DQO Process History

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DQO Training Course Day 1 Module 1
Evolution of the Data Quality Objectives Concept
From Qualitative Concept to Practical
Implementation
Presenter Sebastian Tindall
15 minutes
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Terminal Course Objective

• To understand how the DQO Process has matured
  over time from a qualitative concept to practical
  implementation

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Key Points

• DOE requires integration of the DQO Process into
  all environmental sampling programs
• EPA requires systematic planning and recommends
  using the DQO Process
• There is a well-established misconception that
  DQOs are the PARCC parameters

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EPA QAMS-005/80

• DQO concept first defined in terms of the PARCC
  parameters
• Precision
• Accuracy
• Representativeness
• Completeness
• Comparability

Interim Guidelines and Specifications for
Preparing Quality Assurance Project Plans, EPA,
QAMS-005/80, February 1983
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EPA/540/G-87/003 1987

• Defined DQOs as
• qualitative and quantitative statements which
  specify the quality of the data required to
  support the Agency decisions during remedial
  response activities

• Analytical Levels I - IV
• PARCC Parameters
• Three stages process
• Stage 1 Identify decision types
• Stage 2 Identify data uses and needs
• Stage 3 Design data collection program

Data Quality Objectives for Remedial Response
Activities, EPA/540/G-87/003, March 1987 Data
Quality Objectives for Remedial Response
Activities Example Scenario, EPA/540/G-87/004,
March 1987
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EPA QA/G-41994

• Defined DQOs as
• a systematic planning tool based on the
  Scientific Method for establishing criteria for
  data quality and for developing data collection
  designs

7 Step Process
Step 1 State the Problem
Step 2 Identify Decisions
Step 3 Identify Inputs
Step 4 Specify Boundaries
Step 5 Define Decision Rules
Step 6 Specify Error Tolerances
Step 7 Optimize Sample Design
Guidance for the Data Quality Objectives Process,
EPA QA/G-4, September 1994
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EPA QA/G-42000
Step 1 State the Problem
Step 2 Identify Decisions
Step 3 Identify Inputs
Step 4 Specify Boundaries
Step 5 Define Decision Rules
Step 6 Specify Error Tolerances
Step 7 Optimize Sample Design
Guidance for the Data Quality Objectives
Process, EPA QA/G-4, September 2000
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Misconception

• The term Data Quality Objectives is misleading
  since data quality is only one component of the
  DQO Process
• This underplays the role of DQOs as a Planning
  Process
• More appropriate terms would be
• Planning Quality Objectives (PQOs)
• Systematic Planning Objectives (SPOs)
• Decision-Making Objectives
• (DMOs)

DQOs
PQOs
SPOs
DMOs
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Opinion

• DQO guidance should be housed in a non-data
  section of EPA. This would help eliminate the
  misconception that the DQO Process is simply the
  PARCC parameters.

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EPA Order 5360.1

• EPA organizations covered by the scope of this
  order shall develop, complement, and maintain a
  quality system thatprovides for the following
• Use of a systematic planning approach to develop
  acceptance or performance criteria for all work
  covered by this order (see Section 3.3.8 of the
  EPA Quality Manual for Environmental Programs).

EPA Order 5360.1 A2, May 5, 2000, Section 6A(6)
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EPA 5360.1 Manual

• EPA has developed a systematic planning process
  called the data quality objective process. This
  process is the recommended planning approach for
  many EPA data collection activities.

Quality Manual for Environmental Programs, EPA
Order 5360 A1, May 5, 2000
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DOE-HQSeptember 7, 1994
Institutionalizing the Data Quality Objectives
Process,DOE Letter, DOE EM-263 to all Field
Offices, September 1994
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Implement DQOs . . . Easier said than done

• Grumbly memo directs sites to do DQOs, but...
• No guidance for an implementation mechanism
• Lack of a uniform approach
• Every site began using a different process to
  implement DQOs.
• No guidance on documentation/format
• Lack of documentation format guidance yields
  variable products (defensibility?)

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?!!
Certification of DQO Training
DQO SOP
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Impact

• DOE/EPA Cleanup decisions are vulnerable to
  criticism - if not rejection
• Non-standard approaches/documentation often lack
  clearly stated
• Decision statements (principal study questions)
• Decision rules
• Error tolerances
• Sample design ensuring sample representativeness
• These shortcomings are often revealed in the Data
  Quality Assessment Process

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Challenges

• Unstructured approach to DQOs
• Proves to be quite unmanageable
• Aggravates acceptance
• Perception that DQOs are waste of time and money
• Cultural barrier
• Sampling and Analysis Plans (SAPs) are well
  understood
• DQOs are not

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Challenges (cont.)

• Reality
• DQOs are not the problem
• Flawed approach is the problem
• More was needed
• Merely giving Projects QA/G-4 - not enough

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Implement DQOs - But how??

• Grumbly memo outlined no tactical plan for
  implementing the 7 Steps.
• Every site began using a different process to
  implement DQOs.
• Hanfords response
• An evolutionary process that lead to the
  development of a workable process for
  implementing DQOs.

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DQO Implementation Process

• Highly structured tactical approach to
  implementing the 7 Steps.
• Begins with scoping - a key element.
• Gets early input from regulatory agencies and key
  decision makers.
• Utilizes a facilitator to coordinate everything.
• More details to come.

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History Summary
DOE DQO Tools
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End of Module 1

• Thank you.
• Questions?