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8th Annual California Unified Program Conference 5/24/2017 9th Annual California Unified Program Conference 1 What is a valid Waste Determination? Part II. Analysis or Knowledge of Process? 5/24/2017 9th Annual California Unified Program Conference 2 Most of the Time… 5/24/2017 It’s simple 9th Annual California Unified Program Conference 3 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. 5/24/2017 9th Annual California Unified Program Conference 4 §66262.11 Hazardous 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? 5/24/2017 9th Annual California Unified Program Conference 5 §66262.11 Hazardous 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. 5/24/2017 9th Annual California Unified Program Conference 6 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. 5/24/2017 9th Annual California Unified Program Conference 7 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. 5/24/2017 9th Annual California Unified Program Conference 8 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. 5/24/2017 9th Annual California Unified Program Conference 9 Waste Analysis Plan? http://www.epa.gov/epaoswer/hazwaste/ldr/wap330.pdf 5/24/2017 9th Annual California Unified Program Conference 10 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. 5/24/2017 9th Annual California Unified Program Conference 11 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: 5/24/2017 9th Annual California Unified Program Conference 12 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. 5/24/2017 9th Annual California Unified Program Conference 13 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). 5/24/2017 9th Annual California Unified Program Conference 14 EPA publication SW-846 Test 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 RCRArelated sampling and analysis requirements. 5/24/2017 9th Annual California Unified Program Conference 15 SW 846 http://www.epa.gov/sw-846/sw846.htm 5/24/2017 9th Annual California Unified Program Conference 16 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. 5/24/2017 9th Annual California Unified Program Conference 17 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 5/24/2017 9th Annual California Unified Program Conference 18 Histograph of candy population (normal distribution) Population mean = 2 30 20 10 0 1 5/24/2017 2 3 4 9th Annual California Unified Program Conference 19 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 5/24/2017 9th Annual California Unified Program Conference 20 Histograph of Samples 3 2 1 0 1 5/24/2017 2 3 4 9th Annual California Unified Program Conference 21 Normal Distribution In a normal distribution a Samples 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 The sample population under the true population. magenta curve is an estimate of the true population. 5/24/2017 9th Annual California Unified Program Conference 22 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 The The got 5/24/2017 X axis is the concentration. Y axis is the number of samples. tails are where the people who 100% or 0% on an exam are found. 9th Annual California Unified Program Conference 23 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. 5/24/2017 24 Sample size 5/24/2017 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 25 Terminology Precise, 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 are synonymous with biased samples. They are not representative samples. Poor tool selection or calibration can cause sample bias. 5/24/2017 9th Annual California Unified Program Conference 26 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. 5/24/2017 0 Mean 1012.5 9th Annual California Unified Program Conference 2000 27 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. 0 Sample True Mean Mean 2000 Who can think of another cause for biased samples? 5/24/2017 9th Annual California Unified Program Conference 28 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. 5/24/2017 0 Mean 1012.5 9th Annual California Unified Program Conference 2000 29 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. 5/24/2017 0 Mean 1012.5 9th Annual California Unified Program Conference 2000 30 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. 5/24/2017 9th Annual California Unified Program Conference 31 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. 5/24/2017 9th Annual California Unified Program Conference 32 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? 5/24/2017 9th Annual California Unified Program Conference 33 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. 5/24/2017 9th Annual California Unified Program Conference 34 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? 5/24/2017 9th Annual California Unified Program Conference 35 Is it hazardous? A. Yes, 3 of 4 is good enough. B. No, it’s 100% or nothing. C. More analysis and maybe more samples are required. The answer is C! 5/24/2017 9th Annual California Unified Program Conference 36 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? 5/24/2017 Yes, well… 9th Annual California Unified Program Conference 37 Guess how many samples 4 5 15 20? The answer is 15.31 Where did that number come from? 5/24/2017 9th Annual California Unified Program Conference 38 A Seven step Statistical Process is used to determine number of samples (SW 846 Table 9-1) 1. 