1

MEMORANDUM
Date: June 15, 2015 Subject: Revised Technical Approach for Wool Fiberglass MACT Floo r Calculations 40 CFR 63, Subpart NNN – Final Standards
From: Susan Fairchild, EPA/OAQPS/SPPD/MMG
To: Docket EPA-HQ-OAR-2010-1042

I. Introduction Section 112 of the Clean Air Act (CAA) requires that th e U.S. Environmental Protection Agency (EPA) establish National Emission Standards for Hazardo us Air Pollutants ( NESHAP) for the control of hazardous air pollutants (HAP) emitted from both new and existing major sources in a source category. These standards must reflect the “Maximum Achievable Co ntrol Technology (MACT) floor” for HAP emitted by these sources The method for determining the M ACT floor for a NESHAP is defined for both new and existing sources in CAA section 112(d)(3). Fo r new sources, the MACT floor cannot be less stringent than the emission control that is ac hieved in practice by the best controlled similar source. For existing sources, the MACT floor cannot be less st ringent than the average emission limitation achieved by the best performing 12 percent of existing sources f or source categories with 30 or more sources, or the best performing five sources for source cat egories with fewer than 30 sources. The purpose of this memorandum is to present the data, meth odology, and results of the MACT floor analysis for the Wool Fiberglass Manufacturing N ESHAP, 40 CFR 63, Subpart NNN source category. This MACT floor analysis uses data collected from a nationwide voluntary survey of wool fiberglass manufacturers conducted by EPA in 2010 and a Secti on 114 Information Collection Request (ICR) sent out to the wool fiberglass industry in Novembe r 2011. Data on process operations, 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 se t 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 Fibergla ss Manufacturing NESHAP, 40 CFR part 63, subpart NNN for the RTR for this so urce category. Based on comments received on the November 2011 proposal and data received fro m the 2012 ICR responses, EPA published a supplemental proposal on April 15, 2013 with additiona l revisions to Subpart NNN. This memorandum incorporates changes made since the April 2013 suppl emental proposal and includes changes made as a result of public comments regarding fac ility 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 following existing and newly c onstructed major sources located at a wool fiberglass manufacturing facility: glass-melting fur naces, rotary spin (RS) manufacturing lines that produce bonded building insulation, and flame attenuation (FA ) manufacturing lines that produce 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 sources at bonded RS and FA lines which generally include fiber forming, curing, and collection operations. Both RS and FA manufacturing lines that2

produce nonbonded products are not currently subject to the sta ndards under the applicability section of the rule. The 1999 Wool Fiberglass Manufacturing NESHAP (s ubpart NNN) established particulate matter (PM) emission limits for new and existing furnac es and also established formaldehyde emission limits for new and existing RS lines and for new FA manu facturing lines. The 1999 NESHAP also used a surrogate approach, where the PM emission limit serve s as a surrogate for HAP metals and the formaldehyde emission limit serves as a surrogate for or ganic HAP. The wool fiberglass manufacturing source category includes 30 facilities operating in the United States; 10 of these facilities are major sources and th e remaining 20 facilities are listed as area sources or synthetic minors. Although the North American Insulatio n Manufacturers Association (NAIMA) announced that by the end of 2012, no major sources subject t o Subpart NNN would still exist in this source category (see docket items 0040 and 0052) by phasing out the use of phenol-formaldehyde binders, 1,2 this prediction did not come to pass. The 10 major sources currently operate approximately 29 glass melting furnaces, 13 FA bonded lines and 5 RS bonded line s. The use of PF binders has not been phased out in any FA lines, but has been phased out on 95per cent of RS lines. The specific chemicals, compounds, or groups of compounds desi gnated as HAP are listed in CAA section 112(b). From this list, chromium compounds, hydr ogen fluoride (HF) and hydrogen chloride (HCl) were identified as being emitted from furnac es; and formaldehyde, phenol, and methanol were identified as being emitted from bonded FA and RS line s. The EPA reviewed unit and process designs, operating informa tion, and air emissions data and analyzed several subcategorization recommendations subm itted by industry in their public comments on the proposed rule (76 FR 72770, November 25, 2011). Based on this revi ew, 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 subcate gorization. 3 As a result, the FA line emission limits for phenol, formaldehyde, and methanol would apply to all products manufactured on FA lines. FA lines were tested producing a variety of products, and industry stat ed in their 2010 ICR submittal that these tests were representative of all products manufactured o n FA lines. No additional data on FA lines were submitted to the EPA that led the EPA to establish further subcategories for FA lines. III. Overview of the Proposed MACT Floor Emission Limits in th e 2011 Proposal and 2013 Supplemental Proposal This section provides an overview of the MACT floor limit s 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 lim its for HF and HCl from glassmelting 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 t o 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 proposed to set work practice standa rds for HF and HCl instead of MACT floor emission limits under section 112(h) of the CAA (see Apr il 15, 2013 supplemental proposal).

