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US EPA’S DATA QUALITY OBJECTIVE PROCESS

by Karen A. Storne, O’Brien & Gere Engineers, Inc., 5000 Brittonfield Parkway, Syracuse, NY 13221

The United 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 precise data.

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:

Step 1. State the problem to be resolved. Identify the team members, the general problem, the project budget, the time for the study, and the social/political issues that may impact the project.

Step 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 problem.

Step 3. Identify the inputs to the decision. Identify the variables to be measured and the basis for the action level.

Step 4. Define the boundaries of the study. Define the geographical area, the media of 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.

Step 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 error.

Step 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.

Step 7. Optimize the design for obtaining the data. Choose a sampling design that meets the DQO requirements and the budget.

 

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