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Demonstrations of Method Applicability under a Triad Approach for 

Site Assessment and Cleanup — Technology Bulletin 

August 2008 

Since its inception in 1995, the U.S. Environmental 

Protection Agency’s (EPA) Brownfields Initiative and other 

revitalization efforts have grown into major national 

programs that have changed the way contaminated 

property is perceived, addressed, and managed in the 

United States. In addition, there has been a shift within 

EPA and other environmental organizations in the way 

hazardous waste sites are cleaned up. Increasingly, 

project managers, regulators, technology providers, and 

other stakeholders are recognizing the value of 

implementing a more dynamic and flexible approach to 

site cleanup that focuses on real-time decision-making in 

the field to reduce costs, improve decision certainty, and 

expedite site closeout. The approach, known as Triad, 

uses (1) systematic project planning, (2) dynamic work 

strategies, and (3) real-time measurement technologies 

designed to increase confidence in the project (Figure 1). 

Figure 1. The Triad Approach 

Triad’s best management and technical practices have 

been successfully implemented in a variety of regulatory 

frameworks, including Brownfields, Superfund, the 

Resource Conservation and Recovery Act (RCRA), 

management of underground storage tanks (UST), and 

voluntary cleanup programs. As a result, the EPA 

Brownfields and Land Revitalization Technology Support 

Center (BTSC) is preparing a series of technical bulletins 

to provide additional information on implementing specific 

aspects of the Triad approach. These bulletins are 

intended for technical project managers and team 

members. Non-technical managers or stakeholders may 

also present these bulletins to consultants and service 

providers to ensure Triad best management and technical 

practices are implemented appropriately at their sites. 

These bulletins provide sufficient information for less 

technical project managers and team members to request 

that critical Triad project elements be included in scope of 

work and planning documents. 

Demonstrations of Method Applicability (DMA) are a key 

component of using real-time measurement technologies 

and are presented in this bulletin through: 

1. Answers to frequently asked questions on key 

aspects of DMAs 

2. Examples of DMAs performed at hazardous waste 

sites: 

• Wenatchee Tree Fruit, Wenatchee, Washington 

• Poudre River, Fort Collins, Colorado 

• Fort Lewis Small Arms Firing Range, Fort Lewis, 

Washington 

3. Sources of additional information for communities and 

project teams that desire to implement DMAs and the 

Triad approach. 

Systematic 

Project 

Planning

Dynamic 

Work 

Strategy

Real Time Measurement 

Technologies

Project 

Confidence

Systematic 

Project 

Planning

Dynamic 

Work 

Strategy

Real Time Measurement 

Technologies

Project 

Confidence

About the Brownfields and Land Revitalization 

Technology Support Center (BTSC) 

EPA established the BTSC (see 

www.brownfieldstsc.org) to ensure that brownfields and 

other land revitalization decision-makers are aware of 

the full range of technologies and technical support 

services available for site assessments and cleanups 

and to help them make informed decisions about their 

sites. The center can help federal, state, local, and tribal 

officials evaluate strategies to streamline the site 

assessment and cleanup process at specific sites; 

identify, review, and communicate information about 

complex technology options; evaluate contractor 

capabilities and recommendations; and plan technology 

demonstrations. BTSC is coordinated through EPA’s 

Office of Superfund Remediation and Technology 

Innovation (OSRTI) and works through EPA’s Office of 

Research and Development (ORD) laboratories. The 

center also works closely with EPA’s Office of 

Brownfields Cleanup and Redevelopment and in 

partnership with the U.S. Army Corps of Engineers 

(USACE) and Argonne National Laboratory. 

Localities can submit requests for assistance through 

the EPA Regional Brownfields Coordinators, online, or 

by calling 1-877-838-7220 toll free. For more 

information about the BTSC, contact Carlos Pachon at 

(703) 603-9904 or pachon.carlos@epa.gov.
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What is a DMA? 

A DMA is an initial site-specific performance evaluation for 

a wide range of sampling, testing, and data management 

tools. It is a concept founded in EPA SW-846 guidance 

(www.epa.gov/epaoswer/hazwaste/test/sw846.html) and 

is based on the principles of EPA’s performance-based 

measurement system (PBMS) initiative 

(www.epa.gov/SW-846/pbms.htm). A DMA usually falls 

into one of two categories: (1) a comparison of a fieldbased method with a more established laboratory-based 

method to demonstrate the usefulness of the field-based 

method, or (2) a test to evaluate whether a particular tool 

will work on a specific site. Both types of DMAs may be 

needed at a single site, and the exact format of the DMA 

will depend heavily on the site characteristics, the history 

of investigations at the site, and the intended use of the 

data. 

The DMA serves several different purposes for many 

applications, including showing whether a technology will 

be effective at the intended site, but also to optimize how it 

will be used collaboratively with other information sources 

at the site. DMA data are of particular importance relative 

to understanding the potential effects of matrix 

heterogeneity and sample support on data quality. During 

the DMA, the types and frequency of quality assurance 

and quality control (QA/QC) procedures are often tested 

for adequacy, and preliminary field-based action levels are 

developed for comparison with site decision criteria. 

Methods for data sharing and management are also 

tested to assure a project can proceed in real-time. 

In addition, a DMA can be used to evaluate technologies 

for generating analytical data (or other information) both in 

the field and in an off-site location that will provide 

information appropriate for meeting project decision 

criteria. The ability of technologies such as X-ray 

fluorescence (XRF), immunoassay (IA), ultraviolet (UV) 

fluorescence, and direct sensing tools such as the 

membrane interface probe (MIP) and laser induced 

fluorescence (LIF) to produce decision quality data has 

been well documented (www.epa.gov/ORD/SITE and 

www.epa.gov/etv) and their suitability to a site can be 

evaluated through a DMA. Extensive literature and 

performance data are available for some technologies, 

and these data should be reviewed by project teams 

before a DMA is designed. 

A DMA can also provide information on cost and 

performance that can be used to optimize collaborative 

data collection using technologies for generating analytical 

data (or other information) both in the field and in an offsite location. Additionally, a DMA can offer stakeholders 

an understanding of the site-specific performance of a 

technology while at the same time it provides the basis to 

optimize standard operating procedures (SOPs) for 

deployment. 

DMAs are performed easily and affordably before 

mobilization, or as an early component of a field program. 

Advice on the specific technology and assistance to set up 

a DMA sometimes are available from equipment vendors 

or service providers. 

