1

Date: September 2 9

, 201 4

Subject: Revised Technical Approach for Wool Fiberglass MACT Floor Calculations

40 CFR 63, Subpart NNN

– Supplemental Proposal

EPA Contract No. EP

- D

- 11

- 084 ; EPA Work Assignment No. 2

- 0 9 RTI Project No. 021 3199 .00 2 .00 9

From: Cindy Hancy / Dave Reeves

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

I. Introduction

Section 112 of the Clean Air Act (CAA) requires that the U.S. Environmental Protection Agency (EPA) establish National Emission Standards for Hazardous Air Pollutants ( NESHAP ) for the control of hazardous air pollutants (HAP) emitted from both new and existing major sources in a source category

. The se standards must reflect the “Maximum Achievable Control T echnology (MACT) floor” for HAP emitted by these sou rces 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 simil ar source.
F or existing sources , the MACT floor cannot be less stringent than the average emission limitation achieved by the best performing 12 percent of existing sources for source categories with 30 or more

s ources, or the best perfor ming five sources for source categories with fewer than 30 sources.

The purpose of this memoran dum is to present the data, methodology, and results of the MACT floor analysis for the Wool Fiberglass Manufacturing NESHAP , 40 CFR 63, Subpart NNN source category. This MACT floor analysis uses data collected from a nationwide voluntary survey of wool fiberglass manufactur er s conducted by EPA in 2010 and a Section 114 Information Collection Request (ICR) sent out to the wool fiberglass industry in N ovember 2011

. Data on process o perati ons, emission controls, and air emissions data reported by respondents to the survey and 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”).

On November 25, 2011 EPA proposed revisions to the Wool Fiberglass Manufacturing NESHAP, 40 CFR part 63, subpart NNN for the RTR for this source category

. Based on comments

received on the November 2011 proposal a nd data received from the 2012 ICR responses
, EPA published a supplemental proposal on April 15, 2013 with additional revisions to Subpart NNN. This memorandum incorporates changes made since the April 2013 supplemental proposal and includes changes made as a result of public comments regarding facility information and emissions data.

II. Background Information

The current Wool Fiberglass Production NESHAP (subpart NNN) was promulgated on June 14, 1999 and applies to each of the followin g existing and newly constructed major sources located at a wool fiberglass manufacturing facility: glass

- melting furnaces, rotary spin (RS) manufacturing lines that2

produce bonded building insulation, and flame attenuation (FA) manufacturing lines that pr oduce bonded heavy

- density or pipe insulation. Subpart NNN defines “bonded” as wool fiberglass to which a phenol

- formaldehyde binder has been applied. The use of these types of HAP binders results in formaldehyde, phenol, and methanol emissions from source s at bonded RS and FA lines which generally include fiber forming, curing, and collection operations. Both RS and FA manufacturing lines that produce nonbonded products are not currently subject to the standards. The 1999 Wool Fiberglass Manufacturing NESH AP (subpart NNN) established particulate matter (PM) emission limits for new and existing furnaces and also established formaldehyde emission limits for new and existing RS lines and for new FA manufacturing lines. The 1999 NESHAP also used a surrogate ap proach, where the PM emission limit serves as a surrogate for HAP metals and the formaldehyde emission limit serves as a surrogate for organic HAP. The wool fiberglass manufacturing source category includes 30 facilities operating in the United States; 10 of these facilities are major sources and the remaining 20 facilities are listed as area sources or synthetic minors. The 10 major sources operate approximately 29 glass melting furnaces and less than 30 bonded manufacturing lines. The North American Insu lation Manufacturers Association (NAIMA) announced that by the end of 2012, they predicted no major sources subject to Subpart NNN will exist in this source category (see docket items 0040 and 0052) by phasing out the use of phenol

