DRC-2025-002506

299 South Main Street, Suite 1700 ▪ Salt Lake City, Utah 84111
[phone] ▪ Fax: [phone] ▪ www.energysolutions.com

August 6, 2025 CD-2025-159

Mr. Doug Hansen, Director
Division of Waste Management and Radiation Control
P.O. Box 144880
Salt Lake City, UT 84114-4880

Subject: Groundwater Quality Discharge Permit Number UGW450005 (GWQDP): Request
for Compliance Status Change and Request for Modification of the Groundwater
Protection Level (GWPL) for Ra-226+Ra-228 in Monitoring Well GW-126

Dear Mr. Hansen:

This letter requests a return to baseline monitoring status of compliance monitoring well
GW-126, presently in probable out-of-compliance (POOC) status for the sum of radiums
(Ra-226+Ra-228). This letter also requests modification of GWQDP UGW450005 to revise
the background-based GWPL exception for Ra-226+Ra-228 in well GW-126 groundwater.

The first groundwater sample was collected from monitoring well GW-126 in September
2003. As isotopic radium results have been added to the dataset over time, it has become
apparent that the background concentration of Ra-226+Ra-228 in well GW-126 groundwater
sometimes exceeds the current GWPL, which is equal to the Ground Water Quality Standard
(GWQS) of 5 picoCuries per liter (pCi/L). The occurrence of background concentrations
potentially greater than the GWQS is addressed in GWQDP Part I.B, which states the
Director may revise background concentrations after submittal of additional groundwater
quality data. GWQDP Part I.C states that a GWPL is defined as either the GWQS or the
Director-approved background concentration, should that concentration exceed the GWQS.

To minimize sampling activities that are inconsequential to protection of Clive Facility
groundwater, i.e., additional sampling of background, EnergySolutions requests Division of
Waste Management and Radiation Control (DWMRC) approval of this request to return well
GW-126 to baseline status by October 30, 2025. Continued accelerated monitoring of well
GW-126 does not further inform DWMRC’s assurance that human health and the
environment remain protected. To date, there is no evidence of groundwater contamination
at well GW-126.

On November 30, 2022 (CD-2022-198), EnergySolutions notified DWMRC of the
compliance status change from annual baseline monitoring to POOC status due to
exceedance of the Ra-226+Ra-228 GWPL in the 2022 annual groundwater sample collected
from well GW-126. The most recent compliance status details for well GW-126 are provided
in the First Semi-Annual Accelerated Groundwater Monitoring Report for 2025 (CD-2025-
153, July 30, 2025).

Mr. Doug Hansen
August 6, 2025
CD-2025-159
Page 2 of 5

Background-Based GWPL Exception Calculation
The methodology for the calculation of the background-based GWPL exception is as follows:
1) Define the data set for statistical evaluation (identify and remove outliers and address
duplicate results). EnergySolutions identifies outliers using the box plot screening
method referenced in Section 12.2 of the U.S. EPA’s Statistical Analysis of
Groundwater Monitoring Data at RCRA Facilities, Unified Guidance (USEPA,
2009). Identified outliers removed from the dataset are only those requiring removal
to demonstrate normality.
2) Perform statistical tests to demonstrate normality.
3) Calculate the proposed GWPL exception as the mean plus two standard deviations of
the data set in accordance with GWQDP UGW450005 Parts I.B.1 and I.2.
The statistical tests performed to demonstrate the normality of the GW-126 Ra-226+Ra-228
background data set are the following:
1) Comparison of skewness and kurtosis to values expected from a normal distribution
(absolute value of each is less than a respective critical value),
2) The Shapiro-Wilk test (calculated statistic is greater than critical value).
The Microsoft Excel add-in program, Analyse-it, Version 6.16.2 (Analyse-it Software, Ltd.,
2025), was used to identify outliers, perform normality testing, and to calculate the mean and
standard deviation. Copies of Analyse-it output files are included as Attachment 1 of this
document. Attachment 2 provides additional information on the statistical tests listed above.

In accordance with guidance provided in the U.S. EPA’s Statistical Analysis of Groundwater
Monitoring Data at RCRA Facilities, Unified Guidance (USEPA, 2009), samples collected in
duplicate were addressed by randomly selecting either the result or its duplicate in the
dataset. The random number function of Microsoft Excel was used for this purpose.

