ncasi NATIONAL COUNCIL FOR AIR AND STREAM IMPROVEMENT, INC.

SOUTHERN REGIONAL CENTER

Mailing and Street Address: 

402 SW 140th Terrace, Newberry, FL 32669

Phone (352) 331-1745 

FAX (352) 331-1766

...environmental research for the forest products industry since 1943

July 15, 2011

Mr. Brian Shrager

Energy Strategies Group (C205-01)

Sectors Policies and Program Division

U.S. Environmental Protection Agency

Research Triangle Park, NC 27711

Dear Brian:

In response to EPA’s reconsideration of the boiler MACT and CISWI standards, NCASI is 

submitting two documents which address a number of technical issues with the two standards. 

We want to thank EPA for giving us the opportunity to provide technical comments on the 

standards and would be glad to provide any additional information on specific issues, if 

requested. 

The first document, “Deficiencies in EPA’s Boiler MACT and CISWI Standard Setting 

Methodology and Procedures for Correcting Them,” addresses the issue of detection and 

quantitation capabilities of EPA Methods 5, 10, 23, 26 and 29. It is observed that many of the 

emission tests used for setting the standards had values below the quantitation limits of the EPA 

test methods, thus making their use inappropriate. Therefore many of the standards need to be 

revised to reflect method capabilities so that source operators would be able to demonstrate 

compliance using the specified EPA test methods.

The second document, “Errors in Estimation of the Boiler MACT and CISWI Limits Due to the 

Impact of Non-Independent Data on the Determination of UPL,” raises questions regarding 

EPA’s assumption of data independence in analyzing the emission data. Our analysis of the runby-run data shows the runs in a given test are not necessarily independent (random) observations 

as has been assumed. This conclusion is not surprising as source emission tests are generally 

carried out under relatively steady-state operating conditions over a short period of time and thus 

do not reflect source variability. Unless each run is an independent value, EPA’s current 

statistical methodology for data analysis is invalid. 

The issues identified in the above two documents have significant impacts on the emission 

standards that have been developed, and we urge you to give consideration to these technical 

issues in your review of these standards. We would welcome the opportunity to meet with 

EPA’s technical staff to discuss these issues and provide additional documentation, if desired.
2

Although these comments are being submitted by NCASI, they were prepared with the assistance 

of a team of technical experts working on behalf of ACC, AF&PA, API, AWC, BPA, and CIBO. 

These organizations will be submitting comments in addition to this submission.

Sincerely,

Ashok K. Jain

Southern Regional Manager

cc: Robert Wayland, EPA

Peter Tsirigotis, EPA

Jim Eddinger, EPA

Tim Hunt, AF&PA

John Pinkerton, NCASI
National Council for Air and Stream Improvement

Deficiencies in EPA’s Boiler MACT and CISWI Standard Setting Methodology and 

Procedures for Correcting Them

I. Introduction

In setting the MACT and CISWI emission standards EPA relied on an emissions database 

created from sampling conducted as a part of the 2009 ICR and from older data submitted in 

response to a 2008 boiler survey. A number of industry groups and others have commented on 

the procedures used by EPA to identify the best performers for setting the standards and the 

statistical procedure used by EPA. Among the problems identified are (i) issues with data 

quality, (ii) failure to consider test method capabilities, (iii) use of inappropriate statistical 

procedures, and (iv) source categories. Of these, one overwhelming issue common to all is the 

issue that deals with EPA’s failure to properly consider test method measurement capabilities. 

This document discusses this issue and suggests some ways to minimize the impact of 

consequent problems. 

Among the test methods which were utilized to collect the source emission data used in setting 

the MACT and CISWI standards and are to be used to demonstrate compliance with the 

applicable standards are EPA Methods 5 for particulate matter (PM), 10 for CO, 23 for dioxins 

and furans, 26 for hydrochloric acid, and 29 for mercury and select metals. These methods were 

developed decades ago for sources with then much higher emission rates, and little attention was 

given to method performance near their detection capabilities. However, over the years, as 

source emission rates have decreased and methods have been applied to low-emitting source 

types beyond the method designer’s original intentions, we have run up against the capabilities 

of these test methods to accurately measure source emissions. But EPA has given inadequate 

consideration to this issue and continued to accept all data submitted by sources as being of equal 

significance, resulting in the proposal of emission standards which are beyond the capabilities of 

the test methods employed to show compliance. This issue must be addressed prior to 

promulgating the MACT and CISWI emission standards. 

II. Why Is It Important to Know Measurement Method Quantitation Limits?

Before discussing quantitation limits, it is important to understand related concepts. 

Measurement error is defined as the deviation of a measured value from the true value1. All 

measurements have associated errors. These errors may be systematic (bias) or random 

(precision). Systematic errors are exhibited in a mean of replicate measured values that deviates 

from the true value. Test methods may correct for systematic errors, for example, by subtraction 

of the value measured in a reagent blank from the value measured in a sample or adjustment of 

the measured value in a sample based on the recovery of a tracer compound (spike) added to the 

sample. Random errors are exhibited in replicate measurements which vary from run to run 

around a mean value. Measurement errors contribute to the uncertainty2associated with a 

measured value. Measurement uncertainty is defined variously as the interval around a measured 

value which contains the true value, for a given level of confidence (ASME 1998) or a parameter 

 

1 Measurement error in this context does not imply use of improper or erroneous procedures.

2 Uncertainty here refers to the statistical expression of total measurement error, rather than an inference of 

something which is unknown.
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National Council for Air and Stream Improvement

which characterizes the dispersion of values that could reasonably be attributed to the measured 

value (ISO 1995). Uncertainty in a measured value might be expressed as a standard deviation 

(or multiple of) or as the half-width of an interval having a given level of confidence.

As a fraction of the measured value, the relative magnitude of the random error decreases as the 

value of the measured parameter increases. Thus, at high concentrations, random errors do not 

affect the measured values significantly. However, as the measured value decreases, the 

potential contribution of the random error to the measured value increases, thus decreasing 

confidence in the measured value until the point where the measured value cannot be 

distinguished from random error. Stated another way, as the measured value approaches the 

detection limits, the confidence with which the value can be distinguished from zero decreases. 

Faced with the need to determine whether or not a substance is present in a sample and, if it is 

present, what is its true level, analysts apply two important concepts: limit of detection and limit 

of quantitation. The detection limit of an analytical method for a substance of interest commonly 

is defined as the lowest concentration of the substance that can be distinguished from a blank or 

low concentration sample with 99% confidence. The quantitation limit of a method for a 

substance is defined as the smallest concentration of the substance which can be quantified with 

an acceptable level of uncertainty. In some cases, laboratories may give a “reporting limit” or 

“reporting detection limit” at a level which may or may not be statistically defined, or may be an 

estimate based on the experience and judgment of the analyst. 

Quantitation limits of test methods have great significance when measuring very low 

concentrations of pollutants. In practice, no reported value below a method’s quantitation limit 

should be treated as a quantified value. It should only be treated as a result indicating the 

presence of an analyte that is greater than zero but too low to quantify. This is why the 

American Chemical Society Committee on Environmental Improvement recommended that 

“...quantitative interpretation, decision-making, and regulatory actions should be limited to data 

at or above the limit of quantitation.” (Keith et al. 1983) 

EPA has been well aware of the issues related to method detection and quantitation limits for 

many years, as reflected in EPA’s appointment in 2005 of a federal advisory committee on 

detection and quantitation approaches and their uses in the Clean Water Act Program (Federal 

Register 2005). In 2005, EPA issued a memorandum (EPA 2005) regarding effluent limits 

which states: “The NPDES permit should state that any sample analyzed in accordance with a 

method having appropriate method detection limit (MDL) and minimum level (ML) and found to 

be below the ML will be considered in compliance with the permit limits unless other monitoring 

information indicates a violation.” EPA’s air program has, however, chosen to not address this 

issue adequately. Our search of the literature has identified very few reports on studies to 

determine the detection and quantitation limits of its reference stack test methods, and to date 

EPA has not even proposed a method for determining the detection and quantitation limits for 

these methods. One such report is the ASME ReMAP Project report which examined the 

precision of a number of EPA stack test Methods (Lanier and Hendrix 2001). The findings of 

the ReMAP study are discussed later in the relevant sections of this document. Additionally, 

EPA has produced a guideline for description of in-stack detection limits (ISDL) which defines 

the ISDL for integrated sampling methods as the analytical detection limit divided by the volume 

of flue gas sampled (EPA 1992); however, this ignores measurement errors in the sampling 
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process which often greatly exceed the analytical error. While these issues were not significant 

in the past when the emission standards were set at levels being achieved by a large number of 

sources with a common technology and the emission levels were easy to quantify, the newly 

promulgated MACT and CISWI standards for new sources based on data from a single lowest 

emitting source and for existing sources based on data from a few of the lowest emitting sources 

are so low that they have raised questions regarding the validity of the data used in setting these 

standards and the capabilities of the test methods used. 

III. How Does one Establish the Quantitation Limit of a Test Method?

The issue of establishing quantitation limits has primarily been addressed by analytical chemists, 

who have proposed a number of non-arbitrary, statistical procedures for determining analytical 

method detection and quantitation limits (Keith et al. 1983; Coleman et al. 1997; Corley 2003;

Currie 1999; Voigtman 2008). In 2004 EPA conducted a detailed review of analytical method 

detection and quantitation approaches (EPA 2004). In its simplest form, the detection limit of an 

analytical method is determined by conducting 7 replicate analyses of a very low level sample 

(or blank, ideally of the same overall composition as the sample but without the analyte of 

interest) and multiplying the resulting standard deviation by 3.14 (the Student’s two-tailed tfactor for six degrees of freedom at the 99% confidence level). The detection limit corresponds 

to having less than 1 percent probability of a false positive or false negative detection. The 

quantitation limit, which is also defined as the minimum level of the test method, represents the 

lowest level that can be measured with 30% uncertainty at the 99% confidence level and is 

calculated by multiplying the same standard deviation by 10 (Keith et al. 1983). Alternatively, 

the detection limit as defined above may be multiplied by 10/3.14=3.18 (EPA 2004, pp. 5-35). 

While the above procedures for determining the detection and quantitation limits are adequate 

for analytical methods for sample analysis, source emission test methods have two other 

components, namely source sampling and sample recovery, prior to sample analysis. The errors 

associated with these activities can be significantly higher than the errors associated with 

laboratory analysis. For example, a typical analytical laboratory can weigh filters used in PM 

testing reproducibly to within 0.2 mg. However, when EPA conducted a laboratory study “to 

determine the minimum particulate catch that would be accurate to within ± 10 percent so that 

sample timing can be shortened,” (Shigehara 1996) it concluded that “the minimum PM catch for 

a ± 10 percent accuracy would be 7.2 mg.” Although limited to testing in a single laboratory, the 

study showed that the quantitation capability of EPA Method 5 to achieve 10 percent accuracy 

was 36 times higher than the detection capability of the analytical laboratory when weighing the 

samples obtained from a Method 5 test. This example clearly shows that to determine the 

detection and quantitation limits of source emission test methods EPA must include procedures 

to account for the random errors associated with sampling and sample recovery. Quantitation 

limits achieved by laboratories may only be an insignificant portion of the random errors 

associated with source emission testing. 

