Data Quality Objectives Process For Superfund Fact Sheet

United States
Environmental Protection
Agency
Office of Solid Waste
and Emergency
Response
vvEPA Data Quality Objectives
Process for Superfund
Office of Emergency and Remedial Response (5201)
Hazardous Site Evaluation Division (5204-G)
Publication 9355.9-01 FS
EPA/540/F -93/043
PB94-963205
September 1993
Quick Reference Fact Sheet
EPA Order 5360.1, entitled, Policy and Program Requirements to Implement the Mandatory Quality Assurance Program,
establishes mandatory quality assurance (QA) requirements for Agency environmental data collection activities. The National Oil
and Hazardous Substances Pollution Contingency Plan (NCP) mandates specific Superfund QA requirements. Both documents
emphasize two requirements: (1) Superfund environmental data must be of known quality; and (2) QA plans based on generic or
site-specific procedures are required to obtain the first objective. The Office of Solid Waste and Emergency Response (OSWER)
has developed this fact sheet to promote the Data Quality Objective Process for Superfund: Interim Final Guidance. The focus
here is the development and implementation of a quality system which requires all Superfund activities to develop and operate
management processes and structures for assuring that the data collected are of known quality. The Data Quality Objective
process is an effective means by which managers and technical staff may plan and design a more efficient QA plan and a more
timely sampling and analysis program that is consistent with the integrated site assessment and accelerated response activities of
the Superfund Accelerated Cleanup Model (SACM). Conforming to this guidance will help ensure that site managers generate
data of known quality that are sufficient for their intended use.
This fact sheet describes the Interim Final Guidance on the
Data Quality Objectives Process for Superfund (September
1993)1. This new guidance supersedes previous 1987
Superfund guidance on Data Quality Objectives (DQOs),
Data Quality Objectives for Remedial Response Activities:
Development Process, EPA/540/G-87/003. This fact sheet
also introduces the Guidance for Conducting Environmental
Data Quality Assessment and the DQO Decision Error
Feasibility Trials software.

What are DQOs? DQOs are qualitative and quantitative
statements 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 conditions 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 scientific 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
environmental decision making.

What are the products of the DQO Process?

The products (outputs) of the DQO process are
statements that define data quality criteria and sampling
design performance specifications. The key data quality
criteria state "how good" the data should be and are
expressed as acceptable probabilities of decision errors.
Other data quality criteria include the spatial and
temporal boundaries of the study, and a precise statement
of the environmental conditions that will be studied to
determine the need for remedial or removal actions.
Sampling design performance specification outputs
include the sampling design method, the numbers and
locations of samples and the sample collection method.
U.S. Environmental Protection Agency. 1993. Data Quality Objectives Process for Superfund: Interim Final Guidance. EPA/540/R-93/071.

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What are the benefits of using the DQO Process to plan
Superfund studies?

The DQO Process offers site managers a way to plan field
investigations so that the quality of data collected can be
evaluated with  respect to the data's  intended use.  The
DQO  process provides a method by  which site managers
can estimate how data quality contributes to the quality of
their decisions.

Specifically, the DQO process:
•    helps site managers decide how many samples and
     analyses are required to support defensible decision
     making;
•    helps site managers define where and when samples
     are to be collected;
•    helps  site  managers develop  a  statistical sampling
     design  that allows  the  uncertainty  in  data to  be
     quantified;
•    helps   field personnel   identify  resource-efficient
     sample collection methods;
•    helps laboratory analysts identify resource-efficient
     analytical methods;
•    can drastically  reduce overall  project costs;
•    provides the QA community with a scientific basis for
     defining the right type and number of  quality
     control  and assessment  samples and  associated
     analytical precision and recovery requirements;
•    provides a  structure for clarifying  multiple study
     objectives into specific decisions;
•    encourages  the participation and communication of
     data users and  relevant technical experts  in planning,
     implementation and assessment.

What  role does statistics play in the DQO Process?

The statistical procedures used in the DQO Process provide:
•    a scientific basis for making inferences  about areas of
     a site based  on information contained in environmental
     samples;
•    a basis for defining data quality criteria and assessing
     the achieved data quality for supporting  integrated site
     assessment decisions;
•    a  foundation for defining meaningful quality  control
     procedures that  are based on the intended use of the
     data;
•    quantitative  criteria for knowing  when  site managers
     can  stop sampling (i.e.,  when  the site has  been
     adequately characterized); and
•     a  solid  foundation  for  planning subsequent  data
     collection activities.
 How does the DQO  Process fit into integrated site
 assessment?

 The  DQO process  provides  a  logical framework  for
 planning  multiple field investigations,  thereby improving
 cross-program  response planning  and  allowing  optimal
 cross-program data useability, which are important goals of
 integrated site assessment under the Superfund Accelerated
 Cleanup Model (SACM).   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 worthwhile investment in planning,
 which results in timely and efficient cleanups.

