DEVELOPMENT OF THE MACT FLOORS FOR THE FINAL NESHAP FOR 

PORTLAND CEMENT 

Metals and Minerals Group 

Sector Policies and Programs Division 

Office of Air Quality Planning and Standards 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 

August 6, 2010
2

Development of the MACT Floors for the Final NESHAP for Portland Cement 

1. Introduction 

 The maximum achievable control technology (MACT) standards for existing sources 

must be at least as stringent as the average emissions limitation achieved by sources in the 

MACT pool, that is, the best performing 12 percent of existing sources or the best performing 

5 sources for source categories with less than 30 sources. This level of minimum stringency 

is called the MACT floor. For new sources, MACT standards must be at least as stringent as 

the control level achieved in practice by the best controlled similar source. The MACT floor 

for each of the hazardous air pollutants (HAP) and HAP surrogates (mercury, total 

hydrocarbons, HCl, and particulate matter) are calculated based on the performance of the 

lowest emitting sources in each of the MACT pools. We ranked all of the sources for which 

we had data based on their emissions and identified the lowest emitting 12 percent of the 

sources for which we had data, which, for existing sources, ranged from two kilns for THC to 

11 kilns for mercury. For new source MACT, the floor was based on the performance of the 

lowest emitting source. We then assess variability of the best performers by using a statistical 

formula designed to estimate a MACT floor level that is equivalent to the average of the best 

performing sources based on future compliance tests.. Specifically, the MACT floor limit is 

an upper prediction limit (UPL) calculated with the Student’s t-test using the TINV function 

in Microsoft Excel®. The Student’s t-test has also been used in other EPA rulemakings (e.g., 

NSPS for Hospital/Medical/Infectious Waste Incinerators, NESHAP for Industrial, 

Commercial, and Institutional Boilers and Process Heaters) in accounting for variability. A 

prediction interval for a future observation is an interval that will, with a specified degree of 

confidence, contain the next (or some other pre-specified) randomly selected observation 

from a population. In other words, the prediction interval estimates what the upper bound of 

future values will be, based upon present or past background samples taken. The UPL 

consequently represents the value which we can expect the mean of future observations (3-run 

average for HCl, 30 day average for Hg, PM, HCl (sources not having wet scrubbers or 

otherwise electing CEM-based compliance), and THC) to fall below within a specified level 

of confidence, based upon the results of an independent sample from the same population. 

This formula encompasses all the data point to data point variability. Its predictions derive 
3

from the data set to which it is applied, and thus can be applied to any type of data. 

 The form of the equation differs somewhat depending upon the nature of the data set 

to which it is applied . To this end, EPA carefully evaluated the data sets for each HAP to 

ascertain whether the data were normally distributed, or distributed in some other manner 

(i.e., log normally). To evaluate the distribution of the best performing dataset, we first 

computed the skewness and kurtosis statistics and then conducted the appropriate smallsample hypothesis tests. The skewness statistic (S) characterizes the degree of asymmetry of 

a given data distribution. Normally distributed data have a skewness of 0. A skewness 

statistic that is greater (less) than 0 indicates that the data are asymmetrically distributed with 

a right (left) tail extending towards positive (negative) values. Further, the standard error of 

the skewness statistic (SES) is given by SES = SQRT(6/N) where N is the sample size. 

According to the small sample skewness hypothesis test, if the skewness statistic (S) is greater 

than two times the SES, the data distribution can be considered non-normal. The kurtosis 

statistic (K) characterizes the degree of peakedness or flatness of a given data distribution in 

comparison to a normal distribution. Normally distributed data have a kurtosis of 0. A 

kurtosis statistic that is greater (less) than 0 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 the 

kurtosis statistic (K) is greater than two times the SEK, the data distribution is typically 

considered to be non-normal. 

 For each data set to which the UPL was applied (i.e. the separate data sets for mercury, 

PM, and HCl), we applied the skewness and kurtosis hypothesis tests to both the reported test 

values and the lognormal values of the reported test values. If the skewness (S) and kurtosis 

(K) statistics of the reported data set were both less than twice the SES and SEK, respectively, 

the dataset was classified as normally distributed. If neither of the skewness (S) and kurtosis 

(K) statistics, or only one of these statistics were less than twice the SES or SEK, respectively, 

then the skewness and kurtosis hypothesis tests were conducted for the natural logtransformed data. Then the distribution most similar to a normal distribution was selected as 

the basis for calculating the UPL. If both the reported values and the natural-log transformed 

reported values had skewness (S) and kurtosis (K) statistics that were greater than twice the 

SES or SEK, respectively, the normally distributed dataset was selected as the basis of the 
4

floor to be conservative. If the results of the skewness and kurtosis hypothesis tests were 

mixed for the reported values and the natural log-transformed reported values, we also chose 

the normal distribution to be conservative. We believe this approach is more accurate and 

obtained more representative results than a more simplistic normal distribution assumption. 

Tests for normal distributions were run on each data set as part of the analysis of the 

variability analysis for each pollutant. Normality tests used as part of our analyses included 

Andersonn-Darling, Cramer-von Mises, Lilliefors (Komogorov-Smirnov), and ShapiroFrancia. We determined that the distributions for mercury, HCl and particulate matter were 

approximately a normal distribution, which in turn determined the final form of the UPL 

equation. 

 Specifically, the MACT floor limit is an upper prediction limit (UPL) calculated from: 

UPL = 

Where: 

 = mean of the top performing sources

n = the number of test runs = 

m = the number of future test runs in the compliance average 

s

2

 = pooled variance 

t df, p = quantile t-distribution with df degrees of freedom at p confidence level 

df = degrees of freedom 

1

N

i

i

n

=

∑

1 1

1 i N n

ij

i j

X x

n = =

= ∑∑

( )2 2

1

1

1

n

i

i

s x X

n =

= − − ∑

2

,

1 1 Xt s df p n m

⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠

1

1

N

i

i

df n =

⎛ ⎞ = − ⎜ ⎟ ⎝ ⎠ ∑
5

This calculation was performed using the following Excel functions: 

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

[STDEV(Test Runs in Top 12%) x TINV(2 x probability, n-1 degrees of 

freedom)*SQRT((1/n)+(1/m))], for a one-tailed t-value, probability of 0.01, and sample size of n 

 We are using a 99th confidence UPL. We have chosen the 99th confidence UPL as a 

reasonable upper limit because only one percent of future tests of the MACT pool of lowest 

emitting kilns will exceed the limit if they are performing as well as the emission test data 

indicate (i.e., these kilns will achieve the limit 99 percent of the time in the future). If we did not 

account for variability in this manner and set the limit based solely on the average performance, 

then these kilns could exceed the limit half the time or more. 

EPA was able to reasonably calculate variability for the THC and HCl standards by other 

means. The data set for THC contains sufficient observations to estimate the variability without 

the need of any type of statistical intervals (so no UPL needed to be calculated). The key issue 

for the HCl data set is the analytic detection limit, which dictated the level of the standard 

(although EPA did apply the UPL equation to the underlying data set). The methods used for 

THC and HCl are described in the appropriate sections below. 

 The following sections describe the development of the MACT floors for each of the 

pollutants. The data for each run for the MACT floor sources are given in the appendices to this 

report. 

2. MACT Floor for Mercury 

 The first step in establishing a MACT standard is to determine the MACT floor based on 

the amount of HAP emitted. In the case of mercury emitted by cement kilns, this is not 

necessarily a straightforward undertaking. Single stack measurements represent a snapshot in 

time of a source’s emissions, always raising questions of how representative such emissions are 

of the source’s emissions over time. This problem is compounded in the case of cement kilns, 

because cement kilns do not emit mercury uniformly. For all kilns, the mercury content of the 

feed and fuels varies significantly from day-to-day and even hour-to-hour. For modern preheater 

and preheater/precalciner kilns this problem is compounded because these kilns have in-line raw 

mills. With in-line raw mills, mercury is captured in the ground raw meal in the in-line raw mill 

and this raw meal (containing mercury) is returned as feed to the kiln. Mercury emissions may 
6

remain low during such recycling operations. However, as part of normal operation, a kiln’s raw 

mill must be periodically shut down for maintenance, and mercury-containing exhaust gases 

from the kiln are then bypassed directly to the main control device resulting in significantly 

increased mercury emissions at the stack. The result is that at any given time, mercury emissions 

from such cement kilns are either low or high, but rarely in equilibrium, so that single stack tests 

are likely to either underestimate or overestimate cement kilns’ performance over time. Put 

another way, the single short term stack test data (typically a few hours) are probably not 

indicative of long term emissions performance, and so are not the best indicator of performance 

over time. With these facts in mind, one must carefully consider alternatives other than use of 

single short-term stack test results to quantify kilns’ performance for mercury. 