The Division has previously requested normality testing to remove outliers from the data
(Dane Finerfrock, March 26, 2009) because it is a more conservative method for establishing
background concentrations since outlier data increase the standard deviation of the dataset.
Outlier data are not removed from the permanent well data record and are only omitted in
very specific circumstances for the purpose of establishing GWPLs. EnergySolutions has
established the methodology summarized above based on review and approval from the
Division. This approach provides a defensible and repeatable process for efficiently and
conservatively establishing background-based GWPLs for wells and parameters with
demonstrably higher background levels which cannot meet GWQS.

GW-126 Ra-226+Ra-228
Monitoring well GW-126 has been sampled for 12 consecutive quarters, beginning in
December 2022 when GW-126 entered POOC status for Ra-226+Ra-228. As shown in
Table 1, subsequent to the exceedance observed in the 2022 annual groundwater sample,

Mr. Doug Hansen
August 6, 2025
CD-2025-159
Page 3 of 5

Ra-226+Ra-228 again exceeded the GWPL in samples collected on August 17, 2023; August
20, 2024; and May 13, 2025.

As reported in submittal CD-2022-198, the Ra-226+Ra-228 exceedances observed in well
GW-126 groundwater represent natural concentration fluctuations. Linear regression, shown
in Figure 1, indicates a slight increase of radium levels over time in GW-126. Over the 21-
year period of observation, the Ra-226+Ra-228 concentration increased by 0.24 pCi/L, which
is within the range of the 2 analytical uncertainty. The data in Figure 1 has one outlier
removed with a Ra-226+Ra-228 concentration of 10.6 pCi/L taken on December 15, 2022.
The Ra-228 concentration of this sample was flagged as estimated (“J”) because the
minimum detection level exceeded the reporting limit. A duplicate sample taken at the same
time was reported with a Ra-226+Ra-228 concentration of 4.97 pCi/L. Variation of this
magnitude (10.6 vs 4.97 pCi/L) is anomalous for water samples, and laboratory factors were
the suspected cause. For this reason, Ra-228 in both samples were rerun with reported Ra-
226+Ra-228 concentrations of 4.43 pCi/L and 3.64 pCi/L, respectively. These subsequent
analyses and the duplicate sample data do not support the validity of the original Ra-228
analysis, although validation did not reject it. The purpose of duplicate samples is to provide
secondary supporting evidence for data validity. EPA Methods 903.0 and 904.0 (Ra-226 and
Ra-228, respectively) are chemical precipitation methods based on the chemical yield of
barium sulfate precipitate, and the presence of significant natural barium in samples will
result in a falsely high chemical yield. Clive Facility groundwater is naturally saline with
total dissolved solids (TDS) greater than 40,000 mg/L at well GW-126. High TDS,
particularly higher levels of barium, make these methods challenging for the laboratory. It is
common for EnergySolutions’ current contract radiological laboratory to report that samples
for radium analysis were prepared at a reduced aliquot due to the matrix.

Mr. Doug Hansen
August 6, 2025
CD-2025-159
Page 4 of 5

The GWQS for Ra-226+Ra-228, 5 pCi/L, is a drinking-water based maximum contaminant
level (MCL) which originates from the carcinogenic effects of radium (alpha/beta decay)
when ingested. Each time a well is sampled, more data becomes available, and the
background concentration and distribution of data become better understood. Because the
natural concentration of isotopic radium in Clive Facility groundwater can exceed the MCL,
it is expected, with the collection of additional data, that occasional adjustments to GWPLs
will be required for specific wells. Background concentrations of Ra-226+Ra-228 in Clive
Facility groundwater commonly exceed this MCL, as documented by the approval of
background-based GWPL exceptions for 20 compliance monitoring wells (GWQDP Table
1B).