The question often arises: “How do DMAs fit into the data 

quality objective (DQO) process?” Various aspects of 

DMA planning and implementation fall under a number of 

DQO steps, but the most important one is Step 7: Select 

the most resource-effective sampling and analysis 

strategy that meets the performance criteria. 

 

When is it necessary to perform a DMA? 

DMAs may be used when the project team works with a 

technique that previously has not been used at the site. 

Site-specific factors often may render an otherwise useful 

technique unsuitable and can result in high and 

unnecessary project costs if they are not discovered early 

in the project. A DMA can quickly ascertain whether the 

new technique is suitable for use at the site, allowing the 

project team to identify an alternative if the proposed 

technique is not suitable. Conversely, the project team 

can proceed with confidence, realizing the benefits offered 

by the technique, if the DMA suggests that it will be 

effective. 

A DMA may be necessary when: 

• A project will depend heavily on field-based results to 

make real-time decisions. 

• Experience indicates a technology’s performance is 

variable from site to site. 

• Heterogeneity and the cost of cleanup are high. 

• The chemistry of contaminants is complex. 

• A specific relationship is needed between 

collaborative forms of data sets to support decisionmaking. 

• Stakeholder acceptance requires that the utility of a 

technology or approach be evaluated. 

What are the benefits of performing a DMA? 

A well-planned DMA can simultaneously test, refine, and 

coordinate many project design parameters before full-
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scale project activities are under way. Project design 

parameters often evaluated during a DMA include 

sampling and analytical methods, QA/QC procedures, 

data management, communication and data sharing 

strategies, collaborative or comparative data needs (for 

example, technologies matched with other field tools or 

standard fixed-laboratory analytical methods), project 

staffing, and the overall flow or sequencing of field 

activities. A carefully considered DMA can help an entire 

project run faster and more smoothly, resulting in lower 

costs, and assuring that the data collected will be 

adequate for the intended end use. Both field-based and 

fixed laboratory methods have limitations, and the project 

team should verify their performance during project startup 

to avoid generating data or using equipment that does not 

meet project requirements for precision, accuracy, 

representativeness, completeness, comparability, 

specificity, sensitivity, ruggedness, and reliability. 

Project-specific DMAs guide the project team in selecting 

and optimizing collaborative methods and assuring 

adequate method performance for site conditions and 

decision criteria. DMA results can be used to develop 

project-specific action levels; analytical, sampling, and 

data analysis procedures; QA/QC requirements; and 

additional data requirements to assure the quality of the 

decision. Furthermore, A DMA can help set acceptable 

levels of uncertainty relative to decision thresholds used in 

the field as part of a dynamic field decision strategy. The 

fast pace of real-time cleanup projects makes DMAs 

essential to avoid down time related to problems that arise 

from inadequate planning for sampling and analysis. 

As noted earlier, DMAs can be used to test the suitability 

of technologies for generating any data, whether the 

analysis occurs in the field or in a traditional laboratory. 

Chemical data-oriented DMA tasks involve collecting, 

preparing, and analyzing samples from a site-specific 

matrix (soil, water, air, and tissue, for example). 

DMAs also serve important non-analytical functions and 

can help evaluate whether a project is ready to proceed. 

DMAs can be used to test the preparedness of field 

personnel and service providers, as well as to evaluate 

the adequacy of logistical and data management plans. 

For example, sample throughput and analysis times can 

be more accurately estimated. Likewise, materials and 

personnel needs can be balanced and documentation 

procedures clarified. In addition, instrument compatibility 

and data exchange or upload protocols can be verified 

and debugged as necessary. Assessing logistical 

feasibility in this manner is especially important when the 

project team uses dynamic work strategies. This aspect 

of a DMA can help evaluate practical constraints for work 

at the site in relation to the timing of sample collection and 

analytical throughput, including field analytical equipment, 

labor, sample storage, and the cost and supply of 

consumables. 

The DMA can also be used to “test-drive” real-time 

decision support tools (DSTs). These tools include 

electronic data management procedures, global 

positioning system (GPS) and surveying equipment, and 

modeling, mapping, and data display software. This 

aspect of a DMA will improve the ease of use during fullscale field mobilization by ensuring operators can do the 

job and identifying those aspects of a DST that can be 

improved. In some cases, the project team may decide a 

different DST is more appropriate for one particular portion 

of a project versus another. 

Finally, the DMA can be used to assess the 

appropriateness and performance of proposed generation 

techniques for data other than chemical, such as 

geophysical, geotechnical, or direct sensing or probing 

methods. The presence of site-specific interferences that 

could compromise the performance of these tools can be 

tested as a result. Interferences for geophysical 

techniques could include tree leaf cover, seasonal wetland 

Important functions of a DMA include: 

• Providing assurance that the proposed site 

characterization methods are suitable for the 

specific project. 

• Generating data of known quality. 

• Developing initial relationships between field 

methods or tools and other collaborative methods 

such as fixed laboratory. These relationships are 

used to design and focus the QC program. 

• Testing a preliminary CSM to refine sampling 

protocols if assumptions are found to be incorrect. 

• Setting preliminary field-based action levels to be 

used for real-time decision-making. 

• Establishing the readiness of field personnel, 

equipment, and procedures before full-scale work 

begins at a site. 

• Assessing alternative strategies as contingencies 

should the performance of the intended methods 

be compromised by unanticipated problems. 
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areas, low power lines, fences, shallow bedrock, salts, 

and interlocking sands. Mineralogical interferences or 

other geological conditions can also affect the 

performance of direct-sensing equipment such as LIF 

probes. The presence of some forms of calcite or specific 

clay materials also can impair the utility of LIF. If 

interferences are identified during the DMA, alternative 

strategies for dealing with them can be developed before 

full-scale work is undertaken at the site. 

DMAs for direct-sensing tools include many of the same 

techniques used for evaluating other field analytical tools. 

They can involve building relationships between sensor 

response and analytical data or other forms of comparable 

information such as visible staining, free product, soil 

saturation, or physical characteristics of the matrix. Figure 

2 provides an example of developing relationships 

between LIF response and visual core observations. At 

this site, relative fluorescence for various product types 

such as gasoline, diesel, and oil were used to estimate the 

presence or absence of free product. When used with LIF 

logs to estimate product thickness, these values allowed 

members of the technical project team to estimate 

contaminant mass and optimize locations and depths for a 

product removal system. 

Figure 2: Correlations of LIF response and presence of free 

product. 

What are key concerns to address in designing a 

DMA? 

