DRAFT

Model	for	Predicting	In-Stack	

Response	Functions	for	Beta	

Attenuation-Based	PM	CEMS	and	

Proposed	Alternatives	to	PS-11	and	

P-2	Using	a	QAG

EPRI Communication – Working White Paper

Cooper	Environmental	Service,	LLC

10180	SW	Nimbus	Avenue,	Suite	J6

Portland,	OR	97223

www.cooperenvironmental.com

January 10, 2013
DRAFT

Model	for	Predicting	In-Stack	

Response	Functions for	Beta	

Attenuation-Based PM	CEMS	and	

Proposed	Alternatives	to	PS-11	and	

P-2	Using	a	QAG

Prepared by 

Andrea Geiger, Troy Pittenger, Jamie Berg, and John A. Cooper

Cooper Environmental Services, LLC

Portland, OR

and

Craig Clapsaddle

Mechanical Systems, Inc.

Raleigh, NC

January 10, 2013 
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TABLE OF CONTENTS

SUMMARY 5

1.0 INTRODUCTION 6

2.0 REVIEW	OF	RECENT	BAM-BASED	TEST	RESULTS 8

2.1 Background for CEMS, Testing, and Power Plant 8

2.2 Testing Results at the Bruce Mansfield Power Station 9

2.3 Comparison of QAG and PS-11 Results 12

3.0 PROPOSED	MODELS 15

3.1 Overview 15

3.2. Physical Model 15

 3.3 Mathematical Model 17

 3.4 Model Evaluation 19

4.0 PROPOSED	ALTERNATIVES	TO	PS-11	AND	P-2 22

4.1 PS-11 and P-2 22

4.2 Proposed Alternative: Performance Specification 11b 22

4.3 Proposed Alternative: Procedure 2B 24

4.4 Evaluation of Proposed Alternative Methods 24

4.5 Summary of Benefits of Proposed Alternatives 26

5.0 CONCLUSIONS	AND	RECOMMENDATIONS 27

6.0 REFERENCES 27

Appendix A: Preliminary Application of Alternative Performance Specification 

11B to Examples of Existing BAM-Based PM CEMS Database
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Tables

Table 1. Summary of initial PS-11 Certification correlation test results for the three PM CEMS challenged 

by the QAG (1A, 2B, 3A)

Table 2. Summary of CEMS correlation parameters resulting from QAG measurements

Table 3. Comparison of Linear Model Fit Parameters for Fifteen BAM-Based PM CEMS Developed with 

PS-11 In-Stack or QAG Off-line Measurements (MSI BetaGuard data)

Table 4. Examples of potential variables influencing measured PM CEMS concentration and their 

potential range

Table 5. Comparison of Linear Model Fit Parameters (In Order from Smallest to Largest Slope) for Fifteen 

BAM-Based PM CEMS Correlated with M5 and PS-11 In-Stack Measurements

Table 6. Comparison of PS-11/PS-11B Calibration Correlations for 14 BAM-Based PM CEMS Databases

Figures

Figure 1. Plots of original PS-11 correlation data and ongoing Calibration Results for MSI BetaGuard PM 

CEMS 1A (a), 2B (b), and 3A (c) at the BMPS

Figure 2. Plot of predicted QAG reference aerosol concentration versus measured concentration by 

three different BAM-based PM CEMS exhibiting a highly linear relationship and a slope close to one

Figure 3. Comparison of PS-11/PM CEMS and QAG/PM CEMS response functions for 1A (a), 2B (b) and 

3A (c) PM CEMS at the BMPS

Figure 4. Schematic illustration of proposed physical model of the PM deposit measurements for both 

EPA Reference Method 5 and extractive BAM-based PM CEMS

Appendix A: Preliminary Application of Alternative Performance Specification 11B to Examples of 

Existing BAM-Based PM CEMS Databases
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SUMMARY

Recent quantitative aerosol generator (QAG) testing with three independent beta attenuation monitor 

(BAM)-based particulate matter (PM) continuous emissions monitoring systems (CEMS) has demonstrated 

strong agreement between predicted QAG reference aerosol concentrations and measured PM CEMS aerosol 

concentrations based on PM CEMS factory thin film standard calibrations. The average concentration 

relationship between the QAG (CQAG) reference aerosol concentration and all three of the BAM-based PM 

CEMS (CCEMS) measurements is given by the following linear equation:

CQAG = (1.06 ± 0.02)CCEMS – 1.43 ± 0.61

The linearity of this relationship over a concentration range from zero to 75 mg/m3 was strong, as evidenced 

by a coefficient of determination (R2) of 0.99. In addition, the uncertainty in the individual slopes was about 

2%, while the standard deviation in the mean slope for all three CEMS (1.05 (1A), 1.06 (2B), and 1.07 (3A)) 

was less than 1%.

Results from Environmental Protection Agency (EPA) Performance Specification 11 (PS-11) for multiple BAMbased PM CEMS showed not only high variability between PM CEMS’ calibration slopes, but also a significant 

departure from the ideal 1:1 linear relationship, with some slopes as high as 3.7. The results from both the 

QAG and PS-11 testing indicate that off-line QAG calibrations of PM CEMS cannot be used alone to predict instack response functions for BAM-based PM CEMS. However, based on this data and theoretical 

considerations, a model has been developed that suggests that independent off-line BAM-based PM CEMS

calibrations using the QAG, in conjunction with measurements of PM CEMS probe deposits at typical stack 

conditions, can reliably and accurately estimate in-stack PM concentrations. A key concept of this proposed 

linear model assumes that a PS-11 slope’s deviation from 1.0 is due primarily to losses from probe deposits.

Based on this model, alternatives to PS-11 and Procedure 2 (P-2) are outlined that would not require altering 

plant processes. These alternative specifications and procedures are expected to meet EPA criteria for 

approval of alternative methods. Results from applying the alternative PS-11 method to existing PS-11 data 

from 14 coal fired power plants (CFPP) are encouraging. The results demonstrate that the proposed model of 

incorporating off-line QAG and probe deposit measurements is capable of producing results comparable to 

PS-11, except the QAG method would not require extrapolation at higher concentrations nor modification of 

CFPP processes.

Cooper Environmental Services, LLC recommends that these alternatives (performance specification (PS-11B) 

and an alternative on-going quality assurance procedure (P-2B)) be developed and formalized. Once 

completed, the alternatives should be submitted to the EPA to solicit their comments and contributions 

towards the development of a field demonstration plan. It is further recommended that these field 

demonstration tests be conducted to develop a database to support a request for EPA approval of these 

proposed alternative methods.

