United States
               Environmental Protection
Office of Solid Waste and
Emergency Response
EPA 542-R-01-014
October 2001
V EPA   Current Perspectives in Site Remediation and Monitoring

                D. M. Crumbling1

                The appropriate use of field analytical tech-
                nologies and  dynamic  work  plans could
                dramatically improve the cost-effectiveness of
                environmental restoration activities. EPA's
                Technology Innovation Office (TIO) has been
                developing classroom and Internet web-based
                courses to  promote adoption of these tools
                and strategies. TIO's experience has been that
                a common  language to unambiguously com-
                municate technical concepts is vital if regula-
                tors,  stakeholders, and practitioners are  to
                negotiate, plan, and implement these projects
                to their mutual satisfaction.

                Systematic planning is critical to the success-
                ful implementation of hazardous site charac-
                terization and cleanup projects. EPA's "DQO
                process" has been around for many years, and
                the "DQO" terminology is used extensively.
                Unfortunately, over the years the terminology
                has been used in ambiguous or contradictory
                ways, and this has resulted in confusion about
                what terms mean and how they are to be used.
                It is thus useful to clarify the relationship
                between DQO-related terms as  descriptively
                and concretely as possible. The discussion
                provided here has  been reviewed by the
                primary DQO  and data quality coordinators
                within the  EPA Headquarters offices of the
                Office of Solid Waste, the Office of Emergen-
                cy and Remedial Response, the Office  of
                Environmental Information, and the Quality
                Staff to ensure that the concepts presented are
          consistent with EPA's original intent for DQO
          terminology and with the direction program
          needs are currently taking. Any questions or
          comments about this paper should be directed
          to the EPA Technology Innovation  Office
          through the Clu-In "Comments" form (http://
          cluin.org/gbook.cfm) or to (703) 603-9910.

          This paper does not intend to provide  all-
          inclusive definitions that can be found else-
          where in EPA guidances, nor does it attempt
          to provide all-inclusive coverage of each
          topic. It is intended to provide, as briefly yet
          unambiguously as possible, a basic conceptual
          understanding of DQO-related terms in a way
          that facilitates systematic project planning
          in the context of site cleanups. A  list of
          descriptions  for  DQO-related  terms  and
          concepts appears first in this paper, followed
          by a more intensive discussion of the working
          interrelationships between these concepts. It
          is entirely possible that other parties use terms
          other than these to communicate the same
          concepts. The  actual  terms used are less
          important than the ability of parties involved
          in site cleanup projects to have a "meeting of
          minds" and clearly communicate  the con-
          cepts,  since the concepts  are  basic  to  the
          scientific validity of environmental decisions
          and to the data that support those decisions. A
          common conceptual framework could help all
          within the hazardous waste community better
          communicate our goals and results, fostering
          more cost-effective planning and implemen-
          tation of projects.
                 EPA, Technology Innovation Office

Descriptions for DQO-Related Terms

Data Quality Objectives (DQO) Process

This is a systematic, iterative, and flexible planning
process  based on  the scientific method.  The  DQO
process was developed by EPA to provide a common
structure  and  terminology to practitioners  designing
environmental data generation operations. The  DQO
process produces quantitative and/or qualitative  state-
ments (called "the DQOs," see below) that express the
project-specific decision goals. The DQOs then are used
to guide the design of sampling and analysis plans that
will be able to cost-effectively produce the "right kind of

An important part of the DQO process is developing an
understanding of how uncertainties  can impact the
decision-making process. A systematic planning process,
such as the DQO process, identifies what the goals are
and what the consequences may be if the decisions are
made in error. It is within the  realm of values (not
science) for decision-makers (as  representatives of
society as a  whole) to estimate  how  certain (i.e.,
confident) they want to be before making decisions that
will either impact, or be impacted  by, environmental
conditions. When technically feasible, an expression of
statistical certainty may be desirable because it can be
more "objective" (if it is  done in a technically valid
manner). But in the environmental field, mathematical
(e.g., statistical)  treatment of "uncertainty" may not
always  be  technically feasible  or even  necessary.
Qualitative expressions of decision confidence through
the exercise of professional judgment (such as a "weight
of evidence" approach) may well be sufficient, and in
some cases, may be the only  option  available. An
important part of systematic planning is identifying the
information gaps that could cause a decision to be made
in error. If the existence of information gaps increases
the  likelihood  of decision  error  beyond what  it
acceptable, then it may be desirable to fill those gaps, if
it is feasible  to do so. Planning  how  to  gathering
environmental data that can acceptably fill information
gaps is the purpose of the DQO process.  Decision-
makers should also keep in mind that, on occasional,
systematic planning may indicate that it may be more
cost-effective to simply go ahead and make the decision
to take the most conservative (protective) action, rather
than spend the resources neededto scientifically "prove"
whether the protective action is absolutely necessary or

Sampling and analysis plans lay out the strategy to be
used to gather needed data. Steps 1 through 6 of the
DQO process provide the structure to help a project
team articulate their project goals  and decisions, the
project's constraints (time, budget, etc.), and how much
uncertainty they can tolerate in the final decision. These
things must be thoroughly understood before the task of
developing the data-gathering plans that can meet those
goals within the given constraints is begun. Developing
project-specific sampling (i.e., determining the number
of samples, their locations, their  volume,  etc.)  and
analysis (i.e., selecting and modifying, as needed, the
analytical  preparation,  cleanup,  and  determinative
methods, the analytical QA/QC protocols, etc.) plans is
the very last step (Step 7—"optimize the design") of the
DQO process.

