United States
               Environmental Protection
               Agency
Office of Solid Waste and
Emergency Response
(5102G)
EPA 542-R-01-016
October 2001
www.epa.gov
www.clu-in.org
V EPA   Current Perspectives in Site Remediation and Monitoring
                USING THE TRIAD APPROACH TO IMPROVE  THE COST-
                EFFECTIVENESS OF HAZARDOUS WASTE SITE CLEANUPS


                D. M. Crumbling1
                Executive Summary

                U.S. EPA's Office of Solid Waste and Emergency Response is promoting more effective
                strategies for characterizing, monitoring, and cleaning up hazardous waste sites. In particular,
                the adoption of a new paradigm holds the promise for better decision-making at waste sites. This
                paradigm is based on using an integrated triad of systematic planning, dynamic work plans, and
                real-time measurement technologies to plan and implement data collection and technical
                decision-making at hazardous waste sites. A central theme of the triad approach is a clear focus
                on overall decision quality as the overarching goal of proj ect quality assurance, requiring careful
                identification and management of potential causes for errors in decision-making (i.e.,  sources
                of uncertainty).
                Perspective

                EPA's Office of Solid Waste and Emergency
                Response (OSWER) manages the Superfund,
                RCRA Corrective Action, Federal Facilities,
                Underground Storage Tank, and Brownfields
                programs. "Smarter solutions" for the techni-
                cal evaluation and clean up of such contam-
                inated sites can take two major forms. One is
                through the adoption of new technologies and
                tools; the other is to modernize the strategy by
                which tools are deployed. Both are connected
                in a feedback loop,  since strategy shifts are
                both  fueled by and fuel the  evolution of
                innovative technology. In the area of hazar-
                dous waste site monitoring and measurement,
                new technologies have become available with
                documented  performance   showing  them
                capable of substantially improving the cost-
                effectiveness of site characterization.

                The current traditional  phased engineering
                approach to site investigation (mobilize staff
          and equipment to a site, take samples to send
          off to a lab, wait for results to come back and
          be  interpreted, then re-mobilize to collect
          additional samples, and repeat one or more
          times) can be incrementally improved by the
          occasional use of on-site analysis to screen
          samples so that expensive off-site analysis is
          reserved for more critical samples.  Yet, as
          discussed elsewhere, integration of new tools
          into site cleanup practices faces an array of
          obstacles [1]. If the cost savings promised by
          new technologies is to be realized, a funda-
          mental change in thinking is needed. Faster
          acceptance of cost-effective characterization
          and monitoring tools among practitioners is
          even more  important now that Brownfields
          and Voluntary Cleanup Programs are gaining
          in importance.  For these programs that focus
          on site redevelopment and reuse, factors such
          as  time, cost,  and quality  are  of prime
          concern. Modernization of the fundamental
          precepts underlying characterization  and
          cleanup practices offers cost savings of about
                 EPA, Technology Innovation Office

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50% while simultaneously improving the quality of site
decision-making.

The idealized model for an innovation-friendly system
that produces defensible site decisions at an affordable
cost would have the following characteristics:

  it would be driven by achieving performance, rather
   than by  complying with checklists that do not add
   value;

  it would use transparent, logical reasoning to articu-
   late project goals, state assumptions,  plan site
   activities, derive conclusions, and make defensible
   decisions;

   it would value the need for a team of technical
   experts  in  the  scientific,  mathematical,  and
   engineering disciplines required  to  competently
   manage the complex issues of hazardous waste sites;

   it would require regular continuing education of its
   practitioners, especially in rapidly evolving areas of
   practice;

   its practitioners would be able to logically evaluate
   the  appropriateness of an innovative technology
   with respect to project-specific conditions and prior
   technology performance, with  residual areas of
   uncertainty being identified and addressed; and

   it   would   reward   responsible risk-taking by
   practitioners who would not fear to ask, "why don't
   we look into...?" or "what if we tried...?"

What form might such an idealized model take ? A maj or
step toward this goal would involve institutionalizing the
triad of systematic planning, dynamic work plans, and
real-time analysis as the foundation upon which cost-
effective, defensible site decisions and actions are built.
None of the  concepts in the triad are new, but the boost
given by computerization to technology advancement in
recent years is now providing strategy options that did
not exist before. Pockets of forward-thinking practition-
ers are already successfully using this triad; the concept
is proven.

