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Data quality objectives in environmental research planning.

A R Batterman1, S L Batterman, K M Jensen

  • 1US Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, Minnesota, USA. batterman.allan@epa.gov

Quality Assurance (San Diego, Calif.)
|February 24, 2001
PubMed
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This study highlights the U.S. Environmental Protection Agency's (EPA) Data Quality Objectives process, ensuring environmental research data is credible and usable. It links research goals to final products through systematic planning and experimentation.

Area of Science:

  • Environmental Science
  • Ecology
  • Environmental Chemistry

Background:

  • The U.S. Environmental Protection Agency (EPA) developed a systematic seven-step research planning process.
  • The National Health and Environmental Effects Research Laboratory (NHEERL), Mid-Continent Ecology Division, applies this process.
  • Introductory materials are based on "Guidance for the Data Quality Objectives Process, EPA QA/G-4."

Purpose of the Study:

  • To present highlights of a Data Quality Objectives (DQO) course.
  • To demonstrate the link between the EPA's seven-step research planning process and environmental research efforts.
  • To illustrate how the DQO process ensures data quality.

Main Methods:

  • Review of EPA's seven-step research planning process.

Related Experiment Videos

  • Application of the DQO process to environmental research case studies.
  • Analysis of decision-making during systematic planning and experimentation.

    • Main Results:

    • The DQO process effectively links research goals and objectives to the final research product.
    • Case studies demonstrate practical application of the DQO process.
    • The systematic planning approach ensures data are of known, credible, defensible, and usable quality.

    Conclusions:

    • The Data Quality Objectives process is crucial for successful environmental research.
    • Implementing the DQO process enhances the reliability and utility of environmental data.
    • This approach supports informed decision-making in environmental protection efforts.