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Demonstration of
Two Long-Term Groundwater Monitoring
Optimization Approaches

Report with Appendices
            Compliance boundary
         Groundwater flow direction
                                         The nearest
                                         downgradient
                                         receptor

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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

                                            iv

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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.	
                                             13

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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

                                             14

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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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Two distinct monitoring zones are recognized in the groundwater system beneath the Logistics
Center area.  Most groundwater monitoring wells are completed in the upper monitoring zone (the
"Upper Vashon" zone); relatively few monitoring wells are completed in the lower monitoring zone
(the  "Lower  Vashon" zone).   An  LTMO evaluation of the groundwater extraction  system and
associated monitoring network at the Logistics Center was completed by the Fort Lewis project team
in May 2001 (Appendices  C and D); the refined monitoring program generated as a result of this
evaluation is known as the LOGRAM program.  Based  on the results of the LOGRAM LTMO
evaluation, 24 monitoring  wells were added to the  Logistics Center monitoring program, and 11
previously  sampled monitoring wells  were removed from the  program (a  net increase  of 13
monitoring wells); sampling frequencies generally were  reduced.  The revised Logistics Center
monitoring program (LOGRAM), which was  initiated in December 2001, includes 72 wells  — 51
monitoring wells (29 wells  sampled quarterly, 3 wells sampled semi-annually, and 19 wells sampled
annually), and 21  extraction wells (6 wells sampled quarterly and 15 wells sampled annually).  The
reduction in sampling frequency at a number of wells produced a net reduction in the total number of
primary samples collected  and analyzed per year, from 236 samples to  180 samples. All samples
from the monitoring and extraction wells are analyzed for volatile organic compounds (VOCs) using
U.S. EPA Method SW8260B.

3.2      RESULTS OF LTMO EVALUATION COMPLETED USING MAROS TOOL

Because  extensive  historical  data were  not  available  for the new  wells  installed during
implementation of the current LOGRAM monitoring program, the MAROS tool was used to evaluate
data from the 59 wells that remained in the monitoring program in  September 2001  (21  extraction
wells and 38  groundwater monitoring wells; Appendix  C) included in  the  original monitoring
program, and was  not used to evaluate the LOGRAM program. The detailed results of the MAROS
evaluation of the  groundwater monitoring program at the Fort Lewis Logistics Center area  are
presented in Appendices C (Section C1.5) and D-l, and are summarized in this subsection.

Prior to the evaluation,  five wells that potentially  would provide  "redundant"  information  were
identified on the basis of qualitative considerations (Appendices C and D-l); these were not included
in the moment analysis or in the spatial evaluation.  Historic monitoring results from all monitoring
and extraction wells were included in the temporal evaluation. However, results from groundwater
extraction wells were not used in the spatial evaluation; and the results from two monitoring wells
completed in the  lower part of the Lower Vashon subunit also were  excluded  from the spatial
evaluation, because these two wells were considered to be within a different monitoring zone than the
other monitoring wells (Appendix D-l).

Application  of the  Mann-Kendall and  linear-regression  temporal  trend  evaluation  methods
(Appendices B and C) indicated that the extent and concentrations of TCE in groundwater at the
Logistics Center source area (the EGDY) probably are decreasing (GSI, 2003a).  TCE concentrations
in groundwater at  most of the extraction wells located northwest of the EGDY source area also are
probably decreasing.  The results of the moment analysis indicated that the location of the center of
mass of the plume has remained essentially unchanged, and that the extent of TCE in groundwater
has decreased over time, providing further evidence that the plume is  stable under current conditions.
The evaluation of  overall plume stability indicated that the extent of TCE in groundwater is stable or
decreasing, resulting in the  recommendation that a monitoring strategy appropriate for a "Moderate "
design category be adopted  (Appendices C and D).
                                          19

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The results of detailed spatial analyses using the Delaunay method (Appendices C and D) indicated
that 8 monitoring wells could be removed from the original monitoring program (which included 38
monitoring wells)  without  significant loss  of information.  However, the accompanying well-
sufficiency analysis  indicated that there  is  a high  degree  of uncertainty  in  predicted TCE
concentrations in six areas within the network where the available historical sampling information
may be inadequate; new monitoring wells were recommended for installation in these six areas (GSI,
2003a).  These six locations recommended for installation of new wells correspond to six wells that
had been installed and were being monitored in conjunction with the LOGRAM program (Appendix
C). All groundwater extraction wells were recommended for retention in the refined monitoring
program.  The results of the sampling-frequency optimization analysis completed using MAROS
(Appendices C and D) indicated that most wells in the monitoring network  could be  sampled less
frequently than in the current (LOGRAM) monitoring program.  The results  of the data-sufficiency
evaluation, completed using power-analysis methods, indicated that RAO concentrations of TCE in
groundwater have nearly been achieved at the compliance boundary.

