1494000 1494200 1494400
Xc (ft)
Date Constituent Xc (ft)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
1,494,603
1,494,202
1,494,121
1,493,859
1,493,930
1,493,916
1,494,099
1,493,962
1,494,275
1,494,159
1,494,203
1,494,410
1,493,883
1,494,115
1,493,885
1,494,269
1,494,208
1,494,370
1,494,268
1,494,517
1,494,002
1,494,049
1,494,089
1,494,069
1494600
Yc (ft) Distance
653,396
653,479
653,510
653,957
653,609
653,576
653,667
653,760
653,457
653,539
653,477
653,674
654,112
653,399
653,910
653,633
653,700
653,452
653,750
653,565
653,738
653,892
653,903
653,844
from Source (ft)
2,031
2,436
2,522
2,941
2,736
2,737
2,601
2,764
2,359
2,496
2,434
2,322
2,993
2,492
2,897
2,432
2,515
2,269
2,482
2,178
2,719
2,742
2,712
2,703
Number of Wells
35
35
35
35
34
35
35
35
35
35
36
35
27
34
34
34
34
34
34
34
32
32
32
32
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 1 of 2
-------
MAROS First Moment Analysis
Effective Date
Constituent
Xc (ft)
Yc (ft) Distance from Source (ft) Number of Wells
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 2 of 2
-------
MAROS Second Moment Analysis
Project: Fort Lewis Upper Aquifer Julia Aziz
Pierce County Washington
COC: TRICHLOROETHYLENE (TCE)
Change in Plume Spread Over Time
<& J5 & £ & ^ J3 <& <& £ ^ *.
&<£>&$> &
Mann Kendall S Statistic:
1 -134
Confidence in
Trend:
| 100.0%
Coefficient of Variation:
j 0.60
Second Moment
Trend:
I n
1
Constituent Sigma XX (sq ft) Sigma YY (sq ft) Number of Wells
12/1/1995 TRICHLOROETHYLENE (TCE) 5,012,168 4,898,123 35
3/1/1996 TRICHLOROETHYLENE (TCE) 3,998,297 3,918,813 35
6/1/1996 TRICHLOROETHYLENE (TCE) 4,143,839 3,999,799 35
9/1/1996 TRICHLOROETHYLENE (TCE) 4,433,515 4,219,867 35
12/1/1996 TRICHLOROETHYLENE (TCE) 4,802,967 4,688,850 34
3/1/1997 TRICHLOROETHYLENE (TCE) 4,252,709 4,187,670 35
6/1/1997 TRICHLOROETHYLENE (TCE) 4,299,841 4,196,466 35
9/1/1997 TRICHLOROETHYLENE (TCE) 4,496,534 4,271,155 35
12/1/1997 TRICHLOROETHYLENE (TCE) 4,423,644 3,966,113 35
3/1/1998 TRICHLOROETHYLENE (TCE) 4,635,910 4,388,878 35
6/1/1998 TRICHLOROETHYLENE (TCE) 4,125,427 4,095,919 36
9/1/1998 TRICHLOROETHYLENE (TCE) 4,360,842 4,323,315 35
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 1 of 2
-------
MAROS Second Moment Analysis
Effective Date
Constituent
Sigma XX (sq ft) Sigma YY (sq ft)
Number of Wells
12/1/1998
3/1/1999
6/1/1999
9/1/1999
12/1/1999
3/1/2000
6/1/2000
9/1/2000
12/1/2000
3/1/2001
6/1/2001
9/1/2001
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
18,123,344
3,944,965
4,691,727
4,143,586
3,920,347
3,837,803
4,118,134
4,031,575
3,930,191
3,655,290
3,740,729
3,739,485
14,198,215
3,637,538
4,316,807
3,888,306
3,548,080
3,394,424
3,606,414
3,675,232
3,512,792
3,356,920
3,297,856
3,318,478
27
34
34
34
34
34
34
34
32
32
32
32
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events)
The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
11/22/2002
Page 2 of 2
-------
MAROS Spatial Moment Analysis Summary
Project; Fort Lewis Upper Aquifer
Location: Pierce County
Effective Date
TRICHLOROETHYLENE
12/1/1995
3/1/1996
6/1/1996
9/1/1996
12/1/1996
3/1/1997
6/1/1997
9/1/1997
12/1/1997
3/1/1998
6/1/1998
9/1/1998
12/1/1998
3/1/1999
6/1/1999
9/1/1999
12/1/1999
3/1/2000
6/1/2000
9/1/2000
12/1/2000
3/1/2001
6/1/2001
9/1/2001
Oth Moment
Estimated
Mass (Kg)
(TCE)
3.2E+02
4.4E+02
4.3E+02
5.7E+02
4.1E+02
4.4E+02
4.6E+02
4.8E+02
5.1E+02
4.0E+02
4.8E+02
6.8E+02
5.4E+02
5.4E+02
4.0E+02
6.5E+02
6.3E+02
6.0E+02
6.1E+02
6.9E+02
5.1E+02
5.5E+02
5.6E+02
5.8E+02
1st Moment (Center of
Xc (ft)
1,494,603
1,494,202
1,494,121
1,493,859
1,493,930
1,493,916
1,494,099
1,493,962
1,494,275
1,494,159
1,494,203
1,494,410
1,493,883
1,494,115
1,493,885
1,494,269
1,494,208
1,494,370
1,494,268
1,494,517
1,494,002
1,494,049
1,494,089
1,494,069
Yc (ft)
653,396
653,479
653,510
653,957
653,609
653,576
653,667
653,760
653,457
653,539
653,477
653,674
654,112
653,399
653,910
653,633
653,700
653,452
653,750
653,565
653,738
653,892
653,903
653,844
Source
Distance (ft)
2,031
2,436
2,522
2,941
2,736
2,737
2,601
2,764
2,359
2,496
2,434
2,322
2,993
2,492
2,897
2,432
2,515
2,269
2,482
2,178
2,719
2,742
2,712
2,703
Julia Aziz
Washington
2nd Moment
Sigma XX
(sq ft)
5,012,168
3,998,297
4,143,839
4,433,515
4,802,967
4,252,709
4,299,841
4,496,534
4,423,644
4,635,910
4,125,427
4,360,842
18,123,344
3,944,965
4,691 ,727
4,143,586
3,920,347
3,837,803
4,118,134
4,031 ,575
3,930,191
3,655,290
3,740,729
3,739,485
Sigma YY
(sq ft)
4,898,123
3,918,813
3,999,799
4,219,867
4,688,850
4,187,670
4,196,466
4,271,155
3,966,113
4,388,878
4,095,919
4,323,315
14,198,215
3,637,538
4,316,807
3,888,306
3,548,080
3,394,424
3,606,414
3,675,232
3,512,792
3,356,920
3,297,856
3,318,478
Number of
Wells
35
35
35
35
34
35
35
35
35
35
36
35
27
34
34
34
34
34
34
34
32
32
32
32
MAROS Version 2, 2002, AFCEE
Friday, November 22, 2002
Page 1 of 2
-------
Fort Lewis Upper Aquifer
Location: Pierce County
Julia Aziz
Washington
Moment Type Consituent
Zeroth Moment: Mass
TRICHLOROETHYLENE (TCE)
1st Moment: Distance to Source
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma XX
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma YY
TRICHLOROETHYLENE (TCE)
Coefficient
of Variation
0.19
0.09
0.60
0.49
Mann-Kendall
S Statistic
128
-2
-134
-146
Confidence
in Trend
99.9%
51.0%
100.0%
100.0%
Moment
Trend
I
S
D
D
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.25 Saturated Thickness: Uniform: 60 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
Note: The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
Friday, November 22, 2002
Page 2 of 2
-------
MAROS Site Results
Project: Fort Lewis Upper Aquifer
Location: Pierce County
User Defined Site and Data Assumptions:
Julia Aziz
Washington
Hydrogeology and Plume Information:
Down-gradient Information:
Groundwater
Seepage Velocity: 547 5 ft/yr
Current Plume Length: 10000 ft
Current Plume Width 4000 ft
Number of Tail Wells: 108
Number of Source Wells: 8
Source Information:
Distance from Edge of Tail to Nearest:
Down-gradient receptor: 1000 ft
Down-gradient property: 200 ft
Distance from Source to Nearest:
Down-gradient receptor: 10000ft
Down-gradient property: 1000 ft
Source Treatment: No Current Site Treatment
NAPL is not at this site.
Consolidation Assumptions:
Time Period: 11/1/1995 to 10/1/2001
Consolidation Period: No Time Consolidation
Consolidation Type: Geometric Mean
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Plume Information Weighting Assumptions:
Consolidation Step 1. Weight Plume Information by Chemical
Summary Weighting: Weighting Applied to All Chemicals Equally
Consolidation Step 2. Weight Well Information by Chemical
Well Weighting: No Weighting of Wells was Applied.
Chemical Weighting: No Weighting of Chemicals was Applied.
Note: These assumptions made when consolidating the historical mentoring lumping the Wells and COCs.
1.
Preliminary Monitoring System Optimization Results: Based on site classification, source treatment and Monitoring System
Category the following suggestions are made for site Sampling Frequency, Duration of Sampling, and Well Density. These
criteria take into consideration: Plume Stability, Type of Plume, and Groundwater Velocity.
coc
Tail Source Level of
Stability Stability Effort
Sampling
Duration
Sampling
Frequency
Sampling
Density
TRICHLOROETHYLENE (TCE)
NT
M
Sample 4 more years Biannually (6 months)
>50
Note:
Plume Status: (I) Increasing; (Pl)Probably Increasing; (S) Stable; (NT) No Trend; (PD) Probably Decreasing; (D) Decreasing
Design Categories: (E) Extensive; (M) Moderate; (L) Limited (N/A) Not Applicable, Insufficient Data Available
Level of Monitoring Effort Indicated by Analysi I Moderate
2,
MAROS Version 2, 2002, AFCEE
Friday, November 22, 2002
Page 1 of 2
-------
Moment Type Consituent
Zeroth Moment: Mass
TRICHLOROETHYLENE (TCE)
1st Moment: Distance to Source
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma XX
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma YY
TRICHLOROETHYLENE (TCE)
Coefficient
of Variation
0.19
0.09
0.60
0.49
Mann-Kendall
S Statistic
128
-2
-134
-146
Confidence
in Trend
99.9%
51 .0%
100.0%
100.0%
Moment
Trend
I
S
D
D
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.25
Saturated Thickness: Uniform: 60 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
MAROS Version 2, 2002, AFCEE
Friday, November 22, 2002
Page 2 of 2
-------
MAROS Sampling Frequency Optimization Results
Fort Lewis
Seattle
The Overall Number of Sampling Events
"Recent Period" defined by evetns:
Well
TRICHLOROETHYLENE (TCE)
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41 a
LC-44a
LC-49
LC-51
LC-53
LC-64a
MAROS Version 2, 2002, AFCEE
: 24
From 4th Quarter 1999
12/1/1999
Recommended
Sampling Frequency
Annual
Quarterly
Quarterly
Annual
Biennial
SemiAnnual
Biennial
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Biennial
Biennial
Annual
Quarterly
Biennial
Annual
Quarterly
SemiAnnual
Annual
Quarterly
Annual
SemiAnnual
Quarterly
Quarterly
Quarterly
Monday, November
Meng
Washington
To 3rd Quarter 2001
9/1/2001
Frequency Based
on Recent Data
Annual
Quarterly
Annual
Annual
Annual
SemiAnnual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Quarterly
SemiAnnual
Annual
Quarterly
Annual
SemiAnnual
Quarterly
SemiAnnual
Quarterly
18, 2002
Frequency Based
on Overall Data
Annual
SemiAnnual
Quarterly
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Quarterly
Annual
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
SemiAnnual
SemiAnnual
Quarterly
Quarterly
Page 1 of 2
-------
Fort Lewis
Seattle
Meng
Washington
Well
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
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-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-04
T-08
T-12b
T-13b
Recommended
Sampling Frequency
Annual
Annual
Annual
Biennial
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Biennial
Quarterly
Annual
Annual
Annual
Annual
Frequency Based Frequency Based
on Recent Data on Overall Data
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Note: Sampling frequency is determined considering both recent and overall concentration trends. Sampling Frequency is the
final recommendation; Frequency Based on Recent Data is the frequency determined using recent (short) period of monitoring
data; Frequency Based on Overall Data is the frequency determined using overall (long) period of monitoring data. If the "recent
period" is defined using a different series of sampling events, the results could be different.
MAROS Version 2, 2002, AFCEE
Monday, November 18, 2002
Page 2 of 2
-------
• MAROS Sampling
Fort Lewis
Location: Seattle
Sampling Events Analyzed:
I Location Optimization Results I
Meng
Washington
From 4th Quarter 1999 to 3rd Quarter 2001
12/1/1999
Well
TRICHLOROETHYLENE
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136b
LC-137b
LC-149C
LC-14a
LC-165
LC-19a
LC-26
LC-41 a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-66b
LC-73a
PA-381
PA-383
T-04
T-08
T-12b
T-13b
X (feet)
(TCE)
1493904.00
1490857.00
1493994.00
1496486.63
1490017.75
1490585.63
1491418.00
1490373.75
1491411.00
1496354.88
1496179.63
1498352.88
1489560.00
1491769.63
1495139.00
1497563.00
1491874.50
1493248.00
1493877.00
1495357.00
1494335.00
1496588.25
1492172.00
1488270.38
1490584.00
1490422.00
1489309.00
1486709.00
1490605.00
1488281.00
Y (feet)
657303.00
657293.00
655896.00
652634.44
657038.50
657662.75
658353.44
658841.19
657023.69
652485.88
652691 .44
651059.25
658337.00
659713.06
653095.00
651895.00
655151.06
656872.00
654135.00
651777.00
651926.00
652433.13
656883.00
656103.75
655045.00
654112.00
660114.00
658646.00
660206.38
659071 .00
Removable?
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9/1/2001
Average
Slope Factor*
0.255
0.357
0.083
0.263
0.682
0.254
0.666
0.372
0.233
0.073
0.144
0.539
0.338
0.426
0.024
0.567
0.142
0.102
0.116
0.076
0.090
0.310
0.179
0.235
0.248
0.478
0.033
0.014
0.622
0.084
Minimum
Slope Factor*
0.113
0.277
0.038
0.101
0.634
0.000
0.623
0.288
0.213
0.000
0.060
0.440
0.277
0.380
0.004
0.320
0.075
0.011
0.099
0.009
0.021
0.115
0.160
0.177
0.203
0.439
0.002
0.010
0.189
0.062
Maximum
Slope Factor*
0.365
0.463
0.148
0.453
0.709
0.617
0.688
0.451
0.259
0.169
0.210
0.728
0.412
0.569
0.076
0.717
0.175
0.158
0.172
0.134
0.129
0.475
0.215
0.273
0.282
0.517
0.079
0.022
0.701
0.098
Eliminated?
n
n
0
n
n
n
n
n
n
0
n
n
n
n
0
n
0
0
0
0
n
n
n
n
n
n
n
n
n
0
MAROS Version 2, 2002, AFCEE
Monday, November 18, 2002
Page 1 of 2
-------
Project: Fort Lewis Meng
Seattle State: Washington
Average Minimum Maximum
Well X (feet) Y (feet) Removable? Slope Factor* Slope Factor* Slope Factor* Eliminated?
Note: The Slope Factor indicates the relative importance of a well in the monitoring network at a given sampling event; the larger the SF
value of a well, the more important the well is and vice versa; the Average Slope Factor measures the overall well importance in the
selected time period; the state coordinates system (i.e., X and Y refer to Easting and Northing respectively) or local coordinates systems
may be used; wells that are NOT selected for analysis are not shown above.
* When the report is generated after running the Excel module, SF values will NOT be shown above.
MAROS Version 2, 2002, AFCEE Monday, November 18, 2002 Page 2 of 2
-------
MAROS Risk-Based Power Analysis for Site Cleanup
Project: Fort Lewis Upper Aquifer
Seattle
Meng
State: Washington
Parameters:
Groundwater Flow Direction: 140 degrees Distance to Receptor: 1000 feet
From Period: 2nd Quarter 1998 to 3rd Quarter 2001
6/1/1998 9/1/2001
Selected Plume
Centerline Wells:
Well
T-13b
LC-14a
LC-66b
LC-49
LC-19a
LC-137b
The distance
from the well
Distance to Receptor (feet)
1931.0
3382.6
6318.1
9390.6
11025.9
12082.4
is measured in the Groundwater Flow Angle
to the compliance boundary.
Normal Distribution Assumption Lognormal Distribution Assumption
Sample
Sample Event Szje
Sample
Mean
Sample
Stdev.
TRICHLOROETHYLENE (TCE)
2nd Quarter 1998
3rd Quarter 1998
4th Quarter 1998
1st Quarter 1999
2nd Quarter 1999
3rd Quarter 1999
4th Quarter 1999
1st Quarter 2000
2nd Quarter 2000
3rd Quarter 2000
4th Quarter 2000
1st Quarter 2001
2nd Quarter 2001
3rd Quarter 2001
35
35
28
35
35
35
36
36
36
36
36
36
36
36
5.04E-03
1 .07E-02
4.27E-02
7.03E-03
8.21 E-03
6.16E-03
6.16E-03
5.55E-03
6.86E-03
4.80E-03
5.13E-03
4.92E-03
3.40E-03
4.05E-03
6.42E-03
1 J5E-02
4.36E-02
9.55E-03
9.30E-03
7.16E-03
7.66E-03
7.21 E-03
8.53E-03
5.80E-03
6.17E-03
6.05E-03
4.16E-03
5.00E-03
Cleanup Expected Celanup
Status Power Samp|e size status
Expected Alpha Expectec
Power Sample Size Level Power
Cleanup Goal = 0.005
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Attained
Not Attained
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
0.075
S/E
0.059
0.739
0.304
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
>100
S/E
>100
43
>100
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Note: #N/C means "not conducted" due to a small sample size (N<4) or that the mean concentration is much greater than the cleanup
level; Sample Size is the number of sampling locations used in the power analysis; Expected Sample Size is the number of concentration
data needed to reach the Expected Power under current sample variability.
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 1 of
-------
Risk-Based Power Analysis — Projected Concentrations
Project: Fort Lewis Upper Aquifer
Seattle
From Period 6/1/1998 to
Sampling
Event
Effective
Date
9/1/2001
Well
Distance from
Observed
Concentration
(mg/L)
Name: Meng
Washington
the most downgradient well to
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
recep 1000 feet
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-144a
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-49a
LC-51
LC-53
LC-64a
6.000E-04
3.200E-02
3.400E-02
1 .700E-02
6.000E-04
2.450E-04
6.000E-04
1 .900E-02
7.300E-02
2.800E+00
7.800E+01
7.000E-02
1 .OOOE-01
1 .200E-01
4.300E-03
3.400E-02
6.000E-04
6.000E-04
4.700E-02
4.500E-01
6.000E-04
2.000E-01
9.700E-02
7.600E-02
1.400E-04
1 .800E-01
1 .400E-02
2.600E-01
8.900E-02
1.500E-01
1.500E-01
7.500E-01
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
11261.0
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
9398.3
12040.1
11161.4
12561.5
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
7.158E-05
7.468E-03
3.064E-03
4.827E-04
1 .608E-04
6.502E-05
1 .506E-04
6.573E-03
1 .434E-02
7.276E-02
2.216E+00
1.991E-03
3.075E-03
3.685E-03
1.319E-04
1 .323E-03
8.425E-06
8.480E-06
1 J72E-02
1.108E-02
1 J92E-04
8.328E-03
4.041 E-03
3.168E-03
2.733E-06
2.256E-02
1 J82E-03
1 J35E-02
5.925E-03
4.663E-03
6.007E-03
2.006E-02
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 1 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-01
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
5.900E-02
9.600E-02
1 .200E-01
4.800E-04
1.100E-02
5.500E-02
3.700E-02
2.000E-02
4.500E-03
5.300E-03
2.700E-03
1 .500E-01
4.500E-01
7.000E-01
1.100E-01
1 .500E-02
1.100E-01
3.000E-02
6.700E-02
8.800E-02
9.600E-02
7.500E-02
6.800E-02
6.700E-02
3.300E-02
5.000E-04
1 .600E-01
1 .900E-03
5.200E-03
2.300E-03
4.600E-03
1.500E-03
4.400E-02
1.200E-01
1.100E-02
6.000E-04
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
10934.0
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
10572.4
2217.7
2048.1
1000.0
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
1 .578E-03
1 .556E-02
1.941E-02
1.591E-04
2.960E-03
1 .425E-02
9.501 E-03
5.091 E-03
1.135E-03
1 .325E-03
6.689E-04
6.414E-03
1.221E-02
1 .906E-02
3.223E-03
4.025E-03
3.221 E-03
8.030E-03
1 J89E-02
2.348E-02
2.558E-02
1 .993E-02
1 J93E-02
1.751E-02
5.393E-03
7.125E-05
7.593E-03
1 .003E-03
2.881 E-03
1 J24E-03
2.636E-03
2.225E-04
1.192E-02
1 .384E-02
4.500E-04
1 .840E-04
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 2 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-49a
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
3.000E-04
1 .OOOE-04
2.400E-02
5.400E-02
2.800E+00
1.100E+02
9.800E-02
5.500E-01
3.300E-01
1 .580E-02
1 .OOOE-04
4.000E-04
1.100E-01
2.900E-01
2.000E-04
1 .567E-01
1 .200E-01
4.515E-02
1. OOOE-04
1 .450E-01
2.000E-02
2.600E-01
9.200E-02
2.000E-01
2.100E-01
5.800E-01
8.000E-02
1 .200E-01
4.800E-01
8.000E-04
9.700E-03
5.600E-02
3.500E-02
2.000E-02
5.900E-03
5.600E-03
3.100E-03
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
9398.3
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
9.121E-05
2.892E-05
9.257E-03
1 .253E-02
1 .058E-01
4.501 E+00
4.014E-03
2.417E-02
1 .448E-02
6.926E-04
2.174E-06
8.749E-06
4.585E-02
1 .044E-02
6.763E-05
9.037E-03
6.925E-03
2.607E-03
2.922E-06
2.249E-02
3.145E-03
2.290E-02
8.086E-03
8.874E-03
1.170E-02
2.249E-02
3.102E-03
2.344E-02
9.360E-02
2.970E-04
2.986E-03
1 .666E-02
1 .033E-02
5.857E-03
1.714E-03
1.614E-03
8.861 E-04
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 3 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
LX-17
LX-18
LX-19
LX-2
LX-21
LX-3
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
T-13b
LC-05
LC-06
LC-108
LC-116b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-19a
LC-19b
LC-19c
6.900E-01
1 .OOOE+00
1 .200E-01
1 .600E-02
1 .500E-01
3.500E-02
9.600E-02
1 .200E-01
1 .OOOE-01
7.500E-02
7.400E-02
5.800E-02
4.000E-04
1 .700E-03
1 .500E-02
3.700E-03
6.200E-03
1.800E-02
6.700E-02
1 .500E-01
3.000E-04
1 .800E-02
7.700E-02
1 .OOOE+00
4.550E+01
8.000E-02
4.800E-02
3.950E-02
2.300E-02
1 .OOOE-04
3.000E-04
4.600E-02
1.100E-01
1 .900E-01
7.800E-02
5.300E-02
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2217.7
2048.1
1000.0
1931.0
5047.2
8348.3
12354.3
4601 .7
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
11025.9
11024.1
11022.5
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
2.709E-02
3.940E-02
5.049E-03
4.913E-03
6.307E-03
1 .072E-02
2.933E-02
3.662E-02
3.044E-02
2.268E-02
2.220E-02
1.141E-02
6.960E-05
9.577E-04
8.830E-03
2.856E-03
3.762E-03
1 .356E-02
4.192E-02
7.494E-02
2.317E-04
1 .464E-02
5.608E-02
4.911E-01
2.273E+01
3.998E-02
2.436E-02
2.004E-02
1.167E-02
4.356E-05
1 .308E-04
3.804E-02
5.345E-02
1 .023E-01
4.199E-02
2.854E-02
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 4 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
3/1/1999
3/1/1999
3/1/1999
3/1/1999
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
LC-03
LC-05
LC-06
LC-108
1 .700E-01
1 .800E-02
3.000E-01
1.400E-01
1.600E-01
1 .200E+00
4.100E-02
1 .400E-01
1 .200E-01
5.500E-03
6.700E-02
1 .400E-02
1 .700E-02
5.100E-03
5.000E-03
2.500E-03
8.700E-01
1 .OOOE+00
2.000E-01
1 .400E-02
1 .300E-01
3.500E-02
9.800E-02
1 .200E-01
5.500E-02
8.300E-02
3.950E-02
2.600E-02
9.000E-04
2.000E-03
3.200E-03
2.700E-03
8.000E-04
6.000E-03
9.800E-03
6.400E-03
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2217.7
2048.1
1000.0
7375.0
5047.2
8348.3
12354.3
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
1.134E-01
1 .205E-02
1 J70E-01
7.119E-02
8.548E-02
5.926E-01
2.025E-02
9.821 E-02
8.415E-02
4.259E-03
5.150E-02
1 .074E-02
1 .302E-02
3.900E-03
3.816E-03
1 .905E-03
4.308E-01
4.956E-01
1 .005E-01
1 .083E-02
6.534E-02
2.707E-02
7.576E-02
9.274E-02
4.248E-02
6.402E-02
3.041 E-02
1 .827E-02
6.157E-04
1 J66E-03
2.852E-03
2.553E-03
1 .326E-04
1 J54E-03
1.281E-03
3.152E-04
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 5 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
2.000E-04
3.000E-04
5.000E-04
9.500E-03
4.500E-02
1 .400E+00
1 .200E+02
1.100E-01
3.700E-02
5.500E-02
1 .600E-02
1 .OOOE-04
1 .OOOE-04
4.000E-02
5.000E-01
1 .OOOE-04
2.200E-01
3.300E-01
5.100E-02
3.000E-04
1 .700E-01
1 .300E-02
2.500E-01
1.800E-01
1.800E-01
1.100E+00
5.600E-02
1.100E-01
1 .600E-01
1 .350E-03
1 .300E-02
7.800E-02
5.000E-02
3.300E-02
5.400E-03
6.000E-03
3.500E-03
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
6.570E-05
9.774E-05
1 .554E-04
3.873E-03
1.137E-02
6.397E-02
5.911E+00
5.424E-03
1 .949E-03
2.894E-03
8.410E-04
2.716E-06
2.731 E-06
1 J54E-02
2.183E-02
3.601 E-05
1 .498E-02
2.247E-02
3.474E-03
1 .076E-05
2.938E-02
2.276E-03
2.535E-02
9.569E-03
1.185E-02
5.150E-02
2.622E-03
2.362E-02
3.431 E-02
5.308E-04
4.286E-03
2.490E-02
1 .584E-02
1 .038E-02
1 .686E-03
1 .858E-03
1 .076E-03
Yes
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 6 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
LX-17
LX-18
LX-19
LX-2
LX-21
LX-3
LX-4
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
5.800E-01
9.600E-01
1 .400E-01
1 .400E-02
1.100E-01
3.200E-02
9.200E-02
1 .300E-01
4.500E-02
8.600E-02
8.300E-02
4.200E-02
1 .800E-03
1 .600E-03
5.300E-03
2.500E-03
5.300E-03
4.500E-04
2.200E-02
5.000E-02
2.000E-02
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
2.100E-02
8.000E-02
5.600E-01
1 .OOOE+02
5.000E-02
9.500E-02
8.000E-02
4.000E-04
1 .OOOE-04
1 .OOOE-04
5.800E-02
1 .850E-01
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2217.7
2048.1
1000.0
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
2.748E-02
4.563E-02
7.080E-03
4.604E-03
5.559E-03
1 .050E-02
3.013E-02
4.250E-02
1 .468E-02
2.787E-02
2.670E-02
9.083E-03
3.466E-04
9.319E-04
3.217E-03
1 .959E-03
3.310E-03
1 .068E-04
8.219E-03
9.810E-03
1 J96E-03
4.102E-05
4.075E-05
3.924E-05
1 .024E-02
2.660E-02
4.737E-02
8.983E+00
4.495E-03
9.004E-03
7.576E-03
3.785E-05
5.577E-06
5.602E-06
2.998E-02
1 .509E-02
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 7 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
1 .OOOE-04
9.000E-02
9.000E-02
5.400E-02
1. OOOE-04
1 .500E-01
1 .750E-02
1.000E-01
9.000E-02
1.000E-01
5.100E-01
6.400E-02
7.150E-02
7.000E-02
4.500E-04
9.800E-03
6.300E-02
4.500E-02
2.700E-02
6.800E-03
7.500E-03
4.400E-03
3.900E-01
5.400E-01
1 .200E-01
1 .200E-02
9.300E-02
2.500E-02
5.700E-02
9.950E-02
9.400E-02
8.300E-02
7.400E-02
6.900E-02
5.200E-02
2.000E-03
1 .650E-03
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2217.7
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
4.415E-05
1 .047E-02
1 .048E-02
6.288E-03
6.969E-06
3.679E-02
4.338E-03
1.601E-02
8.593E-03
1.133E-02
4.399E-02
5.521 E-03
2.087E-02
2.041 E-02
2.132E-04
4.032E-03
2.526E-02
1 J93E-02
1 .070E-02
2.678E-03
2.935E-03
1.712E-03
3.396E-02
4.714E-02
1.101E-02
4.927E-03
8.527E-03
1 .025E-02
2.332E-02
4.069E-02
3.841 E-02
3.385E-02
3.003E-02
2.783E-02
1 .526E-02
5.351 E-04
1.071 E-03
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 8 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
6/1/1999
6/1/1999
6/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
8.800E-03
3.100E-03
5.500E-03
1.200E-03
4.400E-02
1.200E-01
4.000E-04
1 .OOOE-04
3.000E-04
1 .OOOE-04
2.200E-02
9.100E-02
2.000E+00
1 .300E+02
8.100E-02
2.700E-01
2.100E-01
8.100E-03
1 .OOOE-04
1 .OOOE-04
6.200E-02
2.800E-01
1 .OOOE-04
1 .700E-01
1 .200E-01
4.600E-02
1. OOOE-04
1 .900E-01
1 .800E-02
1.700E-01
1.600E-01
1.700E-01
3.700E-01
3.600E-02
8.250E-02
1 .200E-01
2048.1
1000.0
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
-1 .95E-04
-1 .95E-04
-1 .95E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
5.901 E-03
2.551 E-03
3.774E-03
1.518E-04
1 .069E-02
1.155E-02
1 .253E-05
2.779E-05
8.257E-05
2.607E-05
7.837E-03
1 .870E-02
5.747E-02
4.073E+00
2.541 E-03
9.137E-03
7.098E-03
2.734E-04
1 .579E-06
1 .589E-06
2.402E-02
7.636E-03
3.089E-05
7.727E-03
5.457E-03
2.093E-03
2.175E-06
2.522E-02
2.425E-03
1 .222E-02
5.472E-03
7.438E-03
1 .093E-02
1 .064E-03
1 .406E-02
2.041 E-02
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 9 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
8.000E-04
1 .400E-02
6.100E-02
4.000E-02
2.300E-02
7.300E-03
4.000E-03
4.800E-01
5.350E-01
1 .OOOE-01
1.100E-02
1.100E-01
2.400E-02
5.900E-02
1 .OOOE-01
9.200E-02
8.300E-02
7.200E-02
6.900E-02
4.400E-02
2.100E-03
2.400E-03
1 .OOOE-02
3.800E-03
5.300E-03
8.500E-04
2.700E-02
1.100E-01
3.400E-02
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
2.850E-02
1 .OOOE-01
1 .975E+00
1 .800E+02
3830.2
4553.1
4684.8
4715.9
4746.4
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2217.7
2048.1
1000.0
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
2.734E-04
3.906E-03
1 .640E-02
1 .066E-02
6.079E-03
1 .896E-03
1 .030E-03
1 .438E-02
1 .609E-02
3.229E-03
3.061 E-03
3.549E-03
6.661 E-03
1 .634E-02
2.767E-02
2.542E-02
2.287E-02
1 .970E-02
1 .872E-02
7.559E-03
3.157E-04
1 .289E-03
5.632E-03
2.871 E-03
3.084E-03
1.152E-04
6.878E-03
1.146E-02
1.196E-03
2.901 E-05
2.874E-05
2.727E-05
1.051E-02
2.167E-02
6.393E-02
6.334E+00
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 10 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
5.000E-02
5.700E-02
1 .300E-01
3.000E-04
1 .OOOE-04
1 .OOOE-04
5.200E-02
1 .700E-01
1 .OOOE-04
1 .700E-01
7.300E-02
4.700E-02
1. OOOE-04
1 .600E-01
3.700E-02
2.700E-01
8.000E-02
2.300E-01
4.300E-01
9.000E-03
1 .OOOE-01
1 .300E-01
1 .200E-03
1.100E-02
6.300E-02
3.600E-02
2.200E-02
6.800E-03
3.400E-03
5.500E-01
7.900E-01
1.100E-01
1 .500E-02
1.100E-01
2.400E-02
6.000E-02
1 .OOOE-01
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
1.761E-03
2.161E-03
4.922E-03
1.135E-05
1.815E-06
1 .826E-06
2.080E-02
5.232E-03
3.213E-05
8.571 E-03
3.682E-03
2.372E-03
2.473E-06
2.272E-02
5.332E-03
2.120E-02
3.064E-03
1.118E-02
1 .430E-02
2.994E-04
1 .808E-02
2.347E-02
4.251 E-04
3.203E-03
1 J70E-02
1 .003E-02
6.080E-03
1 .848E-03
9.161E-04
1 .854E-02
2.672E-02
3.986E-03
4.357E-03
3.983E-03
6.954E-03
1 J35E-02
2.889E-02
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 11 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
9.300E-02
8.800E-02
7.700E-02
6.800E-02
2.800E-02
1 .500E-03
1 .200E-02
3.000E-03
4.400E-03
5.400E-03
6.500E-04
1.100E-02
6.800E-02
5.100E-03
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
2.500E-02
8.300E-02
1 .250E+00
1 .900E+02
9.800E-02
6.100E-02
1 .350E-01
2.000E-04
1 .OOOE-04
1 .OOOE-04
5.800E-02
3.800E-01
1 .OOOE-04
1 .800E-01
8.300E-02
3.900E-02
1. OOOE-04
1 .500E-01
1 .400E-02
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2048.1
1000.0
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
2.683E-02
2.532E-02
2.200E-02
1 .927E-02
5.103E-03
2.403E-04
6.889E-03
2.288E-03
1 .962E-03
3.200E-03
8.653E-05
2.767E-03
6.937E-03
1 J40E-04
2.868E-05
2.842E-05
2.695E-05
9.135E-03
1 J73E-02
3.921 E-02
6.485E+00
3.349E-03
2.245E-03
4.961 E-03
7.342E-06
1 J50E-06
1.761E-06
2.300E-02
1.133E-02
3.180E-05
8.831 E-03
4.074E-03
1.915E-03
2.391 E-06
2.093E-02
1 .982E-03
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 12 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
2.000E-01
1.700E-01
1.700E-01
3.900E-01
1 .800E-02
1.100E-01
1 .300E-01
1.100E-03
1.100E-02
1.100E-01
4.300E-02
3.000E-02
8.200E-03
4.300E-03
4.600E-01
7.700E-01
1 .OOOE-01
1 .300E-02
9.700E-02
2.200E-02
5.100E-02
9.000E-02
8.800E-02
8.400E-02
7.600E-02
6.800E-02
4.700E-02
1 .400E-03
8.500E-03
2.600E-03
1 .OOOE-04
4.600E-03
8.000E-04
2.400E-02
1.400E-01
2.400E-02
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2048.1
1000.0
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
1 .534E-02
6.320E-03
8.037E-03
1 .257E-02
5.804E-04
1 .958E-02
2.310E-02
3.860E-04
3.168E-03
3.056E-02
1.184E-02
8.194E-03
2.202E-03
1.145E-03
1 .503E-02
2.525E-02
3.515E-03
3.733E-03
3.407E-03
6.303E-03
1 .458E-02
2.570E-02
2.510E-02
2.390E-02
2.147E-02
1 .905E-02
8.433E-03
2.206E-04
4.855E-03
1 .978E-03
4.425E-05
2.713E-03
1 .223E-04
6.636E-03
1 .670E-02
1 .032E-03
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
Yes
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 13 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
1 .OOOE-04
4.000E-04
1 .OOOE-04
2.100E-02
1 .OOOE-01
1 .450E+00
1 .600E+02
9.000E-02
5.400E-02
1.100E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
6.700E-02
2.800E-01
1 .OOOE-04
1 .700E-01
7.000E-02
4.200E-02
7.500E-05
1 .600E-01
4.200E-02
2.200E-01
1.500E-01
9.500E-02
3.400E-01
1 .800E-02
1 .OOOE-01
1.100E-01
9.000E-04
9.200E-03
7.700E-02
5.100E-02
3.600E-02
7.600E-03
4.500E-03
4.000E-01
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4809.2
4840.1
12512.4
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
3.124E-05
1 .239E-04
2.948E-05
8.221 E-03
2.375E-02
5.766E-02
6.882E+00
3.875E-03
2.491 E-03
5.069E-03
4.603E-06
2.308E-06
2.322E-06
2.831 E-02
1 .062E-02
3.439E-05
1 .025E-02
4.223E-03
2.535E-03
2.316E-06
2.554E-02
6.798E-03
2.012E-02
6.987E-03
5.535E-03
1 .387E-02
7.343E-04
2.004E-02
2.200E-02
3.393E-04
2.885E-03
2.335E-02
1 .534E-02
1 .075E-02
2.233E-03
1.312E-03
1 .652E-02
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 14 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
LX-18
LX-19
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
5.800E-01
9.200E-02
1 .300E-02
9.600E-02
2.000E-02
5.000E-02
1 .OOOE-01
1 .OOOE-01
9.600E-02
8.900E-02
8.000E-02
6.600E-02
1 .600E-03
1 .200E-02
2.400E-03
1 .OOOE-04
4.800E-03
1.800E-02
4.800E-02
1. OOOE-01
2.400E-02
1 .OOOE-04
4.100E-03
1 .OOOE-04
6.200E-02
9.100E-02
2.050E+00
1 .900E+02
8.300E-02
3.300E-01
2.100E-01
3.000E-04
1 .OOOE-04
1 .OOOE-04
5.200E-02
2.300E-01
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
2048.1
1000.0
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12661.3
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
12847.7
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
2.403E-02
4.067E-03
4.066E-03
4.241 E-03
6.242E-03
1 .557E-02
3.112E-02
3.108E-02
2.977E-02
2.741 E-02
2.445E-02
1 .332E-02
2.861 E-04
7.122E-03
1 .860E-03
4.680E-05
2.935E-03
1 .689E-03
9.506E-03
6.868E-03
4.559E-04
2.310E-05
9.367E-04
2.147E-05
1 .903E-02
1 .488E-02
3.529E-02
3.611E+00
1 .579E-03
6.850E-03
4.352E-03
6.210E-06
8.677E-07
8.740E-07
1 J57E-02
3.729E-03
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
Yes
No
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 15 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-9
PA-381
PA-383
T-04
T-08
1 .OOOE-04
1 .800E-01
9.800E-02
5.300E-02
1. OOOE-04
1 .800E-01
2.700E-02
2.300E-01
1.600E-01
2.100E-01
2.500E-01
2.100E-02
8.000E-02
1.100E-01
7.000E-04
7.600E-03
6.400E-02
3.500E-02
2.300E-02
5.300E-03
5.800E-03
2.900E-03
4.700E-01
6.550E-01
8.800E-02
9.800E-03
1 .OOOE-01
2.000E-02
5.600E-02
6.300E-02
8.900E-02
8.300E-02
6.700E-02
3.500E-02
1 .OOOE-03
8.300E-03
2.200E-03
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4653.9
6283.1
6758.7
2048.1
1000.0
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
2.607E-05
5.236E-03
2.852E-03
1 .543E-03
1 .252E-06
1 J85E-02
2.724E-03
1.131E-02
3.362E-03
5.849E-03
4.443E-03
3.733E-04
1 .056E-02
1 .449E-02
2.048E-04
1 J64E-03
1 .424E-02
7.709E-03
5.016E-03
1.145E-03
1 .240E-03
6.138E-04
8.486E-03
1.188E-02
1.731E-03
2.267E-03
1 .965E-03
4.613E-03
1 .288E-02
1 .448E-02
2.043E-02
1 .899E-02
1 .505E-02
4.663E-03
1.144E-04
4.302E-03
1 .596E-03
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 16 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2000
3rd Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
9/1/2000
9/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
1 .OOOE-04
3.750E-03
2.000E-03
7.600E-02
4.600E-02
4.200E-03
1 .OOOE-04
5.700E-03
1 .OOOE-04
2.200E-02
1 .OOOE-01
7.500E+01
1 .OOOE-01
2.800E-01
2.800E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
5.000E-02
1 .OOOE-04
1 .OOOE-01
1.100E-01
3.700E-02
4.700E-02
8.000E-02
3.000E-02
3.300E-01
1.700E-01
2.700E-01
1 .OOOE-02
2.350E-02
8.300E-02
1 .300E-01
9.000E-04
4.900E-03
3.550E-02
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
-3.21 E-04
-3.21 E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
3.842E-05
2.018E-03
2.140E-04
1 .646E-02
3.664E-03
9.934E-05
2.505E-05
1.413E-03
2.338E-05
7.208E-03
1 .807E-02
1 J75E+00
2.369E-03
7.202E-03
7.192E-03
2.565E-06
1.128E-06
1.136E-06
1 J94E-02
2.808E-05
3.538E-03
3.894E-03
1.310E-03
7.497E-04
9.014E-03
3.436E-03
1.916E-02
4.423E-03
9.168E-03
2.221 E-04
5.221 E-04
1 .226E-02
1.916E-02
2.819E-04
1 .233E-03
8.582E-03
Yes
No
No
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 17 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
LX-11
LX-12
LX-13
LX-14
LX-15
LX-17
LX-18
LX-19
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
1 .600E-02
1 .OOOE-02
2.900E-03
2.700E-03
1 .550E-03
2.900E-01
3.825E-01
5.500E-02
6.000E-03
5.000E-02
1 .350E-02
3.600E-02
5.250E-02
5.500E-02
5.000E-02
3.900E-02
4.300E-02
1.100E-03
8.000E-03
2.900E-03
1 .OOOE-04
4.900E-03
1.500E-03
8.300E-02
6.700E-02
1 .300E-02
1 .OOOE-04
1 .400E-02
1 .OOOE-04
2.100E-02
9.700E-02
1 .900E+02
1.100E-01
2.700E-01
2.500E-01
1 .OOOE-04
4715.9
4746.4
4777.3
4809.2
4840.1
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4653.9
6283.1
6758.7
2048.1
1000.0
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12352.7
12348.8
12077.8
12082.4
12086.6
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
3.832E-03
2.373E-03
6.817E-04
6.286E-04
3.575E-04
6.538E-03
8.658E-03
1 .345E-03
1 .505E-03
1.221E-03
3.377E-03
8.983E-03
1 .309E-02
1 .369E-02
1.241E-02
9.517E-03
6.404E-03
1.418E-04
4.300E-03
2.142E-03
4.051 E-05
2.729E-03
1 .486E-04
1 J06E-02
4.893E-03
2.704E-04
2.388E-05
3.309E-03
2.224E-05
6.622E-03
1 .653E-02
3.954E+00
2.292E-03
6.125E-03
5.663E-03
2.262E-06
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
No
Yes
No
No
No
Yes
No
Yes
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 18 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
1 .OOOE-04
1 .OOOE-04
5.800E-02
1 .OOOE-04
1 .600E-01
8.600E-02
4.400E-02
3.000E-04
1 .900E-01
3.400E-02
2.400E-01
1.500E-01
2.200E-01
8.600E+00
1 .600E-02
6.700E-02
1.100E-01
7.000E-04
1.100E-02
4.300E-02
2.600E-02
2.000E-02
6.500E-03
5.900E-03
3.000E-03
8.000E-01
1 .200E-01
1 .400E-02
9.200E-02
2.300E-02
5.800E-02
9.100E-02
8.200E-02
7.900E-02
7.300E-02
6.100E-02
2.300E-02
14796.4
14773.9
3382.6
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
9.674E-07
9.743E-07
2.009E-02
2.688E-05
5.047E-03
2.714E-03
1 .389E-03
4.152E-06
1 .986E-02
3.615E-03
1 .264E-02
3.443E-03
6.651 E-03
1 .676E-01
3.120E-04
9.264E-03
1.518E-02
2.107E-04
2.640E-03
9.901 E-03
5.929E-03
4.517E-03
1 .454E-03
1 .307E-03
6.579E-04
1 .590E-02
2.583E-03
3.349E-03
1 .979E-03
5.487E-03
1 .380E-02
2.163E-02
1 .946E-02
1 .869E-02
1.714E-02
1.418E-02
3.209E-03
Yes
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 19 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
8.000E-04
1 .200E-02
2.400E-03
1 .OOOE-04
4.250E-03
1.500E-03
4.100E-02
7.400E-02
1 .600E-02
1 .OOOE-04
1.100E-02
1 .OOOE-04
2.200E-02
9.900E-02
1 .800E+02
9.200E-02
3.500E-01
3.200E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
3.500E-02
1 .OOOE-04
1 .600E-01
4.500E-02
6.200E-02
3.000E-04
2.000E-01
2.800E-02
2.400E-01
1.500E-01
1.900E-01
1 .400E+01
2.200E-02
6.800E-02
1.100E-01
6758.7
2048.1
1000.0
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
5644.7
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
9.615E-05
6.315E-03
1 J54E-03
3.927E-05
2.320E-03
1.102E-04
6.865E-03
3.850E-03
2.015E-04
1 .984E-05
2.157E-03
1.831E-05
5.974E-03
1 .342E-02
2.268E+00
1.161E-03
4.862E-03
4.438E-03
1 .385E-06
5.305E-07
5.347E-07
1 .057E-02
2.268E-05
3.226E-03
9.077E-04
1.251E-03
2.385E-06
1.561E-02
2.227E-03
8.633E-03
2.112E-03
3.651 E-03
1 .639E-01
2.576E-04
7.277E-03
1.174E-02
Yes
No
No
Yes
No
Yes
No
No
No
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 20 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
6.000E-04
1 .OOOE-02
3.900E-02
2.100E-02
1 .600E-02
5.600E-03
4.700E-03
2.400E-03
1 .200E-01
1.100E+00
1 .050E+00
1 .300E-01
1 .300E-02
9.600E-02
2.700E-02
5.400E-02
8.550E-02
7.900E-02
7.200E-02
8.200E-02
5.500E-02
3.600E-02
8.000E-04
1 .500E-01
8.800E-03
1 .900E-03
1 .OOOE-04
4.000E-03
2.200E-03
7.300E-02
6.100E-02
4.000E-03
1 .OOOE-04
1 .400E-02
1 .OOOE-04
1 .350E-02
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
10934.0
12512.4
12499.3
12245.1
4563.1
12247.8
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
10572.4
2048.1
1000.0
2981 .5
1931.0
7375.0
5047.2
8348.3
12354.3
4567.9
4601 .7
4795.4
3681 .9
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
1 .546E-04
1 .995E-03
7.424E-03
3.954E-03
2.980E-03
1 .032E-03
8.562E-04
4.324E-04
2.499E-03
1.310E-02
1 .256E-02
1 J02E-03
2.584E-03
1 .256E-03
5.350E-03
1 .067E-02
1 .687E-02
1 .557E-02
1.414E-02
1 .595E-02
1 .059E-02
3.891 E-03
7.307E-05
3.551 E-03
4.261 E-03
1 .333E-03
3.479E-05
2.019E-03
1 J56E-04
1 .294E-02
3.487E-03
5.792E-05
2.089E-05
2.891 E-03
1 .932E-05
3.821 E-03
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
No
Yes
No
No
Yes
Yes
No
Yes
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 21 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
1.100E-01
2.500E+02
1 .250E-01
4.100E-01
3.050E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
4.600E-02
1 .OOOE-04
1 .700E-01
1 .400E-01
6.800E-02
2.000E-03
1 .900E-01
3.000E-02
2.500E-01
1.600E-01
1.900E-01
1 .900E+01
1 .550E-02
6.200E-02
1 .300E-01
8.000E-04
1 .OOOE-02
4.600E-02
2.000E-02
1 .300E-02
5.300E-03
4.200E-03
2.300E-03
1 .400E-01
7.800E-01
1 .200E+00
1 .600E-01
1.100E-02
1 .OOOE-01
5644.7
12352.7
12348.8
12077.8
12082.4
12086.6
14796.4
14773.9
3382.6
4190.8
11025.9
11024.1
11022.5
13654.1
7203.5
7149.5
9390.6
12040.1
11161.4
12561.5
12560.8
6311.6
6318.1
3830.2
4553.1
4684.8
4715.9
4746.4
4777.3
4809.2
4840.1
10934.0
12512.4
12499.3
12245.1
4563.1
12247.8
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
1 .589E-02
3.622E+00
1.813E-03
6.526E-03
4.847E-03
1 .587E-06
6.268E-07
6.317E-07
1 .443E-02
2.377E-05
3.881 E-03
3.198E-03
1 .554E-03
1 .855E-05
1 .608E-02
2.587E-03
9.997E-03
2.580E-03
4.141E-03
2.562E-01
2.091 E-04
7.124E-03
1 .490E-02
2.152E-04
2.100E-03
9.232E-03
3.971 E-03
2.555E-03
1 .030E-03
8.077E-04
4.377E-04
3.298E-03
1 .070E-02
1 .653E-02
2.405E-03
2.302E-03
1 .502E-03
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 22 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Observed
Concentration
(mg/L)
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-04
T-08
T-12b
T-13b
2.100E-02
5.200E-02
7.200E-02
7.800E-02
7.600E-02
6.800E-02
5.400E-02
3.500E-02
1 .OOOE-03
1 .500E-01
8.800E-03
2.500E-03
1 .OOOE-04
3.850E-03
4571 .9
4580.3
4583.3
4587.7
4597.5
4623.3
4653.9
6283.1
6758.7
10572.4
2048.1
1000.0
2981 .5
1931.0
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
4.381 E-03
1 .082E-02
1 .496E-02
1.618E-02
1 .572E-02
1 .394E-02
1 .095E-02
4.061 E-03
9.858E-05
4.000E-03
4.361 E-03
1 J74E-03
3.598E-05
1 .986E-03
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Note: Projected Concentrations that are below the user-specified detection limit are indicated by a check mark to its right; for sampling events
with less than 3 selected plume centerline wells, NO projected concentrations are calculated because no regression coefficient is available.
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 23 of 2:
-------
MAROS Risk-Based Power Analysis for Site Cleanup
Project: Fort Lewis Upper Aquifer
Seattle
Meng
State: Washington
Parameters:
Groundwater Flow Direction: 140 degrees Distance to Receptor: 2000 feet
From Period: 2nd Quarter 1998 to 3rd Quarter 2001
6/1/1998 9/1/2001
Selected Plume
Centerline Wells:
Well
T-13b
LC-14a
LC-66b
LC-49
LC-19a
LC-137b
The distance
from the well
Distance to Receptor (feet)
2931.0
4382.6
7318.1
10390.6
12025.9
13082.4
is measured in the Groundwater Flow Angle
to the compliance boundary.
Normal Distribution Assumption Lognormal Distribution Assumption
Sample
Sample Event Szje
Sample
Mean
Sample
Stdev.
TRICHLOROETHYLENE (TCE)
2nd Quarter 1998
3rd Quarter 1998
4th Quarter 1998
1st Quarter 1999
2nd Quarter 1999
3rd Quarter 1999
4th Quarter 1999
1st Quarter 2000
2nd Quarter 2000
3rd Quarter 2000
4th Quarter 2000
1st Quarter 2001
2nd Quarter 2001
3rd Quarter 2001
35
35
28
35
35
35
36
36
36
36
36
36
36
36
3.78E-03
8.22E-03
4.03E-02
5.51 E-03
6.76E-03
4.65E-03
4.70E-03
4.22E-03
5.32E-03
3.48E-03
3.79E-03
3.60E-03
2.38E-03
2.87E-03
4.81 E-03
1 .35E-02
4.12E-02
7.49E-03
7.65E-03
5.41 E-03
5.84E-03
5.48E-03
6.61 E-03
4.21 E-03
4.56E-03
4.43E-03
2.92E-03
3.55E-03
Cleanup Expected Celanup
Status Power Samp|e size status
Expected Alpha Expectec
Power Sample Size Level Power
Cleanup Goal = 0.005
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Attained
Not Attained
Attained
Attained
Attained
0.436
S/E
S/E
S/E
S/E
0.102
0.091
0.211
S/E
0.690
0.475
0.594
1.000
0.972
97
S/E
S/E
S/E
S/E
>100
>100
>100
S/E
49
88
63
9
18
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
Not Attained
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
S/E
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Note: #N/C means "not conducted" due to a small sample size (N<4) or that the mean concentration is much greater than the cleanup
level; Sample Size is the number of sampling locations used in the power analysis; Expected Sample Size is the number of concentration
data needed to reach the Expected Power under current sample variability.
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 1 of
-------
Risk-Based Power Analysis — Projected Concentrations
Project: Fort Lewis Upper Aquifer
Seattle
From Period 6/1/1998 to
Sampling
Event
Effective
Date
9/1/2001
Well
Distance from
Observed
Concentration
(mg/L)
Name: Meng
Washington
the most downgradient well to
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
recep 2000 feet
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-144a
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-49a
LC-51
LC-53
LC-64a
6.000E-04
3.200E-02
3.400E-02
1 .700E-02
6.000E-04
2.450E-04
6.000E-04
1 .900E-02
7.300E-02
2.800E+00
7.800E+01
7.000E-02
1 .OOOE-01
1 .200E-01
4.300E-03
3.400E-02
6.000E-04
6.000E-04
4.700E-02
4.500E-01
6.000E-04
2.000E-01
9.700E-02
7.600E-02
1.400E-04
1 .800E-01
1 .400E-02
2.600E-01
8.900E-02
1.500E-01
1.500E-01
7.500E-01
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
12261.0
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
10398.3
13040.1
12161.4
13561.5
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
5.365E-05
5.598E-03
2.296E-03
3.618E-04
1 .205E-04
4.873E-05
1.129E-04
4.927E-03
1 .075E-02
5.454E-02
1.661E+00
1 .492E-03
2.305E-03
2.762E-03
9.885E-05
9.917E-04
6.315E-06
6.356E-06
1 .329E-02
8.307E-03
1 .344E-04
6.242E-03
3.029E-03
2.374E-03
2.048E-06
1.691E-02
1 .336E-03
1 .300E-02
4.441 E-03
3.495E-03
4.502E-03
1 .504E-02
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 1 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
2nd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
6/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-01
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
5.900E-02
9.600E-02
1 .200E-01
4.800E-04
1.100E-02
5.500E-02
3.700E-02
2.000E-02
4.500E-03
5.300E-03
2.700E-03
1 .500E-01
4.500E-01
7.000E-01
1.100E-01
1 .500E-02
1.100E-01
3.000E-02
6.700E-02
8.800E-02
9.600E-02
7.500E-02
6.800E-02
6.700E-02
3.300E-02
5.000E-04
1 .600E-01
1 .900E-03
5.200E-03
2.300E-03
4.600E-03
1.500E-03
4.400E-02
1.200E-01
1.100E-02
6.000E-04
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
11934.0
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
11572.4
3217.7
3048.1
2000.0
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.88E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
1.183E-03
1.166E-02
1 .455E-02
1.193E-04
2.219E-03
1 .068E-02
7.121E-03
3.816E-03
8.509E-04
9.930E-04
5.014E-04
4.807E-03
9.150E-03
1 .429E-02
2.416E-03
3.017E-03
2.414E-03
6.019E-03
1.341E-02
1 J60E-02
1.917E-02
1 .494E-02
1 .344E-02
1.313E-02
4.042E-03
5.340E-05
5.691 E-03
7.514E-04
2.160E-03
1 .292E-03
1 .976E-03
1.718E-04
9.203E-03
1 .068E-02
3.474E-04
1.421E-04
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 2 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-49a
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
3.000E-04
1 .OOOE-04
2.400E-02
5.400E-02
2.800E+00
1.100E+02
9.800E-02
5.500E-01
3.300E-01
1 .580E-02
1 .OOOE-04
4.000E-04
1.100E-01
2.900E-01
2.000E-04
1 .567E-01
1 .200E-01
4.515E-02
1. OOOE-04
1 .450E-01
2.000E-02
2.600E-01
9.200E-02
2.000E-01
2.100E-01
5.800E-01
8.000E-02
1 .200E-01
4.800E-01
8.000E-04
9.700E-03
5.600E-02
3.500E-02
2.000E-02
5.900E-03
5.600E-03
3.100E-03
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
10398.3
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
7.042E-05
2.232E-05
7.147E-03
9.677E-03
8.167E-02
3.475E+00
3.099E-03
1 .866E-02
1.118E-02
5.347E-04
1 .679E-06
6.754E-06
3.539E-02
8.060E-03
5.221 E-05
6.977E-03
5.346E-03
2.012E-03
2.256E-06
1 J36E-02
2.428E-03
1 J68E-02
6.242E-03
6.851 E-03
9.030E-03
1 J36E-02
2.395E-03
1.810E-02
7.226E-02
2.293E-04
2.306E-03
1 .286E-02
7.976E-03
4.522E-03
1 .323E-03
1 .246E-03
6.841 E-04
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 3 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
3rd Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
9/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
LX-17
LX-18
LX-19
LX-2
LX-21
LX-3
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
T-13b
LC-05
LC-06
LC-108
LC-116b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-19a
LC-19b
LC-19c
6.900E-01
1 .OOOE+00
1 .200E-01
1 .600E-02
1 .500E-01
3.500E-02
9.600E-02
1 .200E-01
1 .OOOE-01
7.500E-02
7.400E-02
5.800E-02
4.000E-04
1 .700E-03
1 .500E-02
3.700E-03
6.200E-03
1.800E-02
6.700E-02
1 .500E-01
3.000E-04
1 .800E-02
7.700E-02
1 .OOOE+00
4.550E+01
8.000E-02
4.800E-02
3.950E-02
2.300E-02
1 .OOOE-04
3.000E-04
4.600E-02
1.100E-01
1 .900E-01
7.800E-02
5.300E-02
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3217.7
3048.1
2000.0
2931 .0
6047.2
9348.3
13354.3
5601 .7
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
12025.9
12024.1
12022.5
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-2.59E-04
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
2.092E-02
3.042E-02
3.898E-03
3.793E-03
4.869E-03
8.279E-03
2.264E-02
2.827E-02
2.350E-02
1.751E-02
1.714E-02
8.811E-03
5.373E-05
7.394E-04
6.817E-03
2.205E-03
2.904E-03
1 .282E-02
3.963E-02
7.085E-02
2.190E-04
1 .384E-02
5.301 E-02
4.642E-01
2.149E+01
3.780E-02
2.303E-02
1 .894E-02
1.103E-02
4.118E-05
1 .237E-04
3.596E-02
5.053E-02
9.669E-02
3.970E-02
2.698E-02
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 4 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
4th Quarter 1998
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
12/1/1998
3/1/1999
3/1/1999
3/1/1999
3/1/1999
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
LC-03
LC-05
LC-06
LC-108
1 .700E-01
1 .800E-02
3.000E-01
1.400E-01
1.600E-01
1 .200E+00
4.100E-02
1 .400E-01
1 .200E-01
5.500E-03
6.700E-02
1 .400E-02
1 .700E-02
5.100E-03
5.000E-03
2.500E-03
8.700E-01
1 .OOOE+00
2.000E-01
1 .400E-02
1 .300E-01
3.500E-02
9.800E-02
1 .200E-01
5.500E-02
8.300E-02
3.950E-02
2.600E-02
9.000E-04
2.000E-03
3.200E-03
2.700E-03
8.000E-04
6.000E-03
9.800E-03
6.400E-03
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3217.7
3048.1
2000.0
8375.0
6047.2
9348.3
13354.3
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-5.62E-05
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
1 .072E-01
1.139E-02
1 .674E-01
6.730E-02
8.081 E-02
5.602E-01
1.914E-02
9.285E-02
7.955E-02
4.026E-03
4.869E-02
1.016E-02
1.231 E-02
3.687E-03
3.608E-03
1.801E-03
4.073E-01
4.685E-01
9.504E-02
1 .024E-02
6.177E-02
2.559E-02
7.162E-02
8.767E-02
4.016E-02
6.052E-02
2.875E-02
1 J27E-02
5.821 E-04
1 .669E-03
2.696E-03
2.413E-03
1 .039E-04
1 .374E-03
1 .004E-03
2.470E-04
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 5 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
2.000E-04
3.000E-04
5.000E-04
9.500E-03
4.500E-02
1 .400E+00
1 .200E+02
1.100E-01
3.700E-02
5.500E-02
1 .600E-02
1 .OOOE-04
1 .OOOE-04
4.000E-02
5.000E-01
1 .OOOE-04
2.200E-01
3.300E-01
5.100E-02
3.000E-04
1 .700E-01
1 .300E-02
2.500E-01
1.800E-01
1.800E-01
1.100E+00
5.600E-02
1.100E-01
1 .600E-01
1 .350E-03
1 .300E-02
7.800E-02
5.000E-02
3.300E-02
5.400E-03
6.000E-03
3.500E-03
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
5.149E-05
7.660E-05
1.218E-04
3.035E-03
8.910E-03
5.013E-02
4.633E+00
4.251 E-03
1 .527E-03
2.268E-03
6.591 E-04
2.128E-06
2.140E-06
1 .375E-02
1.711E-02
2.822E-05
1.174E-02
1.761E-02
2.723E-03
8.434E-06
2.302E-02
1 J84E-03
1 .987E-02
7.500E-03
9.291 E-03
4.036E-02
2.055E-03
1.851E-02
2.689E-02
4.160E-04
3.359E-03
1 .952E-02
1 .242E-02
8.134E-03
1.321 E-03
1 .456E-03
8.432E-04
Yes
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 6 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
1st Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
3/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
LX-17
LX-18
LX-19
LX-2
LX-21
LX-3
LX-4
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
5.800E-01
9.600E-01
1 .400E-01
1 .400E-02
1.100E-01
3.200E-02
9.200E-02
1 .300E-01
4.500E-02
8.600E-02
8.300E-02
4.200E-02
1 .800E-03
1 .600E-03
5.300E-03
2.500E-03
5.300E-03
4.500E-04
2.200E-02
5.000E-02
2.000E-02
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
2.100E-02
8.000E-02
5.600E-01
1 .OOOE+02
5.000E-02
9.500E-02
8.000E-02
4.000E-04
1 .OOOE-04
1 .OOOE-04
5.800E-02
1 .850E-01
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3217.7
3048.1
2000.0
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-2.44E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
2.154E-02
3.576E-02
5.549E-03
3.608E-03
4.357E-03
8.230E-03
2.361 E-02
3.331 E-02
1.150E-02
2.184E-02
2.092E-02
7.118E-03
2.717E-04
7.304E-04
2.521 E-03
1 .535E-03
2.594E-03
8.783E-05
6.762E-03
8.071 E-03
1 .478E-03
3.375E-05
3.353E-05
3.228E-05
8.425E-03
2.188E-02
3.897E-02
7.391 E+00
3.698E-03
7.409E-03
6.233E-03
3.114E-05
4.589E-06
4.609E-06
2.467E-02
1 .242E-02
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 7 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
6/1/1999
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
1 .OOOE-04
9.000E-02
9.000E-02
5.400E-02
1. OOOE-04
1 .500E-01
1 .750E-02
1.000E-01
9.000E-02
1.000E-01
5.100E-01
6.400E-02
7.150E-02
7.000E-02
4.500E-04
9.800E-03
6.300E-02
4.500E-02
2.700E-02
6.800E-03
7.500E-03
4.400E-03
3.900E-01
5.400E-01
1 .200E-01
1 .200E-02
9.300E-02
2.500E-02
5.700E-02
9.950E-02
9.400E-02
8.300E-02
7.400E-02
6.900E-02
5.200E-02
2.000E-03
1 .650E-03
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3217.7
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
-1 .95E-04
3.633E-05
8.617E-03
8.620E-03
5.174E-03
5.734E-06
3.027E-02
3.569E-03
1.317E-02
7.070E-03
9.325E-03
3.619E-02
4.542E-03
1.717E-02
1 .679E-02
1 J54E-04
3.317E-03
2.078E-02
1 .476E-02
8.801 E-03
2.203E-03
2.415E-03
1 .408E-03
2.794E-02
3.879E-02
9.058E-03
4.054E-03
7.016E-03
8.431 E-03
1.919E-02
3.348E-02
3.160E-02
2.785E-02
2.471 E-02
2.290E-02
1 .256E-02
4.402E-04
8.808E-04
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 8 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 1999
2nd Quarter 1999
2nd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
6/1/1999
6/1/1999
6/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
8.800E-03
3.100E-03
5.500E-03
1.200E-03
4.400E-02
1.200E-01
4.000E-04
1 .OOOE-04
3.000E-04
1 .OOOE-04
2.200E-02
9.100E-02
2.000E+00
1 .300E+02
8.100E-02
2.700E-01
2.100E-01
8.100E-03
1 .OOOE-04
1 .OOOE-04
6.200E-02
2.800E-01
1 .OOOE-04
1 .700E-01
1 .200E-01
4.600E-02
1. OOOE-04
1 .900E-01
1 .800E-02
1.700E-01
1.600E-01
1.700E-01
3.700E-01
3.600E-02
8.250E-02
1 .200E-01
3048.1
2000.0
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
-1 .95E-04
-1 .95E-04
-1 .95E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
4.856E-03
2.099E-03
3.105E-03
1.147E-04
8.075E-03
8.729E-03
9.465E-06
2.099E-05
6.239E-05
1 .970E-05
5.921 E-03
1.413E-02
4.342E-02
3.077E+00
1.919E-03
6.903E-03
5.362E-03
2.066E-04
1.193E-06
1.201E-06
1.815E-02
5.769E-03
2.333E-05
5.838E-03
4.123E-03
1.581 E-03
1 .644E-06
1 .905E-02
1 .832E-03
9.233E-03
4.134E-03
5.620E-03
8.261 E-03
8.039E-04
1 .062E-02
1 .542E-02
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 9 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
3rd Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
9/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-01
T-04
T-08
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
8.000E-04
1 .400E-02
6.100E-02
4.000E-02
2.300E-02
7.300E-03
4.000E-03
4.800E-01
5.350E-01
1 .OOOE-01
1.100E-02
1.100E-01
2.400E-02
5.900E-02
1 .OOOE-01
9.200E-02
8.300E-02
7.200E-02
6.900E-02
4.400E-02
2.100E-03
2.400E-03
1 .OOOE-02
3.800E-03
5.300E-03
8.500E-04
2.700E-02
1.100E-01
3.400E-02
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
2.850E-02
1 .OOOE-01
1 .975E+00
1 .800E+02
4830.2
5553.1
5684.8
5715.9
5746.4
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3217.7
3048.1
2000.0
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.80E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
2.065E-04
2.951 E-03
1 .239E-02
8.056E-03
4.593E-03
1 .432E-03
7.780E-04
1 .086E-02
1.215E-02
2.440E-03
2.312E-03
2.682E-03
5.033E-03
1 .234E-02
2.090E-02
1.921E-02
1 J28E-02
1 .488E-02
1.414E-02
5.711 E-03
2.385E-04
9.737E-04
4.255E-03
2.169E-03
2.330E-03
8.789E-05
5.245E-03
8.737E-03
9.121E-04
2.212E-05
2.192E-05
2.080E-05
8.015E-03
1 .652E-02
4.875E-02
4.831 E+00
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 10 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
5.000E-02
5.700E-02
1 .300E-01
3.000E-04
1 .OOOE-04
1 .OOOE-04
5.200E-02
1 .700E-01
1 .OOOE-04
1 .700E-01
7.300E-02
4.700E-02
1. OOOE-04
1 .600E-01
3.700E-02
2.700E-01
8.000E-02
2.300E-01
4.300E-01
9.000E-03
1 .OOOE-01
1 .300E-01
1 .200E-03
1.100E-02
6.300E-02
3.600E-02
2.200E-02
6.800E-03
3.400E-03
5.500E-01
7.900E-01
1.100E-01
1 .500E-02
1.100E-01
2.400E-02
6.000E-02
1 .OOOE-01
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
1 .343E-03
1 .648E-03
3.754E-03
8.654E-06
1 .384E-06
1 .393E-06
1 .586E-02
3.990E-03
2.450E-05
6.537E-03
2.808E-03
1 .809E-03
1 .886E-06
1 J33E-02
4.067E-03
1.617E-02
2.337E-03
8.525E-03
1.091E-02
2.283E-04
1 .379E-02
1 J90E-02
3.242E-04
2.443E-03
1 .350E-02
7.651 E-03
4.637E-03
1 .409E-03
6.987E-04
1.414E-02
2.038E-02
3.040E-03
3.323E-03
3.037E-03
5.303E-03
1 .323E-02
2.203E-02
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 11 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
4th Quarter 1999
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
12/1/1999
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
9.300E-02
8.800E-02
7.700E-02
6.800E-02
2.800E-02
1 .500E-03
1 .200E-02
3.000E-03
4.400E-03
5.400E-03
6.500E-04
1.100E-02
6.800E-02
5.100E-03
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
2.500E-02
8.300E-02
1 .250E+00
1 .900E+02
9.800E-02
6.100E-02
1 .350E-01
2.000E-04
1 .OOOE-04
1 .OOOE-04
5.800E-02
3.800E-01
1 .OOOE-04
1 .800E-01
8.300E-02
3.900E-02
1. OOOE-04
1 .500E-01
1 .400E-02
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3048.1
2000.0
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.71 E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
2.046E-02
1.931E-02
1 .678E-02
1 .470E-02
3.892E-03
1 .833E-04
5.254E-03
1 J45E-03
1 .496E-03
2.441 E-03
6.583E-05
2.105E-03
5.278E-03
1 .324E-04
2.182E-05
2.162E-05
2.050E-05
6.950E-03
1 .349E-02
2.983E-02
4.934E+00
2.547E-03
1 J08E-03
3.774E-03
5.585E-06
1.331E-06
1 .339E-06
1 J50E-02
8.618E-03
2.419E-05
6.718E-03
3.099E-03
1 .457E-03
1.819E-06
1 .592E-02
1 .508E-03
No
No
No
No
No
Yes
No
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 12 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
1st Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
3/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
2.000E-01
1.700E-01
1.700E-01
3.900E-01
1 .800E-02
1.100E-01
1 .300E-01
1.100E-03
1.100E-02
1.100E-01
4.300E-02
3.000E-02
8.200E-03
4.300E-03
4.600E-01
7.700E-01
1 .OOOE-01
1 .300E-02
9.700E-02
2.200E-02
5.100E-02
9.000E-02
8.800E-02
8.400E-02
7.600E-02
6.800E-02
4.700E-02
1 .400E-03
8.500E-03
2.600E-03
1 .OOOE-04
4.600E-03
8.000E-04
2.400E-02
1.400E-01
2.400E-02
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3048.1
2000.0
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.73E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
1.167E-02
4.808E-03
6.114E-03
9.565E-03
4.416E-04
1 .490E-02
1 J58E-02
2.936E-04
2.410E-03
2.325E-02
9.010E-03
6.234E-03
1 .675E-03
8.709E-04
1.143E-02
1.921E-02
2.674E-03
2.840E-03
2.592E-03
4.795E-03
1.109E-02
1 .955E-02
1.910E-02
1.818E-02
1 .633E-02
1 .449E-02
6.416E-03
1 .678E-04
3.694E-03
1 .505E-03
3.367E-05
2.064E-03
9.478E-05
5.144E-03
1 .294E-02
7.999E-04
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
No
Yes
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 13 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-14
LX-1 5
LX-1 7
1 .OOOE-04
4.000E-04
1 .OOOE-04
2.100E-02
1 .OOOE-01
1 .450E+00
1 .600E+02
9.000E-02
5.400E-02
1.100E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
6.700E-02
2.800E-01
1 .OOOE-04
1 .700E-01
7.000E-02
4.200E-02
7.500E-05
1 .600E-01
4.200E-02
2.200E-01
1.500E-01
9.500E-02
3.400E-01
1 .800E-02
1 .OOOE-01
1.100E-01
9.000E-04
9.200E-03
7.700E-02
5.100E-02
3.600E-02
7.600E-03
4.500E-03
4.000E-01
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5809.2
5840.1
13512.4
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
2.422E-05
9.604E-05
2.285E-05
6.373E-03
1.841E-02
4.469E-02
5.335E+00
3.004E-03
1.931E-03
3.929E-03
3.568E-06
1 J89E-06
1 .800E-06
2.194E-02
8.230E-03
2.666E-05
7.947E-03
3.274E-03
1 .965E-03
1 J95E-06
1 .980E-02
5.270E-03
1 .560E-02
5.416E-03
4.290E-03
1 .075E-02
5.692E-04
1 .553E-02
1 J06E-02
2.630E-04
2.236E-03
1.810E-02
1.189E-02
8.330E-03
1.731E-03
1.017E-03
1.281E-02
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 14 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
2nd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
6/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
LX-18
LX-19
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-134
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-162
5.800E-01
9.200E-02
1 .300E-02
9.600E-02
2.000E-02
5.000E-02
1 .OOOE-01
1 .OOOE-01
9.600E-02
8.900E-02
8.000E-02
6.600E-02
1 .600E-03
1 .200E-02
2.400E-03
1 .OOOE-04
4.800E-03
1.800E-02
4.800E-02
1. OOOE-01
2.400E-02
1 .OOOE-04
4.100E-03
1 .OOOE-04
6.200E-02
9.100E-02
2.050E+00
1 .900E+02
8.300E-02
3.300E-01
2.100E-01
3.000E-04
1 .OOOE-04
1 .OOOE-04
5.200E-02
2.300E-01
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
3048.1
2000.0
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13661.3
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
13847.7
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-2.55E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
1 .863E-02
3.153E-03
3.152E-03
3.288E-03
4.838E-03
1 .207E-02
2.412E-02
2.409E-02
2.307E-02
2.125E-02
1 .895E-02
1 .033E-02
2.218E-04
5.521 E-03
1 .442E-03
3.627E-05
2.275E-03
1 .226E-03
6.897E-03
4.983E-03
3.308E-04
1 .676E-05
6.796E-04
1 .558E-05
1.381E-02
1 .079E-02
2.560E-02
2.620E+00
1.146E-03
4.970E-03
3.158E-03
4.505E-06
6.296E-07
6.341 E-07
1 .275E-02
2.706E-03
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
Yes
No
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 15 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
3rd Quarter 2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
9/1/2000
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-9
PA-381
PA-383
T-04
T-08
1 .OOOE-04
1 .800E-01
9.800E-02
5.300E-02
1. OOOE-04
1 .800E-01
2.700E-02
2.300E-01
1.600E-01
2.100E-01
2.500E-01
2.100E-02
8.000E-02
1.100E-01
7.000E-04
7.600E-03
6.400E-02
3.500E-02
2.300E-02
5.300E-03
5.800E-03
2.900E-03
4.700E-01
6.550E-01
8.800E-02
9.800E-03
1 .OOOE-01
2.000E-02
5.600E-02
6.300E-02
8.900E-02
8.300E-02
6.700E-02
3.500E-02
1 .OOOE-03
8.300E-03
2.200E-03
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5653.9
7283.1
7758.7
3048.1
2000.0
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
-3.21 E-04
1.891E-05
3.799E-03
2.070E-03
1.120E-03
9.082E-07
1 .295E-02
1 .976E-03
8.203E-03
2.439E-03
4.244E-03
3.224E-03
2.709E-04
7.662E-03
1.051E-02
1 .486E-04
1 .280E-03
1 .033E-02
5.593E-03
3.640E-03
8.304E-04
8.995E-04
4.453E-04
6.157E-03
8.617E-03
1 .256E-03
1 .645E-03
1 .426E-03
3.347E-03
9.347E-03
1.051E-02
1 .482E-02
1 .378E-02
1 .092E-02
3.383E-03
8.298E-05
3.122E-03
1.158E-03
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 16 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2000
3rd Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
9/1/2000
9/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
1 .OOOE-04
3.750E-03
2.000E-03
7.600E-02
4.600E-02
4.200E-03
1 .OOOE-04
5.700E-03
1 .OOOE-04
2.200E-02
1 .OOOE-01
7.500E+01
1 .OOOE-01
2.800E-01
2.800E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
5.000E-02
1 .OOOE-04
1 .OOOE-01
1.100E-01
3.700E-02
4.700E-02
8.000E-02
3.000E-02
3.300E-01
1.700E-01
2.700E-01
1 .OOOE-02
2.350E-02
8.300E-02
1 .300E-01
9.000E-04
4.900E-03
3.550E-02
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
-3.21 E-04
-3.21 E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
2.788E-05
1 .464E-03
1 .580E-04
1.216E-02
2.706E-03
7.337E-05
1 .850E-05
1 .044E-03
1 J27E-05
5.323E-03
1 .335E-02
1.311E+00
1 J50E-03
5.319E-03
5.311E-03
1 .895E-06
8.333E-07
8.391 E-07
1 .325E-02
2.074E-05
2.613E-03
2.876E-03
9.677E-04
5.537E-04
6.658E-03
2.538E-03
1.415E-02
3.266E-03
6.771 E-03
1.641 E-04
3.856E-04
9.051 E-03
1.415E-02
2.082E-04
9.105E-04
6.338E-03
Yes
No
Yes
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 17 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
4th Quarter 2000
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
12/1/2000
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
LX-11
LX-12
LX-13
LX-14
LX-15
LX-17
LX-18
LX-19
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-9
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
1 .600E-02
1 .OOOE-02
2.900E-03
2.700E-03
1 .550E-03
2.900E-01
3.825E-01
5.500E-02
6.000E-03
5.000E-02
1 .350E-02
3.600E-02
5.250E-02
5.500E-02
5.000E-02
3.900E-02
4.300E-02
1.100E-03
8.000E-03
2.900E-03
1 .OOOE-04
4.900E-03
1.500E-03
8.300E-02
6.700E-02
1 .300E-02
1 .OOOE-04
1 .400E-02
1 .OOOE-04
2.100E-02
9.700E-02
1 .900E+02
1.100E-01
2.700E-01
2.500E-01
1 .OOOE-04
5715.9
5746.4
5777.3
5809.2
5840.1
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5653.9
7283.1
7758.7
3048.1
2000.0
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13352.7
13348.8
13077.8
13082.4
13086.6
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.03E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
2.830E-03
1 J52E-03
5.035E-04
4.642E-04
2.640E-04
4.829E-03
6.395E-03
9.931 E-04
1.112E-03
9.021 E-04
2.494E-03
6.634E-03
9.666E-03
1.011E-02
9.167E-03
7.029E-03
4.730E-03
1 .048E-04
3.176E-03
1 .582E-03
2.992E-05
2.016E-03
1 .086E-04
1 .247E-02
3.576E-03
1 .977E-04
1 J46E-05
2.418E-03
1 .626E-05
4.840E-03
1 .208E-02
2.890E+00
1 .675E-03
4.477E-03
4.139E-03
1 .653E-06
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
Yes
No
Yes
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 18 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
1 .OOOE-04
1 .OOOE-04
5.800E-02
1 .OOOE-04
1 .600E-01
8.600E-02
4.400E-02
3.000E-04
1 .900E-01
3.400E-02
2.400E-01
1.500E-01
2.200E-01
8.600E+00
1 .600E-02
6.700E-02
1.100E-01
7.000E-04
1.100E-02
4.300E-02
2.600E-02
2.000E-02
6.500E-03
5.900E-03
3.000E-03
8.000E-01
1 .200E-01
1 .400E-02
9.200E-02
2.300E-02
5.800E-02
9.100E-02
8.200E-02
7.900E-02
7.300E-02
6.100E-02
2.300E-02
15796.4
15773.9
4382.6
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
7.071 E-07
7.121E-07
1 .468E-02
1 .965E-05
3.689E-03
1 .984E-03
1.016E-03
3.035E-06
1 .452E-02
2.642E-03
9.239E-03
2.517E-03
4.862E-03
1 .225E-01
2.280E-04
6.771 E-03
1.109E-02
1 .540E-04
1 .929E-03
7.237E-03
4.333E-03
3.302E-03
1 .063E-03
9.550E-04
4.809E-04
1.162E-02
1 .888E-03
2.448E-03
1 .446E-03
4.010E-03
1 .009E-02
1.581E-02
1 .423E-02
1 .366E-02
1 .252E-02
1 .037E-02
2.345E-03
Yes
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 19 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
1st Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
3/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
PA-383
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
8.000E-04
1 .200E-02
2.400E-03
1 .OOOE-04
4.250E-03
1.500E-03
4.100E-02
7.400E-02
1 .600E-02
1 .OOOE-04
1.100E-02
1 .OOOE-04
2.200E-02
9.900E-02
1 .800E+02
9.200E-02
3.500E-01
3.200E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
3.500E-02
1 .OOOE-04
1 .600E-01
4.500E-02
6.200E-02
3.000E-04
2.000E-01
2.800E-02
2.400E-01
1.500E-01
1.900E-01
1 .400E+01
2.200E-02
6.800E-02
1.100E-01
7758.7
3048.1
2000.0
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
6644.7
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.13E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
7.028E-05
4.615E-03
1 .282E-03
2.870E-05
1 .696E-03
7.731 E-05
4.818E-03
2.702E-03
1.414E-04
1 .392E-05
1.514E-03
1 .285E-05
4.192E-03
9.415E-03
1 .592E+00
8.148E-04
3.412E-03
3.114E-03
9.718E-07
3.723E-07
3.753E-07
7.415E-03
1.591 E-05
2.264E-03
6.371 E-04
8.782E-04
1 .674E-06
1 .095E-02
1 .563E-03
6.059E-03
1 .482E-03
2.562E-03
1.150E-01
1 .808E-04
5.107E-03
8.242E-03
Yes
No
No
Yes
No
Yes
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 20 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
2nd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
6/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-04
T-08
T-12b
T-13b
LC-03
LC-05
LC-06
LC-108
LC-111b
LC-116b
LC-122b
LC-128
6.000E-04
1 .OOOE-02
3.900E-02
2.100E-02
1 .600E-02
5.600E-03
4.700E-03
2.400E-03
1 .200E-01
1.100E+00
1 .050E+00
1 .300E-01
1 .300E-02
9.600E-02
2.700E-02
5.400E-02
8.550E-02
7.900E-02
7.200E-02
8.200E-02
5.500E-02
3.600E-02
8.000E-04
1 .500E-01
8.800E-03
1 .900E-03
1 .OOOE-04
4.000E-03
2.200E-03
7.300E-02
6.100E-02
4.000E-03
1 .OOOE-04
1 .400E-02
1 .OOOE-04
1 .350E-02
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
11934.0
13512.4
13499.3
13245.1
5563.1
13247.8
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
11572.4
3048.1
2000.0
3981 .5
2931 .0
8375.0
6047.2
9348.3
13354.3
5567.9
5601 .7
5795.4
4681 .9
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.54E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
1 .085E-04
1 .400E-03
5.210E-03
2.775E-03
2.092E-03
7.240E-04
6.009E-04
3.035E-04
1 J54E-03
9.194E-03
8.817E-03
1.194E-03
1.813E-03
8.812E-04
3.754E-03
7.486E-03
1.184E-02
1 .092E-02
9.921 E-03
1.120E-02
7.429E-03
2.731 E-03
5.128E-05
2.492E-03
2.991 E-03
9.358E-04
2.442E-05
1.417E-03
1 .246E-04
9.184E-03
2.475E-03
4.111E-05
1 .483E-05
2.052E-03
1 .372E-05
2.712E-03
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
No
Yes
No
No
Yes
Yes
No
Yes
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 21 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-137C
LC-149C
LC-149d
LC-14a
LC-165
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-64b
LC-66a
LC-66b
LC-73a
LX-1
LX-10
LX-11
LX-1 2
LX-1 3
LX-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-2
LX-21
1.100E-01
2.500E+02
1 .250E-01
4.100E-01
3.050E-01
1 .OOOE-04
1 .OOOE-04
1 .OOOE-04
4.600E-02
1 .OOOE-04
1 .700E-01
1 .400E-01
6.800E-02
2.000E-03
1 .900E-01
3.000E-02
2.500E-01
1.600E-01
1.900E-01
1 .900E+01
1 .550E-02
6.200E-02
1 .300E-01
8.000E-04
1 .OOOE-02
4.600E-02
2.000E-02
1 .300E-02
5.300E-03
4.200E-03
2.300E-03
1 .400E-01
7.800E-01
1 .200E+00
1 .600E-01
1.100E-02
1 .OOOE-01
6644.7
13352.7
13348.8
13077.8
13082.4
13086.6
15796.4
15773.9
4382.6
5190.8
12025.9
12024.1
12022.5
14654.1
8203.5
8149.5
10390.6
13040.1
12161.4
13561.5
13560.8
7311.6
7318.1
4830.2
5553.1
5684.8
5715.9
5746.4
5777.3
5809.2
5840.1
11934.0
13512.4
13499.3
13245.1
5563.1
13247.8
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
1.128E-02
2.571 E+00
1 .287E-03
4.632E-03
3.440E-03
1.126E-06
4.449E-07
4.484E-07
1 .024E-02
1 .687E-05
2.755E-03
2.270E-03
1.103E-03
1.316E-05
1.141E-02
1 .836E-03
7.096E-03
1.831E-03
2.939E-03
1.819E-01
1 .484E-04
5.057E-03
1 .058E-02
1 .527E-04
1 .490E-03
6.553E-03
2.819E-03
1.813E-03
7.314E-04
5.733E-04
3.106E-04
2.341 E-03
7.593E-03
1.173E-02
1 J07E-03
1 .634E-03
1 .066E-03
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
Yes
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 22 of 2:
-------
Project: Fort Lewis Upper Aquifer
Location: Seattle
Name: Meng
Washington
Sampling
Event
Effective
Date
Well
Observed
Concentration
(mg/L)
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
3rd Quarter 2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
9/1/2001
LX-3
LX-4
LX-5
LX-6
LX-7
LX-8
LX-9
PA-381
PA-383
RW-1
T-04
T-08
T-12b
T-13b
2.100E-02
5.200E-02
7.200E-02
7.800E-02
7.600E-02
6.800E-02
5.400E-02
3.500E-02
1 .OOOE-03
1 .500E-01
8.800E-03
2.500E-03
1 .OOOE-04
3.850E-03
5571 .9
5580.3
5583.3
5587.7
5597.5
5623.3
5653.9
7283.1
7758.7
11572.4
3048.1
2000.0
3981 .5
2931 .0
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
-3.43E-04
3.110E-03
7.677E-03
1 .062E-02
1.149E-02
1.116E-02
9.893E-03
7.774E-03
2.883E-03
6.997E-05
2.839E-03
3.095E-03
1 .259E-03
2.554E-05
1.410E-03
No
No
No
No
No
No
No
No
Yes
No
No
No
Yes
No
No
No
No
No
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Note: Projected Concentrations that are below the user-specified detection limit are indicated by a check mark to its right; for sampling events
with less than 3 selected plume centerline wells, NO projected concentrations are calculated because no regression coefficient is available.
