ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

 

 ii
EPA 542-R-05-003 

 www.cluin.org/optimization 

 May 2005

ROADMAP

TO

LONG-TERM MONITORING

OPTIMIZATION

 
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 

not intended, nor can it be relied upon, to create any rights enforceable by any party in litigation 

with the United States or any other party. The information provided may be revised periodically 

without public notice. Use or mention of trade names does not constitute endorsement or 

recommendation for use. Standards of Ethical Conduct do not permit EPA to endorse any private 

sector product or service. 

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 

via the web site, by mail, or by fax from the following source: 

EPA/National Service Center for Environmental Publications 

P.O. Box 42419 

Cincinnati, OH 45242-2419

Telephone: (513) 489-8190 or (800) 490-9198 

Fax: (513) 489-8695 

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 rubin.ellen@epa.gov

 ii
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 
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 et al., 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 costeffectiveness 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 et al., 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 

 1
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). 

 2
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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. 

 3
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

Figure 2.0.1 LTMO Steps Flowchart, Associated Key Points, and Corresponding Roadmap 

Sections 

 4
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 

 5
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 al., 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”). 

 6
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 of in-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 programspecific, 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). 

 7
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 timeconsuming 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 

 8
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 

− Database 

− Well construction information −

− Site maps

Determine spatial distribution of

monitoring points 

Analytical data and 

COC sampling results 

− Database 

− Monitoring reports 

− Site investigation reports 

Define concentrations of COCs in

space and time,

Confirm primary COCs, 

Verify data quality

Potentiometric surface 

configuration −

groundwater flow

direction, velocity, and 

gradient

− Recent monitoring report 

− Document providing facility 

and site information (e.g., 

CSM, remedial investigation

[RI] or RCRA facility 

investigation [RFI] report, or

similar) 

− Database 

Evaluate direction and rate of

groundwater movement and 

contaminant migration

Hydrogeologic conditions − Document providing facility 

and site information (e.g., 

RI/RFI or similar document) 

− CSM 

− Hydrogeologic testing results 

Identify geologic or other controls on

occurrence and movement of

groundwater and dissolved COCs

Well completion intervals 

and hydrogeologic zone 

− Database 

− Well construction diagrams

− Drilling logs

Determine depth of sample collection

in groundwater system and potential 

hydrogeologic and stratigraphic zones 

Cleanup goals and 

regulatory limits 

− ROD 

− Decision document 

− RI/RFI 

Establish cleanup limits and areas of

concern requiring monitoring

Potential receptor and

compliance point 

locations

− RI/RFI 

− ROD 

− Site map

− Site visit 

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, property

boundaries) 

− Site map⎯AutoCAD 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 

 9
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

Data Needed Potential Data Source(s) Purpose 

Geochemical data − Database Identify natural attenuation parameters 

Wells with NAPL present − Database, historical monitoring

reports 

Identify data values that potentially 

should be excluded from the analysis 

Current program costs, 

including analytical, field 

mobilization, sample 

collection, data 

management and

reporting, and waste 

management 

− Laboratory invoices 

− Project budget, schedule, and 

labor projections for monitoring

projects 

− Site personnel; professional 

judgment 

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 degree of difficulty in implementing optimization

recommendations, 

• 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 

 10
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 al., 1990). All approaches to the design, evaluation, and 

optimization of effective groundwater monitoring programs must acknowledge and 

account for the dynamic nature of groundwater systems, as affected by natural 

phenomena (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. 

 11
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 spatialstatistical 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 al., 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 et al., 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 

 12
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

• 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

Reasons for Removing a Well

From a Monitoring Network

Well is needed to further characterize the site or 

monitor changes in contaminant concentrations

through time. 

Well provides spatially redundant information with a 

neighboring well (e.g., same constituents, and/or

short distance between wells). 

Well is important for defining the lateral or 

vertical extent of contaminants. 

Well has been dry for more than two years, and there 

is no expectation for the water levels to recover in the 

foreseeable future. 