The optimized monitoring program generated using the MAROS tool includes 57  wells, with 19
sampled  quarterly,  2 sampled  semiannually, 30  sampled  annually, and  6 sampled  biennially
(Appendices C and D).  Adoption of the optimized program would result in collection and analysis of
113 samples per year, as compared with collection and analysis of 180 samples per year in the  current
LOGRAM monitoring program (Table  3.1)  and  236  samples per year in  the original  sampling
program.  Implementing these recommendations could lead to a 37-percent reduction in the number
of samples collected and analyzed annually, as compared with the current LOGRAM program, or a
52-percent reduction in the number of samples collected and analyzed, as compared with the original
program (Table 3.1).  Assuming a cost per sample of $500 for collection and chemical analyses
(based on information provided by the U.S. Army Corps of Engineers [USAGE, 2001]), adoption of
the monitoring program as  optimized using the MAROS tool is projected to result in savings of
approximately $33,500 per year as  compared with the LOGRAM  program (Table 3.1).   (The
estimated cost per sample is based on information provided by facility personnel in conjunction with
efforts to estimate potential cost savings resulting from optimization of the monitoring program, and
includes costs associated  with sample collection and analysis, data compilation and reporting, and
handling of materials generated as investigation-derived waste [IDW]  during sample collection [e.g.,
purge water].)  The  optimized program remains adequate to  delineate the  extent of TCE  in
groundwater,  and to monitor changes in the plume over time (GSI, 2003a).

3.3      RESULTS OF LTMO EVALUATION COMPLETED USING THREE-TIERED APPROACH

The three-tiered approach was used to evaluate the original monitoring program at the  Logistics
Center area (which included 59 wells), and also was used to evaluate the current LOGRAM program
(which includes 72 wells).  Because extensive historical data were not available for the new wells
included in the LOGRAM program, temporal  analyses were not  used in  evaluating the new
LOGRAM wells - only qualitative and spatial evaluations of that program were completed for these
wells, and as a consequence, the results of evaluation  of the two programs  are  not directly
comparable.  The  detailed  results  of the three-tiered  evaluation of the  groundwater monitoring
programs at the Fort Lewis Logistics Center area are presented in Appendices C (Section C1.6) and D
(Appendix D-l), and are summarized in this subsection.
                                          20

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                    Table 3.1: Results of Optimization Demonstrations at
                        Logistics Center Area Fort Lewis, Washington
Monitoring-Program Feature
Wells sampled quarterly
Wells sampled semi-annually
Wells sampled annually
Wells sampled biennially
Wells sampled every 3 years
Total wells included in LTM program
Total number of samples (per year)
Annual 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).
                                          22

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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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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.
                                          28

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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

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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

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(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

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            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

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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

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     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

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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

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         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

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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
                    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.
                                             40

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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

-------
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

-------
and subsequent implementation of optimization recommendations, must be made on a site-specific
basis. Factors to be considered in deciding whether to proceed with an LTMO evaluation include:

   •   The projected level of effort necessary to conduct the evaluation;

   •   The resources available for the evaluation (e.g.,  quality and quantity of data, staff having the
       appropriate technical capabilities);

   •   The anticipated degree of difficulty in implementing optimization recommendations; and

   •   The potential benefits (e.g., cost savings) that  could result  from an optimized monitoring
       program.

