1 

MEMORANDUM 

To: Nathan Topham, U.S. Environmental Protection Agency, OAQPS 

From: Mike Burr, Donna Lazzari, and Danny Greene, ERG 

Date: December 16, 2011 

Subject: MACT Floor Analysis for the Secondary Lead Smelting Source Category 

This memorandum presents the methodology and results of the maximum achievable 

control technology (MACT) floor determinations for dioxins and furans (D/F) and total 

hydrocarbons (THC) for the Secondary Lead Smelting source category. The EPA has reviewed 

this memorandum and it reflects the agency’s analysis and consideration. The EPA is therefore 

adopting this memorandum as its own. 

The EPA determined the MACT floors using emissions data obtained from the recently 

implemented Information Collection Request (ICR). We ranked emissions test averages for each 

of the sources, calculated MACT floor averages, and conducted emissions test data variability 

analysis to estimate these best performing sources’ emissions performance over time, 

considering each HAP (THC and D/F) separately. The MACT floors are determined based on the 

results of these analyses. The EPA also conducted the beyond the floor analyses required by 

section 112 (d)(2), to evaluate possible standards that would be more stringent than the MACT 

floor. The organization of this memo is as follows: 

1.0 Summary of Existing Limits 

2.0 Background on MACT Floor Methodology 

3.0 MACT Floor Determination Methodology 

4.0 Beyond the Floor 

5.0 Summary of MACT Floor Analysis 

Appendix A: Total Hydrocarbons Emissions Data Used in the MACT Floor Calculation 

Appendix B: Dioxin and Furan Emissions Data Used in the MACT Floor Calculation 

Appendix C: Toxic Equivalency Factors used to Calculate Toxic Equivalency Quotient 

1.0 SUMMARY OF EXISTING MACT LIMITS 

The existing National Emissions Standard for Hazardous Air Pollutants (NESHAP) for 

the Secondary Lead Smelting source category (40 CFR part 63, subpart X) established emissions 
2 

limits for lead (as a surrogate for non-mercury metal hazardous air pollutants (HAP)) and THC 

(as a surrogate for organic HAP) based on furnace type. Table 1-1 summarizes the emissions 

limits in the existing NESHAP. 

Table 1-1. Emissions Limits in the 1997 Secondary Lead Smelting NESHAP. 

Furnace Configuration 

Lead Compounds THC

Existing 

Sources 

New 

Sources 

Existing 

Sources 

New 

Sources 

Collocated Blast and 

Reverberatory furnace:

When both furnaces 

operating

2.0 mg/dscm

20 ppmv

When reverberatory 

furnace not operating

360 ppmv 70 ppmv

Blast 360 ppmv 70 ppmv

Reverberatory

Rotary Not Applicable

Electric

As shown in Table 1-1, the current NESHAP does not contain THC emissions limits for 

three furnace groups: (1) reverberatory furnaces not collocated with blast furnaces, (2) rotary 

furnaces, and (3) electric furnaces. Since organic HAP is emitted from these furnace types, we 

have calculated THC MACT floors and conducted beyond the floor analyses for THC from these 

three furnace types in this analysis. The existing NESHAP also does not set emissions limits for 

D/F. We therefore calculated MACT floors for D/F based on the same furnace types as THC. 

This is appropriate as the formation of D/F, like THC, is highly temperature dependent.1 We note 

that although the D/F and THC MACT floor calculations for rotary furnaces are presented in this 

document, the EPA is delaying promulgation of D/F and THC emissions limits for rotary 

furnaces due to insufficient and unrepresentative emissions test data. The information presented 

here for rotary furnaces is based on two test runs during one test period for one rotary furnace. 

This data was determined by the EPA to be insufficient to establish MACT floor limits. See the 

final rule preamble and comment response document available in the docket (EPA-HQ-OAR2011-0344) for more information.

2.0 OVERVIEW OF MACT STANDARD DEVELOPMENT METHODOLOGY 

2.1 MACT Determination

Section 112(d) of the CAA requires the EPA to set emissions standards for HAP emitted 

by major stationary sources. The MACT standards for existing sources must be at least as 

stringent as the average emissions limitation achieved by the best performing 12 percent of 

existing sources (for which the Administrator has emissions information) or the five bestperforming sources for source categories with less than 30 sources (CAA section 112(d)(3)(A) 

 

1 ERG. Technology Review for the Secondary Lead Smelting Source Category. December 16, 2011. 
3 

and (B)). This level of minimum stringency is called the MACT floor. For new sources, MACT 

standards must be at least as stringent as the control level achieved in practice by the best 

controlled similar source (CAA section 112(d)(3)). These requirements constitute the MACT 

floor for new and existing sources. The EPA may not consider costs or other impacts in 

determining the MACT floors. The EPA must consider cost, non-air quality health and 

environmental impacts, and energy requirements in connection with any standards that are more 

stringent than the MACT floor (beyond-the-floor controls). 

MACT analyses involve an assessment of the emissions from the best-performing units in 

a source category. The assessment can be based on actual emissions data, on knowledge of the 

air pollution control in place in combination with actual emissions data, or on state regulatory 

requirements that may enable the EPA to estimate the actual performance of the regulated units 

and other relevant emissions information. For each source category, the assessment involves a 

review of actual emissions data with an appropriate accounting for best performing sources’ 

emissions variability. Other methods of estimating emissions can be used provided that the 

methods can be shown to provide reasonable estimates of the emissions performance of a source 

or sources. 

In a March 2007 court decision (Sierra Club v. EPA, 479 F. 3d 875 [DC Cir. 2007]) 

(Brick MACT), the EPA was directed to set MACT floors conforming to the statute, with these 

specific directives: 

• Floors for existing sources must reflect the average emissions limitation achieved 

by the best-performing 12 percent of existing sources, not levels the EPA 

considers to be achievable by all sources (479 F. 3d at 880–81). 

