1 

MEMORANDUM 

TO: Jim Eddinger, U.S. Environmental Protection Agency, OAQPS/SPPD

FROM: Amanda Singleton and Brandon Long, ERG

DATE: February 2011

SUBJECT: Revised MACT and GACT Floor Analysis for the Industrial, Commercial, and

Institutional Boilers National Emission Standards for Hazardous Air Pollutants –

Area Source

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

achievable control technology (MACT) floor determinations for mercury (Hg) and polycyclic organic

matter (POM) at area source boilers.  This memo also discusses generally achievable control

technologies (GACT) for non‐Hg metallic urban hazardous air pollutants (HAP) and organic urban HAP.  

Emissions data for area source boilers is limited, as these units often fall below permitting thresholds.  

We determined the MACT floors using data obtained from four sources: state operating permits,

emission test results reported at area sources in the recently implemented Information Collection

Request (ICR) approved under OMB Control No. 2060‐0616 ICR number 2286.01, additional data

provided with comments to the proposed rule promulgated in April 2010, and mercury data from the

utility industry.1, 2, 3

  We ranked emission test averages for each boiler, calculated MACT floor averages,

and conducted emissions data variability analyses.  We determined the MACT floor emission limits

based on the results of these analyses.   

We determined GACT based on a review of state regulations and common work practices for

existing units and PM data from the ICR and comments. For new units we reviewed related combustion

regulations under the New Source Performance Standards program. 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 on MACT Floor and GACT Methodology
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2.0 Data Available to Estimate MACT Floors

3.0 MACT Floors for Existing Sources

3.1  Methodology for Floors for Existing Sources

3.2  Incorporating Data Variability

3.3  Calculating Emission Limits

4.0 MACT Floors for New Sources

5.0 Determining GACT Standards  

5.1  Additional Data Sources Available for Assessing GACT

5.2  GACT for Existing Units

5.3  GACT for New Units

Appendix A: Ranked Emission Data for MACT Floor Analysis

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

Appendix C: UPL Calculations for New Sources

Appendix D: Control Device Data used to Establish GACT for Particulate Matter at Existing Sources

Appendix E: Emission Data used to Establish GACT for Particulate Matter at New Sources

1.0 Background on MACT Floor and GACT Methodology 

Industrial boilers and institutional/commercial boilers were two different source categories

included in the area source list published on July 19, 1999 (64 FR 38721).  In addition, both industrial

boilers and institutional/commercial boilers are on the list of section 112(c)(6) source categories

published on April 10, 1998 (63 FR 17830).  Section 112(c)(6) of the Clean Air Act (CAA) requires EPA to

list categories of sources accounting for not less than 90 percent of emissions of each of seven listed

pollutants.  These two categories are on the 112(c)(6) list because of emissions of mercury and

polycyclic organic matter (POM).  Section 112(c)(6) also requires that source categories accounting for

emissions of the HAP listed in section 112(c)(6) be subject to standards under sections 112(d)(2) and

(d)(4).   

EPA estimates that they have subjected to regulation or will regulate 90.3 percent of the 172.3

tons in the 1990 emissions inventory for mercury.  Coal‐fired area source boilers provide an additional

0.72 percent.  Regulation of these boilers under MACT provides an anticipated margin to ensure that the

obligations under CAA section 112(c)(6) are met.  Consequently, MACT control was evaluated for

mercury from coal area source boilers.  Similarly, to meet the 90 percent threshold for POM, it is

necessary to regulate coal area source boilers under MACT using CO as a surrogate.  GACT controls were

evaluated at biomass and liquid boilers for CO and at biomass, liquid, and coal boilers for PM.   

This NESHAP covers the source categories defined as industrial boilers and institutional and

commercial boilers at locations identified as an area source of HAP.  Both of the source categories
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included in this proposal are on the Integrated Urban Air Toxics Strategy Area Source Category List.   

A boiler is defined as an enclosed device using controlled flame combustion and having the

primary purpose of recovering thermal energy in the form of steam or hot water. An area source of HAP

emissions is any stationary source or group of stationary sources located within a contiguous area and

under common control that does not emit or have the potential to emit any single HAP at a rate of 10

tons or more per year or any combination of HAP at a rate of 25 tons or more a year.

