UNITED STATES ENVIRONMENTAL PROTECTION AGENCY RESEARCH TRIANGLE PARK, 

### NC 27711



### MEMORANDUM 

## SUBJECT: Approach for 

### Applying the 

### upper prediction 

### Limit to 

### Limited OFFICE OF AIR QUALITY PLANNING AND STANDARDS

Datasets 

### DATE:
FROM:

TO: September 

### 3,2014

Barretr Parker and 

### Brian 

### Shrager 

# [( O"t Office of

### Air Quality Planning and'slandards


### Docket 

### ID No. EPA-HQ-OAR-20 10-0895

The 

### UPL approach addresses

### variability of emissions data 

### from the best 

### performing source or sources 

### in setting 

### MACT standards. The 

### UPL also accounts 

### for uncertainty associated 

### with emission values 

### in a dataset, 

### which can be influenced by components such asthe number

### of samples available 

### for developing 

### MACT standards andthe number 

### of samples that

### will be collected 

### to assess compliance 

### with the emission 

### limit. The 

### UPL approach has been used 

### in many environmental science applications.l'2'3'4'5,6 

### As explained 

### in more 

### detail 

### in memorandum entitled "Use 

### ofthe Upper 

### Prediction 

### Limit for Calculating 

### MACT Floors", which is located 

### in the docket 

### forthis action, the 

### EPA uses the 

### UPL approach 

### to reasonably estimatethe emissions performance 

### of the best 

### performing source or sources

### to establish 

### MACT floor standards. Sample size (the number 

### of values 

### in a

### particular dataset) is an 

### important component 

### of the 

### UpL approach. First, the use

### of the 

### UPL approach requires us

### to 

### identify the 

### distribution ofthe data, that is,whether a

### particular dataset is 

### normally or 

### non-normally distributed (see the memorandum 

### entitled "Use of the Upper Prediction 

### Limit for Calculating 

### MACT Floors", which

I Gibbons, R. D. (1987), Statistical Prediction Inlervalsfor the Evaluation of Grouncl-I4tater ettatity. Groundwater, 25: 455465 and Hart, Barbara F. and Janet Chaseling, Optimizing Landfilt Ground ItraterAnatytes-New South Wales, Australia, Groundwater Mon itoring& Remediatio n, 2003, 23, 2. 2 Vy'an, Can; Xu, Zhao; Pinson, Pierre; Dong, ZhaoYang;Wong, Kit Po. Optimal Prediction Intervals of Wind Power Generation.20l4.IEEE Transactions on Power Systems, ISSN 0885-8950, 29(3): pp. l166

## - 1114. 3 Khosravi, Abbas; Mazloutni, Ehsan; Nahavandi, Saeid; Creighton, Doug; van Lint,J. W. C. prediction Intervals to Account for Utlcertainties in Travel Time Prediction.201l. IEEE Transactions on Intelligent Tr.anspoftation Systerns, ISSN 1524-9050, 12(2):537 

## - 541. aAshkan Zarnani; Petr Musilek; Jana Hecl<enbergerova.20 14. Clustering nurnerical weather forecasts to obtain statistical prediction intervals. Meteorological Applications, ISSN 1350-4821.21(3): 605. 5 Rayer, Stefan; Smith, Stanley I(; Tayman 

### , Jeff.2009. Empirical Prediction Intervalsfor County population Forecasts. Poptrlation Research and Policy Review, 28(6):773 -793. 6Nicholas A Som; Nicolas PZegre; Lisa M Ganio; Arne E Skaugset. 2012. Conected prediction intervals for change detection in paired watershed studies. Hydrologicalsciences Journal, ISSN0262-6661,5j(l): 134_ 143

lntemet Address (URL) 

### . htþ://www.epa.gov RscycledRecyclable

. Printed with Vegetable Oil Based lnks on Recycled Paper (Mlnimum 25% Postconsumer)is located 