The Triad's First Component: Systematic Planning

Most organizational  mission  statements  pledge  a
commitment to quality. EPA is no different. EPA Order
5360.1 CHG 2 requires that work performed by, or on
behalf of, EPA  be  governed by a mandatory quality
system to ensure the technical validity of products or
services  [2]. A fundamental aspect of the mandatory
quality system is thoughtful, advance planning. The EPA
Quality Manual for Environmental Programs explains
that "environmental data operations shall be planned
using a systematic planning process that is based on the
scientific method. The planning process shall be based
on a common sense, graded approach to ensure that the
level of  detail in planning is commensurate with the
importance  and  intended use  of the work and  the
available resources" [3].

Systematic  planning  is  the scaffold around  which
defensible site decisions are constructed. The essence of
systematic planning is asking the right questions and
strategizing how best to answer them. It requires that for
every planned  action the responsible individual can
clearly answer  the  question, "Why am I doing this?"
First and foremost, planning requires that key decision-
makers collaborate with  stakeholders to  resolve clear
goals for a proj ect. A team of multi-disciplinary, experi-
enced technical staffthen works to translate those goals
into  realistic  technical  objectives.  The  need  for
appropriately educated,  knowledgeable  practitioners
from all disciplines relevant to the site's needs is vital to
cost-effective project success.

Multi-disciplinary Technical Team

During the planning phase, the most resource-effective
characterization tools for collecting data are identified
by technically qualified staff who are familiar with both
the established and innovative technology tools of their
discipline.  For example, the hydrogeologist will be
conversant not only with  the  performance and cost
issues of well drilling techniques, but also with the more
innovative and (generally)  less costly  direct push
technologies entering common use. The sampling design
expert will  understand  how  uncertainties  due to
sampling considerations (where, when, and how samples
are collected) impact the  representativeness  of data
generated from those samples,  and thus the ability of
those samples to provide  accurate site information [4].
The team's analytical chemist will not only know the
relative merits of various traditional sample preserva-
tion, preparation, and analysis  methods, but also the
strengths and  limitations  of innovative techniques,
including on-site analytical options. The  chemist's
responsibilities include designing the quality control
(QC) protocols that reconcile project-specific data needs
with the abilities of the selected analytical tools. When
risk assessment is part of a project, involvement of the
risk assessor at the beginning of proj ect planning is vital

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to ensure that a meaningful data will be available for
risk assessment purposes. Other technical experts might
include (depending on the nature of the project) regula-
tory experts, soil scientists, geochemists,  statisticians,
wildlife biologists, ecologists, and others.

When  project planners wish to express the desired
decision confidence objectively and rigorously in terms
of a statistical certainty level, statistical expertise is
required to translate that overall decision goal into data
generation strategies. Demonstrating overall statistical
confidence in decisions based on environmental data
sets will require the cost-effective blending of

   the number of samples,

   the expected variability in the matrix (i.e., matrix
    heterogeneity),

   the  analytical  data   quality (e.g., precision,
    quantitation limits, and other attributes of analytical
    quality) [5],

   the expected contaminant concentrations (i.e., how
    close are they expected to be to regulatory limits),

   the  sampling  strategy (e.g.,  grab samples vs.
    composites; a  random  sampling  design vs.  a
    systematic design), and

   the costs.

Since sampling design and analytical strategy interact to
influence the statistical confidence in  final decisions,
collaboration between an analytical chemist, a sampling
expert, and a statistician is key to selecting a  final
strategy that can achieve project goals accurately, yet
cost-effectively. Software tools are also available now to
assist technical experts to develop sampling and analysis
designs.  Although they can be powerful tools, neither
statistics nor software programs  can be used as "black
boxes." A knowledgeable user must be able to verify
that key assumptions  hold true in order to draw sound
conclusions  from statistical analyses and  software
outputs.

