ROADMAP
LONG-TERM MONITORING
OPTIMIZATION
US Army Corps
of Engineers
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
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EPA 542-R-05-003
www.cluin.org/optimization
May 2005
ROADMAP
TO
LONG-TERM MONITORING
OPTIMIZATION
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
NOTICE
This information represents the views of the authors and has undergone EPA and external review
by experts in the field. Not all references are known to have been peer reviewed.
This document is not an U.S. EPA policy, guidance or regulation. It does not create or impose
any legally binding requirements or establish U.S. EPA policy or guidance. The information is
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The U.S. Environmental Protection Agency funded the preparation of this document by The
Parsons Corporation under EPA Contract No. 68-C-02-092 to Dynamac Corporation, Ada,
Oklahoma, and by the U.S. Army Corps of Engineers, and their contractor, Mitretek.
A limited number of printed copies of the report are available free of charge and may be ordered
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For further information about this report, please contact the EPA's Office of Superfund
Remediation and Technology Innovation:
Kathy Yager Ellen Rubin
(617) 918-8362 (703) 603-0141
yager.kathleen@epa.gov mbin.ellen@epa.gov
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TABLE OF CONTENTS
1.0 INTRODUCTION 1
1.1. BENEFITS OF OPTIMIZATION OF
LONG-TERM MONITORING PROGRAMS 1
1.2. PURPOSE 2
1.3. SCOPE 2
2.0 STEPS INVOLVED IN LONG-TERM MONITORING OPTIMIZATION 3
2.1 CLEARLY DEFINE AND DOCUMENT CURRENT MONITORING PROGRAM 5
2.1.1 Components of a Monitoring Program 5
2.1.2 Document/Refine LTM Program Objectives 6
2.1.3 Identify Parameters/Constituents to be Monitored and Methods
Used for Measuring those Constituents/Parameters 7
2.1.4 Document Sampling Locations and Frequency of Monitoring 7
2.2 EXAMINE EXISTING DATA 8
2.3 DETERMINE IF SITE is A CANDIDATE FOR A DETAILED LTMO
EVALUATION 10
2.4 DETERMINE THE TYPE OF EVALUATION 11
2.4.1 General Considerations in LTMO 11
2.4.2 Considerations in Qualitative Evaluation 12
2.4.3 Considerations for Quantitative Analysis of Temporal Trends.... 14
2.4.4 Considerations for the Quantitative Spatial Analysis of Monitoring
Networks
15
2.4.5 Other Considerations 15
2.5 SELECT THE LTMO METHOD(S)/TOOL(S) 15
2.5.1 LTMO Guidance Documents 16
2.5.2 LTMO Tools & Standardized Approaches 16
2.5.3 Current Research in LTMO 17
2.6 PERFORM THE OPTIMIZATION 21
2.6.1 LTMO Preparation and Implementation 21
2.6.2 Optimization of Other Aspects of the LTM Program 21
2.7 ASSESS AND IMPLEMENT RESULTS 22
2.7.1 Examine Results of LTMO Evaluation 22
2.7.2 Implementation Steps 23
2.7.3 Cost to Implement Recommendations 23
2.7.4 Benefits of Flexibility in Planning and Decision Documents 24
2.7.5 Periodic Re-Evaluation of LTM Programs and Validation of
LTMO Recommendations 25
2.7.6 Considerations in Reviewing LTMO Analyses 25
3.0 REFERENCES 28
APPENDIX: LTMO RESOURCES 30
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
1.0 INTRODUCTION
1.1. BENEFITS OF OPTIMIZATION OF LONG-TERM MONITORING PROGRAMS
Costs for groundwater monitoring during remediation represent a significant, persistent,
and growing burden for the private entities and government agencies responsible for
environmental remediation projects, especially as remedies are determined and
implemented.
The U.S. Environmental Protection Agency (U.S. EPA) (2004a) defines monitoring as
"... 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.. .directly
related to the management objectives for the site in question."
Long-term monitoring (LTM) is defined here as monitoring conducted after some active,
passive, or containment remedy has been selected and put in place, and is used to evaluate
the degree to which the remedial measure achieves its objectives (e.g., removal of
groundwater contaminants, restoration of groundwater quality, etc.). 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 etal., 2000). However, site characterization
networks often are not perfectly suited for LTM, because they were installed with a
different purpose - to define the nature and extent of the problem when there were many
unknowns about the site.
In some cases, the money spent on LTM yields incomplete information on the performance
of the remedy. In other situations, money spent on monitoring yields more information than
is necessary to make decisions about the operation of the remedy or the progress toward
closeout. LTM optimization (LTMO) offers an opportunity to improve the cost-
effectiveness of the LTM effort by assuring that monitoring achieves its objectives with an
appropriate level of effort. The optimization may identify inadequacies in the monitoring
program, and recommend changes to protect against potential impacts to the public and the
environment. LTMO may also reduce costs. This is especially true as the remedy
progresses, monitored parameters become more predictable, and the extent of
contamination diminishes. Decreases in monitoring frequency, locations, and analytical
requirements can result in substantial cost savings, and such reductions can be implemented
in ways to maintain adequate understanding of the site conditions to make site decisions.
Optimization techniques have been applied to the design of monitoring networks for site
characterization, detection monitoring, and compliance monitoring (Loaiciga etal., 1992).
In practice, however, optimization techniques are most often applied 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. In addition, optimization of a long-term monitoring
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
program can provide significant benefits, due to the typically long time periods and
relatively high cost of LTM programs.
Optimization of LTM programs need not be limited to the subsurface and can extend to the
monitoring performed for the operation of the above-ground treatment processes. In fact,
optimization of this monitoring can be done quickly and relatively easily, and has
potentially has significant cost-saving implications. Though this is not the focus of this
document, it should be considered as part of an LTMO effort.
1.2. PURPOSE
The primary goals of this Roadmap are to assist site managers in:
Understanding the steps involved in conducting a LTMO,
. Determining whether a monitoring program could benefit from a LTMO
assessment,
Identifying potential strategies for applying optimization techniques and evaluating
which are appropriate for a program, and
Accessing more information and resources about LTMO tools, methods, and
approaches.
1.3. SCOPE
This roadmap focuses on optimization of established long-term monitoring programs for
groundwater. Tools and techniques discussed concentrate on methods for optimizing the
monitoring frequency and spatial (three-dimensional) distribution of wells (i.e., physical
program optimization). Other LTMO methods focusing on areas such as the list of analytes,
the sampling and analytical methods, and data management are important items for
consideration, but are not detailed in this document.
The LTMO techniques discussed here can be described as qualitative or quantitative or
some combination of these techniques. Qualitative LTMO evaluations rely on the use of
professional judgment to assess the adequacy of the monitoring network and sampling
frequency, whereas quantitative LTMO approaches use numerical and statistical
approaches to recommend changes. There are advantages to both. The general approaches
are discussed further in Section 2.4 (Determine the Type of Evaluation) and specific
processes and tools are introduced in Section 2.5 (Select the LTMO Methods/Tools).
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2.0 STEPS INVOLVED IN
LONG-TERM MONITORING OPTIMIZATION
This section presents the seven steps involved in LTMO. These seven steps are detailed in
Sections 2.1 through 2.7. Figure 2.0.1 presents a flowchart of the LTMO steps, along with
the associated Roadmap sections and key considerations for each phase of the evaluation.
1. Clearly Define and Document the Current Monitoring Program.
Define monitoring objectives, parameters/constituents measured, sampling and
analytical methods, frequency and location of sampling, and monitoring program costs.
In addition, ensure that the monitoring program meets both Federal and State regulatory
requirements. This information is used to establish the baseline conditions of the
monitoring evaluation to be completed during the LTMO.
2. Examine Existing Data.
Determine the amount, types and quality of data available to discover data gaps and
decide what types of analyses will be feasible. Ensure that the data are defensible, come
from reputable sources, and meet the purpose for which they were collected.
3. Determine If the Site Is a Candidate for a Detailed LTMO.
Establish whether the site meets minimum threshold criteria for LTMO. The potential
success of implementing LTMO recommendations can be greatly enhanced by
introducing and discussing the idea of optimization with site managers and stakeholders
early in the LTMO process.
4. Determine the Type of Evaluation.
Evaluate whether a stand-alone qualitative evaluation or a qualitative evaluation, with
supporting quantitative temporal and/or spatial statistical analysis, is appropriate for the
site.
5. Select the LTMO Methods/Tools.
Assess and select the LTMO methods and tools available to optimize the monitoring
program.
6. Perform the Optimization.
Apply the selected tools and methods to develop recommendations for the monitoring
program's optimal well distribution and sampling frequency.
7. Assess and Implement the Results.
Check the reasonableness of the LTMO results, confirm stakeholder buy-in, and
implement the recommendations.