- formaldehyde

binders. 1 O nce a facility has phased out its use of these HAP binders on bonded lines, it may apply to the appropriate permitting authority to no longer be subject to the MACT Subpart NNN standard under two definitions of the MACT rule: the applicability definition a nd the definition of ‘bonded’. The specific chemicals, compounds, or groups of compounds designated as HAP are listed in CAA section 112(b). From this list, chromium compounds, hydrogen fluoride (HF) and hydrogen chloride (HCl) were identified as being em itted from furnaces; and formaldehyde, phenol, and methanol were identified as being emitted from bonded FA and RS lines. The EPA reviewed unit and process designs, operating information, air emissions data and analyzed several subcategorization

recommend ations submitted by industry in their public comments on the proposed rule (76 FR 72770, November 25, 2011). Based on this review, the EPA concluded in the 2011 proposal that sufficient information exists to continue subcategorization of bonded lines into both FA and RS lines, but that beyond this level, there are no significant design or operational differences at wool fiberglass facilities that warrant further subcategorization. 2 As a result, the FA line emission limits for phenol, formaldehyde, and metha nol would apply to all products manufactured on FA lines.

III. Overview of the Proposed MACT Floor Emission Limits in the 2011 Proposal and 2013 Supplemental Proposal

This section provides an overview of the MACT floor limits that were proposed in the 2011 and 2013 proposal packages

.

A. Hydrogen Fluoride (HF) and Hydrogen Chloride (HCl)

In the November 2011 proposal

, EPA proposed MACT floor limits for HF and HCl from glass

melting furnaces

. The EPA reviewed its analysis of acid gas test data based on comments it received on the proposed HF and HCl emission limits and found that up to 80 percent of the HF and HCl test data in

the floor were reported as below the detection limit ( BDL ) of the required test method . Based on this

information

, the EPA propos ed to se t work practice standards for HF and HCl instead of MACT floor

1 Letters from A. Crane, NAIMA to C. Hancy, RTI (June 8. 2011) and S. Fairchild, U.S. EPA, (August 2, 2011).

2 FA lines are no longer subcategorized into pipe and heavy density products, as described in the November 2011 and April 2013 proposals. RS lines would still only apply to wool fiberglass manufactured for residential insulation, as indicated in the

1999 MACT standard.3

emission limits under section 112(h) of the CAA (see April 15, 2013 supplemental proposal )

.

B. Formaldehyde, Phenol and Methanol

Formaldehyde, p henol and methanol are emitted HAP from bonded lines at wool fiberglass manufacturing facilities. In the November 2011 proposal, t he EPA proposed to remove the surrogacy relationship and proposed establishing limits for phenol and methanol instead

. The EPA prop o sed separate formaldehyde, phenol, and me thanol emission limits for combined curing and collection operations at RS and FA lines based on data submitted by the industry in their voluntary information

collection effort of 2010

. After the 2011 proposal

, the EPA received information from industry

cl arifying that the proposed limits for formaldehyde, phenol and methanol were based on a data set that

included test data for lines that had phased out the use of phenol/formaldehyde binders a nd were therefore no longer subject to the rule. The Agency remov ed the erroneous data from the dataset and recalculated the MACT floor for RS lines based on the bonded lines at major sources and synthetic

minor s ources in the April 2013 supplemental proposal

.

IV. Proposed MACT Floor Changes in the 2014 Supplemental Propos al

Since the April 2013 supplemental proposal, the EPA has updated the MACT floor approach for existing and new sources that are based on limited dataset s ( e.g., containing fewer than 7 but more than

2 data points ) in order to address questions raised in the D .C. Circuit Court of Appeals decision in National Association of Clean Water Agencies v. EPA (NACWA)

. This updated approach affected all of the new source limits for formaldehyde, methanol, and phenol which w ere based on the best performing

source and wh ere the MACT floor consisted of limited datasets. The details of the updated MACT floor approach for limited data sets can be found in the memorandum “Approach for Applying the Upper

Prediction Limit to Limited Datasets” (Fairchild, 2014). Table 1 provides an overview of the limits that have been proposed for wool fiberglass since the November 2011 proposal and Sections I V

- VI of this memorandum present the methodology of the MACT floor analysis for the Wool Fibe rglass Manufacturing NESHAP

.