MAROS Version 2, 2002, AFCEE
Friday, March 21, 2003
Page 23 of 2:
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DRAFT FINAL
THREE-TIERED
GROUND WATER MONITORING NETWORK
OPTIMIZATION EVALUATION
FOR THE
FORT LEWIS LOGISTICS CENTER, WASHINGTON
Prepared for
US Environmental Protection Agency
May 2003
Denver, Colorado
-------
EXECUTIVE SUMMARY
This report presents a description and evaluation of the groundwater monitoring
program associated with the Logistics Center at Fort Lewis, Washington. The monitoring
program at this site was evaluated to identify potential opportunities to streamline
monitoring activities while still maintaining an effective monitoring network. This
evaluation is being conducted as part of an independent assessment of monitoring
network optimization (MNO) methods by the US Environmental Protection Agency
(USEPA) and the Air Force Center for Environmental Excellence (AFCEE).
Groundwater monitoring programs have two primary objectives (U.S. Environmental
Protection Agency [USEPA], 1994b; Gibbons, 1994):
1. Evaluate long-term temporal trends in contaminant concentrations (temporal
objective)', and
2. Evaluate the extent to which contaminant migration is occurring (spatial
objective).
The relative success of any remediation system (including the monitoring network) is
judged based on the degree to which it achieves the stated objectives of the system.
Designing an effective groundwater monitoring program involves locating monitoring
points and developing a site-specific strategy for groundwater sampling and analysis that
maximizes the amount of relevant information that can be obtained while minimizing
incremental costs. The effectiveness of a monitoring network in achieving the two
primary monitoring objectives can be evaluated quantitatively using statistical
techniques; qualitative evaluation also is important to allow consideration of such factors
as hydrostratigraphy, locations of potential receptor exposure points with respect to a
dissolved contaminant plume, and the direction(s) and rate(s) of contaminant migration.
Groundwater monitoring has been conducted at the Logistics Center on a quarterly
basis since December 1995. As part of this quarterly monitoring program, 38 monitoring
wells and 21 groundwater extraction wells have been sampled. In May 2001, US Army
Corps of Engineers (USAGE) published a Draft Logistics Center (FTLE-33) Remedial
Action Monitoring Optimization Report that presents the results of a MNO evaluation
conducted for the groundwater extraction and treatment system in operation at the
Logistics Center. Based on the MNO evaluation, USAGE (2001) recommended adding
24 monitoring wells (18 existing and 6 new wells) to the sampling network and removing
11 previously sampled monitoring wells from the sampling network (a net increase of 13
monitoring wells), and generally reducing sampling frequencies. The revised Logistics
ES-1
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Center remedial action monitoring network (LOGRAM) consists of 72 wells— 51 Vashon
Aquifer monitoring wells and 21 extraction wells.
The chemical analytical data used in the evaluation of the remedial action (RA)
monitoring program were compiled using groundwater monitoring results for the nearly 7
years of quarterly sampling events performed from February 1995 through December
2001 provided by the Seattle district of the U.S. Army Corps of Engineers. Sampling
results for four chlorinated solvent compounds (i.e., tetrachloroethene [PCE],
trichloroethene [TCE], czs-l,2-dichloroethene [DCE], and vinyl chloride [VC]) were
utilized in the MNO analysis. TCE is the primary contaminant of concern in
groundwater at the Logistics Center, and TCE sampling results were the primary data
used to conduct the three-tiered monitoring network optimization due to the magnitude
and spatial extent of TCE concentrations in groundwater at Fort Lewis compared to the
other detected compounds.
The general objective of the project was to optimize the Fort Lewis Upper Aquifer
long-term monitoring network by applying a three-tiered MNO approach to assess the
degree to which the monitoring network addresses each of the two primary objectives of
monitoring listed above and other important considerations. The three-tiered MNO
evaluation described in this report examines the 83 wells (21 extraction wells and 62
monitoring wells) included in both the original and the revised LOGRAM monitoring
networks. The specific objectives of the project included:
« Apply a qualitative methodology that considers factors such as
hydrostratigraphy, locations of potential receptors with respect to the dissolved
plume, and the direction(s) and rate(s) of contaminant migration to establish the
frequency at which monitoring should be conducted, and if each well should be
retained in or removed from the monitoring program.
« Conduct a Mann-Kendall statistical analysis to determine the temporal trends of
chemicals of concern over time, and apply an algorithm to determine the
relevance of the trends within the monitoring network.
. Determine the relative amount of spatial information contributed by each
monitoring well by performing a spatial statistical analysis utilizing kriging error
predictions.
« Combine and evaluate the results of the three analyses to establish the frequency
at which monitoring should be conducted, as well as the number and locations of
wells in the monitoring network.
Results from the three-tiered monitoring network optimization for the Fort Lewis site
indicate that 6 of the 72 monitoring and extraction wells included in the revised
LOGRAM program could be removed from the groundwater LTM program with little
loss of information. In addition, the three-tiered analysis supports the deletion of 9 of the
11 wells removed from the LTM program as a result of the USAGE (2001) MNO
ES-2
022/742479/Fort Lewis Draft Final.doc
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evaluation; continued monitoring of the remaining 68 wells is recommended, as well as
the addition of one well to monitor leading edge of southwestern lobe of the plume near
Murray Creek. A refined monitoring program, consisting of 69 wells (16 sampled
quarterly, 7 sampled semi-annually, 17 sampled annually, 14 sampled biennially, and the
15 1-5 extraction wells sampled every 3 years) would be adequate to address the two
primary objectives of monitoring. This refined monitoring network would result in an
average of 107 sampling events per year, compared to 180 events per year in the current
LOGRAM monitoring program and 236 yearly events in the original sampling program.
Implementing these recommendations for optimizing the monitoring program at the Fort
Lewis Logistics Center could reduce site monitoring costs by $36,500 a year (more than
40%) from the revised LOGRAM LTM plan, and $64,500 (approximately 55%) from the
original LTM program (based on a per sample cost of $500 (USAGE, 2001).
ES-3
022/742479/Fort Lewis Draft Final.doc
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TABLE OF CONTENTS
Page
EXECUTIVE SUMMARY ES-1
SECTION 1 -INTRODUCTION 1-1
SECTION 2 - SITE BACKGROUND INFORMATION 2-1
2.1 Site Description 2-1
2.2 Geology and Hydrogeology 2-2
2.2.1 Geology 2-2
2.2.2 Hydrogeology 2-3
2.3 Nature and Extent of Contamination 2-4
2.4 Remedial Systems 2-5
SECTION 3 - LONG-TERM MONITORING PROGRAM AT FORT LEWIS 3-1
3.1 Description of Original and Revised Monitoring Program 3-1
3.2 Summary of Analytical Data 3-5
SECTION 4 - QUALITATIVE MNO EVALUATION 4-1
4.1 Methodology for Qualitative Evaluation of Monitoring Network 4-2
4.2 Results of Qualitative MNO Evaluation 4-4
4.2.1 Monitoring Network and Sampling Frequency 4-4
4.2.1.1 Upper Vashon Aquifer 4-4
4.2.1.2 Lower Vashon Aquifer 4-13
4.2.2 Laboratory Analytical Program 4-15
4.2.4 LTM Program Flexibility 4-16
SECTION 5 - TEMPORAL STATISTICAL EVALUATION 5-1
5.1 Methodology for Temporal Trend Analysis of Contaminant Concentrations 5-1
5.2 Temporal Evaluation Results 5-7
SECTION 6 - SPATIAL STATISTICAL EVALUATION 6-1
6.1 Geostatistical methods for evaluating Monitoring networks 6-1
6.2 Spatial Evaluation of Monitoring Network at Fort Lewis Logistics Center 6-4
6.3 Spatial Statistical Evaluation Results 6-8
6.3.1 Kriging Ranking Results 6-8
6.3.2 Additional Well Analysis 6-12
SECTION 7 - SUMMARY OF THREE-TIERED MONITORING
NETWORK EVALUATION 7-1
SECTION 8 - REFERENCES 8-1
-i-
022/742479/Fort Lewis Draft Final.doc
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TABLE OF CONTENTS (Continued)
LIST OF TABLES
No. Title Page
3.1 Original and Revised Groundwater Monitoring Programs 3-2
3.2 Summary of Occurrence of Groundwater Contaminants of Concern 3-8
4.1 Monitoring Network Optimization Decision Logic 4-3
4.2 Monitoring Frequency Decision Logic 4-4
4.3 Qualitative Evaluation of Groundwater Monitoring Network 4-5
5.1 Results of Temporal Trend Analysis of Groundwater Monitoring Results 5-8
6.1 Results of Geostatistical Evaluation Ranking of Upper Vashon Aquifer
Wells by Relative Value of TCE Information 6-9
7.1 Summary of Evaluation of Current Groundwater Monitoring Program 7-3
LIST OF FIGURES
No. Title Page
1.1 Fort Lewis Logistics Center Study Area 1-3
3.1 Original and Revised Monitoring Networks 3-7
4.1 Qualitative Evauation Recommendations for Upper Vashon Wells 4-7
4.2 Qualitative Evaluation Recommendations for Lower Vashon Wells 4-14
5.1 TCE Concentrations Through Time at Well LC-132 5-2
5.2 Conceptual Representation of Temporal Trends and Temporal Variations
in Concentrations 5-3
5.3 Conceptual Representation of Continued Monitoirng at Location Where
No Temporal Trend in Concentrations is Present 5-6
5.4 Mann-Kendall Temporal Trend Analysis for Concentrations of TCE 5-11
5.5 Temporal Trend Decision Rationale Flowchart 5-13
6.1 Idealized Semvariogram Model 6-3
6.2 Impact of Missing Wells on Predicted Standard Error 6-7
6.3 Results of Geostatistical Analysis Showing Relative Value of
Informationon TCE Distribution in the Upper Vashon Aquifer 6-10
6.4 Location of "New" Wells Relative to Predicted Standard Error of TCE
Distribution 6-11
-11-
022/742479/Fort Lewis Draft Final.doc
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ACRONYMS AND ABBREVIATIONS
AFCEE
ASCE
bgs
coc
DCA
DCE
DNAPL
EGDY
ESRI
ft/day
ft/ft
GIS
1-5
LOGRAM
LNAPL
LTM
ug/L
MAMC
MCL
MNO
NAPL
PCE
POL
RA
TCA
TCE
USAGE
USEPA
vc
voc
Air Force Center for Environmental Excellence
American Society of Chemical Engineers
below ground surface
contaminant of concern
dichloroethane
dichloroethene
dense nonaqueous-phase liquid
East Gate Disposal Yard
Environmental Systems Research Institute, Inc.
foot (feet) per day
foot per foot
geographical information system
Interstate 5
revised remedial action monitoring plan
implementation in December, 2001
light nonaqueous-phase liquid
long-term monitoring
microgram(s) per liter
Madigan Army Medical Center
maximum contaminant level
monitoring network optimization
nonaqueous-phase liquid
tetrachloroethene
petroleum, oils, and lubricants
remedial action
trichloroethane
trichloroethene
United States Army Corps of Engineers
United States Environmental Protection Agency
vinyl chloride
volatile organic compound
proposed for
-111-
022/742479/Fort Lewis Draft Final.doc
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SECTION 1
INTRODUCTION
Groundwater monitoring programs have two primary objectives (U.S. Environmental
Protection Agency [USEPA], 1994; Gibbons, 1994):
1. Evaluate long-term temporal trends in 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
2. Evaluate the extent to which contaminant migration is occurring, particularly if
a potential exposure point for a susceptible receptor exists (spatial objective).
The relative success of any remediation system and its components (including the
monitoring network) must be judged based on the degree to which it achieves the stated
objectives of the system. Designing an effective groundwater monitoring program
involves locating monitoring points and developing a site-specific strategy for
groundwater sampling and analysis so as to maximize the amount of relevant information
that can be obtained while minimizing incremental costs. Relevant information is that
required to effectively address the temporal and spatial objectives of monitoring. The
effectiveness of a monitoring network in achieving these two primary objectives can be
evaluated quantitatively using statistical techniques. In addition, there may be other
important considerations associated with a particular monitoring network that are most
appropriately addressed through a qualitative assessment of the network. The qualitative
evaluation may consider such factors as hydrostratigraphy, locations of potential receptor
exposure points with respect to a dissolved contaminant plume, and the direction(s) and
rate(s) of contaminant migration.
1-1
022/742479/Fort Lewis Draft Final.doc
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This report presents a description and evaluation of the groundwater monitoring
program associated with the Logistics Center at Fort Lewis, Washington. The
groundwater monitoring program at this site was evaluated to identify potential
opportunities to streamline monitoring activities while still maintaining an effective
monitoring network. This effort is being conducted as part of an independent assessment
of monitoring network optimization methods by the USEPA and the Air Force Center for
Environmental Excellence (AFCEE). A three-tiered approach, consisting of a qualitative
evaluation, an evaluation of temporal trends in contaminant concentrations, and a
statistical spatial analysis, was conducted to assess the degree to which the monitoring
network addresses each of the two primary objectives of monitoring, and other important
considerations. The results of the three evaluations were combined and used to assess the
optimal frequency of monitoring and the spatial distribution of the components of the
monitoring network. The results of the analysis were then used to develop
recommendations for optimizing the monitoring program at Fort Lewis.
1-2
022/742479/Fort Lewis Draft Final.doc
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O
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-------
SECTION 2
SITE BACKGROUND INFORMATION
The location, operational history, geology, and hydrogeology of Fort Lewis are briefly
described in the following subsections. This information was obtained primarily from the
U.S. Army Corps of Engineers (USAGE, 2002).
2.1 SITE DESCRIPTION
Fort Lewis is located near the southern end of Puget Sound in Pierce County,
approximately 11 miles south of Tacoma and 17 miles northeast of Olympia,
Washington. The Logistics Center occupies approximately 650 acres of the Fort Lewis
Military Reservation. The installation lies on generally level glacial outwash deposits,
and is bounded on the northwest by Interstate 5 and on the south and southwest by
Murray Creek. Murray Creek discharges into American Lake, approximately 2 miles
northwest of the EGDY.
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 (Figure
1.1). Between 1946 and 1960, waste solvents (primarily TCE) and petroleum, oils, and
lubricants (POL) from cleaning, degreasing, and maintenance operations were disposed
of in trenches at the EGDY. Liquid wastes have contaminated soils and groundwater at
and downgradient from this former landfill, which is no longer active. 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 shores of American Lake. The well network used to monitor the magnitude
and extent of this plume, and to assess the performance of remedial systems installed to
2-1
022/742479/Fort Lewis Draft Final.doc
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address source-area and groundwater contamination (as described in Section 3), is the
subject of this MNO evaluation.
2.2 GEOLOGY AND HYDROGEOLOGY
2.2.1 Geology
The study area is underlain by a complex sequence of glacial and non-glacial
Quaternary sediments up to 2,000 feet thick. At least three glacial and three non-glacial
units have been identified in the sediments that occur above sea level in the study area.
Of primary interest for this evaluation is the uppermost water-bearing zone, termed the
Vashon Aquifer. The stratigraphic units that comprise the Vashon Aquifer include the
Vashon Drift, Olympia beds, and Pre-Olympia Drift.
Vashon Drift deposits typically extend from at or near the ground surface to depths of
approximately 60 to 95 feet below ground surface (bgs). However, in the region of a
deep erosional trough, these deposits extend to approximately 230 feet bgs. Lithologies
encountered in the Vashon Drift consist primarily of sands and gravels, which
occasionally are silty. The Vashon Drift deposits consist, from youngest to oldest, of the
following units:
• Vashon Recessional Outwash—sandy, cobbly gravel extending from at or near the
ground surface to depths of 5 to 50 feet bgs (typically less than 30 feet bgs).
• Vashon Till and Ice-Contact Deposits—Well-graded gravel in a matrix of sand,
silt, and clay, ranging from 4 to 35 feet in thickness. This unit is present beneath
much of the study area, but is absent at some locations.
• Vashon Advance Outwash—Medium to coarse sandy gravel with cobbles and fine
to medium sand.
• Vashon Glaciolacustrine Silt/Clay—Very stiff to hard clayey silt varying in
thickness from 10 to 150 feet.
2-2
022/742479/Fort Lewis Draft Final.doc
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The Olympia beds, which underlie the Vashon Drift, consist of alluvial sands and
gravels with silt, silty gravel, scattered wood, and peat. This unit, which is present in
some areas beneath the northern portion of the EGDY, may be up to 40 feet thick. The
Pre-Olympia Drift, which typically is 10 to 70 feet thick, consists of very fine to coarse
sand with lenses of gravelly sand and sandy silt, sandy gravel with cobbles, and silty
gravel with sand and clay seams.
2.2.2 Hydrogeology
The Vashon Aquifer, also termed the Upper Aquifer, is unconfined. The aquifer
materials consist of Vashon outwash deposits and Pre-Olympia drift deposits; the Vashon
till and ice-contact deposits, glaciolacustrine silts/clays, and Olympia beds may act
locally as discontinuous aquitards within the Vashon Aquifer. The depth to groundwater
is spatially variable, but generally ranges from 5 to 25 feet bgs throughout most of the
study area. The elevation of the water table fluctuates approximately 5 to 6 feet
seasonally, and by as much as 14.7 feet over periods of several years. The majority of
dissolved contamination associated with the EGDY source area occurs within the Vashon
Aquifer, and the groundwater monitoring network assessed in this report consists of wells
screened in the Vashon Aquifer. There is evidence that contamination has migrated into
the underlying confined Sea Level Aquifer as well, however this monitoring network
optimization focuses solely on the Vashon Aquifer.
The Vashon Aquifer is subdivided into Upper and Lower Vashon subunits, although
regionally these subunits are considered to comprise a single unconfined aquifer. The
Upper and Lower Vashon aquifer subunits are separated by the Vashon Till, which acts
as a discontinuous aquitard (USAGE, 2001). The stratigraphic units comprising the
Lower Vashon Aquifer are laterally discontinuous: they are present beneath the EGDY
and in the area north and east of well LC-41 (Figure 1.1), but are absent between the
EGDY and well LC-41.
The hydraulic conductivity of the permeable Vashon Aquifer units, which are the
primary pathways for groundwater and contaminant migration, ranges from 10 to more
2-3
022/742479/Fort Lewis Draft Final.doc
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than 1,000 feet per day (ft/day). Groundwater flow within the Vashon Aquifer is
regionally towards the northwest; however, flow directions vary locally and seasonally.
Murray Creek, a northwesterly-flowing stream that meanders south and west of the
EGDY and discharges into American Lake (Figure 1.1), likely influences local
groundwater gradients in the shallow part of the Vashon Aquifer. For example, the
extension of the dissolved contaminant plume to the southwest from the EGDY indicates
a southwesterly groundwater flow component (toward Murray Creek) in this area; the
flow direction beneath the upgradient half of the EGDY also may be locally variable.
However, the overall regional flow direction remains relatively constant, toward the
northwest. Measured horizontal hydraulic gradients within the Vashon Aquifer typically
range from 0.001 to 0.004 foot per foot (ft/ft). Horizontal groundwater flow velocities
are estimated to range from 0.05 to 15.2 ft/day.
2.3 NATURE AND EXTENT OF CONTAMINATION
TCE has been identified as the major contaminant dissolved in groundwater beneath
the Logistics Center, based on its widespread detection in wells across the site (URS,
2000). Other contaminants of concern (COCs) in groundwater include cis-1,2-
dichloroethene (czs-l,2-DCE), tetrachloroethene (PCE), 1,1,1-TCA, and vinyl chloride
(VC) (USACE and URS, 2002). TCE, DCE, and TCA have been detected consistently in
many wells, whereas PCE and VC have been only sporadically detected in a few wells.
The former waste-disposal trenches at the EGDY are the apparent source of the
groundwater contamination. Three major dense non-aqueous-phase liquid (DNAPL)
source areas have been identified within the EGDY. DNAPL was detected primarily in
the Vashon recessional outwash deposits above the uppermost Vashon till unit, and the
extent of DNAPL in the study area is currently assumed to be limited to the EGDY. In
addition to these three DNAPL source areas, light NAPL (LNAPL) was detected during
exploratory trenching and sonic drilling. The extent of the LNAPL is unknown.
In the Vashon Aquifer, groundwater contaminated with TCE at concentrations
exceeding the federal drinking-water maximum contaminant level (MCL) of 5
micrograms per liter (ug/L) extend 2 miles downgradient from the source area (i.e., the
2-4
022/742479/Fort Lewis Draft Final.doc
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EGDY) to American Lake, where it is presumed to discharge. A lobe of the TCE plume
extends west-southwest from the source area toward Murray Creek, reflecting a local
westerly hydraulic gradient. A portion of the westward lobe of the TCE plume extends to
a gaining reach of Murray Creek, where contaminated groundwater discharges to the
stream bed (Figure 1.1).
The 5-ug/L isopleth of the TCE plume has remained relatively stable since it was
defined during the remedial investigation, which was completed in 1990. The margin of
the westward lobe of the plume, however, was poorly defined until recently. Therefore, it
is not known if this portion of the plume is stable, expanding, or contracting.
Concentrations of TCE and cis-l,2-DCE have remained relatively constant in most
monitored wells since the late 1980s. Some wells (primarily extraction wells and
monitoring wells near extraction wells) have exhibited slight decreasing trends, while
other wells within the interior of the plume have exhibited slight increasing trends over
time.
2.4 REMEDIAL SYSTEMS
The engineered remedial action (RA) for contaminated groundwater at the Fort Lewis
Logistics Center includes groundwater extraction and treatment, and recharge of treated
groundwater via infiltration galleries back into the Upper Aquifer. Operation of the
treatment systems began in August 1995. One aquifer cleanup objective for the facility is
to restore the Upper Aquifer to MCLs by reducing the TCE concentration to less than 5
ug/L within 30 to 40 years (URS, 2000).
Two groundwater extraction well fields and associated treatment plants and recharge
systems have been constructed at the Logistics Center: the Interstate 5 (1-5) system and
the East Gate system. The objective of the 1-5 system, which consists of 15 extraction
wells and 4 infiltration galleries, is to prevent further migration of contaminated
groundwater in the Upper Aquifer across the installation boundary. The East Gate
system, which comprises primary and secondary extraction well fields, a recharge well,
and a infiltration trench field, is designed to remove and treat contaminated groundwater
2-5
022/742479/Fort Lewis Draft Final.doc
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from the Upper Aquifer directly downgradient from the source area in the former EGDY.
The 2-well secondary extraction well field is located approximately 1,500 feet
downgradient from the 4-well primary field (URS, 2000).
2-6
022/742479/Fort Lewis Draft Final.doc
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SECTION 3
LONG-TERM MONITORING PROGRAM AT FORT LEWIS
The groundwater monitoring program at Fort Lewis was examined to identify
potential opportunities for streamlining monitoring activities while still maintaining an
effective RA monitoring program. The monitoring program at Fort Lewis is reviewed in
the following subsections.
3.1 DESCRIPTION OF ORIGINAL AND REVISED LOGRAM
MONITORING PROGRAM
Groundwater monitoring has been conducted at the Logistics Center on a quarterly
basis since December 1995. As part of this quarterly monitoring program, 38 monitoring
wells and 21 groundwater extraction wells were sampled, resulting in a total of 59 wells
and 236 primary analytical samples per year (Table 3.1). As described by USAGE
(2001), the following data quality objectives were developed for the RA monitoring
program:
• Confirm that the primary EGDY extraction wells are capturing all dissolved COCs
migrating from the former landfill source area;
• Confirm that the secondary EGDY wells are capturing groundwater with high
contaminant concentrations (> 200 ug/L) in the Vashon Aquifer between the
primary and secondary EGDY well fields;
• Confirm that the 1-5 extraction well field is intercepting the Vashon Aquifer
contaminant plume upgradient from 1-5 and between the 1-5 well field and the 1-5
infiltration gallery;
3-1
022/742479/Fort Lewis Draft Final.doc
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TABLE 3.1
ORIGINAL AND REVISED GROUNDWATER MONITORING PROGRAMS
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS, WASHINGTON
Well ID
Screened Interval
(fbtoc) "'
Hydrologic
Unit"
Original Monitoring Network Wells
LC-03
LC-05
LC-06
LC-14a
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-66a
LC-66b
LC-73a
LC-108
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-149c
LC-149d
LC-165
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-lllb
LC-116b
LC-122b
LC-128
LC-137c
LC-64b
20-60
19-59.6
20-60
42.5-52.5
45-55
25-35
65-75
11.5-36
84.7-93.9
17-32
43-47.5
26.5-32
26.5-31.5
25-30
34.5-39.5
68-73
40-45
60-65.5
40-49.6
31-40.5
55-74.5
35-44.5
40-60
38-47.93
60-80
40-45
47-57
47-57
55-65
66-76
59.1-64.1
75.3-80.3
105-125
107-127
112-132
134-154
105-125
74-79
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-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-21
RW-1
72.5-92.5
70-100
60.5-88.5
64-94
54.5-72, 85-95
58-88
52-65, 72-92
58-88
58.5-88.5
59-89
67-78,85-99, 104-111
55-85
68.5-99.5
62-92
66-96
42-72
34.5-54.5
31-41
53-83
51.6-81.8
41.6-66.2
UV
uv
UV
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
LV
LV
LV
LV
LV
LV
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
Original
Sampling
Frequency c
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Wells Added to Monitoring Network in December 2001
FL2
FL3
FL4B
FL6
LC-16
LC-20
LC-24
LC-34
LC-57
35-40
37.5-42.5
32-37
47-57
18.5-58.5
37.5-47.5
26-46
30-35
29.3-34.3
UV
UV
UV
UV
UV
UV
UV
UV
UV
None
None
None
None
None
None
None
None
None
Revised Sampling
Frequency d/
Quarterly
Annually
Semi -Annually
Annually
Quarterly
None
None
Annually
Annually
None
Annually
None
Annually
Quarterly
None
Annually
None
None
None
Quarterly
Annually
None
Quarterly
Annually
None
None
Annually
Annually
Annually
Semi-Annually
Quarterly
Semi-Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Annually
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
022/742479/3-tiered Ft Lewis Tables.xls/Table 3.1
3-2
-------
TABLE 3.1 (Continued)
ORIGINAL AND REVISED GROUNDWATER MONITORING PROGRAMS
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS, WASHINGTON
Well ID
LC-61b
LC-167
NEW-1
NEW-2
NEW-3
NEW-4
NEW-5
NEW-6
T-06
T-llb
FL4A
LC-41b
MAMC1
MAMC6
T-10
Screened Interval
(fbtoc) "'
55-60
40-50
-
-
-
60-70
74.2-79.2
123-133
130.1-139.3
-
-
104-114
Hydrologic
Unit"
UV
uv
UV
uv
uv
uv
uv
uv
uv
uv
LV
LV
LV
LV
LV
Original
Sampling
Frequency c
None
None
None
None
None
None
None
None
None
None
None
None
None
None
None
Revised Sampling
Frequency d/
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
ft btoc = feet below top of well casing.
b/ UV = Upper Vashon Aquifer; LV = Lower Vashon Aquifer.
Sampling frequency prior to December 2001.
^ Sampling frequency as revised in December 2001 (USAGE, 2001).
022/742479/3-tieredFt Lewis Tables.xls/Table 3.1
3-3
-------
• Determine if TCE concentrations in the Vashon Aquifer contaminant plume
downgradient from the 1-5 infiltration gallery are decreasing to less than 5 ug/L;
• Assess the lateral and vertical extent and concentration of the COCs monitor
changes through time in both the Vashon and Salmon Springs Aquifers;
• Confirm that the remedial systems are ensuring that COC concentrations in
Murray Creek remain below cleanup goals for COCs (i.e., < 80 ug/L TCE) in
surface water;
• Assess COC concentrations in extraction well effluent; and
• Monitor the COC mass-removal rate and total mass of COCs removed for each of
the remedial system components (primary and secondary EGDY well fields and I-
5 well field).
In May 2001, US ACE published a Draft Logistics Center (FTLE-33) Remedial Action
Monitoring Optimization Report that presents the results of a MNO evaluation conducted
for the groundwater extraction and treatment system in operation at the Logistics Center.
As part of the MNO evaluation, the analytical data for TCE were assessed to see if a
reduction in sampling frequency from quarterly to semiannually or annually was
warranted. A corresponding non-statistical sampling-location analysis was performed to
assess which monitoring wells were best suited for continued RA monitoring based on a
synthesis of spatial uniqueness with average TCE concentration uniqueness. None of the
extraction wells was considered for elimination from the remedial-action monitoring
network.
Based on the MNO evaluation, USAGE (2001) recommended adding 24 monitoring
wells (18 existing and 6 new wells) to the sampling network and removing 11 previously
sampled monitoring wells from the sampling network (a net increase of 13 monitoring
wells), and generally reducing sampling frequencies (Table 3.1). The revised Logistics
Center remedial action monitoring network (LOGRAM) consists of 72 wells— 51 Vashon
Aquifer wells (29 sampled quarterly, 3 semiannually, and 19 annually), and all 21
3-4
022/742479/Fort Lewis Draft Final.doc
-------
extraction wells (6 sampled quarterly and 15 annually). By reducing the sampling
frequency in some of the monitoring wells, the total number of primary analytical
samples per year was reduced from 236 to 180 (a 23% reduction). The monitoring
program changes were recommended for implementation in December 2001, which
marked the 25th quarter of sampling.
The three-tiered MNO evaluation described in this report examines the 83 wells (21
extraction wells and 62 monitoring wells) included in both the original and the
LOGRAM monitoring networks (Table 3.1). Figure 3.1 shows the locations of these
wells, and their status per the revised monitoring program (USAGE, 2001).
3.2 SUMMARY OF ANALYTICAL DATA
The chemical analytical data used in the evaluation of the RA monitoring program
were compiled using groundwater monitoring results for the nearly 7 years of quarterly
sampling events performed from February 1995 through December 2001. This analytical
data, along with water level and well location information was provided to Parsons Mr.
Richard Smith from the Seattle district of the U.S. Army Corps of Engineers. The
database was processed to remove duplicate data measurements by retaining the
maximum result of the duplicate samples. Analytical data exists for 74 of the 83 wells of
included in the original and/or revised monitoring networks. Extensive data (greater than
20 sampling rounds) are available for the 21 extraction wells and the 38 monitoring wells
originally included in the sampling program. However, only limited data (typically from
fewer than four sampling rounds) are available for the 18 existing wells added to the
monitoring network in December 2001. No sampling data were available for 9 of the
wells added to the program in 2001, including the 6 NEW wells, wells MAMC 1, MAMC
6andT-llb.
Extensive sampling results for four chlorinated solvent compounds (i.e., PCE, TCE, cis-
1,2-DCE, and VC) were included in the analytical database provided to Parsons. Table
3.2 presents a summary of the occurrence of these four analytes in groundwater based on
the data collected during the sampling events from February 1995 through December
3-5
022/742479/Fort Lewis Draft Final.doc
-------
2001. As indicated in Table 3.2, TCE is the primary contaminant of concern in
groundwater at the Logistics Center. TCE has been detected in almost 90% of samples,
and has exceeded the MCL of 5 |ug/L over 73% of the time. TCE has been detected in 71
of the 74 wells that have been sampled, and has exceeded the MCL in 56 of these wells.
cis-l,2-DCE is second most prevalent compound and has been detected in over 80% of
samples; however, detected concentrations of cis-l,2-DCE have exceeded standards in
less than 6% of samples. Additionally, although PCE and VC have been detected on site
at several wells, groundwater measurements have rarely exceeded standards for these
compounds.
TCE sampling results were the primary data used to conduct the three-tiered
monitoring network optimization due to the magnitude and spatial extent of TCE
concentrations in groundwater at Fort Lewis compared to the other detected compounds.
3-6
022/742479/Fort Lewis Draft Final.doc
-------
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SECTION 4
QUALITATIVE MNO EVALUATION
An effective groundwater monitoring program will provide information regarding
contaminant plume migration and changes in chemical concentrations through time at
appropriate locations, enabling decision-makers to verify that contaminants are not
endangering potential receptors, and that remediation is occurring at rates sufficient to
achieve RA objectives within a reasonable time frame. The design of the monitoring
program should therefore include consideration of existing receptor exposure pathways,
as well as exposure pathways arising from potential future use of the groundwater.
Performance monitoring wells located upgradient, within, and immediately
downgradient from a plume provide a means of evaluating effectiveness of a groundwater
remedy relative to performance criteria. Long-term monitoring (LTM) of these wells
also provides information about migration of the plume and temporal trends in chemical
concentrations. Groundwater monitoring wells located at greater distances downgradient
from a plume (i.e., sentry wells) are used to evaluate possible changes in the extent of the
plume and, if warranted, to trigger a contingency response action if contaminants are
detected.
Primary factors to consider when developing a groundwater monitoring program
include at a minimum:
• Aquifer heterogeneity,
• Types of contaminants,
• Distance to potential receptor exposure points,
• Groundwater seepage velocity,
4-1
022/742479/Fort Lewis Draft Final.doc
-------
• Potential surface-water impacts, and
• The effects of the remediation system.
These factors will influence the locations and spacing of monitoring points and the
sampling frequency. Typically, the greater the seepage velocity and the shorter the
distance to receptor exposure points, the more frequently groundwater sampling should
be conducted.
One of the most important purposes of LTM is to confirm that the contaminant plume
is behaving as predicted. Graphical and statistical tests can be used to evaluate plume
stability. If a groundwater remediation system or strategy is effective, then over the long
term, groundwater-monitoring data should demonstrate a clear and meaningful
decreasing trend in concentrations at appropriate monitoring points. The current
groundwater monitoring program at the Fort Lewis Logistics Center was evaluated to
identify potential opportunities to assess the recent MNO results (USAGE, 2001) and, if
appropriate, to further refine the RA monitoring program.
4.1 METHODOLOGY FOR QUALITATIVE EVALUATION OF
MONITORING NETWORK
The three-tiered MNO evaluation of the Logistics Center groundwater LTM program
considered information for 83 wells in the Logistics Center study area that were included
in the original and/or revised groundwater monitoring networks (38 monitoring wells in
the original network, 24 existing and new wells added to the monitoring network in
December 2001, and the 21 groundwater extraction wells screened in the Vashon
Aquifer). These wells are listed in Table 3.1, and their locations are depicted on Figure
3.1. Subsets of these wells were evaluated in the temporal and spatial tiers of the MNO
evaluation, as required by the statistical and geostatistical methods employed (see
Sections 5 and 6). Wells screened in the underlying Salmon Springs Aquifer were not
included in this MNO evaluation.
Multiple factors were considered in developing recommendations for continuation or
cessation of groundwater monitoring at each well. In some cases, a recommendation was
4-2
022/742479/Fort Lewis Draft Final.doc
-------
made to continue monitoring a particular well, but at a reduced frequency. A
recommendation to discontinue monitoring at a particular well based on the information
reviewed does not necessarily constitute a recommendation to physically abandon the
well. A change in site conditions might warrant resumption of monitoring at some time
in the future at wells that are not currently recommended for continued sampling.
Typical factors considered in developing recommendations to retain a well in, or remove
a well from the monitoring program are summarized in Table 4.1. Typical factors
considered in developing recommendations for monitoring frequency are summarized in
Table 4.2.
TABLE 4.1
MONITORING NETWORK OPTIMIZATION DECISION LOGIC
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER, WASHINGTON
Reasons for Retaining a Well in
Monitoring Network
Well is needed to further characterize the
site or monitor changes in contaminant
concentrations through time
Well is important for defining the lateral or
vertical extent of contaminants
Well is needed to monitor water quality at
compliance point or receptor exposure
point (e.g., domestic well)
Well is important for defining background
water quality
Reasons for Removing a Well From
Monitoring Network
Well provides spatially redundant
information with a neighboring well (e.g.,
same constituents, and/or short distance
between wells)
Well has been dry for more than 2 yearsa/
Contaminant concentrations are
consistently below laboratory detection
limits or cleanup goals
Well is completed in same water-bearing
zone as nearby well(s)
a/ Water-level measurements in dry wells should continue,
becomes re-wetted.
and groundwater sampling should be resumed if the well
4-3
022/742479/Fort Lewis Draft Final.doc
-------
TABLE 4.2
MONITORING FREQUENCY DECISION LOGIC
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER, WASHINGTON
Reasons for Increasing
Sampling Frequency
Groundwater velocity is high
Change in contaminant concentration
would significantly alter a decision or
course of action
Well is close to source area or operating
remedial system
Cannot predict if concentrations will
change significantly over time
Reasons for Decreasing
Sampling Frequency
Groundwater velocity is low
Change in contaminant concentration
would not significantly alter a decision or
course of action
Well is distal from source area or remedial
system
Concentrations are not expected to change
significantly over time, or contaminant
levels have been below groundwater
cleanup objectives for some prescribed
period of time
4.2 RESULTS OF QUALITATIVE MNO EVALUATION
The results of the qualitative evaluation of the 83 monitoring and extraction wells
screened in the Vashon Aquifer at the Fort Lewis Logistics Center included in the
original and/or revised monitoring programs are summarized in Table 4.3, and described
in the following subsections. The table includes recommendations for retaining or
deleting each existing monitoring well, and for changing the sampling frequency, and
lists the rationale for the recommendations.
4.2.1 Monitoring Network and Sampling Frequency
The following subsections describe the recommended groundwater sampling locations
and frequencies in the Upper and Lower Vashon Aquifer.
4.2.1.1 Upper Vashon Aquifer
The Upper Vashon Aquifer wells recommended for retention in the Logistics Center
LTM program are identified in Table 4.3 and on Figure 4.1, and are discussed in the
following paragraphs.
4-4
022/742479/Fort Lewis Draft Final.doc
-------
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TABLE 4.3 (Continued)
QUALITATIVE EVALUATION OF GROUNDWATER MONITORING NETWORK
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS, WASHINGTON
Rationale
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Source-Area Monitoring Wells. Sampling of monitoring wells LC-64a and LC-
136a, located in the EGDY source area (Figure 4.1), facilitates assessment of
maximum TCE concentrations present in source-area groundwater, as well as
assessment of the effectiveness of source-removal actions (i.e., the recent drum
removal action and the in situ soil heating activities in the NAPL areas, scheduled
to begin in 2004). Given the prior and planned future source removal actions,
relatively frequent (i.e., quarterly) monitoring of these wells is reasonable to gauge
the effect of the RAs on source-area groundwater quality. Available
hydrogeologic data suggest that groundwater velocities within the cone of
depression of the primary EGDY extraction well field may be relatively high (i.e.,
greater than 10 ft/day). Therefore, the effects of source removal efforts may
become apparent relatively quickly as highly contaminated groundwater is
extracted from the aquifer and replaced by less-contaminated influent groundwater
from surrounding areas. Currently, these wells are monitored on a quarterly basis.
Source-area well LC-136b is screened in a deeper, lower-concentration zone than
its shallower paired well LC-136a. Continued monitoring of this well is
recommended to assess the vertical extent of substantially elevated TCE
concentrations detected in groundwater from LC-136a. However, given the
substantially lower TCE concentrations in LC-136b, a less-frequent (i.e., annual)
monitoring frequency is recommended (Table 4.3).
Plume Interior Wells. These wells are located within the TCE plume in areas
where dissolved TCE concentrations are relatively elevated (e.g., along the
longitudinal axis of the TCE plume, defined as the northwest/southeast-trending
zone containing the highest TCE concentrations along the primary flow axis of the
plume). Specific wells recommended for continued monitoring include FL2, LC-
06, LC-14a, LC-19a, LC-41a, LC-49, LC-53, LC-66b, and PA-381 (Figure 4.1).
Monitoring of these wells tracks the magnitude of the dissolved TCE plume over
time.
4-8
022/742479/Fort Lewis Draft Final.doc
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According to USAGE (2001), the rate at which the COCs migrate in groundwater
at the site is sufficiently slow that significant concentration changes do not occur
between quarterly monitoring periods. TCE concentrations detected in the Upper
Aquifer throughout the monitoring network indicates that the plume footprint has
not expanded or decreased significantly and has remained largely stable since
startup of the groundwater extraction systems in August 1995 (USAGE and URS,
2002). Given that the plume is hydraulically controlled, and has been relatively
stable in terms of areal extent and magnitude over the post-RA monitoring period,
annual monitoring is recommended for most of these wells. This frequency should
be adequate to monitor changes in the magnitude of the plume over time. The only
recommended exception to this monitoring frequency is for well PA-381, where
biennial (i.e., every other year) monitoring is recommended. This well, which is
located near the inferred western edge of the TCE plume along Murray Creek, near
the Madigan Army Medical Center (MAMC), has exhibited stable TCE
concentrations over 38 sampling events from 1986 to December 2001. Therefore,
relatively infrequent monitoring is recommended.
Well LC-14a is located downgradient from the 1-5 extraction system and
upgradient from the 1-5 system infiltration gallery. Given that sampling results for
this well are indicative of the effectiveness of the extraction system at minimizing
or preventing northwesterly migration of contamination across the line of
extraction wells, a more frequent monitoring frequency for this well was
considered. However, TCE data for this well collected from 1995 to 2001 indicate
a decreasing trend. Therefore, more-frequent monitoring is not recommended
unless hydraulic conditions change (e.g., the collective groundwater extraction rate
at the 1-5 extraction system decreases substantially, indicating a lower degree of
plume capture).
• Newly-Installed Monitoring Wells. Six Upper Vashon monitoring wells installed
in 2002 were only recently included in the LTM program; therefore, groundwater
analytical results for these wells were not available during this MNO evaluation.
4-9
022/742479/Fort Lewis Draft Final.doc
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These wells include NEW-1 through NEW-6 (Figure 4.1). Quarterly monitoring
of these wells for a 1-year period is recommended to establish baseline
concentrations. At the end of the first year of monitoring, the four quarters of data
should be reviewed, and the monitoring frequency should be revised as appropriate
(or monitoring should be discontinued) using the guidelines discussed in this
section. For example, plume boundary wells should be assigned a relatively low
monitoring frequency, as described in the following paragraph.
• Upgradient and Cross-Gradient Wells. These wells are located near the upgradient
(eastern or southern) or crossgradient (northeastern or southwestern) edges of the
TCE plume and define the approximate location of the 5-ug/L TCE concentration
contour over time. Starting at and including upgradient well cluster LC-149c/d at
the southeastern end of the plume, and moving around the plume in a clockwise
direction, these wells include LC-57, NEW-2, FL4B, LC-34, NEW-6, PA-383,
FL6, LC-03, NEW-5, LC-20, LC-24, and LC-26 (Figure 4.1). Excluding the new
wells (discussed above), several of these wells have been sampled many times
since the mid- to late 1980s, and results have consistently indicated that the plume
is not expanding in the cross- or upgradient directions. For example, well PA-383
was sampled 29 times from 1986 through December 2001, and TCE
concentrations in the 28 samples in which this compound was detected ranged
from 0.4 to 3.4 ug/L. Given the apparent stability of the TCE plume, the
recommended sampling frequency for these cross- and upgradient wells is
biennial.
. Downgradient Wells. Wells NEW-4, T-08, T-13b, LC-61b, and LC-167) are
located downgradient from the leading edge of the primary TCE plume, and act as
sentry wells that facilitate assessment of plume migration over time (Figure 2).
TCE results for T-08 and T-13b do not indicate a temporal trend, and insufficient
data were available to assess contaminant concentration trends at wells LC-61b
and LC-167. The consistently low TCE concentrations and lack of temporal trends
in T-08 and T-13b indicate that the plume is stable, neither expanding nor receding
4-10
022/742479/Fort Lewis Draft Final.doc
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significantly. However, given the presence of relatively steep hydraulic gradients,
high hydraulic conductivities (Table 5-2 in USAGE and URS, 2002), and
correspondingly high groundwater and solute migration velocities in this area, a
conservative semiannual monitoring schedule for these three wells is
recommended should hydraulic conditions change and plume expansion occur in
the future. Well T-12B also is potentially downgradient from the main TCE
plume. However, this well is almost 1,500 feet from the estimated location of the
5-ug/L TCE concentration contour, and semiannual monitoring of LC-167 should
provide an early warning of plume expansion in this direction. Therefore, biennial
monitoring of T-12b is recommended unless data for LC-167 indicate plume
expansion in this direction. Currently, the monitoring program does not include a
well to monitor the leading (southern) edge of the southwestern lobe of the TCE
plume, which appears to extend south of Murray Creek in this are (Figure 4.1).
Existing well LC-180, not currently monitored on a routine basis, should be
considered for incorporation into the LTM program to monitor plume stability in
this area.
Wells Located in Plume Extension North of 1-5. Wells T-04, T-06, and T-llb are
located north of 1-5 in what appears to be an extension of the TCE plume that is
inferred to be separated from the main plume by a zone of lower TCE
concentrations (i.e., < 5 ug/L) (Figure 4.1). This portion of the plume is inferred
to discharge into American Lake. Monitoring of wells T-06 and T-llb was
renewed in December 2001 (T-06) and March 2002 (T-llb) after a sampling
hiatus of 8 to 13 years. Completion of four quarters of monitoring is
recommended to reestablish the baseline for these wells. The monitoring
frequency should then be adjusted appropriately following review of the first-year
results. Preliminary results for these wells indicate the presence of TCE
concentrations that slightly exceed the 5-ug/L MCL. If concentrations appear to
be stable, then annual monitoring of these wells would be appropriate. Annual
monitoring is recommended for well T-04, based on the stable concentrations of
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022/742479/Fort Lewis Draft Final.doc
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TCE detected over the past few years (8 to 12 ug/L) and the lack of a temporal
trend.
• Extraction Wells. Continued quarterly monitoring of the groundwater extraction
wells LX-17 through LX-21 in the primary EGDY well field (Figure 4.1) is
recommended to assess the effects of recent and pending source-removal activities
on groundwater quality. Given the source-area location of these extraction wells,
the average contaminant mass-removal rate is relatively high, and RA-related
changes should be closely monitored. Extraction wells LX-16 and RW-1 are
located in the secondary EGDY well field, approximately 1,000 feet downgradient
from the EGDY source area. Assuming an average hydraulic conductivity in the
vicinity of the primary and secondary EGDY well fields of 120 ft/day (average of
the geometric mean hydraulic conductivity values for these well fields derived
from pumping tests (Table 5-2 in USAGE and URS, 2002), a hydraulic gradient
between the EGDY and the secondary well field of 0.0013 ft/ft, and an average
effective porosity of 0.30, the average groundwater velocity between the EGDY
and the secondary well field is 0.5 ft/day. The TCE migration rate would probably
be slower due to the effects of retardation. Therefore, the effects of source-
removal at the EGDY may not be apparent at the secondary East Gate well field
for years. Therefore, less frequent (i.e., semiannual) monitoring of LX-16 and
RW-1 is recommended to track mass-removal rates at these wells given the
historically stable TCE concentrations detected in their effluent.
The primary purpose of the 1-5 extraction system is hydraulic control of the
downgradient portion of the plume rather than mass removal. In 2001, the average
TCE concentration in the 1-5 extraction well field effluent was approximately 50
ug/L (computed using the maximum concentration detected in the effluent from
each well during the four quarters ending in December 2001). In contrast, the
average TCE concentration in the primary and secondary EGDY extraction wells
during the same period was approximately 900 ug/L. Therefore, annual sampling
of the 1-5 extraction well effluent for VOCs is not recommended. Instead, the
4-12
022/742479/Fort Lewis Draft Final.doc
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effectiveness of this well field at preventing further downgradient migration of the
plume should be assessed via a combination of periodic sampling of monitoring
wells located downgradient from the line of extraction wells and capture zone
analyses using measured water levels. The 1-5 extraction wells could be sampled
relatively infrequently (e.g., once every 2 to 3 years) to assess contaminant
concentrations in the effluent and confirm that continued pumping of all of the
wells, and operation of the air stripper, are necessary.
4.2.1.2 Lower Vashon Aquifer
Compared with the Upper Vashon Aquifer, a relatively small number of wells are
screened within the Lower Vashon Aquifer. Eleven Lower Vashon wells currently are
included in the LTM program, and continued monitoring of 10 of these wells is
recommended for the reasons summarized in Table 4.3.
Two of the Lower Vashon wells (LC-64b and LC-137c) are located in or immediately
adjacent to the EGDY source area (Figure 4.2). TCE concentrations detected in
groundwater samples collected from these wells between December 1995 and December
2001 are much lower than the paired Upper Vashon wells, and exhibit a statistical
decreasing trend (see Section 5). The beneficial effects of source removal activities in
this area will likely be evident more rapidly in the Upper Vashon Aquifer than in the
Lower Vashon. Therefore, an annual monitoring frequency for these two Lower Vashon
wells is recommended.
Other than source-area well LC-64b, wells LC-41b and LC-128 are the only Lower
Vashon wells that contain TCE at concentrations exceeding the 5-ug/L MCL. Annual
monitoring of these wells is recommended, similar to the annual monitoring schedule
recommended for wells in the interior of the Upper Vashon TCE plume.
Wells MAMC 1 and MAMC 6 are new, and have been monitored for less than 1 year.
Similar to wells NEW-1 through NEW-6 in the Upper Vashon Aquifer, quarterly
monitoring of these wells for a 1-year period is recommended to establish baseline
concentrations. At the end of the first year of monitoring, the four quarters of data should
4-13
022/742479/Fort Lewis Draft Final.doc
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be reviewed, and the monitoring frequency should be revised as appropriate, or
monitoring should be discontinued.
Similar to Upper Vashon well FL4B (Figure 4.1), Lower Vashon well FL4A is located
near the estimated southwestern boundary of the contaminant plume, and the low and
stable magnitude of detected TCE concentrations supports a very low-frequency (i.e.,
biennial) monitoring schedule.
Lower Vashon Aquifer wells LC-11 Ib, LC-116b, and LC-122b are located in the line
of extraction wells comprising the 1-5 well field, but are screened at greater depths than
the extraction wells. In addition, these wells are located potentially downgradient from
TCE contamination detected in Lower Vashon well LC-41b (Figure 4.2). TCE
concentrations at wells LC-1 lib and LC-122b have been less than 2 ug/L over 25
sampling events since 1993, and recent concentrations exhibit a decreasing trend.
Therefore, removal of well LC-122b from the RA monitoring program is recommended,
and low-frequency (i.e., biennial) monitoring of well LC-1 lib is recommended because
of its potentially increasing cis-l,2-DCE concentrations. In contrast, TCE concentrations
at LC-116b slightly exceed the 5-ug/L MCL and have recently exhibited a statistical
increasing trend. Therefore, continued annual monitoring of this well is recommended.
Lower Vashon well T-10 is located off-site (i.e., northwest of 1-5) and potentially
downgradient from TCE contamination detected in the Lower Vashon at well LC-128.
Similar to sentry wells screened in the Upper Vashon, a conservative semiannual
monitoring schedule is recommended for this sentry well, should hydraulic conditions
change or plume expansion occur in the future.
4.2.2 Laboratory Analytical Program
Groundwater samples have been analyzed for VOCs using USEPA Method SW8260B
since the 12th quarter of monitoring (September 1998) (USACE and URS, 2002). The
previous analytical program had used USEPA Methods SW8010A and SW8260.
Beginning in the 24th quarter (September 2001), selected samples have been analyzed for
VC using USEPA Method SW8260B modified for select ion monitoring (SIM) in
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022/742479/Fort Lewis Draft Final.doc
-------
response to elevated Method 8260B reporting limits for VC (up to 4,000 ug/L) and other
compounds due to the high TCE concentrations in several samples (USAGE and URS,
2002).
Because the characterization of conditions in the Fort Lewis Logistics Center
groundwater plume has been largely completed, groundwater samples collected from
monitoring wells could be analyzed for selected COCs using Method 802IB, rather than
the currently-used Method 8260B. Method 8021B can be used to analyze for the primary
COCs at the site, and could potentially result in a considerable reduction in analytical
costs. The cost for analysis of a groundwater sample using Method 802IB (a gas-
chromatographic [GC] method) can be substantially lower than the cost of analysis using
Method 8260B (a gas-chromatographic/mass-spectrographic [GC/MS] method),
especially if the target analyte list is reduced. USEPA Method 8260B/SIM could still be
used for samples from wells that contain substantially elevated TCE concentrations.
4.2.4 LTM Program Flexibility
The LTM program recommendations made in Sections 4.2.1 through 4.2.3 are based
on available data regarding current (and expected future) site conditions. Changing site
conditions (e.g., lengthy malfunction or significant adjustment of the groundwater
extraction/infiltration systems) could affect plume behavior. Therefore, the LTM
program should be reviewed if hydraulic conditions change significantly, and revised as
necessary to adequately track changes in plume magnitude and extent over time.
4-16
022/742479/Fort Lewis Draft Final.doc
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SECTION 5
TEMPORAL STATISTICAL EVALUATION
Temporal data (chemical concentrations measured at different points in time) can be
examined graphically, or using statistical tests, to evaluate dissolved-contaminant plume
stability. If removal of chemical mass is occurring in the subsurface as a consequence of
attenuation processes or operation of a remediation system, mass removal will be
apparent as a decrease in chemical concentrations through time at a particular sampling
location, as a decrease in chemical 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.
5.1 METHODOLOGY FOR TEMPORAL TREND ANALYSIS OF
CONTAMINANT CONCENTRATIONS
Temporal chemical-concentration data can be evaluated by plotting contaminant
concentrations through time for individual monitoring wells (Figure 5.1), or by plotting
contaminant concentrations versus downgradient distance from the contaminant source
for several wells along the groundwater fiowpath, over several monitoring events.
Plotting temporal concentration data is recommended for any analysis of plume stability
(Wiedemeier and Haas, 2000); however, visual identification of trends in plotted data
may be a subjective process, particularly if (as is likely) the concentration data do not
exhibit a uniform trend, but are variable through time (Figure 5.2).
The possibility of arriving at incorrect conclusions regarding plume stability on the
basis of visual examination of temporal concentration data can be reduced by examining
temporal trends in chemical concentrations using various statistical procedures, including
5-1
022/742479/Fort Lewis Draft Final.doc
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FIGURE 5.1
TCE CONCENTRATIONS THROUGH TIME
AT WELL LC-132
THREE-TIERD MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER, WASHINGTON
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regression analyses and the Mann-Kendall test for trends. The Mann-Kendall
nonparametric test (Gibbons, 1994) is well-suited for evaluation of environmental data
because the sample size can be small (as few as four data points), no assumptions are
made regarding the underlying statistical distribution of the data, and the test can be
adapted to account for seasonal variations in the data. The Mann-Kendall test statistic
can be calculated at a specified level of confidence to evaluate whether a temporal trend
is exhibited by contaminant concentrations detected through time in samples from an
individual well. If a trend is identified, a nonparametric slope of the trend line (change in
concentration per unit time) also can be estimated using the test procedure. A negative
slope (indicating decreasing contaminant concentrations through time) or a positive slope
5-2
022/742479/Fort Lewis Draft Final.doc
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Trend
Increasing Trend
No Trend
Confidence Factor
HIGH
Confidence Factor
LOW
Variation
LOW
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5.2
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Monitoring Network Optimization
Fort Lewis Logistics Center, Washington
draw\739732\diffusion\williamsA.cdr pg1 nap 4/3/02
-------
(increasing concentrations through time) provides statistical confirmation of temporal
trends that may have been identified visually from plotted data (Figure 5.2).
The relative value of information obtained from periodic monitoring at a particular
monitoring well can be evaluated by considering the location of the well with respect to
the dissolved contaminant plume and potential receptor exposure points, and the presence
or absence of temporal trends in contaminant concentrations in samples collected from
the well. The degree to which the amount and quality of information that can be obtained
at a particular monitoring point serve the two primary (i.e., temporal and spatial)
objectives of monitoring must be considered in this evaluation. For example, the
continued non-detection of a target contaminant in groundwater at a particular monitoring
location provides no information about temporal trends in contaminant concentrations at
that location, or about the extent to which contaminant migration is occurring, unless the
monitoring location lies along a groundwater fiowpath between a contaminant source and
a potential receptor exposure point. Therefore, a monitoring well having a history of
contaminant concentrations below detection limits may be providing little or no useful
information, depending on its location.
A trend of increasing contaminant concentrations in groundwater at a location between
a contaminant source and a potential receptor exposure point may represent information
critical in evaluating whether contaminants are migrating to the exposure point, thereby
completing an exposure pathway. Identification of a trend of decreasing contaminant
concentrations at the same location may be useful in evaluating decreases in the areal
extent of dissolved contaminants, but does not represent information that is critical to the
protection of a potential receptor. Similarly, a trend of decreasing contaminant
concentrations in groundwater near a contaminant source may represent important
information regarding the progress of remediation near, and downgradient from the
source, while identification of a trend of increasing contaminant concentrations at the
same location does not provide as much useful information regarding contaminant
conditions. By contrast, the absence of a temporal trend in contaminant concentrations at
a particular location within or downgradient from a plume indicates that virtually no
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022/742479/Fort Lewis Draft Final.doc
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additional information can be obtained by continued monitoring of groundwater at that
location, in that the results of continued monitoring through time are likely to fall within
the historic range of concentrations that have already been detected (Figure 5.3).
Continued monitoring at locations where no temporal trend in contaminant
concentrations is present serves merely to confirm the results of previous monitoring
activities at that location. The relative amounts of information generated by the results of
temporal trend evaluation at monitoring points near, upgradient from, and downgradient
from contaminant sources are presented schematically as follow:
Monitoring Point Near Contaminant Source
Relatively less information Nondetect or no trend
Relatively more information
Increasing trend in concentrations
Decreasing trend in concentrations
Monitoring Point Upgradient from Contaminant Source
Relatively less information Nondetect or no trend
Relatively more information
Decreasing trend in concentrations
Increasing trend in concentrations
022/742479/Fort Lewis Draft Final.doc
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Monitoring Network Optimization
Fort Lewis Logistics Center, Washingon
draw\739732\diffusion\williamsA.cdr pg2 nap 4/3/02
-------
Monitoring Point Downgradient from Contaminant Source
Relatively less information Decreasing trend in concentrations
Nondetect or no trend
Relatively more information Increasing trend in concentrations
5.2 TEMPORAL EVALUATION RESULTS
The analytical data for groundwater samples collected the 59 wells in the original
monitoring program from February 1995 through December 2001 at the Fort Lewis
Logistics Center were examined for temporal trends using the Mann-Kendall test. Wells
for which results from fewer than four sampling events were available (i.e., the 24 wells
added to the monitoring program in December 2001; see Table 3.1) did not meet the
minimum data requirements for the Mann-Kendall test, and therefore were not evaluated.
The objective of the evaluation was to identify those wells having increasing or
decreasing concentration trends for each COC, and to consider the quality of information
represented by the existence or absence of concentration trends in terms of the location of
each monitoring point.
Summary results of Mann-Kendall temporal trend analyses for COCs in groundwater
samples from wells in the TCE plume area are presented in Table 5.1. As implemented,
the algorithm used to evaluate concentration trends assigned a value of "ND"(not
detected) to those wells with sampling results that were consistently below analytical
detection limits through time, rather than assigning a surrogate value corresponding to the
detection limit - a procedure that could generate potentially-misleading and anomalous
"trends" in concentrations. In addition, a value of "
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trace detections that were less than practical quantitation limits, and 21 measurements in
which TCE was not detected during the sampling events from 1995 to 2001. In the
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be retained because they provide information on the effectiveness of the source-area
RA(s). A flow chart of the decision logic applied to the temporal trend analysis results is
presented in Figure 5.5.
Table 5.1 summarizes recommendations to retain 18 of the original 38 monitoring
wells and 6 of the 21 extraction wells in a revised monitoring program for the Logistics
Center TCE plume. Note that the recommendations provided in Table 5.1 are based on
the evaluation of temporal statistical results only, and must be used in conjunction with
the results of the qualitative and spatial evaluations to generate final recommendations
regarding retention of monitoring points in the LTM program, and the frequency of
monitoring at particular locations at the Fort Lewis Logistics Center.
5-12
022/742479/Fort Lewis Draft Final.doc
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FIGURE 5.5
TEMPORAL TREND
DECISION RATIONALE
FLOW CHART
Monitoring Network Optimization
Fort Lewis Logistics Center
Denver, Colorado
-------
SECTION 6
SPATIAL STATISTICAL EVALUATION
Spatial statistical techniques also can be applied to the design and evaluation of
groundwater monitoring programs to assess the quality of information generated during
monitoring, and to evaluate monitoring networks. Geostatistics, or the Theory of
Regionalized Variables (Clark, 1987; Rock 1988; American Society of Civil Engineers
[ASCE] Task Committee on Geostatistical Techniques in Hydrology, 1990a and 1990b),
is concerned with variables having values dependent on location, and which are
continuous in space, but which vary in a manner too complex for simple mathematical
description. Geostatistics is based on the premise that the differences in values of a
spatial variable depend only on the distances between sampling locations, and the relative
orientations of sampling locations — that is, the values of a variable (e.g., chemical
concentrations) measured at two locations that are spatially "close together" will be more
similar than values of that variable measured at two locations that are "far apart".
6.1 GEOSTATISTICAL METHODS FOR EVALUATING MONITORING
NETWORKS
Ideally, application of geostatistical methods to the results of the groundwater
monitoring program at Fort Lewis Logistics Center could be used to estimate COC
concentrations at every point within the dissolved contaminant plume, and also could be
used to generate estimates of the "error," or uncertainty, associated with each estimated
concentration value. Therefore, the monitoring program could be "optimized" by using
available information to identify those areas having the greatest uncertainty associated
with the estimated plume extent and configuration. Conversely, sampling points could be
successively eliminated from simulations, and the resulting uncertainty examined, to
evaluate if significant loss of information (represented by increasing error or uncertainty
6-1
022/742479/Fort Lewis Draft Final.doc
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in estimated chemical concentrations) occurs as the number of sampling locations is
reduced. Repeated application of geostatistical estimating techniques, using tentatively
identified sampling locations, then could be used to generate a sampling program that
would provide an acceptable level of uncertainty regarding the distribution of COCs with
the minimum possible number of samples collected. Furthermore, application of
geostatistical methods can provide unbiased representations of the distribution of COCs
at different locations in the subsurface, enabling the extent of COCs to be evaluated more
precisely.
Fundamental to geostatistics is the concept of semivariance [y(h)], which is a measure
of the spatial dependence between samples (e.g., chemical concentrations) in a specified
direction. Semivariance is defined for a constant spacing between samples (h) by:
Y(h) = — T.[g(x) - g(x + h)]2 Equation 1
2n
Where:
y(h) = semivariance calculated for all samples at a distance h from each other;
g(x) = value of the variable in sample at location x;
g(x + h) = value of the variable in sample at a distance h from sample at location x;
and
n = number of samples in which the variable has been determined.
Semivariograms (plots of y(h) versus K) are a means of depicting graphically the range
of distances over which, and the degree to which, sample values at a given point are
related to sample values at adjacent, or nearby, points, and conversely, indicate how close
together sample points must be for a value determined at one point to be useful in
predicting unknown values at other points. For h = 0, for example, a sample is being
compared with itself, so normally y(Q) = 0 (the semivariance at a spacing of zero, is
zero), except where a so-called nugget effect is present (Figure 6.1), which implies that
6-2
022/742479/Fort Lewis Draft Final.doc
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FIGURE 6.1
IDEALIZED SEMVARIOGRAM MODEL
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER, WASHINGTON
3500 1
3000
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-------
most closely follows the real data. Fitting a theoretical model to calculated semivariance
points is accomplished by trial-and-error, rather than by a formal statistical procedure
(Davis, 1986; Clark, 1987; Rock, 1988). If a "good" model fit results, then tfh) (the
semivariance) can be confidently estimated for any value of h, and not only at the
sampled points.
6.2 SPATIAL EVALUATION OF MONITORING NETWORK AT FORT
LEWIS LOGISTICS CENTER
TCE was used as the indicator chemical for the spatial evaluation of the groundwater
monitoring network at Fort Lewis because this COC has the largest percentage and
spatial distribution of measurements that exceeded groundwater MCLs. The Upper
Vashon Aquifer wells were considered separately from the Lower Vashon Aquifer wells
for the spatial analysis due to the hydrogeological conditions discussed in Section 2.2.
The combined original and revised monitoring networks include a total of 11 Lower
Vashon wells. MAMC 1 and MAMC 6 do not have analytical results for the sampling
period selected for evaluation, and well T-10 was not sampled recently. Because a
minimum of 10 wells is required for the kriging analysis (and 30 or more data points are
strongly preferred to ensure a more rigorous analysis), a kriging analysis could not be
conducted for the Lower Vashon wells. The original and revised monitoring networks
contain a total of 51 Upper Vashon wells (Table 3.1). However, only wells that were
sampled in September or December 2001 (the most recent analytical data available) were
included in the kriging analysis because a spatial "snapshot" is required in order to
conduct the geospatial statistical analysis.
A kriging analysis was conducted only on those Upper Vashon Aquifer monitoring
wells with recent analytical data. Extraction wells were excluded from the analysis
because some of them have multiple screen intervals and because of their nature, they
monitor water drawn from a wider region as opposed to the "point" sampled obtained
from the monitoring wells. Of the 51 Upper Vashon monitoring wells, wells NEW-1
through -6 do not have analytical data, and wells T-l Ib and FL2 do not have current TCE
6-4
022/742479/Fort Lewis Draft Final.doc
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measurements. Additionally, well LC-41a was not included in the analysis because is
screened deeper than the other Upper Vashon wells, and because this well is considered a
"spatially unique window" to the Upper Salmon Springs Aquifer (USAGE, 2001). Thus,
2001 TCE measurements from 42 of the Upper Vashon wells were used to develop the
semivariogram model. The commercially available geostatistical software package
Geostatistical Analyst™ (an extension to the Arc View® geographic information system
[GIS] software package) (Environmental Systems Research Institute, Inc. [ESRI], 2001)
was used to develop a semivariogram model depicting the spatial variation in TCE
concentrations in groundwater for 42 wells in the Upper Vashon aquifer the Fort Lewis
Logistics Center.
As semivariogram models were calculated for TCE (Equation 1), considerable scatter
of the data was apparent during fitting of the models. Several data transformations
(including a log transformation) were attempted to obtain a representative semivariogram
model. Ultimately, the concentration data were transformed to "rank statistics," in which
the 42 values were ranked according to their concentration from 1 to 42 (tie values were
assigned the median rank of the set). Transformations of this type can be less sensitive to
outliers, skewed distributions, or clustered data than semivariograms based on raw
concentration values, and thus may enable recognition and description of the underlying
spatial structure of the data in cases where ordinary data are too "noisy".
The TCE rank statistics were used to develop a semivariogram that most accurately
modeled the spatial distribution of the data. Figure 6.2 shows the semivariogram model
in comparison to the site data. The best-fit semivariogram had the following parameters:
« Exponential Model
. Range: 2,500 feet
. Sill: 150
. Nugget: 10
6-5
022/742479/Fort Lewis Draft Final.doc
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FIGURE 6.2
FORT LEWIS UPPER VASHION SEMVARIOGRAM MODEL
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS LOGISTICS CENTER, WASHINGTON
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After this semivariogram model had been developed, it was used in the kriging system
implemented by the Geostatistical Analyst™ software package (ESRI, 2001) to develop
kriging realizations (estimates of the spatial distribution of TCE in groundwater at Fort
Lewis), and to calculate the associated kriging prediction standard errors. The median
kriging standard deviation was obtained from the standard errors calculated using the
entire 42-well Upper Vashon monitoring network for the Logistics Center. Next, each of
the 42 monitoring wells was sequentially removed from the network, and for each
resulting 41-well network configuration, a kriging realization was completed using the
TCE concentration rankings from the remaining 41 wells. The "missing well"
monitoring network realizations were used to calculate prediction standard errors, and the
median kriging standard deviations were obtained for each "missing well" realization and
compared with the median kriging standard deviation for the "base-case" realization
(obtained using the complete 42-well monitoring network), as a means of evaluating the
amount of information loss (as indicated by increases in kriging error) resulting from the
use of fewer monitoring points.
Figure 6.3 illustrates the spatial evaluation procedure by showing kriging prediction
standard error maps for three kriging realizations. Each map shows the predicted
6-6
022/742479/Fort Lewis Draft Final.doc
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standard error associated with a given group of wells based on the semivariogram
parameters discussed above. Lighter colors represent areas with lower spatial
uncertainty, and darker colors represent areas with higher uncertainty; regions in the
vicinity of wells (i.e., data points) have the lowest associated uncertainty. Map A on
Figure 6.4 shows the predicted standard error map for the "base-case" realization in
which all 42 wells are included. Map B shows the realization in which well PA-383 was
removed from the monitoring network, and Map C shows the realization in which well
LC-132 was removed. Figure 6.3 shows that when a well is removed from the network,
the predicted standard error in the vicinity of the missing well increases. If a "removed"
(missing) well is in an area with several other wells (e.g., well LC-132; Map B on Figure
6.3), the predicted standard error may not increase as much as if a well (e.g., PA-381;
Map C) is missing from an area with fewer surrounding wells.
If removal of a particular well from the monitoring network caused very little change
in the resulting median kriging standard deviation (less than about 1%), that well was
regarded as contributing only a limited amount of information to the LTM program.
Likewise, if removal of a well from the monitoring network produced larger increases in
the kriging standard deviation, this was regarded as an indication that the well contributes
a relatively greater amount of information, and is relatively more important to the
monitoring network. At the conclusion of the kriging realizations, each well was ranked
from 1 (providing the least information) to 42 (providing the most information), based on
the amount of information (as measured by changes in median kriging standard
deviation) the well contributed toward describing the spatial distribution of TCE, as
shown in Table 6.1. Wells providing the least amount of information represent possible
candidates for removal from the monitoring network at the Logistics Center.
6.3 SPATIAL STATISTICAL EVALUATION RESULTS
6.3.1 Kriging Ranking Results
Figure 6.5 and Table 6.1 present the ranking of monitoring locations based on the
relative value of TCE information provided by each well, as calculated based on the
6-8
022/742479/Fort Lewis Draft Final.doc
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TABLE 6.1
RESULTS OF GEOSTATISTICAL EVALUATION
RANKING OF UPPER VASHON AQUIFER WELLS
BY RELATIVE VALUE OF TCE INFORMATION
THREE-TIERED MONITORING NETWORK OPTIMIZATION
FORT LEWIS, WASHINGTON
Well ID ^
T-08
LC-66b
T-04
LC-66a
LC-64a
LC-61b
LC-26
LC-19c
LC-19b
LC-19a
LC-167
LC-14a
LC-149d
LC-149c
LC-137b
LC-137a
LC-136b
LC-136a
LC-132
LC-108
LC-05
Kriging Ranking b/
1
2
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12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
12
Well ID
LC-51
LC-44a
FL6
T-06
LC-57
FL3
LC-53
LC-34
LC-24
LC-165
LC-16
LC-03
LC-73a
LC-20
PA-381
FL4b
T-12b
PA-383
LC-49
LC-06
T-13b
Kriging Ranking
23
23
23
26
26
26
28.5
28.5
30
32
32
32
34.5
34.5
36.5
36.5
38.5
38.5
40
41
42
Upper Vashon wells T-l Ib, FL2, and wells NEW-1 through -6 were
not included in the kriging rankings because they did not have current
analytical results to contribute to the spatial distribution analysis. Well
LC-41a was excluded because its screened interval is in a unit that is not
representative of the Upper Vashon Aquifer.
1= least relative amount of information; 42= most relative amount of
information.
c Tie values receive the median ranking of the set.
022/742479/3-tiered Ft Lewis Tables.xls/Table 6.1
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kriging realizations. Examination of these results indicate that monitoring wells in close
proximity to several other monitoring wells (red color coding on Figure 6.5) generally
provide relatively lesser amounts of information than do wells at greater distances from
other wells, or wells located in areas having limited numbers of monitoring points (blue
color coding on Figure 6.5). This is intuitively obvious, but the analysis allows the most
valuable and least valuable wells to be identified quantitatively. For example, Table 6.1
identifies the 21 wells (ranked 1-12) that provide the relative least amount of information
(potential candidates for removal from the monitoring program) and the 9 wells (ranked
34-42) that provide the greatest amount of information (candidates for retention in the
monitoring program) regarding the occurrence and distribution of TCE in groundwater in
the Upper Vashon Aquifer. Wells ranked from 23 to 32 fall in the "intermediate" range
and receive no recommendation for removal or retention.
6.3.2 Additional Well Analysis
The kriging predicted standard error map and plume delineation also can be used to
evaluate the addition of new wells to the monitoring program. Figure 6.5 shows the eight
Upper Vashon wells not included in the kriging ranking analysis, along with the predicted
standard error map for the kriging realization containing the "base-case" data from the
other 42 wells in the Upper Vashon aquifer. The map also shows an estimate of the
extent of the TCE plume (as defined by the 5-|ug/L isopleth) (USAGE, 2001). The lighter
yellow shading represents areas with less spatial uncertainty, and the darker shading
represents area with greater spatial uncertainty. Figure 6.4 shows that, with the potential
exception of well FL2, all of the new wells are located in areas with higher spatial
uncertainty. Once a final monitoring network program is established, a similar standard
error map could be created to determine the optimal locations for the new wells. For
example, well NEW-5 could provide better spatial information if it were shifted about
500 feet to the south, based on the predicted standard error map shown on Figure 6.4.
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022/742479/Fort Lewis Draft Final.doc
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SECTION 7
SUMMARY OF THREE-TIERED MONITORING NETWORK
EVALUATION
The 83 wells included in the original and/or revised groundwater monitoring programs
at the Fort Lewis Logistics Center were evaluated using qualitative hydrogeologic and
RA knowledge, temporal statistical techniques, and spatial statistics. At each tier of the
evaluation, monitoring points that provide relatively greater amounts of information
regarding the occurrence and distribution of COCs in groundwater were identified, and
were distinguished from those monitoring points that provide relatively lesser amounts of
information. In this section, the results of the evaluations are combined to generate a
refined monitoring program that could potentially provide information sufficient to
address the primary objectives of monitoring, at reduced cost. Monitoring wells not
retained in the refined monitoring network could be removed from the monitoring
program with relatively little loss of information. The results of the evaluations were
combined and summarized in accordance with the following decision logic:
1. Each well retained in the monitoring network on the basis of the qualitative
hydrogeologic evaluation is recommended to be retained in the refined
monitoring program.
2. Those wells recommended for removal from the monitoring program on the
basis of all three evaluations, or on the basis of the qualitative and temporal
evaluations (with no recommendation resulting from the spatial evaluation)
should be removed from the monitoring program.
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3. If a well is recommended for removal based on the qualitative evaluation and
recommended for retention based on the temporal or spatial evaluation, the final
recommendation is based on a case-by-case review of well information.
