0223.04.020 1 Date: January 4, 2011

To: Brian Shrager, US EPA

From: Amanda Singleton, ERG

Subject: Revised MACT Floor Analysis (2011) for the Industrial, Commercial, and Institutional Boilers

and Process Heaters National Emission Standards for Hazardous Air Pollutants

– Major Source

The purpose of this memorandum is to present the revised methodology and results of the

maximum achievable control technology (MACT) floor determinations for boilers and process heaters at

major sources of hazardous air pollutants (HAP). We determined the MACT floors using data obtained

from two recently implemented Information Collection Requests (ICRs) approved under OMB Control

No. 2060

‐ 0616: ICR numbers 2286.01 and 2286.03, as well as new data and data corrections received

before, during and after the public comment period on the proposal. 1,2,3 Data from both phases—the

2008 Combustion Survey and the 2009 Test Plan for boilers and process heaters—were analyzed for

currently operating boilers and process heaters. We ranked emission test averages for each boiler and

process heater, calculated MACT floor averages, and conducted emission data variability analyses. We

determined the MACT floor emission limits based on the results of these analyses. The following

sections address the methodology used to calculate pollutant limits for existing and new sources. The

memorandum is organized as follows:

1.0 Background of MACT Floor Methodology

2.0 Floors for Existing Sources

2.1 Methodology for MACT Floors for Existing Sources

2.2 Incorporating Data Variability



### 2.3 Calculating Emission Limits



### 2.4 Data Not Considered in the MACT Floor Analysis for Existing Units

3.0 Floors for New Sources

3.1 Methodology for MACT Floors for New Sources

Appendix A: Hg and HCl Variability as a Function of Fuel Variability

Appendix B: Ranked Emission Data for MACT Floor Analysis

Appendix C: Upper Prediction Limit (UPL) Calculations for Existing Sources

Appendix D: Analysis of Representative Method Detection Levels (RDL)

Appendix E: Summary of Statistical, Fuel, and RDL Variability for Existing Sources

Appendix F: UPL Calculations for New Sources

Appendix G: Summary of Statistical, Fuel, and RDL Variability for New Sources0223.04.020 2 1.0 Background of MACT Floor Methodology

Section 112 of the Clean Air Act (CAA), as amended in November 1990, requires the U.S.

Environmental Protection Agency (EPA) to set emission standards for HAP emitted by major stationary

sources based on the performance of the MACT. Pollutants emitted by boilers and process heaters

include particulate matter (PM); mercury (Hg); non

‐ mercury metallic HAP; halogenated compounds (HCl,

HF, and Cl 2 ); and organic HAP, including volatile organic compounds, dioxin, and polycyclic organic

matter.

On September 13, 2004, under authority of section 112 of the CAA

, EPA promulgated national

emission standards for hazardous air pollutants (NESHAP) for new and existing

industrial/commercial/institutional boilers and process heaters. On June 19, 2007 the United States

Court of Appeals for the District of Columbia Circuit (DC Circuit) vacated the NESHAP for

industrial/commercial/institutional boilers and process heaters. The facture occurred simultaneously

with the vacatur of the CISWI solid waste definition rule (see Natural Resources Defense Council v. EPA,

489 F. 3d 1250, 1257–61 [D.C. Cir. 2007]). In this decision, the court determined that any combustion

device firing “any” solid waste material is subject to Section 129 instead of Section 112. As a result, EPA

has categorized units as either waste burning units, or boilers and process heaters, consistent with the

EPA definition for nonhazardous solid waste. The new MACT floor analyses do not consider emissions

from waste burning units in the ranking analysis. If boilers and process heaters were burning small

amounts of waste materials or had indicated that they would stop burning materials that were

considered to be solid wastes, it is possible that a unit firing waste materials is in the boiler inventory,

but any emission test data based on tests when the unit was firing any amount of waste materials was

not included in the analysis. In the Emission database, the table ‘Data: Emission Test’ and the field ‘Test

Burning Waste? Opt 3‘ indicates whether or not the test is firing waste materials, as defined under the

proposed solid waste definition. In the ‘Data: Fuel Analysis’ table the field ‘In Waste Inventory? Opt 3’

denotes if a certain fuel is covered by the final solid waste definition. 4

The revised MACT floor analysis discussed in this memorandum factors in other recent court

decisions that impacted the MACT floor analysis methodology used in the 2004 vacated rule. The March

2007 court decision ( Sierra Club v. EPA, 479 F. 3d 875 [DC Cir. 2007]) ( Brick MACT ) found the following:

 Floors for existing sources must reflect the average emission limitation achieved by the best

‐ performing 12 percent of existing sources, not levels EPA considers to be achievable by all

sources (479 F. 3d at 880–81).0223.04.020 3  EPA cannot set floors of “no control.” The court reiterated its prior holdings, including National

Lime Association, confirming that EPA must set floor standards for all HAP emitted by the major

source, including those HAP that are not controlled by at

‐ the

‐ stack control devices (479 F. 3d at

883).

 EPA cannot ignore non

‐ technology factors that reduce HAP emissions. Specifically, the court

held that “EPA’s decision to base floors exclusively on technology even though non

‐ technology

factors affect emissions violates the Act” (479 F. 3d at 883).



###  EPA may not use emission levels of the worst performers to estimate variability of the best

performers without a demonstrated relationship between the two (479 F. 3d at 882).

2.0 Floors for Existing Sources

2.1 Methodology for MACT Floors for Existing Units

For existing sources, MACT cannot be less stringent than the average emission limitation

achieved by the best performing 12 percent of existing sources (for which emission information is

available) for categories and subcategories with 30 or more sources or the best performing five sources

for subcategories with less than 30 sources. The size of the subcategory was determined according to

the number of units in the boiler and process heater inventory, not the number of units within each

subcategory that had emission data available.

The revised MACT floor analysis has the following differences from the analysis conducted for

the 2010 proposal for the Industrial, Commercial, and Institutional Boilers and Process Heaters National

Emission Standards for Hazardous Air Pollutants:

1) Variability factors for Hg and chlorine (Cl) were assessed using fuel variability only from units

in the top 12 percent. Fuel variability was not used for units outside the top 12 percent.

However, in contrast to the proposed approach all fuel analysis data reported from top

performing units were analyzed instead of only the maximum reported fuel sample from

each unit.

2) Statistical variability was incorporated using the 99% Upper Prediction Limit (UPL) for all

pollutants except for CO, and a 99.9% UPL statistic was used to establish the MACT floor for

CO.

3) The computed UPLs in each subcategory were compared to a level three times the Method

Detection Limit (MDL) and if the computed UPL was less than this level the floor was

established no less than three times the method detection level.0223.04.020 4 4) Modifications were made to the default distribution selection of each floor dataset and the

statistical approach for lognormally distributed data was adjusted to calculate a UPL on the

arithmetic mean in lieu of a geometric mean.

5) New subcategories were added based on comments received during the public comment

period to account for limited use units, hybrid suspension grate burners, and facilities

located outside of the lower 48 contiguous states. A solid fuel subcategory consolidated the

fuel

‐ based HAP emission limits from units firing coal, other fossil solids, or bio

‐ based solids.

6) The threshold criterion for considering performance tests from co

‐ fired units was increased

from 10 percent of a fuel type to 90 percent of a fuel type.

7) Instrument calibration and measurement error were incorporated into the CO analysis

before computing a floor.