At least four aspects of a data-focused DMA should be 

considered during its design, including the following: 

• What are critical aspects of the preliminary 

conceptual site model (CSM) that should be tested to 

assure project success? These aspects may include 

assumptions about the locations and nature of 

suspected releases, the degree of matrix 

heterogeneity, and the impact of sample processing 

bias. Understanding matrix heterogeneity is critical in 

evaluating the number of samples required for 

statistically based sampling designs. Without some 

information about the specific site and performance of 

the analytical method, the appropriate number of 

samples needed to achieve a desired level of 

decision confidence cannot be correctly identified. 

• Is it important to evaluate the cost-effectiveness and 

bias of multi-increment sampling? It may be important 

to compare the cost-effectiveness and bias of 

traditional grab sample collection and mathematical 

averaging procedures against a multi-increment 

(physical averaging) sampling strategy. 

• Do changes in sample support (the size, shape, and 

orientation of a sample) dramatically affect analytical 

data results? Is the level of effort associated with 

various sample preparation and analysis techniques 

worth the benefit of higher precision, accuracy, or 

control of bias? 

• Are project decisions of a qualitative or quantitative 

nature? In some cases, a “yes or no” answer is all 

that is needed, while for others a more quantitative 

result is needed. For example, in some cases the 

decision is only whether free product is present or 

absent in the subsurface. In contrast, risk estimation 

often requires data in the form of quantitative 

concentration results. 

DMAs should be designed to address those issues that 

most often provide the greatest source of uncertainty: 

sample heterogeneity and short-scale spatial variability. 

The resulting mismatch between the volume of the sample 

analyzed and where it is collected versus the data result 

that will be extrapolated to a significantly larger volume of 

material can be significant. Therefore, the DMA should 

appraise sampling designs (such as multi-increment 

designs), sample collection techniques (such as low-flow 

purging of ground water wells versus passive diffusion 

samplers), and sample preparation procedures (such as in 

situ versus ex situ readings and options for sample 

homogenization or fractionization based on soil properties 

such as particle size). 

A DMA often involves “split samples” that are carefully 

prepared to minimize matrix heterogeneity and analyzed 

by two or more different techniques to establish 

relationships. Parametric and non-parametric techniques 

are commonly used to evaluate these relationships and 

establish decision quality for collaborative data sets. 

Presence of free 

product unlikely

Presence of 

free product 

likely

Presence of free 

product unlikely

Presence of 

free product 

likely

Free Product At >50% Relative 

Fluorescence for Gasoline

Free Product At >75% Relative 

Fluorescence for Oil 

Presence of free 

product unlikely

Presence of 

free product 

likely

Presence of free 

product unlikely

Presence of 

free product 

likely

Free Product At >50% Relative 

Fluorescence for Gasoline

Free Product At >75% Relative 

Fluorescence for Oil 
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Parametric statistical methods use assumptions about the 

data’s underlying statistical distribution shape (normal, 

lognormal, or other). If those assumptions are invalid, the 

statistical conclusions may not be reliable. Nonparametric techniques do not require that as many 

assumptions be true, so they are more broadly applicable 

to the properties of environmental data. 

Comparability is quantified by establishing the frequency 

that results from different techniques agree with each 

other with respect to a declared reference point. Different 

points of reference can be used, but the most common 

strategy in Triad projects is to establish comparability in 

terms of the decision being made on the data. These data 

may require quantitative comparability (such as if or when 

two data sets are combined to calculate risk assessment 

parameters) or qualitative comparability for agreement at 

the compliant or non-compliant decision threshold. 

A comparability DMA can be used to demonstrate the 

suitability of field-based technologies or project-specific 

modifications to improve the performance of an 

established fixed-laboratory method. The techniques to 

be compared include sampling related methods as well as 

analytical methods. Understanding the effects of sample 

heterogeneity is extremely important when samples will be 

split for the different analytical methods to be evaluated. 

A valid comparison of data sets requires thorough 

homogenization of samples before they are split to ensure 

both methods see the same sample characteristics that 

will be used to make a decision. To understand method 

differences, known or blind QA samples (spikes, 

replicates, reference materials, or blanks) are also often 

subjected to comparative analysis to assure technical 

team members that both methods are providing 

representative results. 

What data deficiencies can be addressed using a 

DMA? 

Site-specific method reporting limits (MRLs), precision, 

bias, false positive rates, and false negative rates can be 

assessed through the DMA process. For example, MRLs 

and sample reporting limits can be tested by analyzing 

samples spiked with known amounts of target 

contaminants and comparing site-specific matrices to find 

the lowest concentration that can be reliably detected and 

quantified. 

Data from laboratory reference methods and from a fieldbased method should be compared to see whether they 

produce data that lead to the same project decision based 

on established field-based action levels or decision rules. 

The ability of two methods to agree for decision-making is 

an important parameter to examine when comparing 

analytical methods, especially when methods with lower 

analytical performance such as immunoassay methods 

(which measure several closely related analytes and 

report a single result for the group) are being compared 

with methods with higher performance, such as gas 

chromatography (GC) and mass spectrometry (MS) 

(which are usually able to distinguish between closely 

related analytes and measure each). In this way, the 

DMA is a critical component in Triad’s efforts to manage 

the analytical contribution to decision uncertainty. 

How can DMA use weight of evidence and 

collaborative data sets? 

Terms such as “weight of evidence” or “multiple lines of 

evidence” and “collaborative data sets” have been 

developed to describe these layered data sets. From a 

Triad perspective, there is a distinction between the two. 

“Weight (or lines) of evidence” refers to combining 

information from various different sources into a holistic 

picture (that is, a CSM). For example, historical 

information may be used in conjunction with geological, 

hydrogeological, chemical, and geophysical data to predict 

contaminant fate and transport. 

On the other hand, “collaborative data sets” or 

“collaborative methods” refer specifically to the strategy of 

using two (or more) analytical methods to measure the 

“same” analyte or a surrogate of an analyte. For example, 

total uranium can be measured by XRF, gamma 

spectroscopy, and alpha spectroscopy. Collaborative 

methods are paired so that the strengths of one method 

can compensate for the limitations of the other. 

Frequently, a field method is selected for its ability to 

provide a much higher density of data points than an 

expensive laboratory method. However, the laboratory 

method will generally achieve better detection limits and 

accuracy than the field method. A DMA should be 

designed to guide the “marriage” of the techniques to 

produce reliable information that is not biased by the 

effects of heterogeneity or analytical inaccuracy. 