1 Letters from A. Crane, NAIMA to C. Hancy, RTI (Jun e 8. 2011) and S. Fairchild, U.S. EPA, (August 2, 2011). 2 Once a facility has phased out its use of these HAP binde rs on bonded lines, it may apply to the appropriate permi tting authority to no longer be subject to the MACT Subpart NN N standard under two definitions of the MACT rule: the applicability definition and the definition of ‘bonded’. 3 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 fibergla ss manufactured for residential insulation, as indicate d in the 1999 MACT standard.3

B. Formaldehyde, Phenol and Methanol Formaldehyde, phenol and methanol are the HAP that are emitted from bonded lines at wool fiberglass manufacturing facilities. In the November 2011 p roposal, the EPA proposed to remove the surrogacy relationship and proposed establishing limits for phen ol and methanol instead. The EPA proposed separate formaldehyde, phenol, and methanol emissi on limits for combined curing and collection operations at RS and FA lines based on data s ubmitted by the industry in their voluntary information collection effort of 2010. After the 2011 propo sal, the EPA received information from industry clarifying 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 t he use of phenol/formaldehyde binders and were therefore no longer subject to the rule. The Agency remove d the erroneous data from the dataset and recalculated the MACT floor for RS lines based on the bo nded lines at major sources and synthetic minor sources in the April 2013 supplemental proposal. IV. Proposed MACT Floor Changes in the 2014 Supplemental Proposal Since the April 2013 supplemental proposal, the EPA has updated the MACT floor approach for existing and new sources that are based on limited datasets (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 ph enol which were based on the best performing source and where the MACT floor consisted of limited dat asets. The details of the updated MACT floor approach for limited data sets can be found in the memora ndum “Approach for Applying the Upper Prediction Limit to Limited Datasets” (Fairchild, 2014). T able 1 provides an overview of the limits that have been proposed for wool fiberglass since the Novembe r 2011 proposal and Sections IV-VI of this memorandum present the methodology of the MACT floor an alysis for the Wool Fiberglass Manufacturing NESHAP.

Table 1 – Overview of New and Existing Source UPL L imits for Rotary Spin Lines Proposed in 2014

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 0.06 6 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. Changes Since the 2014 Supplemental Proposal We did not receive any data or information in response to the November 13, 2014 supplemental proposal that warranted revising the MACT floors for FA lines at wool fiberglass manufacturing facilities. Consequently, the EPA is finalizing the form aldehyde, methanol, and phenol emissions limits for existing, new, and reconstructed FA lines, as proposed in 2014. However, as noted in the section V.H of the preamble to the final rule, we are not establ ishing limits under CAA sections 112(d)(2)and (3) for rotary spin (RS) lines because we believe that the data that we relied on in proposing these standards are not sufficiently related to current operatio ns or emissions from RS bonded lines. The emissions and process data available to EPA were collec ted beginning in 2003, and since that time,4