### in the docket 

### for this action). 

### To determinethe 

### distribution of the data, we use 

### wellestablished tests 

### (kurtosis and skewness tests), and thesetests require at least three data points. 

### In prior rulemakings, we usedthe 

### kurtosis equation that is a

### built-in function in Excel software, but that equation requires at least 4values 

### in order 

### to avoid 

### dividing by zero,

### which precluded its use 

### for datasets

### of 3, 

### However, we 

### recentlyfurther reviewed the 

### application of the 

### UPL where data are

### limited, and concluded that 

### it is appropriate to use another 

### kurlosis estimatorT that provides a 

### meaningful result 

### with

### just 3values. The use of

### this estimator is 

### important, as many, 

### if not most, 

### of our new source emissions 

### limits are based on 3 samples collected 

### during anemissions test.
Once we determine the data 

### distribution, the appropriate equation

### to be used 

### in the 

### UPL approach is selected based on the data 

### distribution, We recognize that the use 

### of the 

### UPL approach 

### for limited datasets introduces some amount 

### of uncertainty 

### in the 

### calculation of 

### MACT standards, and,therefore, we are

### taking additional steps, discussed 

### inthis document, 

### to ensure that the 

### level ofthe 

### MACT standards is reasonable. We also note that after 

### MACT standards are promulgated, we are required to 

### review those standards 

### periodically, and 

### for such reviews, we 

### typically have 

### significant additional 

### HAP emissions data

### from the 

### intervening years 

### of compliance 

### with which to further assess the actual performance 

### ofthevarious emission sources. Regardless 

### of the

### distribution ofthe data, 

### UPL equations have three

### well-dehned components: an average, the t-score and a rneasure 

### of 

### variability that includes the actual

### variability of the data, the sample size, andthe number 

### of data points that are averaged together 

### to determine compliance 

### with a

### particular emission 

### limit. A t-score 

### is avalue that estimates the uncertainty and

### variability for a certain confidence 

### level associated 

### with a

### specific number 

### of data points. For a constant confidence 

### level,t-scores decrease asthe number 

### of samples increase. This means that, 

### if the mean and variance remain constant, 

### UPL values

### for a

### particular confidence level decrease asthe number 

### of samples increase. 

### Figure 1and

### Table 1showthe t-scores

### for various sample sizes, and demonstrate that thet-score is 

### highly variable atthe smallest sample sizes and becomes 

### relatively constant once the sample size is larger than a

### few data 

### points. Consequently,
we recognize that we need to take special care when smaller, 

### limited sample sizes are usedto establish emission 

### limits, asthet-scores can have adisproportionate 

### effect, overwhelming the other components 

### of the emission 

### limit calculation. 

### In addition, we recognize that 

### for a sample size 

### of fewer than three data points, 

### which has a

### very larget-score and precludes the appropriate selection 

### of a

### distribution, we should not develop emission 

### limits using the 

### UPL. In other words, 

### if fewer than 3 data 

### points areavailable 

### for use 

### in determining anemission 

### limit for aparticular source, and no other data 

### from sources 

### in the subcategory areavailable, we 

### would have to establish a

### different procedure 

### for establishing the 

### MACTfloor that does 

### not rely onthe 

### UPL,

7 Doric D, Nikolic-Doric E, Jevremovic V and Malisic J.2009. On measuring skewness and kurtosis. Qual Quant. 43:48 I-493. DOI I 0. 1007/sl | 135-007 -9128-9,