• The EPA cannot set floors of “no control.” The court reiterated its prior holdings, 

confirming that the EPA must set floor standards for all HAP emitted by the 

major source, including those HAP that are not controlled by at-the-stack control 

devices (479 F. 3d at 883). 

• The EPA cannot ignore non-technology factors that reduce HAP emissions. 

Specifically, the court held that “EPA’s decision to base floors exclusively on 

technology even though non-technology factors affect emissions violates the Act” 

(479 F. 3d at 883). 

• The EPA may not use emissions levels of the worst performers to estimate 

variability of the best performers without a demonstrated relationship between the 

two (479 F. 3d at 882). 

2.2 Considering Variability in Establishing Emissions Limits

Section 112(d)(3)(B) of the CAA requires that the EPA determine the emissions control 

that is achieved in practice by the average of the best-controlled top five sources when 

establishing the MACT floor for units in a source category with 30 or fewer units. The EPA must 
4 

exercise its judgment, based on an evaluation of the relevant factors and available data, to 

determine the level of emissions control that has been achieved by the average of the bestperforming top five units under variable conditions. The Court has recognized that the EPA may 

consider variability in estimating the degree of emissions reduction achieved by best-performing 

sources and in setting MACT floors (see Mossville Envt’l Action Now v. EPA, 370 F.3d 1232, 

1241-42 (D.C. Cir 2004)), holding that the EPA may consider emissions variability in estimating 

performance achieved by best-performing sources and may set the floor at a level that bestperforming sources can expect to meet “every day and under all operating conditions.”

The data base here consists of short-term emissions test data. Such data are not fully 

representative of the range of operating conditions that the best-performing facilities face on a 

day-to-day basis. In statistical terms, each test produces a limited data sample, not a complete 

enumeration of the available data for performance of the unit over a long period of time. EPA, 

therefore, adjusted the short-term data to account for these varying conditions in order to 

properly estimate sources’ performance over time. The types of variability that the EPA attempts 

to account for include operational distinctions between and within tests at the same unit. 

“Between-test variability” can occur even where conditions appear to be the same when 

two or more tests are conducted. Variations in emissions may be caused by different settings for 

emissions testing equipment, different field teams conducting the testing, differences in sample 

handling or different laboratories analyzing the results. Identifying an achieved emissions level 

for best performing sources needs to account for these differences between tests for “a uniform 

standard [to] be capable of being met under most adverse conditions which can reasonably be 

expected to recur[.]” (See National Lime Ass’n v. EPA, 627 F.2d at 431, n. 46.) 

The same types of differences leading to between-test variability also cause variations in 

results between various runs comprising a single test, or “within-test variability.” A single test at 

a unit usually includes at least three separate test runs. (See 40 CFR 63.7(e)(3) for MACT 

standards under CAA Section 112.) Each data point should be viewed as a snapshot of actual 

performance. Along with an understanding of the factors that may affect performance, each of 

these snapshots gives information about the normal, and unavoidable, variation in emissions that 

would be expected to recur over time. 

Variability can be accounted for using different statistical methods. For example, recent 

rules have used the upper limit (UL) or the upper prediction limit (UPL) to evaluate variability as 

part of the process for assessing sources’ performance over time, as part of the process of 

determining which sources are best performing, their level of performance, and ultimately 

determining the MACT floor emissions limits. A UL is based on the distribution of the available 

emissions observations (e.g., test runs), and does not embody the predictive aspect that a UPL 
5 

does. A prediction interval (e.g., a UPL) for a future observation is an interval that will, with a 

specified degree of confidence, contain the next (or some other pre-specified) randomly selected 

observation from a population. In other words, the prediction interval estimates what future 

values will be, based on present or past measurements. Given this definition, the UPL represents 

the value that the mean of three future test run observations (three-run average) can be expected 

to fall below, based on the results of the independent sample of size (n) from the same 

population. Therefore, should a future test condition be selected randomly from any of these 

sources (i.e., average of three runs), one can be 99 percent confident that the reported level will 

fall below a MACT floor emissions limit calculated using a UPL, assuming representativeness of 

the underlying data set. For these reasons, we used the UPL to account for variability in our 

MACT floor calculations.

2 We did, however, examine alternative statistical methods to account 

for variability when calculating MACT floors for the rotary furnace group due to limited 

emissions data, as discussed further in section 3.2 of this memorandum. 

3.0 MACT DETERMINATION FOR SECONDARY LEAD SMELTING SOURCE 

CATEGORY 

The following sections outline the steps taken in determining the MACT floors for the 

Secondary Lead Smelting source category. We calculate emissions test averages and rank the top 

sources from lowest to highest emitter of the HAP in question (section 3.1), perform statistical 

analyses to account for variability in emissions data for that HAP (section 3.2), perform analyses 

to assess the impact of below detection level (BDL) emissions data (section 3.3), and calculate 

the 99% UPL for each furnace group (section 3.4). 

3.1 Ranking the Best Performing Five Sources

The emissions data used to calculate the MACT floors for THC and D/F were obtained 

using an ICR survey sent to the six companies that own the existing secondary lead smelting 

facilities in June 2010. The emissions test data used in our MACT floor calculations for THC 

and D/F, including individual emissions test runs, are presented in Appendices A and B, 

respectively. 

The MACT floor emissions limits for THC were calculated for the following furnace 

groups: (1) reverberatory furnaces not collocated with blast furnaces and reverberatory furnaces 

mixed with electric furnaces and (2) rotary furnaces. The MACT floor emissions limits for D/F 

were calculated for the following furnace groups: (1) reverberatory furnaces not collocated with 

 

2 Because a source must demonstrate compliance with the MACT floor using the average of a three-run test, the 

appropriate test condition to use to assess variability is three test runs. If a source had to demonstrate compliance by 

showing that each individual test run was below the MACT floor emissions limit, it would be appropriate to use a 

future test condition of one test run. 
6 

blast furnaces and reverberatory furnaces mixed with electric furnaces, (2) blast furnaces, (3) 

collocated blast and reverberatory furnaces, and (4) rotary furnaces. 

