# ncasi NATIONAL COUNCIL FOR AIR AND STREAM IMPROVEMENT, INC.
SOUTHERN REGIONAL CENTER Mailing and Street Address:
402 SW 140 th Terrace, Newberry, FL 32669

Phone (352) 331

- 1745 FAX (352) 331

- 1766

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

### July 15, 2011

M r. 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 capabilitie s 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 ne ed 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 run

by

- run data shows the runs in a given test are not necessarily independent (random) obs ervations 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 considera tion 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, NCASINational 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. O f 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 t his 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 emiss ion 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 ori ginal 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 signif icance, 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 value 1 

### . All

measurements have associa ted 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 subtractio n 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 wh ich vary from run to run

around a mean value. Measurement errors contribute to the uncertainty 2 

### associated with a measured value. Measurement uncertainty is defined variously as the interval around a measured value which contains the true value, for a gi ven 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

.2

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 e rrors 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 mea sured 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 de fined 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 c an 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 j udgment 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 sh ould 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, deci sion

- 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 app ointment 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 NPDE S 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

i nformation 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 st ack 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 sampling3

National Council for Air and Stream Improvement 

### process which often greatly exceed the analytical error. While these issues were not signific ant 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 procedur es for determining analytical method detection and quantitation limits (Keith e t 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 (E PA 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) a nd multiplying the resulting standard deviation by 3.14 (the Student’s two

- tailed t

factor 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 nega tive 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 dev iation 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 an alysis. 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 weig hing 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 sa mple 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, an d 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 samp ling trains and sampling4

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 routi ne 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 199 9 ; 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 limi t.

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 stand ards and will require sources to conduct performance tests to demonstrate compliance with PM, CO, PCDD/F, HCl, and mercury

emission standards. However, s everal of the Boiler MACT and CISWI emission limits are so low that it would not be possible to demons trate 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 metho d.

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 fil ter 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 an d 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 requi red 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 r un 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 Ark ley’s studies were limited in scope in that they did not examine the impacts of stack traverse and stack gas stratification on the5

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 limi ts 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.”

Usin g 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 pro posed 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 limit s described above. EPA

nevertheles s 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/10 6 

### Btu. For liquid fuel units, the best performing unit was selected based o n a 3

- run test with reported PM values of 0.00072, 0.00041, and 0.00040 lb/10 6 

### Btu (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 /10 6 

### Btu for solid fuel units and 0.0013 lb/10 6 

### Btu for liquid fuel boilers. This bring s 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/10 6 

### Btu, the PM ma ss 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 pr actice 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 pr oblem 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 e stablishes

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, a fter 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 ga ses 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 mate rial during the run or during sample recovery. EPA Method 5 does not specify how to account for decreases in filter mass6

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

compa nies 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 weigh t 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 var ied 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 fr om 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.

T o address th e Method 5

- related problems described ab ove, 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

t hose 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 CO 2 

### concentration if a CO 2 

### 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 t he 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 t he 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 subcategor y shows a significant fraction of

measurements wer e below the 10 ppm practical quantitation limit of the wet chemistry reference7

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

ques tions 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

ma terial 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 standar ds 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 P rocedure 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 em ission s 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 WHO

TEF/2005 TEQs for the six runs on these two boilers. The mean and 99% upper prediction limit ( UPL ) value s for the six runs w ere 1.64E

- 03 and 3.82E

- 03 ng TEQ/dscm @ 7% O 2 

### , respectively.8

National Council for Air and Stream Improvement Table 1. PCDD/F Emissions For 2 Biomass Stokers in Top 12%

PCDD/PCDFs in ng/dscm @ 7% O 2 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%

O 2 

### . 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

co nfidence 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% O 2 

### . 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% O 2 

### .)9

National Council for Air and Stream Improvement Table 2. Estimation of Detection Levels For Each Run

- 2 Biomass Stokers in Top 12%

PCDD/PCDFs in ng/dscm @ 7% O 2 Georgia Pacific, Madison, GA Boralex Stratton Energy, ME 1

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 methodolog y 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 calculatio ns, 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 limits10

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 associa ted 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 t he 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 laboratori es be used to analyze samples and National Quantitation limits should be set at the upper 95% confidence limit for normally distributed data

or 95 th 

### percentile of the laboratory quantitation limit data, if data are non

- normally distributed (Federal Advisor y 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 95 th 

### percentile values of detection limits for each of the 17 PCDD/F isomers. Befo re 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 95 th


### 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 in cluded in this analysis

.