### o

L 

### o o Ut !

# l

# T-score

# vs Number 

# of Samples

35

30

25

20

15

10

5

0

## 23456789 Number 

## of Samples
Figure 1. Sample sizesversus 

### one-sided,99 percent confidence 

### interval t-scores

### for normal 

### distributions

Table 

### l. Degrees 

### of Freedom, T-Score 

### for a One-Tailed, 99 Percent Confidence 

### Interval, and Percent 

### Difference in T-Score 

### for Each Sample Size 35% reductionfrom 3 to4 samples

17% reductionfrom 4 to 5 samples

10% reductionfrom 5 to 6 samples

7% reduction from 6to 7 samples

5% reductionfrom 7to 8 samples

Sample Size (n) Degrees 

### of Freedom (n1) t-score (99

percent) Relative Percent Differenceu 2 1 31.82 J 2 

### 696 78 4 3 4.54 35
5 4 3.75 

### t7 6 5 3.36 

### l0 7 6 

### 3.t4 7 8 7 3.00 5 9 8 

### 290 3 uThe relative percent 

### difference between the t-score and the t-score shown on the preceding 

### rowIn the case 

### of a dataset

### with a 

### normal distribution, the equation 

### to calculate the 

### UPL is as



### follows: 

### UPL-x*t7çn_t),(r_a)l

where

# 7: average or mean

### of test run data; 

### t[(n-t),(t-u.)] 

# : t score, the one-tailed 

### tvalue 

### ofthe Student's 

### t distribution 

### for aspecific degree

### offreedom 

### (n-l) and confidence 

### level

## (l- cr);



# o: level 

### of signihcance expressed as adecimal 

### (e.g.,|Yo signihcance 

# : 0.01), note that confidence 

## level: 100

# - (ø

## x 100);

s2

# : vadance 

### of the dataset (test run);



### n

# : number 

### of values (test runs) 

### in the dataset; and 

# m: number 

### of values used

### to calculate the test average (generally 3).



### As shown 

### in the equation, the sample size 

### (n) directly affects the 

### UPL calculations 

### intwo ways. 

### First, as mentioned earlier, the t-score 

### is selected based onthe sample size. Second, the sample size is a separate 

### variable in the equation, and as

### with thet-score, asthe sample size gets larger 

### (all othervariables being held constant),the 

### UPL gets smaller 

### (i.e., approaches the mean value). The sample size also influences both the mean 

### (f,) andvariance (s2) 

### in that larger sample sizes provide better estimates 

### of the 

### population mean and variance. The confidence 

### level also influences the t-score, and is represented 

### in the 

### UPL equation by the subscript 

### "1- ü,." As noted in the memorandum 

### entitled "Use of the Upper Prediction 

### Limit for Calculating 

### MACT Floors", 

### which is located 

### in the docket 

### for this action, we

### typically choose the 99 percent 

### UPL, which is another 

### way 

### of saying

### that a equals 0.01,
Note that the 

### UPL equation 

### for a 

### lognormal distribution uses az-score, instead 

### of at-score.8 As 

### with the 

### normal 

### UPL equation, the 

### lognormal 

### UPL equation is affected 

### by sample size and the variance 

### of the data, andthe basic concepts 

### outlined in 

### this discussion apply 

### to datasets regardless 

### ofthe 

### distribution.
In summary, sample size is 

### important for 

### UPL calculations, as

### it is a

### key component 

### of the

### tscore value and the 

### calculation of the mean and variance 

### of the sample. For the reasons presented above, 

### for datasets 

### below a certain size, 

### further evaluation iswarranted 

### to ensure thatthe results are a

### valid representation 

### of the performance 

### of the emission sources whose data are included 

### in the dataset.The remainder 

### of this document provides our assessment

### ofwhat constitutes a

8Bhaumik, D.I(. and Gibbons, R.D."An Upper Prediction Limit for theArithmetic Mean of a Lognormal Random Variable", Technom etric s, 46, 2004, 239-248.

