1 

MEMORANDUM 

To: Amy Hambrick, U.S. EPA, Sector Policies and Programs Division/Natural 

Resources and Commerce Group 

From: Eastern Research Group, Inc. 

Date: January 2011 

Subject: Revised MACT Floor Analysis for the Sewage Sludge Incinerator Source 

Category 

1.0 INTRODUCTION AND SUMMARY OF RESULTS 

The U.S. Environmental Protection Agency (EPA), under section 129 of the Clean Air 

Act (CAA), is required to regulate emissions of nine pollutants from sewage sludge incineration 

(SSI) units. The nine pollutants are: hydrogen chloride (HCl), carbon monoxide (CO), lead (Pb), 

cadmium (Cd), mercury (Hg), particulate matter (PM), total mass basis dioxins/furans (TMB 

PCDD/PCDF) and toxic equivalency basis dioxin/furans (TEQ PCDD/PCDF), nitrogen oxides 

(NOx), and sulfur dioxide (SO2). The purpose of this memorandum is to present the methodology 

used to determine the maximum achievable control technology (MACT) floor limits for SSI units. 

 The MACT floor analysis was conducted using emissions data obtained from responses to an 

Information Collection Request (ICR). Emissions test information for each of the Section 129 

pollutants was analyzed, MACT floor averages were calculated, and then a data variability 

analysis was conducted. The MACT floor emissions limits were determined based on the results 

of these analyses. 

Table 1-1 summarizes the MACT floor limits for existing sources regulated by the SSI 

emission guidelines (EG) and the MACT floor limits for new sources regulated by the SSI new 

source performance standards (NSPS). The table presents limits for two subcategories under the 

EG and the NSPS: multiple hearth incineration units (MH) and fluidized bed incineration units 

(FB). For both the MH and FB new source subcategories, the best performing FB unit was used 

as the best performing similar source. The discussion of the SSI subcategories is presented in 

section 3.0. 

Table 1-1. Summary of MACT Floor Limits for Sewage Sludge Incinerators 

Pollutant Units 

EG NSPS 

FB MH FB MH 

Cd mg/dscm 0.0016 0.095 0.0011 0.0024 

Pb mg/dscm 0.0074 0.30 0.00062 0.0035 

Hg mg/dscm 0.037 0.28 0.0010 0.15 

PM mg/dscm 18 80 9.6 60 

HCl ppmvd 0.51 1.2 0.24 1.2 
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Table 1-1. Summary of MACT Floor Limits for Sewage Sludge Incinerators 

(Continued) 

Pollutant Units 

EG NSPS 

FB MH FB MH 

SO2 ppmvd 15 26 5.3 26 

NOx ppmvd 150 220 30 210 

CO ppmvd 64 3,800 27 52 

PCDD/PCDF 

TMB 

ng/dscm 1.2 5.0 0.013 0.045 

PCDD/PCDF 

TEQ 

ng/dscm 0.10 0.32 0.0044 0.0022 

The memorandum is organized as follows: 

2.0 Background of MACT Floor Methodology 

3.0 Development of Subcategories for SSI Source Category 

4.0 MACT Floor Determination Methodology 

5.0 Summary of Results 

6.0 References 

2.0 BACKGROUND OF MACT FLOOR METHODOLOGY 

All standards established pursuant to CAA Section 129(a)(2) must reflect MACT, the 

maximum degree of reduction in emissions of certain listed air pollutants that the Administrator, 

taking into consideration the cost of achieving such emission reduction, and any non-air quality 

health and environmental impacts and energy requirements, determines is achievable for each 

category. This level of control is referred to as a MACT standard. 

The minimum level of stringency is called the MACT “floor,” and CAA Section 129(a)(2) 

sets forth differing levels of minimum stringency that EPA’s standards must achieve, depending 

on whether they regulate new or existing sources. For new units, the MACT floor cannot be less 

stringent than the emission control that is achieved in practice by the best controlled similar unit. 

Emission standards for existing units may be less stringent than standards for new units, but 

cannot be less stringent than the average emission limitation achieved by the best performing 12 

percent of units in the category. These requirements constitute the MACT floor for new and 

existing sources; however, 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 
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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 [DC Cir. 2007]) 

(Brick MACT) EPA was directed to set MACT floors with the following guidance: 

• Floors for existing sources must reflect the average emission limitation achieved by the 

best-performing 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, including 

National Lime Association, 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 nontechnology factors affect emissions violates the Act” (479 F. 3d at 883). 

• EPA may not use emission levels of the worst performers to estimate variability of the best 

performers without a demonstrated relationship between the two (479 F. 3d at 882). 

These decisions were incorporated into the MACT floor analysis for SSI units. 

3.0 DEVELOPMENT OF SUBCATEGORIES FOR SSI SOURCE CATEGORY 

The Clean Air Act allows EPA to subcategorize a source category based on differences in 

class, type, or size. There are two types of incinerators currently used to combust sewage sludge 

(or biosolids): MH units and FB units. Of the 204 sewage sludge incineration units in operation, 

60 use the FB design, while 144 use the MH design. The two types are significantly different 

combustor designs. 

A MH unit consists of a vertical cylinder containing from 6 to 12 horizontal hearths and a 

rotating center shaft with rabble arms. Biosolids enter the top hearth and flows downward while 

combustion air flows from the bottom to the top. The MH is divided into three zones. The upper 

hearths comprise the drying zone in which biosolids, water, and some organic compounds are 

evaporated. The middle hearths comprise the combustion zone. The exposure to the combustion 

gas and biosolids to high temperature is only in this section and residence time of the gas is short. 