This document presents a conceptual physical and mathematical model approach for determining in-stack 

response functions. In addition, outlines of alternatives to EPA’s PS-11 for initial certification and calibration 

and EPA’s P-2 for on-going quality assurance of BAM-based PM CEMS are presented. Also presented here are 

discussions of the data and theoretical considerations to support model conclusions and proposed alternative 

procedures. 
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1.0 Introduction

Recent EPA regulations for electric generating units (EGUs) require plant operators to limit emissions of 

hazardous air pollutant (HAP) metals. Operators have several paths for demonstrating compliance 

under this new regulation. One of the preferred compliance demonstration paths is to monitor total PM 

emissions with a PM CEMS as an EPA-accepted surrogate for HAP metals emissions. Initial PM CEMS 

certification with performance specification 11 (PS-11) and on-going QA with procedure 2 (P-2) are 

difficult, costly, and are typically unable to establish an adequate linear operating range. Cooper 

Environmental Services LLC (CES) has developed a quantitative aerosol generator (QAG) similar to 

reference gas generators and certified reference calibration gas cylinders. CES’ QAG has been proposed 

as a reference aerosol source that could be used in alternative performance specifications and on-going 

QA procedures, which would be easier to implement, less costly, and provide a larger dynamic range of 

validated linearity. Using a QAG in an alternative certification and/or on-going QA procedure would 

most likely require off-line calibration of a candidate CEMS with a QAG-generated aerosol and then infer

an in-stack CEMS response function from these off-line QAG-based calibrations.

Results from recent QAG field tests with BAM-based PM CEMS provided encouraging results that

support the potential of this off-line calibration approach (Pittenger et al., 2012). For example, in these 

recent field tests, a QAG-generated reference aerosol challenged three previously PS-11 certified BAMbased PM CEMS off-line by comparing predicted QAG reference concentrations to reported PM CEMS 

concentrations, and by comparing the QAG-/CEMS-based regression parameters to PS-11-/CEMS-based 

regression parameters. The average concentration relationship between all three of the PM CEMS 

(CCEMS) and the QAG (CQAG) concentration measurements is given by the following linear equation:

CQAG = (1.06 ± 0.02)CCEMS – 1.43 ± 0.61.

The linearity of the QAG/PM CEMS relationship over the concentration range from zero to 75 mg/m3

was strong, as evidenced by a coefficient of determination (R2) of 0.99. In addition, the uncertainty in 

the individual slopes was about 2%, while the standard deviation in the mean slope for all three CEMS 

(1.05 (1A), 1.06 (2B), and 1.07 (3A)) was less than 1%. This high level of agreement is even more 

impressive when one considers that the PM CEMS factory calibration is based on thin film standards,

while the QAG-predicted concentrations are based on NIST-traceable components and in-field 

determination of transport efficiency. The independent nature of the QAG’s predicted concentration 

and the PM CEMS’ measured concentration supports the high accuracy, precision, stability, and 

reproducibility of both the QAG and the PM CEMS. As evidenced by the data, these three PM CEMS’ 

sensors, which have been in operation for about three years, provide an accurate measure of PM mass 

captured on the CEMS filter tape.

The linearity of the relationship between these three PM CEMS and EPA Method 5B (M5B) was also 

quite good with a coefficient of determination of 0.99. However, although the off-line response 

functions for the three BAM-based PM CEMS were nearly identical, the in-stack slopes measured with 
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M5B for these same three PM CEMS were 1.17 (CEMS 1A), 1.19 (CEMS 2B), and 1.63 (CEMS 3A) with an 

average slope uncertainty of about 4%. Although the slope uncertainty as determined with M5B is 

higher than that obtained with the QAG (4% versus 1%), the more important issue is why these three 

nearly identical PM CEMS and off-line QAG response functions are reporting such significantly different 

slopes with M5B compared to the QAG.

The difference between in-stack and off-line response functions is particularly important for the 

development of alternatives to PS-11 and P-2 based on off-line QAG measurements. For example, any 

off-line measurements with the QAG would have suggested similar in-stack response functions, whereas 

the PS-11/M5B data show the in-stack response functions are clearly different, and quite significantly for 

the CEMS exhibiting a slope of 1.63. It is important to note that this high slope (1.63) is not an outlier;

on the contrary, numerous other high slopes have been reported (Mechanical Systems, Incorporated 

(MSI), BetaGuard PM CEMS data, presented in Subsection 2.3 of this report) for this same type of PM 

CEMS, with one slope as high as 3.7. It can be assumed from the results of the three BAM-based PM 

CEMS tested that off-line QAG calibration for these other BAM-based PM CEMS would be similar to the 

QAG-determined slope values. Additionally, these measurements (QAG, CEMS, and M5B) can be 

considered to be precise, independent (NIST, thin film, reference method), and accurate measurements 

and predictions of PM concentration. Furthermore, these recorded differences between slopes are real 

and statistically significant with, in some cases, differences exceeding well over five standard deviations 

in the slope measurements. As such, these differences need to be well understood before an 

alternative method to PS-11 and P-2 can be developed based on off-line QAG measurements and 

proposed to the EPA.

The objective of this work is to develop alternative procedures to PS-11 and P-2 based on off-line PM 

CEMS reference aerosol measurements, such as those generated by a QAG. The objective of this 

specific document is to provide a foundation for further discussions of how the QAG might be used in 

this capacity. Our approach to this problem has been to first develop conceptual physical and 

mathematical models to explain the results from these three independent measurements and 

understand why the data fit a linear model so well. Then, based on this new understanding, we propose

an alternative performance specification and procedure that uses off-line measurements in conjunction 

with probe- and transport-loss measurements. Section 2.0 reviews the details of these recent QAG/PM 

CEMS/M5 test results and their implications. In Section 3.0, physical and mathematical models are 

proposed that form the foundation for understanding the current field data and the development of 

alternatives to PS-11 and P-2. Based on this new understanding, preliminary outlines of alternatives to 

PS-11 and P-2 are proposed and discussed in Section 4 followed by considerations of implications, 

recommendations, and conclusions.
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2.0 Review	of	Recent BAM-Based	Test	Results

2.1 Background	for	CEMS,	Testing,	and	Power	Plant

2.1.1 Bruce Mansfield	Power	Plant

The BAM-based PM CEMS testing occurred near Shippingport, Pennsylvania at the Bruce Mansfield 

Power Station (BMPS), which is a coal-fired power plant owned and operated by First Energy. BMPS has 

three pulverized coal-fired boilers producing 835 megawatts (MW) each, for a combined site total of 

2505 MW. It is the largest coal plant owned and operated by First Energy. At full capacity, the plant’s 

generating units can produce 56-million kilowatt-hours of electricity daily. 

Units 1 and 2 exhaust through a 950-foot stack and are equipped with several emission controls, 

including a wet venturi scrubber for sulfur dioxide (SO2) and PM controls, low- nitrogen oxide (NOx)

burner/separated over-fired air integration system (LNB/SOFA), selective catalytic control, and a sodium 

bisulfite injection system. Unit 3 exhausts through a 600-foot stack and is controlled with an 

electrostatic precipitator (ESP), followed by a horizontal weir wet scrubber for PM and SO2 control. It is 

also equipped with an LNB/SOFA, selective catalytic control, and a sodium bisulfite injection system 

(Hatch, 2012). Each unit has two exhaust flues with a PM CEMS located on each flue. The QAG was 

interfaced with one CEMS from each unit (1A, 2B, and 3A). Units 1 and 2 both have a typical PM level 

between 15 and 25 milligrams per wet standard cubic meter (mg/wscm); the stack gas is not saturated 

with water. Unit 3 has a typical PM level between 5 and 15 mg/wscm and is saturated with free water 

droplets.