Because facilities could have multiple boilers on‐site that are different capacities, burn different

fuels, or have different combustors or levels of add‐on control devices, the MACT floor and GACT were

determined by grouping boilers designed to fire similar fuel types, coal, biomass, and liquid, and

evaluating reported emission data for each particular subcategory.   

2.0 Data Available to Estimate MACT Floors 

The affected inventory of area source boilers was estimated using a boiler inspector inventory

from 13 states, extrapolated nationwide.4 This boiler inspector dataset is focused on boiler safety items

and contact information and it does not contain emission data for any of the units. To analyze numerical

emission data, EPA identified four viable data sources: state operating permits, emission test results

reported at area sources in the recently implemented Information Collection Request (ICR) approved

under OMB Control No. 2060‐0616 ICR number 2286.01, additional data sent with comments to the

proposed rule promulgated in 2010, and mercury data from the utility industry. Based on the four data

sources considered for the analysis, the following data were available:

Data Number of Coal Boilers

POM Emission Data None

CO Emission Data 16

Mercury Emission Data 7

Boilers Using Add‐On Control Technology for Hg 5

In addition, the following information was not available.  There are no state regulations

applicable mercury or POM; there are no state permits specific for mercury or POM; and there is no

surrogate for Hg.  
4 

3.0 MACT Floors for Existing Sources 

3.1 Methodology for Floors for Existing Sources 

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

the area boiler NESHAP proposed in 2010.

1. MACT floor limits are only applicable to coal units for Hg and CO; no MACT limits were

set for biomass or liquid boilers.

2. A fuel variability factor was incorporated into the Hg floor limits.

3. Statistical variability was incorporated using the 99% Upper Prediction Limit (UPL) for Hg

and the 99.9% UPL for CO.

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.  The new statistical method is the

Bhaumik and Gibbons approach.

5. The threshold criteria for considering the use of performance tests for the MACT floor

analysis from co‐fired units was increased from 10 percent of a fuel type to 90 percent

of a fuel type.

Using the average nation‐wide projections from the 13‐state boiler inspector inventory, there

are 182,671 boilers at area sources of HAP. Of these, 10,958 boilers are designed to fire biomass, 3,710

boilers are designed to fire coal, and 168,003 boilers are designed to fire liquids. 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 data is available) for categories and subcategories with 30 or more

sources or the best performing 5 sources for subcategories with less than 30 sources.  The size of the

subcategory was determined according to the number of units in the boiler inventory, not the number

of units within each subcategory that had emission data available, and all subcategories contain more

than 30 sources.

We identified the lowest emission test average (mean) for each pollutant at each boiler. We did

not include any data from units that indicated they were permanently shut down. For units with a mix of

detection levels and measured values we used the average of the measured values and the reported

numerical detection level to calculate an average. If a boiler reported three non‐detect levels, we

averaged the numerical detection levels. 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 that was
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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 was not used in the MACT floor

analysis.  

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 designed to burn (coal, biomass, or liquid).

Because emissions of fuel‐based HAP (e.g., mercury) depend on the fuel used rather than the design of

the combustion chamber, MACT floors for fuel‐based HAP were based on data from all boilers designed

to burn the same type of fuel. For boilers 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 coal, the unit was classified as

designed to burn coal. If the test was fired with at least 90 percent biomass, the unit was classified as

designed to burn biomass. If the test was fired with at least 90 percent liquid, the unit was classified as

designed to burn liquid.  

Emissions of POM and CO fluctuate according to the design of the combustion chamber.

Although combustor design subcategorization was considered in the proposed NESHAP for Industrial,

Commercial, and Institutional Boilers and Process Heaters at area sources of HAP, there is not enough

emission data available to further subcategorize according to combustor design. Further, the 13‐state

boiler inspector inventory does not include information on the combustor designs of these units and we

are unable to identify the distribution of combustor designs at area source boilers. Given the limited

emission data available to calculate a MACT floor limit, the boiler combustor design was not a factor in

developing the subcategories for the MACT floor.

We then identified minimum emission test averages in the best performing 12 percent for each

subcategory. To determine the number of boilers 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. This roundup approach is

consistent with the approach used by statisticians in survey sampling.5

An additional step was necessary for coal CO to create limits which were representative of the

population of boilers.  The best performing 12 percent for coal CO were two units which used petroleum

coke as their fuel, which has different emission characteristics than coal.  Due to this, these two units

were excluded from the calculations and the next two units were used as the best performing 12

percent.   
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3.2 Incorporating Data Variability 

In order to capture the emission levels achieved by the best performing sources over the long

term, data variability was used.  This section discusses the statistical variability and fuel analysis

variability incorporated into the MACT floor analysis.  