The lower hearths form the cooling zone, where ash is cooled as its heat is transferred to the 

incoming combustion air. Multiple hearth units are sensitive to any change in the feed, such as 

feed moisture and feed rate. Since the emissions of CO and organic compounds are dependent on 

the temperature of the top hearth, any changes occurring in the biosolids input can cause 

operational upset with momentary drop in top hearth temperature and increase in emissions.1
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In an FB unit, the reactor is a vertical steel shell comprised of four sections. The lower 

section is called the windbox and acts as a plenum in which combustion air is received. Above the 

windbox is a refractory arch. The section above the refractory arch is filled with sand and is 

called the bed area or combustion zone. Hot air is distributed homogeneously throughout the 

fluidized bed. The intensive mixing of the solid and gas in the fluidized state results in a high heat 

transfer resulting in rapid combustion of the biosolids. The section above the bed is the freeboard 

or disengagement zone. The freeboard provides six to seven seconds of gas residence time which 

complete the combustion of any volatile hydrocarbons escaping from the bed.1 

The differences between the two combustor designs result in significant differences in 

emissions, size of the flue gas stream, ability to handle variability in the feeds, control of 

temperature and other process variables, auxiliary fuel use, and other characteristics. Generally, 

FB units have lower emissions of organic compounds, PCDD/PCDF, and CO than MH units due 

to the combustion temperature, mixing, and residence time differences.1,2 This comparison is 

demonstrated in EPA’s Compilation of Emission Factors (also referred to as AP-42)2, and in the 

emissions information gathered to support developing these proposed standards.3 Intermittent 

operations, involving frequent shutdown and startup, are generally easier and more rapid for FB 

units than MH units. Additionally, FB units have better capability of handling feeds with varying 

moisture and volatile contents. Lower excess air and auxiliary fuel is required to operate FB units 

resulting in smaller flue gas flow rates and consequently smaller sized downstream control 

devices. 

To reflect the differences in their combustion mechanisms, two subcategories, FB and 

MH, were developed for existing and new SSI sources. 

4.0 MACT FLOOR DETERMINATION METHODOLOGY 

Section 129(a)(2) of the CAA requires that EPA determine the emission control that is 

achieved in practice by the best controlled similar unit when establishing the MACT floor for new 

units, and the average emission limitation achieved by the best performing 12 percent of units 

when establishing the MACT floor for existing units. EPA must exercise its judgment, based on 

an evaluation of the relevant factors and available data, to determine the level of emission control 

that has been achieved by the best performing SSI 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 EPA may consider emission 

variability in estimating performance achieved by best-performing sources and may set the floor at 

a level that best-performing sources can expect to meet “every day and under all operating 

conditions”). 

Maximum Achievable Control Technology 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.4 EPA, therefore, often 

needs to adjust the short-term data to account for these varying conditions. The types of 
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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 emission level for 

best performing sources needs to account for these differences between tests, in order for “a 

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

reasonably be expected to recur[.]” (See NLA I, 627 F.2d at 431, n. 46.) (See also Portland 

Cement Ass’n, 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 and 40 CFR 60.8(f) for NSPS under CAA Section 111.) 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 determine the 

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

observations (e.g., test runs), and does not embody a 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 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 background samples taken. Given this definition, the UPL 

represents the value 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), we can be 99 percent confident that the reported level will 

fall below a MACT floor emission limit calculated using a UPL. Since 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 emission limit, it would be 

appropriate to use a future test condition of one. 

The UPL is an appropriate statistical tool to use in determining variability in the SSI data. 

For this source category, where there is a limited sampling of the source category and we do not 

have test data from all of the SSI units in the best performing 12% for each subcategory, the 

predictive aspect of the UPL calculation is especially important. (See sections 4.1 through 4.3 for 

further discussion of the dataset and the statistical approach.) 
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4.1 MACT Floor Analyses Dataset

As stated earlier, the CAA requires that MACT for new sources be no less stringent than 

the emission control achieved in practice by the best controlled similar unit. For existing sources, 

the CAA requires that MACT be no less stringent than the average emission limitation achieved 

by the best performing 12 percent of units in a source category. Because the number of units in 

different subcategories may be different, the number of units that represent the best performing 12 

percent of sources in different subcategories may be different. Also, mathematically, the number 

of units that represent the best performing 12 percent of the units in a subcategory will not always 

be an integer. To ensure that each MACT standard is based on at least 12 percent of the units in 

a subcategory, EPA has determined that it is appropriate to always round up to the nearest integer 

when 12 percent of a given subcategory is not an integer. For example, if 12 percent of a 

subcategory is 4.1, the standards will be based on the best performing five units even though 

rounding conventions would normally lead to rounding down to four units. As discussed earlier, 

there are 204 SSI units, composed of 144 multiple hearth units and 60 fluidized bed units. This 

procedure results in a top 12 percent comprising 18 MH units and eight FB units. 