2. 3. 4. 5. 6. 7. 5/24/2017 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 39 Statistics, the last time… 5/24/2017 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 40 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: 5/24/2017 Heterogeneous, or Close to the regulatory threshold 9th Annual California Unified Program Conference 41 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. 5/24/2017 0 Sample Mean 1012.5 9th Annual California Unified Program Conference 2000 42 Normal Distribution Variance The variance is the sum of the differences between the sample values and the mean, squared. The variance sets the boundaries of the distribution. 5/24/2017 variance 0 Mean 9th Annual California Unified Program Conference 2000 43 Standard Deviation Standard Deviation The standard deviation is the square root of the variance. variance 0 Mean 1012.5 5/24/2017 9th Annual California Unified Program Conference 2000 44 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 5/24/2017 9th Annual California Unified Program Conference 2000 45 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: 5/24/2017 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 46 Step 1. The Mean Add the results of all samples and divide by the number of samples Sample A=1000ppm Sample C=980ppm Sample B=1050ppm Sample D=1020ppm MEAN A+B+C+D =(1000+1050+980+1020)/4 = 4050/4 = 1012.5 ppm 5/24/2017 A+B C+D B+D A+C = 2050/2 = 1025 ppm = 2000/2 = 1000 ppm = 2070/2 = 1035 ppm = 1980/2 = 990 ppm 9th Annual California Unified Program Conference 47 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 5/24/2017 9th Annual California Unified Program Conference 48 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 5/24/2017 9th Annual California Unified Program Conference 49 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 5/24/2017 9th Annual California Unified Program Conference 50 Step 5 Confidence Interval A=1000ppm, B=1050ppm, C= 980ppm, D=1020 ppm Confidence Interval = Mean ±(student “t”)(standard error) A+B+C+D: 1012.5 ± (1.638)(14.93) = 1012.5 ± 25.46 (988 to 1038). 80 of 100 samples should have concentrations between 988 and 1038 ppm. A+B: 1025 ± (3.078)(25.07) = 1025 ± 77 (948 to 1102) C+D: 1000 ± (3.078)(20.06) = 1000 ± 62 (938 to 1062) B+D: 1035 ± (3.078)(15.04) = 1035 ± 46 (989 to 1081) A+C: 1010 ± (3.078)(10.03) = 1010 ± 31 (979 to 1041) 5/24/2017 9th Annual California Unified Program Conference 51 Step 6. Is the Variance >the Mean? If the variance is not greater than the mean, go to step 7. A + B + C + D: 891.67 is not > 1012.5 If the variance is greater than the mean , you have to transform the data. An example follows for samples A & B. 5/24/2017 A+B: C+D: B+D: A+C: 1250 is > 1025 800 is not > 1000 450 is not > 1035 200 is not > 990 9th Annual California Unified Program Conference 52 Is the Variance > the Mean? Mean 0 Variance 0 If variance is > mean then part of the population is less than zero, i.e. with samples A & B the population is between -225 and 2275. You can’t have a concentration of less than zero so you have to transform the data. 5/24/2017 9th Annual California Unified Program Conference 53 Not more math! 5/24/2017 OK, we won’t transform the data, here But in your handout the next four slides take the square root and go through the steps 1 to 6 and square the data to return to real numbers. Go to step 7. 9th Annual California Unified Program Conference 54 Transform the data if the variance is >the mean Usually data is transformed into a smaller number by taking either the log or the square root of the value. Step 1a. Transforming the mean 10001/2 = 31.62 1/2 = 32.40 1050 Total 64.02/2 = 32.01 5/24/2017 9th Annual California Unified Program Conference 55 Transforming the Variance & Standard Deviation Step 2a. Transforming the Variance Variance = (sample A - mean)2 + (sample B - mean)2 Number of samples - 1 A+B: (31.62 – 32.01)2 + (32.4 - 32.01) 2 = 0.304 1 Step 3a. Transforming the Standard Deviation Standard Deviation = Variance 1/2 A+B = (0.304)1/2 = 0.5515 5/24/2017 9th Annual California Unified Program Conference 56 Transforming the Standard Error and Confidence Interval Step 4a. Transforming the Standard Error Standard Error = Standard Deviation (Number of samples) 1/2 A+B: 0.5515/1.41 = 0.39 Step 5a. Transforming the Confidence Interval Mean ±(student “t”)(standard error) A+B: 32.01 ± (3.078)(.39) = 32.01 ± 1.20 5/24/2017 9th Annual California Unified Program Conference 57 Step 6a Variance > Mean A+B: The transformed variance (0.304) is not greater than the transformed mean (32.01). Now go to the last step #7. 