 An alternative to short term stack test data would be to use mercury continuous 

monitoring data over a longer time period. Because no cement kilns in the U.S. have continuous 

mercury monitors, this option was not available. However, mercury is an element, and can 

neither be created nor destroyed in a cement kiln. Therefore, all the mercury that enters a kiln 

has to leave the kiln in some fashion. The available data indicate that almost no mercury leaves 

the kiln as part of the clinker (product). Therefore, over the long term all the mercury leaves the 

kiln as a stack emission with three exceptions: 

1. If instead of returning all particulate captured in the particulate control device to the kiln, 

the source removes some of it from the circuit entirely, i.e., the kiln wastes some cement 

kiln dust (CKD); or 

 

2. The kiln is equipped with an alkali bypass, which means all CKD captured in the alkali 

bypass PM control is wasted, and/or; 

3. If the kiln has a wet scrubber (usually for SO2 control), the scrubber will remove some 

mercury which will end up in the gypsum generated by the scrubber. 

 Based on these facts, the most accurate method available to determine long term mercury 

emissions performance is to do a total mass balance. EPA did so for the final rule by obtaining 

data on all the kiln mercury inputs (i.e., all raw materials and all fuels) for a large group of kilns, 

and assuming all mercury that enters the kiln is emitted with the three exceptions noted above. 

Pursuant to letters mandating data gathering, issued under the authority of section 114 of the Act, 

30 days of daily data were obtained for kiln mercury concentrations in each individual raw 

material, fuel, and CKD for 89 kilns, along with annual mass inputs and the amount of material 
7

collected in the PM control device (or alkali PM control device) that is wasted rather than 

returned to the kiln. 

These data were submitted to EPA as daily concentrations for the inputs, i.e., samples of 

all inputs were taken daily and analyzed daily for their mercury content. We took the daily 

averages, calculated a mean concentration, and multiplied the mean concentration by annual 

materials use to calculate an annual mercury emission for each of the 89 kilns (normalized by 

amount of total feed). If the facility wasted CKD, we subtracted out the annual mercury that left 

the system in the CKD. If the facility had a wet scrubber (the only control device currently in 

use among the sampled kilns with any substantial mercury capture efficiency), we subtracted out 

the annual mercury attributable to use of the scrubber. There are five cement kilns using wet 

scrubbers and EPA has removal efficiencies for four of these scrubbers (based on inlet/outlet 

testing conducted at EPA’s request concurrent with the input sampling). We attributed a 

removal efficiency for the scrubber on the fifth kiln based on the average removal efficiency of 

the other four scrubbers. 

For each kiln, we took this (calculated) annual emission and then divided by total inputs 

to calculate an average emission factor – the average projected emission rate for each kiln. We 

then ranked each kiln from lowest average emission factor to highest. The resulting emissions 

factors for 87 of the 89 ranged (relatively continuously) from 8 to 300 pounds of mercury per 

million tons of feed. Two kilns showed considerably higher numbers, approximately 1200 and 

2000 pounds per ton of feed. These two facilities have atypically high mercury contents in the 

limestone in their proprietary quarries which are the most significant contributors to the high 

mercury emissions. By conducting a total mass balance for mercury and then assuming that all 

mercury inputted is emitted (minus conservatively estimated removals for scrubber usage and 

dust wastage), EPA made a near worst case assumption as to kilns’ mercury emission levels. 

The kilns were then ranked from best to worst based on the extrapolated mercury emissions, 

normalized to clinker production. 

Based on these data and ranking methodology, the existing source MACT floor would be 

the average of the lowest emitting 12 percent of the kilns for which we have data, which would 

be the 11 kilns with lowest emissions (as calculated). Following proposal, we received 

additional data on raw materials usage for these best performing kilns and used that data to revise 

our average mercury emissions estimates from the best performing kilns at proposal (Docket 
8

item EPA-HQ-OAR-2002-0051-2845). Specifically, for each of the top 11 performing kilns at 

proposal, industry submitted new information characterizing forseeable kiln operations changes 

not captured in the 30-day sampling, including data on annual usage amounts for raw feed 

materials, each fuel, as well as the amount of cement kiln dust wasted. EPA has examined and 

accepted these data. These data are presented in Docket item EPA-HQ-OAR-2002-0051-2855. 

Using this new information, EPA revised the daily mercury emissions for the top performing 

kilns. In some cases, estimated emissions increased. At proposal, average mercury emissions of 

the top 11 kilns ranged from7.14 to 23.92 lb/MM tons of feed and the overall average was 16.6 

lb/MM tons feed. Using the revised mercury emissions, average mercury emissions of the top 11 

kilns ranged from 8.48 to 25.91 lb/MM tons feed with an overall average of 19.21 lb/MM tons 

feed. The new usage data also showed that during the 30-day sampling period, kiln 1233 was 

producing a type of cement that, because of the mix of materials and amounts being used, 

resulted in lower than typical mercury emissions. Like a similar kiln at the facility, kiln 1233 

typically produces Type I/II clinker which would result in higher mercury emissions. At 

proposal, emissions of mercury from kiln 1233 were estimated to be 7.14 lb/MM tons feed. After 

adjusting the mix of raw materials and fuel using the new data for kiln 1233, mercury emissions 

were estimated to be 44.5 lb/MM tons feed, now outside the range of the top performing kilns. 

As a result, kiln 1233 was dropped and replaced by the next best performing kiln, kiln 1594. 

In addition, following proposal, we received new information that has caused us to 

reconsider how we accounted for intra-quarry mercury variability for the best performing kilns. 

Industry commented that there were sites with quarries in close proximity to each other where 

we have sampling data from the ICR we sent out. Because these quarries come from a common 

geologic limestone formation, it is reasonable to assume that the variability of mercury levels 

across the formation are comparable (although actual mercury levels are not). See US EPA, 

Memorandum “Intra-quarry Variability Estimate for Mercury”, July, 2010. Using the mercury 

content of daily samples from each of the three proximate quarries, we calculated the mean and 

standard deviation of each quarry and then the average of the means and standard deviations. 

From this an average relative standard deviation (RSD) was calculated. The RSD was then used 

to calculate a variance for the quarries from the two best performers which was combined with 

the variance calculated for their daily kiln emissions. Like the kiln emissions data, the quarry 

data were also corrected for temporal autocorrelation. The details of these calculations are 
9

presented in Appendix A. 

EPA further applied these estimates of intra-quarry variability to the mercury data for the 

other best performing kilns (i.e. applied the same RSD to the other best performing sources). 

EPA did so to more robustly characterize long-term variability of these sources’ quarries’ 

mercury levels. The quarries have a 50 to 100 year supply of limestone, and applying a 30-day 

standard based on 30 days of sampling consequently may not fully capture the variations in 

mercury levels in the quarries. The fact that intra-quarry variability of the two lowest emitting 

sources increased somewhat after examination with other quarries in the common geologic 

formation confirms that there can be further variability. Since the intra-quarry variability comes 

from quarries servicing the two lowest emitting kilns, EPA would not expect intra-quarry 

variability to be lower for the other best performing sources (and there are no additional data 

from best performers from which to estimate long-term quarry variability). 

Using the equations described earlier, we then calculated the upper prediction limit based 

on the 30-day average proposed compliance period (i.e., m = 30). Because the mercury 

concentration data was collected over 30 consecutive days of sampling raw materials, daily 

mercury emissions are considered to be temporally correlated. In other words, the daily mercury 

data are not independent of the results of the previous day. For each kiln, a test for temporal 

correlation was performed and confirmed that the results of daily sampling were correlated. In 

the presence of temporal correlation between pairs of consecutive measurements (e.g. in the case 

of rolling averages or averages of consecutive test runs), one can estimate the first-order 

autocorrelation as: 

( )( )

( )

1

( 1)

1

1

2

1

i

i

n

ij i i j i

j

n

ij i

j

x xx x

r

x x

−

+

=

=

− −

=

−

∑

∑

The UPL is then calculated by adjusting the variance by multiplying the variance by (1+temporal 

correlation) as follows: 

s

2

 * (1+r1). 