The statistician is concerned with controlling the overall
(or summed) variability (i.e., uncertainty) in the  final
data set, and with the interpretability of that final data
set with respect to the decisions to  be made.  The
statistician does this during project planning by addres-
sing issues related to  "sample support" (a concept that
involves ensuring that the  physical  dimensions  of
samples are representative of the original matrix in the
context of the investigation), by selecting a statistically
valid sampling design, and by estimating how analytical
variability could impact the overall variability. The field
sampling  expert is responsible for implementing the
sampling  design while controlling contributions to the
sampling  variability as  actual sample locations are
selected  and as  specimens  are  actually collected,
preserved, and transported to the analyst. The analytical
chemist is responsible for controlling components of
variability and uncertainty that stem from the analytical
side (such as analyte extraction, concentration,  and
instrumental determinative analysis), but also for over-
seeing aspects of sample preservation, storage, homo-
genization, and  possibly subsampling (if done by the
analyst). The analytical chemist should select analytical
methods  that  can  meet  the analytical variability
(precision) limits  estimated by the  statistician. The
chemist must be able to evaluate the relative merits of
methods  for  their detection  capacity (detection or
quantitation  limits), specificity (freedom  from inter-
ferences), and selectivity (uniqueness of the analytes
detected), and match those properties to the data type
and quality needed by all the data users involved with
the project. Finally, the chemist is responsible for desig-
ning an analytical  QC program that will establish that
the analytical data sets are of known and documented
quality.

Controlling  the various  sources  of analytical  and
sampling  uncertainties (assuming no clerical or data
management errors) ensures that data of known overall
quality are generated. Since the single largest source of
uncertainty  in contaminated  site decisions generally
stems from matrix heterogeneity, increasing the samp-
ling density is critical to improving decision confidence.

Managing Uncertainty as a Central Theme

Project planning documents should be organized around
the theme of managing the overall decision uncertainty.
The purpose of systematic planning, such as EPA's Data
Quality  Objectives (DQO)  process  used for  the
systematic planning of environmental data collection, is
to first articulate clear goals for the anticipated project,
and then  to  devise cost-effective strategies that can
achieve those goals. Project planning documents [such
as work management plans, quality assurance  project
plans (QAPPs),  sampling and analysis plans (SAPs),
etc.] should be written so that the reader can explicitly
identify what those decisions  are and what sources of
uncertainty could potentially cause those decisions to be
made  in  error.  The  balance  of  project  planning

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documents should discuss the rationale and procedures
for managing each major source of uncertainty to the
degree necessary to achieve the overall decision quality
(i.e., decision confidence and defensibility) desired by
project managers and stakeholders.

After completion of the proj ect, summary reports should
clearly discuss the project  goals that were  actually
achieved, the decisions that were made, the uncertainties
that  actually impacted  project decision-making, the
strategies used to manage these uncertainties, and the
overall confidence in  the project outcome (which is a
function of what uncertainties remain).

Conceptual Site Model

Using  all  available information, the technical  team
develops a conceptual site model (CSM) that crystallizes
what is already known about the site and identifies what
more must be known in order to achieve the project's
goals. A  single project may have more than one CSM.
Different CSM formulations are used to depict exposure
pathways for risk assessment, the  site's  geology or
hydrogeology, contaminant concentrations in surface or
subsurface  soils,  or  other conceptual  models  of
contaminant deposition, transport, and fate. Depending
on the specifics of the project, CSMs may take the form
of graphical representations, cross-sectional maps, plan-
view maps, complex  representations of contaminant
source terms, migration pathways,  and receptors, or
simple diagrams or verbal descriptions. The team uses
the  CSM(s)  to  direct  field work  that gathers the
necessary information to close the information gaps that
stand in  the way of making site decisions.  Data not
needed to inform site  decisions will not be collected.
(Although this sounds elementary, the one-size-fits-all
approach used by many practitioners routinely leads to
the  collection of costly data  which  are ultimately
irrelevant to the project's outcome.) The CSM will
evolve as site work progresses and data gaps are filled.
The  CSM thus serves several purposes: as a planning
and  organizing instrument,  as a modeling  and data
interpretation tool,  and as  a communication device
among the team, the decision-makers, the stakeholders,
and the field personnel.

Systematic planning  provides the  structure  through
which  foresight  and  multi-disciplinary  technical
expertise improves the scientific quality of the work and
avoids blunders that  sacrifice time, money, and the
public  trust. It guides careful, precise communication
among participants and compels them to move beyond
the ambiguities of vague,  error-prone generalizations
[5]. Systematic planning requires unspoken assumptions
to be openly acknowledged and tested in the context of
site-specific constraints and goals, anticipating problems
and preparing contingencies. It should be required for all
proj ects requiring the generation or use of environmental
data [6].