Table 1

– Overview of New and Existing Source UPL Limits for Rotary Spin Lines

Pollutant Existing Source MACT Floor Limits (lb/ton of glass pulled) New Source MACT Floor Limits (lb/ton of glass pulled)

Current

Rule 2011

Proposal 2013

Supp

. Proposal 2014

Supp

. Proposal Current

Rule 2011

Proposal 2013 Supp

.

Proposal 2014 Supp

.

Proposal
Formaldehyde 1.2 0.17 0.19 0.19 0.8 0.020 0.087 0.066

Phenol NA 0.19 0.26 0.26 NA 0.0011 0.063 0.060

Methanol NA 0.48 0.83 0.83 NA 0.00067 0.61 0.29

V. MACT Floor Approach

A. Upper Prediction Limit (UPL)

The MACT floor analysis for wool fiberglass manufacturing uses a UPL approach to identify the average emission limitation achieved by th os e best performing sources because this approach4

incorporates the average performance of the sources and the variability of the performance during testing conditions. The UPL represents the value which one can expect the mean of a specified number of future observations ( e.g. , 3

- run average) to fall below for the specified level of confidence, based upon the results of an independent sample from the same population. The level of confidence represents the level of protection afforded to facilities whose emissions are in line with the best perform ers. For example, a 99 percent level of confidence means that a facility whose emissions are in line 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 severa l 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 UPL estimates what the upp er bound of future values will be, based upon present or past background samples taken. The UPL approach encompasses all the data point

- to

- data point variability. The predictions derive from the data set to which it is applied, and, thus, can be applied t o any type of data.

The UPL is a value derived from widely accepted and commonly used statistical principles, and represents the upper end of a prediction interval. 3 The UPL is used in a wide variety of industries and professions, such as manufacturing, finance, healthcare, etc

. and is calculated using one of several equations that must be selected based on certain characteristics of each dataset. 4 The most important parameters in the UPL calculation are the mean (average) performance of the unit or units, the distribution of the dataset, the variance in the measured emission values (that is, the individual data points), and the sample size. The equations that are use d to calculate the UPL are explained in more

detail below

.

The re are different equations for calculating a UPL based on the distribution of the data set

. Data can be normally distributed, lognormally distributed, or neither and thus have a skewed/unkno wn distribution. The EPA implemented the statistical tests of skewness and kurtosis to determine the

distribution of each source’s data.

The skewness statistic (S) characterizes the degree of asymmetry of a given data distribution. Normally distributed da ta have an S value of zero. An S value that is greater than zero indicates that the data are asymmetrically distributed with a right tail extending towards positive values; similarly, an S value that is less than zero indicates that the data are asymmetric ally distributed with a left tail extending towards negative values. The value of S can be approximated using the following equation (which is implemented in Microsoft Excel using the “ SKEW ” function ) :

where:

n = sample size

3 National Institute of Standards and Technology, NIST/SEMATECH e

- Handbook of Statistical Methods

,
< http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/predlimi.htm >.

4 See, e.g., Gibbons, Robert D. and Coleman, David E. (2001). Statistical Methods for Detection and Quantification of

Environmental Contamination , New Yor k: Wiley, pp 21

- 31, and Luko, S.N. and D.V. Neubauer, Statistical Intervals Part 2.
Standardization News , October/November 2011. <http:www.astm.org/standardization

- news/data

- points/statistical

- intervals

part

- 2

- so11.html>.5 = mean of MACT floor pool

x i = individual source mean

stdev =

As part of determining whether a data distribution can be considered normal, the S value must be compared to t he standard error of the skewness statistic (SES) . The SES value can be approximated using the following equation :

Using the standard skewness hypothesis test, if the absolute value of S is less than two times the

absolute value of SES, the skewness of the data can be considered 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 K value of three. A K value that is greater than thre e indicates a relatively peaked distribution. A K value that is less than three indicates a relatively flat distribution. The estimate of K can be calculated using one of two ways depending on the number o f data points in the data set . For data sets with more than three run values,

kurtosis can be estimated using the following equation (which is implemented in Microsoft Excel ® using the “ KURT ” function ) :