The results of the qualitative, temporal, and spatial evaluations are summarized in Table
7.1. These results indicate that 15 of the 83 monitoring wells could be removed from the
groundwater LTM program with little loss of information. The justification for the
recommendations for the 5 wells that fell into case 3 of the decision logic is as follows:
« LC-05 and LC-132 are both recommended for removal from the monitoring
program based on the qualitative and spatial evaluations (Tables 4.3 and 6.1), but
are recommended for retention based on the on the temporal analysis, which
showed their TCE concentrations to be increasing (Table 5.1). However, these
wells provide redundant data because of their relative physical proximity (about
500 feet; Figure 5.4). It is recommended that LC-132 be retained and that well
LC-05 be removed from the program because LC-132 has higher TCE
concentrations that are increasing at a faster rate than the concentrations at well
LC-05.
« Well LC-51 is recommended for removal from the monitoring program based on
the qualitative evaluation (Table 4.3) and for retention in the temporal analysis
due to increasing TCE trends (Table 5.1). The decision was made to recommend
removal of well LC-51 from the monitoring program because wells FL2 and LC-
53 provide adequate plume coverage in the area, and LC-53 also exhibits an
increasing TCE concentration trend, as well as higher TCE concentrations than
those measured at well LC-51.
« Well LC-73a is recommended for removal from the monitoring program based on
the qualitative and temporal analyses, and for retention on basis of the spatial
analysis. A decision was made to recommend removal of the well from the
monitoring program because the well is outside the area of pertinent spatial
information (i.e., outside of the 5-ug/L TCE isopleth and across Murray Creek).
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TABLE 7.1
SUMMARY OF EVALUATION OF CURRENT GROUNDWATER MONITORING PROGRAM
MONITORING NETWORK OPTIMIZATION
FORT LEWIS, WASHINGTON
Well ID
Hydro logic
Unit"7
Revised
Sampling
Frequency b/
hialitative Evaluatio
Remove
Retain
Tern
Remove
poral
Retain
Spatial Evaluation
Remove
Retain
Summary
Remove
Retain
Recommended
Monitoring
Frequency
Original Monitoring Network Wells
LC-03
LC-05
LC-06
LC-14a
LC-19a
LC-19b
LC-19c
LC-26
LC-41a
LC-44a
LC-49
LC-51
LC-53
LC-64a
LC-66a
LC-66b
LC-73a
LC-108
LC-132
LC-136a
LC-136b
LC-137a
LC-137b
LC-149c
LC-149d
LC-165
PA-381
PA-383
T-04
T-08
T-12b
T-13b
LC-lllb
LC-116b
LC-122b
LC-128
LC-137c
LC-64b
UV
uv
UV
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
uv
LV
LV
LV
LV
LV
LV
Quarterly
Annually
Semi-Annually
Annually
Quarterly
None
None
Annually
Annually
None
Annually
None
Annually
Quarterly
None
Annually
None
None
None
Quarterly
Annually
None
Quarterly
Annually
None
None
Annually
Annually
Annually
Semi-Annually
Quarterly
Semi-Annually
Annually
Annually
Annually
Annually
Annually
Annually
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
NI"7
NI
NI
NI
NI
NI
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
Biennially
Annually
Annually
Annually
-
-
Annually
Annually
-
Annually
Quarterly
Annually
-
-
Annually
Quarterly
Annually
-
-
Biennially
Biennially
Biennially
Biennially
Annually
Semi-Annually
Biennially
Semi-Annually
Biennially
Annually
-
Annually
Annually
Annually
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-14
LX-1 5
LX-1 6
LX-1 7
LX-1 8
LX-1 9
LX-21
RW-1
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
EW
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Annually
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
NI
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
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-Annually
Quarterly
Quarterly
Quarterly
Quarterly
Semi-Annually
Wells Added to Monitoring Network in December 2001
FL2
FL3
FL4B
FL6
LC-16
LC-20
LC-24
LC-34
LC-57
LC-61b
LC-167
NEW-1
NEW-2
NEW-3
UV
UV
UV
UV
UV
UV
UV
UV
UV
UV
UV
uv
uv
uv
Annually
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
V
V
V
V
V
V
V
V
V
V
V
V
V
V
NA"
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
Annually
-
Biennially
Biennially
Biennially
Biennially
Biennially
Biennially
Semi-Annually
Semi-Annually
Quarterly
Quarterly
Quarterly
022/742479/3-tieied Ft Lewis Tables.xls/Table 7.1
7-3
-------
TABLE 7.1 (Continued)
SUMMARY OF EVALUATION OF CURRENT GROUNDWATER MONITORING PROGRAM
MONITORING NETWORK OPTIMIZATION
FORT LEWIS, WASHINGTON
Well ID
NEW-4
NEW-5
NEW-6
T-06
T-llb
FL4A
LC-41b
MAMC1
MAMC6
T-10
Hydro logic
Unit17
UV
uv
UV
uv
uv
LV
LV
LV
LV
LV
Revised
Sampling
Frequency b/
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
hialitative Evaluatio
Remove
Retain
V
V
V
V
V
V
V
V
V
V
Tern
Remove
poral
Retain
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Spatial Evaluation
Remove
Retain
NI
NI
NI
NI
NI
Summary
Remove
V
V
V
V
V
V
V
V
V
V
Retain
Recommended
Monitoring
Frequency
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Biennially
Annually
Quarterly
Quarterly
Semi-Annually
Proposed Additional Well
LC-lSO
UV
None
ADD
NA
NI
ADD
Annually
UV=Upper Vashon Aquifer; LV= Lower Vashon Aquifer; EW=(Vashon Aquifer) Extraction Well.
Sampling frequency established by the remedial action monitoring network optimization report (USAGE, 2001).
c NA = Fewer than four samples; not applicable for temporal trend analysis.
^ NI = Extraction well or Lower Vashon well not included in spatial analysis.
022/742479/3-tiered Ft Lewis Tables.xls/Table 7.1
7-4
-------
A refined monitoring program, consisting of 69 wells (16 sampled quarterly, 7
sampled semi-annually, 17 sampled annually, 14 sampled biennially, and the 15 1-5
extraction wells sampled every 3 years) would be adequate to address the two primary
objectives of monitoring. This refined monitoring network would result in 107 sampling
events per year, compared to 180 events per year in the current LOGRAM monitoring
program and 236 yearly events in the original sampling program. Implementing these
recommendations for optimizing the RA monitoring program at the Fort Lewis
Logistics Center could reduce site monitoring costs by $36,500 a year (more than 40%)
from the LOGRAMLTM strategy, and $64,500 (approximately 55%) from the original
LTMprogram (based on a per sample cost of $500 (USAGE, 2001)). Additional cost
savings could be realized if groundwater samples collected from select wells (e.g., wells
along the lateral and upgradient plume margins) were analyzed for a short list of
halogenated VOCs using Method 802 IB instead of Method 8260B.
7-5
022/742479/Fort Lewis Draft Final.doc
-------
SECTION 8
REFERENCES
American Society of Civil Engineers (ASCE) Task Committee on Geostatistical
Techniques in Hydrology. 1990a. Review of geostatistics in geohydrology - I.
Basic concepts. Journal of Hydraulic Engineering 116(5):612-632.
ASCE Task Committee on Geostatistical Techniques in Hydrology. 1990b. Review of
geostatistics in geohydrology - II. Applications. Journal of Hydraulic
Engineering 116(6): 63 3-65 8.
Clark, I. 1987. Practical Geostatistics. Elsevier Applied Science, Inc., London.
Environmental Systems Research Institute, Inc. (ESRI). 2001. ArcGIS Geostatistical
Analyst Extension to ArcGIS 8 Software, Redlands, CA.
Gibbons, R.D. 1994. Statistical Methods for Groundwater Monitoring. John Wiley &
Sons, Inc., New York.
Rock, N.M.S. 1988. Numerical Geology. Springer-Verlag. New York, New York
US Army Corps of Engineers (USACE). 2001. Draft Logistics Center (FTLE-33)
Remedial Action Monitoring Network Optimization Report. May.
US ACE and URS Corporation. 2002. Draft Field Investigation Report Phase II Remedial
Investigation, East Gate Disposal Yard, Fort Lewis, Washington. DSERTS no.
FTLE-67. July.
US Environmental Protection Agency (USEPA). 1994. Methods for Monitoring Pump-
and-Treat Performance. Office of Research and Development. EPA/600/R-
94/123.
URS Corporation. 2000. Draft Engineering Evaluation/Cost Analysis, East Gate
Disposal Yard and Logistics Center, Fort Lewis, Washington. September.
Wiedemeier, T.H., and P.E. Haas. 2000. Designing Monitoring Programs to Effectively
Evaluate the Performance of Natural Attenuation. Air Force Center for
Environmental Excellence (AFCEE). August.
8-1
022/742479/Fort Lewis Draft Final.doc
-------
APPENDIX D-2
OPTIMIZATION OF MONITORING PROGRAM
AT
LONG PRAIRIE GROUND WATER CONTAMINATION
SUPERFUND SITE, MINNESOTA
-------
G-2236-15
2.0
Long Prairie, Minnesota
. -f>
*•
to
Air Force Center for Environmental
May 8, 2003
Groundwater Services, Inc.
2211 Norfolk, Suite 1000, Houston, Texas 77098-4044
-------
V
GROUNDWATER
SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER OUTWASH AQUIFER MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE SITE
Long Prairie, Minnesota
Prepared
by
Groundwater Services, Inc.
2211 Norfolk, Suite 1000
Houston, Texas 77098
(713)522-6300
GSI Job No. G-2236
Revision No. DRAFT
Date: 5/08/03
-------
GSI Job No. G-2236-15 GROUNDWATER
February 19, 2003 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER OUTWASH AQUIFER MONITORING NETWORK
OPTIMIZATION, LONG PRAIRIE SITE
Long Prairie, Minnesota
Table of Contents
Executive Summary 1
Project Objectives 1
Results 2
1.0 Introduction 4
1.1 Geology/Hydrogeology 5
1.2 Remedial Action 5
2.0 MAROS Methodology 7
2.1 MAROS Conceptual Model 7
2.2 Data Management 8
2.3 Site Details 8
2.4 Data Consolidation 9
2.5 Overview Statistics: Plume Trend Analysis 9
2.5.1 Mann-Kendall Analysis 10
2.5.2 Linear Regression Analysis 10
2.5.3 Overview Plume Analysis 11
2.5.4 Moment Analysis 12
2.6 Detailed Statistics: Optimization Analysis 13
2.6.1 Well Redundancy Analysis- Delaunay Method 14
2.6.2 Well Sufficiency Analysis - Delaunay Method 15
2.6.3 Sampling Frequency- Modified CES Method 15
2.6.4 Data Sufficiency - Power Analysis 17
3.0 Site Results 19
3.1 Data Consolidation 19
3.2 Overview Statistics: Plume Trend Analysis 20
3.2.1 Mann-Kendall/Linear Regression Analysis 20
3.2.2 Moment Analysis 21
3.2.3 Overview Plume Analysis 23
3.3 Detailed Statistics: Optimization Analysis 24
3.3.1 Well Redundancy Analysis 24
3.3.2 Well Sufficiency Analysis 25
3.3.3 Sampling Frequency Analysis 26
3.3.4 Data Sufficiency - Power Analysis 27
4.0 Summary and Recommendations 29
Long Prairie Site Q MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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GSIJobNo. G-2236-15
February 19, 2003
GROUNDWATER
SERVICES, INC.
Tables
Table 1 Sampling Locations Used in the MAROS Analysis
Table 2 Mann-Kendall Analysis Decision Matrix
Table 3 Linear Regression Analysis Decision Matrix
Table 4 Upper Outwash Aquifer Site-Specific Parameters
Table 5 Results of Upper Outwash Aquifer Trend Analysis
Table 6 Redundancy Analysis Results - Delaunay Method
Table 7 Sampling Frequency Analysis Results - Modified CES
Table 8 Selected Plume Centerline Wells for Risk-Based Site Cleanup Evaluation
- Power Analysis
Table 9 Plume Centerline Concentration Regression Results - Power Analysis
Table 10 Risk-Based Site Cleanup Evaluation Results - Power Analysis
Table 11 Summary of MAROS Sampling Optimization Results
Figures
Figure 1 Upper Outwash Aquifer Groundwater Monitoring Network
Figure 2 MAROS Decision Support Tool Flow Chart
Figure 3 MAROS Overview Statistics Trend Analysis Methodology
Figure 4 Decision Matrix for Determining Provisional Frequency
Figure 5 Upper Outwash Aquifer PCE Mann-Kendall Trend Results
Figure 6 Upper Outwash Aquifer PCE Linear Regression Trend Results
Figure 7 Upper Outwash Aquifer PCE Mann-Kendall Trend Results, Recovery
Wells
Figure 8 Upper Outwash Aquifer PCE Linear Regression Trend Results, Recovery
Wells
Figure 9 Upper Outwash Aquifer PCE First Moment (Center of Mass) Over Time
Figure 10 Upper Outwash Aquifer PCE plume contoured with 1999 and 2002 data:
With "B" Zone Wells Only
Figure 11 Upper Outwash Aquifer PCE plume contoured with 1999 data: before
optimization and after optimization
Figure 12 Upper Outwash Aquifer Well Sufficiency Results
Appendices
Appendix A: Upper Outwash Aquifer Long Prairie site Historical PCE Maps
Appendix B: Upper Outwash Aquifer Long Prairie site MAROS 2.0 Reports
Long Prairie Site
Long Prairie, Minnesota
MAROS 2.0 Application
Monitoring Network Optimization
-------
GROUNDWATER
February 19, 2003 SERVICES, INC.
MAROS 2.0 APPLICATION
UPPER OUTWASH AQUIFER MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE SITE
EXECUTIVE SUMMARY
Long-term monitoring programs, whether applied for process control, performance
measurement, or compliance purposes, require large scale data collection effort and
time commitment, making their cumulative costs very high. With the increasing use of
risk-based goals and natural attenuation in recent years as well as the move toward
long-term closure upon completion of cleanup activities, the need for better-designed
long-term monitoring plans that are cost-effective, efficient, and protective of human and
ecological health has greatly increased. The Monitoring and Remediation Optimization
System (MAROS) methodology provides an optimal monitoring network solution, given
the parameters within a complicated groundwater system which will increase its
effectiveness. By applying statistical techniques to existing historical and current site
analytical data, as well as considering hydrogeologic factors and the location of potential
receptors, the software suggests an optimal plan along with an analysis of individual
monitoring wells for the current monitoring system. This report summarizes the findings
of an application of the MAROS 2.0 software to the Upper Outwash Aquifer long-term
monitoring well networks at the Long Prairie Site in Long Prairie, Minnesota.
The primary constituent of concern at the site is tetrachloroethylene (PCE) which is
analyzed at a total of 44 wells consisting of 31 monitoring wells, 3 city wells, and 10
extraction wells (Figure 1). Sampling frequency for these wells varies: extraction wells
were generally sampled quarterly while monitoring wells were generally sampled
semiannually or annually since the implementation of the long-term monitoring plan in
1996. For some wells, sampling was even terminated for 3 years before they were
sampled again in October 2002. This resulted in some monitoring wells having only 5 ~
7 data records during the 7-year period (from 1996 to 2002). The historical PCE data for
all or in some cases a subset of wells were analyzed using the MAROS 2.0 software in
order to: 1) gain an overall understanding of the plume stability, and 2) recommend
changes in sampling frequency and sampling locations without compromising the
effectiveness of the long-term monitoring network.
Project Objectives
The general objective of the project was to optimize the Long Prairie long-term
monitoring network and sampling plan applying the MAROS 2.0 statistical and decision
support methodology. The key objectives of the project included:
Determining the overall plume stability through trend analysis and moment
analysis;
Evaluating individual well PCE concentration trends over time;
Long Prairie Site 1 MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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GROUNDWATER
February 19, 2003 SERVICES, INC.
Addressing adequate and effective sampling through reduction of redundant
wells without information loss and addition of new wells for future sampling;
• Assessing future sampling frequency recommendations while maintaining
sufficient plume stability information;
Evaluating risk-based site cleanup status using data sufficiency analysis.
Results
The MAROS 2.0 sampling optimization software/methodology has been applied to the
Long Prairie's existing monitoring program as of October 2002. Results from the
temporal trend analysis, moment analysis, sampling location determination, sampling
frequency determination, and data sufficiency analysis indicate that:
• Site monitoring wells were divided into source wells and tail wells where source
wells are near the dry cleaner site or have historically elevated concentrations of
PCE.
• 2 out of 4 source wells and 24 out of 27 tail wells have a Probably Decreasing,
Decreasing, or Stable trend. Both of the statistical methods used to evaluate
trends (Mann-Kendall and Linear Regression) gave similar trend estimates for
each well.
• 7 out of 10 recovery wells have Probably Decreasing or Decreasing trends. Both
the Mann-Kendall and Linear Regression methods gave similar trend estimates
for each well.
• The dissolved mass shows stability over time, whereas the center of mass shows
increase in distance over time in relation to the source location, and the statistical
distribution of the plume in the x and y directions show a show a relatively stable
trend over time. The results from the moment analysis are dependent on a
changing dataset over time due to the change in the wells sampled over the
sampling period analyzed. Overall these results indicate that the plume is not
increasing in size.
• Overall plume stability results indicate that a monitoring system of "Moderate"
intensity is appropriate for this plume compared to "Limited" or "Extensive"
systems due to a stable Upper Outwash Aquifer plume.
• The well redundancy optimization tool, using the Delaunay method aided with a
qualitative analysis, indicates that 12 existing monitoring wells (27% of all) may
not be needed in the current monitoring system without compromising the
accuracy of the monitoring network.
• The well sufficiency optimization tool, based on the Delaunay method, indicates
that there are no areas within the existing monitoring network that have
Long Prairie Site 2 MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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GROUNDWATER
February 19, 2003 SERVICES, INC.
significantly high uncertainty in the PCE concentration estimation. Therefore, no
new monitoring wells are recommended.
• Application of the well sampling frequency determination tool, the Modified CES
method, leads to significant reduction in sampling frequency. Among the 44
wells in the current monitoring system, 19 are recommended for annual sampling
and 25 for biennial sampling. Considering only wells that have been sampled
consistently up to October 2002 (26 wells) and the sampling frequency reduction
alone, a reduction of approximately 57% in total samples each year can be
achieved.
• The MAROS Data Sufficiency (Power Analysis) application indicates that the
monitoring record has sufficient statistical power to conclude that the site has
attained cleanup goal at (or farther than) the "hypothetical statistical compliance
boundary" located near the most downgradient well at the site. As the plume
shrinks, this hypothetical statistical compliance boundary will move upgradient
gradually.
The recommended long-term monitoring strategy results in a significant reduction in
sampling costs and allows site personnel to develop a better understanding of plume
behavior over time. A reduction in the number of redundant wells will still maintain
adequate delineation of the plume as well as knowledge of the plume state over time.
The MAROS optimized plan results in a monitoring network of 32 wells: 16 sampled
annually, and 16 sampled biennially. The MAROS optimized plan would result in 24
samples per year, compared to 51 samples per year if all the monitoring wells in the
current network were sampled every year. Implementing these recommendations could
lead to a 52% reduction from the current monitoring plan in terms of the samples to be
collected per year. The reduction in the number of redundant wells and decreased
sampling frequency is expected to result in a moderate cost savings over the long-term
at the Long Prairie site. An approximate cost savings estimate range from $2,700 to
$7,560 per year (based on an average per sample cost range of $100 to $280) is
projected while still maintaining adequate delineation of the plume as well as knowledge
of the plume state over time.
Long Prairie Site 3 MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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GROUNDWATER
February 19, 2003 SERVICES, INC.
1.0 INTRODUCTION
Long-term monitoring programs, whether applied for process control, performance
measurement, or compliance purposes, require large scale data collection effort and
time commitment, making their cumulative costs very high. With the increasing use of
risk-based goals and natural attenuation in recent years as well as the move toward
long-term closure upon completion of cleanup activities, the need for better-designed
long-term monitoring plans that are cost-effective, efficient, and protective of human and
ecological health has greatly increased. AFCEE's Monitoring and Remediation
Optimization System (MAROS) methodology provides an optimal monitoring network
solution, given the parameters within a complicated groundwater system which will
increase its effectiveness. By applying statistical techniques to existing historical and
current site analytical data, as well as considering hydrogeologic factors and the location
of potential receptors, the software suggests an optimal plan along with an analysis of
individual monitoring wells for the current monitoring system. This report summarizes
the findings of an application of the MAROS 2.0 software to the Upper Outwash Aquifer
long-term monitoring well network at the Long Prairie site, Long Prairie, Minnesota.
1.1 Geology/Hydrogeology
The Long Prairie groundwater contamination site is a groundwater plume of chlorinated
organic compounds (mostly Tetrachlorothene (PCE)) located below portions of the city
of Long Prairie, Minnesota.
The subsurface in the vicinity of Long Prairie consists of a series of glacial till and
outwash deposits approximately 700 feet thick. The aquifer system near Long Prairie
consists of two water-bearing outwash units. However, in some areas the separating
aquitard is not present between the upper and lower outwash units within the outwash
valley. A sandy clay till aquitard separates the two outwash units on the eastern side of
the city. Leakage is inhibited primarily by the low vertical hydraulic conductivity of the till.
The lower outwash is in direct contact with the upper outwash unit below the western
side of Long Prairie (Barr, 2001). The uppermost geologic unit is silty sand with some
coarser sand and gravel (glacial outwash deposit), the most prolific aquifer in the area.
The aquifer is essentially a wedge of outwash sand and gravel approximately 60 feet
thick near municipal well #4 and thins to less than 5 feet to the southeast and maybe
locally absent beyond. Underlying the glacial outwash sediments is glacial till composed
of sandy clay with varying concentrations of gravel. The till extends to a depth of at least
200 ft bgs and appears to be continuous beneath the site. The backlot, where the PCE
release occurred, is located over an area where the till is present between the two
outwash units and it near the western limit of the till. The saturated thickness of the
upper outwash is only about 10 feet near the source, near MW-10.
The groundwater table in the vicinity of the source area is significantly higher possibly
due to a continuation of the water table as it comes into town from the south in a broad,
shallow valley that contains the Charlotte Lake. Just north of this area, the lower till
pinches out, so the aquifer is much thicker and water elevations are largely controlled by
Long Prairie Site 4 MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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GROUNDWATER
February 19, 2003 SERVICES, INC.
the Long Prairie River. The water table configuration suggests that the groundwater
discharges to the Long Prairie River from the area of well MW-7A and south
(approximately Northwesterly direction). North of well MW-5A, the groundwater flow
direction appears to be approximately parallel with the river (approximately Northerly
direction). Base wells, domestic production wells, extraction wells, and regional
pumping affect local groundwater flow directions. Groundwater elevations range from
1280 to 1290 ft msl at the northern and southern edges of the area, respectively. The
groundwater seepage velocity is approximately 472 ft/yr. For a detailed description of
site geology and hydrogeology refer to Barr (2001).
1.2 Remedial Action
The Long Prairie site is has an approximate 7,000 square foot source area with a one-
half mile long ground water plume located on Long Prairie, Todd County, Minnesota.
The chlorinated plume originates in the commercial area of Long Prairie and extends
through an older residential area of the city. The source of PCE in the groundwater was
a dry-cleaning facility which operated from 1978 until mid-1984, located near well MW-
10. The site was discovered in 1983 during the Minnesota Department of Health's VOC
state wide analysis of public water supplies. The municipal water supply of Long Prairie
was found to be contaminated with PCE and its degradation products trichloroethylene
(TCE) and cis-1,2-dichloroethylene (DCE). The United States Environmental Protection
Agency (U.S. EPA) placed the site on the National Priority List (NPL) in 1986. The
Remedial Investigation/Feasibility Study (RI/FS) and the Remedial Design/Remedial
Action (RD/RA) were conducted under a Multi-Site Cooperative Agreement between
U.S. EPA and the Minnesota Pollution Control Agency (MPCA).
A soil vapor extraction system was installed in 1997 to remove PCE in the soil above the
water table in the alley adjacent to the dry cleaning business, the source area. This
system operated until the end of 1999. A groundwater recovery system and GAC plant
was started up in 1996, with two additional recovery wells added in 1999. The system is
still operating today and consists of nine groundwater recovery wells in the plume area
for groundwater flow control. The objective of the remediation is to restore the Upper
Outwash aquifer to drinking water standards by reducing the PCE concentration to less
than the MCL (5 ppb) as well as preventing the spread of the plume to wells presently
unaffected, including the city of Long Prairie municipal supply well 6.
The groundwater long-term monitoring plan started in 1996 consists of 31 monitoring
wells, 3 city wells, and 10 extraction wells (Figure 1). The monitoring well naming
convention includes: "a" wells, shallow wells screened at the water table; "b" wells, mid-
depth wells screened at the base of the upper outwash; and "c" wells, deep wells
screened in the lower outwash. The monitoring system is used for performance
monitoring and compliance monitoring with the following goals: 1) plume containment
monitoring to confirm that the plume remains hydraulically controlled; and 2) plume
reduction monitoring to verify progress toward achieving cleanup goals.
The sampling frequency for the long-term monitoring wells varies: extraction wells have
generally been sampled quarterly while monitoring wells were generally sampled
Long Prairie Site 5 MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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GROUNDWATER
February 19, 2003 SERVICES, INC.
semiannually or annually since the implementation of the long-term monitoring plan in
1996. For some wells, sampling was even terminated for 3 years before they were
sampled again in October 2002. This resulted in some monitoring wells having only 5 ~
7 data records during the 7-year period (from 1996 to 2002). The historical PCE data for
all or in some cases a subset of wells were analyzed using the MAROS 2.0 software in
order to: 1) gain an overall understanding of the plume stability, and 2) recommend
changes in sampling frequency and sampling locations without compromising the
effectiveness of the long-term monitoring network.
Long Prairie Site 6 MAROS 2.0 Application
Long Prairie, Minnesota Monitoring Network Optimization
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2.0 MAROS METHODOLOGY
The MAROS 2.0 software used to optimize the LTM network at the Long Prairie site is
explained in general terms in this section. MAROS is a collection of tools in one
software package that is used in an explanatory, non-linear fashion. The tool includes
models, statistics, heuristic rules, and empirical relationships to assist the user in
optimizing a groundwater monitoring network system while maintaining adequate
delineation of the plume as well as knowledge of the plume state over time. Different
users utilize the tool in different ways and interpret the results from a different viewpoint.
For a detailed description of the structure of the software and further utilities, refer to the
MAROS 2.0 Manual (Aziz et al. 2002).
2.1 MAROS Conceptual Model
In MAROS 2.0, two levels of analysis are used for optimizing long-term monitoring plans:
1) an overview statistical evaluation with interpretive trend analysis based on temporal
trend analysis and plume stability information; and 2) a more detailed statistical
optimization based on spatial and temporal redundancy reduction methods (see Figure 2
for further details). In general, the MAROS method applies to 2-D aquifers that have
relatively simple site hydrogeology. However, for a multi-aquifer (3-D) system, the user
could apply the statistical analysis layer-by-layer.
The overview statistics or interpretive trend analysis assesses the general monitoring
system category by considering individual well concentration trends, overall plume
stability, hydrogeologic factors (e.g., seepage velocity, and current plume length), and
the location of potential receptors (e.g., property boundaries or drinking water wells). The
analysis relies on temporal trend analysis to assess plume stability, which is then used
to determine the general monitoring system category. Since the temporal trend analysis
focuses on where the monitoring well is located, the site wells are divided into two
different zones: the source zone or the tail zone. The source zone includes areas with
non-aqueous phase liquids (NAPLs), contaminated vadose zone soils, and areas where
aqueous-phase releases have been introduced into ground water. The tail zone is
usually the area downgradient of the contaminant source zone. Although this
classification is a simplification of the well location, this broadness makes the user aware
on an individual well basis that the concentration trend results can have a different
interpretation depending on the well location in and around the plume. The location and
type of the individual wells allows further interpretation of the trend results, depending on
what type of well is being analyzed (e.g., remediation well, leading plume edge well, or
monitoring well). General recommendations for the monitoring network frequency and
density are suggested based on heuristic rules applied to the source and tail trend
results.
The detailed statistics level of analysis or sampling optimization, on the other hand,
consists of a well redundancy analysis and well sufficiency analysis using the Delaunay
method, a sampling frequency analysis using the Modified Cost Effective Sampling
(CES) method and a data sufficiency analysis using power analysis. The well
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redundancy analysis is designed to minimize monitoring locations and the Modified CES
method is designed to minimize the frequency of sampling. The data sufficiency
analysis uses power analysis to assess the sampling record to determine if the current
monitoring network and record is sufficient in terms of evaluating risk-based site target
level status.
2.2 Data Management
In MAROS, ground water monitoring data can be imported from simple database-format
Microsoft® Excel spreadsheets, Microsoft Access tables, previously created MAROS
database archive files, or entered manually. Compliance monitoring data interpretation in
MAROS is based on historical ground water monitoring data from a consistent set of
wells over a series of sampling events. Statistical validity of the concentration trend
analysis requires constraints on the minimum data input of at least four wells (ASTM
1998) in which COCs have been detected. Individual sampling locations need to include
data from at least the six most-recent sampling events. To ensure a meaningful
comparison of COC concentrations over time and space, both data quality and data
quantity need to be considered. Prior to statistical analysis, the user can consolidate
irregularly sampled data or smooth data that might result from seasonal fluctuations or a
change in site conditions.
Imported ground water monitoring data and the site-specific information entered in Site
Details can be archived and exported as MAROS archive files. These archive files can
be appended as new monitoring data becomes available, resulting in a dynamic long-
term monitoring database that reflects the changing conditions at the site (i.e.
biodegradation, compliance attainment, completion of remediation phase, etc.).
2.3 Site Details
Information needed for the MAROS analysis includes site-specific parameters such as
seepage velocity and current plume length. Part of the trend analysis methodology
applied in MAROS focuses on where the monitoring well is located, therefore the user
needs to divide site wells into two different zones: the source zone or the tail zone. The
source zone includes areas with non-aqueous phase liquids (NAPLs), contaminated
vadose zone soils, and areas where aqueous-phase releases have been introduced into
ground water. The source zone generally contains locations with historical high ground
water concentrations of the COCs. The tail zone is usually the area downgradient of the
contaminant source zone. It is up to the user to make further interpretation of the trend
results, depending on what type of well is being analyzed (e.g., remediation well, leading
plume edge well, or monitoring well).
MAROS allows the analysis of up to 5 COCs concurrently and users can pick COCs
from a list of compounds existing in the monitoring data, or select COCs based on
recommendations provided in MAROS based on toxicity, prevalence, and mobility of
compounds.
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2.4 Data Consolidation
Typically long-term monitoring raw data have been measured irregularly in time or
contain many non-detects, trace level results, and duplicates. Therefore, before the data
can be further analyzed, raw data are filtered, consolidated, transformed, and possibly
smoothed to allow for a consistent dataset meeting the minimum data requirements for
statistical analysis mentioned previously.
MAROS allows users to specify the period of interest in which data will be consolidated
(i.e., monthly, bi-monthly, quarterly, semi-annual, yearly, or a biennial basis). In
computing the representative value when consolidating, one of four statistics can be
used: median, geometric mean, mean, and maximum. Non-detects can be transformed
to one half the reporting or method detection limit (DL), the DL, or a fraction of the DL.
Trace level results can be represented by their actual values, one half of the DL, the DL,
or a fraction of their actual values. Duplicates are reduced in MAROS by one of three
ways: assigning the average, maximum, or first value. The reduced data for each COC
and each well can be viewed as a time series in a graphical form on a linear or semi-log
plot generated by the software.
2.5 Overview Statistics: Plume Trend Analysis
Within the MAROS software there are historical data analyses that support a conclusion
about plume stability (e.g., increasing plume, etc.) through statistical trend analysis of
historical monitoring data. Plume stability results are assessed from time-series
concentration data with the application of three statistical tools: Mann-Kendall Trend
analysis, linear regression trend analysis and moment analysis. The two trend methods
are used to estimate the concentration trend for each well and each COC based on a
statistical trend analysis of concentrations versus time at each well (Figure 2). These
trend analyses are then consolidated to give the user a general plume stability and
general monitoring frequency and density recommendations (see Figure 3 for further
step-by-step details). Both qualitative and quantitative plume information can be gained
by these evaluations of monitoring network historical data trends both spatially and
temporally. The MAROS Overview Statistics are the foundation the user needs to make
informed optimization decisions at the site. The Overview Statistics are designed to
allow site personnel to develop a better understanding of the plume behavior over time
and understand how the individual well concentration trends are spatially distributed
within the plume. This step allows the user to gain information that will support a more
informed decision to be made in the next level or detailed statistics optimization analysis
(Figure 2).
2.5.1 Mann-Kendall Analysis
The Mann-Kendall test is a non-parametric statistical procedure that is well suited for
analyzing trends in data over time. The Mann-Kendall test can be viewed as a
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nonparametric test for zero slope of the first-order regression of time-ordered
concentration data versus time. The Mann-Kendall test does not require any
assumptions as to the statistical distribution of the data (e.g. normal, lognormal, etc.)
and can be used with data sets which include irregular sampling intervals and missing
data. The Mann-Kendall test is designed for analyzing a single groundwater constituent,
multiple constituents are analyzed separately. The Mann-Kendall S statistic measures
the trend in the data: positive values indicate an increase in concentrations over time
and negative values indicate a decrease in concentrations over time. The strength of the
trend is proportional to the magnitude of the Mann-Kendall statistic (i.e., a large value
indicates a strong trend). The confidence in the trend is determined by consulting the S
statistic and the sample size n in a Kendall probability table such as the one reported in
Hollander and Wolfe (1973).
The concentration trend is determined for each well and each COC based on results of
the S statistic, the confidence in the trend, and the Coefficient of Variation (COV). The
decision matrix for this evaluation is shown in Table 2. A Mann-Kendall statistic that is
greater than 0 combined with a confidence of greater than 95% is categorized as an
Increasing trend while a Mann-Kendall statistic of less than 0 with a confidence between
90% and 95% is defined as a Probably Increasing trend, and so on.
Depending on statistical indicators, the concentration trend is classified into six
categories:
• Decreasing (D),
• Probably Decreasing (PD),
• Stable (S),
• No Trend (NT),
• Probably Increasing (PI)
• Increasing (I).
These trend estimates are then analyzed to identify the source and tail region overall
stability category (see Figure 2 for further details).
2.5.2 Linear Regression Analysis
Linear Regression is a parametric statistical procedure that is typically used for
analyzing trends in data over time. Using this type of analysis, a higher degree of
scatter simply corresponds to a wider confidence interval about the average log-slope.
Assuming the sign (i.e., positive or negative) of the estimated log-slope is correct, a level
of confidence that the slope is not zero can be easily determined. Thus, despite a poor
goodness of fit, the overall trend in the data may still be ascertained, where low levels of
confidence correspond to "Stable" or "No Trend" conditions (depending on the degree of
scatter) and higher levels of confidence indicate the stronger likelihood of a trend. The
linear regression analysis is based on the first-order linear regression of the log-
transformed concentration data versus time. The slope obtained from this log-
transformed regression, the confidence level for this log-slope, and the COV of the
untransformed data are used to determine the concentration trend. The decision matrix
for this evaluation is shown in Table 3. To estimate the confidence in the log-slope, the
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standard error of the log-slope is calculated. The coefficient of variation, defined as the
standard deviation divided by the average, is used as a secondary measure of scatter to
distinguish between "Stable" or "No Trend" conditions for negative slopes. The Linear
Regression Analysis is designed for analyzing a single groundwater constituent; multiple
constituents are analyzed separately, (up to five COCs simultaneously). For this
evaluation, a decision matrix developed by Groundwater Services, Inc. is also used to
determine the "Concentration Trend" category (plume stability) for each well.
Depending on statistical indicators, the concentration trend is classified into six
categories:
Decreasing (D),
Probably Decreasing (PD),
Stable (S),
No Trend (NT),
Probably Increasing (PI)
Increasing (I).
The resulting confidence in the trend, together with the log-slope and the COV of the
untransformed data, are used in the linear regression analysis decision matrix to
determine the concentration trend. For example, a positive log-slope with a confidence
of less than 90% is categorized as having No Trend whereas a negative log-slope is
considered Stable if the COV is less than 1 and categorized as No Trend if the COV is
greater than 1.
2.5.3 Overall Plume Analysis
General recommendations for the monitoring network frequency and density are
suggested based on heuristic rules applied to the source and tail trend results.
Individual well trend results are consolidated and weighted by the MAROS software
according to user input, and the direction and strength of contaminant concentration
trends in the source zone and tail zone for each COC are determined. Based on
i) the consolidated trend analysis,
ii) hydrogeologic factors (e.g., seepage velocity), and
iii) location of potential receptors (e.g., wells, discharge points, or property
boundaries),
the software suggests an general optimization plan for the current monitoring system in
order to efficiently effectively monitor in the future. A flow chart of the MAROS
methodology utilizing trend analysis results and other site-specific parameters to form a
general sampling frequency and well density recommendation is outlined in Figure 3. For
example, a generic plan for a shrinking petroleum hydrocarbon plume (BTEX) in a slow
hydrogeologic environment (silt) with no nearby receptors would entail minimal, low
frequency sampling of just a few indicators. On the other hand, the generic plan for a
chlorinated solvent plume in a fast hydrogeologic environment that is expanding but has
very erratic concentrations over time would entail more extensive, higher frequency
sampling. The generic plan is based on a heuristically derived algorithm for assessing
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future sampling duration, location and density that takes into consideration plume
stability. For a detailed description of the heuristic rules used in the MAROS software,
refer to the MAROS 2.0 Manual (Aziz et al. 2002).
2.5.3 Moment Analysis
An analysis of moments can help resolve plume trends, where the zeroth moment shows
change in dissolved mass vs. time, the first moment shows the center of mass location
vs. time, and the second moment shows the spread of the plume vs. time. Moment
calculations can predict how the plume will change in the future if further statistical
analysis is applied to the moments to identify a trend (in this case, Mann Kendall Trend
Analysis is applied). The trend analysis of moments can be summarized as:
• Zeroth Moment: Change in dissolved mass over time
• First Moment: Change in the center of mass location over time
• Second Moment: Spread of the plume over time
The role of moment analysis in MAROS is to provide a relative measure of plume
stability and condition. Plume stability may vary by constituent, therefore the MAROS
moment analysis can be used to evaluate multiple COCs simultaneously in order to
provide used to provide a quick way of comparing individual plume parameters to
determine the size and movement of constituents relative to one another. Moment
analysis in the MAROS software can also be used to assist the user in evaluating the
impact on plume delineation in future sampling events by removing identified
"redundant" wells from a long-term monitoring program (this analysis was not performed
as part of this study, for more details on this application of moment analysis refer to the
MAROS 2.0 Manual (Aziz et al. 2002).
The zeroth moment is a mass estimate. The zeroth moment calculation can show high
variability over time, largely due to the fluctuating concentrations at the most
contaminated wells as well as varying monitoring well network. Plume analysis and
delineation based exclusively on concentration can exhibit a fluctuating degree of
temporal and spatial variability. The mass estimate is also sensitive to the extent of the
site monitoring well network over time. The zeroth moment trend over time is determined
by using the Mann-Kendall Trend Methodology. The zeroth Moment trend test allows
the user to understand how the plume mass has changed over time. Results for the
trend include: Increasing, Probably Increasing, No Trend, Stable, Probably Decreasing,
Decreasing or Not Applicable (Insufficient Data). When considering the results of the
Zeroth moment trend, the following factors should be considered which could effect the
calculation and interpretation of the plume mass over time: 1) Change in the spatial
distribution of the wells sampled historically 2) Different wells sampled within the well
network over time (addition and subtraction of well within the network). 3) Adequate
versus inadequate delineation of the plume over time.
The first moment estimates the center of mass, coordinates (Xc and Yc) for each
sample event and COC. The changing center of mass locations indicate the movement
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of the center of mass over time. Whereas, the distance from the original source location
to the center of mass locations indicate the movement of the center of mass over time
relative to the original source. Calculation of the first moment normalizes the spread by
the concentration indicating the center of mass. The first moment trend of the distance to
the center of mass over time shows movement of the plume in relation to the original
source location over time. Analysis of the movement of mass should be viewed as it
relates to 1) the original source location of contamination 2) the direction of groundwater
flow and/or 3) source removal or remediation. Spatial and temporal trends in the center
of mass can indicate spreading or shrinking or transient movement based on seasonal
variation in rainfall or other hydraulic considerations. No appreciable movement or a
neutral trend in the center of mass would indicate plume stability. However, changes in
the first moment over time do not necessarily completely characterize the changes in the
concentration distribution (and the mass) over time. Therefore, in order to fully
characterize the plume the First Moment trend should be compared to the Zeroth
moment trend (mass change over time).
The second moment indicates the spread of the contaminant about the center of mass
(Sxx and Syy), or the distance of contamination from the center of mass for a particular
COC and sample event. The Second Moment represents the spread of the plume over
time in both the x and y directions. The Second Moment trend indicates the spread of
the plume about the center of mass. Analysis of the spread of the plume should be
viewed as it relates to the direction of groundwater flow. An increasing trend in the
second moment indicates an expanding plume, whereas a declining trend in the plume
indicates a shrinking plume. No appreciable movement or a neutral trend in the center of
mass would indicate plume stability. The second moment provides a measure of the
spread of the concentration distribution about the plume's center of mass. However,
changes in the second moment over time do not necessarily completely characterize the
changes in the concentration distribution (and the mass) over time. Therefore, in order to
fully characterize the plume the Second Moment trend should be compared to the zeroth
moment trend (mass change over time).
2.6 Detailed Statistics: Optimization Analysis
Although the overall plume analysis shows a general recommendation regarding
sampling frequency reduction and general sampling density, a more detailed analysis is
also available with the MAROS 2.0 software in order to allow for further reductions on a
well-by-well basis for frequency, well redundancy, well sufficiency and sampling
sufficiency. The MAROS Detailed Statistics allows for a quantitative analysis for spatial
and temporal optimization of the well network on a well-by-well basis. The results from
the Overview Statistics should be considered along with the MAROS optimization
recommendations gained from the Detailed Statistical Analysis described previously.
The MAROS Detailed Statistics results should be reassessed in view of site knowledge
and regulatory requirements as well as in consideration of the Overview Statistics
(Figure 2).
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The Detailed Statistics or Sampling Optimization MAROS module can be used to
determine the minimal number of sampling locations and the lowest frequency of
sampling that can still meet the requirements of sampling spatially and temporally for an
existing monitoring program. It also provides an analysis of the sufficiency of data for
the monitoring program.
Sampling optimization in MAROS consists of four parts:
• Well redundancy analysis using the Delaunay method
• Well sufficiency analysis using the Delaunay method
• Sampling frequency determination using the Modified CES method
• Data sufficiency analysis using statistical power analysis.
The well redundancy analysis using the Delaunay method identifies and eliminates
redundant locations from the monitoring network. The well sufficiency analysis can
determine the areas where new sampling locations might be needed. The Modified CES
method determines the optimal sampling frequency for a sampling location based on the
direction, magnitude, and uncertainty in its concentration trend. The data sufficiency
analysis examines the risk-based site cleanup status and power and expected sample
size associated with the cleanup status evaluation.
2.6.1 Well Redundancy Analysis - Delaunav Method
The well redundancy analysis using the Delaunay method is designed to select the
minimum number of sampling locations based on the spatial analysis of the relative
importance of each sampling location in the monitoring network. The approach allows
elimination of sampling locations that have little impact on the historical characterization
of a contaminant plume. The delaunay methodology application assumes that the
current sampling network adequately delineates the plume (bounding wells have non-
detect values) and that if a hydraulic containment system is currently in operation, this
will continue. An extended method or wells sufficiency analysis, based on the Delaunay
method, can also be used for recommending new sampling locations. Details about the
Delaunay method can be found in Appendix A.2 of the MAROS Manual (AFCEE 2002).
Well redundancy analysis uses the Delaunay triangulation method to determine the
significance of the current sampling locations relative to the overall monitoring network.
The Delaunay method calculates the network Area and Average concentration of the
plume using data from multiple monitoring wells. A slope factor (SF) is calculated for
each well to indicate the significance of this well in the system (i.e. how removing a well
changes the average concentration.)
The well redundancy optimization process is performed in a stepwise fashion. Step one
involves assessing the significance of the well in the system, if a well has a small SF
(little significance to the network), the well may be removed from the monitoring network.
Step two involves evaluating the information loss of removing a well from the network. If
one well has a small SF, it may or may not be eliminated depending on whether the
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information loss is significant. If the information loss is not significant, the well can be
eliminated from the monitoring network and the process of optimization continues with
fewer wells. However if the well information loss is significant then the optimization
terminates. This sampling optimization process allows the user to assess "redundant"
wells that will not incur significant information loss on a constituent-by-constituent basis
for individual sampling events.
Before applying the Delaunay method for spatial redundancy analysis, it is important to
select the appropriate set of wells for analysis, i.e., only the wells that contribute to the
spatial delineation of the plume. For example, if wells are far from the plume and
contribute little or nothing to the delineation of the plume (e.g., some sentry wells or
background wells far from the plume), they should be excluded from the analysis. One
reason not to use these wells is that these wells usually are on the boundary of the
triangulation and are hard to be eliminated since the Delaunay method protects
boundary wells from being easily removed. The elimination status of these wells, in fact,
should be determined from the regulatory standpoint. Another well type that could be
excluded from analysis is one of a clustered well set because the Delaunay method is a
two-dimensional method. Generally, only one well is picked from the clustered well set to
represent the concentration at this point. This well can be the one that has the highest
concentration or is screened in the representative aquifer interval with the geologic unit.
Data from clustered wells can also be averaged to form a single sample and then used
in the Delaunay method.
2.6.2 Well Sufficiency Analysis - Delaunav Method
The well sufficiency analysis, using the Delaunay method, is designed to recommend
new sampling locations in areas within the existing monitoring network where there is a
high level uncertainty in plume concentration. Details about the well sufficiency analysis
can be found in Appendix A.2 of the MAROS Manual (AFCEE 2002).
In many cases, new sampling locations need to be added to the existing network to
enhance the spatial plume characterization. In MAROS, the method for determining new
sampling locations recommends the area for a possible new sampling location where
there is a high level of uncertainty in concentration estimation. The Slope Factor (SF)
values obtained from the redundancy reduction described above are used to calculate
the concentration estimation error at each triangle area formed in the Delaunay
triangulation. The estimated SF value at each triangle area is then classified into four
levels: Small, Moderate, Large, or Extremely large because the larger the estimated SF
value, the higher the estimation error at this area. Therefore, the triangle areas with the
estimated SF value at the Extremely large or Large level are candidate regions for new
sampling locations.
The results from the Delaunay method and the method for determining new sampling
locations are derived solely from the spatial configuration of the monitoring network and
the spatial pattern of the contaminant plume. No parameters such as the hydrogeologic
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conditions are considered in the analysis. Therefore, professional judgement and
regulatory considerations must be used to make final decisions.
2.6.3 Sampling Frequency Determination - Modified CES Method
The Modified Cost Effective Sampling (MCES) method optimizes sampling frequency for
each sampling location based on the magnitude, direction, and uncertainty of its
concentration trend derived from its recent and historical monitoring records. The MCES
estimates the lowest-frequency sampling schedule for a given groundwater monitoring
location yet still provide needed information for regulatory and remedial decision-making.
The Modified CES method was developed on the basis of the Cost Effective Sampling
(Ridley et al. 1995). Details about the Modified CES method can be found in Appendix
A.3 of the MAROS Manual (AFCEE 2002).
In order to estimate the least frequent sampling schedule for a monitoring location that
still provides enough information for regulatory and remedial decision-making, MCES
employs three steps to determine the sampling frequency. The first step involves
analyzing frequency based on recent trends (Figure 4). A preliminary location sampling
frequency (PLSF) is determined based on the trends determined by rates of change
from linear regression and Mann-Kendall analysis of the most recent monitoring data.
The variability of the sequential sampling data is accounted for by the Mann-Kendall
analysis. The PLSF is then adjusted based on overall trends. If the long-term history of
change is significantly greater than the recent trend, the frequency may be reduced by
one level. Otherwise, no change could be made. The final step in the analysis involves
reducing frequency based on risk. Since not all compounds in the target being
assessed are equally harmful, frequency is reduced by one level if recent maximum
concentration for compound of high risk is less than 1/2 of the Maximum Concentration
Limit (MCL). The result of applying this method is a suggested sampling frequency
based on recent sampling data trends and overall sampling data trends.
The finally determined sampling frequency from the Modified CES method can be
Quarterly, Semiannual, Annual, and Biennial. Users can further reduce the sampling
frequency to, for example, once every three years, if the trend estimated from Biennial
data (i.e., data drawn once every two years from the original data) is the same as that
estimated from the original data.
2.6.4 Data Sufficiency Analysis - Power Analysis
Statistical power analysis is a technique for interpreting the results of statistical tests. It
provides additional information about a statistical test: 1) the power of the statistical test,
i.e., the probability of finding a difference in the variable of interest when a difference
truly exists; and 2) the expected sample size of a future sampling plan given the
minimum detectable difference it is supposed to detect. For example, if the mean
concentration is lower than the cleanup goal but a statistical test cannot prove this, the
power and expected sample size can tell the reason and how many more samples are
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needed to result in a significant test. The additional samples can be obtained by a
longer period of sampling or an increased sampling frequency. Details about the data
sufficiency analysis can be found in Appendix A.6 of the MAROS Manual (AFCEE 2002).
When applying the MAROS power analysis method, a hypothetical statistical compliance
boundary (HSCB) is assigned to be a line perpendicular to the groundwater flow
direction (see figure below). Monitoring well concentrations are projected onto the
HSCB using the distance from each well to the compliance boundary along with a decay
coefficient. The projected concentrations from each well and each sampling event are
then used in the risk-based power analysis. Since there may be more than one sampling
event selected by the user, the risk-based power analysis results are given on an event-
by-event basis. This power analysis can then indicate if target are statistically achieved
at the HSCB. For instance, at a site where the historical monitoring record is short with
few wells, the HSCB would be distant; whereas, at a site with longer duration of
sampling with many wells, the HSCB would be close. Ultimately, at a site the goal would
be to have the HSCB coincide with or be within the actual compliance boundary
(typically the site property line).
^ ^.:::::::::::::::::::::::::::::::::::::::
L
o
Groundwater flow direction
Concentrations
projected to this
line
* -f
The nearest
downgradient
receptor
In order to perform a risk-based cleanup status evaluation for the whole site, a strategy
was developed as follows.
• Estimate concentration versus distance decay coefficient from plume centerline
wells.
• Extrapolate concentration versus distance for each well using this decay
coefficient.
• Comparing the extrapolated concentrations with the compliance concentration
using power analysis.
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Results from this analysis can be Attained or Not Attained, providing a statistical
interpretation of whether the cleanup goal has been met on the site-scale from the risk-
based point of view. The results as a function of time can be used to evaluate if the
monitoring system has enough power at each step in the sampling record to indicate
certainty of compliance by the plume location and condition relative to the compliance
boundary. For example, if results are Not Attained at early sampling events but are
Attained in recent sampling events, it indicates that the recent sampling record provides
a powerful enough result to indicate compliance of the plume relative to the location of
the receptor or compliance boundary.
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3.0 SITE RESULTS
The groundwater long-term monitoring plan for Long Prairie was started in 1996. The
monitoring plan consisted of performance monitoring and compliance monitoring with the
following goals:
1) plume containment monitoring to confirm that the PCE plume remains
hydraulically controlled; and
2) plume reduction monitoring to verify progress toward achieving cleanup goals.
31 monitoring wells, 3 city wells, and 10 extraction wells were included in the long-term
monitoring network as of 2002 (Figure 1). The monitoring well naming convention
includes: "a" wells, shallow wells screened at the water table; "b" wells, mid-depth wells
screened at the base of the upper outwash; and "c" wells, deep wells screened in the
lower outwash. The sampling frequency for the long-term monitoring wells varies:
extraction wells have generally been sampled quarterly while monitoring wells were
generally sampled semiannually or annually since the implementation of the long-term
monitoring plan in 1996. For some wells, sampling was even terminated for 3 years
before they were sampled again in October 2002. This resulted in some monitoring
wells having only 5 ~ 7 data records during the 7-year period (from 1996 to 2002).
Monitoring data from 1996 to 2002 were used for the detailed optimization analysis, with
a subset of this data used in some of the analyses.
In applying the MAROS methodology to develop a revised monitoring strategy for the
Long Prairie site, many site and dataset parameters were applied. General site
assumptions include:
• All wells that were part of the network in between 1996 and 2002 were
considered in the temporal concentration trend analysis.
• Four chemicals of concern (COCs) that have been historically present at the site:
tetrachloroethene (PCE), trichloroethene (TCE), cis-1,2-dichloroethene (cis-1,2-
DCE), and vinyl chloride (VC), however, PCE is the predominant chemical and
has been used as an indicator parameter in the MAROS analyses.
• All source/tail assignments were made based on the PCE plume. Source wells
were selected based on historically elevated concentrations of PCE, near the dry
cleaner site in the vicinity of well MW-10.
• Site-specific hydrogeologic parameters related to the upper outwash aquifer
including groundwater flow direction, seepage velocity, saturated thickness,
porosity, receptor locations, can be found in the Table 4.
• Monitoring data from 1996 to 2002 were used for the "overall" trend analysis in
the sampling frequency optimization and other analyses in the MAROS detailed
optimization analysis.
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3.1 Data Consolidation
In MAROS, ground water monitoring data can be imported from simple database-format
Microsoft® Excel spreadsheets, Microsoft® Access tables, previously created MAROS
database archive files, or entered manually. The historical monitoring data from Long
Prairie were received in Excel database format. The columns in the file where formatted
to the MAROS Access file import format and then imported into the MAROS software
using the import tool. The long-term monitoring raw data contained many non-detects,
trace level results, and duplicates. Therefore, in the MAROS software the raw data are
filtered, consolidated, and the period of interest was specified (i.e. monitoring data from
1996 to 2002) as well as the wells of interest for the zone of interest. For statistical
evaluation of the data, a representative value for each sample point in time is needed.
MAROS has many automated options to choose how these values are assigned. For
the Long Prairie data, non-detects values were chosen to be set to the minimum
detection limit, allowing for uniform detection limits over time. Trace level results were
chosen to be represented by their actual values and duplicates samples were chosen to
be assigned the average of the two samples. The reduced data for each well were
viewed as a time series in a graphical form on a linear or semi-log plot generated by the
software.
3.2 Overview Statistics: Plume Trend Analysis
3.2.1 Mann-Kendall/Linear Regression Analysis
The goal of the Mann-Kendall and Linear Regression temporal trend analysis is to
assess the historical trend in the concentrations over time. These trend estimates are
then analyzed to identify the source and tail region overall stability category as well as
gaining an understanding of the individual well concentrations overtime (see Figure 2 for
further details). The PCE historical data for monitoring wells the Upper Outwash Aquifer
as well as the recovery wells were assessed for trends. No data consolidation was
performed to condense the sampling into regular sample intervals.
Only 31 monitoring wells and 9 recovery wells had sufficient data within the time period
of 1996 to 2002 (at least 6 sample events) to assess the trends in the wells. Trend
results from the Mann-Kendall and Linear Regression temporal trend analysis for both
Upper Outwash Aquifer monitoring wells and extraction wells are given in Table 5. The
monitoring well trend results show that 2 out of 4 source wells and 24 out of 27 tail wells
have a Probably Decreasing, Decreasing, or Stable trend. Both methods gave similar
trend estimates for each well. The recovery well trend results show that 7 out of 10 wells
have a Probably Decreasing, Decreasing, or Stable trend. Both methods gave similar
trend estimates for each well. When considering the spatial distribution of the trend
results (Figures 5 and 6 - maps created in ArcGIS from MAROS results), the majority of
the decreasing trend results are located in the interior of the plume or near the source,
indicating a decreasing source. Another area of decreasing trends is in the vicinity of
the line of recovery or plume containment wells (Figures 7 and 8 - maps created in
ArcGIS from MAROS results).
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Well Type
Source
Tail
Recovery
Zone A MAROS Trend Analysis
PD, D, S
2 of 4 (50%)
24 of 27 (90%)
7 of 10 (70%)
I, PI
Oof 4(0%)
3 of 27 (10%)
Oof 10(0%)
Note: Decreasing (D), Probably Decreasing (PD), Stable (S), Probably Increasing (PI), and Increasing (I)
Although monitoring wells and recovery wells are present in the well network, these well
trend results need to be treated differently for the purpose of individual trend analysis
interpretation primarily due to the different course of action possible for the two types of
wells. For monitoring wells, strongly decreasing concentration trends may lead the site
manager to decrease their monitoring frequency, as well look at the well as possibly
attaining its remediation goal. Conversely, strongly decreasing concentration trends in
recovery wells may indicate ineffective or near-asymptotic contamination extraction,
which may in turn lead to either the shutting down of the well or a drastic change in the
extraction scheme. Other reasons favoring the separation of these two types of wells in
the trend analysis interpretation is the fact that they produce very different types of
samples. On average, the extraction wells possess screens that are twice as large and
extraction wells pull water from a much wider area than the average monitoring well.
Therefore, the potential for the dilution of extraction well samples is far greater than
monitoring well samples.
3.2.2 Moment Analysis
The moment analysis in the MAROS software was applied at the Long Prairie site in
order to gain a better understanding of the overall plume stability in the Upper Outwash
Aquifer. Monitoring well data from 1996 to 2002 were used for the moment analysis, the
wells utilized for the analysis are listed in Table 1. Sampling frequency for these wells
was very irregular, therefore, the spatial moment analyses were based on sampling
events redefined on a yearly basis, that is, data collected between January 1st and
December 31st of a year were treated as if from the same sampling event performed on
July 1st of that year, with the geometric mean result utilized for each location.
Moment trend results from the Zeroth, First, and Second Moment analyses for the Zone
A monitoring well network were varied. Moment Trend results from the moment trend
analysis for the selected Upper Outwash well dataset are given in the Moment Analysis
Report, Appendix B. Approximately 17 wells were used in the moment analysis. Wells
with redundant spatial concentration information were not utilized in the moment analysis
(i.e. MW-1A).
The zeroth moment analysis showed a stable trend (no change in dissolved mass) over
time (Appendix B). The zeroth moment or mass estimate can show high variability over
time, largely due to the fluctuating concentrations at the most contaminated wells as well
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as a varying monitoring well network. In order to reduce the fluctuating factors that could
influence a mass trend, the data were consolidated to annual sampling and the zeroth
moment trend evaluated. Another factor to consider when interpreting the mass
increase over time is the change in the spatial distribution of the wells sampled
historically. At the Long Prairie site there were changes in the well distribution over time,
due to addition and subtraction of wells from the well network as well as changes in
sampling frequency.
Moment
Type
Zeroth
First
Second
Mann -Kendall Trend Analysis
Trend
Stable
Increasing
Stable to No Trend
Comment
The amount of dissolved mass has not fluctuated appreciably over time.
This matches results in Table 5, where 15% of wells had stable trends.
The center of mass moved away from the source area over along the
direction of groundwater flow.
Stable to no trend, indicating that wells representing very large areas
both on the tip and the sides of the plume show little change in
concentrations. The shape of the plume is relatively constant over time.
The first moment, or center of mass, for each sample event in the Upper Outwash
aquifer had an increasing distance relative to the approximate source location, see
Figure 9, as well as the MAROS First Moment Reports in Appendix B. The center of
mass showed some movement forward along the direction of groundwater flow. These
spatial and temporal trends in the center of mass distance from the source location can
indicate transient movement based on season variation in rainfall or other hydraulic
considerations. With appreciable movement in the center of mass as is the case at Long
Prairie as well as a stable to decreasing source (zeroth moment and individual well
trend analysis results), there is an indication that the near source area is remediating
faster (on a mass basis) than the other areas of the plume. So although the plume is
stable the relative concentrations in the source area are decreasing faster than the other
areas of the plume. This concentration decrease can be seen in comparing the1996
and 2002 maps in Appendix A.
The second moment, or spread of the plume over time in both the x and y directions for
the sample events, showed a stable to no trend, Appendix B. The second moment
provides a measure of the spread of the concentration distribution about the plume's
center of mass. Analysis of the spread of the plume indicates stability in the plume in
the y direction and stable to no trend in the x direction, indicating that wells representing
very large areas both on the tip and the sides of the plume show little change in
concentrations over time and that the overall shape of the plume is relatively constant
with time. This stable trend in the spread of the plume strengthens the individual well
Mann-Kendall and Linear Regression trend analysis spatially, where most of the tail
wells showed a decreasing or probably decreasing PCE concentration trend.
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3.2.3 Overview Statistics: Plume Analysis
In evaluating overall plume stability, the trend analysis results and all monitoring wells
were assigned "Medium" weights within the MAROS software (as described in Figure 3),
assuming equal importance for each well and each trend result in the overall analysis.
Overview Statistics Results:
• Overall trend for Source region: Stable,
• Overall trend for Tail region: near Stable,
• Overall results from moment analysis indicate a stable to decreasing plume,
• Overall monitoring intensity needed: Moderate.
These results matched with the judgment based on the visual comparison of PCE
plumes over time, as well as the Moment Analysis. The PCE concentrations observed
over the history of monitoring at the site are plotted in Appendix A. The PCE plume
observed in 1996 was very similar to that of 2002, indicating that the PCE plume is
relatively stable over time.
For a generic plume, the MAROS software indicates:
• No recommendation for sampling frequency
• Upper Outwash Aquifer may need 35 wells for the sampling network
These MAROS results are for a generic site, and are based on knowledge gained from
applying the MAROS Overview Statistics. There is no recommendation for frequency of
sampling for the whole monitoring network due to some uncertainty in the trends and the
presence of an active remediation system. Also, the recommended the number of wells
seems high when applied to the entire site. So, although the overall plume trend
analysis shows a stable plume, no general sampling frequency recommendation was
assessed by the MAROS software. Therefore, a more detailed analysis was performed
using the MAROS 2.0 software in order to allow for possible reductions on a well-by-well
basis, frequency and well redundancy analysis were conducted. These overview
statistics were also used when evaluating a final recommendation for each well after the
detailed statistical analysis was applied.
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3.3 Detailed Statistics: Optimization Analysis
From May 1996 to October 2002, a total of 44 wells were sampled, among which there
are 31 monitoring wells, 3 city wells, and 10 extraction wells (Table 1). Sampling
frequency for these wells varies: extraction wells were generally sampled quarterly while
monitoring wells were generally sampled semiannually or annually. A brief sampling
history for these wells is summarized in the last column of Table 1. All 44 wells were
used in the MAROS sampling optimization analysis. In the well redundancy and well
sufficiency analyses, the monitoring wells and some of the city wells were used (mostly
"B" wells, i.e., wells screened in the middle of the aquifer). In the sampling frequency
analysis, all 44 wells were analyzed. In data sufficiency analysis, only monitoring wells
were used. Results for well redundancy and sufficiency analyses, sampling frequency
analysis, and data sufficiency analysis are detailed in the following sub-sections.
3.3.1 Well Redundancy Analysis - Delaunav Method
The goal of the well redundancy analysis is to identify wells that are redundant within
monitoring network as candidates for removal from the sampling plan. The approach
allows elimination of sampling locations that have little impact on the historical
characterization of a groundwater plume. The analysis assumes that the current state of
hydraulic containment at the site will continue and than the monitoring network
adequately delineates the plume.
A monitoring network of 17 monitoring wells was used in the well redundancy analysis.
Clustered wells that are screened in different zones of the aquifer and had equivalent
duplicates or lower concentrations were excluded from the analysis (Table 1 lists the
wells excluded and the 17 wells used in the analysis). For example, wells MW-2A and
MW-2C are screened above and below the aquifer zone in which MW-2B is screened
(the middle zone or "B" zone), respectively, and both had concentrations lower than MW-
2B. Therefore, MW-2B instead of MW-2A and MW-2C was used in the well redundancy
analysis. In most cases, the "B" zone wells were used. But for well cluster MW-11A,
MW-1B, and MW-11C, MW-11C was used because it had more sampling records for
analysis and had the same concentration level as MW-2B. The well redundancy
analysis was conducted with the latest 3 years' sampling events (May 1999 to October
2002). The results show that no monitoring wells can be eliminated from this 17-well
network (Table 6).
However, after a qualitative consideration of the need for plume and site characterization
and the wells' concentration history, 9 monitoring wells (all "A" zone wells) can be
eliminated (Table 6). Using similar qualitative analysis, 3 extraction wells in the source
area can be eliminated since their concentrations were always below MCL or DL (Table
6). This resulted in a total of 12 wells recommended for removal, that is, a reduction of
27% in the well network (12 out of 44 wells).
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Well removal candidates include:
Monitoring wells
• MW-1A
• MW-2A
• MW-3A
• MW-4A
• Mw-5A
Monitoring wells
• MW-6A
• MW-11A
• MW-16A
• MW-18A
Extraction wells
• RW-1A
• RW-1B
• RW-1C
Eliminating the above 12 wells has negligible influence on spatial plume characterization
(Figure 10). For monitoring wells that have sampling ports at different levels ("A", "B", or
"C" zones), only "B" zone or "C" zone wells were used in the 2-D plume contouring. "B"
zone and "C" zone wells were generally higher in PCE concentrations than their
corresponding "A" zone wells, resulting in conservative estimates of the spatial plume
distribution. All monitoring wells that are candidates for elimination from the monitoring
network, generally "A" zone wells, plume contouring was not affected and therefore the
plumes before and after well elimination (Figure 10) are identical.
In considering the MAROS redundancy analysis results, other factors needed to be
taken into consideration before recommending well removals. Recent concentrations in
7 of the 9 wells were all below detection limits (MW-1A, MW-3A, MW-5A, MW-11A, MW-
15A, MW-16A, MW-18A), indicating that these wells do not and probably will not
contribute to the vertical plume delineation. In the case of MW-2A and MW-4A, these
were eliminated because their concentrations were all lower than their corresponding "B"
zone wells. Since the dissolved PCE plume was originated from DNAPL, without vertical
upward movement of groundwater, it typically tends to migrate downward vertically and
stay at the bottom of the aquifer. Therefore, although the "A" zone wells are candidates
for elimination, their corresponding "B" (or "C") zone wells were kept. Also, in plume
contouring for the purpose of delineating the plume extent horizontally, using "B" wells
can provide a more conservative estimate (i.e., a larger plume) than using "A" zone wells
due to their typically higher concentrations. For example, Figure 11 depicts the
approximate PCE plumes observed in May 1999 and October 2002 using data from "B"
zone wells only. In order to monitor the possible vertical migration of the plume in the
aquifer, all "C" zone wells were kept, ensuring enough information will be available for
plume characterization vertically. In considering the recovery wells, three wells were
identified for removal from the monitoring network because these 3 wells (RW-1A, RW-
1B, and RW-1C) have been extracting groundwater have been consistently below
detection limits or the MCL.
3.3.2 Well Sufficiency Analysis - Delaunav Method
The well sufficiency analysis for recommending new sampling locations was performed
using the same set of monitoring wells as in the well redundancy analysis. With this
analysis, areas within the monitoring well network where there is high uncertainty for
predicting concentrations are recommended for additional sample locations within the
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existing well network. The SF values obtained from the well redundancy analysis were
used to generate Figure 12, which indicates the triangular regions for placing new
sampling locations. It is seen that almost all triangular regions (except one to the east of
the plume source area) have M (medium) estimation errors. The region with L (large)
result has a SF value of 0.602, which is not significantly larger than the M regions whose
estimated SF values range from 0.3 to 0.6. Considering that the plume is stable to
decreasing (Figure 10 and Section 3.2 results) and that the source of contamination has
been remediated (Barr, 2001), a new sampling location to the east of the plume source
area is not necessary. Therefore, no new locations were recommended.
3.3.3 Sampling Frequency Analysis- Modified CES Method
Results from the sampling frequency analysis for the 31 monitoring wells, 3 city wells,
and 10 extraction wells are given in Table 7. Some of the annual or quarterly sampling
frequency recommendations were due to insufficient overall or recent data (i.e., less
than 6 data records), which prevented the MAROS estimation of concentration trend
using overall or recent monitoring data for some wells. After considering the MAROS
results along with the historical and recent concentration levels at these wells, final
sampling frequency recommendations are provided in Table 7. For the monitoring well
system well, considering all 31 wells prior to the well redundancy analysis, 18 wells can
be sampled biennially, and 13 annually. All 3 city wells are recommended to be sampled
biennially. For the recovery well system, considering all 10 wells prior to the well
redundancy analysis, 4 can be sampled biennially, and 6 annually. If only considering
wells that have been sampled consistently up to October 2002 (27 wells) and the sample
frequency reduction alone, a reduction of approximately 44% in total samples per year
can be achieved (see the breakdown table below).
Current Sampling Frequency
Frequency
Quarterly
Annually
Total samples per year
Number of Wells
8
19
51
Recommended Sampling Frequency
Frequency
Biennial
Annual
Total samples per year
Number of Wells
9
18
22.5
In some cases, the frequency recommendations from the MAROS software were not
adopted due to data inadequacy. Sampling frequencies for some of the wells were
irregular, ranging from quarterly to annual during the period between 1996 and 2002. For
some wells, sampling was even terminated for 3 years before they were sampled again
in October 2002. This resulted in some monitoring wells having only 5 ~ 7 data records
available during the 7-year period (from 1996 to 2002). Because the minimum data
requirement for the sampling frequency trend analysis is 6 sampling events, the overall
or recent trends for many wells could not be estimated. This resulted in frequency
results that were solely estimated from the overall data trend. For instance, well MW-3B
has only 3 concentration records between May 1999 and October 2002, making the
estimation of recent trend impossible. In cases of data inadequacy, the MAROS
frequency analysis will assign conservative results, i.e., semiannual or annual instead of
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annual or biennial. However, with the incorporation of a qualitative assessment of the
concentration levels and concentration history for these wells, more reasonable
sampling frequencies were recommended (Table 7). For example, well MW-3B was
suggested for a biennial sampling because all its historical concentrations were below
the detection limit. Considering the plume stability which remains stable according to the
overview statistical analysis, it is unlikely that the plume will show rapid changes over the
long-term. Therefore, keeping the frequency of wells at annual and biennial level will
continue to allow for adequate plume delineation.
3.3.4 Data Sufficiency - Power Analysis
In the MAROS data sufficiency analysis, statistical power analysis was used to assess
the sufficiency of monitoring plans for detecting difference between the mean
concentration and cleanup goal. Results from the analysis indicate remediation
progress from the risk-based standpoint at a hypothetical statistical compliance
boundary (HSCB). The power and expected sample size associated with the target level
evaluation may indicate the need for expansion or redundancy reduction of future
sampling plans.
In the risk-based site cleanup evaluation, two analyses were performed (see Appendix B
for all related MAROS reports). In the first analysis, the distance from the most
downgradient well (MW-15 A and B) to the nearest downgradient receptor (HSCB) was
assumed to be 10 ft (just upgradient of the Long Prairie river). The general groundwater
flow angle is to the North. Selected plume centerline wells are MW-15B, MW-16B, MW-
17B, MW-4B, and MW-2B (Table 8). Sampling events from May 1999 to October 2002
were selected for the analysis (Table 9). Among these only 5 sampling events have
sufficient data for plume centerline concentration regression. Regression coefficients for
the 5 sampling events range from 1.5 x 10"3 to 5.4 x 10"3 per ft, all with high confidence
(Table 9 and see Appendix B for individual well projected concentration values). The
second analysis used the same parameters except that the distance to receptor was
assumed to be -100 ft, i.e., assuming that the HSCB is 100 ft upgradient of the most
downgradient well.
Table 10 shows the risk-based site cleanup status at selected sampling events for both
analyses (i.e., HSCB at 10 ft and HSCB at -100 ft downgradient of the monitoring
system). The results show that the risk-based site cleanup status in most cases for both
analyses is "attained", i.e., the projected mean site concentration at the HSCB is
statistically significantly lower than the target level. Similarly, the associated power is
high and the expected sample size is relatively small. The "not attained" results, all have
low power (<0.5) but not S/E (significantly exceed) status, indicate that the projected
mean site concentration at the HSCB is lower than the target level but is not statistically
significant. The "not attained" results can be explained by the small dataset (18
samples) available for these sampling events (September 2000 and September 2001),
while all other sampling events have more than 20 samples for analysis. Therefore, the
results indicate that the site is clean at the HSCB that is -100 ft downgradient (equal to
100 ft upgradient of the monitoring system from the risk-based standpoint). Also, with
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the plume stable to shrinking over time, the HSCB will move upgradient gradually. The
HSCB is getting tighter and tighter as the monitoring record increases. In general,
monitoring networks become more powerful over time. This analysis indicates that the
monitoring system is working because it is powerful enough to accurately reflect the
location of the plume relative to the compliance point. Therefore, the current monitoring
network is sufficient in terms of evaluating risk-based site target level status, if the pump-
and-treat remedial system continues to contain the plume and keeps reducing the
contaminant concentration.
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4.0 SUMMARY AND RECOMMENDATIONS
In recent years, the high cost of long-term monitoring as part of active or passive
remediation of affected ground water has made the design of efficient and effective
ground water monitoring plans a pressing concern. Periodically updating and revising
long-term monitoring programs with changing conditions at the site can mean
considerable savings in site monitoring costs. The MAROS decision-support software
presented in this report assists in revising existing long-term monitoring plans based on
the historical and current monitoring data and plume behavior over time.
The MAROS 2.0 sampling optimization software/methodology has been applied to the
Long Prairie existing long-term monitoring program as of October 2002. The
optimization results and subsequent recommendations allow for optimization of the
spatial and temporal groundwater monitoring system in place at the Long Prairie site.
The current long-term monitoring network could be optimized through reduction in both
sampling locations and sampling frequency (results are summarized in Table 11 and
MAROS Reports in Appendix B).
Overview Statistics
Both the Mann-Kendall and Linear Regression temporal trend methods gave similar
trend estimates for each well. Results from the temporal trend analysis indicate that
90% of the plume tail and edge area monitoring wells in the Upper Outwash Aquifer
indicate a Probably Decreasing, Decreasing, or Stable PCE concentration trend,
whereas only about half of the wells in the source area have similar trends. The trend
results for the recovery wells along the centerline of the plume indicate most wells have
Probably Decreasing, or Decreasing concentrations over time. These temporal trend
results were applied to the
Results from the moment trend analysis give evidence of a stable plume as well, with the
dissolved mass showing stability over time, whereas the center of mass shows
movement away from the source area due to decreasing source area concentrations
and the plume spread shows stability over time. Overall plume stability temporal results
recommend a moderate monitoring strategy due to the stable PCE plume. The overview
results are relatively generic and not well-by-well specific, therefore, a detailed statistical
analysis with a well-by-well analysis was performed.
Detailed Statistics
Further analysis from the well redundancy analysis using the Delaunay method indicate
that 12 monitoring wells could be eliminated from the original monitoring network of 44
wells without any significant loss of plume information (Table 11). The well sufficiency
analysis indicated no need for adding new wells into the current monitoring system. The
resulting reduction in sampling locations would therefore be 27% (12 out of 44).
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The sampling frequency optimization analysis using the modified CES method, indicated
that the wells in the monitoring system could be sampled at annual or biennial
frequency, lower than the current sampling frequency overall. If only considering wells
that have been sampled consistently up to October 2002 (27 wells) and the sample
frequency reduction alone, this is a reduction of approximately 47% in total samples per
year.
Data sufficiency analysis using power analysis methods, shows that the site has reached
the target cleanup levels (0.005 mg/L for PCE) at the HSCB that is 100 ft upgradient
from the most downgradient well. With the plume stable to shrinking over time, the
HSCB will move upgradient gradually. This analysis indicates that the monitoring
system is working because it is powerful enough to accurately reflect the location of the
plume relative to the compliance boundary. This analysis shows the sufficiency of the
monitoring system in terms of evaluating risk-based site target level status if the pump-
and-treat remedial system continues to contain the plume and keeps reducing the
contaminant concentration in the aquifer.
The recommended long-term monitoring strategy results in a reduction in sampling costs
and allows site personnel to develop a better understanding of plume behavior over
time. A reduction in the number of redundant wells is expected to result in a moderate
cost savings over the long-term at the Long Prairie site. Overall, the MAROS optimized
plan consists of 32 wells: 16 sampled annually, and 16 sampled biennially ($6,720). The
MAROS optimized plan would result in 24 samples per year, compared to 51 samples
per year in the current monitoring program (Table 11). Implementing these
recommendations could lead to a 52% reduction from the current monitoring plan in
terms of the samples to be collected per year. An approximate cost savings estimate
range from $2,700 to $7,560 per year (based on an average per sample cost range of
$100 to $280) is projected while still maintaining adequate delineation of the plume as
well as knowledge of the plume state over time.
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CITED REFERENCES
Air Force Center for Environmental Excellence (AFCEE), 2002. Monitoring and
Remediation Optimization System (MAROS) 2.0 Software Users Guide.
Air Force Center for Environmental Excellence (AFCEE), 1997, AFCEE Long-Term
Monitoring Optimization Guide, http://www.afcee.brooks.af.mil.
Aziz, J, Ling, M., Newell, C., Rifai, H., and Gonzales, J., 2002, MAROS: a Decision
Support System for Optimizing Monitoring Plans, Ground Water, In Press.
Barr, 1999, 1997/1998 Annual Report, Long Prairie GW Remediation System July 1999
Barr, 2000, 1998/1999 Annual Report, Long Prairie GW Remediation System Feb 2000
Barr, 2001, 1999/2000 Annual Report, Long Prairie GW Remediation System March
2001
Barr, 2002, 2000/2001 Annual Report, Long Prairie GW Remediation System April 2002
Gilbert, R. O., 1987, Statistical Methods for Environmental Pollution Monitoring, Van
Nostrand Reinhold, New York, NY, ISBN 0-442-23050-8.
Ridley, M.N. et al., 1995. Cost-Effective Sampling of Groundwater Monitoring Wells, the
Regents of UC/LLNL, Lawrence Livermore National Laboratory.
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MAROS 2.0 APPLICATION
MONITORING NETWORK OPTIMIZATION
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TABLES
Table 1 Sampling Locations Used in the MAROS Analysis
Table 2 Mann-Kendall Analysis Decision Matrix
Table 3 Linear Regression Analysis Decision Matrix
Table 4 Upper Outwash Aquifer Aquifer Site-Specific Parameters
Table 5 Results of Upper Outwash Aquifer Trend Analysis
Table 6 Redundancy Analysis Results - Delaunay Method
Table 7 Sampling Frequency Analysis Results - Modified CES
Table 8 Selected Plume Centerline Wells for Risk-Based Site Cleanup Evaluation
- Power Analysis
Table 9 Plume Centerline Concentration Regression Results - Power Analysis
Table 10 Risk-Based Site Cleanup Evaluation Results - Power Analysis
Table 11 Summary of MAROS Sampling Optimization Results
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GSI Job No. G-2236-15
Issued: 2/19/03
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If
GROUNDWATER
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Table 1
Sampling Locations Used in the MAROS Analysis
Long Prairie Site
Long Prairie, Minnesota
Well
Name
BAL2B
BAL2C
CW-3
CW-6
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
Used in Delaunay Analysis?
No, city well and far from the
plume
Yes
Yes
No, city well and far from the
plume
No, screened above and
duplicates MW-1B
Yes
No, screened above MW-2B
and lower in concentration
Yes
No, screened below MW-2B
and lower in concentration
No, screened above and
duplicates MW-3B
Yes
No, screened above MW-4B
and lower in concentration
Yes
No, screened below MW-4B
and lower in concentration
No, screened above and
duplicates MW-5B
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling data
available since 1996)
Sampled only once in October 2002, below DL
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below DL
Sampled quarterly on average; all records were
below DL
Sampled quarterly until March 2000, then sampled
quarterly since April 2002; all records were below DL
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below DL
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below DL
Sampled annually since 1997
Sampled annually since 1997
Sampled annually since 1 997 except when in 1999 it
was sampled several times
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below
MCL or DL
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below DL
Sampled only in 1999, then sampled in October
2002
Sampled annually since 1997
Sampled annually except when in 1999 it was
sampled several times
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below DL
Notes:
MCL= the maximum contaminant level of PCE (0.005 mg/L)
DL = detection limit
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GSI Job No. G-2236-15
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Page 2 of 3
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Table 1
Sampling Locations Used in the MAROS Analysis
Long Prairie Site
Long Prairie, Minnesota
Welt
Name
MW-5B
MW-6A
MW-6B
MW-6C
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
Used in Detaunay
Analysis?