The Second Component of the Triad: Dynamic Work
Plans

When experienced practitioners use systematic planning
combined with informed understanding about the likely
fate  of pollutants in  the subsurface  and advanced
technology, an extremely powerful strategy emerges for
the effective execution of field activities. Terms associa-
ted with this strategy include  expedited, accelerated,
rapid, adaptive, or streamlined site characterization. Its
cornerstone is the use of dynamic work plans. Formu-
lated as a decision tree during the planning phase, the
dynamic work plan adapts site activities to track the
maturing conceptual site model, usually on a daily basis.
Contingency plans are developed to accommodate even-
tualities that are considered reasonably likely to occur
during the  course of  site work, such as equipment
malfunction, the unanticipated (but possible) discovery
of additional contamination,  etc. Dynamic work plans
have been championed and successfully demonstrated
for over 10 years by a number of parties [7, 8]. Success
hinges on the presence of experienced practitioners in
the field to  "call the shots" based on the decision logic
developed during the planning stage and to cope with
any unanticipated issues. For small uncomplicated sites,
or for discrete  tasks  within complex sites, project
management  can  be  streamlined so  smoothly that
characterization activities blend seamlessly into cleanup
activities.

Just as the design of a dynamic work plan requires the
first component of the triad (systematic  planning) to
choreograph   activities  and   build   contingencies,
implementation  of a dynamic work  plan generally
requires the third member of the triad (real-time genera-
tion and interpretation of site data) so that data results
are available fast enough to support the rapidly evolving
on-site decision-making inherentto dynamic work plans.

The Third Component: Real-Time Analysis

Real-time decision-making requires real-time informa-
tion. There are  a variety of ways real-time data can be
generated, ranging from very short turnaround from a
conventional  laboratory (off-site  analysis)  to on-site
mobile laboratories using conventional analytical instru-

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mentation to "hand-held" instrumentation set up in the
back of a van or under a tent in the field. For many
projects, on-site analysis in some manner will be the
most cost-effective option, although this will always
depend on many factors, including the target analyte list
and the nature of the decisions to be made at a particular
project. On-site analysis  can be  performed within the
standard phased engineering approach; however, it does
not achieve its full potential for cost- and time-savings
except in the context of dynamic work  plans. All
sampling and analysis designs should be designed with
thoughtful technical input from systematic planning, but
the nature of field analytical methods and the critical
role they play in the context of dynamic work plans
makes systematic planning vital so that the most approp-
riate sampling  and measurement tools are selected and
suitably operated.

Data collection is not an end in itself: its purpose is to
supply information. There has been a counter-productive
tendency to fixate solely upon the quality of data points,
without asking whether  the  information quality  and
representativeness of the data set was either sufficient or
matched to the planned uses of the data. On-site analysis
can never eliminate the need for traditional laboratory
services; but  the judicious blending  of intelligent
sampling design, dynamic work plans, and on-site analy-
sis, supplemented by traditional laboratory testing as
necessary, can assemble information-rich data sets much
more   effectively  than  total  reliance  on  fixed  lab
analyses. The  lower costs and  real-time information
value of field analysis permits much greater confidence
in the  representativeness  of data sets  due to greater
sampling density and the ability to delineate a hot spot
or "chase a plume" in real-time [4]. When the gathering
of reliable information to guide defensible site decisions
is a clear priority, field analytical technologies offer a
much more valuable contribution than is implied when
the concept is downplayed as "field screening." The cost
advantages of on-site analysis  extend well beyond
possible  "per  sample" savings,  since the use of the
integrated triad approach maximizes the chances that the
project will be done right the first time over the shortest
possible time frame.

Informative data sets that accurately represent true site
conditions across the proj ect' s lifetime (from assessment
to characterization through remediation and close-out)
never happen by accident. No matter whether the on-site
generated data are expected to be used for "screening"
purposes or for  "definitive" decision-making, good
analytical chemistry practice must be followed and QC
protocols  must  be  designed   carefully.  Analytical
chemists are the trained professionals best able to
construct valid QC protocols that will integrate 1) the
site-specific data needs and uses, 2) any site-specific
matrix issues, and 3) the strengths and limitations of a
particular  analytical  technology.  Ignoring  these
considerations risks a chain of errors that waste effort
and  money:  faulty  data  sets  lead  to  erroneous
conclusions, that, in turn, lead to flawed site decisions
and/or ineffectual remedial actions. Good decisions rely
on representative data sets that are of known quality.
Therefore, the expertise of an analytical chemist must go
along when analytical methods are taken to the field,
whether in absentia as a written site-specific Standard
Operating Procedure (SOP) that atechnician will follow,
or in person as an instrument operator or supervising
field chemist.

