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MEMORANDUM

TO: Conrad Chin, U.S. Environmental Protection Agency, OAQPS

FROM: Beth Friedman, Jeff Harris, and Brad Nelson, EC/R

DATE: October 24, 2011

SUBJECT: Draft MACT Floor Analysis for the Ferroalloys Production Source Category

This memorandum presents the methodology and results of the maximum achievable control technology 

(MACT) floor determinations for particulate matter (PM), hydrochloric acid (HCl), total polycyclic 

aromatic hydrocarbons (PAH), mercury (Hg), and formaldehyde (HCHO) from electric arc furnaces 

(EAF), and PM from product sizing operations for the Ferroalloys Production source category. 

We determined the MACT floors using emissions test data obtained from the 2010 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. We determined the MACT floors based on the results of 

these analyses. We 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: Detailed Emissions Test Data
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1.0 SUMMARY OF EXISTING MACT LIMITS

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

Production source category (40 CFR part 63, subpart XXX) established emissions limits for PM (as a 

surrogate for metal hazardous air pollutants (HAP)). Table 1-1 summarizes the emissions limits in the 

existing NESHAP.

Table 1-1. Emissions Limits in the Ferroalloys NESHAP.

New or Reconstructed or 

Existing Source

Affected Source Applicable PM Emission 

Standards

New or reconstructed Submerged arc furnace 0.23 kilograms per hour per 

megawatt (kg/hr/MW) (0.51 

pounds per hour per megawatt 

(lb/hr/MW) or 35 milligrams per 

dry standard cubic meter 

(mg/dscm) (0.015 grains per dry 

standard cubic foot (gr/dscf)

Existing Open submerged arc furnace producing 

ferromanganese and operating at a furnace 

power input of 22 megawatts (MW) or less

9.8 kg/hr (21.7 lb/hr) 

Existing Open submerged arc furnace producing 

ferromanganese and operating at a furnace 

power input greater than 22 MW

13.5 kg/hr (29.8 lb/hr) 

Existing Open submerged arc furnace producing 

silicomanganese and operating at a furnace 

power input greater than 25 MW

16.3 kg/hr (35.9 lb/hr) 

Existing Open submerged arc furnace producing 

silicomanganese and operating at a furnace 

power input of 25 MW or less

12.3 kg/hr (27.2 lb/hr) 

Existing Semi-sealed submerged arc furnace (primary, 

tapping, and vent stacks) producing 

ferromanganese

11.2 kg/hr (24.7 lb/hr) 

New, reconstructed, or 

existing

Metal oxygen refining process 69 mg/dscm (0.03 gr/dscf)

New or reconstructed Individual equipment associated with the 

product crushing and screening operation

50 mg/dscm (0.022 gr/dscf)

Existing Individual equipment associated with the 

product crushing and screening operation

69 mg/dscm (0.03 gr/dscf)
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As shown in Table 1-1, the current NESHAP does not specify emissions limits for pollutants other than 

PM. Therefore, pursuant to sections 112(d)(2) and (3) of the Clean Air Act (CAA), we have calculated 

MACT floors and conducted beyond the floor analyses for Hg, PAH, formaldehyde, and HCl. Because 

MACT has already been established for PM (metal HAP), we conducted a technology review for those 

emissions. However, we used the MACT floor methodology to evaluate potential revised emissions 

limits based on the recent test program. For addtional detail, see Draft Technology Review for the 

Ferroalloys Production Source Category.

2.0 OVERVIEW OF MACT STANDARD DEVELOPMENT METHODOLOGY

2.1 MACT Determination

Section 112(d) of the CAA requires 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 best-performing sources for source categories with less than 30 

sources (CAA section 112(d)(3)(A) and (B)). The Ferroalloy Production source category consists of 

fewer than 30 sources. Where, as here, there are five or fewer sources, we base the MACT floor limit on 

the average emissions limitation achieved by those sources for which we have data. 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. EPA may not 

consider costs or other impacts in determining the MACT floors. 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 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 emissions variability. Other methods of estimating emissions can be used 

provided that the methods can be shown to provide reasonable estimates of the actual emissions 

performance of a source or sources. In a March 2007 court decision (Sierra Club v. EPA, 479 F. 3d 875 
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[DC Cir. 2007]) (Brick MACT), 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 bestperforming 12 percent of existing sources, not levels EPA considers to be achievable by all 

sources (479 F. 3d at 880–81).