Yes
No, screened above MW-6B
and generally lower in
concentration
Yes
No, screened below MW-6B
and lower in concentration
Yes
No, screened above and
duplicates MW-11C
No, screened above and
duplicates MW-11C
Yes, it has more records
than MW-11B
Yes
Yes
No, screened below MW-
14B and lower in
concentration
No, screened above and
duplicates MW-5B
Yes
No, screened above MW-5B
and lower in concentration
Yes
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling data
available since 1996)
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below DL
Sampled annually since 1997
Sampled annually since 1997
Sampled annually since 1 998 except when in 1999 it
was sampled several times
Sampled semiannually until October 1999, then
sampled in October 2002
Sampled only in 1999, then sampled in October
2002; all records were below DL
Sampled annually since 1999; all records were
below DL
Sampled annually since 1 997 except when in 1999 it
was sampled several times; all records were below
MCL or DL
Sampled semiannually until October 1999, then
sampled in October 2002; all records were below
MCL or DL
Sampled semiannually until October 1999, then
sampled annually
Sampled semiannually until October 1999, then
sampled annually; all records were below MCL or DL
Sampled semiannually since October 1998, then
sampled annually since October 1999; all records
were below DL
Sampled semiannually since October 1998, then
sampled annually since October 1999; all records
were below DL
Sampled 5 times between 1998 and 1999, then
sampled in October 2002; all records were below
MCL or DL
Sampled 5 times between 1998 and 1999, then
sampled annually
Notes:
MCL= the maximum contaminant level of PCE (0.005 mg/L)
DL = detection limit
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 3 of 3
If
GROUNDWATER
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Table 1
Sampling Locations Used in the MAROS Analysis
Long Prairie Site
Long Prairie, Minnesota
Welt
Name
MW-17B
MW-18A
MW-18B
MW-19B
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
Used in Detaunay
Analysis?
Yes
No, screened above MW-
18B and lower in
concentration
Yes
Yes
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling data
available since 1996)
Sampled 4 times between 1998 and 1999, then
sampled annually
Sampled 4 times between 1998 and 1999, then
sampled in October 2002; all records were below DL
Sampled 4 times between 1998 and 1999, then
sampled in October 2002; all records were below
MCL but higher than DL
Sampled 4 times between 1998 and 1999, then
sampled annually; all records were below DL
Sampled quarterly on average until 1999, then
sampled in October 2002
Sampled quarterly on average until 1999, then
sampled in October 2002
Sampled only once in October 2002
Sampled quarterly
Sampled quarterly until 99, then sampled annually
Sampled quarterly
Sampled quarterly
Sampled quarterly
Sampled quarterly since 1999
Sampled quarterly since 1999
Notes: MCL= the maximum contaminant level of PCE (0.005 mg/L)
DL = detection limit
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GSI Job No. G-2236-15
Issued 2/19/03
Page 1 of 1
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Mann-Kendall
Mann-Kendall
Statistic
S>0
S>0
S>0
S<0
S<0
S<0
S<0
Table 2
Analysis Decision Matrix
Confidence in the
Trend
> 95%
90 - 95%
< 90%
< 90% and COV > 1
< 90% and COV < 1
90 - 95%
> 95%
(Aziz, et. al., 2002)
Concentration Trend
Increasing
Probably Increasing
No Trend
No Trend
Stable
Probably Decreasing
Decreasing
Table 3
Linear Regression Analysis Decision Matrix (Aziz, et. al., 2002)
Confidence in the
Trend
Log-slope
Positive
Negative
< 90%
90 - 95%
> 95%
No Trend
Probably Increasing
Increasing
COV < 1 Stable
COV > 1 No Trend
Probably Decreasing
Decreasing
-------
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CD
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-------
GSIJobNo. G-2236-15
Issued 2/19/03
Page 1 of 2
TABLE 5
Upper Outwash Aquifer Trend Analysis
Long Prairie Site
Long Prairie, Minnesota
Notes:
1. Consolidation of data included non-detect values set to the minium detection limit (0.001 mg/L)
and duplicate data for the quarter were averaged.
2. All wells that were part of the network in between 1996 and 2002 with more than 4 sample events were analyzed.
3. RW = Recovery Well; MW = Monitoring Well; CSW = City Supply Well
4. Decreasing (D), Probably Decreasing (PD), Stable (S), No Trend (NT), Probably Increasing (PI), and Increasing (I)
5. S = Source Zone Well; T = Tail Zone Well
6. Overall Trend is calculated from a weighted average of the Linear Regression and Mann-Kendall Trends.
For further details on this methodolgy refer to the MAROS Manual Appendix A.8.
mtOUNDWATEK
SE1¥!C1S, IMC
Well
CW-3
CW-6
BAL2C
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MV\MB
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
MW-10
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-19B
RW-1A
RW-1B
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
Well
Type3
CSW
CSW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
RW
RW
RW
RW
RW
RW
RW
RW
RW
Well
Category ''
T
T
T
T
T
S
S
S
T
T
T
T
T
T
T
T
T
S
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
S
T
T
T
T
T
T
Mann-Kendall
Trend 4
S
S
S
S
S
NT
NT
S
NT
S
D
S
S
S
NT
D
PD
D
S
S
NT
PD
S
S
S
S
S
D
S
S
S
PD
NT
D
D
D
D
D
D
NT
Linear
Regression
Trend 4
S
I
S
S
S
NT
NT
S
NT
S
D
S
S
S
NT
D
D
D
S
PD
NT
D
S
D
D
S
NT
D
S
I
I
NT
NT
D
D
D
D
D
D
NT
Overall
Trend 6
S
PI
S
S
S
NT
NT
S
NT
S
D
S
S
S
NT
D
D
D
S
S
NT
D
S
PD
PD
S
S
D
S
PI
PI
S
NT
D
D
D
D
D
D
NT
Number
of
Samples
24
14
8
8
8
6
6
8
8
8
6
8
8
8
6
6
7
8
4
8
9
10
11
7
7
6
8
7
5
5
7
12
12
25
15
25
25
25
12
12
Number
of
Detects
0
0
0
0
0
6
6
5
1
0
6
8
0
0
5
6
7
8
0
1
2
10
1
0
0
1
8
7
0
5
0
11
1
25
6
25
25
25
12
12
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 3
If
GROUNDWATER
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Table 6
Well Redundancy Analysis Results - Delaunay Method
Long Prairie Site
Long Prairie, Minnesota
Well
Name
BAL2B
BAL2C
CW-3
CW-6
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
Well Used in
Analysis?
No, city well and far
from the plume
Yes
Yes
No, city well and far
from the plume
No, screened above
and duplicates MW-1B
Yes
No, screened above
MW-2B and lower in
concentration
Yes
No, screened below
MW-2B and lower in
concentration
No, screened above
and duplicates MW-3B
Yes
No, screened above
MW-4B and lower in
concentration
Yes
No, screened below
MW-4B and lower in
concentration
No, screened above
and duplicates MW-5B
MAROS Well
Redundancy
Analysis
Result
-
Keep
Keep
-
-
Keep
-
Keep
-
-
Keep
-
Keep
-
-
MAROS
Interpreted
Well
Redundancy
Keep
Keep
Keep
Keep
Eliminate
Keep
Eliminate
Keep
Keep
Eliminate
Keep
Eliminate
Keep
Keep
Eliminate
Comments
City well monitoring
Downgradient sentry well
City well monitoring
City well monitoring
Duplicates MW-1B and concentrations
below DL
Cross gradient sentry well
Monitors only the upper zone at MW-4 and
concentrations dropped to below MCL
Inside plume well and close the source
Monitors the lower zone at MW-2 for
possible downward or lower zone
contaminant migration
Duplicates MW-3B and historical
concentrations below MCL or DL
Downgradient of the source
Monitors only the upper zone at MW-4 and
lower in concentration than MW-4B
Inside plume well
Monitors the lower zone at MW-4 for
possible downward or lower zone
contaminant migration
Duplicates MW-5B and historical
concentrations below DL
Notes: Sampling events from May 1999 to October 2002 were used in the analysis
InsideSF = 0.1, HullSF = 0.01, AR = CR = 0.95
MCL = the maximum contaminant level of PCE (0.005 mg/L)
DL = detection limit
-------
GSI Job No. G-2236-15
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Table 6
Well Redundancy Analysis Results - Delaunay Method
Long Prairie Site
Long Prairie, Minnesota
Well
Name
MW-5B
MW-6A
MW-6B
MW-6C
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
Well Used in Analysis?
Yes
No, screened above
MW-6B and generally
lower in concentration
Yes
No, screened below
MW-6B and lower in
concentration
Yes
No, screened above and
duplicates MW-1 1C
No, screened above and
duplicates MW-1 1C
Yes, it has more records
than MW-11B
Yes
Yes
No, screened below
MW-14B and lower in
concentration
No, screened above and
duplicates MW-5B
Yes
No, screened above
MW-5B and lower in
concentration
Yes
MAROS Well
Redundancy
Analysis
Result
Keep
-
Keep
-
Keep
-
-
Keep
Keep
Keep
-
-
Keep
-
Keep
MAROS
Interpreted
Well
Redundancy
Keep
Eliminate
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Eliminate
Keep
Comments
Cross gradient sentry well
Monitors only the upper zone at MW-4
and lower in concentrations than MW-6B
Inside plume well
Monitors the lower zone at MW-6 for
possible downward or lower zone
contaminant migration
Continues to monitor the source for
abnormal conditions
Monitors only the upper zone at MW-1 1
and concentrations below DL
Monitors the middle zone at MW-1 1
Monitors the lower zone at MW-1 1 for
possible downward or lower zone
contaminant migration
Inside plume well
On plume edge
Monitors the lower zone at MW-1 4 for
possible downward or lower zone
contaminant migration
Monitors the upper zone at MW-1 5 since it
is just upgradient of the Long Prairie river
Downgradient sentry well just upgradient
of the Long Prairie river
Monitors only the upper zone at MW-1 6
and concentrations below MCL or DL
Downgradient well on plume edge
Notes: Sampling events from May 1999 to October 2002 were used in the analysis
InsideSF = 0.1, HullSF = 0.01, AR = CR = 0.95
MCL = the maximum contaminant level of PCE (0.005 mg/L)
DL = detection limit
-------
GSI Job No. G-2236-15
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Table 6
Well Redundancy Analysis Results - Delaunay Method
Long Prairie Site
Long Prairie, Minnesota
Well
Name
MW-17B
MW-18A
MW-18B
MW-19B
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
Well Used in
Analysis?
Yes
No, screened above
MW-18B and lower in
concentration
Yes
Yes
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
No, recovery well
MAROS Well
Redundancy
Analysis
Result
Keep
-
Keep
Keep
-
-
-
-
-
-
-
-
-
-
MAROS
Interpreted
Well
Redundancy
Keep
Eliminate
Keep
Keep
Eliminate
Eliminate
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Comments
Inside plume well
Monitors only the upper zone at MW-18 and
concentrations below DL
Downgradient well on plume edge
Downgradient well close to plume tail
Source has been cleaned up and
concentrations below MCL or DL
Source has been cleaned up and
concentrations below MCL or DL
Source has been cleaned up and
concentrations below MCL or DL
Recovery well for performance monitoring
Recovery well for performance monitoring
Recovery well for performance monitoring
Recovery well for performance monitoring
Recovery well for performance monitoring
Recovery well for performance monitoring
Recovery well for performance monitoring
Notes: Sampling events from May 1999 to October 2002 were used in the analysis
InsideSF = 0.1, HullSF = 0.01, AR = CR = 0.95
MCL = the maximum contaminant level of PCE (0.005 mg/L)
DL = detection limit
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 3
If
GROUNDWATER
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Table 7
Sampling Frequency Analysis Results - Modified CES
Long Prairie Site
Long Prairie, Minnesota
Well
Name
BAL2B
BAL2C
CW-3
CW-6
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MAROS
Frequency
Based on
Recent
Trend'11
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
MAROS
Frequency
Based on
Overall
Trend121
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
MAROS
Recommended
Frequency'31
Annual
Annual
Biennial
Biennial
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
MAROS
Interpreted
Sampling
Frequency
Result
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
Biennial
Biennial
Annual
Annual
Annual
Biennial
Comments
Concentrations below DL (the MAROS
result was due to insufficient data)
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Historical concentrations below DL
Historical concentrations below DL
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Recent concentrations above MCL
Recent concentrations above MCL
Recent concentrations below MCL but
above DL
Historical concentrations below MCL or DL
(the MAROS result was due to insufficient
recent data)
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Recent concentrations above MCL (the
MAROS result was due to insufficient data)
Recent concentrations above MCL
Recent concentrations above MCL
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Notes: 1) The frequency determined by MAROS based on the analysis of recent data (May 1999 ~ October 2002)
2) The frequency determined by MAROS based on the analysis of overall data (May 1996 ~ October 2002)
3) The frequency finally recommended by MAROS after considering recent and overall frequency results as well
as the rates of change in these trends
Rate parameters used are 0.5MCL/year, 1 .OMCL/year, and 2.0MCL/yearfor Low, Medium, and High rates,
respectively; MCL = the maximum contaminant level of PCE (0.005 mg/L); DL = detection limit
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 2 of 3
If
GROUNDWATER
SERVICES, INC.
Table 7
Sampling Frequency Analysis Results - Modified CES
Long Prairie Site
Long Prairie, Minnesota
Well
Name
MW-5B
MW-6A
MW-6B
MW-6C
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MAROS
Frequency
Based on
Recent
Trend11'
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Frequency
Based on
Overall
Trend'2'
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Recommended
Frequency'3'
Annual
Annual
Annual
Annual
Quarterly
Annual
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Annual
MAROS
Interpreted
Sampling
Frequency
Result
Biennial
Annual
Annual
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Comments
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Recent concentrations above MCL
Recent concentrations above MCL
Recent concentrations above MCL
Historical concentrations have dropped
significantly but recent concentrations still
above MCL (the MAROS result was due to
insufficient recent data)
Historical concentrations below DL (the
MAROS result was due to insufficient data)
Historical concentrations below DL
Historical concentrations below MCL or DL
Historical concentrations below MCL or DL
Recent concentrations above MCL
Historical concentrations below MCL or DL
Historical concentrations below DL
Historical concentrations below DL
Historical concentrations below MCL or DL
Recent concentrations above MCL
Notes: 1) The frequency determined by MAROS based on the analysis of recent data (May 1999 ~ October 2002)
2) The frequency determined by MAROS based on the analysis of overall data (May 1996 ~ October 2002)
3) The frequency finally recommended by MAROS after considering recent and overall frequency results as well
as the rates of change in these trends
Rate parameters used are 0.5MCL/year, 1 .OMCL/year, and 2.0MCL/yearfor Low, Medium, and High rates,
respectively; MCL = the maximum contaminant level of PCE (0.005 mg/L); DL = detection limit
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 3 of 3
If
GROUNDWATER
SERVICES, INC.
Table 7
Sampling Frequency Analysis Results - Modified CES
Long Prairie Site
Long Prairie, Minnesota
Well
Name
MW-17B
MW-18A
MW-18B
MW-19B
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
MAROS
Frequency
Based on
Recent
Trend11'
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Frequency
Based on
Overall
Trend'2'
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Recommended
Frequency'3'
Annual
Annual
Annual
Biennial
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Annual
Annual
Annual
MAROS
Interpreted
Sampling
Frequency
Result
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Annual
Annual
Annual
Annual
Annual
Comments
Recent concentrations above MCL
Historical concentrations below DL (the
MAROS result was due to insufficient data)
Historical concentrations below MCL (the
MAROS result was due to insufficient data)
Historical concentrations below DL
Historical concentrations below MCL or DL
(the MAROS result was due to insufficient
recent data)
Historical concentrations below DL (the
MAROS result was due to insufficient recent
data)
Recent concentrations below DL (the
MAROS result was due to insufficient data)
Recent concentrations above MCL
Recent concentrations below DL
Recent concentrations above MCL
Recent concentrations above MCL
Recent concentrations above MCL
Recent concentrations above MCL
Recent concentrations above MCL
Notes: 1) The frequency determined by MAROS based on the analysis of recent data (May 1999 ~ October 2002)
2) The frequency determined by MAROS based on the analysis of overall data (May 1996 ~ October 2002)
3) The frequency finally recommended by MAROS after considering recent and overall frequency results as well
as the rates of change in these trends
Rate parameters used are 0.5MCL/year, 1 .OMCL/year, and 2.0MCL/yearfor Low, Medium, and High rates,
respectively; MCL = the maximum contaminant level of PCE (0.005 mg/L); DL = detection limit
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 8
Selected Plume Centerline Wells
Risk-Based Site Cleanup Evaluation - Power Analysis
Long Prairie Site
Long Prairie, Minnesota
Well Name
MW-15B
MW-16B
MW-17B
MW-4B
MW-2B
Distance from Well to Receptor (ft)
HSCB = 10ft
10.0
520.6
1063.2
1998.1
2897.1
HSCB = -100ft
-100
410.6
953.2
1888.1
2787.1
Notes: Groundwater flow angle is to the north/northwest (assumed 90 degrees
counterclockwise from East in this analysis); Distance from Well to Receptor
refers to the most downgradient well's Distance to the Hypothetical Statistical
Compliance Boundary (HSCB).
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GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 9
Plume Centerline Concentration
Regression Results - Power Analysis
Long Prairie Site
Long Prairie, Minnesota
Sampling Event
May 1999
July 1999
September 1999
October 1999
March 2000
June 2000
September 2000
October 2000
December 2000
March 2001
May 2001
September 2001
November 2001
January 2002
April 2002
July 2002
October 2002
Number of
Center! ine Welts
5
0
2
3
0
0
5
0
0
0
0
5
0
0
0
0
5
Regression
Coefficient (1/ft)
-2.31 E-03
-
-
-5.38E-03
-
-
-1.50E-03
-
-
-
-
-1.51 E-03
-
-
-
-
-1.24E-03
Confidence in
Coefficient
97.9%
-
-
91.5%
-
-
92.7%
-
-
-
-
92.1%
-
-
-
-
91.3%
Notes: Regression is on natural log concentration of PCE versus distance from source
centerline wells shown in Table M4; no regression was performed for sampling event
with less than 3 centerline wells.
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 10
Risk-Based Site Cleanup Evaluation Results - Power Analysis
Long Prairie Site
Long Prairie, Minnesota
Sampling Event
May 1999
October 1999
September 2000
September 2001
October 2002
Sample
Size
30
25
18
18
34
Distance to HSCB = 10 ft
Cleanup
Status
Attained
Attained
Not Attained
Attained
Attained
Power
1.000
1.000
0.414
0.685
1.000
Expected
Sample Size
7
<=3
54
25
8
Distance to HSCB = -100 ft
Cleanup
Status
Attained
Attained
Not Attained
Not Attained
Attained
Power
0.992
1.000
0.211
0.432
0.998
Expected
Sample Size
12
<=3
>100
50
11
Notes: The power analysis used for this application assumes normality of data. Distance to the Hypothetical
Statistical Compliance Boundary (HSCB) is the distance from the most downgradient well to the
HSCB; S/E = extrapolated result significantly exceeds the target level (0.005 mg/L).
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 3
If
GROUNDWATER
SERVICES, INC.
Table 11
Summary of MAROS Sampling Optimization Results
Long Prairie Site
Long Prairie, Minnesota
Well
Name
BAL2B
BAL2C
CW-3
CW-6
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MAROS
Welt
Category"1
T
T
T
T
T
T
S
S
S
T
T
T
T
T
T
Current
Sampling
Frequency'2'
Biennial*
Annual
Quarterly
Quarterly
Biennial*
Biennial*
Annual
Annual
Annual
Biennial*
Biennial*
Biennial*
Annual
Annual
Biennial*
MAROS
Trend
Result'31
NA
S
S
PI
S
S
NT
NT
S
NT
S
NA
D
S
S
MAROS
Interpreted Well
Redundancy
and Well
Sufficiency
Results
Keep
Keep
Keep
Keep
Eliminate
Keep
Eliminate
Keep
Keep
Eliminate
Keep
Eliminate
Keep
Keep
Eliminate
MAROS
Interpreted
Sampling
Frequency
Results
Biennial
Biennial
Biennial
Biennial
-
Biennial
-
Annual
Annual
-
Biennial
-
Annual
Annual
-
Comments
Concentration below DL & city well
monitoring
Historical concentrations below DL &
downgradient sentry well
Historical concentrations below DL & city
well monitoring
Historical concentrations below DL & city
well monitoring
Duplicates MW-1B & historical
concentrations below DL
Historical concentrations below DL &
cross gradient sentry well
Monitors only the upper zone at MW-4 and
concentrations dropped to below MCL
Recent concentrations above MCL &
inside plume well
Recent concentrations below MCL but
above DL & monitors the lower zone at
MW-2
Duplicates MW-3B & historical
concentrations below MCL or DL
Historical concentrations below DL &
downgradient of the source
Monitors only the upper zone at MW-4 and
lower in concentration than MW-4B
Recent concentrations above MCL &
inside plume well
Recent concentrations above MCL &
monitors the lower zone at MW-4
Duplicates MW-5B & historical
concentrations below DL
Notes: (1) S = Source well, T = Tail well
(2) Sampling frequency based on recent sampling results, 'assumed biennial due to lack of data in 2000 and 2001
(3) D = Decreasing, PD = Probably Decreasing, S = Stable, NT = No Trend, PI = Probably Increasing, I = Increasing
MCL = the maximum contaminant level of PCE (0.005 mg/L), DL = detection limit,"-" = Not Applicable
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 2 of 3
If
GROUNDWATER
SERVICES, INC.
Table 11
Summary of MAROS Sampling Optimization Results
Long Prairie Site
Long Prairie, Minnesota
Welt
Name
MW-5B
MW-6A
MW-6B
MW-6C
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MAROS
Welt
Category'11
T
T
T
T
S
T
T
T
T
T
T
T
T
T
T
Current
Sampling
Frequency'2'
Biennial*
Annual
Annual
Annual
Annual
Biennial*
Annual
Annual
Biennial*
Annual
Annual
Annual
Annual
Biennial*
Annual
MAROS
Trend
Result'3'
S
NT
D
D
D
NA
S
PD
NT
D
S
PD
PD
S
S
MAROS
Interpreted Well
Redundancy and
Well Sufficiency
Results
Keep
Eliminate
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Eliminate
Keep
MAROS
Interpreted
Sampling
Frequency
Results
Biennial
-
Annual
Annual
Annual
-
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
-
Annual
Comments
Historical concentrations below DL & cross
gradient sentry well
Monitors only the upper zone at MW-4 and
lower in concentrations than MW-6B
Recent concentrations above MCL &
inside plume well
Recent concentrations above MCL &
monitors the lower zone at MW-6
Recent concentrations above MCL &
monitors the source
Monitors only the upper zone at MW-1 1
and concentrations below DL
Historical concentrations below DL &
monitors the middle zone at MW-1 1
Historical concentrations below MCL or DL
& Monitors the lower zone at MW-1 1
Historical concentrations below MCL or DL
& Inside plume well
Recent concentrations above MCL & on
plume edge
Historical concentrations below MCL or DL
& monitors the lower zone at MW-1 4
Historical concentrations below DL &
monitors the upper zone at MW-1 5
Historical concentrations below DL &
downgradient sentry well just upgradient of
the Long Prairie river
Monitors only the upper zone at MW-1 6 &
concentrations below MCL or DL
Recent concentrations above MCL &
downgradient well on plume edge
Notes: (1) S = Source well, T = Tail well
(2) Sampling frequency based on recent sampling results, 'assumed biennial due to lack of data in 2000 and 2001
(3) D = Decreasing, PD = Probably Decreasing, S = Stable, NT = No Trend, PI = Probably Increasing, I = Increasing
MCL = the maximum contaminant level of PCE (0.005 mg/L), DL = detection limit
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 3 of 3
If
GROUNDWATER
SERVICES, INC.
Table 11
Summary of MAROS Sampling Optimization Results
Long Prairie Site
Long Prairie, Minnesota
Welt
Name
MW-17B
MW-18A
MW-18B
MW-19B
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
MAROS
Welt
Category'11
T
T
T
T
T
T
T
S
T
T
T
T
T
T
Current
Sampling
Frequency'2'
Annual
Biennial*
Biennial*
Annual
Biennial*
Biennial*
Biennial*
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
MAROS
Trend
Result'3'
D
S
PI
PI
S
NT
NA
D
D
D
D
D
D
NT
MAROS
Interpreted Well
Redundancy and
Well Sufficiency
Results
Keep
Eliminate
Keep
Keep
Eliminate
Eliminate
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
MAROS
Interpreted
Sampling
Frequency
Results
Annual
-
Biennial
Biennial
-
-
-
Annual
Biennial
Annual
Annual
Annual
Annual
Annual
Comments
Recent concentrations above MCL &
inside plume well
Monitors only the upper zone at MW-18 &
concentrations below DL
Historical concentrations below MCL &
downgradient well on plume edge
Historical concentrations below DL &
downgradient well close to plume tail
Source has been cleaned up and
concentrations below MCL or DL
Source has been cleaned up and
concentrations below MCL or DL
Source has been cleaned up and
concentrations below MCL or DL
Recent concentrations above MCL &
recovery well for performance monitoring
Recent concentrations below DL &
recovery well for performance monitoring
Recent concentrations above MCL &
recovery well for performance monitoring
Recent concentrations above MCL &
recovery well for performance monitoring
Recent concentrations above MCL &
recovery well for performance monitoring
Recent concentrations above MCL &
recovery well for performance monitoring
Recent concentrations above MCL &
recovery well for performance monitoring
Notes: (1) S = Source well, T = Tail well
(2) Sampling frequency based on recent sampling results, 'assumed biennial due to lack of data in 2000 and 2001
(3) D = Decreasing, PD = Probably Decreasing, S = Stable, NT = No Trend, PI = Probably Increasing, I = Increasing
MCL = the maximum contaminant level of PCE (0.005 mg/L), DL = detection limit
-------
February 19 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
FIGURES
MAROS 2.0 APPLICATION
MONITORING NETWORK OPTIMIZATION
Long Prairie Site
Long Prairie, Minnesota
Figure 1 Upper Outwash Aquifer Groundwater Monitoring Network
Figure 2 MAROS Decision Support Tool Flow Chart
Figure 3 MAROS Overview Statistics Trend Analysis Methodology
Figure 4 Decision Matrix for Determining Provisional Frequency
Figure 5 Upper Outwash Aquifer PCE Mann-Kendall Trend Results
Figure 6 Upper Outwash Aquifer PCE Linear Regression Trend Results
Figure 7 Upper Outwash Aquifer PCE Mann-Kendall Trend Results, Recovery
Wells
Figure 8 Upper Outwash Aquifer PCE Linear Regression Trend Results, Recovery
Wells
Figure 9 Upper Outwash Aquifer PCE First Moment (Center of Mass) Over Time
Figure 10 Upper Outwash Aquifer PCE plume contoured with 1999 and 2002 data:
With "B" Zone Wells Only
Figure 11 Upper Outwash Aquifer PCE plume contoured with 1999 data: before
optimization and after optimization
Figure 12 Upper Outwash Aquifer Well Sufficiency Results
-------
.
-------
GSI Job No. G-2236-15
Issued: 2/19/03 GROUNDWATER
Page 1 of 1 SERVICES, INC.
MAROS: Decision Support Tool
MAROS is a collection of tools in one software package that is used in an explanatory, non-linear fashion. The tool
includes models, geostatistics, heuristic rules, and empirical relationships to assist the user in optimizing a
groundwater monitoring network system while maintaining adequate delineation of the plume as well as knowledge
of the plume state over time. Different users utilize the tool in different ways and interpret the results from a different
viewpoint.
Overview Statistics
What it is: Simple, qualitative and quantitative plume information can be gained through evaluation of monitoring
network historical data trends both spatially and temporally. The MAROS Overview Statistics are the foundation the
user needs to make informed optimization decisions at the site.
What it does: The Overview Statistics are designed to allow site personnel to develop a better understanding of the
plume behavior over time and understand how the individual well concentration trends are spatially distributed within
the plume. This step allows the user to gain information that will support a more informed decision to be made in the
next level of optimization analysis.
What are the tools: Overview Statistics includes two analytical tools:
1) Trend Analysis: includes Mann-Kendall and Linear Regression statistics for individual wells and results in
general heuristically-derived monitoring categories with a suggested sampling density and monitoring
frequency.
2) Moment Analysis: includes dissolved mass estimation (0th Moment), center of mass (1st Moment), and
plume spread (2nd Moment) over time. Trends of these moments show the user another piece of
information about the plume stability over time.
What is the product: A first-cut blueprint for a future long-term monitoring program that is intended to be a
foundation for more detailed statistical analysis.
T
Detailed Statistics
What it is: The MAROS Detailed Statistics allows for a quantitative analysis for spatial and temporal optimization of
the well network on a well-by-well basis.
What it does: The results from the Overview Statistics should be considered along side the MAROS optimization
recommendations gained from the Detailed Statistical Analysis. The MAROS Detailed Statistics results should be
reassessed in view of site knowledge and regulatory requirements as well as the Overview Statistics.
What are the tools: Detailed Statistics includes four analytical tools:
1) Sampling Frequency Optimization: uses the Modified CES method to establish a recommended future
sampling frequency.
2) Well Redundancy Analysis: uses the Delaunay Method to evaluate if any wells within the monitoring
network are redundant and can be eliminated without any significant loss of plume information.
3) Well Sufficiency Analysis: uses the Delaunay Method to evaluate areas where new wells are
recommended within the monitoring network due to high levels of concentration uncertainty.
4) Data Sufficiency Analysis: uses Power Analysis to assess if the historical monitoring data record has
sufficient power to accurately reflect the location of the plume relative to the nearest receptor or
compliance point.
What is the product: List of wells to remove from the monitoring program, locations where monitoring wells may
need to be added, recommended frequency of sampling for each well, analysis if the overall system is statistically
powerful to monitor the plume.
Figure 2. MAROS Decision Support Tool Flow Chart
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
In Ml
i
(21 S>* of
V fiy
of
tor lip wtll Had by
C IK
• fi '
« (HJ ——
» _____________
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tor
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(I
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Md
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faj
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mm
*
»
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Figure 3:
MAROS Overview Statistics Trend Analysis Methodology
CtmsHJtttlon
Fvei SsSwent
-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 1
INC.
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-------
GSI JobNo. G-2236-15
Issued: 2/19/03
Page 1 of 1
IMC
Potential areas for
new locations are
indicated by triangles
with a high SF level.
Estimated SF Lew el:
S -
M -
L -
MSB
t\\ \ -C^
j\\ \ M-
/ I \
-i ee
M
BAL2C
' M llMW-SB
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\ ¥
£4
CW-3
Figure 12. Well Sufficiency Analysis for possible new sampling locations. Areas with L
or E symbols are candidate regions for placing new wells.
-------
February 19 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
MONITORING NETWORK OPTIMIZATION
Long Prairie Site
Long Prairie, Minnesota
APPENDICES
Appendix A: Upper Outwash Aquifer Long Prairie site Historical PCE Maps
Appendix B: Upper Outwash Aquifer Long Prairie site MAROS 2.0 Reports
-------
February 19 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
MONITORING NETWORK OPTIMIZATION
Long Prairie Site
Long Prairie, Minnesota
APPENDIX A: Upper Outwash Aquifer Long Prairie Historical PCE Maps
Appendix A: Upper Outwash Aquifer Long Prairie site Historical PCE Maps
-------
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-------
February 19 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
MONITORING NETWORK OPTIMIZATION
Long Prairie Site
Long Prairie, Minnesota
APPENDIX B: Upper Outwash Aquifer Long Prairie MAROS 2.0 Reports
Linear Regression Statistics Summary
Mann-Kendall Statistics Summary
Spatial Moment Analysis Summary
Zeroth, First, and Second Moment Reports
Plume Analysis Summary
Site Results Summary
Sampling Location Optimization Results
Sampling Frequency Optimization Results
Risk-Based Power Analysis - Plume Centerline Concentrations
Risk-Based Power Analysis - Site Cleanup Status
-------
MAROS Linear Regression Statistics Summary
Project: Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
Time Period: 5/20/1996 to 10/14/2002
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Average
Source/ Cone
Tail (mg/L)
Median
Cone
(mg/L)
Standard
Deviation
All
Samples
"ND" ?
Coefficient
Ln Slope of Variation
Confidence Concentration
in Trend Trend
TETRACHLOROETHYLENE(PCE)
MW-2A
MW-2B
MW-2C
RW-3
MW-10
MW-11B
MW-17B
MW-16B
MW-16A
MW-15B
MW-15A
MW-14C
MW-14B
MW-18B
MW-11C
MW-19B
MW-11A
CW-6
CW-3
BAL2C
BAL2B
MW-13C
MW-6A
RW-8
RW-7
RW-6
RW-5
RW-4
RW-1C
RW-1B
RW-1A
MW-18A
MW-6B
RW-9
MW-5B
MW-5A
MW-4C
MW-4B
MW-4A
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
1 .8E-02
4.0E-01
4.2E-03
8.6E-02
2.0E+01
4.0E-04
1.3E-01
1.1E-02
5.2E-04
4.0E-04
4.0E-04
4.6E-04
2.2E-01
2.2E-03
5.3E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
8.1E-04
8.6E-02
2.5E-02
5.2E-02
4.3E-02
1.6E-01
9.8E-04
4.0E-04
8.7E-04
2.5E-03
4.0E-04
2.8E-01
1 .4E-02
4.0E-04
4.0E-04
1.1E-01
2.2E-01
2.0E-02
7.3E-03
6.7E-02
4.3E-03
8.7E-02
9.3E-01
4.0E-04
1 .4E-01
1 .1 E-02
4.0E-04
4.0E-04
4.0E-04
4.0E-04
2.1E-01
2.4E-03
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.0E-04
4.1 E-02
2.3E-02
4.0E-02
4.1 E-02
1 .6E-01
4.0E-04
4.0E-04
4.0E-04
1 .7E-03
4.0E-04
1 .5E-01
1 .3E-02
4.0E-04
4.0E-04
1 .2E-01
1 .9E-01
2.0E-02
2.9E-02
5.4E-01
3.6E-03
4.7E-02
5.3E+01
O.OE+00
5.5E-02
7.4E-03
2.9E-04
O.OE+00
O.OE+00
2.1E-04
9.2E-02
5.5E-04
3.5E-04
O.OE+00
O.OE+00
O.OE+00
8.6E-12
O.OE+00
O.OE+00
8.3E-04
1 .4E-01
1 .2E-02
3.0E-02
3.2E-02
7.0E-02
8.3E-04
O.OE+00
1 .6E-03
3.4E-03
O.OE+00
3.8E-01
2.3E-03
O.OE+00
O.OE+00
4.1 E-02
1 .3E-01
1 .8E-02
No
No
No
No
No
Yes
No
No
No
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
No
No
Yes
No
No
Yes
Yes
No
No
No
-9.4E-04
-1 .2E-03
-8.6E-04
-8.3E-04
-3.4E-03
O.OE+00
-8.8E-04
5.5E-05
-2.6E-04
-2.6E-34
-2.6E-34
-5.0E-05
-3.7E-04
2.6E-06
-4.2E-04
1.1E-34
O.OE+00
1 .6E-34
O.OE+00
O.OE+00
O.OE+00
-2.4E-04
-1.1E-03
-1 .OE-03
-7.2E-04
-1.1E-03
-3.5E-04
-8.4E-04
O.OE+00
-4.9E-04
-4.6E-04
O.OE+00
-2.5E-03
1 .7E-04
O.OE+00
O.OE+00
-3.1E-04
-7.9E-04
O.OE+00
1.59
1.35
0.86
0.55
2.63
0.00
0.43
0.66
0.55
0.00
0.00
0.46
0.41
0.26
0.67
0.00
0.00
0.00
0.00
0.00
0.00
1.03
1.66
0.48
0.57
0.75
0.44
0.84
0.00
1.87
1.39
0.00
1.35
0.17
0.00
0.00
0.39
0.57
0.00
84.0%
86.6%
82.0%
100.0%
99.6%
100.0%
99.8%
53.2%
74.6%
100.0%
100.0%
62.4%
97.4%
100.0%
94.1%
100.0%
0.0%
100.0%
100.0%
100.0%
0.0%
69.5%
74.8%
100.0%
100.0%
100.0%
99.6%
99.9%
0.0%
89.4%
87.1%
100.0%
100.0%
86.1%
100.0%
100.0%
87.5%
99.8%
0.0%
NT
NT
S
D
D
S
D
NT
S
D
D
S
D
I
PD
I
N/A
I
S
S
N/A
NT
NT
D
D
D
D
D
N/A
NT
NT
S
D
NT
S
S
S
D
N/A
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 1 of 2
-------
project; Long Prairie Site
Julia Aziz
Todd County
Minnesota
Well
Average
Source/ Cone
Tail (mg/L)
Median
Cone
(mg/L)
Standard
Deviation
All
Samples
"ND" ?
Coefficient
Ln Slope of Variation
Confidence Concentration
in Trend Trend
TETRACHLOROETHYLENE(PCE)
MW-3B
MW-3A
MW-1B
MW-1A
MW-6C
T
T
T
T
T
4.0E-04
9.3E-04
4.0E-04
4.0E-04
8.4E-02
4.0E-04
4.0E-04
4.0E-04
4.0E-04
1 .OE-01
O.OE+00
1 .5E-03
O.OE+00
O.OE+00
7.1E-02
Yes
No
Yes
Yes
No
O.OE+00
-5.8E-04
O.OE+00
O.OE+00
-1 .6E-03
0.00
1.61
0.00
0.00
0.84
100.0%
87.2%
100.0%
100.0%
98.2%
S
NT
S
S
D
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events); COV = Coefficient of Variation
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 2 of 2
-------
MAROS Mann-Kendall Statistics Summary
Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
Time Period: 5/20/1996 to 10/14/2002
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Source/
Tail
Number of
Samples
Number of
Detects
Coefficient
of Variation
Mann-Kendall
Statistic
Confidence
in Trend
All
Samples Concentration
"ND" ? Trend
TETRACHLOROETHYLENE(PCE)
MW-2B
MW-10
MW-2A
MW-2C
RW-3
MW-13C
MW-18B
MW-18A
MW-17B
MW-16B
MW-16A
MW-15B
MW-15A
MW-1B
MW-14B
BAL2B
MW-11C
MW-1 1 B
MW-11A
RW-8
CW-6
CW-3
BAL2C
MW-14C
MW-4C
RW-5
RW-1C
RW-1B
RW-1A
MW-6C
MW-6B
MW-6A
MW-19B
MW-5A
MW-1 A
MW-4B
MW-4A
MW-3B
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
6
8
6
8
25
9
5
5
7
8
6
7
7
8
10
1
8
4
2
12
14
24
8
11
8
25
1
12
12
7
6
6
7
8
8
6
2
8
6
8
6
5
25
2
5
0
7
8
1
0
0
0
10
0
1
0
0
12
0
0
0
1
8
25
0
1
11
7
6
5
0
0
0
6
2
0
1.35
2.63
1.59
0.86
0.55
1.03
0.26
0.00
0.43
0.66
0.55
0.00
0.00
0.00
0.41
0.00
0.67
0.00
0.00
0.48
0.00
0.00
0.00
0.46
0.39
0.44
0.00
1.87
1.39
0.84
1.35
1.66
0.00
0.00
0.00
0.57
0.00
0.00
-7
-28
-5
-11
-173
-3
-3
0
-14
-4
-3
0
0
0
-18
0
-7
0
0
^6
0
0
0
-2
-11
-132
0
-11
-24
-10
-15
0
0
0
0
-12
0
0
86.4%
100.0%
76.5%
88.7%
100.0%
58.0%
67.5%
40.8%
97.5%
64.0%
64.0%
43.7%
43.7%
45.2%
93.4%
0.0%
76.4%
37.5%
0.0%
100.0%
47.8%
49.0%
45.2%
53.0%
88.7%
99.9%
0.0%
74.9%
94.2%
90.7%
99.9%
42.3%
43.7%
45.2%
45.2%
98.2%
0.0%
45.2%
No
No
No
No
No
No
No
Yes
No
No
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes
No
Yes
Yes
Yes
No
No
No
Yes
No
No
No
No
No
Yes
Yes
Yes
No
No
Yes
NT
D
NT
S
D
NT
S
S
D
S
S
S
S
S
PD
N/A
S
S
N/A
D
S
S
S
S
S
D
N/A
NT
PD
PD
D
NT
S
S
S
D
N/A
S
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 1 of 2
-------
Project: Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
Source/
Well Tail
Number of
Samples
Number of
Detects
Coefficient
of Variation
Mann-Kendall
Statistic
Confidence
in Trend
All
Samples
"ND" ?
Concentration
Trend
TETRACHLOROETHYLENE(PCE)
MW-3A T
RW-6 T
RW-9 T
RW-7 T
RW-4 T
MW-5B T
8
25
12
25
15
8
1
25
12
25
6
0
1.61
0.75
0.17
0.57
0.84
0.00
-7
-234
2
-255
-61
0
76.4%
100.0%
52.7%
100.0%
99.9%
45.2%
No
No
No
No
No
Yes
NT
D
NT
D
D
S
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-
Due to insufficient Data (< 4 sampling events); Source/Tail (S/T)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 2 of 2
-------
MAROS Statistical Trend Analysis Summary
Project: Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
Time Period: 5/20/1996 to 10/14/2002
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Source/
Tail
Number Number
of of
Samples Detects
Average Median
Cone. Cone.
(mg/L) (mg/L)
All
Samples
"ND" ?
Mann-
Kendall
Trend
Linear
Regression
Trend
TETRACHLOROETHYLENE(PCE)
BAL2B
BAL2C
CW-3
CW-6
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-19B
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
RW-1A
T
T
T
T
S
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
S
S
S
T
T
T
T
T
T
T
T
T
T
T
1
8
24
14
8
2
4
8
9
10
11
7
7
6
8
7
5
5
7
8
8
6
6
8
8
8
2
6
8
8
8
6
6
7
12
0
0
0
0
8
0
0
1
2
10
1
0
0
1
8
7
0
5
0
0
0
6
6
5
1
0
2
6
8
0
0
5
6
7
11
4.0E-04
4.0E-04
4.0E-04
4.0E-04
2.0E+01
4.0E-04
4.0E-04
5.3E-04
8.1E-04
2.2E-01
4.6E-04
4.0E-04
4.0E-04
5.2E-04
1.1E-02
1 .3E-01
4.0E-04
2.2E-03
4.0E-04
4.0E-04
4.0E-04
1 .8E-02
4.0E-01
4.2E-03
9.3E-04
4.0E-04
2.0E-02
2.2E-01
1.1E-01
4.0E-04
4.0E-04
8.6E-02
2.8E-01
8.4E-02
2.5E-03
4.0E-04
4.0E-04
4.0E-04
4.0E-04
9.3E-01
4.0E-04
4.0E-04
4.0E-04
4.0E-04
2.1E-01
4.0E-04
4.0E-04
4.0E-04
4.0E-04
1.1E-02
1.4E-01
4.0E-04
2.4E-03
4.0E-04
4.0E-04
4.0E-04
7.3E-03
6.7E-02
4.3E-03
4.0E-04
4.0E-04
2.0E-02
1.9E-01
1.2E-01
4.0E-04
4.0E-04
4.1E-02
1.5E-01
1 .OE-01
1 .7E-03
Yes
Yes
Yes
Yes
No
Yes
Yes
No
No
No
No
Yes
Yes
No
No
No
Yes
No
Yes
Yes
Yes
No
No
No
No
Yes
No
No
No
Yes
Yes
No
No
No
No
N/A
S
S
S
D
N/A
S
S
NT
PD
S
S
S
S
S
D
S
S
S
S
S
NT
NT
S
NT
S
N/A
D
S
S
S
NT
D
PD
PD
N/A
S
S
I
D
N/A
S
PD
NT
D
S
D
D
S
NT
D
S
I
|
S
S
NT
NT
S
NT
S
N/A
D
S
S
S
NT
D
D
NT
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 1 of 2
-------
MAROS Statistical Trend Analysis Summary
Well
Number Number Average Median AM Mann-
Source/ of of Cone. Cone. Samples Kendall
Tail Samples Detects (mg/L) (mg/L) "ND" ? Trend
Linear
Regression
Trend
TETRACHLOROETHYLENE(PCE)
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
T
T
S
T
T
T
T
T
T
12
1
25
15
25
25
25
12
12
1
0
25
6
25
25
25
12
12
8.7E-04
4.0E-04
8.6E-02
9.8E-04
1 .6E-01
4.3E-02
5.2E-02
2.5E-02
1 .4E-02
4.0E-04
4.0E-04
8.7E-02
4.0E-04
1 .6E-01
4.1E-02
4.0E-02
2.3E-02
1 .3E-02
No
Yes
No
No
No
No
No
No
No
NT
N/A
D
D
D
D
D
D
NT
NT
N/A
D
D
D
D
D
D
NT
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable
(N/A); Not Applicable (N/A) - Due to insufficient Data (< 4 sampling events); No Detectable Concentration (NDC)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 2 of 2
-------
MAROS Spatial Moment Analysis Summary
Project; Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
Effective Date
Oth Moment
1st (Center of
Estimated
Mass (Kg) Xc (ft)
Source
Yc (ft) Distance (ft)
2nd Moment
Sigma XX
(sq ft)
(Spread)
Sigma YY
(sq ft)
Number of
Wells
TETRACHLOROETHYLENE(PCE)
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
7/1/2001
7/1/2002
4.7E+00
2.4E+01
2.9E+01
2.1E+01
1.5E+01
1.3E+01
7.3E+00
510,952
510,886
510,757
510,842
510,698
510,705
510,983
173,519
173,907
174,134
174,212
174,623
174,626
174,558
1,199
1,342
1,408
1,523
1,779
1,786
1,880
865,855
458,802
484,020
483,456
516,718
477,288
537,329
2,257,891
271,873
567,076
544,788
488,253
427,496
849,155
8
12
17
17
10
10
17
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 1 of 2
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Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
Moment Type Consituent
Zeroth Moment: Mass
TETRACHLOROETHYLENE(PCE)
1st Moment: Distance to Source
TETRACHLOROETHYLENE(PCE)
2nd Moment: Sigma XX
TETRACHLOROETHYLENE(PCE)
2nd Moment: Sigma YY
TETRACHLOROETHYLENE(PCE)
Coefficient
of Variation
0.54
0.17
0.26
0.88
Mann-Kendall
S Statistic
-7
21
1
-3
Confidence
in Trend
80.9%
100.0%
50.0%
61.4%
Moment
Trend
S
I
NT
S
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.30 Saturated Thickness: Uniform: 60 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
Note: The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 2 of 2
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MAROS Zeroth Moment Analysis
Project: Long Prairie Site
Location: Todd County
Julia Aziz
Minnesota
COC: TETRACHLOROETHYLENE(PCE)
Change in Dissolved Mass Over Time
Date
/ / /
3.5E+01
2.5E+01 -
*-»,
S 2.0E+01
Sg 1.5E+01
S
1.0E+01
5.0E+00 ^
O.OE+00
Porosity: 0.30
Saturated Thickness:
Uniform: 60 ft
Mann Kendall S Statistic:
-7
Confidence in
Trend:
I 80.9%
Coefficient of Variation:
I 0.54
Zeroth Moment
Trend:
Data Table:
Effective Date
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
7/1/2001
7/1/2002
Constituent
TETRACHLOROETHYLENE(PCE)
TETRACHLOROETHYLENE(PCE)
TETRACHLOROETHYLENE(PCE)
TETRACHLOROETHYLENE(PCE)
TETRACHLOROETHYLENE(PCE)
TETRACHLOROETHYLENE(PCE)
TETRACHLOROETHYLENE(PCE)
Estimated
Mass (Kg)
4.7E+00
2.4E+01
2.9E+01
2.1E+01
1.5E+01
1.3E+01
7.3E+00
Number of Wells
8
12
17
17
10
10
17
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events); ND = Non-detect. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
2/13/2003
Page 1 of 1
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-------
MAROS First Moment Analysis
Project: Long Prairie Site
Location: Todd County
Julia Aziz
State: Minnesota
COC: TETRACHLOROETHYLENE(PCE)
Change in Location of Center of Mass Over Time
I/ H / UU '
174600 •
174500 •
174400 •
32 174300 •
>_ 174200 •
174100 •
174000 •
173900 •
173 800 •
•»*OWSQO
»07
* 07/98
* 07/97
• 07/96
Flow Direction:
,01 w
\
\
Source
Coordinate:
X: I 509,844
1
Y: j 173,062
510650 510700 510750 510800 510850 510900 510950 511000
Xc (ft)
Effective Date Constituent Xc (ft) Yc (ft) Distance from Source (ft) Number of Wells
7/1/1996 TETRACHLOROETHYLENE(P 510,952 173,519 1,199 8
7/1/1997 TETRACHLOROETHYLENE(P 510,886 173,907 1,342 12
7/1/1998 TETRACHLOROETHYLENE(P 510,757 174,134 1,408 17
7/1/1999 TETRACHLOROETHYLENE(P 510,842 174,212 1,523 17
7/1/2000 TETRACHLOROETHYLENE(P 510,698 174,623 1,779 10
7/1/2001 TETRACHLOROETHYLENE(P 510,705 174,626 1,786 10
7/1/2002 TETRACHLOROETHYLENE(P 510,983 174,558 1,880 17
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
2/13/2003
Page 1 of 1
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MAROS First Moment Analysis
Project: Long Prairie Site
Location: Todd County
COC: TETRACHLOROETHYLENE(PCE)
Julia Aziz
Minnesota
Distance from Source to Center of Mass
Mann Kendall S Statistic:
Date
g
t
3
O
CO
E
O
(0
to
5
/ / / / / / /
1.8E+03 •
1.6E+03 -
1.4E+03 -
1.2E+03 -
1 np-i-n^
'
8.0E+02 -
6.0E+02 •
4.0E+02 -
2.0E+02 -
n np+nn .
*
_
»
*
A
I 21
Confidence in
Trend:
100.0%
Coefficient of Variation:
First Moment Trend:
Data Table:
Effective Date
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
7/1/2001
7/1/2002
Constituent
TETRACHLOROETHYLENE(P
TETRACHLOROETHYLENE(P
TETRACHLOROETHYLENE(P
TETRACHLOROETHYLENE(P
TETRACHLOROETHYLENE(P
TETRACHLOROETHYLENE(P
TETRACHLOROETHYLENE(P
Xc (ft)
510,952
510,886
510,757
510,842
510,698
510,705
510,983
Yc (ft)
173,519
173,907
174,134
174,212
174,623
174,626
174,558
Distance from Source (ft)
1,199
1,342
1,408
1,523
1,779
1,786
1,880
Number of Wells
8
12
17
17
10
10
17
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
2/13/2003
Page 1 of 1
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MAROS Second Moment Analysis
Project: Long Prairie Site
Todd County
COC: TETRACHLOROETHYLENE(PCE)
Change in Pli
10000000
1000000
£- 100000
JE 10000
CM
V 1000
w
100
10
1
mnnnnn _i_
tr
-2.
?• 100000 -
1
innnn - -
Data Table:
Effective Date
jme Spread Over Time
Date
*
; . • * * * *
Date
c?> <^ <$> <§> <§* «?> c?
%r %r 4r %r %r 4r %r
\y \y \y \y \y \y \y
•
******
Julia Aziz
Minnesota
Mann Kendall S Statistic:
? i -3
Confidence in
Trend:
| 61.4%
Coefficient of Variation:
|| 0.88
Second Moment
Trend:
I S
Mann Kendall S Statistic:
Confidence in
Trend:
| 50.0%
Coefficient of Variation:
j 0.26
Second Moment
Trend:
1 NT
i
Constituent Sigma XX (sq ft) Sigma YY (sq ft) Number of Wells
7/1/1996 TETRACHLOROETHYLENE(P 865,855 2,257,891 8
7/1/1997 TETRACHLOROETHYLENE(P 458,802 271,873 12
7/1/1998 TETRACHLOROETHYLENE(P 484,020 567,076 17
7/1/1999 TETRACHLOROETHYLENE(P 483,456 544,788 17
7/1/2000 TETRACHLOROETHYLENE(P 516,718 488,253 10
7/1/2001 TETRACHLOROETHYLENE(P 477,288 427,496 10
7/1/2002 TETRACHLOROETHYLENE(P 537,329 849,155 17
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events)
The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
2/13/2003
Page 1 of 1
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MAROS Site Results
Project: Long Prairie Site
Location: Todd County
User Defined Site and Data Assumptions:
Julia Aziz
Minnesota
Hydrogeology and Plume Information:
Groundwater
Seepage Velocity: 475 ft/yr
Current Plume Length: 2100 ft
Current Plume Width 1000 ft
Number of Tail Wells: 15
Number of Source Wells: 2
Source Information:
Source Treatment: SVG
NAPL is not at this site.
Down-gradient Information:
Distance from Edge of Tail to Nearest:
Down-gradient receptor: 1000 ft
Down-gradient property: 1 ft
Distance from Source to Nearest:
Down-gradient receptor: 3000ft
Down-gradient property: 1 ft
Consolidation Assumptions:
Time Period: 10/31/1996 to 10/14/2002
Consolidation Period: Yearly
Consolidation Type: Geometric Mean
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Plume Information Weighting Assumptions:
Consolidation Step 1. Weight Plume Information by Chemical
Summary Weighting: Weighting Applied to All Chemicals Equally
Consolidation Step 2. Weight Well Information by Chemical
Well Weighting: No Weighting of Wells was Applied.
Chemical Weighting: No Weighting of Chemicals was Applied.
Note: These assumptions made when consolidating the historical mentoring lumping the Wells and COCs.
1.
Preliminary Monitoring System Optimization Results: Based on site classification, source treatment and Monitoring System
Category the following suggestions are made for site Sampling Frequency, Duration of Sampling, and Well Density. These
criteria take into consideration: Plume Stability, Type of Plume, and Groundwater Velocity.
coc
Tail Source Level of
Stability Stability Effort
Sampling
Duration
Sampling
Frequency
Sampling
Density
TETRACHLOROETHYLENE(PCE)
M
35
Remove treatment No Recommendation
system if previously
reducing concentation
Note:
Plume Status: (I) Increasing; (Pl)Probably Increasing; (S) Stable; (NT) No Trend; (PD) Probably Decreasing; (D) Decreasing
Design Categories: (E) Extensive; (M) Moderate; (L) Limited (N/A) Not Applicable, Insufficient Data Available
Level of Monitoring Effort Indicated by Analysi I Moderate
2,
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 1 of 2
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Coefficient Mann-Kendall Confidence
Moment Type Consituent of Variation S Statistic in Trend
Moment
Trend
Zeroth Moment: Mass
TETRACHLOROETHYLENE(PCE) 0.54 -7 80.9%
S
1st Moment: Distance to Source
2nd
2nd
TETRACHLOROETHYLENE(PCE) 0.17 21 100.0%
Moment: Sigma XX
TETRACHLOROETHYLENE(PCE) 0.26 1 50.0%
Moment: Sigma YY
TETRACHLOROETHYLENE(PCE) 0.88 -3 61.4%
I
NT
S
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.30 Saturated Thickness: Uniform: 60 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
MAROS Version 2, 2002, AFCEE
Thursday, February 13, 2003
Page 2 of 2
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1 MAROS Sampling Location Optimization Re suits 1
Long Prairie site
Location: Todd County
Sampling Events Analyzed: From May 1999
5/20/1999
Parameters used: Constituent
TETRACHLOROETHYLENE(PCE
Meng
Minnesota
to October 2002
10/14/2002
Inside SF Hull SF Area Ratio Cone. Ratio
0.1 0.01 0.95 0.95
Average Minimum Maximum
Well X (feet) Y (feet) Removable? Slope Factor* Slope Factor* Slope Factor*
Eliminated?
TETRACHLOROETHYLENE(PCE)
BAL2C 511820.13 175912.75 0
CW-3 511983.13 173568.70 0
MW-10 509843.81 173061.80 0
MW-11C 511661.88 175323.95 0
MW-13C 510827.19 174770.72 0
MW-14B 511182.03 174835.77 0
MW-15B 510155.53 176497.39 0
MW-16B 510567.59 175986.83 0
MW-17B 510889.94 175444.19 0
MW-18B 510844.34 176150.06 0
MW-19B 510391.47 175606.44 0
MW-1B 509586.91 173544.45 0
MW-2B 510177.97 173610.33 0
MW-3B 510256.16 173355.97 0
MW-4B 510892.31 174509.33 0
MW-5B 510198.19 175015.45 0
MW-6B 510524.09 174085.47 0
0.488 0.463 0.519
0.628 0.470 0.720
0.498 0.338 0.752
0.678 0.629 0.730
0.726 0.690 0.770
0.450 0.288 0.619
0.474 0.291 0.588
0.269 0.011 0.437
0.506 0.378 0.627
0.270 0.244 0.296
0.624 0.556 0.704
0.685 0.587 0.776
0.463 0.281 0.588
0.680 0.532 0.793
0.357 0.225 0.468
0.490 0.409 0.637
0.206 0.090 0.447
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
Note: The Slope Factor indicates the relative importance of a well in the monitoring network at a given sampling event; the larger the SF
value of a well, the more important the well is and vice versa; the Average Slope Factor measures the overall well importance in the
selected time period; the state coordinates system (i.e., X and Y refer to Easting and Northing respectively) or local coordinates systems
may be used; wells that are NOT selected for analysis are not shown above.
* When the report is generated after running the Excel module, SF values will NOT be shown above.
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of 1
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MAROS Sampling Frequency Optimization Results
Project: Long Prairie site
Location: ToddCounty
The Overall Number of Sampling Events: 28
"Recent Period" defined by events: From May 1999
5/20/1999
Meng
Minnesota
To October 2002
10/14/2002
"Rate of Change" parameters used:
Well
Constituent Cleanup Goal Low Rate Medium Rate High Rate
TETRACHLOROETHYLENE(PCE 0.005
Units: Cleanup Goal is in mg/L; all rate parameters are
Recommended
Sampling Frequency
0.0025 0.005 0.01
in mg/L/year.
Frequency Based Frequency Based
on Recent Data on Overall Data
TETRACHLOROETHYLENE(PCE)
BAL2B
BAL2C
CW-3
CW-6
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-1 7B
MW-18A
MW-18B
MW-19B
MW-1 A
MW-1B
MW-2A
MW-2B
MW-2C
Annual
Annual
Biennial
Biennial
Quarterly
Annual
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of2
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Project: Long Prairie site
Location: ToddCounty
Meng
State: Minnesota
Well
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
Recommended
Sampling Frequency
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Annual
Annual
Annual
Frequency Based
on Recent Data
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Frequency Based
on Overall Data
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Note: Sampling frequency is determined considering both recent and overall concentration trends. Sampling Frequency is the
final recommendation; Frequency Based on Recent Data is the frequency determined using recent (short) period of monitoring
data; Frequency Based on Overall Data is the frequency determined using overall (long) period of monitoring data. If the "recent
period" is defined using a different series of sampling events, the results could be different.
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 2 of2
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[Regression of Plume Centerline Concentrations |
Long Prairie site
Location: Todd County
Groundwater Flow Direction: 90 degrees
From Period: 5/20/1999 to 10/14/2002
Selected Plume We||
Centerline Wells:
MW-15B
MW-16B
MW-1 7B
MW-4B
MW-2B
Meng
Minnesota
Distance to Receptor: 1 0 feet
Distance to Receptor (feet)
10.0
520.6
1063.2
1998.1
2897.1
The distance is measured in the Groundwater Flow Angle
from the well to the compliance boundary.
Sample Event Effective Date
Number of Regression Confidence in
Centerline Wells Coefficient (1 /ft) Coefficient
TETRACHLOROETHYLENE(PCE)
May 1999 5/20/1999
July 1999 7/7/1999
September 1 999 9/1 5/1 999
October 1999 10/26/1999
March 2000 3/15/2000
June 2000 7/1/2000
September 2000 9/10/2000
October 2000 10/26/2000
December 2000 12/31/2000
March 2001 3/23/2001
May 2001 5/22/2001
September 2001 9/15/2001
November 2001 11/29/2001
January 2002 1/31/2002
April 2002 4/3/2002
July 2002 7/25/2002
October 2002 10/14/2002
5 -2.31 E-03
0 O.OOE+00
2 O.OOE+00
3 -5.38E-03
0 O.OOE+00
0 O.OOE+00
5 -1 .50E-03
0 O.OOE+00
0 O.OOE+00
0 O.OOE+00
0 O.OOE+00
5 -1.51 E-03
0 O.OOE+00
0 O.OOE+00
0 O.OOE+00
0 O.OOE+00
5 -1 .24E-03
97.9%
0.0%
0.0%
91.5%
0.0%
0.0%
92.7%
0.0%
0.0%
0.0%
0.0%
92.1%
0.0%
0.0%
0.0%
0.0%
91 .3%
Note: when the number of plume centerline wells is less than 3, no analysis is performed and all related values
are set to ZERO; Confidence in Coefficient is the statistical confidence that the estimated coefficient is
different from ZERO (for details, please refer to "Conference in Trend" in Linear Regression Analysis).
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of 1
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Risk-Based Power Analysis — Projected Concentrations
Long Prairie site
Location: Todd County
From Period:
Sampling
Event
5/20/1999 to
Effective
Date
10/14/2002
Well
Meng
Minnesota
Distance from the most downgradient well to receptor: 10 feet
Observed
Concentration
(mg/L)
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
BAL2C
CW-3
CW-6
MW-10
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
RW-1A
RW-1B
5.000E-04
5.000E-04
5.000E-04
9.900E-02
5.000E-04
5.000E-04
3.500E-01
5.000E-04
5.000E-04
5.000E-04
5.000E-04
1 .450E-02
2.000E-01
5.000E-04
2.200E-03
5.000E-04
5.000E-04
7.600E-02
9.700E-01
5.100E-03
5.000E-04
5.000E-04
7.400E-03
2.300E-01
1.700E-01
5.000E-04
5.000E-04
3.750E-01
2.600E-01
2.100E-01
1 .500E-03
5.000E-04
594.6
2938.7
430.4
3445.6
1183.4
1736.7
1671.6
1671.6
10.0
10.0
520.6
520.6
1063.2
357.3
357.3
2962.9
2962.9
2897.1
2897.1
2897.1
3151.4
3151.4
1998.1
1998.1
1998.1
1491.9
1491.9
2421.9
2421.9
2421.9
3468.7
3409.4
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
1 .268E-04
5.684E-07
1 .852E-04
3.495E-05
3.260E-05
9.099E-06
7.400E-03
1 .057E-05
4.886E-04
4.886E-04
1 .505E-04
4.363E-03
1.721E-02
2.193E-04
9.647E-04
5.375E-07
5.375E-07
9.511E-05
1.214E-03
6.382E-06
3.479E-07
3.479E-07
7.368E-05
2.290E-03
1 .693E-03
1 .600E-05
1 .600E-05
1 .404E-03
9.737E-04
7.864E-04
5.020E-07
1.919E-07
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
May 1999
May 1999
May 1999
May 1999
May 1999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
RW-3
RW-4
RW-5
RW-6
RW-7
BAL2C
CW-3
CW-6
MW-10
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-19B
MW-1A
MW-1B
MW-2C
MW-3A
MW-3B
MV\MC
MW-5A
MW-5B
MW-6C
RW-1A
RW-1B
RW-3
RW-4
RW-5
RW-6
1.350E-01
5.000E-04
1.900E-01
4.100E-02
4.000E-02
5.000E-04
5.000E-04
5.000E-04
6.500E-02
5.000E-04
5.000E-04
2.100E-01
5.000E-04
5.000E-04
5.000E-04
5.000E-04
2.000E-03
1.400E-01
5.000E-04
1 .200E-03
5.000E-04
5.000E-04
5.000E-04
3.400E-03
5.000E-04
5.000E-04
1.400E-01
5.000E-04
5.000E-04
1.100E-01
1 .OOOE-03
5.000E-04
8.750E-02
5.000E-04
2.200E-01
4.400E-02
2922.2
2414.7
1730.8
2364.6
1686.5
594.6
2938.7
430.4
3445.6
1183.4
1736.7
1671.6
1671.6
10.0
10.0
520.6
520.6
1063.2
357.3
357.3
901.0
2962.9
2962.9
2897.1
3151.4
3151.4
1998.1
1491.9
1491.9
2421.9
3468.7
3409.4
2922.2
2414.7
1730.8
2364.6
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
1 .594E-04
1 .904E-06
3.505E-03
1 .753E-04
8.172E-04
2.045E-05
6.882E-11
4.943E-05
5.863E-10
8.627E-07
4.407E-08
2.626E-05
6.253E-08
4.738E-04
4.738E-04
3.045E-05
1.218E-04
4.611E-04
7.323E-05
1 .757E-04
3.939E-06
6.041 E-11
6.041 E-11
5.854E-10
2.193E-11
2.193E-11
3.027E-06
1 .643E-07
1 .643E-07
2.436E-07
7.966E-12
5.478E-12
1.316E-08
1.151E-09
2.002E-05
1 .326E-07
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 2 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
October 1 999
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
10/26/1999
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
RW-7
CW-3
MW-11B
MW-11C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16B
MW-17B
MW-19B
MW-2A
MW-2B
MW-2C
MV\MB
MV\MC
MW-6A
MW-6B
MW-6C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
CW-3
MW-11B
MW-11C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16B
MW-17B
MW-19B
3.600E-02
5.000E-04
5.000E-04
5.000E-04
1.400E-01
5.000E-04
5.000E-04
5.000E-04
1 .400E-02
1.000E-01
5.000E-04
1.100E-02
7.100E-02
5.000E-04
1.400E-01
1.150E-01
5.400E-02
4.700E-02
2.100E-02
5.100E-02
5.000E-04
7.400E-02
2.000E-02
3.100E-02
3.000E-02
1 .600E-02
5.000E-04
5.000E-04
5.000E-04
1.300E-01
5.000E-04
5.000E-04
5.000E-04
3.700E-03
9.200E-02
5.000E-04
1686.5
2938.7
1183.4
1183.4
1671.6
1671.6
10.0
10.0
520.6
1063.2
901.0
2897.1
2897.1
2897.1
1998.1
1998.1
2421.9
2421.9
2421.9
2922.2
2414.7
1730.8
2364.6
1686.5
1096.9
535.7
2938.7
1183.4
1183.4
1671.6
1671.6
10.0
10.0
520.6
1063.2
901.0
-5.38E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
4.156E-06
6.121E-06
8.490E-05
8.490E-05
1.144E-02
4.086E-05
4.926E-04
4.926E-04
6.418E-03
2.033E-02
1 .296E-04
1 .433E-04
9.251 E-04
6.515E-06
7.015E-03
5.762E-03
1 .434E-03
1 .248E-03
5.576E-04
6.399E-04
1 .342E-05
5.534E-03
5.787E-04
2.477E-03
5.799E-03
7.170E-03
5.942E-06
8.390E-05
8.390E-05
1 .045E-02
4.017E-05
4.925E-04
4.925E-04
1 .687E-03
1.851E-02
1 .285E-04
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
No
No
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 3 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
MW-2A
MW-2B
MW-2C
MW-4B
MW-4C
MW-6A
MW-6B
MW-6C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
BAL2B
BAL2C
CW-3
CW-6
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-19B
MW-1A
MW-1B
5.200E-03
3.800E-02
5.000E-04
1.400E-01
6.450E-02
5.400E-02
3.700E-02
1 .200E-02
2.550E-02
5.000E-04
1.025E-01
1 .250E-02
2.800E-02
1 .950E-02
1 .400E-02
5.000E-04
5.000E-04
5.000E-04
5.000E-04
3.800E-02
5.000E-04
5.000E-04
5.000E-04
5.000E-04
1.100E-01
5.000E-04
5.000E-04
5.000E-04
5.000E-04
2.000E-02
4.000E-02
5.000E-04
2.400E-03
5.000E-04
5.000E-04
5.000E-04
2897.1
2897.1
2897.1
1998.1
1998.1
2421.9
2421.9
2421.9
2922.2
2414.7
1730.8
2364.6
1686.5
1096.9
535.7
318.8
594.6
2938.7
430.4
3445.6
1183.4
1183.4
1183.4
1736.7
1671.6
1671.6
10.0
10.0
520.6
520.6
1063.2
357.3
357.3
901.0
2962.9
2962.9
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
6.580E-05
4.809E-04
6.327E-06
6.875E-03
3.167E-03
1 .399E-03
9.587E-04
3.109E-04
3.107E-04
1.310E-05
7.533E-03
3.531 E-04
2.200E-03
3.728E-03
6.240E-03
3.362E-04
2.385E-04
1 .288E-05
2.926E-04
5.210E-04
1.146E-04
1.146E-04
1.146E-04
5.754E-05
1 .373E-02
6.240E-05
4.938E-04
4.938E-04
2.615E-04
1 .046E-02
1 .065E-02
3.205E-04
1 .538E-03
1 .629E-04
1 .250E-05
1 .250E-05
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 4 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
9.700E-04
4.100E-02
6.900E-03
5.000E-04
5.000E-04
3.300E-02
7.700E-02
5.100E-02
5.000E-04
5.000E-04
5.000E-04
4.700E-03
2.500E-02
1 .OOOE-03
5.000E-04
5.000E-04
2.000E-02
5.000E-04
8.400E-02
9.500E-03
2.200E-02
1 .800E-02
1 .500E-02
2897.1
2897.1
2897.1
3151.4
3151.4
1998.1
1998.1
1998.1
1491.9
1491.9
2421.9
2421.9
2421.9
3468.7
3409.4
3412.5
2922.2
2414.7
1730.8
2364.6
1686.5
1096.9
535.7
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
2.633E-05
1.113E-03
1 .873E-04
9.887E-06
9.887E-06
2.743E-03
6.400E-03
4.239E-03
7.804E-05
7.804E-05
2.452E-05
2.305E-04
1 .226E-03
1 .332E-05
7.171E-06
7.144E-06
5.261 E-04
2.474E-05
9.739E-03
5.003E-04
2.695E-03
4.594E-03
7.699E-03
Yes
No
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Note: Projected Concentrations that are below the user-specified detection limit are indicated by a check mark to its right; for sampling events
with less than 3 selected plume centerline wells, NO projected concentrations are calculated because no regression coefficient is available.
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 5 of 5
-------
MAROS Risk-Based Power Analysis for Site Cleanup
Project: Long Prairie site
Todd County
Meng
Minnesota
Parameters:
Groundwater Flow Direction: 90 degrees Distance to Receptor: 10 feet
From Period: May 1999 to October 2002
5/20/1999
Selected Plume
Centerline Wells:
10/14/2002
Well
MW-15B
MW-16B
MW-1 7B
MW-4B
MW-2B
The distance is
from the well to
Distance to Receptor (feet)
10.0
520.6
1063.2
1998.1
2897.1
measured in the Groundwater Flow Angle
the compliance boundary.
Normal Distribution Assumption Lognormal Distribution Assumption
Sample Event
Sample Sample
Szie Mean
Sample
Stdev.
TETRACHLOROETHYLENE(PCE)
May 1999
October 1999
September 2000
September 2001
October 2002
30
25
18
18
34
1 .34E-03
7.66E-05
3.15E-03
2.51 E-03
1 .66E-03
3.38E-03
1 .54E-04
5.40E-03
4.86E-03
3.44E-03
Cleanup
Status
Expected Celanup
Power Samp|e Size status
Expected
Power Sample Size
Alpha Expected
Level Power
Cleanup Goal = 0.005
Attained
Attained
Not Attained
Attained
Attained
1.000
1.000
0.414
0.685
1.000
7
<=3
54
25
8
Not Attained
Not Attained
Not Attained
Not Attained
Attained
S/E
S/E
S/E
S/E
0.728
S/E
S/E
S/E
S/E
42
0.05
0.05
0.05
0.05
0.05
0.8
0.8
0.8
0.8
0.8
Note: #N/C means "not conducted" due to a small sample size (N<4) or that the mean concentration is much greater than the cleanup
level; Sample Size is the number of sampling locations used in the power analysis; Expected Sample Size is the number of concentration
data needed to reach the Expected Power undercurrent sample variability.
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of 1
-------
-------
Risk-Based Power Analysis — Projected Concentrations
Long Prairie site
Location: Todd County
From Period:
Sampling
Event
5/20/1999 to
Effective
Date
10/14/2002
Well
Meng
Minnesota
Distance from the most downgradient well to receptor: -100 feet
Observed
Concentration
(mg/L)
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
May 1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
BAL2C
CW-3
CW-6
MW-10
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-1A
MW-1B
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
RW-1A
RW-1B
5.000E-04
5.000E-04
5.000E-04
9.900E-02
5.000E-04
5.000E-04
3.500E-01
5.000E-04
5.000E-04
5.000E-04
5.000E-04
1 .450E-02
2.000E-01
5.000E-04
2.200E-03
5.000E-04
5.000E-04
7.600E-02
9.700E-01
5.100E-03
5.000E-04
5.000E-04
7.400E-03
2.300E-01
1.700E-01
5.000E-04
5.000E-04
3.750E-01
2.600E-01
2.100E-01
1 .500E-03
5.000E-04
484.6
2828.7
320.4
3335.6
1073.4
1626.7
1561.6
1561.6
-100.0
-100.0
410.6
410.6
953.2
247.3
247.3
2852.9
2852.9
2787.1
2787.1
2787.1
3041.4
3041.4
1888.1
1888.1
1888.1
1381.9
1381.9
2311.9
2311.9
2311.9
3358.7
3299.4
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
1 .635E-04
7.326E-07
2.387E-04
4.505E-05
4.202E-05
1.173E-05
9.538E-03
1 .363E-05
6.297E-04
6.297E-04
1 .939E-04
5.624E-03
2.218E-02
2.826E-04
1 .243E-03
6.927E-07
6.927E-07
1 .226E-04
1 .564E-03
8.226E-06
4.485E-07
4.485E-07
9.496E-05
2.952E-03
2.182E-03
2.062E-05
2.062E-05
1.810E-03
1 .255E-03
1.014E-03
6.471 E-07
2.473E-07
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
No
No
Yes
No
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
May 1999
May 1999
May 1999
May 1999
May 1999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
October 1 999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
5/20/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
10/26/1999
RW-3
RW-4
RW-5
RW-6
RW-7
BAL2C
CW-3
CW-6
MW-10
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-19B
MW-1A
MW-1B
MW-2C
MW-3A
MW-3B
MV\MC
MW-5A
MW-5B
MW-6C
RW-1A
RW-1B
RW-3
RW-4
RW-5
RW-6
1.350E-01
5.000E-04
1.900E-01
4.100E-02
4.000E-02
5.000E-04
5.000E-04
5.000E-04
6.500E-02
5.000E-04
5.000E-04
2.100E-01
5.000E-04
5.000E-04
5.000E-04
5.000E-04
2.000E-03
1.400E-01
5.000E-04
1 .200E-03
5.000E-04
5.000E-04
5.000E-04
3.400E-03
5.000E-04
5.000E-04
1.400E-01
5.000E-04
5.000E-04
1.100E-01
1 .OOOE-03
5.000E-04
8.750E-02
5.000E-04
2.200E-01
4.400E-02
2812.2
2304.7
1620.8
2254.6
1576.5
484.6
2828.7
320.4
3335.6
1073.4
1626.7
1561.6
1561.6
-100.0
-100.0
410.6
410.6
953.2
247.3
247.3
791.0
2852.9
2852.9
2787.1
3041.4
3041.4
1888.1
1381.9
1381.9
2311.9
3358.7
3299.4
2812.2
2304.7
1620.8
2254.6
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-2.31 E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
-5.38E-03
2.055E-04
2.454E-06
4.518E-03
2.259E-04
1 .053E-03
3.693E-05
1.243E-10
8.929E-05
1 .059E-09
1 .559E-06
7.962E-08
4.744E-05
1.130E-07
8.560E-04
8.560E-04
5.500E-05
2.200E-04
8.329E-04
1 .323E-04
3.175E-04
7.116E-06
1.091E-10
1.091E-10
1 .057E-09
3.962E-1 1
3.962E-1 1
5.469E-06
2.968E-07
2.968E-07
4.401 E-07
1 .439E-1 1
9.897E-12
2.377E-08
2.080E-09
3.617E-05
2.396E-07
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 2 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
October 1 999
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2000
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
10/26/1999
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/10/2000
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
RW-7
CW-3
MW-11B
MW-11C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16B
MW-17B
MW-19B
MW-2A
MW-2B
MW-2C
MV\MB
MV\MC
MW-6A
MW-6B
MW-6C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
CW-3
MW-11B
MW-11C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16B
MW-17B
MW-19B
3.600E-02
5.000E-04
5.000E-04
5.000E-04
1.400E-01
5.000E-04
5.000E-04
5.000E-04
1 .400E-02
1.000E-01
5.000E-04
1.100E-02
7.100E-02
5.000E-04
1.400E-01
1.150E-01
5.400E-02
4.700E-02
2.100E-02
5.100E-02
5.000E-04
7.400E-02
2.000E-02
3.100E-02
3.000E-02
1 .600E-02
5.000E-04
5.000E-04
5.000E-04
1.300E-01
5.000E-04
5.000E-04
5.000E-04
3.700E-03
9.200E-02
5.000E-04
1576.5
2828.7
1073.4
1073.4
1561.6
1561.6
-100.0
-100.0
410.6
953.2
791.0
2787.1
2787.1
2787.1
1888.1
1888.1
2311.9
2311.9
2311.9
2812.2
2304.7
1620.8
2254.6
1576.5
986.9
425.7
2828.7
1073.4
1073.4
1561.6
1561.6
-100.0
-100.0
410.6
953.2
791.0
-5.38E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.50E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
7.508E-06
7.217E-06
1.001E-04
1.001E-04
1 .349E-02
4.818E-05
5.808E-04
5.808E-04
7.568E-03
2.398E-02
1 .529E-04
1 .690E-04
1.091E-03
7.682E-06
8.272E-03
6.795E-03
1.691E-03
1.471E-03
6.575E-04
7.546E-04
1 .583E-05
6.526E-03
6.823E-04
2.921 E-03
6.838E-03
8.455E-03
7.014E-06
9.904E-05
9.904E-05
1 .233E-02
4.742E-05
5.814E-04
5.814E-04
1 .992E-03
2.185E-02
1.516E-04
Yes
Yes
Yes
Yes
No
Yes
No
No
No
No
Yes
Yes
No
Yes
No
No
No
No
No
No
Yes
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 3 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
September 2001
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
9/15/2001
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
MW-2A
MW-2B
MW-2C
MW-4B
MW-4C
MW-6A
MW-6B
MW-6C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
BAL2B
BAL2C
CW-3
CW-6
MW-10
MW-11A
MW-11B
MW-11C
MW-13C
MW-14B
MW-14C
MW-15A
MW-15B
MW-16A
MW-16B
MW-17B
MW-18A
MW-18B
MW-19B
MW-1A
MW-1B
5.200E-03
3.800E-02
5.000E-04
1.400E-01
6.450E-02
5.400E-02
3.700E-02
1 .200E-02
2.550E-02
5.000E-04
1.025E-01
1 .250E-02
2.800E-02
1 .950E-02
1 .400E-02
5.000E-04
5.000E-04
5.000E-04
5.000E-04
3.800E-02
5.000E-04
5.000E-04
5.000E-04
5.000E-04
1.100E-01
5.000E-04
5.000E-04
5.000E-04
5.000E-04
2.000E-02
4.000E-02
5.000E-04
2.400E-03
5.000E-04
5.000E-04
5.000E-04
2787.1
2787.1
2787.1
1888.1
1888.1
2311.9
2311.9
2311.9
2812.2
2304.7
1620.8
2254.6
1576.5
986.9
425.7
208.8
484.6
2828.7
320.4
3335.6
1073.4
1073.4
1073.4
1626.7
1561.6
1561.6
-100.0
-100.0
410.6
410.6
953.2
247.3
247.3
791.0
2852.9
2852.9
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.51E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
7.768E-05
5.676E-04
7.469E-06
8.116E-03
3.739E-03
1 .652E-03
1.132E-03
3.670E-04
3.667E-04
1 .546E-05
8.892E-03
4.169E-04
2.597E-03
4.401 E-03
7.366E-03
3.855E-04
2.735E-04
1 .478E-05
3.355E-04
5.974E-04
1.314E-04
1.314E-04
1.314E-04
6.599E-05
1 .574E-02
7.155E-05
5.663E-04
5.663E-04
2.999E-04
1 .200E-02
1.221E-02
3.675E-04
1 .764E-03
1 .868E-04
1 .434E-05
1 .434E-05
Yes
No
Yes
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 4 of 5
-------
Project: Long Prairie site
Location: Todd County
Meng
State: Minnesota
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TETRACHLOROETHYLENE(PCE)
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
October 2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
10/14/2002
MW-2A
MW-2B
MW-2C
MW-3A
MW-3B
MW-4A
MW-4B
MW-4C
MW-5A
MW-5B
MW-6A
MW-6B
MW-6C
RW-1A
RW-1B
RW-1C
RW-3
RW-4
RW-5
RW-6
RW-7
RW-8
RW-9
9.700E-04
4.100E-02
6.900E-03
5.000E-04
5.000E-04
3.300E-02
7.700E-02
5.100E-02
5.000E-04
5.000E-04
5.000E-04
4.700E-03
2.500E-02
1 .OOOE-03
5.000E-04
5.000E-04
2.000E-02
5.000E-04
8.400E-02
9.500E-03
2.200E-02
1 .800E-02
1 .500E-02
2787.1
2787.1
2787.1
3041.4
3041.4
1888.1
1888.1
1888.1
1381.9
1381.9
2311.9
2311.9
2311.9
3358.7
3299.4
3302.5
2812.2
2304.7
1620.8
2254.6
1576.5
986.9
425.7
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
-1.24E-03
3.019E-05
1 .276E-03
2.148E-04
1.134E-05
1.134E-05
3.145E-03
7.339E-03
4.861 E-03
8.949E-05
8.949E-05
2.812E-05
2.643E-04
1 .406E-03
1 .528E-05
8.223E-06
8.192E-06
6.033E-04
2.837E-05
1.117E-02
5.737E-04
3.091 E-03
5.268E-03
8.829E-03
Yes
No
Yes
Yes
Yes
No
No
No
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Note: Projected Concentrations that are below the user-specified detection limit are indicated by a check mark to its right; for sampling events
with less than 3 selected plume centerline wells, NO projected concentrations are calculated because no regression coefficient is available.
