MEMORANDUM



### DATE: August 6,2010 FROM: Keith Bamett, EPA/OAR/OAQPS/SPPD/MMG TO: Docket EPA-HQ-OAR-2002-0051, National Emissions Standards for Hazardous Air Pollutants for Portland Cement Manufacturing.
Record of contactswith HectorYbanez concerning calculation of mercury floors. Note that Mr. Ybanez is an employee of Holcim, USA. However, during much of this discussion hewas acting as a representative of the Portland Cement Association (PCA) charged by the PCA towork with EPA onvariability issues.
On March 25,210 Hector Ybanez of Holcim USA sent anemail (attached) proposing a simplified version of the mercuryfloor calculation. Inthis version, the daily mercury numbers would be adjusted to accountfor intra-quarry variability and local coals. I held subsequent conversations and meetingswith Mr. Ybanez and Dr. Mills, a statistician who wasconsulting for the Portland CementAssociation. The outcome

### of that meeting was a July 22,2010 email which contained Mr. Ybanez'sfinal calculations. In their original public comments (#2845), the Portland Cement Association stated that an upper tolerance limit (UTL) was the proper statistical calculation to use to develop a MACT floor. EPA's response wasthat an upper predictive limit (UPL) was the more appropriate statistical calculation. Our reasoning was that the upper predictive limit is a better indicator whenyou aretrying to determine future averages. The UTL is more appropriate when you aretrying to determine the highest future value. The industry did correctly note that there was an error in the UPL equation that EPA us at proposal. In this final rule, we corrected this error. EPA rnetwith PCA and Industry representatives on May 5,2010, including Mr. Ybanez, Dr. Mills, John Holmes (consultant to the PCA). The information presented is attached. In this meeting industry still suggested the UTL was a better predictor of the 99'r' percentile, but they also indicated that the UPL could be usedif the variance was correctly accounted for in the underlying data to which the UPL equation is applied. (See slide 11). EPA representatives including myself also discussed these issues with Mr. Ybanez on April 4, May 6, and May 12of 2010. On June 25,2010, Mr. Ybanez send anemail (attached). In that email he indicated that in his view therewere three main sources ofvariability that need to be accounted for,which were intra-quaffy variability, variability of local coal mines, and auto-correlation

(which accountsfor the fact that the mercury measurements are on consecutive days). In that email, he again claimed that the UPL being used by EPA underestimated variability. However, atthat time hewas unaware that EPA had changed our calculation nto a pooled variance, which increased the UPL, and he had dropped his contention that the UTL should be used. The frnal calculations prepared by PCA were sentto EPA onJuly 22,2010. (Attached). In this calculation, the mercury floor was calculated using aproportional variance and including afactor for intra-quarry variability of 0.45. It also included avariance to accountfor varying mercury contentsof local coals. In EPA'sfinal mercury floor calculation we included pooled variance and afactor for intra-quarry variability, but we used avalue of 0.57, versus the 0.45 factor used by Mr. Ybanez. We did not include variability of local coals for reasons discussed in the preamble tothefïnal 

### rule. Because we pooled the variance, theform of the UPL equation EPA usedwould produce essentially the same result astheform used by Mr. Ybanez. My Ybanez's ftnal result was a mercury MACT floor of 78 lb/MM tonsfeed. But this number included variance for thedifferent mercury contents present in local coals. Attached to this memo in reverse chronological order are three emails and the presentation from the May 7,2010 meeting. The email attachments are included behind the three emails.