Statistical Variability7, 8

After identifying the units with 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 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.  

EPA evaluated several confidence intervals for the upper prediction limit (UPL); 99 percent, 99.5

percent, and 99.9 percent.  It was determined that 99 percent UPL is appropriate for fuel based HAP

(mercury) and a 99.9 percent UPL is appropriate for combustion dependent HAP (CO); CO fluctuates

more with load and a higher UPL is warranted to capture these variations.  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 (or 99.5 or 99.9) 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 Hospital/Medical/Infectious Waste Incinerator

rulemaking, 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.
7 

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.9 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, 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. For CO and Hg the lognormal method was used to calculate the floor, but both

the normal and lognormal methods are shown.  Note that the equations below use 99% UPL as an

example, but for CO calculations 99.9% would be used instead.
8 

Computing the UPL for Normally Distributed Data

For datasets that were determined to be normally distributed, the UPL is computed using a

student’s t‐test statistic. The t‐test was used in the promulgated Hospital/Medical/Infectious Waste

Incinerators 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*(1‐0.99),n‐1)          (Equation 1)

Where:

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

Next, the average (or sample mean) and sample standard deviation of the test runs were

calculated. We calculated the 99% 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 = standard deviation of emission data

t (0.99, n‐1) = the t‐statistic

x = mean of emissions test data

This calculation was performed using the following 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  

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
9 

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   E y log( ) 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.   

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 β2z, which represents the flatness of the z distribution, and √β1z, which measures the

skewness of the z distribution, was calculated as:

 

222

2

432 ˆˆˆ

2 2 ˆ

233 1 3 1

1 z

eee

m m e





          

         (Equation 3)

Where:

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

  2 2 ˆ ˆ

1

1 2

z

e e

m

 



                (Equation 4)

The two parameters calculated using Equations 12 and 13 above were used to identify the density of Z:

     2 4

1 3 2 336

() 1 3 () 6 24

z z

G

z z

f z zz z

  

   

    

 

    (Equation 5)

The 99 percentile is calculated as:

  .99    2 4

1 3 2

0

336

1 3 ( ) .99 6 24

z z z z z

zz z

  

        

         (Equation 6)
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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 7)

Next , we solved the following equation for 1

m

i

i

x

x

m

 

        (Equation 8)

2ˆ

2

22 2

ˆ

1

.99 2 4

2 22 ˆˆ ˆ ˆˆ ˆ ˆ ( 1) 2( 1)

m

i

i

x me

z

me e m e

m m

 

    





 





 

      

         (Equation 9)

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:   

2ˆ 22 2 2

2 4 ˆ .99 2 22 ˆˆ ˆ ˆˆ ˆ ˆ ( 1) 2( 1)

z UPL e me e m e

m nn

          

            (Equation 10)

Where:

μ^ = mean of test runs

n = number of test runs

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

Fuel Analysis Variability

To capture the mercury fuel content variability, a fuel variability factor is applied to give a more

representative floor limit.  This is necessary because the amount of mercury emitted from the boiler

depends on the amount of mercury contained in the fuel.  The best performing 12 percent for the coal

Hg subcategory is a single unit from New York.  Although there is no mercury data for this specific unit,

there is a significant amount of mercury fuel analysis data available for Eastern bituminous coal, which

would cover the sources of coal available to the unit.  There were 24,107 data points available, and the

fuel variability factor was calculated by dividing the maximum Hg value by the mean Hg value.  The fuel
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variability factor equals 10.9.  This fuel variability factor was then multiplied by the 99 percent UPL value

to arrive at the final Hg MACT floor limit.

3.3 Calculating Emission Limits 

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

UPL values less than 100 to two significant figures. This approach allows for an appropriate level of

precision depending on the scale of the measured value. It should be noted that if the UPL values were

rounded down, then the possibility exists that the best‐performing units that comprise the MACT floor

may not be able to achieve the emission limit on an ongoing basis; rounding up is appropriate for

establishing emission standards that must be complied with at all times.  In all cases, the significant

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

emission limits are summarized in Table 1 below; it was determined that only coal would have MACT

floors for existing units. The data ranked and analyzed for the MACT floor at existing units is shown in

Appendix A, the UPL floor calculations for each subcategory and each pollutant are shown in Appendix

B.  The mercury fuel analysis data is provided in the docket.3

  

Table 1: MACT Floor Emission Limits for Existing Units

Subcategory 

Hg 

(lb/MMBtu) 

CO 

(ppm @3% O2) 

COAL 4.8 E-06 400 

Notes:  

 Red text indicates at least one test run used to calculate the MACT floor is based on a reported detection

limit.