Experience suggests that optimization of a monitoring program  should be considered for most  sites
where  the LTM programs are based on monitoring points and/or sampling frequencies that were
established during site characterization, or for sites where more  than about 50 samples are collected
and analyzed on  an annual basis.  Because  it is likely that monitoring programs can benefit from
periodic evaluation as environmental programs evolve, monitoring program optimization also should
be undertaken periodically, rather than being regarded  as a one-time event.  Overall site conditions
should be relatively stable, with no large changes in remediation  approaches occurring or anticipated.
For sites at which response decisions are being validated or refined (e.g., during periodic remedy-
performance reviews), optimization of the LTM program should be postponed until adjustments to
the response have been implemented and evaluated. Successful application of either LTMO approach
to the site-specific evaluation of a monitoring program is directly dependent upon the amount and
quality of the available  data - results from a minimum of four  to six separate sampling events are
necessary to support a temporal analysis, and results  collected at a minimum of about six (for a
MAROS evaluation) to 15 (for a three-tiered evaluation) separate monitoring points are necessary to
support a spatial analysis. It also is necessary to develop an adequate CSM, describing site-specific
conditions (e.g., direction and rate of groundwater movement, locations of contaminant sources and
potential receptor exposure points) prior to applying either approach; the extent of contaminants in
the subsurface at  the site also must be adequately delineated before  the monitoring program can be
optimized.

Typically, a program manager should anticipate incurring costs on the order of $6,000 to  $10,000 to
complete an LTMO evaluation using one of the two approaches presented in this demonstration, at
the level of detail of the case-study examples used in the demonstration (Sections 3, 4, and 5; and
Appendices  C and D).   Consequently,  an LTMO evaluation may  be  cost-prohibitive for smaller
monitoring programs.   Assuming  a payback period of three  years,  potential  cost savings  of
approximately $2,000 to $3,300 per year must be realized if optimization of a monitoring program is
to be cost-effective.  Because the costs associated with collection  and analysis of a groundwater
sample (including prorated mobilization costs, and  costs for field sampling, management  of water
produced during sampling, laboratory analyses, QA/QC, and reporting) using conventional sampling
technologies (bailer or purge pump) can range from about $200 per sample to more than $500 per
sample (U.S. Air  Force, 2004),  an LTMO evaluation that can be used to reduce the total number of
samples collected at a site by about 5 to 10 samples per annum should be cost-effective.
                                             43

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                                7.0    REFERENCES
Air Force Center for Environmental Excellence (AFCEE).   2000.  Monitoring and Remediation
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AFCEE.  2002.  Monitoring and Remediation Optimization System (MAROS) User's Guide, Version
      2.0. October.
American Society of Civil Engineering (ASCE) Task Committee on Geostatistical Techniques in
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      Hydraulic Engineering 1 16(5):612-632.
ASCE Task Committee on Geostatistical Techniques  in Hydrology. 1990b. Review of geostatistics
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Bartram, J., and R. Balance. 1996.  Water Quality Monitoring. E&FN Spon. London.
Cameron,  K., and P.  Hunter.  2002.   Optimization of LTM  Networks Using GTS:  Statistical
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Cameron, K. and P. Hunter.  2002.  Using spatial models and kriging techniques to optimize long-
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Cameron, K. and P. Hunter.  2004.  Optimizing LTM Networks with GTS:  Three New Case Studies,
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      Optimization  —   Proceedings  of  the  Federal  Remediation  Technologies  Roundtable
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Clark, I. 1987. Practical Geostatistics.  Elsevier Applied Science, Inc. London.
Cieniawski,  S.E.,  J.W.  Eheart,  and  S. Ranjithan.   1995.   Using genetic algorithms to solve  a
      multiobjective groundwater monitoring problem.  Water Resources Research 3 1 (2):399-409.
Davis, J.C.   1986.  Statistics and Data Analysis in Geology.  John Wiley & Sons, Inc.  New York,
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Deutsch, C.V., and A.G. Journel. 1998. GSLIB - Geostatistical Software Library and User's Guide.
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Dresel,  E.P., and  C. Murray.   1998.  Groundwater  monitoring network design using stochastic
      simulation.  Geological Society of America Abstracts with Programs  30(7):181.

Englund, E., and A. Sparks.  1992.  GEO-EAS (GEO_statistical Environmental Assessment software),
      Program Version 1.2.1 and User's Guide. US Environmental Protection Agency. EPA/600/4-
      88/033a.

Environmental Systems  Research  Institute,  Inc. (ESRI).   2001.   ArcGIS Version 8 Software.
      Redlands, California.