### limited dataset and

### what additional analyses areappropriate 

### for ensuring thatthe 

### UPL

calculations are

### valid representations 

### ofthe performance 

### of units as reflected 

### in the available data.

What is a 

### limited dataset?


### While there are 

### different options

### for choosing the number 

### of samples that represents a 

### limited dataset 

### (or small sample size), we can usedistinctions based on

### our observations 

### in Figure 1 and 

### Table 

## I as an aid 

### in def,rning 

### what constitutes a

### limited dataset.

### As discussed earlier, the t-score is an 

### influential parameter that can have a

### particularly large 

### influence on an emission 

### limit developed 

### from a 

### limited dataset.

### As such, thet-score is a key parameter 

### for identifying what constitutes a 

### limited dataset 

### for purposes 

### of 

### MACT analyses. 

### As shown 

### in Figure 

## I and

### Table

## l, above, 

### atthe 99 percent confidence level there is a clear

### distinction between t-score values 

### for sample sizes 

### of 3 and 

### fewerwhen compared

### to t-score values 

### for sample sizes 

### of more than 6.As 

### Figure 

## I and

### Table 1show, the t-score changes 

### drastically

### from sample size equal 

### to 2

### to sample size equal 

### to 3. The changes 

### inthe t-score are considerably lessdramatic asthe sample size approaches 

### 9 datapoints and larger.


### As noted earlier, we

### will not use the 

### UPL approach 

### for datasets 

### with fewerthan 3values because 

### of the 

### high degree 

### of uncerlainty that results

### from the size 

### of the dataset and the relatedt-score. 

### In fact, thet-score (see 

### Figure 

## I and

### table 1)

### for a dataset 

### of 3 is 78 percent 

### lower than at-score 

### for adataset 

### of 2, meaning that 

### given an equal variance, the component 

### of the 

### UPL that includes thet-score and variance 

### would be more than 78 percent lesswhen evaluating a

### 3-point dataset compared 

### to a

### 2-point dataset, The table and

### figure also showthat the 

### relative change of thetscore 

### for each

### additional data 

### point decreases asthe sample size gets larger. 

### As sample size increases, uncertainty decreases, but there is always some amount 

### of uncertainty.The discussion that

### follows presents our 

### examination ofthet-score atvarious sample sizes. We 

### rely onthis examination to help 

### identify asample size 

### belowwhich we

### would further evaluate the data and the application 

### of the 

### UPL to ensure that the amount 

### of uncertainty is

### within a reasonable range, Based on

### Table 1,the 

### lower bound 

### ofthe sample sizewhere such 

### further evaluation is needed is 3values, and a reasonable value (where the size 

### of the 

### t-statistic has 

### much less influence on the eventual emission 

### limit) lies between 4 and 9values.


### An additionalfactor 

### to consider isthat, 

### in the context 

### of 

### MACT analyses, emission tests 

### typically include 3test runs (independent data points), and,therefore, 

### our 

### MACT floor dataset size

### typically is a

### multiple of 3. For this reason, we

### first considered sample sizesthat are 

### multiples of 3when 

### identifying the size 

### belowwhich further scrutiny is needed. 

### As can be calculated 

### from the t-scores shown 

### inTable 1,the relative 

### difference between the t-score 

### for 6 data points and the 

### t-score for 

### 9 data points is about 14 percent ((3.36-2.90)13.36). The relative difference between the t-scores

### for 7 and 9 data points is about 8 percent ((3.14-2.90)13.14). Given that the test methods that are used

### to generate the data

### typically are accurate 

### to

### within 

### l0 to 20 percente
,

### this difference in t-score (between datasets 

### with 7 and 9 runs) is less than the 

### uncerlainty in the test methods and

### would not have a large 

### influence on the magnitude 

### of an emission 

### limit. Conversely, comparing thet-score 

### for 3 runs to that 

### for 7 runs, the relative

e(ReMAP): PHASE 

### l, Precision of Manual Stack Emission Measurements; American Society of Mechanical Engineers, Research Committee on Industrial and Municipal Waste, February 200 I