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 5 of 5
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MAROS Risk-Based Power Analysis for Site Cleanup
Project: Long Prairie site
Todd County
Meng
Minnesota
Parameters:
Groundwater Flow Direction: 90 degrees Distance to Receptor: -100 feet
From Period: May 1999 to October 2002
5/20/1999
Selected Plume
Centerline Wells:
10/14/2002
Well
MW-15B
MW-16B
MW-1 7B
MW-4B
MW-2B
The distance is
from the well to
Distance to Receptor (feet)
-100.0
410.6
953.2
1888.1
2787.1
measured in the Groundwater Flow Angle
the compliance boundary.
Normal Distribution Assumption Lognormal Distribution Assumption
Sample Event
Sample Sample
Szie Mean
Sample
Stdev.
TETRACHLOROETHYLENE(PCE)
May 1999
October 1999
September 2000
September 2001
October 2002
30
25
18
18
34
1 J3E-03
1 .38E-04
3.71 E-03
2.97E-03
1 .90E-03
4.35E-03
2.79E-04
6.36E-03
5.74E-03
3.95E-03
Cleanup
Status
Expected Celanup
Power Samp|e Size status
Expected
Power Sample Size
Alpha Expected
Level Power
Cleanup Goal = 0.005
Attained
Attained
Not Attained
Not Attained
Attained
0.992
1.000
0.211
0.432
0.998
12
<=3
>100
50
11
Not Attained
Not Attained
Not Attained
Not Attained
Attained
S/E
S/E
S/E
S/E
0.591
S/E
S/E
S/E
S/E
60
0.05
0.05
0.05
0.05
0.05
0.8
0.8
0.8
0.8
0.8
Note: #N/C means "not conducted" due to a small sample size (N<4) or that the mean concentration is much greater than the cleanup
level; Sample Size is the number of sampling locations used in the power analysis; Expected Sample Size is the number of concentration
data needed to reach the Expected Power undercurrent sample variability.
MAROS Version 2, 2002, AFCEE
Wednesday, February 12, 2003
Page 1 of 1
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DRAFT FINAL
THREE-TIERED
GROUND WATER MONITORING NETWORK
OPTIMIZATION EVALUATION
FOR
LONG PRAIRIE GROUND WATER CONTAMINATION
SUPERFUND SITE, MINNESOTA
Prepared for
US Environmental Protection Agency
MAY 2003
Denver, Colorado
-------
EXECUTIVE SUMMARY
This report presents a description and evaluation of the groundwater monitoring
program associated with the Long Prairie Ground Water Contamination Superfund Site in
Long Prairie, Minnesota. Groundwater at the site was contaminated by release of dry-
cleaning solvents into the primary drinking water aquifer. The monitoring program at
this site was evaluated to identify potential opportunities to streamline monitoring
activities while still maintaining an effective monitoring network. This evaluation is
being conducted as part of an independent assessment of monitoring network
optimization (MNO) methods by the US Environmental Protection Agency (USEPA) and
the Air Force Center for Environmental Excellence (AFCEE).
Objectives
Groundwater monitoring programs have two primary objectives (USEPA, 1994;
Gibbons, 1994):
1. Evaluate long-term temporal trends in contaminant concentrations (temporal
objective)', and
2. Evaluate the extent to which contaminant migration is occurring (spatial
objective).
The relative success of any remediation system (including the monitoring network) is
judged based on the degree to which it achieves the stated objectives of the system.
Designing an effective groundwater monitoring program involves locating monitoring
points and developing a site-specific strategy for groundwater sampling and analysis that
maximizes the amount of relevant information that can be obtained while minimizing
incremental costs. The effectiveness of a monitoring network in achieving the two
primary monitoring objectives can be evaluated quantitatively using statistical
techniques. Qualitative evaluation also is important to allow consideration of such
factors as hydrostratigraphy, locations of potential receptor exposure points with respect
to a dissolved contaminant plume, and the direction(s) and rate(s) of contaminant
migration.
The general objective of the project was to optimize the long-term groundwater
monitoring network at the Long Prairie site by applying a three-tiered MNO approach to
assess the degree to which the monitoring network addresses each of the two primary
objectives of monitoring listed above and other important considerations. The three-
ES-1
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tiered MNO evaluation described in this report examines the 44 wells included in the
Long Prairie monitoring network. The specific objectives of the project were as follow:
« Apply a qualitative methodology that considers factors such as
hydrostratigraphy, locations of potential receptors with respect to the dissolved
plume, and the direction(s) and rate(s) of contaminant migration, to establish the
frequency at which monitoring should be conducted, and if each well should be
retained in or removed from the monitoring program.
« Conduct a Mann-Kendall statistical analysis to determine the temporal trends of
contaminants of concern (COCs) over time, and apply an algorithm to determine
the relevance of the trends within the monitoring network.
. Determine the relative amount of spatial information contributed by each
monitoring well by performing a spatial statistical analysis utilizing kriging error
predictions.
« Combine and evaluate the results of the three analyses to establish the frequency
at which monitoring should be conducted, as well as the optimal number and
locations of wells in the monitoring network.
Current Monitoring Program
The purposes of groundwater monitoring at Long Prairie are 1) to monitor progress
toward achieving the remedial action objectives set forth in the Record of Decision (as
amended), and 2) to gather adequate information to determine the status and effectiveness
of the groundwater extraction and treatment system. Wells are classified as recovery
(i.e., extraction) wells, monitoring wells, and municipal water-supply wells. The
purposes of the wells included in groundwater monitoring program are to:
« Evaluate the effectiveness of the groundwater recovery system on controlling the
plume and improving regional groundwater quality
. Confirm protection of the city of Long Prairie water supply wells (Barr, 2002).
The most recent monitoring event, conducted during October 2002, involved sampling
of 44 wells in the Long Prairie contaminant plume area, including 10 recovery wells, 2
municipal water-supply wells, and 32 monitoring wells. Several of the monitoring wells
are installed as clusters at a single location, but screened at different depths. The "A"
wells are screened across the water table, the "B" wells are screened at the based of the
upper glacial outwash deposits, and the "C" wells are screened in the lower outwash
deposits. Typically, about half of the wells sampled during the most recent monitoring
event are routinely sampled as part of the groundwater monitoring program; for example,
in the 2001 and 2000 sampling rounds, a subset of 26 of the 44 wells was sampled. This
subset included quarterly sampling of the 6 active extraction wells (RW3 through RW9,
ES-2
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excluding RW4) and city well CW3, and annual sampling of 19 monitoring wells
(including RW4). In 2002, city well CW6 was added to the quarterly sampling schedule.
The "current" sampling plan includes the 25 extraction and monitoring wells sampled
during scheduled 2000 and 2001 monitoring events, plus city wells CW3 and CW6. The
three-tiered MNO evaluation in described in this report was used to examine the 44-well
network monitored during the October 2002 sampling event and to develop optimization
recommendations. The resulting optimized well network is then compared to the
currently monitored 27-well network.
Optimization Findings
The Long Prairie groundwater monitoring program was evaluated using results for
sampling events performed from May 1996 through October 2002. The analytical
database provided to Parsons contained from 1 to 29 sampling results for each constituent
for each of the 44 wells in the Long Prairie region. The primary COCs identified for the
Long Prairie plume are PCE, TCE, and cis-l,2-DCE. PCE is the primary COC because it
has been detected at the highest concentrations and has the broadest distribution in
groundwater at the site. PCE sampling results were used to conduct the spatial
component of the three-tiered MNO evaluation.
Results from the three-tiered MNO evaluation of the 2002 program for Long Prairie
indicate that 18 of the 44 wells could be removed from the groundwater monitoring
program with little loss of information. Based on these recommendations, the "current"
sampling plan (the monitoring and extraction wells included in the 2000 and 2001
sampling schedules, plus CW3 and CW6), could be optimized by removing 4 of the 27
active monitoring wells, and adding 3 additional area wells. A refined monitoring
program, consisting of 26 wells (2 to be sampled quarterly, 6 to be sampled semi-
annually, 14 to be sampled annually, and 4 to be sampled biennially) would be adequate
to address the two primary objectives of monitoring. This refined monitoring network
would result in an average 36 sampling events per year, compared to 51 events per year
under the current monitoring program. Implementing these recommendations for
optimizing the LTM monitoring program at Long Prairie could reduce current LTM
annual monitoring by over 29 percent. Additionally, based on a per well sampling cost
ranging from$l 00 to $280, these recommendations could reduce costs by an average of
$1500 to $4,200 per year.
ES-3
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TABLE OF CONTENTS
Page
ACRONYMS AND ABBREVIATIONS iv
EXECUTIVE SUMMARY ES-1
SECTION 1 -INTRODUCTION 1-1
SECTION 2 - SITE BACKGROUND INFORMATION 2-1
2.1 Site Description 2-1
2.2 Geology and Hydrogeology 2-3
2.3 Nature and Extent of Contamination 2-6
2.4 Remedial Systems 2-7
SECTION 3 - LONG-TERM MONITORING PROGRAM AT LONG
PRAIRIE 3-1
3.1 Description of Monitoring Program 3-1
3.2 Summary of Analytical Data 3-2
SECTION 4 - QUALITATIVE MNO EVALUATION 4-1
4.1 Methodology for Qualitative Evaluation of Monitoring Network 4-2
4.2 Results of Qualitative MNO Evaluation 4-3
4.2.1 Monitoring Network and Sampling Frequency 4-3
4.2.1.1 Extraction Wells 4-4
4.2.1.2 Municipal Water-Supply Wells 4-6
4.2.1.3 Monitoring Wells 4-7
4.2.2 Laboratory Analytical Program 4-8
4.2.3 LTM Program Flexibility 4-8
-i-
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TABLE OF CONTENTS (Continued)
Page
SECTION 5 - TEMPORAL STATISTICAL EVALUATION 5-1
5.1 Methodology for Temporal Trend Analysis of Contaminant
Concentrations 5-1
5.2 Temporal Evaluation Results 5-7
SECTION 6 - SPATIAL STATISTICAL EVALUATION 6-1
6.1 Geostatistical methods for evaluating Monitoring networks 6-1
6.2 Spatial Evaluation of Monitoring Network at Long Prairie 6-4
6.3 Spatial Statistical Evaluation Results 6-10
6.3.1 Kriging Ranking Results 6-10
SECTION 7 - SUMMARY OF THREE-TIERED MONITORING
NETWORK EVALUATION 7-1
SECTION 8 - REFERENCES 8-1
LIST OF TABLES
No. Title Page
3.1 Groundwater monitoring Program 3-3
3.2 Summary of Occurrence of Groundwater Contaminants of Concern 3-5
4.1 Monitoring Network Optimization Decision Logic 4-3
4.2 Monitoring Frequency Decision Logic 4-4
4.3 Qualitative Evaluation of Groundwater Monitoring Network 4-5
5.1 Results of Temporal Trend Analysis of COC Concentrations in Current
Groundwater Monitoring Network 5-8
6.1 Results of geostatistical Evaluation Ranking Wells by Relative Value of
Information 6-6
7.1 Summary of Evaluation of Current Groundwater Monitoring Program 7-4
-11-
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TABLE OF CONTENTS (CONTINUED)
LIST OF FIGURES
No. Title Page
2.1 Current Monitoring Well Network and Sampling Frequencies 2-5
5.1 PCE Concentrations Through Time at Well MW6B 5-2
5.2 Conceptual Representation of Temporal Trends and Temporal Variation
in Concentrations 5-3
5.3 Conceptual Representation of Continued Monitoring at Location Where
No Temporal Trend in Concentration is Present 5-6
5.4 Mann-Kendall Temporal Trend Analysis for Concentrations of PCE 5-11
5.5 Temporal Chemical Concentration Trend Decision Rationale Flowchart 5-12
6.1 Idealized Semvariogram Model 6-3
6.2 Idealized Semivariogram Model 6-9
6.3 Results of Geostatistical Analysis Showing Relative Value of Spatial
Information of PCE Distribution in Select Wells 6-11
-111-
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ACRONYMS AND ABBREVIATIONS
AFCEE
ASCE
bgs
CAH
BSD
coc
DCE
ESRI
GAC
Gpm
GIS
LTM
ug/L
MCL
MCPA
MDH
MNO
NPL
O&M
OU
PCE
PQL
RAO
ROD
SVE
TCE
USEPA
vc
voc
Air Force Center for Environmental Excellence
American Society of Chemical Engineers
below ground surface
chlorinated aliphatic hydrocarbon
explanation of significant difference
contaminant of concern
dichloroethene
Environmental Systems Research Institute, Inc.
granular activated carbon
gallons per minute
geographical information system
long-term monitoring
microgram(s) per liter
maximum contaminant level
Minnesota Pollution Control Agency
Minnesota Department of Health
monitoring network optimization
National Priorities List
operations and maintenance
operable unit
tetrachloroethene
practical quantitation limit
remedial action objective
Record of Decision
soil vapor extraction
trichloroethene
United States Environmental Protection Agency
vinyl chloride
volatile organic compound
-IV-
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SECTION 1
INTRODUCTION
Groundwater monitoring programs have two primary objectives (U.S. Environmental
Protection Agency [USEPA], 1994; Gibbons, 1994):
1. Evaluate long-term temporal trends in 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
2. Evaluate the extent to which contaminant migration is occurring, particularly
if a potential exposure point for a susceptible receptor exists (spatial
objective).
The relative success of any remediation system and its components (including the
monitoring network) must be judged based on the degree to which it achieves the stated
objectives of the system. Designing an effective groundwater monitoring program
involves locating monitoring points and developing a site-specific strategy for
groundwater sampling and analysis so as to maximize the amount of relevant information
that can be obtained while minimizing incremental costs. Relevant information is that
required to effectively address the temporal and spatial objectives of monitoring. The
effectiveness of a monitoring network in achieving these two primary objectives can be
evaluated quantitatively using statistical techniques. In addition, there may be other
important considerations associated with a particular monitoring network that are most
appropriately addressed through a qualitative assessment of the network. The qualitative
evaluation may consider such factors as hydrostratigraphy, locations of potential receptor
exposure points with respect to a dissolved contaminant plume, and the direction(s) and
rate(s) of contaminant migration.
1-1
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This report presents a description and evaluation of the groundwater monitoring
program associated with the Long Prairie Ground Water Contamination Superfund Site
(Long Prairie), Minnesota. A 44-well monitoring network was evaluated to identify
potential opportunities to streamline monitoring activities while still maintaining an
effective monitoring program. This evaluation is being conducted as part of an
independent assessment of monitoring network optimization (MNO) methods by the
USEPA and the Air Force Center for Environmental Excellence (AFCEE). A three-
tiered approach, consisting of a qualitative evaluation, an evaluation of temporal trends in
contaminant concentrations, and a statistical spatial analysis, was conducted to assess the
degree to which the monitoring network addresses each of the two primary objectives of
monitoring, and other important considerations. The results of the three evaluations were
combined and used to assess the optimal frequency of monitoring and the spatial
distribution of the components of the monitoring network. The results of the analysis
were then used to develop recommendations for optimizing the monitoring program at
Long Prairie.
1-2
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SECTION 2
SITE BACKGROUND INFORMATION
The location, operational history, geology, and hydrogeology of Long Prairie are
briefly described in the following subsections.
2.1 SITE DESCRIPTION
The city of Long Prairie, Minnesota is a small farming community of fewer than 5,000
residents, and is located in Todd County in central Minnesota, about 120 miles northwest
of Minneapolis/St. Paul. The Long Prairie site comprises a 0.16-acre source area of
contaminated soil and an elongate plume of dissolved chlorinated aliphatic hydrocarbons
(CAHs) in the drinking-water aquifer underlying the north-central part of the city of Long
Prairie in central Minnesota. The source of the groundwater contamination was a dry-
cleaning establishment, formerly located in the city's commercial district at 243 Central
Street in the city of Long Prairie, which operated from 1949 through 1984. The
contamination resulted from the discharge of spent dry-cleaning solvents, primarily
tetrachloroethene (PCE), into the subsurface via a shallow, makeshift "french drain."
The contaminated soils served as a source of groundwater contamination, and a dissolved
CAH plume has migrated northward at least 3,600 feet from the source area, extending
beneath an older residential neighborhood and to within 500 feet of the Long Prairie
River.
The contamination was discovered in 1983, during a survey of municipal drinking-
water-supply wells for synthetic organic contaminants. PCE and other CAHs, including
trichloroethene (TCE) and czs-l,2-dichloroethene (DCE), were detected in two (CW4 and
CW5) of the five Long Prairie municipal water-supply wells, which are screened in the
lower unit of the Long Prairie Sand Plain aquifer. Eight of 21 residential wells sampled
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also were contaminated. Because the detected concentrations of CAHs exceeded federal
maximum contaminant levels (MCLs) or other risk-based levels, the Minnesota
Department of Health (MDH) recommended that the two affected city wells be removed
from service, and issued a health advisory for a 15-block area in the northern part of the
city. The Minnesota Pollution Control Agency (MCPA) ordered that bottled water be
supplied for the 350 residents on city water or private wells in the advisory area. A new
municipal well (CW6) was installed in the deeper outwash deposits northeast of (outside)
the contaminant plume in 1984.
After enforcement activities failed to identify any viable potentially responsible parties
from among the three owners of the dry-cleaning property, a Multi-Site Cooperative
Agreement was signed on September 4, 1984 between MPCA and the US Environmental
Protection Agency (USEPA) to implement a remedial investigation and feasibility study.
Based on the results of the RI/FS, the Long Prairie Groundwater Contamination Site was
promulgated to the National Priorities List (NPL) in 1985, and a Record of Decision
(ROD) was signed in 1988.
The ROD and subsequent explanations of significant difference (ESDs) identify the
following remedial action objectives (RAOs) for groundwater:
• Provide a safe drinking-water supply for current and future users of the Long
Prairie San Plain aquifer by
- Restoring the aquifer by reducing the major contaminant (PCE) concentrations
to a health-based concentration of 5 micrograms per liter (ug/L) or less,
- Providing an alternate water supply to persons using the contaminated part of
the aquifer; and
- Reducing soil PCE concentrations to 1,200 micrograms per kilogram or less to
maintain an acceptable groundwater risk level (< 1x10"6) due to PCE leaching
from soils;
2-2
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• Prevent the spread of contaminated groundwater to wells currently unaffected by
the CAH contamination, including municipal well CW6; and
• Prevent adverse effects on aquatic organisms in Long Prairie River due to
implementation of remedial actions by obtaining a PCE concentration of 5 ug/L or
less in treatment system effluent discharged to the river.
Pursuant to achieving these RAOs, the ROD identified three operable units (OUs).
OU1 addresses groundwater contamination through extraction of CAH-contaminated
groundwater via nine extraction wells (RWs 1 through 9), treatment of the extracted
water using granular activated carbon (GAC) filtration, and discharge of treated water to
the Long Prairie River. Installation of the pump-and-treat system, with a 250-gallon-per-
minute (gpm) treatment capacity, was completed in August 1997, and is intended to
restore aquifer quality to MCLs, and to prevent further migration and discharge of the
CAH plume to the Long Prairie River. The source area soils were addressed through soil
vapor extraction (SVE) under OU2, which operated from June 1997 through 1999, when
it was decommissioned. OU3 comprises an alternative water supply system, which
provided municipal water hookups to residents with private wells within the health-
advisory area; these hookups were completed in 1996.
The OU1 groundwater remedial system performance is monitored through quarterly to
annual sampling of a series of monitoring and city wells, and operation and maintenance
(O&M) monitoring of the extraction and treatment systems. The monitoring program is
fully described in Section 3.
2.2 GEOLOGY AND HYDROGEOLOGY
The city of Long Prairie is situated at an elevation of 1,300 feet above mean sea level
(amsl) on the eastern bank of the Long Prairie River. The sediments underlying the city
consist of a series of glacial till and outwash deposits nearly 700 feet thick, deposited in a
large valley along the Long Prairie River. Outwash sediments in the valley are composed
of coarse sands and gravels of deposited during two events, which are separated by finer
2-3
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grained tills. The lower outwash is incised into the lower till, which forms the base of
that water-bearing unit. The lower outwash appears to extend to the east of the river, but
does not appear to be present west of the river. Above the lower outwash is a younger till
(the upper Wadena till). The surficial upper outwash unit incises into the lower outwash
unit through the Wadena till just east of the Long Prairie River, where the outwash
deposits form a single hydrogeologic unit. However, the till is intact along the eastern
side of the outwash valley, and where present, acts as an aquitard between the two
outwash units. The upper outwash unit pinches out at the eastern edge of the glacial
valley (Barr, 2002). The aquifer is recharged by inflow from upgradient lakes and
precipitation. Because of the high transmissivity of the outwash deposits, and the fact
that the river is "under-fit" in the much larger underlying glacial valley, the influence of
the Long Prairie River on groundwater flow in the site area may limited.
At the Long Prairie site, the solvent release occurred in an area in which the upper till
is present between the upper and lower outwash units. However, the saturated thickness
of the upper outwash at the source area (well MW10A) is only about 10 feet (Barr, 2002).
The till pinches out just north of the source area, and the CAH plume in groundwater is
present in both the shallow and deeper portions of the outwash deposits in the incised
channel west of the western edge of the upper till. However, because the upper and lower
outwash units are in direct hydraulic communication where the confining till is absent,
there is a pathway for contaminant migration to be drawn into the city wells screened in
the lower outwash unit to the east. The upper till is present east of the longitudinal axis
of the north/south-trending CAH plume; municipal wells CW3 and CW6 are screened in
the lower outwash deposits below the upper till.
Where the upper till unit is absent (along the incised upper outwash channel),
groundwater in the outwash aquifer occurs under water-table (unconfined) conditions.
Groundwater flow directions in the upper and lower outwash deposits generally parallel
the channel of the Long Prairie River; from the PCE source area, groundwater flows
northeast to the vicinity of extraction wells RW5 and RW7, then flows west-northwest
toward the Long Prairie River (Figure 2.1). In September 2001, the water table was
2-4
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measured at elevations ranging from about 1,287 feet amsl near the source area at the
south end of the CAH plume, to 1,282 feet amsl at the northern end of the plume
(MW15A) (Barr, 2002). The average hydraulic gradient along the plume flowpath (i.e.,
between MW10A in the source area and MW16A near the plume toe) in the upper
outwash aquifer in September 2001 was approximately 0.0012 foot per foot. Using this
gradient, the calibrated hydraulic conductivity value of 426 feet per day (ft/day) used in
the MODFLOW model constructed for the site (Bangsund, 2003), and an estimated
effective porosity for sand and gravel of 0.30, the average advective groundwater flow
velocity in the upper outwash aquifer is estimated to be 1.7 ft/day. The vertical gradients
in the incised channel deposits are negligible, but appear to be slightly downward at the
northern extent of the CAH plume, suggesting that the plume may not directly threaten
wetlands in that area (Barr, 2002).
Where the upper till is present, groundwater in the lower outwash deposits is under
confined to semi-confined conditions, with a generally northwesterly groundwater flow
direction. Hydraulic properties of the lower glacial outwash deposits are inferred to be
comparable to those of the upper outwash deposits. Groundwater flow directions are
influenced locally by pumping of the city water-supply wells, as well as by operation of
the OU1 extraction wells.
2.3 NATURE AND EXTENT OF CONTAMINATION
The source of contamination at the Long Prairie site was discharge of dry-cleaning
solvents directly into glacial outwash deposits at the site of the former dry cleaning
establishment. Available records indicate that 2,200 gallons of PCE were used during the
period of operation from 1949 through 1984. While PCE was the primary solvent
disposed of, trace amounts of chlorinated ethanes also have been detected. The waste
solvents percolated through the coarse outwash soils at the source to the water table in the
Long Prairie Sand Plain aquifer, and subsequently migrated as dissolved constituents in
groundwater. PCE and its daughter products TCE and cis-1,2-DCE have been detected in
a plume about 1,000 feet wide and up to 3,600 feet long. In October 2002, the CAH
plume extended from the source area, near the inactive RW1A/1B/1C extraction well
2-6
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cluster, approximately 3,200 feet downgradient to the northwest, to nested monitoring
well pair MW18A/B (Figure 2.1).
Contamination has been detected throughout the saturated thickness of the upper
glacial outwash deposits, and also historically has been detected in the lower outwash
deposits beneath the upper till at city well CW3 (Figure 2.1). Maximum historical
concentrations of PCE in groundwater were as high as 150,000 ug/L. Recent monitoring
data indicate that maximum PCE concentrations have decreased to around 100 ug/L in
the core of the plume, and that PCE is no longer present at detectable concentrations in
the lower outwash deposits east of the incised channel. However, contamination persists
throughout the saturated upper outwash deposits within the incised channel (along the
centerline of the plume), and the overall extent of the plume, as defined by the 5-ug/L
isolpleth for PCE, has not changed significantly since pumping began (Barr, 2002). In
October 2002, PCE concentrations in the plume ranged from 2.4 ug/L at the northern end
of the plume (well MW18B) to 110 ug/L near the center of the plume, at well MW14B
(Figure 2.1). In the shallow portion of the upper outwash deposits in the source area,
PCE was detected at 38 ug/L at well MW10A.
2.4 REMEDIAL SYSTEMS
As discussed in Section 2.1, an SVE system (OU2) was installed to remove PCE from
vadose-zone soils in the source area. The SVE system was pilot tested and completed in
June 1997, and operated continuously through the end of 1999, when it was disassembled
due to the low magnitude of extracted PCE concentrations.
The OU1 groundwater extraction system consists of 10 recovery wells, 6 of which are
currently operational. The RAOs for the OU1 groundwater extraction and treatment
system include restoring aquifer quality to MCLs, and preventing further migration and
discharge of the CAH plume to the Long Prairie River (Section 2.1). Initially, seven
recovery wells (RW1A/1B/1C/3/4/6/7) were installed, and closed municipal well CW5
was retrofitted to become RW5. This eight-well extraction system began operating in
May 1996. Two additional recovery wells (RW8 and 9) were installed in September
2-7
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1999. The recovery wells are distributed along the axis of the plume, and the system is
designed to recover and treat up to 250 gpm of groundwater. According to the First Five-
Year Review Report (MPCA, 2002), operation of 4 of the 10 recovery wells (RW1A,
RW1B, RW1C, and RW4) was discontinued starting in 2000 to allow for higher pumping
rates at wells closer to the center of the plume. Recovered groundwater is discharged to
the Long Prairie River following treatment with GAC.
2-8
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SECTION 3
LONG-TERM MONITORING PROGRAM AT LONG PRAIRIE
The current groundwater monitoring program at Long Prairie and the 44 wells
sampled during the comprehensive October 2002 monitoring event were examined to
identify potential opportunities for streamlining monitoring activities while still
maintaining an effective performance and compliance monitoring program. The 2002
and the current (2000/2001) monitoring programs at Long Prairie are reviewed in the
following subsections.
3.1 DESCRIPTION OF MONITORING PROGRAM
The purposes of monitoring at Long Prairie are to monitor progress toward achieving
the RAOs set forth in the ROD and ESDs, and to gather adequate information to
determine the effectiveness of the groundwater recovery and treatment system. Wells are
classified as recovery (extraction) wells, monitoring wells, and city wells. The
monitoring program is designed to:
« Evaluate the effectiveness of the groundwater extraction system on controlling the
plume and improving regional groundwater quality
. Confirm protection of the city of Long Prairie water-supply wells. (Barr, 2002)
The October 2002 sampling event included 44 wells in the Long Prairie region.
Several of the monitoring wells are installed as clusters at a single location, and screened
at different depths. The "A" wells are screened across the water table, the "B" wells are
screened at the based of the upper glacial outwash deposits, and the "C" wells are
screened in the lower outwash deposits. Typically, about half of the wells sampled
during the most recent monitoring event are routinely sampled as part of the groundwater
3-1
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monitoring program; for example, in the 2001 and 2000 sampling rounds, a subset of 26
of the 44 wells was sampled. This subset included quarterly sampling of the 6 active
extraction wells (RW3 through RW9, excluding RW4) and city well CW3, and annual
sampling of 19 monitoring wells (including RW4). In 2002, city well CW6 was added to
the quarterly sampling schedule. In the second quarter of 2000, the suite of volatile
organic compounds (VOCs) for which groundwater samples were analyzed was reduced
from the MDH 465E list to the COCs: DCE, PCE, TCE, and vinyl chloride (VC).
Additionally, a gas chromatograph (GC) analytical method (assumed to be USEPA
Method 802IB) is now used instead of the gas chromatograph/mass spectrometer
(GC/MS) method (assumed to be USEPA Method SW8260B) formerly required.
The locations of 44 Long Prairie wells sampled in October 2002 are shown in relation
to the COC plume on Figure 2.1. Table 3.1 lists these wells with their screened intervals,
well type, and designation. The "current" sampling plan includes the 18 monitoring
wells and 7 recovery wells sampled during scheduled monitoring events in 2000 and
2001, as well as city wells CW3 and CW6. The three-tiered MNO evaluation in
described in this report examines the 44 wells sampled during October 2002, and
compared the recommended optimized well network to the 27-well network included in
the "current" monitoring program.
3.2 SUMMARY OF ANALYTICAL DATA
The Long Prairie groundwater monitoring program was evaluated using results for
sampling events performed from May 1996 through October 2002. These analytical data
were provided to Parsons by Ms. Jonelle Branca, the Data Management Coordinator for
Barr Engineering (MPCA's environmental contractor). The database was processed to
remove duplicate data by retaining the maximum result for each duplicate sample pair.
The analytical database provided to Parsons contained from 1 to 29 sampling results for
each constituent for each of the 44 wells in the Long Prairie site area. As discussed in
Section 2.3, the primary COCs identified for the Long Prairie plume are PCE, TCE, and
cis-l,2-DCE, therefore, the MNO evaluation focused on these constituents.
3-2
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TABLE 3.1
GROUNDWATER MONITORING PROGRAM
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
Well ID
Screened Interval
(ft bgs) *
Sampling
Frequency
Monitoring Wells in 2000/2001 Sampling Plan
MW2A
MW2B
MW2C
MW4B
MW4C
MW6A
MW6B
MW6C
MW10A
MW11B
MW11C
MW14B
MW14C
MW15A
MW15B
MW16B
MW17B
MW19B
15-20
31-35
48-53
31.5-35.5
42.0-46.0
12.0-17
31.0-37
46.0-59
16-21
50-55
21-26
50-55
12
33
25.3
33
26.5
City Water-Supply Wells
CW3
CW6
67-85
53-76
Recovery Wells
RW1A
RW1B
RW1C
RW3
RW4
RW5
RW6
RW7
RW8
RW9
15-30
13-45
12.5-23.5
17-52
10.0-50
41-56
10.0-55.5
15-45
30-40
25-35
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly
Quarterly
NA"'
NA
NA
Quarterly
Annual
Quarterly
Quarterly
Quarterly
Quarterly
Quarterly
Other Area Wells Sampled in October 2002
BAL2B
BAL2C
MW1A
MW1B
MW3A
MW3B
MW4A
MW5A
MW5B
MW11A
MW13C
MW16A
MW18A
MW18B
57-65
40-50
9.5-14.5
30-35
17.8-22.8
30-35
10.0-15
4.5-9.5
31.0-35
50-55
15.5
15.1
35
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
ft bgs = feet below ground surface
Reduced sampling frequency established prior to
022/742479/LongPraiTieTablesPinal.xls/Table3.1
3-3
-------
Table 3.2 presents a summary of the occurrence of the three primary COCs in Long
Prairie groundwater based on the data collected from 33 site and area monitoring wells
during the period from May 1996 through October 2002. The data summarized in Table
3.2 exclude results for the recovery wells (with the exception of inactive extraction well
RW4, which is sampled annually as a monitoring well) and city wells CW3 and CW6.
As indicated in this table and discussed in Section 2.3, PCE is the primary COC based on
its broad distribution at concentrations exceeding its MCL of 5 |u.g/L. PCE has been
detected in approximately 39 percent of groundwater samples, and has exceeded its MCL
in approximately 33 percent of the samples. PCE has been detected in 20 of the 33
monitoring wells in the Long Prairie site area, and has exceeded the MCL at 14 of these
wells. One of PCE's reductive-dechlorination daughter products, cis-l,2-DCE, is another
prevalent compound on site, and has been detected in 44 percent of the collected samples.
However, detected concentrations of cis-l,2-DCE have exceeded the MCL of 70 ug/L in
only about 1 percent of samples. TCE, another PCE daughter product, has been detected
at Long Prairie in approximately 35 percent of samples. Detected concentrations of TCE
have exceeded its MCLs of 5 |ug/L in approximately 21 percent of the samples.
PCE sampling results were the primary data used to conduct the qualitative and spatial
components of the three-tiered MNO evaluation due to the magnitude and spatial extent
of PCE concentrations in groundwater at Long Prairie compared to the other detected
compounds.
3-4
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ROUNDWATER CONTAMI
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RED MONITORING NETWORK
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SECTION 4
QUALITATIVE MNO EVALUATION
An effective groundwater monitoring program will provide information regarding
contaminant plume migration and changes in chemical concentrations through time at
appropriate locations, enabling decision-makers to verify that contaminants are not
endangering potential receptors, and that remediation is occurring at rates sufficient to
achieve RAOs within a reasonable time frame. The design of the monitoring program
should therefore include consideration of existing receptor exposure pathways, as well as
exposure pathways arising from potential future use of the groundwater.
Performance monitoring wells located upgradient, within, and immediately
downgradient from a plume provide a means of evaluating the effectiveness of a
groundwater remedy relative to performance criteria. Long-term monitoring (LTM) of
these wells also provides information about migration of the plume and temporal trends
in chemical concentrations. Groundwater monitoring wells located downgradient from
the leading edge of a plume (i.e., sentry wells) are used to evaluate possible changes in
the extent of the plume and, if warranted, to trigger a contingency response action if
contaminants are detected.
Primary factors to consider when developing a groundwater monitoring program
include at a minimum:
• Aquifer heterogeneity,
• Types of contaminants,
• Distance to potential receptor exposure points,
4-1
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• Groundwater seepage velocity and flow direction(s),
• Potential surface-water impacts, and
• The effects of the remediation system.
These factors will influence the locations and spacing of monitoring points and the
sampling frequency. Typically, the greater the seepage velocity and the shorter the
distance to receptor exposure points, the more frequently groundwater sampling should
be conducted.
One of the most important purposes of LTM is to confirm that the contaminant plume
is behaving as predicted. Graphical and statistical tests can be used to evaluate plume
stability. If a groundwater remediation system or strategy is effective, then over the long
term, groundwater-monitoring data should demonstrate a clear and meaningful
decreasing trend in concentrations at appropriate monitoring points. The current
groundwater monitoring program at Long Prairie was evaluated to identify potential
opportunities to LTM optimization.
4.1 METHODOLOGY FOR QUALITATIVE EVALUATION OF
MONITORING NETWORK
The three-tiered MNO evaluation of the Long Prairie groundwater LTM program
considered information for the 44 wells sampled during the October 2002 sampling
round. These wells, their respective screened intervals, and their current monitoring
frequencies are listed in Table 3.1, and their locations are depicted on Figure 2.1.
Multiple factors were considered in developing recommendations for continuation or
cessation of groundwater monitoring at each well. In some cases, a recommendation was
made to continue monitoring a particular well, but at a reduced frequency. A
recommendation to discontinue monitoring at a particular well based on the information
reviewed does not necessarily constitute a recommendation to physically abandon the
well. A change in site conditions might warrant resumption of monitoring at some time
in the future at wells that are not currently recommended for continued sampling.
4-2
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Typical factors considered in developing recommendations to retain a well in, or remove
a well from, the monitoring program are summarized in Table 4.1. Typical factors
considered in developing recommendations for monitoring frequency are summarized in
Table 4.2.
TABLE 4.1
MONITORING NETWORK OPTIMIZATION DECISION LOGIC
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
Reasons for Retaining a Well in
Monitoring Network
Well is needed to further characterize the
site or monitor changes in contaminant
concentrations through time
Well is important for defining the lateral or
vertical extent of contaminants
Well is needed to monitor water quality at
compliance point or receptor exposure
point (e.g., domestic well)
Well is important for defining background
water quality
Reasons for Removing a Well From
Monitoring Network
Well provides spatially redundant
information with a neighboring well (e.g.,
same constituents, and/or short distance
between wells)
Well has been dry for more than 2 yearsa/
Contaminant concentrations are
consistently below laboratory detection
limits or cleanup goals
Well is completed in same water-bearing
zone as nearby well(s)
a/ Water-level measurements in dry wells should continue,
becomes re-wetted.
and groundwater sampling should be resumed if the well
4.2 RESULTS OF QUALITATIVE MNO EVALUATION
The results of the qualitative evaluation of the 44 wells in the Long Prairie plume
vicinity are described in this subsection. Recommendations for optimizing the well
network are developed, and the basis for each recommendation is provided.
4.2.1 Monitoring Network and Sampling Frequency
The results of the qualitative evaluation of the 18 monitoring wells, 7 extraction wells,
and 2 municipal water-supply wells currently included in the LTM program at the Long
Prairie Superfund Site are included are summarized in Table 4.3, and described in the
following subsections. Other site wells that are not currently monitored on a regular
4-3
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basis (i.e., 3 extraction wells and 14 monitoring wells) also are included in Table 4.3 for
completeness. The table includes recommendations for retaining or removing each well,
and for changing the sampling frequency, and lists the rationale for the recommendations.
TABLE 4.2
MONITORING FREQUENCY DECISION LOGIC
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
Reasons for Increasing
Sampling Frequency
Groundwater velocity is high
Change in contaminant concentration
would significantly alter a decision or
course of action
Well is close to source area or operating
remedial system
Cannot predict if concentrations will
change significantly over time
Reasons for Decreasing
Sampling Frequency
Groundwater velocity is low
Change in contaminant concentration
would not significantly alter a decision or
course of action
Well is distal from source area or remedial
system
Concentrations are not expected to change
significantly over time, or contaminant
levels have been below groundwater
cleanup objectives for some prescribed
period of time
4.2.1.1
Extraction Wells
Six of the 10 groundwater extraction wells at the site (RW3 and RW5 through RW9)
are currently operating and are sampled quarterly. A seventh inactive extraction well
(RW4) is sampled annually, and the remaining three extraction wells (source-area wells
RW1A/1B/1C) are not sampled.
Continued sampling of the six active extraction wells is recommended to facilitate
periodic calculation of contaminant mass-removal rates and assessment of remedial
progress and system optimization needs. However, historical sampling data for these
wells indicate that temporal concentration trends could be adequately determined from
semiannual monitoring. Therefore, reduction in the sampling frequency for these wells
from quarterly to semiannually is recommended. This frequency also should be adequate
4-4
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to achieve the extraction-system performance monitoring objectives stated above. In
addition, reduction in the sampling frequency for inoperative well RW4 from annual to
biennial (every other year) is recommended. This well was sampled 18 times from May
1996 through October 2002. Trace-level (i.e., almost exclusively < 3 ug/L)
concentrations of COCs were detected until late 1997; since then, COCs have for the
most part not been detected. Given this monitoring history, relatively infrequent
monitoring of RW4 is sufficient to define the southwestern boundary of the CAH plume
over time in this area.
Extraction wells RW1A/1B/1C are located in the source area and provide groundwater
quality data for the depth interval ranging from about 10 to 43 feet below the ground
surface. Wells RW1A and IB were sampled regularly from May 1996 through October
1999, and were sampled again in October 2002. Available data indicate that RW1C was
only sampled once (in October 2002). Since May 1996, COCs either have not been
detected in these wells, or have been present at trace concentrations (i.e., < 3 ug/L).
Given that monitoring well MW10A is located immediately adjacent to these extraction
wells, and is screened near the water table, where any fresh influx of contaminants from
the vadose zone would first be detected, continued monitoring of this well should be
sufficient to monitor groundwater quality in the source area. This well has historically
had higher COC concentrations than the adjacent extraction wells.
4.2.1.2 Municipal Water-Supply Wells
Two municipal water-supply wells, CW3 and CW6, currently are sampled on a
quarterly basis. Available data indicate that the CAH plume is not migrating eastward in
the lower outwash deposits toward these wells, and the only COCs detected in samples
from these wells have been very low-magnitude (< 1 ug/L) detections of cis-l,2-DCE in
CW3. This observation is supported by the prevailing groundwater flow direction to the
northwest, and by the lack of significant historical contaminant concentrations at other
lower-outwash wells (i.e., MW3B, MW11C, and BAL2C) along the eastern margin of the
COC plume (Figure 2.1). Therefore, from a purely technical standpoint, quarterly
sampling of CW3 and CW6 is probably not necessary. However, continuation of
4-6
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"goodwill" monitoring at this frequency is recommended to reassure the public about the
safety of the municipal water supply (Table 4.3).
4.2.1.3 Monitoring Wells
Continued sampling of 14 of the 18 monitoring wells included in the current LTM
program is recommended. Wells MW2A, MW2C, MW6A, and MW14C are
recommended for removal from the LTM program. As indicated in Table 4.3, the aquifer
zones monitored by MW2A and MW2C have consistently contained lower COC
concentrations than the zone monitored by clustered well MW2B. Although this is
important information to obtain for initial plume characterization purposes, continued
monitoring of only the highest-concentration zone (well MW2B) is adequate to track the
magnitude of contaminant concentrations within the plume over time. Similarly,
continued monitoring of well MW14B should be adequate to track the plume magnitude
at the location of the 14B/14C well cluster over time, given the consistently low (to non-
detect) COC concentrations at MW14C.
During the first few sampling events for well cluster MW6A/6B/6C, the highest
concentrations of PCE, TCE, and cis-l,2-DCE were detected in groundwater from the
intermediate-depth well (MW6B). More recently, however, COC concentrations at each
of the three wells in this cluster have been similar, suggesting that continued sampling of
each well is not necessary. Continued sampling of MW6C is recommended because
recent samples from this well have contained the highest PCE concentrations. Continued
sampling of MW6B also is recommended because this well is screened in the vertical
interval that has historically contained the most elevated contaminant concentrations.
Removal of MW6A from the LTM program is recommended because the data provided
by sampling of this well does not provide any additional useful information that cannot
be obtained from other shallow wells downgradient from the MW6 cluster (Figure 2.1)..
Less-frequent monitoring is recommended for one monitoring well currently sampled
on an annual basis (MW19B). Retention of this well in the LTM program is
recommended due to its location in the wetland area adjacent to the Long Prairie River (a
4-7
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potentially sensitive ecological receptor exposure area). However, the recommendation
to reduce the monitoring frequency is based on the low magnitude and stable nature of
COC concentrations detected to date. There is no reason to assume that COC
concentrations at this location could increase significantly unless the current pumping
regime is interrupted or altered.
Low-frequency (e.g., biennial) monitoring of two wells that currently are not included
in the LTM program is recommended. Samples from MW13C, which is located adjacent
to a potentially sensitive wetland area, have exhibited an increasing trend for cis-1,2-
DCE. If continued infrequent monitoring of this well does not indicate a continuation of
this trend, then monitoring should be discontinued. Sampling of well MW18B also
enables periodic evaluation of COC concentrations at the edge of the wetland area. In
addition, the location of this well near the downgradient plume toe facilitates continued
evaluation of plume dynamics (i.e., expanding, receding, steady-state).
4.2.2 Laboratory Analytical Program
The 2000/2001 Annual Report (Barr, 2002) indicates that the target analyte list (TAL)
was reduced to DCE, PCE, TCE, and VC in the second quarter of 2000, and that a
relatively inexpensive GC method that provides low detection limits is now used.
Parsons assumes that the presence of other VOCs at concentrations of potential concern
was ruled out based on previous analytical results obtained for a more extensive TAL
(MDH 465E list). If this is the case, then the current laboratory analytical program
appears to be reasonably optimized, and no further recommendations are provided. If
this is not the case, then the potential presence of other VOCs that could have been
present in the PCE as contaminants should be assessed.
4.2.3 LTM Program Flexibility
The LTM program recommendations summarized in Table 4.3 are based on available
data regarding current (and expected future) site conditions. Changing site conditions
(e.g., lengthy malfunction or significant adjustment of the groundwater extraction
system) could affect plume behavior. Therefore, the LTM program should be reviewed if
4-8
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hydraulic conditions change significantly, and revised as necessary to adequately track
changes in plume magnitude and extent over time.
4-9
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SECTION 5
TEMPORAL STATISTICAL EVALUATION
Chemical concentrations measured at different points in time (temporal data) can be
examined graphically, or using statistical tests, to evaluate dissolved-contaminant plume
stability. If removal of chemical mass is occurring in the subsurface as a consequence of
attenuation processes or operation of a remediation system, mass removal will be
apparent as a decrease in chemical concentrations through time at a particular sampling
location, as a decrease in chemical concentrations with increasing distance from chemical
source areas, and/or as a change in the suite of chemicals detected through time or with
increasing migration distance.
5.1 METHODOLOGY FOR TEMPORAL TREND ANALYSIS OF
CONTAMINANT CONCENTRATIONS
Temporal chemical-concentration data can be evaluated for trends by plotting
contaminant concentrations through time for individual monitoring wells (Figure 5.1), or
by plotting contaminant concentrations versus downgradient distance from the
contaminant source for several wells along the groundwater fiowpath, over several
monitoring events. Plotting temporal concentration data is recommended for any analysis
of plume stability (Wiedemeier and Haas, 2000); however, visual identification of trends
in plotted data may be a subjective process, particularly if (as is likely) the concentration
data do not exhibit a uniform trend, but are variable through time (Figure 5.2).
5-1
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FIGURE 5.1
PCE CONCENTRATIONS THROUGH TIME
AT WELL MW6B
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
PCE Concentration (^g/L)
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The possibility of arriving at incorrect conclusions regarding plume stability on the
basis of visual examination of temporal concentration data can be reduced by examining
temporal trends in chemical concentrations using various statistical procedures, including
regression analyses and the Mann-Kendall test for trends. The Mann-Kendall
nonparametric test (Gibbons, 1994) is well-suited for evaluation of environmental data
because the sample size can be small (as few as four data points), no assumptions are
made regarding the underlying statistical distribution of the data, and the test can be
adapted to account for seasonal variations in the data. The Mann-Kendall test statistic
can be calculated at a specified level of confidence to evaluate whether a statistically
significant temporal trend is exhibited by contaminant concentrations detected through
time in samples from an individual well. If a trend is identified, a nonparametric slope of
the trend line (change in concentration per unit time) also can be estimated using the test
5-2
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Trend
Increasing Trend
No Trend
Confidence Factor
HIGH
Confidence Factor
LOW
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HIGH
5.2
OF
IN
Monitoring Network Optimization
Long Prairie, Minnesota
draw\739732\diffusion\williamsA.cdr pg1 nap 4/3/02
-------
procedure. A negative slope (indicating decreasing contaminant concentrations through
time) or a positive slope (increasing concentrations through time) provides statistical
confirmation of temporal trends that may have been identified visually from plotted data
(Figure 5.2).
The relative value of information obtained from periodic monitoring at a particular
monitoring well can be evaluated by considering the location of the well with respect to
the dissolved contaminant plume and potential receptor exposure points, and the presence
or absence of temporal trends in contaminant concentrations in samples collected from
the well. The degree to which the amount and quality of information that can be obtained
at a particular monitoring point serve the two primary (i.e., temporal and spatial)
objectives of monitoring must be considered in this evaluation. For example, the
continued non-detection of a target contaminant in groundwater at a particular monitoring
location provides no information about temporal trends in contaminant concentrations at
that location, or about the extent to which contaminant migration is occurring, unless the
monitoring location lies along a groundwater fiowpath between a contaminant source and
a potential receptor exposure point. Therefore, a monitoring well having a history of
contaminant concentrations below detection limits may be providing little or no useful
information, depending on its location.
A trend of increasing contaminant concentrations in groundwater at a location between
a contaminant source and a potential receptor exposure point may represent information
critical in evaluating whether contaminants are migrating to the exposure point, thereby
completing an exposure pathway. Identification of a trend of decreasing contaminant
concentrations at the same location may be useful in evaluating decreases in the areal
extent of dissolved contaminants, but does not represent information that is critical to the
protection of a potential receptor. Similarly, a trend of decreasing contaminant
concentrations in groundwater near a contaminant source may represent important
information regarding the progress of remediation near, and downgradient from the
source, while identification of a trend of increasing contaminant concentrations at the
same location does not provide as much useful information regarding contaminant
5-4
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conditions. By contrast, the absence of a temporal trend in contaminant concentrations at
a particular location within or downgradient from a plume indicates that virtually no
additional information can be obtained by continued monitoring of groundwater at that
location, in that the results of continued monitoring through time are likely to fall within
the historic range of concentrations that have already been detected (Figure 5.3).
Continued monitoring at locations where no temporal trend in contaminant
concentrations is present serves merely to confirm the results of previous monitoring
activities at that location. The relative amounts of information generated by the results of
temporal-trend evaluation at monitoring points near, upgradient from, and downgradient
from contaminant sources are presented schematically as follow:
Monitoring Point Near Contaminant Source
Relatively less information
Nondetect or no trend
Increasing trend in concentrations
Relatively more information
Decreasing trend in concentrations
Monitoring Point Upgradient from Contaminant Source
Relatively less information
Nondetect or no trend
Relatively more information
Decreasing trend in concentrations
Increasing trend in concentrations
5-5
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c
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Monitoring Network Optimization
Long Prairie, Minnesota
draw\739732\diffusion\williamsA.cdr pg2 nap 4/3/02
-------
Monitoring Point Downgradient from Contaminant Source
Relatively less information Decreasing trend in concentrations
Nondetect or no trend
Relatively more information Increasing trend in concentrations
5.2 TEMPORAL EVALUATION RESULTS
The analytical data for groundwater samples collected from the 44 wells in Long
Prairie LTM program from May 1996 through October 2002 were examined for temporal
trends using the Mann-Kendall test. The objective of the evaluation was to identify those
wells having increasing or decreasing concentration trends for each COC, and to consider
the quality of information represented by the existence or absence of concentration trends
in terms of the location of each monitoring point.
Summary results of Mann-Kendall temporal trend analyses for COCs in groundwater
samples from wells in the PCE plume area are presented in Table 5.1. As implemented,
the algorithm used to evaluate concentration trends assigned a value of "ND" (not
detected) to those wells with sampling results that were consistently below analytical
detection limits through time, rather than assigning a surrogate value corresponding to the
detection limit - a procedure that could generate potentially misleading and anomalous
"trends" in concentrations. The color-coding of the Table 5.1 entries denotes the
presence/absence of temporal trends, and allows those monitoring points having
nondetectable concentrations, decreasing or increasing concentrations, or no discernible
trend in concentrations to be readily identified. The four wells that had fewer than four
analytical results for each of the COCs (wells RW1C, BAL2B, MW4A and MW11A)
5-7
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TABLE 5.1
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could not be analyzed using the Mann-Kendall trend analysis, and have a "<4meas"
designation. Figure 5.4 displays the Mann-Kendall results thematically for PCE by well;
the analytical results for PCE in October 2002 are also presented.
The basis for the decision to remove or retain a well in the monitoring program based
on the value of its temporal information is described in the "Rationale" column of Table
5.1. In general, monitoring wells at which detected chemical concentrations display no
discernible temporal trends (e.g., wells MW2A, MW2C, MW4C, MW6A, MW18B)
represent points generating the least amount of useful information, and can be
recommended for removal from the monitoring network. Monitoring wells that are not
considered "sentry" wells (e.g., wells MW1A, MW1B, MW3B, MW5A, and MW18A) at
which concentrations of COCs consistently have been non-detected or
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FIGURE 5.5
TEMPORAL CHEMICAL
CONCENTRATION TREND
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CHART
Monitoring Network Optimization
Long Prairie. Minnesota
PARSONS
Denver, Colorado
-------
SECTION 6
SPATIAL STATISTICAL EVALUATION
Spatial statistical techniques also can be applied to the design and evaluation of
groundwater monitoring programs to assess the quality of information generated during
monitoring, and to evaluate monitoring networks. Geostatistics, or the Theory of
Regionalized Variables (Clark, 1987; Rock 1988; American Society of Civil Engineers
[ASCE] Task Committee on Geostatistical Techniques in Hydrology, 1990a and 1990b),
is concerned with variables having values dependent on location, and which are
continuous in space, but which vary in a manner too complex for simple mathematical
description. Geostatistics is based on the premise that the differences in values of a
spatial variable depend only on the distances between sampling locations, and the relative
orientations of sampling locations — that is, the values of a variable (e.g., chemical
concentrations) measured at two locations that are spatially "close together" will be more
similar than values of that variable measured at two locations that are "far apart".
6.1 GEOSTATISTICAL METHODS FOR EVALUATING MONITORING
NETWORKS
Ideally, application of geostatistical methods to the results of the groundwater
monitoring program at Long Prairie could be used to estimate COC concentrations at
every point within the dissolved contaminant plume, and also could be used to generate
estimates of the "error," or uncertainty, associated with each estimated concentration
value. Thus, the monitoring program could be optimized by using available information
to identify those areas having the greatest uncertainty associated with the estimated
plume extent and configuration. Conversely, sampling points could be successively
eliminated from simulations, and the resulting uncertainty examined, to evaluate if
significant loss of information (represented by increasing error or uncertainty in
6-1
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estimated chemical concentrations) occurs as the number of sampling locations is
reduced. Repeated application of geostatistical estimating techniques, using tentatively
identified sampling locations, then could be used to generate a sampling program that
would provide an acceptable level of uncertainty regarding the distribution of COCs with
the minimum possible number of samples collected. Furthermore, application of
geostatistical methods can provide unbiased representations of the distribution of COCs
at different locations in the subsurface, enabling the extent of COCs to be evaluated more
precisely.
Fundamental to geostatistics is the concept of semivariance [y(h)], which is a measure
of the spatial dependence between samples (e.g., chemical concentrations) in a specified
direction. Semivariance is defined for a constant spacing between samples (h) by:
1 2
y(h) = — zL[g(x) - g(x + h) ] Equation 6-1
2n
Where:
y(h) = semivariance calculated for all samples at a distance h from each other;
g(x) = value of the variable in sample at location x;
g(x + h) = value of the variable in sample at a distance h from sample at location x;
and
« = number of samples in which the variable has been determined.
Semivariograms (plots of y(h) versus h) are a means of depicting graphically the range
of distances over which, and the degree to which, sample values at a given point are
related to sample values at adjacent, or nearby, points, and conversely, indicate how close
together sample points must be for a value determined at one point to be useful in
predicting unknown values at other points. For h = 0, for example, a sample is being
compared with itself, so normally y(0) = 0 (the semivariance at a spacing of zero, is
6-2
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FIGURE 6.1
IDEALIZED SEMIVARIOGRAM MODEL
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
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Distance (ft)
zero), except where a so-called nugget effect is present (Figure 6.1), which implies that
sample values are highly variable at distances less than the sampling interval. As the
distance between samples increases, sample values become less and less closely related,
and the semivariance, therefore, increases, until a "sill" is eventually reached, where y(h)
equals the overall variance (i.e., the variance around the average value). The sill is
reached at a sample spacing called the "range of influence," beyond which sample values
are not related. Only values between points at spacings less than the range of influence
can be predicted; but within that distance, the semivariogram provides the proper
weightings, which apply to sample values separated by different distances.
When a semivariogram is calculated for a variable over an area (e.g., concentrations of
PCE in the groundwater plume at Long Prairie), an irregular spread of points across the
6-3
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semivariogram plot is the usual result (Rock, 1988). One of the most subjective tasks of
geostatistical analysis is to identify a continuous, theoretical semivariogram model that
most closely follows the real data. Fitting a theoretical model to calculated semivariance
points is accomplished by trial-and-error, rather than by a formal statistical procedure
(Davis, 1986; Clark, 1987; Rock, 1988). If a "good" model fit results, then tfh) (the
semivariance) can be confidently estimated for any value of h, and not only at the
sampled points.
6.2 SPATIAL EVALUATION OF MONITORING NETWORK AT LONG
PRAIRIE
PCE was used as the indicator chemical for the spatial evaluation of the groundwater
monitoring network at Long Prairie because this COC has the largest spatial distribution
of measurements that exceeded groundwater MCLs. The most recent (October 2002)
validated analytical data available at the start of this MNO evaluation were used in the
kriging evaluation because a spatial "snapshot" is required in order to conduct the
geospatial statistical analysis.
Of the 44 wells sampled in October 2002, 16 were included in the kriging evaluation.
Although the OU1 extraction wells have historically been used to define the plume
extent, data from extraction wells are not appropriate for use in a kriging analysis because
they represent COC concentrations averaged over the area within the well's capture zone,
and thus are not point specific, nor temporally discrete; the recovery wells are also
typically screened across a longer screening interval than the site monitoring wells.
Similarly, city wells CW3 and CW6 were excluded from the analysis because they also
are pumping wells. Kriging predicts concentrations over a two-dimensional surface and
thus including data from multiple co-located wells screened at different depths is not
appropriate. In this application, the well within each cluster of well with the highest
concentration of PCE was retained for use in the geostatistical evaluation; this
methodology is consistent with the values displayed on plume maps in the Long Prairie
annual reports (Barr, 2001; Barr, 2002). On the whole, of the clustered wells, the "B"
zone wells had the highest October 2002 PCE concentrations and were included in the
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spatial analysis; however, the "C" zone well MW6C was included from the MW6 cluster.
The 16 wells analyzed are shown in Figure 6.3 and listed in Table 6.1.
The commercially available geostatistical software package Geostatistical Analyst™
(an extension to the Arc View® geographic information system [GIS] software package)
(Environmental Systems Research Institute, Inc. [ESRI], 2001) was used to develop a
semivariogram model depicting the spatial variation in PCE concentrations in
groundwater for the 16 Long Prairie area wells.
As semivariogram models were calculated for PCE (Equation 6-1), considerable
scatter of the data was apparent during fitting of the models. Several data
transformations (including a log transformation) were attempted to obtain a
representative semivariogram model. Ultimately, , the concentration data were
transformed to "rank statistics," in which the 16 wells were ranked from 1 to 16 (tie
values were assigned the median rank of the set) according to their October 2002 PCE
concentration. Transformations of this type can be less sensitive to outliers, skewed
distributions, or clustered data than semivariograms based on raw concentration values,
and thus may enable recognition and description of the underlying spatial structure of the
data in cases where ordinary data are too "noisy".
The PCE rank statistics were used to develop a semivariogram that most accurately
modeled the spatial distribution of the data. Anisotrophy was incorporated into the model
to adjust for the directional influence of groundwater to the northeast. The model was
unable to account for the "dog-leg" shift in groundwater flow direction to the northwest
in the northern half of the plume (Figure 2.1). Figure 6.2 shows the semivariogram
model in comparison to the site data. The large amount of scatter in the data due to the
small number of wells and shifting groundwater direction makes it difficult to develop a
representative semivariogram model. Thus, the geostatistical evaluation at this site can
not be as rigorous as at other sites with more wells and more consistent hydrology. The
best-fit semivariogram had the following parameters:
6-5
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TABLE 6.1
RESULTS OF GEOSTATISTICAL EVALUATION RANKING OF WELLS BY
RELATIVE VALUE OF PCE INFORMATION
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
Well ID ^
MW19B
MW13C
MW4B
MW16B
MW6C
MW2B
MW3B
BAL2C
MW18B
MW10A
MW11B
MW15B
MW14B
MW5B
MW17B
MW1B
Kriging Ranking
1
2
3.5
3.5
5.5
5.5
7
8
9.5
9.5
11
12
13
14.5
14.5
16
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Clustered wells included in the spatial analysis are those with the highest
relative October 2002 PCE concentration.
1= least relative amount of information; 16= most relative amount of information.
c Tie values receive the median ranking of the set.
Well in the "intermediate" range; received no recommendation for removal/exclusion or retention/addition
(see Section 6.2).
022/742479/LongPrairieTablesFinal.xls/Table 6.1
6-6
-------
Circular model
Range: 1200 feet
Sill: 17
Nugget: 10
Anisotrophy:
- Minor Range: 400 feet
- Direction: 20 degrees
FIGURE 6.2
LONG PRAIRIE SEMVARIOGRAM MODEL
THREE-TIERED MONITORING NETWORK OPTIMIZATION
LONG PRAIRIE, MINNESOTA
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After this semivariogram model had been developed, it was used in the kriging system
implemented by the Geostatistical Analyst™ software package (ESRI, 2001) to develop
kriging realizations (estimates of the spatial distribution of PCE in groundwater at Long
Prairie), and to calculate the associated kriging prediction standard errors. The median
kriging standard deviation was obtained from the standard errors calculated using the
entire 16-well monitoring network for Long Prairie. Next, each of the 16 wells was
sequentially removed from the network, and for each resulting well network
6-7
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configuration, a kriging realization was completed using the PCE concentration rankings
from the remaining 15 wells. The "missing-well" monitoring network realizations were
used to calculate prediction standard errors, and the median kriging standard deviations
were obtained for each "missing-well" realization and compared with the median kriging
standard deviation for the "base-case" realization (obtained using the complete 16-well
monitoring network), as a means of evaluating the amount of information loss (as
indicated by increases in kriging error) resulting from the use of fewer monitoring points.
Figure 6.3 illustrates the spatial-evaluation procedure by showing kriging prediction
standard-error maps for three kriging realizations. Each map shows the predicted
standard error associated with a given group of wells based on the semivariogram
parameters discussed above. Lighter colors represent areas with lower spatial
uncertainty, and darker colors represent areas with higher uncertainty; regions in the
vicinity of wells (i.e., data points) have the lowest associated uncertainty. Map A on
Figure 6.3 shows the predicted standard error map for the "base-case" realization in
which all 16 wells are included. Map B shows the realization in which well MW13C was
removed from the monitoring network, and Map C shows the realization in which well
MW17B was removed. Figure 6.3 shows that when a well is removed from the network,
the predicted standard error in the vicinity of the missing well increases (as indicated by a
darkening of the shading in the vicinity of that well). If a "removed" (missing) well is in
an area with several other wells (e.g., well MW13C; Map B on Figure 6.2), the predicted
standard error may not increase as much as if a well (e.g., MW17B; Map C) is removed
from an area with fewer surrounding wells. If removal of a particular well from the
monitoring network caused very little change in the resulting median kriging standard
deviation (less than about 1 percent), that well was regarded as contributing only a
limited amount of information to the LTM program. Likewise, if removal of a well from
the monitoring network produced larger increases in the kriging standard deviation, this
was regarded as an indication that the well contributes a relatively greater amount of
information, and is relatively more important to the monitoring network.
6-8
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At the conclusion of the kriging realizations, each well was ranked from 1 (providing the
least information) to 16 (providing the most information), based on the amount of
information (as measured by changes in median kriging standard deviation) the well
contributed toward describing the spatial distribution of TCE, as shown in Table 6.1.
Wells providing the least amount of information represent possible candidates for
removal from the monitoring network at the Long Prairie.
6.3 SPATIAL STATISTICAL EVALUATION RESULTS
6.3.1 Kriging Ranking Results
Figure 6.4 and Table 6.1 present the ranking of the evaluated subset of monitoring
locations based on the relative value of recent PCE information provided by each well, as
calculated based on the kriging realizations. Examination of these results indicate that
monitoring wells in close proximity to several other monitoring wells (e.g., red color
coding on Figure 6.4) generally provide relatively lesser amounts of information than do
wells at greater distances from other wells, or wells located in areas having limited
numbers of monitoring points (e.g., blue color coding on Figure 6.4). This is intuitively
obvious, but the analysis allows the most valuable and least valuable wells to be
identified quantitatively. For example, Table 6.1 identifies the four wells ranked at or
below 3.5 (wells MW19B, MW13C, MW4B, MW16B) that provide the relative least
amount of information, and the four wells ranked at or above 13 (wells MW14B, MW5B,
MW17B, MW1B) that provide the greatest amount of relative information regarding the
occurrence and distribution of PCE in groundwater among those wells included in the
kriging analysis. The four lowest-ranked wells are potential candidates for removal from
the Long Prairie groundwater monitoring program, and the four highest-ranked wells are
candidates for retention in the monitoring program, intermediate ranked wells receive no
recommendation for removal or retention in the monitoring program based on the spatial
analysis.
6-10
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SECTION 7
SUMMARY OF THREE-TIERED MONITORING NETWORK
EVALUATION
The 44 wells sampled in October 2002 at Long Prairie were evaluated using
qualitative hydrogeologic and extraction-system information, temporal statistical
techniques, and spatial statistics. At each tier of the evaluation, monitoring points that
provide relatively greater amounts of information regarding the occurrence and
distribution of COCs in groundwater were identified, and were distinguished from those
monitoring points that provide relatively lesser amounts of information. In this section,
the results of the evaluations are combined to generate a refined monitoring program that
potentially could provide information sufficient to address the primary objectives of
monitoring, at reduced cost. Monitoring wells not retained in the refined monitoring
network could be removed from the monitoring program with relatively little loss of
information. The results of the evaluations were combined and summarized in
accordance with the following decision logic:
1. Each well retained in the monitoring network on the basis of the qualitative
hydrogeologic evaluation is recommended to be retained in the refined
monitoring program.
2. Those wells recommended for removal from the monitoring program on the
basis of all three evaluations, or on the basis of the qualitative and temporal
evaluations (with no recommendation resulting from the spatial evaluation)
should be removed from the monitoring program.
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3. If a well is recommended for removal based on the qualitative evaluation and
recommended for retention based on the temporal or spatial evaluation, the final
recommendation is based on a case-by-case review of well information.
The results of the qualitative, temporal, and spatial evaluations are summarized in Table
7.1. These results indicate that 18 of the 44 wells sampled in October 2002 could be
excluded from the groundwater monitoring program with little loss of information.
These results further suggest that the "current" sampling plan (the 19 monitoring wells
and 6 active recovery wells included in the 2000 and 2001 sampling schedules, plus
water-supply wells CW3 and CW6) could be optimized by removing four of the 27 wells
now in the LTM program, and adding three wells not currently included in the program.
Two wells sampled in October 2002, but not included in the current monitoring program,
fall into case 3 of the decision logic (as listed above). Justifications for the
recommendation to continue to exclude these wells from routine sampling are as follow:
• Well MW1B was recommended for continued exclusion from the monitoring
network based on the qualitative and temporal evaluations, and for addition to the
program based on the spatial evaluation. This well should not be added to the
monitoring program because it is crossgradient from (west of) the plume, and well
RW4 provides adequate monitoring of the western boundary of the plume.
• Well MW5B also was recommended for continued exclusion from the monitoring
network based on the qualitative evaluation, and addition to the program based on
the temporal and spatial evaluations. Well MW5B should be added to the
scheduled monitoring program to determine if the recent low detection of cis-1,2-
DCE truly reflects an increasing concentration trend.
A refined monitoring program, consisting of 26 wells (2 to be sampled quarterly, 6 to be
sampled semi-annually, 14 to be sampled annually, and 4 to be sampled biennially)
would be adequate to address the two primary objectives of monitoring. This refined
monitoring network would result in an average of 36 sampling events per year, compared
to 51 events per year under the current (2000/2001) monitoring program. Implementing
7-2
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these recommendations for optimizing the LTM monitoring program at Long Prairie
could reduce current LTM annual monitoring by more than 29 percent. Additionally,
based on a per well sampling cost ranging from$l 00 to $280, these recommendations
could reduce costs by an average of $1500 to $4,200 per year.
7-3
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SECTION 8
REFERENCES
American Society of Civil Engineers (ASCE) Task Committee on Geostatistical
Techniques in Hydrology. 1990a. Review of geostatistics in geohydrology - I.
Basic concepts. Journal of Hydraulic Engineering 116(5):612-632.
ASCE Task Committee on Geostatistical Techniques in Hydrology. 1990b. Review of
geostatistics in geohydrology - II. Applications. Journal of Hydraulic
Engineering 116(6): 63 3-65 8.
Barr Engineering (Barr). 2001. 1999/2000 Annual Report (September 1999 through
October 2000) Long Prairie Groundwater Remediation System, prepared for
Minnesota Pollution Control Agency, March.
Barr. 2002. 2000/2001 Annual Report (September 2000 through October 2001) Long
Prairie Groundwater Remediation System, prepared for Minnesota Pollution
Control Agency, March.
Clark, I. 1987. Practical Geostatistics. Elsevier Applied Science, Inc., London.
Environmental Systems Research Institute, Inc. (ESRI). 2001. ArcGIS Geostatistical
Analyst Extension to ArcGIS 8 Software, Redlands, CA.
Gibbons, R.D. 1994. Statistical Methods for Groundwater Monitoring. John Wiley &
Sons, Inc., New York.
Minnesota Pollution Control Agency (MPCA). 2002. First Five-Year Review Reprotfor
Long Prairie Ground Water Contamination Superfund Site, September.
8-1
S:\ES\SHARED\CEN\MNO\EPA\LONGPRAIRIE\WRITEUP\LongPrairieMNODraftFinal.doc
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Rock, N.M.S. 1988. Numerical Geology. Springer-Verlag. New York, New York
USEPA. 1994. Methods for Monitoring Pump-and-Treat Performance. Office of
Research and Development. EPA/600/R-94/123.
Wiedemeier, T.H., and P.E. Haas. 2000. Designing Monitoring Programs to Effectively
Evaluate the Performance of Natural Attenuation. Air Force Center for
Environmental Excellence (AFCEE). August.
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APPENDIX D-3
OPTIMIZATION OF MONITORING PROGRAM
AT
OPERABLE UNIT D
McCLELLAN AIR FORCE BASE, CALIFORNIA
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G-2236-15
2.0
Air
Sacramento Valley, California
to
Air Force Center for Environmental
June 2, 2003
Groundwater Services, Inc.
2211 Norfolk, Suite 1000, Houston, Texas 77098-4044
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V
GROUNDWATER
SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
MCCLELLAN AIR FORCE BASE
Sacramento Valley, California
Prepared
by
Groundwater Services, Inc.
2211 Norfolk, Suite 1000
Houston, Texas 77098
(713)522-6300
GSI Job No. G-2236
Revision No. DRAFT
Date: 6/02/03
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GSI Job No. G-2236-15 GROUNDWATER
January 15, 2003 SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK
OPTIMIZATION, MCCLELLAN AIR FORCE BASE
Sacramento Valley, California
Table of Contents
Executive Summary 1
Project Objectives 1
Results 2
1.0 Introduction 4
1.1 Geology/Hydrogeology 4
1.2 Remedial Action 5
2.0 MAROS Methodology 7
2.1 MAROS Conceptual Model 7
2.2 Data Management 8
2.3 Site Details 8
2.4 Data Consolidation 9
2.5 Overview Statistics: Plume Trend Analysis 9
2.5.1 Mann-Kendall Analysis 10
2.5.2 Linear Regression Analysis 10
2.5.3 Overall Plume Analysis 11
2.5.4 Moment Analysis 12
2.6 Detailed Statistics: Optimization Analysis 13
2.6.1 Well Redundancy Analysis- Delaunay Method 14
2.6.2 Well Sufficiency Analysis - Delaunay Method 15
2.6.3 Sampling Frequency- Modified CES Method 15
2.6.4 Data Sufficiency - Power Analysis 16
3.0 Site Results 18
3.1 Data Consolidation 19
3.2 Overview Statistics: Plume Trend Analysis 19
3.2.1 Mann-Kendall/Linear Regression Analysis 19
3.2.2 Moment Analysis 21
3.2.3 Overall Plume Analysis 24
3.3 Detailed Statistics: Optimization Analysis 25
3.3.1 Well Redundancy Analysis 25
3.3.2 Well Sufficiency Analysis 27
3.3.3 Sampling Frequency Analysis 28
3.3.4 Data Sufficiency - Power Analysis 29
4.0 Summary and Recommendations 31
McClellanAFB Q MAROS 2.0 Application
Sacramento Valley, California Monitoring Network Optimization
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Tables
Table 1 Sampling Locations Used in the MAROS Analysis
Table 2 Mann-Kendall Analysis Decision Matrix
Table 3 Linear Regression Analysis Decision Matrix
Table 4 Zone A & B Aquifer Site-Specific Parameters
Table 5 Results of Zone A McClellan OU D Trend Analysis
Table 6 Results of Zone B McClellan OU D Trend Analysis
Table 7 Zone A Redundancy Analysis Results - Delaunay Method
Table 8 Zone B Redundancy Analysis Results - Delaunay Method
Table 9 Zone A Sampling Frequency Analysis Results - Modified CES
Table 10 Zone B Sampling Frequency Analysis Results- Modified CES
Table 11 Zone A Risk-Based Site Cleanup Evaluation Results - Power Analysis
Table 12 Zone A Selected Plume Centerline Wells for Risk-Based Site Cleanup
Evaluation - Power Analysis
Table 13 Zone A Plume Centerline Concentration Regression Results - Power
Analysis
Table 14 Summary of MAROS Sampling Optimization Results
Figures
Figure 1 Zone A McClellan OU D Groundwater Monitoring Network
Figure 2 Zone B McClellan OU D Groundwater Monitoring Network
Figure 3 MAROS Decision Support Tool Flow Chart
Figure 4 MAROS Overview Statistics Trend Analysis Methodology
Figure 5 Decision Matrix for Determining Provisional Frequency
Figure 6 Zone A McClellan OU D TCE Mann-Kendall Trend Results
Figure 7 Zone A McClellan OU D TCE Linear Regression Trend Results
Figure 8 Zone B McClellan OU D TCE Mann-Kendall Trend Results
Figure 9 Zone B McClellan OU D TCE Linear Regression Trend Results
Figure 10 Zone AB McClellan OU D TCE Mann-Kendall Trend Results, Extraction
Wells
Figure 11 Zone AB McClellan OU D TCE Linear Regression Trend Results,
Extraction Wells
Figure 12 Zone A McClellan OU D TCE First Moment (Center of Mass) Over Time
Figure 13 Zone B McClellan OU D TCE First Moment (Center of Mass) Over Time
Figure 14 Zone A Well Sufficiency Results
Appendices
Appendix A: Zone A and Zone B McClellan OU D Historical TCE Maps
Appendix B: Zone A and Zone B McClellan OU D MAROS 2.0 Reports
McClellan AFB
Sacramento Valley, California
MAROS 2.0 Application
Monitoring Network Optimization
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GROUNDWATER
June 2, 2003 SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
MCCLELLAN AIR FORCE BASE
EXECUTIVE SUMMARY
Long-term monitoring programs, whether applied for process control, performance
measurement, or compliance purposes, require large scale data collection effort and
time commitment, making their cumulative costs very high. With the increasing use of
risk-based goals and natural attenuation in recent years as well as the move toward
long-term closure upon completion of cleanup activities, the need for better-designed
long-term monitoring plans that are cost-effective, efficient, and protective of human and
ecological health has greatly increased. The Monitoring and Remediation Optimization
System (MAROS) methodology provides an optimal monitoring network solution, given
the parameters within a complicated groundwater system which will increase its
effectiveness. By applying statistical techniques to existing historical and current site
analytical data, as well as considering hydrogeologic factors and the location of potential
receptors, the software suggests an optimal plan along with an analysis of individual
monitoring wells for the current monitoring system. This report summarizes the findings
of an application of the MAROS 2.0 software to Zone A and Zone B long-term monitoring
well networks in Operating Unit (OU) D at the McClellan Air Force Base in Sacramento
Valley, California.
The primary constituent of concern at the site is trichloroethylene (TCE) which is
analyzed at 32 and 14 monitoring wells respectively in the OU D Zone A and Zone B
well networks (Figures 1 and 2). Monitoring wells in both Zones A and B have been
sampled for TCE irregularly, ranging from quarterly to annually and in some cases,
biennially (every two years) since the implementation of the long-term monitoring plan in
1990. By December 2000, 40 sampling events had been carried out at the site,
however, many wells have only 5 analyses. The historical TCE data for all or in some
cases a subset of wells were analyzed using the MAROS 2.0 software in order to: 1)
gain an overall understanding of the plume stability, and 2) recommend changes in
sampling frequency and sampling locations without compromising the effectiveness of
the long-term monitoring network.
Project Objectives
The general objective of the project was to optimize the McClellan OU D long-term
monitoring network and sampling plan applying the MAROS 2.0 statistical and decision
support methodology. The key objectives of the project included:
Determining the overall plume stability through trend analysis and moment
analysis;
Evaluating individual well TCE concentration trends over time;
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Addressing adequate and effective sampling through reduction of redundant
wells without information loss and addition of new wells for future sampling;
• Assessing future sampling frequency recommendations while maintaining
sufficient plume stability information;
Evaluating risk-based site cleanup status using data sufficiency analysis.
Results
The MAROS 2.0 sampling optimization software/methodology has been applied to the
McClellan's existing monitoring program as of December 2000. Historical data from
2001 was not used in this analysis due to anomalous TCE concentrations from the
passive diffusion sampling technique utilized only in 2001. Results from the temporal
trend analysis, moment analysis, sampling location determination, sampling frequency
determination, and data sufficiency analysis indicate that:
• Site monitoring wells were divided into source wells and tail wells where source
wells are in the vicinity of NAPL or have historically elevated concentrations of
TCE.
• 9 out of 10 source wells and 11 out of 22 tail wells in Zone A have a Probably
Decreasing, Decreasing, or Stable trend. Both of the statistical methods used to
evaluate trends (Mann-Kendall and Linear Regression) gave similar trend
estimates for each well.
• 0 out of 1 source wells and 3 out of 13 tail wells in Zone B have a Probably
Decreasing, Decreasing, or Stable trend. The majority of the wells in Zone B
have no trend in the historical data. However, as of 2000, only one of the wells in
Zone B is actually above the MCL for TCE. Both of the statistical methods used
to evaluate trends (Mann-Kendall and Linear Regression) gave similar trend
estimates for each well.
• 5 out of 6 source area extraction wells in Zone AB have a Decreasing trend. Both
the Mann-Kendall and Linear Regression methods gave similar trend estimates
for each well.
• The dissolved mass shows stability over time, whereas the center of mass and
the plume spread show no trend over time. The results from the moment
analysis are not very strong due to the change in the wells sampled over the
sampling period analyzed.
• Overall plume stability results lead to the MAROS analysis system to indicate
that a monitoring system of "Moderate" intensity is appropriate for this plume
compared to "Limited" or "Extensive" systems due to a stable or decreasing
plume in both Zone A and Zone B.
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• The well redundancy optimization tool, using the Delaunay method, indicates that
3 existing monitoring wells may not be needed for Zone A plume monitoring and
can likely be eliminated from the existing Zone A monitoring network of 32 wells
without compromising the accuracy of the monitoring network. Similarly, two
existing monitoring wells may be eliminated from the existing Zone B monitoring
network of 14 wells.
• The well sufficiency optimization tool, using the Delaunay method, indicates that
there are no areas within the existing monitoring network that have high
uncertainty in the TCE concentration estimation. Therefore, no new monitoring
wells are recommended for Zones A or B.
• Application of the well sampling frequency determination tool, the Modified CES
method, leads to results that are in agreement with the sampling frequency
currently in use at the site. Therefore, no sampling frequency reduction is
recommended for the wells in the current monitoring network.
• The MAROS Data Sufficiency (Power Analysis) application indicates that the
monitoring record has sufficient statistical power to conclude that the site has
attained cleanup goal at (or farther than) the "hypothetical statistical compliance
boundary" located 100 feet downgradient of the most downgradient well at the
site. As more sampling records accumulate, this hypothetical statistical
compliance boundary will get closer and closer to the downgradient wells of the
monitoring system.
The recommended long-term monitoring strategy results in a moderate reduction in
sampling costs and allows site personnel to develop a better understanding of plume
behavior over time. The MAROS optimized plan results in a monitoring network of 47
wells: 17 sampled annually, and 30 sampled biennially. The MAROS optimized plan
would result in 32 samples per year, compared to 34 samples per year (17 annual and
34 biennial) in the current McClellan sampling program. Implementing these
recommendations could lead to a 6% reduction from the current monitoring plan in terms
of the samples to be collected per year. A reduction in the number of redundant wells is
expected to result in a moderate cost savings over the long-term at McClellan Air Force
Base. An approximate cost savings estimate of $300 per year is projected while still
maintaining adequate delineation of the plume as well as knowledge of the plume state
over time.
