1

MEMORANDUM

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

Resources and Commerce Group

From : Eastern Research Group, Inc.

Date: January 2011

Subject: Revised MACT Floor Analysis for the Sewage Sludge Incin erator 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 poll utants 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 (TE Q PCDD/PCDF), nitrogen oxides (NO
x ), and sulfur dioxide (SO 2 ). The purpose of this memorandum is to present the meth odology used to determine the maximum achievable control techno logy (MACT) floor limits for SSI units. The MACT floor analysis was conducted using emissions da ta 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 calcul ated, and then a data variability analysis was conducted. The MACT floor emissions limi ts were determined based on the results of these analyses.

Table 1-1 summarizes the MACT floor limits for existin g 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 o f the SSI subcategories is presented in section 3.0.

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

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 1.22 Table 1-1. Summary of MACT Floor Limits for Sewage Sludge Inci nerators (Continued)

Pollutant Units EG NSPS
FB MH FB MH
SO 2 ppmvd 15 26 5.3 26
NO
x 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) m ust reflect MACT, the maximum degree of reduction in emissions of certain list ed air pollutants that the Administrator, taking into consideration the cost of achieving such emi ssion 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 “fl oor,” 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 strin gent than standards for new units, but cannot be less stringent than the average emission li mitation achieved by the best performing 12 percent of units in the category. These requirements co nstitute 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 h ealth and environmental impacts, and energy requirements in connection with any standards tha t are more stringent than the MACT floor (beyond-the-floor controls).

MACT analyses involve an assessment of the emission s 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 e missions data, or on state regulatory3

requirements that may enable EPA to estimate the actua l performance of the regulated units and other relevant emissions information. For each sour ce category, the assessment involves a review of actual emissions data with an appropriate accounting fo r emissions variability. Other methods of estimating emissions can be used provided that the met hods can be shown to provide reasonable estimates of the actual emissions performa nce 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 follow ing guidance:

• Floors for existing sources must reflect the average em ission 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 s et floor standards for all HAP emitted by the major source, including those HAP that ar e not controlled by at-the-stack control devices (479 F. 3d at 883).

• EPA cannot ignore non-technology factors that reduce HA P emissions. Specifically, the court held that “EPA’s decision to base floors exclusiv ely 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 performe rs to estimate variability of the best performers without a demonstrated relationship between t he 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 CATEG ORY

The Clean Air Act allows EPA to subcategorize a source category based on differences in class, type, or size. There are two types of inciner ators currently used to combust sewage sludge (or biosolids): MH units and FB units. Of the 204 sewage s ludge incineration units in operation, 60 use the FB design, while 144 use the MH design. The two ty pes are significantly different combustor designs.

A MH unit consists of a vertical cylinder containing f rom 6 to 12 horizontal hearths and a rotating center shaft with rabble arms. Biosolids en ter 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, wa ter, and some organic compounds are evaporated. The middle hearths comprise the combustion z one. The exposure to the combustion gas and biosolids to high temperature is only in this sec tion and residence time of the gas is short. The lower hearths form the cooling zone, where ash i s cooled as its heat is transferred to the incoming combustion air. Multiple hearth units are sens itive 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 temper ature and increase in emissions. 14

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 wh ich 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 dis tributed homogeneously throughout the fluidized bed. The intensive mixing of the solid and gas i n 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 sev en seconds of gas residence time which complete the combustion of any volatile hydrocarbons e scaping 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 ha ndle variability in the feeds, control of temperature and other process variables, auxiliary fuel us e, 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 gene rally 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 mechan isms, 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 t he 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 da ta, 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 estima ting the degree of emissions reduction achieved by best-performing sources and in setting MACT f loors. (See Mossville Envt’l Action Now v. EPA, (370 F.3d 1232, 1241-42 D.C. Cir 2004), holding EPA may cons ider 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 techno logy-based standards are necessarily derived from short-term emissions test dat a, but such data are not representative of the range of operating conditions that the best performing fa cilities face on a day-to-day basis. In statistical terms, each test produces a limited data sam ple, 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 of5

variability that EPA attempts to account for include ope rational distinctions between and within tests at the same unit.

