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1 of 21 From qualitative concept to practical implementation. Evolution of the Data Quality Objectives Concept DOE EM-5 DQO Training Workshop - Day 2 Appendix.

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Presentation on theme: "1 of 21 From qualitative concept to practical implementation. Evolution of the Data Quality Objectives Concept DOE EM-5 DQO Training Workshop - Day 2 Appendix."— Presentation transcript:

1 1 of 21 From qualitative concept to practical implementation. Evolution of the Data Quality Objectives Concept DOE EM-5 DQO Training Workshop - Day 2 Appendix A End Show

2 2 of 21 Evolution of the DQO Concept n 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. End Show

3 3 of 21 EPA QAMS-005/80 n 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 End Show

4 4 of 21 EPA/540/G-87/003 EPA/540/G-87/ n 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 End Show

5 5 of 21 Major Elements: –Analytical Levels I - IV –PARCC Parameters –Three stage DQO Process: n Stage 1: Identify decision types n Stage 2: Identify data uses and needs n 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 EPA/540/G-87/003 EPA/540/G-87/ n Stage 3: Design data collection program End Show

6 6 of 21 EPA QA/G n Supercedes previous DQO guidance. n 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 End Show

7 7 of 21 EPA QA/G Step 4: Specify Boundaries Step 2: Identify Decisions Step 3: Identify Inputs Step 1: State the Problem Step 5: Define Decision Rules Step 6: Specify Error Tolerances Step 7: Optimize Sample Design Presents a new 7-Step DQO Process. EPA, 1994, Guidance for the Data Quality Objectives Process, EPA QA/G-4, September End Show

8 8 of 21 Misconception n The term Data Quality Objectives is misleading since “data quality” is only one component of the DQO process. n This underplays the role of DQOs as a Planning Process n More appropriate terms would be: –Planning Quality Objectives (PQOs) –Systematic Planning Objectives (SPOs) –Decision Making Objectives (DMOs) DQOs PQOsSPOs DMOs End Show

9 9 of 21 Opinion n 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. End Show

10 10 of 21 DOE-HQ September 7, 1994 DOE Letter, DOE EM-263 to all Field Offices, 1994, Institutionalizing the Data Quality Objectives Process, September n 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

11 11 of 21 Implement DQOs... Easier said than done n Grumbly memo directs sites to do DQOs, but... –No guidance for an implementation mechanism. n Lack of a uniform approach results in an unwieldy process. –No guidance on documentation/format. n Lack of documentation format guidance yields variable products (defensibility?). End Show

12 12 of 21 Impact n DOE Cleanup decisions are vulnerable to criticism - if not rejection. –Non-standard approach/documentation often lack clearly stated: n Decision Statements (Principal Study Questions) n Decision Rules n Error Tolerances n Sample Design –These shortcomings are revealed in the Data Quality Assessment Process. End Show

13 13 of 21 Challenges at Hanford n Unstructured approach to DQOs –proves to be quite unmanageable. –aggravates acceptance. –Perception that DQOs are waste of time and money. n Cultural barrier –SAPs are well understood. –DQOs are not. End Show

14 14 of 21 EPA QA-G4 Certification of DQO Training ?*!! DQO SOP End Show

15 15 of 21 End Show

16 16 of 21 Challenges at Hanford (continued) n Reality: –DQOs are not the problem. –Flawed approach is the problem. –More was needed. n Merely giving Projects QA/G-4 - not enough. End Show

17 17 of 21 Highly structured, tactical approach to implementing the overall DQO Process. 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. End Show

18 18 of 21 - Workbook captures the inputs/outputs of the 7-Step Process. 1 st Draft provides Strawman - Visual Sample Plan used in DQO meeting to what-if sample designs. Tools further streamline the implementation... Tools further streamline the implementation... - Scoping Checklist to ensure a good start. More details to come... More details to come... (Module 9) End Show

19 19 of 21 History Summary ERC DQO Tools PARCC 3 Stage Process 7 Step Process ERC DQO Implementation Process End Show

20 20 of 21 Contacts: n Sebastian C. Tindall Bechtel Hanford Inc George Washington Way, HO-02 Richland, WA n Elizabeth M. (Liz) BowersDepartment of Energy Jadwin Avenue, A2-15 Richland, WA n James R. Davidson, Jr. Davidson & Davidson, Inc Gage Blvd., Suite 205 Kennewick, WA End Show

21 21 of 21 End of Module End Show


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