Additionally, alternative analytical methods, particularly 

any that provide results in "real time," can be used to 

optimize the decision making process. For example, the 

real-time decisions and high data density possible with 

field methods can reduce the volume of material removed 

during cleanup by more precisely defining and confirming 

the actual contamination footprint. Real-time data can in 

this way improve confidence in the decision and limit 

“surprises” after a project is complete. 
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What is involved in performing a DMA? 

Designing an appropriate DMA is specific to the 

technology or technique being employed and to the 

project, the site matrix or matrices, the effects of 

heterogeneity, sample support (the size, shape, and 

orientation of a sample), the expected use of the data, and 

a myriad of other factors. During systematic planning, the 

project team may evaluate potential candidate 

technologies or strategies for use at the site. 

Technologies and strategies that can improve project 

efficiency and the CSM, increase data density, and reduce 

uncertainties associated with decision making are most 

often targeted. Project teams are encouraged to employ 

the services of an experienced Triad practitioner when 

technologies or strategies are short listed. Although there 

is no generic format for designing a DMA, a number of 

activities are often involved. 

• Evaluating the strengths and limitations of 

technologies or techniques to be used on site 

samples. 

• Evaluating sample support, throughput, ease of use, 

manipulation and storage of data, and other logistics 

so that the process is optimized. 

• Collecting and analyzing QC samples to evaluate the 

uncertainties that are the largest contributors to total 

measurement error. Project resources can then be 

allocated to control for those activities with the 

greatest effect (Figures 3 and 4). 

Figure 3. Uncertainty Sources and Associated QC Samples 

Figure 4. Output from an Uncertainty Evaluation 

• Collecting information to establish initial relationships 

with data from the fixed laboratory or other 

collaborative information. The collaborative 

relationship (data comparability) can be evaluated 

using a variety of options. 

• Completing parametric statistical techniques, such as 

linear regression (Figure 5). Although commonly 

applied, caution should be used with linear 

regression. The correlation coefficient (R2) is 

universally used as a measure of a good relationship 

between two methods, but can be misleading. 

Examining the slope and y-intercept can be far more 

informative and less distorted by isolated high values. 

Figure 5. Sample Linear Regression 

• Using non-parametric techniques (often more useful 

for establishing comparability). These techniques are 

“common sense,” but still powerful aids to decision 

making. Examples include scatter plots, calculating 

decision error rates and establishing investigation 

levels to use with the alternative technique (Figure 6). 
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Figure 6. Sample Investigation Levels 

Figure 7. Typical Outputs of Investigation Levels and 

Decisions Bins Using Non-Parametric Methods 

• Using “decision bins” to establish investigation levels 

can guide confident decisions made on field data. 

(Figure 7). 

• Identifying potential interferences, bias, false positive 

and false negative rates, and other issues. 

Depending on project timelines, the Quality 

Assurance Project Plan (QAPP) can be formally or 

informally updated and optimized as a result of the 

DMA to manage QA issues and produce data of 

known quality. The plan should also identify steps to 

address violations of QC criteria should they occur 

during the full-scale field effort. 

• Using data collected during the DMA as the input 

values to construct a statistical sampling design. One 

of the acknowledged pitfalls associated with using 

classical statistical tools in sampling design is that 

project teams seldom have a sound estimate of total 

measurement error to use in establishing sample 

quantities, grid sizes, and other factors. With results 

from a DMA, project teams can use classical 

statistical tools (such as the Visual Sample Plan 

software, http://vsp.pnl.gov/) more effectively in 

sampling design because they have generated sitespecific information on method error. 

• Evaluating site-specific method error helps establish 

initial collaborative relationships that can be refined 

as the program progresses. These relationships 

provide a framework for indicating problematic 

samples or “out of control” QC issues. 

• Providing insight into how the full set of data may be 

statistically evaluated. Statistical methods such as 

those described in the guidance on Data Quality 

Assessment (EPA 2006b) may be examined for 

effectiveness and used to test basic project data 

assumptions, contaminant distributions, and sampling 

designs planned for use at a site. 

• Testing the suitability of data visualization and 

management strategies. 

How are results of an analytical DMA applied? 

If the DMA is properly designed, the data can be used for 

the following: 

• Support development or refinement of the CSM 

• Estimate matrix and contaminant concentration 

variability at different spatial scales 

• Identify potential interferences 

• Ascertain whether particle size is correlated with 

contaminant concentrations 

• Evaluate the value and effectiveness of different 

sample collection and processing techniques to 

optimize SOPs 

• Establish a comparability relationship between two 

measurement systems 

• Establish proper decision logic and sequencing of 

data collection 

In this section, readers will be introduced to some of the 

basics of environmental decision making. Understanding 

the context in which decisions are made is essential for 

discerning how managing associated uncertainties affects 

the decisions. Managing decision uncertainties is 

important to developing realistic and protective field-based 

action levels for a site. Overly protective standards can 

significantly increase project costs, while less stringent 

standards may lead to controversy or surprises later in the 

project. 

Under Triad, use of collaborative data becomes essential 

to provide sufficient density, and these data sets are then 

compared to improve decision certainty. A variety of 

methods can be used to assess the comparability 

between measurement systems. “Measurement system” 

refers to the combination of techniques used to collect and 
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process a sample in addition to the actual analysis. For 

example, analysis of split samples that have been wellhomogenized (which, depending on the matrix and 

analyte, may require grinding to a uniform particle size), 

will be only part of the measurement system compared. 

The overall measurement system for both XRF and the 

compared laboratory technique must be considered for 

technologies such as XRF spectrometers that can be used 

for in situ sampling and where the effect of sample 

properties cannot be removed from the analytical process. 

Although there are a variety of mechanisms to assess the 

comparability of data sets from two different measurement 

methods, traditional statistical techniques are widely used 

to begin the process of exploring the data. Traditional 

statistical evaluations of data sets can include summary 

statistics and statistical plots (box and whisker plots, 

histograms, and probability plots) to evaluate distributional 

characteristics of the sample population (normal, 

lognormal, or other) that decide what types of statistical 

manipulations are warranted. 

Field-based and fixed-laboratory results can be compared 

by developing correlation scatter plots, or by calculating 

best-fit lines and correlation coefficients to describe the 

mathematical relationship between the data sets. If a field 

method is shown to be biased high uniformly across a site, 

the bias might be used to provide a natural safety factor 

when compared with regulatory limits. Alternatively, if the 

bias is highly consistent and predictable across samples 

and concentrations, adjustment for the bias is a possibility. 