Well is needed to monitor water quality at a 

compliance point or receptor exposure point (e.g., 

sentinel well for municipal wells). 

Contaminant concentrations are consistently below 

laboratory detection limits or cleanup goals. 

Well is important for defining background water 

quality. 

Table 2.4.2 Qualitative Monitoring Frequency Decision Logic 

Reasons for 

Increasing Sampling Frequency 

Reasons for 

Decreasing Sampling Frequency 

Groundwater velocity is high. Groundwater velocity is low. 

Change in concentration would significantly alter a 

decision or course of action. 

Change in concentration would not significantly alter 

a decision or course of action. 

Well is close to source area or operating remedy. Well is far from 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. 

Concentrations are not expected to change 

significantly over time, or contaminant levels have 

been below cleanup objectives for some period of

time. 

 13
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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) nonparametric 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 rulebased 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 averageconcentration 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. 

 14
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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. 

 15
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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. 

• Naval Facilities Engineering Service Center (NFESC) Guide to Optimization 

Groundwater 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 Monitoring 

Optimization Guide, Version 1.1, 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, 

 16
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 Federal Remediation Technologies Roundtable (FRTR) website and on the U.S. 

EPA’s Clean Up Information CLU-IN Remediation Optimization website (see Appendix 

for references and additional information).

 17
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

Table 2.5.1 LTMO Tools and Approaches Methodology and Data Requirements 

LTMO 

Tool/Approach Overview Frequency Optimization Methodology Spatial Distribution Methodology Data Requirem

Cost Effective Sampling (CES) 

CES is a methodology for reviewing and assessing the lowest-frequency sampling schedule for a given groundwater monitoring location.

Rule-based decision algorithm based on trend, variability, and magnitude statistics recommends optimal frequency at each well. 

Not included 

− At least 6 

quarterly monitoriresults p

− Clean do

gradient wells" 

Geostatistical Temporal/SpatialOptimizationAlgorithm (GTS) 

GTS is a spatial and temporal algorithm developed byAFCEE that utilizes geostatistical methods to optimize sampling frequency and to define the network of essential sampling locations. The GTS algorithmincorporates a decision pathway analysis that incorporates both spatial and temporal components and is used to identify spatial and temporal redundancies in existing monitoring networks.

1) Iterative thinning approach reconstructs baseline trends with fewersamples to determine optimalfrequency on a well-by-well basis.2) Temporal variogram is applied to determine composite autocorrelation and optimal site-wide frequency.

Weighting scheme utilizing locally weighted quadratic regression examines multiple "time slices" to identify redundant wells based on cost-accuracy trade-off curves. 

− More tha

events pe(tempora

− Greater t

wells (sp

Monitoring and Remediation OptimizationSystem (MAROS) 

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 softwarerecommends optimal future sampling frequency,location and density, as well as providing information on the plume state over time. 

Modified cost-effective sampling method (rule-based decision algorithmbased on trend, variability, and magnitude statistics) recommends optimal frequency for each well. 

Weighting scheme utilizing Delaunay triangulation identifies redundant wells.Can evaluate multiple chemicals at one time. 

− More tha

events pe(tempora

− Greater t

wells per (spatial) 

Parsons 3-Tiered LTMO 

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 programoptimization.

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.

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.

− More tha

events pe(tempora

− Greater t

wells per (spatial) 
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

LTMO 

Tool/Approach Overview Frequency Optimization Methodology Spatial Distribution Methodology Data Requirem

Adaptive EnvironmentalMonitoringSystem (AEMS) 

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 explicitlyaccount for uncertainty in the historical data. A suite of spatial and/or temporal models can be built fromhistorical data and used within the redundancy analyses to find the optimal set of samples that meet userspecified 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.

Genetic algorithms are used to search for optimal designs given a temporal interpolation model or multiple spatial interpolation models built fromhistorical data.