.difference 

### in thet-score 

### is 55 percent ((6.96-3.14)16.96), 

### indicating thatthere is a

### possibility,

especially 

### if the variance is high, that thet-score

### for 3test runs may 

### introduce alarge amount 

### of uncertainty 

### in anemission 

### limit, and,therefore, 

### additional analysis 

### of a dataset 

### ofthis size is warranted. We also conducted the same type 

### of evaluation on datasets between

### 4 and 6 points, and 

### while the change 

### in t-score 

### for datasets

### of these sizes could be considered

### to be

### sufficiently small, 

### to be conservative, we conclude that a 

### limited dataset,

### for purposes 

### of 

### MACT analyses, is a dataset that includes at least 3test runs and

### fewer thanT test runs. Because 

### of the uncertainty associated 

### with datasets at or below 

### this size, 

### further evaluation is warranted, and thetypes

### of additional analyses that we may choose 

### to conduct areexplained below.
V/hat approach 

### will the 

### EPA 

### follow for 

### MACT floors based on 

### limited datasets?
When a 

### MACT floor for either 

### existing or new sources is based on

### fewer thanT data points, we 

### will further evaluate each 

### individual dataset 

### in order 

### to ensurethat the 

### uncertainty associated 

### with a 

### limited dataset does 

### not cause the calculated emission 

### limit to be so 

### high that 

### it does not reflect the average performance 

### of the units upon 

### which the 

### limit is based after accounting 

### for variability in the emissions 

### of those units. The evaluation 

### will include one or more 

### of the 

### following, depending onthe 

### specific dataset: 

### confirming thatthe data 

### distribution was selected 

### correctly; after 

### confirming the data 

### distribution, ensuring that we use the most appropriate 

### UPL equationl0, and, as necessary, comparing 

### UPL equation components 

### for the 

### individual unit upon 

### which a new source 

### floor is based 

### with those 

### of the 

### units in the 

### existing source 

### floor to determine 

### if our 

### identification of the best 

### unit is reasonable. Each

### ofthe 

### additional evaluations are discussed below. 

### Confirming the data 

### distribution is 

### important because 

### UPL equations andthe emission 

### limits derived 

### from those 

### UPL equations depend onthe 

### distribution. In prior rulemakings, we had not 

### identified away 

### of ascertaining the data 

### distribution for units 

### with three samples, sowe either assumed a 

### normal or 

### lognormal distribution. This 

### particularly affected 

### new source emissions 

### limits, many 

### ofwhich were based on three samples. 

### However, we have since 

### identified and adopted an established technique that compares the 

### ratio of skewness 

### tothe standard error 

### of the skewness 

### for both the 

### raw and 

### logtransformed data. The 

### lower of the 

### two ratios identifies the data

### distribution that best represents the sample set.

### This 

### ability to more 

### precisely ascertain the data 

### distribution enables us

### to select the best 

### optionfor the 

### UPL equation given the small sample size.


### After confirming that 

### we selected the best 

### distribution based on the available data and ensuring that we usedthe correct equation 

### to calculate a

### MACTfloor value, wethen examine key variables that factor 

### into the calculated 

### floor values, Thevariables that arefactored 

### into the emission 

### limit include the

### following: average, t-score, confidence level, variance, number 

### of test runs (i.e., sample size), and number 

### of runs 

### in eachtest (i.e., the number 

### of data points that are averaged together 

### to determine compliance 

### with a

### particular emission 

### limit). The considerations related 

### to each 

### variable are discussed below.

r0 For datasets with lognormal distributions, we recently determined that the most appropriate UPL equation, which is especially important for the srnallest datasets that we use, is based on an approach described in Bhaumik, 2004, op. cit.The average, 