Field analytical chemistry has made significant advances
in scientific rigor and credibility. Computerization,
miniaturization, photonics (e.g., lasers and fiber optics),
materials research, immunochemistry, microwave tech-
nologies and a host of other chemical, biological, and
physical  science disciplines  are  contributing  to  a
multiplicity of technology improvements and innova-
tions for analytical chemistry in general, and for the
specialized practice of on-site analytical chemistry in
particular.  When compared to the convenience  and
control offered  by  fixed  laboratory  analysis,  field
analysis offers unique challenges to its practitioners,
leading to the blossoming of a recognized subdiscipline.
Field analysis now has its own dedicated international
conferences, a peer-reviewed journal (Field Analytical
Chemistry and Technology, published by Wiley Inter-
Science), and university-based research centers. There
is a small but  growing number of companies offering
specialized on-site analytical services and  consulting
expertise to the  environmental community, and their
professional standards and practices will be addressed
by the newly  formalized  Field Activities Committee
within  the  National  Environmental   Laboratory
Accreditation Council (NELAC).

Environmental chemists are not alone in recognizing the
potential of field analysis.  Even the pharmaceutical
industry is taking their analytical methods to the field to
screen for new  drugs  in marine  and  terrestrial
ecosystems. "Who would have thought we could do this
much in situ now? When we first started, people said we
were crazy," marveled a University of Illinois chemistry
professor. While acknowledging that "on-site analysis
may seem the stuff of science fiction," he predicted that
the pace  of technological advances  will make  it
commonplace  for the pharmaceutical industry within

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five years [9].  Will the same be true for the environ-
mental remediation industry?

On-site interpretation of data is greatly  facilitated by
decisions support software tools using classical statis-
tical analysis and geostatistical mapping algorithms.
Laptop PCs may be used to manage data and produce 2-
or 3-dimensional  images  representing  contaminant
distributions, including an assessment of the statistical
reliability of the projections. Cost-benefit and risk-
management analyses  produced within  minutes can
allow decision-makers to weigh options at branch points
of the  dynamic  work plan,  or to  select optimum
sampling locations that can give the "most bang for the
characterization buck" by minimizing  decision uncer-
tainty.  The  graphical output of the software greatly
facilitates meaningful communication of site issues and
decisions with regulators and the public. As with all
tools, users  need to  understand possible pitfalls and
consult with experts as necessary to avoid misapplica-
tions that could lead to faulty outputs.

Experience  with the Triad Approach

In the  early  1990s, the Department of Energy (DOE)
articulated  the concepts of the  triad  approach as
Expedited Site Characterization (ESC) [10]. In addition,
DOE  linked dynamic work plans with  systematic
planning with the intent of speeding up Superfund site
investigations and feasibility studies at DOE sites in an
approach called SAFER (Streamlined Approach for
Environmental Restoration). Showing the  acceptance of
this paradigm among remediation experts, ASTM has
issued  three  guides describing various applications of
expedited or accelerated approaches [11,  12, 13].

In 1996-1997,  EPA  Region  1  and Tufts  University
coordinated  with  the  U.S  Air Force to  conduct a
demonstration of a dynamic site investigation using real-
time results generated by  a mobile laboratory to
delineate residual soil contamination at Hanscom Air
Force Base.  The project showed that innovative tech-
nologies combined with an  adaptive sampling and
analysis program could drastically reduce the time and
cost, while increasing the confidence, of  site decisions
[14].