Everett,  L.G.  1980.  Groundwater Monitoring — Guidelines and Methodology for Developing and
      Implementing a Groundwater Quality Monitoring Program.   General  Electric  Company.
      Schenectady, New York.
                                           44

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Franke, O.L. (ed.)  1997.  Conceptual Frameworks for Ground-Water-Quality Monitoring.  Ground-
       Water Focus Group of the Intergovernmental Task Force on Monitoring  Water Quality.
      Denver, Colorado.  August.
Gibbons, R.D.  1994.  Statistical Methods for Groundwater Monitoring. John Wiley & Sons, Inc.
      New York, New York.

Gibbons, R.D. and D.E. Coleman.  2002.  Statistical Methods for Detection and Quantification of
      Environmental Contamination.  John Wiley & Sons, Inc. New York, New York.
Griffin, D.A.  1996. The Need for Spatial Statistics, in Arlinghaus, S.L., ed., Practical Handbook of
      Spatial Statistics. CRC Press LLC.  Boca Raton, Florida.
Groundwater  Services, Inc. (GSI).  2003a.  MAROS 2.0 Application - Upper Aquifer Monitoring
      Network Optimization, Fort Lewis Logistics Center, Pierce County, Washington.  Final.  April.
GSI.  2003b.  MAROS 2.0 Application — Upper Outwash Aquifer Monitoring Network Optimization,
      Long Prairie Site, Long Prairie, Minnesota. Final. May.
GSI.  2003c.  MAROS 2.0 Application - Upper Aquifer Monitoring Network Optimization, Operable
       Unit D, McClellan Air Force Base, California.  Final. April.
Hirsch, R.M., R.B. Alexander, and R.A.  Smith.  1991.  Selection of methods for the detection and
      estimation of trends in water quality.  Water Resources Research 27(5):803-813.
Hudak, P.P. 2000. Effects of two-dimensional mass  transport modeling on groundwater monitoring
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Hudak, P.P., H.A. Loaiciga,  and F.A. Schoolmaster.  1993.  Application of geographic information
      systems to groundwater monitoring network design. Water Resources Bulletin 29(3):383-390.
Kelly, B.P.  1996.  Design of a Monitoring Well Network for the City of Independence, Missouri,
       Well Field Using Simulated Ground-Water Flow Paths and Travel Times.  US  Geological
      Survey Water-Resources Investigation Report 96-4264.
Lapham,  W.W., P. D. Wilde, and M.T. Koterba. 1996.  Guidelines and Standard Procedures for
      Studies  of Ground-Water Quality:   Selection and Installation of Wells, and Supporting
      Documentation. US Geological Survey Water-Resources Investigations Report 96-4233.
Ling, M., .S. Rifai, C.J. Newell, J.J. Aziz, and J.R. Gonzales.  2003.  Groundwater monitoring plans
      at  small-scale  sites  -  an  innovative  spatial  and  temporal  methodology.   Journal of
      Environmental Monitoring 5(1): 126-134.
Loaiciga, H.A., R.J.  Charbeneau, L.G. Everett, G.E. Fogg,  B.F. Hobbs, and S. Rouhani.   1992.
      Review of  ground-water  quality  monitoring  network  design.   Journal  of Hydraulic
      Engineering 118(1):11-37.
Makeig, K.S.  1991.  Regulatory Mandates for Controls on Ground-Water Monitoring, in Nielsen,
      D.M., ed.  Practical Handbook of Ground-Water Monitoring.  Lewis Publishers, Inc.  Chelsea,
      Michigan.
National  Research Council  (NRC).   1993.   Alternatives for  Groundwater Cleanup.   National
      Academy of Sciences,  National Academy Press. Washington, D.C.

NRC.  1999.  Environmental Cleanup at Navy Facilities - Risk-Based Methods. National Academy
      of Sciences, National Academy Press. Washington, D.C.
                                            45

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Parsons Corporation (Parsons). 2000.  Remedial Process Optimization Report for Operable Unit 1,
      Hill Air Force Base, Utah.  Final. December.
Parsons.  2003a. Comparative Evaluation of the Monitoring and Remediation Optimization System
      (MAROS) Tool. Draft.  January.

