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8 th Annual California Unified Program Conference

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  1. 8th Annual California Unified Program Conference Advanced Hazardous Waste Inspector Training 9th Annual California Unified Program Conference

  2. What is a valid Waste Determination? Part II. Analysis or Knowledge of Process? 9th Annual California Unified Program Conference

  3. Most of the Time… • It’s simple 9th Annual California Unified Program Conference

  4. But when it isn't simple, who makes the waste determination? The Generator • The person whose act or process produces hazardous waste or whose act first causes a hazardous waste to become subject to regulation.” • A hazardous waste Generator must comply with the requirements of Title 22 CCR, Division 4.5, Chapter 12. 9th Annual California Unified Program Conference

  5. §66262.11Hazardous Waste Determination • First, the generator must determine if it is a waste. • §66261.2 Definition of a waste • § 66261.3 Definition of a hazardous waste • §66261.4 Materials which are not waste • §25143.2 Excluded recyclable materials • Next, the generator must determine if it is a hazardous waste. • Is it listed in article 4 or in Appendix X of Chapter 11? • Or does it exhibit any of the characteristics set forth in article 3 of Chapter 11? 9th Annual California Unified Program Conference

  6. §66262.11Hazardous Waste Determination (cont). • The Generator can make a hazardous waste determination by: • (1) Testing; or • (2) Applying knowledge of the hazard characteristic of the waste in light of the materials or the processes used. • This is also called waste analysis. 9th Annual California Unified Program Conference

  7. Waste Analysis • The cornerstone of a hazardous waste program is the ability of facility personnel to identify properly, through waste analysis, all the wastes they generate, treat, store, or dispose of. Waste analysis involves identifying or verifying the chemical and physical characteristics of a waste by performing a detailed chemical and physical analysis of a representative sample of the waste, or in certain cases, by applying acceptable knowledge of the waste. 9th Annual California Unified Program Conference

  8. Testing • Accurate analytical data is required to comply with Chapter 18, LDR requirements. • A written Waste Analysis Plan (WAP) is required for: • TSDFs, • PBR Treatment and, • Generators treating hazardous to meet LDR standards. 9th Annual California Unified Program Conference

  9. A Waste Analysis Plan • Establishes consistent internal management mechanism(s) for properly identifying wastes on site. • Ensures that waste analysis participants have identical information (e.g., a hands-on operating manual), promoting consistency and decreasing errors. • Ensures that facility personnel changes or absences do not lead to lost information. • Reduces your liabilities by decreasing the instances of improper handling or management of wastes. 9th Annual California Unified Program Conference

  10. Waste Analysis Plan? http://www.epa.gov/epaoswer/hazwaste/ldr/wap330.pdf 9th Annual California Unified Program Conference

  11. Article 3. Characteristics of Hazardous Waste§66261.20.General • (a) A waste, as defined in section 66261.2, which is not excluded from regulation as a hazardous waste pursuant to section 66261.4(b), is a hazardous waste if it exhibits any of the characteristics identified in this article • (c) Sampling and testing pursuant to this article shall be in accord with the chapter nine of SW-846, the Department will consider samples obtained using any of the other applicable sampling methods specified in Appendix I of this chapter to be representative samples. 9th Annual California Unified Program Conference

  12. Characteristic Wastes • §66261.21 (a) A waste exhibits the characteristic of ignitability if representative samples of the waste have any of the following properties: • §66261.22(a) A waste exhibits the characteristic of corrosivity if representative samples of the waste have any of the following properties: • §66261.23(a) A waste exhibits the characteristic of reactivity if representative samples of the waste have any of the following properties: • §66261.24 (a) A waste exhibits the characteristic of toxicity if representative samples of the waste have any of the following properties: 9th Annual California Unified Program Conference

  13. Representative Sample • §66260.10. Definitions. "Representative sample" means a sample of a universe or whole (e.g., waste pile, lagoon, ground water) which can be expected to exhibit the average properties of the universe or whole. 9th Annual California Unified Program Conference

  14. Note:Enforcement Sample • A regulator does not necessarily need a representative sample to support an enforcement action. • The primary reason is that the data quality objectives (DQOs) of the enforcement agency often may be legitimately different from those of a waste handler. • A sample taken for enforcement is used to demonstrate that the waste exceeds a standard (e.g. STLC). 9th Annual California Unified Program Conference

  15. EPA publication SW-846Test Methods for Evaluating Solid Waste, Physical/Chemical Methods OSW's official compendium of approved analytical and sampling methods for use in complying with RCRA regulations. SW-846 primarily is a guidance document that sets forth acceptable, although not required, methods for the regulated and regulatory communities to use for RCRA-related sampling and analysis requirements. 9th Annual California Unified Program Conference

  16. SW 846 http://www.epa.gov/sw-846/sw846.htm 9th Annual California Unified Program Conference

  17. SW 846, Chapter 9,Sampling Plan • SW 846 assumes that: • The concentration of a contaminant in individual samples will exhibit a normal distribution. • Simple random sampling is the most appropriate sampling strategy. • As more information is accumulated, greater consideration can be given to different sampling strategies. • Start with simple random sampling and assume a normal distribution. 9th Annual California Unified Program Conference