Information collection request surveys were sent to nine municipalities operating SSI units 

to collect emissions information. To select the surveyed owners, EPA reviewed the inventory of 

SSI units for the control devices being operated, and identified a subset of units expected to have 

the lowest emissions based on the type of unit and the installed air pollution controls. EPA 

believes these controls achieve the most reductions possible for the CAA Section 129 pollutants, 

and thereby allow EPA to identify for each pollutant the units with the lowest emissions. For 

example, units were selected that operated more than one of the following technologies: activated 

carbon injection to reduce Hg and PCCD/PCDF; regenerative thermal oxidizers or afterburners to 

reduce CO and organics; wet electrostatic precipitators to reduce fine particulate; high efficiency 

scrubbers such as packed bed scrubbers and impingement tray scrubbers to reduce PM, Cd, Pb, 

particulate Hg, and acid gases such as HCl and SO2; and units with multiple control devices that 

could reduce PM, Cd, Pb, particulate Hg, such as venturi scrubber in combination with 

impingement scrubbers and wet ESPs or with another particulate control device. 

EPA considered whether non-technology factors, such as the characteristics of the waste 

combusted, would influence which sources are the best performing. In contrast to regulated 

MWC units or CISWI units, SSI units receive a more homogenous type of waste to burn. There 

are variations in the amount of each of the CAA Section 129 pollutants present depending on the 

regions, climates, and sources of wastewater (commercial or residential). The variations are not 

as significant as variations that would occur if different types of materials were combusted, e.g., 

sewage sludge, coal, gas, oil, wood, municipal waste, etc. All SSI units are required to meet the 

EPA’s Office of Water SSI discharge and emission requirements (40 CFR part 503). Part 503 

establishes daily average concentration limits for Pb, Cd, and other metals in sewage sludge that is 

disposed of by incineration. Part 503 also requires that SSI units meet the National Emission 

Standards for Beryllium and Hg in subparts C and E, respectively, of 40 CFR part 61. In order to 

meet the 40 CFR part 503 standards, facilities are already incorporating management practices 

and measures to reduce waste and limit the concentration of pollutants in the sludge sent to SSI 

units, such as segregating contaminated and uncontaminated wastes and establishing discharge 

limits or pre-treatment standards for non-domestic users discharging wastewater to publicly 

owned treatment works (POTWs). Thus, non-technology measures to reduce emission are 
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already being taken. As a result, the data used to develop the MACT emission limits reflects the 

control technologies used at each facility, and the other HAP emission reduction approaches, such 

as management practices each facility is following to comply with the part 503 standards. 

The nine companies selected to receive the ICR operate 20 SSI units with the most 

efficient pollution controls that would reduce emissions of CAA section 129 pollutants of interest. 

 Therefore, the sources identified for testing and the resulting emission information received from 

the surveyed SSI units represent the best performing SSI units. Six of the nine operate MH units, 

resulting in 13 MH units surveyed. Three of the nine operate FB units, resulting in seven FB units 

surveyed. Some owners of multiple units at a facility provided information for less than the total 

number they operated, e.g. 1 unit instead of 2, because not all units were in operation during the 

test period. Of those 20 units from the nine surveyed municipalities, EPA collected data from 17 

units that were in operation (11 MH units and six FB units). The surveyed information was 

supplemented with test information for nine MH SSI units collected from state environmental 

agencies public databases. In total, emissions information were collected from six FB units and 

20 MH units from facilities responding to the ICR and additional test reports provided by state 

environmental agencies. Attachment H identifies the control devices used at FB and MH units 

with advanced controls and identifies the nine companies that were sent ICR surveys. 

Although emissions information was collected from six FB units and 20 MH units, not 

every test report contained information on all pollutants. Except for PCDD/PCDF and SO2, test 

information for most of the nine CAA 129 pollutants was available from six FB units. 

PCDD/PCDF data was provided for five FB units, and SO2 data were only available for four units. 

 One facility (operating two FB units) provided “zero” values for SO2 emissions, without 

providing detection limits. Depending on the pollutant, the number of MH units with emissions 

information ranges from five to 19. Table 4-1 presents the distribution of emissions information 

for the CAA section 129 pollutants. The floor analysis was then conducted using all the quality 

assured emissions information for each pollutant in each subcategory (e.g, all six FB units for Cd, 

and all 14 MH units for Cd), as this information includes emissions data from the population of 

best performing units. For Hg, data from the top 18 MH units (or 12% of the 144 MH units in 

the total inventory) were used. 

Table 4-1. Summary of the Number of Units with 

Test Information on the Section 129 Pollutants 

Subcategory Pb Cd Hg PM HCl SO2 NOx CO PCDD/PCDF 

FB 6 6 6 6 6 4 6 6 5 

MH 14 14 19 18 7 11 8 9 5 

Test results from each of these units are based on the results of at least three individual 

runs per test, meaning that one would expect MACT floor calculations based on a population of 

21 FB runs (7 FB units * 3 runs per FB unit) and on a population of 60 MH runs (20 MH unit * 3 

runs per MH unit). 

While EPA does not have actual emissions test data for the population of units that 

represent the best performing 12 percent, the statistical technique described below is the approach 
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used to establish the existing source MACT floor. The MACT floor calculations are based on all 

of the actual quality assured data received (for example, a population of 15 MH runs from five 

MH incinerators for PCDD/PCDF). Because the emission data are normally distributed, or can be 

transformed to be normally distributed (using the log-normal transformation of the data as 

described in Section 4.3), a statistical technique can be employed to determine the minimum 

number of observations needed to accurately characterize the distribution of the best performing 

12 percent of units in each subcategory. EPA used this statistical technique because of the lack of 

data from the full set of the best performing 12 percent of sources. While Congress adopted 

identical language describing the MACT floor calculation in section 129(a)(2) as it did in section 

