Understanding Analytical Environmental Data kenneth.niswonger@state.co.us

1. Understanding Analytical Environmental Datakenneth.niswonger@state.co.us

3. Complex and Confusing • Interested in low concentrations of targets • Heterogeneous samples - variable results • Matrix interference on analysis • Regulations don’t address these problems

4. Complex and Confusing • What do you need? • Why do you need it? • How will you use it? • Bad or good decisions can come from it?

5. Complex and Confusing • All data have error. • Nobody can afford absolute certainty. • Tolerable error rates (99 % vs. 95 % certainty) • Without DQOs, decisions are uninformed. • Uninformed decisions - conservative and expensive

6. Appendix IA Parameters • Dissolved Anions Method 300 or 9056 (pay attention to hold times) + Alkalinity Method 310.1 • 48 hour hold on NO3- and NO2- (May need 353.1, 353.2, 353.3) • Dissolved Cations Method 6010B/6020 • Field Parameters • Specific Conductance Method 160.1 • pH Method(s) 150.1 or 9040B • Temperature Method 170.1 • TOC (Not field parameter) Lab Method 9060 • Ask for what you need and want

7. Appendix IB Parameters • Total Elements Method 6010B/6020 • Volatiles • Method 8260B • Method 624 • Ask for what you want • Communicate, communicate, communicate

8. DQO Approach: 3 Phases • Planning • Data Quality Objectives (Why sample?) • Quality Assurance Project Plan (“QAPP”) • Implementation • Field Data Collection (Sampling) • Quality Assurance/Quality Control Activities • Assessment • Data Validation • Quality Assurance/Quality Control Activities

9. Much Work Remains to be Done before We Can Announce Our Total Failure to Make any Progress

10. Implementation

11. Assessment

12. Environmental Data:What does this information tell us?(Reading between the Regulatory Lines)

13. Why monitor? Why do statistical analysis? Understand the hydrological setting. Detect and deal with environmental impacts. Understand risks and liabilities. Focus resources. Reduce monitoring costs.

17. “A” horizon Topsoil, organic material Zone of leaching “B” horizon Zone of accumulation “C” horizon Parent material ( rock, gravel, sand) The soil profile of a dark brown Chernozemic soil formed under native grassland

18. Detection Monitoring Includes all Appendix I parameters (Appendix IA and IB). May be modified, in consultation with local governing body to delete any Appendix I parameter on a Site Specific Basis, if Removed constituents not reasonably expected to be derived from waste

19. Detection Monitoring May add parameters, if Acceptable analytical method, Commercially available calibration standard, Analyte is chemically stable, Reasonable sample collection and preservation technique Reasonable expectation of detection, and is a good indicator and possible precursor to other more hazardous constitutents that might Be released later.

20. Detection Monitoring Department considerations in modifying Appendix I parameters: Types, quantities, and concentrations of constituents in waste managed at the SWDS and facilities Mobility, stability, and persistence of constituents, or their reaction products in the unsaturated zone beneath the MSWLF unit.

21. Detection Monitoring Department may specify a monitoring frequency during the active life and post-closure. Minimum of semi-annually, unless approved by the Department. Considerations: Lithology of the saturated and unsaturated zone Hydraulic conductivity of groundwater Groundwater flow rates and minimum distance of travel Resource value of the groundwater

22. Background Data Owner/operator must acquire a minimum of Eight Quarterly Samples From each well and analyzed for Appendix IA and IB constituents. Owner/operator must specify in the operating record, one or more statistical tests for each hazardous constituent. Changes in these statistical tests shall be reviewed and approved within two weeks of the request and entered into the operating record.

23. Background Data Owner/operator must acquire a minimum of Eight Quarterly Samples From each well and analyzed for Appendix IA and IB constituents. Owner/operator must specify in the operating record, one or more statistical tests for each hazardous constituent. Changes in these statistical tests shall be reviewed and approved within two weeks of the request and entered into the operating record.

24. Statistically Significant Increase over Background Documentation in Operating Record indicating which constituent is above Background, and forward the Documentation to the Department and local Governing Body within 14 days. Begin Assessment Monitoring, or Provide an Alternative Source Demonstration Error in sampling, analysis, or natural variations in water Certified by a qualified groundwater scientist If not successfully demonstrated begin Assessment Monitoring in 90 days.