“Between-test variability” can occur even where condi tions appear to be the same when two or more tests are conducted. Variations in emissi ons may be caused by different settings for emissions testing equipment, different field teams conducti ng 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 differ ences between tests, in order for “a uniform standard [to] be capable of being met under most a dverse conditions which can reasonably be expected to recur[.]” (See NLA I, 627 F.2d a t 431, n. 46.) (See also Portland Cement Ass’n, 486 F.2d at 396 noting industry point that “a sin gle test offered a weak basis” for inferring that plants could meet the standards.)

The same types of differences leading to between-test v ariability 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 perform ance. Along with an understanding of the factors that may affect performance, each of the se snapshots gives information about the normal, and unavoidable, variation in emissions that w ould be expected to recur over time.

Variability can be accounted for using different statist ical 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 dis tribution of the available emission observations (e.g., test runs), and does not embody a pre dictive aspect that a UPL does. A prediction interval (e.g., a UPL) for a future observati on 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 predic tion 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 o bservations (three-run average) can be expected to fall below, based on the results of the inde pendent sample of size (n) from the same population. Therefore, should a future test condition be s elected randomly from any of these sources (i.e., average of three runs), we can be 99 perce nt confident that the reported level will fall below a MACT floor emission limit calculated usin g a UPL. Since a source must demonstrate compliance with the MACT floor using the average of a t hree-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 det ermining variability in the SSI data. For this source category, where there is a limited sam pling 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 i mportant. (See sections 4.1 through 4.3 for further discussion of the dataset and the statistical appr oach.)6

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 t he average emission limitation achieved by the best performing 12 percent of units in a source cat egory. 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 diff erent. 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 ba sed on at least 12 percent of the units in a subcategory, EPA has determined that it is appropriate t o 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 bes t 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 ni ne municipalities operating SSI units to collect emissions information. To select the sur veyed owners, EPA reviewed the inventory of SSI units for the control devices being operated, and iden tified 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 po ssible for the CAA Section 129 pollutants, and thereby allow EPA to identify for each pollutant th e 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; regenerativ e thermal oxidizers or afterburners to reduce CO and organics; wet electrostatic precipitators t o reduce fine particulate; high efficiency scrubbers such as packed bed scrubbers and impingement tray s crubbers to reduce PM, Cd, Pb, particulate Hg, and acid gases such as HCl and SO 2 ; and units with multiple control devices that could reduce PM, Cd, Pb, particulate Hg, such as venturi scrub ber in combination with impingement scrubbers and wet ESPs or with another parti culate control device.

EPA considered whether non-technology factors, such as the characteristics of the waste combusted, would influence which sources are the best perf orming. In contrast to regulated MWC units or CISWI units, SSI units receive a more hom ogenous type of waste to burn. There are variations in the amount of each of the CAA Sect ion 129 pollutants present depending on the regions, climates, and sources of wastewater (commerci al or residential). The variations are not as significant as variations that would occur if differe nt 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 require ments (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 S SI units meet the National Emission Standards for Beryllium and Hg in subparts C and E, respecti vely, of 40 CFR part 61. In order to meet the 40 CFR part 503 standards, facilities are already i ncorporating 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 w astes and establishing discharge limits or pre-treatment standards for non-domestic users discharging wastewater to publicly owned treatment works (POTWs). Thus, non-technology me asures to reduce emission are7

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 oth er 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 r esulting 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 opera te FB units, resulting in seven FB units surveyed. Some owners of multiple units at a facility pr ovided 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 muni cipalities, 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 informa tion were collected from six FB units and 20 MH units from facilities responding to the ICR and additi onal 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 pollutan ts. Except for PCDD/PCDF and SO 2 , 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 SO 2 data were only available for four units. One facility (operating two FB units) provided “zero” va lues for SO 2 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 t he 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 eac h 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 unit s (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 SO 2 NO
x 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 c alculations 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 approach8

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 emissio n data are normally distributed, or can be transformed to be normally distributed (using the log-norm al 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 characteriz e the distribution of the best performing 12 percent of units in each subcategory. EPA used this sta tistical technique because of the lack of data from the full set of the best performing 12 percent o f sources. While Congress adopted identical language describing the MACT floor calculation i n section 129(a)(2) as it did in section 112(d)(3), the latter section includes a provision stating t hat the floor for existing sources cannot be less stringent than “the average emission limitati on achieved by the best performing 12 percent of the existing sources (for which the Administrator h as 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 pe rcent of the existing sources in the category. Therefore, while EPA believes Congress int ended for the MACT floor calculation under each section of the Act to be the same, this dif ference in the text of the two provisions requires us to establish the MACT floor for section 129 s ource categories based on the best performing 12 percent of sources in the category. Because E PA 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 (s hown 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 show s that the number of observations is a function of the total population size (in each subcategor y), the level of confidence, the level of precision, and degree of the variability (expressed as pro bability 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 size a