This kurtosis equation cannot be used for data sets with three run values (n=3) because there will

be a zero in the denominator of the first and second term s

. The kurtosis estimated with the “KURT” function is called excess kurtosis. It is equal to the kurtosis

– 3, so for a normal distribution, excess

kurtosis = 0. F or data sets with three run values

, the excess kurtosis is estimated using the following equation:

As part of determining whether the distribution of the data can be considered normal, the K value must be compared to t he standard error of the kurtosis statistic (SEK) . The SEK value can be approximated using the following equation :

for datasets with n>3, or6 for datasets with n=3

Accord ing to the standard kurtosis hypothesis test, if the absolute value of K is less than two times the

absolute value of SEK, the kurtosis of the data can be considered normal

. For this MACT floor analysis, if the results from both the skewness and kurtosis hypothesis tests

indicated the data were normally distributed, the data were treated as normally distributed in subsequent

UPL calculations. If either of these tests indicated the data were not normally distributed, the data were log

- transformed, and the log

- transformed data were evaluated for k urtosis and skewness. If both tests of the log

- transformed data indicated the log

- transformed data were normally distributed, the data were treated as lognormally distributed in subsequent U PL calculations. If either of these tests indicated the log

- transformed data were not normally distributed, the data were treated as skewed/unknown distribution in subsequent UPL calculations.

For data sets with only three run values, the result of both th e skewness and kurtosis hypothesis tests always indicate the data set is normally distributed. Therefore, to determine the appropriate data distribution for those data sets, the skewness/SES values from raw data were compared to the

skewness/SES values fro m log

- transformed data. The smaller skewness/SES value was selected as an indication of a larger likelihood of the data to come from the corresponding distribution.

After completing the determination of the type of data distribution for each data set, the UPL was calculated using the corresponding equation for the distribution for th ose d ata

. The UPL equations for normal, lognormal, and skewed/unknown distribution data sets are provided below.

Normally Distributed Data Set

where:

UP L = upper prediction limit

= mean of the data calculated as

n = number of test runs =

m = number of future test runs in the compliance average

N = number of sources

s 2

= pooled variance calculated as

t df, 0 .99 = quantile of t

- distribution at the 99 percent level of confidence with degrees of freedom (df) ; this value can be calculated with the Student’s t

- test statistical method, using the “TINV” func ti on in Microsoft Excel® software7

df = degrees of freedom calculated as

Lognormally Distributed Data Set (Bhaumik and Gibbons, 2004)

where:

= = the average of the log transformed data from the data

= = variance estimate of the log transformed data

z 0. 99 = the 99 th

- percentile of the log

- normal distribution estimated using the trapezoidal rule approach from the following equation:

Skewed/Unknown Distribution (Non

- Normal/Non

- Lognormal) (Gayen, 1949)

where:

UPL = upper prediction limit

= mean of the data calculated as

n = number of test runs =

m = number of future test runs in the compliance average

N = number of sources8

s 2

= pooled variance calculated as

t df,p = recalculated quantile of t

- distribution bas ed on specified level of confidence ( i.e.

, 99%)

df = degrees of freedom calculated as

B. Representative Detection Limit (RDL)

The imprecision of t est method measurement s can be a significant component of data variability. At very low emissions levels the inherent imprecision in the pollutant measurement method has a large influence on the reliability of the data underlying a MACT floor emission limit. Of particular concern

are those data that are reported near or below a test method’s pollutant detection capability. Because of
matrix effects, la boratory techniques, sample volume , and other factors, method detection limits normally vary from test to test.

Variability of data due to measuremen t imprecision is inherently and reasonably addressed in calculating a MACT floor emission limit when most of the data are significantly above the method

detection limit. For data sets with a large number of test results below the method detection limit (re ported as method detection limit values), other techniques need to be used to account for data variability. Indeed, under such a shift, the data distribution becomes truncated on the lower end, leading to an artificial overabundance of values occurring at the method detection limit. There is a concern that a

MACT floor emission limit based on a truncated data base ( i.e.