Genetic algorithms are used to search for optimal designs given a spatial orspatiotemporalinterpolation 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. 

− More tha

events pe(tempora

− More tha

events pe(spatial) 

− More tha

events pe(combineand tempoptimiza
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

Table 2.5.2 LTMO Tools and Approaches Implementation Details 

Tool/Approach # Sites Applied Typical Cost to Apply to Site Availability Required Reso

Cost Effective Sampling (CES) ~10 $25-$50k

− Consulting method − Follow methodology presented in references

− High level of skill (i− Minimal skill (subse

Geostatistical Temporal/Spatial Optimization Algorithm (GTS) ~10 ~$25k for average site 

− Currently consulting method − Scaled-down software available in Summer

2005 

− Experienced statistic− Mid-level analyst (s

Monitoring and Remediation Optimization System (MAROS) Unknown* $5-$15k for average site − Free software available for download 

− Mid-level analyst − IBM-compatible PC 

2000 

Parsons 3-Tiered LTMO ~20 $5-$15k for average site 

− Consulting method − Follow methodology presented in references 

− Experienced geolog− Mid-level analyst fa

(quantitative) 

− GIS software (e.g., A

Adaptive Environmental MonitoringSystem (AEMS) 3 $5-$15k for average site

− Currently consulting method − Prototype software available for testing in 

Spring 2005.

− 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.
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 et al., 

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 (a gas-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. 

 21
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

Additionally, groundwater sampling methods have evolved over the past 20 years; 

relatively new sampling methods (http://www.clu-in.org/char1_tech.cfm#new_tech) 

including passive diffusion bag samplers (http://www.diffusionsampler.org/) and 

innovative in-situ and field-based analytical methods (http://www.frtr.gov/site/) 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 data 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 

 22
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 timeconsuming, and typically requires coordination with entities outside of the immediate 

 23
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 

 24
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 

 25
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

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 threedimensional 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. 

 26
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

• 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. 

 27
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., and R. 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, and R.A. Smith. 1991. Selection of methods for the 

detection and estimation of trends in water quality. Water Resources Research

27(5):803-813. 

Hudak, P.F., H.A. Loaiciga, and F.A. Schoolmaster. 1993. Application of geographic 

information systems to groundwater monitoring network design. Water Resources 

Bulletin 29(3):383-390. 

Loaiciga, H.A., R.J. Charbeneau, L.G. Everett, G.E. Fogg, B.F. Hobbs, and S. Rouhani. 

1992. Review of ground-water quality monitoring network design. Journal of 

Hydraulic Engineering 118(1):11-37. 

Makeig, K.S. 1991. Regulatory Mandates for Controls on Ground-Water Monitoring, in

Nielsen, D.M., ed. Practical Handbook of Ground-Water Monitoring. Lewis

Publishers, Inc. Chelsea, Michigan. 

Minsker, B., ed. 2003. Long-Term Groundwater Monitoring-The State of the Art. 

American Society of Civil Engineers. Reston, VA. 

Reed, P.M., B.S. Minsker, and A.J. Valocchi. 2000. Cost-effective long-term

groundwater monitoring design using a genetic algorithm and global mass 

interpolation. Water Resources Research 36(12):3731-3741. 

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
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-04-001a and -001b. 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 

 29
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

APPENDIX

LTMO RESOURCES 

 30
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

LTMO POLICY AND GUIDANCE RESOURCES 

Department of the Navy Policy for Optimizing Remedial and Removal Actions Under the 

Environment Restoration Programs. 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 of MNA 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. 

Final Remedial Process Optimization Handbook prepared for the Air Force Center for 

Environmental Excellence and the Defense Logistics Agency. 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. 

Guide to Optimal 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. 

Long-Term Monitoring Optimization Guide, Final Version 1.1. 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.

 31
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

Technical Guidance for the Long-Term Monitoring of Natural Attenuation Remedies at 

Department of Energy Sites. 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. 

Method for Monitoring Pump-and-Treat Performance. 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. 