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

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).

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).

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 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. EPA must exercise its judgment, based on an 

valuation of the relevant factors and available data, to determine the level of emissions control that has 

been achieved by the average of the best-performing top five units under variable conditions. The Court 

has recognized that 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 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 MACT and other technology-based standards are necessarily derived from short term emissions test 

data, but such data are not 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, often needs to adjust the short-term data to account for these varying conditions in order to 
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properly estimate these sources’ performance over time. The types of variability that 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.; see also v. Ruckelshaus, 486 F.2d at 396 noting industry point that “a 

single test offered a weak basis” for inferring that plants could meet the standards.)

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 

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 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 prespecified) 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 
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for variability in our MACT floor calculations.1 We did, however, employ alternative statistical methods 

to account for variability when calculating MACT floors for acid gases and formaldehyde due to limited 

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

3.0 MACT DETERMINATION FOR FERROALLOYS SOURCE CATEGORY

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

Production source category. We calculated emissions test averages and ranked the top sources (section 

3.1), performed statistical analyses to account for variability in emissions data (section 3.2), performed

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

the 99% UPL for each pollutant (section 3.4) 

3.1 Ranking the Best Performing Sources

The emissions test data used to calculate the MACT floors were obtained using an ICR survey sent to 

the two existing ferroalloys facilities in April 2010. The data used in our MACT floor calculations, 

including individual emissions test runs, is presented in Appendix A.

The MACT floor emissions limits for Hg, PAH, formaldehyde, and HCl were calculated for furnace 

emissions from both facilities. A MACT floor emission limit was also calculated for PM emissions 

from product sizing/crushing operations at both facilities.

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

each affected source for which emissions data was available. The MACT floors for each pollutant were 

calculated using the units shown in Table 3-1.

Because we had emissions test data for less than five sources (there are only two sources in the source 

category and each provided emissions data for a single furnace and a single product sizing operation), 

the average of the best-performing sources was determined as the average of all available emissions test 

data for each pollutant. One plant provided test data for a unique emission source, metal oxygen refining 

(MOR).

 

1 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. 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.
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Table 3-1. Units of MACT Floor for Ferroalloys NESHAP.

Pollutant Units of MACT Floor

Total PM (Furnace) mg/dscm@ 2% CO2

HCl (Furnace) μg/dscm @ 2% CO2

Hg (Furnace) μg/dscm @ 2% CO2

Total PAH (Furnace) μg/dscm @ 2% CO2

Formaldehyde (Furnace) μg/dscm @ 2% CO2

Total PM (MOR) mg/dscm @ 2% CO2

Total PM (Product Sizing) mg/dscm

3.2 Incorporating Data Variability

Data variability was incorporated after ranking the emissions points. Each test run from the top sources 

was used in the variability analysis. By including multiple emissions tests from facilities, 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 Ferroalloys 

Production MACT floor variability analysis. 

The first step included a determination of whether the concentration data from the best performing 

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 
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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 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 (where 

necessary) 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 logtransformed data. The distribution most similar to a normal distribution was selected as the basis for 
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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 logtransformed reported values, the normal distribution was chosen to be protective of the environment.

After determining the distribution of each dataset, a student’s t-test 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 two 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 both 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:

(Eq. 3)

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-1.

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

from the 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 nondetect values was investigated after determining the 99 percent UPL values.

Because only one facility provided useful test data for furnace PAH (the other facility provided PAH 

data based on the analysis of baghouse dust) and only a single facility performs the MOR process, these 

UPLs were calculated using the three test runs available. 