The results are also shown in Table 1. At proposal, the average mercury emissions of the 

top 12 percent of the kilns was 27.4 pounds per million (lb/MM) tons clinker, and the average 

emissions of the best performing source were 11.8 lb/MM ton clinker. After revising our 
10

mercury emissions estimates to account for the new data on inputs and to adjust for intra-quarry 

variability, and reapplying the UPL formula (revised slightly as explained in the preamble to the 

final rule), the averages were 32 and 14 lb/MM tons clinker, respectively, as shown in Table 1. 

Table 1. MACT Floor for Mercury 

Kiln code Mercury emissions (lb/MM ton feed) 

1589 8.48 

1650 9.53 

1315 15.26 

1302 15.28 

1248 16.63 

1259 21.33 

1286 22.65 

1594 25.23 

1435 25.51 

1484 25.51 

1364 25.91 

MACT - Existing Kilns 

Average: lb/MM tons feed (lb/MM 

tons clinker) 

19.21 (31.7) 

Total variance 272.3 

UPL: lb/MM tons feed (lb/MM 

tons clinker) 

32.8(54.1) 

MACT - New Kilns 

Average: lb/MM tons feed (lb/MM 

tons clinker) 

8.48 (14.0) 

Total variance 35.2 

UPL: lb/MM tons feed (lb/MM 

tons clinker) 

12.3 (20.3) 

To reiterate, for the 11 lowest emitting kilns, we calculated a daily emission rate using 

the daily concentration values and annual materials inputs divided by each kiln’s operating 

days.1 The results are shown in Table 1 and represent the average performance of each kiln over 

the 30-day period. We then calculated the average performance of the 11 lowest emitting kilns 

 

1 In the daily calculations, we treated the CKD removal as if it was a control device, and applied 

the overall percent reduction rather that using the daily CKD concentration value. We used this 
11

(19.2 lb/MM tons of feed) and the variance of the daily emission rates of these 11 kilns which is 

a direct measure of the within-source variability of the data set. To further account for long-term 

variability, we calculated the variance of the mercury content in limestone from two of the best 

performing kilns using data on mercury variability from the quarries of several other kilns 

quarrying limestone from the same geologic limestone formation and included this variability 

with the pooled variability of the best performing sources in the MACT floor pool. We further 

applied this revised estimate of intra-quarry variability to the rest of the kilns in the pool of best 

performers. We also adjusted the variance to correct for the temporal correlation among the daily 

measurements. Our analysis of variability thus includes the day-to-day variability in the total 

mercury input to each kiln, the variability in the mercury content of limestone mined from 

quarries over time, the variability of the sampling and analysis methods over the 30-day period, 

and the variability between sources. We calculated the MACT floor (32.8 lb/MM tons feed) 

based on the 99th confidence UPL, as described earlier, from the average performance of the 11 

lowest emitting kilns, Students t-factor, and the total variability, which was adjusted to account 

for the lower variability when using 30 day averages. This floor, normalized to tons of clinker 

production (rather than to feed), is 54.1 lb/MM tons clinker, which rounds to 55 lb/MM tons 

clinker. The calculations are found in Appendix A to this document. 

3. MACT Floor for THC 

 The THC data used to develop the MACT floor were obtained from 15 kilns using 

CEMS to continuously measure the concentration of THC exiting each kiln’s stack. Only three 

kilns, one with a regenerative thermal oxidizer (RTO) and 2 kilns with activated carbon injection 

(ACI), have THC emissions controls which remove or destroy THC. We also obtained THC 

data from manual stack tests, typically based on 3 one-hour runs per test. The CEMS data are 

superior to the results of a single stack test for characterizing the long term performance and in 

determining the best performing kilns with respect to THC emissions for several reasons. The 

manual stack test is of short duration and represents a snapshot in time; consequently, it provides 

only limited information on the variability in emissions over time due to changes in raw material 

feed or in kiln operating conditions. In contrast, the CEMS data include measurements that 

range from 29 consecutive days to almost 800 days of operation for the various kilns. This 

 

approach because if we used daily CKD removal values, some days showed negative mercury 

emissions rates. This is because of the mercury recycling issues discussed above. 
12

extended duration of the CEMS test data gives us confidence that for any particular kiln CEMS 

data will capture the variability associated with the long-term THC emissions data, and thus give 

the most accurate representation of a source’s performance. 

The method used by EPA to calculate the emission limits for THC in the final rule is 

different than that used in the proposed rule. At proposal, we had nearly 700 days of CEMS data 

for one of the top 2 kilns, but only 35 days for the best performing kiln. As a result, the MACT 

floor was calculated using the same UPL formula used for the other pollutants at proposal. 

Following proposal, additional THC CEMS data were received: on the order of several hundred 

days of data for the top performing kilns. As a result, we determined that rather than calculate an 

upper predictive limit, there was sufficient data to directly calculate the 99th percentile for the 

best performing sources. Put another way, because the dataset contained 274 and 668 daily 

measurements, it represents long-term performance and accounts for long-term and day-to-day 

variability. As a result, it was not necessary to estimate variability and calculate an upper 

prediction limit as was done when using smaller, short-term datasets 

In order to set MACT floors, we ranked the kilns based on the average THC emissions 

levels (in ppmv) achieved (i.e., each kiln’s averaged performance, averaged over the number of 

available measurements). This ranking is shown in Table 2 for the top 2 kilns (top 12 percent of 

the 12 for which we have data). 

Table 2. MACT Floor for THC 

Kiln Average (ppmvd at 7% O2, 

propane) 

Number of 

readings 

Kiln type In-line 

raw mill 

Kiln 1 4.7 668 Precalciner yes 

Kiln 2 5.7 274 Precalciner yes 

Existing Source 

Average 

5.2 

Existing Source 

99th percentile 

24 

New Source 

Average 

4.7 

New Source 99th

percentile 

24 

The average performance of the best performing 12 percent of kilns (2 kilns) is 5.2 

ppmvd THC (a daily average expressed as propane at 7 percent oxygen). As just explained, 
13

because of the large amount of daily CEM measurements (274 and 668 daily measurements), we 

calculated the 99th percentile using 30-day rolling average THC emissions. We calculated the 

99th percentile of the two lowest emitting kilns and calculated the MACT floor for existing 

sources of 24 ppmvd. The MACT floor for new sources is based on the lowest emitting similar 

source. Calculating the 99th percentile for the best performing source also results in a limit of 24 

ppmvd because the dataset for this kiln has more variability although a lower mean emissions 

than the other MACT floor kiln. 

To calculate the MACT floor for existing sources, the entire distribution of THC CEM 

data for the top 2 kilns (942 daily readings) are ranked from high to low. The value that 99 

percent of the data are below, or that 1 percent are above, is the 99th percentile. Because the total 

number of readings is 942, if you calculate the top 1 percent (0.01 * 942), the 99th percentile is 

the 9.42th value Round to 10 and count down the readings to the 10th value, in this case 23.5 and 

round to 24. For MACT new, the result is the same because the top performing kiln had the 

highest daily readings although its overall average was less.For the 668 readings the 99th

percentile is the 7th value down from the maximum value, or 23.5, which rounds to 24. 

As an alternative to the THC limit, EPA proposed a floor for non-dioxin organic HAP, 

based on measuring the organic HAP itself rather than the THC surrogate. Test reports for 

cement kilns were reviewed where both individual organic HAP and THC were measured. At 

proposal we determined that organic HAP averaged 24 percent of the THC. Since proposal we 

have reevaluated these data and recalculated an average organic HAP concentration of 35 

percent. The specific organic compounds that were used in the recalculation include benzene, 

toluene, styrene, xylene (ortho-, meta-, and para-), acetaldehyde, formaldehyde, and naphthalene. 

These were the organic HAP species that were measured along with THC in the cement kiln 

emissions tests that were reviewed. Based on this percentage, and the fact that the THC emission 

floor is now 24 ppmvd, the alternative organic HAP floor is 9 ppmvd, corrected to 7 percent 

oxygen (or 19 percent oxygen for raw material dryers), for new and existing sources. 