To determine the MACT floor emissions limits, the average pollutant concentration was 

calculated for each affected source within each furnace group. To maintain consistency with the 

current NESHAP, the MACT floors for THC were calculated in units of parts per million by 

volume, expressed as propane and adjusted to 4% carbon dioxide (ppmv propane @ 4% CO2). 

The D/F MACT floors were calculated on a toxic equivalency (TEQ) basis in units of nanograms 

per dry standard cubic meter adjusted to 7 percent oxygen (ng/dscm @ 7% O2) to maintain 

consistency with previously-promulgated D/F standards. The TEQ was calculated using toxic 

equivalency factors (TEFs) recommended by the World Health Organization3(WHO) in 2005; 

these factors are presented in Appendix C. Because we had emissions test data for less than five 

sources for both pollutants for each furnace group, the average of the best-performing sources 

was determined as the average of all available emissions test data for each furnace group and 

pollutant. More specifically, each affected source for which emissions data were available fell 

within the top five (or fewer) performing sources of the respective furnace group. As described 

in the control technology review memo (Summary of the Technology Review for the Secondary 

Lead Smelting Source Category), the control technologies applied at these facilities (for which 

we have emissions test data) are the same or better than the controls applied at similar affected 

sources in the industry. Tables 3-1 and 3-2 present the average concentration of THC and D/F, 

respectively, for each affected source (for which emissions data were available) within each 

furnace group considered in this analysis. 

Table 3-1. Average Concentrations of THC 

Furnace Group Company/Facility Average THC 

Concentration 

Rank 

Reverberatory furnaces not 

collocated with blast furnaces and 

reverberatory furnaces mixed with 

electric furnaces 

RSR 3.30 1 

Quemetco IN 7.76 2 

Rotary Furnaces RSR 257 1 

THC in parts per million by volume expressed as propane and adjusted to 4 percent CO2. 

Table 3-2. Average Concentrations of TEQ 

Furnace Group Company/Facility Average TEQ 

Concentration 

Rank 

Reverberatory furnaces not collocated 

with blast furnaces and reverberatory 

furnaces mixed with electric furnaces 

Quemetco IN 0.08 1

RSR 0.09 2

Quemetco IN 0.12 3

Blast Furnaces 

Exide Forest City 1.3 1

Sanders Lead 76.4 2 

 

3Van den Berg et al. “The 2005 World Health Organization reevaluation of human and mammalian toxic 

equivalency factors for dioxins and dioxin-like compounds.” Toxicol. Sci. 93 (2006): 223–241. 
7 

Collocated Blast and Reverberatory 

Furnaces 

East Penn 0.19 1 

Rotary Furnaces RSR 0.14 1

TEQ in nanograms per dry standard cubic meter adjusted to 7 percent O2. 

3.2 Incorporating Data Variability

Data variability was incorporated after ranking the emissions points and determining the 

top five (or fewer) best-performing sources. Each test run from the top sources was used in the 

variability analysis. By including multiple emissions tests from units with a test average from the 

top five facilities, the EPA can evaluate intra-unit variability of emissions tests over time, 

considering variability in control device performance and unit operations. As discussed 

previously, the UPL was used for the Secondary Lead Smelting MACT floor variability analysis. 

The first step included a determination of whether the concentration data from the bestperforming sources were normally or log-normally distributed. If the data were normally 

distributed (e.g., similar to a typical bell curve), further variability analyses were conducted on 

the data set. If the data were not normally distributed (for example if the data were asymmetric or 

skewed to the right or left), the type of distribution (e.g., log-normal) was determined and a data 

transformation (e.g., taking the natural log of the data) was performed to normalize the data prior 

to conducting the variability analysis. Two statistical measures, skewness and kurtosis, were 

examined to determine if the data were normally or log-normally distributed. 

The skewness statistic (S) characterizes the degree of asymmetry of a given data 

distribution. Normally distributed data have a skewness of zero. A non-zero S value indicates 

that the data are asymmetrically distributed. A positive S value means that the data distribution 

has a right tail extending toward positive values. A negative S value means that the data 

distribution has a left tail extending toward negative values. Further assessment of data normality 

can be made using the standard error of the skewness statistic (SES) shown in Equation 1: 

 = 

 

 

(Eq. 1) 

Where: 

N = the sample size. 

According to the small sample skewness hypothesis test, if S is greater than two times the 

SES, the data distribution can be considered non-normal. 

The kurtosis statistic (K) characterizes the degree of peakedness or flatness of a given 

data distribution in comparison to a normal distribution. Normally distributed data have a 

kurtosis of zero. A K value that is greater than zero indicates a relatively peaked distribution 
8 

while a K value less than zero indicates a relatively flat distribution. The normality of the data 

can be further evaluated using the standard error of the kurtosis statistic (SEK) shown in 

Equation 2: 

 = 

 

 

(Eq. 2)

Where: 

N = the sample size. 

According to the small sample kurtosis hypothesis test, if K is greater than two times the 

SEK, the data distribution is typically considered to be non-normal. 