3.3 Use of Non-Detect Values

At very low emissions levels where emissions tests result in non-detect (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.

We applied the following procedures to account for the effect of measurement imprecision associated 

with a database that includes ND values, and to address the concerns associated with use of a small data 

set, such as the Ferroalloys Production data set for acid gases. As a first step, we reviewed an HCL 

emissions data set for the industrial boilers rule, which represents several hundred emissions tests used 

in the floor calculations (i.e., best performers) for the boilers rule. We presume that data for the best 

performing units also reflect the capabilities of high quality testing companies and laboratories. Further, 

the method detection levels calculated from larger data sets are more representative of the inherent 

measurement variability both within and between testing companies than the limited Ferroalloys 

Production dataset is. We believe that emissions tests conducted with these methods for most 

combustion operations (e.g., fossil fuel, biomass, and waste fired units; brick and clay kilns; Portland 

cement kilns), including ferroalloys production, should produce method detection levels very similar to 

the level of 60 μg/dscm that is the result of this review. 
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Accordingly, we used this value (60 μg/dscm) as the representative method detection level (RDL) for 

HCl. 

Because no such data was available for formaldehyde, we used the following methodology to determine 

the RDL. We identified all of the available reported pollutant specific method detection levels and 

calculated the arithmetic mean value. We deemed the resulting mean of the method detection levels to 

be the RDL. 

The second step in the process is to calculate three times the RDL and compare that value to the 

calculated floor or emissions limit. If three times the representative method detection level was less than 

the calculated floor or emissions limit calculated from the UPL, we would conclude that measurement 

variability was adequately addressed. The calculated floor or emissions limit would need no adjustment. 

If, on the other hand, the value equal to three times the representative method detection level was greater 

than the UPL, we would conclude that the calculated floor or emissions limit does not account entirely 

for measurement variability. If indicated, we substituted the value equal to three times the representative 

method detection level to apply as the adjusted floor or emissions limit. This adjusted value would 

ensure measurement variability is adequately addressed in the floor or the emissions limit.

3.4 MACT Floor and Variability Analysis Results

3.4.1 Existing Sources

Tables 3-2 through 3-8 present the results of the existing source MACT floor and variability analyses.

As described in section 3.3, because of data reported below the detection limit, three times the RDL for 

HCl, and formaldehyde was compared to the calculated UPL. For HCl, the calculated UPL was higher 

than three times the RDL, and thus was selected as the MACT floor for existing sources. For 

formaldehyde, the calculated UPL was less than three times the RDL, and thus three times the RDL was 

selected as the MACT floor for existing sources. Because only a single facility performs MOR 

operations, the UPL is calculated based on the three test runs from that facility.

The test data for PAH were obtained from an outlet stack test from one of the facilities and a baghouse 

dust analysis from the other facility. The outlet PAH concentrations for the stack test were calculated 

using the analytical results and the calculated stack test parameters. The outlet PAH concentrations for 
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the baghouse dust data were calculated using a mass balance around the baghouse by assuming the 

control efficiency of the PAH capture. Because of the uncertainty of the calculated PAH outlet 

concentrations from the baghouse dust data, only the outlet stack test data were used to calculate the 

UPL for PAH. 

Table 3-2. Existing Source MACT Floor and Variability Analysis Results for Furnace PM Total PM (Furnace)

Total Facility Count 2 # Facilities

Ave Top 2- All Runs 8.0 (mg/dscm @ 2% CO2)

Min Run in Top 2 1.2 (mg/dscm @ 2% CO2)

Max Run in Top 2 16.8 (mg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.29

Std Error of Skewness (SES) 1.00

Skewness Test (reported values)

S/SES 0.29

normal S/SES<2

Kurtosis (K) -2.14

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values)

K/SEK -1.07

normal K/SEK<2

99% UPL (normal distribution) 23.44 (mg/dscm @ 2% CO2)

Table 3-3. Existing Source MACT Floor and Variability Analysis Results for Furnace HCl HCl (Furnace)

