From Qualitative Concept to Practical Implementation

1. DQO Training Course Day 1 Module 1 Evolution of the Data Quality Objectives Concept From Qualitative Concept to Practical Implementation Presenter: Sebastian Tindall (20 minutes) (10 minute break)

2. Terminal Course Objective To understand how the DQO Process has matured over time from a qualitative concept to practical implementation

3. 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

4. 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

5. 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 EPA/540/G-87/0031987 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 5 of 23

6. EPA QA/G-41994 7 Step Process: • Defined DQOs as: “…a systematic planning tool based on the Scientific Method for establishing criteria for data quality and for developing data collection designs” 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 6 of 23

7. 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

9. Misconception DQOs • 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) PQOs SPOs DMOs

10. 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. 10 of 23

11. EPA Order 5360.1 • “EPA organizations covered by the scope of this order shall develop, complement, and maintain a quality system that…provides 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)

12. 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

13. } • Thomas Grumbly memo: • “…it is the policy of…(EM) to apply up-front planning…to ensure safer, better, faster, and cheaper environmental sampling…It is EM policy that the…(DQO) process be used in all environmental projects...” DOE-HQSeptember 7, 1994 Institutionalizing the Data Quality Objectives Process,DOE Letter, DOE EM-263 to all Field Offices, September 1994

14. 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?)

15. EPA QA-G4 ?*!! Certification of DQO Training DQO SOP

16. 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 • These shortcomings are often revealed in the Data Quality Assessment Process

17. 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

18. Challenges (cont.) • Reality: • DQOs are not the problem • Flawed approach is the problem • More was needed • Merely giving Projects QA/G-4 - not enough

19. 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. • Hanford’s response: • An evolutionary process that lead to the development of a workable process for implementing DQOs. 19 of 23

20. 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…. 20 of 23

22. History Summary PARCC 3-Stage Process 7-Step Process DOE DQO Implementation Process DOE DQO Tools

23. End of Module 1 Thank you. Questions? We will now take a 10 minute break. Please be back in 10 minutes.