### (y.

## -= Final HG Floor for Existing Kilns - lN DOCKET



### ..--'ãr Hector.Ybanez to: Keith 

### Bainett o7t22t2o1o12:38PM 

### History: This message has been fon¡rarded.
Keith 

### I have 

### attached two excel- 

### fil-es - one 

### is the autocorreLation 

### for Hg using 

### over one 

### year 

### of kil-n feed

### data for each Hg Fl-oor source. The 

### other file is the variability calculations which lncl-ude the 

### variabil-ity f

### or:



### intra-quarry for each 

### of the lL sources,' 

### the variability of nearby coaL mines 

### that supply each

### of the Ll" 

### sources; and 

### auto-correlation. Subsequent 

### to our submittal, RTI 

### provided comments on Hol-cim's proposal-. Holcim 

### incorporated most 

### of the RTI comments 

### in to our caLculations. 

### Basically, w€'veworked 

### cooperativelywith EPA/RTI, and 

### I think that 

### significant progress has been 

### made. I want 

### to thank you

### for giving us Chat 

### opportunity Currentl-y, al-l- 

### issues have been resol-ved

### with the exception 

### of how the 

### "totaL variance" is cal-cul-ated. RTI assumes

### that the variation in the data 

### set is equal between

### sources, and hence 

### this assumption does 

### not al-l-ow

### for between source 

### variability to be 

### considered. Furthermore RTI assumes

### that 

### the variance become 

### smaller as more 

### data 1s 

### col-Iected. In reality, as more 

### data is col-l-ected 

### the J-arger

### the variance 

### - we have demonstrated 

### that point 

### to EPA and RTI 

### using reaL data. 

### Conversely,

### to RTI assumptions, 

### the sourceswhich make up 

### the mercury 

### floors al-so 

### contain an unequal- 

### or unbal-ance 

### dataset. In an 

### effort to determine 

### the best approach 

### of cal-culating an 

### overaLÌ 

### variance, we 

### contacted SAS 

### Institute and 

### inquired as

### to the best statistical approach to use when

### determining an 

### overall- variance 

### for such 

### a data 

### set. The SAS 

### Institute indicated the correct way

### of handl-ing an unbal-ance 

### dataset was 

### through use

### of the Proc Mixed 

### or Proc Varcomp procedures 

### (in other words

### the variance needs

### to be weighted 

### to account 

### for variability between sources and

### within source 

### variability). This issue has been discussedwith RTI 

### but it remains unresol-ved. To 

### aid j-n

### this discussion, we decided 

### to test RTI's UPL and assumptions 

### using avail-abl-e 

### long-term THC CEM

### data. Testing such 

### a model- shouLd 

### provide reaÌ confirming evidence 

### as 

### to it functionality in a real worl-d 

### situation. Whatwediscovered 

### j-s 

### that RTI's UPL and assumptions grossJ-y underestimate 

### the actual- 99th 

### percenti.i-e. That 

### in itseLf ; should

### force RTI 

### to re-eval-uate 

### their assumptions.Once 

### again, I would 

### like to thank you

### for your 

### efforts and we

### arewilling

to continue 

### to work on 

### this matter 

### with EPA and RTI.

(See 

### attached 

### fil-e: HgAuto 

### Correlation.xls) (See

### attached 

### file: HG Floors 

### for Exisiting Kilns with Varabil-ity Included.xls)

Hector Ybanez Corporate Manager, EnvironmentaL 

### I22 West Carpenter Freeway 

### Suite 485 

### Irving, Texas 75039 Phone (21,4) 524

### - 28L4 

### CeIt (9'72l, 268-tL39 Hector. Ybanez@holcim. com hybanezGgmail. com www. holcim. com,/us 

### This e-mail- 

### is confidential and intended 

### only for the use 

### of the above named addressee. 

### If you have 

### received 

### this e-mail in error, please delete 

### it immediately and 

### notify us 

### by e-maiL 

### or telephone.From:

To: Cc: Date: Subject: Heetor.Ybanez@holcim.com

Peter Tsirigotis/RTP/USEPA/US@EPA Keith BarnetURTP/USEPA/US@EPA 0612512010 04:16 PM follow-up PUT lN DOCKET