112(d)(3), the latter section includes a provision stating that the floor for existing sources cannot 

be less stringent than “the average emission limitation achieved by the best performing 12 percent 

of the existing sources (for which the Administrator has emissions information)[.]” Section 129, 

however, simply states that the existing source floor cannot be less stringent than the average 

emission limitation achieved by the best performing 12 percent of the existing sources in the 

category. Therefore, while EPA believes Congress intended for the MACT floor calculation 

under each section of the Act to be the same, this difference in the text of the two provisions 

requires us to establish the MACT floor for section 129 source categories based on the best 

performing 12 percent of sources in the category. Because EPA does not have that data at this 

time, the statistical technique described below is the only manner in which we can establish the 

existing source floor on that basis. This technique (shown in Equation 1) is necessary to provide 

the assurance that the characteristics of the sampled data set mirror those of the best performing 

12 percent of units in the source category. Equation 1 shows that the number of observations is a 

function of the total population size (in each subcategory), the level of confidence, the level of 

precision, and degree of the variability (expressed as probability of occurrence).5 

Equation 1: 

n = N * Z * Z * p * q / [(E * E * (N – 1)) + (Z * Z * p * q)] 

where: 

 n = minimum number of observations or sample size, 

 N = population sizea, 

 Z = the Z-score associated with a specific confidence level, 

 E = level of precision or error tolerance, 

 p = degree of variability of the observations, and 

 q = 1 – p. 

The number of runs test associated with the total population is N, since EPA set the 

confidence level at 90% for this source category, the Z-score is 1.645); as EPA’s level of 

precision for this case is 20%, E = 0.2; and the degree of variability of the observations is 0.5 

 

a For this calculation, the population size is the number of test runs that correspond to 12% of units in each subcategory 

(the MACT floor population). Performance tests consist of 3 individual test runs. For the FB subcategory, 12% of 60 

units is 8 units, so the population size is 24 observations (8 FB * 3 runs per FB). For the MH subcategory, the 

population size is 54 observations (18 MH * 3 runs per MH). 
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(which means p and q are both 0.5). Note that a variability of 0.5 maximizes the number of 

observations required for collection. Therefore, using a variability of 0.5 is a conservative 

approach in that its use overestimates the minimum number of observations which may actually 

overestimate the minimum sample size. 

In order to assess whether or not the number of observations collected adequately 

characterizes the population, Equation 1 was applied to each subcategory for each pollutant. If 

the number of observations collected equaled or exceeded the minimum number of observations 

calculated using Equation 1, then the MACT floor calculations of the sampled data set are 

consistent with what the MACT floor calculations would have been had they been performed on 

the complete data set from the best performing 12% of the population. Table 4-2 demonstrates 

(for the pollutants with the lowest number of observations) the data utilized by EPA meets or 

exceeds the number of observations necessary to provide an accurate representation of that data 

distributed from the best performing 12% of the source population as determined using 

Equation 1.

Table 4-2. Minimum Size Sample Calculation 

Subcategory Population size, N 

Minimum number of 

observations (calculated 

using Equation 1) 

Number of observations 

collected and used (for 

pollutants with lowest 

number of test data) 

FB 24 11 12

MH 58 14 15 

For units whose emissions testing results were below detection levels and who provided 

numerical detection levels, EPA used the detection levels in the MACT floor analysis. If a source 

reported a “zero” or a negative value, that run was not used to calculate the average of the test. 

Each data set used to calculate the MACT floor limits were reviewed for outliers. If emission 

levels appeared to be three standard deviations from the mean, the test reports containing the 

emissions information were reviewed to determine if there were any irregularities during the 

operation of the SSI unit, or during the testing or analysis. Information from three sources was 

removed from the MACT floor calculations during the review. The HCl emissions for test run 1 

conducted by Columbia Metro were two orders of magnitude greater than the other two test runs. 

 Calls to the facility indicated that a data entry error was made for test run 1. The SO2 emissions 

from Ypsilanti were two orders of magnitude greater than all the other SO2 test information. A 

review of the source test report indicated a different test method was used than used at all the 

other facilities providing SO2 data. Additionally, the reported emissions in the report could not be 

replicated using the raw data. The CO emissions reported from one run from tests conducted at 

one of The Metropolitan Council’s St. Paul units was 3 standard deviations greater than the other 

two test runs in the test. The high value was also 3 standard deviations greater than other tests 

conducted at the unit. A review of the source test report did not indicate any irregularities during 

the test run. However, because the value was not consistent with the other tests conducted at the 

facility and also greater than 3 standard deviations from the other tests, this data point was 

removed from the MACT floor analysis. 
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The MACT floor analysis for PM was conducted on emissions measured using only EPA 

Test Method 5, or equivalent State test methods. Although PM emissions were reported to be 

collected using other test methods, such as OTM 27, the results from the different test methods 

were not combined because they do not measure equivalent PM fractions or species. 

4.2 MACT Floor Calculation

For each unit providing emissions information for a pollutant, the average (mean) of the 

test runs for all the tests conducted on the unit was calculated. If one test were conducted 

consisting of three test runs, the emissions from the unit would be the average of the three test 

runs. If two tests were conducted each consisting of three test runs, the average of all 6 test runs 

would be calculated. 