During Step 7, pre-existing site information  should be
sought and evaluated so that uncertainties that could
impact the sampling and analysis plan can be evaluated
as much as possible prior to finalizing the  plan. For
example, existing information about the mechanism(s)
of contaminant(s) distribution and their likely environ-
mental fate (degradation  and/or  redistribution in the
environment) can be used to develop a conceptual model
for the variability of contaminant concentrations and the
media that should be sampled. Knowledge or suspicion
that other contaminants may be present in the samples
can guide consideration of alternate  analytical methods
able to cope with any analytical interferences that might
arise. [More details about the development of sampling
and  analytical plans  can be  found in  the article,
"Guidelines for  Preparing SAPs  Using Systematic
Planning  and  PBMS"  (Jan/Feb  2001  issue  of
Environmental Testing & Analysis;  also available as a
pdf file on http://cluin.org/charl_edu.cfm#syst_plan).]

It should be noted that the DQO process is a systematic
planning process focused on generating project data.
The term "systematic planning" is often used to encom-
pass the broad range of project activities that includes
more  than  just  data  generation and  interpretation
activities. (See "Systematic Planning" below.) [More
thorough discussions of DQOs and details of the DQO
process can be found in various EPA Quality Assurance
documents available on EPA's Quality Staff website:

Data Quality Objectives

DQOs are qualitative and quantitative statements that
translate  non-technical project  goals into  technical
project-specific decision goals. Project planners derive
these technical DQOs from the non-technical social,

economic, and/or regulatory objectives of the environ-
mental  program under which  the  project  is  being
implemented. DQOs are goal-oriented statements that
establish the (technical) "bar" for overall decision
quality or tolerable decision error in accordance with
the (non-technical)  objectives driving the project.
The DQOs for any particular proj ect may, or may not, be
highly specific in naming target elements, target media,
and action  levels along with the intended uses of the
data.  Project DQOs may be articulated at different
technical levels depending on the intended audience. For
communication with public stakeholders or  in non-
technical settings, DQOs will usually be summarized as
simple,  less technical statements. For communication
between technical practitioners, proj ect DQOs should be
articulated in as specific and technically-detailed manner
as possible to avoid  ambiguities that could  cause
confusion or misunderstandings. In either case, DQOs
summarize  the outputs of the DQO (planning) process.
Example statements below  provide a flavor of simply
worded DQO statements that summarize the highly
technical statements that would lie behind the simply
worded summaries and give them substance.  The most
important  point to note is that in no case do DQO
statements directly set criteria for the quality of data
that will be gathered  during implementation of the
project. The process of determining the quality of data
that will be needed to meet the  project decision goals
(i.e., "meet the DQOs") must be done after the DQOs
are established.  Quantitative DQOs express decision
goals using numbers, such as quantitative expressions of
decision certainty. Qualitative DQOs express decisions
goals  without specifying those goals in a quantitative
 Example  of  a less detailed,  quantitative DQO:
 Determine with greater than 95% confidence that
 contaminated surface  soil will not pose  a human
 exposure hazard.

 Example  of a  more  detailed,  quantitative DQO:
 Determine to a 90% degree of statistical certainty
 whether or not the concentration of mercury in each
 bin of soil is less than 96 ppm.

 Example of a detailed, qualitative DQO: Determine the
 proper disposition of each bin of soil in real-time using
 a dynamic work plan and a field method able to turn-
 around lead (Pb) results on the soil samples within 2
 hours of sample collection.
Even when expressed in technical terms, DQOs should
express "what"  (i.e., what  decision) the data will
ultimately support, but should not specify "how" that
data will be generated (e.g., which analytical methods
are to be  used). Despite the  name, "Data  Quality
Objectives," DQOs should be thought of as statements
that express the project objectives (or decisions) that the
data (and  its associated quality) will be expected to
support. As project objectives, DQOs serve to guide the
eventual determination of the data quality that is needed
to make good decisions, but DQOs themselves should
not attempt to directly define the specifics of that
data quality.  Doing  so short-circuits the systematic
planning process, hindering  the  ability of project
planners to optimize data collection  designs to make
projects  more  cost-effective  (Step  7 of the  DQO
process). Various terms have  been  used that more
intuitively  express the originally intended concept of
"DQO," including "Decision  Quality  Objectives";
"Decision Confidence Objectives (DCOs—used in the
context of compliance monitoring—WTQA 2001 short
course, "Regulation  Writing  under PBMS"); and
"Project Quality Objectives (PQOs—used  by  EPA
Region 1  in  their Quality Assurance  Project Plan

A discussion of the analytical flexibility inherent to U.S.
EPA's waste programs and  to  SW-846,  the methods
manual used by these programs, is found  in the paper,
Current  Perspectives  in  Site  Remediation  and
Monitoring: The Relationship between SW-846, PBMS
and Innovative  Analytical  Technologies [document
number EPA 542-R-01-015; available on http://cluin.