Parsons.  2003b.  Three-Tiered Groundwater Monitoring Network Optimization Evaluation for the
      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.
Reed, P.M. and B.S. Minsker.  2004.  Striking the  balance: Long term groundwater monitoring
      design for  multiple, conflicting objectives.   Journal of Water Resources and Planning
      Management 13 0(2): 140-149.
Reed, P.M., B.S. Minsker, and D.E.  Goldberg.  2001.  A multiobjective approach to cost effective
      long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with
      historical data. Journal of Hydroinformatics 3:71-89.
Reed, P.M., B.S. Minsker, and D.E. Goldberg.  2003.  Simplifying multiobjective optimization II:
      An automated design methodology for the nondominated sorted  genetic algorithm.   Water
      Resources Research 39(7): 1196.
Reed, P.M., B.S.  Minsker, and A.J. Valocchi.  2000.    Cost-effective long-term  groundwater
      monitoring design using a genetic algorithm and global mass interpolation.  Water Resources
      Research 36(12):3731-3741.

Ridley, M.N., V.M. Johnson, and R.C. Tuckfield. 1995.  Cost-Effective Sampling of Ground Water
      Monitoring  Wells.  Lawrence Livermore National Laboratory (LLNL).  Report UCRL-JC-
      118909.
Rock, N.M.S. 1988. Numerical Geology. Springer-Verlag. New York, New York.
Sheskin, D.J.  2000. Handbook of Parametric and Nonparametric Statistical Procedures.  Chapman
      & Hall/CRC, Inc.  Boca Raton, Florida. 2nd ed.
Schock, S.C., E. Mehnert, and D.P. McKenna. 1989.  Design of a Sampling System for Agricultural
      Chemicals in  Rural, Private  Water-Supply Wells, in Proceedings of  the  Third National
      Outdoor  Action  Conference  on Aquifer Restoration,  Ground  Water  Monitoring,  and
      Geophysical Methods. May 22 - 25.  Orlando,  Florida. National Water Well Association.

Sen, P.K.  1968.   Estimates of the regression coefficient based on Kendall's tau.  Journal of the
      American Statistical Association 63:1379-1389.
Storck, P., A.J. Valocchi, and J.W. Eheart.   1995.   Optimal Location  of Monitoring Wells  for
      Detection of Groundwater Contamination in Three-Dimensional Heterogeneous Aquifers, in
      Wagner,  B.J., T.H. Illangasekare, and K.H. Jensen, eds., Models for Assessing and Monitoring
      Groundwater  Quality  -  Proceedings of the  Prague  Conference  of the International
      Association of Hydrological Sciences.  Boulder, Colorado.  IAHS Publication No. 227.
Tuckfield, R.C., E.P. Shine, R.A. Hiergesell, M.E. Denham, S. Reboul, and C. Beardsley.  2001.
      Using Geoscience  and  Geostatistics  to Optimize Groundwater Monitoring Networks at  the
      Savannah River Site.  U.S. Department  of Energy Publication No. WSRC-MS-2001-00145.


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U.S. Air Force.   2004.   Comprehensive Results  Report for  the  Passive  Diffusion Sampler
      Demonstration. Draft. August.
U.S. Army Corps of Engineers (USAGE). 2001. Draft Logistics Center (FTLE-33) Remedial Action
      Monitoring Network Optimization Report. May.
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      Objectives Process.   U.S. EPA  Office of Research  and Development.  EPA QA/G-4.
      EPA/600/R-94/055. September.
U.S. EPA.  1994b.  Methods for Monitoring Pump-and-Treat Performance.  U.S. EPA Office  of
      Research and Development. EPA/600/R-94/123.
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U.S. EPA.  2000. Drinking Water Standards and Health Advisories.  U.S. EPA Office of Water.
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      Van Nostrand Reinhold.  New York, New York.
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      Performance of Natural Attenuation.  AFCEE.  August.
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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.


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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).

                                            A-4

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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.
                                            A-10

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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.
                                           B-l

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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.)
                                           B-2

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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

                                             B-3

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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-

                                           B-4

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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.
                                            B-5

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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

                                            B-7

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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:

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      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
                                           B-9

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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)
                                           B-10

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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

                                           B-ll

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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

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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)
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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

-------
                                   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.
                                        C-l

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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
                                          C-2

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13   *   x
c   I   r
is   1
05  t
ffl   s
                                                                                 y"
                                                                              V
                                                                                                               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
                                   C-6

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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
                                 C-7

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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.