Total Facility Count 2 # Facilities

Ave Top 2- All Runs 495.8 (μg/dscm @ 2% CO2)

Min Run in Top 2 341.34 (μg/dscm @ 2% CO2)

Max Run in Top 2 659.26 (μg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.05

Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES 0.05

normal S/SES<2

Kurtosis (K) -2.47

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values)

K/SEK -1.24

normal K/SEK<2

99% UPL (normal distribution) 808.65 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 60

3x RDL 180
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Table 3-4. Existing Source MACT Floor and Variability Analysis Results for Furnace Hg Hg (Furnace)

Total Facility Count 2 # Facilities

Ave Top 2- All Runs 24.33 (μg/dscm @ 2% CO2)

Min Run in Top 2 0.52 (μg/dscm @ 2% CO2)

Max Run in Top 2 48.06 (μg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.02

Std Error of Skewness (SES) 1.00

Skewness Test (reported values)

S/SES 0.02

normal S/SES<2

Kurtosis (K) -3.20

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values)

K/SEK -1.60

normal K/SEK<2

99% UPL (normal distribution) 79.92 (μg/dscm @ 2% CO2)

Table 3-5. Existing Source MACT Floor and Variability Analysis Results for Furnace PAH PAH (Furnace)

Total Facility Count 2 # Facilities

Top 1 1 # Facilities

Ave Top 1- All Runs 402.94 (μg/dscm @ 2% CO2)

Min Run in Top 1 305.00 (μg/dscm @ 2% CO2)

Max Run in Top 1 458.82 (μg/dscm @ 2% CO2)

Top 2 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) -1.68

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES -1.19

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 886.87 (μg/dscm @ 2% CO2)
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Table 3-6. Existing Source MACT Floor and Variability Analysis Results

for Furnace Formaldehyde

Formaldehyde (Furnace)

Total Facility Count 2 # Facilities

Ave Top 2- All Runs 67.02 (μg/dscm @ 2% CO2)

Min Run in Top 2 41.87 (μg/dscm @ 2% CO2)

Max Run in Top 2 91.01 (μg/dscm @ 2% CO2)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) -0.17

Std Error of Skewness (SES) 1.00

Skewness Test (reported values) S/SES -0.17

normal S/SES<2

Kurtosis (K) 0.81

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values)

K/SEK 0.40

normal K/SEK<2

99% UPL (normal distribution) 106.21 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 67

3x RDL 201

Table 3-7. Existing Source MACT Floor and Variability Analysis Results

for Product Sizing PM

Total PM (Product Sizing)

Total Facility Count 2 # Facilities

Ave Top 2- All Runs 4.87 (mg/dscm)

Min Run in Top 2 2.21 (mg/dscm)

Max Run in Top 2 10.03 (mg/dscm)

Top 2 Run Count 6 # of runs

Sample Size (n) 6 # of runs

Skewness (S) 0.99

Std Error of Skewness (SES) 1.00

Skewness Test (reported values)

S/SES 0.99

normal S/SES<2

Kurtosis (K) -0.85

Std Error of Kurtosis (SEK) 2.00

Kurtosis Test (reported values)

K/SEK -0.43

normal K/SEK<2

99% UPL (normal distribution) 12.68 (mg/dscm)
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Table 3-8. Existing Source MACT Floor and Variability Analysis Results

for MOR PM

Total PM (MOR)

Total Facility Count 1 # Facilities

Top 1 1 (mg/dscm @ 2% CO2)

Ave All Runs 1.10 (mg/dscm @ 2% CO2)

Min Run 0.61 (mg/dscm @ 2% CO2)

Max Run 1.60 # of runs

Run Count 3 # of runs

Skewness (S) 0.13

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES 0.10

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 3.9 (mg/dscm @ 2% CO2)

3.4.2 New Sources

New source limits were determined by selecting the top performing source. A variability analysis similar 

to that used for existing sources was then performed to calculate a 99% UPL using the three run test data 

from the top source. As described in section 3.4.1, because only one facility provided useful test data for 

furnace PAH (the other provided PAH data based on analysis of baghouse dust), the calculated new 

source UPL for PAH is the same as for existing sources. Because only a single facility performs MOR 

operations, the new source UPL is calculated based on the three test runs from that facility, and is the 

same as for existing sources. As described in Section 1, as MACT has already been established for PM

from crushing and screening operations, we conducted a technology review for those emissions and

adopted the same limit for new and existing crushing and screening sources. 