  18. Population • 90 bags of candy • 10 bags contain 0 pieces of (0 pieces) • 20 bags contain 1 piece of (20 pieces) • 30 bags contain 2 pieces of (60 pieces) • 20 bags contain 3 pieces of (60 pieces) • 10 bags contain 4 pieces of (40 pieces) • Population mean is 180/90 = 2 9th Annual California Unified Program Conference

  19. Histograph of candy population (normal distribution) Population mean = 2 30 20 10 0 1 2 3 4 9th Annual California Unified Program Conference

  20. Random sample • Four samples from web 14, 37, 40, 81 (90 bags) • Four samples from web 7, 19, 35, 41 (50 bags) • Six samples from web 3, 24, 64, 71, 76 , 90 9th Annual California Unified Program Conference

  21. Histograph of Samples 3 2 1 0 1 2 3 4 9th Annual California Unified Program Conference

  22. Normal Distribution In a normal distribution a bell shaped curve is used to represent the boundaries of the population. The “true” population (under the blue curve) is never known, but precise and unbiased samples will provide an accurate estimate of the true population. Samples The sample population under the magenta curve is an estimate of the true population. 9th Annual California Unified Program Conference

  23. A Bell Curve has Tails! I left the tails off most of the diagrams because I couldn’t figure out how to draw them! • The X axis is the concentration. • The Y axis is the number of samples. • The tails are where the people who got 100% or 0% on an exam are found. 9th Annual California Unified Program Conference

  24. Reliable Waste Analysis • Reliable information concerning the chemical properties of a solid waste is needed for comparison with applicable regulatory thresholds. • If chemical information is to be considered reliable, it must be accurate and sufficiently precise. • Accuracy (no bias) is usually achieved by incorporating randomness into the sample selection process. • Sufficient precision is most often obtained by selecting an appropriate number of samples.

  25. Sample size • Small samples (A) cause the constituent of interest to be under-represented in most samples and over-represented in a small proportion of samples. Larger samples (B) more closely reflect the parent population. • Sometimes you sample a large portion or even the entire population, so you don’t need statistics to determine a confidence interval. 9th Annual California Unified Program Conference

  26. TerminologyPrecise, Accurate & Biased • Precise means all of the samples are similar; they form a “tight group” on the graph. Taking more samples or taking larger samples will increase the sample precision. • Accurate or unbiased means that you’re taking truly random samples. Properly planned random samples are accurate and unbiased samples. • Inaccurate samples aresynonymous with biased samples. They are not representative samples. Poor tool selection or calibration can cause sample bias. 9th Annual California Unified Program Conference

  27. Biased & Imprecise Samples Biased samples do not represent the true population. The biases could result from poor tool selection or contamination. Imprecise samples have a lot of variation. More samples should decrease variation. 0 Mean 1012.5 2000 9th Annual California Unified Program Conference

  28. Biased & Precise Samples A poor sampling plan could lead to biased or inaccurate samples. Poor tool selection, poor sampling design or contamination are some causes. Biased sampling shifts the population curve. Sample Mean True Mean 0 2000 Who can think of another cause for biased samples? 9th Annual California Unified Program Conference

  29. Unbiased & Imprecise Samples Unbiased samples are Random samples. Random samples fall inside the bell curve that represents the true population. Take more samples to increase the precision. 0 Mean 1012.5 2000 9th Annual California Unified Program Conference

  30. Unbiased & Precise Samples Unbiased samples are a function of randomness. Random sampling requires proper plan design and tool selection. Precise samples are a function of the number of samples. 0 Mean 1012.5 2000 9th Annual California Unified Program Conference

  31. Waste Analysis (Testing)To evaluate the physical and chemical properties of a solid waste • The initial -- and perhaps most critical -- element is the sampling plan. • Analytical studies, with their sophisticated instrumentation and high cost, are often perceived as the dominant element. • But analytical data generated by a scientifically defective sampling plan have limited utility. 9th Annual California Unified Program Conference

  32. SW 846 • Waste characterization requires a representative sample. • At least two samples of a material are required for any estimate of precision. • SW 846 uses an 80% confidence interval as an acceptable degree of sampling accuracy and precision. • Normally data from four representative samples is the minimum required to achieve an 80% confidence interval. 9th Annual California Unified Program Conference

  33. How many samples are enough? An example • A business wants to dispose of a pile of used blast medium. It has been reused and it is well mixed. It might have been used to remove paint with lead pigment. • Is it hazardous? • Testing or knowledge of process? • It might have lead? – Knowledge?? • How many samples do need for testing? • Four? 9th Annual California Unified Program Conference

  34. Sampling Plan • Make a 3-D grid of the pile. Number each area of the grid. • Select four numbers randomly. Random number generators are on the web, tables or in textbooks. • Sample from the four areas represented by the number. • Analyze the samples using TTLC. 9th Annual California Unified Program Conference