sources have phased out the use of a phenol/formaldehyde binde r from approximately 95 percent of the lines on which it was previously used. We have also found out that sources often can no longer either identify the products that were tested or the lines on whi ch those products had been manufactured. Moreover, when sources can identify the products that we re tested, those products are now produced using a HAP-free binder, and the product lines that now opera te using a phenol/formaldehyde binder do not bear similarity in size, end use, production rate or percent loss on ignition (LOI) to the tested product line. As a result, the data no longer represent current industry conditions, most notably the significant reduction in the use of phenol-formaldehyde binders in wool fiberglass manufacturing. While we are still authorized under CAA section 112(b) to set emission s limits using the data available to us, we also have the option to use our authority under CAA section 114 to collect new data upon which to base emissions limits, and we have chosen to exercise this option in this case based on the specific and unusual circumstances of the wool fiberglass industry’s RS lines. Consequently, we have issued a CAA section 114 ICR to wool fiberglass facilities to obtain upda ted formaldehyde, methanol, and phenol emissions and process data for RS manufacturing lines.
VI. MACT Floor Approach A. Upper Prediction Limit (UPL) The MACT floor analysis for wool fiberglass manufacturi ng uses a UPL approach to identify the average emission limitation achieved by those best perfor ming sources because this approach 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 o f confidence, based upon the results of an independent sample from the same po pulation. The level of confidence represents the level of protection afforded to facilities whose em issions are in line with the best performers. For example, a 99 percent level of confidence means that a f acility whose emissions are in line with the best performers has one chance in 100 of exceeding the floor l imit. A prediction interval for a single future observation (or an average of several test observati ons) is an interval that will, with a specified degree of confidence, contain the next (or the average of some othe r pre-specified number of) randomly selected observation(s) from a population. In other words, the UP L estimates what the upper 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 predi ctions derive from the data set to which it is applied, and, thus, can be applied to any type of data. The UPL is a value derived from widely accepted and commonl y used statistical principles, and represents the upper end of a prediction interval. 4 The UPL is used in a wide variety of industries and professions, such as manufacturing, finance, healthcare, e tc. and is calculated using one of several equations that must be selected based on certain character istics of each dataset. 5 The most important parameters in the UPL calculation are the mean (aver age) performance of the unit or units, the distribution of the dataset, the variance in the meas ured emission values (that is, the individual data points), and the sample size. The equations that are used to calculate the UPL are explained in more detail below.

4 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 >. 5 See, e.g., Gibbons, Robert D. and Coleman, David E. (2001). Statistical Methods for Detection and Quantification of Environmental Contamination , New York: 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/standardizati on-news/data-points/statistical-intervalspart-2-so11.html>.5

There are different equations for calculating a UPL ba sed on the distribution of the data set. Data can be normally distributed, lognormally distribut ed, or neither and thus have a skewed/unknown distribution. The EPA implemented the statistical tes ts 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 dat a distribution. Normally distributed data 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 asymmetrically distributed with a left tail exten ding towards negative values. The value of S can be approximate d using the following equation (which is implemented in Microsoft Excel using the “SKEW” function ):


## ( )( ) 3

1 2 1 

## ∑

=      

−

− − = n

i i stdev x x
n n n Skewness

where: n = sample size



### x = mean of MACT floor pool

x i = individual source mean

stdev = 

## ( )

( ) 

## ∑

− − 1 2

n x x i

As part of determining whether a data distribution can be considered normal, the S value must be compared to the 

### standard error of the skewness statistic (SES). The SES value can be approximated using the following equation: 

## ( ) ( ) ( )( )( )

3 1 2 1 6 + +

− − ×
= n

n n n n SES

Using the standard skewness hypothesis test, if the absol ute value of S is less than two times the absolute value of SES, the skewness of the data can be con sidered 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 three indicates a rela tively 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 of data points in the data set. F or 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):6 

## ( ) ( )( )( ) ( ) ( )( )

3 2 1 3 3 2 1 1 2 4

− − −

−                

−

− − − +
= 

## ∑ n n n stdev x x
n n n n n Kurtosis

This kurtosis equation cannot be used for data sets with thr ee run values (n=3) because there will be a zero in the denominator of the first and second ter ms. The kurtosis estimated with the “KURT” function is called excess kurtosis. It is equal to the kurt osis – 3, so for a normal distribution, excess kurtosis = 0. For data sets with three run values, the ex cess kurtosis is estimated using the following equation: 

## ( ) ( )( ) 3 1 4 1 4

−

− +

= 

## ∑

= stdev n x x Kurtosis n

i i

As part of determining whether the distribution of the dat a can be considered normal, the K value must be compared to the 

### standard error of the kurtosis statistic (SEK). The SEK value can be approximated using the following equation:



# ( ) ( )( )

5 3 )3 )(2 ( )1

( 24 2
+

− +

− − = n

n n n n n SEK for datasets with n>3, or

n SEK 24

= for datasets with n=3
According 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 co nsidered 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 wer e treated as normally distributed in subsequent UPL calculations. If either of these tests indicated t he data were not normally distributed, the data were log-transformed, and the log-transformed data were evaluat ed for kurtosis and skewness. If both tests of the log-transformed data indicated the log-transformed dat a were normally distributed, the data were treated as lognormally distributed in subsequent UPL cal culations. If either of these tests indicated the log-transformed data were not normally distributed, the da ta were treated as skewed/unknown distribution in subsequent UPL calculations. For data sets with only three run values, the result of both the skewness and kurtosis hypothesis tests always indicate the data set is normally distri buted. Therefore, to determine the appropriate data distribution for those data sets, the skewness/SES val ues from raw data were compared to the skewness/SES values from log-transformed data. The smal ler skewness/SES value was selected as an indication of a larger likelihood of the data to come fro m the corresponding distribution. After completing the determination of the type of data di stribution for each data set, the UPL was calculated using the corresponding equation for the distri bution for those data. The UPL equations for normal, lognormal, and skewed/unknown distribution data s ets are provided below.7

