From: Hector.Ybanez@holcim.com

To: Keith Barnett/RTP/USEPA/US@EPA, Sharon Nizich/RTP/USEPA/US@EPA

Date: 12/18/2011 06:35 PM

Subject: follow up on UPL

Keith and Sharon

During our December 6th meeting we discussed the ability of the UPL to

predict the 99th percentile. Holcim used PM CEM data from four kilns and

showed that the UPL underestimates the actual 99th percentile by more than

50 percent. EPA pointed out that Holcim was making an incorrect

comparison. EPA believes if the entire data set is used then the actual

99th percentile would most likely be found in the worst performer within

the floor. EPA, further stated, that the UPL does not predict the 99th

percentile of the data set but rather the 99th percentile of the best

performers within the floor. Based on this “new information”, Holcim

recalculated the actual 99th percentile and the UPL of the best performers

within the data set. Once again, it was concluded the UPL significantly

underestimates the actual 99th percentile for the best performers between

40 and 50 percent.

Holcim has provided examples using long-term THC CEM data, PM CEM data from

6 European kilns, and most recently 4 Holcim kilns to show that the UPL

does not work. In addition, the utility sector provided EPA a similar

comparison using long-term Hg CEM data to show that the UPL underestimates

the real value. Therefore, we can conclude that the UPL equation only

works in two occasions. The first is when the long-term data set is

calculated as 30-day averages and the UPL is used the predict the 99th

percentile (i.e., m=1). In this configuration, the UPL is most accurate

plus or minus 1 to 5 percent. The second instance is when there is no

long-term data to verify the UPL and one has to assume it works.

If EPA continues to use the UPL to predict the 99th percentile of 30-day

average from daily averages, they should provide a CEM data set to prove

the equation works. Otherwise, EPA should reconsider the use of such a

flawed and grossly underestimating empirical method.

The four kilns used in the most recent example might be a mixture of LLL,

CISWI and EEE kilns; however, the type of kiln is not important, Holcim’s

point is the inability of the UPL equation.

If you have any questions or comments please contact me.
thank you

(See attached file: UPL Evaluation.ppt)

----------------------------

Hector Ybanez

Corporate Manager, Environmental

122 West Carpenter Freeway

Suite 535

Irving, Texas 75039

Phone (214) 524 - 2814

Cell (972) 268-1139

hybanez@gmail.com

mailto:Hector.Ybanez@holcim.com

http://www.holcim.us
UPL and the 99th Percentile
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Holcim PM CEM Example

N Mean Variance EPA UPL % Difference *

All hourly Data 9824 0.020 0.00043 0.030 -53.1

Random A Set 1 631 0.020 0.00036 0.029 -55.1

Random A Set 2 561 0.020 0.00036 0.029 -54.1

Random A Set 3 611 0.021 0.00042 0.031 -52.3

Random B Set 1 1213 0.020 0.00039 0.029 -54.0

Random B Set 2 1195 0.020 0.00043 0.030 -52.8

Random B Set 3 1191 0.020 0.00040 0.030 -53.9

Random C Set 1 1810 0.020 0.00039 0.030 -53.6

Random C Set 2 1822 0.020 0.00049 0.031 -52.1

Random C Set 3 1807 0.020 0.00039 0.029 -54.6

Random D Set 1 2339 0.020 0.00041 0.030 -53.3

Random D Set 2 2323 0.020 0.00040 0.029 -54.2

Random D Set 3 2247 0.020 0.00041 0.030 -53.4

Random E Set 1 3908 0.020 0.00041 0.030 -53.5

Random E Set 2 3821 0.020 0.00049 0.031 -51.7

Random E Set 3 4048 0.020 0.00040 0.029 -53.9

 * The column represents the percent differences between the UPL value and the 

actual 99th percentile.
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UPL Using 30-day Averages

0.020286

0.000266

N 4

m 1

SQRT(Variance*(1/n+ 0.016322

t 2.586536

UPL 0.0625

Actual 99th Percenti 0.0640

Overall Mean

Variance
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UPL – EPA’s Approach

a b c d e

a(i) b(i) c(i) d(i) e(i)

99th

a(n) b(n) c(n) d(n) e(n)

Mean (i) 1 1.5 2.5 4 4.5

Overall Mean 2.7

Overall Variance

Actual 99th Percentile

UPL Only Sources better than 2.7

So the UPL is predicting the 99th percentile for only source a, b, and c

EPA states that the difference is between the actual 99th percentile and the 

UPL 99th of the best performers

Kiln Floor Sources
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Holcim PM CEM Data applying EPA “thinking”

UPL still under-estimated by significant percent

Only Best Performers All Data

0.01193317 0.020286

0.000145771 0.000428

3 4

30 30

0.00220856 0.003784

2.576460585 2.57633

0.0176 0.0300

0.0434 0.063966

Percent Differences (%) -59.39% -53.05%

Mean

Variance

N

m

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

t

UPL

Actual 99th Percentile of 30-day Average
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Holcim Group Support 28.06.2012/Ad

Holcim PM CEM Data applying EPA “thinking 

UPL Using 30-day Averages

0.020286203

9.89704E-05

N 3

m 1

SQRT(Variance*(1/n+0.009960663

t 2.588114378

UPL 0.0461

Actual 99th Percentil 0.0434

Overall Mean

Variance