From qualitative concept to practical implementation.

1. End Show DOE EM-5 DQO Training Workshop- Day 2 Appendix A Evolution of the Data Quality Objectives Concept From qualitative concept to practical implementation.

2. End Show Evolution of the DQO Concept • Objectives: • To illustrate how the DQO Process has matured over time from a qualitative concept to practical implementation. • To reinforce DOE’s requirement for integrating the DQO Process into all environmental sampling programs. • To dispel the misconception that DQOs are the PARCC parameters.

3. End Show EPA QAMS-005/80 • DQO concept first defined in terms of the PARCC parameters: • Precision • Accuracy • Representativeness • Completeness • Comparability EPA, 1983, Interim Guidelines and Specifications for Preparing Quality Assurance Project Plans, QAMS-005/80, February

4. End Show EPA/540/G-87/003EPA/540/G-87/0041987 • Defined DQOs as: • “…qualitative and quantitative statements which specify the quality of the data required to support the Agency decisions during remedial response activities” EPA, 1987, Data Quality Objectives for Remedial Response Activities, EPA/540/G-87/003, March EPA, 1987, Data Quality Objectives for Remedial Response Activities: Example Scenario, EPA/540/G-87/004, March

5. End Show EPA/540/G-87/003EPA/540/G-87/0041987 Major Elements: • Analytical Levels I - IV • PARCC Parameters • Three stage DQO Process: • Stage 1: Identify decision types • Stage 2: Identify data uses and needs • Stage 3: Design data collection program • Stage 3: Design data collection program EPA, 1987, Data Quality Objectives for Remedial Response Activities, EPA/540/G-87/003, March EPA, 1987, Data Quality Objectives for Remedial Response Activities: Example Scenario, EPA/540/G-87/004, March

6. End Show EPA QA/G-41994 • Supercedes previous DQO guidance. • Defined DQOs as: “…a systematic planning tool based on the Scientific Method for establishing criteria for data quality and for developing data collection designs” EPA, 1994, Guidance for the Data Quality Objectives Process, EPA QA/G-4, September

7. End Show EPA QA/G-41994 Presents a new 7-Step DQO 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 EPA, 1994, Guidance for the Data Quality Objectives Process, EPA QA/G-4, September

8. End Show 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

9. End Show 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. } • 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...” End Show DOE-HQSeptember 7, 1994 DOE Letter, DOE EM-263 to all Field Offices, 1994, Institutionalizing the Data Quality Objectives Process, September

11. End Show 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 results in an unwieldy process. • No guidance on documentation/format. • Lack of documentation format guidance yields variable products (defensibility?).

12. End Show Impact • DOE Cleanup decisions are vulnerable to criticism - if not rejection. • Non-standard approach/documentation often lack clearly stated: • Decision Statements (Principal Study Questions) • Decision Rules • Error Tolerances • Sample Design • These shortcomings are revealed in the Data Quality Assessment Process.

13. End Show Challenges at Hanford • Unstructured approach to DQOs • proves to be quite unmanageable. • aggravates acceptance. • Perception that DQOs are waste of time and money. • Cultural barrier • SAPs are well understood. • DQOs are not.

14. EPA QA-G4 ?*!! End Show Certification of DQO Training DQO SOP

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16. End Show Challenges at Hanford(continued) • Reality: • DQOs are not the problem. • Flawed approach is the problem. • More was needed. • Merely giving Projects QA/G-4 - not enough.

17. End Show • Highly structured, tactical approach to implementing the overall DQO Process. • Identify Projects requiring DQOs. • Begins with Scoping - a key element. • Gets early input from regulatory agencies and key decision makers. • Utilizes a facilitator to coordinate everything • Global Issues identified and resolved prior to DQOs.

18. End Show • Tools further streamline the implementation . . . • Scoping Checklist to ensure a good start. • Workbook captures the inputs/outputs of the 7-Step Process.1st Draft provides Strawman • Visual Sample Plan used in DQO meeting to what-if sample designs. More details to come . . . (Module 9)

19. History Summary PARCC 3 Stage Process 7 Step Process ERC DQO Implementation Process End Show ERC DQO Tools

20. End Show Contacts: • Sebastian C. Tindall Bechtel Hanford Inc. 509-372-9195 3350 George Washington Way, HO-02 sctindal@mail.bhi-erc.com Richland, WA 99352 • Elizabeth M. (Liz) Bowers Department of Energy 509-373-9276 825 Jadwin Avenue, A2-15 Elizabeth_M_Liz_Bowers@RL.gov Richland, WA 99352 • James R. Davidson, Jr. Davidson & Davidson, Inc. 509-374-4498 8390 Gage Blvd., Suite 205 davidson@owt.com Kennewick, WA 99336

21. End Show End of Module