United Slates
Environmental Protection
Agency f
9355.9-03
PB94-963207
FPA 5n-F-P3-
November 1993
Superfund
What are DQOs?
DQOs are qualitative and quantitative state-
ments derived from the outputs of each step
of the DQO Process that:
(1) Clarify the study objective;
(2) Define the most appropriate type of data
to collect;
(3) Determine the most appropriate condi-
tions from which to collect the data; and
(4) Specify acceptable levels of decision
errors that will be used as the basis for
establishing the quantity and quality of
data needed to support the decision.
The DQOs are then used to develop a scien-
tific and resource-effective sampling design.
What is the Data Quality Objective
Process?
The DQO Process is a scientific and legally
defensible data collection planning process to
help users decide what type, quality, and
quantity of data will be sufficient for envi-
ronmental decision making.
How does the DQO Process address
early and time-critical sampling
activities?
The DQO Process should be used to plan all
significant Superfund field investigations,
regardless of the sampling objectives. For
early and time-critical decisions, a less rigor-
ous approach to DQO development is consis-
tent with Superfund policy on accelerated
response activities.
Where can more information about
the Superfund DQO guidance, the
DQO process, and QA training be
found?
For more information on Superfund DQO
guidance contact Duane Geuder, QA Manager
for the Office of Emergency and Remedial
Response (OERR) at 703-603-8891. EPA
provides quarterly DQO and DQA training
and quality assurance workshops at the EPA
Institute (202-260-3297). The training courses
are a series of presentations and exercises that
engage the audience in actual DQO develop-
ment and DQA activities.
EPA Data Quality
Objectives
Process for
Superfund
DQO
DQO
DQO
DQC
DQi
DC
How does the DQO Process fit into
integrated site assessment?
By emphasizing the need to place limits on
the probability of taking incorrect actions, the
DQO Process complements the integrated site
assessment objective of evaluating the need
for action. The DQO Process places a worth-
while investment in planning, which results in
timely and efficient cleanups.
Copies of the DQO Fact Sheet and related
Superfund DQO documents can be obtained
from:
National Technical Information
Service (NTIS) 703-487-4650
Interim Final Guidance PB94-963203
Workbook PB94-963204
Fact Sheet PB94-963205
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1
State the Problem
Purpose
Summarize the contamination problem that
will require new environmental data, and
identify the resources available to resolve
the problem.
• Identify members of the scoping team.
• Develop/refine the conceptual site
model.
• Define the exposure scenarios.
• Specify available resources.
• Write a brief summary of the
contamination problem.
£ Identify the Decision
Purpose
Identify the decision that requires new
environmental data to address the contami-
nation problem.
Identify the key decision for the current
phase or stage of the project.
Identify alternative actions that maybe
taken based on the findings of the field
investigation.
Identify relationships between this
decision and any other current or
subsequent decisions.
3 Identify Inputs
Purpose
Identify the information needed to support
the decision, and specify which inputs
require new environmental measurements.
Identify the informational inputs needed
to resolve the decision.
Identify sources for each informational
input, and list those inputs that are
obtained through environmental
measurements.
Define the basis for establishing
contaminant-specific action levels.
Identify potential sampling approaches
and appropriate analytical methods.
The Data Quality Objectives
Process
1. State the Problem
*
2. Identify the Decision
3. Identify Inputs to the Decision
4. Define the Study Boundaries
*
5. Develop a Decision Rule
*
6. Specify Limits on Decision Errors
**
7. Optimize the Design for Obtaining Data
Sampling and Analysis Plan
Development
Implementation
Data Quality Assessment
Define Boundaries
5 Develop a Decision Rule
Develop a logical "if...then..." statement that
defines the conditions that would cause the
decision maker to choose among the
alternative actions.
Specify the parameter of interest (such as
mean, median, or proportion).
Specify the preliminary action level for
the decision.
Combine the outputs of the previous
DQO steps into an "if...then..." decision
rule that includes the parameter of
interest, the action level, and the
alternative actions.
Purpose
Specify the spatial and temporal aspects of
the environmental media that the data must
represent to support the decision.
• Define the geographic areas of the field
investigation.
• Specify the characteristics that define the
population of interest.
• Divide the population into strata having
'relatively homogeneous characteristics.
• Define the scale of decision making. •
• Determine the time frame to which the
decision applies.
• Determine when to collect samples.
• Identify practical constraints that may
hinder sample collection (reconsider
previous steps as necessary).
Optimize the Design
Identify the most resource-effective sampling
and analysis design for generating data that
are expected to satisfy the DQOs.
Review the DQO outputs and existing
environmental data. '
Develop general sampling and analysis
design alternatives.
For each design alternative, verify that
the DQOs are satisfied.
Select the most resource-effective design
that satisfies all of the DQOs.
Document the operational details and
theoretical assumptions of the selected
design in the Sampling andAnalysis
Plan.
Specify Limits on
Decision Errors
Purpose
Specify the decision maker's acceptable
limits on decision errors, which are used to
establish appropriate performance goals for
limiting uncertainty in the data.
Activities
• Determine the range of contaminant
levelsthatmaybe encountered a t the
site.
• Define both types of decision errors and
identify the potential consequences of
each.
• Specify a range of contaminant levels
over which the consequences of decision
errors are relatively minor (the gray
region).
Assign acceptable limits on decision
errors above and below the gray region.
Check for consistency.
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