The 99 percent UPL calculated for HCl, and formaldehyde based on the best-performing source is less 

stringent than the corresponding MACT floor for existing furnaces based on the 99 percent UPL 

calculated for both sources. We determined that the use of this method is not appropriate in this situation 

because the high variability and small data pool would result in a new source MACT floor limit that is 

less stringent than the limit based on the UPL calculated from the larger data pool for existing sources.

Accordingly, in these cases, the 99 percent UPL based on both sources was used for our analyses.
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As described in section 3.3, because of data reported below the detection limit, three times the RDL for 

HCl, and formaldehyde was compared to the calculated UPL. For HCl, the calculated UPL was higher 

than three times the RDL, and thus was selected as the new source MACT floor. For formaldehyde, the 

calculated UPL was less than three times the RDL, and thus three times the RDL was selected as the 

new source MACT floor.

Results of the new source variability analyses are presented in Tables 3-9 through 3-15.

Table 3-9. New Source MACT Floor and Variability Analysis Results for Furnace PM Total PM (Furnace)

Total Facility Count 2 # Facilities

Ave Top 1- All Runs 2.38 (mg/dscm @ 2% CO2)

Min Run in Top 1 1.20 (mg/dscm @ 2% CO2)

Max Run in Top 1 3.62 (mg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) 0.23

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES 0.16

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 23.44 (mg/dscm @ 2% CO2)
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Table 3-10. New Source MACT Floor and Variability Analysis Results for Furnace HCl HCl (Furnace)

Total Facility Count 2 # Facilities

Ave Top 1- All Runs 469.41 (μg/dscm @ 2% CO2)

Min Run in Top 1 341.34 (μg/dscm @ 2% CO2)

Max Run in Top 1 659.26 (μg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) 1.43

Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES 1.01

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 1423.18 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 60 μg/dscm

3x RDL 180 μg/dscm

Table 3-11. New Source MACT Floor and Variability Analysis Results for Furnace Hg Hg (Furnace)

Total Facility Count 2 # Facilities

Ave Top 1- All Runs 3.12 (μg/dscm @ 2% CO2)

Min Run in Top 1 0.52 (μg/dscm @ 2% CO2)

Max Run in Top 1 4.77 (μg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) -1.55

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES -1.09

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 16.06 (μg/dscm @ 2% CO2)
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Table 3-12. New Source MACT Floor and Variability Analysis Results for Furnace PAH PAH (Furnace)

Total Facility Count 2 # Facilities

Top 1 1 # Facilities

Ave Top 1- All Runs 402.94 (μg/dscm @ 2% CO2)

Min Run in Top 1 305.00 (μg/dscm @ 2% CO2)

Max Run in Top 1 458.82 (μg/dscm @ 2% CO2)

Top 2 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) -1.68

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES -1.19

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 886.87 (μg/dscm @ 2% CO2)

Note: Because only one useful data set was available, the calculated new source UPL for PAH is the same 

as for existing sources.

Table 3-13. New Source MACT Floor and Variability Analysis Results

for Furnace Formaldehyde

Formaldehyde (Furnace)

Total Facility Count 2 # Facilities

Ave Top 1- All Runs 56.09 (μg/dscm @ 2% CO2)

Min Run in Top 1 41.87 (μg/dscm @ 2% CO2)

Max Run in Top 1 68.80 (μg/dscm @ 2% CO2)

Top 1 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) -0.49

Std Error of Skewness (SES) 1.41

Skewness Test (reported values) S/SES -0.35

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 133.00 (μg/dscm @ 2% CO2)