Given the possibilities of introducing random errors during the sampling, sample recovery, and 

sample analysis steps in an air emission testing method, the appropriate methodology for 

establishing the detection and quantitation limits of a test method would consist of the following 

three steps: (1) Simultaneous sampling using multiple, paired sampling trains and sampling 
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National Council for Air and Stream Improvement

teams on sources operating at very low emission rates, (2) Sampling recovery by each team 

consistent with its practice, and (3) Sample analysis by different analytical laboratories which 

analyze the samples as a part of their routine analytical practice and not as a special study 

project. The data obtained from such a study would allow one to calculate method standard 

deviations and estimate the quantitation limit of the test method using appropriate statistics for 

the source which is studied (Rigo and Chandler 1999; Lanier and Hendrix 2001). It is only when 

such studies are carried out at many different sources using different testing and analytical 

laboratories that one can get a good understanding of the method quantitation limit. 

IV. Test Methods Used to Collect Data Used in Setting the MACT and CISWI 

Standards and Their Limitations

As described earlier, EPA used data obtained with source emission test methods 5, 10, 23, 26 and 

29 for setting many of the MACT and CISWI standards and will require sources to conduct 

performance tests to demonstrate compliance with PM, CO, PCDD/F, HCl, and mercury 

emission standards. However, several of the Boiler MACT and CISWI emission limits are so 

low that it would not be possible to demonstrate compliance with the promulgated emission 

standards using the test methods specified in the rules. Since EPA used the data in its Boiler 

MACT and CISWI data sets and performed a statistical analysis to arrive at the promulgated 

standards, this raises questions regarding the validity of the reported test data for the best 

performing units and the appropriateness of the statistical procedures used to determine the 

emission limits from reported tests. These issues are discussed below for each test method.

A. EPA Method 5 for Measuring PM Emissions

Nearly all filterable PM data in EPA’s databases were obtained with EPA Method 5. Method 5 

utilizes a probe to extract isokinetically a measured volume of the stack gas which passes 

through a glass fiber filter maintained at a temperature of 248 ± 25 ̊F. The particulate matter 

collected in the probe and on the filter is recovered and weighed. The method requires two 

separate weight determinations – one for the amount of the material collected in the probe and 

the other for the increase in the filter weight. The moisture and oxygen contents of the stack are 

determined to enable the calculation of PM concentration in the stack gas in grains/dscf corrected 

to a specified level of oxygen. If emissions are required to be reported in lb/MMBtu heat input, 

stack gas flow rate and heat input rate or fuel F-factor information are utilized.

Although EPA Method 5 has been in use over 35 years, the promulgated method as published in 

40 CFR Part 60, Appendix A does not include any information on detection and quantitation 

limits of the method. We are aware of only one EPA study addressing Method 5 detection limits 

(Shigehara 1996). This study concluded that “the minimum PM catch for a ± 10 percent 

accuracy would be 7.2 mg.” It should be noted that this was a laboratory study and did not 

include random errors associated with field testing. In a study partially funded by EPA and 

published by Arkley (2000), data are presented where two single-point Method 5 trains were run 

simultaneously on several different sources to examine source emission variability. NCASI 

(2002) conducted an analysis of these data and concluded that the quantitation limits of Method 

5 based on Arkley’s work would be 6 mg. It is noteworthy that Arkley’s studies were limited in 

scope in that they did not examine the impacts of stack traverse and stack gas stratification on the 
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National Council for Air and Stream Improvement

detection and quantitation limits of EPA Method 5. It would be reasonable to expect that the 

detection and quantitation limits measured by Arkley would have been higher had the stack 

variability been included in his study. The ASME ReMAP study did not examine Method 5 

performance at stack gas PM concentrations less than 10 mg/dscm but concluded that (1) the 

standard deviation of low-level PM measurements with EPA Method 5i was not a function of 

stack gas PM concentration, (2) the standard deviation ranged between 1.265 and 1.655 

mg/dscm, and (3) the “inherent precision of Method 5 should be similar to that for Method 5i.” 

Using the above value of the standard deviation, the quantitation limit of Method 5i ( and, 

consequently Method 5) calculated as 10 times the standard deviation would range from 13 (12.6 

rounded up) to 17 mg (16.5 rounded up). 

In comments to EPA on the proposed MACT standards, NCASI noted several best performers 

for filterable PM had sampling runs where the collected PM mass was less than 6 mg, which is 

the lowest of the above three estimates of Method 5 quantitation limits described above. EPA 

nevertheless accepted the reported values and identified the best performing solid fuel boiler 

based on a 3-run emission test, where the emission rate for each run was reported as 0.0002 

lb/106Btu. For liquid fuel units, the best performing unit was selected based on a 3-run test with 

reported PM values of 0.00072, 0.00041, and 0.00040 lb/106Btu (the lowest value was tagged as 

detection limit limited (DLL)), i.e., one or more measurements were below the detection limit. 

The resulting new source limits were 0.0011 lb/106Btu for solid fuel units and 0.0013 lb/106Btu 

for liquid fuel boilers. This brings into question the validity of using these reported values for 

setting emission limits.

Compliance with the PM emission limits must be demonstrated with annual Method 5 (or 17) 

tests consisting of three runs, each with a minimum sample volume of 3 dscm, or with a PM 

CEMS that meets the requirements of PS-11. If the filterable PM emission level is 

approximately equivalent to an emission rate of 0.001 lb/106Btu, the PM mass collected during 

sampling run would be in the range of 2.8 to 4.4 mg. The Method 5 quantitation limit is over 6 

mg. Thus Method 5 results would not be sensitive enough to demonstrate compliance with the 

standard. Furthermore, since normal industry practice is to install control devices that have a 

compliance margin of safety, actual PM levels needed to be achieved would be lower than the 

emission limit. This introduces even more uncertainty into the Method 5 results. If EPA decides 

to remedy this problem by requiring higher gas volumes to be sampled, i.e. more than 3 dscm, 

then a new set of Method 5 data obtained with the extended sampling period must be generated. 

Alternatively, a limit could be set at the Method 5 level of quantitation, once EPA establishes 

that value. 

In addition to the test method detection and quantitation limits, another major shortcoming of 

EPA Method 5 is the absence of any discussion of the treatment of negative changes in the 

weight of the PM filter. In EPA Method 5, after the PM samples have been recovered from the 

probe and the filter and the residue have been dried, the analyst is left with (a) a sample of the 

material collected in the probe and (b) the filter which is used to capture the particulate matter in 

the gases being sampled. These two samples are weighed and tare weights are subtracted from 

each to obtain increases in mass of the two fractions as a result of sampling. In some cases the 

increase in the filter mass is negative, suggesting loss of filter material during the run or during 

sample recovery. EPA Method 5 does not specify how to account for decreases in filter mass 
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National Council for Air and Stream Improvement

when calculating the total mass collected. Our review of the PM test reports found many 

instances where the increase in the weight of the filter after testing was negative. In such cases, 

some testing companies added the negative increase in the filter mass to the positive value for the 

probe residue to obtain the net mass of the captured particulate matter, whereas other testing 

companies treated the negative change in filter weight as zero and reported the mass of the 

residue collected in the probe as the net mass captured. In either case, the random error 

represented by small variations in net filter weight and net probe rinse weight above and below 

zero is under-represented in these approaches. Random error in the probe rinse results is 

typically much greater than in the filter results. This is evident in Shigehara’s (1996) laboratory 

study in which 14 probe rinse blank results varied between 0.5 and 5.3 mg (excluding one outlier 

at 19.3 mg) and dominated the total (filter + probe rinse) error. In Method 5, a single probe rinse 

recovery reagent (acetone wash bottle) blank is collected and analyzed, and the results are 

subtracted from the sample results. Acetone recovery blank results routinely vary between zero 

and 1 mg and are occasionally as high as 3 mg. It is clear that results in this range are dominated 

by random errors.

To address the Method 5-related problems described above, we recommend that EPA should (1) 

revise Method 5 by specifying that all negative filter weight change measurements obtained 

during sample analysis should be treated as zero, and (2) establish the quantitation limit of 

Method 5 at no less than 8 mg (7.2 mg from the Shigehara study rounded up to a single digit). 

Once this is accomplished, EPA should review all the data in its emissions database to identify 

the runs where PM mass less than the quantitation limit of the method were collected, replace 

those values with the quantitation limit values, and set the standards. As a minimum, EPA 

should set no standards where the mass of PM collected during a run would be less than the 

quantitation limit of the test method. 

B. Method 10 for Measuring Carbon Monoxide 

EPA Method 10 is an instrumental method for measuring CO emissions from stacks. The 

method provides for a multitude of alternatives, including correction for stack gas CO2

concentration if a CO2 trap is used ahead of the analyzer. The CO analyzer itself has to meet the 

performance specification for analyzers in EPA Method 7E. 

Although facilities are required to use Method 10 for demonstrating compliance, EPA has not 

provided any information on the detection and quantitation limits of Method 10 in the write-up 

of the method. In the write-up of Method 10A, which is used for determining the CO emissions 

in certifying CEMs at petroleum refineries and is available as an alternative for certifying the 

accuracy of a CO CEMS, it has, however, been stated in Section 13.4 that the detection limit of 

the method “is 3 ppmv based on a change in concentration equal to three times the standard 

deviation of the reagent blank solution.” This would imply that the quantitation limit of the wet 

chemistry method based on reagent blank solution analysis is 10 ppmv. Since the quantitation 

limit of the reference method is 10 ppm, the quantitation limit of the CO CEMs which are 

certified with Method 10A would have to be substantially higher than the quantitation limit of 

the wet chemistry method used to certify its accuracy. An examination of the CO emission data 

for the best performing boilers in the liquid subcategory shows a significant fraction of 

measurements were below the 10 ppm practical quantitation limit of the wet chemistry reference 
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National Council for Air and Stream Improvement

method described in Method 10A. In addition, a few of the CO concentrations for best 

performing units in the coal and biomass subcategories were under 10 ppm. This raises 

questions regarding the validity of the data used to set the standards. 

We recommend that EPA should establish the quantitation limit of Method 10 at no less than 10 

ppm, and revise its floor analysis as appropriate for both the Boiler MACT and CISWI energy 

recovery units. 