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1.0 INTRODUCTION
Long-term monitoring programs, whether applied for process control, performance
measurement, or compliance purposes, require large scale data collection effort and
time commitment, making their cumulative costs very high. With the increasing use of
risk-based goals and natural attenuation in recent years as well as the move toward
long-term closure upon completion of cleanup activities, the need for better-designed
long-term monitoring plans that are cost-effective, efficient, and protective of human and
ecological health has greatly increased. AFCEE's Monitoring and Remediation
Optimization System (MAROS) methodology provides an optimal monitoring network
solution, given the parameters within a complicated groundwater system which will
increase its effectiveness. By applying statistical techniques to existing historical and
current site analytical data, as well as considering hydrogeologic factors and the location
of potential receptors, the software suggests an optimal plan along with an analysis of
individual monitoring wells for the current monitoring system. This report summarizes
the findings of an application of the MAROS 2.0 software to the current Zone A and
Zone B OU D long-term monitoring well network at the McClellan Air Force Base,
Sacramento Valley, California.
1.1 Geology/Hydrogeology
McClellan Air Force Base (AFB) is located in the Sacramento Valley, approximately
seven miles northeast of Sacramento, California. The site has been divided into 8
operable units (OUs). Locations of these OUs at McClellan AFB are available in Figure
2-2 in the GMPF (Radian Corporation 1997). OU D is located in the northwest corner of
McClellan AFB.
The subsurface of McClellan AFB consists of alluvial and fluvial sediments, stretching
from the ground surface to a depth of 450 feet below ground surface (bgs). The ground
surface elevation in OU D is about 62 ft above mean sea level (msl). The subsurface
beneath McClellan AFB has been divided into the vadose zone and five monitoring
zones (A, B, C, D, and E, from shallowest to deepest) on the basis of lithologic, geologic,
and hydrologic characteristics. The monitoring zones are used to track the horizontal
migration of contaminants and to monitor local variations in hydraulic gradient. A
generalized geologic cross-section illustrating the designated monitoring zones is
presented in Figure 2-3 of the GMPF (Radian Corporation 1997). In OU D, the plume
has only impacted monitoring Zones A and B. Monitoring Zone A of OU D has an
average depth of 35 ft, ranging from 99 to 134 ft bgs; monitoring Zone B of OU D has an
average depth of 60 ft, ranging from 134 to 194 ft bgs (Table 2-3 in the GMPF, Radian
Corporation 1999).
Base-wide data collected during remedial investigations and groundwater sampling
efforts indicate that groundwater from 100 to 425 feet bgs beneath McClellan AFB is one
hydraulic system. Fine-grained deposits used to define the monitoring zones are not
continuous and allow groundwater movement and contaminant migration between
monitoring zones. The water elevation within the aquifer system has been declining
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continuously for approximately 50 years due to overdrawing by irrigation, pumping for
municipal water supply, and extraction wells. Within the last decade, water levels in
Zone A across the base have been declining at a rate of 1 to 2 feet per year.
Groundwater elevations rise and fall by an average of 5 feet due to seasonal
fluctuations. Groundwater elevations range from -38 to -40 ft msl at the northern and
southern edges of OU D, respectively, and the horizontal hydraulic gradient at OU D is
about 0.0006 ft/ft (1Q02 Monitoring Report, URS 2002).
Flow directions in the hydraulic system have varied over the past 8 decades, but have
persisted in a south to southwesterly direction over the past decade. The groundwater
flow direction for both monitoring Zone A and Zone B is predominantly toward the South-
Southwest and the groundwater seepage velocity is approximately 35 ft/yr. Base wells,
domestic production wells, extraction wells, and regional pumping affect local
groundwater flow directions. The vertical hydraulic gradients between monitoring zones
A and B are predominantly upward in the winter and downward during the rest of the
year. The horizontal hydraulic conductivity of layered sediments is about 5 to 15 times
the vertical hydraulic conductivity. For a detailed description of site geology and
hydrogeology refer to Radian Corporation (1997).
1.2 Remedial Action
McClellan Air Force Base was established in 1936 as an aircraft repair depot and supply
base. McClellan AFB and the OUs that have been established were put on the National
priorities List (NPL) in 1988. OU D located in the northwestern portion of the base is
approximately 192 acres and consists primarily of several disposal pits, sludge/oil pits,
fuel solvent pits, runway access, and an Industrial Wastewater Treatment Plant sludge
land farm. The Rl for OU D was complete in 1994 and indicated TCE is the main
constituent of concern for a single plume that extended approximately 1,750 feet off-
base to the northwest in Zones A and B.
A pump-and-treat system with 6 extraction wells was installed in OU D in 1987,
continuing to the present. The objective of the remediation is to restore Zones A and B
to drinking water standards by reducing the TCE concentration to less than the MCL (5
ppb). The groundwater long-term monitoring plan consists of 33 monitoring wells in Zone
A, 14 monitoring wells in Zone B and 6 extraction wells in Zone AB. It consists of
performance monitoring and compliance monitoring with the following goals: 1) plume
containment monitoring to confirm that the plume remains hydraulically controlled; and
2) plume reduction monitoring to verify progress toward achieving cleanup goals.
The number of monitoring wells that are currently sampled include 32 Zone A monitoring
wells, 14 Zone B monitoring wells, and 6 extraction wells screened in both Zone A and
Zone B (Figures 1 and 2). The sampling frequency for these wells has been very
irregular, ranging from quarterly or annual in 1990 to annual or biennial in 2000, although
the sampling was conducted on a quarterly basis. The frequency of sampling at the
base has been changed over time as part of the McClellan AFB Remediation Monitoring
Decision Logic implemented as part of the Installation Restoration Program (IRP)
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Groundwater Monitoring Plan (Radian Corporation, 1997). Some monitoring wells have
only 5 ~ 7 data records available during this period. This resulted in only a portion of the
wells being sampled on each quarterly sampling event. The MAROS 2.0 analysis
performed for this study utilizes the data from the current McClellan long-term monitoring
plan (1990 to 2000) and is summarized in Table 1. The 2001 data were not used
because a new sampling technique was being tested (passive diffusion sampling) in
sample collection and results from these sample events were not consistent with
previous results.
McClellan Air Force Base
Sacramento Valley, California
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2.0 MAROS METHODOLOGY
The MAROS 2.0 software used to optimize the LTM network at the McClellan AFB is
explained in general terms in this section. MAROS is a collection of tools in one
software package that is used in an explanatory, non-linear fashion. The tool includes
models, statistics, heuristic rules, and empirical relationships to assist the user in
optimizing a groundwater monitoring network system while maintaining adequate
delineation of the plume as well as knowledge of the plume state over time. Different
users utilize the tool in different ways and interpret the results from a different viewpoint.
For a detailed description of the structure of the software and further utilities, refer to the
MAROS 2.0 Manual (Aziz et al. 2002).
2.1 MAROS Conceptual Model
In MAROS 2.0, two levels of analysis are used for optimizing long-term monitoring plans:
1) an overview statistical evaluation with interpretive trend analysis based on temporal
trend analysis and plume stability information; and 2) a more detailed statistical
optimization based on spatial and temporal redundancy reduction methods (see Figure 2
for further details). In general, the MAROS method applies to 2-D aquifers that have
relatively simple site hydrogeology. However, for a multi-aquifer (3-D) system, the user
could apply the statistical analysis layer-by-layer.
The overview statistics or interpretive trend analysis assesses the general monitoring
system category by considering individual well concentration trends, overall plume
stability, hydrogeologic factors (e.g., seepage velocity, and current plume length), and
the location of potential receptors (e.g., property boundaries or drinking water wells). The
analysis relies on temporal trend analysis to assess plume stability, which is then used
to determine the general monitoring system category. Since the temporal trend analysis
focuses on where the monitoring well is located, the site wells are divided into two
different zones: the source zone or the tail zone. The source zone includes areas with
non-aqueous phase liquids (NAPLs), contaminated vadose zone soils, and areas where
aqueous-phase releases have been introduced into ground water. The tail zone is
usually the area downgradient of the contaminant source zone. Although this
classification is a simplification of the well location, this broadness makes the user aware
on an individual well basis that the concentration trend results can have a different
interpretation depending on the well location in and around the plume. The location and
type of the individual wells allows further interpretation of the trend results, depending on
what type of well is being analyzed (e.g., remediation well, leading plume edge well, or
monitoring well). General recommendations for the monitoring network frequency and
density are suggested based on heuristic rules applied to the source and tail trend
results.
The detailed statistics level of analysis or sampling optimization, on the other hand,
consists of a well redundancy analysis and well sufficiency analysis using the Delaunay
method, a sampling frequency analysis using the Modified Cost Effective Sampling
(CES) method and a data sufficiency analysis using power analysis. The well
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redundancy analysis is designed to minimize monitoring locations and the Modified CES
method is designed to minimize the frequency of sampling. The data sufficiency
analysis uses power analysis to assess the sampling record to determine if the current
monitoring network and record is sufficient in terms of evaluating risk-based site target
level status.
2.2 Data Management
In MAROS, ground water monitoring data can be imported from simple database-format
Microsoft® Excel spreadsheets, Microsoft Access tables, previously created MAROS
database archive files, or entered manually. Compliance monitoring data interpretation in
MAROS is based on historical ground water monitoring data from a consistent set of
wells over a series of sampling events. Statistical validity of the concentration trend
analysis requires constraints on the minimum data input of at least four wells (ASTM
1998) in which COCs have been detected. Individual sampling locations need to include
data from at least the six most-recent sampling events. To ensure a meaningful
comparison of COC concentrations over time and space, both data quality and data
quantity need to be considered. Prior to statistical analysis, the user can consolidate
irregularly sampled data or smooth data that might result from seasonal fluctuations or a
change in site conditions.
Imported ground water monitoring data and the site-specific information entered in Site
Details can be archived and exported as MAROS archive files. These archive files can
be appended as new monitoring data becomes available, resulting in a dynamic long-
term monitoring database that reflects the changing conditions at the site (i.e.
biodegradation, compliance attainment, completion of remediation phase, etc.).
2.3 Site Details
Information needed for the MAROS analysis includes site-specific parameters such as
seepage velocity and current plume length. Part of the trend analysis methodology
applied in MAROS focuses on where the monitoring well is located, therefore the user
needs to divide site wells into two different zones: the source zone or the tail zone. The
source zone includes areas with non-aqueous phase liquids (NAPLs), contaminated
vadose zone soils, and areas where aqueous-phase releases have been introduced into
ground water. The source zone generally contains locations with historical high ground
water concentrations of the COCs. The tail zone is usually the area downgradient of the
contaminant source zone. It is up to the user to make further interpretation of the trend
results, depending on what type of well is being analyzed (e.g., remediation well, leading
plume edge well, or monitoring well).
MAROS allows the analysis of up to 5 COCs concurrently and users can pick COCs
from a list of compounds existing in the monitoring data, or select COCs based on
recommendations provided in MAROS based on toxicity, prevalence, and mobility of
compounds.
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2.4 Data Consolidation
Typically long-term monitoring raw data have been measured irregularly in time or
contain many non-detects, trace level results, and duplicates. Therefore, before the data
can be further analyzed, raw data are filtered, consolidated, transformed, and possibly
smoothed to allow for a consistent dataset meeting the minimum data requirements for
statistical analysis mentioned previously.
MAROS allows users to specify the period of interest in which data will be consolidated
(i.e., monthly, bi-monthly, quarterly, semi-annual, yearly, or a biennial basis). In
computing the representative value when consolidating, one of four statistics can be
used: median, geometric mean, mean, and maximum. Non-detects can be transformed
to one half the reporting or method detection limit (DL), the DL, or a fraction of the DL.
Trace level results can be represented by their actual values, one half of the DL, the DL,
or a fraction of their actual values. Duplicates are reduced in MAROS by one of three
ways: assigning the average, maximum, or first value. The reduced data for each COC
and each well can be viewed as a time series in a graphical form on a linear or semi-log
plot generated by the software.
2.5 Overview Statistics: Plume Trend Analysis
Within the MAROS software there are historical data analyses that support a conclusion
about plume stability (e.g., increasing plume, etc.) through statistical trend analysis of
historical monitoring data. Plume stability results are assessed from time-series
concentration data with the application of three statistical tools: Mann-Kendall Trend
analysis, linear regression trend analysis and moment analysis. The two trend methods
are used to estimate the concentration trend for each well and each COC based on a
statistical trend analysis of concentrations versus time at each well (Figure 2). These
trend analyses are then consolidated to give the user a general plume stability and
general monitoring frequency and density recommendations (see Figure 3 for further
step-by-step details). Both qualitative and quantitative plume information can be gained
by these evaluations of monitoring network historical data trends both spatially and
temporally. The MAROS Overview Statistics are the foundation the user needs to make
informed optimization decisions at the site. The Overview Statistics are designed to
allow site personnel to develop a better understanding of the plume behavior over time
and understand how the individual well concentration trends are spatially distributed
within the plume. This step allows the user to gain information that will support a more
informed decision to be made in the next level or detailed statistics optimization analysis
(Figure 2).
2.5.1 Mann-Kendall Analysis
The Mann-Kendall test is a non-parametric statistical procedure that is well suited for
analyzing trends in data over time. The Mann-Kendall test can be viewed as a
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nonparametric test for zero slope of the first-order regression of time-ordered
concentration data versus time. The Mann-Kendall test does not require any
assumptions as to the statistical distribution of the data (e.g. normal, lognormal, etc.)
and can be used with data sets which include irregular sampling intervals and missing
data. The Mann-Kendall test is designed for analyzing a single groundwater constituent,
multiple constituents are analyzed separately. The Mann-Kendall S statistic measures
the trend in the data: positive values indicate an increase in concentrations over time
and negative values indicate a decrease in concentrations over time. The strength of the
trend is proportional to the magnitude of the Mann-Kendall statistic (i.e., a large value
indicates a strong trend). The confidence in the trend is determined by consulting the S
statistic and the sample size n in a Kendall probability table such as the one reported in
Hollander and Wolfe (1973).
The concentration trend is determined for each well and each COC based on results of
the S statistic, the confidence in the trend, and the Coefficient of Variation (COV). The
decision matrix for this evaluation is shown in Table 2. A Mann-Kendall statistic that is
greater than 0 combined with a confidence of greater than 95% is categorized as an
Increasing trend while a Mann-Kendall statistic of less than 0 with a confidence between
90% and 95% is defined as a Probably Increasing trend, and so on.
Depending on statistical indicators, the concentration trend is classified into six
categories:
• Decreasing (D),
• Probably Decreasing (PD),
• Stable (S),
• No Trend (NT),
• Probably Increasing (PI)
• Increasing (I).
These trend estimates are then analyzed to identify the source and tail region overall
stability category (see Figure 2 for further details).
2.5.2 Linear Regression Analysis
Linear Regression is a parametric statistical procedure that is typically used for
analyzing trends in data over time. Using this type of analysis, a higher degree of
scatter simply corresponds to a wider confidence interval about the average log-slope.
Assuming the sign (i.e., positive or negative) of the estimated log-slope is correct, a level
of confidence that the slope is not zero can be easily determined. Thus, despite a poor
goodness of fit, the overall trend in the data may still be ascertained, where low levels of
confidence correspond to "Stable" or "No Trend" conditions (depending on the degree of
scatter) and higher levels of confidence indicate the stronger likelihood of a trend. The
linear regression analysis is based on the first-order linear regression of the log-
transformed concentration data versus time. The slope obtained from this log-
transformed regression, the confidence level for this log-slope, and the COV of the
untransformed data are used to determine the concentration trend. The decision matrix
for this evaluation is shown in Table 3. To estimate the confidence in the log-slope, the
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standard error of the log-slope is calculated. The coefficient of variation, defined as the
standard deviation divided by the average, is used as a secondary measure of scatter to
distinguish between "Stable" or "No Trend" conditions for negative slopes. The Linear
Regression Analysis is designed for analyzing a single groundwater constituent; multiple
constituents are analyzed separately, (up to five COCs simultaneously). For this
evaluation, a decision matrix developed by Groundwater Services, Inc. is also used to
determine the "Concentration Trend" category (plume stability) for each well.
Depending on statistical indicators, the concentration trend is classified into six
categories:
Decreasing (D),
Probably Decreasing (PD),
Stable (S),
No Trend (NT),
Probably Increasing (PI)
Increasing (I).
The resulting confidence in the trend, together with the log-slope and the COV of the
untransformed data, are used in the linear regression analysis decision matrix to
determine the concentration trend. For example, a positive log-slope with a confidence
of less than 90% is categorized as having No Trend whereas a negative log-slope is
considered Stable if the COV is less than 1 and categorized as No Trend if the COV is
greater than 1.
2.5.3 Overall Plume Analysis
General recommendations for the monitoring network frequency and density are
suggested based on heuristic rules applied to the source and tail trend results.
Individual well trend results are consolidated and weighted by the MAROS software
according to user input, and the direction and strength of contaminant concentration
trends in the source zone and tail zone for each COC are determined. Based on
i) the consolidated trend analysis,
ii) hydrogeologic factors (e.g., seepage velocity), and
iii) location of potential receptors (e.g., wells, discharge points, or property
boundaries),
the software suggests an general optimization plan for the current monitoring system in
order to efficiently effectively monitor in the future. A flow chart of the MAROS
methodology utilizing trend analysis results and other site-specific parameters to form a
general sampling frequency and well density recommendation is outlined in Figure 3. For
example, a generic plan for a shrinking petroleum hydrocarbon plume (BTEX) in a slow
hydrogeologic environment (silt) with no nearby receptors would entail minimal, low
frequency sampling of just a few indicators. On the other hand, the generic plan for a
chlorinated solvent plume in a fast hydrogeologic environment that is expanding but has
very erratic concentrations over time would entail more extensive, higher frequency
sampling. The generic plan is based on a heuristically derived algorithm for assessing
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future sampling duration, location and density that takes into consideration plume
stability. For a detailed description of the heuristic rules used in the MAROS software,
refer to the MAROS 2.0 Manual (Aziz et al. 2002).
2.5.3 Moment Analysis
An analysis of moments can help resolve plume trends, where the zeroth moment shows
change in dissolved mass vs. time, the first moment shows the center of mass location
vs. time, and the second moment shows the spread of the plume vs. time. Moment
calculations can predict how the plume will change in the future if further statistical
analysis is applied to the moments to identify a trend (in this case, Mann Kendall Trend
Analysis is applied). The trend analysis of moments can be summarized as:
• Zeroth Moment: Change in dissolved mass over time
• First Moment: Change in the center of mass location over time
• Second Moment: Spread of the plume over time
The role of moment analysis in MAROS is to provide a relative measure of plume
stability and condition. Plume stability may vary by constituent, therefore the MAROS
moment analysis can be used to evaluate multiple COCs simultaneously in order to
provide used to provide a quick way of comparing individual plume parameters to
determine the size and movement of constituents relative to one another. Moment
analysis in the MAROS software can also be used to assist the user in evaluating the
impact on plume delineation in future sampling events by removing identified
"redundant" wells from a long-term monitoring program (this analysis was not performed
as part of this study, for more details on this application of moment analysis refer to the
MAROS 2.0 Manual (Aziz et al. 2002).
The zeroth moment is a mass estimate. The zeroth moment calculation can show high
variability over time, largely due to the fluctuating concentrations at the most
contaminated wells as well as varying monitoring well network. Plume analysis and
delineation based exclusively on concentration can exhibit a fluctuating degree of
temporal and spatial variability. The mass estimate is also sensitive to the extent of the
site monitoring well network over time. The zeroth moment trend over time is determined
by using the Mann-Kendall Trend Methodology. The zeroth Moment trend test allows
the user to understand how the plume mass has changed over time. Results for the
trend include: Increasing, Probably Increasing, No Trend, Stable, Probably Decreasing,
Decreasing or Not Applicable (Insufficient Data). When considering the results of the
Zeroth moment trend, the following factors should be considered which could effect the
calculation and interpretation of the plume mass over time: 1) Change in the spatial
distribution of the wells sampled historically 2) Different wells sampled within the well
network over time (addition and subtraction of well within the network). 3) Adequate
versus inadequate delineation of the plume over time.
The first moment estimates the center of mass, coordinates (Xc and Yc) for each
sample event and COC. The changing center of mass locations indicate the movement
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of the center of mass over time. Whereas, the distance from the original source location
to the center of mass locations indicate the movement of the center of mass over time
relative to the original source. Calculation of the first moment normalizes the spread by
the concentration indicating the center of mass. The first moment trend of the distance to
the center of mass over time shows movement of the plume in relation to the original
source location over time. Analysis of the movement of mass should be viewed as it
relates to 1) the original source location of contamination 2) the direction of groundwater
flow and/or 3) source removal or remediation. Spatial and temporal trends in the center
of mass can indicate spreading or shrinking or transient movement based on seasonal
variation in rainfall or other hydraulic considerations. No appreciable movement or a
neutral trend in the center of mass would indicate plume stability. However, changes in
the first moment over time do not necessarily completely characterize the changes in the
concentration distribution (and the mass) over time. Therefore, in order to fully
characterize the plume the First Moment trend should be compared to the Zeroth
moment trend (mass change over time).
The second moment indicates the spread of the contaminant about the center of mass
(Sxx and Syy), or the distance of contamination from the center of mass for a particular
COC and sample event. The Second Moment represents the spread of the plume over
time in both the x and y directions. The Second Moment trend indicates the spread of
the plume about the center of mass. Analysis of the spread of the plume should be
viewed as it relates to the direction of groundwater flow. An increasing trend in the
second moment indicates an expanding plume, whereas a declining trend in the plume
indicates a shrinking plume. No appreciable movement or a neutral trend in the center of
mass would indicate plume stability. The second moment provides a measure of the
spread of the concentration distribution about the plume's center of mass. However,
changes in the second moment over time do not necessarily completely characterize the
changes in the concentration distribution (and the mass) over time. Therefore, in order to
fully characterize the plume the Second Moment trend should be compared to the zeroth
moment trend (mass change over time).
2.6 Detailed Statistics: Optimization Analysis
Although the overall plume analysis shows a general recommendation regarding
sampling frequency reduction and general sampling density, a more detailed analysis is
also available with the MAROS 2.0 software in order to allow for further reductions on a
well-by-well basis for frequency, well redundancy, well sufficiency and sampling
sufficiency. The MAROS Detailed Statistics allows for a quantitative analysis for spatial
and temporal optimization of the well network on a well-by-well basis. The results from
the Overview Statistics should be considered along with the MAROS optimization
recommendations gained from the Detailed Statistical Analysis described previously.
The MAROS Detailed Statistics results should be reassessed in view of site knowledge
and regulatory requirements as well as in consideration of the Overview Statistics
(Figure 2).
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The Detailed Statistics or Sampling Optimization MAROS module can be used to
determine the minimal number of sampling locations and the lowest frequency of
sampling that can still meet the requirements of sampling spatially and temporally for an
existing monitoring program. It also provides an analysis of the sufficiency of data for
the monitoring program.
Sampling optimization in MAROS consists of four parts:
• Well redundancy analysis using the Delaunay method
• Well sufficiency analysis using the Delaunay method
• Sampling frequency determination using the Modified CES method
• Data sufficiency analysis using statistical power analysis.
The well redundancy analysis using the Delaunay method identifies and eliminates
redundant locations from the monitoring network. The well sufficiency analysis can
determine the areas where new sampling locations might be needed. The Modified CES
method determines the optimal sampling frequency for a sampling location based on the
direction, magnitude, and uncertainty in its concentration trend. The data sufficiency
analysis examines the risk-based site cleanup status and power and expected sample
size associated with the cleanup status evaluation.
2.6.1 Well Redundancy Analysis - Delaunav Method
The well redundancy analysis using the Delaunay method is designed to select the
minimum number of sampling locations based on the spatial analysis of the relative
importance of each sampling location in the monitoring network. The approach allows
elimination of sampling locations that have little impact on the historical characterization
of a contaminant plume. The delaunay methodology application assumes that the
current sampling network adequately delineates the plume (bounding wells have non-
detect values) and that if a hydraulic containment system is currently in operation, this
will continue. An extended method or wells sufficiency analysis, based on the Delaunay
method, can also be used for recommending new sampling locations. Details about the
Delaunay method can be found in Appendix A.2 of the MAROS Manual (AFCEE 2002).
Well redundancy analysis uses the Delaunay triangulation method to determine the
significance of the current sampling locations relative to the overall monitoring network.
The Delaunay method calculates the network Area and Average concentration of the
plume using data from multiple monitoring wells. A slope factor (SF) is calculated for
each well to indicate the significance of this well in the system (i.e. how removing a well
changes the average concentration.)
The well redundancy optimization process is performed in a stepwise fashion. Step one
involves assessing the significance of the well in the system, if a well has a small SF
(little significance to the network), the well may be removed from the monitoring network.
Step two involves evaluating the information loss of removing a well from the network. If
one well has a small SF, it may or may not be eliminated depending on whether the
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information loss is significant. If the information loss is not significant, the well can be
eliminated from the monitoring network and the process of optimization continues with
fewer wells. However if the well information loss is significant then the optimization
terminates. This sampling optimization process allows the user to assess "redundant"
wells that will not incur significant information loss on a constituent-by-constituent basis
for individual sampling events.
Before applying the Delaunay method for spatial redundancy analysis, it is important to
select the appropriate set of wells for analysis, i.e., only the wells that contribute to the
spatial delineation of the plume. For example, if wells are far from the plume and
contribute little or nothing to the delineation of the plume (e.g., some sentry wells or
background wells far from the plume), they should be excluded from the analysis. One
reason not to use these wells is that these wells usually are on the boundary of the
triangulation and are hard to be eliminated since the Delaunay method protects
boundary wells from being easily removed. The elimination status of these wells, in fact,
should be determined from the regulatory standpoint. Another well type that could be
excluded from analysis is one of a clustered well set because the Delaunay method is a
two-dimensional method. Generally, only one well is picked from the clustered well set to
represent the concentration at this point. This well can be the one that has the highest
concentration or is screened in the representative aquifer interval with the geologic unit.
Data from clustered wells can also be averaged to form a single sample and then used
in the Delaunay method.
2.6.2 Well Sufficiency Analysis - Delaunav Method
The well sufficiency analysis, using the Delaunay method, is designed to recommend
new sampling locations in areas within the existing monitoring network where there is a
high level uncertainty in plume concentration. Details about the well sufficiency analysis
can be found in Appendix A.2 of the MAROS Manual (AFCEE 2002).
In many cases, new sampling locations need to be added to the existing network to
enhance the spatial plume characterization. In MAROS, the method for determining new
sampling locations recommends the area for a possible new sampling location where
there is a high level of uncertainty in concentration estimation. The Slope Factor (SF)
values obtained from the redundancy reduction described above are used to calculate
the concentration estimation error at each triangle area formed in the Delaunay
triangulation. The estimated SF value at each triangle area is then classified into four
levels: Small, Moderate, Large, or Extremely large because the larger the estimated SF
value, the higher the estimation error at this area. Therefore, the triangle areas with the
estimated SF value at the Extremely large or Large level are candidate regions for new
sampling locations.
The results from the Delaunay method and the method for determining new sampling
locations are derived solely from the spatial configuration of the monitoring network and
the spatial pattern of the contaminant plume. No parameters such as the hydrogeologic
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conditions are considered in the analysis. Therefore, professional judgement and
regulatory considerations must be used to make final decisions.
2.6.3 Sampling Frequency Determination - Modified CES Method
The Modified Cost Effective Sampling (MCES) method optimizes sampling frequency for
each sampling location based on the magnitude, direction, and uncertainty of its
concentration trend derived from its recent and historical monitoring records. The MCES
estimates the lowest-frequency sampling schedule for a given groundwater monitoring
location yet still provide needed information for regulatory and remedial decision-making.
The Modified CES method was developed on the basis of the Cost Effective Sampling
(Ridley et al. 1995). Details about the Modified CES method can be found in Appendix
A.3 of the MAROS Manual (AFCEE 2002).
In order to estimate the least frequent sampling schedule for a monitoring location that
still provides enough information for regulatory and remedial decision-making, MCES
employs three steps to determine the sampling frequency. The first step involves
analyzing frequency based on recent trends (Figure 4). A preliminary location sampling
frequency (PLSF) is determined based on the trends determined by rates of change
from linear regression and Mann-Kendall analysis of the most recent monitoring data.
The variability of the sequential sampling data is accounted for by the Mann-Kendall
analysis. The PLSF is then adjusted based on overall trends. If the long-term history of
change is significantly greater than the recent trend, the frequency may be reduced by
one level. Otherwise, no change could be made. The final step in the analysis involves
reducing frequency based on risk. Since not all compounds in the target being
assessed are equally harmful, frequency is reduced by one level if recent maximum
concentration for compound of high risk is less than 1/2 of the Maximum Concentration
Limit (MCL). The result of applying this method is a suggested sampling frequency
based on recent sampling data trends and overall sampling data trends.
The finally determined sampling frequency from the Modified CES method can be
Quarterly, Semiannual, Annual, and Biennial. Users can further reduce the sampling
frequency to, for example, once every three years, if the trend estimated from Biennial
data (i.e., data drawn once every two years from the original data) is the same as that
estimated from the original data.
2.6.4 Data Sufficiency Analysis - Power Analysis
Statistical power analysis is a technique for interpreting the results of statistical tests. It
provides additional information about a statistical test: 1) the power of the statistical test,
i.e., the probability of finding a difference in the variable of interest when a difference
truly exists; and 2) the expected sample size of a future sampling plan given the
minimum detectable difference it is supposed to detect. For example, if the mean
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concentration is lower than the cleanup goal but a statistical test cannot prove this, the
power and expected sample size can tell the reason and how many more samples are
needed to result in a significant test. The additional samples can be obtained by a
longer period of sampling or an increased sampling frequency. Details about the data
sufficiency analysis can be found in Appendix A.6 of the MAROS Manual (AFCEE 2002).
When applying the MAROS power analysis method, a hypothetical statistical compliance
boundary (HSCB) is assigned to be a line perpendicular to the groundwater flow
direction (see figure below). Monitoring well concentrations are projected onto the
HSCB using the distance from each well to the compliance boundary along with a decay
coefficient. The projected concentrations from each well and each sampling event are
then used in the risk-based power analysis. Since there may be more than one sampling
event selected by the user, the risk-based power analysis results are given on an event-
by-event basis. This power analysis can then indicate if target are statistically achieved
at the HSCB. For instance, at a site where the historical monitoring record is short with
few wells, the HSCB would be distant; whereas, at a site with longer duration of
sampling with many wells, the HSCB would be close. Ultimately, at a site the goal would
be to have the HSCB coincide with or be within the actual compliance boundary
(typically the site property line).
®-
L
c>
Groundwater flow direction
Concentrations
projected to this
•* line
The nearest
downgradient
receptor
In order to perform a risk-based cleanup status evaluation for the whole site, a strategy
was developed as follows.
• Estimate concentration versus distance decay coefficient from plume centerline
wells.
• Extrapolate concentration versus distance for each well using this decay
coefficient.
• Comparing the extrapolated concentrations with the compliance concentration
using power analysis.
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Results from this analysis can be Attained or Not Attained, providing a statistical
interpretation of whether the cleanup goal has been met on the site-scale from the risk-
based point of view. The results as a function of time can be used to evaluate if the
monitoring system has enough power at each step in the sampling record to indicate
certainty of compliance by the plume location and condition relative to the compliance
boundary. For example, if results are Not Attained at early sampling events but are
Attained in recent sampling events, it indicates that the recent sampling record provides
a powerful enough result to indicate compliance of the plume relative to the location of
the receptor or compliance boundary.
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3.0 SITE RESULTS
The groundwater long-term monitoring plan for McClellan AFB was started in 1990. The
monitoring plan consisted of performance monitoring and compliance monitoring with the
following goals:
1) plume containment monitoring to confirm that the TCE plume remains
hydraulically controlled; and
2) plume reduction monitoring to verify progress toward achieving cleanup goals.
32 monitoring wells in Zone A were included in the long-term monitoring network as of
2000 along with 14 monitoring wells in Zone B, and 6 extraction wells screened in both
Zone A and Zone B (Figures 1 and 2). The sampling frequency for these wells has been
irregular, ranging from quarterly or annual in 1990 to annual or biennial in 2000, although
the sampling was conducted on a quarterly basis. Some monitoring wells have only 5 ~
7 data records available during this period. This resulted in only a portion of the wells
being sampled on each quarterly sampling event.
Monitoring data from 1990 to 2000 were used for the detailed optimization analysis, with
a subset of this data used in some of the analyses. The 2001 data were not used in the
overview analysis or the detailed analysis because a new sampling technique was being
tested (passive diffusion sampling) in sample collection and results from these sample
events were not consistent with previous results.
In applying the MAROS methodology to develop a revised monitoring strategy for the
McClellan AFB Zones A and B, many site and dataset parameters were applied. General
site assumptions include:
• All wells that were part of the network in between 1990 and 2000 were
considered in the temporal concentration trend analysis.
• Five chemicals of concern (COCs) that have been historically present at the site:
tetrachloroethene (PCE), trichloroethene (TCE), cis-1,2-dichloroethene (cis-1,2-
DCE), and 1,2-dichloroethane (DCA), however, TCE is the only constituent that
is currently above the MCL in the OU D plume.
• All source/tail assignments were made based on the TCE Plume. Source wells
were selected based on historically elevated concentrations of TCE.
• Site-specific hydrogeologic parameters related to Zones A and B including
groundwater flow direction, seepage velocity, saturated thickness, porosity,
receptor locations, can be found in the Table 4.
• Monitoring data from 1990 to 2000 were used for the "overall" trend analysis in
the sampling frequency optimization, and data from January, 1995 to December,
2000 "recent" trend analysis and other analyses in the MAROS detailed
optimization analysis.
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• There will be a continuation of the current hydraulic containment system in place
for the near future of the monitoring network.
• The current monitoring network adequately delineates the plume at the site for
the constituent of concern, TCE.
3.1 Data Consolidation
In MAROS, ground water monitoring data can be imported from simple database-format
Microsoft® Excel spreadsheets, Microsoft® Access tables, previously created MAROS
database archive files, or entered manually. The historical monitoring data from
McClellan were received in Excel database format. The URS, 2002 report defined the
wells into Zone A, Zone B and Zone AB (URS 2002, Tables 3.2 and 3.3). The columns
in the file where formatted to the MAROS Access file import format and then imported
into the MAROS software using the import tool. The long-term monitoring raw data
contained many non-detects, trace level results, and duplicates. Therefore, in the
MAROS software the raw data are filtered, consolidated, and the period of interest was
specified (i.e. monitoring data from 1990 to 2000) as well as the wells of interest for the
zone of interest. The MAROS analysis was applied separately for Zone A and Zone B
monitoring networks. For statistical evaluation of the data, a representative value for
each sample point in time is needed. MAROS has many automated options to choose
how these values are assigned. For the McClellan data, non-detects values were
chosen to be set to the minimum detection limit, allowing for uniform detection limits over
time. Trace level results were chosen to be represented by their actual values and
duplicates samples were chosen to be assigned the average of the two samples. The
reduced data for each well were viewed as a time series in a graphical form on a linear
or semi-log plot generated by the software.
3.2 Overview Statistics: Plume Trend Analysis
3.2.1 Mann-Kendall/Linear Regression Analysis
The goal of the Mann-Kendall and Linear Regression temporal trend analysis is to
assess the historical trend in the concentrations over time. These trend estimates are
then analyzed to identify the source and tail region overall stability category as well as
gaining an understanding of the individual well concentrations overtime (see Figure 3 for
further details). The TCE historical data for monitoring wells in both Zones A and B as
well as the extraction wells in Zone AB were assessed for trends. No data consolidation
was performed to condense the sampling into regular sample intervals.
Zone A
All 32 monitoring wells in Zone A had sufficient data within the time period of 1990 to
2000 (greater than 6 sample events) to assess the trends in the wells. Trend results
from the Mann-Kendall and Linear Regression temporal trend analysis for Zone A
monitoring wells are given in Table 5. The monitoring well trend results for Zone A show
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that 9 out of 10 source wells and 9 out of 22 tail wells have a Probably Decreasing,
Decreasing, or Stable trend. Both methods gave similar trend estimates for each well.
When considering the spatial distribution of the trend results (Figures 6 and 7 - maps
created in ArcGIS from MAROS results), the majority of the decreasing or stable trend
results are located near in the source area, indicating a decreasing source region
concentration. Areas with no trend tended to be in the tail or edge of the plume where
the wells have been sampled less frequently.
Well Type
Source
Tail
Extraction (Zone AB)
Zone A MAROS Trend Analysis
PD, D, S
9 of 10 (90%)
1 1 of 22 (50%)
5 of 6 (83%)
I, PI
Oof 10(0%)
1 of 22 (5%)
0 of 6 (0%)
Note: Decreasing (D), Probably Decreasing (PD), Stable (S), Probably Increasing (PI), and Increasing (I)
Zone B
All 14 monitoring wells in Zone B had sufficient data within the time period of 1990 to
2000 (greater than 6 sample events) to assess the trends in the wells. Trend results
from the Mann-Kendall and Linear Regression temporal trend analysis for Zone B
monitoring wells are given in Table 6. The majority of the wells in Zone B have no trend
in the historical data. However, as of 2000, only one of the wells in Zone B is actually
above the MCL for TCE. Both of the statistical methods used to evaluate trends (Mann-
Kendall and Linear Regression) gave similar trend estimates for each well. When
considering the spatial distribution of the trend results (Figures 8 and 9 - maps created
in ArcGIS from MAROS results), the majority of the decreasing or stable trend results
are located near in the source area, indicating a decreasing source region concentration.
Areas with no trend tended to be in the tail or edge of the plume where the wells have
been sampled less frequently.
Well Type
Source
Tail
Zone B MAROS Trend Analysis
PD, D, S
0 of 1 (0%)
6 of 13 (50%)
1, PI
1 of 1 (100%)
1 of 13(8%)
Note: Decreasing (D), Probably Decreasing (PD), Stable (S), Probably Increasing (PI), and Increasing (I)
Zone AB
All 6 extraction wells had sufficient data within the time period of 1990 to 2000 (greater
than 6 sample events) to assess the trends in the wells. Trend results from the Mann-
Kendall and Linear Regression temporal trend analysis for Zone AB extraction wells are
given in Table 5. The extraction well trend results show that 5 out of 6 wells have a
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Probably Decreasing, Decreasing, or Stable trend. Both methods gave similar trend
estimates for each well. The extraction wells in the source mostly show decreasing or
probably decreasing trends (Figures 10 and 11 - maps created in ArcGIS from MAROS
results).
Although monitoring wells and extraction wells are present in the well network, these
well trend results need to be treated differently for the purpose of individual trend
analysis interpretation primarily due to the different course of action possible for the two
types of wells. For monitoring wells, strongly decreasing concentration trends may lead
the site manager to decrease their monitoring frequency, as well look at the well as
possibly attaining its remediation goal. Conversely, strongly decreasing concentration
trends in extraction wells may indicate ineffective or near-asymptotic contamination
extraction, which may in turn lead to either the shutting down of the well or a drastic
change in the extraction scheme. Other reasons favoring the separation of these two
types of wells in the trend analysis interpretation is the fact that they produce very
different types of samples. Typically extraction wells possess screens that are much
larger than those of the average monitoring well. Therefore, the potential for the dilution
of extraction well samples is far greater than monitoring well samples.
3.2.2 Moment Analysis
The moment analysis in the MAROS software was applied at the McClellan site in order
to gain a better understanding of the overall plume stability in both Zones A and B.
Monitoring well data from 1990 to 2000 were used for the moment analysis. Sampling
frequency for these wells was very irregular, ranging from quarterly or annual in 1990 to
annual or biennial in 2000. Therefore, all Zone A spatial moment analyses were based
on sampling events redefined on a yearly basis, that is, data collected between January
1st and December 31st of a year were treated as if from the same sampling event
performed on July 1st of that year, with the geometric mean result utilized for each
location. Whereas, all Zone B spatial moment analyses were based on sampling events
redefined on a biennial basis, that is, data collected between January 1st and December
31st of two years were treated as is from the same sampling event performed on July
1st of the first year, with the geometric mean result utilized for each location.
Zone A
Moment trend results from the Zeroth, First, and Second Moment analyses for the Zone
A monitoring well network were varied. Moment Trend results from the moment trend
analysis for selected Zone A well dataset are given in the Moment Analysis Report,
Appendix B. Approximately 32 wells were used in the Zone A moment analysis. Wells
with redundant spatial concentration information were not utilized in the moment analysis
(i.e. MW-1041).
The zeroth moment analysis showed a stable trend (no change in dissolved mass) over
time (Appendix B). The zeroth moment or mass estimate can show high variability over
time, largely due to the fluctuating concentrations at the most contaminated wells as well
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as a varying monitoring well network. In order to reduce the fluctuating factors that could
influence a mass trend, the data were consolidated to annual sampling and the zeroth
moment trend evaluated. Another factor to consider when interpreting the mass
increase over time is the change in the spatial distribution of the wells sampled
historically. At the McClellan OU D site there were changes in the well distribution over
time, due to addition and subtraction of wells from the well network as well as changes in
sampling frequency. Therefore, the results from the MAROS mass trend over tim at the
site should be evaluated along with the trend analysis results. The trend in mass is more
likely decreasing over time, in accordance with the decreasing trend results (decreasing
concentrations) seen in the majority of wells in the source area.
Moment
Type
Zeroth
First
Second
Zone A Mann-Kendall Trend Analysis
Trend
Zone A
Stable to Decreasing
No Trend
No Trend
Comment
The amount of dissolved mass has decreased over time.
The center of mass remained in relatively the same location through
time, with slight movement forward or backward along the direction of
groundwater flow.
Stable to no trend, indicating that wells representing very large areas
both on the tip and the sides of the plume show little conclusive change
in concentrations.
The first moment, or center of mass, for each sample event in Zone A remained
relatively stable to no trend in distance relative to the approximate source location, see
Figure 12, as well as the MAROS First Moment Reports in Appendix B. The center of
mass remained in relatively the same location through time, with slight movement
forward or backward along the direction of groundwater flow. These spatial and
temporal trends in the center of mass distance from the source location can indicate
transient movement based on season variation in rainfall or other hydraulic
considerations. With no appreciable movement or a neutral trend in center of mass as is
the case at McClellan there is additional confirmation separate from the individual well
trend analysis, that the plume is relatively stable to decreasing. This stable center of the
mass indicates that both the mass and mass movement over time, the plume itself is
stable.
Zone B
Moment trend results from the Zeroth, First, and Second Moment analyses for the Both
the Zone B monitoring well network were similar. Moment Trend results from the
moment trend analysis for selected Zone B monitoring well dataset are given in the
Moment Analysis Report, Appendix B. Approximately 12 wells were used in the Zone B
moment analysis. Wells with redundant spatial concentration information were not
utilized in the moment analysis (i.e. MW-1003 and MW-1028).
The zeroth moment analysis showed a stable trend (no change in dissolved mass) over
time (Appendix B). The zeroth moment or mass estimate can show high variability over
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time, largely due to the fluctuating concentrations at the most contaminated wells as well
as a varying monitoring well network. In order to reduce the fluctuating factors that could
influence a mass trend, the data were consolidated to biennial sampling and the zeroth
moment trend evaluated. Another factor to consider when interpreting the mass
increase over time is the change in the spatial distribution of the wells sampled
historically. At the McClellan OU D site there were changes in the well distribution over
time, due to addition and subtraction of wells from the well network as well as changes in
sampling frequency. . Therefore, the results from the MAROS mass trend over time at
the site should be evaluated along with the trend analysis results. The trend in mass is
more likely decreasing over time, in accordance with the decreasing trend results
(decreasing concentrations) seen in the majority of wells.
The first moment, or center of mass, for each sample event in Zone B remained
relatively stable in distance relative to the approximate source location, see Figure 13,
as well as the MAROS First Moment Reports in Appendix B. The center of mass
remained in relatively the same location through time, with slight movement forward or
backward along the direction of groundwater flow. Similar to Zone A, these spatial and
temporal trends in the center of mass distance from the source location can indicate
transient movement based on season variation in rainfall or other hydraulic
considerations. With no appreciable movement or a neutral trend in center of mass as is
the case at McClellan there is additional confirmation separate from the individual well
trend analysis, that the plume is relatively stable to decreasing. This stable center of the
mass indicates that both the mass and mass movement over time, the plume itself is
stable.
Moment
Type
Zeroth
First
Second
Zone B Mann-Kendall Trend Analysis
Trend
Zone B
Stable to Decreasing
Stable
No Trend
Comment
The amount of dissolved mass has decreased over time.
The center of mass remained in relatively the same location through
time, with slight movement forward or backward along the direction of
groundwater flow.
Stable to no trend, indicating that wells representing very large areas
both on the tip and the sides of the plume show little conclusive change
in concentrations.
As was the case with the Zone A moment analysis, the second moment, or spread of the
plume over time in both the x and y directions for the sample events in Zone B, showed
no trend over time, Appendix B. The second moment provides a measure of the spread
of the concentration distribution about the plume's center of mass. Analysis of the
spread of the plume indicates no trend in the plume, indicating that wells representing
very large areas both on the tip and the sides of the plume show highly variable
concentrations over time or that the wells have not been sampled consistently enough to
show a clear trend. The no trend results indicate that at the McClellan OU D site the
highly variable well network from changes in the well distribution over time, due to
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addition and subtraction of wells from the well network as well as changes in sampling
frequency, results in non-specific trend results.
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3.2.3 Overview Statistics: Plume Analysis
Overview Statistics Results:
• Overall trend for Source region: Stable,
• Overall trend for Tail region: near Stable (No Trend),
• Overall results from moment analysis indicate a stable to decreasing plume,
• Overall monitoring intensity needed: Moderate.
In evaluating overall plume stability, the trend analysis results and all monitoring wells
were assigned "Medium" weights within the MAROS software (as described in Figure 4),
assuming equal importance for each well and each trend result in the overall analysis.
These results matched with the judgment based on the visual comparison of TCE
plumes over time, as well as the Moment Analysis. The TCE concentrations observed
over the history of monitoring at the site are plotted in Appendix A. The Zone A TCE
plume observed in 1995 was very similar to that of 2000, indicating that the TCE plume
is relatively stable over time, even when the individual well concentration trends in the
MAROS analysis indicate a near stable overall plume trend.
For a generic plume, the MAROS software indicates:
• No recommendation for sampling frequency
• Zone A may need 25 wells for sampling network
• Zone B may need 25 wells for sampling network
These MAROS results are for a generic site, and are based on knowledge gained from
applying the MAROS Overview Statistics. There is no recommendation for frequency of
sampling for the whole monitoring network due to some uncertainty in the trends and the
presence of an active remediation system. Also, the recommended the number of wells
seems high when applied to each zone individually. So, although the overall plume
trend analysis shows a near stable plume, no general sampling frequency
recommendation was assessed by the MAROS software. Therefore, a more detailed
analysis was performed using the MAROS 2.0 software in order to allow for possible
reductions on a well-by-well basis, frequency and well redundancy analysis were
conducted. These overview statistics were also used when evaluating a final
recommendation for each well after the detailed statistical analysis was applied.
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3.3 Detailed Statistics: Optimization Analysis
Monitoring data from 1990 to 2000 were used for the optimization analysis. 2001 data
were not used because passive diffusion sampling was used in sample collection, with
anomalous results. Wells used in the analysis include 32 Zone A monitoring wells, 14
Zone B monitoring wells and 6 extraction wells screened in Zone AB (Table 1).
Due to the variable and irregular sampling frequency as described in Section 1.2, some
monitoring wells have only 5-7 data records available from 1990 to 2000. This
resulted in only a portion of the wells being sampled on each quarterly sampling event.
Therefore, all spatial analyses (sampling location determination and risk-based site
cleanup evaluation) were based on sampling events redefined on a yearly basis, that is,
data collected between January 1st and December 31st of a year were treated as coming
from the same sampling event performed on July 1st of that year.
In the well redundancy and well sufficiency analyses, only the monitoring wells were
used. For the sampling frequency analysis, both the monitoring wells and the extraction
wells were analyzed. Only Zone A was analyzed with the data sufficiency analysis for
evaluating the risk-based site cleanup. The data sufficiency or power analysis for Zone
B was not performed because Zone B has only one monitoring well with concentrations
above MCL. Results for well redundancy, well sufficiency, sampling frequency, and data
sufficiency analyses are detailed in the following sub-sections.
3.3.1 Well Redundancy Analysis - Delaunav Method
The goal of the well redundancy analysis is to identify wells that are spatially redundant
within monitoring network as candidates for removal from the sampling plan. The
approach allows elimination of sampling locations that have little impact on the historical
characterization of a contaminant plume.
Zone A
Among the 32 Zone A monitoring wells, 31 were used in the well redundancy analysis
(Table 1). MW-1041 was excluded from the analysis because it duplicates MW-1042
both spatially and in concentration levels over time. The Delaunay analysis was
conducted with yearly averages from the latest 6 years of data (1995 to 2000). The
MAROS results show that 3 monitoring wells (MW-14, MW-241, and MW-72) are
candidates for elimination from the existing long-term monitoring network (Table 7).
These wells are overall most redundant in the past 6 years, from the standpoint of their
contribution to the spatial definition of the plume.
After consideration of the MAROS recommendations and the need for plume and site
characterization, 3 wells were recommended for elimination from the existing 32-well
Zone A monitoring network, resulting in a reduction of 9%.
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Well removal candidates include (Table 7):
MW-1041
MW-14
MW-241
A qualitative confirmation was performed to assess that eliminating these 3 wells from
the 32-well monitoring network would not cause inadequate plume delineation and
spatial concentration representation. The Zone A TCE plume observed in 2000 was
hand contoured before and after removal of the 3 wells resulting in no significant plume
size or concentration changes, indicating that the information loss in by eliminating these
wells would be negligible. Also, the TCE plume shown in Figure 14 generated based on
the existing and optimized networks using 1999 data agree with each other quite well,
indicating that eliminating these wells from the monitoring network does not show any
significant loss of information. Therefore, information gained from the MAROS trend
analysis and a qualitative assessment of the concentration history of the wells, indicated
these wells could be removed from the Zone A monitoring network without significant
loss of information.
The MAROS software suggested eliminating MW-72 from the monitoring network,
however, there were site-specific reasons to keep the well within the monitoring network
(Table 7). Well MW-72 currently (as of the 2000 sampling) has concentration greater
than the MCL for TCE and the well is located on the plume centerline and is the basis for
risk-based power analysis for attainment at the compliance boundary. Similarly, there
was a well that was not used in the Delaunay analysis that is clustered with an
equivalent duplicate, MW-1041 is very close to MW-1042 with similar concentrations and
concentration trends over time (Table 1 and Table 7). The clustered well MW-1041 with
similar screen intervals, concentration trends, and concentration ranges to the nearby
well, MW-1042, is suggested for elimination without having used them in the MAROS
well redundancy analysis.
Zone B
Among the 14 Zone B monitoring wells, 12 were used in the Delaunay analysis (Table
1). MW-1003 and MW-1028 were excluded from the analysis because they duplicate
MW-1001 and MW-1027, respectively. The Delaunay analysis was conducted with
yearly averages from the latest 6 years (1995 to 2000). The MAROS results show that
no monitoring wells that could be eliminated from the existing long-term monitoring
network (Table 8).
After consideration of the MAROS recommendations and the need for plume and site
characterization, 2 wells were recommended for elimination from the existing 14-well
monitoring network, resulting in a reduction of 14%.
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Well removal candidates include (Table 8):
MW-1003
MW-1028
A qualitative confirmation was performed to assess that eliminating these 2 wells from
the 14-well monitoring network would not cause inadequate plume delineation and
spatial concentration representation. The Zone B TCE plume observed in 2000 was
hand contoured before and after removal of the 2 wells resulting in no significant plume
size or concentration changes, indicating that the information loss by eliminating these
wells would be negligible. Also, the TCE plume shown in Figure 15 generated based on
the existing and optimized networks using 1999 data agree with each other quite well,
indicating that eliminating these wells from the monitoring network does not show any
significant loss of information. Therefore, information gained from the MAROS trend
analysis and a qualitative assessment of the concentration history of the wells, indicated
these wells could be removed from the Zone B monitoring network without significant
loss of information.
Although these wells (MW-1003 and MW-1028) were not used in the well redundancy
analysis they were clustered wells with equivalent duplicates very close to a well that
had similar concentrations (lower than the MCL or DL) and concentration trends over
time, which indicated these wells could be eliminated without significant loss of
information. These clustered wells with similar screen intervals, concentration trends,
and concentration ranges to a nearby well were suggested for elimination without having
used them in the MAROS well redundancy analysis. However, information gained from
the MAROS trend analysis and a qualitative assessment of the concentration history of
the well, indicated these wells could be eliminated without significant loss of information.
Zones A & B
An additional redundancy analysis was performed on the McClellan well network that
excluded the wells on the extreme periphery of the network (Zone A: far up-gradient
wells: MW-1041, 1042, MW-1064; far cross-gradient wells: MW-237, MW-1026; and far
down-gradient well: MW-350; Zone B: far up-gradient wells MW-1043 and MW-1010;
far cross-gradient wells MW-1027 and MW-1028). The above analysis included these
wells in the MAROS analysis, and these wells were not recommended for removal from
the network as they are required to define the boundary of the Zone A and Zone B
plumes. However, a monitoring network that includes the periphery wells significantly
"over-captures" the plume as these wells are located far from the likely plume boundary.
The MAROS analysis that excluded these periphery wells indicated that additional wells
would be required at the down-gradient edge of the plume to define the boundary of the
plumes in Zone A and B. For example in Zone A, the area East of MW-12 is a likely
location for a new down-gradient boundary well. In the Figure 9 from the well sufficiency
analysis in the next section, almost all triangles outside the plume region are "Medium"
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in estimation errors, those areas that are close to the plume could be potential new
locations if the periphery network wells were removed.
Although the MAROS analysis indicates that new wells might be able to replace the
periphery wells, the decision to stop sampling the periphery wells should be made with
consideration to non-statistical considerations, such as regulatory, community, and/or
public health issues. Non-statistical considerations may indicate that continued
sampling of the periphery wells may be warranted.
3.3.2 Well Sufficiency Analysis - Delaunav Method
The goal of the sampling location determination was to identify wells that are redundant
within the monitoring network as candidates for removal from the sampling plan. The
approach allows elimination of sampling locations that have little impact on the historical
characterization of a contaminant plume. An extended method based on the Delaunay
method can also be used for recommending new sampling locations in areas where
additional plume information is needed.
Zone A
The well sufficiency analysis SF values obtained from the aforementioned analysis were
used to generate Figure 16, which recommends the triangular regions for placing new
sampling locations. It is seen that almost all triangular regions (except one) have S
(small) or M (medium) estimation errors. Also, considering the relatively small size of
the TCE plume, which is adequately delineated by the current monitoring system, the
current sampling locations are sufficient. Zone A well sufficiency analysis was
performed and no new locations are recommended.
Zone B
The Zone B well sufficiency analysis was not performed because only one well (MW-54)
out of the 14 monitoring wells has concentrations above the MCL for TCE. Therefore,
considering the relatively small size of the TCE plume, which is adequately delineated by
the current monitoring system, the current sampling locations are sufficient. No new
locations are recommended for the Zone B monitoring network.
3.3.3 Sampling Frequency Analysis - Modified CES Method
The sampling frequency analysis, using the Modified CES method, was applied to
optimize the sampling frequency for each sampling location based on the magnitude,
direction, and uncertainty of its concentration trend of its recent and historical TCE data.
The Modified Cost Effective Sampling is a temporal analysis that estimates the lowest-
frequency sampling schedule for a given groundwater monitoring location yet still
provide needed information for regulatory and remedial decision-making. In the
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sampling frequency analysis, sampling events were defined on a quarterly basis
(corresponding to the actual sampling schedules) so that all the data records could be
used in the temporal analysis.
Zone A
For the monitoring well system, considering all the wells prior to the well redundancy
analysis, 20 wells are recommended to be sampled biennially, and 12 annually. All 6
extraction wells are recommended to be sampled annually. Because the pre-
optimization sampling frequencies for the Zone A wells were already very low (mostly
annual and biennial), the sampling frequency optimization results in no significant
reduction and in some cases there is an increase in sampling recommended. Results
from the sampling frequency analysis for the 32 Zone A monitoring wells and the 6
extraction wells are given in Table 9. Most of the annual or lower-than-annual sampling
frequency recommendations (Table 9) were due to insufficient recent data (i.e., less than
6 data records), which prevented the MAROS estimation of concentration trend using
recent monitoring data. After considering the MAROS results and the historic and recent
concentration levels at these wells, final sampling frequency recommendations are
provided in Table 9.
In most cases, the frequency recommendations from the MAROS software were not
adopted due to data inadequacy. Sampling frequencies for all the wells was irregular,
ranging between quarterly and biennial from 1990 to 2000 (Table 1). Many monitoring
wells have only 5-7 data records available during an 11-year period (from 1990 to
2000). Because the minimum data requirement for the sampling frequency trend
analysis is 6 sampling events, the recent trends for many wells were not able to be
estimated. This resulted in frequency results that were solely estimated from the overall
data trend, which was not very reliable given the data inadequacy. For instance, well
MW-74 has only two concentration records between 1994 and 2000, making the
estimation of recent trend impossible. In cases of data inadequacy, the MAROS
frequency analysis will always assign conservative results, i.e., quarterly or semiannual
instead of annual or biennial.
However, a qualitative assessment of the concentration levels and concentration history
for these wells resulted in more reasonable sampling frequency recommendations
(Table 9). For example, well MW-1026 was suggested for a biennial sampling because
its concentrations have been below the MCL or DL since 1993. Also, the relative size of
the plume as well as the plume stability which remains stable according to the overview
statistical analysis, it is unlikely that the plume will show rapid changes over the long-
term. Therefore, keeping the frequency of most wells at annual and biennial level will
continue to allow for adequate plume delineation.
Zone B
For the Zone B monitoring well system, assessing all the wells prior to the well
redundancy analysis, 12 wells can be sampled biennially and 2 annually. In all cases
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except for well MW-1028, these sampling recommendations agree with the sampling
frequency currently performed at the site. Results from the Modified CES method for the
14 Zone B monitoring wells are given in Table 10. Similar to the MAROS Zone A
analysis, some Zone B annual sampling frequency recommendations needed to be
adjusted when taking into consideration the historic and recent concentration levels at
these wells.
3.3.4 Data Sufficiency - Power Analysis
The MAROS data sufficiency analysis indicates the current monitoring network is
sufficient in terms of evaluating risk-based site target level status, if the pump-and-treat
remedial system contains the plume and keeps reducing the TCE concentration in the
aquifer. Table 11 shows the risk-based site cleanup status at selected sampling events
for both analyses (i.e., HSCB at 1000 ft and HSCB at 100 ft downgradient of the
monitoring system) assuming normality of the projected data. The cleanup standard has
been "attained" for both HSCBs for all the years in the assessment. The high power
indicates the site-wide TCE concentration level at the HSCB is much lower than the
cleanup goal and the number of sampling points is more than sufficient. This analysis
indicates that the monitoring system is working because it is powerful enough to
accurately reflect the location of the plume relative to the compliance point.
In the MAROS data sufficiency analysis, statistical power analysis was used to assess
the sufficiency of monitoring plans for detecting the difference between the mean
concentration and cleanup goal. Results from the analysis indicate plume location from
the risk-based standpoint at a hypothetical statistical compliance boundary (HSCB). The
power and expected sample size associated with the target level evaluation may indicate
the need for expansion or redundancy reduction of future sampling plans. This analysis
was performed based on sampling events defined on a yearly basis for the Zone A
monitoring well system only.
In the risk-based site cleanup evaluation, two analyses were performed. In the first
analysis, the distance from the most downgradient well to the nearest downgradient
receptor (HSCB) was assumed to be 1000 ft. The general groundwater flow angle is to
the Southwest. Selected plume centerline wells are MW-11, MW-72, MW-91, and MW-
92 (Table 12). The analysis was conducted with yearly averages of the TCE data from
the latest 6 years (1995 to 2000). The second analysis used the same parameters
except that the distance from the HSCB was assumed to be 100 ft. Table 13 shows
plume centerline concentration regression results for each selected sampling event,
which range from 3.8 x 10"5 to 6.8 x 10"4 per ft.
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4.0 SUMMARY AND RECOMMENDATIONS
In recent years, the high cost of long-term monitoring as part of active or passive
remediation of affected ground water has made the design of efficient and effective
ground water monitoring plans a pressing concern. Periodically updating and revising
long-term monitoring programs with changing conditions at the site can mean
considerable savings in site monitoring costs. The MAROS decision-support software
presented in this report assists in revising existing long-term monitoring plans based on
the historical and current monitoring data and plume behavior over time.
The MAROS 2.0 sampling optimization software/methodology has been applied to the
McClellan existing OU D long-term monitoring program as of December 2000. The
optimization results and subsequent recommendations allow for optimization of the
spatial and temporal groundwater monitoring system in place at the McClellan site. The
current long-term monitoring network could be optimized through reduction in sampling
locations (Results are summarized in Table 14).
Overview Statistics
Both the Mann-Kendall and Linear Regression temporal trend methods gave similar
trend estimates for each well. Results from the temporal trend analysis indicate that
90% of the plume source area monitoring wells in Zone A indicate a Probably
Decreasing, Decreasing, or Stable TCE concentration trend, whereas only about half of
the wells in the tail and edges of the plume have similar trends. The majority of the wells
in Zone B have no trend in the historical data. However, as of 2000, only one of the
wells in Zone B is actually above the MCL for TCE. The trend results for the extraction
wells in the source area indicate most wells have Probably Decreasing, or Decreasing
concentrations over time.
Results from the moment trend analysis give some evidence of a stable plume, with the
dissolved mass showing a decrease over time, whereas the center of mass and the
plume spread shows no trend over time, probably due to the change in sample locations
and frequency over time. Overall plume stability temporal results recommend a
moderate monitoring strategy due to the near stable to decreasing OU D TCE plume.
The overview results are relatively generic and not well-by-well specific, therefore, a
detailed statistical analysis with a well-by-well analysis was performed.
Detailed Statistics
Further analysis from the well redundancy spatial analysis using the Delaunay method
optimization indicate that
• 3 monitoring wells could be eliminated from the existing Zone A monitoring
network of 32 wells and
• 2 wells could be eliminated from the existing Zone B monitoring network of
14 wells (Table 14)
without compromising the reliability of the monitoring system.
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In addition, the well sufficiency spatial analysis indicated there are no areas within the
network where there are high uncertainties in the predicted TCE concentration.
Therefore there are no recommended new monitoring wells for Zones A or B. The
sampling frequency optimization analysis using the temporal MCES method, resulted in
sampling frequency that is relatively consistent with the sampling schedule currently in
use at the site for both Zone A and Zone B well networks (Table 14).
Data sufficiency analysis using power analysis methods, shows that the site has
achieved target levels at (or further than) the compliance boundary 100 ft downgradient
from the most downgradient well. This analysis indicates that the monitoring system is
working because it is powerful enough to accurately reflect the location of the plume
relative to the compliance boundary. This shows the sufficiency of the monitoring system
in terms of evaluating risk-based site target level status if the pump-and-treat remedial
system continues to contain the plume and keeps reducing the TCE concentration in the
OU-D plume.
The recommended long-term monitoring strategy results in small reduction in sampling
costs and allows site personnel to develop a better understanding of plume behavior
over time. A reduction in the number of redundant wells is expected to result in a
moderate cost savings over the long-term at McClellan AFB. The MAROS optimized
plan consists of 47 wells: 17 sampled annually, and 30 sampled biennially. The MAROS
optimized plan would result in 32 samples per year, compared to 34 samples per year
(17 annual and 34 biennial) in the current sampling program. Implementing these
recommendations could lead to a 6% reduction from the current monitoring plan in terms
of the samples to be collected per year. An approximate cost savings estimate of $300
per year is projected while still maintaining adequate delineation of the plume as well as
knowledge of the plume state over time.
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CITED REFERENCES
Air Force Center for Environmental Excellence (AFCEE), 2002. Monitoring and
Remediation Optimization System (MAROS) 2.0 Software Users Guide.
Ridley, M.N. et al., 1995. Cost-Effective Sampling of Groundwater Monitoring Wells, the
Regents of UC/LLNL, Lawrence Livermore National Laboratory.
Air Force Center for Environmental Excellence (AFCEE), 1997, AFCEE Long-Term
Monitoring Optimization Guide, http://www.afcee.brooks.af.mil.
Aziz, J, Ling, M., Newell, C., Rifai, H., and Gonzales, J., 2002, MAROS: a Decision
Support System for Optimizing Monitoring Plans, Ground Water, In Press.
Gilbert, R. O., 1987, Statistical Methods for Environmental Pollution Monitoring, Van
Nostrand Reinhold, New York, NY, ISBN 0-442-23050-8.
Radian Corporation, 1997. Installation Restoration Program, Groundwater Monitoring
Plan - Final.
Radian Corporation, 1999. Five Year Review Report, McClellan Air Force Base - Final.
URS, 2002. Former McClellan Air Force Base Installation Restoration Program,
Groundwater Monitoring Program - Quarterly Report - 1st Quarter 2002.
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January 15, 2003
GSI Job No. G-2236-15
V
GROUNDWATER
SERVICES, INC.
TABLES
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
McClellan AFB
Sacramento Valley, California
Table 1 Sampling Locations Used in the MAROS Analysis
Table 2 Mann-Kendall Analysis Decision Matrix
Table 3 Linear Regression Analysis Decision Matrix
Table 4 Zone A & B Aquifer Site-Specific Parameters
Table 5 Results of Zone A McClellan OU D Trend Analysis
Table 6 Results of Zone B McClellan OU D Trend Analysis
Table 7 Zone A Redundancy Analysis Results - Delaunay Method
Table 8 Zone B Redundancy Analysis Results - Delaunay Method
Table 9 Zone A Sampling Frequency Analysis Results - Modified CES
Table 10 Zone B Sampling Frequency Analysis Results - Modified CES
Table 11 Zone A Selected Plume Centerline Wells for Risk-Based Site Cleanup
Evaluation - Power Analysis
Table 12 Zone A Plume Centerline Concentration Regression Results - Power
Analysis
Table 13 Zone A Risk-Based Site Cleanup Evaluation Results - Power Analysis
Table 14 Summary of MAROS Sampling Optimization Results
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Table 1
Sampling Locations Used in the MAROS Analysis
McClellan AFB OU-D
Sacramento Valley, California
Welt
Name
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
Monitoring
Zone 1
A
A
A
A
A
lABorA
lABorA
lABorA
lABorA
lABorA
lABorA
lABorA
lABorA
A
A
Used in Delaunay
Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling data
available since 1 990)
Sampled annually; recent data were <= TL but
>=MCL
Sampled annually; recent data were <= TL but
>=MCL
Sampled annually; it is an interior plume well
Sampled annually since 90, biennially since 99
because data fell below MCL
Sampled annually; it is an interior plume well
Sampled annually; recent data were <= TL but
>=MCL
Sampled annually, then biennially since 99
because data fell below MCL
Sampled annually, then biennially since 99
because data fell below MCL
Sampled annually on average, then biennially
since 01 because data fell below MCL
Sampled annually or less frequently since 91 ,
then biennially since 01 because data fell below
DL
Sampled annually; recent data were <= TL but
>=MCL
Sampled quarterly the first 6 quarters, then
annually on average, then biennially since 01
because data fall below MCL
Sampled quarterly the first 6 quarters, then
biennially on average, then annually since 99
because data were <= TL but >= MCL
Sampled annually, then biennially since 99
because data fell below MCL
Sampled annually, then biennially since 99
because data fell below MCL
Note: 1) Monitoring Zones assigned as shown in URS, 2002, 1Q02 report (Tables 3.2 and 3.3).
2) TL = the upper 90% tolerance limit from Groundwater Monitoring Plan Final (Radian Corporation 1997)
3) MCL = the maximum contaminant level of TCE, DL = detection limit, IAB = intermediate zone between zone
A and Zone B, AB = both zone A and zone B
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 2 of 2
If
GROUNDWATER
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Table 1
Sampling Locations Used in the MAROS Analysis
McClellan AFB OU-D
Sacramento Valley, California
Welt
Name
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
Monitoring
Zone
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
Used in Delaunay
Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No, duplicates MW-
1042
Yes
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling
started in 1990)
Sampled annually, then biennially since 95
because data fell below MCL
Sampled annually, then biennially since 95
because data fell below MCL
Sampled quarterly the first year, then annually on
average, then biennially since 99 because data
fall below MCL
Sampled annually since 93, then biennially since
96 because data fell below MCL
Sampled annually since 93, then biennially since
99 because data fell below MCL
Sampled annually since 93; recent data were <=
TL but >= MCL
Sampled annually since 93; recent data were <=
TL but >= MCL
Sampled at least annually since 95, then
biennially since 99 because data fell below MCL
Sampled at least annually since 95, then
biennially since 98 because data were <= MCL
Sampled quarterly since 97, then biennially since
99 because data fell below MCL
Sampled quarterly since 99, then biennially since
00 because data fell below MCL
Sampled quarterly in the first 2 years, then
biennially because data fell below MCL
Sampled biennially since data were <= MCL
Sampled biennially since data were <= MCL
Sampled annually in the first 4 years, then
biennially since 95 because data fell below MCL
Note: 1) Monitoring Zones assigned as shown in URS, 2002, 1Q02 report (Tables 3.2 and 3.3).
2) TL = the upper 90% tolerance limit from Groundwater Monitoring Plan Final (Radian Corporation 1997)
3) MCL = the maximum contaminant level of TCE, DL = detection limit, IAB = intermediate zone between zone
A and Zone B, AB = both zone A and zone B
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 3 of 2
If
GROUNDWATER
SERVICES, INC.
Table 1
Sampling Locations Used in the MAROS Analysis
McClellan AFB OU-D
Sacramento Valley, California
Welt
Name
MW-1064
MW-1073
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
Monitoring
Zone
A
A
AB
AB
AB
AB
AB
AB
B
B
B
B
B
B
B
Used in Delaunay
Analysis?
Yes
Yes
No, screened in
zones A and B
No, screened in
zones A and B
No, screened in
zones A and B
No, screened in
zones A and B
No, screened in
zones A and B
No, screened in
zones A and B
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling
started in 1990)
Sampled quarterly in the first 6 quarters, then
biennially since 93 because data fell below MCL
Sampled biennially since 93 because data were
<=MCL
Sampled annually except between 94 and 96 it
was sampled quarterly
Sampled annually except between 94 and 96 it
was sampled quarterly
Sampled annually except between 94 and 96 it
was sampled quarterly
Sampled annually except between 94 and 96 it
was sampled quarterly
Sampled annually except between 94 and 96 it
was sampled quarterly
Sampled annually except between 94 and 96 it
was sampled quarterly
Sampled annually on average, then biennially
since 95 because data fell below MCL
Sampled biennially because data were below
MCL or DL
Sampled annually on average, then annually
since 98; recent data were <= TL but >= MCL
Sample quarterly in the first 6 quarters, then
annually, then biennially since 99 because data
fell below MCL
Sample quarterly in the first 6 quarters, then
annually, then biennially since 01 because data
fell below MCL
Sample quarterly in the first 5 quarters, then
annually, then biennially since 97 because data
fell below MCL
Sampled annually, then biennially since 95
because data fell below MCL
Note: 1) Monitoring Zones assigned as shown in URS, 2002, 1Q02 report (Tables 3.2 and 3.3).
2) TL = the upper 90% tolerance limit from Groundwater Monitoring Plan Final (Radian Corporation 1997)
3) MCL = the maximum contaminant level of TCE, DL = detection limit, IAB = intermediate zone between zone
A and Zone B, AB = both zone A and zone B
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 4 of 2
If
GROUNDWATER
SERVICES, INC.
Table 1
Sampling Locations Used in the MAROS Analysis
McClellan AFB OU-D
Sacramento Valley, California
Well
Name
MW-105
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
Monitoring
Zone
B
B
B
B
B
B
B
Used in Delaunay
Analysis?
Yes
Yes
No, duplicates MW-
1001
Yes
Yes
No, duplicates MW-
1027
Yes
Used in Modified
CES Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Summary of Sampling History (sampling
started in 1990)
Sampled annually on average, then biennially
since 01 because data fell below MCL
Sampled annually on average, then biennially
since 96 because data fell below MCL or DL
Sampled annually on average, then biennially
since 01 because data fell below MCL
Sampled biennially on average, then biennially
since 01 because data fell below MCL or DL
Sampled biennially on average, then biennially
since 99 because data fell below MCL or DL
Sampled annually on average, then biennially
since 99 because data fell below MCL or DL
Sampled annually, then biennially since 95
because data fell below MCL or DL
Note: 1) Monitoring Zones assigned as shown in URS, 2002, 1Q02 report (Tables 3.2 and 3.3).
2) TL = the upper 90% tolerance limit from Groundwater Monitoring Plan Final (Radian Corporation 1997)
3) MCL = the maximum contaminant level of TCE, DL = detection limit, IAB = intermediate zone between zone
A and Zone B, AB = both zone A and zone B
-------
GSI Job No. G-2236-15
Issued 1/15/03
Page 1 of 1
If
GROUNDWATER
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Mann-Kendall
Mann-Kendall
Statistic
S>0
S>0
S>0
S<0
S<0
S<0
S<0
Table 2
Analysis Decision Matrix
Confidence in the
Trend
> 95%
90 - 95%
< 90%
< 90% and COV > 1
< 90% and COV < 1
90 - 95%
> 95%
(Aziz, et. al., 2002)
Concentration Trend
Increasing
Probably Increasing
No Trend
No Trend
Stable
Probably Decreasing
Decreasing
Table 3
Linear Regression Analysis Decision Matrix (Aziz, et. al., 2002)
Confidence in the
Trend
Log-slope
Positive
Negative
< 90%
90 - 95%
> 95%
No Trend
Probably Increasing
Increasing
COV < 1 Stable
COV > 1 No Trend
Probably Decreasing
Decreasing
-------
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-------
GSIJobNo. G-2236-15
Issued 1/15/03
Page 1 of 2
TABLE 5
Results of Zone A Trend Analysis
McClellan Air Force Base OU D
Sacramento Valley, California
Notes:
1. Consolidation of data included non-detect values set to the minium detection limit (0.001 mg/L)
and duplicate data for the quarter were averaged.
2. All wells that were part of the network in between 1990 and 2000 were analyzed.
If HV1CI-S. tXC-
Well
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
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
EW-73
EW-86
EW-87
EW-83
EW-84
EW-85
Well
Type3
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
MW
EW
EW
EW
EW
EW
EW
Well
Category '"
S
S
S
S
S
S
T
T
T
T
S
T
T
T
T
T
T
T
T
T
S
S
T
S
T
T
T
T
T
T
T
T
S
S
S
S
S
S
Mann-Kendall
Trend 4
D
D
D
D
D
D
NT
I
S
S
D
D
I
NT
NT
NT
D
NT
NT
NT
D
D
D
NT
S
S
PD
NT
NT
S
NT
NT
D
D
I
NT
D
D
Linear
Regression
Trend 4
D
D
D
D
D
D
PD
I
S
S
D
D
I
NT
NT
NT
D
NT
NT
NT
D
D
D
NT
S
NT
PD
NT
NT
S
PI
NT
D
D
I
NT
D
D
Overall
Trend 6
D
D
D
D
D
D
S
I
S
S
D
D
I
NT
NT
NT
D
NT
NT
NT
D
D
D
NT
S
S
PD
NT
NT
S
PI
NT
D
D
I
NT
D
D
Number
of
Samples
11
11
12
12
11
8
9
10
11
6
10
10
10
11
10
8
10
12
7
9
12
12
8
9
6
4
12
7
7
7
8
6
15
15
15
16
16
16
Number
of
Detects
11
11
12
12
11
8
3
9
11
0
10
10
4
4
2
6
9
9
5
5
12
12
5
9
4
3
6
5
1
1
4
5
15
15
15
16
16
16
-------
GSIJobNo. G-2236-15
Issued 1/15/03
Page 1 of 1
GSQUNDWATE!
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TABLE 6
Results of Zone B Trend Analysis
McClellan Air Force Base OU D
Sacramento Valley, California
Well
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-105
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
Well
Type3
T
T
S
T
T
T
T
T
T
T
T
T
T
T
Well
Category !
T
T
S
T
T
T
T
T
T
T
T
T
T
T
Mann-Kendall
Trend 4
NT
S
I
NT
NT
S
NT
NT
S
PD
S
NT
S
NT
Linear
Regression
Trend 4
NT
PD
I
NT
NT
S
NT
I
D
D
S
NT
S
NT
Overall
Trend 6
NT
S
I
NT
NT
S
NT
PI
PD
D
S
NT
S
NT
Number
of
Samples
10
8
10
12
15
12
8
8
10
11
5
6
10
6
Number
of
Detects
6
1
8
5
7
2
2
2
1
3
0
2
2
1
Notes:
1. Consolidation of data included non-detect values set to the minium detection limit (0.001 mg/L)
and duplicate data for the quarter were averaged.
2. All wells that were part of the network in between 1990 and 2000 were analyzed.
3. EW = Extraction Well; MW = Monitoring Well
4. Decreasing (D), Probably Decreasing (PD), Stable (S), No Trend (NT), Probably Increasing (PI), and Increasing (I)
5.3 = Source Zone Well; T = Tail Zone Well
6. Overall Trend is calculated from a weighted average of the Linear Regression and Mann-Kendall Trends.
For further details on this methodolgy refer to the MAROS Manual Appendix A.8.
-------
GSI Job No. G-2236-15
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Page 1 of 2
If
GROUNDWATER
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Table 7
Well Redundancy Analysis Results - Delaunay Method
McClellan AFB OU-D Zone A
Sacramento Valley, California
Well Name
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
MW-90
MW-91
MW-92
MW-237
MW-240
Well Used in Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Well
Redundancy
Analysis Result
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
MAROS
Interpreted
Well
Redundancy
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Comments
Spatially redundant
On plume centerline and used in
MAROS data sufficiency
analysis
Notes: 1) Yearly averages from 6 sampling events (1995 ~ 2000) were used in the above analysis
2) InsideSF = 0.20, HullSF = 0.01, AR = CR = 0.95
3)"-" = Not Applicable.
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 2 of 2
If
GROUNDWATER
SERVICES, INC.
Table 7
Well Redundancy Analysis Results - Delaunay Method
McClellan AFB OU-D Zone A
Sacramento, California
Well Name
MW-241
MW-242
MW-350
MW-351
MW-412
MW-458
MW-1004
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
Well Used in Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No: duplicates MW-1042
Yes
Yes
Yes
MAROS Weil
Redundancy
Analysis Result
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
-
Keep
Keep
Keep
MAROS
Interpreted
Well
Redundancy
Eliminate
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Eliminate
Keep
Keep
Keep
Comments
Spatially redundant
Duplicates MW-1042
Notes: Yearly averages from 6 sampling events (1995 ~ 2000) were used in the above analysis
InsideSF = 0.20, HullSF = 0.01, AR = CR = 0.95
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
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Table 8
Well Redundancy Analysis Results - Delaunay Method
McClellan AFB OU-D Zone B
Sacramento Valley, California
Well Name
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-105
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
Well Used in Analysis?
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No: duplicates MW-1001
Yes
Yes
No: duplicates MW-1027
Yes
MAROS Well
Redundancy
Analysis Result
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
-
Keep
Keep
-
Keep
MAROS
Interpreted
Well
Redundancy
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Keep
Eliminate
Keep
Keep
Eliminate
Keep
Comments
Duplicates MW-1003
Duplicates MW-1027
Notes: 1) Yearly averages from 6 sampling events (1995 ~ 2000) were used in the above analysis
2) InsideSF = 0.05 or 0.20, HullSF = 0.01 or 0.05, AR = CR = 0.95
3)"-" = Not Applicable.
-------
GSI Job No. G-2236-15
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Page 1 of 2
If
GROUNDWATER
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Table 9
Sampling Frequency Analysis Results - Modified CES
McClellan AFB OU-D Zone A
Sacramento Valley, California
Well
Name
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
MW-90
MW-91
MW-92
MW-237
MW-240
MAROS
Frequency
Based on
Recent
Trend"1
Annual
Annual
Annual
Annual
Semiannual
Annual
Annual
Annual
Annual
Annual
Annual
Semiannual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Frequency
Based on
Overall
Trend'21
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Semiannual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Recommended
Frequency'31
Annual
Annual
Annual
Annual
Semiannual
Annual
Biennial
Annual
Biennial
Biennial
Annual
Semiannual
Quarterly
Annual
Biennial
Annual
Biennial
Annual
Biennial
Annual
Frequency for
Optimized
Network
Annual
Annual
Annual
-
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Comments
Decreasing trend but still higher than MCL
Decreasing trend but still higher than MCL
Decreasing trend but still higher than MCL
-
Inside-plume well although with slightly
increasing trend
Concentrations higher than MCL
All historical concentrations below MCL or
DL
All historical concentrations (except one)
below MCL or DL
All historical concentrations below MCL or
DL
All historical concentrations below MCL or
DL
Recent concentrations higher than MCL
Recent concentrations higher than MCL
(the MAROS result was due to insufficient
recent data)
Recent concentrations higher than MCL
(the MAROS result was due to insufficient
recent data)
Recent concentrations below MCL or DL
All historical concentrations below MCL or
DL
Recent concentrations (except one) below
MCLorDL
Recent concentrations below MCL or DL
Recent concentrations (except one) below
MCLorDL
All historical concentrations below MCL or
DL
All historical concentrations below MCL or
DL
Notes: 1) The frequency determined by MAROS based on the analysis of recent data (data between 1994 and 2000)
2) The frequency determined by MAROS based on the analysis of overall data (data between 1990 and 2000)
3) The frequency finally recommended by MAROS after considering recent and overall frequency results as well
as the rates of change in these trends Rate parameters used are 0.5MCL/year, 1.0MCL/year, and 2.0MCL/year
for Low, Medium, and High rates, respectively; the MCL of TCE is 0.005 mg/L
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 2 of 2
If
GROUNDWATER
SERVICES, INC.