### which 

### typically is calculated asthe mean value 

### of a dataset, does not require



### additional consideration. Once

### it is calculated, 

### it is used

### to rank the performance 

### of units, and also isthe keyvalue upon 

### which each emission 

### limit is based.

### As such, average values have already been compared

### within a

### particular dataset, and 

### additional assessment is not necessary. 

### However, in the

### overall assessment

### of variables, when 

### multiple best 

### performing units have emission averages that are 

### similar, we may 

### look to othervariables 

### like thevariance to help to 

### inform our decision as

### to which 

### unit isthe single best performer.


### As discussed earlier, thet-score is dependent on the sample size, and, therefore, we discuss these 

### two variables together.

### As shown earlier 

### in Figure 1, asthe sample size decreases, the t-statistic increases. 

### This is 

### important because the

### t-statistic is 

### multiplied by thevariance and afactor that 

### involves the sample size and the number 

### of test runs used

### for compliance 

### (i.e.,the

### "m" term 

### in the 

### UPL equation), For the smallest datasets (3 data points) that we consider under the 

### UPL approach, a largevariance coupled 

### with at-scorethat 

### issignificantly greater than the t-score

### for
a dataset

### of 7 or more can create asituation wherethe emission 

### limit includes alarge amount

### of uncertainty, 

### In such cases, we use

### ourtechnical expertise 

### to assess 

### what level of emissions could 

### realistically be expected

### from the type 

### of unit, controls, and other relevant factors, and

### would ensure that the emission 

### limit is reasonable.
The variance is another key variable that we

### would evaluate. 

### Generally speaking, 

### if our evaluation showed a

### very small amount 

### of

### variability (e,g., asmall variance), the emission 

### limit would not contain an unacceptable amount 

### of uncertainty and

### variability because asmall variance

### would result 

### in an emission 

### limit that

### would be reasonably close 

### to the mean 

### ofthe data. 

### With larger datasets, 

### it is more 

### likely thatthe demonstrated variance is

### truly representative 

### of the 

### variation in the source's emissions 

### simply because there is more evidence 

### of the source's emissions over 

### time. Onthe other hand,when a 

### limited dataset includes a large amount

### of variability (e.g., a large variance), we 

### would need 

### to carefully evaluate the data. 

### For instance, consider the case where the 

### pool of best 

### performing units all have 

### similar averages and the 

### unit identified asthe single best 

### performer (based on average emissions 

### only) has a 

### limited dataset and avariancethat greatly exceeds thevariance 

### ofthe other 

### similarly-controlled best-performing units. 

### In such a case, the 

### MACT floor analysis may

### yield anemission 

### limit for that 

### unit (i.e,, the new source 

### MACTfloor) that is higher than the

### existing source 

### MACT floor, which is an 

### indicatorthat 

### further analysis iswarranted. Careful consideration 

### of variance 

### will allów us to better ascertain 

### which 

### unit should be considered the best performer.

Another 

### tool that 

### could be used

### to adjust unreasonable emission 

### limits isthe selection 

### of the confidence 

### level. In certain instances, we may determine that a 

### limited dataset includes such a 

### high degree

### of

### variability that the 99 percent conf,rdence 

### level results 

### in an emission 

### limit that our experience suggests is higher thanthe average emissions 

### limitation achieved by the best 

### performing source 

### or sources after accounting 

### for

### variability. In such cases, we may choose to select a confidence 

### level 

### of 95 percent 

### or 90 percent. 

### As anexample, consider a

### unit identified as the best 

### performing new source 

### with a specif,rc process or 

### control device that has been demonstrated 

### to operate 

### far more 

### efficiently (thus having the 

### potentialto lower emissions) 

### in sirnilar units, processes, or 

### control devices. Under these 

### conditions,we may choose to acknowledge the better operation 

### by lowering the confidence 

### level (which lowers the emissions 

### limit).Establishing and using 

### this approach on a case-by-case basis 

### for limited datasets 

### will ensure

consistent 