Data Quality

Data quality is a term that tends to be rather vaguely
understood in the environmental community, despite its
importance to the decision-making process. In addition,
the term "data" is used to refer to many different kinds
of information that is derived from very different kinds
of data generation procedures. In the contextofthe DQO
process, "data" generally refers to the measurement of
some physical or chemical environmental property. Of
greatest concern to the management of hazardous waste
and contaminated sites is the measurement of toxic (or
potentially toxic) chemicals in environmental media to
which receptors maybe exposed. In this context, "good"
data quality tends to be linked in many minds with using
the most  sensitive or precise analysis procedures
available. However, this view of data quality produces
problems because the information value of that kind of
data is limited not so much by the analytical procedures
used (although that is  certainly possible), but  by the

difficult task  of ensuring  representative sampling in
heterogeneous environmental matrices.

Fortunately, EPA has recently clarified its intended
meaning for the term "data quality" in its broadest sense
by defining it as "the totality of features and characteris-
tics of data that bear on its ability to meet the stated or
implied needs and expectations of the customer " (i.e.,
the data user). [This definition appears in the 2000
version of the Office of Environmental Information's
Quality Management Plan, entitledManagement System
for Quality}  Recent  EPA guidance  reinforces this
understanding of data quality by stating that "...data
quality, as a concept, is meaningful only when it relates
to the intended use of the  data. Data quality does not
exist in a vacuum; one must know in what context a data
set is to be used in order to establish a relevant yardstick
for judging whether or not the data  set is  adequate"
[from page 0-1 of Guidance for Data Quality Assess-
ment: Practical Methods for Data Analysis (QA/G-9
QAOO Update). EPA 600/R-96/084; http://www.epa.gov/
quality/qs-docs/g9-final .pdfj.

Linking data quality directly to  the data's intended use
provides a firm foundation for building a vocabulary that
distinguishes the various components of data quality.
For example,  since analytical data are generated from
samples, pre-analytical considerations (such as sample
representativeness and sample integrity) are crucial
when determining whether data are of sufficient quality
to meet the user's need to make correct decisions. Data
quality can be broken broadly into  the components of
analytical  quality  (how  reliable  is  the  analytical
procedure) and  representativeness (selection  of the
samples and of the analytical method is appropriate to
the intended use of the data). Non-representative sample
selection produces "bad" data (misleading or meaning-
less information), even if the analytical quality on those
samples was perfect.

Data  Quality Indicators

DQIs are qualitative and quantitative measures of data
quality "attributes." Quality attributes are the descriptors
(i.e., the words) used to express various properties of
analytical data. DQIs are the measures of the  individual
data  characteristics   (the  quality  attributes)  that
collectively tend  to be grouped under the general term
"analytical data quality." For instance, the data quality
attribute of analytical  sensitivity can by measured  by
different  DQIs,  such  as instrument detection  limit,
sample detection limit, or quantification limit, each of
which can be  defined somewhat differently depending
on the program or laboratory. See EPA QA/G-5 (1998
version) for more discussion on the topic of DQI (http://
www.epa.gov/quality/qs-docs/g5-final.pdf).   Another
guidance document, EPA/G-5i, will explicitly discuss
DQIs in much greater detail. EPA/G-5i is currently
under development. Look for a peer-review draft to be
posted in the future at http://www.epa.gov/quality/qa_

Quality attributes (and the facets of data quality that
they  describe) include  (but  are not limited to) the

 •  Selectivity/specificity (describes what analytes the
    technique can "see"  and discriminate from  other
    target analytes or from similar-behaving, but non-
    target, substances);

 •  Sensitivity  [depending  whether "detection" or
    "quantification" is specified, describes  the lowest
    concentration, or increment  of concentration, that
    the technique is able to detect (although quantifica-
    tion may be highly uncertain) or quantitate with
    greater confidence];

 •  Bias (describes whether the technique produces
    results with a predictable  deviation from the "true"

 •  Precision (describes how  much random error there
    is in the measurement process or how reproducible
    the technique is);

 •  Completeness  (describes  whether  valid data is
    produced for all the submitted samples, or just some
    fraction thereof); and

 •  Comparability (describes whether two data sets can
    be considered to be  equivalent with respect to a
    common goal).

The  familiar   "PARCC  parameters"  have   been
considered to consist of 5 principal DQIs that include
measures of precision, accuracy (used in this context to
denote bias),  representativeness, comparability, and
completeness. Sensitivity ("S") may also be included as
a principal DQI. Precision, bias, and sensitivity describe
properties that are measured quantitatively through an
appropriate  analytical quality control (QC) program.
Comparability between data sets  generated by different
analytical methods can also be established through the
use of  relevant QC samples, such  as  standardized
performance evaluation  (PE) or certified  reference

material  (CRM)  samples run  by both  methods, in
addition  to  other  comparisons  of  each  method's
performance  (sensitivity,  selectivity,  precision, bias,

The term "representativeness" can be used to address
either the analytical aspect or the  sampling aspect of
sample analysis. Analytical methods must be selected
and designed to be representative of the parameter of
interest. Positive (i.e., causing an analytical result to be
biased high) or negative (i.e., causing an analytical result
to be biased low) interferences and unrecognized non-
selectivity for a particular target analyte can result in a
non-representative interpretation of analytical results,
leading to decision errors. For example, immunoassay
tests  for environmental  contaminants  are usually
designed to give results that are biased high, and the kits
frequently cross-react  with daughter  products  of the
parent  contaminant  or  other  structurally  similar
compounds. A potential user of an immunoassay kit who
does not recognize these characteristics will risk serious
misinterpretation of the kit's test results. On the other
hand, users who do understand this will seek to use these
characteristics to their advantage, or will manage the
inherent  uncertainties through  a demonstration of
method  applicability (see below) and an  appropriate
quality control protocol.