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

95 th 

### percentile detection limit in Table 3 . Also shown is the QL in WHO

- TEF/2005 TEQ units. The data show that using the 95 th 

### percentile values

, the quantitation limit of the analytical component of Method 23 would be 0.1 1 ng/dscm

.11

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% O 2

Congener N 1

N 1 Min. Max. 95th %ile 2

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

1 No. of Observations; 2 Detection Limit

Table 4. Summary of Quantitation Limits f or E ach PCDD/F Isomer Based on the

95 th Percentile Detection Limits Determined in Table 3, ng/dscm at 7% O 2

Congener N 1 95th %ile 2

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

1 No. of Observations; 2 Quantitation Limit = 3 X Detection Limit12

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

f ractions of all observations in the entire Boiler MACT PCDD/F database that w ere below the

95 th 

### percentile analytical method quantitation limits as shown in Table 4. I t is seen that even when the source sampling and recovery

- related variabilities are ignored, on average 94.6% o f all data were below the quanti tation 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 95 th Percentile Quantitation Limits As Shown in Table 4

Congener N 1 95th %ile 2 95th %ile 3

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%

1 Total 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 act ivities 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 sourc es 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 boile r 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 note d13

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 ac id 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 perfo rmers 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 con sideration 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%

O 2 

### 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 0.03515

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/10 6 

### Btu

8/26/09 2 0.0002 lb/10 6 

### Btu

8/27/09 3 0.0002 lb/10 6 

### Btu

HCl INAlcoaWarrick

Unit#3 8/7/08 1 0.0000143 lb/10 6 

### Btu ND

8/7/08 2 0.0000157 lb/10 6 

### Btu ND

Hg IAUofIowa

EP7 Boiler 11 2/15/06 1 1.26E

- 08 lb/10 6 

### Btu

2/21/06 2 7.54E

- 09 lb/10 6 

### Btu ND

2/21/06 3 2.27E

- 08 lb/10 6 

### Btu

CO (Dutch Oven/Suspension Burner) GATempleInlandThomson

BW

- B001 1/30/03 1 5.5 ppm @

3% O 2 

### Use 30

1/30/03 2 1.4 ppm @

3% O 2 

### Use 30

1/30/03 3 5.4 ppm @

3% O 2 

### Use 30

(continued on next page)16

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%

O 2



### 7/29/09 2 61 ppm @ 3%

O 2



### 7/30/09 3 99 ppm @ 3%

O 2



### Dioxin (biomass

fluidized bed) GATempleInlandRome

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

O 2 

### (TEQ) DLL *

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

O 2 

### (TEQ) DLL

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

O 2 

### (TEQ) DLL

Dioxin (biomass stoker) MEBoralexStratton

Boiler#1 10/20/09 1 0.00076 ng/dscm@7%

O 2 

### (TEQ) DLL

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

O 2 

### (TEQ) ADL **

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

O 2 

### (TEQ) DLL

*DLL

– detection limit limited

**ADL

– above detection limit17

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 rea gent 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 calcu late the mercury concentration in μg/m 3 

### in the stack gas. This concentration is multiplied by the appropriate fuel F

- factor, corrected for the stack gas oxygen (or CO 2 

### ) content, to obtain the mercury emission rate in

lb/10 12 

### Btu.

A critical aspect of Met hod 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 95 th 

### percentile of detection limits reported by different laboratories and multiplying those values by three , t he results in Table 8 show that for

f ractions 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 associat ed 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

95 th 

### 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 re sults to as low as 3 μg/dscm, the resulting standard deviation of mercury measurements would be 0.55 μg/dscm. As discussed earlier,

m ultiplying this value by 10 to obtain the quantitation limit of the method results in a quantitation limit of 6 μg/dscm f or 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 C ISWI 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 tar gets 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 com pliance 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 s eries 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 pr obe 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 on19

National Council for Air and Stream Improvement 

### a biomass boiler based on the maximum sample volume of 2 m 3 

### on 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 impi nger 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 measurement s 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 ex amination 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 KMnO 4 

### impingers are processed, one obtains two 500 mL sub

samples for mercury analysis. In the case of ASTM Method D6784

- 02, the processing of the

KMnO 4 

### 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 emissio n 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 re

examine all the emission test results in its database to identify data below test method quantitation limits and make appropriate corrections to the data . On ly 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. Referenc es

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.20

National Council for Air and Stream Improvement 

### Arkley, S. 2000. Compliance problems encountered in testing par ticulate 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 f or 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. h ttp://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 Technolo gy, 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). D ecember 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 O rganization 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. 199 9 . Quantitation limits for reference methods 23, 26, and

29. J. Air & Waste Management 49:399

- 410.21

National Council for Air and Stream Improvement 

### Shigehara, R.T. 1996. Min imum Detection Limi t 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. 201 1

. 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 Determination s

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 pr ediction limit (UPL) values for each subcategory using the following methodology 1 :

First, for each subcategory, t he t

‐ statistic was calculated for the appropriate confidence level using the following Excel equation:

t

‐ statistic = TINV(2*(1

‐ x),n

‐ 1) (Eq uation 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, NO

x and SO 2 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 b oiler, 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 best

performing sources in E PA’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 inde pendent 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 tha t are correlated with each other then the statistics used by EPA would be incorrect.