4.0 Floors for New Sources 

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

Section 3.1, was used to calculate the floor for new sources, with two exceptions. 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% or 99.9% 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. Similar to the approach used in the HMIWI rulemaking, if the emission
12 

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 ensure that future sources can repeatedly meet the limit during their

compliance tests. The next lowest unit was used in one instance:   

1) Coal, CO – top performing unit only had two test runs. Used test data from second best

unit, which had 3 runs.

Table 2 summarizes the MACT floor emission limits for new sources; for both limits 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 C.  

Table 2: MACT Floor Emission Limits for New Units

Subcategory 

Hg 

(lb/MMBtu) 

CO 

(ppm @3% O2) 

COAL 4.8 E-06 400 

Notes:  

 For both Hg and CO, the calculated limit for new sources was less stringent than the calculated limit for

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

 Red text indicates at least one test run used to calculate the MACT floor is based on a reported detection

limit.

5.0 Determining GACT Standards 

5.1 Additional Data Sources Available for Assessing GACT 

Of the 13 states for which we had boiler inspector data, EPA was able to gather information

from one state environmental agency on air pollution control devices for permitted boilers in the state.  

EPA obtained an additional permit database from the state of Pennsylvania, although EPA did not have

boiler inspector data for this state.

EPA had a 1996 document from the Pennsylvania Department of Environmental Protection

(DEP), which provided a list of facilities in Pennsylvania with permitted boilers.  This list included

information on fuel type, unit capacity, and control devices.  Given the age of this document, EPA made

several calls to facilities that were considered to be potential area sources affected by this rule.  As a

result of this data gathering effort, it was determined none of the coal‐fired boilers in several of the
13 

school districts on the list were still in operation.  The contacts indicated that due to increased insurance

rates on the old boilers, and additional recordkeeping requirements from the state DEP, the boilers have

been replaced with natural gas units.  Additionally, these contacts stated that this switchover was

common practice for several other school districts in similar situations.  Since many of the solid fuel

boilers on this list were no longer operating, this data source was considered obsolete for identifying

GACT levels of control for boilers at area sources.   

EPA requested that Pennsylvania DEP provide an updated version of the list of facilities with

permitted boilers, and EPA used this list to identify control devices for various fuel and size

combinations.  This list contained data for approximately 4,000 permitted boiler records.10  The

Pennsylvania database contained information on the facility name and location, fuel, rated heat input

capacity, and air pollution control device.  This database consists of units at facilities that would

potentially require a permit to operate in the state of Pennsylvania, which included data on 93 facilities

with permitted solid fuel boilers.   

A second state permit data base from the Wisconsin Department of Natural Resources (DNR)

contained approximately 1,731 permitted boiler records, which included data on 108 facilities with

permitted solid fuel boilers.  The Wisconsin database incorporated information on the facility name and

location, unit installation date, SCC code, fuel, rated heat input capacity, and air pollution control device.  

This database only consisted of units at facilities that are large enough to potentially require a permit

from Wisconsin DNR. 11

5.2 GACT for Existing Units 

The revised GACT analysis for existing units has the following difference from the analysis

conducted for the area boiler NESHAP proposed in 2010.

1. A GACT management practice for CO is applicable to existing biomass and liquid boilers

instead of a MACT limit; a tune‐up is required.

For existing coal and biomass area source boilers, the add‐on control technology generally being

used for removing HAP other than mercury and POM is multiclones.  We found that this technology is

minimally effective in controlling such metallic HAP emissions.

Multiclones are mechanical separators that use velocity differential across the cyclones to

separate particles.  A multiclone uses several smaller diameter cyclones to improve efficiency.  