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Figure 2.0.1 LTMO Steps Flowchart, Associated Key Points, and Corresponding Roadmap
Sections
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Can rsco^rritnded changes be made without
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Need 44 fsr
-Need 6-15 samping w
Sect«n 2,4
QUW-iTATiVE-* cppiow professional judgment to
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QUANTITATIVE-* Empbj'S satisfies and
2.7
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
2.1 CLEARLY Define and Document Current Monitoring Program
It is necessary that the primary elements of the existing monitoring program be clearly
defined and documented prior to determining if a LTMO evaluation of the program is
appropriate, or before conducting a LTMO effort. In some instances, the components of
the current LTM program may not have been clearly defined or documented (e.g., in a
formal monitoring plan). If the current plan is not well defined, a LTMO evaluation may
facilitate the development of an appropriate plan. This section highlights the monitoring
program components to be defined to establish the program baseline conditions,
including objectives, monitoring constituents, sampling location and frequencies, and
level of effort. These aspects are investigated and evaluated as a preliminary effort to
assess the potential value of a LTMO effort and to support the LTMO effort if it is
deemed appropriate. Furthermore, the relationship between the monitoring program and
the current conceptual site model (CSM) should be identified. The CSM may be
described in site sampling plans, risk assessments, or other documents.
2.1.1 Components of a Monitoring Program
The U.S. EPA (2004a) 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, consistent with the CSM,
3. Formulate monitoring decision rules,
4. Design the monitoring plan:
Identify the volume and characteristics of the earth material targeted for
sampling,
Select the target parameters and analytes, including field parameters/analytes
and laboratory analytes,
Define the spatial and temporal sampling strategy, including the number of
wells necessary to be sampled to meet program objectives, sampling
methods, and the schedule for repetitive sampling of selected wells, and
Select the wells to be sampled.
5. Conduct monitoring, evaluate and characterize the results, and
6. Establish the management decision.
In this paradigm, a long-term 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. The conceptual site model is a mental construct (though sometimes documented in
a chart or schematic) of the means by which contaminants were introduced into the
environment and the fate and movements of the contaminants in liquid, dissolved, vapor,
or solid phases from the release to points at which the contaminants are extracted for
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treatment or at which people or ecological receptors can be exposed to them. 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 (e.g., assess whether a selected response action is/is not achieving
its objectives). The following sections discuss aspects of the monitoring program that
need to be researched prior to considering LTMO.
2.1.2 Document/Refine LTM Program Objectives
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). Therefore, 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. 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.
An effective LTM 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. Thus, the two primary
objectives of LTM programs can be expressed as follows:
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).
The design and optimization of a monitoring program therefore considers existing
receptor exposure pathways, as well as exposure pathways arising from potential future
use of the groundwater. These general objectives are often expressed formally in a
decision document (e.g., Record of Decision [ROD] or monitoring plan) as a series of
site-specific objectives, tailored to a particular LTM program. The LTM objectives
should have been discussed with and endorsed by the project stakeholders early in the
development of the LTM program. If the objectives of the LTM program have not been
clearly articulated, they should be developed and accepted by all stakeholders prior to the
initiation of an LTM optimization effort. It also may be necessary to clearly articulate or
refine the monitoring decision(s) that the monitoring program is intended to support (e.g.,
"The response action is/is not achieving the objectives established for the response
action"), and the decision rules used to evaluate the results of monitoring, as they apply
to the decision (e.g., "decreases in contaminant concentrations near the source area of
50% or more are an indication that the response action is achieving its objectives").
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2.1.3 Identify Parameters/Constituents To Be Monitored and Methods for
Measuring Them
Target parameters and analytes typically will include those constituents that are known or
suspected to be contaminants of concern (COCs) at a particular site. COCs usually are
identified in a decision document (e.g., ROD) for that 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 ofin-situ degradation of organic chemicals) or the performance of a selected
remedy (Makeig, 1991). The process of determining COCs is important early in the
optimization process. Data analyses that can help identify pertinent COCs include
quantifying statistics such as frequency of detects, frequency of detects exceeding
environmental criteria, and frequency of detects across distinct sampling locations (i.e.
how many wells are showing detects and over how much space). Emphasis should be
given to constituents that are also more toxic and mobile.
Usually, several different sampling and/or analytical methods for detecting or measuring
a particular parameter or constituent are available. The method that is selected for an
LTM program is that method which provides sufficient precision and accuracy to satisfy
program data quality objectives (DQOs) and also the stated objectives of the monitoring
program, at the lowest cost or with the least level of effort. A LTMO will be dependent
on the suitability and comparability of the historical data collected. The LTMO itself may
assess the issues of optimal sampling and analytical methods further and develop
recommendations for changes (see section 2.6.2).
2.1.4 Document Sampling Locations and Frequency of Monitoring
Designing an effective long-term 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 information obtained to effectively address
the temporal and spatial objectives of monitoring, while minimizing incremental costs.
Hydrogeologic units are part of the basic framework of a CSM; thus, the volume of earth
material targeted for groundwater monitoring should be defined in terms of
hydrogeologic units. The number of wells sampled, and the locations selected for
sampling, depend primarily on the known or anticipated spatial variability in groundwater
conditions and quality (which, in turn, depends to a large degree on the differences
among hydrogeologic units), because if spatial variability is great, it is a good idea for a
larger number of wells to be sampled to assess that variability (Franke, 1997). Criteria
used to identify wells that are suitable for inclusion in an LTM program are program-
specific, and are related primarily to the locations in three dimensions (with respect to
contaminant sources and potential receptor locations) of individual wells, and the
purposes for which a well was installed (e.g., a well installed strictly as a monitoring
point may be suited for purposes for which a groundwater extraction well is not suitable)
(Franke, 1997).
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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 will be redundant
(Zhou, 1996). Therefore, prior to initiating a LTMO evaluation, it is recommended the
frequency of sample collection at each monitoring point be documented, along with the
rationale for sampling at each location.
2.2 EXAMINE EXISTING DATA
While the monitoring program objectives establish the baseline and "big picture"
considerations for a LTMO, the data availability and format determine the feasible
type(s) and level of detail of the evaluation. Successful application of any LTMO
approach to the site-specific evaluation of a monitoring program is directly dependent
upon the amount and quality of the available data. For example, a site with insufficient
data collection or poor quality data could potentially benefit from a qualitative LTMO
that recommends an improved sampling plan; however, a more sophisticated qualitative
and quantitative analysis requires a certain amount of historical data. For any 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. For instance, many LTMO tools are "data driven" and the effort to
develop and cleanup the datasets prior to the tool application can often be more than 75%
of the overall effort to accomplish the optimization.
Table 2.2.1 presents priority and useful information, potential data sources and the
associated purpose of the data required to conduct a LTMO. The first step in the analysis
would be to get a general feel for the types and formats of data available in order to
determine if a LTMO analysis is possible (Section 2.3). If an LTMO is deemed
appropriate, then more rigorous data gathering and processing can take place. Ideally, the
available site and monitoring data listed in Table 2.2.1 can be used to revisit the
comprehensive CSM over time, which should include extent and nature of the plume as
well as the hydrogeologic conditions. The data collected must be evaluated as it is
gathered to make site decisions in a timely fashion. This would include periodic
qualitative review of the LTM program. In addition, other available information can be
used to characterize important institutional considerations. It is important to involve site
personnel, site managers, and stakeholders in the LTMO process, as they can provide
essential information about regulatory issues, political issues, and other qualitative
information that drives monitoring priorities but might not be available in other
information sources.
Along with acquiring and processing the data, it is important to evaluate the data in terms
of quality and comparability. The defensibility and usability of the data should be
verified. This is important when the data are obtained from multiple sources, and is
especially true when preparing to assess temporal trends. For example, detection limits of
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sampling results can be examined to see if they change over time and/or if they are
adequately low to enable effective decision making.
Table 2.2.1 Data Requirements Checklist
Data Needed
Potential Data Source(s)
Purpose
Priority Information
Current monitoring
program description
- Monitoring program plan
- Recent monitoring report
Establish baseline conditions, purpose
of monitoring program, rationale for
monitoring wells, and sampling and
analytical methods
Well locations and
coordinates
Analytical data and
COC sampling results
Potentiometric surface
configuration -
groundwater flow
direction, velocity, and
gradient
Hydrogeologic conditions
Well completion intervals
and hydrogeologic zone
Cleanup goals and
regulatory limits
Potential receptor and
compliance point
locations
- Database
- Well construction information -
- Site maps
- Database
- Monitoring reports
- Site investigation reports
- Recent monitoring report
- Document providing facility
and site information (e.g.,
CSM, remedial investigation
[RI] or RCRA facility
investigation [RFI] report, or
similar)
- Database
- Document providing facility
and site information (e.g.,
RI/RFI or similar document)
- CSM
- Hydrogeologic testing results
- Database
- Well construction diagrams
- Drilling logs
- ROD
- Decision document
- RI/RFI
- RI/RFI
- ROD
- Site map
- Site visit
Determine spatial distribution of
monitoring points
Define concentrations of COCs in
space and time,
Confirm primary COCs,
Verify data quality
Evaluate direction and rate of
groundwater movement and
contaminant migration
Identify geologic or other controls on
occurrence and movement of
groundwater and dissolved COCs
Determine depth of sample collection
in groundwater system and potential
hydrogeologic and stratigraphic zones
Establish cleanup limits and areas of
concern requiring monitoring
Identify areas and/or migration
directions of concern, e.g., nearby
public supply wells
Useful Information
Logistical/policy
considerations
- Site personnel
- Stakeholders
Identify regulatory /public priorities
and potential for program
implementation
Site features (roads,
building, rivers, properly
boundaries)
Site mapAutoCAD drawings
or GIS layers (in real
coordinates, if possible)
Site visit
Create spatial context for monitoring
program,
Develop base map of site for LTMO
reporting
Water levels through time -
Database
Historical monitoring reports
Identify dry wells,
Evaluate seasonal effects
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Data Needed
Geochemical data
Wells with NAPL present
Current program costs,
including analytical, field
mobilization, sample
collection, data
management and
reporting, and waste
management
Potential Data Source(s)
- Database
- Database, historical monitoring
reports
- Laboratory invoices
- Project budget, schedule, and
labor projections for monitoring
projects
- Site personnel; professional
judgment
Purpose
Identify natural attenuation parameters
Identify data values that potentially
should be excluded from the analysis
Establish a baseline and quantify
potential cost changes based on
optimization results
2.3 DETERMINE IF SITE is A CANDIDATE FOR A DETAILED LTMO EVALUATION
The decisions regarding whether to conduct a LTMO evaluation, which approach to
apply, and the degree of regulatory-agency involvement in the LTMO evaluation and
implementation of optimization recommendations are made on a site-specific basis.