C. Method 23 for Measuring PCDD/F

In Method 23 for measuring stack PCDD/F emissions, the stack gas sample is withdrawn 

isokinetically using a glass-lined probe. The sample passes through a glass fiber filter and an 

adsorbent trap where PCDD/Fs are captured. A dry gas meter is used to measure the total 

volume of the sampled stack gas. PCDD/Fs captured in the probe, on the filter and the adsorbent 

material are recovered, cleaned, separated on a high resolution gas chromatographic column, and 

analyzed using high resolution mass spectrometer. Although this method includes significant 

information on calibration and use of internal standards, EPA has not developed any information 

on the quantitation limits of this method when used for testing stacks. Consequently, the 

analytical laboratories evaluate individual isomer peaks in terms of their own detection capability 

based on analyzing calibration standards and report the results accordingly. Method 23 also 

specifies in Section 9.9 that all measurements below the detection limit should be treated as zero 

“for the purpose of calculating the total concentration of PCDDs and PCDFs in the sample.”

MACT EPA Procedure for Setting the PCDD/F Standards

In order to establish the PCDD/F emission limit for biomass stoker boilers, EPA chose from its 

database only those stokers that burned at least 90% biomass. This resulted in only nine boilers 

with PCDD/F emissions data. Assuming that all measurements below the detection limits were 

present at their respective detection limits, EPA identified the best performing 12% sources with 

the lowest measured dioxin emissions in TEQs. This resulted in identifying 2 boilers, namely 

the Georgia Pacific, Madison, GA and Boralex Stratton Energy, Stratton, ME boilers as best 

performers, each with 3 runs. Table 1 shows the detailed isomer-specific data and the WHOTEF/2005 TEQs for the six runs on these two boilers. The mean and 99% upper prediction limit 

(UPL) values for the six runs were 1.64E-03 and 3.82E-03 ng TEQ/dscm @ 7% O2, respectively.
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Table 1. PCDD/F Emissions For 2 Biomass Stokers in Top 12%

PCDD/PCDFs in ng/dscm @ 7% O2

Georgia Pacific, Madison, GA Boralex Stratton Energy, ME

Pollutant Name Run 1 Run 2 Run 3 Run 1 Run 2 Run 3

1,2,3,4,6,7,8 HpCDD 1.55E-03 8.99E-04 6.82E-04 4.31E-03 5.39E-03 1.83E-03

1,2,3,4,6,7,8 HpCDF 2.79E-03 1.88E-03 1.69E-03 3.66E-03 8.10E-03 1.64E-04

1,2,3,4,7,8 HxCDD 6.32E-04 5.72E-04 5.03E-04 4.21E-05

1,2,3,4,7,8 HxCDF 1.10E-03 5.62E-04 2.98E-04 4.63E-04 1.76E-03

1,2,3,4,7,8,9 HpCDF 5.01E-04 4.10E-04 6.18E-04 8.14E-05 1.28E-03 1.99E-04

1,2,3,6,7,8 HxCDD 6.11E-04 5.50E-04 5.13E-04 4.35E-04

1,2,3,6,7,8 HxCDF 7.49E-04 2.10E-04 2.79E-04 5.76E-04 1.81E-03

1,2,3,7,8 PeCDD 6.55E-04 3.12E-04 5.62E-04 4.24E-04 1.31E-04

1,2,3,7,8 PeCDF 8.14E-04 7.07E-04 5.99E-04 4.89E-04 1.41E-03

1,2,3,7,8,9 HxCDD 6.65E-04 6.21E-04 5.45E-04

1,2,3,7,8,9 HxCDF 5.17E-04 2.70E-04 3.52E-04 1.43E-04

2,3,4,6,7,8 HxCDF 5.87E-04 2.19E-04 2.96E-04 3.76E-04 2.15E-03

2,3,4,7,8 PeCDF 6.97E-04 6.70E-04 5.91E-04 6.25E-04 1.82E-03 3.97E-05

2,3,7,8-TCDD 1.69E-03 4.57E-04 3.30E-04 1.10E-04 6.32E-04

2,3,7,8-TCDF 8.85E-04 5.18E-04 4.32E-04 3.17E-03 2.12E-03 7.52E-04

Total OCDD 4.96E-03 3.95E-03 3.13E-03 1.83E-02 1.71E-02 7.44E-03

Total OCDF 3.39E-03 2.37E-03 1.71E-03 4.66E-03 8.31E-03 7.94E-04

TEQs using ND=DL 3.21E-03 1.38E-03 1.44E-03 7.48E-04 2.12E-03 8.74E-04

 shaded entries are non-detects shown at the detection limit

 ND – non-detect; DL – detection limit

On pages 14 and 15 of the ERG memo (Singleton 2011), under “Measurement Detection Level 

Variability,” a procedure is laid out whereby the calculated UPL is compared to a computed 

value that was developed to account for the effect of measurement imprecision associated with a 

database that includes method detection level data. For each test run, a detection level (DL) is 

estimated (in TEQ units) by replacing each congener value by the average of all the “flagged” 

values below detection limit (BDL) or detection limit limited values for that run and then 

recalculating the run TEQ. Table 2 shows the results of these calculations for the six runs shown 

in Table 1. The maximum detection limit (DL) value is estimated to be 1.92E-03 ng/dscm @ 7% 

O2. The EPA procedure then stipulates that the maximum DL cannot be greater than the mean of 

the pollutant emissions values from the best performers, and this largest value becomes the 

representative method detection limit (RDL) for each pollutant emission’s dataset. The larger of 

the 3 x RDL value and the computed floor value or emissions limit (e.g., the 99 percent 

confidence UPL) becomes the limit. Thus, since the mean (1.64E-03) is less than the maximum 

DL (1.92E-03), the 3 x RDL value for biomass stokers becomes 3 x 1.64E-03 or 4.92E-03 

ng/dscm @ 7% O2. Since this value is higher than the calculated floor derived from the UPL 

calculations (3.82E-03), it becomes the basis for the floor. (Note this value compares favorably 

with the EPA derived limit of 5.00E-03 ng/dscm @ 7% O2.)
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Table 2. Estimation of Detection Levels For Each Run - 2 Biomass Stokers in Top 12%

PCDD/PCDFs in ng/dscm @ 7% O2

Georgia Pacific, Madison, GA Boralex Stratton Energy, ME1

Pollutant Name Run 1 Run 2 Run 3 Run 1 Run 2 Run 3

1,2,3,4,6,7,8 HpCDD 6.08E-04 4.60E-04 5.45E-04

1,2,3,4,6,7,8 HpCDF 6.08E-04 4.60E-04 5.45E-04

1,2,3,4,7,8 HxCDD 6.08E-04 4.60E-04 5.45E-04

1,2,3,4,7,8 HxCDF 6.08E-04 4.60E-04 5.45E-04

1,2,3,4,7,8,9 HpCDF 6.08E-04 4.60E-04 5.45E-04

1,2,3,6,7,8 HxCDD 6.08E-04 4.60E-04 5.45E-04

1,2,3,6,7,8 HxCDF 6.08E-04 4.60E-04 5.45E-04

1,2,3,7,8 PeCDD 6.08E-04 4.60E-04 5.45E-04

1,2,3,7,8 PeCDF 6.08E-04 4.60E-04 5.45E-04

1,2,3,7,8,9 HxCDD 6.08E-04 4.60E-04 5.45E-04

1,2,3,7,8,9 HxCDF 6.08E-04 4.60E-04 5.45E-04

2,3,4,6,7,8 HxCDF 6.08E-04 4.60E-04 5.45E-04

2,3,4,7,8 PeCDF 6.08E-04 4.60E-04 5.45E-04

2,3,7,8-TCDD 6.08E-04 4.60E-04 5.45E-04

2,3,7,8-TCDF 6.08E-04 4.60E-04 5.45E-04

Total OCDD 6.08E-04 4.60E-04 5.45E-04

Total OCDF 6.08E-04 4.60E-04 5.45E-04

DL (in TEQs) 1.92E-03 1.45E-03 1.72E-03 0.0E+00 0.0E+00 0.0E+00

 

1

no entries for the Boralex unit since the DLs for all NDs were unavailable in the original report;

Deficiencies in EPA’s Procedures for Establishing Biomass Stoker PCDD/F Limits

There are a number of major deficiencies in EPA’s methodology for establishing biomass stoker 

PCDD/F emission limits:

(1) As seen in Tables 1 and 2, no analytical detection limit data were available for the 

Boralex Stratton Energy boiler. Thus EPA treated those values as zero in selecting that 

boiler as a best performer. This error biased the results low.

(2) EPA’s methodology for establishing the quantitation limits for different isomers is not 

valid, as described below.

In its September 18, 2009 guidance to sources obligated to perform dioxin/furan testing in 

response to the 2009 ICR, EPA directed sources to “calculate the in-stack emissions rate for any 

analytical measurement below detection level using the relevant detection level as the “real 

value,” in direct conflict with the Method 23 requirement. When this approach was used to 

establish PCDD/F emissions, because most of the congeners were present below the method 

detection limit, the sources with the lowest analytical method detection limit became the best 

performers. Thus, by default, the lowest reported analytical detection limits became the RDL in 

EPA’s calculations, which then became the basis for determining the quantitation limit 

(determined to be three times the RDL).

In using the above approach, EPA seems to have assigned the lowest analytical detection limit 

values obtained at one or two laboratories for one or two sets of data as method detection limits
10

National Council for Air and Stream Improvement

which were multiplied by 3 to obtain method quantitation limits. As described earlier, Method 

23 consists of extracting a known volume of the sample from the stack to capture the target 

pollutant on a filter and a sorbent, recovering the collected sample, processing the collected 

samples, and, finally, analyzing the samples. Each of these steps can introduce random errors to 

the final measurement. In its analysis of data, EPA has ignored the errors associated with the 

sampling and recovery aspects of Method 23 and has misapplied the detection limits reported by 

the best performing laboratories as analytical method detection limits. 

For EPA, the correct approach for determining the quantitation limit of the analytical component 

of Method 23 would be to follow the majority recommendation of its own Federal Advisory 

Committee on Method Detection and Quantitation which recommended that to set the detection 

limit of a test method a minimum of 6 to 7 laboratories be used to analyze samples and National 

Quantitation limits should be set at the upper 95% confidence limit for normally distributed data 

or 95th percentile of the laboratory quantitation limit data, if data are non-normally distributed

(Federal Advisory Committee 2007). To accomplish this, NCASI reassembled and reanalyzed 

all the PCDD/F emissions data in the industrial boiler MACT database and attempted to establish 

the 95th percentile values of detection limits for each of the 17 PCDD/F isomers. Before these 

percentile values were determined, the entire body of data comprising the detection limits for 

each non-detect isomer was checked for suspected “outliers” using the Rosner’s test. This test 

can be used to detect up to 10 outliers for sample sizes of 25 or more. However, the suspected 

outliers at the 99% confidence interval were discarded from the dataset only after the data were 

plotted graphically and the outliers confirmed by “visual observation.” Table 3 provides the 95th

percentile values of the detection limits for each of the 17 PCDD/F isomers corresponding to all 

the PCDD/F emissions data in EPA’s industrial boiler emissions database. Data for a total of 

110 sources, including boilers firing biomass, gas, and coal and CISWI units, were included in 

this analysis.