The skewness and kurtosis hypothesis tests were applied to both the reported test values 

and the lognormal values of the reported test values. If the S and K statistics of the reported data 

set were both less than twice the SES and SEK, respectively, the data set was classified as 

normally distributed. If neither of the S and K statistics or only one of these statistics was less 

than twice the SES or SEK, respectively, skewness and kurtosis hypothesis tests were conducted 

for the natural log-transformed data. The distribution most similar to a normal distribution was 

selected as the basis for calculating the UPL. If both the reported values and the natural-log 

transformed reported values had S and K statistics that were greater than twice the SES or SEK, 

respectively, the normally distributed data set was selected as the basis of the floor to be 

protective of the environment. If the results of the skewness and kurtosis hypothesis tests were 

mixed for the reported values and the natural log-transformed reported values, the normal 

distribution was chosen to be protective of the environment. 

After determining the distribution of each dataset, a student’s t­test statistic was 

determined to calculate the UPL value. The t-statistic test, which can be found in standard 

statistical reference tables or by using the Excel function “TINV,” requires the degrees of 

freedom and the desired confidence interval. The degrees of freedom was calculated as one less 

than the number of test runs for the top five facilities and the confidence interval used was 99 

percent. 

Following the determination of the student’s t-test statistic, the 99 percent UPL values for 

each pollutant were calculated using the test run data for top five best-performing facilities. 

Because compliance with the MACT floor emissions limit is based on the average of a 3-run test, 

the UPL was calculated using the following equation: 

 = ̅+ (0.99, − 1) × 

 × 

 

 

+

 

 

 (Eq. 3)
9 

Where: 

n = the number of test runs (i.e., sample size), 

m = the number of test runs in the compliance average, 

s = standard deviation of the emissions test data, 

 = mean, i.e., average of the emissions test data, and 

t (0.99, n – 1) = t-statistic for 99% significance and a sample size of n. 

Equation 3 was applied to the normally distributed data sets. However, for the 

transformed data, the average and the standard deviation of the test runs were replaced with the 

average and the standard deviation of the transformed data. The exponential function of Equation 

3 was calculated to obtain the 99 percent UPL value. 

The 99 percent UPL represents the value that one can expect the mean of future 3-run 

performance tests from the top five (or fewer) best-performing facilities to fall below with 99 

percent confidence, based upon the results of the independent sample of observations from the 

same best performing sources. Imprecision due to ND values was investigated after determining 

the 99 percent UPL values. 

As noted above, and as explained in the preamble to the final rule, because the data for 

THC and D/F from rotary furnaces contains only two test runs, the EPA has determined that 

these data are insufficient to realistically determine sources’ performance (as well as not being 

representative of other types of furnaces). The analysis presented in this memo for these sources 

confirms this conclusion, yielding results (floor standards allowing over 7 % concentration of 

THC in emissions) which are not a valid measure of performance. This is not a reflection of the 

statistical tools, but an illustration that these statistical approaches are dependent on the data 

input to them. 

3.3 Use of Non-Detect Values

At very low emissions levels where emissions tests result in ND values, the inherent 

imprecision in the pollutant measurement method has a large influence on the reliability of the 

data underlying the regulatory floor emissions limit. Because of sample and emissions matrix 

effects, laboratory techniques, sample size and other factors, method detection levels normally 

vary from test to test for any specific test method and pollutant measurement. The confidence 

level that a value measured at the detection level is greater than zero is about 99 percent. The 

expected measurement imprecision for an emissions value occurring at or near the method 

detection level is about 40 to 50 percent. Pollutant measurement imprecision decreases to a 

consistent level of 10 to 15 percent for values measured at a level about 3 times the method 

detection level. 
10 

One approach that can be applied to account for variations in concentration 

measurements in this situation starts with defining a method detection level that is representative 

of the data. The first step in this approach is to identify the highest test-specific method detection 

level reported in a data set that is also equal to or less than the average emissions calculated for 

the data set; this is termed the representative detection level (RDL). This approach has the 

advantage of relying on the data collected to develop the floor emissions limit, while to some 

degree, minimizing the effect of a test(s) with an inordinately high method detection level (e.g., 

the sample volume was too small, the laboratory technique was insufficiently sensitive or the 

procedure for determining the detection level was other than that specified). 

The second step is to determine the value equal to 3 times the RDL and compare it to the 

calculated 99 percent UPL value. If 3 times the RDL were less than the calculated UPL value, 

we would conclude that measurement variability is adequately addressed and we would not 

adjust the calculated UPL value. If, on the other hand, the value equal to 3 times the RDL was 

greater than the calculated UPL value, we would conclude that the calculated UPL value does 

not account entirely for measurement variability. Therefore, the use of the value equal to 3 times 

the method detection level in place of the UPL value ensures that the floor emissions limit 

accounts for measurement variability and imprecision. 

The following additional procedures were followed for calculating the D/F (TEQ) MACT 

floors. To calculate a limit on a TEQ basis for a test run with some, but not all values reported as 

ND, we first calculated the mean of the values that were flagged as ND for each test run. The 

toxic TEF for each congener was multiplied by the mean of the flagged values to produce a TEQ 

adjusted flagged value for each congener. A list of the TEF values used in the analysis is 

presented in Appendix C. The sum of the TEQ adjusted flagged values was used as the detection 

level for each run. To calculate a detection limit for a run where all values were reported as ND, 

each ND value was multiplied by the TEF for the specific congener. The TEQ adjusted ND 

values were summed and used as the detection limit for the test run. We then continued with the 

second step discussed above to determine if measurement variability was adequately addressed 

in setting the limit. 

With regards to the emissions data used in our MACT floor determinations, we did not 

receive any BDL emissions data for THC, and thus the above analyses were not necessary. For 

D/F, approximately 67 percent of the individual congener emissions data was above detection 

level (ADL). Additionally, the 99 percent UPL was in all cases greater than the 3 times the RDL. 

