Turning Data into Information

1. Turning Data into Information Limitations and Solutions Richard Burrows

2. First, a little history • Lead in Albacore: Guide to Lead Pollution in Americans • Science, Vol 207, March 1980 p1167 • Typical results for fresh albacore muscle were around 400 ng/g Pb • Typical results for albacore muscle from lead soldered cans were around 700-1000 ng/g • Therefore, the canning process approximately doubles the concentration of lead in tuna?

3. Actually, when analyzed using clean preparation techniques and isotope dilution ICPMS the concentration of lead in fresh albacore muscle was found to be approx 0.3 ng/g • Highly regarded government and commercial laboratories at the time were overestimating the concentration of lead in fresh tuna by over1000 X.

4. Issues with Detection Limits

5. Issues with Detection Limits • MDL • Short term • Small data set • No consideration of blank bias • Assumes constant variance

6. If there is no blank bias, then no problem, but

7. .39 Episode 6000 data, Chromium by 200.8 EPA MDL 0.073 EPA ML 0.2

8. The more sensitive the method, the bigger the problem with ignoring blank bias in the detection limit determination • ICPMS • Method 1668 PCBs • Method 1631 Mercury • SIM analysis

9. Method blank detect rates, multi lab study 8270C 0.3% 8270 SIM 6.4% 8260B 2% 8021B 16%, ICPMS 8%

10. Solutions to Detection Limits Take Blank Bias into Account!! Consider long term variability Keep non-constant variance in mind Consider qualitative identification criteria

11. A Better MDL Detection/Quantitation Federal Advisory Committee Procedure

12. Issues with Quantitation Limits

13. Issues with Quantitation Limits • Until recently, no requirement for a prepped standard at the quantitation limit • Based on MDL • Precision based on statistical prediction • 3 times MDL, therefore 10%RSD

14. Solutions to Quantitation Limits • Recent method and regulatory updates • SW-846 Update V • LLOQ standard, prepped, per quarter at least, must be within 50% of true value, or generate in house limits • Drinking water methods • 7 replicates initially, at MRL, prediction interval within 50-150% • Texas PQL • 8 spikes at the PQL • 10% RSD, metals; 20% RSD, volatiles; 30% RSD semivolatiles

15. Solutions to Quantitation Limits • DQFAC procedure • 7 replicates at QL, then quarterly verifications • Limits not defined in the procedure

16. DQFAC • What we need a procedure to do: • Provide an explicit, verifiable estimate of bias at the quantitation limit • Provide an explicit, verifiable estimate of precision at the quantitation limit • Provide that qualitative identification criteria defined in the method are met at the quantitation limit • Assess multi and inter laboratory variability when data from more than one laboratory is used

17. A Better Quantitation Limit

18. A Better Quantitation Limit

19. Ongoing verification

20. Ongoing verification assessment (continues

21. Determination of Precision and Accuracy Criteria GUESS There will be poor performers….. Step 1

22. Determination of Precision and Accuracy Criteria VERIFY Step 2 Spike at multiple levels around the anticipated quantitation limit

23. Evaluation levels Metals

24. Arsenic

25. o-Xylene

26. Vinyl acetate

27. Objectives • NOT the lowest quantitation limits that can be achieved • Reasonable limits that are relevant to groundwater monitoring criteria and can be achieved by most labs • PQLs that can be verified by data analyzed at the PQL

28. Next Steps Gather additional data bracketing expected quantitation limits 30 plus labs involved Attempt to mimic real world conditions Large data set will be available in about 6 months

29. Summary IQE was used – other procedures could be used TCEQ collects ongoing verification data Don’t need low ppb levels for minerals Some analytes will not meet desired MQOs Spiking at the PQL

30. Problems with solutions to Quantitation limits • Key points • Spike at or very close to the quantitation limit

31. Problems with solutions to Quantitation limits • Precision is highly dependent on how the data is generated • Method 8260 • 70 analytes, spiked at 0.2 ug/L, one batch • Average RSD = 8.2% • Multiple batches, multiple instruments, spikes aged after preparation to simulate holding time

32. Issues with Calibration

33. Issues with Calibration Analyze at least 5 points RSD, linear regression, quadratic regression r, r2 > 0.990 (0.995)

34. The curve that cannot fail

35. Calibration issues RSE = 179% r= 0.997, r2 = 0.994

36. Dalapon RSE = 63%

37. Solutions to Calibration • Calculate “readback” for each level • Recent drinking water methods • Recent SW-846 methods • Pros • Provides an indication of the error introduced at each level • Conceptually straightforward • Cons • Lots of numbers! • Difficult to compare different curve types • Need to be careful with criteria

38. Solutions to calibration • RSE • Extends applicability of RSD (used for average curve) to all other curve types • Pros • Allows easy comparison of curve types • Will indicate failing calibration if any point (high or low concentration) has a high deviation from the curve • Can use same criteria as RSD • Cons • Not currently available in most chromatographic data systems

39. RSE examples

40. Guidelines Establishing Test Procedures for the Analysis of Pollutants Under the CleanWater Act; Analysis and Sampling Procedures When a regression curve is calculated as an alternative to using the average response factor, the quality of the calibration may be evaluated using the Relative Standard Error (RSE). The acceptance criterion for the RSE is the same as the acceptance criterion for Relative Standard Deviation (RSD), in the method. RSE is calculated as:

41. Issues with Sample Matrix

42. 8081A 15 pesticides identified Which are real?

44. 8330B

45. Solutions to Sample Matrix ICPMS – instrumentation advances Complex chromatograms – possible techniques exist, but are not used because of cost – GC/GC Cleanups

46. 2D GC

47. Blank Acid Matrices and IPA in ICPMS No Gas Mode Color of spectrum indicates which matrix gave each interfering peak ClO ArN2H, SO2H Ar2, Ca2, ArCa, S2O, SO3 CO2 ClO 2E5cps SO, SOH ArO, CaO ArC SO2, S2, ArCl ArN Multiple polyatomic interferences affect almost every mass – Interferences are matrix-dependent CO2H ArC S2, SO2 Cl2 ArOH, CaOH ArS, Cl2 Br, Ar2H Br, Ar2H Ar2 ArCO, ArCN ArCl CaO, NaCl ClN2, CaOH, ArNa NaClH S2, SO2 ArS, Cl2 CaO SN ClO, NaS Ar2 Cl2H CaO, NaCl ArS 45 50 55 60 65 70 75 80 Mass No Gas Mode • Unspiked 5% HNO3+ 5% HCl + 1% H2SO4 + 1% IPA Matrix • Unspiked Matrix – ALL peaks are due to polyatomic interferences Page

48. Blank Acid Matrices and IPA in He Mode Color of spectrum indicates which matrix gave each interfering peak 2E5cps • Unspiked 5% HNO3+ 5% HCl + 1% H2SO4 + 1% IPA Matrix • ALL polyatomic interferences are removed in He Mode (same cell conditions) ALL polyatomic interferences are removed in He Mode Is sensitivity still OK? 45 50 55 60 65 70 75 80 Mass He Mode

49. Matrix Mix with Spike (10ppb) in He Mode Consistent sensitivity and perfect template match for all elements 2E5cps • 10ppb Spike in 5% HNO3+ 5% HCl + 1% H2SO4 + 1% IPA Matrix • Consistent high sensitivity for all isotopes of all elements in He Mode Good signal for all spike elements at 10ppb Spike. Perfect template fit for all elements – no residual interferences and no loss of analyte signal by reaction 45 50 55 60 65 70 75 80 Mass He Mode

50. False Positive Probability In Real Data