Date: September 29, 2011

Subject: Technical Approach for Wool Fiberglass MACT Floor Calculations

EPA Contract No. EP-D-06-118; EPA Work Assignment No. 4-10

RTI Project No. 0210426.004.010

From: Cindy Hancy

Dave Reeves

To: Susan Fairchild, EPA/OAQPS/SPPD/MMG

Introduction

Section 112 of the Clean Air Act (CAA) requires that the U.S. Environmental Protection Agency (EPA) 

to establish National Emission Standards for Hazardous Air Pollutants (NESHAP) for the control of the 

hazardous air pollutants (HAP) emitted from both new and existing major sources in a source category. 

These standards must reflect the maximum degree of reduction in the HAP emissions that is achievable. 

The minimum level of control is referred to as the “Maximum Achievable Control Technology (MACT)

floor.” The method for determining the MACT floor for a NESHAP is defined for both new and 

existing sources in CAA section 112(d)(3). For new sources, the MACT floor cannot be less stringent 

than the emission control that is achieved in practice by the best-controlled similar source. For existing 

sources, the MACT floor cannot be less stringent than the average emission limitation achieved by the 

best-performing 12% of existing sources for source categories with 30 or more sources, or the bestperforming five sources for source categories with fewer than 30 sources.

The purpose of this memorandum is to present the data, the methodology, and the results of the MACT 

floor analysis for the Wool Fiberglass Production NESHAP source category. This analysis is part of 

EPA's obligation under CAA section 112(f)(2) and 112(d)(6) to conduct a residual risk and technology

review. The MACT floor analysis is also being performed in response to a petition for rulemaking by

the Natural Resources Defense Council and Sierra Club that states EPA failed to set emission limits for 

HAPs (phenol and methanol) emitted by wool fiberglass facilities and challenges some HAP surrogacies 

used in the current wool fiberglass NESHAP. This MACT floor analysis uses data collected from a

nationwide voluntary Information Collection Request (ICR) of wool fiberglass manufacturers conducted 

by EPA in 2010. Data on process operations, emission controls, and air emissions data reported by 

respondents to the ICR were compiled into a Microsoft Access data base that serves as the data set used 

for this MACT floor analysis (referred to in this memorandum as the “ICR data set”).

Background Information

The current Wool Fiberglass Production NESHAP was promulgated on June 14, 1999 and applies to 

each of the following existing and newly constructed sources located at a wool fiberglass manufacturing 

facility: All glass-melting furnaces, rotary spin (RS) manufacturing lines that produce bonded building 

insulation, and flame attenuation (FA) manufacturing lines producing bonded pipe insulation. The rule 

also applies to new FA manufacturing lines producing bonded heavy-density products. RS and FA 
manufacturing lines that produce nonbonded products are not currently subject to the standards. The 

1999 Wool Fiberglass Production NESHAP sets particulate matter (PM) emission limits for new and 

existing furnaces and sets formaldehyde limits for new and existing RS and FA manufacturing lines. 

The current NESHAP also uses the surrogate approach, where PM serves as a surrogate for HAP metals 

and formaldehyde serves as a surrogate for organic HAPs.

The wool fiberglass production source category currently consists of 29 facilities in the US; combined, 

these 29 facilities operate approximately 79 furnaces and listed over 100 process lines. This furnace 

count does not include pot & marble furnaces and not all RS and FA manufacturing lines produce a 

bonded product. By definition in the current rule Bonded refers to wool fiberglass to which a phenolformaldehyde binder has been applied. The use of these types of binders results in formaldehyde, 

phenol, and methanol emissions from sources at bonded RS and FA lines which generally consist of 

forming, curing, and collection operations. The stack configurations of RS and FA bonded lines vary at 

facilities, as it is common industry practice to vent more than one process unit to common 

ductwork/controls. The proposed amendments to the rule, as a result of this review, will include the 

addition of methanol and phenol limits for RS and FA bonded lines, as well as, a revision to 

formaldehyde limits in order to reflect the emission levels achieved by the current best performing 

sources.

The specific chemicals, compounds, or groups of compounds designated as HAP are listed in CAA 

section 112(b). From this list, the following additional HAPs were identified as being emitted from 

furnaces and will be regulated for new and existing sources in the proposed rule: hydrogen fluoride (HF)

and hydrogen chloride (HCl). The current PM limit is also being revised and a new chromium limit is 

being proposed as a result of the technology review on the wool fiberglass industry. Because PM and 

Chromium limits will be proposed as a technology review finding, the details of these limits are 

discussed in the Section 112(d)(6) Technology Review for Wool Fiberglass NESHAP memorandum.