2.1.2	 BAM-based	PM CEMS

The PM monitors tested at BMPS were MSI BetaGuard PM CEMS. The primary components of a 

BetaGuard PM CEMS include a probe and transport system to deliver a representative PM sample to a 

reel-to-reel filter tape sampler. The measured flow through the filter tape and the PM mass deposited 

are measured and used to calculate the stack gas PM concentration. The principle behind BAM-based

PM CEMS is that the intensity of a beam of beta particles is reduced as it passes through PM collected 

on a filter media. A baseline beta intensity of blank filter media is collected and compared to the beta 

intensity after sample collection. The attenuation of beta ray intensity between the baseline and postsample filter provides a measure of the PM deposit mass. 

MSI BetaGuard CEMS are capable of measuring on a wet or dry basis and have dual carbon-14 (

14C) beta 

sensor designs, a seven-point thin film factory beta calibration, a four-point flow calibration, and 

automatic daily beta and flow calibration checks. The three PM CEMS tested were all installed and PS-11 

certified about three years prior to QAG testing and each CEMS met all on-going QA requirements 

defined in P-2. The PM CEMS’ sampler (reel-to-reel tape drive) and mass sensor (beta source and 

detector) are located between 45 (1A) and 80 (2B, 3A) feet away from the probe outlet, which were 

identical for all three CEMS. A P-2 response correlation audit (RCA) for CEMS 1A and 2B was performed 

before the QAG testing of those PM CEMS, and a P-2 relative response audit (RRA) for CEMS 3A was 

performed after QAG testing. All P-2 criteria were met for all three of the PM CEMS tested.
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2.2 		Testing	Results	at	the	Bruce	Mansfield	Power	Station

2.2.1 PS-11	Results

Results from initial PS-11 certification tests for the three PM CEMS of interest (1A, 2B, and 3A) are

summarized below in Table 1. These results show a very linear relationship (R2=0.992, 0.983 and 0.981)

between the PM CEMS factory calibration concentration and the M5-measured PM concentration; all 

three CEMS met PS-11 initial certification test criteria. The slopes for 1A and 2B CEMS correlations are 

similar (1.17 ±0.02 and 1.19 ±0.02) and within one standard deviation of each other. However, the PS11 correlation slope for CEMS 3A is significantly different (1.63 ± 0.053) from the first two PM CEMS, and 

all three are statistically different from 1.00 by well over five standard deviations. It is generally thought 

that this divergence from one is due to differences between the thin films used to calibrate the PM 

CEMS sensor in the factory and such factors as in-stack aerosol characteristics, probe location, particle 

losses in sampling system, etc. (Jahnke, 2000). The EPA considers these differences are compensated 

for by using the in-stack calibration based on simultaneous M5 and PM CEMS concentration

measurements, as defined in PS-11. As discussed in this report, we hypothesize that deviations in slope

from 1.00 are due primarily to PM losses (deposits) in the probe and transport before the PM aerosol 

reaches the filter tape and beta gauge sensor, while most of the other possible influencing variables 

contribute primarily to the systematic constant bias term or random variability, i.e. data scatter.

Table 1. Summary of initial PS-11 Certification correlation test results for the three PM CEMS challenged 

by the QAG (1A, 2B, 3A) 

CEMS ID 1A 2B 3A

Parameter

R

2

(CoD) 0.992 0.983 0.982

m (slope) 1.17 ± 0.025 1.19 ± 0.043 1.63 ± 0.054

b (intercept) 0.32 ± 0.500 -2.04 ± 0.962 -1.17 ± 0.532

COL (mg/wscm) 45.6 62.5 38.0

CoD = Coefficient of Determination; COL = Continuous Operating Limit

2.2.2 P-2	On-Going	Results

As noted above, the initial PS-11 certification test results were strongly linear. In addition, the on-going 

P-2 QA test results were within linear confidence intervals, as illustrated in Figure 1a through 1c, where

original PS-11 correlation data (black diamonds) are compared to subsequent P-2 data for RRA (red 

squares and blue triangles), and RCA (black circles) tests conducted since initial installation and 

certification. All of the P-2 data fit well within EPA-accepted correlation regression line limits (dashed 

red lines; ± 25% of the numerical emission limit value from the correlation regression line) and were 

within EPA-defined tolerance and confidence intervals. These graphs show that these CEMS remained 

stable and precise over a period of several years. They were also operating well, and in compliance, at 

the time of and immediately following the QAG tests as evidenced with the successful RRA and RCA 

tests. 
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2.2.3 QAG	Testing	Results

The three BMPS operating PM CEMS were taken off-line and challenged with QAG-generated, NISTtraceable aerosols of known concentrations. The results of this comparison (QAG-predicted 

concentration versus PM CEMS measured concentration) are summarized in Table 2 and Figure 2. These 

results show very similar correlation parameters for all three of the CEMS with a strong linear 

relationship (R2>0.985) up to about 75 mg/m3. The average concentration relationship between the 

three PM CEMS (CCEMS) and the predicted QAG (CQAG) concentration is given by the following linear 

equation:

CQAG = (1.06 ± 0.02) CCEMS – 1.43 ± 0.61 Equation 1

This linear equation is an average of QAG testing of three BAM-based PM CEMS; summarized in Table 2

below:

Figure 1. Plots of original PS-11 

correlation data and ongoing Calibration 

Results for MSI BetaGuard PM CEMS 1A 

(a), 2B (b), and 3A (c) at the BMPS
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Table 2. Summary of CEMS correlation parameters resulting from QAG measurements 

Figure 2. Plot of predicted QAG reference aerosol concentration versus measured concentration by three 

different BAM-based PM CEMS exhibiting a highly linear relationship and a slope close to one

The linearity of the relationship over the concentration range from zero to 75 mg/m3 was very strong, as 

represented by a coefficient of determination (R2) of 0.99. The uncertainty in the individual slopes was 

about 2%, while the uncertainty in the mean slope was less than 1%. As previously mentioned, this level 

of agreement is very impressive, considering that the CEMS factory calibration is based on thin film

standards, while the QAG-predicted concentrations are based on NIST-traceable components and infield determination of transport efficiency. The linearity of the relationship between the PM CEMS and 

EPA M5B was also quite good, with a coefficient of determination of 0.99, but the slopes for these same 

three PM CEMS were 1.17, 1.19 and 1.63 with an average slope uncertainty of 2 to 4%. Although the 

slope uncertainty, as determined with M5B, is higher than that obtained with the QAG, the more 

important issue is why these three, nearly identically performing PM CEMS and response functions are 

reporting significantly different slopes with M5B compared to the QAG and thin film-based PM CEMS 

PM CEMS ID 1A 2B 3A

Parameter

R

2

(CoD) 0.985 0.994 0.993

m (slope) 1.077 ± 0.04 1.054 ± 0.025 1.063 ± 0.024

b (intercept) -3.103 ± 1.483 -1.391 ± 1.01 -0.281 ± 0.955

y = 1.06x - 1.43

R2 = 0.9899

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

QA

G

C

onc (mg/wscm)

PM CEMS (mg/wscm)
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calibrations. As noted previously, the primary contribution to correlation slopes that deviate from one

in this BAM-based PM CEMS application is hypothesized to be due to probe and transport loss of PM 

prior to being sampled and measured with the filter tape and beta gauge.