, 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 populat ion 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 n umber of test runs that correspond to 12% of units in each subcategory (the MACT floor population). Performance tests con sist of 3 individual test runs. For the FB subcateg ory, 12% of 60 units is 8 units, so the population size is 24 obse rvations (8 FB * 3 runs per FB). For the MH subcat egory, the population size is 54 observations (18 MH * 3 runs per MH).9

(which means p and q are both 0.5). Note that a variabi lity 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 numbe r of observations which may actually overestimate the minimum sample size.

In order to assess whether or not the number of obser vations collected adequately characterizes the population, Equation 1 was applied to each subcategory for each pollutant. If the number of observations collected equaled or exceeded t he minimum number of observations calculated using Equation 1, then the MACT floor calculatio ns of the sampled data set are consistent with what the MACT floor calculations woul d 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 observat ions) 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 populat ion 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 de tection 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 w ere 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 t here were any irregularities during the operation of the SSI unit, or during the testing or analy sis. Information from three sources was removed from the MACT floor calculations during the revi ew. The HCl emissions for test run 1 conducted by Columbia Metro were two orders of magnitude gre ater than the other two test runs. Calls to the facility indicated that a data entry err or was made for test run 1. The SO 2 emissions from Ypsilanti were two orders of magnitude greater than all the other SO 2 test information. A review of the source test report indicated a different t est method was used than used at all the other facilities providing SO 2 data. Additionally, the reported emissions in the repor t 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 s tandard deviations greater than the other two test runs in the test. The high value was also 3 s tandard deviations greater than other tests conducted at the unit. A review of the source test repor t did not indicate any irregularities during the test run. However, because the value was not cons istent 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.10

The MACT floor analysis for PM was conducted on emissi ons 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 re sults 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 poll utant, the average (mean) of the test runs for all the tests conducted on the unit was ca lculated. 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 th ree 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 t est 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 performi ng (i.e., lowest emitting) 12 percent of sources. For the SSI source category, all the quality a ssured emissions information from the ICR responses and additional test reports collected were used i n 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 develope d for new sources. For the FB new source subcategory, the best performing FB unit wa s used as the best performing similar source. For the MH new source subcategory, the best per forming 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 (mea n) from 3 test runs for the pollutant was identified. The MACT floor was calculated as the avera ge 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, the n 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 cur ve), then further variability analyses could be conducted on the data set. If the data were not normall y distributed (for example if the data were asymmetric or skewed to the right or left), then the t ype 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 a nalysis. 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 o f zero. A skewness statistic that is11

greater (less) than zero indicates that the data are a symmetrically 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 sampl e 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. No rmally distributed data have a kurtosis of zero. A kurtosis statistic that is greater (less) tha n 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 distr ibution 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, re spectively, 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 th e SES or SEK, respectively, then the skewness and kurtosis hypothesis tests were conducted for the natura l log-transformed data. If both the reported values and the natural-log transformed reported val ues 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 ske wness and kurtosis hypothesis tests were mixed for the reported values and the natural log-transformed re ported values, the log-normal distribution was chosen.

After determining the distribution of each dataset, a st udent’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 m ore appropriate for smaller sample sizes. The t-statistic is calculated using the f ollowing 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 perce nt

Next, the average (or sample mean) and sample standard dev iation 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 eac h 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 ove r time, considering variability in control device12

performance, unit operations, and fuels fired during the tes t. 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 li mit 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 Ex cel 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 sampl e size of n

Lognormal distribution: 99% UPL = EXP{AVERAGE(Natural Log V alues 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 (w ith 2 x probability), probability of 0.01, and sample size of n

The 99 percent UPL represents the value which one can ex pect 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 UP L values were rounded up to two significant figures. For example, a value of 1.42 would be rounded to 1.5 (a s has been done for other CAA section 129 rules) because a limit of 1.4 would be lower tha n 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 r ecent report. One facility, with three units, provided results of emissions test conducted for com pliance reports spanning a 10 year period. This facility also uses the most advanced polluti on controls on their FB units in the subcategory. The one facility comprises 70 percent of th e 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 stri ngent limits more akin to a new source MACT13

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 UP L is calculated from a weighted mean and weighted variance as described below.