To conduct the existing source MACT floor analysis for each pollutant, individual SSI 

units in each subcategory for which EPA had emissions test data were ranked based on their 

average emissions levels of the pollutant from lowest to highest. The MACT floor was calculated 

as the average of the test runs from the best performing (i.e., lowest emitting) 12 percent of 

sources. For the SSI source category, all the quality assured emissions information from the ICR 

responses and additional test reports collected were used in the MACT floor calculation. That is, 

for each pollutant the MACT floor emissions level was calculated as the average emission limit for 

all the test runs from the quality assured emissions data collected. 

As discussed in section 3.0, two subcategories were developed for new sources. For the 

FB new source subcategory, the best performing FB unit was used as the best performing similar 

source. For the MH new source subcategory, the best performing MH unit was used as the best 

performing similar source. 

To conduct the new source MACT floor analysis for each pollutant, the SSI unit in each 

subcategory with the lowest emission test average (mean) from 3 test runs for the pollutant was 

identified. The MACT floor was calculated as the average emission level of the test runs from the 

best performing single unit (i.e., lowest emitting). If the lowest emitting source provided 

emissions information from less than 3 test runs, then the MACT floor was calculated from the 

next lowest emitting source with at least 3 test runs. This procedure was followed because 

compliance is based on the average of 3 test runs. 

4.3 Incorporating Data Variability

The first step in the statistical analysis includes a determination of whether the data used 

for each MACT floor calculation were normally or log-normally distributed. If the data were 

normally distributed (e.g., similar to a typical bell curve), then further variability analyses could be 

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), then the type of distribution (e.g., log-normal) was 

determined and a data transformation was performed (e.g., taking the natural log of the data) 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 skewness statistic that is 
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greater (less) than zero indicates that the data are asymmetrically distributed with a right (left) tail 

extending toward positive (negative) values. Further, the standard error of the skewness statistic 

(SES) is given by SES = SQRT(6/N) where N is the sample size. According to the small sample 

skewness hypothesis test, if the skewness statistic (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 kurtosis statistic that is greater (less) than zero indicates a relatively peaked (flat) 

distribution. Further, the standard error of the kurtosis statistic (SEK) is given by SEK = 

SQRT(24/N) where N is 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 nonnormal. 

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 

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

normally distributed. An EPA guidance document shows that environmental data are more likely 

to be log-normally distributed than normally distributed.6 If neither of the S and K statistics or 

only one of these statistics was less than twice the SES or SEK, respectively, then the skewness 

and kurtosis hypothesis tests were conducted for the natural log-transformed data. 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 log-normally distributed dataset was selected 

as the basis of the floor. If the results of the skewness and kurtosis hypothesis tests were mixed 

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

distribution was chosen. 

After determining the distribution of each dataset, a student’s t-test statistic was used. 

The t-test was used in the promulgated hospital and medical waste incineration (HMWI) 

rulemaking and proposed Portland Cement rulemaking, and it is more appropriate for smaller 

sample sizes. The t-statistic is calculated using the following Excel equation: 

Equation 2: 

t-statistic = TINV(2*(1-0.99),n-1) 

Where: 

n = the number of test runs from units in the top 12 percent 

Next, the average (or sample mean) and sample standard deviation of the test runs were 

calculated. 

For the existing source variability analysis, all the quality assured emission test runs 

reported for the best performing 12 percent of units in each subcategory were identified. By 

including multiple emission tests from units with a test average in the top 12 percent, EPA can 

evaluate intra-unit variability of emission tests over time, considering variability in control device 
12 

performance, unit operations, and fuels fired during the test. As discussed previously, the UPL 

was used for the SSI MACT floor variability analysis.

For the existing source analysis, the 99% UPL values was calculated for each pollutant 

and for each subcategory using the test run data for those units in the best-performing 12 percent. 

Since the compliance with the MACT floor emission limit is based on the average of a three-run 

test, the UPL is calculated by: 

Equation 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 emission test data 

x = mean, i.e., average of the emission test data 

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

This calculation was performed using the following two Excel functions: 

Normal distribution: 99% UPL = AVERAGE (Test Runs in Top 12%) + [STDEV(Test Runs in 

Top 12%) x TINV(2 * 0.99, n-1 degrees of freedom)*SQRT((1/n)+(1/3))], for a one-tailed tvalue (with 2 x probability), probability of 0.01, and sample size of n 

Lognormal distribution: 99% UPL = EXP{AVERAGE(Natural Log Values of Test Runs in Top 

12%) + [STDEV(Natural Log Values of Test Runs in Top 12%) x TINV(2 * 0.99, n-1 degrees of 

freedom) * SQRT((1/n)+(1/3))]}, for a one-tailed t-value (with 2 x probability), probability of 

0.01, and sample size of n 

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

performance tests from the best-performing 12 percent of sources to fall below with 99 percent 

confidence, based upon the results of the independent sample of observations from the same best 

performing sources. In establishing the limits, the UPL values were rounded up to two significant 

figures. For example, a value of 1.42 would be rounded to 1.5 (as has been done for other CAA 

section 129 rules) because a limit of 1.4 would be lower than the calculated MACT floor value. 

The SSI emissions database for FB units contains data from six units at four facilities. The 

entities surveyed were requested to provide recent (within the previous five years) emissions test 

reports. Most survey recipients provided only the most recent report. One facility, with three 

units, provided results of emissions test conducted for compliance reports spanning a 10 year 

period. This facility also uses the most advanced pollution controls on their FB units in the 

subcategory. The one facility comprises 70 percent of the Cd and Pb data, 90 percent of the CO 

and Hg data, and 75 percent of the HCl data and PM data. As a result, the existing source 

MACT floors calculated using the UPL methodology, and all the test run data from the one 

facility, effectively results in calculating more stringent limits more akin to a new source MACT 
13 

floor than an existing source MACT floor. In order to adequately incorporate the emissions from 

all the SSI units in the FB subcategory, a weighted UPL was used for calculating the existing 

source MACT floors for the final rule. The weighted UPL is calculated from a weighted mean 

and weighted variance as described below. 