Peter


### I would 

### like to thank you 

### for taking time from

### your 

### hectic schedul"e

### to meet and 

### discuss Mercury 

### floors and

### the UPL. The purpose 

### of this email is to 

### provide you wit,h 

### a cursory summary 

### of Lhe 

### major take-a-ways from our discussion. As you know, Holcim 

### took iE upon 

### iLself to offer you 

### a simpJ-e method 

### to accounÈ

### for most,

### of the variability associated 

### with the 11 sources which make

### up 

### the mercury 

### fLoor. The approach incLuded Ehe

### variability for:

intra-quarry for each

### of Èhe L1 sources; 

### the variability of nearby 

### coal mines 

### that supply each

### of the 11 

### sources; and 

### auto-correlation. SubsequenE

### to our submiÈEal, RTI 

### provided comments on HoLcim's proposal. Holcim incorporaEed most 

### of thê RTI commenÈs 

### in to our calculations. Basically, w€'veworked 

### cooperatively with EPA/RTI, and 

### I think that significant progress has been 

### made. I want

### to thank you 

### for giving us thaE opporEuniEy. 

### Currently, aLl issues have been 

### resolved wit.h 

### the exception 

### of how the 

### "total variance" is calculaEed. RTI assumes

### that thevariation in the data 

### set is equal between 

### sources, and hence

### t,his assumption does 

### not allow for beEweensource

### variability to be 

### considered. Furthermore RTI assumes

### that the variance become smaLler 

### as more 

### data is collected. In reality, as more 

### data is col-l-ected Èhe 

### larger the variance - we have demonstrat,ed 

### that point Eo EPAand RTI 

### using real data. 

### Conversely, 

### to RTf assumptions, 

### the sourceswhich make up 

### the mercury 

### floors also contain an unequal 

### or unbaLance

### dat,aset.' In an 

### effort Èo determine 

### the best apprgach 

### of caLculating an overalJ- 

### variance, h¡e

### contacted SAS 

### InstiÈute and 

### inquired as 

### to the besL 

### statistical approach to

use when 

### determining an

### overall variance 

### for such 

### a data 

### set. The SAS 

### Instit,ute indicated tshe 

### correct way

### of handling an unbalance daÈaset was 

### through use 

### of the Proc Mixed 

### or Proc Varcomp procedures 

### (in other words

### the variance needs 

### to be weighÈed

### to accounE

### for variabilit,y between sources and

### wit.hin source

### variability)

. This issue has been discussed 

### with RTI 

### but it remains unresoLved. To

### aid in this discussion, we decided 

### to test RTI's UPL and assumpÈions 

### using available long-term THC CEM

### data. Testing such 

### a modeL should 

### provide real confirming evidence 

### as Èo 

### it functionality in a real world 

### situation. I¡Ihat we discovered 

### is that RTI's UPLand assumptions 

### grossly underestimate 

### the acÈual 

### 99th 

### percentile. That 

### in it,self,. should

### force RTI Lo re-evaLuate 

### their assumpÈions.
Once 

### again, I would 

### like to thankyou

### for your efforts and r^¡e

### arewilling t,o 

### continue 

### to work 

### on

### this matterwith EPA and RTI.

# PCA Presentation



# IJpper 

# Predictive 

# Limit (IJPL)

and 

# IJpper Tolerance 

# Limit (IJTL) NESHAP Rulemaking

May

# 7,2010

# Contents
o Statistical Intervals 

# o 

# lJtilizinglong-term THC 

# CEM datatotest 

# UPL and 

# UTLApproach 

# o Conceptual 

# Discussion on 

# Mercury

Slide 1

# Statistical Intenrals ' Statistical intervals are 

## common 

## practice 

## in

# working

# with data.

## There is a

## difference between a

## confidence 

## interval 

# (CI), a

## p-rediction 

## interval (PI), and a

## tolerance 

## interval 

# (TI). A CI contains a

## value, such as

### the mean,

## with a

## specified degree

# of confidence. 

## A PI contains a

## certain number 

## (k) 

## of future observations 

## with a

## specified 

## probability. 

## A

## TI contains 

## aspecified 

## proportion of a

## population

## with a

## specified degree 

## of confidence.



# . Therefore: a. a 95o/o

## CI for an 

## unknown quantity is a 

### range 

## which has a 95o/o 

### chance 

## of containing the

## truevalue 

## of the 

## quantity; b. a 99% 

## PI

# will contain a

## value

## with gg%probability; and c. a99o/o

## TI contains 99%

## of the values 

## with a 

### stated 

## confidence (such as 

## es%).


# Slide 2

# Distributions Testing 

# Using



# Long-teffn 

# CEM data

Slide 3

# 99th 

# Percentile 

# of Long-Te{m



# THC

# CEMDATA

This data set is usedto calculate the UPL and 

### UTL in the

### following slides.

Rankino RndmKilnCo Name DataField Counl Mean 99th 

### lhrcentile

1 

### t520 Lehieh Union Bridse E02-001 30-Dav Rolline Averaee 668 4.69007 

### ,\ 23.65'\ 2 THC-1 Suwannee Bradford, FL 1 30-Dav Rollins Averase 274 5.68426 

### \,,,0.u0

# i ,,j 3 L226 HolcimTrident 30-Day Rolling Average 762 5.69003 

### 'ffi€--/ 4 L239 TXI Midlothian Kiln 5 30-Dav Rolline Averaee 444 s.8292 10.17 5 1316 Holcim Mason Citv Kiln 2 30-Dav Rolline Averaee 70L 10.9063 28.57