4. MACT Floor for HCl 

In the proposed rule EPA based the proposed HCl emission limit for major sources on 

HCl data measured at 27 kilns using Method 321. The data in parts per million by volume on a 

dry basis (ppmvd) corrected to 7 percent oxygen (O2) were ranked by emissions level and the 

top 12 percent (4 kilns) lowest emitting kilns identified as best performing existing sources. The 
14

calculated MACT floors were 2 ppmvd and 0.1 ppmvd respectively. Subsequent to proposal, we 

received comments that indicated we had inappropriately (albeit inadvertently) included certain 

natural area sources in the MACT floor analysis. In addition, many of the source tests were not 

actually EPA Method 321 tests; others lacked important quality assurance information. As a 

result, we issued letters under CAA section 114 authority requiring facilities that were major 

sources and that had previously submitted data to retest their facilities. New Method 321 test 

results were obtained for 21 kilns. The results were ranked and the best performing (lowest 

emitting) 12 percent (3 kilns) were selected as the MACT floor kilns. 

Some of the test results contained values that were reported as below the detection limit, 

including kilns that were among the top performers. With respect to nondetect values, the 

literature contains numerous statistical methods for estimating values below the detection limit 

and determining statistical values (such as the mean, standard deviation, and upper confidence 

limit) of sample populations containing these nondetect values. A number of these approaches 

were considered and a limited amount of data analyses were conducted using such statistical 

methods. The approach use here is to use a straightforward, non-statistical approach for handling 

nondetect values, that is, to establish the nondetect value at the detection limit for data analysis. 

The data from the best performing three sources, as determined by average emissions 

during the test, are shown below in Table 3. The average of the three lowest emitting kilns is 

0.41 ppmvd. The variability for the 3 lowest emitting kilns includes the run-to-run variability of 

three runs for each stack test and the variability across the 3 lowest emitting kilns. We calculated 

the 99th confidence UPL from the performance of the 3 lowest emitting kilns as described earlier 

(see Appendix D for the calculations). 

Table 3. HCl MACT Floor 

Kiln HCl Emissions (ppmvd at 7% O2) 

1 0.34 

2 0.44 

3 0.46 

MACT – Existing 

Average (Top 3) 0.41 

Variance 0.02 

UPL 0.52 

MACT – New 

Average 0.34 

Variance 0.0 
15

Kiln HCl Emissions (ppmvd at 7% O2) 

UPL 0.34 

However, these results are very close to the detection limit for analytic method 321 

actually calculated in the field for HCl -- from 0.2 to 0.3 parts per million by volume (ppmv) as 

measured in the stack. The expected measurement imprecision for an emissions value occurring 

at or near the method detection level is in fact about 40 to 50 percent. This large measure of 

analytic uncertainty decreases as measured values increase: pollutant measurement imprecision 

decreases to a consistent relative 10 to 15 percent for values measured at a level about three (3) 

times the method detection level. (See American Society of Mechanical Engineers, Reference 

Method Accuracy and Precision (ReMAP): Phase 1, Precision of Manual Stack Emission 

Measurements, CRTD Vol. 60, February 2001.) Therefore, if three times the representative 

method detection level were less than the calculated floor emissions limit, we would conclude 

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

floor emissions limit. If, on the other hand, the value equal to three times the representative 

method detection level were greater than the calculated floor emissions limit, we would conclude 

that the calculated floor emissions limit does not account entirely for measurement variability. 

That is the case here with HCl. The calculated existing source UPL (not accounting for the 

inherent analytical variability in the measurements) is 0.52 ppmvd for existing sources and 0.34 

ppmvd for the best performing source (see Table 3 above). 

Thus, because the measured levels of HCl are very close to the reported minimum 

detection levels actually calculated in the field for this method -- from 0.2 to 0.3 parts per million 

by volume (ppmv) as measured in the stack -- in order to account of measurement variability, we 

multiplied the highest reported minimum detection level (0.3 ppmv) by a factor of three which 

results in a level of 0.9 ppmv. This represents the lowest level that can be reliably measured 

using this test method, and we therefore believe that it is the lowest level we can set as the 

MACT limit taking the appropriate analytic test measurement variability into account. 

Converting this level to a dry basis at 7 percent oxygen results in a floor of 3 ppmvd for existing 

sources. As with the average HCl emissions for the top three kilns, the average emissions for the 

best performing kiln, 0.34 ppmvd, is also very close the reported minimum detection levels. To 

account for measurement variability, we multiplied the highest reported detection limit (0.3 
16

ppmv) by three to get a level of 0.9 ppmv and adjusted this to a dry basis at 7 percent oxygen 

resulting in a floor for new sources of 3.0 ppmvd. 

5. MACT Floor for PM 

We compiled PM stack test data for 45 kilns from the period 1998 to 2007. EPA ranked 

the data by emissions level and the lowest emitting 12 percent, 6 kilns, was used to develop the 

proposed MACT floors of 0.085 and 0.08 lb/ton clinker for new and existing sources 

respectively. 

Following proposal, one commenter submitted additional data for their kiln that was 

among the best performing kilns. The commenter provided PM test results for 2002 and 2007 as 

well as new PM run data for tests conducted in 2004, which EPA did not have at proposal.. 

Adding in the additional test run data increased the weighted average emissions (assuming the 

raw mill is on 90 percent of the time and off 10 percent of the time) for the kiln from 0.0108 

lb/ton clinker to 0.0216 lb/ton clinker. Even with the increase in average emissions, the kiln is 

still among the top six performing kilns. In addition, at proposal we noted that for two of the best 

performing facilities we had only one emissions test. We did not think that this was enough data 

to fully characterize variability in emissions. Therefore, we requested these sources to submit 

additional PM emission test data. Data for two additional PM tests per kiln (a total of 6 

additional runs per kiln) were provided. With the additional data for the two kilns, average 

emission for one kiln increased from 0.0053 to 0.034 lb/ton clinker; for the second kiln, average 

emissions increased from 0.008 to 0.013 lb/ton clinker. As a result of the revision to the PM 

emissions for these three kilns, average emissions for the top 6 performing kilns increased from 

0.01 lb/ton clinker at proposal to 0.02 lb/ton of clinker. For the best performing kiln, the average 

increased from 0.005 lb/ton clinker at proposal to 0.01 lb/ton clinker. The results of the new 

MACT analysis using the 99th confidence UPL are shown in Table 4. 

The average emissions for the top six kilns based on short-term PM tests ranged from 

0.007 to 0.037 lb/ton clinker. Accounting for variability using the 99th confidence UPL as 

described above, we calculated an existing source MACT floor of 0.04 lb/ton clinker and a new 

source MACT floor of 0.01 lb/ton clinker (see App. C for calculations). 

Table 4. PM MACT Floor 

Kiln PM Emissions (lb/ton clinker) 
17

1 0.01 

2 0.01 

3 0.01 

4 0.03 

5 0.04 

6 0.04 

MACT - Existing 

Average 0.02 

Variance 0.001 

UPL 0.04 

MACT - New 

Average 0.01 

Variance 0.00001 

UPL 0.01 

Another change made since proposal is that we have changed the compliance requirement 

to require a PM CEMS. This requires that we establish an averaging period. We chose a period 

of 30 days (rolling average) to be consistent with requirements for mercury and THC, and 

because PM emissions on a lb/ton basis are affected by raw mill cycles (typically encompassed 

within 30-days, see 74 FR at 21144) for kilns with in-line raw mills. We have converted the 

concentrations obtained from 3-hour tests into 30-day values by means of the UPL equation 

previously described. It should be noted that due to the longer averaging periods, the actual limit 

will be a lower number compared to the shorter compliance interval in the proposed rule (30 

days versus a three hour test). This damping of variability when a longer averaging period is 

used is well established where continuous monitors have been used to measure emissions, and is 

also accounted for in the “m” term of the UPL equation. 
18

Appendix A. Daily Mercury Emissions Data for the Top 12 Percent (11 kilns) 

 Daily Mercury Emissions (lb/MM tons feed) for Each Kiln (coded) 