5/24/2017 9th Annual California Unified Program Conference 58 Step 7. Determine the number of samples (n) The Regulatory Threshold (RT) using TTLC for lead is 1000 ppm. n = (student t)2(variance) (RT – mean)2d Use the square root of the RT for lead (36.62) for transformed data. 5/24/2017 9th Annual California Unified Program Conference 59 n = (student t)2(variance) (RT – mean)2 A + B + C + D: (1.638)2 (892) = 15.31 (1000 – 1012.5)2 A+B: (3.078)2 (0.304) = 7.73 samples C+D: (3.078)2 (800) = B+D: 4.27 (1000 – 1035)2 (3.078)2 (200) = 18.94 A+C: 5/24/2017 (1000 – 1000)2 (3.078)2 (450) = (32.62 – 32.01)2 (1000 – 990)2 9th Annual California Unified Program Conference 60 So, fewer samples are required if, The waste is essentially homogenous or Well above or below the threshold 5/24/2017 9th Annual California Unified Program Conference 61 Other Types of Sampling 5/24/2017 Stratified random sampling Systematic random sampling Authoritative sampling 9th Annual California Unified Program Conference 62 Stratified random sampling Stratified random sampling is appropriate if a batch of waste is known to be non-randomly heterogeneous. An example is a pile of blast media. One layer is from blasting lead paint, the next layer is from blasting new aluminum parts prior to painting. Another example is a stripping tank that is used to clean different parts and is periodically changed. The waste could vary from batch to batch. Stratification may occur over space (locations or points in a batch of waste) and/or time (each batch of waste). The units in each stratum are numerically identified, and a simple random sample is taken from each stratum. 5/24/2017 9th Annual California Unified Program Conference 63 Systematic random sampling Systematic random sampling, in which the first unit to be collected from a population is randomly selected but all subsequent units are taken at fixed space or time intervals. An example of systematic random sampling is the sampling along a pipeline at 20 feet intervals. The advantages of systematic random sampling are the ease with which samples are identified and collected and, sometimes, an increase in precision. The disadvantages of systematic random sampling are the poor accuracy and precision that can occur when unrecognized trends or cycles occur in the population. 5/24/2017 9th Annual California Unified Program Conference 64 Authoritative Sampling Authoritative Sampling - Sufficient information is available to accurately assess the chemical and physical properties of a waste, authoritative sampling (AKA judgment sampling) can be used to obtain valid samples. This type of sampling involves the selection of sample locations based on knowledge of waste distribution and waste properties (e.g., homogeneous process streams). The rationale for the selection of sampling locations is critical and should be well documented. An example is an inspector taking one sample a discarded liquid that appears to be gasoline (color & odor) to verify that it is gasoline and has a flash point below 140 °F. 5/24/2017 9th Annual California Unified Program Conference 65 Enforcement Sampling RCRA Waste Sampling Draft Technical Guidance, EPA530-D-02-002 (draft), August 2002, page 10 & 11, {RCRA online # 50940} 2.2.4 Enforcement Sampling and Analysis The sampling and analysis conducted by a waste handler during the normal course of operating a waste management operation might be quite different than the sampling and analysis conducted by an enforcement agency. 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. Consider an example to illustrate this potential difference in approach: Many of RCRA’s standards were developed as concentrations that should not be exceeded (or equaled) or as characteristics that should not be exhibited for the waste or environmental media to comply with the standard. In the case of such a standard, the waste handler and enforcement officials might have very different objectives. 5/24/2017 9th Annual California Unified Program Conference 66 Enforcement Sampling An enforcement official, when conducting a compliance sampling inspection to evaluate a waste handler’s compliance with a “do not exceed” standard, take only one sample. Such a sample may be purposively selected based on professional judgment. This is because all the enforcement official needs to observe – for example to determine that a waste is hazardous – is a single exceedance of the standard. EPA530-D-02-002 (draft), August 2002, Page 11 {RCRA online # 50940} 5/24/2017 9th Annual California Unified Program Conference 67 Enough on Sampling? 5/24/2017 What about knowledge of process? 9th Annual California Unified Program Conference 68 Quick Break Take 5 Next Speaker John Misleh 5/24/2017 9th Annual California Unified Program Conference 69 Waste Determination 5/24/2017 9th Annual California Unified Program Conference 70 Waste Determination by: Process Knowledge Knowledge of Process (KOP) Generator Knowledge 5/24/2017 9th Annual California Unified Program Conference 71 Waste Determination CCR 66262.11 (b) the generator may determine that the waste is not a hazardous waste by either: (1) testing…; or (2) applying knowledge of the hazard characteristic of the waste in light of the materials or the processes used and the characteristics set forth in article 3 of chapter 11 of this division. 