The   representativeness  of  sample   selection  and
collection is complicated by the extreme heterogeneity
of many of the matrices encountered in the environmen-
tal field. The concentrations of contaminants in soils,
sediments, waste streams, and other matrices can vary
tremendously on  even small scales in both space and
time. Samples must be representative of the "true" site
conditions in the context of the decision to be made
based on those samples. If the decision is not specified,
a representative sampling design cannot be selected.
Sample   representativeness   also   includes sample
preservation and subsampling issues.

Comparability  and representativeness are  critically
important to  the  scientifically valid  interpretation of
analytical data, but estimating both requires the exercise
of professional judgment in BOTH the science genera-
ting the  data (e.g., analytical chemistry) and  in the
science involved in interpreting and using the data (e.g.,
using the data to model contaminant extent or migration
or to design a treatment system).

As noted above, there may be more than one DQI for a
single data quality attribute. For example, the attribute
of precision can be   measured using  mathematical
formulas for relative percent difference (RPD), relative
standard deviation (RSD),  standard  deviation (SD),
variance (SD2), and a variety of other calculations that
can quantitatively express the degree of random fluctua-
tion in a measurement process. The selection of a
particular DQI  to measure  a specific  data quality
attribute (for example,  selecting  RPD to  measure
precision) is a matter of:

 •  Convention (what are people  used to seeing or

 •  The characteristics of the analytical method (for
    example, does the method generate  continuous or
    discontinuous data?);

 •  The data set  being evaluated (for example, the
    formula for RPD cannot handle more than 2 values,
    whereas the formula for RSD can handle multiple
    values); or

 •  The intended use for the  data (which determines
    how extensively the quality of a data set must be
    documented, and what form  of documentation is
    most useful to the data user).

The language of "data quality attributes"  and "data
quality indicators" provides data generators and  data
users with the ability to establish the comparability of
different data sets and whether data are of "known and
documented quality" commensurate with intended data

Measurement Quality Objectives

MQOs are project-specific analytical parameters derived
from project-specific DQOs. MQOs include acceptance
criteria for the data quality indicators (DQIs—see
above) that  are important to the  project,  such as
sensitivity (e.g., what detection or quantitation limit is
desired),  selectivity (i.e,  what  analytes are  to be
targeted), analytical precision, etc. MQOs can be used
to establish  the "bar" for data performance para-
meters. MQOs are derived by considering the level of
analytical performance needed to actually achieve the
project goals  (as expressed in the DQOs).

However,  project  MQOs are  not  intended  to be
technology- or method-specific. As with DQOs, MQOs
specify "what" the level of data performance should be,
but not "how" that level  of data performance will be
achieved. In other words,  although MQOs provide the
criteria for how good the  data must be, MQOs do not

specify exactly how the data must be produced, and so
MQOs  do not  specify  what analytical  method  or
technology is to be used.

In actual practice during project planning, the planning
team's analytical chemist will naturally be considering
which specific technologies may be applicable even in
the  early stages of project planning.  Evaluating and
refining analytical options is  a significant part of the
iterative nature of systematic planning which seeks the
most resource-effective work strategy that can achieve
the  stated project goals (i.e., the project DQOs). The
project chemist  should  explore whether available
innovative  analytical technologies might achieve the
project MQOs (i.e., the needed data quality) to the same
degree as the conventional technology, yet be able to do
so in a way that is more  resource-effective for the
project because of lower per-sample costs, economies of
scale, or more rapid turnaround times that could support
real-time project decision-making.

The following are examples of what MQOs "look like":

 • An MQO for one project might read: "The overall
    precision of lead measurements taken on the soil in
    the bins must be less than 50% RPD when at  least
    10 samples are taken from each bin."

 • An MQO for a different project might read: "The
    measurement method to be chosen must be able to
    detect the presence of compounds X, Y, and  Z in
    groundwater at a quantitation limit of 10 Fg/L with
    a recovery range of 80-120% and a precision of

A large part of the variability in environmental data (and
thus  in  overall  decision  uncertainty)  stems from
sampling considerations.  MQOs  should be developed
with this fact in  mind, and requirements for analytical
MQOs  should be  derived in conjunction with the
development  of the sampling design. The team  or
individual setting the MQOs should balance the relative
contributions from  analytical uncertainties and from
sampling uncertainties. In many environmental media
(especially solid media),  matrix  heterogeneity causes
sampling variability to overwhelm analytical variability.
Insisting on perfectly precise analyses on a few tiny
samples taken across a large heterogeneous matrix is
meaningless since two adjacent samples will probably
provide very different results. Which sample is selected,
and  hence the  project  decision  influenced  by  that
sample's results, will be a matter of chance. This "luck
of the draw" can only be controlled by obtaining a better
understanding of the contaminant distribution, and that
is dependent on increasing the  density of sample