Based on the 2008 Combustion Survey and additional new data submitted during the public

comment period, there are 13,840 boilers/process heaters currently in operation at major sources in the

United States and United States territories. Emission test data and additional Hg and Cl fuel analysis data

from uncontrolled liquid units were ranked and analyzed. These data were obtained from the public

comment period, Boiler Survey, when available, or when requested under the ICR phase II testing plan,

and compiled into an emissions database. 2,4 We used this database to develop the MACT floors.

We identified the lowest emission test average (mean) for each pollutant at each boiler/process

heater. We did not include any data from units that indicated in their ICR responses that they were

permanently shut down or other situations where the data was no longer representative of current

operating conditions, as discussed in Section 2.4 below. For units with a mix of detection levels and

measures, we used the reported numerical detection level. If a source reported “zero” or simply “ND” as

the value for a test run, that run was not used to calculate the average of the test. Similarly, if a unit

reported data in the ICR that were not standardized to the appropriate units of measure, or did not

provide operating and stack test parameters necessary to standardize the emissions data, the data were

not used in the MACT floor analysis. For uncontrolled liquid units, we included reported fuel analysis

data for Hg and Cl (converted to HCl) in the ranking analysis for the MACT floors for units designed to

burn liquid fuels. Details on how these data were standardized are discussed in another memorandum. 6

Next, we ranked the minimum stack test means for each pollutant in each subcategory. We

conducted this ranking according to the fuels the unit was burning during the test for Hg, PM, and HCl.

Because emissions of fuel

‐ based HAP (e.g., Hg, acid gases, filterable particulate) depend on the fuel used0223.04.020 5 rather than the design of the combustion chamber, MACT floors for fuel

‐ based HAP were based on data

from all boilers and process heaters firing a minimum threshold of each fuel group. For the

recommended MACT floor analysis the fuel groups were solid, liquid and other process gases and for the

alternative MACT floor analysis the fuel groups were coal/fossil solid, biomass/bio

‐ based solid, liquid,

and other process gases. For boilers/process heaters burning multiple fuel types, we reviewed the

emission database to determine the relative heat input percentages each fuel category contributed

during the test. If a test was fired with at least 90 percent of the heat input from solid fuels, the test was

assigned and ranked in the MACT floor for units designed to solid fuels. If a test was fired with at least

90 percent of the heat input from liquid fuels, the test was assigned and ranked in the MACT floor for

units designed to burn liquid fuels or non

‐ continental liquid fuels, depending on the location of the unit.

If a test was fired with at least 90 percent of the heat input from other gaseous fuels (gas 2), the test

was assigned and ranked in the MACT floor for units designed to burn other gaseous fuels. Based on the

results of the selected gas specification for mercury, which will determine whether a gaseous fuel

belongs in the gas 1 or other gas 2 fuel category, we determined that most gaseous fuels other than

coke oven gas and blast furnace gas would pass the fuel specifications in the final rule, so only tests

firing coke oven gas either alone or in combination with blast furnace gas or other gaseous fuels were

considered when calculating the MACT floors for the other process gas (gas 2) subcategory.

Emissions of organic HAP, including CO and dioxin/furans, fluctuated according to the design of

the combustion chamber. Combustor design subcategories considered in this analysis were Pulverized

Coal (PC), Fluidized Bed (FB), and Stoker/Other for units designed to burn coal and other fossil solids and

Stoker/Sloped Grate/Other, FB, Dutch Oven (including Suspension Burners), Fuel Cells, and hybrid

grate/suspension for units designed to burn biomass/bio

‐ based solids. Because of these differences,

MACT floors for dioxin/furans and CO were based on unique combustor design characteristics in

addition to the type of solid fuel the boiler is designed to burn. We conducted a ranking according to the

combustor design categories at solid fuel units and according to the fuel the unit was burning during the

test. If the test was fired with at least 90 percent biomass the test data was assigned and ranked in one

of the five combustor design subcategories for biomass and bio

‐ based solids. If the test was fired with

at least 90 percent coal or other fossil solids, the test was classified and ranked in one of the three

combustor design groups for coal and other fossil solids. If a test was fired with at least 90 percent liquid

fuels it was classified and ranked in either the liquid or non

‐ continental liquid subcategories, depending

on the location of the unit. If a test was fired with at any amount of heat input from coke oven gas in

combination with other gaseous fuels, the test was classified and ranked in the MACT floor units0223.04.020 6 designed to burn other process gases (gas 2). Some units reported test burns on more than one material

or blend of materials in different tests. For example, units burning oil during process interruptions might

have submitted test results when firing coke oven gas and when firing fuel oil. The emission test data

based on fuel oil would have been characterized and ranked in the “boilers designed to burn liquids”

subcategory, but the emission test data based on coke oven gas would have been ranked and analyzed

in the “boilers designed to burn other gas” subcategory. Given the ability for units to switch

subcategories based on their actual annual heat input, we determined that it is appropriate to rank and

analyze tests separately, based on what materials were fired during the test.

Emissions of CO were ranked according to either their reported measured values, or the

adjusted values that incorporate measurement calibration error. The methodologies used to adjust CO

measurements to incorporate instrument measurement error are discussed in another memorandum. 5

If an emission test result was conducted on a common stack, and the units feeding that stack

were boilers and process heaters in the same subcategory, each unit venting to a common stack was

treated as a separate data point. For example, if PM emissions were reported at a common stack for

boilers #1 and #2, we would have counted the results of those tests twice, to represent data from two

units. Although some commenters suggested that this approach underestimates emissions variability,

EPA determined that emissions from common stacks for boilers in the same subcategory are relevant to

the analysis and incorporating these data as separate records avoids under estimating the number of

sources for which data is available.

We then identified minimum emission test averages in the best performing 12 percent (or top 5

units) for each subcategory. To determine the number of boilers/process heaters in the best

‐ performing

12 percent, we multiplied the number of sources with emission data in each subcategory by 12 percent

and rounded up to the nearest whole integer. For example, 12 percent of a category with 103 emission

test averages is 12.36, so we would have averaged the emission test data from the top 13

boilers/process heaters. This roundup approach is consistent with the approach used by statisticians in

survey sampling. 7



### 2.2 Incorporating Data Variability

Four types of data variability were evaluated to determine the emission levels achieved by the

best performing sources over the long term. This section discusses the statistical variability, method

detection level variability, fuel analysis variability, and boiler operating load variability considered and

incorporated into the MACT floor analysis.0223.04.020 7 

### Statistical Variability. 10,11

After identifying the units, based on minimum emission test averages, in the top 12 percent (or

top 5 units), we identified all the emission test runs reported for those top

‐ performing boilers/process

heaters to be in the same subcategory. By including multiple emission tests from units with a test

average in the top 12 percent, EPA can evaluate intra

‐ unit variability of emission tests over time,

considering variability in control device performance, unit operations, and fuels fired during the test.

Some commenters disagreed with considering all test runs from a best performer in the statistical

analysis, while others agreed with this approach. EPA determined that consideration of all test runs for a

unit is an appropriate method for assessing the operational and test

‐ to

‐ test variability at the same unit,

when additional data was made available to the Agency.

We evaluated various confidence intervals for the upper prediction limits (UPL), including 99

percent, 99.5 percent, and 99.9 percent. A prediction interval for a future observation is an interval that

will, with a specified degree of confidence, contain the next (or some other pre

‐ specified) randomly

selected observation from a population. In other words, the prediction interval estimates what future

values will be, based on present or past background samples taken. Given this definition, the UPL

represents the value we can expect the mean of three future observations (three

‐ run average) to fall

below, based on the results of the independent sample of size (n) from the same population. In other

words, if we were to randomly select a future test condition from any of these sources (i.e., average of

three runs), we can be 99 percent confident that the reported level will fall below a MACT floor emission

limit calculated using a UPL. Since a source must demonstrate compliance with the MACT floor using the

average of a three

‐ run test, the appropriate test condition to use to assess variability is 3. If a source

had to demonstrate compliance by showing that each individual test run was below the MACT floor

emission limit, it would be appropriate to use a future test condition of 1.