3.4 MACT Floor and Variability Analysis Results

Tables 3-3 through 3-8 present the results of the existing source MACT floor and 

variability analysis for THC and D/F for each furnace group. 
11 

Table 3-3. Existing Source MACT Floor and Variability Analysis Results for THC – 

Reverberatory Furnaces Not Collocated with Blast Furnaces and Reverberatory Furnaces 

Mixed with Electric Furnaces 

Parameter Normal Log-normal

Number of Test Runs 6 6

Average (ppmv) 5.53 2.04

Median (ppmv) 5.75 1.74

Standard Deviation (ppmv) 2.72 0.58

Minimum (ppmv) 2.12 0.75

Maximum (ppmv) 8.83 2.18

Skewness -0.14 -0.62

SE Skewness 1.00 1.00

Skewness Test normal normal

Kurtosis -1.86 -1.50

SE Kurtosis 2.00 2.00

Kurtosis Test normal normal

99.0% t-statistic for UPL 3.36 3.36

99.0% UPL (ppmv) 12.01 30.64

UPL Value Using Normally Distributed Data

99% UPL (ppmv) 12.01

UPL Value Using LN Transformed Data

99% UPL (ppmv) 30.64

ppmv – Parts per million volume expressed as propane, adjusted to 4 percent CO2

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

Table 3-4. Existing Source MACT Floor and Variability Analysis Results for THC – 

Rotary Furnaces* 

Parameter Normal Lognormal

Sample Size 2 2

Average (ppmv) 256.73 5.54

Median (ppmv) 256.73 5.54

Standard Deviation (ppmv) 49.54 0.19

Minimum (ppmv) 221.70 5.40

Maximum (ppmv) 291.77 5.68

Skewness N/A N/A

SE Skewness 1.73 1.73

Skewness Test N/A N/A
12 

Parameter Normal Lognormal

Kurtosis N/A N/A

SE Kurtosis 3.46 3.46

Kurtosis Test N/A N/A

99.0% t-statistic for UL 2.62 2.62

99.0% UL (ppmv) 601.79 983.49

99.0% UPL (ppmv) 1695.91 71649.41

UL and UPL Values Using Normally Distributed Data

99% UL (ppmv) 601.79

99% UPL (ppmv) 1695.91

UL and UPL Values Using LN Transformed Data

99% UL (ppmv) 983.49

99% UPL (ppmv) 74649.41

ppmv – Parts per million volume expressed as propane, adjusted to 4 percent carbon dioxide 

SE – Standard error (applied to skewness or kurtosis) 

UL - Upper limit 

UPL – Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

*Data is not sufficient to establish MACT floor limits as represented in this table 

Table 3-5. Existing Source MACT Floor and Variability Analysis Results for D/F – 

Reverberatory Furnaces Not Collocated with Blast Furnaces and Reverberatory Furnaces 

Mixed with Electric Furnaces 

Parameter Normal Lognormal

Sample Size 9 9

Average (ng/dscm) 0.10 -2.41

Median (ng/dscm) 0.08 -2.52

Standard Deviation (ng/dscm) 0.04 0.42

Maximum (ng/dscm) 0.17 -1.80

Minimum (ng/dscm) 0.04 -3.23

Skewness 0.60 -0.40

SE Skewness 0.82 0.82

Skewness Test normal normal

Kurtosis -0.21 0.72

SE Kurtosis 1.63 1.63

Kurtosis Test normal normal

99.0% t-statistic for UPL 2.82 2.82

99.0% UPL (ng/dscm) 0.17 0.20
13 

Parameter Normal Lognormal

UPL Value Using Normally Distributed Data

99% UPL (ng/dscm) 0.17

UPL Value Using LN Transformed Data

99% UPL (ng/dscm) 0.20

ng/dscm – Nanograms per dry standard cubic meter adjusted to 7 percent oxygen 

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

Table 3-6. Existing Source MACT Floor and Variability Analysis Results for D/F – Blast 

Furnaces 

Parameter Normal Lognormal

Sample Size 6 6

Average (ng/dscm) 38.83 2.23

Median (ng/dscm) 1.44 0.37

Standard Deviation (ng/dscm) 52.27 2.19

Minimum (ng/dscm) 1.20 0.18

Maximum (ng/dscm) 134.51 4.90

Skewness 1.52 0.14

SE Skewness 1.00 1.00

Skewness Test normal normal

Kurtosis 2.18 -2.89

SE Kurtosis 2.00 2.00

Kurtosis Test normal normal

99.0% t-statistic for UPL 3.36 3.36

99.0% UPL (ng/dscm) 163.20 1714.90

UPL Value Using Normally Distributed Data

99% UPL (ng/dscm) 163.20

UPL Value Using LN Transformed Data

99% UPL (ng/dscm) 1714.90

ng/dscm – Nanograms per dry standard cubic meter adjusted to 7 percent oxygen 

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

Table 3-7. Existing Source MACT Floor and Variability Analysis Results for D/F – 

Collocated Blast and Reverberatory Furnaces 

Parameter Normal Lognormal
14 

Parameter Normal Lognormal

Sample Size 3 3

Average (ng/dscm) 0.19 -1.68

Median (ng/dscm) 0.21 -1.55

Standard Deviation (ng/dscm) 0.04 0.23

Minimum (ng/dscm) 0.14 -1.95

Maximum (ng/dscm) 0.21 -1.54

Skewness -1.73 -1.73

SE Skewness 1.41 1.41

Skewness Test Normal normal

Kurtosis N/A N/A

SE Kurtosis 2.83 2.83

Kurtosis Test N/A N/A

99.0% t-statistic for UPL 6.96 6.96

99.0% UPL (ng/dscm) 0.42 0.70

UPL Value Using Normally Distributed Data

99% UPL (ng/dscm) 0.42

UPL Value Using LN Transformed Data

99% UPL (ng/dscm) 0.70

ng/dscm – Nanograms per dry standard cubic meter adjusted to 7 percent oxygen 

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

Table 3-8. Existing Source MACT Floor and Variability Analysis Results for D/F – Rotary 