One common use of a DMA is to demonstrate that a lessrigorous analytical method correlates well with an 

established method. Best-fit lines and correlation 

coefficients may be used for this purpose. If correlation is 

lower than expected, a DMA may show that decision error 

is low enough that the less-rigorous method is still 

acceptable for the purpose of increasing sampling density 

to delineate contaminant footprints and control data 

variability on large and small spatial scales. This ability is 

valuable since matrix heterogeneity and small-scale 

variability are often the largest contributor to total 

measurement error in environmental data. 

In real life, the “true value” is unknown. Any data result, 

no matter how good the analytical method, is an 

approximation of the true value. If sample heterogeneity 

and interferences are controlled, the more sophisticated 

analytical method will be closer to the “truth.” However, 

the expense of these methods can limit the number of 

samples that can be analyzed. Non-representative data 

that are biased by matrix problems can lead an unwary 

decision maker into costly decision errors. Using two 

analytical methods helps ensure that matrix heterogeneity 

does not mislead the decision maker. One analytical 

method (usually the fixed-laboratory method) will produce 

more accurate measures of concentration, and that 

method is used as the surrogate for the “true value.” The 

other method (usually a field method) produces a more 

accurate representation of sample representativeness, but 

at the expense of data accuracy. Therefore, in a DMA, 

traditional laboratory results are assigned as the “true 

value” (y axis) in Figures 8 through 10. Field results are 

assigned as the “estimated value” (x axis). “Investigation 

levels” are selected through the process of comparing the 

two sets of data and minimizing the likelihood of decision 

errors. Field results that fall above and below the upper 

and lower investigation levels can support confident 

decisions. Field results that fall within the concentration 

range between the investigation levels require analysis by 

the more accurate laboratory method. 

Figure 8 illustrates two types of decision errors possible 

when two sample analysis methods are compared. 

Assume all data points fall within the ellipse. “False 

positive” decision errors (also called “false dirty” decision 

errors) occur when a data result falls above an action level 

when the true result is below the action level, and the 

decision maker undertakes unnecessary remediation. 

“False negative” decision errors (also called “false clean” 

decision errors), occur when a data result is below the 

action level when the true result is actually above the 

action level. If the decision maker accepts the data at 

face value, erroneous decisions are possible that 

potentially increase risk to human or ecological receptors. 

Figure 8. Misclassification Ellipse 

Xc denotes the action level, areas I and II indicate the false positive and the 

false negative decision error zone, respectively. Areas AA and BB indicate 

zones of consistent decisions between the data results and the true values.
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Figure 9 shows how using a safety factor below the action 

level can reduce the false clean (false negative) decision 

error rate. 

Figure 10 shows how safety factors both above and below 

the action level reduces both the false clean and false 

dirty decision errors. 

Figure 9. Misclassification Ellipse with Safety Factor.

Xc denotes the action level, area I indicates the false positive error zone, and 

area II indicates the false negative error zone that has been reduced by the 

safety factor. Areas AA and BB indicate zones of consistent decisions between 

the data results and the true values. 

Figure 10. Multiple Investigation Levels. 

Estimated Value

True Value

Action 

Level

II

I

Upper 

Investigation 

Level

Lower 

Investigation 

Level

Both types of errors are reduced. Shaded area represents data results falling 

between the investigation levels that require more accurate analysis. 

What else should project managers and technical staff 

consider when planning a DMA? 

Refining decision criteria and decision logic based on the 

results of a DMA can significantly improve project 

performance. Results from the DMA should be integrated 

as quickly as possible into work strategies to assure 

project efficiency (See EPA 542-F-05-008, “Use of 

Dynamic Work Strategies Under a Triad Approach for Site 

Assessment and Cleanup — Technology Bulletin, 

www.brownfieldstsc.org/pdfs/DWSBulletin.pdf). 

Project managers must identify resource needs to support 

real-time decision-making during the DMA. These 

resources include DSTs and associated expertise. For 

example, a DST may be required to assist in developing 

and verifying field-based action levels. 

Project managers and technical staff should refine the 

type and level of field documentation required based on 

the DMA. Site-specific work plans and SOPs for field 

methods with sufficient flexibility can be easily revised as 

more is learned about a site, even after the DMA is 

complete. Team member responsibilities should be 

consistent and modified as needed based on the DMA. 

Creativity and flexibility in procurement and contracting is 

often needed for a DMA, or in response to a DMA. 

Review EPA’s procurement guide at 

www.brownfieldstsc.org/pdfs/procurement.pdf as a 

starting point for procurement and contracting strategies 

for Triad investigations. Unitizing or classifying costs per 

analytical sample or borehole, for example, is an 

illustration of a financial strategy that allows project 

planners to accurately track costs in real time as a 

dynamic investigation progresses. Potential vendors may 

provide free resources for the DMA to market and 

demonstrate the applicability of the technology, reducing 

the cost to the project — most commonly with newer and 

relatively unproven technologies. 

Workload balancing and task sequencing are examples of 

strategies used to ensure that project team members are 

aware of the project’s time-critical tasks. Team members 

should work together to prioritize each task so that no task 

slows the entire effort while others (drillers, samplers, or 

analytical chemists) are idled, but still billing time. A DMA 

can provide important information on potential bottlenecks 

in data generation and flow, so that effective field 

coordination and sequencing strategies can be developed 

for the main field investigation. 

How is a DMA documented? 

A DMA is documented through a variety of formal and 

informal means. Project plans such as Sampling and 

Analysis Plans (SAPs) and QAPPs are formal documents 

that undergo mandatory review. When they are written for 

a Triad investigation, these documents outline the DMA to 

refine the data collection schemes and strategies to 

manage uncertainty. Site-specific SOPs for field methods 

are useful for documenting the outcome of a DMA in the 
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form of exact procedures to be used at a given site, 

especially when there are deviations from vendorrecommended procedures or published methods (such as 

SW-846 methods). 

More informal methods, such as memoranda of 

understanding, meeting notes, project Web sites, 

E-rooms, and electronic bulletin boards, also document 

the DMA process. These informal methods are 

particularly useful to document stakeholder participation 

and buy-in for Triad investigations. Informal discussions 

with stakeholders after the DMA can also be useful to 

accelerate document comment, revision, and submittal. 

Regardless of the method used to document a DMA, good 

records are essential to scientifically validating and legally 

defending selection and use of analytical methods and, 

ultimately, the conclusions based on data generated in the 

field. Project teams are strongly encouraged to plan for 

and complete DMA documentation. 