  35. Sample Results • The TTLC for lead is 1000 mg/kg. • Sample A contains 1000 mg/kg. Is sample A hazardous waste? • Is the waste pile hazardous? • Sample B contains 1050 mg/kg, sample C contains 980 mg/kg and sample D contains 1020 mg/kg. • Is the waste pile hazardous? 9th Annual California Unified Program Conference

  36. Is it hazardous? • Yes, 3 of 4 is good enough. • No, it’s 100% or nothing. • More analysis and maybe more samples are required. The answer is C! 9th Annual California Unified Program Conference

  37. More Analysis? • Yes, more analysis. • The samples were pretty close, A contains 1000 mg/kg, B contains 1050, C: 980 & D: 1020. A range of only 70 mg/kg. • Do we need more samples? • Yes, well… 9th Annual California Unified Program Conference

  38. Guess how many samples 4 5 15 20? The answer is 15.31 Where did that number come from? 9th Annual California Unified Program Conference

  39. A Seven step Statistical Process is used to determine number of samples (SW 846 Table 9-1) • Determine the mean • Determine the variance • Determine the standard deviation • Determine the standard error • Determine the confidence interval • Determine if the variance is > the mean • Determine the appropriate number of samples. 9th Annual California Unified Program Conference

  40. Statistics, the last time… • I would have gotten a PHD if I liked math. • Give it a chance! • It’s just addition, multiplication and division. • Oh, and square roots, but you can use a calculator. 9th Annual California Unified Program Conference

  41. If you really hate Numbers Pretend to listen, it’s the polite thing to do, and remember: • You need at least four (4) samples. • More samples may be required if the waste is: • Heterogeneous, or • Close to the regulatory threshold 9th Annual California Unified Program Conference

  42. Step #1: The Mean Samples A: 1000ppm B: 1050ppm C: 980ppm D: 1020ppm The sample mean is the average value of the samples. It’s an estimate. The true mean is never known. 0 Sample Mean 1012.5 2000 9th Annual California Unified Program Conference

  43. Normal DistributionVariance The variance is the sum of the differences between the sample values and the mean, squared. variance The variance sets the boundaries of the distribution. 0 Mean 2000 9th Annual California Unified Program Conference

  44. Standard Deviation Standard Deviation The standard deviation is the square root of the variance. variance 0 Mean 1012.5 2000 9th Annual California Unified Program Conference

  45. Normal Distribution CI 80% Confidence Interval (CI) If you take 100 samples, 80 should fall inside the boundaries of the 80% CI. variance 0 Mean 1012.5 2000 9th Annual California Unified Program Conference

  46. Normally you would evaluate all four samples • All four randomly selected samples must be considered in a valid statistical analysis. • In the following example, four sets of two will also be analyzed to illustrate the effects of: • Decreasing the variance in concentration in the samples. • Increasing number of samples. • The relationship of the mean to the Regulatory Threshold (RT). 9th Annual California Unified Program Conference

  47. Step 1. The Mean • Add the results of all samples and divide by the number of samples Sample A=1000ppm Sample B=1050ppm Sample C=980ppm Sample D=1020ppm MEAN • A+B+C+D =(1000+1050+980+1020)/4 = 4050/4 = 1012.5 ppm • A + B = 2050/2 = 1025 ppm • C + D = 2000/2 = 1000 ppm • B + D = 2070/2 = 1035 ppm • A + C = 1980/2 = 990 ppm 9th Annual California Unified Program Conference

  48. Step 2. The Variance Variance = (sample A - mean)2 + (sample B - mean)2 +(..) Number of samples - 1 • (1000-1012.5)2+(1050-1012.5) 2 +(980-1012.5) 2 +(1020-1012.5)2 3 • (12.5)2 + (37.5)2 + (32.5)2 + (7.5)2 = 2675/3 = 891.67 3 A+B: (1000-1025)2+ (1050-1025) 2 =1250 1 C+D: ( 980 - 1000) 2 + (1020 - 1000) 2 = 800 1 B+D: (1050 - 1035) 2 + (1020 - 1035) 2 = 450 1 A+C: (1000 - 990) 2 + ( 980 - 990) 2 = 200 1 9th Annual California Unified Program Conference

  49. Step 3 Standard Deviation A=1000 ppm, B=1050 ppm, C= 980 ppm, D=1020 ppm Standard Deviation = Variance 1/2 • The variance of A+B+C+D is 891.67; the square root of 891.67 (standard deviation) = 29.86 • A+B: Variance = 1250; standard deviation = 35.35 • C+D: Variance = 800; standard deviation = 28.28 • B+D: Variance = 450; standard deviation = 21.21 • A+C: Variance = 200; standard deviation = 14.14 9th Annual California Unified Program Conference

  50. Step 4 Standard Error A=1000ppm, B=1050ppm, C= 980ppm, D=1020 ppm Standard Error = Standard Deviation (Number of samples) ½ • Standard error ABCD= 29.86/(4)1/2 = 14.93 • Standard error A + B = 35.35/1.41 = 25.07 • Standard error C + D = 28.28/1.41 = 20.06 • Standard error B + D = 21.21/1.41 = 15.04 • Standard error A + C = 14.14/1.41 = 10.03 9th Annual California Unified Program Conference