Top 2 30-Dav Rolline Averase 942 4.97925 23.7 Too 5 30-Dav Rollino Averaoe 2183 7.39371 27.4 

## ?ßr.i


# Slide 4

# EPA 

# UPL Equations

Equation 

### Assumption Calculated

THC 

### ppmvd 

### @7% O2



### JPL Equation 

### in Proposal 

# [?l = ¡ndûn

### * 

## ktft, ilf,.i n (future values)=f,Q 

### îean=average of group 

### îeans 10.43
UPL=meüil+tü
mfuturevalues 

### tlew UPL 

### Equation (EPA 

### evised) n (future values)=36 nean=average of group 

### neans 8.29



### \./ 

# Slide 5

# Issues

# with'New UPL" o EPA "new 

# IJPL equation" appears

# to have 

# corrected the issue

# within-source

# variability o EPA "new 

# UPL Equation" does 

# not change 

# the

# calculation

# ofthe between-source 

# o

# Additionally, 

# itwrongly divided twice by 

# N 

# variúility. which attenuates

# the emissions 

# standard 

# deviation between best 

# performing units 

# by an

# extra

# factor

# of 1/sqrt(N)-3.3.
Slide 6

# Corrected 

# IJPL Equation
Calculated THC 

### ppmvd (õ.7o/o

### 02 rrected UPL Equation (2009 EPA 

### Unified Guidance)

P

### L 

## : rr* an* 

### fi-*/*",-,

## t'¡[=) for wrtransformed dzta assumed 

### normally distributed m

### future r¡alues 

### (future values)=1 

### n-grand mean


### -alm= 0.99 

### dence limit (future values)=39 

### s¿¡=grârìd mean


### -olm= 0.9996 nce limit

The above PL is calculating the one data 

### point which represents the 99th percentile, therefore, "m." should 

### be 1. 

# Slide 7

# Corrected 

# UPL Equation
THC 

### ppmvd @.7% 

### 02 UPL Equation (2009 EPA 

### Unified Guidance)



## PL: meãn+ å*,o_r$ 

### foruntransformed data assrmed 

### normal distrib ute d me¿uì 

### of p futr.rre values 

### (future 

### values) =1 

### s¿¡=grâhd mean -a=99o/o 

### cumulative bility 

### of p

### future (future values)=tQ 

### ¡=grârìd mean -a=99o/o 

### cumulative robability n

### of p

### future

The above equation isthe same equatio.n used 

### in Boiler MACT. s 

### th 

# Slide 8

# IJTL

Calculation (2009 EPA Unified Guidance)
TL = mean +-r(¡¡, y, 1- c).s for untransficrrned data

. &)Ìn oJîsquaretotal r,,(tota¡ varrañce) r(n,f ,l- ar) :one sided norma!. tolerance factor y :cov erage coefficient 1- ø: cor¡fidence level TL: explmean+ r(n,y,7- a).sl fornatural log transformed data

. St¡tnqf squnretotal yr(total eattætce) K<n,f ,7- a): one sided normal tolerance faclor / :coverage coefficient
1- a: cor¡fidencre lev el 