The Ra-226+Ra-228 dataset listed in Table 1 includes all radium data for the entire duration
of monitoring at well GW-126. Along with the Ra-226+Ra-228 dataset, summary statistics
and results of normality testing are also provided in Table 1. The absolute values of
skewness and kurtosis are less than respective critical values and the Shapiro-Wilk statistic is
greater than the critical value. Based on these statistical measures, Ra-226+Ra-228
concentrations in groundwater at GW-126 are normally distributed. One outlier, at the high
end of the dataset, and discussed previously, was identified and removed from the dataset
prior to this analysis (Table 1). This is a conservative approach since outliers increase the
standard deviation of the data. Section 5.2.3 of the U.S. EPA’s Statistical Analysis of
Groundwater Monitoring Data at RCRA Facilities, Unified Guidance (USEPA, 2009) states:
If an outlier value with much higher concentration than other background observations is not
removed from background prior to statistical testing, it will tend to increase both the
y = 3E-05x + 3.2177
R² = 0.0099
0
1
2
3
4
5
6
7
9/1/2003 5/28/2006 2/21/2009 11/18/2011 8/14/2014 5/10/2017 2/4/2020 10/31/2022
Ra
-
2
2
6

+

R
a
-
2
2
8

(
p
C
i
/
L
)
Figure 1: GW-126 Sum of Radiums
Mr. Doug Hansen
August 6, 2025
CD-2025-159
Page 5 of 5
background sample mean and standard deviation. A subsequent compliance well test against
this background limit will be much less likely to identify an exceedance.
The mean and standard deviation were calculated and used to develop the proposed GWPL
exception of 5.78 pCi/L (Table 2). Including the outlier, three Ra-226+Ra-228 results (6.3%
of all results) in the dataset exceed this value.
Table 2 – Summary of Background-Based
Ra-226+Ra-228 GWPL Exception at Well GW-126
Well Parameter Current GWPL
Exception
Proposed GWPL
Exception
GW-126 Ra-226+Ra-228 5.00 pCi/L 5.78 pCi/L
pCi/L – picoCuries per liter
As stated above, EnergySolutions requests DWMRC approval of this request to return well
GW-126 to annual groundwater sampling by October 30, 2025. Continued collection of
additional background water-quality data is not necessary to provide assurance of the
protection of human health and the environment at well GW-126.
Should you have any questions regarding this submission please feel free to contact me at
[phone].
Sincerely,
Mathew R. Schon
Manager, Groundwater and Environmental Program
I certify under penalty of law that this document and all attachments were prepared under my direction or supervision in accordance
with a system designed to assure that qualified personnel properly gather and evaluate the information submitted. Based on my inquiry
of the person or persons who manage the system, or those persons directly responsible for gathering the information, the information
submitted is, to the best of my knowledge and belief, true, accurate, and complete. I am aware that there are significant penalties for
submitting false information, including the possibility of fine and imprisonment for knowing violations.
Table 1 - Well GW-77 Sum of Radium (Ra-226+Ra-228) Data and Statistical Evaluation
Well ID Date
Used in
Stat. Eval.
GW-126 9/25/2003 Y 1.2 +0.4 2.9 +0.7 4.1 +0.8
GW-126 3/24/2004 Y 1.6 +0.79 2.5 +0.5 4.1 +0.9
GW-126 9/22/2004 Y 1.4 +0.75 2.7 +0.5 4.1 +0.9
GW-126 3/24/2005 Y 1.4 +0.88 2.7 +0.6 4.2 +1.1
GW-126 9/26/2005 Y 1.51 +0.31 2.68 +0.41 4.2 +0.51