Table 4 summarizes the quantitation limits (QL) for the 17 PCDD/F isomers calculated at 3 x the 

95th percentile detection limit in Table 3. Also shown is the QL in WHO-TEF/2005 TEQ units. 

The data show that using the 95th percentile values, the quantitation limit of the analytical 

component of Method 23 would be 0.11 ng/dscm.
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National Council for Air and Stream Improvement

Table 3. Summary of Detection Limit Statistics for All Non-Detects

in the Boiler MACT PCDD/F Database, ng/dscm at 7% O2

Congener N

1

N

1

Min. Max. 95th %ile2

1,2,3,4,6,7,8 HpCDD 53 1.68E-04 1.60E-02 1.60E-02

1,2,3,4,6,7,8 HpCDF 108 1.80E-04 2.50E-02 1.60E-02

1,2,3,4,7,8 HxCDD 162 5.65E-05 1.65E-02 1.22E-02

1,2,3,4,7,8 HxCDF 87 4.66E-05 1.60E-02 1.35E-02

1,2,3,4,7,8,9 HpCDF 189 4.63E-06 1.60E-02 1.11E-02

1,2,3,6,7,8 HxCDD 134 4.99E-05 1.61E-02 1.28E-02

1,2,3,6,7,8 HxCDF 100 4.29E-05 1.60E-02 1.28E-02

1,2,3,7,8 PeCDD 168 1.75E-04 1.60E-02 1.19E-02

1,2,3,7,8 PeCDF 113 2.56E-05 1.60E-02 1.28E-02

1,2,3,7,8,9 HxCDD 153 8.04E-04 1.80E-02 1.28E-02

1,2,3,7,8,9 HxCDF 181 0.00E+00 1.60E-02 1.14E-02

2,3,4,6,7,8 HxCDF 126 4.85E-05 1.60E-02 1.22E-02

2,3,4,7,8 PeCDF 110 2.00E-04 1.62E-02 1.35E-02

2,3,7,8-TCDD 210 0.00E+00 1.60E-02 8.72E-03

2,3,7,8-TCDF 81 2.95E-06 1.17E-02 1.00E-02

Total OCDD 41 5.50E-04 3.20E-02 3.20E-02

Total OCDF 84 2.02E-04 2.02E-02 1.61E-02

In WHO-TEF/2005 TEQs 3.53E-02

 1No. of Observations; 2Detection Limit

Table 4. Summary of Quantitation Limits for Each PCDD/F Isomer Based on the 

95th Percentile Detection Limits Determined in Table 3, ng/dscm at 7% O2

Congener N

1

95th %ile2

1,2,3,4,6,7,8 HpCDD 53 4.80E-02

1,2,3,4,6,7,8 HpCDF 108 4.80E-02

1,2,3,4,7,8 HxCDD 162 3.66E-02

1,2,3,4,7,8 HxCDF 87 4.05E-02

1,2,3,4,7,8,9 HpCDF 189 3.33E-02

1,2,3,6,7,8 HxCDD 134 3.84E-02

1,2,3,6,7,8 HxCDF 100 3.84E-02

1,2,3,7,8 PeCDD 168 3.57E-02

1,2,3,7,8 PeCDF 113 3.84E-02

1,2,3,7,8,9 HxCDD 153 3.84E-02

1,2,3,7,8,9 HxCDF 181 3.42E-02

2,3,4,6,7,8 HxCDF 126 3.66E-02

2,3,4,7,8 PeCDF 110 4.05E-02

2,3,7,8-TCDD 210 2.62E-02

2,3,7,8-TCDF 81 3.00E-02

Total OCDD 41 9.60E-02

Total OCDF 84 4.84E-02

In WHO-TEF/2005 TEQs 1.06E-01

 1No. of Observations; 2Quantitation Limit = 3 X Detection Limit
12

National Council for Air and Stream Improvement

To compare the reported PCDD/F concentrations to the quantitation limits of the analytical 

component of Method 23, in Table 5 NCASI has tabulated for each of the 17 congeners the 

fractions of all observations in the entire Boiler MACT PCDD/F database that were below the 

95th percentile analytical method quantitation limits as shown in Table 4. It is seen that even 

when the source sampling and recovery-related variabilities are ignored, on average 94.6% of all 

data were below the quantitation levels of the analytical component of Method 23. Thus, a very 

large fraction of all the data was always below the respective quantitation limits for each isomer. 

Table 5. Fraction of Observations in the Entire Boiler MACT PCDD/F Database 

Below the 95th Percentile Quantitation Limits As Shown in Table 4

Congener N

1

95th %ile295th %ile3

1,2,3,4,6,7,8 HpCDD 327 303 92.7%

1,2,3,4,6,7,8 HpCDF 324 303 93.5%

1,2,3,4,7,8 HxCDD 327 313 95.7%

1,2,3,4,7,8 HxCDF 327 312 95.4%

1,2,3,4,7,8,9 HpCDF 327 318 97.2%

1,2,3,6,7,8 HxCDD 327 308 94.2%

1,2,3,6,7,8 HxCDF 327 313 95.7%

1,2,3,7,8 PeCDD 327 316 96.6%

1,2,3,7,8 PeCDF 327 311 95.1%

1,2,3,7,8,9 HxCDD 327 315 96.3%

1,2,3,7,8,9 HxCDF 327 315 96.3%

2,3,4,6,7,8 HxCDF 327 310 94.8%

2,3,4,7,8 PeCDF 327 303 92.7%

2,3,7,8-TCDD 327 317 96.9%

2,3,7,8-TCDF 327 292 89.3%

Total OCDD 322 295 91.6%

Total OCDF 320 309 96.6%

94.6% 1Total number of valid observations excluding outliers; 2

number below QL in Table 4; 3% of data < QL

It should be noted that the above analysis is based only on the analytical method quantitation 

limit and does not include random errors associated with sample collection and recovery of the 

samples. These activities are expected to increase the quantitation limit of Method 23. The best 

available source of EPA Method 23 variability data is the ASME ReMAP study which analyzed 

the available PCDD/F emission test results from simultaneous testing of emission sources and 

concluded that the standard deviation of PCDD/F measurements using Method 23 was constant 

at PCDD/F levels below 1 ng/dscm as TEQs and had an expected mean value of 0.027 ng/dscm. 

As described in the EPA revised assessment document (EPA 2004), when the standard deviation 

is multiplied by 10, this gives a PCDD/F quantitation limit in TEQs of 0.27 ng/dscm for EPA 

Method 23, which would be rounded up to 0.3 ng/dscm to make it consistent with the ERG 

memo addressing EPA’s procedure for setting the boiler MACT standards (Singleton 2011). 

This estimate of the quantitation limit of EPA Method 23 is approximately 2.5 times the 

quantitation limit of the analytical component of Method 23. For comparison, it was noted
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National Council for Air and Stream Improvement

earlier that the ratio of the overall quantitation limits of Method 5 and the quantitation limit of 

the analytical component of Method 5 was 36. 

We recommend that prior to setting Boiler MACT and CISWI standards for PCDD/F, EPA 

should reanalyze its data set by taking into consideration the Method 23 PCDD/F quantitation 

limit in TEQs of 0.3 ng/dscm and not set any standards below the quantitation limit. 

D. Method 26/26A for HCl Emissions

EPA Method 26 utilizes a heated probe to extract stack gases which pass through a filter and a 

series of impingers containing dilute sulfuric acid followed by dilute sodium hydroxide 

solutions. HCl contained in the stack gas is captured in the sulfuric acid impinger. The impinger 

content is analyzed for its chloride content using ion chromatography. 

In section 13 of EPA Method 26, under method performance, it has been stated that (1) the ion 

chromatography instrument detection limit is 0.2 μg/mL, which translates to 0.1 ppm HCl, and 

(2) at a stack gas concentration of 3.9 ppm, the laboratory relative standard deviation was 

determined to be 6.2 percent or 0.24 ppm. 

Since no between laboratory standard deviation data are provided, it is not possible to assess the 

overall method detection and quantitation limit. However, if one uses the low level HCl standard 

deviation of 0.24 ppm, this would suggest a quantitation limit no less than 2.4 ppm. 

As shown in Table 6, the best performing CISWI source had a reported stack gas HCl 

concentration of 0.068 ppm. Similarly, the best performing source for HCl in the solid fuel 

category shown in Table 7 reported HCl emission rates of 0.0000143 and 0.0000157 lb/MMBtu 

or approximately 0.011 ppm. These data, if they were valid, clearly show a need for 

reassessment of data prior to setting the emission standards. However, there were serious data 

errors involving the best performing Alcoa units as identified and explained in the CIBO Petition 

for Reconsideration, p. 19-22 (attached for reference), such that one unit (Boiler 2) has no valid 

HCl emission data and thereby drops completely from the emission data ranking list and the 

other unit (Boiler 3) drops considerably in the best performing rank. 

Other top HCl performers have listed emission rates that demonstrate similar deficiencies 

relative to the quantitation limit, thus also requiring a reassessment of data prior to setting 

emission standards. 