### application 

### of emissions 

### limit development procedures, 

### which

### will mitigate the 

### additional uncertainty that 

### could otherwise result 

### from calculating 

### MACTfloor standards 

### with 

### limited dafa.tl

FIow 

### didwe a datasets 

### to 

### limited d lovs source category?

For the 

### ferroalloys source category, we have 

### limited datasets 

### for the

### following pollutants and subcategories: 

### PAHs

### for existing and new furnaces producing ferromanganese 

### (FeMn); PAHs 

### for new furnaces 

### producing silicon manganese 

### (SiMn); mercury 

### for new furnaces 

### producing SiMn; mercury 

### for existing and new furnaces producing 

### FeMn; and 

### HCI for new furnaces 

### producing FeMn or SiMn. Therefore, we evaluated these 

### specific datasets 

### to determine whether 

### it is appropriate 

### to make any 

### modifications to the approach used

### to calculate 

### MACT floors for each 

### ofthese datasets.
For each dataset, we 

### performed the steps 

### outlined above, 

### including: ensuring that we selected the data 

### distribution that best represents each dataset; ensuringthat the

### corect equation 

### for the 

### distribution was then applied 

### tothe data; and comparing 

### individual components 

### of each 

### limited dataset 

### to determine 

### if the standards based on 

### limited datasets reasonably represent the performance 

### ofthe 

### units included 

### in the dataset, The results 

### of each analysis are presented below.

The 

### MACT floor dataset

### for PAHs from existing furnaces 

### producing FeMn includes 6test runs 

### from 2 furnaces. 

### This subcategory includes 

### only two units, and the

### CAA specihesthat the 

### existing source 

### MACTfloor for subcategories 

### with fewer than 30 sources shall not be less stringent than 

### "the average emission 

### limitation achieved by the best 

### performing 5 sources." 

### However, since there are 

### only 2 

### units in the subcategory and we have data

### for both units, the data 

### from both units serve asthe basis 

### for the 

### MACT floor. 

### After determining that the dataset is best represented 

### by a

### normal distribution and ensuring that we usedthe correct equation 

### for the 

### distribution, we considered the selection 

### of a 

### lower confidence 

### level

### for determining the emission 

### limit by evaluating whether the calculated 

### limit reasonably represents the performance 

### of the units upon

### which 

### it is based. 

### In this case, where 

### two units make up the 

### pool of best performers, the calculated emission 

### limit is about 

### twice theshort-term average emissions

### from the best 

### performing sources, 

### indicating thatthe emission 

### limit is not unreasonable compared to the actual performance 

### of the units upon 

### which the

### limit is based and is

### within the range that we see when we evaluate larger data sets using our 

### MACTfloor calculation procedures, Therefore, we determined that no changes 

### to our standard 

### floor calculation procedure are warranted 

### for this

IIWe notethat in the recent D.C. Cirbuit Court of Appeals decision in National Assn. of Clean Vy'ater Agencies v. EPA (NACWA), which involved challenges to EPA's MACT standardsfor sewage sludge incinerators, the couft identified two instanceswhere we replaced a new source floor value with the existing source value because the new source value was less stringentthan the existing source value (the "anomalous result"). See 734 F.3d 1l15. The procedures outlined here would eliminate the anomalous result in both instances in the rule at issue in NACWA. The first instance inwhich the courl in the NACWA decision identihed the anomalous result is where we established an ernission limit using the UPL on a dataset that included only 2 dafa points. The other instance in which the courl identified the anomalous result is where we did not apply the most appropriate lognormal UPL equation.