In light of comments made during the proposal we first determined the distribution of the test

run data for the best

‐ performing 12 percent of units within each subcategory prior to calculating UPL

values. To evaluate the distribution of the best performing dataset, we computed the skewness and

kurtosis statistics and then conducted the appropriate small

‐ sample hypothesis tests. Some commenters

suggested additional more complex methods for determining the distribution of a dataset, such as the

Shapiro

‐ Wilkes method. However due to the small nature of the datasets available for the MACT floor

calculations, EPA determined these more advanced methods, which are commonly used for larger

datasets, were not necessary in this analysis.0223.04.020 8 The skewness statistic (S) characterizes the degree of asymmetry of a given data distribution.

Normally distributed data have a skewness of 0. A skewness statistic that is greater (less) than 0

indicates that the data are asymmetrically distributed with a right (left) tail extending toward positive

(negative) values. Further, the standard error of the skewness statistic (SES) is given by SES = SQRT(6/N)

where N is the sample size. According to the small sample skewness hypothesis test, if the skewness

statistic (S) is greater than two times the SES, the data distribution can be considered non

‐ normal.

The kurtosis statistic (K) characterizes the degree of peakedness or flatness of a given data

distribution in comparison to a normal distribution. Normally distributed data have a kurtosis of 0. A

kurtosis statistic that is greater (less) than 0 indicates a relatively peaked (flat) distribution. Further, the

standard error of the kurtosis statistic (SEK) is given by SEK = SQRT(24/N) where N is the sample size.

According to the small sample kurtosis hypothesis test, if K is greater than two times the SEK, the data

distribution is typically considered to be non

‐ normal.

We applied the skewness and kurtosis hypothesis tests to both the reported test values and the

lognormal values of the reported test values. If the S and K statistics of the reported dataset were both

less than twice the SES and SEK, respectively, the dataset was classified as normally distributed. If

neither of the S and K statistics or only one of these statistics was less than twice the SES or SEK,

respectively, then the skewness and kurtosis hypothesis tests were conducted for the natural log

‐

transformed data. Then the distribution most similar to a normal distribution was selected as the basis

for calculating the UPL. If both the reported values and the natural

‐ log transformed reported values had

S and K statistics that were greater than twice the SES or SEK, respectively, the lognormally distributed

dataset was selected as the basis of the floor, consistent with guidelines from EPA Guidance document,

Data Quality Assessment: Statistical Methods for Practitioners EPA QA/G

‐ 9S which notes that a

lognormal distribution is a commonly used distribution for most environmental contaminant data. a This

guidance document also notes that the lognormal is an important probability distribution when

analyzing environmental data where normality cannot be assumed. If the results of the skewness and

kurtosis hypothesis tests were mixed for the reported values and the natural log

‐ transformed reported

values, or if the dataset was too small to apply skewness or kurtosis tests, we also chose the lognormal

distribution, consistent with the guidance noted above. We believe this approach is more accurate and

obtained more representative results than a more simplistic normal distribution assumption.

Computing the UPL for Normally Distributed Data0223.04.020 9 For datasets that were determined to be normally distributed, we computed the UPL using a student’s

t

‐ test statistic. The t

‐ test was used in the promulgated HMWI rulemaking and proposed Portland

Cement rulemaking, and it is more appropriate for smaller sample sizes. The t

‐ statistic is calculated 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)

Next, for the floors that were based on a single type of data, e.g. emission stack tests, the average (or

sample mean) and sample standard deviation of the test runs were calculated. We calculated the 99,

99.5 and 99.9 percent UPL values based on the test run data for those units in the best

‐ performing 12

percent. Since the compliance with the MACT floor emission limit is based on the average of a three

‐ run

test, the UPL is calculated by:

(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

This calculation was performed using the following Microsoft Excel function:

Normal distribution: 99% UPL = AVERAGE (Test Runs in Top 12%) + [STDEV(Test Runs in Top 12%) x

TINV(2 * 0.99, n

‐ 1 degrees of freedom)*SQRT((1/n)+(1/3))], for a one

‐ tailed t

‐ value (with 2 x

probability), probability of 0.01, and sample size of n

For floors that were based on more than one type of data, e.g., emission stack tests and fuel analysis

data from uncontrolled liquid fuel units, a separate set of analysis were conducted to calculate a pooled

variance from the two different populations of data. First, the averages for the stack test data were0223.04.020 10 calculated using the averages of the individual test runs. If a source had more than 1 stack test (e.g. 6

test runs), then separate averages were calculated for each test. Next, the average of the fuel analysis

data were calculated, assuming that the u

‐ th source has

3 u 

### n data points. The averages of stack test and

fuel analysis data were considered to be a sample of averages of outcomes that come from the same

population with mean μ and the variance, 2 

### 

. The averages obtained from stack tests and fuel analysis

data were assumed to come from distributions with the same average, but with variances given by 2



### 3 

and 2

3 u 

### n 

, respectively. The UPL equation for a floor based on a combination of fuel analysis and

emission stack test data is then calculated using the following equation:



#     Var Var Mean future m-test runs

df 

### Xt X   (Equation 3)

where

3 1

1 1

1 3



### 2 2 n n

i u

i u 

### x f

n n x f

X   

### 



  

#  

(Equation 4)

where

1

1

1 n

i

i 

### x

x

n  

###  

# 

and 3

1

3 n

u

u 

### f

f

n  

###  

# 

(Equations 5a and 5b)

The pooled variance of the two source means are calculated as:



#   2

1 3 

### 1 1 Var Var

2 4 xf s

X

n n     

          (Equation 6)



##   

#  

   1 33 1

3 1 2 2

1 1 11 2

1 3

1 1 

### 1 1 i u n nn n

ki i uj u

ik uj

n n

i u

i u



### x x ff

s

n n   

  

###  



  

#  

  (Equation 7)0223.04.020 11 

##    3 1

1 3

1 1 

### 1 1 n n

i u

i u 

### df n n

  

###   



#   (Equation 8)

Where:

uj 

### f is the j

‐ th fuel analysis data value for the ujth

‐ source with only fuel analysis data

1 i 

### n is the total number of test

‐ runs in the ith

‐ source with only stack tests data

1 

### n is the total number of sources with only stack tests data

3 

### n is the total number of sources fuel analysis data

3 u 

### n is the total number of sources fuel analysis data in the u

‐ th source with only fuel analysis data

1

1 1 

### 1 i n

i ki

k i 

### x x

n

 

###  

#  (Equation 9)

Where:

ki 

### x is the k

‐ th test value for the j

‐ th source with only stack test data

3

1 3 

### 1 u n

u uj

j u 

### f f

n

 

###  

#  (Equation 10)

Then the UPL will be equal to:

2

1 3 

### 11 1 1

4 df 

### Xt s

nn m      

            (Equation 11)

Where:



### m is the number of test

‐ runs from the new source. T df is the student t

‐ statistic as described in Equation

1 above.0223.04.020 12 Computing the UPL for Lognormally Distributed Data

For lognormally distributed datasets, we revised the UPL computation in light of comments that a simple

lognormal transformation of the dataset results in a UPL calculated on the geometric mean of a dataset

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

calculation. To adjust the calculation EPA considered a scale bias correction approach as well as a new

UPL equation based on a Bhaumik and Gibbons 2004 paper, which calculates “An Upper Prediction Limit

for the Arithmetic Mean of a Lognormal Random Variable”. EPA selected the Bhaumik and Gibbons

2004 approach which addresses commenters concerns with the proposed computations. In this

approach, y is a lognormal random variable, that means 2 

### log( ) ~ ( , ) y N   where 

#   log( ) E y   and



#   2 

### var log( ) y  

. We assume 1 

### ,..., n 

### y y is a random sample from the lognormal distribution. The goal

is to construct the upper prediction limit (UPL) for the mean of m future observations of the lognormal

distribution: 1 m

i

i 

### x

x

m  

###  

# 

. For values of n and m less than 30, as is the case for the test condition of three

future test runs to demonstrate compliance with the MACT floor, we use the Gram

‐ Charlier Type A

series to compute the UPL. The equations used to compute UPL according to the Bhaumik Gibbons

approach are detailed below.