-------
            C    g
a*
o>
                  TJ    131    JJ

                                                                                       F,

                                                                                       i
                                                                                                                •a
                                                                                                                O
                                                                         Oi

                                                                           6

-------
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

                                          C-10

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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

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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

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  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

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   •   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

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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

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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

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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
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•/
^
^
^
^
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v'
•/
v'
v'
•/
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v'
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                                         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

-------
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

-------
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

-------
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

-------
 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

-------
                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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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

-------
       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

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       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

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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

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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

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      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

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            APPENDIX D

    ORIGINAL MONITORING PROGRAM
         OPTIMIZATION REPORTS
                  BY
GROUNDWATER SERVICES, INC. AND PARSONS

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            APPENDIX D-l

  OPTIMIZATION OF MONITORING PROGRAM
                   AT
FORT LEWIS LOGISTICS CENTER, WASHINGTON

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                                                         G-2236-15
                 2.0
Pierce County, Washington
           to
Air
April 7, 2003
Groundwater Services, Inc.

2211 Norfolk, Suite 1000,  Houston, Texas 77098

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                                                          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

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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

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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

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                                                                    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

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                                                                    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.

Fort Lewis Logistics Center                     2                 MAROS 2.0 Application
Pierce County, Washington                                      Monitoring Network Optimization

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                                                                   GROUNDWATER
January 75, 2003                                                    SERVICES, INC.
   •   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).
Fort Lewis Logistics Center                    23                MAROS 2.0 Application
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January 75, 2003
                                                                      If
                                                                    GROUNDWATER
                                                                    SERVICES, INC.
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
Fort Lewis Logistics Center
Pierce County, Washington
24
MAROS 2.0 Application
Monitoring Network Optimization

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                                                                     GROUNDWATER
January 75, 2003                                                      SERVICES, INC.
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.
Fort Lewis Logistics Center                    25                MAROS 2.0 Application
Pierce County, Washington                                      Monitoring Network Optimization

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                                                                     GROUNDWATER
January 75, 2003                                                      SERVICES, INC.
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

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                                                                   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

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                                                                          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

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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

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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

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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

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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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Figure 3:
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:

-------
                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

022/742479/Fort Lewis Draft Final.doc

-------
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

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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
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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-

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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-
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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-
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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  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
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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
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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
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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
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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
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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
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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
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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
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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
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30-35
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Annually
Quarterly
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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
-


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60-70
74.2-79.2
123-133
130.1-139.3
-
-
104-114
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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
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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

-------
      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

-------
      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

-------
      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
                                     4-11
022/742479/Fort Lewis Draft Final.doc

-------
      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

-------
      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
                                    4-15
022/742479/Fort Lewis Draft Final.doc

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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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Date
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
     Variation
       HIGH
                                                                               5.2

                                                                                             OF

                                                                        IN
                                                               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
                                      5-4
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
                                      5-5

-------
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                                                                              Results
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                                                                               IS
                                                                        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
absence of 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
•S 2500 -

-------
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

-------
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

-------
                                  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
12C/
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.
                                     6-12
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.
                                     7-1
022/742479/Fort Lewis Draft Final.doc

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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).
                                     7-2
022/742479/Fort Lewis Draft Final.doc

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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

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                                                          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

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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.

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

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             APPENDIX D-2

   OPTIMIZATION OF MONITORING PROGRAM
                   AT
LONG PRAIRIE GROUND WATER CONTAMINATION
        SUPERFUND SITE, MINNESOTA

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                                                            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

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                                                               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

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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

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                                                                    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
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       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.
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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 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

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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

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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.
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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.
Long Prairie Site
Long Prairie, Minnesota
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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.
Long Prairie Site                             IS                MAROS 2.0 Application
Long Prairie, Minnesota                                         Monitoring Network Optimization

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February 19, 2003                                                    SERVICES, INC.
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.
Long Prairie Site
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February 19, 2003                                                     SERVICES, INC.
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.
Long Prairie Site                           23                MAROS 2.0 Application
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February 19, 2003                                                     SERVICES, INC.
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).
Long Prairie Site                           24                MAROS 2.0 Application
Long Prairie, Minnesota                                        Monitoring Network Optimization