There are many different types of weighting procedures. The most straightforward 

methodology is to base it on the number of data points (i.e., test runs) from each SSI unit. This 

weighting scheme ensures that no facility in the MACT best performers pool is over-represented 

in the computation of the MACT floor. The first step in weighting procedure is to assign a 

weighting factor to each test run by multiplying each observation for source i and run j with a 

weight term, wij , as shown in equation 4: 

Equation 4: 

Where: 

Mi = the number of observations (i.e., runs) for source i and 

N = the number of best performing sources in the MACT pool. 

The second step is to calculate the mean and total variance for the weighted data from the 

weight terms using equations 5 and 6: 

Equation 5: 

Weighted Mean: 

Equation 6: 

Weighted Variance: 

 

Where: 













×















=

M N

w

i

ij

1 1

∑∑

∑∑

= =

= =

= N

i

M

j

ij

N

i

M

j

ij ij

weighted

i

i

w

w x

x

1 1

1 1

( )

( )

K

K w

w x x

N

i

M

j

ij

N

i

M

j

weighted

ij ij U

weighted

i

i

∑∑

∑∑

= =

= =

−

−

=

1 1

1 1

2

1

ν
14 

 = the total number of observations in the MACT best performers pool. 

When the weights are equal to one, the above equations reduce to those for un-weighted 

data, as expected. As shown in equation 7, the weighted mean and weighted variance are then 

used in the UPL calculation instead of the simple (i.e., un-weighted) mean and variance. 

Equation 7: 

For MH units, there are more emissions data from a larger number of facilities/units. For 

example, we have data on Cd and Pb from 11 facilities with 14 units, Hg from 11 facilities with 18 

units. The MACT floor calculations are not skewed by 1 or 2 units or facilities. Consequently, 

the MACT floor for existing MH units does not need to be calculated using a weighted UPL. 

To determine the MACT floor for new sources, a UPL calculation similar to that for 

existing sources was conducted, except the best performing unit’s data within a subcategory was 

used to calculate the MACT floor emission limit for each pollutant instead of the average of the 

best performing 12 percent of units. The best performing unit was identified as the lowest 

emitting source with at least 3 test runs. In summary, the approach ranks individual SSI units 

based on actual performance and establishes MACT floors based on the best performing source 

for each pollutant and subcategory, with an appropriate accounting of emissions variability. In 

other words, the UPL was determined for the data set of individual test runs for the single best 

performing source for each regulated pollutant from each subcategory. 

4.4 Use of Non-Detect Values

Non-detect values comprise more than 50 percent of the emission data for HCl from FB 

units and PCDD/PCDF from both MH and FB units and may influence the MACT floor analysis 

for other pollutants. EPA developed a methodology to account for the imprecision introduced by 

incorporating non-detect data into the MACT floor calculation. 

At very low emission levels where emission tests result in non-detect values, the inherent 

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

underlying the regulatory floor emission limit. Because of sample and emission 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 emission 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 three times the method detection level. 

∑=

=

N

i

K Mi

1

( ) ( ) 















= + − × × +

i i

weighted

i

weighted

n m

UPL x t n

1 1

99.0 , 1 ν
15 

One approach that can be applied to account for measurement variability in this situation 

starts with defining a method detection level that is representative of the data used in the data 

pool. The first step in this approach would be identify the highest test-specific method detection 

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

data set. This approach has the advantage of relying on the data collected to develop the floor 

emission 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 three times the representative method 

detection level and compare it to the calculated floor emission limit. If three times the 

representative method detection level were less than the calculated floor emission limit, we would 

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

calculated floor emission limit. If, on the other hand, the value equal to three times the 

representative method detection level were greater than the calculated floor emission limit, we 

would conclude that the calculated floor emission limit does not account entirely for measurement 

variability. Therefore, the use the value equal to three times the method detection level in place of

the calculated floor emission limit ensures that the floor emission limit accounts for measurement 

variability and imprecision. 

The approach discussed above was used to calculate the proposed MACT floor limit for 

HCl from FB units, and Hg from MH units. The following additional procedures were followed 

for PCDD/PCDF total mass basis (TMB) and toxic equivalency (TEQ) basis limits. To calculate 

a TMB limit, all the 17 congeners of interest were identified and non-detect values that are 

associated with each were indicated. The mean of the non-detect values was calculated and 

multiplied by 17 (for the total number of congeners of interest). The mean value was then used as 

the detection limit of the run. Then, each data set was reviewed to identify the highest testspecific method detection level reported that was also equal to or less than the average emission 

level (i.e., unadjusted for probability confidence level) calculated for the data set. The second 

step discussed above and also used for HCl was used to set the limit. 

To calculate a limit on a TEQ basis, first, the mean of the non-detect values was 

calculated. Then the toxic equivalency factor (TEF) for each congener was multiplied by the 

mean to determine the TEQ for each congener. TEQs for each congener were summed to 

calculate a TEQ sum value. The TEQ sum was then used as the detection limit for the test run. 

The second step discussed above and also used for HCl was used to set the limit. 