Depending on how the term is being applied and the
sources of uncertainty that impact  an  environmental
decision,  measurement quality  objectives  may  be
interpreted to include assessment of the performance of
the entire measurement system, including the uncertain-
ties  in the  data introduced  by sampling.  This  is
especially true if there are more sources of uncertainty
in making the actual decision than just evaluating the
immediate  data package.  For example,  making risk
management decisions is based not only on site-specific
data sets, but also on the non-site-specific toxicological
data sets used to derive the  various reference values,
etc., all of which have their own associated uncertain-
ties. However,  in  some  usage,  the  term  MQO  is
restricted  to the analytical side  of the  measurement
process, and the broader concept of DQO or decision
confidence  objective  (DCO) is  used to include the
sampling considerations. This terminology may be used
in activities such  as permit compliance monitoring
where  there is no  perceived "uncertainty"  in the
regulatory limit itself (once it has been established  by
the permit). In this case, the "project decision" involves
demonstrating only that the permitted material  is in
compliance  to  some  specified  level   of  decision
confidence.  If usage of terminology such as DQO,
MQO, DCO,  etc. in  a  particular   situation  is
ambiguous  (as many times it is), parties should strive
to clarify what meaning is intended. Parties should
also strive to clarify how sampling uncertainties are
accounted for  in data generation, assessment, and

Whether sampling considerations are evaluated as part
of MQOs (as the entire measurement system)  or as part
of DQOs  (or some  other term expressing the overall
decision uncertainty), the importance of including the
sampling component when assessing overall data quality
cannot be overemphasized. It is possible to isolate the
performance of various parts of the  measurement
system, and to determine the relative contributions from
the various  sampling components versus the various
analytical   components.  [Discussions  about  the
partitioning  of decision uncertainty can  be  found in
various statistical or sampling documents available  on
http://cluin.org/chartext_edu.htm#stats. Since soils tend
to illustrate  a "worst case scenario" for non-gaseous
environmental media, the following documents present
valuable guiding principles: the 1990 A Rationale for the
Assessment of Errors in the Sampling of Soils, and the

1989 Soil Sampling Quality Assurance User's Guide.
This topic is also discussed in the paper, "Applying the
Concept of Effective Data to Environmental Analyses
for Contaminated Sites," EPA 542-R-01-013; available
from http://cluin.org/tiopersp/].

Demonstration of Method Proficiency

A Demonstration of Method Proficiency shows that a
particular operator or laboratory  has the  appropriate
training and equipment to accurately perform a method.
The demonstration may be done by using Performance
Evaluation (PE) samples, or by using known concentra-
tions of analytes spiked into a clean matrix. The purpose
of a demonstration of proficiency is to ensure that the
performance of the operators and equipment is capable
of producing  data of known quality.  [Proficiency
demonstrations are discussed in Chapter 2 of the SW-
846 Manual, available at http://www.epa.gov/epaoswer/

Demonstration of Method Applicability

A Demonstration of Method Applicability involves a
laboratory study, pilot study, field trial, or other kind of
activity  that  establishes the  appropriateness  and
performance capability of a particular method for a site-
specific matrix  and application.  The purpose  of a
demonstration of method applicability is to ensure that
a particular method or method modification can produce
data  of known  quality,  able to  meet  the project's
decision goals, on the site-or project-specific samples to
be tested.

Systematic Planning

Systematic planning for project decision-making is the
process of clearly defining and articulating:

 • What the goals (i.e., primary decisions) of a project
   will be (including how much uncertainty will be
   tolerated in those decisions);

 • Identifying what  potential sources  of error and
   uncertainty could lead to an  erroneous decision;

 • Developing   strategies  to manage   each of the
   identified uncertainties and avoid decision errors;

 • Planning the most resource-effective means for
   implementing those strategies.
Strategies for managing uncertainties include identifying
information or knowledge gaps and deciding how to fill
those  gaps.   Locating  and  interpreting  historical
information or pre-existing data is one possible way to
fill certain knowledge gaps. Collecting  new  data is
another  way  to fill information  gaps.  Systematic
planning then evaluates:

 •  What types and amounts of data will be needed to
    address the information gaps; and

 •  What mix of sampling and analytical technologies
    can address both sampling and analytical uncertain-
    ties to  optimization the data  collection design to
    maximize overall cost-effectiveness for the project.