Are DMAs difficult, time-consuming, or costly? 

Generally, DMAs can be completed with 20 or fewer 

samples, but the level of effort can be scaled to the 

magnitude of expected site work. However, more data 

can be extremely valuable in the case of many real-time 

technologies where costs per sample are relatively low. If 

linear regressions will be generated, a good rule of thumb 

is at least 10 paired samples; however, 20 or more can 

provide exceedingly robust statistical evaluations. A key 

concept for analytical DMAs is to focus sample pairs 

around action or decision points (for example, 5 low 

values, 5 higher values, and 10 in areas around action 

levels). Using real-time measurements provides a level of 

assurance that samples submitted for fixed-laboratory 

comparative analysis are in the range of interest. Data 

sets with high percentages of non-detected pairs are not 

beneficial for statistical evaluations. 

Information collected during the DMA will provide a basis 

for establishing QA/QC protocol, sample support, 

preliminary relationships for collaborative data sets, load 

balancing, and sequencing field activities. The DMA 

results are a means for optimizing use of resources and 

become part of the final data set that will be compiled to 

reach project decision points. 

The cost and time required for a well-designed DMA are 

usually a small fraction of the cost of a full-scale field 

program. When the project team designs a DMA, the cost 

and time allotted should be proportionate to the impact of 

the DMA on reduction in uncertainty about a site condition. 

It is expected that relationships evaluated under the DMA 

will continue to be refined as more data become available. 

Another cost savings consideration is the use of archived 

material (where appropriate) for comparative analyses that 

may have already been completed. These samples 

provide the advantage that concentration ranges of 

contaminants of concern (COCs) to target samples in the 

primary areas of interest will be known. 

In any project, the methods being used will be under 

scrutiny. When a DMA is not conducted on a limited 

number of samples, the data collected during the full site 

investigation must be used to demonstrate method 

performance. The DMA therefore provides an opportunity 

to change tactics affordably if a method does not perform 

as expected, compared with the alternative of having to 

change tactics after the full site investigation. 

Appropriate professional expertise and good 

communication among team members about their data 

needs are critical to planning a successful DMA. The cost 

of a DMA is recouped many times over though cost 

avoidance of unusable data and recharacterization efforts. 

Are DMAs appropriate in all cases? 

Some investigation and cleanups may prescribe set 

sample numbers or recommend limited sampling through 

guidance. In these cases, DMAs may not be appropriate. 

Similarly, resources may not be adequate at some sites 

with limited grant funding to accommodate a DMA. 

Projects with adequate resources to employ established 

mobile or fixed-laboratory methods at sufficient density 

may be inappropriate, while those that require method 

modifications or careful examination of sampling and 

spatial uncertainties may benefit significantly from DMAs. 

Even if only fixed-laboratory methods are used, a DMA 

may be considered if there is any question about matrix 

interference effects. A relatively limited number of pilot 

samples can save large sums of money by detecting 

extraction issues and interference problems at the start of 

a program. 

It should be noted, however, that a DMA is beneficial for 

most applications precisely because a particular field 

analytical technique, direct-sensing tool, or innovative 

strategy is identified as potentially applicable to costeffectively increase data density, refine the CSM, or 

address small-scale variability and matrix heterogeneity. 

In some cases, sampling locations for an early 

assessment are obvious (for example, areas of visible 
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staining, product, lagoons, or discharge points), while 

other cases are more complicated, and identifying 

appropriate sampling locations is a problem. Depending 

on the nature of the suspected contamination, some 

sample material can be archived and potentially used later 

as part of a DMA for an expanded assessment or 

additional investigation. 

Adding limited additional cost for analyzing field analytical 

methods, direct-sensing tools, or other innovative 

technologies does not significantly raise project expenses 

at sites with elevated expenditures associated with 

collecting deeper subsurface samples. In these cases, 

inexpensive analytics and direct sensing tools can provide 

greater vertical density to help target locations for more 

expensive traditional laboratory samples. Furthermore, 

the increased density can support a more efficient design 

of cleanup strategies when required, leading to lower 

project lifecycle costs. 

Finally, the definition of a Brownfield — “a property, 

redevelopment, or reuse which may be complicated by the 

presence or potential presence, of a hazardous 

substance, pollutant, or contaminant” — underscores the 

need for higher data density and collaborative data sets 

that accompany DMAs. Regardless of whether significant 

contamination or just the perception of contamination is 

present at a property, DMAs used to optimize sampling 

schemes with innovative tools provide a higher data 

density that facilitates timely revitalization. These welldesigned data sets are particularly helpful to address 

stakeholder concerns and provide a level of comfort that 

allows developers, insurance partners, risk partners, 

public stakeholders, state agencies, local agencies, and 

others to be involved, invested, and reassured with a 

project outcome. 

EXAMPLES OF DMA IMPLEMENTATION 

Example #1: Immunoassay: Wenatchee Tree Fruit 

Test Plot Site, Wenatchee, Washington 

The Wenatchee Tree Fruit Test Plot area contained soil 

contaminated with pesticide compounds from agricultural 

research conducted from 1966 through the mid-1980s. 

The U.S. Public Heath Service (PHS) and EPA used the 

test plot area to evaluate the effectiveness of various land 

disposal methods for pesticides. In 1997, USACE 

conducted an integrated site characterization and 

remediation program that allowed characterization, 

excavation, and segregation of contaminated material in 

real time. Work was completed under a voluntary cleanup 

program with regulatory oversight of the Washington 

Department of Ecology. 

A DMA was conducted to provide critical input to the 

project design because the project would use IA methods 

to drive the dynamic work strategy. The DMA was 

structured to evaluate both the utility of the IA kits and to 

develop field-based action levels. 

Highlights of the DMA

The DMA confirmed that the IA test kits were intentionally 

biased 100 percent high by the manufacturer to reduce 

the chance of false negative results. The DMA 

accommodated the response of the kits to structurally 

similar compounds beyond the target compounds. Taking 

into consideration the high bias and correlations with 

fixed-laboratory results, the DMA showed that the DDT 

test kit result exceeding 5 parts per million (ppm) could 

indicate elevated levels of DDT, DDE, or DDD. Likewise a 

cyclodiene kit response of 0.1 ppm indicated the 

possibility that regulatory action levels for endrin or 

dieldrin were exceeded. Therefore, these values were 

selected as the investigation levels to make decisions 

based on the kit results. 