We recommend that EPA should reanalyze its data by taking into consideration the quantitation 

limit of Method 26 and not set any standards below the 2.4 ppm level described above. 
14

National Council for Air and Stream Improvement

Table 6. CISWI NSPS Limits

Best Performing Unit

Avg. at 7% 

O2 NDs

99% 

UPL Limit

HCl TXInternationalPaperQueenCity

PB02

0.068 ppm 1 of 3 0.54 0.45

Hg LAIPMansfield

2-78 No. 2 Power Boiler

3.2E-05 

mg/dscm

0 of 3 0.0028 0.00033

Pb TNPackagingCorpCounce

Combination Boiler #2

0.00045 

mg/dscm

2 of 3 0.0030 0.0031

Cd LAIPMansfield

2-78 No. 2 Power Boiler

1.4E-04 

mg/dscm

0 of 3 2.84 0.00051

PM NYBlackRiverGen

E000001, E000002, E000003 

Shared Stack

2.6 mg/dscm 0 of 6 1054 250

Dioxin/furan (TEQ) Biomass TXInternationalPaperQueenCity

PB02

0.0005 

ng/dscm

0 of 3 0.01 0.011

Dioxin/furan (TEQ) Coal TNEastman

Boiler 18

0.0086 

mg/dscm

3 of 3 0.035 0.035
15

National Council for Air and Stream Improvement

Table 7. Emission Tests Used to Identify the Best Performing Solid Fuel Boiler for Various Pollutants

Pollutant ID Date Run

Reported 

Value Units Qualifier

PM IAArchersDanielsMidlandDesMoinesAsea 

Boiler #1

8/26/09 1 0.0002 lb/106Btu

8/26/09 2 0.0002 lb/106Btu

8/27/09 3 0.0002 lb/106Btu

HCl INAlcoaWarrick

Unit#3

8/7/08 1 0.0000143 lb/106Btu ND

8/7/08 2 0.0000157 lb/106Btu ND

Hg IAUofIowa

EP7 Boiler 11

2/15/06 1 1.26E-08 lb/106Btu

2/21/06 2 7.54E-09 lb/106Btu ND

2/21/06 3 2.27E-08 lb/106Btu

CO (Dutch 

Oven/Suspension

Burner)

GATempleInlandThomson

BW-B001

1/30/03 1 5.5 ppm @ 

3% O2

Use 30

1/30/03 2 1.4 ppm @ 

3% O2

Use 30

1/30/03 3 5.4 ppm @ 

3% O2

Use 30

(continued on next page)
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National Council for Air and Stream Improvement

Table 7 (Cont’d). Emission Tests Used to Identify the Best Performing Solid Fuel Boiler for Various Pollutants

Pollutant ID Date Run

Reported 

Value Units Qualifier

CO (biomass 

fluidized 

bed)

GATempleInlandRome

WF

7/29/09 1 136 ppm @ 3% 

O2

7/29/09 2 61 ppm @ 3% 

O2

7/30/09 3 99 ppm @ 3% 

O2

Dioxin 

(biomass 

fluidized 

bed)

GATempleInlandRome

WF

7/29/09 1 0.019 ng/dscm@7% 

O2 (TEQ) DLL*

7/29/09 2 0.0042 ng/dscm@7% 

O2 (TEQ) DLL

7/30/09 3 0.0057 ng/dscm@7% 

O2 (TEQ) DLL

Dioxin 

(biomass 

stoker)

MEBoralexStratton

Boiler#1 10/20/09 1 0.00076 ng/dscm@7% O2 (TEQ) DLL

10/20/09 2 0.0022 ng/dscm@7% 

O2 (TEQ) ADL**

10/21/09 3 0.00088 ng/dscm@7% 

O2 (TEQ) DLL

 *DLL – detection limit limited

 **ADL – above detection limit
17

National Council for Air and Stream Improvement

E. Method 29 and Ontario Hydro Method for Mercury Emission Testing

The promulgated regulations allow the use of several different methods for mercury emission 

testing. These comments address EPA Methods 29 and ASTM Method D6784, which is 

generally known as the Ontario Hydro method. There are significant quantitation limit 

related issues with these test methods that have not been adequately addressed by EPA.

EPA Method 29

In EPA Method 29, stack gas is drawn isokinetically from the stack through a sampling probe 

followed by an external heated filter to capture the particulate mercury. The gases leaving 

the filter pass through a series of impingers which capture gaseous mercury. The volume of 

the gases drawn from the stack is measured via a dry gas meter. The probe, filter and 

impinger contents are recovered by transferring the captured material, including the filter 

catch, wash solutions, and impinger contents, to several containers for digestion and further 

processing for analysis. This processing of the samples results in five sub-samples which are 

identified as sub-samples 1B, 2B, 3A, 3B and 3C. These samples are analyzed separately 

using cold vapor atomic absorption spectroscopy. A series of reagent blanks are also 

analyzed. The blank-corrected mercury concentrations (μg/L) in the five sub-samples are 

multiplied by the amount of sample liquid, and the calculated masses are summed. The sum 

is divided by the volume of stack gases sampled to calculate the mercury concentration in 

μg/m3in the stack gas. This concentration is multiplied by the appropriate fuel F-factor, 

corrected for the stack gas oxygen (or CO2) content, to obtain the mercury emission rate in 

lb/1012Btu. 

A critical aspect of Method 29 is the need to sum five different measurements (1B, 2B, 3A, 

3B and 3C), each with its own detection limit, to obtain the total mass of mercury in the 

sample being analyzed. In reporting the results, Method 29 does not require separate 

reporting of mercury masses in each of the five sub-samples, nor does the method provide 

guidance as to how any non-detect values of these sub-samples should be handled when 

calculating the total mass of mercury in the samples. In the absence of such guidance, testing

companies have used a variety of different approaches. Some testing companies treat all 

non-detect fractions as zero, others treat the non-detect values at one-half the detection limit, 

and some use a combination for no apparent reason. It is, therefore, essential that EPA 

amend Method 29 to (1) specify how the non-detects should be treated in calculating 

emissions, and (2) establish the detection and quantitation limits of different mercury 

fractions obtained in Method 29 samples.

Using the data in EPA’s boiler MACT data set to estimate the quantitation limits of fractions 

1B, 2B, 3A, 3B and 3C using the 95th percentile of detection limits reported by different 

laboratories and multiplying those values by three, the results in Table 8 show that for 

fractions 1B, 2B, 3A, 3B and 3C the quantitation limits would be 0.2, 0.92, 0.4, 0.7 and 0.27 

μg, respectively. This gives a quantitation limit of 2.50 μg for the analytical component of 

Method 29. One needs to add the effect of the random errors associated with sample 

collection and recovery to this estimate to obtain an estimate of the quantitation limit of 

Method 29. 
18

National Council for Air and Stream Improvement

Table 8. Summary Statistics for Hg Sample Fraction-Specific Quantitation

Limits in Examined Data Sets

Quantitation Limit, μg

Sample Fraction 1B 2B 3A 3B 3C

Minimum 0.015 0.2 0.0048 0.025 0.025

Maximum 0.4 1 0.44 1 0.28

95th Percentile 0.2 0.92 0.4 0.7 0.27

One source of the Method 29 variability data is the ASME ReMAP study. The results of that 

study suggest that the standard deviation of mercury measurements was a function of the 

stack mercury content. This study included data with stack mercury concentrations as low as 

5 μg/dscm. Even if we extrapolate the results to as low as 3 μg/dscm, the resulting standard 

deviation of mercury measurements would be 0.55 μg/dscm. As discussed earlier, 

multiplying this value by 10 to obtain the quantitation limit of the method results in a 

quantitation limit of 6 μg/dscm for Method 29 for a typical run where the sample volume is

one cubic meter. These quantitation limit estimates are significantly higher than the mercury 

emission limits for new boilers burning liquid fuels, and mercury emission limits for existing 

and new CISWI sources.

We recommend that EPA should re-examine its mercury emission data, appropriately 

identify all measurements below method quantitation limits, and analyze the data to develop 

mercury emission standards for sources covered by Boiler MACT and CISWI standards. 

Additionally, we recommend that EPA should re-examine the requirement in Table 1 of the 

Boiler MACT standards that new liquid-fired boilers achieve mercury detection limits of 0.1 

μg/dscm because it may not be possible to achieve these targets when EPA Method 29 is 

used for stack testing.

ASTM Method D6784-02 Data for Mercury Emission Testing

In the promulgated regulations, EPA has specified that as an alternative to using EPA 

Method 29, ASTM Method D6784-02 may be used to demonstrate compliance with the 

mercury emission standards. This ASTM method is similar to EPA Method 29 in that it also 

utilizes a probe and filter assembly to capture any mercury present in the solid phase. The 

gaseous oxidized or elemental mercury is captured in a series of three impingers containing 

KCl, an empty impinger, and three potassium permanganate impingers. The processing of 

the sample results in five sub-samples containing mercury from the stack gases. These 

consist of two digested samples (filter and probe residue) and three impinger samples 

(impingers 1-3, impinger 4, and impingers 5-7). The volumes of these samples vary 

depending upon the stack volume and moisture content and sample dilution required during 

digestion. The estimated volumes of the sub-sample that would be obtained during a run on 
19

National Council for Air and Stream Improvement

a biomass boiler based on the maximum sample volume of 2 m3on a dry basis would be 

approximately 100 mL each for the filter and the probe wash samples, 1 liter for the impinger 

1-3 sample, 150 mL for the impinger 4 sample and 500 mL for the impinger 5-7 sample. 

Aliquots of these samples are analyzed using CVAA, and the amount of mercury in the 

samples is corrected for sample dilution.

Similar to Method 29, ASTM D6784-02 does not specify the quantitation limit of the test 

method nor does it address how measurements below the method detection limits should be 

treated in calculating source emissions. This would create a challenge for sources with low 

levels of mercury in the flue gases.

Additionally, an examination of the two methods would show that the resulting volumes of 

the sub-samples obtained with the two methods are different. Thus, depending upon how 

measurements below the method detection limit are treated, one would get different results 

from the two methods when sample fractions are non-detect. For example, in Method 29 

when the contents of the KMnO4 impingers are processed, one obtains two 500 mL subsamples for mercury analysis. In the case of ASTM Method D6784-02, the processing of the 

KMnO4 impinger solution would result in a single 500-600 mL sample. Thus, if the 

detection limits of the analyzers used with the two methods are the same and measurements 

below the method detection limits are reported at the method detection limit, the emission 

rate reported for non-detect measurements when Method 29 is used would be nearly twice 

that reported for the ASTM method.

Based on the above inconsistencies between the two methods and the fact that almost all the 

mercury emission data in EPA’s database were obtained with Method 29, we recommend 

that EPA should re-examine its inclusion of ASTM Method D6784-02 among the methods 

approved for demonstrating compliance with the mercury emission standards.

V. Summary

In setting the MACT and CISWI standards, EPA has accepted all the test results while either 

ignoring the quantitation capabilities of the test methods or using an approach towards 

establishing quantitation limits which is neither supported in the literature nor is statistically 

meaningful. This has resulted in setting unrealistically low emission standards for which 

compliance cannot be demonstrated using the specified EPA test methods. EPA should reexamine all the emission test results in its database to identify data below test method

quantitation limits and make appropriate corrections to the data. Only after this has been 

accomplished can EPA perform appropriate statistical analysis of the data and set MACT and 

CISWI emission standards for PM, CO, PCDD/F, HCl and Hg. 

VI. References

American Society of Mechanical Engineers (ASME). 1998. Test uncertainty, instruments 

and apparatus. ANSI/ASME PTC 19.1-1998. New York, NY: American Society of 

Mechanical Engineers.
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National Council for Air and Stream Improvement

Arkley, S. 2000. Compliance problems encountered in testing particulate matter at low 

concentration sources. Draft for Managing Title V Compliance Conference, February 

2000.

Coleman, D., Auses, J., and Grams, N. 1997. Regulation – from an industry perspective or 

relationships between detection limits, quantitation limits, and significant digits. 

Chemometrics and Intelligent Laboratory Systems 37:71-80.

Corley, J. 2003. Best practices in establishing detection and quantification limits for 

pesticide residues in foods. In Handbook of Residue Analytical Methods for 

Agrochemicals. John Wiley & Sons Ltd. 

Currie, L.A. 1999. Detection and quantitation limits: origins and historical overview. 

Analytica Chimica Acta 391:127-134.