Factors to be considered in deciding whether to proceed with a 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
recommendations,
degree of difficulty in implementing optimization
The potential benefits (e.g., projected savings in cost or level of effort) that could
result from an optimized monitoring program, and
Perceived problems with the current LTMO program on the part of the project
team or stakeholders.
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 20 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, LTM program optimization also should be considered periodically,
rather than being regarded as a one-time event. The periodic assessment of remedy
effectiveness, such as a CERCLA five-year review, offers a good framework in which to
perform LTMO.
In general, overall site conditions should be relatively stable before LTMO is conducted,
and no major changes in remediation approaches should be occurring or anticipated in the
next year or two. LTMO should be part of any system-wide optimization effort for sites
at which response-action decisions are being validated or refined (e.g., during periodic
remedy-performance reviews). The implementation of recommendations from
optimization of the LTM program should be considered in light of the other
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recommended adjustments to the remediation. If there are major changes in the
subsurface aspects of the remediation, the LTMO recommendations may be best
implemented after the other remedial measures have been implemented and evaluated.
Successful application of any LTMO approach to the site-specific evaluation of a
monitoring program is directly dependent upon the amount and quality of the available
data. Minimum data requirements include:
Results from four to six separate sampling events (to support a temporal
analysis),
Results collected at six to 15 separate monitoring points (to support a spatial
analysis)., and
An adequate CSM, describing site-specific conditions (e.g., direction and rate of
groundwater movement, locations of contaminant sources and potential receptor
exposure points - Section 2.1.1).
If a CSM does not exist, the team conducting the LTMO should develop one based on the
available site characterization data. A potential recommendation of the LTMO may be to
gather the necessary additional data to refine the CSM. In addition, it is extremely
beneficial to delineate the extent of contaminants in the subsurface at the site before the
monitoring program can be optimized, though the process of LTMO may help identify
data gaps and the need for additional monitoring. The certainty in results of a temporal or
spatial analysis increases with an increased number of sampling events or monitoring
points, respectively. The minimum numbers of events or points mentioned above must be
considered in light of the spacing in time or space and the time period and area over
which the data were collected. The data should not be highly clustered in time or space
and should span the scope of the problem (e.g. over years or the footprint of the
groundwater plume).
2.4 DETERMINE THE TYPE OF EVALUATION
2.4.1 General Considerations in LTMO
Historically, most monitoring programs have been designed and evaluated based on
qualitative insight into the characteristics of the hydrologic system using professional
judgment (Zhou, 1996). Groundwater systems by nature are variable in space and through
time, and it may be difficult to account for much of the existing variability using
quantitative techniques (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 (e.g., changes in groundwater levels and the resulting rates or directions of
groundwater movement) and anthropogenic changes (e.g., changes in nearby pumping,
introduction and movement of contaminants) (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 2.1.2), a LTMO evaluation should address the temporal
and spatial characteristics of groundwater-quality data. Temporal and spatial data may be
most rigorously evaluated using temporal and spatial-statistical techniques, respectively.
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However, there may be other considerations that are best addressed through qualitative
evaluation. Therefore, an LTM program can be evaluated and optimized using
qualitative or quantitative approaches. Because it is possible to consider numerous factors
simultaneously in a qualitative evaluation, this usually is considered to be the primary
approach for evaluating an LTM program, with the results of temporal or spatial-
statistical evaluations used to support the results of a qualitative evaluation. Even if the
focus of the LTMO is a rigorous quantitative analysis, a qualitative review of the results
is recommended to assess the impacts of site hydrogeologic characteristics and
stakeholder considerations.
If attenuation or removal of contaminant mass is occurring in the subsurface as a
consequence of natural processes or operation of an engineered remedy, attenuation or
mass removal will be apparent as a:
Decrease in contaminant concentrations through time at a particular sampling
location,
Decrease in contaminant concentrations with increasing distance from chemical
source areas, and/or
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.
2.4.2 Considerations in Qualitative Evaluation
In a qualitative evaluation, the numbers and locations of wells and frequency of sample
collection are examined in the context of site-specific conditions. This is done to ensure
that the program is capable of generating information regarding contaminant migration
and changes in chemical concentrations through time and to ensure that the objectives of
the monitoring program (Section 2.1) are satisfied. Additional considerations such as the
list of analytes, sampling method(s), analytical methods, and mechanisms used for data
management and reporting can also be assessed during the qualitative evaluation (Section
2.6.2). The relative performance of the monitoring program is assessed from calculations
and judgments made without the use of quantitative methods (Hudak et a/., 1993).
Sampling locations are evaluated by considering contaminant behavior and
hydrogeologic and other conditions within, and at locations distal from the source(s) of
contaminants (e.g., Schock etal., 1989). The ultimate configuration of the monitoring
program, including the location of wells and frequency of monitoring, is subject to the
investigator's understanding of:
The properties and behavior of the groundwater system,
The ways in which these properties influence the movement and fate of
contaminants, and the resultant contaminant distributions, and
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What constitutes an "optimal" monitoring program, given the monitoring
objectives, probable contaminant migration pathways, receptor exposure points,
and travel times.
These factors will influence the locations and spacing of monitoring points, and the
sampling frequency. All monitoring points that are sampled periodically in conjunction
with the LTM program under consideration should be included in a qualitative
evaluation. Multiple factors can be considered in developing recommendations for
continued monitoring, additional monitoring, or cessation of monitoring at each
monitoring point or well. In some cases, a recommendation may be made to continue
monitoring at a particular well, but at a less frequent interval than at present. Typical
factors considered in developing recommendations to retain, add, or remove a well from
the monitoring program are summarized in Table 2.4.1; typical factors considered in
developing recommendations for monitoring frequency are summarized in Table 2.4.2.
These tables are meant to assist in understanding the nature of qualitative analysis so a
decision can be made regarding the appropriateness of this approach to a site. These
tables can also be of use in guiding the actual performance of qualitative analysis.
Table 2.4.1 Qualitative Monitoring Network Optimization Decision Logic
Reasons for Retaining or Adding a Well
in a 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 a
compliance point or receptor exposure point (e.g.,
sentinel well for municipal wells).
Well is important for defining background water
quality.
Reasons for Removing a Well
From a 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 two years, and there
is no expectation for the water levels to recover in the
foreseeable future.
Contaminant concentrations are consistently below
laboratory detection limits or cleanup goals.
Table 2.4.2 Qualitative Monitoring Frequency Decision Logic
Reasonsfor
Increasing Sampling Frequency
Groundwater velocity is high.
Change in concentration would significantly alter a
decision or course of action.
Well is close to source area or operating remedy.
Whether concentrations will change significantly
over time cannot be predicted, or there is no ready
explanation for recent irregular or contradictory
data.
Reasonsfor
Decreasing Sampling Frequency
Groundwater velocity is low.
Change in concentration would not significantly alter
a decision or course of action.
Well is far from source area or operating remedy.
Concentrations are not expected to change
significantly over time, or contaminant levels have
been below cleanup objectives for some period of
time.
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A qualitative evaluation is complete when recommendations regarding retention in, or
removal from, the program and the frequency of sample collection have been generated
for every sampling location (well) in the monitoring program, and other broader program
considerations (Section 2.6.2) have been assessed and documented. Qualitative
approaches to the evaluation of a monitoring program range from relatively simple to
complex, but often are subjective. Furthermore, the degree to which the LTM program
satisfies the spatial and temporal objectives of the program may not be easily evaluated
by qualitative methods.
2.4.3 Considerations for Quantitative Analysis of Temporal Trends
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.
The temporal objective of LTM (evaluate contaminant concentrations in groundwater
through time; Section 2.1.2) 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). 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, the Mann-Kendall test (Gibbons, 1994), and Sen's (1968) non-
parametric test for the slope of a trend, are widely applied (Hirsch et al., 1991). 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 average concentrations. Other quantitative methods
use a different means for temporal analysis. They may recommend a sample frequency
based on an analysis of the portion of historical concentration trends that can be
reconstructed when sampling events are iteratively removed from the monitoring system.
The minimum frequency of past sampling events that can indicate the same general
concentration trend as has been observed is used as a basis for the recommended future
sample frequency.