Day 1248 1259 1286 1302 1315 1364 1435 1484 1589 1650 1594 

1 0.32 18.88 27.01 16.82 16.67 26.00 30.50 30.50 0.29 13.22 13.60 

2 15.53 15.77 28.73 12.97 12.76 28.27 27.52 27.52 7.96 7.64 30.05 

3 17.43 20.71 28.67 9.49 9.57 27.59 29.07 29.07 8.88 9.53 32.88 

4 16.57 22.61 27.95 10.43 10.58 37.25 24.64 24.64 8.44 1.71 32.59 

5 18.30 19.67 7.66 9.44 9.64 28.78 23.82 23.82 9.33 7.66 28.73 

6 17.06 25.90 7.49 11.47 11.71 7.72 24.52 24.52 8.69 0.32 34.14 

7 16.57 22.63 7.58 13.59 13.83 42.98 25.14 25.14 8.44 14.12 32.33 

8 21.00 21.21 8.12 12.85 12.72 25.28 26.46 26.46 10.71 13.70 23.38 

9 16.57 5.07 23.93 4.30 4.38 24.67 25.49 25.49 8.44 12.72 35.00 

10 16.57 44.96 22.61 12.04 12.29 25.39 38.60 38.60 8.44 11.24 32.91 

11 16.57 34.37 23.06 21.41 20.55 24.24 24.65 24.65 8.44 10.56 31.44 

12 16.57 13.46 24.21 13.65 13.82 23.34 29.45 29.45 8.44 9.82 27.48 

13 16.57 11.57 25.25 12.44 12.54 25.04 25.04 25.04 8.44 12.92 28.55 

14 20.45 13.90 21.47 16.52 16.66 23.65 29.91 29.91 10.43 13.70 25.19 

15 16.57 27.43 25.53 22.02 22.51 26.83 1.43 1.43 8.44 9.46 25.63 

16 16.99 25.55 25.89 12.31 12.31 27.19 5.07 5.07 8.66 7.66 10.01 

17 16.60 21.95 29.34 15.39 15.11 24.25 25.97 25.97 8.46 12.36 28.03 

18 16.57 12.10 26.03 13.51 13.59 25.28 29.30 29.30 8.44 13.98 30.27 

19 16.57 12.83 7.60 3.75 3.82 24.83 26.93 26.93 8.44 9.40 25.85 

20 16.57 9.18 24.26 9.40 9.59 23.88 26.63 26.63 8.44 8.68 33.36 

21 16.57 8.74 23.68 10.88 11.06 23.69 24.82 24.82 8.44 7.60 29.10 

22 16.57 17.63 19.77 12.45 12.35 21.74 24.11 24.11 8.44 7.62 26.91 

23 16.65 47.30 21.97 11.76 11.89 26.04 22.00 22.00 8.48 7.63 29.70 

24 19.14 33.53 22.80 11.73 11.75 7.28 22.00 22.00 9.76 8.20 30.02 

25 21.37 19.11 23.23 60.15 61.31 26.36 22.00 22.00 10.90 11.27 10.91 

26 16.57 25.09 31.24 20.59 19.93 36.12 35.74 35.74 8.44 8.93 11.87 

27 16.57 14.94 30.64 13.96 14.10 28.96 24.47 24.47 8.44 7.64 14.04 

28 16.57 17.89 32.58 21.90 21.19 29.82 38.69 38.69 8.44 7.60 13.49 

29 16.30 24.46 28.53 12.74 11.85 29.05 27.28 27.28 8.30 15.72 

30 16.58 39.85 28.41 27.66 26.93 26.93 8.45 13.75 

31 12.95 23.83 23.83 

32 24.11 24.11 

33 27.28 27.28 

34 24.82 24.82 

35 24.47 24.47 
19

Calculation of Mercury 99th Confidence UPL- Existing 

Kiln 1248 1259 1286 1302 1315 13 64 1435 1484 1589 1650 1594 

ni = number runs = 30 31 29 30 30 29 35 35 30 28 30 

 

ni-1 29 30 28 29 29 28 34 34 29 27 29 

 

 = sum of (ni-1) 326 

 

 

means 16.63 21.33 22.65 15.28 15.26 25.91 25.51 25.51 8.48 9.53 25.23 

 

 

Mean 19.21 

#source mean = mean of 

source means 

 

N = Number sources= 11 

#because UPL is based on source 

means 

m= number future 

runs = 30 

 

t-statistic = quantile t-distribution with df degrees of freedom 

at .99 confidence level = 2.337 =tinv((1-.99)*2,df) 

 

 

 

Assuming Correlated variables 

Correlation =correl(data-last,data - first obs) 

 

 0.202 0.201 0.543 0.102 0.084 -0.187 0.238 0.236 0.190 0.195 0.532 

Variance = Variance pooled data adjusted 

for temporal correlation 109.30 #calculated as variance*(1+average(temporal correlation)) 

Daily Mercury Concentrations (ppm) in

Limestone by Quarry

Kiln ID # 1239 1248 1376

   0.0795 0.0265

   0.0233 0.02 0.0197

   0.0296 0.0212 0.0214

   0.0245 0.02 0.0326

   0.02 0.0225 0.0225

   0.0495 0.02 0.02

   0.0462 0.02 0.0337

1

1 N

T i

i

X x

N =

= ∑

1 1

( 1)

N Mi

ij

i j

df n = == − ∑∑

1

1 i n

i ik

i k

x x

n =

= ∑

df t
20

   0.0315 0.0263 0.0365

   0.0264 0.02 0.0344

   0.0294 0.02 0.0313

   0.0263 0.02 0.0312

   0.0242 0.02 0.028

   0.0407 0.02 0.0317

   0.0309 0.0256 0.0207

   0.0235 0.02 0.0274

   0.0295 0.0206 0.0263

   0.0673 0.02 0.0211

   0.0625 0.02 0.0177

   0.0624 0.02 0.0318

   0.0899 0.02 0.0246

   0.0835 0.02 0.0289

   0.122 0.02 0.0232

   0.0998 0.02 0.0292

   0.0991 0.0237 0.0302

   0.109 0.0269 0.036

   0.13 0.02 0.0291

   0.104 0.02 0.0296

   0.0589 0.02 0.0265

   0.0729 0.02 0.0257

   0.0856 0.02 0.0255

Avg   0.059 0.021 0.027 0.036

Stdev       0.021

RSD       0.573

correlation 0.8052  ‐0.0011 0.2282 0.3441

intra quarry variance = (RSD*Mean)2 = (0.38*19.21)2 121.29

adjustment for temporal correlation= 53.17*(1+0.3441) 163.03

total variance = 109.30+71.47 272.3

 

SQRT(Variance*(1/n+1/m)) 5.816 

 

UPL-- source mean and variance pooled 

data 32.81 Lb/MM tons feed 
21

99th Confidence UPL – MACT New 

 

MACT NEW 

Kiln 1589 

ni = number runs = 30 

 

ni-1 29 

 

 = sum of (ni-1) 29 

 

 

mean 8.48 

 

 

Mean 8.48 #source mean = mean of source means 

 

n to be used with 1 source=ni 30 

m= number future runs = 30 

 

t-statistic = quantile t-distribution with df degrees of freedom at .99 confidence 

level = 2.46 

=tinv((1-

.99)*2,df) 

 

 

 

 

Assuming Correlated variables 

Correlation = correl(data-last,data - first obs) 0.190 

    

Stdev= 1.708 

Variance data =     2.917 

Variance adjusted for temporal correlation = 3.472

#calculated as variance*(1+average(temporal 

correlation)) 

Intraquarry variance =     23.634 

adjustment for temporal correlation=     31.766 

total variance     35.239 

SQRT(Variance*(1/n+1/m)) =SQRT(35,239(1/30+1/30) 1.533 

 

 

UPL-- source mean and variance pooled data 12.25 

1

1 N

T i

i

X x

N =

= ∑

1 1

( 1)