Environmental Protection Agency (EPA). 1992. Description of in-stack detection limit. 

Guideline Document 038. Research Triangle Park, NC: Technology Transfer 

Network/Emission Measurement Center. http://www.epa.gov/ttn/emc/guidlnd.html

(accessed July 8, 2011).

Environmental Protection Agency (EPA). 2004. Revised assessment of detection and 

quantitation approaches. Washington, DC: Engineering and Analysis Division, Office 

of Science and Technology, Office of Water. EPA-821-B-04-005. 

Federal Advisory Committee. 2007. Report of the federal advisory committee on detection 

and quantitation approaches and uses in Clean Water Act Programs submitted to the 

Environmental Protection Agency (EPA). December 2007. 

Federal Register. 2005. Establishment of a federal advisory committee to examine detection 

and quantitation approaches in Clean Water Act programs. Federal Register, Vol. 70, 

No. 92, p. 25565. Friday, May 13, 2005. 

International Organization for Standardization (ISO). 1995. Uncertainty of measurement --

Part 3: Guide to the expression of uncertainty in measurement. Geneva, Switzerland: 

International Organization for Standardization (GUM:1995). 

Keith, L., Crummett, W., Deegan, J., Jr., Libby, R., Taylor, J., and Wentler, G. 1983. 

Principles of environmental analysis. Analytical Chemistry 55:2210-2218.

Lanier, W.S., and Hendrix, C.D. 2001. Final report -- Reference method accuracy and 

precision (ReMAP): Phase 1 Precision of manual stack emission measurements. 

Prepared for ASME International, Washington, DC. 

Rigo, H.G., and Chandler, A.J. 1999. Quantitation limits for reference methods 23, 26, and 

29. J. Air & Waste Management 49:399-410.
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National Council for Air and Stream Improvement

Shigehara, R.T. 1996. Minimum Detection Limit for Method 5. EPA Contract No. 68-D2-

0163, Work Assignment 3-06. Prepared for M. Ciolek, EMAD, OAQPS, EPA Research 

Triangle Park, NC.

Singleton, A. ERG. 2011. Revised MACT floor analysis (2011) for the industrial, 

commercial, and institutional boilers and process heaters national emission standards for 

hazardous air pollutants – major source. 

Voigtman, E. 2008. Limits of detection and decision. Part 1. Spectrochimica Acta Part B

63:115-128. 
National Council for Air and Stream Improvement

Errors in Estimation of the Boiler MACT and CISWI Limits Due to the Impact 

of non-Independent Data on UPL Determinations

The recently promulgated MACT standards for boilers and CISWI standards for energy recovery units 

(ERUs) are based on multiple sets of three 1-hour runs from the best performing sources in each 

subcategory. These standards were calculated by determining 99, 99.5 or 99.9% upper prediction limit 

(UPL) values for each subcategory using the following methodology1:

First, for each subcategory, the t‐statistic was calculated for the appropriate confidence level using the 

following Excel equation:

t‐statistic = TINV(2*(1‐x),n‐1) (Equation 1)

Where:

n = the number of test runs from units in the top 12 percent

x = the selected confidence level (0.99, 0.995, 0.999)

Second, using the individual test run data for the best performers in each source category, the sample 

mean and standard deviation of the test runs were calculated.

Finally, the UPL was calculated using the following equation:

(Equation 2)

 

Where:

n = the number of test runs

m = the number of test runs in the compliance average

s

2

= the combined variance of the sample of test runs from top performing units

bar x = the sample mean of test runs from top performing units”

In arriving at the UPLs that represented the Boiler MACT standards for CO, HCl, Hg, PM and PCDD/Fs, 

or the CISWI standards for Pb, Cd, NOx and SO2 in addition to CO, HCl, Hg, PM and PCDD/Fs, EPA 

considered in its analysis all the runs corresponding to boilers that made up the top 12% performers. 

Typically, three consecutive runs were conducted for a pollutant on a boiler, the average of the 3 runs 

used to determine the top 12% of boilers, with each run considered to be an independent data point.

The above calculation for the UPL is based on the underlying assumption that the data for the bestperforming sources in EPA’s data set are independent observations and the three runs carried out during 

the future performance test would also be independent observations. However, EPA did not make any 

attempt to determine whether the data in its data sets were composed of independent observations and, by 

inference, it assumed that the future 3-run tests would consist of three independent observations. This is 

an extremely important issue, because if the data set is not composed of independent observations but 

contains data that are correlated with each other then the statistics used by EPA would be incorrect. 

 

1Revised MACT Floor Analysis (2011) for the Industrial, Commercial, and Institutional Boilers and Process Heaters 

National Emission Standards for Hazardous Air Pollutants – Major Source, Memo from Brian Shrager, US EPA to 

Amanda Singleton, ERG January 4, 2001.
2

National Council for Air and Stream Improvement

To check for independence of individual observations, one can utilize an inherent property of population 

variance, namely, that if a population of random variables has mean and standard deviation (SD) σ, a 

new population composed of the means of m samples each from the same population will also have a 

mean but will have a reduced standard deviation equal to σ/√m. Applying this principle to our case 

with n supposedly independent one hour test runs and n/3 three run test averages, the SD of the three test 

run averages should be equal to the SD of the one hour test runs divided by the √ of 3 (or SDn = 0.58 * 

SDo). If the n observations are not truly independent, the standard deviation of the means of 3 

observations would be relatively unchanged (0.85 to 1.0).

Impact on CO Limit for Biomass Stokers

Table 1 shows the effect on the SD when the 48 test runs (16 tests) comprising the top 12% for the 

biomass stoker category (Boiler MACT data) are first assumed to be 48 independent observations and 

then assumed to be 16 observations with the 3 runs corresponding to each test averaged as one data point. 

It is seen that while the SD for the smaller data set (n = 16) should have been reduced to 1/√3 or 58% of 

the SD for the larger data set (n = 48), instead it remains fairly constant (0.98). This implies that the 48 

data points were strictly not independent data points but rather the data set contained only 16 independent 

data points. This is consistent with the fact that when 3 test runs are conducted consecutively, within a 

short period of time, on a boiler operating under similar conditions, the result for each of these runs is not 

expected to be statistically different from the average of the 3 runs (Note that the 16 data points are 

clearly independent since they represent tests conducted on boilers at significantly different time periods). 

Similar analysis on more extensive data on a variety of boilers is shown below to further strengthen this 

observation.

Table 1. Effect of Averaging Time on Standard Deviation - 48 Test Runs 

For 16 Data Sets in the Boiler MACT Biomass Stoker Top 12% Category

No. of Data 

Points

Averaging 

Time

CO 

ppm @ 3% O2

Ratio of 3 hr avg SD 

to 1 hr avg SD

mean (X) stdev (X) Actual Expected1

48 1 hr 197.0 88.3

16 3 hr 180.9 86.6 0.98 0.58

1

For a set of independent data, the 3 hr avg SD should be = 1/√3 or 58% of the 1 hr avg SD

Thus, in equation 2, instead of using n = 48 and m = 3 as currently done by EPA to derive the 99.9 

percent UPL value (CO limit), the UPL should be recalculated using the means of three run tests as 

independent observations and assigning n = 16 and m = 1. Using n = 48 and m = 3 (log-normalized data), 

equation 2 currently yields a 99.9 percent UPL value of 476 ppm @ 3% O2 (Note that EPA adjusted this 

value to 481 ppm by applying the Bhaumik and Gibbons correction1,2 which was then rounded upwards to 

the promulgated limit of 490 ppm). If one used n = 16 and m = 1 (log-normalized data) instead to 

correctly reflect the fact that there were only 16 independent observations, equation 2 would yield a 99.9 

percent UPL value of 1081 ppm @ 3% O2 (619 ppm with the Bhaumik and Gibbons correction).

 

2

adjusts for the fact that a simple lognormal transformation of a dataset results in a UPL calculated on the geometric 

mean of the dataset instead of the arithmetic mean which can introduce bias into the resulting UPL calculation
3

National Council for Air and Stream Improvement

To further examine the issue of independence of hourly stack gas CO measurements, NCASI examined 

the data from a 4-month study (10-1-10 to 1-31-11) conducted at a pulp mill biomass stoker unit during 

which one minute average CO emissions were recorded using a CEMS. A 2007 EPA Method 10 for this 

boiler (MSGPNewAugusta) had a 3-run average of 102 ppm (which EPA adjusted to130 ppm), placing it 

among the 12 boilers which comprised the top 12% performers for CO in the biomass stoker category. A 

total of 2904 1-hour averages and 960 3-hour averages were generated from the 4 month-long CEM data. 

Table 2 shows the standard deviations for the 1-hour and 3-hour average CO concentrations. The data 

show that the standard deviation for the 960 3-hour averages was almost unchanged (92%) when 

compared to the standard deviation of the 2904 1-hour averages, while it should have dropped to close to 

58% (1/√3). This long-term CEM data analysis clearly shows that one hour averages and three hour 

averages are truly not independent observations, further lending credence to the fact that EPA should have 

used n =16 and m = 1 when estimating the 99.9% UPL value for biomass stokers as per equation 2.

Table 2. Effect of Averaging Time on the Standard Deviation – Four Month 

Duration CO CEMS Data for the MSGPNewAugusta Biomass Stoker Unit

No. of Data 

Points

Averaging 

Time

CEM CO 

ppm @ 3% O2

Ratio of 3 hr avg SD 

to 1 hr avg SD

mean (X) stdev (X) Actual Expected1

2,904 1 hr 339.3 138.7

960 3 hr 339.1 127.8 0.92 0.58

1

For a set of independent data, the 3 hr avg SD should be = 1/√3 or 58% of the 1 hr avg SD

Impact on CO Limit for Biomass FBCs

Table 3 shows the effect on the SD when the 15 test runs comprising the top 12% floor for the biomass 

FBC category are first assumed to be 15 independent runs and then assumed to be 5 independent runs 

with the 3 runs corresponding to each test averaged as one data point. It is seen that while the SD for the 

smaller 5 run data set should have been reduced to 1/√3 or 58% of the SD for the 15 run data set, instead 

it remains fairly constant (97%). This suggests that the 15 run results were not independent data points 

but rather the data set contained only 5 independent data points.

Table 3. Effect of Averaging Time on Standard Deviation - 15 Test Runs 

For 5 Data Sets in the Boiler MACT Biomass FBC Top 12% Category

No. of Data 

Points

Averaging 

Time

CEM CO 

ppm @ 3% O2

Ratio of 3 hr avg SD 

to 1 hr avg SD

mean (X) stdev (X) Actual Expected1

15 1 hr 118.7 93.8

5 3 hr 118.7 91.1 0.97 0.58

1

For a set of independent data, the 3 hr avg SD should be = 1/√3 or 58% of the 1 hr avg SD
4

National Council for Air and Stream Improvement

Thus, in equation 2, instead of using n = 15 and m = 3 (log-normalized data) as currently done by EPA to 

derive the 99.9 percent UPL value (CO limit), the UPL should be recalculated using n = 5 and m = 1. 