Decisions regarding sampling frequency are made using a simulation approach or a rule-
based approach. In a simulation approach, a computer model is used to simulate the
movement of contaminants in the environment, and the optimal frequency of sampling at
a particular monitoring point is estimated based on the rate of change in contaminant
concentrations calculated by the model. In a rule-based approach, a decision rule is
established and is used together with the results of the trend, periodicity, or average-
concentration evaluations to select the sampling frequency at a particular monitoring
location. For example, a decision rule may state that if a trend of increasing contaminant
concentrations in groundwater is identified at a monitoring point near a potential receptor
exposure point, an increase in the frequency of sampling at that location is warranted.
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2.4.4 Considerations for the Quantitative Spatial Analysis of Monitoring Networks
Spatial techniques that can be applied to the design and evaluation of monitoring
programs fall into several categories - simulation, geostatistical, and analytical
approaches (Minsker, 2003). Simulation approaches fit historical data to computer
models and simulate the evolution of contaminant plumes. Geostatistical and analytical
methods use only historical data to interpolate contaminant concentrations throughout the
region of interest and, in some cases, in multiple periods of time. Geostatistical methods
typically use kriging, while analytical methods apply other interpolation methods, such as
Delaunay triangulation.
Once a plume model is created using one of these approaches, it can then be incorporated
into a numerical optimization algorithm that uses formal mathematical techniques to
derive an optimal monitoring network configuration (e.g., Reed et al., 2000).
Alternatively, ranking methods can be used to select monitoring configurations using
"rules-of-thumb" rather than formal optimization. Monitoring points that are identified as
contributing relatively little information to the program, based on a spatial evaluation, are
candidates for removal from the program.
More significantly, if areas having significant uncertainty in contaminant concentrations
are identified in the spatial evaluation, it may be necessary to install and sample
additional monitoring points in these areas. This is especially true where the uncertainty
exists near the limits of the plume and near potential receptors (American Society of Civil
Engineering [ASCE], 1990a and 1990b).
2.4.5 Other Considerations
A site manager desiring to optimize a LTM program may not possess the technical
capabilities necessary to complete a qualitative, temporal, or spatial evaluation. In this
circumstance, it may be necessary to seek outside expertise (e.g., a contracted or in-house
specialist) to provide the necessary capabilities. Although this may necessitate additional
expenditures, in addition to completing the required technical evaluation(s), an individual
or firm not otherwise directly involved in the LTM program can also provide an
independent and possibly fresh and more objective review of aspects of the LTM
program, or of the overall environmental program. This can be a distinct advantage if
disincentives exist for an incumbent contractor responsible for the LTM program to
optimize or otherwise change aspects of the program.
The concepts discussed are geared toward the subsurface monitoring networks, but could
also apply to the collection of data from points within above-ground treatment systems.
In this case, the frequency of sampling and locations of sampling within the treatment
"train" can be modified to more efficiently meet the needs of sampling.
2.5 SELECT THE LTMO METHOD(S)/TOOL(S)
There is no definitively "right" way to conduct a LTMO; multiple guidance documents,
tools, and standardized methods and approaches which utilize qualitative, temporal,
and/or spatial-statistical methods have been applied successfully to a range of sites.
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Application of any approach to an existing LTM program can be used to generate
recommendations for changes in sampling frequency, and in the numbers and locations of
monitoring points that are sampled. Whatever approach is used should be applied within
the framework of a clearly articulated decision structure that has been accepted by all
stakeholders. 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. The most significant
advantage conferred by any optimization approach is the fact that they are used to apply
consistent, well-documented procedures, which incorporate formal decision tools, to the
process of evaluating and optimizing monitoring programs.
2.5.1 LTMO Guidance Documents
The primary available LTMO guidance documents are discussed briefly below.
Additional information about these guidance documents, additional guidance references,
and full web page addresses are presented in the Appendix.
. NavalJiacijM
Groimdwater Monitoring, January 2000
This guide includes information on how to both design new monitoring programs
and optimize existing programs. It covers a broad range of issues including physical
program optimization (e.g., frequency and location), analytical & field protocols and
data management and reporting, and includes a summary of several optimization
case studies.
. Air Force Center for Environmental Excellence Long-Term Monitor ing
OjjtimizatiorjGuicje, Version_L]_, October, 1997
This guide includes information on developing a LTM work plan, collecting data
and documenting the existing LTM program, optimization strategies, and evaluation
of cost savings.
2.5.2 LTMO Tools & Standardized Approaches
The specific optimization approach selected for a given site depends on several factors,
including the amount and type of existing data, available resources, and size of the
monitoring program. A significant number of monitoring programs have been optimized
using various LTMO methods at a range of sites. Current academic research tends to
consider primarily numerical simulation and formal optimization approaches, while
"readily available" tools and approaches tend to focus more on rule-based algorithms and
professional judgment. The ASCE monograph "Long-Term Groundwater Monitoring,
The State of the Art" (2003) details a range of methods and presents a substantial list of
case studies and results. Table 2.5.1 highlights the methodology and data requirements
for readily available standardized approaches and tools that have been applied to multiple
sites. Table 2.5.2 lists implementation details for these approaches, including cost,
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required resources and availability. Additional references and contact information for
these tools are available in the Appendix.
2.5.3 Current Research in LTMO
All of the LTMO approaches discussed in the preceding subsections are evolving, as they
are continuously being refined as experience is gained during application to real-world
sites and LTM programs. Several of the approaches are labor intensive; the most
significant future enhancement to these approaches will be development of procedures to
improve the automation of parts of the evaluation. Some approaches utilize only a single
contaminant (an "indicator" contaminant that has relatively high toxicity and/or mobility)
or a subset of the actual COCs in the evaluation; these approaches may be expanded to
address multiple contaminants at several time periods. Approaches that rely on numerical
optimization may benefit from development or refinement of the optimization algorithms
that are used in the computer program. Finally, all of the LTMO approaches incorporate a
framework for making decisions regarding the number and locations of monitoring
points, and the frequency of sample collection, on the basis of qualitative, temporal,
and/or spatial evaluations. These decision frameworks are being improved on the basis of
experience, and could be improved further by applying insight from formal logic, systems
engineering, and information theory. Developments in LTMO approaches will be tracked
on the FedendJjgmedjjttiQtLTedin^^ and on the UJS,
EPA's Clean Up Information CLU-IN Remediation Optimization website (see Appendix
for references and additional information).
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Table 2.5.1 LTMO Tools and Approaches Methodology and Data Requirements
LTMO
Tool/Approach
Cost Effective
Sampling (CES)
Geostatistical
Temporal/Spatial
Optimization
Algorithm (GTS)
Monitoring and
Remediation
Optimization
System (MAROS)
Parsons 3 -Tiered
LTMO
CES is a methodology for reviewing and assessing the
lowest-frequency sampling schedule for a given
groundwater monitoring location.
GTS is a spatial and temporal algorithm developed by
AFCEE that utilizes geostatistical methods to optimize
sampling frequency and to define the network of
essential sampling locations. The GTS algorithm
incorporates a decision pathway analysis that
incorporates both spatial and temporal components and
is used to identify spatial and temporal redundancies in
existing monitoring networks.
The MAROS public domain software was developed in
accordance with the AFCEE Long-Term Monitoring
Optimization guide. MAROS is a decision support tool
based on statistical methods applied to site-specific data
that accounts for relevant current and historical site data
as well as hydrogeologic factors. The software
recommends optimal future sampling frequency,
location and density, as well as providing information
on the plume state over time.
The 3-Tiered LTMO consists of a qualitative
evaluation, an evaluation of temporal trends in
contaminant concentrations, and a statistical spatial
analysis. The results of the three evaluations are
combined to assess the degree to which the monitoring
network addresses the primary objectives of monitoring.
A decision algorithm is applied to assess the optimal
frequency of monitoring and the spatial distribution of
the components of the monitoring network, and to
develop recommendations for monitoring program
optimization.
Frequency Optimization
Methodology
Rule-based decision algorithm based
on trend, variability, and magnitude
statistics recommends optimal
frequency at each well.
1) Iterative thinning approach
reconstructs baseline trends with fewer
samples to determine optimal
frequency on a well-by-well basis.
2) Temporal variogram is applied to
determine composite autocorrelation
and optimal site-wide frequency.
Modified cost-effective sampling
method (rule-based decision algorithm
based on trend, variability, and
magnitude statistics) recommends
optimal frequency for each well.
Qualitative evaluation, temporal
statistical evaluation (Mann-Kendall),
and spatial statistical evaluation are
combined to identify wells for
exclusion or retention and make final
sampling frequency recommendations.
Spatial Distribution
Methodology
Not included
Weighting scheme utilizing
locally weighted quadratic
regression examines
multiple "time slices" to
identify redundant wells
based on cost-accuracy
trade-off curves.
Weighting scheme utilizing
Delaunay triangulation
identifies redundant wells.
Can evaluate multiple
chemicals at one time.
Qualitative evaluation, a
weighting scheme using
kriging, and temporal
evaluation are combined to
identify the relative spatial
value of each well And
make final network
distribution
recommendations.