GW-126 3/23/2006 Y 1.58 +0.63 3 +0.46 4.6 +0.78
GW-126 9/14/2006 Y 1.77 +0.56 3.26 +0.45 5.0 +0.72
GW-126 3/19/2007 Y 1.66 +0.52 1.99 +0.46 3.7 +0.69
GW-126 9/17/2007 Y 1.45 +0.3 2.06 +0.8 3.5 +0.9
GW-126 3/5/2008 Y 1.8 +0.3 2.7 +0.7 4.5 +0.8
GW-126 9/9/2008 Y 1.4 +0.30 2.1 +0.60 3.5 +0.67
GW-126 4/30/2009 Y 1.93 +0.46 2.82 +0.61 4.75 +0.76
GW-126 12/3/2009 Y 1.53 +0.239 2.76 +0.438 4.29 +0.499
GW-126 5/18/2010 Y 1.85 +0.803 2.22 +1.34 4.07 +1.562
GW-126 9/19/2011 Y 1.72 +0.170 2.62 +1.34 4.34 +1.351
GW-126 4/25/2012 Y 1.67 +0.290 2.5 +0.430 4.17 +0.519
GW-126 4/29/2013 Y 1.77 +0.262 2.75 +0.403 4.52 +0.481
GW-126 5/27/2014 Y 2.08 +0.322 2.5 +0.435 4.58 +0.541
GW-126 5/27/2014 Y 1.83 +0.260 2.95 +0.360 4.78 +0.444
GW-126 5/11/2015 Y 1.47 J +0.298 2.74 +0.437 4.21 +0.529
GW-126 5/18/2016 Y 2.04 +0.254 2.84 +0.516 4.88 +0.575
GW-126 5/16/2017 Y 1.76 +0.288 3.13 +0.386 4.89 +0.482
GW-126 5/17/2018 Y 1.65 +0.263 2.25 +0.420 3.90 +0.496
GW-126 6/6/2019 Y 1.87 +0.255 2.9 +0.487 4.77 +0.550
GW-126 6/9/2020 Y 1.30 +0.257 1.72 J +0.385 3.02 +0.463
GW-126 5/10/2021 Y 1.47 +0.190 2.69 +0.440 4.16 +0.479
GW-126 6/2/2022 Y 1.59 +0.255 3.46 J +0.447 5.05 +0.515
GW-126 6/2/2022 Y ND 3.50 J +0.462 5.09 +0.462
GW-126 12/15/2022 Nb 2.80 +0.206 7.81 J +0.413 10.61 +0.462
GW-126 12/15/2022 Y ND 2.17 J +0.392 4.97 +0.392
GW-126 Dup. 12/15/2022 Y 1.04 +0.226 3.39 J +0.398 4.43 +0.458
GW-126 Dup. 12/15/2022 Y ND 2.60 J +0.404 3.64 +0.404
GW-126 3/30/2023 Y 1.42 +0.219 2.39 +0.451 3.81 +0.501
GW-126 6/27/2023 Y 1.81 +0.256 2.60 J +0.435 4.41 +0.505
GW-126 Dup. 6/27/2023 Y 1.52 +0.267 3.39 J +0.428 4.91 +0.504
GW-126 8/17/2023 Y 1.47 +0.240 4.79 +0.470 6.26 +0.528
GW-126 Dup. 8/17/2023 Y 1.49 +0.25 4.92 +0.44 6.41 +0.51
GW-126 11/28/2023 Y 1.41 +0.24 2.72 +0.41 4.13 +0.48
GW-126 3/25/2024 Y 1.65 +0.25 2.28 +0.45 3.93 +0.51
GW-126 Dup 3/25/2024 Y 1.41 +0.21 1.76 +0.40 3.17 +0.45
GW-126 5/16/2024 Y 1.32 +0.21 2.3 +0.48 3.62 +0.52
GW-126 5/16/2024 Y 1.46 +0.24 2.95 +0.51 4.41 +0.56
GW-126 8/20/2024 Y 1.1 +0.26 2.32 +0.43 3.42 +0.50
GW-126 Dup 8/20/2024 Y 1.28 +0.24 3.96 +0.59 5.24 +0.64
GW-126 10/10/2024 Y 1.35 +0.189 2.69 +0.407 4.04 +0.449
GW-126 2/6/2025 Y 1.28 +0.486 3.05 +1.24 4.33 +1.33
GW-126 Dup. 2/6/2025 Y 0.892 +0.273 1.76 +0.546 2.65 +0.610
GW-126 5/13/2025 Y 1.43 +0.235 3.78 +0.401 5.21 +0.465
Summary statistics listed below include only results used in statistical analysis
Radium-226
(pCi/L)
Radium-228
(pCi/L)
Sum of Radiums
(pCi/L)
CD-2025-159 August 6, 2025
Table 1 - Well GW-77 Sum of Radium (Ra-226+Ra-228) Data and Statistical Evaluation
Well ID Date
Used in
Stat. Eval.
Radium-226
(pCi/L)
Radium-228
(pCi/L)
Sum of Radiums
(pCi/L)
Number of samples 48
Minimum 2.65
Maximum 6.41
Median 4.29
Mean 4.34
Standard Deviation 0.722
Variance 0.522
Mean + 2 standard deviations 5.78
Skewness 0.452
2 x standard error of skewness (SES)0.707
Kurtosis 1.369
2 x standard error of kurtosis (SEK)1.414
Shapiro-Wilk test statistic 0.970
Shapiro-Wilk test critical value ( = 0.05) 0.195
Number of analyses greater than proposed GWPL exception (based on all results including outliers) 3
Percent of analyses greater than proposed GWPL exception 6.3%
a For sample pairs (sample plus duplicate), result used in analysis selected randomly.
b Result identified as an outlier, and therefore, not included in analysis.
ND = no data
NC = not calculated
J = result is estimated based on data validation