Argonne National Laboratory's Environmental Assess-
ment Division  (EAD) uses Adaptive Sampling and
Analysis Programs (ASAP) to expedite data collection
in support of hazardous waste site characterization and
remediation. ASAPs rely on "real-time" data collection
and field-based decision-making, using dynamic work
plans to specify the way sampling decisions are to be
made, instead of determining  the exact number and
location of samples before field work begins. EAD
focuses on the decision support aspects of ASAP data
collection, including the management and visualization
of data to answer questions such as: What's the current
extent  of  contamination? What's  the  uncertainty
associated with this extent? Where should sampling take
place next?  When  can sampling stop? A variety of
software  tools are  used  to facilitate real-time  data
collection and interpretation,  including  commercial
databases, standard geographical information system
(GIS) packages,  customized  data visualization and
decision support software based on Bayesian statistics,
and Internet applications to foster real-time communi-
cation and data dissemination. The EAD is documenting
that  ASAP-style  programs consistently yield  cost
savings  of more than 50%  as compared to  more
traditional sampling programs [15].

The U.S. Army Corps of Engineers began institutionali-
zing an integrated approach to systematic planning under
the name "Technical Project Planning (TPP) Process."
Although it does not address dynamic work plans and
on-site analysis directly, the TPP engineering manual
stresses the importance of a multi-disciplinary team that
performs "comprehensive and systematic planning that
will  accelerate progress  to site closeout within all
project constraints" [16].  A 1997 review of 11 initial
projects performed under the TPP approach demonstra-
ted the following successes:

   Met all schedules (and "train-wreck" and "break-
    neck" milestones);

   Improved project focus and communications;

   Improved  defensibility and implementability of
    technical plans;

   Eliminated "excessive" data needs and identified
    "basic" data needs;

   Increased satisfaction of USACE's Customers;

   Improved  relations   and communication with
    regulators; and
   Documented  cost savings of at least $4,430,000
    (total savings  for all 11 projects) [17].

In addition, a well-documented USAGE project using
the  triad  approach in combination with Performance-
Based Measurement System (PBMS) principles (for

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both the field analytical and fixed laboratory methods)
achieved site closure  while  demonstrated an overall
project savings of 50% ($589K actual project cost vs.
$1.2M projected cost)  [18].

The  Florida Department of Environmental Protection
created  the Drycleaning Solvent  Cleanup  Program
(DSCP) to address contamination from small dry cleaner
shops. Under the DSCP, rapid site characterizations are
performed using on-site mobile laboratories and direct
push technologies to characterize soil and ground water
contamination,  assess  cleanup  options,  and install
permanent monitoring wells, all in an average of 10 days
per site. Site characterization costs have been lowered
by an estimated 30 to 50 percent when compared to
conventional assessments [19].

Whether the  focus  of a site investigation is ground
water,  surface  water,  sediment,  soil,  or  waste
characterization, or a combination thereof, the  triad

References
approach has been shown to achieve site closeout faster
and cheaper than traditional phased approaches. The
question becomes: What are the barriers that hinder
wider utilization of this approach? Past reasons no doubt
included the limited selection of rapid turnaround field
analytical and software tools so vital for implementing
dynamic work plans  efficiently. As described earlier
however, recent  years have seen a growing array of
analytical options  able to meet many types of data
quality needs. Technology advancement would be even
more  brisk if  a  paradigm  of logical  evaluation,
acceptance, and use by practitioners and regulators were
the norm. To benefit  from the tools we currently have
and boost our available options, we must modernize
habits that were  established during the infancy of the
environmental remediation industry. Other papers in this
series address the limitations of prescriptive require-
ments for analytical methods and analytical data quality
[4,20].
[1] National Research Council. 1997. Committee on Innovative Remediation Technologies. Innovations in Ground
Water and Soil Cleanup: From Concept to Commercialization, National Academy Press, Washington, DC, pp. 40-75.
http://www.nap. edu

[2] U.S. EPA. 2000. EPA Order 5360.1 CHG 2: Policy and Program Requirements for the Mandatory Agency-wide
Quality System, U.S. Environmental Protection Agency, Washington, DC, May. http://www.epa.gov/qualityl/
qs-docs/53 60-1 .pdf

[3]  U.S. EPA. 2000. EPA 5360 Al: EPA Quality Manual for Environmental Program?,, U.S. Environmental
Protection Agency, Washington, DC, May. http://www.epa.gov/qualityl/qs-docs/5360.pdf

[4]  Crumbling, D.M. 2001. Current Perspectives in Site Remediation and Monitoring: Applying the Concept of
Effective  Data  to Environmental Analyses for  Contaminated  Sites.  EPA   542-R-01-013.   September.
http://cluin. org/tiopersp/