25. Statistical Methods and Requirements Trend analysis Control charts Prediction interval (tolerance intervals) ANOVA comparison with background Other………………………. ------------------------------------------------------------------- Regulations……..Type I error = 0.01 99 % Certainty (for each constituent in each well)

26. Statistical Methods and Requirements Intrawell Statistics, or Interwell Statistics (groups and/or Upgradient – Downgradient)

28. Analyses of Variance (ANOVA)

30. Trend Analysis

31. Control Charts Family of Charts: Shewhart used 3 sigma (3 standard deviations, 98.5 % probability, others have used the Standard error of the Estimate, etc.) 1 sd 67 % of data fits within limits 2 sd 95 % of data fits within limits 3 sd 98.5 % of data fits within limits 4 sd 99 % of data fits within limits “….the fact that the criterion which we happen to use has a fine ancestry in highbrow statistical theorems does not justify its use. Such justification must come from empirical evidence that it works. As the practical engineer might say, the proof of the pudding is in the eating.” Walter A. Shewhart

32. Control Charts Criticisms: Controversial. Operators expected to determine if a special case has occurred. Process in control – 0.27% probability that a point will be out of specs (1/0.0027 or 1 in 370.4) Good at detecting large changes, does not detect small changes efficiently Strengths: May work well for non-parametric data Special control chart CUMSUM does detect small changes

33. Control Charts

34. Control Charts

35. Tolerance Interval A tolerance interval, also known as a tolerance limit, or prediction interval is an interval within which, with some confidence, a specified proportion of a population falls. This differs from a confidence interval in that the confidence interval bounds a population parameter (the mean, for example) with some confidence, while a tolerance interval bounds a population proportion. Criticisms: Difficult to use and interpret…..takes some experience Strengths: Works well on non-parametric data

36. Tolerance Interval

37. Analyses of Variance (ANOVA) Parametric – populations behave as a Normal Distribution Non-parametric – population does not behave Normally Can it be mathematically transformed to behave Normally ? log, antilog, power transformation

38. Hypothesis Testing – Probability and Inferential Statistics Hypothesis: Ho : The Landfill is contributing pollutants in excess of standards, and background. Ha : The Landfill is not contributing pollutants in excess of standards, and background. There are two decisions possible: (1). Accept the null hypothesis (Ho), (2). Reject the null hypothesis (Ho ), equivalent to “accept the alternate hypothesis (Ha)”. There are two possible situations either the null hypothesis (Ho ) is true, or it is false. Because of these facts the possible errors are: Situation Ho is True Ho is False_ Decision Accept Ho correct Type II error (Beta) Reject Ho Type I error (alpha) correct

39. Hypothesis Testing – Probability and Inferential Statistics The Type I (alpha) error occurs when Ho is true, but we reject it. This error would occur when the Landfill is contributing pollutants to water above standards and background, but we conclude that it is not. The consequences of the Type I (alpha) error are the most severe. This error would mislead an understanding of the actual impacts to water resources and public health. In addition, the Type I (alpha) error would be the most embarrassing error to the agency. The Type II (Beta) error occurs when Ho is false, but we accept it. This error would occur when the Landfill is not contributing pollutants above standards and background, but we conclude that it is. The Type II error (Beta) is less embarrassing to the organization, but carries a large opportunity cost by unnecessarily alarming residents of the area and possibly causing unnecessary remediation activities.

40. Hard to imagine good and bad from Groundwater Statistics !!!!!!!

41. Hypothesis Testing – Probability and Inferential Statistics Ho - The two well populations are not statistically equivalent Ha - The two well populations are statistically equivalent 90 % Certainty 95 % Certainty 99 % Certainty Accept Ho Accept Ho Accept Ho

42. Hypothesis Testing – Probability and Inferential Statistics Ho - The two well populations are not statistically equivalent Ha - The two well populations are statistically equivalent 90 % Certainty 95 % Certainty 99 % Certainty Reject HoAccept Ho Accept Ho

43. Hypothesis Testing – Probability and Inferential Statistics Ho - The two well populations are not statistically equivalent Ha - The two well populations are statistically equivalent 90 % Certainty 95 % Certainty 99 % Certainty Reject HoReject HoAccept Ho

44. Hypothesis Testing – Probability and Inferential Statistics Ho - The two well populations are not statistically equivalent Ha - The two well populations are statistically equivalent 90 % Certainty 95 % Certainty 99 % Certainty Reject HoReject HoReject Ho

45. Injecting Common Sense into Statistic Evaluations If determination is that constituent concentration is > Background - Is it consequential ? - Is result above GW standard, or tending toward > GW standard ? - Look over the data, is it cogent? - Is there a failure, or misrepresentation of the statistical protocol? - Resample, errors happen and GW variations are the norm.