Once decisions are made, follow-up actions (such as
remedial activities) may  be  indicated.  Systematic
planning evaluates how data gathering, decision-making,
and follow-up activities may be efficiently ordered or
merged to  minimize  expensive and time-consuming
remobilizations of staff and equipment back to a site. A
dynamic work plan approach develops decision trees or
other articulations of decision logic that guide real-time
decision-making in the field to allow sequential activi-
ties to be  performed in  fewer mobilizations. More
information about dynamic work plans can be found in
A  Guideline for Dynamic  Workplans  and Field Ana-
lytics: The Keys to Cost-Effective Site Characterization
and Cleanup, available from http://cluin.org/download/

The DQO process is a systematic planning approach that
EPA has articulated to aid data collection activities. The
DQO process does not address other aspects of project
planning that are  included under the broader  term
"systematic  planning."   Systematic  planning   also
includes developing the work plans that will coordinate
and guide  site  operations  related to cleanup,  worker
safety, waste removal and disposal, public involvement
and other activities needed to achieve projectgoals. Key
to successful systematic planning is the involvement of
sufficient  technical  expertise,  generally  provided
through a multi-disciplinary team, that represents the
scientific  and  engineering  disciplines  needed to
adequately address all project issues. For example, the
U.S. Army Corps  of Engineers  uses a systematic
planning process called Technical Project  Planning
(TPP) that  encompasses many of the project activities
that extend  beyond just data collection. The TPP manual
can  be accessed  at  http://www.usace.army.mil/inet/
usace-docs/eng-manuals/em.htm, referto Engineering]
Mfanual] 200-1-2.

EPA has policy requirements that mandate the use of
systematic planning for all  projects performed under
EPA  direction.  EPA does  not  mandate  the  type of
systematic planning to be done, since this necessarily
will vary depending on a wide variety of factors. EPA
policy statements on systematic planning can be found
in Policy and Program Requirements for the Mandatory
Agency-Wide Quality System (EPA Order 5360.1 A2),
available at http://www.epa.gov/quality/qs-docs/5360-

Triad Approach

A strategy for cleaning up hazardous waste sites that
relies on the integration of systematic planning, dynamic
work plans,  and real-time  results (usually provided
through rapid  turnaround on-site  measurements) to
reduce costs and move site work  along  faster while
maintaining or increasing the reliability and protective-
ness of site decisions. [Discussion about the triad app-
roach can be found in the paper, Current Perspectives in
Site Remediation and Monitoring: Using the Triad App-
roach to Improve the Cost-Effectiveness of Hazardous
Waste  Cleanups, EPA  542-R-01-016  available from

The Relationships Among Decision  Goals, DQOs,
MQOs, and QC Protocols

During project  planning,  there  should a  logical
conceptual progression in  the development of decision
goals,  DQOs, MQOs, and QC  acceptance criteria. In
practice, however,  this will be a non-linear, iterative
process where  various options  for implementing a
project are explored, dissected, and recombined,  the
feasibility  and costs for various options are estimated
and weighed, and then the  most promising option is
selected and fully developed into project work plans that
will actually be implemented. As a project's planning
documents (such as work plans, sampling and analysis
plans, quality assurance project plans, health and safety
plans) are  developed and finalized, there should be a
clear presentation of (and the reasoning behind):

  •  The general project decision goals;

  •  The more  detailed,  technical  expression of the
    project goals (the DQOs), and the decision rules that
    will guide project decision-making;

  •  An expression of how much uncertainty decision-
    makers are willing to tolerate in the final project
 •  An  evaluation of the uncertainties (information
    gaps) that could potentially lead to decision errors;

 •  A discussion of the strategies that will be used to
    manage each of those uncertainties to the degree
    needed  to  needed to  accommodate the desired
    decision certainty.

No doubt at least one of those strategies will include the
generation  of analytical chemistry data from environ-
mental samples to fill information gaps. (In contrast, it
may be possible  to  manage  uncertainty without
generating  data simply by "assuming the worst" and
taking the most protective actions. In highly specialized
instances, this might be the most cost-effective strategy
when the cost of sampling and analysis to reach a
"definitive  conclusion," and the  likelihood that action
will  be  required anyway  are  both high.) When data
generation  is planned,  the planning document should

 •  The roles these data are expected to play within the
    context of the project  or how they will be used to
    support project decision-making;

 •  A description of how data  will be assessed and
    interpreted according to the decision rules (e.g., how
    will  the  results be reduced, treated statistically,
    mapped, etc.);

 •  The  goals  for overall data  quality (the overall
    MQOs, where  "data" are measurements  generated
    from samples and sampling  uncertainties must be

 •  How the representativeness of sampling will be
    ensured or assessed (how the various aspects of
    sampling uncertainty will be  managed);

 •  A list of the analytical technologies and methods
    that were selected, and a  description of the data
    attributes (analytes, detection/quantitation  limits,
    requirements for accuracy as bias and precision) that
    is expected to be generated from the listed methods;

 •  The analytical QC protocols and criteria to be used
    with the methods to demonstrate that analytical data
    of  known  quality  are being  generated that are
    suitable for the described intended uses.

At designated completion points  in the project, project

reports  that summarize  work  accomplished to date
should clearly reiterate the project goals and the means
by which these goals would be achieved. Important
uncertainties (that is, those information gaps that bear
directly on decision-making confidence) in the decision-
making process should be identified. The success of the
project  plan in managing  those uncertainties to the
degree desired, and an estimation of the overall decision
uncertainty should be  assessed in the project report.

In the  beginning of a  project, high-level  program
managers often set the broad, non-technical goals for
projects: Forexample, "Given a budget of $X, we want
to clean up this lead contaminated soil in accordance
with all environmental regulations and to the satisfaction
of the residents in the neighborhood." The next question,
of course, is "How do we do that?" So the next step for
the project manager or the planning team is to translate
these broad, non-technical goals into more technically
oriented goals that can address  specific considerations
such as:

 •  Regulations: What are the applicable environmental
    regulations? Are applicable  action levels already in
    place in regulations, or do site-specific action levels
    need to be derived based on risk-drivers? If there is
    more  than  one possible regulatory action level,
    which one should be used?