MACT Floor Analysis - Subcategories

Under CAA section 112(d)(1), EPA has the discretion to “...distinguish among classes, types, and sizes 

of sources within a category or subcategory in establishing...” standards. When separate subcategories 

are established, a MACT floor is determined separately for each subcategory. To determine whether the 

wool fiberglass production facilities warrant subcategorization for the MACT floor analysis, EPA 

reviewed unit and process designs, operating information, and air emissions data compiled in the ICR 

data set and other information collected by the Agency for development of the NESHAP for this source 

category. Based on this review, EPA concluded that there are no significant design and operational 

differences at wool fiberglass facilities that would warrant proposing subcategorization. However, EPA 

did find large differences in chrome contents of refractory bricks used in some glass melting furnaces. 

MACT Floor Analysis Methodology

Existing Sources 

A MACT floor analysis was completed for each proposed regulated pollutant as summarized in Table 1. 
Table 1 – Summary of Proposed Limits

Existing Sources New Sources

Furnaces

HF

EPA is proposing new limit (No current 

limit exists)

EPA is proposing new limit (No current 

limit exists)

HCl

EPA is proposing new limit (No current 

limit exists)

EPA is proposing new limit (No current 

limit exists)

RS Bonded lines

Formaldehyde Revising existing limit Revising existing limit

Phenol

EPA is proposing new limit (No current 

limit exists)

EPA is proposing new limit (No current 

limit exists)

Methanol

EPA is proposing new limit (No current 

limit exists)

EPA is proposing new limit (No current 

limit exists)

FA Bonded Lines

Formaldehyde Revising existing limit Revising existing limit

Phenol

EPA is proposing new limit (No current 

limit exists)

EPA is proposing new limit (No current 

limit exists)

Methanol

EPA is proposing new limit (No current 

limit exists)

EPA is proposing new limit (No current 

limit exists)

The first step in the MACT floor analysis for each regulated source and HAP was to rank each unit (for 

which emissions data was provided) by emission level (lowest to highest) for each pollutant. From this 

ranking, a MACT floor pool of sources was identified for determining the minimum control level 

allowed for the MACT floor, consistent with the criteria defined for new and existing sources by CAA 

section 112(d)(3). For the new source MACT floors, the best-controlled source was identified for which

there were individual source test run data in the ICR data set. For the existing source MACT floors, 

selection of the MACT floor pool size (i.e., number of emission units to be included in the determination 

of the average emission limitation value) was determined on an individual unit category basis as 

described below. 

Furnaces. This category includes more than 30 sources and therefore the top 12% of furnaces 

where used to calculate the UPL. Since there are 80 furnaces industry wide, the top 10 best 

performing furnaces for which we had data for were used in calculating the MACT floor for HF 

and HCl. At some facilities, multiple furnaces are routed to one stack and emission testing was 

performed at the combined stack. In these cases, the lb/ton emission rate from the test report was 

applied to all the sources routed to the stack. 

Bonded RS Manufacturing Lines. Industry wide, this category includes more than 30 sources

and therefore the top 12% of furnaces where used to calculate the UPL. There are approximately

__ bonded RS manufacturing lines, therefore, the top 7 best performing furnaces for which 

emission test data was provided were used in calculating the MACT floor for formaldehyde, 

phenol, and methanol. 

Bonded FA Manufacturing Lines. This category includes less than 30 sources and emission test 

data was submitted for three FA lines. Therefore, all of these 3 sources were used to calculate 

formaldehyde, phenol, and methanol limits for existing sources. For new sources, the best 

performing source was used to calculate the MACT floor for each pollutant. 
The next step in the MACT floor analysis was to account for data variability in the calculations of the

applicable MACT floor limits for the subcategories using the data’s 99% upper prediction limit (UPL). 