, calculated using values at or near the

method detection limit ) may not account adequately for data measurement variability, because the

me asurement error associated with those values provides a large degree of uncertainty.

The expected measurement imprecision for an emissions value occurring at or near the detection limit is about 40 to 50 percent. Relative p ollutant measurement imprecision decreases to a consistent 10 to 15 percent for values measured at a level about three times the method detection limit (ASME, 2001).

The approach taken to account for measurement variability includes def ining a detection limit

that is representative of the data used in establishing a MACT floor emission limitation and also

minimizes the influence of an outlier test

- specific method detection limit value. The EPA has developed a list of RDL from available pollutant

- specific method detection levels (Westlin a nd Merrill , 2011). These RDL values are then multiplied by three to decrease measurement imprecision to around 10 to 15 percent (as noted in the previous paragraph), resulting in values referred to as “3xRDL” values.

The appropriate 3xRDL value is compared to the calculated UPL value for each pollutant and subcategory. The larger of the 3xRDL value and the calculated UPL value is used for the MACT floor emission limit to ensure that measurement variability is adequate ly addressed. This check was part of the variability analysis for all existing MACT floors that had below detection level (BDL) or detection level limited (DLL) run data present in the best performing data sets. For more information regarding the general use of the UPL and why it is appropriate for calculating MACT floors, see the memorandum

“ Use of the Upper Prediction Limit for Calculating MACT Floors” ( Fairchild , 2014).9

VI. MACT Floor Analysis for Wool Fiberglass

A. MACT Floor Calculation Steps

Step 1.

The first step in the MACT floor analysis was to calculate a pollutant

- specific average emission

value for formaldehyde, phenol, and methanol emissions from RS and FA lines . The limits for the wool fiberglass NESHAP are in pounds of pollutant per tons of glass pulled ; the majority of the data (e.g., production and equipment throughput rates) used to calculate the MACT floor limits w ere submitted as confidential business information (CBI) and therefore, the data and calculations are not publicly

available (and are not included in this memorandum).

Step 2.

Once an average emission value was calculated for each source, subcategory and pollutant, all of the individual average emission values were ranked from lowest to highest value.

Step 3.

Based on the rank ing in Step 2

, the best performing sources were identified consistent with the

criteria in CAA section 112(d)(3) for determining MACT floors for new and existing sources. These sources are referred to as the “MACT floor pool.” For the new source MACT flo ors, the best controlled

source was identified. For the existing source MACT floors, the number of sources in the MACT floor pool was determined on an individual subcategory basis, as described in Table s 2 and 3

.

Table 2 . Number of Sources in Existing Source MACT Floor Pool

Subcategory Pollutant No. Sources

Nationwi de No.

Sources
in MACT

Floor

Pool No. of Data

Points in
MACT Floor

Pool Based on

Rotary Spin Formaldehyde

<30 located at Major Sources 5 25 Best

- performing
five sources for

source categories
with fewer than 30

sources Phenol 5 22

Methanol 5 19

Flame

Attenuation Formaldehyde

<30 located at Major Sources 3 9 Best

- performing
five sources for

source categories
with fewer than 30

sources Phenol 3 9

Methanol 3 910 Table 3 . Number of Sources in New Source MACT Floor Pool

Subcategory Pollutant No. Sources

Nationwide No.

Sources
in MACT

Floor

Pool No. of Data

Points in
MACT Floor

Pool Based on

Rotary Spin Formaldehyde
<30 located at Major Sources 1 3 Best

- performing

source Phenol 1 3

Methanol 1 3

Flame

Attenuation Formaldehyde <30 located at Major Sources 1 3 Best

- performing

source Phenol 1 3

Methanol 1 3

Step 4.

Each MACT floor data set (for each subcategory and pollutant) was evaluated for kurtosis and skewness to determine the distribution, as described in Section IV of this memorandum

.

Step 5.

After determinin g the type of data distribution for the data set, the UPL was calculated using the corresponding distribution equation for that data set, as previously described. See Section VII for UPL summary tables for new and existing sources.

Step 6.