### g¿¡=gfâfìd mean 

### -q=95% Cl,

### y-99% n-grand mean transformed data 

### -q=95% Cl,

### y-99%



# Slide 9

# Conclusions

UPL equations 

# in the 

# proposal and

# the 

# most recent 

# IJPL are

# in effor EPA should 

# utilize the 

# correct 

# IJPL (Slide 7)wherethe 

# "future value" is equal

# to one 

# (1).A futurevalue

# of 1

# should be used because 

# the 

# UPL iscalculating the

# one data 

# pointwhich represents 

# the

# future value

# of 99thpercentile. 

# Utilizationof the 

# logtransformed 

# IJTL is a 

# much better estimator 

# ofthe 99th

# percentile Slide 10

# Mercury 

# o EPA should used

# eitherthe 

# UPL on 

# SlidesT or 8 

# o EPA could use

# the

# tlTL to

# obtaiÍtabetter estim ate

# of th

# e 99th

# percentile 

# o Regardless

# if EPA uses

# the 

# UPL or 

# UTL EPA should make allowances 

# to adjust the

# variance 

# term 

# inthe above equations so

# variances 

# not capfure 

# during the 3O-days

# of data

# collection can 

# be 

# inlcuded



# . Those 

## variances should 

## include at a

# minimum; 

# Kiln feed; 

## Intra-quarry; and 

## Coal 

# Mine ICR Data.



# Slide 11q{1.

## _r variability



## .-.,'Ü Hector.Ybanez to: Peter Tsirigotis, Steve Fruh, Keith

## Barnett O3l25l2O1O 11:26 AM 

### H¡story: This message has been forwarded.
Regarding 

### Peter's invitation to provide an exampJ-e

### of how

### variabil-ity couJ-d 

### be incorporated 

### without creating a "Franken-Limit"; Hol-cim 

### offers a simpJ-e approach

### that incl-udes

### variability encountered as 

### part of kiJ-n feed, 

### intra-quarry, and 

### coal from numerous coal- 

### mines. In very simple terms,. Limestone mercury 

### content 

### of the 11 sources 

### is 11".75 

### lblMil-Lion tons 

### of kil-n feed incl-uding any 

### controls, such as mercury removed 

### via CKD and,/or scrubber. 

### Kiln feed standard 

### deviation for each source i-s converted 

### to a 

### relative standard 

### deviation (RSDkiln feed)n 

### Intra-quarry variability is based on 

### the 458 (RSDintra-quarry)n which was developed 

### as 

### part of our comnents 

### for Location A and 

### Location B CaLcul-ate 

### relative standard 

### deviation for coal- mj-nes (RSDcoat mines)n 

### identified at each 

### sources. These 

### are the coal- mines 

### that were 

### identified by John Hol-mes

### for each source. 

### Therefore, the RSDoverall 

### is caLcuLated 

### for each source 

### using 

### the folJ.owing equat ion : Overall- RSDn 

### : {(RSDkilnfeed)2n +(RSDintra-quarry)2n +(RSDcoal mines )2n ) Once an overal-L RSD 

### per source 

### is cal-cul-ated,

### the total varj_ance 

### is calcul-ated by 

### @Total: ?{ ?L

### to 11(RSDn)} The 

### total- variance is converted back

### to an overal-l- standard 

### deviation (S) 

### by multipJ-ying 

### it bythe average 

### kiln feed Hg

### content 

### of the 11 MACT sources. S 

### : @TotaÌ 

### x kiln feed HgJ-b

### HglMillion tons of kiln feed 

### This all-ows

### for the 99th 

### percentile to be calcul-ated MACT 

### Floor lb HglMillion tons of kiln feed 

### : ave. Limestone Hg

### lb HglMil]ion tons of kiln feed +

### t (S/?n) MACT 

### Floor J-b

### HglMillion tons of kiln feed 

### = 11.'15 

### + 2.'7631 x 

### 52.64/?7r MACT 

### Floor lb HglMillion tons of kil-n feed 

### = 55.6 The above 

### equation woul-d generate 

### a MACT FLoor 

### of 55.6 Lb HglMilJ-ion tons 

### of kiln feed. PLease share 

### with your 

### statistlcal- contractors and 

### if you a-lJ- have any 

### questions 

### or conments 

### please contact me.(See

### attached 

### fil-e: Hg VARABILITY Cal-culations HY

### for EPA 03252010.xls)



### Hector Ybanez Corporate Manager, Environmental l-22 West Carpenter Freeway 

### Suite 485 

### Irving, Texas 75039 Phone (21.4) 524 

### - 2814 

### CeLl (972) 268-L139 Hector. YbanezGhof cim. com hybanezGgmaiL. com www. holcim. com/us 

### This e-mail is confidentiaf and intended 

### only for the use

### of the above named addressee. 

### If you have 

### received 

### this e-mail in error, please deLete 

### it immediately and 

### notify us 

### by e-mail or telephone.