N Mi

ij

i j

df n = == − ∑∑

1

1 i n

i ik

i k

x x

n =

= ∑

df t
22

Appendix B. 30-Day Rolling Average THC Data for the Top 5 Kilns

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

Rank = 1 2 

Avg 4.69 5.68 

Count 668 274 

Day ppmvd ppmvd 

1 17.1 4.0568824 

2 18.14 3.8020157 

3 19.22 3.8153705 

4 20.33 3.7492022 

5 21.03 3.6412561 

6 21.89 3.4557194 

7 22.62 3.3972125 

8 23.38 3.230093 

9 23.65 3.096194 

10 23.65 2.8943202 

11 23.62 2.9191445 

12 23.66 2.929303 

13 23.65 2.7791057 

14 24.07 2.6290951 

15 24.07 2.6206282 

16 24.07 2.5969613 

17 24.1 2.4590373 

18 23.82 2.4125101 

19 22.98 2.4740972 

20 21.78 2.3683547 

21 20.67 2.2297787 

22 19.61 2.1772147 

23 18.52 2.1490682 

24 17.35 2.0722489 

25 16.16 2.0097001 

26 14.96 2.0173296 

27 13.97 2.0478656 

28 12.84 1.8592713 

29 11.56 1.7718099 

30 10.42 1.6481029 

31 9.35 1.6600984 

32 8.3 1.6647753 

33 7.22 1.6777869 

34 6.09 1.7157459 

35 5.38 1.7012927 

36 4.53 1.7831105 

37 3.79 1.7749938 

38 3 1.7435064 
23

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

39 2.72 1.7523618 

40 2.72 1.8528243 

41 2.74 1.8745587 

42 2.73 1.8287196 

43 2.72 1.8929991 

44 2.29 1.9163573 

45 2.29 1.9309755 

46 2.31 1.9951586 

47 2.29 2.0832697 

48 2.25 2.0615847 

49 2.26 2.1447898 

50 2.32 2.2084574 

51 2.34 2.2729784 

52 2.36 2.3200585 

53 2.34 2.4529343 

54 2.35 2.5763747 

55 2.37 2.6077566 

56 2.39 2.6115668 

57 2.43 2.6443302 

58 2.45 2.6864886 

59 2.48 2.6901418 

60 2.51 2.7175858 

61 2.54 2.7834102 

62 2.55 2.7988575 

63 2.54 2.7844131 

64 2.55 2.7556139 

65 2.55 2.7474631 

66 2.54 2.6761027 

67 2.52 2.6942049 

68 2.51 2.7786708 

69 2.51 2.8054558 

70 2.5 2.6746115 

71 2.48 2.5774559 

72 2.49 2.6330937 

73 2.5 2.6366482 

74 2.5 2.654228 

75 2.5 2.5689492 

76 2.65 2.4658213 

77 2.7 2.4256624 

78 2.72 2.4062289 

79 2.66 2.3282791 

80 2.68 2.2338986 

81 2.71 2.1078847 
24

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

82 2.71 2.0911697 

83 2.72 1.990936 

84 2.74 1.9580954 

85 2.75 2.0444899 

86 2.78 2.113154 

87 2.8 2.1698202 

88 2.88 2.271258 

89 2.97 2.342729 

90 3.05 2.42801 

91 3.15 2.4860402 

92 3.22 2.6325527 

93 3.31 2.7528638 

94 3.56 2.9134544 

95 3.61 3.0724374 

96 3.66 3.2914749 

97 3.7 3.3788205 

98 3.8 3.4838223 

99 3.91 3.5730951 

100 4.03 3.7561303 

101 4.29 3.9790061 

102 4.46 4.127333 

103 4.6 4.2688426 

104 4.74 4.4394382 

105 4.85 4.636522 

106 4.88 4.7531847 

107 4.98 4.8300837 

108 5.14 4.9286187 

109 5.35 5.0241911 

110 5.48 5.1701087 

111 5.79 5.3075854 

112 6.4 5.3516582 

113 6.72 5.4435108 

114 6.89 5.482075 

115 7.03 5.4984402 

116 7.13 5.5353211 

117 7.22 5.582861 

118 7.3 5.6111879 

119 7.37 5.6634172 

120 7.52 5.7194104 

121 7.63 5.7671738 

122 7.72 5.8211527 

123 7.78 5.833569 

124 7.68 5.809611 
25

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

125 7.78 5.8068563 

126 7.92 5.7811127 

127 7.99 5.8456072 

128 8.06 5.8613223 

129 8.1 5.8706969 

130 8.1 5.7995661 

131 7.96 5.7354873 

132 7.87 5.654189 

133 7.8 5.6203548 

134 7.7 5.6459276 

135 7.64 5.6522155 

136 7.48 5.7014603 

137 7.37 5.7371876 

138 7.24 5.8625154 

139 7.12 5.9163954 

140 7.02 5.9700249 

141 6.78 6.0916819 

142 6.19 6.2516946 

143 5.88 6.3652469 

144 5.69 6.4109941 

145 5.54 6.4879237 

146 5.42 6.633322 

147 5.33 6.6758403 

148 5.23 6.6988303 

149 5.1 6.6906681 

150 4.89 6.7054377 

151 4.67 6.7692588 

152 4.53 6.7146551 

153 4.39 6.7347595 

154 4.26 6.8080041 

155 4.12 6.8771626 

156 3.94 6.8894693 

157 3.93 6.9332526 

158 3.81 6.9478962 

159 3.63 6.9742729 

160 3.58 7.0617108 

161 3.55 7.0999521 

162 3.51 7.1268346 

163 3.43 7.1481451 

164 3.42 7.1092641 

165 3.37 7.0639395 

166 3.33 7.0772165 

167 3.29 7.1151706 
26

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

168 3.21 6.980364 

169 3.57 6.9111816 

170 3.5 6.8227537 

171 3.39 6.7321851 

172 3.36 6.6171682 

173 3.36 6.50758 

174 3.35 6.4680329 

175 3.32 6.3932597 

176 3.32 6.25608 

177 3.33 6.182523 

178 3.32 6.2152927 

179 3.4 6.2564832 

180 3.42 6.2496203 

181 3.42 6.1924991 

182 3.42 6.1493382 

183 3.46 6.1962809 

184 3.46 6.1156167 

185 3.45 6.1993762 

186 3.45 6.2298329 

187 3.37 6.2185358 

188 3.33 6.201005 

189 3.39 6.1903568 

190 3.36 6.1837665 

191 3.31 6.1947846 

192 3.29 6.2558198 

193 3.29 6.2804085 

194 3.27 6.3073427 

195 3.26 6.3498099 

196 3.29 6.3565038 

197 3.32 6.408303 

198 3.37 6.494848 

199 2.93 6.5878223 

200 2.92 6.6972612 

201 2.93 6.818657 

202 2.93 7.0386294 

203 2.96 7.219593 

204 2.98 7.3489157 

205 2.99 7.4402831 

206 2.97 7.5133706 

207 2.93 7.6652406 

208 2.9 7.7852362 

209 2.82 7.8583822 

210 2.76 8.0210188 
27

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

211 2.75 8.2103474 

212 2.71 8.3992999 

213 2.65 8.5215683 

214 2.62 8.7101933 

215 2.59 8.6898827 

216 2.55 8.7864338 

217 2.52 8.7908753 

218 2.46 8.8208676 

219 2.4 8.8003636 

220 2.33 8.7491088 

221 2.27 8.7764279 

222 2.22 8.7704613 

223 2.27 8.8204415 

224 2.27 8.8610244 

225 2.3 8.9058481 

226 2.4 9.038251 

227 2.37 9.119164 

228 2.35 9.2637678 

229 2.31 9.3996026 

230 2.28 9.6276436 

231 2.26 9.7480872 

232 2.24 9.7789116 

233 2.2 9.9194066 

234 2.17 10.032116 

235 2.14 10.16267 

236 2.13 10.275968 

237 2.17 10.364569 

238 2.18 10.256312 

239 2.21 10.239686 

240 2.23 10.208514 

241 2.21 10.150306 

242 2.26 10.195732 

243 2.34 10.144193 

244 2.41 10.106733 

245 2.46 10.033116 

246 2.52 10.061693 

247 2.61 10.052624 

248 2.7 10.070087 

249 2.79 10.201611 

250 2.88 10.30278 

251 2.96 10.361297 

252 3.09 10.561381 

253 3.1 10.709434 
28

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

254 3.15 10.84844 

255 3.15 10.926626 

256 3.03 10.932241 

257 3.05 10.836049 

258 3.07 10.735221 

259 3.1 10.643527 

260 3.14 10.476576 

261 3.16 10.502415 

262 3.16 10.431363 

263 3.16 10.287522 

264 3.16 10.125361 

265 3.17 9.8883895 

266 4.44 9.6618801 

267 4.46 9.5910629 

268 4.57 9.6167294 

269 4.51 9.5816603 

270 4.47 9.5023608 

271 4.48 9.3858903 

272 4.44 9.1917224 

273 4.37 9.1540461 

274 4.3 9.1110344 

275 4.24 

276 4.21 

277 4.15 

278 4.13 

279 4.13 

280 4.07 

281 4.03 

282 3.92 

283 3.82 

284 3.75 

285 3.69 

286 3.69 

287 3.66 

288 3.64 

289 3.61 

290 3.57 

291 3.54 

292 3.53 

293 3.52 

294 3.49 

295 3.47 

296 2.19 
29

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

297 2.12 

298 1.99 

299 1.98 

300 1.99 

301 2.01 

302 2.02 

303 2.03 

304 2.05 

305 2.11 

306 2.13 

307 2.13 

308 2.09 

309 2.03 

310 2.01 

311 2.01 

312 2.01 

313 2.06 

314 2.08 

315 2.11 

316 2.13 

317 2.14 

318 2.16 

319 2.15 

320 2.15 

321 2.15 

322 2.14 

323 2.14 

324 2.15 

325 2.17 

326 2.18 

327 2.19 