Table 9
Sampling Frequency Analysis Results - Modified CES
McClellan AFB OU-D Zone A
Sacramento Valley, California
Well
Name
MW-241
MW-242
MW-350
MW-351
MW-412
MW-458
MW-1004
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
MAROS
Frequency
Based on
Recent
Trend11
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Semiannual
Annual
Annual
Quarterly
Annual
MAROS
Frequency
Based on
Overall
Trend'2'
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Semiannual
Annual
Annual
Quarterly
Annual
MAROS
Recommended
Frequency13'
Annual
Annual
Annual
Annual
Biennial
Biennial
Annual
Semiannual
Biennial
Biennial
Quarterly
Annual
MAROS
Interpreted
Sampling
Frequency
Results
-
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
-
Biennial
Biennial
Biennial
Comments
-
Decreasing trend but still higher than
MCL
Recent concentrations below MCL or
DL
Recent concentrations higher than
MCL
All historical concentrations below MCL
orDL
All historical concentrations below MCL
orDL
All historical concentrations below MCL
or DL (the MAORS result was due to
insufficient recent data)
Concentrations since 93 below MCL or
DL (the MAORS result was due to
insufficient recent data)
-
Upgradient wells with all historical
concentrations below MCL or DL
Upgradient well with recent
concentrations below MCL or DL (the
MAORS result was due to insufficient
recent data)
Recent concentrations below MCL or
DL (the MAORS result was due to
insufficient data)
Extraction wells below:
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Performance monitoring
Performance monitoring
Performance monitoring
Performance monitoring
Performance monitoring
Performance monitoring
Notes: 1) The frequency determined by MAROS based on the analysis of recent data (data between 1994 and 2000)
2) The frequency determined by MAROS based on the analysis of overall data (data between 1990 and 2000)
3) The frequency finally recommended by MAROS after considering recent and overall frequency results as well
as the rates of change in these trends Rate parameters used are 0.5MCL/year, 1.0MCL/year, and 2.0MCL/year
for Low, Medium, and High rates, respectively; the MCL of TCE is 0.005 mg/L
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 10
Sampling Frequency Analysis Results - Modified CES
McClellan AFB OU-D Zone B
Sacramento Valley, California
Well
Name
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-105
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
MAROS
Frequency
Based on
Recent
Trend111
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Frequency
Based on
Overall
Trend'21
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS
Recommended
Frequency'31
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Annual
Annual
Biennial
Biennial
Annual
Annual
Annual
Annual
MAROS
Interpreted
Sampling
Frequency
Results
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
-
Biennial
Biennial
-
Biennial
Comments
All historical concentrations below
MCL or DL (the MAORS result
was due to insufficient recent data)
All historical concentrations below
MCL or DL
Recent concentrations above MCL
All historical concentrations below
MCL or DL
All historical concentrations below
MCL or DL
All historical concentrations below
MCL or DL
All historical concentrations below
MCL or DL (the MAORS result
was due to insufficient recent data)
All historical concentrations below
MCL or DL
All historical concentrations below
MCL or DL
-
All historical concentrations below
MCL or DL (the MAORS result
was due to insufficient data)
All historical concentrations below
MCL or DL (the MAORS result
was due to insufficient recent data)
-
All historical concentrations below
MCL or DL (the MAORS result
was due to insufficient recent data)
Notes: 1) The frequency determined by MAROS based on the analysis of recent data (data between 1995 and 2000)
2) The frequency determined by MAROS based on the analysis of overall data (data between 1990 and 2000)
3) The frequency finally recommended by MAROS after considering recent and overall frequency results as well
as the rates of change in these trends Rate parameters used are 0.5MCL/year, 1.0MCL/year, and 2.0MCL/year
for Low, Medium, and High rates, respectively; the MCL of TCE is 0.005 mg/L
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 11
Risk-Based Site Cleanup Evaluation Results - Power Analysis
McClellan AFB OU-D Zone A
Sacramento Valley, California
Sampling Event
(Yearly Averaged)
1995
1997
1998
1999
Sample
Size
29
20
19
29
Distance to HSCB = 1000 ft
Cleanup Status
Attained
Attained
Attained
Attained
Power
1.0
1.0
1.0
1.0
Distance to HSCB = 100 ft
Cleanup Status
Attained
Attained
Attained
Attained
Power
1.0
1.0
1.0
1.0
Note: The power analysis used for this application assumes normality of data. Distance to the Hypothetical
Statistical Compliance Boundary (HSCB) is the distance from the most downgradient well to the HSCB; S/E
= extrapolated result significantly exceeds the target level (0.005 mg/L).
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 12
Selected Plume Centerline Wells
Risk-Based Site Cleanup Evaluation - Power Analysis
McClellan AFB OU-D Zone A
Sacramento Valley, California
Well Name
MW-92
MW-91
MW-72
MW-11
Distance from Well to Receptor (feet)
1866.2
1996.1
2547.8
3115.4
Note: Groundwater flow angle is to the Southeast; the distance from the most
downgradient well to the nearest downgradient receptor is assumed to be 1000
feet.
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
Table 13
Plume Centerline Concentration
Regression Results - Power Analysis
McClellan AFB OU-D Zone A
Sacramento Valley, California
Sampling Event
(Yearly Averaged)
1995
1996
1997
1998
1999
2000
Number of
Centerline Wells
4
2
3
3
4
2
Regression
Coefficient (1/ft)
-6.77E-03
_
-4.74E-03
-3.84E-03
-4.75E-03
-
Confidence in
Coefficient
99.2%
_
83.5%
88.9%
96.2%
-
Note: Regression is on natural log concentration of TCE versus distance from source
centerline wells shown in Table 12; no regression was performed for sampling event with less
than 3 centerline wells.
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January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
McClellan AFB
Sacramento Valley, California
FIGURES
Figure 1 Zone A McClellan OU D Groundwater Monitoring Network
Figure 2 Zone B McClellan OU D Groundwater Monitoring Network
Figure 3 MAROS Decision Support Tool Flow Chart
Figure 4 MAROS Overview Statistics Trend Analysis Methodology
Figure 5 Decision Matrix for Determining Provisional Frequency
Figure 6 Zone A McClellan OU D TCE Mann-Kendall Trend Results
Figure 7 Zone A McClellan OU D TCE Linear Regression Trend Results
Figure 8 Zone B McClellan OU D TCE Mann-Kendall Trend Results
Figure 9 Zone B McClellan OU D TCE Linear Regression Trend Results
Figure 10 Zone AB McClellan OU D TCE Mann-Kendall Trend Results, Extraction
Wells
Figure 11 Zone AB McClellan OU D TCE Linear Regression Trend Results,
Extraction Wells
Figure 12 Zone A McClellan OU D TCE First Moment (Center of Mass) Over Time
Figure 13 Zone B McClellan OU D TCE First Moment (Center of Mass) Over Time
Figure 14 Zone A Well Sufficiency Results
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GSI Job No. G-2236-15
Issued: 1/15/03 GROUNDWATER
Page 1 of 1 SERVICES, INC.
MAROS: Decision Support Tool
MAROS is a collection of tools in one software package that is used in an explanatory, non-linear fashion. The tool
includes models, geostatistics, heuristic rules, and empirical relationships to assist the user in optimizing a
groundwater monitoring network system while maintaining adequate delineation of the plume as well as knowledge
of the plume state over time. Different users utilize the tool in different ways and interpret the results from a different
viewpoint.
Overview Statistics
What it is: Simple, qualitative and quantitative plume information can be gained through evaluation of monitoring
network historical data trends both spatially and temporally. The MAROS Overview Statistics are the foundation the
user needs to make informed optimization decisions at the site.
What it does: The Overview Statistics are designed to allow site personnel to develop a better understanding of the
plume behavior over time and understand how the individual well concentration trends are spatially distributed within
the plume. This step allows the user to gain information that will support a more informed decision to be made in the
next level of optimization analysis.
What are the tools: Overview Statistics includes two analytical tools:
1) Trend Analysis: includes Mann-Kendall and Linear Regression statistics for individual wells and results in
general heuristically-derived monitoring categories with a suggested sampling density and monitoring
frequency.
2) Moment Analysis: includes dissolved mass estimation (0th Moment), center of mass (1st Moment), and
plume spread (2nd Moment) over time. Trends of these moments show the user another piece of
information about the plume stability over time.
What is the product: A first-cut blueprint for a future long-term monitoring program that is intended to be a
foundation for more detailed statistical analysis.
T
Detailed Statistics
What it is: The MAROS Detailed Statistics allows for a quantitative analysis for spatial and temporal optimization of
the well network on a well-by-well basis.
What it does: The results from the Overview Statistics should be considered along side the MAROS optimization
recommendations gained from the Detailed Statistical Analysis. The MAROS Detailed Statistics results should be
reassessed in view of site knowledge and regulatory requirements as well as the Overview Statistics.
What are the tools: Detailed Statistics includes four analytical tools:
1) Sampling Frequency Optimization: uses the Modified CES method to establish a recommended future
sampling frequency.
2) Well Redundancy Analysis: uses the Delaunay Method to evaluate if any wells within the monitoring
network are redundant and can be eliminated without any significant loss of plume information.
3) Well Sufficiency Analysis: uses the Delaunay Method to evaluate areas where new wells are
recommended within the monitoring network due to high levels of concentration uncertainty.
4) Data Sufficiency Analysis: uses Power Analysis to assess if the historical monitoring data record has
sufficient power to accurately reflect the location of the plume relative to the nearest receptor or
compliance point.
What is the product: List of wells to remove from the monitoring program, locations where monitoring wells may
need to be added, recommended frequency of sampling for each well, analysis if the overall system is statistically
powerful to monitor the plume.
Figure 3. MAROS Decision Support Tool Flow Chart
-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
GROUNDWATER
SERVICES, INC.
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-------
GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
If
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SERVICES, INC.
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Figure 5. Decision Matrix for Determining Provisional Frequency (Figure A.3.1 of the
MAROS Manual (AFCEE 2001))
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GSI JobNo. G-2236-15
Issued: 1/15/03
Page 1 of 1
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SERVICES, INC.
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-------
January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
McClellan AFB
Sacramento Valley, California
APPENDICES
Appendix A: Zone A and Zone B McClellan OU D Historical TCE Maps
Appendix B: Zone A and Zone B McClellan OU D MAROS 2.0 Reports
-------
January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
McClellan AFB
Sacramento Valley, California
APPENDIX A: Zone A and B McClellan AFB Historical TCE Maps
Zone A and Zone B McClellan OU D Historical TCE Maps (1990 - 2001)
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January 15 2003 GROUNDWATER
GSI Job No. G-2236-15 SERVICES, INC.
MAROS 2.0 APPLICATION
ZONE A & B OU D MONITORING NETWORK OPTIMIZATION
McClellan AFB
Sacramento Valley, California
APPENDIX B: Zone A and B McClellan AFB MAROS 2.0 Reports
Linear Regression Statistics Summary
Mann-Kendall Statistics Summary
Spatial Moment Analysis Summary
Zeroth, First, and Second Moment Reports
Plume Analysis Summary
Site Results Summary
Sampling Location Optimization Results
Sampling Frequency Optimization Results
Risk-Based Power Analysis - Plume Centerline Concentrations
Risk-Based Power Analysis - Site Cleanup Status
-------
MAROS Linear Regression Statistics Summary
Project: McClellan Zone A OU D
Location: McClellan AFB
Julia Aziz
California
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Average
Source/ Cone
Tail (mg/L)
Median
Cone
(mg/L)
Standard
Deviation
All
Samples
"ND" ?
Coefficient
Ln Slope of Variation
Confidence Concentration
in Trend Trend
TRICHLOROETHYLENE (TCE)
MW-12
EW-73
MW-72
MW-38D
MW-351
MW-242
MW-241
MW-14
MW-11
MW-10
EW-87
EW-86
EW-85
EW-84
EW-83
MW-15
MW-1042
MW-1041
MW-1004
MW-52
MW-91
MW-90
MW-89
MW-88
MW-76
MW-74
MW-70
MW-1026
MW-53
MW-92
MW-458
MW-412
MW-350
MW-240
MW-237
MW-1073
MW-1064
MW-55
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
5.8E-01
2.6E-01
1.3E-01
1.5E-01
5.2E-03
2.2E-02
4.1E-02
1 .4E+00
9.5E-01
3.4E-01
1.2E-01
1 .2E-02
1.9E-01
4.0E-01
1.0E-01
2.4E-01
1.1E-04
1 .OE-04
2.0E-04
1 .8E-04
2.3E-03
3.1E-03
1 .7E-04
8.6E-03
1 .4E-03
2.5E-03
1 .OE-04
2.7E-03
1 .2E-03
1 .OE-03
2.0E-04
4.2E-04
2.7E-03
5.9E-04
4.2E-04
1 .3E-03
3.7E-03
1 .5E-03
5.8E-01
1 .7E-01
6.7E-02
1 .5E-01
2.6E-03
1 .4E-02
2.1E-02
1.0E+00
4.4E-01
3.3E-01
8.4E-02
7.7E-03
1 .2E-01
2.5E-01
9.6E-02
8.3E-02
1 .OE-04
1 .OE-04
1 .3E-04
1 .OE-04
1 .5E-03
2.4E-04
1 .OE-04
1 .OE-04
1 .OE-04
2.8E-03
1 .OE-04
3.4E-04
4.8E-04
6.3E-04
1 .9E-04
2.7E-04
7.1E-04
1 .OE-04
1 .OE-04
4.9E-04
3.8E-04
1 .7E-03
3.5E-01
2.4E-01
1 .2E-01
6.4E-02
4.9E-03
2.1E-02
5.7E-02
1.5E+00
1.5E+00
2.8E-01
8.2E-02
1 .5E-02
1 .5E-01
3.3E-01
3.1E-02
3.9E-01
2.5E-05
8.7E-06
1 .4E-04
2.8E-04
2.8E-03
4.2E-03
2.0E-04
2.8E-02
2.1E-03
6.7E-04
O.OE+00
3.3E-03
1 .8E-03
1 .4E-03
8.3E-05
3.7E-04
5.0E-03
1 .2E-03
5.7E-04
1 .8E-03
7.1 E-03
9.9E-04
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
-6.9E-04
-7.2E-04
-7.8E-04
-3.7E-04
4.9E-04
-8.8E-04
-1.1 E-03
-2.5E-03
-1 .4E-03
-7.5E-04
4.6E-04
-7.6E-04
-6.6E-04
-1 .1 E-03
2.3E-05
-6.5E-04
^.OE-05
2.9E-05
-3.0E-04
-4.0E-04
-8.9E-04
5.5E-04
3.4E-05
4.9E-04
1.1 E-03
-1 .7E-04
O.OE+00
2.4E-04
7.4E-04
-3.3E-04
1 .2E-03
-2.7E-03
-2.3E-03
-6.9E-05
4.7E-04
-8.2E-04
1 .2E-03
-1 .4E-04
0.60
0.92
0.92
0.42
0.94
0.92
1.41
1.12
1.53
0.83
0.68
1.23
0.79
0.83
0.30
1.64
0.23
0.08
0.69
1.52
1.19
1.37
1.14
3.28
1.53
0.26
0.00
1.26
1.45
1.39
0.42
0.88
1.88
2.07
1.36
1.35
1.92
0.65
100.0%
100.0%
100.0%
98.9%
82.3%
100.0%
99.8%
100.0%
100.0%
100.0%
99.9%
99.6%
99.9%
100.0%
60.7%
99.3%
63.0%
72.6%
94.4%
91 .6%
98.6%
75.7%
56.1%
77.4%
100.0%
97.0%
100.0%
60.2%
99.2%
80.5%
64.9%
85.0%
97.4%
53.8%
72.2%
78.6%
91 .8%
72.5%
D
D
D
D
NT
D
D
D
D
D
I
D
D
D
NT
D
S
NT
PD
PD
D
NT
NT
NT
I
D
S
NT
I
NT
NT
S
D
NT
NT
NT
PI
S
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 1 of 2
-------
project; McClellan Zone A OU D
Julia Aziz
McClellan AFB
California
Well
Average Median
Source/ Cone Cone
Tail (mg/L) (mg/L)
All
Standard Samples Coefficient Confidence Concentration
Deviation "ND" ? Ln Slope of Variation in Trend Trend
TRICHLOROETHYLENE (TCE)
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events); COV = Coefficient of Variation
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 2 of 2
-------
MAROS Mann-Kendall Statistics Summary
McClellan Zone A OU D
Location: McClellan AFB
Julia Aziz
California
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Source/ Number of
Well Tail Samples
TRICHLOROETHYLENE
MW-72
MW-351
MW-38D
MW-242
MW-15
MW-12
MW-241
MW-11
EW-73
MW-10
EW-87
EW-86
EW-85
EW-84
EW-83
MW-14
MW-1041
MW-1073
MW-1042
MW-1026
MW-1004
MW-1064
MW-55
MW-91
MW-90
MW-89
MW-88
MW-76
MW-237
MW-70
MW-240
MW-53
MW-52
MW-458
MW-412
MW-350
MW-92
MW-74
(TCE)
S
s
S
s
s
s
s
s
s
s
s
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
T
10
9
8
12
11
12
12
11
15
11
15
15
16
16
16
12
7
6
7
7
12
8
11
10
8
10
11
10
7
6
9
10
9
4
6
8
12
10
Number of
Detects
10
9
8
12
11
12
12
11
15
11
15
15
16
16
16
12
1
5
1
5
6
4
11
9
6
2
4
4
5
0
5
9
3
3
4
5
9
10
Coefficient
of Variation
0.92
0.94
0.42
0.92
1.64
0.60
1.41
1.53
0.92
0.83
0.68
1.23
0.79
0.83
0.30
1.12
0.08
1.35
0.23
1.26
0.69
1.92
0.65
1.19
1.37
1.14
3.28
1.53
1.36
0.00
2.07
1.45
1.52
0.42
0.88
1.88
1.39
0.26
Mann-Kendall
Statistic
-37
7
-18
-46
-23
-52
-42
-49
-83
-53
60
-53
-84
-105
22
-58
4
-3
-2
-4
-23
6
-11
-31
7
3
-4
20
2
0
0
23
-13
0
-4
-15
-18
-28
Confidence
in Trend
100.0%
72.8%
98.4%
100.0%
95.7%
100.0%
99.8%
100.0%
100.0%
100.0%
99.9%
99.6%
100.0%
100.0%
82.5%
100.0%
66.7%
64.0%
55.7%
66.7%
93.3%
72.6%
77.7%
99.8%
76.4%
56.9%
59.0%
95.5%
55.7%
42.3%
46.0%
97.7%
89.0%
37.5%
70.3%
95.8%
87.5%
99.4%
All
Samples Concentration
"ND" ? Trend
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
D
NT
D
D
D
D
D
D
D
D
I
D
D
D
NT
D
NT
NT
S
NT
PD
NT
S
D
NT
NT
NT
I
NT
S
NT
I
NT
S
S
D
NT
D
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 1 of 2
-------
McClellan Zone A OU D Julia Aziz
Location: McClellan AFB California
All
Source/ Number of Number of Coefficient Mann-Kendall Confidence Samples Concentration
We|| Tai| Samples Detects of Variation Statistic in Trend "ND" ? Trend
TRICHLOROETHYLENE (TCE)
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-
Due to insufficient Data (< 4 sampling events); Source/Tail (S/T)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE Monday, January 13, 2003 Page 2 of 2
-------
MAROS Statistical Trend Analysis Summary
Project: McClellan Zone A OU D
Location: McClellan AFB
Julia Aziz
California
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
TRICHLOROETHYLENE
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1004
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-237
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MW-412
MW-458
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
Source/
Tail
(TCE)
s
s
s
s
s
s
s
T
T
T
T
T
T
S
S
s
s
T
T
S
s
T
S
s
T
T
T
T
T
T
S
T
T
T
T
Number Number Average Median
of of Cone. Cone.
Samples Detects (mg/L) (mg/L)
15
16
16
16
15
15
11
12
7
7
7
8
6
11
12
12
11
7
9
12
12
8
9
8
6
4
9
10
11
6
10
10
10
11
10
15
16
16
16
15
15
11
6
5
1
1
4
5
11
12
12
11
5
5
12
12
5
9
8
4
3
3
9
11
0
10
10
4
4
2
2.6E-01
1 .OE-01
4.0E-01
1 .9E-01
1 .2E-02
1 .2E-01
3.4E-01
2.0E-04
2.7E-03
1 .OE-04
1.1E-04
3.7E-03
1 .3E-03
9.5E-01
5.8E-01
1 .4E+00
2.4E-01
4.2E-04
5.9E-04
4.1E-02
2.2E-02
2.7E-03
5.2E-03
1 .5E-01
4.2E-04
2.0E-04
1 .8E-04
1 .2E-03
1 .5E-03
1 .OE-04
1 .3E-01
2.5E-03
1 .4E-03
8.6E-03
1 .7E-04
1 .7E-01
9.6E-02
2.5E-01
1 .2E-01
7.7E-03
8.4E-02
3.3E-01
1 .3E-04
3.4E-04
1 .OE-04
1 .OE-04
3.8E-04
4.9E-04
4.4E-01
5.8E-01
1.0E+00
8.3E-02
1 .OE-04
1 .OE-04
2.1E-02
1 .4E-02
7.1E-04
2.6E-03
1 .5E-01
2.7E-04
1 .9E-04
1 .OE-04
4.8E-04
1 .7E-03
1 .OE-04
6.7E-02
2.8E-03
1 .OE-04
1 .OE-04
1 .OE-04
All
Samples
"ND" ?
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
Mann-
Kendall
Trend
D
NT
D
D
D
I
D
PD
NT
NT
S
NT
NT
D
D
D
D
NT
NT
D
D
D
NT
D
S
S
NT
I
S
S
D
D
I
NT
NT
Linear
Regression
Trend
D
NT
D
D
D
I
D
PD
NT
NT
S
PI
NT
D
D
D
D
NT
NT
D
D
D
NT
D
S
NT
PD
I
S
S
D
D
I
NT
NT
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 1 of 2
-------
MAROS Statistical Trend Analysis Summary
Source/
Well Tai|
Number
of
Samples
Number
of
Detects
Average
Cone.
(mg/L)
Median
Cone.
(mg/L)
All
Samples
"ND" ?
Mann-
Kendall
Trend
Linear
Regression
Trend
TRICHLOROETHYLENE (TCE)
MW-90
MW-91
MW-92
T
T
T
8
10
12
6
9
9
3.1E-03
2.3E-03
1 .OE-03
2.4E-04
1 .5E-03
6.3E-04
No
No
No
NT
D
NT
NT
D
NT
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable
(N/A); Not Applicable (N/A) - Due to insufficient Data (< 4 sampling events); No Detectable Concentration (NDC)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 2 of 2
-------
MAROS Site Results
Project: McClellan Zone A OU D
Location: McClellan AFB
Julia Aziz
California
User Defined Site and Data Assumptions:
Hydrogeology and Plume Information:
Groundwater
Seepage Velocity: 35 ft/yr
Current Plume Length: 1000 ft
Current Plume Width 600 ft
Number of Tail Wells: 22
Number of Source Wells: 16
Source Information:
Source Treatment: Pump and Treat
NAPL is not at this site.
Down-gradient Information:
Distance from Edge of Tail to Nearest:
Down-gradient receptor: 1000 ft
Down-gradient property: 10ft
Distance from Source to Nearest:
Down-gradient receptor: 6000ft
Down-gradient property: 10 ft
Consolidation Assumptions:
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Plume Information Weighting Assumptions:
Consolidation Step 1. Weight Plume Information by Chemical
Summary Weighting: Weighting Applied to All Chemicals Equally
Consolidation Step 2. Weight Well Information by Chemical
Well Weighting: No Weighting of Wells was Applied.
Chemical Weighting: No Weighting of Chemicals was Applied.
Note: These assumptions made when consolidating the historical mentoring lumping the Wells and COCs.
1.
Preliminary Monitoring System Optimization Results: Based on site classification, source treatment and Monitoring System
Category the following suggestions are made for site Sampling Frequency, Duration of Sampling, and Well Density. These
criteria take into consideration: Plume Stability, Type of Plume, and Groundwater Velocity.
coc
Tail Source Level of
Stability Stability Effort
Sampling
Duration
Sampling
Frequency
Sampling
Density
TRICHLOROETHYLENE (TCE)
PD
M
25
Remove treatment No Recommendation
system if previously
reducing concentation
Note:
Plume Status: (I) Increasing; (Pl)Probably Increasing; (S) Stable; (NT) No Trend; (PD) Probably Decreasing; (D) Decreasing
Design Categories: (E) Extensive; (M) Moderate; (L) Limited (N/A) Not Applicable, Insufficient Data Available
Level of Monitoring Effort Indicated by Analysi I Moderate
2,
MAROS Version 2, 2002, AFCEE
Tuesday, January 14, 2003
Page 1 of 2
-------
Moment Type Consituent
Zeroth Moment: Mass
TRICHLOROETHYLENE (TCE)
1st Moment: Distance to Source
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma XX
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma YY
TRICHLOROETHYLENE (TCE)
Coefficient
of Variation
1.44
1.04
1.26
1.72
Mann-Kendall
S Statistic
-116
48
-18
-18
Confidence
in Trend
91 .8%
87.7%
66.2%
66.2%
Moment
Trend
PD
NT
NT
NT
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.30
Saturated Thickness: Uniform: 30 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
MAROS Version 2, 2002, AFCEE
Tuesday, January 14, 2003
Page 2 of 2
-------
MAROS Zeroth Moment Analysis
McClellan AFB Zone A OU D
Location: McClellan AFB
Julia Aziz
California
COC: TRICHLOROETHYLENE (TCE)
Change in Dissolved Mass Over Time
Date
S
1 9F+ni -
1.0E+01 -
8.0E+00 -
6.0E+00 -
4.0E+00 -
2.0E+00 -
n nF+nn -
*
*
• *
* * * *
Porosity: 0.30
Saturated Thickness:
Uniform: 30 ft
Mann Kendall S Statistic:
-15
Confidence in
Trend:
I 85.9%
Coefficient of Variation:
I 0.96
Zeroth Moment
Trend:
Data Table:
Effective Date
7/1/1990
7/1/1991
7/1/1992
7/1/1993
7/1/1994
7/1/1995
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
Constituent
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
Estimated
Mass (Kg)
1.3E+01
5.8E+00
8.8E-01
2.1E+00
1.2E+01
2.2E+00
1.3E+01
7.1E+00
7.9E-01
9.4E-01
9.0E-01
Number of Wells
20
21
11
25
18
28
15
20
19
29
13
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events); ND = Non-detect. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS First Moment Analysis
McClellan AFB Zone A OU D
Location: McClellan AFB
Julia Aziz
California
COC: TRICHLOROETHYLENE (TCE)
Distance from Source to Center of Mass
2.5E+03
£, 2.0E+03
o 1.5E+03
CO
o
* 1.0E+03
8
w 5.0E+02 -
Q
O.OE+00
Mann Kendall S Statistic:
Date
I 17
Confidence in
Trend:
* * *
*
* *
I 89.1%
Coefficient of Variation:
First Moment Trend:
NT
Data Table:
Effective Date
7/1/1990
7/1/1991
7/1/1992
7/1/1993
7/1/1994
7/1/1995
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
Constituent
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
Xc (ft)
2,166,613
2,166,683
2,166,819
2,166,669
2,166,500
2,166,698
2,168,405
2,167,678
2,166,569
2,166,817
2,167,148
Yc (ft)
366,307
366,147
366,337
366,336
366,370
366,444
366,904
367,043
366,068
366,152
366,103
Distance from Source (ft)
220
56
287
242
322
351
1,918
1,387
100
162
482
Number of Wells
20
21
11
25
18
28
15
20
19
29
13
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS First Moment Analysis
McClellan AFB Zone A OU D
Location: McClellan AFB
Julia Aziz
California
COC: TRICHLOROETHYLENE(TCE)
Change in Location of Center of Mass Over Time
367200
367000 •
366800 •
366600 •
366400 •
366200 •
366000
07/96
'/95
• 07/94
* 07AW/9i
«• 07/97
07/99
Groundwater
Flow Direction:
Source
Coordinate:
X: J 2,166,666
Y: I 366,094
2166000 2166500 2167000 2167500 2168000 2168500
Xc (ft)
Effective Date
Constituent
Xc (ft)
Yc (ft) Distance from Source (ft) Number of Wells
7/1/1990
7/1/1991
7/1/1992
7/1/1993
7/1/1994
7/1/1995
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
2,166,613
2,166,683
2,166,819
2,166,669
2,166,500
2,166,698
2,168,405
2,167,678
2,166,569
2,166,817
2,167,148
366,307
366,147
366,337
366,336
366,370
366,444
366,904
367,043
366,068
366,152
366,103
220
56
287
242
322
351
1,918
1,387
100
162
482
20
21
11
25
18
28
15
20
19
29
13
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) •
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS Second Moment Analysis
McClellan AFB Zone A OU D
McClellan AFB
COC: TRICHLOROETHYLENE (TCE)
Change in Pin
10000000
1000000
£- 100000
Jl 10000
CM
V 1000
w
100
10
1
mnnnnnn _i
1000000 -
£- 100000 -
tr
~ 10000 -
CM
g 1000 -
«
100-
10-
1 .
\me Spread Over Time
Date
* » *
* • * * * * *
*
Date
* * * *
* * * *
• *
Data Table:
Effective Date Constituent Sigma XX (sq ft) Si
7/1/1990 TRICHLOROETHYLENE (TCE) 103,011
7/1/1991 TRICHLOROETHYLENE (TCE) 107,861
7/1/1992 TRICHLOROETHYLENE (TCE) 282,555
7/1/1993 TRICHLOROETHYLENE (TCE) 209,993
7/1/1994 TRICHLOROETHYLENE (TCE) 432,219
7/1/1995 TRICHLOROETHYLENE (TCE) 617,888
7/1/1996 TRICHLOROETHYLENE (TCE) 1,645,598
7/1/1997 TRICHLOROETHYLENE (TCE) 2,180,836
7/1/1998 TRICHLOROETHYLENE (TCE) 509,750
7/1/1999 TRICHLOROETHYLENE (TCE) 983,522
7/1/2000 TRICHLOROETHYLENE (TCE) 1,997,612
Julia Aziz
California
Mann Kendall S Statistic:
J?> I15
Confidence in
Trend:
| 85.9%
Coefficient of Variation:
| 0.91
Second Moment
Trend:
l| NT
5§>
Mann Kendall S Statistic:
j 41
Confidence in
Trend:
| 100.0%
Coefficient of Variation:
I 0.93
Second Moment
Trend:
I '
gmaYY(sqft) Number of Wells
478,732 20
321,324 21
565,080 1 1
382,557 25
101,139 18
803,729 28
2,578,566 15
2,062,276 20
338,208 19
1,859,400 29
596,906 13
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 2
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MAROS Second Moment Analysis
Effective Date
Constituent
Sigma XX (sq ft) Sigma YY (sq ft)
Number of Wells
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events)
The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 2 of 2
-------
MAROS Spatial Moment Analysis Summary
McClellan AFB Zone A OU D
Location: McClellan AFB
Julia Aziz
California
Oth Moment
1st (Center of
2nd Moment
Effective Date
TRICHLOROETHYLENE
7/1/1990
7/1/1991
7/1/1992
7/1/1993
7/1/1994
7/1/1995
7/1/1996
7/1/1997
7/1/1998
7/1/1999
7/1/2000
Estimated
Mass (Kg) Xc (ft)
(TCE)
1.3E+01
5.8E+00
8.8E-01
2.1E+00
1.2E+01
2.2E+00
1.3E+01
7.1E+00
7.9E-01
9.4E-01
9.0E-01
2,166,613
2,166,683
2,166,819
2,166,669
2,166,500
2,166,698
2,168,405
2,167,678
2,166,569
2,166,817
2,167,148
Source
Yc (ft) Distance (ft)
366,307
366,147
366,337
366,336
366,370
366,444
366,904
367,043
366,068
366,152
366,103
220
56
287
242
322
351
1,918
1,387
100
162
482
Sigma XX
(sq ft)
103,011
107,861
282,555
209,993
432,219
617,888
1,645,598
2,180,836
509,750
983,522
1,997,612
Sigma YY
(sq ft)
478,732
321,324
565,080
382,557
101,139
803,729
2,578,566
2,062,276
338,208
1,859,400
596,906
Number of
Wells
20
21
11
25
18
28
15
20
19
29
13
MAROS Version 2, 2002, AFCEE
Thursday, January 02, 2003
Page 1 of 2
-------
McClellan AFB Zone A OU D
Location: McClellan AFB
Julia Aziz
California
Moment Type Consituent
Zeroth Moment: Mass
TRICHLOROETHYLENE (TCE)
1st Moment: Distance to Source
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma XX
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma YY
TRICHLOROETHYLENE (TCE)
Coefficient
of Variation
0.96
1.18
0.93
0.91
Mann-Kendall
S Statistic
-15
17
41
15
Confidence
in Trend
85.9%
89.1%
100.0%
85.9%
Moment
Trend
S
NT
I
NT
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.30 Saturated Thickness: Uniform: 30 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
Note: The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
Thursday, January 02, 2003
Page 2 of 2
-------
1 MAROS Sampling Location Optimization Re suits 1
Project: McClellan OUD ZoneA
McClellan AFB
Sampling Events Analyzed: From 1995
7/1/1995
Parameters used: Constituent
TRICHLOROETHYLENE (TCE)
Meng
California
to 2000
7/1/2000
Inside SF Hull SF Area Ratio Cone. Ratio
0.2 0.01 0.95 0.95
Average Minimum Maximum
Well X (feet) Y (feet) Removable? Slope Factor* Slope Factor* Slope Factor* Eliminated?
TRICHLOROETHYLENE (TCE)
MW-10 2166666.50 366103.81 0
MW-1004 2165862.25 366561.97 0
MW-1026 2168527.25 367760.69 0
MW-1042 2165134.00 368116.88 0
MW-1064 2166045.50 368281.13 0
MW-1073 2165967.75 367415.38 0
MW-11 2166621.50 366694.28 0
MW-12 2167022.25 366593.50 0
MW-14 2166783.25 365890.03 0
MW-15 2166639.25 365844.72 0
MW-237 2164664.25 365534.06 0
MW-240 2165901.00 365548.66 0
MW-241 2166666.00 366093.94 0
MW-242 2166642.50 365855.91 0
MW-350 2169199.50 365231.88 0
MW-351 2167722.25 364740.31 0
MW-38D 2166617.50 366507.66 0
MW-412 2167940.50 364881.28 0
MW-458 2167267.75 365669.84 0
MW-52 2166983.75 366826.34 0
MW-53 2166865.50 366619.34 0
MW-55 2166860.25 366290.91 0
MW-70 2166867.50 366745.16 0
MW-72 2166654.25 366123.69 0
MW-74 2166534.25 366146.22 0
MW-76 2166529.50 366444.34 0
MW-88 2167425.50 366186.38 0
MW-89 2167204.00 366317.66 0
0.208 0.156 0.232 D
0.677 0.529 0.892 D
0.506 0.474 0.538 D
0.533 0.398 0.606 D
0.431 0.258 0.555 D
0.373 0.096 0.908 D
0.299 0.080 0.542 D
0.601 0.387 0.778 D
0.135 0.018 0.404 0
0.254 0.190 0.323 D
0.357 0.261 0.499 D
0.591 0.217 0.703 D
0.169 0.081 0.241 0
0.203 0.081 0.292 D
0.392 0.209 0.661 D
0.416 0.068 0.675 D
0.294 0.162 0.374 D
0.477 0.104 0.721 D
0.300 0.187 0.553 D
0.663 0.538 0.779 D
0.374 0.172 0.458 D
0.430 0.212 0.649 D
0.664 0.534 0.896 D
0.032 0.000 0.070 0
0.264 0.262 0.266 D
0.235 0.136 0.336 D
0.457 0.291 0.549 D
0.651 0.481 0.898 D
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 1 of2
-------
McClellan OUD ZoneA
McClellan AFB
Meng
California
Well
MW-90
MW-91
MW-92
X (feet)
2167220.00
2166909.75
2166911.50
Y (feet)
365966.75
365608.53
365476.94
Removable?
0
0
0
Average
Slope Factor*
0.249
0.331
0.364
Minimum
Slope Factor*
0.021
0.095
0.139
Maximum
Slope Factor*
0.347
0.736
0.505
Eliminated?
D
D
D
Note: The Slope Factor indicates the relative importance of a well in the monitoring network at a given sampling event; the larger the SF
value of a well, the more important the well is and vice versa; the Average Slope Factor measures the overall well importance in the
selected time period; the state coordinates system (i.e., X and Y refer to Easting and Northing respectively) or local coordinates systems
may be used; wells that are NOT selected for analysis are not shown above.
* When the report is generated after running the Excel module, SF values will NOT be shown above.
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 2 of2
-------
MAROS Sampling Frequency Optimization Results
McClellan OUD ZoneA
McClellan AFB
The Overall Number of Sampling Events: 42
"Recent Period" defined by events: From 1994 1st Quarter
Meng
California
To 2000 3rd Quarter
"Rate of Change" f
Well
1/15/1994
larameters used:
8/15/2000
Constituent Cleanup Goal Low Rate Medium Rate High Rate
TRICHLOROETHYLENE (TCE) 0.005
Units: Cleanup Goal is in mg/L; all rate parameters are
Recommended
Sampling Frequency
0.0025 0.005 0.01
in mg/L/year.
Frequency Based Frequency Based
on Recent Data on Overall Data
TRICHLOROETHYLENE (TCE)
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1004
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-237
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
Annual
Annual
Annual
Annual
Annual
Quarterly
Annual
Annual
SemiAnnual
Biennial
Biennial
Quarterly
Annual
Annual
Annual
Annual
SemiAnnual
Biennial
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly Quarterly
Annual
Annual
Annual
Annual
SemiAnnual SemiAnnual
Annual
Annual
Annual
Annual
Quarterly Quarterly
Annual
Annual
Annual
Annual
SemiAnnual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 1 of2
-------
McClellan OUD ZoneB
Location: McClellanAFB
Meng
California
Well
MW-412
MW-458
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
MW-90
MW-91
MW-92
Recommended
Sampling Frequency
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Annual
SemiAnnual
Quarterly
Annual
Biennial
Annual
Biennial
Annual
Frequency Based
on Recent Data
Annual
Annual
Annual
Annual
Annual
Annual
Annual
SemiAnnual
Quarterly
Annual
Annual
Annual
Annual
Annual
Frequency Based
on Overall Data
Annual
Annual
Annual
Annual
Annual
Annual
Annual
SemiAnnual
Quarterly
Annual
Annual
Annual
Annual
Annual
Note: Sampling frequency is determined considering both recent and overall concentration trends. Sampling Frequency is the
final recommendation; Frequency Based on Recent Data is the frequency determined using recent (short) period of monitoring
data; Frequency Based on Overall Data is the frequency determined using overall (long) period of monitoring data. If the "recent
period" is defined using a different series of sampling events, the results could be different.
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 2 of2
-------
MAROS Risk-Based Power Analysis for Site Cleanup
McClellan OUD ZoneA
McClellan AFB
Meng
California
Parameters:
Groundwater Flow Direction: 280 degrees Distance to Receptor: 1000 feet
From Period: 1995 to 2000
7/1/1995 7/1/2000
Sample Event
Selected Plume
Centerline Wells:
N
Well
MW-92
MW-91
MW-72
MW-11
The distance
from the well
Distance
to Receptor (feet)
1866.2
1996.1
2547.8
3115.4
is measured in the Groundwater Flow Angle
to the compliance boundary.
ormal Distribution Assumption
Sample Sample Sample Cleanup Expected
Szie Mean Stdev. Status Power Samp|e size
TRICHLOROETHYLENE (TCE) Cleanup Goal =
1995
1997
1998
1999
29 2.05E-07 9.05E-07 Attained 1
20 1.60E-06 4.97E-06 Attained 1
19 4.06E-06 9.41 E-06 Attained 1
29 4.23E-06 2.21 E-05 Attained 1
= 0.005
000 <=3
000 <=3
000 <=3
000 <=3
Lognormal Distribution Assumption
Celanup Expected Alpha Expected
Status Power Sample Size Level Power
Not Attained S/E S/E 0.05 0.8
Not Attained S/E S/E 0.05 0.8
Attained 1.000 4 0.05 0.8
Attained 1.000 4 0.05 0.8
Note: #N/C means "not conducted" due to a small sample size (N<4) or that the mean concentration is much greater than the cleanup
level; Sample Size is the number of sampling locations used in the power analysis; Expected Sample Size is the number of concentration
data needed to reach the Expected Power undercurrent sample variability.
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 1 of 1
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Risk-Based Power Analysis — Projected Concentrations
McClellan OUD ZoneA
Location : McClellan AFB
From Period:
Sampling
Event
7/1/1995 to
Effective
Date
7/1/2000
Well
Meng
California
Distance from the most downgradient well to receptor: 1000 feet
Observed
Concentration
(mg/L)
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-237
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
3.007E-01
1.057E-01
2.618E-01
2.160E-01
5.140E-03
8.076E-02
2.848E-01
6.150E-05
1 .230E-04
6.150E-05
2.063E-02
3.052E-03
7.106E-01
6.823E-01
1.021E+00
7.601 E-02
6.945E-05
7.484E-05
2.257E-02
1 .425E-02
3.657E-03
4.216E-03
1.937E-01
5.514E-05
4.805E-04
1 .507E-04
6.150E-05
6.046E-02
2.583E-03
2.093E-03
8.494E-05
4.948E-05
2900.5
2852.5
2602.5
2307.1
2261.4
2550.4
2526.1
3834.7
4817.5
4774.7
4778.2
3939.1
3115.4
2946.6
2295.3
2275.7
2312.7
2112.3
2516.5
2286.1
1227.6
1000.0
2932.3
3182.6
2999.3
2676.7
3122.8
2547.8
2590.8
2885.3
2475.6
2643.4
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
8.875E-10
4.316E-10
5.811E-09
3.544E-08
1.150E-09
2.551 E-09
1.061E-08
3.247E-16
8.361 E-1 9
5.585E-19
1.830E-16
7.947E-15
4.893E-10
1 .474E-09
1.814E-07
1 .543E-08
1.097E-11
4.592E-1 1
8.972E-10
2.695E-09
8.974E-07
4.831 E-06
4.607E-10
2.409E-14
7.265E-13
2.023E-12
4.028E-14
1 .944E-09
6.206E-1 1
6.849E-12
4.452E-12
8.328E-13
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 1 of4
-------
McClellan OUD ZoneA
: McClellan AFB
Meng
California
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1995
1995
1995
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1998
1998
1998
1998
1998
1998
1998
7/1/1995
7/1/1995
7/1/1995
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
MW-90
MW-91
MW-92
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1004
MW-1064
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MWM12
MW-72
MW-88
MW-89
MW-90
MW-91
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
5.136E-03
7.550E-04
1 .360E-04
9.730E-02
1.770E-01
1.600E-01
5.880E-02
7.750E-03
2.200E-01
1.430E-01
1 .720E-04
6.520E-04
1 .300E-04
7.190E-02
3.280E-01
2.520E-02
5.175E-02
3.820E-03
4.580E-02
1.150E-02
1 .090E-03
2.570E-03
1.620E-01
2.563E-04
6.660E-02
9.310E-02
1 .940E-04
5.400E-03
3.480E-04
1.210E-01
1.480E-01
5.120E-02
4.080E-02
6.700E-03
2.850E-01
9.990E-02
2295.0
1996.1
1866.2
2900.5
2852.5
2602.5
2307.1
2261.4
2550.4
2526.1
3117.0
4778.2
3939.1
3115.4
2946.6
2295.3
2275.7
2112.3
2516.5
2286.1
1227.6
1000.0
2932.3
1100.9
2547.8
2475.6
2643.4
2295.0
1996.1
2900.5
2852.5
2602.5
2307.1
2261.4
2550.4
2526.1
-6.77E-03
-6.77E-03
-6.77E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
9.147E-10
1.018E-09
4.418E-10
1 .040E-07
2.374E-07
7.020E-07
1 .046E-06
1.713E-07
1 .236E-06
9.011E-07
6.585E-1 1
9.492E-14
1.010E-12
2.773E-08
2.816E-07
4.743E-07
1 .069E-06
1.712E-07
3.021 E-07
2.260E-07
3.237E-06
2.245E-05
1 .488E-07
1 .388E-06
3.787E-07
7.453E-07
7.012E-10
1.018E-07
2.705E-08
1 J54E-06
2.580E-06
2.331 E-06
5.778E-06
1.1 31 E-06
1 .585E-05
6.100E-06
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 2 of4
-------
McClellan OUD ZoneA
: McClellan AFB
Meng
California
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
MW-11
MW-12
MW-14
MW-15
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MWM12
MW-52
MW-53
MW-55
MW-72
MW-88
MW-89
MW-92
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1004
MW-1026
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-237
MW-240
MW-241
MW-242
8.695E-02
2.405E-01
3.905E-03
6.195E-02
9.050E-05
7.670E-03
1 .290E-02
1 .OOOE-04
1.915E-03
1.340E-01
5.224E-04
5.400E-05
3.845E-03
3.580E-04
4.630E-02
9.050E-05
9.050E-05
7.853E-04
6.040E-02
9.975E-02
9.923E-03
4.850E-02
6.630E-03
2.653E-01
7.840E-02
1 .095E-04
3.910E-03
4.330E-04
3.475E-02
8.290E-02
4.990E-03
7.890E-02
4.840E-04
4.645E-05
1 .030E-02
5.680E-03
3115.4
2946.6
2295.3
2275.7
2112.3
2516.5
2286.1
1227.6
1000.0
2932.3
1100.9
3182.6
2999.3
2676.7
2547.8
2475.6
2643.4
1866.2
2900.5
2852.5
2602.5
2307.1
2261.4
2550.4
2526.1
3117.0
3834.7
3939.1
3115.4
2946.6
2295.3
2275.7
2312.7
2112.3
2516.5
2286.1
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
5.520E-07
2.920E-06
5.787E-07
9.899E-06
2.709E-08
4.860E-07
1 .980E-06
8.955E-07
4.111E-05
1.719E-06
7.610E-06
2.649E-10
3.814E-08
1 .226E-08
2.601 E-06
6.709E-09
3.522E-09
6.049E-07
6.351 E-08
1.317E-07
4.291 E-08
8.521 E-07
1 .447E-07
1 .469E-06
4.872E-07
4.120E-11
4.879E-1 1
3.292E-12
1.317E-08
7.002E-08
9.272E-08
1 .609E-06
8.280E-09
2.057E-09
6.700E-08
1.102E-07
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 3 of4
-------
McClellan OUD ZoneA
: McClellan AFB
Meng
California
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
MW-350
MW-351
MW-38D
MW-412
MW-458
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
MW-90
MW-91
MW-92
1 .095E-04
1 .370E-02
7.697E-02
1 .095E-04
1 .202E-04
1 .095E-04
4.420E-04
1 .730E-03
1 .095E-04
2.770E-02
1.610E-03
5.430E-03
1 .095E-04
1 .095E-04
1 .095E-04
3.970E-04
1 .095E-04
1227.6
1000.0
2932.3
1100.9
1994.3
3182.6
2999.3
2676.7
3122.8
2547.8
2590.8
2885.3
2475.6
2643.4
2295.0
1996.1
1866.2
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
3.230E-07
1.190E-04
6.956E-08
5.892E-07
9.320E-09
3.018E-11
2.908E-10
5.260E-09
4.008E-1 1
1 .553E-07
7.358E-09
6.136E-09
8.646E-10
3.900E-10
2.037E-09
3.052E-08
1 .559E-08
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Note: Projected Concentrations that are below the user-specified detection limit are indicated by a check mark to its right; for sampling events
with less than 3 selected plume centerline wells, NO projected concentrations are calculated because no regression coefficient is available.
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 4 of4
-------
Regression of Plume Centerline Concentrations
McClellan OUD ZoneA
Location: McClellanAFB
Meng
California
Groundwater Flow Direction: 280 degrees Distance to Receptor: 1000 feet
From Period: 7/1/1995 to 7/1/2000
Selected Plume
Centerline Wells:
Sample Even
Well Distance to Receptor (feet)
MW-92
MW-91
MW-72
MW-11
1866.2
1996.1
2547.8
3115.4
The distance is measured in the Groundwater Flow Angle
from the well to the compliance boundary.
Number of Regression Confidence in
Effective Date Centerline Wells Coefficient (1 /ft) Coefficient
TRICHLOROETHYLENE (TCE)
1995 7/1/1995 4
1996 7/1/1996 2
1997 7/1/1997 3
1998 7/1/1998 3
1999 7/1/1999 4
2000 7/1/2000 2
-6.77E-03 99.2%
O.OOE+00 0.0%
-4.74E-03 83.5%
-3.84E-03 88.9%
-4.75E-03 96.2%
O.OOE+00 0.0%
Note: when the number of plume centerline wells is less than 3, no analysis is performed and all related values
are set to ZERO; Confidence in Coefficient is the statistical confidence that the estimated coefficient is
different from ZERO (for details, please refer to "Conference in Trend" in Linear Regression Analysis).
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 1 of 1
-------
MAROS Risk-Based Power Analysis for Site Cleanup
McClellan OUD ZoneA
McClellan AFB
Meng
California
Parameters:
Groundwater Flow Direction: 280 degrees Distance to Receptor: 100 feet
From Period: 1995 to 2000
7/1/1995 7/1/2000
Sample Event
Selected Plume
Centerline Wells:
N
Well
MW-92
MW-91
MW-72
MW-11
The distance
from the well
Distance
to Receptor (feet)
966.2
1096.1
1647.8
2215.4
is measured in the Groundwater Flow Angle
to the compliance boundary.
ormal Distribution Assumption
Sample Sample Sample Cleanup Expected
Szie Mean Stdev. Status Power Samp|e size
TRICHLOROETHYLENE (TCE) Cleanup Goal =
1995
1997
1998
1999
29 9.09E-05 4.01E-04 Attained 1
20 1.14E-04 3.54E-04 Attained 1
19 1.29E-04 2.99E-04 Attained 1
29 3.03E-04 1.58E-03 Attained 1
= 0.005
000 <=3
000 <=3
000 <=3
000 <=3
Lognormal Distribution Assumption
Celanup Expected Alpha Expected
Status Power Sample Size Level Power
Not Attained S/E S/E 0.05 0.8
Not Attained S/E S/E 0.05 0.8
Not Attained 0.457 49 0.05 0.8
Attained 0.740 35 0.05 0.8
Note: #N/C means "not conducted" due to a small sample size (N<4) or that the mean concentration is much greater than the cleanup
level; Sample Size is the number of sampling locations used in the power analysis; Expected Sample Size is the number of concentration
data needed to reach the Expected Power undercurrent sample variability.
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 1 of 1
-------
Risk-Based Power Analysis — Projected Concentrations
McClellan OUD ZoneA
Location : McClellan AFB
From Period:
Sampling
Event
7/1/1995 to
Effective
Date
7/1/2000
Well
Meng
California
Distance from the most downgradient well to receptor: 100 feet
Observed
Concentration
(mg/L)
Distance Down
Centerline (ft)
Regression
Coefficient
(1/ft)
Projected
Concentration
(mg/L)
Below
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
7/1/1995
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1026
MW-1041
MW-1042
MW-1064
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-237
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
3.007E-01
1.057E-01
2.618E-01
2.160E-01
5.140E-03
8.076E-02
2.848E-01
6.150E-05
1 .230E-04
6.150E-05
2.063E-02
3.052E-03
7.106E-01
6.823E-01
1.021E+00
7.601 E-02
6.945E-05
7.484E-05
2.257E-02
1 .425E-02
3.657E-03
4.216E-03
1.937E-01
5.514E-05
4.805E-04
1 .507E-04
6.150E-05
6.046E-02
2.583E-03
2.093E-03
8.494E-05
4.948E-05
2000.5
1952.5
1702.5
1407.1
1361.4
1650.4
1626.1
2934.7
3917.5
3874.7
3878.2
3039.1
2215.4
2046.6
1395.3
1375.7
1412.7
1212.3
1616.5
1386.1
327.6
100.0
2032.3
2282.6
2099.3
1776.7
2222.8
1647.8
1690.8
1985.3
1575.6
1743.4
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
-6.77E-03
3.935E-07
1.914E-07
2.577E-06
1 .572E-05
5.098E-07
1.131E-06
4.703E-06
1.440E-13
3.707E-16
2.476E-16
8.115E-14
3.524E-12
2.169E-07
6.535E-07
8.043E-05
6.841 E-06
4.865E-09
2.036E-08
3.978E-07
1.195E-06
3.979E-04
2.142E-03
2.043E-07
1.068E-11
3.221 E-10
8.972E-10
1.786E-11
8.618E-07
2.752E-08
3.037E-09
1 .974E-09
3.693E-10
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 1 of4
-------
McClellan OUD ZoneA
: McClellan AFB
Meng
California
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1995
1995
1995
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1997
1998
1998
1998
1998
1998
1998
1998
7/1/1995
7/1/1995
7/1/1995
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1997
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
MW-90
MW-91
MW-92
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1004
MW-1064
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MWM12
MW-72
MW-88
MW-89
MW-90
MW-91
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
5.136E-03
7.550E-04
1 .360E-04
9.730E-02
1.770E-01
1.600E-01
5.880E-02
7.750E-03
2.200E-01
1.430E-01
1 .720E-04
6.520E-04
1 .300E-04
7.190E-02
3.280E-01
2.520E-02
5.175E-02
3.820E-03
4.580E-02
1.150E-02
1 .090E-03
2.570E-03
1.620E-01
2.563E-04
6.660E-02
9.310E-02
1 .940E-04
5.400E-03
3.480E-04
1.210E-01
1.480E-01
5.120E-02
4.080E-02
6.700E-03
2.850E-01
9.990E-02
1395.0
1096.1
966.2
2000.5
1952.5
1702.5
1407.1
1361.4
1650.4
1626.1
2217.0
3878.2
3039.1
2215.4
2046.6
1395.3
1375.7
1212.3
1616.5
1386.1
327.6
100.0
2032.3
200.9
1647.8
1575.6
1743.4
1395.0
1096.1
2000.5
1952.5
1702.5
1407.1
1361.4
1650.4
1626.1
-6.77E-03
-6.77E-03
-6.77E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-4.74E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
4.056E-07
4.512E-07
1 .959E-07
7.409E-06
1 .692E-05
5.002E-05
7.457E-05
1.221E-05
8.804E-05
6.421 E-05
4.693E-09
6.764E-12
7.200E-1 1
1 .976E-06
2.007E-05
3.380E-05
7.617E-05
1 .220E-05
2.153E-05
1.6 11 E-05
2.307E-04
1 .600E-03
1.061 E-05
9.889E-05
2.698E-05
5.311 E-05
4.997E-08
7.252E-06
1 .927E-06
5.565E-05
8.185E-05
7.397E-05
1 .833E-04
3.589E-05
5.030E-04
1 .935E-04
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 2 of4
-------
McClellan OUD ZoneA
: McClellan AFB
Meng
California
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1998
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1998
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
MW-11
MW-12
MW-14
MW-15
MW-240
MW-241
MW-242
MW-350
MW-351
MW-38D
MWM12
MW-52
MW-53
MW-55
MW-72
MW-88
MW-89
MW-92
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-10
MW-1004
MW-1026
MW-1073
MW-11
MW-12
MW-14
MW-15
MW-237
MW-240
MW-241
MW-242
8.695E-02
2.405E-01
3.905E-03
6.195E-02
9.050E-05
7.670E-03
1 .290E-02
1 .OOOE-04
1.915E-03
1.340E-01
5.224E-04
5.400E-05
3.845E-03
3.580E-04
4.630E-02
9.050E-05
9.050E-05
7.853E-04
6.040E-02
9.975E-02
9.923E-03
4.850E-02
6.630E-03
2.653E-01
7.840E-02
1 .095E-04
3.910E-03
4.330E-04
3.475E-02
8.290E-02
4.990E-03
7.890E-02
4.840E-04
4.645E-05
1 .030E-02
5.680E-03
2215.4
2046.6
1395.3
1375.7
1212.3
1616.5
1386.1
327.6
100.0
2032.3
200.9
2282.6
2099.3
1776.7
1647.8
1575.6
1743.4
966.2
2000.5
1952.5
1702.5
1407.1
1361.4
1650.4
1626.1
2217.0
2934.7
3039.1
2215.4
2046.6
1395.3
1375.7
1412.7
1212.3
1616.5
1386.1
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-3.84E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
1.751E-05
9.266E-05
1 .836E-05
3.141E-04
8.594E-07
1 .542E-05
6.283E-05
2.841 E-05
1 .304E-03
5.453E-05
2.414E-04
8.403E-09
1.210E-06
3.889E-07
8.253E-05
2.129E-07
1.117E-07
1.919E-05
4.548E-06
9.432E-06
3.073E-06
6.103E-05
1 .037E-05
1 .052E-04
3.489E-05
2.951 E-09
3.494E-09
2.358E-10
9.433E-07
5.015E-06
6.640E-06
1.152E-04
5.930E-07
1 .473E-07
4.798E-06
7.895E-06
Yes
Yes
Yes
No
Yes
Yes
Yes
Yes
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 3 of4
-------
McClellan OUD ZoneA
: McClellan AFB
Meng
California
Sampling
Event
Effective
Date
Well
Concentration
(mg/L)
Distance Down
Centerline (ft)
Coefficient
(1/ft)
Concentration
(mg/L)
Detection
Limit?
Used in
Analysis?
TRICHLOROETHYLENE (TCE)
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
7/1/1999
MW-350
MW-351
MW-38D
MW-412
MW-458
MW-52
MW-53
MW-55
MW-70
MW-72
MW-74
MW-76
MW-88
MW-89
MW-90
MW-91
MW-92
1 .095E-04
1 .370E-02
7.697E-02
1 .095E-04
1 .202E-04
1 .095E-04
4.420E-04
1 .730E-03
1 .095E-04
2.770E-02
1.610E-03
5.430E-03
1 .095E-04
1 .095E-04
1 .095E-04
3.970E-04
1 .095E-04
327.6
100.0
2032.3
200.9
1094.3
2282.6
2099.3
1776.7
2222.8
1647.8
1690.8
1985.3
1575.6
1743.4
1395.0
1096.1
966.2
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
-4.75E-03
2.313E-05
8.523E-03
4.982E-06
4.220E-05
6.675E-07
2.161E-09
2.082E-08
3.767E-07
2.870E-09
1.112E-05
5.270E-07
4.395E-07
6.192E-08
2.793E-08
1 .459E-07
2.185E-06
1.117E-06
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Note: Projected Concentrations that are below the user-specified detection limit are indicated by a check mark to its right; for sampling events
with less than 3 selected plume centerline wells, NO projected concentrations are calculated because no regression coefficient is available.
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 4 of4
-------
Regression of Plume Centerline Concentrations
McClellan OUD ZoneA
Location: McClellanAFB
Meng
California
Groundwater Flow Direction: 280 degrees
From Period: 7/1/1995 to 7/1/2000
Distance to Receptor: 100 feet
Selected Plume
Centerline Wells:
Sample Even
Well Distance to Receptor (feet)
MW-92
MW-91
MW-72
MW-11
966.2
1096.1
1647.8
2215.4
The distance is measured in the Groundwater Flow Angle
from the well to the compliance boundary.
Number of Regression Confidence in
Effective Date Centerline Wells Coefficient (1 /ft) Coefficient
TRICHLOROETHYLENE (TCE)
1995 7/1/1995 4
1996 7/1/1996 2
1997 7/1/1997 3
1998 7/1/1998 3
1999 7/1/1999 4
2000 7/1/2000 2
-6.77E-03 99.2%
O.OOE+00 0.0%
-4.74E-03 83.5%
-3.84E-03 88.9%
-4.75E-03 96.2%
O.OOE+00 0.0%
Note: when the number of plume centerline wells is less than 3, no analysis is performed and all related values
are set to ZERO; Confidence in Coefficient is the statistical confidence that the estimated coefficient is
different from ZERO (for details, please refer to "Conference in Trend" in Linear Regression Analysis).
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 1 of 1
-------
MAROS Mann-Kendall Statistics Summary
McClellan Zone B OU D
Location: McClellan AFB
Julia Aziz
California
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Source/ Number of
Well Tail Samples
TRICHLOROETHYLENE
EW-83
EW-84
MW-54
EW-85
EW-86
EW-87
EW-73
MW-1028
MW-1001
MW-1003
MW-1027
MW-59
MW-104
MW-1043
MW-105
MW-19D
MW-51
MW-57
MW-58
MW-1010
(TCE)
S
s
S
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
16
16
10
16
15
15
15
10
10
11
6
12
8
6
8
10
8
12
15
5
Number of
Detects
16
16
8
16
15
15
15
2
1
3
2
2
2
1
2
6
1
5
7
0
Coefficient
of Variation
0.30
0.83
1.46
0.79
1.23
0.68
0.92
0.61
0.03
0.30
0.71
0.31
1.60
0.09
1.94
1.67
0.18
1.44
1.75
0.00
Mann-Kendall
Statistic
22
-105
28
-84
-53
60
-83
-1
-3
-19
3
-1
-7
5
11
7
-7
-3
13
0
Confidence
in Trend
82.5%
100.0%
99.4%
100.0%
99.6%
99.9%
100.0%
50.0%
56.9%
91 .8%
64.0%
50.0%
76.4%
76.5%
88.7%
70.0%
76.4%
55.4%
72.1%
40.8%
All
Samples Concentration
"ND" ? Trend
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
NT
D
I
D
D
I
D
S
S
PD
NT
S
NT
NT
NT
NT
S
NT
NT
S
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-
Due to insufficient Data (< 4 sampling events); Source/Tail (S/T)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 1 of 1
-------
MAROS Linear Regression Statistics Summary
Project: McClellan Zone B OU D
Location: McClellan AFB
Julia Aziz
California
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
Average
Source/ Cone
Tail (mg/L)
Median
Cone
(mg/L)
All
Standard Samples
Deviation "ND" ?
Coefficient
Ln Slope of Variation
Confidence Concentration
in Trend Trend
TRICHLOROETHYLENE (TCE)
EW-83
EW-84
EW-85
EW-86
EW-87
MW-54
EW-73
MW-1027
MW-1001
MW-1010
MW-59
MW-1028
MW-104
MW-1043
MW-105
MW-19D
MW-51
MW-57
MW-58
MW-1003
s
s
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
T
T
T
1.0E-01
4.0E-01
1.9E-01
1 .2E-02
1.2E-01
1 .7E-03
2.6E-01
1 .5E-04
9.9E-05
1 .OE-04
1 .OE-04
1 .3E-04
2.1E-04
1 .OE-04
4.5E-04
5.1E-04
1.1E-04
2.6E-04
3.0E-04
1.1E-04
Note: Increasing (I); Probably Increasing (PI);
Due to insufficient Data (< 4 sampling events)
9.6E-02
2.5E-01
1 .2E-01
7.7E-03
8.4E-02
2.4E-04
1 .7E-01
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .7E-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
3.1E-02
3.3E-01
1 .5E-01
1 .5E-02
8.2E-02
2.5E-03
2.4E-01
1 .OE-04
2.6E-06
O.OE+00
3.2E-05
7.8E-05
3.3E-04
9.4E-06
8.7E-04
8.5E-04
1 .9E-05
3.8E-04
5.3E-04
3.3E-05
No
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
Stable (S); Probably Decreasing (PD);
; COV = Coefficient of Variation
2.3E-05
-1.1E-03
-6.6E-04
-7.6E-04
4.6E-04
1 .2E-03
-7.2E-04
6.6E-05
-3.9E-06
O.OE+00
-2.9E-05
-1 .1 E-04
-3.5E-04
7.1E-05
6.3E-04
1 .8E-04
-8.1 E-05
3.4E-05
6.7E-05
-1.1 E-04
Decreasing (D);
0.30
0.83
0.79
1.23
0.68
1.46
0.92
0.71
0.03
0.00
0.31
0.61
1.60
0.09
1.94
1.67
0.18
1.44
1.75
0.30
No Trend
60.7%
100.0%
99.9%
99.6%
99.9%
99.9%
100.0%
60.4%
100.0%
100.0%
60.9%
75.7%
83.6%
85.4%
98.6%
67.4%
92.4%
55.7%
62.3%
96.6%
(NT); Not Applicable
NT
D
D
D
I
I
D
NT
D
S
S
S
NT
NT
I
NT
PD
NT
NT
D
(N/A) -
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 1 of 1
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MAROS Statistical Trend Analysis Summary
Project: McClellan Zone B OU D
Location: McClellan AFB
Julia Aziz
California
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Well
TRICHLOROETHYLENE
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-104
MW-1043
MW-105
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
Source/
Tail
(TCE)
s
s
s
s
s
s
T
T
T
T
T
T
T
T
T
T
S
T
T
T
Number Number Average Median
of of Cone. Cone.
Samples Detects (mg/L) (mg/L)
15
16
16
16
15
15
10
11
5
6
10
8
6
8
10
8
10
12
15
12
15
16
16
16
15
15
1
3
0
2
2
2
1
2
6
1
8
5
7
2
2.6E-01
1 .OE-01
4.0E-01
1 .9E-01
1 .2E-02
1 .2E-01
9.9E-05
1.1E-04
1 .OE-04
1 .5E-04
1 .3E-04
2.1E-04
1 .OE-04
4.5E-04
5.1E-04
1.1E-04
1 .7E-03
2.6E-04
3.0E-04
1 .OE-04
1 .7E-01
9.6E-02
2.5E-01
1 .2E-01
7.7E-03
8.4E-02
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .OE-04
1 .7E-04
1 .OE-04
2.4E-04
1 .OE-04
1 .OE-04
1 .OE-04
All
Samples
"ND" ?
No
No
No
No
No
No
No
No
Yes
No
No
No
No
No
No
No
No
No
No
No
Mann-
Kendall
Trend
D
NT
D
D
D
I
S
PD
S
NT
S
NT
NT
NT
NT
S
I
NT
NT
S
Linear
Regression
Trend
D
NT
D
D
D
I
D
D
S
NT
S
NT
NT
I
NT
PD
I
NT
NT
S
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable
(N/A); Not Applicable (N/A) - Due to insufficient Data (< 4 sampling events); No Detectable Concentration (NDC)
The Number of Samples and Number of Detects shown above are post-consolidation values.
MAROS Version 2, 2002, AFCEE
Monday, January 13, 2003
Page 1 of 1
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MAROS Site Results
McClellan Zone B OU D
Location: McClellan AFB
Julia Aziz
California
User Defined Site and Data Assumptions:
Hydrogeology and Plume Information:
Groundwater
Seepage Velocity: 35 ft/yr
Current Plume Length: 1000 ft
Current Plume Width 600 ft
Number of Tail Wells: 13
Number of Source Wells: 7
Source Information:
Source Treatment: Pump and Treat
NAPL is not at this site.
Down-gradient Information:
Distance from Edge of Tail to Nearest:
Down-gradient receptor: 1000 ft
Down-gradient property: 10ft
Distance from Source to Nearest:
Down-gradient receptor: 6000ft
Down-gradient property: 10 ft
Consolidation Assumptions:
Time Period: 5/1/1990 to 12/31/2000
Consolidation Period: No Time Consolidation
Consolidation Type: Median
Duplicate Consolidation: Average
ND Values: Specified Detection Limit
J Flag Values : Actual Value
Plume Information Weighting Assumptions:
Consolidation Step 1. Weight Plume Information by Chemical
Summary Weighting: Weighting Applied to All Chemicals Equally
Consolidation Step 2. Weight Well Information by Chemical
Well Weighting: No Weighting of Wells was Applied.
Chemical Weighting: No Weighting of Chemicals was Applied.
Note: These assumptions made when consolidating the historical mentoring lumping the Wells and COCs.
1.
Preliminary Monitoring System Optimization Results: Based on site classification, source treatment and Monitoring System
Category the following suggestions are made for site Sampling Frequency, Duration of Sampling, and Well Density. These
criteria take into consideration: Plume Stability, Type of Plume, and Groundwater Velocity.
coc
Tail Source Level of
Stability Stability Effort
Sampling
Duration
Sampling
Frequency
Sampling
Density
TRICHLOROETHYLENE (TCE)
M
25
Remove treatment No Recommendation
system if previously
reducing concentation
Note:
Plume Status: (I) Increasing; (Pl)Probably Increasing; (S) Stable; (NT) No Trend; (PD) Probably Decreasing; (D) Decreasing
Design Categories: (E) Extensive; (M) Moderate; (L) Limited (N/A) Not Applicable, Insufficient Data Available
Level of Monitoring Effort Indicated by Analysi I Moderate
2,
MAROS Version 2, 2002, AFCEE
Tuesday, January 14, 2003
Page 1 of 2
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Moment Type Consituent
Zeroth Moment: Mass
TRICHLOROETHYLENE (TCE)
1st Moment: Distance to Source
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma XX
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma YY
TRICHLOROETHYLENE (TCE)
Coefficient
of Variation
1.32
0.76
1.68
1.06
Mann-Kendall
S Statistic
-13
-50
-50
-56
Confidence
in Trend
57.7%
93.0%
93.0%
95.1%
Moment
Trend
NT
PD
PD
D
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.30
Saturated Thickness: Uniform: 60 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
MAROS Version 2, 2002, AFCEE
Tuesday, January 14, 2003
Page 2 of 2
-------
MAROS Zeroth Moment Analysis
McClellan AFB Zone B OU D
Location: McClellan AFB
Julia Aziz
California
COC: TRICHLOROETHYLENE (TCE)
Change in Dissolved Mass Over Time
Date
H-.UC-U 1 •
3.5E-01 •
3.0E-01 •
2.5E-01 •
2.0E-01 •
1.5E-01 -
1 nF.ni
i .\ic \i i
5.0E-02 •
n np+nn -
*
*
* *
*
Porosity: 0.30
Saturated Thickness:
Uniform: 60 ft
Mann Kendall S Statistic:
-5
Confidence in
Trend:
I 76.5%
Coefficient of Variation:
I 0.70
Zeroth Moment
Trend:
Data Table:
Effective Date
1/1/1991
1/1/1993
1/1/1995
1/1/1997
1/1/1999
1/1/2001
Constituent
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
Estimated
Mass (Kg)
2.0E-01
1 .5E-01
1 .7E-01
1 .2E-01
3.6E-01
O.OE+00
Number of Wells
12
12
12
7
10
3
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events); ND = Non-detect. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS First Moment Analysis
McClellan AFB Zone B OU D
Location: McClellan AFB
COC: TRICHLOROETHYLENE (TCE)
Julia Aziz
California
Distance from Source to Center of Mass
Date
Mann Kendall S Statistic:
Confidence in
Trend:
o
o
3
CO
E
o
o
o
c
to
1.6E+03 -
1.4E+03 -
1.2E+03 -
1.0E+03 -
8.0E+02 -
6.0E+02 -
4.0E+02 -
2.0E+02 -
n np+nn .
*
» *
*
40.8%
Coefficient of Variation:
I 029
First Moment Trend:
Data Table:
Effective Date
1/1/1991
1/1/1993
1/1/1995
1/1/1997
1/1/1999
1/1/2001
Constituent
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
Xc (ft)
2,167,260
2,167,260
2,167,280
2,167,915
2,167,287
Yc (ft)
367,204
367,030
367,265
367,431
366,773
Distance from Source (ft)
1,138
986
1,201
1,709
799
Number of Wells
12
12
12
7
10
3
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS First Moment Analysis
Project: McClellan AFB Zone B OU D
Location: McClellan AFB
Julia Aziz
California
COC: TRICHLOROETHYLENE (TCE)
Change in Location of Center of Mass Over Time
367400
367300
367200
367000
366900
366800
01/9!
Xc (ft)
Groundwater
Flow Direction:
Source
Coordinate:
X: | 2,166,737
Y: I 366,194
Effective Date
Constituent
Xc (ft)
Yc (ft) Distance from Source (ft) Number of Wells
1/1/1991
1/1/1993
1/1/1995
1/1/1997
1/1/1999
1/1/2001
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
TRICHLOROETHYLENE (TCE)
2,167,260
2,167,260
2,167,280
2,167,915
2,167,287
367,204
367,030
367,265
367,431
366,773
1,138
986
1,201
1,709
799
12
12
12
7
10
3
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events). Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS Second Moment Analysis
McClellan AFB Zone B OU D
McClellan AFB
COC: TRICHLOROETHYLENE (TCE)
Julia Aziz
California
Change in Plume Spread Over Time
Date
Mann Kendall S Statistic:
Confidence in
1000000 -
£- 100000 -
-£- 10000 -
CM
£ 1000 -
CO
10-
1 .
4(1(1(1(1(1(1(1(1
10000000 -
_ 1000000 -
=• 100000 -
5* 10000 -
$ 1000 -
100 -
10-
•1 .
Data Table:
Effective Date
1/1/1991
1/1/1993
1/1/1995
1/1/1997
1/1/1999
1/1/2001
* • * I 59'2%
Coefficient of Variation:
l| 1 -23
Second Moment
Trend:
| NT
Date
/& $> & $> &
•$" •$" •$" •$" •$" Mann Kendall S Statistic:
' * ' I4
Confidence in
Trend:
» » » * | 75.8%
Coefficient of Variation:
Second Moment
Trend:
I NT
1
Constituent Sigma XX (sq ft) Sigma YY (sq ft) Number of Wells
TRICHLOROETHYLENE (TCE) 237,221 766,710 12
TRICHLOROETHYLENE (TCE) 240,845 790,322 12
TRICHLOROETHYLENE (TCE) 231,216 837,265 12
TRICHLOROETHYLENE (TCE) 21,689,115 4,613,693 7
TRICHLOROETHYLENE (TCE) 255,472 251,790 10
TRICHLOROETHYLENE (TCE) 3
Note: Increasing (I); Probably Increasing (PI); Stable (S); Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A) -
Due to insufficient Data (< 4 sampling events)
The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
1/2/2003
Page 1 of 1
-------
MAROS Spatial Moment Analysis Summary
McClellan AFB Zone B OU D
Location: McClellan AFB
Julia Aziz
California
Effective Date
TRICHLOROETHYLENE
1/1/1991
1/1/1993
1/1/1995
1/1/1997
1/1/1999
1/1/2001
Oth Moment
Estimated
Mass (Kg)
(TCE)
2.0E-01
1.5E-01
1.7E-01
1.2E-01
3.6E-01
O.OE+00
1st (Center of
Xc (ft)
2,167,260
2,167,260
2,167,280
2,167,915
2,167,287
Yc (ft)
367,204
367,030
367,265
367,431
366,773
Source
Distance (ft)
1,138
986
1,201
1,709
799
2nd Moment
Sigma XX
(sq ft)
237,221
240,845
231,216
21,689,115
255,472
Sigma YY
(sq ft)
766,710
790,322
837,265
4,613,693
251,790
Number of
Wells
12
12
12
7
10
3
MAROS Version 2, 2002, AFCEE
Thursday, January 02, 2003
Page 1 of 2
-------
McClellan AFB Zone B OU D
Location: McClellan AFB
Julia Aziz
California
Moment Type Consituent
Zeroth Moment: Mass
TRICHLOROETHYLENE (TCE)
1st Moment: Distance to Source
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma XX
TRICHLOROETHYLENE (TCE)
2nd Moment: Sigma YY
TRICHLOROETHYLENE (TCE)
Coefficient
of Variation
0.70
0.29
2.12
1.23
Mann-Kendall
S Statistic
-5
0
4
2
Confidence
in Trend
76.5%
40.8%
75.8%
59.2%
Moment
Trend
S
S
NT
NT
Note: The following assumptions were applied for the calculation of the Zeroth Moment:
Porosity: 0.30 Saturated Thickness: Uniform: 60 ft
Mann-Kendall Trend test performed on all sample events for each constituent. Increasing (I); Probably Increasing (PI); Stable (S);
Probably Decreasing (PD); Decreasing (D); No Trend (NT); Not Applicable (N/A)-Due to insufficient Data (< 4 sampling events).
Note: The Sigma XX and Sigma YY components are estimated using the given field coordinate system and then rotated to align with the
estimated groundwater flow direction. Moments are not calculated for sample events with less than 6 wells.
MAROS Version 2, 2002, AFCEE
Thursday, January 02, 2003
Page 2 of 2
-------
MAROS Sampling Location Optimization Results
Project: McClellan OUD ZoneB
McClellan AFB
Meng
California
Sampling Events Analyzed:
From 1995
7/1/1995
to 2000
7/1/2000
Parameters used:
Constituent
Inside SF Hull SF Area Ratio Cone. Ratio
TRICHLOROETHYLENE (TCE)
0.1
0.01
0.95
0.95
Well
Average Minimum Maximum
X (feet) Y (feet) Removable? Slope Factor* Slope Factor* Slope Factor* Eliminated?
TRICHLOROETHYLENE (TCE)
MW-1001
MW-1010
MW-1027
MW-104
MW-1043
MW-105
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
2165839.75
2165998.00
2168527.25
2166810.00
2165134.50
2168172.25
2167162.75
2166767.25
2166658.50
2166663.50
2166656.25
2166608.50
366560.94 0
368273.56 0
367775.44 0
367362.16 0
368138.75 0
366814.81 0
365619.84 0
366264.69 0
366509.63 0
365863.28 0
366634.56 0
365816.88 0
0.574
0.003
0.262
0.712
0.324
0.571
0.156
0.642
0.556
0.257
0.329
0.377
0.262
0.003
0.000
0.534
0.324
0.279
0.084
0.390
0.292
0.098
0.048
0.025
0.949
0.003
0.481
0.925
0.324
0.921
0.242
0.944
0.873
0.447
0.665
0.799
D
D
D
D
D
D
D
D
D
D
D
D
Note: The Slope Factor indicates the relative importance of a well in the monitoring network at a given sampling event; the larger the SF
value of a well, the more important the well is and vice versa; the Average Slope Factor measures the overall well importance in the
selected time period; the state coordinates system (i.e., X and Y refer to Easting and Northing respectively) or local coordinates systems
may be used; wells that are NOT selected for analysis are not shown above.
* When the report is generated after running the Excel module, SF values will NOT be shown above.
MAROS Version 2, 2002, AFCEE
Friday, December 13, 2002
Page 1 of 1
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MAROS Sampling Frequency Optimization Results
McClellan OUD ZoneB
McClellan AFB
Meng
California
The Overall Number of Sampling Events: 34
"Recent Period" defined by events: From 1995 1st Quarter
To 2000 3rd Quarter
"Rate of Change" f
Well
1/15/1995
larameters used:
8/15/2000
Constituent Cleanup Goal Low Rate Medium Rate High Rate
TRICHLOROETHYLENE (TCE) 0.005
Units: Cleanup Goal is in mg/L; all rate parameters are
Recommended
Sampling Frequency
0.0025 0.005 0.01
in mg/L/year.
Frequency Based Frequency Based
on Recent Data on Overall Data
TRICHLOROETHYLENE (TCE)
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-104
MW-1043
MW-105
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
Annual
Annual
Annual
Annual
Annual
Quarterly
Biennial
Biennial
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Quarterly Quarterly
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Note: Sampling frequency is determined considering both recent and overall concentration trends. Sampling Frequency is the
final recommendation; Frequency Based on Recent Data is the frequency determined using recent (short) period of monitoring
data; Frequency Based on Overall Data is the frequency determined using overall (long) period of monitoring data. If the "recent
period" is defined using a different series of sampling events, the results could be different.
MAROS Version 2, 2002, AFCEE
Monday, December 16, 2002
Page 1 of 1
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DRAFT FINAL
THREE-TIERED
GROUND WATER MONITORING NETWORK
OPTIMIZATION EVALUATION
FOR
OPERABLE UNIT D
MCCLELLAN AIR FORCE BASE, CALIFORNIA
Prepared for
US Environmental Protection Agency
May 2003
Denver, Colorado
-------
EXECUTIVE SUMMARY
This report presents a description and evaluation of the groundwater monitoring
program associated with Operable Unit D (OU D) at McClellan Air Force Base (AFB),
California. The monitoring program at this site was evaluated to identify potential
opportunities to streamline monitoring activities while still maintaining an effective
monitoring network. This evaluation is being conducted as part of an independent
assessment of monitoring network optimization (MNO) methods by the US
Environmental Protection Agency (USEPA) and the Air Force Center for Environmental
Excellence (AFCEE).