Data
Requirements
- At least 6
quarterly
monitoring
results per well
- Clean down-
gradient "guard
wells"
- More than 8
events per well
(temporal)
- Greater than 30
wells (spatial)
(tern orar)6
- Greater than 6
(spatial)
- More than 4
events per well
(temporal)
- Greater than 10
wells per zone
(spatial)
Appropriate Site
Size
Unlimited (well-by
well analysis) within
same operable unit
30 to thousands of
wells
40 to 80 wells
recommended (per
aquifer zone)
10 to 100s of wells
(per aquifer zone)
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LTMO
Tool/Approach
Adaptive
Environmental
Monitoring
System (AEMS)
Overview
AEMS performs sample redundancy analyses and
enables smart online data assessment and adaptive
monitoring of environmental systems. The sample
redundancy analyses use multi-objective optimization to
remove spatial, temporal, or simultaneous spatial and
temporal redundancies, including an option to explicitly
account for uncertainty in the historical data. A suite of
spatial and/or temporal models can be built from
historical data and used within the redundancy analyses
to find the optimal set of samples that meet user-
specified performance objectives. The models can also
be used to automatically assess new data in online
systems, sending alerts when data indicate significant
deviations from recent spatial and/or temporal trends.
The adaptive optimization system can also recommend
optimal locations and times for additional sampling to
respond to any observed anomalies.
Frequency Optimization
Methodology
Genetic algorithms are used to search
for optimal designs given a temporal
interpolation model or multiple spatial
interpolation models built from
historical data.
Spatial Distribution
Methodology
Genetic algorithms are used
to search for optimal
designs given a spatial or
spatiotemporal
interpolation model built
from historical data using
geostatistical, statistical, or
analytical approaches.
AEMS is currently the only
optimization software to
perform simultaneous
spatial and temporal
optimization, as well as
allowing optimal tradeoffs
to be identified among
user-specified performance
objectives and allowing
explicit consideration of
uncertainty.
Data
Requirements
- More than 8
events per well
(temporal)
- More than 1 5
events per well
(spatial)
- More than 30
events per well
(combined spatial
and temporal
optimization)
Appropriate Site
Size
Unlimited
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Table 2.5.2 LTMO Tools and Approaches Implementation Details
Tool/Approach
Cost Effective Sampling (CES)
Geostatistical Temporal/Spatial
Optimization Algorithm (GTS)
Monitoring and Remediation
Optimization System (MAROS)
Parsons 3 -Tiered LTMO
Adaptive Environmental Monitoring
System (AEMS)
# Sites
Applied
-10
-10
Unknown*
-20
3
Typical Cost to Apply
to Site
$25-$50k
$25k for average site
$5-$15k for average site
$5-$15k for average site
$5-$15k for average site
Availability
- Consulting method
- Follow methodology presented in references
- Currently consulting method
- Scaled-down software available in Summer
2005
- Free software available for download
- Consulting method
- Follow methodology presented in references
- Currently consulting method
- Prototype software available for testing in
Spring 2005.
Required Resources and Expertise
- High level of skill (initial setup)
- Minimal skill (subsequent runs)
- Experienced statistician (current form)
- Mid-level analyst (software)
- Mid-level analyst
- IBM-compatible PC with MS Access 2000, Excel
2000
- Experienced geologist (qualitative analyst)
- Mid-level analyst familiar with statistics and GIS
(quantitative)
- GIS software (e.g., Arc View 3.2 or higher)
- Mid-level analyst
* Because MAROS is freeware that can be used for multiple applications, it is unknown how many times it has been applied in LTMO applications.
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2.6 PERFORM THE OPTIMIZATION
2.6.1 LTMO Preparation and Implementation
Optimization of an LTM program should be initiated only after:
The site manager has evaluated the LTM program to determine whether the
program is an appropriate candidate for an LTM optimization effort (Section 2.3),
The level of detail proposed for the evaluation (qualitative, temporal, spatial, or a
combination of the three) has been selected (Section 2.4),
An appropriate tool (MAROS, three-tiered approach, GTS algorithm, or other)
and/or guidance document has been identified (Section 2.5),
The available data have been compiled and examined to ensure that the minimum
data requirements have been met for the level of detail of the evaluation and the
selected tool (Sections 2.2 and 2.4), and
All stakeholders have agreed to the objectives developed for the monitoring
program, the data to be used in the LTM optimization, and the approach to be
followed (including the decision structure used to evaluate monitoring frequency,
sampling locations, and other considerations).
After all of these requirements have been satisfied, the selected approach is applied to the
LTM program. An annotated list of applicable policies and guidance documents,
additional information on LTMO approaches, and links to web pages with more
information and case study applications are included in the Appendix.
2.6.2 Optimization of Other Aspects of the LTM Program
Though the focus of this document is the optimization of the frequency of sampling and
the monitoring network, other aspects of an LTM program also can be evaluated for
potential improvements. These include the list of analytes, sampling method(s), analytical
methods, and mechanisms used for data management and reporting. Typically, LTM
programs are initiated only after site characterization has been completed (Reed etal.,
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 Method
SW8260B (agas-chromatographic/mass-spectrometric [GC/MS] method). If the analytes
to be determined in samples are known (e.g., after an LTM program has been initiated),
Method SW8260B can be replaced by Method SW8021B (a GC method), with
potentially significant cost savings realized on a unit-cost basis. One should be cautious
that all potentially toxic daughter products of the COCs are included in the analytical
suite.
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Additionally, groundwater sampling methods have evolved over the past 20 years;
relatively new (http://www.clu-in.org/charl_tech.cfm#new_tech)
including diffusion (http://www.diffusionsampler.org/) and
(htt^//www.frtr.gov/sitg/) may
offer lower costs and, in many cases, more representative data than methods that
historically have been widely applied. Analytical methods also have been improved
through time, often resulting in associated improvements in analytical detection limits;
therefore, updated or alternative methods should be considered. In particular, the rigorous
fixed-laboratory methods used for site characterization may no longer be necessary for
certain current uses of the data (e.g., treatment plant operational decisions) and lower cost
methods may suffice. Analytical methods should be selected to detect and measure only
those contaminants known to be present at the site, or other constituents/parameters
necessary to assess remedy performance. The quality objectives process developed
by the U.S. EPA (1994) (http://www.epa.gov/quality/ qa_docs.html), and the USACE's
technical project planning process both offer excellent frameworks within which to
reassess these aspects of LTM.
Data generated by analytical laboratories should be transferred and managed
electronically to avoid errors and reduce labor costs in both documentation and analysis
of the results. Geographic information systems (GIS) can be highly effective tools for
managing LTM program data and making those data readily available for interpretation
and presentation. LTMO efforts also should assess these aspects of the program,
particularly at large sites. Such data-management tools may also be evaluated considering
their ability to export the data for use by any LTMO software.
2.7 ASSESS AND IMPLEMENT RESULTS
2.7.1 Examine Results of LTMO Evaluation
The final product of a LTMO evaluation of an existing monitoring program comprises a
series of program refinements, potentially including
. A refined CSM,
Refinements or clarification of program objectives,
Changes in the number and locations of monitoring points,
Increases or reductions in the frequency of sampling at each monitoring point in
the program,
Changes in sampling and/or analytical methods, and
Changes in methods of data handling, management, and reporting.
At the conclusion of the LTMO evaluation, it is beneficial for the refined LTM program
to be examined critically by the project team. This would help ensure that the refined
program is capable of generating sufficient information, at appropriate locations and
frequencies so that the objectives of the program continue to be addressed adequately. In
essence, this "reality check" involves completing a qualitative evaluation of the refined
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LTM program, and comparing the results that the refined program is projected to
generate with the requirements of the refined CSM and the program objectives. Often,
performing an LTMO and assessing and implementing the result can be an iterative
process, in which the initial results identify issues that are addressed in a revised analysis.
If both qualitative and quantitative analyses were performed, the results should be
compared and differences in the recommendations resolved by the project team. The
project team should discuss the disposition of monitoring wells that may be eliminated
from the program. The unneeded wells may prove useful in the future as conditions
evolve and should be secured but not decommissioned. In other cases, particularly where
the plume has shrunk significantly, the wells are very unlikely to contribute in the future
and should be decommissioned in accordance with all state and local requirements. This
requires planning, funding, and coordination.
2.7.2 Implementation Steps
The recommendations generated during a LTMO evaluation should be implemented in a
defensible manner consistent with good project planning, including developing
appropriate documentation for the proposed changes (with approval from stakeholders, as
necessary). Actions that typically would be completed during implementation of LTMO
recommendations are presented in the following checklist:
Continue coordination with stakeholders (as discussed above),
Obtain necessary changes to permits or other decision documents, if required (see
below),
Modify, as necessary, elements of the current sampling and analysis plan (SAP),
including the field sampling plan and quality assurance plan (e.g., modify
monitoring plan, quality assurance project plan [QAPP], decision documents,
O&M contracts, etc.),
Procure necessary equipment, laboratory services, etc.; install additional wells (if
required); or modify existing contracts for sampling and analytical services (as
necessary),
Train field personnel in the revised procedures, and
Assess field experiences or sampling results for potential unexpected
consequences that might be related to implementation of the LTMO
recommendations, and modify the process as needed. This may require additional
coordination with the appropriate regulatory agency or other stakeholders.