1 Revised 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 s tandard 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 SD

n = 0.58 *

SD

o ). 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 whi le 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 r ather 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 r uns 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 exten sive 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 Aver aging

Time CO

ppm @ 3% O 2 R atio of 3 hr avg SD to 1 hr avg SD mean (X) stdev (X) Actual Expected 1

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 assi gning 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% O 2 (Note that EPA adjusted this value to 481 ppm by appl ying the Bhaumik and Gibbons correction 1, 2 which was then ro unded 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% O 2 (619 ppm with the Bhaumik and Gibbons correction ).

2 a djusts for the fact that a simple lognormal transformation of a dataset results in a UPL calculated on the geometri c mean of the dataset instead of the arithmetic mean which can introduce bias into the resulting UPL calculation3

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 f or 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 th e 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 h our 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 Ave raging 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% O 2 R atio of 3 hr avg SD to 1 hr avg SD mean (X) stdev (X) Actual Expected 1

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 Averagin g 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% O 2 R atio of 3 hr avg SD to 1 hr avg SD mean (X) stdev (X) Actual Expected 1

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 SD4

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 yield s a 99.9 percent UPL value of 592 ppm @ 3% O 2 (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% O 2 (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, T emple

- Inland had submitted to EPA 30 days of CO

C EMS data (7/1 to 7/30/08) and a plot of the 30

- day rolling average CO CEM S 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 i s 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% O 2 R atio of 3 hr avg SD to 1 hr avg SD mean (X) stdev (X) Actual Expected 1

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 FBC s

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 represente d startup/shutdown (generally very high O 2 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 b oilers. 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 Se veral FPI Biomass Stoker and FBC Units

1 hr avg CEM CO, ppm @ 3% O 2 3 hr avg CEM CO, ppm

@ 3% O 2 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) Act

ual Expe

cted 1

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%) a 10 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) a 12 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

a mainly 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 a nalyzed 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 w ere 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 d eviation 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 numbe r 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 , f uel c ell and stoker biomass boilers an d 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 no n

- 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% exce pt 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 pollu tants 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 wi th 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

- normal ized data sets in the CISWI MAC T data analysis with m = 3 were not adjusted for by the

Bhaumik

- Gibbons c orrection . 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 E stimating UPL s f or 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%O 2 592 430 430 ln 835 737 n 0.97 41.1% 71.4%

CO Biomass, Fuel Cell 47 17 99.9 ppm@3%O 2 687 688 690 ln 1543 891 ln 0.94 124.8% 29.1%

CO Biomass, Stoker 48 16 99.9 ppm@3%O 2 476 481 490 ln 1081 619 ln 0.98 126.9% 26.3%

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

CO Coal, Pulverized 18 6 99.9 ppm@3%O 2 197 153 160 ln 254 236 n 1.05 28.9% 47.5%

CO Coal, Stoker 30 10 99.9 ppm@3%O 2 291 267 270 ln 257 445 n 1.00

- 11.7% 64.8% Hg 3 Solid 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%O 2 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%O 2 4.79E

- 01 3.79E+00 4.00E+00 ln 1.52E+02 8.72E+00 ln 0.64 6 6

PCDD/F Biomass, Stoker 4 6 2 99.0 ng/dscm @7%O 2 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%O 2 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%O 2 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) 1/2 or 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 M ACT limit equals 5.00E

- 03 (rounded from 4.92E

- 03) and is based on taking 3 times the RDL value

5 Center 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 necessary8

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%

NO X (ppmv) 290 340 311.6 340 7% 0.0%

SO 2 (ppmv) 6.2 650 15.5612 650 151% 0.0%

1 All emission limits are measured at 7% o xygen

ppm v = parts per million by volume; mg/dscm = milligra ms 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 SO 2 (biomass) would change substantially when the run data are analyzed for independence.

Two Example s of What Constitutes Truly Independent Data Set s

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 uti lity boilers ( http://www.epa.gov/ttn/atw/utility/ utilitypg.html ) in relati on to the recently proposed Utility Boiler MACT Rule. P CDD/F emission data for

45 coal

- fired boilers were provide d 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 WHO

TEF/2005 TEQs. Also shown in Table 8 are the same data after the emissions from a rand om 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, t he 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 p oints.

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 sh ows the SD s 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 1 9 discrete points with 3 points each (one with 2 points ),

the SD of these 19 data points falls to 3. 80 E

- 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 TEQs 1 WHO

- TEF/2005 TEQs 1

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

1 TEQs estimated a ssuming 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 R atio of SD s

mean (X) stdev (X) Actual Expected 1

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 observations10

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 tes ts 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, pulve rized 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%. H owever, 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 en ergy recovery units (ERUs) subject to the CISWI rule, CO, SO 2 and NO

x 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 H g limits for solid fuel ERUs, and SO 2 /NO

x 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

- ru n 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 da ta point, the value of m in equation 2 should be set = 1 rather than 3

.