 32
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(1):11-37.

Loaiciga et al. 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. 

Demonstration of Two Long-Term Groundwater Monitoring Optimization Methods – Report 

with Appendices. 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. 

 

Demonstration of Two Long-Term Groundwater Monitoring Optimization Methods. 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 State 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.

Extended Book Description

http://cee.uiuc.edu/emsa/conference/AFCEE_LTM_Extended_Abstract.pdf

 33
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

ADAPTIVE ENVIRONMENTAL MONITORING SYSTEM (AEMS) 

Minsker, B., Groves, P., and Beckmann, B. May 2005. Optimizing Long-Term Monitoring at BP 

Sites Using Multi-Objective Optimization. 

This paper was presented at the ASCE World Water and Environmental Resources Congress, 

Anchorage, AK.

http://cee.uiuc.edu/research/emsa/publications

For more information on AEMS contact: 

Barbara S. Minsker 

RiverGlass Inc. 

minskerconsulting@insightbb.com 

217-417-4198

 34
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. Cost-Effective Sampling of Groundwater Monitoring 

wells: A Data Review & Well Frequency Evaluation.

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. Cost Effective Sampling of 

Ground Water Monitoring Wells. 

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

ridley1@llnl.gov

925-422-3593

 35
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

GEOSTATISTICAL TEMPORAL-SPATIAL MONITORING

OPTIMIZATION ALGORITHM (GTS) 

GTS overview, flowchart and decision path, and detailed description and discussion of GTS. 

http://www.afcee.brooks.af.mil/products/rpo/ 

Cameron, K. & Hunter, P. June 2004. Optimizing LTM Networks with GTS: Three New Case 

Studies.

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_kirk.pdf 

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@qwest.net

719-532-0453

 36
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

MONITORING & REMEDIATION OPTIMIZATION SYSTEM (MAROS) 

MAROS software homepage. 

Maintained by Groundwater Services, Inc., this site includes a description of features and 

copies of the MAROS Software Version 2.0, and User’s guide.

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. Software Spotlight/MAROS: A Decision Support Tool for 

Improving the Cost-Effectiveness of Ground Water Monitoring Plans

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

 37
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

PARSONS 3-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 

Carolyn.Nobel@parsons.com

303-764-8866 

 38
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. and L. 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 metamodel 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. 

 39
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 36(12):3731-3741.

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 costeffective 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 of Hydroinformatics 3:71-89. 

Reed, P.M., B.S. Minsker, and D.E. Goldberg. 2003. “Simplifying multiobjective optimization 

II: An automated design methodology for the nondominated sorted genetic algorithm.” 

Water Resources Research 39(7):1196. 

Rizzo, D., D. Dougherty, and M. Yu. 2000. An Adaptive Long-Term Monitoring and Operations 

System (aLTMOsTM) 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. 

 40
ROADMAP TO LONG-TERM MONITORING OPTIMIZATION MAY 2005 

 

WEB PAGES

Conference on Accelerating Site Closeout, Improving Performance, and Reducing Costs 

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. 

Federal Remediation Technologies Roundtable Optimization Case Study Search

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 Roundtable Monitoring Optimization Webpage

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

NAVFAC Environmental Restoration and BRAC Website

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. 

Navy and Marine Corps Working Group Optimizing Remedial Action Operations

and Long Term Monitoring Website

http://enviro.nfesc.navy.mil/scripts/WebObjects.exe/erbweb.woa/6/wa/DisplayPage?pageShortN

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. 

US Air Force Center for Environmental Excellence (AFCEE) Environmental Restoration 

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

USASCE Environmental Monitoring Checklist 

 http://www.environmental.usace.army.mil/library/guide/rsechk/Envmon.pdf

 41
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 Optimization Page

http://www.cluin.org/optimization 

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 Ground Water and Ecosystems Restoration Research 

Optimal Well Locator (OWL) Version 1.2

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. 

 42