2.7.3 Cost to Implement Recommendations
Revision of existing monitoring program documents can cost thousands of dollars,
though the costs of document revision generally are lower than initial document
preparation. Changes to a monitoring program, including the process of obtaining
necessary approvals, also may require significant amounts of time and must be
accommodated in the project schedule. Modification of decision documents can be time-
consuming, and typically requires coordination with entities outside of the immediate
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project team. Costs for new sampling equipment or data management software (e.g., GIS)
can be significant, and installation or replacement of monitoring wells can represent large
additional costs. Accordingly, implementation of optimization recommendations must be
undertaken in a manner that balances the benefits of optimization with implementation
costs. If an excessive time (say more than several years) is required for any cost savings
to offset these up-front costs, the changes may not be appropriate, and may need to be
deferred for some time. In situations where expansion of the monitoring program is
necessary to meet program objectives, additional costs may be unavoidable.
Experience demonstrates (e.g., U.S. EPA, 2004b) that implementation of the results of a
LTMO evaluation of an existing program of moderate size (30 to 100 samples collected
and analyzed per annum) can result in reductions ranging from 10 to about 50 percent in
the number of samples collected annually. In addition to monitoring reductions, LTMO
evaluations can potentially identify data deficiencies that lead to recommendations for
additional wells and/or sampling. Typically, a program manager should anticipate
incurring costs ranging from perhaps $2,500 (for a qualitative evaluation of a monitoring
program that includes on the order of 20 to 30 monitoring points) to approximately
$25,000 to complete a detailed LTMO evaluation of a larger program, using a
combination of qualitative, temporal, and spatial approaches. Consequently, a detailed
LTMO evaluation may be cost-prohibitive for smaller monitoring programs, although
successful LTMO evaluations have been performed at sites with around 20 monitoring
events per year. Assuming a payback period of three years, potential cost savings ranging
from approximately $800 to $8,300 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 typically range from about $200 per sample to
about $800 per sample (Air Force Center for Environmental Excellence [AFCEE], 2004),
a LTMO evaluation that is able to reduce the total number of samples collected at a site
by about 5 to 15 samples per annum should be cost-effective. Note that costs
incorporating all other associated activities (e.g., mobilization, data validation and
management, reporting) may result in an average per-sample cost of over $1000.
2.7.4 Benefits of Flexibility in Planning and Decision Documents
The use of flexible decision documents and plans are strongly encouraged and this
facilitates the implementation of LTMO recommendations. Modification of the LTM
program can be facilitated in terms of cost and time if decision documents (e.g., the
ROD) or permits are constructed to be adaptable to periodic changes, and incorporate
flexibility in LTM requirements. This can be addressed by acknowledging the need for
periodic review of the response action and associated LTM program in the decision
documents, or by including performance-based monitoring requirements in a decision
document, together with an evaluation process for assessing the degree to which such
requirements are achieved. The decision documents or a site exit strategy document
should also identify a process by which a decision can be made as to when LTM is no
longer needed. It is very important to have a rationale for cessation of LTM identified
and endorsed early in the remediation process. A general SAP, that presents standard site
information and specifies standard procedures, but which is periodically updated with
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specific addenda or that allows actual sampling locations and frequency to be proposed
and periodically modified via a separate document, is beneficial.
2.7.5 Periodic Re-Evaluation of LTM Programs and Validation of LTMO
Recommendations
Although significant benefits can be realized by completing a one-time LTMO
evaluation, additional benefits may result from periodic evaluation and optimization of an
LTM program. At sites where active remediation is in progress, or where natural
attenuation processes are effectively removing contaminant mass, the concentrations and
spatial distribution of contaminants are likely to change over time, resulting in decreases
in the extent of contaminants in groundwater. At other sites where releases from source
areas are uncontrolled, the concentrations or extent of contaminants may increase. In such
situations, periodic re-evaluation of the LTM program can be beneficial in assessing
whether the program remains capable of meeting monitoring objectives. There also may
be external influences that can cause changes in the distribution or extent of
contaminants, such as climatic changes (e.g., drought) or changes in groundwater
extraction rates at nearby locations. It is a good idea for the effectiveness of the LTM
program to be periodically re-evaluated in light of the changes resulting from such
external influences. Periodic re-evaluation can support public and regulatory confidence
in the response action and its associated LTM program, and also may reduce costs
incrementally over time as site conditions are addressed. The time interval between
periodic LTMO evaluations will vary depending upon site conditions; typically,
programs should be evaluated at least every two to five years. Periodic LTMO could be
integrated with or timed to support the CERCLA Five-Year Review process or RCRA
permit reapplication process. In the course of the periodic iterative review, new
optimization approaches may become available such that a totally different tool may be
used. In order to capitalize on new approaches, the original optimization technique or tool
may be modified, transformed or replaced by a future approach. Note that the data
collected under the LTM program should be assessed as it is collected for making timely
site decisions, including urgent changes to the LTM program.
Once LTMO has been accomplished and an optimized network and sampling frequency
has been implemented, some effort may be needed to validate the optimized approach is
performing the expected way. Validation involves limited future and perhaps random
sampling of wells originally slated for removal from the network to verify that they are
predictable in behavior prior to abandonment of the wells, removal of equipment, etc.
The notion of testing the ability of essential wells to predict concentrations in
neighboring areas where redundant wells exist, is important to addressing the proof of
optimization concept.
2.7.6 Considerations in Reviewing LTMO Analyses
The review of LTMO reports involves consideration of many issues. Although this
document cannot address all possible project conditions that would affect the review of
LTMO results, some consistent guiding principles exist. Primarily, the review weighs the
recommended changes to the LTM program in light of what is known about the
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monitoring objectives, hydrogeology, and decision logic of the optimization method and
tools. The evaluation must have been done realistically considering the monitoring
objectives identified by all stakeholders. The recommended monitoring program (sample
frequency and monitoring point network) must be appropriate considering the three-
dimensional nature of the plume, its likely flow paths, and contaminant transport
velocities. Furthermore, the recommended monitoring program should support sampling
requirements addressing key potential exposure points, such as those points that monitor
for the protection of production wells. The approach and methods used for the evaluation
need to be clearly described and should have been based on sound technical logic
appropriate to the project. The approach must be balanced and allow for additional
monitor points and/or more frequent sampling if data gaps exist in the current program.
Some additional issues to be considered during review of LTMO results include:
The quality and comparability of data used in the analysis,
Confirmation of adequate reasons for optimizing the LTM program,
The qualifications of the person(s) performing the evaluation considering the
complexity of the hydrogeology,
Adequacy of the data for quantitative temporal and spatial evaluation (4-6
sampling rounds, >6-15 separate monitoring points, depending on method)
The availability of the results (output) from any software package in an appendix,
Modifications (with rationale) to the sampling methods, chemical analysis, data
management and reporting aspects of the LTM program, in addition to the sample
locations and frequency,
The potential need for some wells targeted for exclusion from the LTM program
to meet other objectives not considered in the LTMO evaluation
. The efficient logistics of performing the recommended site monitoring (e.g.,
would some sampling rounds consist of only a few wells?),
The disposition of wells that are to be removed from the LTMO program (i.e.,
decommissioning or stand-by status), and
Consistency between the recommended changes to the program and the output of
the evaluation process (any differences between the results and the actual
recommendations should be explained).
The review of LTMO reports inevitably requires some qualitative assessment of the
suitability of the proposed monitoring program as a "reality check" on the
recommendations. Some interaction between the reviewer(s) and the analyst(s) will be
typically be required and will likely be beneficial.
A good LTMO report would address the following topics:
A description of the current site conditions and monitoring program, including a
discussion of the media sampled, the hydrogeology as it affects the LTMO, a map
of all monitoring points and tables of the sampling frequencies and analytes.
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A description of the Conceptual Site Model and the objectives of the monitoring
program as understood by the LTM optimization team.
The results of the evaluation of the usability of the existing monitoring data,
including the identification of comparability issues, outliers, and data
management problems.
A discussion of the optimization approach, including description of decision logic
and any quantitative methods used.
The results of the optimization including an optimized sampling plan with
essential wells ranked in order of importance, optimal sampling frequencies, list
of redundant or needed wells, changes to the analytical program, etc.
A discussion of any issues that affect the potential implementation of the
recommendations, including, for example, the consideration of planned changes
to the remedy or land use, required changes to plans, permits, or decision
documents, the need for abandonment of any redundant wells, etc.
Appendices with specifics on the analysis or output from software tools.
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
3.0 REFERENCES
AFCEE. 2004. Comprehensive Results Report for the Passive Diffusion Sampler
Demonstration. Draft. August.
American Society of Civil Engineering (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(5):633-658.
Bartram, J., andR. Balance. 1996. Water Quality Monitoring. E&FN Spon. London.
Everett, L.G. 1980. Groundwater Monitoring-Guidelines and Methodology for
Developing and Implementing a Groundwater Quality Monitoring Program.
General Electric Company. Schenectady, New York.
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.
Hirsch, R.M., R.B. Alexander, andR. 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., H.A. Loaiciga, andF.A. Schoolmaster. 1993. Application of geographic
information systems to groundwater monitoring network design. Water Resources
Bulletin 29(3):383-390.
Loaiciga, H.A., RJ. 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.
Minsker, B., ed. 2003. Long-Term Groundwater Monitor ing-The State of the Art.
American Society of Civil Engineers. Reston, VA.