5/24/2017 9th Annual California Unified Program Conference 72 Waste Determination CCR 66262.11 Two options: Process Knowledge Analysis 5/24/2017 9th Annual California Unified Program Conference 73 Knowledge of Process Why Use Knowledge Listed Waste is a function of how the waste is generated (knowledge) Know that it is Hazardous 5/24/2017 9th Annual California Unified Program Conference 74 Knowledge of Process OSWER 9938.4-03 RCRA Online 50010 5/24/2017 9th Annual California Unified Program Conference 75 Knowledge of Process OSWER 9938.4-03 • Process Knowledge • What goes in + contaminants introduced = what comes out. • Waste Analysis Data from other facilities • Old Analytical Data • A lot of the information that is acceptable to demonstrate knowledge of process (KOP), looks a lot like Analytical Data 5/24/2017 9th Annual California Unified Program Conference 76 Knowledge of Process OSWER 9938.4-03 Process Knowledge • • • • • Material Balances Engineering Production Data Material Safety Data Sheets (1% = 10,000 ppm) Process Kinetic Information and Process Rates Other Engineering Calculations 5/24/2017 9th Annual California Unified Program Conference 77 Knowledge of Process OSWER 9938.4-03 Analytical Data From Other Facilities • Another plant that conducts the same process and is managing the same waste and has Analytical Data. • A TSD that relies on waste analysis Analytical Data from offsite generators. 5/24/2017 9th Annual California Unified Program Conference 78 Knowledge of Process OSWER 9938.4-03 Old Analytical Data • Process and materials must be the same. • Detection limits and equipment have improved. 5/24/2017 9th Annual California Unified Program Conference 79 Knowledge of Process OSWER 9938.4-03 Situations where using KOP may be appropriate: • Constituents are well documented such as for F or K listed waste • Wastes are discarded unused chemicals (P & U listed) • Health & safety issues in sampling (too dangerous to sample) • Physical nature of waste (construction debris) makes sampling impractical 5/24/2017 9th Annual California Unified Program Conference 80 Knowledge of Process OSWER 9938.4-03 Conclusion (EPA Guidance Doc) • Although EPA recognizes that sampling and analysis are not as economical or convenient as using acceptable knowledge, they do usually provide advantages. Because accurate waste identification is such a critical factor for demonstrating compliance with RCRA, misidentification can render your facility liable for enforcement actions. 5/24/2017 9th Annual California Unified Program Conference 81 Knowledge of Process Faxback 11918 Conservative Classification The regulations allow a generator to characterize its waste based on process knowledge, and it is understood that generators may at times characterize their wastes as hazardous conservatively, rather than incur the costs of testing every batch or stream. 5/24/2017 9th Annual California Unified Program Conference 82 Knowledge of Process Faxback 11608 Analyzing Munitions “not specifically required to test their waste” “The determination may be made by either applying knowledge of the waste, the raw materials, and the process used in its generation or by testing” “if they think that the above munitions items would fail the TCLP-extract analysis for lead or dinitrotoluene, then these wastes could be declared as hazardous, and no testing would be necessary.” 5/24/2017 9th Annual California Unified Program Conference 83 Knowledge of Process Faxback 11592, 11579 Limited analytical Labs being unable to determine conclusively that the waste is or is not hazardous . . . It would probably be prudent for the generator to manage those wastes as hazardous waste. 5/24/2017 9th Annual California Unified Program Conference 84 Seeking Concurrence with DTSC? CCR §66260.200 (m) A person seeking Department concurrence with a nonhazardous determination or approval to classify and manage as nonhazardous a waste which would otherwise be a non-RCRA hazardous waste shall supply the following information to the Department: (5) laboratory results including results from all tests required by chapter 11 of this division and a listing of the waste's constituents. Results shall include analyses from a minimum of four representative samples as specified in chapter 9 of "Test Methods for Evaluating Solid Waste, Physical/Chemical Methods," SW-846, 3rd Edition, U.S. Environmental Protection Agency, 1986 (incorporated by reference in section 66260.11 of this chapter); 5/24/2017 9th Annual California Unified Program Conference 85 Is ONE sample good for anything? • Faxback 11907 - Representative sampling (Fluorescent Tubes) “it appears that you tested one spent fluorescent tube to conclude that all of your spent fluorescent tubes are non hazardous. . . . Based on one tube, we have no way to assess the variability between fluorescent lamps. . . A representative selection of lamps randomly chosen should be analyzed to make this determination.” 5/24/2017 9th Annual California Unified Program Conference 86 KOP Documentation OSWER 9938.4-03 “…EPA looks for documentation that clearly demonstrates that the information relied upon is is sufficient to identify the waste accurately and completely.” 5/24/2017 9th Annual California Unified Program Conference 87 KOP Documentation • The generator is very familiar with the waste generation process and the California and Federal hazardous waste laws and regulations. • Detailed chemical information for all the chemicals and materials utilized in the process is available. • A detailed review of the generating process has been completed and the point of generation has been properly been identified. • All documentation utilized to make the determination is included in the operating record associated with the waste stream. • The generator has evaluated the information gathered and made a written determination. 5/24/2017 9th Annual California Unified Program Conference 88 Ten minute Break 5/24/2017 89