EPA’S DATA QUALITY OBJECTIVE PROCESS
by Karen A.
Storne, O’Brien & Gere Engineers, Inc., 5000 Brittonfield Parkway, Syracuse,
States Environmental Protection Agency (USEPA) states that all collected
data have error, no one can afford absolute certainty about the data, and
uninformed decisions associated with data collection tend to be conservative
and expensive USEPA 1997 (at Introduction to Data Quality Objectives,
Quality Assurance Division, Washington D.C., page 4). The USEPA proposed
that, before an environmental data collection project begins, criteria
should be established for decision making that is defendable. To accomplish
this, the USEPA developed the data quality objective, or DQO, process. This
is a systematic planning tool used to establish criteria for data quality,
to define tolerable error rates and to develop a data collection design.
Gathering the information for the DQO process is time-consuming and may
negatively impact the project budget and schedule.
The Quality Assurance Management Staff (QAMS) of the USEPA
developed the DQO process to improve effectiveness, efficiency, and
defensibility of decisions related to environmental data collection, while
minimizing expenditures by eliminating unnecessary duplication or overly
The DQO process is presented in the USEPA’s Guidance for the Data Quality
Objectives Process, EPA QA/G-4, EPA/600/R-96/055, September 1994. The DQO
process results in qualitative and quantitative statements that are
developed through a multi-step process that includes the following:
1. State the problem to be resolved. Identify the team members, the general
the project budget, the time for the study, and the
social/political issues that may impact the project.
2. Identify the decision to be made. Identify the main issue to be resolved,
the alternative actions that would result from each resolution, and the
specific decision statement that must be resolved to address the project
3. Identify the inputs to the decision. Identify the variables to be
measured and the basis for the action level.
4. Define the boundaries of the study. Define the geographical area, the
concern, the homogeneous strata, the time frame, the start
and ending time periods, the scale of the decision, and the practical
constraints for the project.
5. Develop a decision rule. The decision rule involves the population
parameter of interest, the scale of the decision making, the action level,
and the alternative action. Develop the test of the hypothesis and decision
6. Specify the tolerable limits on decision errors. Determine the
consequences of each decision error, the quantitation limits of the error,
the range of the parameter of interest, the grey region, and the acceptable
probability of committing decision errors, or how much error is acceptable
before the data becomes unusable.
7. Optimize the design for obtaining the data. Choose a sampling design that
the DQO requirements and the budget.