Reed, P.M., B.S. Minsker, and AJ. Valocchi. 2000. Cost-effective long-term
groundwater monitoring design using a genetic algorithm and global mass
interpolation. Water Resources Research 36(12):3731-3741.
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
28
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
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.
U.S. EPA. 1994a. Methods for Monitoring Pump-and-Treat Performance. U.S. EPA
Office of Research and Development. EPA/600/R-94/123.
U.S. Environmental Protection Agency (U.S. EPA). 1994b. Guidance for the Data
Quality Objectives Process. U.S. EPA Office of Research and Development. EPA
QA/G-4. EPA/600/R-94/055. September.
U.S. EPA. 2004a. Guidance for Monitoring at Hazardous Waste Sites-Framework for
Monitoring Plan Development and Implementation. U.S. EPA Office of Solid
Waste and Emergency Response. OSWER Directive No. 9355.4-28. January.
U.S. EPA. 2004b. A Demonstration of Two Long-Term Monitoring Optimization
Approaches. U.S. EPA Office of Solid Waste and Emergency Response. EPA 542-
R-04-OOla and -00Ib. July.
Ward, R.C., J.C. Loftis, and G.B. McBride. 1990. Design of Water Quality Monitoring
Systems. Van Nostrand Reinhold. New York, New York.
Wiedemeier, T.H. and P.E. Hass. 1999. Designing Monitoring Programs to Effectively
Evaluate the Performance of Natural Attenuation. AFCEE. August.
Zhou, Y. 1996. Sampling frequency for monitoring the actual state of groundwater
systems. Journal of Hydrology 180(3):301:31
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
APPENDIX
LTMO RESOURCES
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
LTMO POLICY AND GUIDANCE RESOURCES
April 2004.
http://enviro.nfesc.navy.mil/erb/erb_a/regs_and_policy/don-policy-ra-optimiz.pdf
This policy establishes procedures (including the Guide to Optimal Groundwater Monitoring)
for optimizing the screening, evaluation, selection, design, and implementation for long-term
operation and management of response actions conducted under the Navy's Environmental
Restoration program.
Performance Monitoring ofMNA Remedies for VOCs in Ground Water. April 2004.
http://www.epa.gov/ada/download/reports/600R04027/600R04027.pdf
This document is designed to be used during preparation and review and long-term
monitoring plans for sites where MNA has been or may be selected as part of the remedy.
Performance monitoring system design depends on site conditions and site-specific remedial
objectives; this document provides information on technical issues to consider during the
design process. Discussions include details of issues concerning monitoring parameters,
locations, and monitoring frequencies. This document does not provide details of particular
methodologies for sampling, analysis, modeling, or other characterization tools.
June 2001.
http://www.afcee.brooks.af.mil/products/rpo/rpooutreach/rl72/A_Final_RPO_Handbook.pdf
This handbook describes the general regulatory and technical framework for evaluating
existing remediation systems, including a section on monitoring optimization based on the
AFCEE Long-Term Monitoring Optimization Guide.
to Groundwater Monitoring (Interim Final). January 2000.
http://enviro.nfesc.navy.mil/erb/erb_a/support/wrk_grp/raoltm/case_studies/Int_Final_Guide.pdf
This document, prepared for the Naval Facilities Engineering Service Center by Radian
International, is the Navy LTM/RAO Working Group guidance for the optimization of
groundwater monitoring programs. It is based on "lessons learned" from optimization efforts
undertaken by the Navy and Marine Corps, as well as optimization evaluations performed by
the Working Group. The guidance is presented in a question and answer format and includes
example decision criteria, decision flow charts and diagrams, statements of work, statistical
tools and more.
LoJlSiTfJ^^ October, 1999.
http://www.afcee.brooks.af.mil/products/techtrans/PBM/downloads/ltmfiles.exe
This document was prepared to assist Department of Defense (DoD) installation managers in
the optimization of their long-term monitoring (LTM) programs by identifying and applying
the appropriate strategies and optimization tools. These strategies and tools should assure
compliance with data quality objectives (DQOs) and quality assurance (QA) requirements to
improve overall effectiveness while minimizing cost. This file expands to a series of
Microsoft Word 6.0 documents, Microsoft PowerPoint slides and bitmap files.
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__
October 1999.
http://web.em.doe.gov/techguide/
The purpose of this guide is to provide project managers with technical direction on: the role
of monitoring for effective implementation of a natural attenuation remedy; the key
considerations for designing a natural attenuation monitoring network; and statistical
approaches for interpreting monitoring data and refining conceptual site models.
for EPA/600/R-94/123, NTIS Order
Number PB95-125456, 102p. June 1994.
http://cfpub.epa.gov/si/osp_sciencedisplay.cfm?dirEntryID=45536«&ref_site=SI«&kwords=Monitoring
%20Pump%20and%20Treat%20Performance
This publication by EPA's Office of Research and Development provides guidance for
monitoring the effectiveness and efficiency of pump-and-treat remediation systems, with
emphasis on the "pump" part of the technology rather than chemical enhancements. The
report includes sections on monitoring hydraulic containment, monitoring groundwater
restoration, evaluation restoration success/closure, a case study, and references. It includes
performance criteria, monitoring objective, and protocols for evaluating effectiveness of
containment and restoration systems.
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
LTMO APPROACHES & METHODS
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(l):ll-37.
Loaiciga etal. 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:
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;
Surrrogate objectives are used for cost and risk-based criteria; and
The hydrogeologic environment is oversimplified, 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.
EPA-542-R-04-001B. July 2004.
http://clu-in.org/download/char/542-r-04-001b.pdf
This recent report by EPA's Office of Solid Waste and Emergency Response summarizes the
results of a demonstration in which optimization techniques were used to improve the design
of several long-term groundwater monitoring programs. The report discusses the results of
application of the MAROS software tool and the Three-Tiered approach applied by The
Parsons Corporation 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 MNO
approaches. The primary goals of this demonstration were to highlight current strategies for
applying optimization techniques to existing LTM programs, and to assist site managers in
understanding the potential benefits associated with monitoring program optimization.
June 2004.
http://clu-in.org/siteopt/proceedings_04/track_b/tue/04_yager_kathleen.pdf
These slides were presented at the Conference on Accelerating Site Closeout, Improving
Performance, and Reducing Costs Through Optimization in Dallas, TX, and summarize the
demonstration project described in the previous citation.
Minsker, B. (Editor). 2003. Long-Term Groundwater Monitoring-The of the Art.
http://www.pubs.asce.org/HTML/water3.htm
This ASCE publication contains summary of state-of-the-art groundwater monitoring
network designs and was prepared specifically for the needs of analysts and practitioners. The
book includes detailed descriptions of the leading methodologies for groundwater monitoring
network designs and guidance for the implementation in a variety of field conditions, as well
as chapters that address: The Objectives of Long-Term Groundwater Monitoring; Data
Requirements in Groundwater Monitoring Network Design; Case Studies; and Future
Research and Technology Transfer Needs in Groundwater Hydrology and Hydrogeology.
http://cee.uiuc.edu/emsa/conference/AFCEE_LTM_Extended_Abstract.pdf
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
ADAPTIVE ENVIRONMENTAL MONITORING SYSTEM (AEMS)
Minsker, B., Groves, P., and Beckmann, B. May 2005.
This paper was presented at the ASCE World Water and Environmental Resources Congress,
Anchorage, AK.
For more information on AEMS contact:
Barbara S. Minsker
RiverGlass Inc.
217-417-4198
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
COST-EFFECTIVE SAMPLING
Ridley, M. N., and D. MacQueen. 2004. "Sampling Plan Optimization: A Data Review and
Sampling Frequency Evaluation Process." Ground Water Monitoring and Remediation, 24(1),
74-80.
Ridley, M. and MacQueen, D. 2001. C0st-_Effecfive__&
http://www-erd.llnl.gov/library/JC-118909.pdf
Ridley et al. 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.
Ridley, M.N., Johnson, V.M, and Tuckfield, R.C. April 1995.
http://www.llnl.gov/tid/lof/documents/pdf/226247.pdf
Paper presented at HAZMACON, San Jose, CA.
Johnson V.M., Tuckfield, R.C., Ridley, M., and Anderson, R. 1996. "Reducing the Sampling
Frequency of Groundwater Monitoring Wells," Environmental Science & Technology, Vol 30,
No. 1.
For more information on Cost Effective Sampling contact:
Maureen N. Ridley
Lawrence Livermore National Laboratory
ridlcyl@llnl.gov
925-422-3593
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
GEOSTATISTICAL TEMPORAL-SPATIAL MONITORING
OPTIMIZATION ALGORITHM (GTS)
GTS overview, and
http://www.afcee.brooks.af.mil/products/rpo/
Cameron, K. & Hunter, P. June 2004.
This paper was presented at Conference on Accelerating Site Closeout, Improving
Performance, and Reducing Costs Through Optimization, Dallas, TX,
http://clu-in.org/siteopt/proceedings_04/track_b/tue/06_cameron_kirkpdf
Cameron, K.M. & Hunter, P. 2003. "Optimization of LTM networks at AF Plant 6 using
GTS." In Situ and On-Site Bioremediation, 2003: Proceedings of the Seventh International In
Situ and On-Site Bioremediation Symposium (Orlando, FL; June 2003). ISBN 1-57477-139-6,
Columbus, OH: Battelle Press.