There are many different types of weighting procedures. T he 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 obs ervation for source i and run j with a weight term, w ij , as shown in equation 4:

Equation 4:

Where:

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

N = the number of best performing sources in the MACT poo l.

The second step is to calculate the mean and total vari ance for the weighted data from the weight terms using equations 5 and 6:

Equation 5:

Weighted Mean:

Equation 6:

Weighted Variance:

Where: 

### 

    
×        

= N M w

i ij 

### 1 1



## ∑∑

∑∑

= = = = 

### = N

i M

j ij N

i M

j ij ij weighted

i i



### w x w

x

1 1

1 1



# ( )

( )

K w K x x w

N

i M

j ij N

i M

j weighted

U ij ij weighted

i i



# ∑∑ ∑∑

= = = =

− −

=

1 1 1 1 2

1 ν14

= the total number of observations in the MACT b est performers pool.

When the weights are equal to one, the above equati ons reduce to those for un-weighted data, as expected. As shown in equation 7, the wei ghted 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 faciliti es 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 per forming 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 perf orming unit was identified as the lowest emitting source with at least 3 test runs. In summ ary, the approach ranks individual SSI units based on actual performance and establishes MACT fl oors based on the best performing source for each pollutant and subcategory, with an appropr iate accounting of emissions variability. In other words, the UPL was determined for the data se t 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 m ay 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 c alculation.

At very low emission levels where emission tests re sult 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. Be cause of sample and emission matrix effects, laboratory techniques, sample size and other factor s, method detection levels normally vary from test to test for any specific test method and pollu tant measurement. The confidence level that a value measured at the detection level is greater th an zero is about 99 percent. The expected measurement imprecision for an emission value occur ring at or near the method detection level is about 40 to 50 percent. Pollutant measurement impr ecision 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 i M K

1



## ( ) 

# ( )        

+ × ×

− + =

i i weighted
i weighted

m n n t x UPL 1 1 1

, 99.0 ν15

One approach that can be applied to account for mea surement 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 ide ntify 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 th e data set. This approach has the advantage of relyi ng on the data collected to develop the floor emission limit, while to some degree, minimizing th e 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 deter mining 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 fl oor emission limit. If three times the representative method detection level were less tha n 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 l imit 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 th e 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 follo wing additional procedures were followed for PCDD/PCDF total mass basis (TMB) and toxic equi valency (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 t he 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 se t was reviewed to identify the highest testspecific method detection level reported that was a lso 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 mea n 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 us ed as the detection limit for the test run. The second step discussed above and also used for H Cl 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 ac curately reading the reported value. If the span was inconsistent with the reported value, the CO le vels were adjusted to provide a value that was more consistent with the span.

EPA Method 10 is structured such that measurement d ata 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 pr oportional to the site-specific instrument calibration span (i.e., ± 2.0 percent of the calibr ation span value). For instrument calibration span values of 25 ppmv and less, the allowable calibrati on error, bias, or drift values are each ± 0.5

ppmv.

For a measurement made using an instrumental test m ethod, the equivalent of the method detection level can be assessed using: a square roo t formula, the reported calibration span value, and the allowable data quality criteria (i.e. the a llowable calibration error, bias, and drift values)

. The estimated CO measurement error resulting from t he square root formula was adjusted by a factor of three to be consistent with the methodolo gy EPA applied for non-detect data (where limits no less than three times the method detectio n level were established).

The calculations used to estimate the measurement e rror 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 calibra tion error, system bias, and drift requirement for calibration spans greater than 25 p pm

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 calib ration 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 w ith the exact test date in question was not available to the Agency to extract the calibration span or protocol gas certificates. In order to fil l these data gaps, test reports from the same units o n alternate dates or test reports from similar units at the same facility were reviewed to identif y 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. This17

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 inclu de these span requirements but instead requires the tester to choose calibration ranges th at reflect the range of expected emission concentrations at the unit. 8

Span values were input into Equations 8 and 9 in or der to estimate the measurement error for each run. In cases where the reported emission concentrations were lower than their corresponding measurement errors, the default measu rement errors were used in lieu of the reported concentration.