Multiclones have a control efficiency for PM emissions of about 75 percent.  Multiclones are more

efficient in collecting larger particles and their collection efficiency falls off at small particle sizes.  This is
14 

a disadvantage because non‐mercury metallic HAP tend to be on small size particles (i.e., fine particle

enrichment).  Based on emission data obtained during the major source NESHAP development,

multiclones have a control efficiency for non‐mercury metallic HAP of only about 10 percent. 12   

Upon review of the area source boilers in the ICR database, 45% of boilers firing biomass, 16% of

boilers firing coal, and 1% of boilers firing liquid had a multiclone installed. Further, the two permit

databases from Pennsylvania and Wisconsin indicate that 53 percent of coal‐fired boilers in

Pennsylvania have no control, while the remaining boilers have multiclones installed and 8 percent of

biomass boilers in the Wisconsin and Pennsylvania permit databases have no control and the remaining

92 percent of biomass boilers have a multiclone or other mechanical control device installed.  See

Appendix D for the Pennsylvania and Wisconsin data.  Based on the presence of mechanical control

devices at solid fuel units greater than or equal to 10 mmBtu/hr, GACT for existing units was determined

to be a multiclone.  However, although multiclones can achieve reductions in filterable particulate

(primarily larger particles), these controls are not effective at reducing mercury emissions, and only

minimally effective at reducing other non‐Hg metallic HAP. Installation of a multiclone is expected to be

comparable or perhaps even less effective than other pollution prevention GACT such as tune‐ups.

Tune‐ups can improve combustion efficiency and, as a result reduce fuel consumption and emissions of

fuel‐based HAP including Hg.  As a result, although multiclones are a GACT for boilers greater than or

equal to 10 mmBtu/hr, they are not a suggested GACT for this NESHAP due to their limited ability to

reduce emissions of Hg.  

After re‐examining the emission inventory with regard to POM emissions, it is not necessary to

set a MACT limit for CO for biomass and liquid boilers to achieve 90 percent reduction.  With these

boilers already meeting a management practice standard of a tune‐up for metallic HAP, the same level

of GACT is applied for POM emissions.  Therefore, existing biomass and liquid boilers must perform a

tune‐up on their boiler to minimize HAP emissions.

All existing boilers with heat input capacity less than 10 mmBtu/hr must meet a GACT

management practice standard of a tune‐up.  For these units it was determined that it is not appropriate

to perform stack testing due to technical and economic considerations.

5.3 GACT for New Units 

   

The revised GACT analysis for new units has the following differences from the analysis

conducted for the area boiler NESHAP proposed in 2010.

1. Coal and biomass were each separated into two subcategories; boilers with heat input
15 

capacity between 10 and 30 mmBtu/hr, and boilers with heat input capacity of 30

mmBtu/hr or greater.

2. GACT limits for PM and MACT limits for CO are not applicable to new boilers less than

10 mmBtu/hr.  For these boilers a management practice standard of a tune‐up is

required.

3. MACT limits for CO are not applicable to new biomass and liquid boilers.  For these

boilers a management practice standard of a tune‐up is required.

The control technologies currently used by facilities in the source categories that reduce non‐

mercury metallic HAP and PM are fabric filters, multiclones, and electrostatic precipitators (ESP).  We

determined that these controls are generally available and cost effective for new area source boilers.  

New area source boilers with heat input capacity of 10 million Btu/hr or greater are subject to the NSPS

for boilers (either subpart Db or Dc of part 60) which regulate emissions of PM and require performance

testing.  Furthermore, new coal area source boilers will likely require a PM control device to comply with

the proposed mercury MACT standard.

The emissions database contains PM test data for 71 area source boilers obtained from the ICR

survey conducted for major sources.  All of the boilers were greater than 10 million Btu per hour in size.  

In order to develop PM (as a surrogate for non‐mercury metallic HAP) emission limits for the three

subcategories, we compared the PM limits in NSPS subpart Dc with the obtained PM emission data.  We

considered this to be an appropriate methodology because many new area source boilers will be subject

to NSPS subpart Dc.  Consequently, we determined that the PM limits in the NSPS could be used to

establish the PM emission limit for certain new area source boilers.   

For coal and biomass boilers with heat input capacity between 10 and 30 mmBtu/hr it was

determined the NSPS PM limits were not appropriate as GACT.  For these subcategories, PM data for

boilers in the 10 to 30 mmBtu/hr heat input range were further analyzed.  There were several biomass

boilers which used multiclones, so the lowest emitting boiler PM value was selected as the appropriate

GACT level.  For coal boilers there were no units which used control devices, so the lowest PM value

from the available data was selected.  Similarly, for liquid boilers between 10 and 30 mmBtu/hr, their

PM data was looked at, but the lowest PM value was below the NSPS limit so a separate limit was not

determined.

