# DEVELOPMENT OF THE MACT FLOOR S 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 pe rforming 12 percent of existing sources or the best performing 5 sources for source categories wi th 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 sour ce. 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 iden tified the lowest emitting 12 percent of the sources for which we had data, which, for existi ng sources, ranged from two kilns for THC to 11 kilns for mercury. For new source MACT, th e 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 th at is equivalent to the average of the best performing sources based on future compliance test s.. 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/Inf ectious Waste Incinerators, NESHAP for Industrial, Commercial, and Institutional Boilers and Proces s Heaters) in accounti ng 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 predic tion 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 TH C) to fall below within a specified level of confidence, based upon the results of an independent sa mple 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 caref ully evaluated the data sets for each HAP to ascertain whether the data were normally dist ributed, or distributed in some other manner (i.e., log normally). To evaluate the distributi on 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 de gree of asymmetry of a given data distribution. No rmally distributed data have a skewness of 0. A skewness statistic that is greater (less) than 0 indicates th at 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 te st, if the skewness st atistic (S) is greater than two times the SES, the data distributi on can be considered non-normal. The kurtosis statistic (K) characterizes the de gree 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 rela tively 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 th e 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 applie d (i.e. the separate data sets for mercury, PM, and HCl), we applied the skewness and kurto sis hypothesis tests to bo th the reported test values and the lognormal values of the reported test values. If the skewness (S) and kurtosis (K) statistics of the reported da ta set were both less than twic e 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 dist ribution 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 a nd kurtosis hypothesis tests were mixed for the reported values and the natural lo g-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 mo re 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 includedAndersonn-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 tu rn 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 df p 

# Xt s n m ⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠

1 

# 1 N

i i 

# df n

= 

# ⎛ ⎞

= − ⎜ ⎟ ⎝ ⎠



# ∑

# 5 This calculation was performed using the following Excel functions:

Normal distribution: 99 th 

# 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 99 th 

# 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 pe rcent of the time in the future). If we did not account for variability in this manner and set th e limit based solely on the average performance, then these kilns could exceed th e limit half the time or more. EPA was able to reasonably cal culate variability for the TH C 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 statisti cal intervals (so no UPL needed to be calculated). The key issue for the HCl data set is the analytic detection li mit, which dictated the level of the standard (although EPA did apply the UPL equation to the unde rlying data set). The methods used for THC and HCl are described in th e appropriate s ections below. The following sections describe the devel opment of the MACT floors for each of the pollutants. The data for each run for the MACT fl oor sources are given in the appendices to this report. 2. MACT Floor for Mercury The first step in establishing a MACT standa rd is to determine the MACT floor based on the amount of HAP emitted. In the case of me rcury emitted by cement kilns, this is not necessarily a straightforward unde rtaking. Single stack measuremen ts represent a snapshot in time of a source’s emissions, always raising questions of how repr esentative 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 uniforml y. 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 capture d in the ground raw meal in the in-line raw mill and this raw meal (containing merc ury) is returned as feed to the kiln. Mercury emissions may

# 6 remain low during such recycling operations. Ho wever, as part of normal operation, a kiln’s raw mill must be periodically shut down for main tenance, and mercury-containing exhaust gases from the kiln are then bypassed directly to the main control de vice resulting in significantly increased mercury emissions at th e 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 underestim ate 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, a nd so are not the best in dicator of performance over time. With these facts in mind, one must car efully consider alternatives other than use of single short-term stack test results to qua ntify 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 avai lable data indicate that almost no mercury leaves the kiln as part of the clinker (product). Ther efore, over the long term a ll the mercury leaves the kiln as a stack emission with three exceptions: 1. If instead of returning all part iculate captured in the particul ate control device to the kiln, the source removes some of it from the circu it 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 SO 2 