Using n = 15 and m = 3, equation 2 currently yields a 99.9 percent UPL value of 592 ppm @ 3% O2 (430 

ppm applying the Bhaumik and Gibbons correction). If one used n = 5 and m = 1 (log-normalized data) 

instead to correctly reflect the fact that there were only 5 independent observations, equation 2 would 

yield a 99.9 percent UPL value of 835 ppm @ 3% O2 (737 ppm applying the Bhaumik and Gibbons 

correction).

NCASI also analyzed one year’s duration (7/7/09 and 7/7/10) of hourly average CO CEMS data for the 

biomass FBC unit at Temple-Inland, Rome, GA. In addition to the data for a 3 one hour-run test which 

caused this boiler (GATempleInlandRome) to be among the five best performers in its category for 

existing FBCs and the best performer for new FBCs, Temple-Inland had submitted to EPA 30 days of CO 

CEMS data (7/1 to 7/30/08) and a plot of the 30-day rolling average CO CEMS data for the period 

between 7/7/09 and 7/7/10. However, EPA did not make use of the 30-day CO CEMS data. 

A total of 8297 hourly averages and 2767 3-hour averages were generated from the 12 month-long CEM 

data (not submitted to EPA). Table 4 shows the effect on the SD calculation when assuming 8297 hourly 

average data points versus 2767 3-hour average data points. Once again, it is seen that the SD for the 

2767 3-hour averages is about 88% of the SD of the 8297 1-hour averages, when they should have 

dropped to 58%. The longer-term CEM data on this boiler clearly show that one hour averages and three 

hour averages are truly not independent observations, suggesting that EPA should have used n =5 and m 

= 1 when estimating the 99.9% UPL value for biomass FBCs as per equation 2.

Table 4. Effect of Averaging Time on the Standard Deviation – 12 Month 

Duration CO CEMS Data for the GATempleInlandRome Biomass FBC Unit

No. of Data 

Points

Averaging 

Time

CEM CO 

ppm @ 3% O2

Ratio of 3 hr avg SD 

to 1 hr avg SD

mean (X) stdev (X) Actual Expected1

8,297 1 hr 108.0 72.2

2,767 3 hr 108.2 63.8 0.88 0.58

1

For a set of independent data, the 3 hr avg SD should be = 1/√3 or 58% of the 1 hr avg SD

Additional Evidence From Analysis of Long-Term CEMS Data for 12 Stokers and 2 FBCs

NCASI analyzed long-term CO CEMS data that were available for 12 other biomass stoker units and two 

biomass FBCs in the forest products industry (FPI), all of which did not belong to the top 12% performer 

list as determined by EPA to constitute the floor. One hour averages and three-hour averages were 

estimated from these long-term data after deleting data that clearly represented startup/shutdown 

(generally very high O2 levels and low load) and/or boiler/instrument downtime conditions. Table 5 

shows a comparison of the standard deviations obtained for the one-hour average and three-hour average 

long term CEMS data for these 14 boilers. Once again, it is evident that the SDs for the 3 hour average 

blocks of data are not 58% of the SDs for the corresponding one-hour average data sets, suggesting that 

the 1 hour average data points are strictly not independent data points. A more detailed review of any 

long-term CO CEMS data will have to be conducted to determine the minimum number of hours of gap 

that would be needed before a 1-hour average CO value would be considered truly independent.
5

National Council for Air and Stream Improvement

Table 5. Effect of Averaging Time on the Standard Deviation of Long-Term CO CEMS Data for Several FPI Biomass Stoker and FBC Units

1 hr avg CEM CO, ppm 

@ 3% O2

3 hr avg CEM CO, ppm 

@ 3% O2 Ratio of SDs

Mill 

Code

Boiler 

Type fuel mix (% heat basis)

Data 

Duration

No. of 

1 hr 

avgs

mean 

(X)

stdev 

(X)

No. of 

3 hr 

avgs

mean 

(X)

stdev 

(X)

ActualExpected1

Stoker Units

A Stoker wood (75%), oil (6.1%), gas (18.9%) 18 mos 11,898 704.3 358.8 3,989 703.7 329.1 0.92 0.58

B1 Stoker wood (27.6%), coal (48.8%), oil (11.5%), gas (12.1%) 18 mos 12,273 154.8 140.4 4,099 155.2 134.1 0.95 0.58

B2 Stoker wood (29.2%), coal (44.0%), oil (12.6%), gas (14.2%) 18 mos 11,724 74.2 150.3 3,938 143.6 152.4 1.01 0.58

C Stoker wood (80.8%), gas (19.2%) 12 mos 6,049 45.4 79.1 2,103 43.4 67.1 0.85 0.58

E1 Stoker wood (20to80%), coal (20to80%) 12 mos 1,395 1,341 1,354 490 1,390 1,371 1.01 0.58

E3 Stoker wood (<90%), coal (>10%) 12 mos 8,084 386.9 228.8 2,709 387.7 195.4 0.85 0.58

F Stoker wood (16.9%), gas (1.7%), petcoke (81.4%) 6 mos 3,819 24.1 25.7 1,276 24.0 20.0 0.78 0.58

H Stoker wood (72.8%), gas (27.2%) 12 mos 8,164 214.1 205.2 2,754 211.5 177.3 0.86 0.58

I Stoker wood (100%) 3 mos 2,022 305.6 354.0 674 306.1 315.8 0.89 0.58

J Stoker wood (100%)a10 mos 2,616 332.4 136.9 874 334.9 120.4 0.88 0.58

K Stoker wood (100%) 5 mos 3,602 998.5 480.8 1,209 995.8 422.8 0.88 0.58

L Stoker wood (100%) 11 mos 6,799 488.7 728.4 2,290 482.9 614.0 0.84 0.58

avg 422.6 353.9 avg 436.3 352.4 0.97 0.58

FBC Units

D FBC wood (100)a12 mos 8,200 683.6 817.2 2,739 700.5 815.2 1.00 0.58

G FBC wood (80.1%), gas (9.4%), TDF (10.5%) 12 mos 7,599 143.6 162.7 2,596 144.9 166.6 1.02 0.58

avg 413.6 489.9 avg 422.7 490.9 1.01 0.58

amainly bark, some WWTP residuals (sludge)
6

National Council for Air and Stream Improvement

Impact on Limits Derived For Other Boiler MACT Pollutants

The analysis provided above demonstrates how the 3 one-hour run averages in a 3 run test for CO 

emissions from biomass stokers and biomass fluidized bed combustor units are not really independent 

data points and how a reanalysis of these data is warranted using instead the averages of the 3-run tests as 

single data points and with m = 1 (in equation 2 above). There is, of course, no reason to believe that this 

observation is limited to the case with CO emissions from biomass stokers and FBC units. Table 6

provides a summary of similar analysis carried out with several key coal and biomass boiler categories 

and all five Boiler MACT pollutants using run data that represented the top 12% floor as included in the 

EPA Boiler MACT analysis. The data are first analyzed using all n observations where each run is 

treated as an independent observation and with m = 3 in equation 2. Next the Bhaumik-Gibbons 

correction for lognormal data sets is applied and the current limits derived by EPA (where all n 

observations were used and m was set = 3) are reproduced. Next the same calculations are repeated by 

treating the average of 3 consecutive runs (occasionally 2) during a single test as a single data point and 

by setting m = 1 in equation 2. The ratio of the standard deviation of the data with m = 1 to the data with 

m = 3 is then estimated. If this ratio is closer to 1 rather than 0.58, the data set with m = 3 is assumed to 

be comprised of non-independent data and the UPLs are recalculated using m = 1 and n = the number of 2 

or 3-run averages in the top 12% floor.

From the ratios of the standard deviations for the data sets with m = 1 and m = 3 shown in Table 6 it is 

seen that the data sets comprised of (1) CO emissions from FBC, fuel cell and stoker biomass boilers and 

FBC, pulverized and stoker coal boilers, (2) Hg, HCl and PM emissions from all solid fuel boilers, and 

(3) PCDD/F emissions from stoker coal boilers were all found to comprise of non-independent runs since 

these ratios were fairly close to 1 rather than 0.58 as would be required if the original data sets were truly 

comprised of independent data points. However, the data sets comprised of PCDD/F emissions from 

FBC, fuel cell and stoker biomass boilers and pulverized coal boilers showed that the SD ratios reduced to 

around 58%, suggesting that the original data sets were comprised of independent data points and the 

UPLs for these data sets need not have to be recalculated.

In Table 6, the recalculated UPLs based on equation 2 for most the data sets with non-independent run 

data are seen to be generally much higher than the originally promulgated Boiler MACT limits. 

However, on applying the Bhaumik-Gibbons correction for log-normal data sets, this increase becomes 

more tempered, ranging from 26 to 120% except for one case where it decreased by 37%. The reasons 

for this decrease are not obvious.

Impact on Limits Derived For CISWI Pollutants for ERUs

An analysis similar to the one carried out with the Boiler MACT pollutants was carried out for several 

pollutants related to existing ERU units in the final CISWI MACT rule. These results are shown in Table 

7. The limits shown under “Existing Units with m =3” represent the current promulgated limits (99.0% 

UPLs) that were derived using all n observations where each run was treated as an independent 

observation and with m = 3 in equation 2. The limits shown under “Existing Units with m =1” represent 

the recalculated limits with n/3 observations (3 runs per test) and m = 1 in equation 2. The limits were 

recalculated only for cases where the ratio of the SD of the data with m = 1 to the SD of the data with m = 

3 did not reduce to 0.58 (as would be expected with independent data sets) but remained more or less 

unchanged (0.85 to 1.0). It should be noted that unlike in the Boiler MACT analysis, the UPLs derived 

for log-normalized data sets in the CISWI MACT data analysis with m = 3 were not adjusted for by the 

Bhaumik-Gibbons correction. Thus, this correction was not applied for the case with m = 1 either. 
7

National Council for Air and Stream Improvement

Table 6. Results of Analysis for Non-Independent Data When Estimating UPLs for Various Boiler MACT Pollutants and Boiler Categories Pollutant Boiler Category n with m = 3 n with m = 1 % Confidence Limit for UPL Emission Units

UPL value 

with m = 3

B-M Limit

Lognormal/normal ?

UPL value 

with m = 1

Lognormal/normal ?

