Attn of: EM-263 (Carter: 301-427-1677)
Subject: Institutionalizing the Data Quality Objectives Process
for EM's Environmental Data Collection Activities
To balance Department of Energy (DOE) environmental sampling and analysis
costs with the need for sound environmental data that address regulatory
requirements and stakeholder concerns, the Department must implement
approaches to streamline procedures, minimize time requirements, and
eliminate unnecessary costs associated with current environmental sampling
and analysis activities. Accordingly, it is the policy of the Office of
Environmental Management (EM) to apply up-front planning, where practical,
to ensure safer, better, faster, and cheaper environmental sampling and
analysis programs for all EM projects and operations. Specifically, it is
EM policy that the Data Quality Objectives (DQO) process be used in all
environmental projects where there may be a need to collect significant
environmental data. The DQO process has already been adapted to site
characterization and remediation in DOE's Streamlined Approach for
Environmental Restoration (SAFER) program. In addition, the Office of
Waste Management is developing guidance to apply the DQO planning process
to efficiently define and integrate process knowledge information with
sampling and analysis to streamline and expedite the activities needed to
meet regulatory requirements and public concerns.
The DQO Process, defined by the U.S. Environmental Protection Agency (EPA),
is a series of planning steps to identify and design more efficient and
timely data collection programs. The DQO process relies heavily on
customer and supplier communication to define data requirements and
acceptable levels of errors in decision making before major resources are
expended on data collection and to assure the customer (whether internal or
external) is satisfied with the results. The DQO process is outlined in
Attachment A. An example of how to integrate process knowledge into the
DQO process is described in Attachment B.
In October 1993, the EPA issued its interim final "Guidance for Planning
for Data Collection in Support of Environmental Decision Making Using the
Data Quality Objectives Process" (EPA QA/G-4). This document provides
excellent guidance on the steps of the DQO process for developing data
quality criteria and performance specifications for data operations. The
EPA's Superfund program has tailored that guidance to its specific
programmatic needs with its interim final guidance on "Data Quality
Objectives Process for Superfund" (EPA/540/G-93/071, September 1993).
Attachment C contains these two EPA guidance documents.
In your application of the DQO process, expertise of the Analytical Services
Division (ASD), EM-263 is available to provide technical assistance. ASD can
provide consultation on specific issues and help EM programs develop
independent capability for planning sound sampling and analysis projects.
ASD has been providing training and direct technical assistance to the field
in using the DQO process to plan, implement, and assess site characterization,
waste characterization, and new remedial action technology. Implementing the
DQO process has successfully achieved substantial cost savings for waste
characterization and environmental remediation projects at Hanford, Oak Ridge,
and Savannah River.
The DQO process has application for environmental sampling and analysis
planning EM-wide, including environmental restoration, waste management,
facility deactivation, decontamination and decommissioning, and technology
development projects. Some potential EM applications of the DQO process
* Waste Management
- Characterizing waste, using process knowledge verified by minimal
necessary sampling and analysis data, to meet acceptance criteria
for treatment, storage, and disposal
- Designing optimized monitoring networks for groundwater and
surface water discharges, and air emissions
* Environmental Restoration
- Focusing regulatory and public concerns relating to remediation
- Effectively identifying target analytes of concern for remedial
- Determining when remediation has met cleanup levels
* Facility Transition and Management
- Performing characterization assessments, using existing
information or collecting new data, to verify facilities for
acceptance into the EM program
- Evaluating alternative end-state conditions and planning facility
deactivation in preparation for eventual decontamination and
- Designing optimized short- and long-term environmental
surveillance and monitoring
* Decontamination and Decommissioning
- Determining location and levels of facility contamination
- Determining when decontamination and decommissioning is complete
* Technology Development
- Determining what constitutes and acceptably demonstrates success
in technology development and evaluation
For further information and assistance in implementing EM's policy for
institutionalizing the DQO process across environmental data collection
activities, please contact the Analytical Services Division, EM-263,
at 301-427-1677 or 301-903-7945 (voice mail).