RDL (Representative Detection Level) 67

3x RDL 201
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Table 3-14. New Source MACT Floor and Variability Analysis Results

for Product Sizing PM

Total PM (Product Sizing)

Total Facility Count 2 # Facilities

Ave Top 1- All Runs 2.41 (mg/dscm)

Min Run in Top 1 2.21 (mg/dscm)

Max Run in Top 1 2.69 (mg/dscm)

Top 2 Run Count 3 # of runs

Sample Size (n) 3 # of runs

Skewness (S) 1.26

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES 0.89

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 3.83 (mg/dscm)

Table 3-15. New Source MACT Floor and Variability Analysis Results

for MOR PM

Total PM (MOR)

Total Facility Count 1 # Facilities

Top 1 1 (mg/dscm @ 2% CO2)

Ave All Runs 1.10 (mg/dscm @ 2% CO2)

Min Run 0.61 (mg/dscm @ 2% CO2)

Max Run 1.60 # of runs

Run Count 3 # of runs

Skewness (S) 0.13

Std Error of Skewness (SES) 1.41

Skewness Test (reported values)

S/SES 0.10

normal S/SES<2

Kurtosis (K) n/a

Std Error of Kurtosis (SEK) 2.83

Kurtosis Test (reported values)

K/SEK n/a

99% UPL (normal distribution) 3.9 (mg/dscm @ 2% CO2)

Note: Because only one facility performs MOR operations, the calculated new source UPL for PM from MOR is the 

same as for existing sources.
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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 Ferroalloys production source category.

Through our technology review and risk analyses, we identified beyond-the-floor options to achieve 

further reduction in emissions of mercury, PAH, acid gases, and formaldehyde. Additional detail on 

these options is presented below and in Cost Impacts for the Ferroalloys Source Category.

Mercury

For mercury, the beyond-the-floor option we considered is based on setting a MACT limit for existing 

sources based on the performance of the best performing source (i.e., based upon the test data used to 

calculate the MACT floor for new sources) such that the MACT limit for existing sources would be the 

same as the MACT limit for new sources. Compliance would be demonstrated by periodic performance 

testing and continuous parameter monitoring. We estimate that under this beyond the floor option, one 

facility would need to install additional controls such as activated carbon injection to meet the limit of 

16 ug/dscm, and that this would achieve about 420 pounds reduction in mercury emissions. The capitol 

costs are estimated to be $1.7 million, annual costs to be $1.4 million, with an overall cost-effectiveness 

of $3,300 per pound. Overall, we believe these controls are feasible and cost-effective.

PAH 

For PAH emissions, we also evaluated control performance that could be achieved via co-control of 

mercury emissions with activated carbon injection as a beyond-the-floor option. Based on information 

from carbon vendors, an activated carbon system that is designed to achieve a 90 percent reduction in 

mercury emissions (which we believe would be applied to meet the proposed mercury standard 

discussed above) should also achieve a high degree of reduction in PAH with no additional costs.

HCl 

No facilities in the source category use add-on control devices or work practices to limit emissions of 
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HCl beyond what is normally achieved as co-control of the emissions with particulate matter control 

device. There are available add-on control devices, such as dry sorbent injection (DSI) or lime injection 

that is believed to be a viable control technology to reduce HCl emissions from furnaces. The DSI 

control system works by injecting a sodium-based powder such as, Trona (Na2CO3) or sodium 

bicarbonate (NaHCO3) into the flue gas duct upstream of a baghouse used for particulate control. For 

the acid gases, the NaHCO3 or Na2CO3 reacts with HCl to form sodium chloride (NaCl), carbon dioxide 

(CO2) and water (H2O). The NaCl is asolid which can be removed from the exhaust stream using a 

particulate control device. This technology is currently used to reduce acid gas emissions from coalfired boilers and achieves up 90 percent reduction of HCl.