[5] Crumbling, D.M. 2001. Current Perspectives in Site Remediation and Monitoring: Clarifying DQO Terminology
Usage to Support Modernization of Site Cleanup Practices. EPA 542-R-01-014. October, http://cluin.org/tiopersp/

[6]  U.S. EPA. 1998. EPA Office of Inspector General Audit Report:  EPA Had Not Effectively Implemented Its
Superfund Quality Assurance Program, ReportNo.ElSKF7-08-0011-8100240, September 30,199% http://www.epa.
gov:80/oigearth/audit/list998/8100240.pdf

[7]  Burton, J.C. 1993. Expedited Site  Characterization: A Rapid, Cost-Effective Process for Preremedial Site
Characterization, Superfund XIV, Vol.  II, Hazardous Materials Research and Control Institute, Greenbelt, MD, pp.
809-826.

[8]  Robbat, A. 1997. A Guideline for Dynamic Workplans and Field Analytics: The Keys to  Cost-Effective Site
Characterization and Cleanup, sponsored by President Clinton's Environmental Technology Initiative, through the
U.S. Environmental Protection Agency, Washington, DC. http://clu-in.org/download/char/dynwkpln.pdf

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[9] Drollette, D. 1999. Adventures in drug discovery. In Photonics Spectra, September 1999, pp. 86 - 95.

[10] DOE.  1998. Expedited Site Characterization. Innovative Technology Summary Report, OST Reference #77.
Office of Environmental Management, U.S. Department of Energy, December 1998. http://ost.em.doe.gov/ifd/ost/
pubs/cmstitsr.htm. See also http://www.etd.ameslab.gov/etd/technologies/projects/esc/index.html

[11] ASTM. 1998. Standard Practice for Expedited Site Characterization ofVadose Zone and  Ground Water
Contamination at Hazardous Waste Contaminated Sites, D6235-98. Conshohocken, PA. www.astm.org

[12] ASTM. 1998b. Standard Guide for Accelerated Site Characterization for Confirmed or Suspected Petroleum
Releases, E1912-98. Conshohocken, PA. www.astm.org

[13] ASTM. 1996. Standard Provisional Guide for Expedited Site  Characterization of Hazardous Waste
Contaminated Sites, PS85-96. Conshohocken, PA. www.astm.org

[14] U.S. EPA. 1998. Innovations in Site Characterization Case Study: Hanscom Air Force Base, Operable Unit 1
(Sites 1,2, and 3). Washington, DC. EPA 542-R-98-006. See also http://clu-in.org/charl_edu.cfm#site_char

[15] U.S. Department of Energy Environmental Assessment Division (EAD) Adaptive  Sampling and Analysis
Program (ASAP) webpage: http://www.ead.anl.gov/project/dsp_topicdetail.cfm?topicid=23

[ 16] USAGE. 1998. Environmental Quality: Technical Project Planning (TPP) Process (Engineering Manual 200-1 -
2), Washington, DC. August 1998.  http://www.usace.army.mil/inet/usace-docs/eng-manuals/em200-l-2/toc.htm

[17] USAGE. 1999. Personal communication withHeidiNovotny,PE., Technical Liaison Manager, U.S. Army Corps
of Engineers HTRW Center of Expertise, July 22, 1999.

[18] U.S. EPA. 2000. Innovations in Site Characterization Case Study: Site Cleanup of the Wenatchee Tree Fruit Test
Plot  Site  Using  a  Dynamic Work   Plan.  Washington,  DC.  EPA  542-R-00-009.  August.  See  also
http://clu-in. org/charl _edu. cfm#site_char

[19] Applegate,  J.L. and  D.M. Fitton.  1998. Rapid Site Assessment Applied to  the Florida Department of
Environmental Protection's Drycleaning Solvent Cleanup Program, in Proceedings Volume for the First International
Symposium on Integrated Technical Approaches to Site Characterization, Argonne National Laboratory, pp. 77-92.
Paper available at http://cluin.org/charl_edu.cfm#mode_expe

[20] Crumbling, D.M.. 2001. Current Perspectives in Site Remediation and Monitoring: The Relationship Between
SW-846, PBMS, and Innovative Analytical Technologies. EPA 542-R-01-015. August, http://cluin.org/tiopersp/

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