2.3 Comparison	of	QAG	and	PS-11	Results

A difference in slopes between the QAG- and PS-11-calibration results is seen in the data presented in 

sections 2.2.1 through 2.2.3. This difference is particularly important for the development of alternatives

to PS-11 and P-2 based on off-line QAG measurements. For example, any off-line measurements with 

the QAG would have suggested similar in-stack response functions (similar slopes within a few percent), 

whereas the PS-11 data clearly show the in-stack response functions are significantly different for CEMS

3A, which exhibited a slope of 1.63 ± 0.05. It is important to note that this high slope (1.63) is not an 

outlier. On the contrary, numerous other high slopes have been reported for this same type of BAMbased PM CEMS (MSI BetaGuard), one of which exceeded three, as shown in Table 3. Based on the 

stability and reproducibility of the three BMPS PM CEMS tested with the QAG, it can he assumed that

off-line QAG calibrations for these latter CEMS would have been similar to the 1.06 measured with the 

QAG. It can also be drawn from these measurements (QAG, CEMS, M5B) that each instrument/method

is a precise, independent (NIST-QAG, thin film-CEMS factory calibration, M5B), and accurate 

measurement/prediction of PM concentration. In addition, it can be concluded that these recorded

slope differences, which in some cases exceed the ideal linear slope of one by well over five standard 

deviations, are real and statistically significant. As such, these differences need to be well understood 

before an alternative method to PS-11 and P-2, based on off-line QAG measurements, can be proposed

to the EPA.

Table 3 lists correlation parameters from a comparison of concentrations measured by factorycalibrated MSI BetaGuard PM CEMS and Reference Methods 5 or 5B simultaneously. Examples 13 

through 15 show the resulting slopes from PS-11-calibrated CEMS at BMPS, while 16 through 18 show 

the resulting slopes from the same BMPS PM CEMS when calibrated with the QAG. Overall, the QAGcalibrated CEMS results depict more strongly correlated data based on the R2

values, as well as slopes

that are closer to a 1:1 ratio in CEMS response. 

The graphs shown in Figures 3a through 3c provide side-by-side comparison of the correlation plots and 

best-fit linear regression parameters for the three BMPS PM CEMS tested with both PS-11 and the QAG.

Our approach to the problem arising from such statistically different results for the same PM CEMS, 

despite the apparent stability and precision of each respective method’s measurements, has been to 

first develop a conceptual physical model (described in Section 3.0) to explain the results from these 

three independent measurements and understand why they fit a linear model so well, while being so 

statistically different. Then, based on this new understanding, outlines of alternative performance 

specifications and procedures that use off-line measurements are suggested and discussed.
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Table 3. Comparison of Linear Model Fit Parameters for Fifteen BAM-Based PM CEMS Developed with PS-11 

In-Stack or QAG Off-line Measurements (MSI BetaGuard data)

Example Slope ± Uncertainty Intercept ± Uncertainty R

2

% Slope 

Uncertainty Notes*

1 2.053 ± 0.095 + 0.437 ± 0.203 0.971 4.6 IS

2 2.297 ± 0.083 + 0.848 ± 0.240 0.982 3.6 IS

3 1.100 ± 0.082 + 0.376 ± 0.159 0.864 7.5 IS

4 1.805 ± 0.042 + 1.024 ± 0.555 0.991 2.3 IS

5 1.263 ± 0.107 - 0.809 ± 0.280 0.914 8.5 IS

6 1.462 ± 0.045 - 2.689 ± 0.871 0.988 3.1 IS

7 1.529 ± 0.058 - 0.222 ± 0.291 0.977 3.8 IS

8 2.665 ± 0.134 - 0.923 ± 0.852 0.957 5.0 IS

9 1.355 ± 0.088 - 0.493 ± 0.901 0.919 6.5 IS

10 1.488 ± 0.054 - 0.298 ± 0.545 0.977 3.6 IS

11 3.702 ± 0.125 - 0.820 ± 2.582 0.983 3.4 IS

12 1.844 ± 0.114 + 0.058 ± 1.175 0.945 6.2 IS

13 (1A - PS-11) 1.166 ± 0.025 + 0.322 ± 0.501 0.992 2.1 IS

14 (2B - PS-11) 1.190± 0.043 - 2.043 ± 0.962 0.983 3.6 IS

15 (3A - PS-11) 1.625 ± 0.054 - 1.170 ± 0.532 0.982 3.3 IS

16 (1A - QAG) 1.077 ± 0.036 - 3.103 ± 1.483 0.985 3.3 OL

17 (2B - QAG) 1.054 ± 0.025 - 1.3591± 1.01 0.994 2.4 OL

18 (3A - QAG) 1.063 ± 0.024 - 0.81 ± 0.955 0.993 2.3 OL

* IS = In-Stack; OL = Offline
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Figure 3. Comparison of PS-11/PM CEMS and QAG/PM CEMS response functions for 1A (a), 2B (b) and 3A

(c) PM CEMS at the BMPS.

y = 1.169x + 0.3442

R2 = 0.9925

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Method 5 (mg/wscm)

PM CEMS (mg/wscm)

PM CEMS 1A PS-11 Calibration

y = 1.0736x - 2.9605

R2 = 0.9847

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

QA

G

C

onc (mg/wscm)

PM CEMS Conc (mg/wscm)

(a) PM CEMS 1A QAG Calibration

y = 1.05x - 1.35

R2 = 0.994

0

10

20

30

40

50

60

70

80

0 20 40 60 80

QA

G

C

onc (mg/wscm)

PM CEMS Conc (mg/wscm)

PM CEMS 2B QAG Calibration

y = 1.1937x - 1.9916

R2 = 0.9826

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Method 5 (mg/wscm)

PM CEMS Conc. (mg/wscm)

(b) PM CEMS 2B PS-11 Calibration

y = 1.0628x - 0.3636

R2 = 0.9931

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

QA

G

C

onc (mg/wscm)

PM CEMS Conc (mg/wscm)

PM CEMS 3A QAG Calibration

y = 1.6258x - 1.2604

R2 = 0.981

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Method 5 (mg/wscm)

PM CEMS Conc. (mg/wscm)

PM CEMS 3A PS-11 Calibration (c)
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3.0 Proposed	Models