Furnaces* 

Parameter Normal Lognormal

Sample Size 2 2

Average (ng/dscm) 0.14 0.04

Median (ng/dscm) 0.14 0.04

Standard Deviation (ng/dscm) 0.12 0.01

Minimum (ng/dscm) 0.06 0.03

Maximum (ng/dscm) 0.23 0.04

Skewness N/A N/A

SE Skewness 1.73 1.73

Skewness Test N/A N/A

Kurtosis N/A N/A

SE Kurtosis 3.46 3.46
15 

Parameter Normal Lognormal

Kurtosis Test N/A N/A

99.0% t-statistic for UL 6.96 6.96

99.0% UL (ng/dscm) 0.96 1.08

99.0% UPL (ng/dscm) 3.54 1.24

UL and UPL Values Using Normally Distributed Data

99% UL (ng/dscm) 0.96

99% UPL (ng/dscm) 3.54

UL and UPL Values Using LN Transformed Data

99% UL (ng/dscm) 1.08

99% UPL (ng/dscm) 1.24

ng/dscm – Nanograms per dry standard cubic meter adjusted to 7 percent oxygen 

SE – Standard error (applied to skewness or kurtosis) 

UL - Upper Limit 

UPL – Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

*Data is not sufficient to establish MACT floor limits as represented in this table 

New source limits for THC and D/F were determined by selecting the lowest emitting 

source for each furnace group for each HAP. There is no reason to think that control of THC and 

D/F cannot be optimized simultaneously, since the same technology (temperature control) is 

used to reduce emissions of both, and the range of temperature for minimizing THC is consistent 

with that for controlling D/F.4,5 Hence, the EPA sees no technical objection to evaluating each 

HAP separately in determining which source is best performing. A variability analysis similar to 

that used for existing sources was then performed using the 3 run test data from the top source. 

For THC and D/F from rotary furnaces and D/F from collocated blast and reverberatory 

furnaces, the MACT floor was calculated based on emissions data from one source; therefore, 

the new source MACT floor is the same as the existing source MACT floor. As noted above, 

emission limits for D/F and THC from rotary furnace are not being promulgated due to 

insufficient data. Results of the new source variability analyses for the other furnace groups for 

D/F and THC are presented in Tables 3-9 through 3-11. 

Table 3-9. New Source MACT Floor and Variability Analysis Results for THC – 

Reverberatory Furnaces Not Collocated with Blast Furnaces and Reverberatory Furnaces 

Mixed with Electric Furnaces 

Parameter Normal Lognormal

Sample Size 3 3

 

4

Ficarella, Antonio and Laforgia, Domenico.” Numerical Simulation of Flow-Field and Dioxins Chemistry for 

Incineration Plants and Experimental Investigation.” Waste Management, 20 (2000) 27-49. 

5 EPA’s Air Pollution Control Technology Fact Sheet, available at: http://www.epa.gov/ttn/catc/dir1/fthermal.pdf
16 

Parameter Normal Lognormal

Average (ppmv) 3.30 1.13

Median (ppmv) 2.78 1.02

Standard Deviation (ppmv) 1.51 0.44

Minimum (ppmv) 2.12 0.75

Maximum (ppmv) 5.00 1.61

Skewness 1.37 1.02

SE Skewness 1.41 1.41

Skewness Test Normal normal

Kurtosis N/A N/A

SE Kurtosis 2.83 2.83

Kurtosis Test N/A N/A

99.0% t-statistic for UPL 6.96 6.96

99.0% UPL (ppmv) 11.87 37.40

UPL Value Using Normally Distributed Data

99% UL (ppmv) 11.87

UPL Value Using LN Transformed Data

99% UL (ppmv) 37.40

ppmv – Parts per million volume expressed as propane, adjusted to 4 percent carbon dioxide 

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN – Log-Normal 

N/A – Not applicable 

Table 3-10. New Source MACT Floor and Variability Analysis Results for D/F – 

Reverberatory Furnaces Not Collocated with Blast Furnaces and Reverberatory Furnaces 

Mixed with Electric Furnaces 

Parameter Normal Lognormal

Sample Size 3 3

Average (ng/dscm) 0.08 -2.52

Median (ng/dscm) 0.08 -2.52

Standard Deviation (ng/dscm) 0.00 0.04

Minimum (ng/dscm) 0.08 -2.48

Maximum (ng/dscm) 0.08 -2.55

Skewness 0.33 0.28

SE Skewness 1.41 1.41

Skewness Test Normal normal

Kurtosis N/A N/A

SE Kurtosis 2.83 2.83
17 

Parameter Normal Lognormal

Kurtosis Test N/A N/A

99.0% t-statistic for UPL 6.96 6.96

99.0% UPL (ng/dscm) 0.10 0.10

UPL Value Using Normally Distributed Data

99% UPL (ng/dscm) 0.10

UPL Value Using LN Transformed Data

99% UPL (ng/dscm) 0.10

ng/dscm – Nanograms per dry standard cubic meter adjusted to 7 percent oxygen 

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN-Log-Normal 

N/A – Not applicable 

Table 3-11. New Source MACT Floor and Variability Analysis Results for D/F – Blast 

Furnaces 

Parameter Normal Lognormal

Sample Size 3 3

Average (ng/dscm) 1.30 0.26

Median (ng/dscm) 1.23 0.22

Standard Deviation (ng/dscm) 1.43 1.38

Minimum (ng/dscm) 1.20 0.18

Maximum (ng/dscm) 1.44 0.37

Skewness 1.43 1.38

SE Skewness 1.41 1.41

Skewness Test Normal normal

Kurtosis N/A N/A

SE Kurtosis 2.83 2.83

Kurtosis Test N/A N/A

99.0% t-statistic for UPL 6.96 6.96

99.0% UPL (ng/dscm) 9.45 3394.06

UPL Value Using Normally Distributed Data

99% UPL (ng/dscm) 9.45

UPL Value Using LN Transformed Data

99% UPL (ng/dscm) 3394.06

ng/dscm – Nanograms per dry standard cubic meter adjusted to 7 percent oxygen 

SE – Standard error (applied to skewness or kurtosis) 

