1

Date: September 29, 2014

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. 2-09

RTI Project No. 0213199.002.009

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. 

These standards must reflect the “Maximum Achievable Control Technology (MACT) floor” for HAP 

emitted by these sources 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 percent of existing sources for source categories with 30 or more 

sources, or the best performing five sources for source categories with fewer than 30 sources.

The purpose of this memorandum 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 manufacturers conducted by EPA in 2010 and a Section 114 Information Collection Request 

(ICR) sent out to the wool fiberglass industry in November 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 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 and 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 following existing and newly constructed major sources located at a 

wool fiberglass manufacturing facility: glass-melting furnaces, rotary spin (RS) manufacturing lines that 
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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 that 

produce nonbonded products are not currently subject to the standards. The 1999 Wool Fiberglass 

Manufacturing NESHAP (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 approach, 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 Insulation 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 Once 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 and 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 emitted 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 

recommendations 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 methanol 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 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 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 proposed to set 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, phenol and methanol are emitted HAP from bonded lines at wool fiberglass 

manufacturing facilities. In the November 2011 proposal, the EPA proposed to remove the surrogacy 

relationship and proposed establishing limits for phenol and methanol instead. The EPA proposed 

separate formaldehyde, phenol, and methanol 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 

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 the use of phenol/formaldehyde binders and were 

therefore no longer subject to the rule. The Agency removed 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 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 phenol which were based on the best performing 

source and where 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 IV-VI of this 

memorandum present the methodology of the MACT floor analysis for the Wool Fiberglass 

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 those best performing sources because this approach
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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 performers. 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 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 UPL 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 predictions 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 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 used to calculate the UPL are explained in more 

detail below.

There 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/unknown 

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 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 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):

  

3

1 2 1













 

 



n

i

i

stdev

x x

n n

n

Skewness

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 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/standardization-news/data-points/statistical-intervalspart-2-so11.html>.
5

x = mean of MACT floor pool

xi = 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:

  

 2 1 3

6 1

  

 



n n n

n n

SES

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 three 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 of 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):

 

   

 

 2 3

3 1

1 2 3

1

4 2

 































 

  



  n n

n

stdev

x x

n n n

n n

Kurtosis

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 terms. 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. For data sets with three run values, the excess kurtosis is estimated using the following 

equation:

 

  

3

1

4

1

4











n stdev

x x

Kurtosis

n

i

i

As part of determining whether the distribution of the data 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:

 

( 2)( 3) 3 5

24 ( 1)

2

   





n n n n

n n

SEK

for datasets with n>3, or
6

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 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 kurtosis 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 UPL 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 the 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 from 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 those data. The UPL equations for 

normal, lognormal, and skewed/unknown distribution data sets are provided below.

Normally Distributed Data Set













  

n m

UPL x t s df

2 1 1

,0.99

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

ni 1

m = number of future test runs in the compliance average

N = number of sources

s

2 = pooled variance calculated as 

  



 

n

i

i

x x

n

s

1

2 1 2

tdf,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” function in Microsoft Excel® software
7

df = degrees of freedom calculated as 

  1

1

  



N

i

i df n

Lognormally Distributed Data Set (Bhaumik and Gibbons, 2004)

 

 



















    

 



2 1

ˆ ˆ

1

2 4

9 9 2 ˆ ˆ ˆ 2 2 ˆ ˆ 2 0.

ˆ

ˆ 2 2 2

2

n n

me e m e

m

z

UPL e

      





where:



ˆ

= 

n

log y

n

i  i 1

( )

= the average of the log transformed data from the data

2 

ˆ =

   

1

ˆ

1

2



 



n

log y

n

i

i 

= variance estimate of the log transformed data

z0.99 = the 99th-percentile of the log-normal distribution estimated using the trapezoidal rule 

approach from the following equation:

 

  

  0.99

24

3 3 6

3

6

1

0.9 9

0

2 4

1 3 2 















   

   z

z z

z z

z

z z 

 

Skewed/Unknown Distribution (Non-Normal/Non-Lognormal) (Gayen, 1949)













  

n m

UPL x t s df p

2 1 1

,

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

ni 1

m = number of future test runs in the compliance average

N = number of sources
8

s

2 = pooled variance calculated as 

  



 

n

i

i

x x

n

s

1

2 1 2

tdf,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 df n

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 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, laboratory techniques, sample volume, 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 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 

(reported 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 

measurement 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 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 variability includes defining 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 and 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 adequately 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).
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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 were 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 ranking 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 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 basis, as described in Tables 2 and 3.

Table 2. Number of Sources in Existing 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

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 9
10

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 determining 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. As described in Section IV, the 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 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 provides 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 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, 2014) were

followed. 

Tables 4 and 5 present the existing source and new source MACT floor values determined for the wool 

fiberglass source category. 

Table 4. Proposed Existing Source MACT Floors- 2014 Supplemental Proposal

Subcategory Pollutant

Number of 

sources in 

floor

Avg Emissions of 

Existing MACT Floor 

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

Subcategory Pollutant

Number of 

sources in 

floor

Avg Emissions of 

New MACT Floor 

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 0.35
12

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. 

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