CD-2025-159 August 6, 2025
Attachment 1a
Analyse-it Output Files
(with outlier excluded)
v6.16.2
Last updated 5 August 2025 at 11:16 by Mathew R. Schon
Descriptives
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
0
4
8
12
16
22.533.544.555.566.57
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Sum of Radiums (pCi/L)
22.533.544.555.566.57
Sum of Radiums (pCi/L)
v6.16.2
Last updated 5 August 2025 at 11:16 by Mathew R. Schon
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
N 47
Mean 98% CI Mean SE SD Skewness Kurtosis
Sum of Radiums (pCi/L)4.3371 4.0831 to 4.5910 0.10538 0.7224 0.5 1.37
Minimum 1st quartile Median 96% CI 3rd quartile Maximum
Sum of Radiums (pCi/L)2.652 3.9483 4.2900 4.1300 to 4.5200 4.7783 6.410
v6.16.2
Last updated 5 August 2025 at 11:16 by Mathew R. Schon
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
Normality
-3
-2
-1
0
1
2
3
234567
No
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v6.16.2
Last updated 5 August 2025 at 11:16 by Mathew R. Schon
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
Shapiro-Wilk test
W statistic 0.97
p-value 0.1947
Dispersion
SD 0.7224
H0: F(Y) = N(μ, σ)
The distribution of the population is normal with unspecified mean and standard deviation.
H1: F(Y) ≠ N(μ, σ)
The distribution of the population is not normal.
1 Do not reject the null hypothesis at the 5% significance level.
1
Attachment 1b
Analyse-it Output Files
(with all data)
v6.16.2
Last updated 5 August 2025 at 12:05 by Mathew R. Schon
Descriptives
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
0
4
8
12
16
1234567891011
Fr
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Sum of Radiums (pCi/L)
1234567891011
Sum of Radiums (pCi/L)
v6.16.2
Last updated 5 August 2025 at 12:05 by Mathew R. Schon
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
N 48
Mean Mean SE SD Skewness Kurtosis
Sum of Radiums (pCi/L)4.4678 0.16649 1.1535 3.3 16.90
Minimum 1st quartile Median 3rd quartile Maximum
Sum of Radiums (pCi/L)2.652 3.9758 4.3100 4.8383 10.610
Frequency Distribution
Class Frequency
Relative
frequency Density
Cumulative
frequency
Cumulative
relative
frequency
≥2.5 to <3 1 0.021 0.0417 1 0.021
≥3 to <3.5 4 0.083 0.1667 5 0.104
≥3.5 to <4 7 0.146 0.2917 12 0.250
≥4 to <4.5 18 0.375 0.7500 30 0.625
≥4.5 to <5 10 0.208 0.4167 40 0.833
≥5 to <5.5 5 0.104 0.2083 45 0.938
≥5.5 to <6 0 0.000 0.0000 45 0.938
≥6 to <6.5 2 0.042 0.0833 47 0.979
≥6.5 to <7 0 0.000 0.0000 47 0.979
≥7 to <7.5 0 0.000 0.0000 47 0.979
≥7.5 to <8 0 0.000 0.0000 47 0.979
≥8 to <8.5 0 0.000 0.0000 47 0.979
≥8.5 to <9 0 0.000 0.0000 47 0.979
≥9 to <9.5 0 0.000 0.0000 47 0.979
≥9.5 to <10 0 0.000 0.0000 47 0.979
≥10 to <10.5 0 0.000 0.0000 47 0.979
≥10.5 to ≤11 1 0.021 0.0417 48 1.000
v6.16.2
Last updated 5 August 2025 at 12:05 by Mathew R. Schon
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
Normality
-3
-2
-1
0
1
2
3
12.545.578.510
No
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v6.16.2
Last updated 5 August 2025 at 12:05 by Mathew R. Schon
Distribution: Sum of Radiums
Radiums A1:B49
Filter: No filter
Shapiro-Wilk test
W statistic 0.71
p-value <0.0001
H0: F(Y) = N(μ, σ)
The distribution of the population is normal with unspecified mean and standard deviation.
H1: F(Y) ≠ N(μ, σ)
The distribution of the population is not normal.
1 Reject the null hypothesis in favour of the alternative hypothesis at the 5% significance level.
1