# 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 balan ce. EPA did so for the final rule by obtaining data on all the kiln mercury inputs (i.e., all raw materials and all fu els) 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, issu ed 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 devi ce (or alkali PM control device) that is wasted rather than returned to the kiln. These data were submitted to EPA as daily conc entrations 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 fac ility wasted CKD, we subtracted out the annual mercury that left the system in the CKD. If the facility had a wet scrubber (the only cont rol device currently in use among the sampled kilns with any substantial me rcury 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 reque st 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 av erage projected emission rate for each kiln. We then ranked each kiln from lowest average emi ssion factor to highest. The resulting emissions factors for 87 of the 89 ranged (relatively c ontinuously) from 8 to 300 pounds of mercury per million tons of feed. Two kilns showed consid erably higher numbers, approximately 1200 and 2000 pounds per ton of feed. These two facilities ha ve 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 ba lance for mercury and then assuming that all mercury inputted is emitted (minus conservative ly estimated removals for scrubber usage and dust wastage), EPA made a near worst case assu mption as to kilns’ mercury emission levels. The kilns were then ranked from best to wo rst based on the extrapol ated 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 char acterizing forseeable k iln 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 pres ented in Docket item EPA-HQ-OAR-2002-0051-2855. Using this new information, EPA revised the da ily mercury emissions for the top performing kilns. In some cases, estimated emissions increa sed. At proposal, average mercury emissions of the top 11 kilns ranged from7.14 to 23.92 lb/MM t ons of feed and the overall average was 16.6 lb/MM tons feed. Using the revi sed 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 sampli ng 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 material s 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 mercur y 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 a ssume that the variability of mercury levels across the formation are comparable (although actu al 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 deviat ion (RSD) was calculated. The RSD was then used to calculate a variance for the qua rries from the two best performers which was combined with the variance calculated for their daily kiln emi ssions. 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-qu arry 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 ch aracterize 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 consequen tly 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 th e 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 perf orming sources (and there are no additional data from best performers from which to estimate long-term quarry variability). Using the equations described earlier, we th en 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 consecu tive 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 pr evious day. For each kil n, a test for temporal correlation was performed and conf irmed that the results of daily sampling were correlated. In the presence of temporal correla tion between pairs of consecutive measurements (e.g. in the case of rolling averages or averages of consecutive test runs), one can es timate 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+ 

# r 1 

# ). 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 m illion (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 emi ssions (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 di vided by each kiln’s operating days. 1 

# The results are shown in Table 1 and repres ent the average performa nce 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 rem oval 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 ra tes of these 11 kilns which is a direct measure of the within-sou rce variability of the data set. To further account for long-term variability, we calculated the variance of the merc ury content in limestone from two of the best performing kilns using data on mercury variabil ity from the quarries of several other kilns quarrying limestone from the same geologic limes tone formation and incl uded this variability with the pooled variability of the best perfor ming sources in the MACT floor pool. We further applied this revised estimate of intra-quarry variability to the rest of the kiln s in the pool of best performers. We also adjusted the variance to co rrect for the temporal co rrelation 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 99 th 

# 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 th ermal oxidizer (RTO) and 2 kilns w ith 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 singl e stack test for characterizing the long term performance and in determining the best performing kilns with respec t to THC emissions for several reasons. The manual stack test is of short dura tion and represents a sn apshot in time; consequently, it provides only limited information on the variability in emissi ons over time due to changes in raw material feed or in kiln operating conditi ons. In contrast, the CEMS da ta 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 remova l 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 TH C emissions data, and thus give the most accurate representation of a source’s performance. The method used by EPA to calculate the emi ssion 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 formul a used for the other po llutants 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 resu lt, 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, b ecause the dataset contained 274 and 668 daily measurements, it represents long-term performa nce 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 us ing 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., e ach 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 99 th 

# percentile 24

New Source Average 4.7

New Source 99 t h 

# percentile 24

The average performance of the best perfor ming 12 percent of kilns (2 kilns) is 5.2 ppmvd THC (a daily average expressed as propa ne at 7 percent oxygen). As just explained,

