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CLU-IN Studio
United States Environmental Protection Agency
NARPM Presents...The Elements of Analytical Laboratory Data Quality
Sponsored by: EPA Office of Superfund Remediation and Technology Innovation, Analytical Services Branch
Original Time/Date of Presentation:

February 24, 2015, 1:00 PM - 3:00 PM, EST (18:00-20:00 GMT)

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Presentation Overview:

The Elements of Analytical Laboratory Data Quality is a two hour training course for RPMs, OSCs, data validators, and other users of analytical laboratory data. The course will focus on the critical elements that must be considered when documenting the quality and usability of data produced by the analysis of samples from contaminated waste sites. Critical elements discussed in the course include:

  • Chain-of-custody;
  • Proper sample collection, storage, and preservation;
  • Methods and SOPs for preparation and analysis;
  • Documentation of processes that affect samples;
  • Documentation of the peer review process; and
  • Quality control and data validation guidelines.

Some make assumptions about analytical laboratory data quality, while others leave nothing to chance. The basic process of data review is discussed in detail, and several tools that have been developed and are in use by EPA to enhance and document this process are also presented. The benefits of taking this course include:

  • Understanding the importance of planning for the information and data you need (developing your analytical request);
  • Gaining familiarity with the data elements needed to establish data quality,
  • Gaining awareness of available resources to provide guidance in reviewing data, (i.e., National Functional Guidelines);
  • Recognizing signs of improper laboratory practices,
    • Gaining awareness of data quality tools, including sample management tools such as Scribe, Staged Electronic Data Deliverables (SEDD), performance evaluation samples, and electronic data evaluation software
  • Understanding the importance of documenting data quality through use of qualifiers; and
  • Understanding the use of labels to document the level of review applied to data.

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