Attachment H shows a detailed list of each of the t est reports reviewed. The attachment indicates whether a substitution was made to fill d ata gaps for CO. Additional background on CO measurement imprecision and the basis for this CO s pan adjustment methodology can be found in separate memoranda. 9,10

5.0 RESULTS

Table 5-1 summarizes the MACT floor variability ana lysis 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 de tailed calculation of the UPL for Cd, Pb, Hg, filterable PM, HCl, SO 2 , NO
x , and CO at existing FB units.

Attachments B-1 through B-8 contain the data and de tailed calculation of the UPL for Cd, Pb, Hg, filterable PM, HCl, SO 2 , NO
x , and CO at existing MH units.

Attachments C-1 through C-8 contain the data and de tailed calculation of the UPL for Cd, Pb, Hg, filterable PM, HCl, SO 2 , NO
x , and CO at all new FB units.

Attachments D-1 through D-8 contain the data and de tailed calculation of the UPL for Cd, Pb, Hg, filterable PM, HCl, SO 2 , NO
x , and CO at all new MH units.

Attachments E-1 through E-5 summarize the MACT floo r variability analysis for PCDD/PCDF at existing and new FB units.

Attachments F-1 through F-5 summarize the MACT floo r 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 wer e sent ICR surveys.

Attachment H lists CO data extracted from test repo rts and shows the calculations made to determine the appropriate span adjusted values t o use in determining the MACT floors for CO.18

6.0 REFERENCES

1. A Comparison of Fluid Bed and Multiple Hearth Bioso lids Incineration. Ky Dangtran, John Mullen, and Dale Mayrose. Paper presented at the 14 th Annual Residuals and Sludge Management Conference. February 27-March 1, 2000, Boston MA.

2. Compilation of Air Pollutant Emission Factors, Volu me I: Stationary Point and Area Sources. Fifth Edition. U.S. Environmental Protect ion Agency. January 1995.

3. Facility, Unit, and Emissions Test Database for the Sewage Sludge Incineration Source Category. Memorandum from ERG to Amy Hambri ck, 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 Populat ions in Gerontology Research; Robert B. Noble, A. John Bailer, Suzanne R. Kunkel & Jane K. Straker; Health Services Outcomes Research Methodol ogies (2006) 6:59–67

6. Guidance for Data Quality Assessment: Practical Met hods 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/metho d10r06.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 Bria n 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) Limit 1 Avg of top 12% 99% of

UPL Limit 1 Cd mg/dscm 0.00049 0.00151 0.0016 0.030 0.0947 0.09 5 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 0.272 2 0.28 PM mg/dscm 4.3 17.4 18 41.3 79.1 80 HCl ppmvd 0.19 0.507 2 0.51 0.53 1.14 1.2
SO 2 ppmvd 2.8 14.04 15 9.4 25.7 26
NO
x 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 f igures.

2 The value shown is the result of the non-detect an alysis, which results in using the limit that is ba sed 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 Limit 1 Avg of top unit 99% of

UPL Limit 1 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 0.150 2 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 0.51 2 1.2 3

SO 2 ppmvd 0.62 5.26 5.3 1.2 265 26 3

NO
x 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 0.002 2

1 Proposed limits were rounded up to 2 significant f igures.

2 The value shown is the result of the non-detect an alysis, which results in using the limit that is ba sed 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 limit s. Therefore, the EG limits were used.

### Attachment A

Detailed MACT Floor Analysis for Existing FB Sources Cd, Pb, Hg, PM, HCl, SO 2 

### , NO
x 

### , and CO

See MACTFloor_AttachA_FBVariability.xlsx

### Attachment B Detailed MACT Floor Analysis for Existing MH Units Cd, Pb, Hg, PM, HCl, SO 2 

### , NO
x 

### , and CO

See MACTFloor_AttachB_MHVariability.xlsx

### Attachment C

Detailed MACT Floor Analysis for New FB Units Cd, Pb, Hg, PM, HCl, SO 2 

### , NO
x 

### , and CO

See MACTFloor_AttachC_FBVariability_NSPS.xlsx

### Attachment D

Detailed MACT Floor Analysis for New MH Units Cd, Pb, Hg, PM, HCl, SO 2 

### , NO
x 

### , 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 Com panies 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