The capital cost for DSI is estimated to be $10 per kilowatt for coal-fired boilers2. Converting this cost 

to an EAF produces an estimated capital cost of $625,000 for a 25 megawatt EAF3. However, the major 

cost of DSI systems is the sorbent4. The DSI control technology has a relatively high operating cost, 

which ranges from $3.4 to $5 per kilowatt-year ($/kW-yr)5, which averages to a cost of $263,000 a year 

for a 25 megawatt EAF. In addition, there are currently only a few suppliers of Trona, which is 

primarily mined in the western United States. Therefore, transporting the supply of Trona to facilities in 

the eastern United States could increase the delivered cost of Trona from $150 per ton to approximately 

$200 per ton. 

The use of sodium-based DSI also affects the marketability and beneficial use of baghouse fly ash, 

which is currently used in the concrete, wallboard and asphalt products industries as well as road and 

bridge construction. This is because sodium-based dry sorbents are soluble and contribute greatly to the 

available alkali content. As a result, fly ash produced with emissions controls systems employing 

sodium-based DSI technologies exceed the available alkali criteria established by the American 

Association of State Highway and Transportation Officials for concrete. Therefore, the collected fly ash 

from DSI technologies would need to be disposed of in a landfill, and would prevent the current practice

of reusing or selling baghouse dust and the resulting waste reduction benefits. 

 

2 URS, Assessment of Technology Options Available to Achieve Reductions of Hazardous Air Pollutants, April 5, 2011.

3 Assumes 40 percent efficiency for the coal-fired boiler.

4 Sargent & Lundy, IPM Model – Revisions to Cost and Performance for APC Technologies, Dry Sorbent Injection Cost 

Development Methodology Final, August 2010.

5 URS, Assessment of Technology Options Available to Achieve Reductions of Hazardous Air Pollutants, April 5, 2011.
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Formaldehyde 

We have not identified any appropriate beyond-the-floor control technology options specifically for 

formaldehyde. Control technologies used to reduce formaldehyde emissions include thermal incineration 

and catalytic oxidation. However, these control technologies are not technologically or economically 

feasible for EAF emissions because of the high volume of exhaust gas that would need to be treated.

We recognize the potential for some co-control of formaldehyde emissions that would be achieved by 

using activated carbon injection to control mercury emissions, but we were unable to quantify those 

reductions.

5.0 SUMMARY OF MACT FLOOR ANALYSIS

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

up to two significant figures. 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. In all cases, the rounding does not meaningfully change 

the emissions limits. Tables 5-1 and 5-2 summarize the MACT floor limits calculated in this document.
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Table 5-1. Summary of Calculated MACT Floor Limits for Existing Ferroalloys Sources

Pollutant Emission Point

Average for 

Top 2 Sources* 99% UPL 3x RDL

MACT Floor 

Emissions Limit Units

P M Furnace Stack 8.00 23.44 2 4 mg/dscm @ 2% CO2

Hg Furnace Stack 24.33 79.92 1 6 μg/dscm @ 2% CO2

PAH Furnace Stack 402.94 886.87 8 9 μg/dscm @ 2% CO2

HCl Furnace Stack 495.80 808.65 809 μg/dscm @ 2% CO2

HF Furnace Stack 445.80 801.25 802 μg/dscm @ 2% CO2

Formaldehyde Furnace Stack 67.02 201 201 μg/dscm @ 2% CO2

P M Fugitive Collection Stack 8.00 23.44 2 4 mg/dscm @ 2% CO2

P M MOR Stack 1.1 3.9 3.9 mg/dscm @ 2% CO2

P M Product Sizing Stacks 4.87 12.68 1 3 mg/dscm

* Top performing single source for PAH & PM (MOR)

Table 5-2. Summary of Calculated MACT Floor Limits for New or Reconstructed Ferroalloys Sources