For most MACT floors, which were based on a single type of data, e.g., stack tests, the average

(mean, μ^) and combined variance, 2 

### 

, of all the test runs associated with the best performing units

were computed using the average and variance functions in Excel. Next, the expressions β

2z , which

represents the flatness of the distribution z and √β

1z which measures the skewness of the z distribution

was calculated as:



#   222

2 ˆˆˆ 432

2 2

ˆ 

### 233 1 3 1

1 z 

### eee

m m e 

 

###    

     

 (Equation 12)

Where:

m = number of test runs in test condition (3)



#   2 2

ˆ ˆ

1 

### 1 2

z 

### e e

m  



###   

 (Equation 13)0223.04.020 13 The two parameters calculated using Equations 12 and 13 above, were used to identify the density of Z:



#     

#   2 4

2 1 3 

### 336 () 1 3 ()

6 24 z z

G 

### z z f z zz z     

      

  (Equation 14)

The 99 percentile is calculated as calculated as:



#     

#  

.99 2 4

2 1 3

0 

### 336 1 3 ( ) .99

6 24 z z z 

### z z

zz z     

         

#  (Equation 15)

Using the trapezoidal rule, and taking increments ∆ x very small, the 99 percentile was estimated by

numerically integrating the density function to obtain the 99 percent UPL, called

.99 

### z

. For other

confidence levels the density function was numerically integrated to obtain the 99.5 or 99.9 percent

UPL. The trapezoidal rule approximates an integral as follows:

1 

### () () 2 ( ) ()

2 M b

i

a i 

### x f x dx f a f x f b

 

###   

     

#   (Equation 16)

Next, we solved the following equation for 1 m

i

i 

### x

x

m  

###  

# 

(Equation 17)

2

ˆ

2

22 2 ˆ

1

.99 2 4

ˆˆ ˆ ˆˆ 2 22 ˆ ˆ ( 1) 2( 1) m

i

i x me

z

me e m e

m m  

     



  

  

   

   

## 

(Equation 18)

The z

‐ value with the smallest value greater than the selected confidence level was identified. For a

confidence level of 99 percent, we identified the smallest z

‐ value, z .99 with a value greater than 0.99.

This z

‐ value was used to compute the UPL, using this equation:

2

ˆ 22 2

2 2 4

ˆ ˆˆ ˆ ˆˆ 2 22 .99 

### ˆ ˆ ( 1) 2( 1) z UPL e me e m e

m nn      

###  

   

###  

     

   (Equation 19)

Where:0223.04.020 14 μ^ = mean of test runs

n = number of test runs

m = number of test runs in test condition (3)

For lognormally distributed datasets that were based on more than one data type, such as the

HCl and Hg floor calculations for the liquid fuel subcategory, the mean and variance of each data type

were calculated separately and a pooled variance was computed using the lognormal values of the

reported test data and Equations 6, 7, and 8 of this section. This pooled variance, 2 

###  and mean of the

two datasets, μ^, were then used to compute the terms in Equations 12 through 19 of this section.



### Measurement Detection Level Variability

After the UPL were calculated, the UPL was 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. The purposes of this procedure were 1) to define a method detection level (DL)

that is representative of the data used in establishing the floor or emissions limit and that also minimizes

the influence of an outlier test

‐ specific DL value; and 2) use this representative method detection level

(RDL) to assess and ensure that the floor or the emissions limit used in the rulemaking adequately

accounts for measurement variability.

First, the mean of the pollutant emissions values was calculated for each subcategory, (e.g.,

mean of reported HCl emissions from best performing liquid

‐ fired boilers). Next, for each test run (e.g.,

three runs per test per pollutant per emissions unit) in the pollutant emissions dataset, all pollutant

emissions data flagged as below detection level (BDL) or detection level limited (DLL) were identified.

Then, the largest of the BDL or DLL flagged values that is no greater than the mean of the pollutant

emissions values from the best performers was identified and this largest value serves as the

representative method detection level (RDL) for each pollutant emissions dataset.

Next, we multiplied the RDL by 3 (3x RDL) for each subcategory and then compared the 3x RDL to

the computed floor value or emissions limit (e.g., the 99 percent confidence UPL). If the 3x RDL value

was less than or equal to the calculated floor or emissions limit, we used the calculated floor derived

from the UPL calculations above. If the 3x RDL value was greater than the calculated floor or emissions

limit, we used the 3x RDL value as the basis for the floor, adjusted for measurement variability for the

final rule.0223.04.020 15 When applying this procedure to measurements of dioxins and furans (D/F) in terms of toxic

equivalence (TEQ) additional steps were necessary to identify the RDL for the TEQ values. The individual

reported congeners that comprise the TEQ value were reviewed to identify if they were flagged as either

BDL or DLL. The state of the congener data fell into one of two types:

1) If the reported emissions values for all of the congeners for a test run are flagged data

(i.e., BDL, DLL), the reported flagged value for each congener was multiplied by the

appropriate TEQ factor (see http://edocket.access.gpo.gov/2007/pdf/E7

‐ 9015.pdf ) to

produce a flagged TEQ value. Next, the sum of the TEQ adjusted flagged values was

estimated for the test run; and this sum of the TEQ adjusted flagged values represented

the DL for the test run.

2) If some but not all of the reported values for the congeners for a test run are flagged

data, the mean of the flagged congeners was computed (e.g., if there are six flagged

congeners out of 17 reported values, we determined the mean of those six flagged

values); then the mean of the flagged values was multiplied by the appropriate toxic

equivalency factor to produce a TEQ adjusted flagged value for each congener. The

TEQ adjusted flagged values were summed for each test run; and this sum of the TEQ

adjusted flagged values represented the DL for the test run.

Then the maximum test run DL values computed in items 1) and 2) above were identified and used in

place of the individual flagged values for determining the RDL. Appendix D shows the data identified as

RDL for each subcategory.



### Fuel Analysis Variability

The UPL analysis discussed above takes into account intra

‐ unit variability within test runs, and

since the compliance date of the vacated boiler MACT was after the date the rule was vacated, often

there were only one or two tests available at a unit. These tests represent a small snapshot of the

overall unit operations, and based on the fuel analysis data reported in the Boiler Survey, Phase II test

plan, and data reported from public comments, but fuel

‐ related HAP levels in the various fuels can vary

significantly over time. These fuel

‐ related HAP levels are not well

‐ represented in a small sample of

emission stack test results.