Several modifications to the IA kit procedures were made 

based on DMA results. For one, pure methanol was used 

instead of a water-methanol mix, and extraction volumes 

were doubled to 20 milliliters (mL) to bracket action levels 

based on cross reactivity and sensitivity results. The 

Site Facts 

9 Disposal area of an agricultural research facility. 

9 Reuse scenarios not identified. Changing land use 

nearby increased concern that the area should 

undergo investigation and remediation.

9 Principal threats included off-site migration, 

contamination of other media, and direct contact. 

9 COCs included organochlorine, organophosphorous, 

and other pesticide compounds. 
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resulting 20-milliliter (mL) extracts were sufficient to run 

both the DDT and cyclodienes IA analyses. 

Some fixed-laboratory detection methods for collaborative 

data were also modified. The project team used MS 

detection instead of the method specified nitrogen and 

phosphorus detector (NPD) to improve selectivity and 

meet project required quantitation limits for the 

organophosphorus pesticides. Similarly, the team used a 

GC NPD method instead of high-pressure liquid 

chromatography (HPLC) for the carbamate pesticides to 

reduce interference. In addition, the surrogate compound 

was changed to a compound rarely used in agricultural 

applications. Non-target compounds and tentatively 

identified compounds (TICs) from fixed-laboratory 

methods also became crucial to understanding IA kit 

response from a broad range of contaminants. 

During the characterization phase, the project team 

continued to collect a subset of samples for fixedlaboratory analysis. These results, used in conjunction 

with DMA data, indicated that the 5 ppm investigation level 

for the DDT IA kit was overly conservative. With regulator 

approval, the DDT IA investigation level was raised to 10 

ppm to complete excavation and waste segregation. 

No false negative decision errors for the action levels for 

individual pesticides were encountered. A low percentage 

of false positive errors (usually associated with of the 

presence of endosulfans in the samples) was encountered 

for the cyclodiene kit. Use of the DMA and Triad 

principles resulted in an estimated savings of 50 percent 

for total project costs. 

More information on the DMA and Triad work conducted 

for this site is available at the Triad Resource Center Web 

page: www.triadcentral.org. 

Example #2: Poudre River Site, Fort Collins, Colorado 

The Poudre River Site is located in Fort Collins, Colorado, 

along the Cache La Poudre River. The presence of coal 

tar in the river and fuel-related ground water 

contamination prompted EPA to initiate a Targeted 

Brownfields Assessment in May 2003. Two DMAs were 

conducted as part of the Targeted Brownfields 

Assessment. 

Highlights of the DMAs

One DMA focused on demonstrating the capability of 

passive soil gas samplers from EMFLUX (now known as 

Beacon Environmental) to detect volatile organic 

compounds in the subsurface. 

A full-scale soil gas survey was implemented using 333 

devices after the passive soil gas DMA successfully 

demonstrated the use of the EMFLUX passive soil gas 

samplers. The data from the study were used to create 

isoconcentration maps for target analytes, helping to 

refine the CSM and optimize the field investigation drilling 

program. 

A DMA was also performed for a modified EPA SW-846 

Method 8260 used for the analysis of ground water 

samples on site via a mobile laboratory. The project team 

used the results from the ground water DMA to set the 

applicable detection and reporting limits for GC/MS 

results, design appropriate initial calibration and QC 

protocols, and evaluate the types and concentrations of 

contaminants expected in ground water at the site. The 

DMA also provided site-specific information about the 

accuracy and precision of the method. 

Another aspect of the Poudre River Site field program that 

showed the usefulness of a DMA study was geophysical 

survey work. A ground penetrating radar (GPR) survey 

conducted at the site suffered from poor signal penetration 

because of soil conditions. However, the performancebased contract used for the work did not allow the cost of 

this GPR work to be billed against the program. Had the 

GPR vendor conducted a DMA, this problem would likely 

Site Facts 

9 Site includes a former manufactured gas plant (MGP) 

that operated from approximately 1900 to 1930.

9 Site includes a former municipal burn landfill that 

operated from the late 1930s to the early 1960s.

9 Proposed reuse was recreational, commercial, and 

industrial.

9 COCs included chlorinated solvents and petroleumrelated substances.
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have been discovered earlier and at significantly lower 

cost to the vendor. 

More information on the DMA and Triad work conducted 

for this site is available at the Triad Resource Center Web 

page: www.triadcentral.org. 

Example #3: X-ray Fluorescence: Fort Lewis Small 

Arms Firing Range, Fort Lewis, Washington 

In 2003, USACE used the Triad approach to expedite site 

characterization and remediation of contaminated soil at 

the former Evergreen Infiltration Training Range in Fort 

Lewis Washington. A dynamic sampling and analytical 

strategy based on rapid field-based analytical methods 

was used to streamline site activities and save resources 

while increasing confidence in remediation decisions. 

Initial evaluations included a suite of metals associated 

with small arms firing ranges (antimony, arsenic, copper, 

iron, lead, tin, and zinc). The DMA indicated that lead was 

the primary risk driver given regulatory thresholds and site 

action levels. After the DMA, the remaining 

characterization and remediation work at the Fort Lewis 

site focused on lead. 

Highlights of the DMA

At the beginning of the field investigation, a DMA was 

conducted using field-portable XRF and fixed-laboratory 

methodologies (EPA SW-846; sections 6010 and 6020). 

Forty samples were collected and analyzed by both 

methods. The DMA established a strong correlation 

between XRF and laboratory data for lead (see Figure 11), 

even with minimal soil sample homogenization. The 

measured correlation coefficient (R2) between the 

methods was 0.96; however, inspection of the slope and 

y-intercept indicate some loss of linearity. Examining 

concentrations for individual sample pairs indicates that 

XRF results tend to under-report concentrations as 

concentrations increase above percent levels (10,000 

ppm). Under-reporting was not a concern for the project 

team, however, since various action levels for lead were 

all less than 1,000 ppm. 

Figure 11. Correlation Curve.

y = 0.74x + 308.27

R2

 = 0.96

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

0 20000 40000 60000

Fixed-lab Lead Concentration (mg/kg)

XRF Lead Concentration (mg/kg)

XRF data are plotted against data from EPA methods 6010/6020 for Fort Lewis 

Small Arms Firing Range DMA. 

A regression was also generated using results for a 

subset of split samples in the 0 to 1,000 ppm range for 

lead to evaluate XRF performance in this critical area. 

The DMA confirmed that the XRF reliably quantified lead 

concentrations down to 45 milligrams per kilogram 

(mg/kg), and so was accurate in locating both “clean” and 

“dirty” areas. Through the DMA, it was assured that data 

of “known and documented” quality could be produced. 