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February 19, 2003
                                                                      If
                                                                    GROUNDWATER
                                                                    SERVICES, INC.
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
Long Prairie Site
Long Prairie, Minnesota
                                      25
MAROS 2.0 Application
Monitoring Network Optimization

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February 19, 2003
                                                                     If
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                                                                    SERVICES, INC.
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
Long Prairie Site
Long Prairie, Minnesota
                                      26
MAROS 2.0 Application
Monitoring Network Optimization

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                                                                     GROUNDWATER
February 19, 2003                                                     SERVICES, INC.
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

Long Prairie Site                            27                MAROS 2.0 Application
Long Prairie, Minnesota                                         Monitoring Network Optimization

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                                                                      GROUNDWATER
February 19, 2003                                                      SERVICES, INC.
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.
Long Prairie Site                            28                MAROS 2.0 Application
Long Prairie, Minnesota                                         Monitoring Network Optimization

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                                                                     GROUNDWATER
February 19, 2003                                                     SERVICES, INC.
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).
Long Prairie Site                           29                MAROS 2.0 Application
Long Prairie, Minnesota                                        Monitoring Network Optimization

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                                                                    GROUNDWATER
February 19, 2003                                                     SERVICES, INC.
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.
Long Prairie Site                           30               MAROS 2.0 Application
Long Prairie, Minnesota                                        Monitoring Network Optimization

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February 19, 2003                                                    SERVICES, INC.
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.
Long Prairie Site                          31                MAROS 2.0 Application
Long Prairie, Minnesota                                        Monitoring Network Optimization

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February 19,2003
GSI Job No. G-2236-15
 V
GROUNDWATER
SERVICES, INC.
                          MAROS 2.0 APPLICATION
                   MONITORING NETWORK OPTIMIZATION

                               Long Prairie Site
                            Long Prairie, Minnesota
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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Issued: 2/19/03
Page 1 of 3
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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
Issued: 2/19/03
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

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GSI Job No. G-2236-15
Issued: 2/19/03
Page 3 of 3
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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
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Page 1 of 1
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                                     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 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
SERVICES, INC.
                                       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
Issued: 2/19/03
Page 2 of 3
  If
GROUNDWATER
SERVICES, INC.
                                       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
Issued: 2/19/03
Page 3 of 3
  If
GROUNDWATER
SERVICES, INC.
                                       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
SERVICES, INC.
                                          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).

-------
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
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Figure 3:
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-------
GSI Job No. G-2236-15
Issued: 2/19/03
Page 1 of 1
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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
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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

-------
            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

-------
 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

-------
 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

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  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

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                          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-

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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,
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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
                                     2-1
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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

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   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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                                  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
S:\ES\SHARED\CEN\MNO\EPA\LONGPRAIRIE\WRITEUP\LongPrairieMNODraftFinal.doc

-------
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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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Dec-96 Dec-97 Dec-98 Dec-99 Dec-00 Dec-01
Date
   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
                   Variation
                     LOW
     Variation
       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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                                                                               IN
                                                                                IS
                                                                        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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could not be analyzed using the Mann-Kendall trend analysis,  and have a "<4meas"
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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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Increasing Trend?
                               Well in Source Area?
                                                      Recent ND
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                              Cross or Downgradient
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                                                  Downgradient?
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                                         Remove
                                                                    FIGURE 5.5
                                                             TEMPORAL CHEMICAL
                                                           CONCENTRATION TREND
                                                         DECISION RATIONALE FLOW
                                                                      CHART
                                                           Monitoring Network Optimization
                                                         	Long Prairie. Minnesota	
                                                                  PARSONS
                                                                  Denver, Colorado

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                                  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
3500 -

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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
                                      6-4
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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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             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

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      Circular model


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      -   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.
                                     7-1

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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.
                                     8-2

S:\ES\SHARED\CEN\MNO\EPA\LONGPRAIRIE\WRITEUP\LongPrairieMNODraftFinal.doc

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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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GSIJobNo. G-2236-15
January 15, 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       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;
McClellan Air Force Base                      1                MAROS 2.0 Application
Sacramento Valley, California                                    Monitoring Network Optimization

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                                                                     GROUNDWATER
June 2, 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
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.
McClellan Air Force Base                      2                 MAROS 2.0 Application
Sacramento Valley, California                                     Monitoring Network Optimization