The GACT PM emission levels based on NSPS subpart Dc for new area source liquid boilers with

heat input capacity of 10 mmBtu/hr or greater and new area source coal and biomass boilers with heat

input capacity of 30 mmBtu/hr or greater is 0.03 lb/mmBtu.  Based on the data analysis, for coal boilers
16 

with heat input capacity between 10 and 30 mmBtu/hr the GACT PM limit is 0.42 lb/mmBtu, and for

biomass boilers with heat input capacity between 10 and 30 mmBtu/hr the GACT PM limit is 0.07

mmBtu/hr.  Table 3 summarizes the GACT limits.  Of the 20 biomass boilers for which we have PM

emission data, six of these are below that limit.  For coal, 5 out of 19 are below the limit and for liquid,

22 of the 32 are below the limit for PM.  See Appendix E for the GACT PM data used to set the limits.  

Table 3: GACT Limits for New Units

Subcategory 

PM 

(lb/MMBtu) 

COAL ≥ 10 and < 30 MMBtu/hr 0.42 

COAL ≥ 30 MMBtu/hr 0.03 

BIOMASS ≥ 10 and < 30 MMBtu/hr 0.07 

BIOMASS ≥ 30 MMBtu/hr 0.03 

LIQUID ≥ 10 MMBtu/hr 0.03 

As noted for existing biomass and liquid units, it is not necessary to set a MACT limit for CO for

biomass and liquid boilers to achieve 90 percent reduction.  Therefore, new biomass and liquid boilers

must meet GACT standards by performing a tune‐up on their boiler to minimize CO emissions.

For all boilers with neat input capacity less than 10 mmBtu/hr, there are no applicable limits but

they must meet a GACT management practice.  It was determined that new boilers meet the same

rationale as existing boilers less than 10 mmBtu/hr, that due to technical and economic considerations

small boilers should not perform stack testing.  As such, new boilers with heat input capacity less than

10 mmBtu/hr must perform tune‐ups.   

6.0 REFERENCES 

1.  See Docket Entries (EPA‐HQ‐OAR‐2006‐0790) for State Operating Permits for IL, NY, RI, VT,

NH, OH, ME, Area Sources of HAP with Boilers.

2. 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.  

3. See Docket (EPA‐HQ‐OAR‐2006‐0790) for mercury utility industry data.

4. Thirteen‐state Boiler Inspector Inventory. Data compiled 2005‐2006. For a description of the

dataset see April 2010 Memorandum: Development of Model Units for Area Source Boilers.

ERG.

5. Cochran, William G. 1977. Sampling Techniques. Third Edition, John Wiley & Sons. Pages 72‐
17 

87.

6. Singleton, A. ERG. MACT Floor Analysis for the Industrial, Commercial, and Institutional

Boilers and Process Heaters National Emission Standards for Hazardous Air Pollutants – Major

Source. April 2010.

7. Hahn GJ & Meeker WQ. 1991. Statistical Intervals: A Guide for Practitioners. John Wiley &

Sons, Inc.: New York.

8. NIST/SEMATECH. 2009. NIST/SEMATECH e‐Handbook of Statistical Methods,

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

9. 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

10. Pennsylvania Department of Environmental Protection. Permitted boilers in the State of

Pennsylvania as of 4/13/2006.  

11. Wisconsin Department of Natural Resources. Permitted Boilers in the State of Wisconsin as

of 7/15/2004.

12. U.S. Environmental Protection Agency.  Compilation of Air Pollution Emission Factors (AP‐

42), Fifth Edition, Volume 1: Stationary and Point Sources, Chapter 1: External Combustion

Sources. January, 1996.
18 

APPENDIX A

Ranked Emission Data for MACT Floor Analysis

(See Excel spreadsheet)
19 

APPENDIX B

Upper Prediction Limit (UPL) Calculations for Existing Sources

(See Excel spreadsheet)
20 

APPENDIX C

UPL Calculations for New Sources

(See Excel spreadsheet)
21 

APPENDIX D

Control Device Data used to Establish GACT for Particulate Matter at Existing Sources

(See Excel spreadsheet)
22 

APPENDIX E

Emission Data used to Establish GACT for Particulate Matter at New Sources

(See Excel spreadsheet)