UPL - Upper prediction limit 

LN-Log-Normal 

N/A – Not applicable 
18 

4.0 BEYOND THE FLOOR 

To evaluate opportunities for emissions reductions beyond those that would result from 

requiring facilities to meet the MACT floor emissions limits, we typically identify control 

techniques that have the ability to achieve an emissions limit more stringent than the MACT 

floor and evaluate whether the standard should be more stringent based on consideration of the 

factors enumerated in section 112 (d)(2) of the Act. The following paragraphs summarize the 

results of this “beyond-the-floor” analysis for the affected sources in the Secondary Lead 

Smelting source category.

Through our technology review discussed in the Summary of the Technology Review for 

the Secondary Lead Smelting Source Category document available in the docket, we identified 

one beyond-the-floor option to achieve further reduction in emissions of D/F and THC. This 

option is the installation of additional afterburner capacity at the facilities operating blast 

furnaces. This would include operating the currently installed afterburners at a temperature of 

1600 degrees Fahrenheit (°F) with a residence time of 2.5 seconds, or installation of a new 

afterburner with these specifications at any facility currently operating a blast furnace without 

the use of an afterburner or with an afterburner that does not meet the mentioned specifications. 

Based on available information and our analysis described in the technology review, we estimate 

that these conditions would result in significant decreases in D/F and THC emissions. 

Based on the current level of performance identified in the ICR surveys, we believe that 

this beyond-the-floor option would require four facilities to install additional capacity at their 

existing afterburners to operate the units at a temperature of 1600 °F with a residence time of 2.5 

seconds. The estimated total capital cost for the additional controls is $5.9 million, with a total 

annualized cost of $2.9 million. Based on an estimated control efficiency of 98 percent, TEQ 

emissions would be reduced by 31 grams per year and organic HAP emissions by 200 tons per 

year, resulting in a total estimated cost effectiveness of $93,500 per gram of TEQ and $14,500 

per ton of organic HAP. There would also be significant adverse energy implications to use of 

this technology, resulting in additional emissions of NOx and CO2 of 19 and 22,000 tons per 

year, respectively, as well as decreased energy security. There would not be collateral waste or 

water generation from use of these afterburners. 

5.0 SUMMARY OF MACT FLOOR ANALYSIS 

We determined emissions limits for each MACT floor option and pollutant by rounding 

the UPL values less than 1 up to one significant figure and rounding the UPL values between 1 

and 10,000 up to two significant figures. This approach allows for an appropriate level of 

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

were rounded down, then the possibility exists that the best-performing units that comprise the 

MACT floor may not be able to achieve the emissions limit on an ongoing basis (a clear 
19 

indication that these sources’ performance would be characterized improperly). In all cases, the 

significant figure approach and associated rounding does not meaningfully change the emissions 

limits. Tables 5-1 through 5-4 summarize the MACT floor limits calculated in document. 

Table 5-1. Summary of Calculated THC Floor Limits for Existing Sources 

Furnace Group

Average of Top 5

Performers

99% UPL MACT Floor

Emissions Limit

Reverberatory furnaces not collocated with 

blast furnaces and reverberatory furnaces 

mixed with electric furnaces

5.53 12.01 13

Rotary Furnaces 256.73 601.79* 610**

*Calculated MACT floor emissions limit for rotary furnaces calculated based on the UL. 

**Data is insufficient to establish a MACT floor for this source. 

Table 5-2. Summary of Calculated D/F Floor Limits for Existing Sources 

Furnace Group

Average of Top 5

Performers

99% UPL MACT Floor

Emissions Limit

Reverberatory furnaces not collocated with blast 

furnaces and reverberatory furnaces mixed with 

electric furnaces

0.10 0.17 0.2

Blast Furnaces 38.83 163.2 170

Collocated Blast and Reverberatory Furnaces 0.19 0.42 0.5

Rotary Furnaces 0.14 0.96* 1.0**

*Calculated MACT floor emissions limit for rotary furnaces is based on the Upper Limit (UL). 

**Data is insufficient to establish a MACT floor for this source. 

Table 5-3. Summary of Calculated THC Floor Limits for New Sources 

Furnace Group

Average of Top

Performer

99% UPL MACT Floor

Emissions Limit

Reverberatory furnaces not collocated with blast 

furnaces and reverberatory furnaces mixed with 

electric furnaces

3.30 11.81 12

Rotary Furnaces 256.73 601.79* 610**

*Calculated MACT floor emissions limit for rotary furnaces is based on the Upper Limit (UL). 

**Data is insufficient to establish a MACT floor for this source. 

Table 5-4. Summary of Calculated D/F Floor Limits for New Sources 

Furnace Group

Average of Top

Performer

99% UPL MACT Floor

Emissions Limit

Reverberatory furnaces not collocated with blast 

furnaces and reverberatory furnaces mixed with 

electric furnaces

0.08 0.10 0.1

Blast Furnaces 1.30 9.45 10

Collocated Blast and Reverberatory Furnaces 0.19 0.42 0.5
20 

Rotary Furnaces 0.14 0.96* 1.0**

*Calculated MACT floor emissions limit for rotary furnaces is based on the Upper Limit (UL). 

**Data is insufficient to establish a MACT floor for this source. 
21 

Appendix A. Total Hydrocarbons Emissions Data 

Facility ID Emissions Point Furnace Type Sample ID THC (ppmv @ 

4% CO2)

In top 5?