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                                                                    GROUNDWATER
June 2, 2003                                                         SERVICES, INC.
   •   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.
McClellan Air Force Base                      3                 MAROS 2.0 Application
Sacramento Valley, California                                     Monitoring Network Optimization

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                                                                    GROUNDWATER
June 2, 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 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

McClellan Air Force Base                       4                 MAROS 2.0 Application
Sacramento Valley, California                                     Monitoring Network Optimization

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                                                                    GROUNDWATER
June 2, 2003                                                         SERVICES, INC.
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.
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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%.
McClellan Air Force Base                     28                MAROS 2.0 Application
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June 2, 2003                                                         SERVICES, INC.
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"
McClellan Air Force Base                     29                MAROS 2.0 Application
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June 2, 2003                                                         SERVICES, INC.
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.
McClellan Air Force Base                      32                MAROS 2.0 Application
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June 2, 2003                                                         SERVICES, INC.
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.
McClellan Air Force Base                      35                MAROS 2.0 Application
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January 15, 2003
GSI Job No. G-2236-15
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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

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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

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                                        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

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                                        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

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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 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

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GSIJobNo. G-2236-15
Issued 1/15/03
Page 1 of 1
                                                                                                                           GSQUNDWATE!
                                                                                                                           SERVICES, INC.
                                                                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.

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GSI Job No. G-2236-15
Issued: 1/15/03
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.

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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
Issued: 1/15/03
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
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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-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
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GROUNDWATER
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                                      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
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GROUNDWATER
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                                    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.

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GSI Job No. G-2236-15
Issued: 1/15/03
Page 1 of 1
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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
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GROUNDWATER
SERVICES, INC.

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Figure 5. Decision Matrix for Determining Provisional Frequency (Figure A.3.1 of the
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-------
  GSI JobNo. G-2236-15
  Issued: 1/15/03
  Page 1 of 1
                                      If
                                     GROUNDWATER
                                     SERVICES, INC.
Potential areas for
new locations are
indicated by triangles
with a high SF level,     t
Estimated SF Level;
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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

-------
 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

-------
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

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            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

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   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

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 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

-------
 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

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 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

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 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

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            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

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     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

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                          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

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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

                                     ES-2

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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.
                                    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	2-2
      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

                                    -i-

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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
                                    -11-
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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
                                     -111-
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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
                                    -IV-
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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),
                                     1-1
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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.
                                      1-2

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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

                                      2-1
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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.
                                         2-9

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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:

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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

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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

S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.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 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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-------






























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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,

                                    4-10
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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.

                                    4-11
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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
                     HIGH
Confidence Factor
       LOW
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                     LOW
     Variation
       HIGH
                                                                              5.2

                                                                                            OF

                                                                        IN
                                                            OU D Monitoring Network Optimization
                                                                  McClellan AFB, California
draw\739732\diffusion\williamsA.cdr pg1 nap 4/3/02

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(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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                                                                               IS
                                                                     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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       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
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.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:
                                      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

S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

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                                   FIGURE 6.1
                    IDEALIZED SEMVARIOGRAM MODEL
              THREE-TIERED MONITORING NETWORK OPTIMIZATION
                                 OPERABLE UNIT D
                    MCCLELLAN AIR FORCE BASE, CALIFORNIA
3500 -

3000
•g 2500 -
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) 500 1000 1500 2000 2500 3000 3500
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
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

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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
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

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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
             1.48-
             1.11
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100   200   300   400   500   600   700   800
                Distance, h
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
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

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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
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

-------
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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
MW-15
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
2
5C/
5
5
5
5
13.5
13.5
13.5
13.5
13.5
13.5
13.5
13.5
13.5
13.5
13.5
13.5
21.5
21.5
21.5
21.5
24
25
26
27
29.5
29.5
29.5
29.5
32
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
Kriging Ranking b/
1
3.5
3.5
3.5
3.5
8
8
8
8
8
11.5
11.5
13
14.5
14.5
16
17
18.5
18.5
20
21
Remove
V
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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
S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

-------
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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.
                                     6-13

S:\ES\shared\cen\MNO\EPA\McClellan\Writeup\McClellanMNODraftFinal.doc

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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.
                                     7-1

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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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