Developing a Total Uncertainty Program for the JAF Environmental Laboratory- Lessons Learned

1. Developing a Total Uncertainty Program for the JAF Environmental Laboratory-Lessons Learned James Furfaro Entergy NNE White Plains, NY

2. The James A. FitzPatrick (JAF) EnvironmentalLaboratory

3. James A. FitzPatrick Environmental Laboratory • The JAF Environmental Laboratory does environmental analysis for: • -Indian Point Units 2 &3 • -James A. FitzPatrick Plant • -Nine Mile Point Units 1 &2 • -Ginna

4. Self-Assessment of the JAF Environmental Laboratory • Compare the JAF E-lab to an industrystandard of excellence, ANSI N42.23. • ANSI N42.23 presents 11 elements for a laboratory QA program. • The JAF E-lab does not calculate total uncertainties for their analytical values.

5. Approach for Calculating Total Uncertainty (TU) • Use NIST Technical Note 1297 and the GUM. • Calculate TUs for each type of analysis. • Look at the major error components of each analysis. • Break the components down into counting errors and systematicerrors.

6. “Guide To The Expression of Uncertainty in Measurement” (GUM) • The NIST policy includes the approach given in the “Guide To The Expression of Uncertainty in Measurement.” • The GUM was prepared by individuals from many international organizations. (ISO 1995)

7. “Creating A Level of Confidence for the Measurement” y ± k c(y) where : c(y) = the combined standard uncertainty of y. k = coverage factor: 1, 2 or 3. y = the estimate of the quantity you are trying to measure.

8. Root-Sum-Of-The-Squares Methodology

9. Estimate Error Terms -Sample Receipt

10. Sample Preparation

11. Sample Counting and Analysis

12. Data Analysis

13. Another Pair of Hands • In April 2001, hired a contractor to help finish the estimate of the error terms for each individual analysis. • NIST and/or GUM methodology would be used.

14. Problems/Confusion • We were planning on issuing this work as a formal calculation, December 2001. • Our contractor presented us with the calculation. He used a methodology based on Bruckner, LANL. We were told that we couldn’t use the root-sum-of-the squares methodology. • Confusion-NIST uses the root-sum-of-the-squares methodology?

15. A Lesson Learned “When systematic errors are small relative to random errors, the root-sum-of-the-squares methodology provides reasonable estimates of the uncertainty.” (Bruckner,1993)

16. A Lesson Learned (Continued) “When systematic errors are large relative to random errors, the root-sum-of-the-squares methodology leads to uncertainty estimates that are too small.” (Bruckner,1993)

17. Bruckner’s Approach • Empirical formula • It gives realistic estimates when the systematic error is larger than the random error. • Very difficult to use when you have several random and systematic error terms.

18. Four Important Parameters in the GUM • Sensitivity Factors • Avoid/minimize correlating effects • Realistically Estimate Degrees of Freedom • Estimate the type of distribution for Type B evaluation.

19. GUM Method for Estimating TU Estimated standard deviations are summed using the root-sum-of-squares methodology to obtain the total standard deviation (uncertainty) for the measurement.

20. Law of Propagation of Uncertainty (GUM) [c(y)] = y{ [c1 x (x1)/x1]2 + [c2 x (x2)/x2]2 + …[cN x (xN)/xN]2}1/2 where: c(y) = the combined standard unc. of the estimate y. (xi) = the standard unc. of xi. ci = the sensitivity factor.

21. Confidence Interval (GUM) y ± t/2,eff x c(y) where : t/2,eff = t statistic with a level of significance of . eff = the effective degrees of freedom.

22. Normal Distributions 2total = 12 + 22 + 3 2 + …. y ± k total

23. Other Distributions utotal2= u12 + u22 + u3 2 + …. u = estimated standard deviation of a component.

24. Effective Degrees of Freedom (GUM) eff = the effective degrees of freedom.

25. Summary of Individual Components-Gamma Spec.* *Uncertainty components reported at 1 standard deviation.

26. Efficiency Versus Energylog-log plot

27. Summary of Individual Components- Beta Analysis *Uncertainty components reported at 1 standard deviation.

28. Summary of Lessons Learned • Estimating error terms is a very time consuming process. • Try to identify the major random and systematic error terms for each analysis. If you overlook an error term, you can always go back and “sharpen the pencil.”

29. Summary of Lessons Learned (Continued) • The GUM allows you to use the Root-Sum-of-the-Squares Methodology. • When the systematic error component is relatively large compared to the random component, the GUM methodology leads to reasonable estimates of the uncertainty. • It does this through the use of four parameters.

30. Dr. Kenneth Inn says: “Always sharpen the pencil...,

31. …but Don’t Sharpen the Marshmallow.”

32. References • ANSI N42.23. “American National Standard Measurement and Associated Instrumentation Quality Assurance for Radioassay Laboratories, IEEE, NY 1997. • Bruckner, L.A. “Propagation of Variance Uncertainty Calculation for an Autopsy Tissue Analysis” Health Physics, 67 (1):24-33, July 1994 (Appendix C). • Bruckner, L.A. “Including Random and Systematic Errors in Measurement Uncertainty” LA-UR-93-932, Los Alamos National Laboratory, NM, 1993 • “Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results” NiST Technical Note 1297, 1994. • “Guide to the Expression of Uncertainty in Measurement” ISO, 1995.