Objectives
Groundwater monitoring programs have two primary objectives (USEPA, 1994b;
Gibbons, 1994):
1. Evaluate long-term temporal trends in contaminant concentrations (temporal
objective)', and
2. Evaluate the extent to which contaminant migration is occurring (spatial
objective).
The relative success of any remediation system (including the monitoring network) is
judged based on the degree to which it achieves the stated objectives of the system.
Designing an effective groundwater monitoring program involves locating monitoring
points and developing a site-specific strategy for groundwater sampling and analysis that
maximizes the amount of relevant information that can be obtained while minimizing
incremental costs. The effectiveness of a monitoring network in achieving the two
primary monitoring objectives can be evaluated quantitatively using statistical
techniques. Qualitative evaluation also is important to allow consideration of such
factors as hydrostratigraphy, locations of potential receptor exposure points with respect
to a dissolved contaminant plume, and the direction(s) and rate(s) of contaminant
migration.
The general objective of the project was to optimize the OU D LTM network by
applying a three-tiered MNO approach to assess the degree to which the monitoring
network addresses each of the two primary objectives of monitoring listed above and
other important considerations. The three-tiered MNO evaluation described in this report
examines the 51 wells (6 extraction wells and 45 monitoring wells) included in OU D
monitoring network. The specific objectives of the project included:
ES-1
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc
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« Apply a qualitative methodology that considers factors such as
hydrostratigraphy, locations of potential receptors with respect to the dissolved
plume, and the direction(s) and rate(s) of contaminant migration to establish the
frequency at which monitoring should be conducted, and if each well should be
retained in or removed from the monitoring program.
« Conduct a Mann-Kendall statistical analysis to determine the temporal trends of
COCs over time, and apply an algorithm to determine the relevance of the trends
within the monitoring network.
. Determine the relative amount of spatial information contributed by each
monitoring well by performing a spatial statistical analysis utilizing kriging error
predictions.
« Combine and evaluate the results of the three analyses to establish the frequency
at which monitoring should be conducted, as well as the optimal number and
locations of wells in the monitoring network.
Current Monitoring Program
Quarterly sampling of off-Base wells begin in 1984 as part of the Installation
Restoration Program at McClellan AFB. In 1986, the Groundwater Sampling and
Analysis Program was established to support ongoing remedial investigation/feasibility
study activities. In 1996, the Groundwater Monitoring Plan (GWMP) for on- and off-
Base wells was established under the Long-Term Monitoring Program to update the
GSAP and to support groundwater operable unit (GWOU) activities associated with the
Interim Record of Decision (IROD). Under the GWMP, groundwater samples are
collected quarterly from selected wells across the entire Base and analyzed for
contaminants of concern (COCs) associated with the respective plumes. Groundwater
sampling data are used to monitor how plume sizes and shapes are changing in response
to extraction well (EW) pumping to determine if remedial action objectives are being
met.
In the OU D area, groundwater sampling is conducted primarily to monitor areas
where dissolved VOC concentrations exceed MCLs (termed VOC target areas) in
monitoring zones A and B (monitoring of deeper zones in the OU D area is not
performed). In addition, selected wells that are more distant from the VOC target areas
are monitored to confirm and demonstrate that significant concentrations of COCs are not
present. The field sampling plan identifies the wells to be sampled in the area based on
the rationale and decision logic presented in the GWMP (Radian, 1997). Based on the
logic presented in the GWMP, the monitoring frequency and sampling rationale for each
well are continually evaluated, and can change as new sampling data are obtained.
Because the OU D plume is contained by the extraction system and the plume is well
defined, all of the wells associated with the OU D plume are sampled relatively
infrequently (annually or biennially). The Monitoring and Extraction Well Baseline
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Sampling Frequency and Rationale (URS, 2002) identifies 6 extraction wells and 45
monitoring wells to be monitored in the vicinity of the OU D plume, based on
groundwater-quality data collected through the first quarter of 2002.
Optimization Findings
The OU D groundwater monitoring program was evaluated using results for sampling
events performed from April 1990 through August 2001. The analytical database
provided to Parsons contained from 5 to 18 sampling results for each of the 51 wells in
the OU D monitoring program. The primary COCs identified for the OU D plume in the
GWOU IROD are tricholoroethene (TCE), tetrachlroethene (PCE) , cis-1,2-
dicholorethene, (DCE), and 1,2-dichloroethane (DCA); therefore, the MNO evaluation
focused on these constituents. TCE is the COC with the highest concentrations and
largest spatial extent in groundwater at McClellan AFB OU D; therefore TCE sampling
results were the primary data used to conduct the three-tiered MNO.
Results from the three-tiered monitoring network optimization for OU D at McClellan
AFB indicate that 30 of the 51 OU D area wells could be removed from the groundwater
LTM program with little loss of information. A refined monitoring program consisting of
21 wells (13 to be sampled annually, and 8 to be sampled biennially) would be adequate
to address the two primary objectives of monitoring. This refined monitoring network
would result in an average of 17 sampling events per year, compared to 34 events per
year in the current monitoring program. Implementing these recommendations for
optimizing the LTM monitoring program at OU D at McClellan AFB could reduce
current LTM annual monitoring events by 50 percent. Based on analytical costs alone,
implementing these recommendations could save $2550 per year based on an estimate
of $150 per sample analysis. The recommendations provided in this report for removal
of off-Base monitoring wells from the LTM program were based largely on technical
considerations, and should be evaluated in the light of relevant community relations
concerns.
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TABLE OF CONTENTS
Page
ACRONYMS AND ABBREVIATIONS iv
EXECUTIVE SUMMARY ES-1
SECTION 1 INTRODUCTION 1-1
SECTION 2 - SITE BACKGROUND INFORMATION 2-1
2.1 Site Description 2-1
2.2 Geology and Hydrogeology 2-2
2.2.1 Geology 2-2
2.2.2 Local Hydrogeology 2-3
2.3 Nature and Extent of Contamination 2-5
2.4 Remedial Systems 2-8
SECTION 3 - LONG-TERM MONITORING PROGRAM AT OU D 3-1
3.1 Description of Monitoring Program 3-1
3.2 Summary of Analytical Data 3-5
SECTION 4 - QUALITATIVE MNO EVALUATION 4-1
4.1 Methodology for Qualitative Evaluation of Monitoring Network 4-2
4.2 Results of Qualitative MNO Evaluation 4-3
4.2.1 Monitoring Network and Sampling Frequency 4-4
4.2.1.1 Extraction Wells 4-8
4.2.1.2 A-Zone Monitoring Wells 4-9
4.2.1.3 B-Zone Monitoring Wells 4-12
4.2.2 Laboratory Analytical Program 4-12
4.2.3 LTM Program Flexibility 4-13
SECTION 5 - TEMPORAL STATISTICAL EVALUATION 5-1
5.1 Methodology for Temporal Trend Analysis of Contaminant
Concentrations 5-1
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TABLE OF CONTENTS (Continued)
Page
5.2 Temporal Evaluation Results 5-7
SECTION 6 - SPATIAL STATISTICAL EVALUATION 6-1
6.1 Geostatistical methods for evaluating Monitoring networks 6-1
6.2 Spatial Evaluation of Monitoring Network at OU D 6-4
6.3 Spatial Statistical Evaluation Results 6-8
6.3.1 Kriging Ranking Results 6-8
SECTION 7 - SUMMARY OF THREE-TIERED MONITORING
NETWORK EVALUATION 7-1
SECTION 8 - REFERENCES 8-1
LIST OF TABLES
No. Title Page
3.1 Current Groundwater Monitoring Program 3-4
3.2 Summary of Occurrence of Groundwater Contaminants of Concern 3-7
4.1 Monitoring Network Optimization Decision Logic 4-3
4.2 Monitoring Frequency Decision Logic 4-4
4.3 Qualitative Evaluation of Groundwater Monitoring Network 4-5
5.1 Results of Temporal Trend Analysis of Groundwater Monitoring Results 5-10
6.1 Results of Geostatistical Evaluation Ranking of Zone A Wells by
Relative Value of TCE Information 6-10
7.1 Summary of Evaluation of Current Groundwater Monitoring Program 7-3
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TABLE OF CONTENTS (CONTINUED)
LIST OF FIGURES
No. Title Page
3.1 Current Monitoring Well Network and Sampling Procedures 3-3
4.1 Recommended Sampling Frequencies Based on Qualitative Evaluation 4-7
5.1 TCE Concentrations Through Time at Well MW-38D 5-2
5.2 Conceptual Representation of Temporal Trends and Temporal Variations
in Concentrations 5-3
5.3 Conceptual Representation of Continued Monitoring at Location Where
no Temporal Trend in Concentrations is Present 5-6
5.4 Mann-Kendall Temporal Trend Analysis for Concentrations of TCE 5-12
5.5 Temporal Trend Decision Rationale Flow Chart 5-13
6.1 Idealized Semvariogram Model 6-3
6.2 Impact of Missing Wells on Predicted Standard Error 6-7
6.3 Results of Geostatistical Analysis Showing Relative Value of
Information on TCE Distribution in Zone A Wells 6-9
6.4 Results of Geostatistical Analysis Showing Relative Value of Spatial
Information on TCE in Select Zone A Wells 6-12
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ACRONYMS AND ABBREVIATIONS
1,2-DCA
AFB
AFCEE
ASCE
bgs
bmsl
coc
DNAPL
ESRI
ft/day
ft/ft
GIS
GSAP
GWMP
GWOU
IROD
IRP
LNAPL
LTM
LTMP
ug/L
MCL
MNO
OUD
PCE
PQL
RAO
TCA
TCE
USEPA
voc
dichloroethane
Air Force Base
Air Force Center for Environmental Excellence
American Society of Chemical Engineers
below ground surface
below mean sea level
contaminant of concern
dense nonaqueous-phase liquid
Environmental Systems Research Institute, Inc.
foot (feet) per day
foot per foot
geographical information system
Groundwater Sampling and Analysis Program
Groundwater Monitoring Plan
Groundwater Operable Unit
Interim Record of Decision
Installation Restoration Program
light nonaqueous-phase liquid
long-term monitoring
Long-Term Monitoring Program
microgram(s) per liter
maximum contaminant level
monitoring network optimization
Operable Unit D
tetrachloroethene
practical quantitation limit
remedial action objective
trichloroethene
trichloroethene
United States Environmental Protection Agency
volatile organic compound
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SECTION 1
INTRODUCTION
Groundwater monitoring programs have two primary objectives (U.S. Environmental
Protection Agency [USEPA], 1994b; Gibbons, 1994):
1. Evaluate long-term temporal trends in 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
2. Evaluate the extent to which contaminant migration is occurring, particularly if
a potential exposure point for a susceptible receptor exists (spatial objective).
The relative success of any remediation system and its components (including the
monitoring network) must be judged based on the degree to which it achieves the stated
objectives of the system. Designing an effective groundwater monitoring program
involves locating monitoring points and developing a site-specific strategy for
groundwater sampling and analysis so as to maximize the amount of relevant information
that can be obtained while minimizing incremental costs. Relevant information is that
required to effectively address the temporal and spatial objectives of monitoring. The
effectiveness of a monitoring network in achieving these two primary objectives can be
evaluated quantitatively using statistical techniques. In addition, there may be other
important considerations associated with a particular monitoring network that are most
appropriately addressed through a qualitative assessment of the network. The qualitative
evaluation may consider such factors as hydrostratigraphy, locations of potential receptor
exposure points with respect to a dissolved contaminant plume, and the direction(s) and
rate(s) of contaminant migration.
This report presents a description and evaluation of the groundwater monitoring
program associated with Operable Unit D (OU D) McClellan Air Force Base (AFB),
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California. The current monitoring program at this site was evaluated to identify
potential opportunities to streamline monitoring activities while still maintaining an
effective monitoring network. This evaluation is being conducted as part of an
independent assessment of monitoring network optimization (MNO) methods by the
USEPA and the Air Force Center for Environmental Excellence (AFCEE). A three-
tiered approach, consisting of a qualitative evaluation, an evaluation of temporal trends in
contaminant concentrations, and a statistical spatial analysis, was conducted to assess the
degree to which the monitoring network addresses each of the two primary objectives of
monitoring, and other important considerations. The results of the three evaluations were
combined and used to assess the optimal frequency of monitoring and the spatial
distribution of the components of the monitoring network. The results of the analysis
were then used to develop recommendations for optimizing the monitoring program at
OUD.
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SECTION 2
SITE BACKGROUND INFORMATION
The location, operational history, geology, and hydrogeology of OU D at McClellan
AFB are briefly described in the following subsections.
2.1 SITE DESCRIPTION
McClellan AFB is located approximately 7 miles northeast of downtown Sacramento,
California. The installation comprises approximately 3,000 acres and is bounded by the
city of Sacramento on the west and southwest, the unincorporated areas of Antelope on
the north, Rio Linda on the northwest, and North Highlands on the east. OU D is located
in the northwestern part of McClellan AFB, northwest of Building 1069 along Patrol
Road, 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. Historical waste-disposal practices included the use of burial
pits for the disposal and/or burning of these materials.
Fifteen sites that were formerly operated as waste pits are located at OU D. These
waste pits were operational from the mid-1950s through the 1970s (CH2M Hill, 1994a).
In 1956, the first burial pit was created for disposal of sodium valves from aircraft
engines. Additional burial and burn pits were constructed throughout the 1960s and
1970s, and were used for the disposal of refuse, other solid waste, oil, various chemicals,
and industrial sludges. From the late 1970s into the early 1980s, many of the burial and
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burn pits were closed and covered with soil. In 1985, the "Area D" cap was constructed
over waste pits CS-1, CS-2, CS-3, CS-4, CS-5, CS-S, CS-A, and CS-T to reduce the
infiltration of precipitation through the waste pits, thereby also reducing the formation
and migration of leachate to groundwater at the site. Prior to 1985, waste pit CS-4 was
excavated to remove the sludge waste, and pits CS-6 and CS-26 also were excavated and
backfilled. The waste-disposal pits at OU D are no longer used for disposal of waste
products (CH2M Hill, 1992).
Based on evidence of environmental contamination, McClellan AFB was included on
the National Priorities List of Superfund sites in 1987. A single OU was established for
groundwater at the Base, and an Interim Record of Decision (IROD) was signed for the
Base-wide Groundwater OU (GWOU) in 1995 (CH2M Hill, 1995). The IROD specifies
groundwater extraction and treatment as the interim remedy for groundwater at OU D,
thereby endorsing the extraction well system that was installed in 1987 (see Section 2.5).
In 1995, McClellan AFB was recommended for closure under the Base Realignment
and Closure Act (BRAC). The recommendation became effective on September 28,
1995, and the installation was closed in July, 2001. Ongoing environmental restoration
activities are being directed by the Air Force Real Property Agency (formerly the Air
Force Base Conversion Agency).
2.2 GEOLOGY AND HYDROGEOLOGY
2.2.1 Geology
The Central Valley is an elongate basin, bounded on the east and west by nearly-
continuous mountain ranges. Sediments derived from the bordering mountain ranges
have accumulated in the basin for millions of years, and now comprise a sequence of
unconsolidated to partly consolidated deposits that, in places along the western side of the
Valley, may be as much as 30,000 feet thick (Norris and Webb, 1990). The sediments
that fill the Valley thin to the east, but the sequence is probably several thousand feet
thick beneath the city of Sacramento.
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The sediments in the upper few hundred feet of the subsurface beneath the Base
consist of coalescing deposits laid down by fluvial systems of various sizes and
competence that flowed generally from northeast to southwest or west. Soils are
primarily sand, silt, and clay, generally poorly sorted, with localized occurrences of
gravel, generally in the southern part of the Base. The nature of fluvial deposition,
including stream meandering and abandonment/reoccupation of channels, produced
morphologically irregular lenses and strata that are laterally and vertically discontinuous.
The coalescing and intercalating nature of the sediments makes distinction among units,
or stratigraphic correlation over distances greater than a few tens of feet, difficult (CH2M
Hill, 1994a).
2.2.2 Local Hydrogeology
Although the stratigraphy of the sediments beneath McClellan AFB is complex, the
juxtaposed and intercalated strata of sand, silt, clay, and gravel comprise a single
groundwater system. The geologic and hydraulic properties of the upper water-bearing
unit vary over short distances, and the more permeable intervals serve to establish
hydraulic interconnection vertically and horizontally, so that in general, groundwater
movement (and associated advective migration of contaminants) may occur throughout
the groundwater system. The upper unit beneath McClellan AFB has been divided into
the vadose (unsaturated) zone and five monitoring zones below the water table,
distinguished on the basis of general hydraulic characteristics. From shallowest to
deepest, the saturated zones are labeled A, B, C, D, and E (Radian, 1999a). The
monitoring zones at McClellan AFB were designated primarily for the purpose of
indicating the completion intervals of monitoring wells; all the monitoring zones are
hydraulically connected to a greater or lesser degree, and groundwater can move between
adjacent zones (CH2M Hill, 1994a). Generally, the zones dip to the west, and increase in
thickness from east to west. In is entirely possible for two adjacent wells screened at
different depth intervals to be completed within the same monitoring zone, or for two
wells screened at similar depths to be completed in different monitoring zones. These
local variations in the depths of monitoring zones are a consequence of the heterogeneity
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of the sediments beneath the Base, and the relative capacities of different deposits to
transmit water.
The depth to the water table beneath McClellan AFB ranges between about 90 and
110 feet below ground surface (bgs) (CH2M Hill, 1994b and 1999). At OU D, the depth
to shallow groundwater varies from approximately 99 to 102 feet bgs. As a consequence
of the relatively deep water table, surface streams are not in direct hydraulic
communication with the groundwater system beneath the Base (CH2M Hill, 1995).
Water-table elevations have declined at rates ranging from 1 to 2 feet per year during the
past 50 years. Groundwater levels are expected to continue to decline at a rate of about 2
feet per year as a consequence of large-scale groundwater production for industrial,
irrigation, and municipal uses in the Sacramento area (CH2M Hill, 1994b).
The thickness of the A monitoring zone ranges from 9 to 50 feet, and groundwater in
the A zone exists under unconfined conditions. The thickness of monitoring zone B
ranges from 40 to 75 feet, and groundwater in this zone appears to exist under partially
confined conditions. However, if the wells screened in the intermediate zone between the
A and B monitoring zones are reassigned to either the A or B zones as described in
Section 3.1, then the defined thickness of these two zones will increase. Monitoring
wells at OU D have been constructed only in the A and B monitoring zones (Radian,
1999a); therefore, no information is available regarding the deeper monitoring zones at
OUD.
Under natural conditions, prior to installation and operation of the OU D groundwater
extraction system, groundwater typically moved from northeast to south or southwest in
the A monitoring zone, and from north to south in the B monitoring zone (CH2M Hill,
1992). The local directions of groundwater movement beneath OU D currently are
influenced by the groundwater extraction system operating at the site. Groundwater flow
generally is directed radially inward toward the extraction wells (EWs).
Horizontal hydraulic gradients in the groundwater system at OU D range from 0.0008
foot per foot (ft/ft) to 0.0021 ft/ft, with the largest gradients occurring near active EWs.
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Vertical hydraulic gradients have been calculated using pairs of adjacent monitoring
wells that are completed in different monitoring zones. Vertical gradients calculated
using well pairs within that part of the groundwater system influenced by active
groundwater extraction at OU D are generally downward, similar to vertical gradients
that exist between the A and B monitoring zones in other parts of the Base (Radian,
1999a). Downward gradients range in magnitude from about 0.003 ft/ft to 0.04 ft/ft, with
the greatest vertical gradients occurring near active EWs. At distances greater than 1,000
feet from the extraction system, vertical gradients may be directed upward or downward,
depending on local potentiometric conditions. The vertical gradients between monitoring
zones A and B are usually upward in the winter and downward during the rest of the year.
Hydraulic conductivity is the property of a porous medium that describes the capacity
of the medium to transmit water. The horizontal hydraulic conductivity of the sediments
in monitoring zones A and B may range as high as 30 feet per day (ft/day); however, the
weighted average hydraulic conductivity of the sediments in the two zones is somewhat
lower (about 5.6 ft/day) (CH2M Hill, 1994a). The value of horizontal hydraulic
conductivity is about 5 to 15 times greater than the vertical hydraulic conductivity
(CH2M Hill, 1992), indicating that advective groundwater movement beneath OU D
occurs primarily in the horizontal plane. Based on the weighted average of horizontal
hydraulic conductivity for the A and B monitoring zones (5.6 ft/day), the range of
horizontal hydraulic gradients within OU D (about 0.001 to 0.002 ft/ft), and an
approximate effective porosity of 0.15 (a value consistent with the results of prior
investigations at McClellan AFB [CH2M Hill, 1992]), the calculated horizontal advective
velocity of groundwater movement in the A and B monitoring zones at OU D ranges
between about 14 and 30 feet per year (ft/year) (Parsons, 2000).
2.3 NATURE AND EXTENT OF CONTAMINATION
The contaminants of concern (COCs) in groundwater at OU D are exclusively
chlorinated aliphatic hydrocarbons (CAHs). Primary COCs include trichloroethene
(TCE), tetrachloroethene (PCE), czs-l,2-dichloroethene (DCE), and 1,2-dichloroethane
(DCA). These four compounds were identified in the GWOU IROD (CH2M Hill, 1995)
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as Base-wide groundwater COCs based on their frequency of detections, exceedance of
regulatory maximum contaminant levels (MCLs), and health risks. However, a recent
review of OU D-specific results suggests that additional volatile organic compounds
(VOCs) may be COCs in groundwater directly beneath OU D (Parsons, 2000). For
example, 1,1-DCA, 1,1-DCE, 1,1,1-trichloroethene (TCA), and vinyl chloride were
detected in groundwater beneath OU D at frequencies greater than 5 percent, and at
concentrations above their respective MCLs (Radian, 1999b).
A dissolved VOC plume in groundwater, consisting primarily of TCE at
concentrations greater than the federal MCL of 5 micrograms per liter (ug/L), has been
identified in the central and southwestern parts of OU D (CH2M Hill, 1999).
Historically, low concentrations of VOCs also have been detected occasionally in
groundwater samples collected from wells at distances up to approximately 500 feet off-
Base to the northwest. No contaminants have been detected in groundwater samples
from off-Base monitoring wells located west or northwest of OU D since 1995.
However, the dissolved CAH plume sourced at the OU D waste pits has migrated with
regional groundwater flow to the south and southwest, and historically extended off-
Base, to the west of OU D (CH2M Hill, 1994a). The off-Base extension of the plume
since has been hydraulically captured by the OU D groundwater extraction system
(Radian, 1999b).
Groundwater "hot spots" at McClellan AFB are defined as areas where contaminants
are present at concentrations greater than 100 times the applicable MCL. Such hot spots
have been identified near the former waste pits at Sites CS-2, CS-3, CS-5, CS-A, CS-S,
and CS-T, which are the sources of VOCs dissolved in groundwater. The CAHs detected
most frequently at these hot spots include PCE, TCE, czs-l,2-DCE, and 1,2-DCA. Prior
to the commencement of groundwater remediation, several VOCs were detected in
groundwater hot spots at OU D at concentrations that occasionally represented
appreciable fractions of the compounds' solubility in water. Although concentrations
greater than 1 percent of solubility can be an indication of the presence of dense
nonaqueous-phase liquid (DNAPL) (USEPA, 1994a), previous investigators had
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considered the likelihood of VOCs being present in the subsurface at OU D as a DNAPL
to be low (CH2M Hill, 1997).
Parsons (2000) reevaluated the potential presence of DNAPL at OU D as part of a
remedial process optimization (RPO) assessment. The elevated concentrations of VOCs
detected in soil-vapor and groundwater samples collected during earlier phases of
investigation and remediation activities at OU D were interpreted as evidence that CAHs
likely were present as DNAPL in the vadose zone, and possibly below the water table,
prior to implementation of interim remedial measures. Implementation of soil vapor
extraction (SVE) has removed considerable VOC mass from the vadose zone during the
past decade; however, because DNAPL is capable of migrating into "dead-end" soil-pore
spaces that are inaccessible to through-flowing fluids (e.g., air or water) (Pankow and
Cherry, 1996), it is likely that some CAH mass remains in the vadose zone as residual
DNAPL.
In addition, increases in the concentrations of TCE and 1,1-DCE in extracted
groundwater through time suggest that a free or residual DNAPL remains in the
subsurface near or below the water table in the vicinities of wells EW83 and EW87.
However, while the increased flow related to pumping may be enhancing the rate of
dissolution of CAH from a local DNAPL source, the increases in dissolved CAH
concentrations are not likely to be of sufficient magnitude to greatly affect the rate of
mass removal. Therefore, residual DNAPL near or below the water table at OU D may
persist as a continuing source of dissolved contaminants for an extended period of time.
Migration of the VOC plume has been halted as a result of the operation of the
groundwater extraction and treatment system since its installation in 1987, and the OU D
plume is currently regarded as being "fully contained" (Radian, 1999b). In general the
concentrations of dissolved CAH in groundwater have decreased appreciably during the
period of system operation. However, low concentrations of VOCs continue to be
detected sporadically at locations distal from the hot spots, west and southwest of OU D
(CH2M Hill, 1994a). According to the first quarter 2002 (1Q02) Groundwater
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Monitoring Program Quarterly Report (URS, 2002), 15 years of groundwater remediation
systems operation (1987 to 2002) have resulted in the following outcomes:
• The areal extent of the TCE plume has been reduced by 38.7 percent;
• Maximum TCE concentrations have been reduced below 100 ug/L; and
• The plume area defined by the 100-ug/L TCE isopleth has been essentially stable
during the last 11.5 years of extraction system operation.
2.4 REMEDIAL SYSTEMS
The remediation systems currently operating at OU D include an SVE and treatment
system, a groundwater extraction system, and associated monitoring networks. Pilot-
scale testing of SVE at OU D began in March 1993, and continuous system operation was
initiated in March 1994.
The primary objective of the groundwater extraction system at OU D is to prevent off-
Base migration of CAH-contaminated groundwater, thereby eliminating potential
exposure of off-Base receptors. A secondary objective of groundwater remediation, as
expressed in the Base-wide GWOU IROD (CH2M Hill, 1995), is aggressive extraction
and treatment of groundwater to remove contaminant mass.
Operation of the groundwater extraction system at OU D commenced in March 1987.
The current groundwater extraction system in OU D consists of six EWs (EW-73 and
EW-83 through EW-87), five of which are operational. Well EW-84 was removed from
service in August 1997, because its continued operation was judged to be unnecessary in
achieving the objective of plume containment (Radian, 1999a). Current plans call for
well EW-84 to remain off-line indefinitely. All EWs were installed to a depth of about
160 feet bgs, and are fully screened across both the A and B monitoring zones. The
design production rates for the individual wells in the current extraction system range
from 10 gallons per minute (gpm) to 25 gpm; the actual production rates (0 to about 11
gpm) are generally somewhat lower than the design rates. Groundwater extracted at OU
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D is conveyed via pipeline to a 1,500- gpm capacity groundwater treatment plant
(GWTP), which is centrally located at McClellan AFB and treats water extracted from
multiple OUs.
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SECTION 3
LONG-TERM MONITORING PROGRAM AT OU D
The current groundwater monitoring program at OU D was examined to identify
potential opportunities for streamlining monitoring activities while still maintaining an
effective performance and compliance monitoring program. The current monitoring
program at OU D is reviewed in the following subsections.
3.1 DESCRIPTION OF MONITORING PROGRAM
Quarterly sampling of off-Base wells begin in 1984 as part of the Installation
Restoration Program (IRP) at McClellan AFB. In 1986, the Groundwater Sampling and
Analysis Program (GSAP) was established to support ongoing remedial
investigation/feasibility study activities. In 1996, the Groundwater Monitoring Plan
(GWMP) for on- and off-Base wells was established under the Long-Term Monitoring
Program (LTMP) to update the GSAP and to support GWOU IROD activities. Under the
GWMP, groundwater samples are collected quarterly from selected wells across the
entire Base and analyzed for COCs associated with the respective plumes. However,
because groundwater sampling focus areas change from quarter to quarter, each areas of
the Base are targeted for a detailed analysis of groundwater quality once each year.
Groundwater sampling and analysis for wells in the OU D area occurs during the first
quarter of each year (URS, 2001).
Groundwater sampling data are used to monitor how plume sizes and shapes are
changing in response to EW pumping to determine if the following remedial action
objectives (RAOs), as specified in the GWOU IROD (CH2M Hill, 1995) for McClellan
AFB, are being met:
3-1
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• Prevent contaminated groundwater from migrating off-Base;
• Contain hot spots; and
• Prevent downward migration of contamination.
As described in Section 2.4, groundwater sampling data also are used to evaluate
contaminant mass removal rates.
In the OU D area, groundwater sampling is conducted to monitor areas where dissolved
VOC concentrations exceed MCLs (termed VOC target areas) in monitoring zones A and
B (there are no target areas in deeper monitoring zones in the OU D area). The field
sampling plan (FSP) identifies the wells to be sampled in the area based on the rationale
and decision logic presented in the GWMP (Radian, 1997). Based on the logic presented
in the GWMP, the monitoring frequency and sampling rationale for each well are
continually evaluated, and can change as new sampling data are obtained. Because the
OU D plume is contained by the extraction system and the plume is well defined, all of
the wells associated with the OU D plume are sampled relatively infrequently (annually
or biennially). The Monitoring and Extraction Well Baseline Sampling Frequency and
Rationale (URS, 2002) identifies 6 EWs and 45 monitoring wells to be monitored in the
vicinity of the OU D plume, based on groundwater-quality data collected through 1Q02.
Figure 3.1 shows the 51 wells currently monitored at OU D, and their monitoring
frequency. Table 3.1 lists these wells along with their assigned monitoring zone,
designated sampling frequency, and sampling rationale based on the information
provided in the FSP for 1Q02, 2Q02, and 3Q02 (URS, 2002). Wells MW-1010 and MW-
1042 are considered "cross-zone wells" (designated as "AB" in the "Original Zone"
column in Table 3.1); these wells are screened across monitoring zones A and B.
However, sampling results for these wells are assigned to a single monitoring zone based
on water-level and lithologic data, contaminant concentrations, and well construction
information for each cross-zone monitoring well compared to nearby single-zone
monitoring wells. Analytical results for samples collected from well MW-1010 are
assigned to zone B, and sampling results for MW-1042 are assigned to zone A. EWs at
3-2
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TABLE 3.1
CURRENT GROUNDWATER MONITORING PROGRAM
THREE-TIERED MONITORING NETWORK OPTIMIZATION
OPERABLE UNIT D
McCLELLAN AFB, CALIFORNIA
Well ID
Screened Interval
(ft msl) "
Extraction Wells
EW-73
EW-83
EW-84
EW-85
EW-86
EW-87
20.7 to -99.3
24.22 to -95.78
22.8 to -97.2
22.9 to -97.1
17.9 to -102.1
18.1 to -101.9
Zone A Wells
MW-10
MW-11
MW-12
MW-14
MW-15
MW-38D
MW-52
MW-53
MW-55
MW-70
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
-37.82 to -47.82
-36.31 to -46.31
-34.67 to -44.67
-35.52 to -45.52
-4 1.48 to -46.48
-57.32 to -67.32
-80.16 to -90.16
-65.47 to -75.47
-67.46 to -75.47
- 66.77 to -76.77
-58.45 to -68.45
-71.39 to -81.39
-80.23 to -90.23
-38.46 to -48.46
-38.89 to -48.89
-34.94 to -44.94
-35.85 to -45.85
-38.39 to 48.39
-41.55 to -61.55
-40.56 to -60.56
-49.24 to -69.24
-52.25 to -67.25
-32.92 to -52.92
-33.97 to -49.97
-31.76 to -51.76
-40.81 to -60.81
-30.88 to -40.88
-3 1.93 to 4 1.93
-52.87 to -62.87
-80.18 to -90.18
-30.96 to -50.96
-52.6 to -62.6
Zone B Wells
MW-19D
MW-51
MW-54
MW-57
MW-58
MW-59
MW-104
MW-1001
MW-1003
MW-1010
MW-1027
MW-1028
MW-1043
-80.16 to -90.16
-112.96 to -127.96
-81.61 to -91.61
-80.38 to -90.38
-112.63 to -122.63
-106.32 to -116.32
-113.78 to -123.78
-105.25 to -115.25
-77.72 to -87.72
-86.37 to -96.37
-70.47 to -80.47
-117 to -127
-137.09 to -147.09
Original
Zone
IAB
IAB
IAB
IAB
IAB
IAB
A
A
A
A
A
IAB
IAB
IAB
IAB
IAB
A
IAB
IAB
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
AB
A
A
B
B
IAB
IAB
B
B
B
B
IAB
AB
B
B
B
Zone
A/B
A/B
A/B
A/B
A/B
A/B
A
A
A
A
A
A*c/
A*
A*
A*
A*
A
A*
A*
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A*
A
A
B
B
B*
B*
B
B
B
B
B*
B*
B
B
B
Sampling
Frequency
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Biennial
Annual
Annual
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Annual
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Annual
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
Biennial
ft bmsl = feet relative to mean sea level.
Sampling frequency based on 1st Quarter 2002 Ground-water
Monitoring Program Quarterly Report (URS, 2002).
* = Reassigned monitoring zone, per URS (2002).
022/742479/McCllelmTablesDraftFinal.xls/Table 3.1
3-4
-------
OU D extract water from zones A and B, and analytical data for samples from these wells
are used to characterize plumes in both zones. Ten of the monitoring wells are screened
in intermediate zones between A and B monitoring zones (designated as "IAB" in the
"Original Zone" column in Table 3.1), and concentrations from these wells have
historically not been used for plume interpretation. However, these wells are sampled
because the data may provide useful information about the effectiveness of the extraction
system. For this MNO analysis, the IAB wells were assigned to either the A or B zones
based on the recommendations listed in Table 3-3 of the 1Q02 Quarterly Monitoring
Report (URS, 2002).
The six EWs are sampled annually. Of the 32 OU D wells that monitor zone A, 22 are
sampled biennially and 10 are sampled annually. Twelve of the 13 zone B wells are
sampled biennially, and the remaining well is sampled annually. All samples from the
monitoring and extraction wells are analyzed for VOCs by USEPA Method SW8260B.
It should be noted that, in 2001, OU D wells were monitored using passive diffusion
bag samplers (PDBSs); conventional-purge sampling was performed prior to and
following the 2001 sampling event. Minor differences in analytical results may be
attributable to use of the two types of sampling techniques. The three-tiered MNO
evaluation described in this report examines the current monitoring network consisting of
51 wells (17 sampled annually and 34 biennially).
3.2 SUMMARY OF ANALYTICAL DATA
The OU D groundwater monitoring program was evaluated using results for sampling
events performed from April 1990 through August 2001. These analytical data, along
with water level and well location information was provided to Parsons by Ms. Diane
Kiota, the Air Force groundwater program manager at McClellan. The database was
processed to remove duplicate data measurements by retaining the maximum result of
the duplicate samples. The analytical database provided to Parsons contained from 5 to
18 sampling results for each of the 51 wells in the OU D monitoring program. As
discussed in Section 2.3, the primary COCs identified for the OU D plume in the GWOU
3-5
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IROD are TCE, PCE, cis-l,2-DCE, and 1,2-DCA; therefore, the MNO evaluation
focused on these constituents. Other VOCs of concern at the Base were not measured
above their MCLs in the OU D area during recent sampling events (URS, 2002), and
therefore were excluded from the MNO analysis.
Table 3.2 presents a summary of the occurrence of the four primary COCs in OU D
groundwater based on the data collected from April 1990 through August 2001. As
indicated in this table, TCE is the COC detected most frequently and at the greatest
concentrations in groundwater at McClellan AFB OU D. TCE has been detected in
approximately 63 percent of samples, and has exceeded its MCL of 5 |ug/L in
approximately 38 percent of the samples. TCE has been detected in 46 of the 51 wells in
the monitoring program, and has exceeded the MCL in 26 of these wells. One of TCE's
reductive dechlorination daughter products, cis-l,2-DCE, is the second-most prevalent
compound, and has been detected in 36 percent of the collected samples. However,
detected concentrations ofcis-l,2-DCE have exceeded the MCL of 70 ug/L in less than 1
percent of samples. PCE and 1,2-DCA have been detected at OU D in approximately 24
percent and 32 percent of samples, respectively; these compounds have been detected
within the footprint of the TCE plume. Detected concentrations of these COCs have
exceeded their MCLs in approximately 5 percent and 14 percent of the samples,
respectively.
TCE sampling results were the primary data used to conduct the three-tiered
monitoring network optimization due to the magnitude and spatial extent of TCE
concentrations in groundwater at OU D compared to the other detected compounds.
3-6
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SECTION 4
QUALITATIVE MNO EVALUATION
An effective groundwater monitoring program will provide information regarding
contaminant plume migration and changes in chemical concentrations through time at
appropriate locations, enabling decision-makers to verify that contaminants are not
endangering potential receptors, and that remediation is occurring at rates sufficient to
achieve RAOs within a reasonable time frame. The design of the monitoring program
should therefore include consideration of existing receptor exposure pathways, as well as
exposure pathways arising from potential future use of the groundwater.
Performance monitoring wells located upgradient, within, and immediately
downgradient from a plume provide a means of evaluating the effectiveness of a
groundwater remedy relative to performance criteria. Long-term monitoring (LTM) of
these wells also provides information about migration of the plume and temporal trends
in chemical concentrations. Groundwater monitoring wells located downgradient from
the leading edge of a plume (i.e., sentry wells) are used to evaluate possible changes in
the extent of the plume and, if warranted, to trigger a contingency response action if
contaminants are detected.
Primary factors to consider when developing a groundwater monitoring program
include at a minimum:
• Aquifer heterogeneity,
• Types of contaminants,
• Distance to potential receptor exposure points,
• Groundwater seepage velocity,
4-1
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• Potential surface-water impacts, and
• The effects of the remediation system.
These factors will influence the locations and spacing of monitoring points and the
sampling frequency. Typically, the greater the seepage velocity and the shorter the
distance to receptor exposure points, the more frequently groundwater sampling should
be conducted.
One of the most important purposes of LTM is to confirm that the contaminant plume
is behaving as predicted. Graphical and statistical tests can be used to evaluate plume
stability. If a groundwater remediation system or strategy is effective, then over the long
term, groundwater-monitoring data should demonstrate a clear and meaningful
decreasing trend in concentrations at appropriate monitoring points. The current
groundwater monitoring program at McClellan AFB OU D was evaluated to identify
potential opportunities to LTM optimization.
4.1 METHODOLOGY FOR QUALITATIVE EVALUATION OF
MONITORING NETWORK
The three-tiered MNO evaluation of the OU D groundwater LTM program considered
information for 51 wells in the OU D study area that currently are included in the
monitoring program. These wells, their respective monitoring zones, and their current
monitoring frequency are listed in Table 3.1, and their locations are depicted on Figure
3.1.
Multiple factors were considered in developing recommendations for continuation or
cessation of groundwater monitoring at each well. In some cases, a recommendation was
made to continue monitoring a particular well, but at a reduced frequency. A
recommendation to discontinue monitoring at a particular well based on the information
reviewed does not necessarily constitute a recommendation to physically abandon the
well. A change in site conditions might warrant resumption of monitoring at some time
in the future at wells that are not currently recommended for continued sampling.
Typical factors considered in developing recommendations to retain a well in, or remove
4-2
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a well from, the monitoring program are summarized in Table 4.1. Typical factors
considered in developing recommendations for monitoring frequency are summarized in
Table 4.2.
TABLE 4.1
MONITORING NETWORK OPTIMIZATION DECISION LOGIC
THREE-TIERED MONITORING NETWORK OPTIMIZATION
OPERABLE UNIT D
MCCLELLAN AFB, CALIFORNIA
Reasons for Retaining a Well in
Monitoring Network
Well is needed to further characterize the
site or monitor changes in contaminant
concentrations through time
Well is important for defining the lateral or
vertical extent of contaminants
Well is needed to monitor water quality at
compliance point or receptor exposure
point (e.g., domestic well)
Well is important for defining background
water quality
Reasons for Removing a Well From
Monitoring Network
Well provides spatially redundant
information with a neighboring well (e.g.,
same constituents, and/or short distance
between wells)
Well has been dry for more than 2 yearsa/
Contaminant concentrations are
consistently below laboratory detection
limits or cleanup goals
Well is completed in same water-bearing
zone as nearby well(s)
a/ Water-level measurements in dry wells should continue, and groundwater sampling should be resumed if the well
becomes re-wetted.
4.2 RESULTS OF QUALITATIVE MNO EVALUATION
The results of the qualitative evaluation of the 51 monitoring and EWs currently
included in the LTM program at OU D included are summarized in Table 4.3, shown on
Figure 4.1, and described in the following subsections. The table includes
recommendations for retaining or deleting each existing monitoring well, and for
changing the sampling frequency, and lists the rationale for the recommendations. Figure
4.1 shows the current and recommended revised sampling frequency for each well based
on the qualitative evaluation.
4-3
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TABLE 4.2
MONITORING FREQUENCY DECISION LOGIC
THREE-TIERED MONITORING NETWORK OPTIMIZATION
OPERABLE UNIT D
MCCLELLAN AFB, CALIFORNIA
Reasons for Increasing
Sampling Frequency
Groundwater velocity is high
Change in contaminant concentration
would significantly alter a decision or
course of action
Well is close to source area or operating
remedial system
Cannot predict if concentrations will
change significantly over time
Reasons for Decreasing
Sampling Frequency
Groundwater velocity is low
Change in contaminant concentration
would not significantly alter a decision or
course of action
Well is distal from source area or remedial
system
Concentrations are not expected to change
significantly over time, or contaminant
levels have been below groundwater
cleanup objectives for some prescribed
period of time
4.2.1 Monitoring Network and Sampling Frequency
The current LTM plan for OU D specifies annual sampling of the six groundwater
EWs, and annual or biennial sampling of 45 groundwater monitoring wells. These
relatively infrequent sampling frequencies are appropriate given that:
1. The current plume is well-characterized and apparently decreasing in footprint
and concentration as long as the groundwater extraction system continues to
operate;
2. Available data indicate that the advective groundwater flow velocity is
relatively slow (average of 14 and 30 feet per year in the A and B monitoring
zones, as described in Section 2.2.2); and
3. Currently, there are no sensitive groundwater receptors near the plume.
4-4
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The LTM objectives for monitoring and extraction wells differ. Because EWs draw
water from a larger (and sometimes unquantified) volume of the aquifer surrounding the
well, the analytical data cannot be used for plume definition to the same extent as data
from monitoring wells. However, data from EWs can be used to assess contaminant
mass-removal rates and progress toward achieving RAOs, and to facilitate system
optimization decision-making. Because the plume extent and magnitude are generally
well-characterized both laterally and vertically, and because operation of the groundwater
extraction system appears to be providing hydraulic control sufficient to cause reductions
in the areal extent of and COC concentrations within the CAH plume , continued
monitoring of a relatively small number of wells (i.e., fewer than the 51 wells currently
included in the OU D LTM plan) should be sufficient to monitor changes in the plume
extent and magnitude in the future.
4.2.1.1 Extraction Wells
The current operational status of extraction well EW-84 is not known. According to
Parsons (2000), this well was deactivated and there were no plans to reactivate it in the
foreseeable future. However, annual monitoring of this EW is continuing (URS, 2002).
The long screened interval of this well (from 22.8 feet below mean sea level [bmsl] to -
97.2 feet bmsl; see Table 3.1) hinders the usefulness of the analytical data obtained from
this well (this issue also pertains to the other extraction wells) in that it cannot be
confidently associated with either the A or B zones. However, groundwater samples
from this well exhibit COC concentrations greater than MCLs that are likely present
primarily in the uppermost part of the aquifer (i.e., the A monitoring zone) based on
groundwater quality data from both A- and B-zone monitoring wells. Continued
sampling of EW-84 will provide data useful for monitoring the magnitude of COC
concentrations in the west-central plume area and the hydraulic effects of groundwater
extraction. However, if this well remains inactive, its abandonment should be considered
given that it may be acting as a conduit for downward migration of VOCs from the A to
the B zone. As shown in Table 4.3, continued annual monitoring of the other five
(active) EWs is recommended in order to permit periodic calculation of mass removal
4-8
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rates, and to facilitate assessment of remedial progress and system optimization.
However, well EW-86 is currently outside of the 5-ug/L TCE isopleth, and consideration
should be given to discontinuing pumping of this well unless it is required for hydraulic
control of the plume.
4.2.1.2 A-Zone Monitoring Wells
Continued monitoring of only 8 of the 24 A-zone wells currently included in the LTM
program is recommended (Table 4.3 and Figure 4.1). In addition, continued monitoring
of only 2 of the 7 IAB wells reassigned to the A zone, as proposed by URS (2002), is
recommended.
Nine of the A-zone wells (MW-1004, MW-1041, MW-1064, MW-1073, MW-1026,
MW-237, MW-351, MW-412, and MW-350) and one of the lAB-zone wells (MW-1042)
recommended for removal from the LTM program are distant (approximately 650 to
2,200 feet) from the location of the 5-ug/L TCE isopleth as estimated during the 1Q02
sampling event, and also are located hydraulically upgradient or crossgradient from the
OU D plume area. These wells are too far from the plume to provide useful information
on plume magnitude and extent given the localized and hydraulically contained
distribution of elevated TCE concentrations. Well MW-240 also is 700 feet from the
plume as defined by the 5-ug/L TCE isopleth, but is located downgradient from the OU
D waste pits, based on a regional south to southwest groundwater flow direction (Section
2.2.2). Based on the decreasing trends exhibited by TCE concentrations at the
downgradient (southern) edge of the A-zone plume (e.g., at wells MW-14, MW-15, and
MW-242; see Section 5), plume expansion toward well MW-240 is not occurring, and
continued monitoring of this well is not necessary unless hydraulic conditions change.
According to URS, the distant wells listed in the previous paragraph are sampled
primarily for community relations purposes (i.e., to demonstrate that contaminant levels
in these off-Base residential areas are remaining low and are not of concern from a
human health point of view). The recommendation to discontinue monitoring of these
wells is primarily technical and does not take into account political/community relations
4-9
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concerns. The recommendations should be evaluated with these other concerns in mind
and followed as appropriate.
Cessation of monitoring at wells MW-88, MW-91, MW-92, and MW-458, which are
located closer to, but still outside of, the 1Q02 5-ug/L TCE isopleth, also is
recommended. Continued monitoring of these four wells is not recommended because
monitoring of the plume, as defined by the 5-ug/L TCE isopleth, over time can be
accomplished using sampling results from other wells located closer to the affected area
(i.e., MW-89, MW-90, and MW-14; Figure 4.1). Concentrations of the four primary
COCs (PCE, TCE, cis-l,2-DCE, and 1,2-DCA) detected in samples from well MW-89
have never exceeded MCLs, and concentrations detected in MW-90 and MW-14 have not
exceeded MCLs since 1997.
Cessation of monitoring at wells MW-72, MW-241, and MW-242 is recommended
because each of these wells is paired with another A-zone well that monitors a higher-
concentration zone. For example, clustered wells MW-10, MW-72, and MW-241 are
screened from -38 to -48 feet msl, -58 to -68 feet msl, and -49 to -69 feet msl,
respectively. TCE concentrations in samples from MW-72 have decreased steadily from
440 ug/L in April 1990 to 2.95 ug/L in February 2001. Similarly, TCE concentrations in
MW-241 have decreased from 210 ug/L in June 1993 to 2.53 ug/L in February 2001.
During the period from 1990 to 2001, TCE concentrations at MW-10 have decreased
from 1,100 ug/L to 50.9 ug/L. In this case, continued monitoring of MW-10 will allow
assessment of how maximum COC concentrations at these plume-interior locations
change over time. In the similar case of well pair MW-15/MW-242, continued
monitoring of MW-15 will enable monitoring of maximum COC concentrations at this
location.
The remaining four wells recommended for removal from the LTM program (wells
MW-53, MW-55, MW-70, and MW-74) are IAB wells that are screened in intermediate
zones between the A and B monitoring zones, and concentrations from these wells
historically have not been used for plume interpretation. As described in Section 3.1,
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URS (2002) has proposed that these wells be reassigned to the A zone for monitoring and
interpretive purposes (Table 3.1). These four wells have been included in the LTM
program because the data may provide useful information about the effectiveness of the
extraction system.
With the exception of one 6-ug/L concentration of TCE in the sample collected from
MW-53 in February 1998, detected concentrations of the primary COCs in these four
IAB wells have not exceeded MCLs. The maximum concentrations of the four primary
COCs detected in MW-52, MW-55, and MW-74 since the 1990-1991 time frame have
been 4.4 ug/L and 3.8 ug/L, respectively. The primary COCs have never been detected
in MW-70. Given the relatively uncontaminated nature of the zones monitored by these
wells, the data obtained from them does not provide very useful information about the
effectiveness of the extraction system except that it may be limiting downward migration
of dissolved contaminants from the A zone. However, water quality in the portions of the
IAB zone monitored by these five wells has been well characterized, and appears to be
stable over time. Therefore, continued monitoring is not necessary unless the hydraulic
control exerted by the extraction system decreases.
The remaining 10 A-zone wells (MW-10 through MW-15, MW-38D, MW-52, MW-
76, MW-89, MW-90) are recommended for continued sampling because they:
• Monitor relatively elevated COC concentrations within the plume interior, thereby
allowing assessment of the plume magnitude and progress toward aquifer cleanup
goals over time;
• Are useful in tracking plume stability or contraction through time (e.g., as defined
by the 5-ug/L TCE isopleth); or
• Have exhibited increasing trends for one or more of the COCs that should be
monitored over time to assess potential plume expansion either laterally or
vertically.
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4.2.1.3 B-Zone Monitoring Wells
The B-zone VOC plume is largely defined by EWs that are screened in both the A and
B monitoring zones (i.e., the analytical results for these wells are assigned to both the A
and B zones for data assessment and presentation purposes). Compared to the number of
wells screened solely in the A zone, there are relatively few monitoring wells screened
solely in the B zone beneath the A-zone plume (Table 3.1). Data from these B-zone
monitoring wells indicate that COC concentrations in B-zone groundwater beneath the A-
zone plume are relatively low (maximum detected CAH concentration of 6.45 ug/L [for
TCE] from 1990 to 2001). Due to the relatively few B-zone wells, and the importance of
documenting water quality in this zone over time, continued monitoring of four of the
five B-zone wells located in the immediate vicinity of the plume area (wells MW-51,
MW-54, MW-58, and MW-59) is recommended. Given the low magnitude of the COC
concentrations, continued biennial monitoring is appropriate for three of these wells;
annual sampling is recommended for well MW-54, where slight increasing trends for
some COCs have been observed. Continued monitoring of IAB well MW-57 is not
recommended due to its close proximity to B-zone well MW-59. Well MW-19D, located
south of and potentially downgradient from the main plume area (Figure 4.1), near the
estimated southern limit of the extraction system capture zone, serves as a sentry well,
and continued biennial monitoring of this well is recommended.
Continued monitoring of B-zone wells located both distant and hydraulically up- or
crossgradient from the plume area is not recommended. These wells, which include MW-
1003, MW-1001, MW-1043, MW-1010, MW-104, MW-1027, and MW-1028 (Table 3.1
and Figure 4.1), do not provide useful information regarding plume magnitude and
extent.
4.2.2 Laboratory Analytical Program
Groundwater samples from OU D wells are analyzed for VOCs using USEPA Method
SW8260B. Because the characterization of conditions in the OU D groundwater plume
has been largely completed, groundwater samples collected from monitoring wells could
4-12
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be analyzed for selected COCs using Method 802IB, rather than the currently-used
Method 8260B. Method 8021B can be used to analyze for the primary COCs at the site,
and could potentially result in a considerable reduction in analytical costs. Depending on
the laboratory, the cost for analysis of a groundwater sample using Method 802IB (a gas-
chromatographic [GC] method) can be substantially lower than the cost of analysis using
Method 8260B (a gas-chromatographic/mass-spectrographic [GC/MS] method),
especially if the target analyte list is reduced. USEPA Method 8260B should still be used
to analyze samples from the few wells that contain substantially elevated CAH
concentrations, in order to obtain appropriate analyte identifications.
4.2.3 LTM Program Flexibility
The LTM program recommendations made in Sections 4.2.1 are based on available
data regarding current (and expected future) site conditions. Changing site conditions
(e.g., lengthy malfunction or significant adjustment of the groundwater extraction
system) could affect plume behavior. Therefore, the LTM program should be reviewed if
hydraulic conditions change significantly, and revised as necessary to adequately track
changes in plume magnitude and extent over time.
4-13
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SECTION 5
TEMPORAL STATISTICAL EVALUATION
Temporal data (chemical concentrations measured at different points in time) can be
examined graphically, or using statistical tests, to evaluate dissolved-contaminant plume
stability. If removal of chemical mass is occurring in the subsurface as a consequence of
attenuation processes or operation of a remediation system, mass removal will be
apparent as a decrease in chemical concentrations through time at a particular sampling
location, as a decrease in chemical 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.
5.1 METHODOLOGY FOR TEMPORAL TREND ANALYSIS OF
CONTAMINANT CONCENTRATIONS
Temporal chemical-concentration data can be evaluated by plotting contaminant
concentrations through time for individual monitoring wells (Figure 5.1), or by plotting
contaminant concentrations versus downgradient distance from the contaminant source
for several wells along the groundwater fiowpath, over several monitoring events.
Plotting temporal concentration data is recommended for any analysis of plume stability
(Wiedemeier and Haas, 2000); however, visual identification of trends in plotted data
may be a subjective process, particularly if (as is likely) the concentration data do not
exhibit a uniform trend, but are variable through time (Figure 5.2).
The possibility of arriving at incorrect conclusions regarding plume stability on the
basis of visual examination of temporal concentration data can be reduced by examining
temporal trends in chemical concentrations using various statistical procedures, including
5-1
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FIGURE 5.1
TCE CONCENTRATIONS THROUGH TIME
AT WELL MW-38D
THREE-TIERED MONITORING NETWORK OPTIMIZATION
OPERABLE UNIT D
MCCLELLAN AFB, CALIFORNIA
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regression analyses and the Mann-Kendall test for trends. The Mann-Kendall
nonparametric test (Gibbons, 1994) is well-suited for evaluation of environmental data
because the sample size can be small (as few as four data points), no assumptions are
made regarding the underlying statistical distribution of the data, and the test can be
adapted to account for seasonal variations in the data. The Mann-Kendall test statistic
can be calculated at a specified level of confidence to evaluate whether a temporal trend
is exhibited by contaminant concentrations detected through time in samples from an
individual well. If a trend is identified, a nonparametric slope of the trend line (change in
concentration per unit time) also can be estimated using the test procedure. A negative
slope (indicating decreasing contaminant concentrations through time) or a positive slope
5-2
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Trend
Increasing Trend
No Trend
Confidence Factor
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LOW
Variation
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5.2
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McClellan AFB, California
draw\739732\diffusion\williamsA.cdr pg1 nap 4/3/02
-------
(increasing concentrations through time) provides statistical confirmation of temporal
trends that may have been identified visually from plotted data (Figure 5.2).
The relative value of information obtained from periodic monitoring at a particular
monitoring well can be evaluated by considering the location of the well with respect to
the dissolved contaminant plume and potential receptor exposure points, and the presence
or absence of temporal trends in contaminant concentrations in samples collected from
the well. The degree to which the amount and quality of information that can be obtained
at a particular monitoring point serve the two primary (i.e., temporal and spatial)
objectives of monitoring must be considered in this evaluation. For example, the
continued non-detection of a target contaminant in groundwater at a particular monitoring
location provides no information about temporal trends in contaminant concentrations at
that location, or about the extent to which contaminant migration is occurring, unless the
monitoring location lies along a groundwater fiowpath between a contaminant source and
a potential receptor exposure point. Therefore, a monitoring well having a history of
contaminant concentrations below detection limits may be providing little or no useful
information, depending on its location.
A trend of increasing contaminant concentrations in groundwater at a location between
a contaminant source and a potential receptor exposure point may represent information
critical in evaluating whether contaminants are migrating to the exposure point, thereby
completing an exposure pathway. Identification of a trend of decreasing contaminant
concentrations at the same location may be useful in evaluating decreases in the areal
extent of dissolved contaminants, but does not represent information that is critical to the
protection of a potential receptor. Similarly, a trend of decreasing contaminant
concentrations in groundwater near a contaminant source may represent important
information regarding the progress of remediation near, and downgradient from the
source, while identification of a trend of increasing contaminant concentrations at the
same location does not provide as much useful information regarding contaminant
conditions. By contrast, the absence of a temporal trend in contaminant concentrations at
a particular location within or downgradient from a plume indicates that virtually no
5-4
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additional information can be obtained by continued monitoring of groundwater at that
location, in that the results of continued monitoring through time are likely to fall within
the historic range of concentrations that have already been detected (Figure 5.3).
Continued monitoring at locations where no temporal trend in contaminant
concentrations is present serves merely to confirm the results of previous monitoring
activities at that location. The relative amounts of information generated by the results of
temporal-trend evaluation at monitoring points near, upgradient from, and downgradient
from contaminant sources are presented schematically as follow:
Monitoring Point Near Contaminant Source
Relatively less information Nondetect or no trend
Relatively more information
Increasing trend in concentrations
Decreasing trend in concentrations
Monitoring Point Upgradient from Contaminant Source
Relatively less information Nondetect or no trend
Relatively more information
Decreasing trend in concentrations
Increasing trend in concentrations
5-5
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c
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OU D Monitoring Network Optimization
McClellan AFB, California
draw\739732\diffusion\williamsA.cdr pg2 nap 4/3/02
-------
Monitoring Point Downgradient from Contaminant Source
Relatively less information Decreasing trend in concentrations
Nondetect or no trend
Relatively more information Increasing trend in concentrations
5.2 TEMPORAL EVALUATION RESULTS
The analytical data for groundwater samples collected from the 51 wells in OU D
LTM program from April 1990 through August 2001 were examined for temporal trends
using the Mann-Kendall test. The objective of the evaluation was to identify those wells
having increasing or decreasing concentration trends for each COC, and to consider the
quality of information represented by the existence or absence of concentration trends in
terms of the location of each monitoring point.
Summary results of Mann-Kendall temporal trend analyses for COCs in groundwater
samples from wells in the TCE plume area are presented in Table 5.1. As implemented,
the algorithm used to evaluate concentration trends assigned a value of "ND" (not
detected) to those wells with sampling results that were consistently below analytical
detection limits through time, rather than assigning a surrogate value corresponding to the
detection limit - a procedure that could generate potentially misleading and anomalous
"trends" in concentrations. In addition, a value of "
-------
these samples, which is primarily an artifact of the analytical procedures, and could
generate false conclusions regarding concentration trends. The color-coding of the Table
5.1 entries denotes the presence/absence of temporal trends, and allows those monitoring
points having nondetectable concentrations, concentrations below PQLs, decreasing or
increasing concentrations, or no discernible trend in concentrations to be readily
identified. Figure 5.4 thematically displays the Mann-Kendall results for TCE by well
and hydraulic unit; the analytical results for TCE in 2000 and 2001 are also presented.
Several of the wells were only sampled once, and EW-85 and MW-1043 were not
sampled during either of the events in the 2-year period.
The basis of the decision to remove or retain a well in the monitoring program based
on the value of its temporal information is described in the "Rationale" column of Table
5.1. In general, monitoring wells at which detected chemical concentrations display no
discernible temporal trend (e.g., wells MW-53, MW-59 MW-90, MW-237, and MW-
1064 ) represent points generating the least amount of useful information, and can be
recommended for removal from the monitoring network. Monitoring wells upgradient
from the source or crossgradient from the plume (e.g., wells MW-1010, MW-1027, MW-
1041, MW-458) for which concentrations of chemicals consistently have been non-
detected or
-------
monitoring points in the LTM program, and the frequency of monitoring at particular
locations at OU D.
5-9
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TABLE 5
WATER MONITORING RESULTS
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FIGURE 5.5
TEMPORAL TREND
DECISION RATIONALE
FLOW CHART
Monitoring Network Optimization
McClellan AFB. California
Denver, Colorado
-------
SECTION 6
SPATIAL STATISTICAL EVALUATION
Spatial statistical techniques also can be applied to the design and evaluation of
groundwater monitoring programs to assess the quality of information generated during
monitoring, and to evaluate monitoring networks. Geostatistics, or the Theory of
Regionalized Variables (Clark, 1987; Rock 1988; American Society of Civil Engineers
[ASCE] Task Committee on Geostatistical Techniques in Hydrology, 1990a and 1990b),
is concerned with variables having values dependent on location, and which are
continuous in space, but which vary in a manner too complex for simple mathematical
description. Geostatistics is based on the premise that the differences in values of a
spatial variable depend only on the distances between sampling locations, and the relative
orientations of sampling locations — that is, the values of a variable (e.g., chemical
concentrations) measured at two locations that are spatially "close together" will be more
similar than values of that variable measured at two locations that are "far apart".
6.1 GEOSTATISTICAL METHODS FOR EVALUATING MONITORING
NETWORKS
Ideally, application of geostatistical methods to the results of the groundwater
monitoring program at OU D could be used to estimate COC concentrations at every
point within the dissolved contaminant plume, and also could be used to generate
estimates of the "error," or uncertainty, associated with each estimated concentration
value. Thus, the monitoring program could be "optimized" by using available
information to identify those areas having the greatest uncertainty associated with the
estimated plume extent and configuration. Conversely, sampling points could be
successively eliminated from simulations, and the resulting uncertainty examined, to
evaluate if significant loss of information (represented by increasing error or uncertainty
6-1
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in estimated chemical concentrations) occurs as the number of sampling locations is
reduced. Repeated application of geostatistical estimating techniques, using tentatively
identified sampling locations, then could be used to generate a sampling program that
would provide an acceptable level of uncertainty regarding the distribution of COCs with
the minimum possible number of samples collected. Furthermore, application of
geostatistical methods can provide unbiased representations of the distribution of COCs
at different locations in the subsurface, enabling the extent of COCs to be evaluated more
precisely.
Fundamental to geostatistics is the concept of semivariance [y(h)], which is a measure
of the spatial dependence between samples (e.g., chemical concentrations) in a specified
direction. Semivariance is defined for a constant spacing between samples (h) by:
1 2
y(h) = — zL[g(x) - g(x + h) ] Equation 6-1
2n
Where:
y(h) = semivariance calculated for all samples at a distance h from each other;
g(x) = value of the variable in sample at location x;
g(x + h) = value of the variable in sample at a distance h from sample at location x;
and
« = number of samples in which the variable has been determined.
Semivariograms (plots of y(h) versus K) are a means of depicting graphically the range
of distances over which, and the degree to which, sample values at a given point are
related to sample values at adjacent, or nearby, points, and conversely, indicate how close
together sample points must be for a value determined at one point to be useful in
predicting unknown values at other points. For h = 0, for example, a sample is being
compared with itself, so normally y(0) = 0 (the semivariance at a spacing of zero, is
zero), except where a so-called nugget effect is present (Figure 6.1), which implies that
6-2
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FIGURE 6.1
IDEALIZED SEMVARIOGRAM MODEL
THREE-TIERED MONITORING NETWORK OPTIMIZATION
OPERABLE UNIT D
MCCLELLAN AIR FORCE BASE, CALIFORNIA
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Distance (ft)
sample values are highly variable at distances less than the sampling interval. As the
distance between samples increases, sample values become less and less closely related,
and the semivariance, therefore, increases, until a sill is eventually reached, where y(h)
equals the overall variance (i.e., the variance around the average value). The sill is
reached at a sample spacing called the range of influence, beyond which sample values
are not related. Only values between points at spacings less than the range of influence
can be predicted; but within that distance, the semivariogram provides the proper
weightings, which apply to sample values separated by different distances.
When a semivariogram is calculated for a variable over an area (e.g., concentrations of
TCE in the groundwater plume at OU D), an irregular spread of points across the
semivariogram plot is the usual result (Rock, 1988). One of the most subjective tasks of
6-3
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geostatistical analysis is to identify a continuous, theoretical semivariogram model that
most closely follows the real data. Fitting a theoretical model to calculated semivariance
points is accomplished by trial-and-error, rather than by a formal statistical procedure
(Davis, 1986; Clark, 1987; Rock, 1988). If a "good" model fit results, then rfh) (the
semivariance) can be confidently estimated for any value of h, and not only at the
sampled points.
6.2 SPATIAL EVALUATION OF MONITORING NETWORK AT OU D
TCE was used as the indicator chemical for the spatial evaluation of the groundwater
monitoring network at OU D because this COC has the largest detection percentage and
spatial distribution of measurements that exceeded groundwater MCLs. Although the A
and B zones are hydraulically connected, the A-zone wells were considered separately
from the B-zone wells for the spatial analysis because the A and B zone wells are
generally screened in shallower and deeper portions of the aquifer, respectively, and have
historically have been used to create different plume distribution maps.
A kriging analysis was not conducted for the wells because this zone contains too few
wells for a valuable spatial analysis. The monitoring network includes a total of 32
designated A-zone monitoring wells (Table 3.1). Additionally, although the EWs have
historically been used to define the plume extent in both the A and B zones, data from
active EWs are not appropriate for use in a kriging analysis because they represent COC
concentrations averaged over the area within the well's capture zone, and thus are not
point specific, nor temporally discrete; the EWs are also screened across both Zones A
and B. Therefore, the active EWs were excluded from the spatial analysis. The most
recent validated analytical data available at the start of this MNO evaluation (February
2000 or March 2001) were used in the kriging evaluation because a spatial "snapshot" is
required in order to conduct the geospatial statistical analysis. Thus, 2000 and 2001 TCE
measurements from the 32 A-Zone monitoring wells were used to develop the
semivariogram model. The commercially available geostatistical software package
Geostatistical Analyst™ (an extension to the ArcView® geographic information system
6-4
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-------
[GIS] software package) (Environmental Systems Research Institute, Inc. [ESRI], 2001)
was used to develop a semivariogram model depicting the spatial variation in TCE
concentrations in groundwater for the 32 wells completed in the A zone in the OU D
area.
As semivariogram models were calculated for TCE (Equation 6-1), considerable
scatter of the data was apparent during fitting of the models. Several data
transformations (including a log transformation) were attempted to obtain a
representative semivariogram model. Ultimately, the concentration data were transformed
to "rank statistics," in which the 32 wells were ranked according to their 2000 or 2001
TCE concentration from 1 to 32 (tie values were assigned the median rank of the set).
Transformations of this type can be less sensitive to outliers, skewed distributions, or
clustered data than semivariograms based on raw concentration values, and thus may
enable recognition and description of the underlying spatial structure of the data in cases
where ordinary data are too "noisy".
The TCE rank statistics were used to develop a semivariogram that most accurately
modeled the spatial distribution of the data. Figure 6.2 shows the semivariogram model
in comparison to the site data. The best-fit semivariogram had the following parameters:
« Spherical Model
. Range: 600 feet
. Sill: 70
. Nugget: 10
After this semivariogram model had been developed, it was used in the kriging system
implemented by the Geostatistical Analyst™ software package (ESRI, 2001) to develop
kriging realizations (estimates of the spatial distribution of TCE in groundwater at OU
D), and to calculate the associated kriging prediction standard errors. The median kriging
standard deviation was obtained from the standard errors calculated using the entire 32-
well A-zone monitoring network for OU D. Next, each of the 32 wells was sequentially
6-5
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FIGURE 6.2
TCE A-ZONE SEMVARIOGRAM MODEL
THREE-TIERED MONITORING NETWORK OPTIMIZATION
OPERABLE UNIT D
MCCLELLAN AIR FORCE BASE, CALIFORNIA
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removed from the network, and for each resulting well network configuration, a kriging
realization was completed using the TCE concentration rankings from the remaining 31
wells. The "missing-well" monitoring network realizations were used to calculate
prediction standard errors, and the median kriging standard deviations were obtained for
each "missing-well" realization and compared with the median kriging standard deviation
for the "base-case" realization (obtained using the complete 32-well monitoring
network), as a means of evaluating the amount of information loss (as indicated by
increases in kriging error) resulting from the use of fewer monitoring points.
Figure 6.3 illustrates the spatial-evaluation procedure by showing kriging prediction
standard-error maps for three kriging realizations. Each map shows the predicted
standard error associated with a given group of wells based on the semivariogram
parameters discussed above. Lighter colors represent areas with lower spatial
uncertainty, and darker colors represent areas with higher uncertainty; regions in the
vicinity of wells (i.e., data points) have the lowest associated uncertainty. Map A on
Figure 6.3 shows the predicted standard error map for the "base-case" realization in
6-6
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which all A-zone wells are included. Map B shows the realization in which well MW-
1004 was removed from the monitoring network, and Map C shows the realization in
which well MW-55 was removed. Figure 6.3 shows that when a well is removed from
the network, the predicted standard error in the vicinity of the missing well increases (as
indicated by a darkening of the shading in the vicinity of that well). If a "removed"
(missing) well is in an area with several other wells (e.g., well MW-55; Map C on Figure
6.3), the predicted standard error may not increase as much as if a well (e.g., MW-1004;
Map B) is missing from an area with fewer surrounding wells.
If removal of a particular well from the monitoring network caused very little change
in the resulting median kriging standard deviation (less than about 1 percent), that well
was regarded as contributing only a limited amount of information to the LTM program.
Likewise, if removal of a well from the monitoring network produced larger increases in
the kriging standard deviation, this was regarded as an indication that the well contributes
a relatively greater amount of information, and is relatively more important to the
monitoring network. At the conclusion of the kriging realizations, each well was ranked
from 1 (providing the least information) to 32 (providing the most information), based on
the amount of information (as measured by changes in median kriging standard
deviation) the well contributed toward describing the spatial distribution of TCE, as
shown in Table 6.1. Wells providing the least amount of information represent possible
candidates for removal from the monitoring network at the OU D.
6.3 SPATIAL STATISTICAL EVALUATION RESULTS
6.3.1 Kriging Ranking Results
Figure 6.4 and Table 6.1 present the ranking of monitoring locations based on the
relative value of recent TCE information provided by each well, as calculated based on
the kriging realizations. Examination of these results indicate that monitoring wells in
close proximity to several other monitoring wells (e.g., red and yellow color coding on
Figure 6.4) generally provide relatively lesser amounts of information than do wells at
greater distances from other wells, or wells located in areas having limited numbers of
6-8
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TABLE 6.1
RESULTS OF GEOSTATISTICAL EVALUATION RANKING OF ZONE A
WELLS BY RELATIVE VALUE OF TCE INFORMATION
OPERABLE UNIT D
THREE-TIERED MONITORING NETWORK OPTIMIZATION
McCLELLAN AFB, CALIFORNIA
All Zone A Wells
Well ID
MW-10
MW-88
MW-91
MW-90
MW-89
MW-1042
MW-1041
MW-76
MW-74
MW-72
MW-70
MW-55
MW-53
MW-38d
MW-242
MW-241
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MW-14
MW-12
MW-92
MW-52
MW-351
MW-11
MW-458
MW-412
MW-1004
MW-1064
MW-350
MW-237
MW-1073
MW-1026
MW-240
Kriging Ranking ^
1
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5
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OU D TCE Plume Area Zone A Wells
Well Id
MW-92
MW-74
MW-72
MW-241
MW-10
MW-76
MW-70
MW-53
MW-38d
MW-242
MW-52
MW-11
MW-458
MW-14
MW-12
MW-88
MW-91
MW-90
MW-55
MW-89
MW-15
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16
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18.5
20
21
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1= least relative amount of information; 32= most relative amount of information.
1= least relative amount of information; 21= most relative amount of information.
c Tie values receive the median ranking of the set.
Wells in the "intermediate" range and receive no recommendation for removal or retention.
022/742479/McCllelanTablesDraftFinal.xls/Table 6.1
6-10
-------
monitoring points (e.g., blue color coding on Figure 6.4). This is intuitively obvious, but
the analysis allows the most valuable and least valuable wells to be identified
quantitatively. In this analysis, the A-zone wells distant from the TCE plume, as defined
by the 5-ug/L concentration isopleth (MW-1004, MW-1064, MW-350, MW-237, MW-
1073, and MW-240) were identified as providing the greatest relative amount of spatial
information. However, as discussed in Section 4.2.1.2, these wells are located
approximately 650 to 2,200 feet from the 5-ug/L TCE isopleth (Figure 6.4), and most
also are located hydraulically up- or crossgradient from the plume sources (i.e., the OU D
waste pits); they are too far from the plume to provide useful information on plume
magnitude and extent given the very localized and contained nature of dissolved COC
present at concentrations above the respective MCLs. Thus, although these wells provide
spatial information, they not in the region of interest for the OU D plume.
Therefore, a revised kriging analysis was conducted only for those monitoring wells
within 500 feet of the OU D plume, as defined by the 5-ug/L TCE isopleth. Although the
EWs are in this region, they were excluded from the analysis because their screened
intervals span both the A and B zones, and monitor water drawn from the respective EW
capture zones, while the monitoring well provide "point" data representative of water
flowing past the well. Thus, the revised analysis examined the relative spatial
information provided by the 21 monitoring wells in the vicinity of the OU D plume using
the same procedure described in Section 6.2. The best-fit semivariogram for the revised
21-well network had the following parameters:
« Circular Model
. Range: 600 feet
. Sill: 20
« Nugget: 19
Figure 6.5 and Table 6.1 present the ranking of monitoring locations based on the
relative value of TCE information provided by each well, as calculated based on the
kriging realizations for the select group of zone A wells. For example, Table 6.1
6-11
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identifies the five wells ranked at or below 3.5 (wells MW-10, MW-241, MW-92, MW-
74, and MW-72) that provide the relative least amount of information, and the four wells
ranked at or above 18.5 (wells MW-15, MW-55, MW-89, MW-90) that provide the
greatest amount of information regarding the occurrence and distribution of TCE in
groundwater in the zone A wells in the region of interest surrounding the OU D TCE
plume. The five lowest-ranked wells are potential candidates for removal from the OU D
groundwater monitoring program, and the four highest-ranked wells are candidates for
retention in the monitoring program, intermediate ranked wells receive no
recommendation for removal or retention in the monitoring program based on the spatial
analysis.
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SECTION 7
SUMMARY OF THREE-TIERED MONITORING NETWORK
EVALUATION
The 51 wells included in the groundwater monitoring program at OU D were
evaluated using qualitative hydrogeologic and extraction-system information, temporal
statistical techniques, and spatial statistics. At each tier of the evaluation, monitoring
points that provide relatively greater amounts of information regarding the occurrence
and distribution of COCs in groundwater were identified, and were distinguished from
those monitoring points that provide relatively lesser amounts of information. In this
section, the results of the evaluations are combined to generate a refined monitoring
program that potentially could provide information sufficient to address the primary
objectives of monitoring, at reduced cost. Monitoring wells not retained in the refined
monitoring network could be removed from the monitoring program with relatively little
loss of information. The results of the evaluations were combined and summarized in
accordance with the following decision logic:
1. Each well retained in the monitoring network on the basis of the qualitative
hydrogeologic evaluation is recommended to be retained in the refined
monitoring program.
2. Those wells recommended for removal from the monitoring program on the
basis of all three evaluations, or on the basis of the qualitative and temporal
evaluations (with no recommendation resulting from the spatial evaluation)
should be removed from the monitoring program.
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3. If a well is recommended for removal based on the qualitative evaluation and
recommended for retention based on the temporal or spatial evaluation, the final
recommendation is based on a case-by-case review of well information.
The results of the qualitative, temporal, and spatial evaluations are summarized in Table
7.1. These results indicate that 30 of the 51 monitoring wells could be removed from the
groundwater LTM program with little loss of information. The justifications for the
recommendations for the four wells that fall into case 3 of the decision logic are as
follow:
• Well MW-55 should be retained in the monitoring program based on its
contribution to spatial plume-definition information.
• Wells MW-72 and MW-241 are recommended for removal from the monitoring
network based on the qualitative and spatial evaluations, and for retention based on
the temporal evaluation. They should be removed from the monitoring network
because they are monitoring lower concentration portions of the A zone than
clustered well MW-10. However, this recommendation is conditional on having
below MCL concentrations of COCs during three most recent consecutive
monitoring events.
• Well MW-242 is recommended for removal from the monitoring network based on
the qualitative and spatial evaluations, and for retention based on the temporal
evaluation. It should be removed from the monitoring network because it is
monitoring lower concentration portions of the A zone than clustered well MW-
15. However, this recommendation is conditional on having below MCL
concentrations of COCs during three most recent consecutive monitoring events.
A refined monitoring program, consisting of 21 wells (13 to be sampled annually, and
8 to be sampled biennially) would be adequate to address the two primary objectives of
monitoring. This refined monitoring network would result in an average of 17 sampling
events per year, compared to 34 events per year in the current monitoring program.
7-2
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Implementing these recommendations for optimizing the LTM monitoring program
at OU D at McClellan AFB could reduce current LTM annual monitoring costs events
by 50 percent. Based on analytical costs alone, implementing these recommendations
could save $2550 per year based on an estimate of $150 per sample analysis.
Additional cost savings could be realized if groundwater samples collected from select
wells (e.g., wells along the lateral and upgradient plume margins) were analyzed for a
short list of halogenated VOCs using USEPA Method SW8021B instead of USEPA
Method SW8260B.
7-5
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SECTION 8
REFERENCES
American Society of Civil Engineers (ASCE) Task Committee on Geostatistical
Techniques in Hydrology. 1990a. Review of geostatistics in geohydrology - I.
Basic concepts. Journal of Hydraulic Engineering 116(5):612-632.
ASCE Task Committee on Geostatistical Techniques in Hydrology. 1990b. Review of
geostatistics in geohydrology - II. Applications. Journal of Hydraulic
Engineering 116(6): 63 3-65 8.
CH2M Hill. 1992. Field Investigation Work Plan, OU D. Draft Final. Prepared for
McClellan AFB, CA. August.
CH2M Hill. 1994a. Groundwater Operable Unit Remedial Investigation/ Feasibility
Study. Prepared for McClellan AFB. June.
CH2M Hill. 1994b. Operable Unit D Remedial Investigation Report. Final Copy. June.
CH2M Hill. 1995. Groundwater Operable Unite Interim Record of Decision. Prepared
for McClellan AFB, CA.
CH2M Hill. 1997. Groundwater Operable Unit, Phase 2 Work Plan. Prepared for
McClellan AFB, CA. August.
CH2M Hill. 1999. Basewide VOC Feasibility Study Report, Volume 1. Final (Version
3). Prepared for McClellan AFB, CA. July.
Clark, I. 1987. Practical Geostatistics. Elsevier Applied Science, Inc., London.
Environmental Systems Research Institute, Inc. (ESRI). 2001. ArcGIS Geostatistical
Analyst Extension to ArcGIS 8 Software, Redlands, CA.
Gibbons, R.D. 1994. Statistical Methods for Groundwater Monitoring. John Wiley &
Sons, Inc., New York.
Norris, R.M. and R.W. Webb. 1990. Geology of California. John Wiley & Sons, Inc.
New York, NY. 2nd ed.
8-1
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Pankow, J.F., and J.A. Cherry. 1996. Dense Chlorinated Solvents and Other DNAPLs in
Groundwater—History, Behavior, and Remediation. Waterloo Press, University of
Waterloo. Guelph, Ontario (Canada).
Parsons. 2000. Remedial Process Optimization Report for Operable Unit D, McClellan
Air Force Base, California. Draft. Prepared for US Air Force Center for
Environmental Excellence Technology Transfer Division and Department of the
Air Force. October.
Radian. 1997'. Groundwater Monitoring Plan. Final. Prepared for McClellan AFB, CA.
September.
Radian. 1999a. Final Groundwater Monitoring Program Quarterly Report, First
Quarter 1999. June.
Radian. 1999b. Final Groundwater Monitoring Program Quarterly Report, Third
Quarter 1999. June.
Rock, N.M.S. 1988. Numerical Geology. Springer-Verlag. New York, New York
URS. 2001. 1st Quarter 2001 (1Q01) Groundwater Monitoring Program Quarterly
Report. July.
URS. 2002. 1st Quarter 2002 (1Q02) Groundwater Monitoring Program Quarterly
Report. July.
US Environmental Protection Agency (USEPA). 1994a. DNAPL Site Characterization.
R.S. Kerr Environmental Research Laboratory and the Office of Solid Waste and
Emergency Response. Publication 9355.4-16S, EPA/540/F-94/049. September.
USEPA. 1994b. Methods for Monitoring Pump-and-Treat Performance. Office of
Research and Development. EPA/600/R-94/123.
Wiedemeier, T.H., and P.E. Haas. 2000. Designing Monitoring Programs to Effectively
Evaluate the Performance of Natural Attenuation. Air Force Center for
Environmental Excellence (AFCEE). August.
8-2
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