3.1 Overview

As discussed in this report, alternatives to EPA’s PS-11 and P-2 are needed. Although off-line calibration 

of PM CEMS with an independent reference aerosol generator like the QAG holds great promise, off-line 

QAG calibrations have not demonstrated distinct applicability to in-stack response functions for BAMbased PM CEMS (CES 2012). Although the data presented in Section 2.0 for BAM-based PM CEMS like 

the BetaGuard are encouraging, the differences between off-line and in-stack calibrations are large 

relative to the statistical uncertainties. The factors responsible for these statistically significant 

differences need to be understood before reliable alternatives to PS-11 and P-2 can be developed, and 

before they are likely to be accepted by the EPA. One possible starting point for understanding the 

causes of these observed differences is to posit a physical model describing the physical parameters that 

might influence the system being measured (stack gas PM aerosol), the measurement systems (M5, 

M5B, CEMS), and statements of assumptions implicit in a theoretical mathematical model, as described 

in Section 3.2. Following the physical model description, we present a linear mathematical model that

defines BAM-based PM CEMS data variability and a discussion of how this physical model influences the 

correlation parameters, the consistency of field measurements with the model, and model implications.

3.2. Physical	Model

A simplified physical model of our system is schematically illustrated in Figure 4. This system includes 

not only the measurement systems, but also the system being measured since variability in both can 

contribute to observed variability in aerosol PM concentration. 

Figure 4. Schematic illustration of proposed physical model of the PM deposit measurements for both 

EPA Reference Method 5 and extractive BAM-based PM CEMS

Stack gas PM aerosols consists of a suspension of liquid and solid particles of varying size and 

composition traveling at high velocities (typically 30 to 50 mph), at relatively high temperatures (about 
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150° to 350°F) and moisture conditions that range up to super-saturation. These conditions can be 

relatively dynamic, with mass being exchanged between phases, and can be strongly influenced by a 

range of process operations including fuel type, combustion characteristics, emission controls, additives, 

etc. However, in the case of coal-fired power plants, these conditions are generally maintained 

relatively constant so as to assure optimum operational efficiency while consistently meeting required 

emission limits. These aerosol characteristics are important because they can affect PM CEMS sampling 

and transport efficiency as well as contribute to potential measurement artifacts.

The objective of both EPA M5 and PM CEMS is to measure the “true” stack PM concentration. In the 

case of M5, a traverse of the stack is conducted while an isokinetic sample of known flow (volume) is 

drawn through a nozzle, probe, filter and flow monitor. Although a substantial portion of the PM may 

be deposited in the probe, this deposit is recovered as part of the M5 procedure and added to the PM 

deposited on the filter. This total PM mass is divided by the volume sampled to calculate the stack gas 

PM concentration, which, by definition, is assumed to be the “true” stack concentration. Although the 

reference method is good, it is not a perfect measure of the “true” stack concentration. The M5 

reported results are only an estimate of the true stack gas concentration. The reference method (M5) 

will have to varying degrees both systematic and random uncertainties associated with each estimate of 

the true concentration due to such variables as local turbulence, deviations from isokinetic sampling 

conditions, errors in pitch and yaw, flow uncertainties, less than 100% PM recovery from the probe, acid 

filter artifacts, sample handling, contamination, etc. 

The PM CEMS also attempts to collect a representative sample of the stack gas by collecting an 

isokinetic sample, but from a single point in the stack that has been generally selected to represent the 

mean traverse concentration. The PM aerosol is transported through a probe and transport component 

before it is drawn through a filter tape where the PM is deposited and its mass measured with a beta 

attenuation sensor. Although some PM mass is deposited in the CEMS probe and transport 

components, it is not recovered, but is periodically purged from the sampling line with a “blow back” 

step. These PM CEMS limitations are well recognized by the EPA, which is one reason why the EPA 

insists on calibrating PM CEMS with in-stack M5 measurements as part of PS-11. Implicit with this 

calibration requirement is the assumption that aerosol features that might affect PM deposition in the 

CEMS probe are constant with time. There are, of course, other systematic bias and random errors that 

can contribute to the uncertainties and variability of both M5 and PM CEMS measurements, as well as 

process changes, that can vary the characteristics of the aerosol and subsequently the quantity of PM 

CEMS probe deposition.

What is clear from this simplified physical model and the QAG/PM CEMS results summarized in Section 

2.0 is that the PM CEMS beta attenuation sensor provides an accurate, precise, and reproducible 

measure of mass deposited on the CEMS’ filter tape. And, although the in-stack aerosol conditions 

might be significantly different from the off-line QAG aerosol, the BAM-based PM CEMS’ mass sensor 

does not detect this difference, because it does not come in direct contact with the aerosol, but only

quantifies the mass deposited on the filter tape. Consequentially, it is reasonable to assume 1) the 

operation of the beta attenuation sensor functions the same under QAG aerosol conditions as under 

stack gas sampling conditions and 2) differences in BAM-based PM CEMS comparisons between the QAG 
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off-line measurements and M5 in-stack measurements are due primarily to differences up-stream of the 

BAM-based PM mass sensor, assuming a representative sampling location in the stack is chosen. For the 

PM CEMS, these might include differences in probe and transport conditions such as nozzle diameter, 

yaw, pitch, and temperature; transport tubing diameter, length, polish, and bends; sampling flow rate;

filter tape sampling efficiency and chemistry. Stack conditions might include stack gas characteristics 

such as moisture content, PM size distribution, free acid content, presence or absence of droplets, and 

gas temperature. Plant conditions might include plant processes and controls, operating conditions, 

additives, scrubber solution chemistry, coal type, de-mister efficiency, etc.

Thus, the conceptualized physical model that forms the foundation of our attempts to understand the 

contributors to differences in off-line QAG calibrations of PM CEMS and in-stack M5 calibrations of these 

same PM CEMS is a simplified model composed of five major components: 

 PM CEMS beta attenuation sensor

 PM CEMS sampling and transport

 Stack gas aerosol properties

 QAG aerosol

 Reference Method 

These will be discussed in the following subsection as they impact the mathematical form and 

components of the linear calibration equation.

3.3			Mathematical	Model

It is clear from the data presented in Section 2.0 that the relationship between the QAG-predicted 

concentration, the BAM-based PM CEMS measured concentration, and the M5 measured concentration 

is linear as illustrated by the plots presented in Figure 3 and data in Table 3. The mathematical form of

the classical slope/intercept form is noted below in Equation 2:

 =  +  Equation 2

where Y is the QAG-predicted aerosol concentration or M5 measured concentration, m is the slope

(representing influence of variables proportional to X), X is the BAM-based PM CEMS measured 

concentration, and b is the intercept (representing the influence of constants independent of X). 