the

### pollutant and subcategory, and we are proposing that the 

### MACT floor is 1,400 pgldscm 

### for

PAHs 

### from existing furnaces 

### producing FeMn.
The 

### MACT floor dataset 

### for PAHs from new furnaces 

### producing FeMn includes 3test runs

### from asingle furnace (furnace #12 at Eramet) thatwe 

### identihed asthe best 

### performing unit based on average emissions performance. 

### After determining thatthe dataset is best represented 

### by a 

### normal distribution and ensuring that we usedthe correct equation 

### for the 

### distribution, we evaluated the variance 

### of the best 

### performing unit. Our 

### anaþsis showedthat 

### this unit, which was 

### identihed asthe best 

### unit based on average emissions, also had the 

### lowestvariance. Therefore, we determined that the emission 

### limit would reasonably account

### forvariability and that no changesto the standard 

### floor calculation procedure werewarranted 

### for this 

### pollutant and subcategory, andwe are proposing thatthe 

### MACTfloor is 880 prg/dscm

### for PAHs from new furnaces producing FeMn.

The 

### MACT floor dataset

### for PAHs 

### initially identified for new furnaces 

### producing SiMn includes 6test runs

### from asingle furnace (furnace 

### #2 atFelman) that we identif,red asthe best 

### performing unit based on average emissions. 

### After determining that the dataset is best represented 

### by a 

### normal distribution and ensuring that we usedthe correct equation 

### for the 

### distribution, we evaluated thevariance 

### of this 

### unit (furnace #2 at Felman) and concluded that 

### further consideration 

### ofthevariance was warranted. 

### In particular, we noted that thevariance 

### of the dataset

### for this 

### unit was almost 

### twice as large asthe variance 

### ofthe dataset

### for the 

### pool of best 

### performing units that was used

### to calculate the 

### existing source 

### MACT floor. The 

### high degree

### of variance 

### in the dataset

### for the 

### unit 

### with the lowest average prompted us

### to question whether this 

### unit was, 

### in fact, the best 

### performing unit and 

### to evaluate the dataset 

### for the 

### unitwith the next 

### lowest average (furnace 

### #7 atFelman). The dataset 

### for furnace #7 

### includes 3test runs, the furnaces are

### controlled

### with the same type 

### of add-on 

### controltechnology, and

### the average emissions

### from furnace 

### #2 arc 

### only about

### 22 percent 

### lower thanthe average emissions 

### from furnace #7, 

### While we 

### find the average performance 

### of these2 units 

### to be 

### similar, the 

### unit

### with the 

### higher average has avariance more 

### than2 orders 

### of magnitude 

### lower than that 

### ofthe 

### unit 

### with the lower,average, thus 

### indicating that the 

### unit

### with the 

### higher average has afar more consistent level 

### of performance. The 

### combination of components 

### from the 

### unit 

### with the higher average (ftrrnace 

### #7)yields anemissions 

### limit that is 

### lower than that calculated 

### from the dataset 

### of the 

### unit (furnace 

### #2)

### with the 

### lowest average (71.7versus 132.8 

### pgldscm). For these reasons, we determined that the 

### unitwith the lowest average (furnace 

### #2) is not the best 

### performing source 

### for this 

### pollutant and we are instead selecting furnace #7 as fhe best 

### performing source. 

### After selecting the source upon 

### which the new source 

### limit would be based, we next considered whether theselection 

### of a

### different confidence level 

### would be appropriate. 

### Inthis case, we determined thaT a 

### lower confidence 

### level was not warranted given the 

### small amount 

### of

### variability in the data

### for the 

### unit that we 

### identified asthe best performer. Based onthe factors outlined above, we are proposing that the 

### MACT floor is 72 p,gldscm 

### for PAHs from new furnaces 

### producing SiMn.

The 

### MACT floor dataset

### for mercury from existing and new furnaces 

### producing FeMn includes 6test runs 

### from asingle furnace. We 

### first determined thatthe dataset is best represented by a normal 

### distribution and ensured that we usedthe correct equation 

### for the 

### distribution. Because the

### floor for both 

### existing and 

### newfurnaces is based onthe performance 

### of asingle 

### unit, ourevaluation 

### ofthe datawas 

### limited to ensuring thatthe emission 

### limit is a reasonable estimate 

### of

the performance 

### of the 

### unit based on our knowledge about the process and controls. 

### Accordingly, we compared the calculated emission 

### limit to the highest measured value and

### the average shortterm emissions

### from the 

### unit, and found that the calculated emission 

### limit is about2.5times the 

### shoft-term average 

### from the 

### unit, which is

### within the range that we see when we evaluate larger data sets using our 

### MACT floor calculation procedures. The

### fairly wide range 

### in mercury emissions shown by the available data 

### for this best 

### performing unit indicate that

### variability is 

### significant, and we determined that the emission 

### limit is representative 

### of the actual performance 

### of the 

### unit upon

### which the 

### limit is based, considering 

### variability. Therefore, we determined that no changes

### toour standard 

### floor calculation procedure were

### waffantedfor this 

### pollutant and subcategory, and we are proposing that the 

### MACT floor is 170 pgldscm 

### for mercury 

### from existing furnaces 

### producing FeMn. We also note that 

### while we calculated the same 

### MACT floor value 

### for new sources, we are proposing a 

### beyond-the-floor standard 

### for new sources, 

### which is discussed later in 

### this section 

### of this preamble.
The 

### MACTfloor dataset

### for mercury 

### from new furnaces 

### producing 

### SiMn includes 3test runs 

### from asingle furnace (furnace 

### #7 atFelman) that we 

### identified asthe best 

### performing unit based on average emissions.

### After determining that the dataset is best represented 

### by a normal 

### distribution and ensuring that we usedthe correct equation 

### for the 

### distribution, we evaluated the variance 

### of this 

### unit. Our analysis showed thatthis 

### unit, identified asthe best 

### unit based on averuge emissions, also had the lowest variance, 

### indicating consistent performance. Therefore, we determined thatthe emission 

### limit reasonably accounts 

### for

### variability and that no changes to the standard 

### floor calculation procedure were warranted 

### for this 

### pollutant and subcategory, and we are proposing thatthe 

### MACT floor is 

### 4.0 pgldscm 

### for Hg from new furnaces 

### producing SiMn.

The 

### MACTfloor dataset

### for HCIfrom new furnaces 

### producing FeMn or 

### SiMn includes 6 test runs 

### from asingle furnace (furnace #5 at Felman) that we 

### identihed asthe best 

### performing unit based on average emissions. 

### After determining thatthe dataset 

### is best represented 

### by a nonnormal 

### distribution and ensuring that we usedthe correct equation 

### for the 

### distribution, we evaluated thevariance 

### of this best 

### performing unit. Our analysis showed that this 

### unit, identified
asthe best 

### unit based 

### on average emission, also had the lowest variance, 

### indicating consistent performance. Therefore, we determined thatthe emission 

### limit reasonably accounts 

### for variability and that no changes 

### tothe standard 

### floor calculation procedure were warranted 

### for this 

### pollutant and subcategory. We also note that 

### for this standard, the calculated new source 

### floor level was 

### below the level that can be accurately measured (the 

### level that we refer 

### to as "3 times the representative detection 

### level" or 3xRDL). Therefore, we are 

### proposing a new source 

### MACT emission 

### limit of 180 ppm, 

### which isthe 

### 3xRDL value 

### for HCl.

10