Fuel analysis data for Hg and HCl (converted from Cl) were standardized to a lb/million Btu

(lb/mmBtu) basis, using data standardization techniques discussed in another memorandum. 6 Next, the

units that reported fuel analysis data paired with stack tests were identified by using the database field

in the Data: Fuel Analysis table, “FM_analysiswithEmissionTest”. Since the Phase II test plan requested

paired fuel analysis data for each stack test run, the fuel analysis “Sample ID” associated with each fuel0223.04.020 16 analysis were matched up with the individual test run “Test ID.” Since fuel sampling IDs reported in the

spreadsheet and test IDs reported in the Electronic Reporting tool sometimes had different naming

conventions, the IDs of the units in the top 12 percent were reviewed and a secondary test ID column

was created in order to allow these two data tables to be linked and compared.

Single

‐ Fuel Units

For boilers combusting a single fuel during a stack test, a ratio of the inlet HAP content from the

fuel analysis data divided by the fuel analysis data point associated with the paired stack test was

calculated for every fuel analysis data point reported for that unit. Where this ratio is greater than one,

a unit reported long

‐ term fuel analysis data that exceeded the mercury and/or chlorine content when

compared to the fuel burned during the reported performance test. The control efficiency parameter

that was incorporated into the fuel variability analysis at the proposal was disregarded due to public

comments which provided supporting data that control efficiency can vary depending on the fuel

constituents or fuel blends. 12 A simple ratio of fuel analysis data points, comparing the data at the time

of the test to the data used at other times provides a similar relationship of fuel quality overtime

without introducing additional uncertainty surrounding a calculated, and then extrapolated control

efficiency at any given time.

Multi

‐ Fuel Units

For boilers combusting multiple fuels during a test, the HAP content of each individual fuel

varies over time, and the relative heat input contribution of multiple fuel types can also vary over time.

To evaluate the impact of fuel variability at multi

‐ fuel boilers, a similar ratio was computed, but the fuel

analysis used in the ratio was based on the fuel with the predominant Hg or HCl (converted from Cl)

content. The average reported fuel input rate, standardized to mmBtu/hr, for each of the fuels fired

during the test was calculated. Next the Hg or HCl (converted from Cl) content of each fuel was

multiplied by the fuel input rate to calculate an emission rate from each fuel type, on a lb/hr basis. The

fuel resulting in the highest Hg or HCl emissions, on a lb/hr basis, was identified as the predominant

driver of Hg or HCl emissions, and its variability was used to compute the ratios discussed above for

single fuel units.

Review of Outliers

At proposal, the maximum fuel variability factors (FVF) for Hg and HCl were ranked in ascending

order for each subcategory (coal, biomass, and liquid), and the interquartile range (IQR) of the variability0223.04.020 17 factors was computed to identify any variability factors that might be outliers. In response to treatment

of outliers received during the public comment period, in this revised analysis, all fuel variability factors,

not just the maximum factors for each unit were evaluated for outliers. All of the calculated ratios, or

FVF for Hg and HCl were ranked in ascending order for each subcategory (coal, biomass, and liquid).

Because variability is considered only from units identified to be in the top 12 percent, the list of fuel

analysis data was restricted to only those units that were in the top 12 percent for Hg or HCl. No Hg or

HCl fuel analysis variability factors could be calculated for gas

‐ fired units.

A statistical analysis was performed on the ratios of best performing units for each combination

of subcategory and pollutant on both a fuel specific basis and on a unit specific basis. In the fuel

‐ specific

approach, all of the ratios for a specific fuel subcategory were ranked in ascending order and the

distribution of the dataset was assumed to be normal. The empirical rule was then applied to identify

and eliminate outlier values that did not lie within three standard deviations of the mean of all of the

computed ratios in each subcategory. For those subcategory and pollutant combination datasets that

had fuel category outliers, the outliers were identified and removed to create a new dataset from which

the maximum ratios, or FVF were calculated. In the unit

‐ specific approach, all of the ratios

corresponding to fuel analysis data reported by a specific best performing unit in each subcategory were

ranked in ascending order and the empirical rule was used to identify outlier values that did not lie

within three standard deviations of the mean of the fuel analysis ratios reported for that unit. For

subcategory and pollutant combination datasets that had unit

‐ specific outliers, the outliers were

removed to create a new dataset from which the maximum ratios were calculated.

After calculating the FVF ratios on both a fuel

‐ specific and unit

‐ specific basis, we determined

that the fuel

‐ specific basis was most appropriate for further consideration in the MACT floor analysis.

Because fuel variability at existing units is based on a range of fuel types identified to be from a set of

best performing units in a subcategory and not fuels specific to any single unit, assessing outliers on

fuel

‐ specific basis is more appropriate than assessing outliers on a unit

‐ specific basis.

Appendix A shows the calculated FVF analysis for Hg and HCl for each subcategory where data

was available.0223.04.020 18 Developing a Fuel Variability Factor for Each Fuel Category

To calculate the MACT floor emission limit for Hg and HCl, the 99% UPL emission limit was

multiplied by the maximum ratio, or FVF, at units in the top 12 percent of each fuel category, after

removing the outliers discussed above. Appendix A

‐ 2 shows the calculated fuel variability factors used in

the MACT floor analyses for coal/fossil

‐ solid, biomass/bio

‐ based solid, all solid fuels, and liquid

subcategories.

For existing solid fuel units, EPA reviewed the fuel variability in the UPL calculations prior to

multiplying the results by the FVF. In the case of the solid fuel subcategory the fuels used in the top

performing boilers varied widely, including coal, petroleum coke, tire

‐ derived fuel, as well as several

types of biomass fuel. Based on the heterogeneous make up of the best performing units, we

determined that the UPL calculation alone considered sufficient variability in fuel types from best

performing units and it was unnecessary to incorporate additional fuel variability through the use of a

FVF. For new solid fuel units, since the floor was based on the best single unit of the subcategory, we

determined that a FVF was appropriate. The best performing solid unit for mercury is also the best

performing coal unit. This unit has additional test data burning 25% coal and 75% biomass, which is

included in the UPL calculations for this subcategory. Given the variability of coal fired from this best

performing unit, a FVF was multiplied by the new source solid fuel UPL for mercury. For HCl, there is no

available FVF from the best performing unit so no FVF was applied.

For existing and new liquid fuel units, the fuels making up the best performing units

demonstrated less variability in their composition and type, and EPA determined that an additional fuel

variability factor was necessary.



### Load Variability

For combustion

‐ based emissions, such as CO, formaldehyde, and dioxin/furan, the formation of

emissions are dependent on the design of the combustion unit; the MACT floor emission limits for CO

and dioxin/furan are subcategorized according to combustor design for boilers burning solid fuels.

However, the emissions can fluctuate with changes in the operating rate of a boiler, or “boiler load”.

Boiler load was divided into two subcategories, depending on the role of a boiler at a facility. A boiler

providing a relatively constant amount of steam to a facility is considered a “base

‐ loaded” unit, and a

boiler that adjusts its operating parameters to meet varying levels of demand in a plant over time is

referred to a “load

‐ following” unit.0223.04.020 19 Stack tests for CO are conducted at near full

‐ load conditions, and although the MACT floor limits

based on stack test results achieved when operating at or near full load, the MACT floor emission levels

for CO might not be achieved when best performing units are operating at lower loads. EPA assessed the

limited amount of CEM data that was paired with load rates in a separate memorandum. 12 As this memo

discusses there is insufficient data to establish a CO limit that considers lower loads, and the preamble

discusses how the compliance mechanisms for CO were modified in the final rule to address this

concern.