Specifically, the MACT floor limit was determined as the UPL calculated with the Student’s t-test using 

the “TINV” function in Microsoft Excel software. The UPL approach has also been used in other EPA 

rulemakings (e.g., NESHAP for Portland Cement, NSPS for Hospital/Medical/Infectious Waste 

Incinerators, NESHAP for Industrial, Commercial, Institutional Boilers and Process Heaters, and 

NESHAP for Electric Generating Units) to account for variability in emissions data for a specified level 

of confidence. The level of confidence represents the level of protection afforded to facilities whose 

emissions are in line with the best performers. For example, a 99% level of confidence means that a 

facility whose emissions are consistent with the best performers has one chance in 100 of exceeding the 

floor limit. A prediction interval for a single future observation (or an average of several test 

observations) is an interval that will, with a specified degree of confidence, contain the next (or the 

average of some other pre-specified number) of randomly selected observation(s) from a population. In

other words, the upper prediction limit estimates what the upper bound of future values will be, based 

upon present or past background samples taken. The UPL consequently represents the value at which 

we can expect the mean of future observations for the HAP emissions to fall within a specified level of 

confidence, based upon the results of an independent sample from the same population. This method 

accounts for the point-to-point variability in the data. 

The form of the UPL equation differs somewhat depending upon the number of data points and data 

distribution to which it is applied. Attachment A includes a flow diagram that summarizes the UPL

approaches used. To this end, the data sets were evaluated for each HAP to ascertain whether the data 

were normally distributed, or fit another type of distribution (e.g., log normal distribution). According 

to the Central Limit Theorem (Durrett, 1996), when a data set includes 15 or more sources, the UPL is

based on the assumption that the data fit a normal distribution. The Central Limit Theorem states that 

regardless of the shape of the original distribution, if the distribution has a finite mean (μ) and variance 

(σ2), the sampling distribution of the mean approaches a normal distribution with a mean of (μ) and a 

variance of σ2/N as N, the sample size, increases (Durrett, 1996).

The wool fiberglass data sets used to calculate MACT floors varied for each pollutant. When the sample 

size is smaller than 15 and the distribution of the data is unknown, the Central Limit Theorem cannot be 

used to support the normality assumption. Statistical test of the kurtosis, skewness, and goodness of fit 

test are then used to evaluate the normality assumption. The skewness statistic (S) characterizes the 

degree of asymmetry of a given data distribution. Normally distributed data have an S value of 0. An S

value that is greater (less) than 0 indicates that the data are asymmetrically distributed with a right (left) 

tail extending towards positive (negative) values. The standard error of the skewness statistic (SES) 

was also used in determining the normality of the data distribution. 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 K value of 0. A K value that is greater (less) than 0 

indicates a relatively peaked (flat) distribution. The standard error of the kurtosis statistic (SEK) was 

also used in determining the normality of the data distribution.

For each data set to which the UPL was applied (i.e., the separate data sets for each HAP applicable to a 

source), the S and K values were calculated using the reported test values. If both kurtosis and skewness 

tests indicate the data is normally distributed, the UPL was calculated using the UPL pooled variance 

Equation 1. 

 Equation 1
where:

= the average (mean) of the best performing existing sources;

t(p,df) = the t statistic for a confidence level p, and df degrees of freedom;

s

2

= the pooled variance;

n = the total number of test runs (all sources) used in the analysis; and

m = the number of (future) compliance test runs [for run-by-run data, m=3].

Degrees of freedom calculated by:

1

1

 











 

N

i

df ni

 Equation 1a

Mean calculated by:

 



N

i

n

j

ij

i

x

n

X

1 1

1

 Equation 1b

Pooled variance calculated by:

 









n

i

xi X

n

s

1

2

2

1

1

 Equation 1c

If the kurtosis or skewness tests indicate the data was not normally distributed, the data were logtransformed. Once the logs of all the test runs were calculated, new kurtosis and skewness tests were

performed on the log-transformed data. If both kurtosis and skewness tests indicated the log-transformed 

data were normally distributed, the UPL can be calculated using the Equation 2. 

  

















    

 



2( 1)

ˆ ˆ

1

2 4

.99 2 ˆ ˆ ˆ 2 2 ˆ ˆ 2

ˆ

ˆ 2 2 2

2

n n

me e m e

m

Z

UPL e

      





 Equation 2

where:

μ = the average of the best performing existing sources;

Z.99 = the z statistic for a lognormal distribution at 99 percent;

2 ˆ = the variance;

n = the total number of test runs (all sources) used in the analysis; and

m = the number of (future) compliance test runs [for run-by-run data m=3].