Quemetco IN EP1 Reverb/Electric Sample 1 7.94 Y

Quemetco IN EP1 Reverb/Electric Sample 2 6.51 Y

Quemetco IN EP1 Reverb/Electric Sample 3 8.83 Y

RSR EP17 Reverb Sample 1 2.12 Y

RSR EP17 Reverb Sample 2 2.78 Y

RSR EP17 Reverb Sample 3 5.00 Y

ND = Non-Detect 
22 

Appendix B. Dioxins and Furans (TEQ) Emissions Data 

Facility ID Emissions Point Furnace Type Sample ID

TEQ 

(ng/dscm @ 

7% O2)

In top 5?

Quemetco CA WESP Reverb Sample 1 0.08 Y

Quemetco CA WESP Reverb Sample 2 0.08 Y

Quemetco CA WESP Reverb Sample 3 0.08 Y

Quemetco IN EP1 Reverb/Electric Sample 1 0.08 Y

Quemetco IN EP1 Reverb/Electric Sample 2 0.14 Y

Quemetco IN EP1 Reverb/Electric Sample 3 0.13 Y

RSR EP17 Reverb/Electric Sample 1 0.04 Y

RSR EP17 Reverb Sample 2 0.17 Y

RSR EP17 Reverb Sample 3 0.07 Y

Sanders EP5 Blast Sample 1 39.00 Y

Sanders EP5 Blast Sample 2 55.56 Y

Sanders EP5 Blast Sample 3 134.51 Y

Exide Forest City EP01 Blast Sample 1 1.44 Y

Exide Forest City EP01 Blast Sample 2 1.20 Y

Exide Forest City EP01 Blast Sample 3 1.25 Y

East Penn C02 Collocated Blast/Reverb Sample 1 0.21 Y

East Penn C02 Collocated Blast/Reverb Sample 2 0.21 Y

East Penn C02 Collocated Blast/Reverb Sample 3 0.14 Y

TEQ = toxic equivalency quotient 
23 

Appendix C. World Health Organization’s (WHO) Recommended Toxic Equivalency 

Factors (TEF) 

Dioxin/Furan Congener Toxic Equivalency Factor

2,3,7,8-tetrachlorinated dibenzo-p-dioxin 1

1,2,3,7,8-pentachlorinated dibenzo-p-dioxin 0.5

1,2,3,4,7,8-hexachlorinated dibenzo-p-dioxin 0.1

1,2,3,7,8,9-hexachlorinated dibenzo-p-dioxin 0.1

1,2,3,6,7,8-hexachlorinated dibenzo-p-dioxin 0.1

1,2,3,4,6,7,8-heptachlorinated dibenzo-p-dioxin 0.01

octachlorinated dibenzo-p-dioxin 0.001

2,3,7,8-tetrachlorinated dibenzofuran 0.1

2,3,4,7,8-pentachlorinated dibenzofuran 0.05

1,2,3,7,8-pentachlorinated dibenzofuran 0.5

1,2,3,4,7,8-hexachlorinated dibenzofuran 0.1

1,2,3,6,7,8-hexachlorinated dibenzofuran 0.1

1,2,3,7,8,9-hexachlorinated dibenzofuran 0.1

2,3,4,6,7,8-hexachlorinated dibenzofuran 0.1

1,2,3,4,6,7,8-heptachlorinated dibenzofuran 0.01

1,2,3,4,7,8,9-heptachlorinated dibenzofuran 0.01

octachlorinated dibenzofuran 0.001
24 

Appendix D. Secondary Lead Smelters Currently Operating in the U.S and Territories 

FACILITY NAME PARENT COMPANY CITY STATE 

Facility 

Abbreviation 

East Penn 

Manufacturing Co., Inc. 

East Penn Manufacturing 

Co., Inc. 

Lyon Station Pennsylvania East Penn 

EnviroFocus 

Technologies, LLC Gopher Resources, LLC Tampa Florida EnviroFocus 

Gopher Resources, LLC Gopher Resources, LLC Eagan Minnesota Gopher Eagan

Quemetco, Inc., City of 

Industry, Ca 

Eco-Bat Technologies, LTD 

(Quemetco West, LLC) 

City of 

Industry 

California Quemetco CA 

Quemetco, Inc., 

Indianapolis, In 

Eco-Bat Technologies, LTD 

(Eco-Bat Indiana, LLC) Indianapolis Indiana Quemetco IN 

Revere Smelting and 

Refining Corporation 

(RSR) 

Eco-Bat Technologies, LTD 

(Eco-Bat New York, LLC) Middletown New York RSR 

Sanders Lead Company Sanders Lead Company Troy Alabama Sanders Lead

Exide Technologies, 

Baton Rouge, La Exide Technologies, Inc. Baton Rouge Louisiana 

Exide Baton 

Rouge 

Exide Technologies, 

Cannon Hollow 

Recycling Facility 

Exide Technologies, Inc. Forest City Missouri Exide Forest City 

Exide Technologies, 

Frisco, Tx 

Exide Technologies, Inc. Frisco Texas Exide Frisco 

Exide Technologies, 

Muncie, In 

Exide Technologies, Inc. Muncie Indiana Exide Muncie 

Exide Technologies –

Reading Recycling 

Exide Technologies, Inc. Reading Pennsylvania Exide Reading 

Exide Technologies, 

Vernon, Ca 

Exide Technologies, Inc. Vernon California Exide Vernon 

Buick Resource 

Recycling Facility, LLC 

The Doe Run Resources 

Corporation 

Boss Missouri Buick RRF 

The Battery Recycling 

Company 

The Battery Recycling 

Company 

Arecibo Puerto Rico TBR 

Johnson Controls 

Battery Group, Inc. 

Johnson Controls 

International Florence South Carolina JCI