### 2.3 Calculating Emission Limits

We determined emission limits for each MACT floor option and pollutant by rounding up the

UPL values to two significant digits for all pollutants except for dioxin/furan. Two significant figures is

consistent with public comments and their referenced guidance document, “Memorandum:

Performance Test Calculation Guidelines, William Laxton, OAQPS, and John Seitz, OAQPS which suggests

considering no less than two and no more than three significant figures. For dioxin/furan which are

measured in units of nanograms per dry standard cubic meter, and are often comprised of data below

detection levels and additional measurement uncertainty due to the analytical procedures for multiple

congeners, these data were rounded up to one significant figure. Although some commenters disagreed

with always rounding up, the guidance they cited was intended for rounding of performance stack test

data results and not standard setting for MACT floors. Rounding up is appropriate for establishing

emission standards that must be complied with at all times. It should be noted that if the UPL values

were rounded down, then the possibility exists that the best

‐ performing units comprising the MACT

floor might not be able to achieve the emission limit on an ongoing basis. In all cases, the significant

figure approach and associated rounding does not meaningfully change the emission limits. The

emission limits are summarized for each subcategory in Tables 1 and 2. The data ranked and analyzed

for the MACT floor at existing units is shown in Appendix B, and the UPL floor calculations for each

subcategory and each pollutant are shown in Appendix C.

Table 1: Summary of Calculated MACT Floor Limits for Existing Boilers and Process Heaters at Major Sources of HAP under

Recommended Approach

Subcategory Filterable

Particulate

Matter

(lb/MMBtu) Hydrogen

Chloride

(lb/MMBtu) Mercury

(lb/MMBtu) CO

(ppm @ 3% O 2 ) TEQ

Dioxins/Furans

(ng/dscm @ 7%

O 2 )

All Solid Fuels 0.039 0.035 4.6E

‐ 06

Coal/Fossil Solid

P C 160 A 0.0040223.04.020 20 Stoker 270 A 0.003

FB 82 A 0.002

Biomass/Bio

‐ based

Solid

Stoker 490 A 0.005

F B 430 A 0.02

Dutch Oven/

Suspension Burner 470 A 0.2

Fuel Cel l 690 A 4.0

Suspension/Grate 3,500 A 0.2

Liquid 0.0075 0.00033 3.5E

‐ 06 10 A 4.0

Non

‐ Continental

Liquid 0.0075 0.00033 7.8E

‐ 07 160 A 4.0

Process Gas 0.043 0.0017 1.3E

‐ 05 9 A 0.08

Notes:

 Red text indicates at least one test run used to calculate the UPL limit was reported as non

‐ detect data.

 Blue shaded cells indicate limits that incorporate fuel analysis variability. The Hg and HCl floors for liquid units incorporate both the

3*RDL value and fuel analysis variability. These values represent the 3*RDL multiplied by the FVF.

 Green cells indicate limits that are based on the 3*RDL approach.

 Although default maximum calibration errors were used for CO, if data was flagged as non

‐ detect when initially reported it was still

considered to be non

‐ detect data even if the value defaulted to the calibration error.

A CO MACT floor is based on adjusted data that consider measurement error for measurements obtained with an instrument test

method.

Table 2: Summary of Calculated MACT Floor Limits for Existing Boilers and Process Heaters at Major Sources of HAP under

Alternative Approach

Subcategory Filterable

Particulate

Matter

(lb/MMBtu) Hydrogen

Chloride

(lb/MMBtu) Mercury

(lb/MMBtu) CO

(ppm @ 3% O 2 ) TEQ

Dioxins/Furans

(ng/dscm @ 7%

O 2 )

Coal/Fossil Solid 0.053 0.068 4.1E

‐ 05

P C 160 A 0.004

Stoker 270 A 0.003

FB 82 A 0.002

Biomass/Bio

‐ based

Solid 0.038 0.0031 1.2E

‐ 06

Stoker 490 A 0.005

FB 430 A 0.02

Dutch Oven/

Suspension Burner 470 A 0.2

Fuel Cel l 690 A 4.0

Suspension/Grate 3,500 A 0.2

Liquid 0.0075 0.00033 3.5E

‐ 06 10 A 4.0

Non

‐ Continental

Liquid 0.0075 0.00033 7.8E

‐ 07 160 A 4.0

Process Gas 0.043 0.0017 1.3E

‐ 05 9 A 0.08

Notes:

 Red text indicates at least one test run used to calculate the 99% UPL limit was reported as non

‐ detect data.

 Blue shaded cells indicate limits that incorporate fuel analysis variability. The mercury for biomass and liquid as well as the HCl value

for liquid incorporate both the 3*RDL value and fuel analysis variability. These values represent the 3*RDL multiplied by the FVF.

 Green cells indicate limits that are based on the 3*RDL approach.0223.04.020 21  Although default maximum calibration errors were used for CO, if data was flagged as non

‐ detect when initially reported it was still

considered to be non

‐ detect data even if the value defaulted to the calibration error.

A CO MACT floor is based on adjusted data that consider measurement error for measurements obtained with an instrument test

method.

2.4 Filling Data Gaps

For subcategories with no applicable test data for a given pollutant, floor limits from other

subcategories were assigned in order to fill the gaps in data. In both of these instances the subcategories

were created in response to compelling public comments and data provided to create a new

subcategory. Since these subcategories were not envisioned at proposal the 2009 ICR test plan was not

targeted to fill data gaps for these subcategories and data from the most similar subcategories were

used as a proxy to fill data gaps and establish emission limits for these subcategories. The appropriate

action taken to fill these data gaps are described below.

 Non

‐ continental liquid: No test data was available for PM, HCl, or TEQ dioxins/furans. For these

pollutants, the floor for other liquid units was used as a proxy to the floor for this subcategory

since both of these subcategories fire liquid fuels.

 Biomass hybrid suspension/grate: No test data was available for TEQ dioxins/furans. The floor

for TEQ dioxins/furans from biomass dutch oven/suspension burners was used as a proxy to the

floor for this subcategory since both of these subcategories incorporate suspension firing.

2.5 Data Not Considered in the MACT Floor Analysis for Existing Units

For fuel

‐ based HAP, data upstream of the control device were not considered in the MACT floor

analysis because the control device is expected to achieve emission reductions. The location of the test

is denoted in the “Location” field of the Data: EmissionsTest Table. If the test location was not reported,

it was assumed to be downstream of the control device since this data represented the vast majority of

data in the database. The upstream/downstream location was not a limiting factor when identifying

suitable data for CO and dioxin/furan, although in all cases, dioxin/furan emissions were downstream of

the control device.

In response to public comments on concerns with basing MACT floors on co

‐ fired units, the

threshold criteria for considering performance tests from co

‐ fired units was increased since proposal

from 10 percent of a fuel type to 90 percent of a fuel type. Based on feedback received during the

public comment period, not all units are designed to co

‐ fire fuels and EPA determined that units that are

co

‐ firing substantial amounts of fuels that do not belong within a subcategory are not representative of

units firing single fuel types or small amounts of co

‐ firing. For example, if a unit has a test based on 50

percent natural gas and 50 percent biomass, the unit is not representative of the other units in the solid

fuel subcategory and it is not representative of the other units in the gas 1 subcategory. Therefore data

from this test would not be considered in the MACT floor analysis.