The level of data quality was shown to be sufficient for the 

project’s decisions. Although more intensive sample 

preparation or use of substitution methods for nondetected XRF values may have produced a better 

regression, the project’s data needs did not require the 

additional precision. 

More information on the DMA and Triad work conducted 

for this site is available at the Triad Resource Center Web 

page: www.triadcentral.org. 

Site Facts 

9 Site is a former small arms firing range that operated 

over an unknown time period between 1917 and 1965.

9 Proposed reuse was for military barracks.

9 Principal threat is direct contact with contaminated soil.

9 COCs were metals including lead and arsenic. 
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SOURCES OF ADDITIONAL INFORMATION 

The Triad approach is encountering ever greater 

acceptance by regulatory agencies, as well as by 

professional and industry organizations. Communities 

and project teams interested in implementing the Triad are 

encouraged to contact the BTSC for more information on 

these organizations and for successful examples of Triad 

applications. More detailed information on DMA and on 

the Triad approach can be found in the Brownfields 

Technology Primer Series document Using the Triad 

Approach to Streamline Brownfields Site Assessment and 

Cleanup, which is available at www.brownfieldstsc.org. 

Project profiles, case studies, and other information on 

applying the Triad approach can be found at

www.triadcentral.org. The BTSC provides other technical 

bulletins related to best practices embodied in the Triad 

approach such as "Use of Dynamic Work Strategies 

Under a Triad Approach for Site Assessment and Cleanup 

— Technology Bulletin." Additional documents providing 

critical information on related issues such as Green 

Remediation and Vapor Intrusion are also available. 

REFERENCES 

Brownfields and Land Revitalization Technology Support 

Center Web Site. 2008. Accessed August. 

www.brownfieldstsc.org

Fort Lewis, Washington. 2003. “Fort Lewis Agreed Order 

RI Demonstration of Method of Applicability Sampling and 

Analysis Plan Addendum Former Small Arms Ranges.” 

October. Available at: 

www.triadcentral.org/user/doc/TPP-FortLewisDMAMemo.pdf

Gilbert, R. 1987. “Statistical Methods for Environmental 

Pollution Monitoring. Wiley. 

Lesnik, B. 2000. “Method Validation for the Resource 

Conservation and Recovery Act Program.” LC/GC 

Magazine, Volume 18, Number 10. October. 

Pacific Northwest National Laboratory. “Visual Sampling 

Plan Web page.” Accessed August. Available at: 

http://vsp.pnl.gov

Triad Resource Center Web Site. 2008. Accessed July. 

Available at: www.triadcentral.org/index.cfm 

U.S. Environmental Protection Agency (EPA). 2000. 

“Guidance for Data Quality Assessment: Practical 

Methods for Data Analysis” (QA/G-9). EPA 600-R-96-084. 

July. Available at www.epa.gov/oust/cat/epaqag9.pdf 

EPA. 2003. “Using the Triad Approach to Streamline 

Brownfields Site Assessment and Cleanup.” Brownfields 

Technology Primer Series. EPA 542-B-03-002. June. 

Available at: 

http://www.brownfieldstsc.org/pdfs/Triadprimer.pdf 

EPA. 2004a. “Case Study of the Triad Approach: 

Expedited Characterization of Petroleum Constituents and 

PCBs Using Test Kits and a Mobile Chromatography 

Laboratory at the Former Cos Cob Power Plant Site.” 

EPA 542-R-04-008. 

EPA. 2004b. "Innovations in Site Characterization Case 

Study: Expedited Characterization and Excavation of Lead 

in Soil Using X-Ray Fluorescence at the Ross Metals Site, 

Rossville, Fayette County, Tennessee." December. 

EPA. 2005a. “Understanding Dynamic Work Strategies 

Under a Triad Approach for Site Assessment and 

Cleanup—Technology Bulletin.” September. Available at: 

www.brownfieldstsc.org/pdfs/DWSBulletin.pdf

EPA. 2005b. “Understanding Procurement for Sampling 

and Analytical Services Under a Triad Approach.” EPA 

542-R-05-022. June. Available at: 

www.brownfieldstsc.org/pdfs/procurement.pdf

EPA. 2005c. “Use of Field Portable X-Ray Fluorescence 

(FPXRF) and the Triad Approach to Investigate the Extent 

of Lead Contamination at a Small Arms Training Range, 

Fort Lewis, WA.” August. 

EPA. 2006a. “Data Quality Assessment: A Reviewer’s 

Guide.” EPA 240-B-06-002. February. Available at: 

www.epa.gov/quality/qs-docs/g9r-final.pdf 

EPA. 2006b. “Data Quality Assessment: Statistical 

Methods for Practitioners.” EPA 240-B-06-003. February. 

Available at: www.epa.gov/quality/qs-docs/g9s-final.pdf 

EPA. 2006c. “Guidance on Systematic Planning Using 

the Data Quality Objectives Process.” EPA/240/B-06/001. 

February. Available at: www.epa.gov/quality/qs-docs/g4-

final.pdf 
Demonstrations of Method Applicability under a Triad Approach 

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Emergency Response August 2008 

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EPA. 2006d. “SW-846 Manual.” Last updated October 

18, 2006. Available at: 

www.epa.gov/epaoswer/hazwaste/test/sw846.htm 

EPA. 2008a. “Environmental Technology Verification 

(ETV) Web page.” Accessed August. Available at: 

www.epa.gov/etv

EPA. 2008b. “Performance-Based Measurement System 

(PBMS) Web page.” Accessed August. Available at: 

www.epa.gov/SW-846/pbms.htm 

EPA. 2008c. Information Quality Guidelines Web Site. 

Accessed July. Available at: 

www.epa.gov/quality/informationguidelines 

NOTICE AND DISCLAIMER 

This bulletin was prepared by EPA’s Office of Solid Waste 

and Emergency Response under EPA Contract Nos. 68-

W-02-034 and EP-W-07-078. The information in this 

bulletin is not intended to revise or update EPA policy or 

guidance on how to investigate or cleanup Brownfields or 

other revitalization sites. Mention of trade names or 

commercial products does not constitute endorsement or 

recommendation for use. 

This bulletin can be downloaded from EPA’s Brownfields 

and Land Revitalization Technology Support Center at 

www.brownfieldstsc.org. For technical inquiries regarding 

this bulletin please contact Stephen Dyment of EPA at 

(703) 603-9903, or dyment.stephen@epa.gov.