As noted in Section 2.0, most of the variability in the data is driven by the variability in the 

concentration and there is minimal variability associated with general data scatter as evidenced by the 

high observed coefficient of determination (R

2

= 0.99). In addition, the variability between the slopes is 

large (1 to 3.7) relative to the statistical uncertainty (about 0.05) in the slope measurement, while the 

variability in the intercept (1 to 3) is large relative to its statistical uncertainty (about 1). As such, the 

primary focus is on the slope and the variables that are likely responsible for differences between slope 

measurements (calibration factors) made with a QAG-generated aerosol, as compared to slope 

measurements made with in-stack M5 measurements of stack gas aerosols. The primary objective of 
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this section is to review those parameters that are likely to have significant impacts on the slope and 

intercept, and relate them to this linear model. 

As noted above, the true PM concentration (Ctrue) can be expressed as a function of the reported M5 

concentration (C5), plus or minus a systemic bias (C5

B

) and random (C5

R

) uncertainty or error terms, as 

noted below in Equation 3:

Equation 3

Similarly, the true PM concentration can be expressed as a function of the reported PM CEMS 

concentration (CC), its known and unknown bias and random uncertainties or errors, as noted below in 

Equation 4:

Equation 4

Where:



 is the concentration bias due to PM deposited in the probe and transport line



 is an acid artifact bias due to vapor phase acids reacting with a basic filter



 is a single point bias (Assumption: SPB minimized by selecting a CEMS sampling point close 

to traverse average concentration)



 is other unknown bias





is concentration error associated with random measurement variability

Combining Equations 3 and 4, and solving for the M5 reported concentration (C5), we obtain the 

following equation:

Equation 5

The terms on the right side of Equation 5 can be grouped into three categories as indicated in Equation 6

below:

If we assume that probe and transport losses are proportional to PM CEMS reported concentration 

(

 = ), where k equals a constant value, the bias terms ( ±  ± ±



) are assumed 

to be nearly constant, and the third category 

 ± 



 contains random variables for both the CEMS 

and M5; Equation 6 can be simplified to the following classic linear relationship:

 =  ± 

 ± 

 =  + 

 −  ± 

 ± 

 ±		



 =  + 

 −  ± 

 ± 

 ± 

 ± 

 ± 



 = [ + 

] + [− ±  ± ±



] ± [

 ± 



]		

B R
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Equation 7

The random component of variability (“R”) in Equation 7 is actually a function of both the concentration 

(Cc) and constant bias (B) terms. It is important to note that the probe and transport losses are 

proportional to concentration only to a degree, but, in fact, may also be a function of such variables as 

particle size distributions, dissolved and suspended solids in droplets downstream of wet scrubbers, and 

other factors affecting the nature of the stack gas aerosol. In a perfect world CC = Ctrue = C5 where m = 

1, k = 0 and B = 0. More realistically, k and B are not equal to zero and m = 1+k. One can thus conclude 

that the slope of the in-stack PM-CEMS response function can be estimated from a measurement of the 

probe and transport PM deposit relative to the CEMS-measured PM concentration (

/CC); that is 

m=1+

/CC.

3.4			Model	Evaluation

As noted above, a number of variables can influence the measured PM aerosol concentration. It is 

important to estimate the magnitude of the more significant potential variables. Examples of these 

variables and the magnitude of their potential range of impact are listed in Table 4. The CEMS and M5 

measured concentrations in EGU emissions are expected to range from about 1 to 100 mg/m3. BAMbased PM CEMS probe deposits are expected to have the largest influence on deviations from true 

concentration measurements and are expected to range from 10 to about 75% of the “true” 

concentration based on the measured slopes (Table 3) and the above discussed model. Acid artifacts 

(vapor phase acid reactions with glass fiber filters) represent an example of a variable that is likely to be 

relatively constant, independent of the PM concentration and have a magnitude less than about 5% of 

the PM concentration. Although this potential acid artifact could be a higher percentage for sources 

with lower PM concentrations, these lower PM concentrations are typically achieved with wet scrubbers 

which also remove most of the acids that are likely to react with BAM-based PM CEMS filter tapes. The 

single point bias is expected to be small, in part because large differences in PM are not expected in the 

stack at the point where measurements are made, but also because the point selected for the probe 

nozzle is selected to minimize this difference. As such, its magnitude is expected to be substantially less 

than about 5%. Other BAM-based PM CEMS biases are expected to be small because the QAG test bias 

was small for all three of the BAM-based PM CEMS. Method 5 bias is also expected to be small in part 

because it is a reference method. Random errors associated with the CEMS, M5 and QAG are expected 

to be small based on the small amount of data scattered and high coefficient of determination observed 

in the regression plots. Based on these estimates, it seems reasonable to assume/conclude that 

measured M5/BAM-based PM CEMS regression slopes greater than 1 are an indicator of the primary 

influence of probe deposits. Thus, in-stack BAM-based PM CEMS response functions might be 

estimated either by 1) measuring probe deposits directly or 2) by comparing simultaneous M5 and PM 

CEMS in-stack concentration measurements at typical or normal operating conditions, or 3) by 

eliminating or minimizing these losses.

 = (1 + ) +  ± 

m
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Table 4. Examples of potential variables influencing measured PM CEMS concentration and their potential range

Variable

CEMS 

Correction

Percent of PM 

Concentration

Expected 

Range 

(mg/m3) Notes

1

Method 5 C5 -

--- 1-100 Expected for Coal-Fired Utility Boilers

CEMS Cc -

2

CEMS, Probe 

Deposit Cc

PD (+) 10 - 75% 0.01→75

Fraction of Cclost in the probe during 

sampling transport

3

CEMS, Acid 

Artifact Bias Cc

AAB (-) <5% 0.1→5 Function of filter and acid concentration

4

CEMS, Single 

Point Bias Cc

SPB (±) <5% <0.01→5 Correction in PM concentration expected 

to be proportional, but small

5

CEMS, Other 

Unknown 

Bias

Cc

OUB

(±) <5% <0.01→5 Other unknown bias, should be small 

because QAG difference is small

6

Method 5, 

Bias C5

B

(±) <5% <0.01→5 Bias is expected to be small because M5 is 

a federal reference method

7

CEMS, 

Random 

error

Cc

R

(±) <5% <0.01→5

Random CEMS error, such as tape position 

and counting statistics, are expected to be 

small and independent of concentration

8

Method 5, 

Random 

error

C5

R

(±) <10% <0.01→10

Random uncertainties in flow, isokinetic 

sampling, PM recovery, mass 

determination, etc.

The influence of probe loss on the in-stack response function can be seen in the data from the 15 BAMbased PM CEMS listed in Table 3. The PM CEMS/M5 regression data has been re-ordered according to 

increasing slope in Table 5. Additional details regarding the CEMS transport characteristics, PS-11 

correlation technique, Plant/EGU emission controls have been added, as they might impact either the 

in-stack aerosol characteristics or CEMS probe and transport loss. The deposition and aerosol 

characteristics are ranked based on their potential to and/or likelihood of deposition in the probe and 

transport components prior to reaching the filter tape and BAM sensor. Generally speaking, an increase 

in deposition and aerosol characteristic ranking is assumed with an increase in slope. Although this is 

not a perfect correlation, it is supportive of the model discussed earlier in this section where the slope is 

dominated by the fraction of PM deposited in the probe and transport. (Note: this paragraph will be 

edited pending the addition of data in Table 5)
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Probe (d/L) Transport (d/L) Coal Type

b

Saturated? System(s)

c Additives Demister?