 •  Confidence in the outcome: How certain do we need
    to be by the end of the project that we have indeed
    achieved goals such as risk reduction or regulatory
    compliance? How will we demonstrate to regulatory
    agencies or stakeholders that this level of certainty
    has in fact been achieved (i.e., what evidence will be
    used to argue that goals have been achieved)?

 •  Constraints: What are all the constraints that need to
    be accommodated (like  seasonal weather, budget,
    property access, etc.)?

Making sure that no important details are left out of
consideration is the purpose of a systematic planning
process such as EPA's 7-step DQO process" [Detailed
explanation of the DQO process  as applied to hazardous
waste sites can be found in the document, Data Quality
Objectives Process for Hazardous Waste Site Investiga-
tions (QA/G-4HW), available through http://www.epa.
gov/quality/qa_docs.html, and will  not be duplicated
here.] Statements that summarize the answers to these
and other questions constitute "the project DQOs." As
noted earlier in this paper, the project DQOs consist of
the unambiguous technical expressions of the overall
project decision goals.
The next level of technical detail geared toward data
collection involves translating the project DQOs into
project MQOs  [i.e.,  a general characterization of the
kind of information (what parameters or analytes need
to be measured, and what level of overall data quality
for those parameters  is needed) that will be needed to
achieve the project DQOs]. Analytical data quality is
most often only a very small part of the uncertainty that
needs to be  controlled  in  order to  have sufficient
confidence in the actual project decisions.  An honest
examination of the "weak" links contributing to overall
decision certainty may reveal that paying for expensive
"definitive"  analyses contributes  nothing   toward
decreasing  the  overall  uncertainty  in the  project
decisions when there are larger uncertainties due to the
limitations of sampling very heterogeneous media.

Sampling uncertainty  is decreased  when sampling
density   is   increased.   Composite   sampling  may
sometimes be used to increase sampling density while
lowering analytical costs. [Refer to EPA Observational
Economy Series Volume  1:  Composite  Sampling,
EPA/QA G-5S,  and  other statistical documents, all
available from http://cluin.org/chartext_edu.htm#stats].
Although composite  sampling is undesirable  in some
situations and its use  should be carefully considered in
the context of how the  data  will be used,  composite
sampling can be a highly cost-effective and informative
sampling strategy.

Another way to cost-effectively increase  sampling
density is by using less expensive analytical  methods
(perhaps, using screening methods) in association with
a well-planned QA/QC design and limited  traditional
analyses to provide data of known quality matched to the
decision needs of the project. As long as the data quality
can be demonstrated to be compatible with the project's
decision rules, the confidence in  the overall  decision
reliability that is gained by  increasing the sampling
density will not be lost by the use of a screening method.
For more details, see  "Guidelines for Preparing SAPs
Using Systematic Planning and PBMS" in the  January/
February 2001 Environmental Testing & Analysis. The
article is available through http://cluin.org/chartext_edu.
htm#planning. Additional discussion can also be found
in the  issue  paper,  Current  Perspectives  in Site
Remediation and Monitoring: Applying the Concept of
Effective Data to Environmental Analyses for  Contam-
inated Sites, available at http://cluin.org/tiopersp/.

When project planners wish to express desired decision
confidence objectively  and  rigorously in terms  of
statistical certainty (that may have  been specified in the
project  DQOs), statistical expertise  is required  to
translate that goal into strategies that blend the number

of samples, the expected variability in the matrix (i.e.,
heterogeneity), analytical data quality (e.g., precision,
quantitation limits), the expected contaminant concen-
trations (i.e.,  how  close are they expected  to  be  to
regulatory  limits),  sampling  design (e.g.,  grab  vs.
composite), and costs into an interlocking whole. Since
sampling  design and  analytical strategy  interact  to
influence the statistical confidence in final decisions, the
interaction 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 can assist  technical
experts to develop sampling and analysis designs. [See

The statistician is concerned with managing 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
addressing issues related to "sample support" (a concept
that involves  ensuring that the volume,  shape, and
orientation of extracted specimens are representative of
the original matrix under 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 managing
contributions  to  the sampling  variability as  actual
sample  locations are selected  and as specimens are
actually collected. The sampling expert does this by
selecting and using  sampling tools in ways that ensure
that the sample support designated in the sampling plan
is met in the field. The analytical chemist is responsible
for managing components of variability that stem from
the  analytical  side  (including  aspects   of sample
preservation,  storage,  homogenization, subsampling,
analyte extraction,  concentration,   and instrumental
determinative analysis). The analytical chemist should
select analytical methods that can meet the analytical
variability limits estimated by the statistician, and design
an  analytical QC program  that defensibly establishes
that those goals were met in the final data set.

