75 ABW Civil Engineering Group
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Statistical Methods for Hazardous Waste Characterization at Hill Air Force Base
March 23, 2006
Karl C. Nieman, Ph.D. and Timothy L. Buck
75 CEG/CEV801-777-5788
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Overview
Hazardous Waste Characterization at Hill AFBHistory and Cost savings
Regulatory Background and ImplicationsMethodologyImplementation
Use of Waste Information Tracking System (WITS)Guidelines and Recommendations
Future Use
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Haz Waste Management at Hill AFB
267 Generating Sites1157 potential waste streams that could be characterized Use of statistical characterization of waste began in 1986Goal: provide defensible, cost effective characterization
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Hazardous Waste Processing
0
1000
2000
3000
4000
5000
6000
7000
8000
Samples Sent to LabContainers Processed
Samples Analyzed and Containers Processed (2001-2005)(Excludes Recycled Containers)
20012002200320042005
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Cost Savings Using Statistical Analysis
Hill AFB shipped 5,454 containers of waste subject to characterization in CY 2005
Analysis would cost:
$2,727,000
(at $500/analysis)
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Cost Savings Using Statistical Analysis
By using statistical analysis 3,924 containers were NOT sampled
Total Cost Savings:
$1,962,000(72% cost savings)
Plus additional savings of labor costs for sampling
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Regulatory Background
Generators are required to make a hazardous waste determination by 40 CFR 262.11 by either:
•Testing•Knowledge
40 CFR 261.20-24 requires a “representative sample” if testing is used
A representative sample is “a sample of a universe or whole which can be expected to exhibit the average properties of the universe or whole” [40CFR 260.10]
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Described in EPA’s SW-846 (Chapter 9, “Sampling Plan”)http://www.epa.gov/epaoswer/hazwaste/test/pdfs/chap9.pdf
Calculate basic statistics from the data set (mean, variance, standard deviation, standard error) assuming a normal distribution
Calculate an upper confidence limit (90% confidence)-use this upper limit to characterize the concentration of the waste stream
Regulatory Background
mean
upper confidence limit
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O G D E N A I R L O G I S T I C S C E N T E RConfidence Limit on a Normal Distribution
Adapted From RCRA Waste Sampling-Draft Technical Guidance, August 2002
Regulatory limit
Regulatory limit
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Does the statistical approach protect the generator from regulatory action if waste is “mischaracterized”?
NOBut neither does sampling every container
2002 draft documentation from EPA indicates that regulators need only show that one grab sample exceeds the regulatory limit to prove lack of compliance [RCRA Waste Sampling-DraftTechnical Guidance, August 2002]
Characterizing waste using an upper confidence limit about the mean is arguably the most practical and protective method to ensure compliance
Regulatory Implications
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Example Calculation
Regulatory LimitFor Cadmium =1.0 ppm
1
)( 2
12
−
−=∑=
n
xxs
n
ii
nss
x=
xstxCL 1.0+=
Variance
Standard error
Confidence interval
Equations:CadmiumSample # ppm (TCLP)
1 0.012 0.013 0.224 0.015 0.626 0.197 0.078 0.239 1.92
10 4.18
sample mean= 0.746variance(S2)= 1.791182222 Upper Confidence Limit
90% conf = 0.58531825 1.3395% conf = 0.775768873 1.52
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Using WITS to Calculate Statistics
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1-Select a Waste Site…
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2-Select a Waste Stream…
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3-Select a Date Range for the Calculation…
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4-View Results for All Analytes…
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5-View All Data for an Analyte…
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6-Transfer Chemicals that Exceed Limits to the Site Plan…
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Guidelines and Recommendations
Use a minimum of 4 samples
Use verification sampling to monitor potential changes (yearly, or 1 in 10 on high volume waste streams)
Check for outliers, but don’t automatically throw them out
Ensure that proper sampling practices are used
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Use the 95% confidence limit to be conservative
Be cautious with non-hazardous determinations
Don’t let waste producers know which containers will be sampled
Work with regulators so they are comfortable with your characterization practices
Guidelines and Recommendations
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Evaluation and refinement of current system will continue
ESOHMIS software will support statistical characterization
Future Use
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AcknowledgementsTim Buck- Hill AFB/EM AssistBlair Armstrong- Hill AFBWayne Downs- Hill AFBChuck Ramsey-Envirostat
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Questions?
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NormalityFrom SW-846 Chapter 9-on NormalityThe validity of a CI for the true mean (μ) concentration of a chemicalcontaminant of a solid waste is, as previously noted, based on the assumptionthat individual concentrations of the contaminant exhibit a normaldistribution. This is true regardless of the strategy that is employed tosample the waste. Although there are computational procedures for evaluatingthe correctness of the assumption of normality, those procedures are meaningfulonly if a large number of samples are collected from a waste. Because samplingplans for most solid wastes entail just a few samples, one can do little morethan superficially examine resulting data for obvious departures from normality(this can be done by simple graphical methods), keeping in mind that even ifindividual measurements of a chemical contaminant of a waste exhibit aconsiderably abnormal distribution, such abnormality is not likely to be thecase for sample means, which are our primary concern. One can also compare themean of the sample (x¯) with the variance of the sample (s2). In a normallydistributed population, ¯x would be expected to be greater than s2 (assumingthat the number of samples [n] is reasonably large). If that is not the case,the chemical contaminant of concern may be characterized by a Poisondistribution (0 is approximately equal to s2) or a negative binomialdistribution (0 is less than s2). In the former circumstance, normality canoften be achieved by transforming data according to the square roottransformation. In the latter circumstance, normality may be realized throughuse of the arcsine transformation. If either transformation is required, allsubsequent statistical evaluations must be performed on the transformed scale.
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Example Using EPA ProUCL
Cadmiumppm (TCLP)
0.010.010.220.010.620.190.070.231.924.18
RECOMMENDATION Data follow gamma distribution (0.05) Use Adjusted Gamma UCL
Analysis for ProUCL software
Normal Distribution Test Shapiro-Wilk Test Statisitic 0.624586 Shapiro-Wilk 5% Critical Value 0.842 Data not normal at 5% significance level 95% UCL (Assuming Normal Distribution) Student's-t UCL 1.521817 Gamma Distribution Test A-D Test Statistic 0.481521 A-D 5% Critical Value 0.795813 K-S Test Statistic 0.227105 K-S 5% Critical Value 0.284442 Data follow gamma distribution at 5% significance level 95% UCLs (Assuming Gamma Distribution) Approximate Gamma UCL 2.434987 Adjusted Gamma UCL 3.055041 Lognormal Distribution Test Shapiro-Wilk Test Statisitic 0.912492 Shapiro-Wilk 5% Critical Value 0.842 Data are lognormal at 5% significance level
Input Output