# 13 because of the large amount of daily CEM meas urements (274 and 668 daily measurements), we calculated the 99 th 

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

# percentile of the two lowest emitting kiln s 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 99 th 

# 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 so urces, the entire dist ribution of THC CEM data for the top 2 kilns (942 daily readings) ar e ranked from high to low. The value that 99 percent of the data are below, or that 1 percent are above, is the 99 th 

# percentile. Because the total number of readings is 942, if you calcu late the top 1 percent (0.01 * 942), the 99 th 

# percentile is the 9.42th value Round to 10 and count down the readings to the 10 th 

# value, in this case 23.5 and round to 24. For MACT new, the result is the sa me because the top performing kiln had the highest daily readings although its overall average was less.For the 668 readings the 99 th 

# percentile is the 7 th 

# value down from the maximum value, or 23.5, which rounds to 24. As an alternative to the TH C 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 individu al organic HAP and THC were measured. At proposal we determined that orga nic 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 pa ra-), 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 inappropr iately (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 im portant 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 result s were ranked and the best performing (lowest emitting) 12 percent (3 kilns) were selected as the MACT floor kilns. Some of the test results contai ned values that were reported as below the detection limit, including kilns that were among the top perfor mers. 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 cont aining 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 st raightforward, 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 s ources, 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 em itting kilns includes the ru n-to-run variability of three runs for each stack test and the variability across the 3 lowest emitting kilns. We calculated the 99 th 

# 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% O 2 

# )

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% O 2 

# ) UPL 0.34

However, these results are very close to the detection limit for analytic method 321 actually calculated in the fiel d for HCl -- from 0.2 to 0.3 parts per million by volume (ppmv) as measured in the stack. The expected measurem ent 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 relativ e 10 to 15 percent for values meas ured 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.) Ther efore, if three times the representative method detection level were less than the calculated floor emi ssions limit, we would conclude that measurement variability is adequately addr essed and we would not adjust the calculated floor emissions limit. If, on the other hand, the value equal to three tim es the representative method detection level were greater than the calcu lated floor emissions limit, we would conclude that the calculated floor emissi ons 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 measur ements) 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 calcula ted 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 th at it is the lowest level we can set as the MACT limit taking the appropria te analytic test measuremen t variability into account. Converting this level to a dry ba sis at 7 percent oxygen results in a floor of 3 ppmvd for existing sources. As with the average HCl emissions for th e 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 adju sted this to a dry ba sis 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 k ilns 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 commente r 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 r un 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. Th erefore, 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 th e 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 be st 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 99 th 

# 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. Accoun ting for variability using the 99 th 

# confidence UPL as described above, we calculated an existing sour ce 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 affect ed 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 l onger 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 Da ta 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 99 th 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) i M N ij i j 

## df n

= = 

## = −



# ∑∑

1



# 1 i 

### n i ik k i 

# 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 99 th 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) i M N ij i j 

# df n

= = 

# = −



# ∑∑

1 

# 1 i 

### n i ik k i 

# 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 i k k i 

### x x n = 

### = 

# ∑ 1 N

i i n

= 

## ∑ N

2

, 

## 1 1 df p 

## Xt s 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 k i 

### x x n = 

### = 

# ∑ 1 N

i i 

### n

= 

# ∑ N

2

, 

## 1 1 df p 

## Xt s n m ⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠

# 43 Appendix D. Top 12 Percen t (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 de sired 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 k i 

### x x n = 

### = 

# ∑ 1 N

i i 

### n

= 

# ∑ N

2

, 

## 1 1 df p 

## Xt s 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 k i 

## x x n = 

## = 

# ∑ 1 N

i i 

## n

= 

# ∑ N

2

, 

## 1 1 df p 

## Xt s n m ⎛ ⎞ + + ⎜ ⎟ ⎝ ⎠