Managing the various sources of analytical and  sampling
uncertainties (assuming no clerical or data management
errors) ensures that data of known quality are generated.
Sometimes there may be only a single option  available
for a certain task, so the selection  process is simple.
Other times there may be more two or more options and
cost/efficiency considerations can drive selection of the
equipment and/or the design.  It should be obvious that
staff expertise (training and practical experience directly
relevant to the techniques under consideration) is very
important to project success.
The data characteristics that will control analytical and
sampling uncertainty are articulated in the MQOs.
Thus the MQOs specify "how good" the data must be at
a general level. MQOs are contrasted with DQOs, which
specify "how  good" the decision  must  be.  DQOs
certainly are the ultimate drivers of how good the data
must be, but DQOs themselves do not directly express
data quality  characteristics.  Sometimes,  as project
planning  progresses  or as  project implementation
proceeds, it is discovered that a DQO is unattainable
given the realities of the site conditions, the availability
of suitable technology, and  financial constraints.  In
collaboration with regulators and stakeholders, revision
of the project DQOs may be  required. For example, it
may be discovered that current technology for a certain
analyte is unable to provide the data needed to support
risk decisions at a desired 10~6 cancer risk level. When
a  risk-based  DQO  is  unachievable  with  current
technology, an MQO  known to be achievable with
currently available technology may be substituted for the
DQO. In other words, if it is clear that the ideal decision
goal (the DQO)  is unattainable, data quality  goals
(MQOs) based on the best available technology may be
substituted for the ideal DQO until a time when newer
technologies  become available. It is important to note
that the technology or method itself is NOT specified by
the regulatory MQO. This allows the flexibility required
for market incentives to encourage the development of
technologies that can meet or exceed that same level of
data quality more economically.

Although project MQOs are  not  meant  to specify
particular methods or technologies, they do serve  to
guide the selection of the technologies that can most
cost-effectively meet the  DQOs. As instrumentation is
selected  (based on factors such as  the  type  of data
needed, the turnaround time needed to support project
activities, the expertise and  infrastructure required  to
operate it, and costs), and as the analytical strategy for
the   project   is   perfected  (perhaps   including  a
"demonstration of method applicability"),  analytical
method SOPs and QC protocols are developed that are
both method- and  project-specific (i.e., tailoring an
analytical method's performance to  meet the  specific
data needs of the project). A QC protocol identifies the
analytical parameter or DQI to be controlled, the limits
within which results for that parameter are acceptable,
and the corrective action procedures to be followed if
those acceptance limits are exceeded. QC acceptance
criteria should be very specific and should be designed
such that if the QC acceptance criteria are consistently
met, the project MQOs will be achieved, which means
that  the resulting data will be sufficient to meet the
project DQOs and support the project decisions.

For  example, an  overall MQO for precision  [for
example, a statistically derived objective of less than
<50% RPD between side-by-side (collocated) samples]
may be partitioned into the  primary  components of
variability that contribute  to the overall variability.
[Discussions about the partitioning of variability can be
found in the Rationale for the Assessment of Errors in
the Sampling of Soils document, available athttp://cluin.
org/chartext_edu.htm#stats .]  In the  QC protocol, QC
samples  are used  to  monitor and document these
measures of variability. The QC acceptance criteria are
used to specify the maximum allowable variation in each
component, and they might be expressed something like

 •  Analytical  (instrumental) precision: "XRF instru-
    ment precision shall be determined using no fewer
    than 7 replicate analyses of a homogenized sample
    with a lead concentration near 400 ppm (the action
    level).  The resulting RSD should be less  than

 •  Combined  analytical and sample preparation
    precision: "Laboratory duplicates (prepared from a
    single sample with at least 150 ppm lead) should
    have RPDs less than "35%."

 •  Combined analytical, sample preparation, and
    sample collection  precision:  "Field  duplicates
    (collocated samples collected from a single location
    with  at least 150  ppm lead, with each  sample
    collected, prepared, and analyzed separately) should
    have  RPDs less than "50% (unless matrix hetero-
    geneity is demonstrated to exceed the anticipated

                              The figure below serves to  illustrate the conceptual
                              progression that comprises the development of a design
                              for generating data based on well-defined project goals.
                              As stated earlier, while conceptually this process is
                              linear, in real-life, the development of a design is highly
                              iterative, as portrayed by the circular arrows. The figure
                              shows that the conceptual progression starts with the
                              project-specific decision goals, and then moves "down-
                              hill" from broader, higher level goals to narrower, more
                              technically detailed articulations of the data quality
                              needs. Project decisions  are translated into project-
                              specific DQOs; then into project-specific MQOs; then
                              into the technology/method selection and development
                              of a  method-specific QC protocol that blends  the
                              QA/QC  needs of the  technology with the project-
                              specific QA/QC needs of the project. Finally, data are

                              Then the process reverses. The actual raw data must
                              then be assessed against the project MQOs to document
                              that the quality of the data generated do indeed meet the
                              decision-making needs of the project. The final step in
                              the chain  is interpreting  the  data  into  meaningful
                              information (such as  a  statistical expression of a
                              contaminant  concentration as  an average  across an
                              exposure unit) that is  fed into the decision-making
                              process (e.g., further action is or is not needed). If the
                              "downhill" process has been conscientiously followed,
                              there is a very strong likelihood that the "uphill" process
                              of data assessment and interpretation will show that the
                              data are of known and documented quality, and are fully
                              adequate to support the project decisions.
                                 The  DQO Process

All data                         are                the
                  Project Decisions
                  will            to and directly support the
                  Project Decisions.