QA/QC and QUALIFIERS

1. QA/QC and QUALIFIERS LOU ANN FISHER CITY OF STILLWATER, OK lafisher@stillwater.org

2. What is a qualifier? One that satisfies requirements or meets a specified standard

3. Why do we need a qualifier? • Validate results • Verify standards are met • Support the case

4. How do we get verification? • Quality assurance stamp How do we get the stamp? • Incorporate quality control How do we show good quality control? • Run blanks, known stds, use precision

5. Quality Assurance - Is defined as a set of principles and techniques that if followed and performed exactly according to the written guidelines for sample collection through analysis will produce data of known and defensible quality.

6. Quality Assurance • In any inquiry or in any court of law, your quality assurance program is useless w/o documentation of the proper control and application of all factors which affect the final results of the analysis. Data Life Cycle

7. A good quality assurance program will include: • Internal quality control • External quality control (quality assessment) • Consistent periodic review of QA Plan • Standard Methods – only those methods accepted by Standard Methods or the regulatory agency

8. Internal Quality Control • Internal quality control methods consist of: • Certification of operator competency • Recovery of known additions 10% of the time • Analysis of externally supplied samples • Analysis of reagent blanks 5% of sample load • Calibration with standards • Analysis of duplicates 10% of the time • Control charts

9. External Quality Control (termed as quality assessment) • External quality (assessment) control consists of: • Performance evaluation samples • Performance audits • Laboratory inter-comparison samples

10. Data Quality Indicators • The primary indicators of data quality are: • Bias – The measure of systematic error • The method – measured by lab inter-comparison studies – diff between grand average and true value • Lab’s method use – measured by diff between lab’s average recovery and true value

11. Data Quality Indicators • Precision – The measure of closeness of results in multiple duplicate samples, repeated analysis of a stable standard or repeated analysis of known additions to samples. Precision is documented by Internal Quality Control

12. Data Quality Indicators • Accuracy – Combination of bias and precision. Accuracy is documented by External Quality Control The external quality control (quality assessment) is a check on whether the laboratory has and practices an acceptable internal quality control program.

13. ODEQ Def’n of Qualifier • Qualifiers are added to the final analytical data reports in an effort to best describe the quality of the sample data to the end user.

14. ODEQ Def’n of Qualifier • Qualifiers may include reasons for sample rejection upon receipt or following analysis or any unusual or nonconforming sample conditions noted as well as additional details.

15. ODEQ Def’n of Qualifier • Analytical qualifiers are “flags” applied by the primary analyst or analyst verifying the data following data reduction and are noted in the qualifier column on the analytical report.

18. Data Quality Evaluation • Gather All Data Available from the Sampling Investigation and Sort by Medium • Evaluate the Analytical Methods Used

19. "Evaluation", as defined in 27A O.S. § 2-4-101, - means a review of the quality control and quality assurance procedures, recordkeeping, reporting procedures, methodology, personal qualifications, equipment, facilities, and analytical technique of a laboratory for measuring, or establishing specific parameters

21. Data Quality Evaluation • Evaluate the Quality of Data with Respect to Sample Quantitation Limits

23. Data Quality Evaluation • Evaluate the Quality of Data with Respect to Qualifiers and Codes

26. Data Quality Evaluation • Evaluate the Quality of Data with Respect to Blanks

27. Types of Blanks Trip blank – Used to indicate potential contamination due to migration of VOC’s Field blank – Used to determine if certain field procedures result in cross-contamination Laboratory Calibration blank – Used to indicate contamination in the instruments themselves Laboratory Reagent or Method blank – Used to determine contamination in the reagents or glassware

29. Data Quality Evaluation • Evaluate Tentatively Identified Compounds

31. Data Quality Evaluation • Compare Potential Contamination with Background

34. Data Quality Evaluation • Develop a Set of Data for Use in the Risk Assessment

36. Data Quality Evaluation • Further Limit the Number of Chemicals to Be carried Through the Risk Assessment if Appropriate • Summarize and Present Data

37. Integrating planning, sampling, and analysis with subsequent data verification, data validation, and data quality assessment is essential. If you are going to run a test, make sure you can back the results with proof; situation, time frame, trends, notification… RESULTS and QUALIFIERS tell it all.

38. Thank you, now I can rest.