328 2.19 

329 2.18 

330 2.15 

331 2.1 

332 2.05 

333 

334 

335 

336 

337 

338 

339 
30

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

340 

341 

342 

343 

344 

345 

346 

347 

348 

349 

350 

351 

352 

353 

354 

355 

356 

357 

358 

359 

360 

361 

362 

363 

364 

365 

366 1.78963301 

367 1.9883969 

368 1.92473024 

369 1.88291079 

370 1.87099413 

371 1.84571635 

372 1.68703725 

373 1.67025153 

374 1.65623885 

375 1.7207298 

376 1.72657702 

377 1.73925757 

378 1.78168813 

379 1.80600757 

380 1.85366035 

381 1.92059091 

382 1.9676048 
31

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

383 1.96009091 

384 1.96538257 

385 1.98272979 

386 1.99512745 

387 1.96778023 

388 1.93290523 

389 1.92921079 

390 2.04747468 

391 2.0938219 

392 2.11140524 

393 2.12137746 

394 2.11823857 

395 2.10348857 

396 1.88067544 

397 1.67443933 

398 1.70535599 

399 1.73548099 

400 1.77689766 

401 1.84657822 

402 1.88225877 

403 1.89943933 

404 1.90320322 

405 1.86051243 

406 1.90019298 

407 1.92066521 

408 1.93117909 

409 1.92688465 

410 1.91927354 

411 1.92199576 

412 1.96310687 

413 1.9944541 

414 1.98134298 

415 2.00491243 

416 2.04219532 

417 2.11004255 

418 2.19855644 

419 2.30084387 

420 2.23539943 

421 2.21787165 

422 2.24931609 

423 2.27745498 

424 2.31852822 

425 2.38606988 
32

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

426 2.4582921 

427 2.54168099 

428 2.58850044 

429 2.64045877 

430 2.68823655 

431 2.79462544 

432 2.90402821 

433 2.94000044 

434 2.95469927 

435 3.02823728 

436 3.13580672 

437 3.18254283 

438 3.22773728 

439 3.2644345 

440 3.33236505 

441 3.37928172 

442 3.38688778 

443 3.40387389 

444 3.49795723 

445 3.52073501 

446 3.57641556 

447 3.84006834 

448 3.94088779 

449 3.99927909 

450 4.11869576 

451 4.29376521 

452 4.43639021 

453 4.59358465 

454 4.82945586 

455 5.01915031 

456 5.21362253 

457 5.44490031 

458 5.77416419 

459 5.97291299 

460 6.02705187 

461 5.9348491 

462 5.88252724 

463 5.89463835 

464 5.9235923 

465 5.88192563 

466 5.77587008 

467 5.74525897 

468 5.73456452 
33

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

469 5.71305063 

470 5.62712007 

471 5.55778674 

472 5.52379179 

473 5.4527767 

474 5.35214971 

475 5.28539138 

476 5.18680804 

477 4.90334971 

478 4.75915526 

479 4.63682374 

480 4.49130985 

481 4.32333763 

482 4.18936541 

483 4.01583763 

484 3.78665707 

485 3.58150429 

486 3.3630043 

487 3.08469391 

488 2.74666613 

489 2.53092845 

490 2.43620472 

491 2.35732787 

492 2.26424129 

493 2.18470106 

494 2.11889646 

495 2.0743275 

496 2.05480163 

497 2.01925566 

498 1.9234798 

499 1.87188497 

500 1.86037635 

501 1.82324991 

502 1.7512729 

503 1.77795518 

504 1.82065838 

505 1.84249172 

506 1.83162965 

507 1.79703195 

508 1.78206068 

509 1.74877045 

510 1.71611241 

511 2.25186158 
34

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

512 2.2097064 

513 2.19806847 

514 2.17360008 

515 2.1804248 

516 2.18720641 

517 2.18831772 

518 2.19057347 

519 2.19301887 

520 2.19741824 

521 2.23364642 

522 2.26376501 

523 2.28204687 

524 2.31367187 

525 2.34693576 

526 2.39450521 

527 2.45326909 

528 2.5717691 

529 2.6528941 

530 2.69953125 

531 2.7537407 

532 2.82199035 

533 2.84849319 

534 2.81024958 

535 2.8215447 

536 2.87110462 

537 2.89791621 

538 2.94291621 

539 2.99384013 

540 3.0386952 

541 2.35983393 

542 2.40070349 

543 2.46773248 

544 2.5636745 

545 2.59550421 

546 2.6512832 

547 2.69211653 

548 2.71633755 

549 2.73213959 

550 2.73853452 

551 2.74835336 

552 2.78430375 

553 2.83676219 

554 2.86361001 
35

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

555 2.88163537 

556 2.86693698 

557 2.85698954 

558 2.78892234 

559 2.76333931 

560 2.81998485 

561 3.0045808 

562 3.02003546 

563 3.08036001 

564 3.15287973 

565 3.21878121 

566 3.24503001 

567 3.39060693 

568 3.41605077 

569 3.76315013 

570 4.30266936 

571 4.77877513 

572 5.30747706 

573 5.75214053 

574 6.23626873 

575 6.94712343 

576 7.42461906 

577 7.93928252 

578 8.65505175 

579 9.30048008 

580 10.1971307 

581 11.687932 

582 12.5692621 

583 13.5540057 

584 14.9008191 

585 16.1747775 

586 16.6283814 

587 16.6328291 

588 16.3742689 

589 16.0380294 

590 16.1192377 

591 16.0677294 

592 16.0741322 

593 16.0512572 

594 16.0367988 

595 15.9782711 

596 15.9951738 

597 15.9347155 
36

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

598 15.951442 

599 15.6690392 

600 15.2417753 

601 14.9093031 

602 14.4813586 

603 14.1049697 

604 13.7420308 

605 13.1817623 

606 12.7980022 

607 12.3655717 

608 11.7857522 

609 11.2293772 

610 10.46603 

611 9.20493276 

612 8.47941888 

613 7.64108554 

614 6.48718057 

615 5.41287561 

616 4.57779228 

617 4.14654227 

618 3.93334782 

619 3.81852838 

620 3.69330616 

621 3.62684783 

622 3.61152838 

623 3.59761172 

624 3.54880616 

625 3.54640338 

626 3.5128756 

627 3.48857005 

628 3.48509783 

629 3.48111929 

630 3.45878596 

631 3.39177207 

632 3.36721651 

633 3.37361929 

634 3.27903596 

635 3.23703596 

636 3.20868874 

637 3.21084151 

638 3.16428596 

639 3.17003596 

640 3.16542485 
37

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

641 3.16939707 

642 3.19111929 

643 3.24406374 

644 3.27417485 

645 3.20550758 

646 3.37684091 

647 3.35803536 

648 3.34538258 

649 3.31181314 

650 3.33475758 

651 3.31650758 

652 3.30399369 

653 3.27917424 

654 3.24903536 

655 3.22006313 

656 3.19791036 

657 3.2091048 

658 3.20332702 

659 3.19673611 

660 3.74606112 

661 3.74293234 

662 3.74139067 

663 3.69456458 

664 3.67890044 

665 3.66613655 

666 3.65939717 

667 3.66141661 

668 3.70180947 

669 3.73136723 

670 3.76925635 

671 3.78609327 

672 3.75051383 

673 3.6917137 

674 3.66349614 

675 3.76888088 

676 3.53184575 

677 3.54661395 

678 3.61258324 

679 3.71712246 

680 3.74543219 

681 3.82112767 

682 3.90108917 

683 4.00431398 
38

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

684 4.16977764 

685 4.35763622 

686 4.51960817 

687 4.64312945 

688 4.78461438 

689 5.05241643 

690 2.69098579 

691 2.73445567 

692 2.73265292 

693 2.94179811 

694 3.24724263 

695 3.36823527 

696 

697 

698 

699 

700 

701 

702 

703 

704 

705 

706 

707 

708 

709 

710 

711 

712 

713 

714 

715 

716 

717 

718 

719 

720 

721 

722 

723 

724 

725 

726 2.56336259 
39

Company Lehigh Suwannee 

Location Union Bridge Branford, FL 

Kiln E02-001 1 

727 2.40374949 

728 2.3252356 

729 2.33374949 

730 2.37194949 

731 2.38003283 

MACT Pool 

Kilns 

THC

MACT Pool Avg 99th Percentile 

Top 2 5.2 23.5 

New 4.7 23.7 
40

Appendix C. PM Data for Top 12 Percent (6 kilns)