For source data sets that contained BDL data, there was an additional step taken after the subcategory UPL was calculated. A s described in Section IV , t he results of the UPL calculation were compared to the 3xRDL value of the relevant performance test method(s) to ensure that data measurement variability is addressed.

The 3xRDL values in the guidance document (Westlin and Merrill , 2011) were given in units of concentration (micrograms per dry standard cubic meter [μg/dscm]). The emission limits for the proposed wool fiberglass NESHAP are in units of pound per ton (lb/ton) of glass. In order to compare

the calculated UPL to the 3xRDL, the 3xRDL value was converted to the units of the emission limits using a ratio of the emission data in the units of interest ( e.g. , lb/ton glass) to the emission data in μg/dscm (uncorrected)

.

The converted 3xRDL value was then compare d to the calculated UPL in the same units of measure. If the calculated UPL value was less than the 3xRDL value, the 3xRDL value was used as the basis for the standard because the 3xRDL value provides the lowest emission rate that can be measured using th e performance test method while maintaining a relative method precision on the order of 10 to 20 percent of the measured value. If the UPL value was equal to or greater than the 3xRDL value, then

the UPL value was used as the basis for the standard.11

Step 7.

For limited data sets with less than seven data points, the steps outlined in the memorandum “Approach for Applying the Upper Prediction Limit to Limited Datasets” ( Fairchild , 20 14 ) were

followed.

Table s 4 and 5 present the existing source and new sour ce MACT floor values determined for the wool fiberglass source category.

Table 4 . Proposed Existing Source MACT Floors

- 2014 Supplemental Proposal

Subc ategory Pollutant Number of
sources in

floor Avg Emissions of

Existing MACT F loor

Pool (lb/ton) 99% UPL MACT Floor (lb/ton)

Rotary Spin Formaldehyde 5 0.13 0.19

Phenol 5 0.13 0.26

Methanol 5 0.40 0.83

Flame Formaldehyde 3 3.5 5.6

Attenuation Phenol 3 0.51 1.4

Methanol 3 0.30 0.50

Table 5 . Proposed New Source MACT Floors

- 2014 Supplemental Proposal

Subc ategory Pollutant Number of
sources in

floor Avg Emissions of

New MACT F loor Source (lb/ton) 99% UPL MACT Floor (lb/ton)

Rotary Spin Formaldehyde 1 0.06 0.066

Phenol 1 0.05 0.060

Methanol 1 0.2 0.29

Flame Formaldehyde 1 2.2 2.6

Attenuation Phenol 1 0.2 0.44

Methanol 1 0.2 0.3512

References:

ASME (founded as the American Society of Mechanical Engineers). 2001. Reference Method Accuracy and Precision (ReMAP): Phase 1, Precision of Manual Stack Emission Measurements

. CRTD Vol. 60, February 2001.

Bhaumik, D.K. and Gibbons, R.D. 2004. “An Upper Prediction Limit for the Arithmetic Mean of a Lognormal Random Variable.” Technometrics , 46(2):239

- 248.

Gayen, A. K. 1949. “The Distribution of Stude nt’s t in Random Samples of any Size Drawn from Non

Normal Universes.” Biometrika , Vol. 36, No. 3/4 (Dec. 1949), pp. 353

- 369.

Fairchild, S . 2014. Use of the Upper Prediction Limit for Calculating MACT Floors . Memorandum from Susan Fairchild, EPA/OAQPS/SPPD, to Do cket ID No . EPA

- HQ

- OAR

- 2010

- 1041

. July 2014

.

Fairchild, S. 2014. Approach for Applying the Upper Prediction Limit to Limited Datasets

. Memorandum from Susan Fairchild, EPA/OAQPS/SPPD, to Do cket ID No

. EPA

- HQ

- OAR

- 2010

- 1041

. October 2014

.

Westlin, P. and Merrill, R. 2011. Data and procedure for handling below detection level data in analyzing various pollutant emissions databases for MACT and RTR emissions limits

. Memorandum from Peter Westlin, SPPD/ MPG and Raymond Merrill, AQAD/MTG, to SPPD management and MACT rule writers. December 13, 2011.