4.4 CO Span Adjustments

Carbon monoxide values obtained from emission test reports were reviewed to determine 

whether the span of the test used was capable of accurately reading the reported value. If the span 

was inconsistent with the reported value, the CO levels were adjusted to provide a value that was 

more consistent with the span. 

EPA Method 10 is structured such that measurement data quality relative to the 

calibration span of the instrument can be assessed. For example, the allowable calibration error, 
16 

system bias, and drift requirements are directly proportional to the site-specific instrument 

calibration span (i.e., ± 2.0 percent of the calibration span value). For instrument calibration span

values of 25 ppmv and less, the allowable calibration error, bias, or drift values are each ± 0.5 

ppmv. 

For a measurement made using an instrumental test method, the equivalent of the method 

detection level can be assessed using: a square root formula, the reported calibration span value, 

and the allowable data quality criteria (i.e. the allowable calibration error, bias, and drift values). 

The estimated CO measurement error resulting from the square root formula was adjusted by a 

factor of three to be consistent with the methodology EPA applied for non-detect data (where 

limits no less than three times the method detection level were established). 

The calculations used to estimate the measurement error are shown in Equations 8 and 9: 

Equation 8: 

For tests where calibration span is greater than 25 ppm: 

Where: 

0.02*calibration span = Method 10 allowable calibration error, system bias, and drift 

requirement for calibration spans greater than 25 ppm 

Equation 9: 

For tests where calibration span is 25 ppm or less:

= 0.9 

Where: 

0.5 = Method 10 allowable calibration error, system bias, and drift requirement for 

calibration spans less than or equal to 25 ppm 

In order to develop a basis for measurement error, instrument calibration spans in available 

test reports were reviewed. In most cases, a calibration span was specified in the report. If no 

span value was reported, the maximum concentration from a protocol gas certificate was used in 

lieu of the span. In several cases a test report with the exact test date in question was not 

available to the Agency to extract the calibration span or protocol gas certificates. In order to fill 

these data gaps, test reports from the same units on alternate dates or test reports from similar 

units at the same facility were reviewed to identify a likely calibration span for the units in 

question. If alternate reports were available, the report with the date closest to the test date 

associated with the CO runs in question was used. Where no span values could be found, it was 

assumed that if the test was conducted on or before May, 2008, the associated CO span would be 

1000 ppm, and tests conducted after May 2008 would have a CO span of 100 ppm. This 
17 

assumption was made because, before revisions were made to Method 10 in May of 2008, it was 

common that units were using the prescriptive span guidance that was listed in the old method.7

The current version of EPA Method 10 does not include these span requirements but instead 

requires the tester to choose calibration ranges that reflect the range of expected emission 

concentrations at the unit.8 

Span values were input into Equations 8 and 9 in order to estimate the measurement error 

for each run. In cases where the reported emission concentrations were lower than their 

corresponding measurement errors, the default measurement errors were used in lieu of the 

reported concentration. 

Attachment H shows a detailed list of each of the test reports reviewed. The attachment 

indicates whether a substitution was made to fill data gaps for CO. Additional background on CO 

measurement imprecision and the basis for this CO span adjustment methodology can be found in 

separate memoranda.9,10

5.0 RESULTS 

Table 5-1 summarizes the MACT floor variability analysis results for existing units. Table 

5-2 summarizes the MACT floor variability analysis results for new units. 

Attachments A-1 through A-8 contain the data and detailed calculation of the UPL for Cd, 

Pb, Hg, filterable PM, HCl, SO2, NOx, and CO at existing FB units. 

Attachments B-1 through B-8 contain the data and detailed calculation of the UPL for Cd, 

Pb, Hg, filterable PM, HCl, SO2, NOx, and CO at existing MH units. 

Attachments C-1 through C-8 contain the data and detailed calculation of the UPL for Cd, 

Pb, Hg, filterable PM, HCl, SO2, NOx, and CO at all new FB units. 

Attachments D-1 through D-8 contain the data and detailed calculation of the UPL for Cd, 

Pb, Hg, filterable PM, HCl, SO2, NOx, and CO at all new MH units. 

Attachments E-1 through E-5 summarize the MACT floor variability analysis for 

PCDD/PCDF at existing and new FB units. 

Attachments F-1 through F-5 summarize the MACT floor variability analysis for 

PCDD/PCDF at existing and new MH units. 

Attachment G identifies the control devices used at FB and MH units with advanced 

controls and identifies the nine companies that were sent ICR surveys. 

Attachment H lists CO data extracted from test reports and shows the calculations made 

to determine the appropriate span adjusted values to use in determining the MACT floors for CO. 
18 

6.0 REFERENCES 

1. A Comparison of Fluid Bed and Multiple Hearth Biosolids Incineration. Ky Dangtran, 

John Mullen, and Dale Mayrose. Paper presented at the 14th Annual Residuals and 

Sludge Management Conference. February 27-March 1, 2000, Boston MA. 

2. Compilation of Air Pollutant Emission Factors, Volume I: Stationary Point and Area 

Sources. Fifth Edition. U.S. Environmental Protection Agency. January 1995. 

3. Facility, Unit, and Emissions Test Database for the Sewage Sludge Incineration 

Source Category. Memorandum from ERG to Amy Hambrick, U.S. EPA. June 2010. 

4. Natrella, Experimental Statistics, National Bureau of Standards Handbook 91, chapter 

1 revised ed., 1966.

5. Sample Size Requirements for Studying Small Populations in Gerontology Research; 

Robert B. Noble, A. John Bailer, Suzanne R. Kunkel & Jane K. 