 How does the  DQO Process address early  and  time-
 critical activities?

 The DQO Process should  be used to plan  all significant
 Superfund field investigations,  regardless of the sampling
 objectives.  Application of the  DQO Process to early and
 time-critical  sampling  activities,  such  as time-critical
 removals   and  early  remedial  actions,  allows  On-Scene
 Coordinators,   Site Assessment Managers,  and Remedial
 Project Managers to generate data of known quality that are
 sufficient for their intended use. For these early and time-
 critical  decisions,  a  less rigorous  approach  to  DQO
 development  is  consistent  with  Superfund  policy  on
 accelerated response activities.

 How can  site managers acquire statistical support for
 field  investigation   planning   and   Data   Quality
 Assessment?

 Site managers  can  access  statistical  support  through
 Regional  and Headquarters  quality  assurance  staff, the
 alternative remedial contracting strategy (ARCS) contracts
 and other Superfund contracts.

EPA  has  also  developed the following  software  and
guidance to provide additional support.

PC Software for DQO Decision Error Feasibility Trials

EPA  has  developed an  interactive PC-based software
package "DQO Decision Error Feasibility Trials"  to help
site managers develop feasible and affordable DQOs. Site
managers  can use this statistical tool to quickly vary their
DQOs, such as limits on decision errors, then see how these
changes  affect the  number  of  samples and  resources
required.

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2. IDENTIFY THE DECISION
Purpose

Identify the decision that requires
new environmental data to
address the contamination
problem.

Activities

• Identify the key decision for the
current phase or stage of the project.
• Identify alternative actions that may
be 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.
Activities
• 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.
1. STATE THE PROBLEM
Purpose
Summarize the contamination problem
that will require new environmental data
and identify the resources available to
resolve the problem.

Activities
• 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.
4. DEFINE BOUNDARIES
Specify the spatial and temporal
aspects of the environmental media
that the data must represent to support
the decision.
Activities

• Define the geographical areas of the field
investigation.
• Specify the characteristics that define the
population of interest.
• Divide the population into strata having
relatively homogenous 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).
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Sampling and Analysis Plan
Development
Implementation
Data Quality
Assessment
5. DEVELOP A DECISION RULE

Purpose

Develop a logical "if...then..." statement that
defines the conditions that would cause the
decision maker to choose among 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.
6. 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 levels that
may be encountered at 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.
7. OPTIMIZE THE DESIGN
Purpose

Identify the most resource-effective sampling and
analysis design for generating data that are expected
to satisfy the DQOs.

Activities

• 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
and Analysis Plan.

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 Data Quality Assessment
Points to Remember
 EPA  has  developed a Data Quality  Assessment (DQA)
 guidance   document   (Guidance   on   Conducting
 Environmental Data Quality Assessment, EPA QA/G-9) that
 explains how a site manager can evaluate whether the data
 satisfy the pre-specified DQOs.

 What is  the  link between quality  control  and DQO
 development?

 DQOs define complete data  collection design performance
 specifications. DQOs are  the driving component of quality
 assurance project plans (QAPPs), which are required as part
 of the Sampling and Analysis Plan for each data collection
 operation.  A quality  control (QC) program  is a required
 element of the QAPP and is  defined based on DQOs.  QC
 programs  provide real-time measurements and  monitoring
 of data collection operations to facilitate corrective action
 support subsequent data validation and assessments.
 QC programs can also be used to evaluate whether expected
 decision error rates have been met.
    The DQO Process is a scientific and legally defensible
    data collection planning approach.
    The DQO Process is  a  framework  for  organizing
    existing QA planning procedures.
    The DQO Process helps produce data of appropriate
    quality for making defensible decisions.
    The DQO Process helps identify the
    most effective use of field investigation
    resources.
    The DQO Process helps develop a statistical sampling
    design  which  allows  site  managers  to  quantify
    uncertainty in data and control the probabilities of
    decision errors.
    The DQO Process provides a basis for defining QA/QC
    programs  and  associated  analytical  precision  and
    recovery requirements.
    The DQO Process leads to identification of efficient
    sampling and analytical  methodologies.
    The DQO Process can drastically reduce overall project
    costs.
 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
 development and DQA activities.
    Copies of this Fact Sheet and related DQO documents
can be obtained from:

    National Technical Information Service (NTIS)
    5285 Port Royal Road
    Springfield, VA 22161
    703-487-4650
    Interim Final Guidance
    Workbook
    Fact Sheet
PB94-963203
PB94-963204
PB94-963205
xvEPA
 United States
 Environmental Protection
 Agency
 Washington, D.C. 20460

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