Cameron, K. & Hunter, P. 2002. "Using spatial models and kriging techniques to optimize
long-term ground-water monitoring networks: a case study." Environmetrics, 13, 629-656.
The GTS Optimization Algorithm was applied to the evaluation and optimization of two
existing monitoring programs at the Massachusetts Military Reservation (MMR), Cape Cod,
Massachusetts. 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.
Cameron, K. 2004. "Better optimization of long-term monitoring networks." Bioremediation
Journal, 8 (3-4): 89-107.
This article presents examples of GTS highlighting improved methods to measure both cost
and accuracy of baseline estimates, chose optimal subsets of the existing data, and flexibility
and adaptability of the optimization scheme.
For more on GTS information contact:
Kirk Cameron
MacStat Consulting, LtD
kcmacstat@qwe st. net
719-532-0453
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
MONITORING & REMEDIATION OPTIMIZATION SYSTEM (MAROS)
Maintained by Groundwater Services. Inc., this site includes a description of features and
copies of the MAROS SoftwargJVcrsion_2j), and Uscrs_guidc.
http://www.gsi-net.com/software/Maros.htm
Aziz, J.J., M. Ling, H.S. Rifai, C.J. Newell, and J.R. Gonzales. 2003. "MAROS: A decision
support system for optimizing monitoring plans." Ground Water 41, no. 3: 355-367.
Wu, J. and D. Guvanasen.
http://www.ngwa.org/publication/softspot/sf03-5.htm
This article, which appears on the National Ground Water Association (NGWA) web site,
reviews the MAROS software (Beta Version 2.0), discusses what the reviewers found, what
they liked, and what they did not like.
For more information on MAROS contact:
Mindy Vanderford
Groundwater Services, Inc.
mvanderford@gsi-net.com
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
PARSONS S-TIERED LTMO APPROACH
Nobel, C. and J.A. Anthony. 2004. "Three-Tiered Approach to Long Term Monitoring
Program Optimization." Bioremediation Journal, Vol. 8, Issue 3-4:147-165.
This paper discusses the three-tiered approach methodology, including data compilation and
site screening, qualitative evaluation decision logic, temporal trend evaluation, and spatial
statistical analysis, illustrated using the results of a case study site. Additionally, results of
multiple applications of the three-tiered LTMO approach are summarized, and future work is
discussed.
Nobel, C. June 2004. Three-Tiered Approach to Long Term Monitoring Optimization
Workshop.
These slides were presented at Conference on Accelerating Site Closeout, Improving
Performance, and Reducing Costs Through Optimization, Dallas, TX, June 2004
For more information on the 3 -Tiered Approach contact:
Carolyn Nobel
Parsons
303-764-8866
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
OTHER APPROACHES
Dresel, E.P., and C. Murray. 1998. "Groundwater monitoring network design using stochastic
simulation." Geological Society of America Abstracts with Programs 30(7): 181.
Dresel and Murray used a ranking approach 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.
Francone, F.D. andL. Deschaine. 2004. "Extending the boundaries of design
optimization by integrating fast optimization techniques with machine-code-based,
linear genetic programming." Information Science 161(3-4): 99-120.
Optimized models of complex physical systems are difficult to create and time consuming to
optimize. The physical and business processes are often not well understood and are therefore
difficult to model. The models are often too complex to be well optimized with available
computational resources. Too often approximate, less than optimal models result. This work
presents an approach to this problem that blends three well-tested components. First: Linear
Genetic Programming (LGP) is applied to those portions of the system that are not well
understood. Second: those portions of the system are simulated.. Finally: the resulting meta-
model is optimized using Evolution Strategies (ES). ES is a fast, general-purpose optimizer
that requires little pre-existing domain knowledge. Results and examples are presented where
this approach can greatly improve the development and optimization of complex physical
systems.
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.
Ling et al. 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 well-siting procedure was used to locate possible new
sampling points for an inadequately delineated plume via regression analysis of plume
centerline concentrations and estimation of plume dispersivity values. The sampling
frequency determination procedure was used to generate recommendations regarding the
future frequency of sampling for each sampling location based on the direction, magnitude,
and uncertainty of the concentration trend derived from representative historical
concentration data. Although the methodology was designed for small-scale sites, it is
adaptable for large-scale site applications. The methodology was applied to a small petroleum
hydrocarbon-contaminated site with a network of 12 monitoring wells to demonstrate its
effectiveness and validity.
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
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 3 6( 12): 3 731 -3 741.
A simulation approach for optimizing existing monitoring programs was developed and
applied 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 et al, extended this work using several different mathematical
optimization algorithms to address multi-objective monitoring optimization problems (see
reference below).
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 130(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 ofHydroinformatics 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.
Rizzo, D., D. Dougherty, and M. Yu. 2000.
System (aLTMOsrM) for Optimization in Environmental Management.
This paper was delivered at the ASCE Joint Water 2000 Conference. Rizzo, Dougherty and
Yu describe aLTMOs, and integrated monitoring and operations optimization system that
utilizes kriging methods, artificial neural networks and Extended Kalman filtering to assess
and optimization long term monitoring network performance and cost. The paper describes
the system, provides a brief methodology review of long-term monitoinrg optimization,
presents a brief benefit-cost analysis, and discussess an application at an Army facility in
Massachusetts.
http://www.subterra.com/downloads/ASCE2000JWC.pdf
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.
The operational efficiency of groundwater monitoring networks at the U.S. Department of
Energy's Savannah River Site was reviewed in order to optimize the number of groundwater
wells needed for monitoring the plumes of the principal constituent of concern,
trichloroethylene (TCE). A multidisciplinary approach, combining geochemistry,
geohydrology, geostatistics, and regulatory knowledge were used to evaluate whether or not a
well should remain on the current sampling schedule. 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.
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
WEB PAGES
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Through Optimization, Dallas, TX, June 2004.
http://clu-in.org/siteopt/ataglance.htm
This web page includes an agenda for the conference as well as links slides from several
LTMO-themed presentations.
http://costperformance.org/optimization/search.cfm
A searchable database of case studies of specific optimization efforts at FRTR member sites,
including several LTMO case studies.
Federal Remediation Technologies
http://www.frtr.gov/optimization/monitoring.htm
Links to approaches for increasing efficiency, reducing cost, identifying uncertainty, and
increasing reliability of long-term monitoring including data quality objectives, long-term
monitoring, well placement and sampling frequency, optimized field sampling procedures,
contaminants of concern and indicator parameters, and data management and data evaluation
http://enviro.nfesc.navy.mil/scripts/WebObjects.exe/erbweb.woa#slide_show_end
This web site is a resource for Navy Remedial Project Managers (RPMs) and other
environmental professionals involved in environmental cleanup. It includes groundwater
monitoring optimization resources such as a description of monitoring changes that might
prompt LTMO, links to related policies, and case studies.
and Long Term Monitoring Website
http://enviro.nfesc.navy.mi1/scripts/WebObjects.exe/erbweb.woa/6/wa/DisplayPage7pageShortN
ame=RAO%2FLTMgt+Workgroup&PageID=165&wosid=PNaOWVZBAkHzJL86g56tOM
As part of the Navy's overall Installation Restoration (IR) program, the Navy and Marine
Corps Working Group was formed in April 1998. The goal is to develop guidance for
optimizing Remedial Action Operation (RAO) and Long Term Monitoring (LTM) phases of
site cleanup projects. This site reports on their progress and has links to Navy
LTM/Groundwater Monitoring Optimization Guidance and case studies.
Products Page
http://www.afcee.brooks.af.mil/products/rpo/default.asp
This page provides access to the Long-Term Monitoring Optimization Guide, as well as links
to the other AFCEE Environmental Restoration products including MAROS, GTS, and the
RPO Handbook.
US Army Corps of Engineers Remedial System Evaluation Checklist Page
http://www.environmental.usace.army.mil/library/guide/rsechk/Envmon.pdf
http://www.environmental.usace.army.mil/library/guide/rsechk/Envmon.pdf
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ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005
The US Army Corps of Engineers developed the Remediation System Evaluation process to
assist in the holistic optimization of remedial actions. The primary tools for the process are a
set of checklists, including one that addresses environmental monitoring. This checklist
guides the user through a qualitative evaluation of the current monitoring program and
suggests ways to optimize the program.
US Environmental Protection Agency "Clu-In" Remediation Page
http://www.cluin.org/optimizatioM
This website is a resource for EPA optimization efforts. It provides information on
optimization-related EPA demonstration projects as well as optimization-related fact sheets
developed by EPA.
US Environmental Protection Agency Water
http://www.epa.gov/ada/csmos/models/owl.html
The OWL program is a simple tool to evaluate existing monitoring well networks and assist
in the selection of new monitoring well locations. The program uses ground-water elevation
measurements to evaluate variations in ground-water flow magnitude and direction over time
and calculate corresponding plume migration paths. A simple analysis combining the
potential locations of the plume and the coverage of monitoring wells at a site allows the user
to evaluate whether existing monitoring wells are optimally located, and to optimize the
placement of new monitoring wells to better characterize plume location and future
movement. The program accomplishes these tasks using simple algorithms and typically
available field data.
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