Pollutant Emission Point

Average for 

Top Source 99% UPL 3x RDL

MACT Floor 

Emissions Limit Units

P M Furnace Stack 2.38 9.26 9.2 mg/dscm @ 2% CO2

Hg Furnace Stack 24.33 79.92 1 6 μg/dscm @ 2% CO2

PAH Furnace Stack 402.94 886.87 8 9 μg/dscm @ 2% CO2

HCl Furnace Stack 495.80 808.65 809 μg/dscm @ 2% CO2

HF Furnace Stack 445.80 801.25 802 μg/dscm @ 2% CO2

Formaldehyde Furnace Stack 67.02 201 201 μg/dscm @ 2% CO2

P M Fugitive Collection Stack 8.00 23.44 2 4 mg/dscm @ 2% CO2

P M MOR Stack 1.10 3.91 3.9 mg/dscm @ 2% CO2

P M Product Sizing Stacks 4.87 12.68 1 3 mg/dscm
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Appendix A: Detailed Emissions Test Data

Test Facility Pollutant Sample Location Test Method

Run 1 

Concentration 

Run 2 

Concentration 

Run 3 

Concentration 

AVG 

Concentration Units Notes

Felman P M Furnace #2 Baghouse Outlet Method 5D 2.32 1.20 3.62 2.38 mg/dscm@ 2% CO2

Eramet P M Furnace #12 Scrubber Exhaust Method 5 10.95 13.09 16.84 13.62 mg/dscm@ 2% CO2 The blank acetone rinse fraction reported a weight gain of 0.4 mg, therefore the acetone 

samples were weight corrected.

Felman HCl Furnace #2 Baghouse Outlet Method 26A 341.34 407.63 659.26 469.41 μg/dscm @ 2% CO2 The run and blank samples were reported below the reporting limit. The reporting limit 

was used to estimate the HCl concentrations and emissions.

Eramet HCl Furnace #12 Scrubber Exhaust Method 26A 591.89 584.07 390.61 522.19 μg/dscm @ 2% CO2 The corrected lab result was blank corrected using the detected value for the H20 blank 

sample which was 0.08 mg/L.

Felman Hg Furnace #2 Baghouse Method 29 0.52 4.77 4.07 3.12 μg/dscm @ 2% CO2 Several of the front and back half fractions were reported below the detection limit. The 

method reporting limit was used to calculate the concentration.

Eramet Hg Furnace #12 Scrubber Exhaust Method 29 48.06 41.87 46.68 45.53 μg/dscm @ 2% CO2 This pollutant was reported below the detection limit in most of the fractions for Runs 1, 

2, 3. The detected fractions and the detection limit for the fractions reported below the 

detection limit were used to calculate the concentration. Felman Total PAH Furnace #2 Baghouse Method 8270D 0.09 0.12 0.17 0.13 μg/dscm @ 2% CO2 The test data for PAH was obtained from an analysis of the baghouse dust.

Eramet Total PAH Furnace #12 Scrubber Exhaust Method HRPAH 305.00 445.00 458.82 402.94 μg/dscm @ 2% CO2 The test data for PAH was obtained from the scrubber outlet exhaust using Method 0010. 

The test results were field blank corrected

Felman Formaldehyde Furnace #2 Baghouse Outlet Method 316 41.87 57.59 68.80 56.09 μg/dscm @ 2% CO2 The analytical results for Runs 1, 2, and 3 were reported below the detection limit. The 

reporting limit was used to estimate the concentration.

Eramet Formaldehyde Furnace #12 Scrubber Exhaust Method 316 74.38 68.49 91.01 77.96 μg/dscm @ 2% CO2

Felman PM Sizing System #2 Method 29 2.21 2.69 2.33 2.41 mg/dscm The concentration and emission rate were revised based on a PM correction submittal by 

the facility in an email dated 12/22/2010.

Eramet PM C2P Baghouse Exhaust Method 29 7.78 10.03 4.18 7.33 mg/dscm The blank acetone rinse fraction reported a weight gain of 0.4 mg, therefore the acetone 

samples were weight corrected.

Eramet P M MOR Baghouse Exhaust Method 29 1.60 0.61 1.09 1.10 mg/dscm@ 2% CO2 The blank acetone rinse fraction reported a weight gain of 0.4 mg, therefore the acetone 

samples were weight corrected.