Thomas P. Grumbly
Assistant Secretary for
Data Quality Objectives (DQO) Planning Process
The DQO planning process consists of seven key steps:
1. State the Problem: Stakeholders work together to define their
concerns and issues based on descriptions of the site, waste
stream, issue, etc., and agree on the question or problem to be
2. Identify the Decision: Stakeholders design the answer or result
that will answer the question or solve the problem, including the
threshold level for action.
3. Identify Inputs to the Decision: Stakeholders define the
measurements needed to answer the question.
4. Define the Boundaries: Stakeholders define the time and space
circumstances covered by the decision.
5. Develop a Decision Rule: Technical staff and stakeholders develop
the formulation to obtain the needed data (quality and quantity)
and to identify acceptability or confidence in the ultimate
6. Specify Acceptable Limits on Decision Errors: In concert with
Step 5, stakeholders define the tolerance for making incorrect
7. Optimize Data Design: Technical staff identifies the most
resource effective data collection design.
Implementation of the DQO process forces data suppliers and data users to
consider the following questions:
* What decision has to be made ?
* What type and quality of data are required to support the decision?
* Why are new data needed for the decision ?
* How will new data be used to make the decision ?
The DQO planning process has several notable strengths. It brings together
the right players (stakeholders and technical staff) at the right time to gain
consensus and commitment about the scope of the project. This interaction
results in a clear understanding of the problem, the actions needed to address
that problem, and the level of uncertainty that is acceptable for making
decisions. Through this process, data collection and analysis are optimized
so only those data needed to address the appropriate questions are collected.
Similarly, SAFER represents an enhancement of the DQO planning process by
combining planning with remedy selection and implementation, resulting in a
process for addressing all facets of an environmental restoration project.
DECIDING WHEN TO APPLY PROCESS KNOWLEDGE OR PERFORM SAMPLING AND ANALYSIS FOR
-- EMPLOYING THE DATA QUALITY OBJECTIVES (DQO) PROCESS
Efficient characterization of DOE's large number of waste streams relies on
information obtained from process knowledge (PK), sampling and analysis (S&A),
or from a combination of both. PK means applying knowledge of the hazard
characteristic of the waste in light of the materials or the processes used
(40 CFR 262.11(a)(2)). This would include knowledge about the origin,
storage, use, and potential exposure of the waste material. For example, if
the feed stocks for making a product are known, the likely range of chemical
components in the waste streams can be predicted. This information may be
sufficient to address regulatory concerns.
To the extent applicable, using PK to characterize waste is more cost
effective than S&A. What is needed is a logical approach to identify the
sufficiency of PK to characterize a particular waste and what is the minimum
amount of new S&A data necessary. The Data Quality Objectives (DQO) planning
process focuses on these specific questions. DQO planning helps stakeholders
decide what questions require characterization information and determine
whether those questions can be answered by existing PK, if the PK must be
supported by new S&A data, or if new data are required because PK is entirely
inadequate. The first several steps of the DQO process help determine whether
existing PK is adequate to characterize the waste. If PK is determined
insufficient, the last few steps of the DQO process lay out the new data needs
and optimize the S&A design.
Attached is a flow chart illustrating the basic framework for applying DQO
planning for waste characterization. First the issues and regulatory drivers
that form the basis for the need to characterize a waste are identified. Then
the questions, possible answers, and data needs for addressing those issues
and drivers are formulated. Waste characterization issues and regulatory
drivers may involve determining whether a waste stream meets treatment,
storage, or disposal waste acceptance criteria. Questions to address these
issues focus on whether PK satisfactorily determines the waste content.
Questions about validity of PK may include how accurate is the knowledge of
the waste generation process, can the waste be traced to the point of origin,
has any significant degradation of the waste occurred, has anything been added
to the waste, and has there been adequate QA/QC of the PK determination.
Once questions and data needs are determined, relevant existing data are
compiled. Existing data include both PK or S&A information already available.
Existing data may be obtained from manifests of the subject waste or waste
generated from areas or processes matching the subject waste, or from
knowledge about the specific process that generated the waste. When all
relevant existing information is compiled, it is evaluated to determine if it
is sufficient to meet the data needs established during the first several
steps of the DQO process. If existing information is sufficient, no further
characterization should be required. If not, then new data are necessary, and
a S&A plan based on the determined data needs should be designed.