SD ratio

2

m=1/ m=3

% increase in 

B-M Limit

w/o B-G 

Correctn

1

With B-G 

Correctn

1

w/o B-G 

Correctn

1

With B-G 

Correctn

1

w/o B-G 

Correction

1

w/B-G 

Correction

1

CO Biomass, FBC 15 5 99.9 ppm@3%O2 592 430 430 ln 835 737 n 0.97 41.1% 71.4%

CO Biomass, Fuel Cell 47 17 99.9 ppm@3%O2 687 688 690 ln 1543 891 ln 0.94 124.8% 29.1%

CO Biomass, Stoker 48 16 99.9 ppm@3%O2 476 481 490 ln 1081 619 ln 0.98 126.9% 26.3%

CO Coal, FBC 6 2 99.9 ppm@3%O2 284 81.4 82 ln 7150 180 n 1.29 2417.6% 119.5%

CO Coal, Pulverized 18 6 99.9 ppm@3%O2 197 153 160 ln 254 236 n 1.05 28.9% 47.5%

CO Coal, Stoker 30 10 99.9 ppm@3%O2 291 267 270 ln 257 445 n 1.00 -11.7% 64.8%

Hg3Solid Fuel 151 56 99.0 lb/MMBtu 1.93E-06 4.58E-06 4.60E-06 ln 3.40E-06 2.87E-06 ln 0.96 76.2% -37.6%

HCl Solid Fuel 186 65 99.0 lb/MMBtu 6.40E-03 3.48E-02 3.50E-02 ln 4.64E-02 6.23E-02 ln 0.96 625.0% 78.0%

PM Solid Fuel 471 157 99.0 lb/MMBtu 1.91E-02 3.86E-02 3.90E-02 ln 5.93E-02 5.31E-02 ln 0.93 210.5% 36.2%

PCDD/F Biomass, FBC 15 5 99.0 ng/dscm @7%O2 1.60E-02 1.80E-02 2.00E-02 ln 2.20E-02 2.60E-02 n 0.53 6 6

PCDD/F Biomass, Fuel Cell 15 5 99.0 ng/dscm @7%O2 4.79E-01 3.79E+00 4.00E+00 ln 1.52E+02 8.72E+00 ln 0.64 6 6

PCDD/F Biomass, Stoker46 2 99.0 ng/dscm @7%O2 5.22E-03 4.20E-03 5.00E-03 ln 2.22E-02 4.12E-03 n 0.58 6 6

PCDD/F Coal, Pulverized 6 2 99.0 ng/dscm @7%O2 3.85E-03 3.60E-03 4.00E-03 ln 1.07E-02 3.21E-03 n 0.36 6 6

PCDD/F Coal, Stoker 6 2 99.0 ng/dscm @7%O2 3.00E-03 2.83E-03 3.00E-03 ln 1.90E-02 3.82E-03 n 1.11 533.3% 27.3%

1

adjusted for the Bhaumik-Gibbons (B-G) correction which accounts for the fact that UPLs based on lognormal transformations result in geometric means rather than arithmetic means

2

if the data set with n observations and m = 3 comprised of truly independent observations, this ratio should have been close to 1/3)1/2or 0.58

3

for Hg, the UPL estimate with B-G correction turns out to be lower for the m = 1 case, which is contrary to expectations and needs to be investigated further

4

actual Boiler MACT limit equals 5.00E-03 (rounded from 4.92E-03) and is based on taking 3 times the RDL value

5Center for Health Statistics - Algorithm provided by Bhaumik-Gibbons to estimate Lognormal Prediction Limit for the Arithmetic Mean of n future samples (http://healthstats.org/lpl.html)

6

the ratio of the SD when m = 1 to that when m = 3 for these categories is close to 0.58, implying the individual runs I a 3 run set are independent and no adjustment of the UPL is necessary
8

National Council for Air and Stream Improvement

Table 7. Impact on UPL Calculation For CISWI ERUs Due to Non-Independent Run Data

Existing Units With m = 1

Existing Units With m = 3 Percent Change

Pollutant (units) 1 Biomass Coal Biomass Coal Biomass Coal

HCl (ppmv) 0.45 0.45 5.34 5.34 1087% 1087%

CO (ppmv) 490 59 1800 66 267% 12%

Pb (mg/dscm) 0.0036 0.0036 0.0435 0.0435 1108% 1108%

Cd (mg/dscm) 0.00051 0.00051 0.00051 0.00051 0.0% 0.0%

Hg (mg/dscm) 0.00033 0.00033 0.00033 0.00033 0.0% 0.0%

PM filterable (mg/dscm) 250 250 250 250 0.0% 0.0%

NOX (ppmv) 290 340 311.6 340 7% 0.0%

SO2 (ppmv) 6.2 650 15.5612 650 151% 0.0%

1All emission limits are measured at 7% oxygen

ppmv = parts per million by volume; mg/dscm = milligrams per dry standard cubic meter

The recalculated CISWI limits for ERUs in Table 7 show that the promulgated limits for HCl (biomass 

and coal), CO (biomass), Pb (biomass and coal) and SO2 (biomass) would change substantially when the 

run data are analyzed for independence.

Two Examples of What Constitutes Truly Independent Data Sets

To illustrate how data sets with independent observations behave when the SD of a subset of such a data 

set is compared with the SD of the original data set, consider two examples. The first is the PCDD/F 

emission data compiled in the ICR database for utility boilers (http://www.epa.gov/ttn/atw/utility/ 

utilitypg.html) in relation to the recently proposed Utility Boiler MACT Rule. PCDD/F emission data for 

45 coal-fired boilers were provided in this database, with single tests of 8 hr duration conducted on each 

boiler. Table 8 shows the total PCDD/F emissions for these 45 boilers in units of lb/MMBtu WHOTEF/2005 TEQs. Also shown in Table 8 are the same data after the emissions from a random set of 3 

boilers are averaged, thus resulting in only 15 data points. Means and standard deviations for both data 

sets are calculated and these are also shown in Table 8. It is seen that while the mean for both sets of data 

remains fairly unchanged, the SD for the dataset with m = 3 is reduced to exactly 58% of the SD for the 

original population that had 45 data points. This demonstrates that the average emissions representing 

each of the 45 boilers in this boiler data set are truly independent data points.

Next consider the Hg emissions data for solid fuel boilers in the current floor of the Boiler MACT rule

(see Table 6). The floor included a total of 151 hourly averages representing 56 tests from 38 boilers, 

each with typically 3 runs. Table 9 shows the SDs of the data set when assuming all 151 runs to be 

independent observations and the data set with the 56 test averages. The SD for the 56 test averages falls 

to 6.41E-07 lb/TBtu from 6.64E-07 lb/TBtu for the 151 run averages, or a drop of only 4%. It should 

have dropped by close to 42% if the 151 runs were truly independent observations. When the 56 data 

points (test averages) are further collapsed into 19 discrete points with 3 points each (one with 2 points), 

the SD of these 19 data points falls to 3.80E-07 or about 59% of the set with 56 points. This shows how 

the 56 points constituting test run averages were indeed truly independent points.
9

National Council for Air and Stream Improvement

Table 8. Coal Utility Boiler PCDD/D Emissions - Single Tests on 45 Boilers

Boiler No. Each Boiler

Avg of 3 

Boilers Boiler No. Each Boiler

Avg of 3 

Boilers

WHO-TEF/2005 TEQs1 WHO-TEF/2005 TEQs1

1 1.38E-14 24 3.88E-11

2 1.92E-13 6.87E-14 25 4.74E-13

3 0.00E+00 26 4.60E-13 5.66E-13

4 6.15E-13 27 7.64E-13

5 2.05E-14 4.90E-13 28 0.00E+00

6 8.34E-13 29 6.84E-13 5.76E-13

7 4.22E-14 30 1.04E-12

8 2.37E-13 3.93E-13 31 4.64E-16

9 9.01E-13 32 2.13E-14 7.25E-15

10 0.00E+00 33 0.00E+00

11 0.00E+00 2.02E-15 34 2.16E-12

12 6.07E-15 35 3.62E-12 1.97E-12

13 1.35E-13 36 1.26E-13

14 2.71E-12 1.85E-12 37 2.81E-13

15 2.71E-12 38 0.00E+00 9.39E-14

16 8.75E-14 39 1.22E-15

17 8.67E-14 2.96E-13 40 2.96E-15

18 7.14E-13 41 3.14E-14 1.74E-14

19 0.00E+00 42 1.77E-14

20 7.11E-14 1.60E-13 43 0.00E+00

21 4.10E-13 44 1.38E-13 4.61E-14

22 8.44E-13 45 0.00E+00

23 1.87E-14 1.32E-11

Ratio

mean 1.32E-12 1.32E-12 1.00

stdev 5.77E-12 3.35E-12 0.58

 

1TEQs estimated assuming all NDs = 0

Table 9. Effect of Averaging Time on the Standard Deviation of 

Mercury Emissions From Solid Fuel Boilers in the Boiler MACT Floor

No. of Data 

Points

No. of 1 hr

runs per 

average

Hg Emissions 

lb/TBtu Ratio of SDs

mean (X) stdev (X) Actual Expected1

161 1 5.04E-07 6.64E-07

56 3 4.90E-07 6.41E-07 0.96 0.58

19 9 4.90E-07 3.80E-07 0.59 0.58

1

For a set of independent observations, the SD of n/3 observations = 58% of the SD of n observations
10

National Council for Air and Stream Improvement

Summary

In its analysis of emissions data used for setting the Boiler MACT and CISWI standards, EPA made an 

inherent assumption that all the tests results were independent observations. However, an examination of 

the data suggests that in many instances 3-run tests did not give three independent results. When 

corrected for this error, the Boiler MACT limits for most solid fuel pollutant categories were determined 

to be higher. For example, the CO limits for FBC, fuel cell, and stoker biomass units and FBC, 

pulverized and stoker coal units increased by 71%, 29%, 26%, 120%, 48% and 65%, respectively; the 

HCl and PM emission limits for solid fuel boilers increased by 78% and 36%, respectively; and the 

PCDD/F emission limits for coal stoker units increased by 27%. However, the PCDD/F limits for FBC, 

fuel cell and stoker biomass units as well as pulverized coal units remained unchanged since the original 

data sets were shown to comprise of independent observations and no recalculation was warranted. 

For existing energy recovery units (ERUs) subject to the CISWI rule, CO, SO2 and NOx limits for 

biomass units increased by 267%, 151% and 7%, respectively. HCl and Pb limits for solid fuel ERUs 

(biomass and coal) increased by 1087% and 1108%, respectively. PM, Cd and Hg limits for solid fuel 

ERUs, and SO2/NOx limits for coal-burning ERUs remain unchanged, since the original data sets were 

comprised of independent observations.

Conclusion

The above analysis of individual one hour run average emissions that represented the top 12% performers 

in several of the key biomass and coal boiler categories for all five Boiler MACT pollutants and several 

CISWI pollutants for ERUs suggests that individual one hour runs where the runs are conducted 

sequentially in a typical three-run test are not always truly independent observations and thus only the 

average emission rates obtained from such three run tests should be used for calculating the UPL values. 

Further, since a future three run test would also constitute one independent data point, the value of m in 

equation 2 should be set = 1 rather than 3.