Normally Distributed Data Set       + + = m

n s t x UPL df 1

1 2 99.0,

where: UPL = upper prediction limit



### x = mean of the data calculated as 

## ∑ ∑

= = = N

i n j ij i x n x 1 1 1

n = number of test runs = 

# ∑

= N i i 

### n 1 m = number of future test runs in the compliance avera ge N = number of sources

s 2 = pooled variance calculated as 

## ( ) 

## ∑

= − = n

i i x x n s 1 2 2 1

t df,0.99 = quantile of t-distribution at the 99 percent level of c onfidence with degrees of

freedom (df); this value can be calculated with the Studen t’s t-test statistical method, using the “TINV” function in Microsoft Excel® software df = degrees of freedom calculated as 

# ( ) 1 1 

### − = 

# ∑

= N i i 

### n df

Lognormally Distributed Data Set (Bhaumik and Gibbons, 2004)



# ( ) ( )        

− + +

− + = + + + 

### 1 2 ˆ ˆ 1 4 2

ˆ ˆ 2 2 ˆ ˆ ˆ 2 99.0 2 ˆ

ˆ 2 2 2 2



### n n e m e me m z e UPL σ σ σ μ σ σ μ σ μ



### where:

μ

ˆ 

### = 

### n y

log n i i 

# ∑

= 1 

### ) ( = the average of the log transformed data from the data

2 ˆ σ 

### = 

## ( ) ( ) 1 ˆ 1 2



### − − 

# ∑

= 

### n y log n i i 

### μ = variance estimate of the log transformed data

### 8

z 0.99 

### = the 99 th 

### -percentile of the log-normal distribution estimate d using the trapezoidal rule

approach from the following equation: 

# ( ) ( ) 

# ( ) ( ) 99.0

24 6 3 3 3 6 1 99.0 0 4 2 2 3 1 

### =         

### +

− − +

− − 

# ∫ z z z z z z z

z φ

β β

Skewed/Unknown Distribution (Non-Normal/Non-Lognormal) (Gayen, 1949)       + + = m

n s t x UPL p

df 1 1 2

,


### where: UPL = upper prediction limit

x = mean of the data calculated as 

## ∑ ∑

= = = N

i n j ij i x n x 1 1 1



### n = number of test runs = 

# ∑

= N i i 

### n 1 

### m = number of future test runs in the compliance a verage N = number of sources

s 2 

### = pooled variance calculated as 

## ( ) 

## ∑

= − = n

i i x x n s 1 2 2 1



### t df,p 

### = recalculated quantile of t-distribution based on specified level of confidence ( i.e. , 99%) df = degrees of freedom calculated as 

# ( ) 1 1 

### − = 

# ∑

= N i i 

### n df

B. Representative Detection Limit (RDL) The imprecision of test method measurements can be a significant component of data variability. At very low emissions levels the inherent imprecisi on 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 te st method’s pollutant detection capability. Because of matrix effects, laboratory techniques, sample volum e, and other factors, method detection limits normally vary from test to test. Variability of data due to measurement imprecision is inherently and reasonably addressed in calculating a MACT floor emission limit when most o f the data are significantly above the method detection limit. For data sets with a large number of test results below the method detection limit (reported as method detection limit values), other techniques need to be used to account for data variability. Indeed, under such a shift, the data d istribution becomes truncated on the lower end, lea ding

