1 of 23

From Qualitative Concept to Practical Implementation

Evolution of the Data Quality Objectives Concept

DQO Training Course Day 1

Module 1

15 minutes

Presenter: Sebastian Tindall

2 of 23

To understand how the DQO Process has matured over time from a qualitative concept

to practical implementation

Terminal Course Objective

3 of 23

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 of 23

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 of 23

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

5 of 23

6 of 23

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”

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

7 Step Process:

Guidance for the Data Quality Objectives Process, EPA QA/G-4, September 1994

6 of 23

7 of 23

EPA QA/G-42000

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

Guidance for the Data Quality Objectives Process, EPA QA/G-4, September 2000

8 of 23

9 of 23

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 SPOsDMOs

10 of 23

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 of 23

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 of 23

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 of 23

DOE-HQSeptember 7, 1994

Institutionalizing the Data Quality Objectives Process,DOE Letter, DOE EM-263 to all Field Offices, September 1994

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...”

}

14 of 23

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 of 23

EPAQA-G4

Certification of

DQO Training

?*!!

DQO

SOP

16 of 23

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

17 of 23

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 of 23

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 of 23

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 of 23

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

21 of 23

22 of 23

History Summary

DOE DQO Tools

PARCC

3-Stage Process

7-Step Process

DOE DQO Implementation Process

23 of 23

End of Module 1

Thank you.

Questions?