3 1.100 Varying unit load Bitu ESP, SCR, WFGD, WESP NH3 or CO(NH2)2,

lime

13 1.166 ___ / 45' Pulv No SCR, LNB, SOFA, SBS, WVS NH3, NaHSO3, lime14 1.190 ___ / 80' Pulv No SCR, LNB, SOFA, SBS, WVS lime, NH3, NaHSO3

5 1.263 Slight ESP detuning PRB ESP, SCR, WFGD NH3, lime9 1.355 ESP detuning, Paired trains Bitu ESP, SCR, WFGD NH3, lime6 1.462 ESP detuning Bitu ESP, SCR, WFGD NH3, lime

10 1.488 ESP detuning Bitu ESP, SCR, WFGD NH3, lime

7 1.529 Baghouse detuning Bitu BH, SCR, WFGD, WESP NH3, lime

15 1.625 ___ / 80' Pulv Yes ESP, SCR, LNB, SOFA, SBS, HWS NH3, lime, NaHSO3

4 1.805 ESP detuning Bitu & PRB ESP, SCR, WFGD NH3, lime

12 1.844 ESP detuning LSSB ESP -

1 2.053

B3 Injection of plant ash

upstream of ID fan

Waste SCR, BH NH3, Plant Ash

2 2.297

B3 Injection of plant ash

upstream of ID fan

Waste SCR, BH NH3, Plant Ash

8 2.665 ESP detuning, Paired trains Bitu ESP, SCR, WFGD NH3, lime

11 3.702 ESP detuning LSSB ESP w/ SO3 injection -

b. Bitu = bituminous, Pulv = pulverized, PRB = Powder River Basin Coal, LSSB = Low Sulfur, Sub-Bitominous.c. SCR = Selective Catalytic Reduction, ESP = Electrostatic Precipitator, WFGD = Wet Flue Gas Desulfurization, WESP = Wet Electrostatic Precipitator, WVS = Wet Venturi Scrubber, LNB = Low Nitrogen Oxide Burner, SOFA = SeparateOver-Fired Air, SBS = Sodium Bisulfate Injection System, HWS = Horizontal Weir Scrubber.Units are listed in estimated order of emission control process.d. Aerosol ranking would be a further subjective rating tool to illustrate the likelihood of deposition onto probe due to the type of aerosol, a ranking of ‘10’ being highly likely and a ranking of ‘1’ being very unlikely. Saturatedaerosols resulting from the use of wet scrubbers, for example, would be more likely to deposit due to the high presence of dissolved solid droplets and water and therefore would receive a high rating.Ex. = Example number, same as shown in Table 3.m = slope for PS-11 calibration results, shown in Table 3 of this reportCEMS = Continous Emissions Monitoring Systemd / L = diameter / Lengtha. Deposition ranking is a subjective rating tool to illustrate the likelihood of deposition onto probe and transport lines due to the characteristics of the CEMS, a ranking of ‘10’ being highly likely and a ranking of ‘1’ being unlikely.Examples of parameters which might determine the ranking are as follows: probe / transport diameter, probe / transport length, number of bends in the system, and the aerosol’s velocity.Table 5. Comparison of Linear Model Fit Paramenters (In Order from Smallest to Largest Slope) for Fifteen BAM-Based PM CEMS Correlated with M5 and PS-11 In-Stack Measurements

Ex. m

CEMS Transport Characteristics

Dep. Rank

a

PS-11 Correlation, Mid/High

PM Conc. Technique

Plant Charactertistics Plant Emission Control Aerosol

Rank

d
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4.0 		Proposed	Alternatives	to	PS-11	and	P-2

4.1 PS-11	and	P-2

PS-11 establishes procedures and performance requirements that must be met to establish initial 

certification of PM CEMS’ ability to demonstrate a facility is in compliance with emission limits. It is 

different from performance specifications for gaseous pollutants for several reasons. First, the type and 

characteristics of PM aerosols in stack gas can vary from source to source. Some PM CEMS, such as light 

scattering-based CEMS, are sensitive to such characteristics as particle size changes, water droplets, 

dissolved and suspended solids within droplets, stack gas velocity changes, etc. Secondly, PS-11 is 

different because EPA-approved PM standard calibration aerosols are not available to independently 

certify a PM CEMS’ in-stack response function. Thirdly, PM aerosols are different from other gaseous 

pollutants because a significant fraction of PM can be lost in the sampling and transport components of 

a PM CEMS. 

A key component of PS-11 is a calibration procedure based on simultaneous M5 and PM CEMS 

measurements over a range of concentrations. This initial correlation test (calibration) is performed to 

develop a relationship between PM CEMS’ response and the manual reference method (M5) results 

over a range of PM concentrations. A minimum of 15 valid runs must be conducted, each consisting of 

simultaneous PM CEMS and M5 measurements covering the full range of PM concentrations. It is 

important to note that this initial correlation test is not an independent check of accuracy, but an on-site 

calibration of the PM CEMS that is dependent on the aerosol characteristics at the time of the tests at 

the specific facility. It is this initial correlation test that is so onerous, due to the need to attain a range 

of in-stack PM concentrations that are only obtained by modifying normal plant operations. It is this 

characteristic of PS-11 that is proposed for modification in the proposed alternative method (PS-11B) 

outlined in Subsection 4.2.

Procedure 2 establishes minimum requirements for evaluating the effectiveness of PM CEMS quality 

control (QC) and quality assurance (QA) procedures, and assesses PM CEMS data quality by estimating 

data accuracy. One relevant tool in P-2 used to perform this function is a relative correlation audit 

(RCA). The RCA is similar to the initial correlation test in PS-11 except only 12 simultaneous M5 test runs 

are required in P-2, instead of the 15 test runs required in PS-11. Initial correlation tests are required at 

the time the PM CEMS is installed whereas RCAs are required to be performed periodically as defined by 

regulations over the lifetime of the CEMS. 

The following outlines of proposed alternative specification (PS-11B) and procedure (P-2B) focus on

modifications to the initial correlation tests and on-going RCA tests and assume most other aspects

remain as stated in the original specifications and procedures.

4.2 Proposed	Alternative:	Performance	Specification	11b	

As noted earlier, off-line QAG calibrations of BAM-based PM CEMS cannot be used alone to accurately 

and reliably predict in-stack response functions for PM CEMS. However, as established in Subsection 

3.3, the in-stack response function slope (m) could be determined if the ratio of CEMS probe deposit 
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(

) to CEMS reported concentration (Cc) could be determined; i.e. m = 1+ /CC. It is this realization 

that forms the basis of the following outline for a proposed alternative performance specification to PS11.

Outline for Proposed Alternative PS-11B

Alternative performance specification (PS-11B) might consist of the following five modified steps to the 

initial correlation tests:

1. Perform an off-line independent certification of the PM CEMS linear range using QAGgenerated reference aerosols at the site (e.g. CFPP) of PM CEMS installation.