### 9

to an artificial overabundance of values occurring at the method detection limit. There is a concern t hat 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 measurement error associated with those values prov ides a large degree of uncertainty. The expected measurement imprecision for an emissio ns value occurring at or near the detection limit is about 40 to 50 percent. Relative pollutant 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 varia bility includes defining a detection limit that is representative of the data used in establis hing a MACT floor emission limitation and also minimizes the influence of an outlier test-specific method detection limit value. The EPA has develop ed a list of RDL from available pollutant-specific met hod detection levels (Westlin and Merrill, 2011). These RDL values are then multiplied by three to de crease measurement imprecision to around 10 to 15 percent (as noted in the previous paragraph), resul ting in values referred to as “3xRDL” values. The appropriate 3xRDL value is compared to the calc ulated 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 variabili ty is adequately addressed. This check was part of the variability analysis for all existing MACT floo rs that had below detection level (BDL) or detectio n level limited (DLL) run data present in the best pe rforming data sets. For more information regarding the general use of the UPL and why it is appropriat e for calculating MACT floors, see the memorandum “Use of the Upper Prediction Limit for Calculating MACT Floors” (Fairchild, 2014).
VI. MACT Floor Analysis for FA Lines at Wool Fiberglass Manufac turing Facilities 

### A. MACT Floor Calculation Steps Step 1. 

### The first step in the MACT floor analysis was to ca lculate a pollutant-specific average emission value for formaldehyde, phenol, and methanol emissi ons from FA lines. The limits for the wool fiberglass NESHAP are in pounds of pollutant per to ns of glass pulled; the majority of the data (e.g. , production and equipment throughput rates) used to calculate the MACT floor limits were submitted as confidential business information (CBI) and therefo re, 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 e ach source, subcategory and pollutant, all of the individual average emission values were ranked from lowest to highest value. Step 3. 

### Based on the ranking in Step 2, the best performing sources were identified consistent with the criteria in CAA section 112(d)(3) for determining M ACT floors for new and existing sources. These sources are referred to as the “MACT floor pool.” For the new source MACT floors, 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 ba sis, as described in Tables 2 and 3.

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

Table 3. Number of Sources in New Source MACT Floo r Pool

Subcategory Pollutant No. Sources Nationwide No.

Sources in MACT Floor Pool No. of Data

Points in MACT Floor Pool Based on
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 was evaluated for kurtosis and skewness to determine the distribution, as described in Section IV of this memorandum. Step 5. 

### After determining the type of data distribution for the data set, the UPL was calculated using the corresponding distribution equation for that data s et, as previously described. See Section VII for U PL 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. As described in Se ction IV, the results of the UPL calculation were compared to the 3xRDL value of the relevant perform ance 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 me ter [μg/dscm]). The emission limits for the proposed wool fiberglass NESHAP are in units of pou nd per ton (lb/ton) of glass. In order to compare the calculated UPL to the 3xRDL, the 3xRDL value wa s 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). Subcategory Pollutant No. Sources Nationwide No.

Sources in MACT Floor Pool No. of Data

Points in MACT Floor Pool Based on

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 9

### 11

The converted 3xRDL value was then compared 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 prov ides the lowest emission rate that can be measured using the 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 standar d. Step 7. 

### For limited data sets with less than seven data poi nts, the steps outlined in the memorandum “Approach for Applying the Upper Prediction Limit t o Limited Datasets” (Fairchild, 2014) were followed. Tables 4 and 5 present the existing source and new source MACT floor values determined for FA lines in the wool fiberglass source category.

Table 4. Proposed Existing Source MACT Floors- 2014 Supplemen tal Proposal

Subcategory Pollutant Number of sources in floor Avg Emissions of

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

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 Su pplemental Proposal

Subcategory Pollutant Number of sources in floor Avg Emissions of

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

Flame Formaldehyde 1 2.2 2.6 Attenuation Phenol 1 0.2 0.44

Methanol 1 0.2 0.35

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 P rediction Limit for the Arithmetic Mean of a Lognormal Random Variable.” Technometrics , 46(2):239-248. Gayen, A. K. 1949. “The Distribution of Student’s t in Random Samples of any Size Drawn from NonNormal Universes.” Biometrika , Vol. 36, No. 3/4 (Dec. 1949), pp. 353-369. Fairchild, S. 2014. Use of the Upper Prediction Limit for Calculating M ACT Floors . Memorandum from Susan Fairchild, EPA/OAQPS/SPPD, to Docket ID No. E PA-HQ-OAR-2010-1041. July 2014.

### 12

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

. Memorandum from Susan Fairchild, EPA/OAQPS/SPPD, to Docket ID No. EPA-HQ-OAR-2010- 1041. October 2014. Westlin, P. and Merrill, R. 2011. Data and procedure for handling below detection lev el 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, 201 1.