Some facilities submitted emission test data based on previous control configurations that are

no longer installed on the unit. Emission data reported while using these previous control configurations

were not used to establish the MACT floor. Table 3 shows which control configurations were not

considered in the MACT floor analysis. Any emissions associated with the control configurations at these

units were not considered in the analysis, except the emissions for CO, as CO is not expected to be

affected by the control devices currently installed on the unit.0223.04.020 22 Table 3: Deleted Control Configurations for MACT Floor Analysis

FacilityID UnitID DeleteRecord Reason for Removing

NCNC_DukeUniversity_Durham 7754-01 Fabric Filter Dry injection added to FF in

2007

OHMedicalCenterCo1915 B003 & B004 Fabric Filter Dry injection added to FF in

2007

MSMasoniteLaurel BB-003 Wet Scrubber Wet scrubber removed in 2007

MIMichiganSugarCaro Boilers #1 & #2 No HAP APCD

Control Venturi installed in 2007

ARGeorgiaPacificCrossett93 SN-WB1 Wood

Boiler Wet Scrubber Wet scrubber no longer

installed at unit

SCNewSouthCamden WWB1 Cyclone or

Multiclone ESP installed in 2007

SCNewSouthCoConway WWB1 Cyclone or

Multiclone ESP installed in 2007

For uncontrolled light liquid units with fuel analysis data for Hg or HCl, the fuel analysis data

were only used if there was not an emission test available for that unit for Hg or HCl. Using both fuel

analysis and emission test data associated with the same unit would be double

‐ counting emission data

and would be inconsistent with the approach to use the minimum available emission data for each unit

to identify the units in the top 12 percent.

Some emission data for CO or dioxin/furan were based on common stacks that are fed from

units in more than one subcategory. Because the CO and dioxin/furan MACT floors are based on

combustor design, only those emissions are based on units of the same combustor. If the combustor

design classification in the Data: EmissionsTest table listed “Multiple Combustor Designs,” this data was

not used in the MACT floor calculations for CO or dioxin/furan.

Finally, there were some specific data reported that were excluded from the MACT floor analysis

for site specific reasons. The data table “Data: EmissionsTest” in version 6 of the emission test database

contains two fields, “Ignore for Floor?” and “Note” which flag data that were disregarded from the

analysis and provides a reason why the data was not considered in the analysis.

After receiving a short extension to the court

‐ ordered deadline we re

‐ evaluated the floor

calculations to consider data from Fibrominn, LLC (Facility ID: MNFibrominn, Unit ID: EU

‐ 001). This unit

is a stoker boiler that had been classified as a solid waste incinerator due to the combustion of poultry

litter along with hog fuel, but was reclassified as a boiler combusting biomass and bio

‐ based solids after

further consideration of the final solid waste definition. Standardized test data for this boiler was

available for filterable PM, HCl, CO, and TEQ dioxin/furan. Test data was provided for Hg, but could not

be standardized from its reported format to units of lb/MMBtu because exhaust temperatures were not

reported with the data. The boiler was not a top performer for any pollutant and did not cause a change

in the number of top performers for filterable PM, HCl, and CO. However, the inclusion of TEQ

dioxin/furan data increased the number of biomass

‐ fired stokers in the top 12 percent from one to two

boilers. Due to rounding conventions, the newly calculated TEQ dioxin/furan emission limit did not differ0223.04.020 23 from the emission limit calculated without the MNFibrominn data represented in Table 1 above. Since

the addition of data from this boiler did not cause changes to any of the previously calculated emission

limits, the floor rankings and calculations presented in the appendices to this memorandum do not

contain the MNFibrominn test data.

3.0 Floors for New Sources

3.1 Methodology for MACT Floor at New Units

The same methodology used to calculate the MACT floors for existing sources, as discussed in

Section 2.0, was used to calculate the floor for new sources, with four exceptions. Additional exceptions

needed to be considered for new source limits to ensure that the floor computations are representative

of the best controlled similar source for an entire subcategory. First, if the calculated floor for new

sources was less stringent than the calculated floor for existing sources, the floor corresponding to the

same subcategory for existing sources was used as the basis for the floor for new sources. Although the

minimum average test run for the best performing source resulted in the lowest three

‐ run average test,

the 99% UPL

‐ based limit incorporated variability between test runs. As the sample size—in this case the

number of test runs—gets smaller, the t

‐ statistic increases. When the sample size of test runs is small,

and there is a large variability between test runs, the calculated limit using the UPL approach can be

larger than the variability among a larger set of test runs from units in the best performing 12 percent,

especially if the performance of the best performing units is comparable. Similar to the approach used in

the HMIWI rulemaking and the proposal for this final rule, if the emission limit for new sources was less

stringent than the emission limit for existing sources in the same subcategory, we decided to use

existing source limits for new sources.

Second, if the lowest emitting unit had less than three test runs, the unit with the next lowest

emissions based on at least a three

‐ run test average was used as the basis for the MACT floor from new

units. Using the lowest emitting unit with three test runs ensures that adequate variability can be

incorporated into the limit to guarantee that future sources can repeatedly meet the limit during their

compliance tests. There were no instances where the top ranked unit was disregarded due to not having

three test runs.

Third, if the top ranked unit was represented by an emission test based on co

‐ firing of fuels that

are not in the same subcategory, this test was not considered to be representative of the best similar

source for the subcategory. The unit with the next lowest emissions that is based on a test firing 100

percent of the fuel category was used in the basis of the floor.

1) For the CO MACT floor for new units designed to burn process gas, the second lowest

test average was used because the lowest test average was based on test data co

‐ firing

fuels from other subcategories.

2) For the Hg MACT floor for new units designed to burn process gas, the second lowest

test average was used because the lowest test average was based on test data co

‐ firing

fuels from other subcategories.0223.04.020 24 3) For the PM MACT floor for new units designed to burn process gas, the second lowest

test average was used because the lowest test average was based on test data co

‐ firing

fuels from other subcategories.

Fourth, if the top ranked unit was based on a process

‐ specific fuel that could not be reasonably

purchased or obtained by other sources in the subcategory, we selected the unit with the next lowest

emissions as the basis of the floor calculations.

1) For the CO MACT floor for new fluidized bed boilers designed to burn biomass, the third

lowest test average was used. The top two best performing test averages were from

units burning deinking residuals and industrial sludge, respectively. Since these fuels are

specific to a process and are not routinely available to other units in the category, they

do not represent the best performing similar source for the subcategory.

2) For the Hg MACT floor for new boilers designed to burn solid fuel, the second lowest

test average was used. The top performing test average was from a unit burning 100

percent tire derived fuel. This test does not reflect normal operating conditions for the

unit in question, which typically combusts a mixture of hog fuel, bituminous coal, and

natural gas. Since this does not represent typical operating conditions for the unit in

question, it does not represent the best performing similar source for the subcategory.

3) For the HCl MACT floor for new boilers designed to burn liquid fuel, the ninth lowest

test average was used. The top seven best performing test averages were fuel analysis

data. The eighth best performing test average was from a unit burning anhydrides

waste. Since this fuel is specific to a process and not routinely available to other units in

the category, it does not represent the best performing similar source for the

subcategory.

Similar to the existing source MACT floor analysis, some subcategories did not have any data available to

establish a MACT floor and the computed floors from the most similar subcategories were used as a

proxy to fill these data gaps.

1) Non

‐ continental liquid: No test data for PM, HCl, or TEQ dioxins/furans. For all

pollutants, the floor for other liquid units was used as the floor for this subcategory.

2) Biomass suspension/grate: No test data for TEQ dioxins/furans. The floor for TEQ

dioxins/furans from biomass dutch oven/suspension burners was used as the floor for

this subcategory.

