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 1 0-0895

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 as the number of

samples available for developing MACT standards and the 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 of the Upper Prediction Limit for Calculating MACT Floors", which is located in

the docket for this action, the EPA uses the UPL approach to reasonably estimate the 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 of the 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 Itrater Anatytes-New South

Wales, Australia, Groundwater Mon itoring & Remediatio n, 2003, 23, 2.

2 Vy'an, Can; Xu, Zhao; Pinson, Pierre; Dong, Zhao Yang; 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.

a Ashkan 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 Intervals for County population

Forecasts. Poptrlation Research and Policy Review, 28(6):773 -793. 6 Nicholas A Som; Nicolas P Zegre; Lisa M Ganio; Arne E Skaugset. 2012. Conected prediction intervals for

change detection in paired watershed studies. Hydrologicalsciences Journal, ISSN 0262-6661,5j(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 determine the distribution of the data, we use wellestablished tests (kurtosis and skewness tests), and these tests require at least three data points. In

prior rulemakings, we used the kurtosis equation that is a built-in function in Excel software, but

that equation requires at least 4 values in order to avoid dividing by zero, which precluded its use

for datasets of 3, However, we recently further 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 3 values. 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 an emissions 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 in this document, to ensure

that the level of the 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 of the various emission sources.

Regardless of the distribution of the 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, and the number of data points that are averaged together to determine

compliance with a particular emission limit. A t-score is a value 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 as the number of samples increase. This means

that, if the mean and variance remain constant, UPL values for a particular confidence level

decrease as the number of samples increase. Figure 1 and Table 1 show the t-scores for various

sample sizes, and demonstrate that the t-score is highly variable at the 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 used to

establish emission limits, as the t-scores can have a disproportionate 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 large t-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 are available for use in determining an emission limit for a particular

source, and no other data from sources in the subcategory are available, we would have to

establish a different procedure for establishing the MACT floor that does not rely on the 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. 1 007/sl | 135-007 -9128-9,
oL

o

o

Ut!

lT-score vs Number of Samples

35

30

25

20

15

10

5

0

23456789

Number of Samples

Figure 1. Sample sizes versus 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% reduction from 3 to 4 samples

17% reduction from 4 to 5 samples

10% reduction from 5 to 6 samples

7% reduction from 6 to 7 samples

5% reduction from 7 to 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 row
In 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 t value of the Student's t distribution for a specific

degree offreedom (n-l) and confidence level (l- cr);

o: level of signihcance expressed as a decimal (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 in two ways.

First, as mentioned earlier, the t-score is selected based on the sample size. Second, the sample

size is a separate variable in the equation, and as with the t-score, as the sample size gets larger

(all other variables being held constant), the UPL gets smaller (i.e., approaches the mean value).

The sample size also influences both the mean (f,) and variance (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 a z-score, instead of a t-score.8 As

with the normal UPL equation, the lognormal UPL equation is affected by sample size and the

variance of the data, and the basic concepts outlined in this discussion apply to datasets

regardless of the 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 is warranted to ensure that the 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 of what constitutes a

8 Bhaumik, D.I(. and Gibbons, R.D."An Upper Prediction Limit for the Arithmetic Mean of a Lognormal Random

Variable", Technom etric s, 46, 2004, 239 -248.
limited dataset and what additional analyses are appropriate for ensuring that the UPL

calculations are valid representations of the 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 use distinctions 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, the t-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 fewer when compared to t-score values for sample sizes of more than 6. As

Figure I and Table 1 show, the t-score changes drastically from sample size equal to 2 to sample

size equal to 3. The changes inthe t-score are considerably less dramatic as the sample size

approaches 9 datapoints and larger.

As noted earlier, we will not use the UPL approach for datasets with fewer than 3 values because

of the high degree of uncerlainty that results from the size of the dataset and the related t-score.

In fact, the t-score (see Figure I and table 1) for a dataset of 3 is 78 percent lower than a t-score

for a dataset of 2, meaning that given an equal variance, the component of the UPL that includes

the t-score and variance would be more than 78 percent less when evaluating a 3-point dataset

compared to a 2-point dataset, The table and figure also show that the relative change of the tscore for each additional data point decreases as the 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 of the t-score at various sample sizes. We rely on this

examination to help identify a sample size below which 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 of the sample size where such further evaluation is needed is

3 values, and a reasonable value (where the size of the t-statistic has much less influence on the

eventual emission limit) lies between 4 and 9 values.

An additional factor to consider is that, in the context of MACT analyses, emission tests

typically include 3 test runs (independent data points), and, therefore, our MACT floor dataset

size typically is a multiple of 3. For this reason, we first considered sample sizes that are

multiples of 3 when identifying the size below which further scrutiny is needed. As can be

calculated from the t-scores shown in Table 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 the t-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 the t-score is 55 percent ((6.96-3.14)16.96), indicating that there is a possibility,

especially if the variance is high, that the t-score for 3 test runs may introduce alarge amount of

uncertainty in an emission limit, and, therefore, additional analysis of a dataset of this 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 3 test runs and fewer thanT test runs. Because of the uncertainty

associated with datasets at or below this size, further evaluation is warranted, and the types of

additional analyses that we may choose to conduct are explained 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 ensure that 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 on the specific dataset: confirming that the 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 of the additional evaluations are

discussed below.