PM Emissions (lb/ton clinker) for Top Performing 6 Kilns   

Lehigh Cement,

Union Bridge,

MD

Lehigh,

Evansville, PA

(Kiln #2)

Lafarge,

Whitehall, PA

Lehigh,

Evansville, PA

(Kiln #1)

Lafarge, Sugar

Creek, MO

Mitsubishi,

Lucerne Valley,

CA

                 

0.0032 0.0041 0.0098 0.0101 0.0004 0.0028

0.0035 0.0065 0.0066 0.0059 0.0049 0.0099

0.0002 0.0054 0.0041 0.0083 0.0085 0.0048

0.0069 0.073 0.0057 0.005 0.0255880 0.0030

0.0032 0.011 0.0028 0.017 0.0199726 0.0031

0.0035 0.004 0.0028 0.005 0.0210066 0.0025

0.1226 0.102 0.0059 0.027 0.0187630 0.0081

0.1092 0.062 0.0053 0.027 0.0253316 0.0081

0.1176 0.069 0.0047 0.020 0.0247617 0.0063

0.0319     0.0117    0.0261849 0.0101

0.0319     0.0108    0.0279137 0.0104

0.0437     0.0105    0.0271205 0.0136

      0.0122    0.0300652    

      0.0087    0.0314742    

      0.0108    0.0279057    

            0.0198    

            0.0134    

            0.0086    

            0.0652    

            0.0152    

            0.0145    

            0.0380571    

            0.0299579    

            0.0719015    

            0.0636305    

            0.0709737    

            0.0970582    

            0.0439    

            0.0587    

            0.0409    
41

99th Confidence UPL ‐ MACT Existing

Lehigh 

Cement, 

Union 

Bridge, MD

Lehigh, 

Evansville, 

PA (Kiln 

#2)

Lafarge, 

Whitehall, 

PA

Lehigh, 

Evansville, 

PA (Kiln 

#1)

Lafarge, 

Sugar 

Creek, MO

Mitsubishi, 

Lucerne 

Valley, CA

ni = number test runs = 12 9 15 9 30 12

Total Number of sources = 6

n =Total # test runs = 87

86

This step is for QC only:

means 0.04 0.04 0.01 0.01 0.03 0.01

0.02

Mean =

Pooled Variance = 0.0008

m= number future runs = 30

Term1 0.0448

Term2 0.0000

Squared root Term2 0.005994

NOTE: the pvalue for the t-statistic is calculated as: 2*alpha, where 1-alpha is desired confidence, so if 99% confidence is desired then alpha=0.01 and 2*alpha=2*(0.01)

t‐statistic = quantile t‐distribution with df degrees of freedom at .99 confidence level = 2.370493194

UPL  POOLED VARIANCE = 0.038400879

( )2 2

1

1

1

n

i

i

s x X n == − − ∑

1 1

1 i N n

ij

i j

X x n = == ∑∑

1 1

n m

+

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1

N

i

i

df n =

⎛ ⎞ = − ⎜ ⎟ ⎝ ⎠ ∑

df t

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1 i n

i ik

i k

x x n == ∑

1

N

i

i

n

=

∑

N

2

,

1 1 Xt s df p n m

⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠
42

99th Confidence UPL ‐ MACT New

MACT NewMitsubishi, 

Lucerne 

Valley, CA

ni = number test runs = 12

Total Number of sources = 1

n =Total # test runs = 12

11

This step is for QC only:

means 0.0069

0.01

Mean =

Pooled Variance = 0.0000

m= number future runs = 30

Term1 0.1167

Term2 0.0000

Squared root Term2 0.0012593

NOTE: the pvalue for the t-statistic is calculated as: 2*alpha, where 1-alpha is desired confidence, so if 99% confidence is desired then alpha=0.01 and 2*alpha=2*(0.01)

t‐statistic = quantile t‐distribution with df degrees of freedom at .99 confidence level = 2.718079183

UPL  POOLED VARIANCE = 0.010302471

( )2 2

1

1

1

n

i

i

s x X n == − − ∑

1 1

1 i N n

ij

i j

X x n = == ∑∑

1 1

n m

+

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1

N

i

i

df n =

⎛ ⎞ = − ⎜ ⎟ ⎝ ⎠ ∑

df t

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1 i n

i ik

i k

x x n == ∑

1

N

i

i

n

=

∑

N

2

,

1 1 Xt s df p n m

⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠
43

Appendix D. Top 12 Percent (4 kilns) for HCl 

Runs

Captital Cement, 

San Antonio, TX 

Alamo, San 

Antonio, TX Holcim, Midlothian, TX 

1 <0.34 0.54 <0.42

2 <0.34 0.66 <0.42

3 <0.34 0.78 <0.43

4 <0.34 0.25 <0.46

5 <0.34 0.25 <0.47

6 <0.34 0.27 <0.41
44

99th Confidence UPL ‐ MACT Existing

Captital 

Cement, 

San 

Antonio, TX

Alamo, 

San 

Antonio, 

TX

Holcim, 

Midlothian

, TX

ni = number test runs = 666

Total Number of sources = 3

n =Total # test runs = 18

17

This step is for QC only:

means 0.34 0.46 0.44

0.41

Mean =

Pooled Variance = 0.0189

m= number future runs = 30

Term1 0.0889

Term2 0.0017

Squared root Term2 0.0410249

NOTE: the pvalue for the t-statistic is calculated as: 2*alpha, where 1-alpha is desired confidence, so if 99% confidence is desired then alpha=0.01 and 2*alpha=2*(0.01)

t‐statistic = quantile t‐distribution with df degrees of freedom at .99 confidence level = 2.566933975

UPL  POOLED VARIANCE = 0.52

( )2 2

1

1

1

n

i

i

s x X n == − − ∑

1 1

1 i N n

ij

i j

X x n = == ∑∑

1 1

n m

+

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1

N

i

i

df n =

⎛ ⎞ = − ⎜ ⎟ ⎝ ⎠ ∑

df t

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1 i n

i ik

i k

x x n == ∑

1

N

i

i

n

=

∑

N

2

,

1 1 Xt s df p n m

⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠
45

99th Confidence UPL ‐ MACT New

Captital 

Cement, 

San 

Antonio, TX

ni = number test runs = 6

Total Number of sources = 3

n =Total # test runs = 6

5

This step is for QC only:

means 0.34

0.34

Mean =

Pooled Variance = 0.0000

m= number future runs = 30

Term1 0.2000

Term2 0.0000

Squared root Term2 0

NOTE: the pvalue for the t-statistic is calculated as: 2*alpha, where 1-alpha is desired confidence, so if 99% confidence is desired then alpha=0.01 and 2*alpha=2*(0.01)

t‐statistic = quantile t‐distribution with df degrees of freedom at .99 confidence level = 3.364929997

UPL  POOLED VARIANCE = 0.34

( )2 2

1

1

1

n

i

i

s x X n == − − ∑

1 1

1 i N n

ij

i j

X x n = == ∑∑

1 1

n m

+

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1

N

i

i

df n =

⎛ ⎞ = − ⎜ ⎟ ⎝ ⎠ ∑

df t

2 1 1 s n m

⎛ ⎞ ⎜ ⎟ + ⎝ ⎠

1

1 i n

i ik

i k

x x

n =

= ∑

1

N

i

i

n

=

∑

N

2

,

1 1 Xt s df p n m

⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