 

Mean is calculated by:

n

x

n

i

 i

 1 

 Equation 2a

Variance is calculated by:
 

1

ˆ

1

2

2









n

x x

n

i

i



 Equation 2b

If the raw data and the log-transformed data were not normally distributed and n ≥13, the UPL was

calculated using the UPL pooled variance with skewness adjustment in Equation 3. Note this 

adjustment cannot be used if the number of individual runs is less than 13. 













 















 



n m

t s

n

Skew s

X df p

1 1

6

2

,

2

 Equation 3

where:

= the average of the best performing existing sources;

t(p,df) = the t statistic for a confidence level p, and df degrees of freedom;

s

2

= the pooled variance;

n = the total number of test runs (all sources) used in the analysis;

m = the number of (future) compliance test runs [for run-by-run data m=3]; and

Skew = the skewness of the test runs used in the analysis.

If the raw data and the log-transformed data were not normally distributed and n was less than 13 the 

UPL was calculated using the UPL pooled variance in Equation 1, but instead of using the Excel TINV 

formula to find the t statistic t(p,df), a trial and error method was used to find the t static that gives a 99 

percent confidence level by correcting the probability values (p) using Equation 4

Corrected Probability = P0(t) + Skewness x Pλ3(t) – Kurtosis x Pλ4(t) + λ3

2

xP λ3/2(t) Equation 4

Adjustment for Below Detection Level Emissions Data

Prior to calculating the UPLs, test runs that were below detection limit (BDL) were identified. If a 

dataset contained any BDL values, the UPL was calculated using BDL values as reported in the test 

report. After the UPL was completed for a particular source and pollutant, a comparison of the UPL and 

the representative method detection level (RDL) was performed. The first step in the comparison was

to, for a particular emissions unit category, find the mean of the emissions data set, which was calculated 

during the UPL calculations. The second step was to identify the largest BDL value of the dataset that 

was no greater than the mean of the entire dataset. This value was the RDL for the pollutant data set of 

the emission unit category. The RDL was then multiplied by 3 and the product is compared to the UPL. 

If the 3x RDL value was less than or equal to the calculated UPL value, the UPL value was used as the 

proposed MACT Floor Limit. If the 3x RDL value was greater than the calculated UPL value, the 3x 

RDL value was used as the proposed MACT Floor Limit. If no BDL values were included in the 

pollutant data set for a particular unit type, these steps were not performed. 

MACT Floor Analysis Results
The MACT floors for new and existing sources are summarized in Table 2. When calculated MACT 

floors for new sources are less stringent than the MACT floor calculated for existing sources, the 

existing MACT floor was used for both new and existing sources. 

Mean of 

Sources

Used to 

Calculate 

UPL

(lb/ton)

99% UPL

Calculated 

for 

Existing 

Sources

(lb/ton)

99% UPL

Calculated 

for New 

Sources

(lb/ton)

Furnaces

Hydrogen Fluoride 0.00079 0.0020 0.00078

Hydrogen Chloride 0.00071 0.0015 0.00078

RS Lines

Formaldehyde 0.090 0.17 0.020

Phenol 0.057 0.19 0.0011

Methanol 0.16 0.48 0.00067

FA Lines

Formaldehyde 3.53 5.55 3.32

Phenol 0.51 1.36 0.46

Methanol 0.30 0.50 0.50
8

Attachment A
9

Determine number of 

sources in MACT floor

Are 

there at least 15 

sources in MACT 

floor?

Assume normal 

distribution

Calculate MACT floor 

using UPL Pooled 

Variance Equation 1

Test for normal 

distribution using 

kurtosis and skewness

Does sample have 

normal distribution?

Calculate MACT floor 

using UPL pooled 

Variance with skewness 

adjustment Equation 3

Use UPL as MACT floor

Are any data 

BDL or DLL?

Log transform data

Test data for lognormal 

distribution using 

kurtosis and skewness

Does sample 

have lognormal 

distribution?

Calculate MACT floor 

using UPL Lognormal 

Equation 2

Determine RDL

Compare UPL to 3 x 

RDL

Use greater of UPL and 

3 x RDL as MACT floor

Yes

No

Yes

No

No

Yes

Yes

MACT Floor Flow Chart

Does the dataset 

contain 13 or more data 

points (n≥13)?

Calculate UPL by 

adjusting T statistic 

values using probability 

trial and error. 

No

No

Yes