Straker; Health Services Outcomes Research Methodologies (2006) 6:59–67

6. Guidance for Data Quality Assessment: Practical Methods for Data Analysis” 

EPA/600/R-96/084, July 2000. 

7. USEPA, Emissions Measurement Center. Old Version of EPA Method 10. [Online] 

http://www.epa.gov/ttn/emc/promgate/m-10.pdf. 

8. USEPA, Emissions Measurement Center. US EPA Method 10 (Revised). [Online] 

2008 May. http://www.epa.gov/ttn/emc/promgate/method10r06.pdf. 

9. Westlin, Peter. Measurement Imprecision for Carbon Monoxide. s.l. : EPA Emissions 

Measurement Center, January 2011. 

10. Incorporating Measurement Error in Reported Carbon Monoxide (CO) and Total 

Hydrocarbon (THC) Data. Memorandum from ERG to Brian Shrager, U.S. EPA. 

January 2011. 
19 

Table 5-1. Summary of MACT Floor Results for Existing Units

Pollutant Units 

FB Units MH Units 

Avg of top 

12%

(weighted) 

99% of 

UPL 

(weighted) Limit1

Avg of top 

12%

99% of 

UPL Limit1

Cd mg/dscm 0.00049 0.00151 0.0016 0.030 0.0947 0.095 

Pb mg/dscm 0.0026 0.00736 0.0074 0.082 0.295 0.30 

Hg mg/dscm 0.010 0.0367 0.037 0.095 0.2722 0.28 

PM mg/dscm 4.3 17.4 18 41.3 79.1 80 

HCl ppmvd 0.19 0.5072 0.51 0.53 1.14 1.2 

SO2 ppmvd 2.8 14.04 15 9.4 25.7 26 

NOx ppmvd 56 143 150 130 217 220 

CO ppmvd 27 63.2 64 1,015 3,775 3,800 

PCDD/PCDF ng/dscm 0.199 1.15 1.2 0.69 4.95 5.0 

PCDD/PCDF 

TEQ 

ng/dscm 0.016 0.0998 0.10 0.047 0.314 0.32 

1

 Proposed limits were rounded up to 2 significant figures. 

2

 The value shown is the result of the non-detect analysis, which results in using the limit that is based on 3 

times the highest detection limit that is less than the average. 

Table 5-2. Summary of MACT Floor Results for New Units 

Pollutant Units 

FB Units MH Units 

Avg of top 

unit 

99% of 

UPL Limit1

Avg of top 

unit 

99% of 

UPL Limit1

Cd mg/dscm 0.00017 0.001098 0.0011 0.00033 0.00231 0.0024 

Pb mg/dscm 0.00031 0.000618 0.00062 0.00084 0.00343 0.0035 

Hg mg/dscm 0.00036 0.000992 0.0010 0.051 0.1502 0.15 

PM mg/dscm 1.41 9.53 9.6 1.2 59.9 60 

HCl ppmvd 0.044 0.2301 0.24 0.201 0.512 1.23

SO2 ppmvd 0.62 5.26 5.3 1.2 265 263

NOx ppmvd 14.9 29.6 30 68.5 206 210 

CO ppmvd 8.21 26.93 27 27.6 51.4 52 

PCDD/PCDF ng/dscm 0.0109 0.0128 0.013 0.0095 0.0447 0.045 

PCDD/PCDF 

TEQ 

ng/dscm 0.0025 0.00431 0.0044 0.00095 0.00212 0.0022 

1

 Proposed limits were rounded up to 2 significant figures. 

2

 The value shown is the result of the non-detect analysis, which results in using the limit that is based on 3 

times the highest detection limit that is less than the average. 

3

 Limit calculated for NSPS was higher than for EG. NSPS limits cannot be less stringent than EG limits. 

Therefore, the EG limits were used.
Attachment A 

Detailed MACT Floor Analysis for Existing FB Sources 

Cd, Pb, Hg, PM, HCl, SO2, NOx, and CO 

See MACTFloor_AttachA_FBVariability.xlsx
Attachment B 

Detailed MACT Floor Analysis for Existing MH Units 

Cd, Pb, Hg, PM, HCl, SO2, NOx, and CO 

See MACTFloor_AttachB_MHVariability.xlsx 
Attachment C 

Detailed MACT Floor Analysis for New FB Units 

Cd, Pb, Hg, PM, HCl, SO2, NOx, and CO 

See MACTFloor_AttachC_FBVariability_NSPS.xlsx
Attachment D 

Detailed MACT Floor Analysis for New MH Units 

Cd, Pb, Hg, PM, HCl, SO2, NOx, and CO 

See MACTFloor_AttachD_MHVariability_NSPS.xlsx
Attachment E 

Detailed MACT Floor Analysis for New and 

Existing FB and MH Sources 

PCDD/PCDF 

See MACTFloor_AttachE_FBVariability_DF.xlsx 
Attachment F 

Detailed MACT Floor Analysis for New and 

Existing MH Sources 

PCDD/PCDF 

See MACTFloor_AttachF_MHVariability_DF.xlsx
Attachment G 

List of SSI Units with Advanced Control Devices and Companies that Received 

the ICR Survey 

See MACTFloor_AttachG_Units w advanced controls.xls 
Attachment H 

Attachment H. CO Spans and Estimated Measurement Error 

See MACTFloor_AttachH_CO_SpanAdjustments.xlsx