Attachment 2
Statistical Test Information

Attachment 2 Statistical Test Information
This attachment contains additional information on the statistical tests applied to demonstrate normality of data sets used in the development of background-based Groundwater Protection
Level (GWPL) exceptions for Clive Facility groundwater. Some of the information provided in this attachment is taken from a January 30, 2009 Technical Memorandum prepared for EnergySolutions by Enchemica LLC, submitted to Utah Water Quality Board and Division of Radiation Control on February 2, 2009 (CD09-0031).
Skewness and Kurtosis Tests Tests of skewness and kurtosis are included in the tests for normality listed in Data Quality Assessment: Statistical Methods for Practitioners (EPA 2006). Both skewness and kurtosis are zero for a normal distribution, but because of chance variations, these statistics depart
from zero for most data sets. These statistical values can be best understood relative to the characteristic bell-shaped curve formed on a histogram by a normally distributed data set. Skewness measures whether data are symmetrically distributed about the mean, which would be expected for a normal distribution. Negative skewness indicates the presence of too many values at the lower end of the distribution range, whereas positive skewness indicates too
many values at the higher end of the range compared to a normally distributed data set. Kurtosis compares the height of the “peak” of the distribution to the height expected from a normal distribution. Positive kurtosis indicates that the data distribution is more “peaked” than expected; negative kurtosis indicates the data distribution is “flatter” than expected from
a normal distribution. The critical values used to determine whether the absolute values of skewness and kurtosis are greater than expected from a normal distribution are equal to two times the standard error of skewness (SES) and kurtosis (SEK) (Brown 1997): SES = (6/N)0.5
SEK = (24/N)0.5 Where N is the number of observations in the data set. Absolute values of skewness and kurtosis higher than the critical values of two times the SES and two times the SEK,
respectively, indicate that the data are relatively unlikely to represent a normal distribution.
Shapiro-Wilk Test The Shapiro-Wilk test is included in the tests for normality listed in Data Quality
Assessment: Statistical Methods for Practitioners (EPA 2006). The Shapiro-Wilk test is
often assumed to be a superior test for normality (EPA 2009) and is specifically suited to smaller data sets. The Shapiro-Wilk test calculates a statistic (W) that indicates whether a data set has a normal distribution. This test is based on the assumption that ordered values in the data set should be highly correlated with corresponding quantiles taken from a normal
distribution (EPA 2009). Small values of W are evidence of departure from normality, whereas a high value (approaching 1) indicates that the data are likely to be normally distributed. If the calculated W statistic is greater than the critical value, the data are likely to
Mr. Doug Hansen August xx, 2025 CD-2025-xxx Attachment 2 Page 2 of 2 be normally distributed. For the Shapiro-Wilk test, critical values represent limits that are a function of the size of the data set and a 5% probability (significance level, α) that a normally distributed population would be incorrectly identified as having a non-normal distribution. The result of the test was compared to the appropriate critical value to determine if there was
a 95% or greater probability that the population was normally distributed.
References Brown, J.D, 1997. Skewness and Kurtosis. JALT Testing & Evaluation SIG Newsletter
1:16-18. EPA (U.S. Environmental Protection Agency), 2009. Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities. Unified Guidance. Office of Resource Conservation and Recovery, EPA/530/R-09-007, March 2009.
EPA (U.S. Environmental Protection Agency), 2006. Data Quality Assessment: Statistical Methods for Practitioners. Office of Environmental Information, EPA/240/B-06/00, February 2006.
National Institute of Standards and Technology (NIST), 2006. N1ST/SEMATECH e-Handbook of Statistical Methods, www.itl.nist.gov/div898/handbook/.