Tables 4 and 5 summarize the MACT floor emission limits for new sources, and the light green

highlighted cells represent the limits where the corresponding limit for existing sources in the same

subcategory were used as a basis for the limit at new sources. The UPL floor calculations for each

subcategory and each pollutant for new sources are shown in Appendix E.0223.04.020 25 To assess fuel variability, the same procedures were used; however, only fuel analysis variability

from the top

‐ performing unit in each subcategory was considered in the analysis.

Table 4: Summary of MACT Floor Limits for New Boilers and Process Heaters at Major Sources of HAP under Recommended

Approach

Subcategory Filterable

Particulate

Matter

(lb/MMBtu) Hydrogen

Chloride

(lb/MMBtu) Mercury

(lb/MMBtu) CO

(ppm @ 3% O 2 ) TEQ

Dioxins/Furans

(ng/dscm @ 7%

O 2 )

All Solids 0.0011 0.0022 3.5E

‐ 06

Coal/Fossil Solid

P C 12 A 0.003

Stoker 6 A 0.003

FB 18 A 0.002

Biomass/Bio

‐ based

Solid

Stoker 160 A 0.005

FB 260 0.02

Dutch Oven/

Suspension Burner 470 A 0.2

Fuel Cel l 470 0.003

Suspension/Grate 1,500 A 0.2

Liquid 0.0013 0.00033 2.1E

‐ 07 3 A 0.002

Non

‐ Continental

Liquid 0.0013 0.00033 7.8E

‐ 07 51 A 0.002

Process Gas 0.0067 0.0017 7.9E

‐ 06 3 A 0.08

Notes:

• Red text indicates at least one test run used to calculate the 99% UPL limit was reported as non

‐ detect data.

• Blue shaded cells indicate limits that incorporate fuel analysis variability.

• Yellow shaded cells indicate limits where the calculated limit for new sources was less stringent than the calculated limit for existing

sources, so the limit for existing sources was used as the basis for the limit at new sources.

• Green cells indicate limits that are based on the 3*RDL approach.

• Although default maximum calibration errors were used for CO, if data was flagged as non

‐ detect when initially reported it was still

considered to be non

‐ detect data even if the value defaulted to the calibration error.

A CO MACT floor is based on adjusted data that consider measurement error for measurements obtained with an instrument test

method.

Table 5: Summary of MACT Floor Limits for New Boilers and Process Heaters at Major Sources of HAP under Alternative

Approach

Subcategory Filterable

Particulate

Matter

(lb/MMBtu) Hydrogen

Chloride

(lb/MMBtu) Mercury

(lb/MMBtu) CO

(ppm @ 3% O 2 ) TEQ

Dioxins/Furans

(ng/dscm @ 7%

O 2 )

Coal/Fossil Solid 0.0011 0.0022 5.5E

‐ 08

P C 12 A 0.003

Stoker 6 A 0.003

FB 18 A 0.002

Biomass/Bio

‐ based

Solid 0.0034 0.0031 2.8E

‐ 07

Stoker 160 A 0.005

FB 260 0.02

Dutch Oven/ 470 A 0.20223.04.020 26 Suspension Burner

Fuel Cel l 470 0.003

Suspension/Grate 1,500 A 0.2

Liquid 0.0013 0.00033 2.1E

‐ 07 3 A 0.002

Non

‐ Continental

Liquid 0.0013 0.00033 7.8E

‐ 07 3 A 0.002

Process Gas 0.0067 0.0017 7.9E

‐ 06 3 A 0.08

Notes:

• Red text indicates at least one test run used to calculate the 99% UPL limit was reported as non

‐ detect data.

• Blue shaded cells indicate limits that incorporate fuel analysis variability.

• Yellow shaded cells indicate limits where the calculated limit for new sources was less stringent than the calculated limit for existing

sources, so the limit for existing sources was used as the basis for the limit at new sources.

• Green cells indicate limits that are based on the 3*RDL approach.

• Although default maximum calibration errors were used for CO, if data was flagged as non

‐ detect when initially reported it was still

considered to be non

‐ detect data even if the value defaulted to the calibration error.

A CO MACT floor is based on adjusted data that consider measurement error for measurements obtained with an instrument test

method.0223.04.020 27 6.0 References

1. Phase I ICR 2008 Combustion Survey. [ICR No. 2286.01]. OMB Control No. 2060

‐ 0616

‐

Information Collection Effort for Facilities with Combustion Units. Approved by OMB on August

1, 2008.

2. Phase II ICR Test Plan Requesting Emission Testing from Boilers, Process Heaters, and CISWI. [ICR

No. 2286.03]. OMB Control No. 2060

‐ 0616

‐ Information Collection Effort for Facilities with

Combustion Units. Approved by OMB on May 21, 2009.

3. Gibson, G. ERG. Handling and Processing of Corrections and New Data in the EPA ICR Databases.

January 2011.

4. Emissions Database for Boilers and Process Heaters Containing Stack Test, CEM, & Fuel Analysis

Data Reported under ICR No. 2286.01 & ICR No. 2286.03 (version 6)

5. Singleton, A. and G. Gibson, ERG. Incorporating Measurement Error in Reported Carbon

Monoxide (CO) and Total Hydrocarbon (THC) Data. January 2011.

6. Gibson, G. and A. Singleton, ERG. Revised Standardization of Reported Data from the

Questionnaire for Boilers, Process Heaters, Incinerators and Other Combustion Units. January

2011.

7. Cochran, William G. 1977. Sampling Techniques. Third Edition, John Wiley & Sons. Pages 72

‐ 87.

8. Hahn GJ & Meeker WQ. 1991. Statistical Intervals: A Guide for Practitioners

. John Wiley & Sons,

Inc.: New York.

9. NIST/SEMATECH. 2009. NIST/SEMATECH e

‐ Handbook of Statistical Methods

. <http://www.itl.nist.gov/div898/handbook/> August 4, 2010.

10. Air Tox Environmental Company, Inc. Test Report Boiler MACT Demonstration Test Program for

Cytec Industries. October 2009.

11. EPA. Data Quality Assessment: Statistical Methods for Practitioners. EPA QA/G

‐ 9S. February

2006. Available on

‐ line at: <http://www.epa.gov/quality/qs

‐ docs/g9s

‐ final.pdf>

12. National Council for Air and Stream Improvement, Inc. “Proposed Emission Standards for

Hazardous Air Pollutant for Major Sources: Industrial, Commercial and Institutional Boilers and

Process Heaters.” Appendices. August 23, 2010. Docket Comment Number EPA

‐ HQ

‐ OAR

‐ 2002

‐

0058

‐ 2804.10223.04.020 28 APPENDIX A

Ranked Emission Data for MACT Floor Analysis

(See Excel spreadsheet)0223.04.020 29 APPENDIX B

Ranked Emission Data for MACT Floor Analysis

(See Excel spreadsheet)0223.04.020 30 APPENDIX C

Upper Prediction Limit (UPL) Calculations for Existing Sources

(See Excel spreadsheet)0223.04.020 31 APPENDIX D

Analysis of Representative Method Detection Levels (RDL)

(See Excel spreadsheet)0223.04.020 32 APPENDIX E

Summary of Statistical, Fuel, and RDL Variability for Existing Sources

(See Excel spreadsheet)0223.04.020 33 APPENDIX F

UPL Calculations for New Sources

(See Excel spreadsheet)0223.04.020 34 APPENDIX G

Summary of Statistical, Fuel, and RDL Variability for New Sources

(See Excel spreadsheet)