Confirming the data distribution is important because UPL equations and the emission limits

derived from those UPL equations depend on the distribution. In prior rulemakings, we had not

identified a way of ascertaining the data distribution for units with three samples, so we either

assumed a normal or lognormal distribution. This particularly affected new source emissions

limits, many of which were based on three samples. However, we have since identified and

adopted an established technique that compares the ratio of skewness to the standard error of the

skewness for both the raw and log transformed 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 option for 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 used the correct equation to calculate a MACT floor value, we then examine key

variables that factor into the calculated floor values, The variables that are factored 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 each test (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 as the 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 is the key value 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 other variables like the variance to help to

inform our decision as to which unit is the single best performer.

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

two variables together. As shown earlier in Figure 1, as the sample size decreases, the t-statistic

increases. This is important because the t-statistic is multiplied by the variance and a factor 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 large variance coupled with a t-score that is significantly greater than the t-score for

a dataset of 7 or more can create a situation where the 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., a small variance), the emission limit

would not contain an unacceptable amount of uncertainty and variability because a small

variance would result in an emission limit that would be reasonably close to the mean of the data.

With larger datasets, it is more likely that the 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. On the 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 as the single best performer (based on average emissions only) has a limited dataset

and a variance that greatly exceeds the variance of the other similarly-controlled best-performing

units. In such a case, the MACT floor analysis may yield an emission limit for that unit (i.e,, the

new source MACT floor) that is higher than the existing source MACT floor, which is an

indicator that further analysis is warranted. 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 is the 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 than the 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 an example, 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 potential to 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 MACT floor standards with

limited dafa.tl

FIow did we 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; ensuring that the corect equation for the

distribution was then applied to the data; and comparing individual components of each limited

dataset to determine if the standards based on limited datasets reasonably represent the

performance of the 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 6 test runs

from 2 furnaces. This subcategory includes only two units, and the CAA specihes that the

existing source MACT floor 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 as the basis for the MACT floor. After determining that the dataset is best

represented by a normal distribution and ensuring that we used the 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 the short-term average emissions from

the best performing sources, indicating that the 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 MACT floor calculation procedures, Therefore,

we determined that no changes to our standard floor calculation procedure are warranted for this

I I We note that 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 standards for sewage sludge incinerators, the couft

identified two instances where 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 in which 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 3 test runs from

a single furnace (furnace #12 at Eramet) that we identihed as the best performing unit based on

average emissions performance. After determining that the dataset is best represented by a

normal distribution and ensuring that we used the correct equation for the distribution, we

evaluated the variance of the best performing unit. Our anaþsis showed that this unit, which was

identihed as the best unit based on average emissions, also had the lowest variance. Therefore,

we determined that the emission limit would reasonably account for variability and that no

changes to the standard floor calculation procedure were warranted for this pollutant and

subcategory, and we are proposing that the MACT floor 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

6 test runs from a single furnace (furnace #2 atFelman) that we identif,red as the best performing

unit based on average emissions. After determining that the dataset is best represented by a

normal distribution and ensuring that we used the correct equation for the distribution, we

evaluated the variance of this unit (furnace #2 at Felman) and concluded that further

consideration of the variance was warranted. In particular, we noted that the variance of the

dataset for this unit was almost twice as large as the variance of the 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 unit with the next

lowest average (furnace #7 atFelman). The dataset for furnace #7 includes 3 test runs, the

furnaces are controlled with the same type of add-on control technology, and the average

emissions from furnace #2 arc only about 22 percent lower than the average emissions from

furnace #7, While we find the average performance of these 2 units to be similar, the unit with

the higher average has a variance more than2 orders of magnitude lower than that of the unit

with the lower,average, thus indicating that the unit with the higher average has a far more

consistent level of performance. The combination of components from the unit with the higher

average (ftrrnace #7) yields an emissions limit that is lower than that calculated from the dataset

of the unit (furnace #2) with the lowest average (71.7 versus 132.8 pgldscm). For these reasons,

we determined that the unit with 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 the selection of a different confidence level would be appropriate. In this 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 as the best performer. Based on the 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

6 test runs from a single furnace. We first determined that the dataset is best represented by a

normal distribution and ensured that we used the correct equation for the distribution. Because

the floor for both existing and new furnaces is based on the performance of a single unit, our
evaluation of the data was limited to ensuring that the 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 about 2.5 times 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 to our standard floor calculation procedure were waffanted for 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 MACT floor dataset for mercury from new furnaces producing SiMn includes 3 test runs

from a single furnace (furnace #7 atFelman) that we identified as the best performing unit based

on average emissions. After determining that the dataset is best represented by a normal

distribution and ensuring that we used the correct equation for the distribution, we evaluated the

variance of this unit. Our analysis showed that this unit, identified as the best unit based on

averuge emissions, also had the lowest variance, indicating consistent performance. Therefore,

we determined that the 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 that the MACT floor is 4.0 pgldscm for Hg from new furnaces producing

SiMn.

The MACT floor dataset for HCI from new furnaces producing FeMn or SiMn includes 6 test

runs from a single furnace (furnace #5 at Felman) that we identihed as the best performing unit

based on average emissions. After determining that the dataset is best represented by a nonnormal distribution and ensuring that we used the correct equation for the distribution, we

evaluated the variance of this best performing unit. Our analysis showed that this unit, identified

as the best unit based on average emission, also had the lowest variance, indicating consistent

performance. Therefore, we determined that the emission limit reasonably accounts for

variability and that no changes to the 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 is the 3xRDL value for HCl.

10