Independently certifying the linear range of the BAM-based PM CEMS using a QAG-generated 

reference aerosol has the potential to establish the linear range well beyond the emission limit 

without requiring modification of plant processes and is independent of the original thin-film 

calibration of the BAM-based PM CEMS. The QAG reference aerosol concentrations will be 

collected from four (4) distinct ranges: <10%, 20-40%, 50-70%, and 80-120% of the PM CEMS’ 

span or PM emissions limit. At least four (4) QAG data points per concentration range, for a total 

of 16 data points, should be performed for initial certification.

2. Determine probe and transport losses

Determine the “k” constant (

/CC) for stack gas at typical operating conditions for the EGU 

and PM CEMS being tested. This ‘k” factor is relatively constant as a function of concentration,

as evidenced by the high degree of linearity of the BAM-based PM CEMS demonstrated both 

with the QAG and M5 correlations.

The “k” factor can be determined either directly by measuring the probe deposit or by multiple

simultaneous paired M5 and PM CEMS measurements (M5 = total, 

 = total - CC ). The total 

number of simultaneous tests will depend on the specific acceptance criteria determined for 

alternative PS-11B.

3. Calculate the in-stack response function.

Calculate the in-stack response function based on data collected in steps 1 and 2, where the 

slope (m) equals 1+k (m = 1+k)

4. Perform independent certification of the BAM-based PM CEMS in-stack response function 

using simultaneous M5 measurements.

Complete 3 runs of M5 by collecting PM data from a full stack traverse at normal, “as found”

operating conditions. The number of traverse points for each stack is determined based on 

stack diameter and shape (circular versus rectangular), as described in EPA Method 1. Using the 

response function determined in Step 3, calculate the average PM CEMS concentration and 

uncertainties during the time of M5 traverse measurements and compare the two results. 
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5. Compare intercept of M5 results to the QAG PM CEMS results.

If the two results are within one standard deviation of each other, make no adjustments to the 

calculated response function. If it is outside these limits, but within three standard deviations, 

adjust the intercept to match the M5 results. If these results differ by more than three standard 

deviations, repeat steps 1 - 4. Additional acceptance criteria will be defined as the alternatives 

are further developed.

The remainder of alternative PS-11b would be essentially the same as stated in the original PS-11.

4.3 Proposed	Alternative:	Procedure	2B

The key modification to P-2 in the proposed alternative Procedure 2B (P-2B) is the replacement of the 

current RCA tests with the same five steps noted in the PS-11B outline above, except there will be 12 

repetitions across the linear concentration range (Step 1). This on-going QA/QC certification test would 

be performed on the same schedule as the current P-2 test schedule.

4.4 Evaluation	of	Proposed	Alternative	Methods

Additional PS-11 datasets for MSI BetaGuard PM CEMS are available, as noted in Subsection 2.3, Table 2. 

Results from QAG testing of three MSI BetaGuard PM CEMS at the BMPS determined that there was 

very little difference in the off-line, QAG-based response functions; i.e. coefficients of determination for 

all three PM CEMS were greater than 0.985 and the slopes were within ±2% of each other. Because of 

the similarity in the QAG measurements of these three PM CEMS, which were manufactured at different 

times, it is reasonable to assume the factory calibrations for the other 13 PM CEMS, for which we have 

PS-11 data, would also be similar. As such, it is possible to apply PS-11B to these 13 sets of PS-11 data to 

demonstrate the application of the proposed model and alternative PS-11B. 

Table 6 summarizes key aspects of the PS-11 and PS-11B comparison. The entirety of the application of 

proposed alternative PS-11B to previously collected PS-11 data is provided in Appendix A of this report.

The PS-11 data was provided by MSI, the manufacturer of the BetaGuard PM CEMS. The PS-11B data 

was calculated using steps similar to the five steps outlined for PS-11B in Section 4.2. The steps applied 

to the MSI data are summarized below:

Step 1. Except for those PM CEMS where QAG data was collected (CEMS 1A, 2B, and 3A), the 

off-line CEMS response was assumed to equal the average slope of the three BMPS PM CEMS 

(1.06x – 1.43), as described in the first paragraph of this section.

Step 2. The probe/transport loss constant (k) was calculated by determining the probe deposit 

as the difference between individual, paired PM CEMS and M5 or M5B concentration 

measurements (normal operating range assumed). The “k” value was then calculated by dividing 

the probe deposit amount by the PM CEMS concentration. (Note: the slope of the in-stack PMCEMS response function is estimated from a measurement of the probe and transport PM 

deposit relative to the CEMS-measured PM concentration (

/CC); that is m=1+/CC.). The 
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individual k-values were then averaged to create the k-constant for use in the next step of this 

process.

Step 3. The in-stack slope (m = 1 +k) was calculated using the formula defined in Section 3.3 of 

this report. The intercept (b) was evaluated using three approaches: 1)b set equal to zero, 2) b 

set equal to the offline QAG intercept, and 3) b defined by the M5 measured concentration at 

normal operating conditions. The in-stack concentration using the calculated slope with b=0 and 

b=M5 normalized were similar and best matched the PS-11 value. The percent errors in the 

predicted concentration at the emission limit in Table 6 were calculated using the zero intercept 

option.

Step 4. The independent certification of BAM-based PM CEMS in-stack response function was

simulated using the true concentration calculated in Step 3. The standard deviation for each 

concentration was estimated, and the difference between the M5 (PS-11) concentration and PS11B concentration was determined.

Step 5. The results from Step 4 are compared to the PM emission limit for each CFPP to see if EPA criteria 

are met.

Table 6. Comparison of PS-11 & PS-11B Calibration Correlations For 14 BAM-Based PM CEMS a

Example

Slopes % Error of PS11b Slope from 

PS-11

PM Emission 

Limit at CFPP

% Error in the 

Predicted Conc. 

at the Emission 

Limit PS-11 PS-11b

1 2.053 ± 0.095 2.130 ± 0.352 3.7 9.9 0.8

2 2.297 ± 0.083 2.575 ± 0.190 12.1 9.9 -2.3

4 1.805 ± 0.042 1.903 ± 0.203 5.4 53.2* -3.2

5 1.263 ± 0.107 1.019 ± 0.204 19.3 6.9* 12.4

6 1.462 ± 0.045 1.333 ± 0.104 8.8 55.5* 4.8

7 1.529 ± 0.058 1.433 ± 0.117 6.3 11.8 4.8

8 2.665 ± 0.134 2.392 ± 0.334 10.2 29.0 8.2

9 1.355 ± 0.088 1.163 ± 0.134 14.2 27.0 14.7

10 1.488 ± 0.054 1.430 ± 0.224 3.9 37.8* 3.3

11 3.702 ± 0.125 3.698 ± 0.455 0.1 182.6* -0.3

12 1.845 ± 0.114 1.896 ± 0.197 2.8 43.1* -2.6

13 (1A)b1.169 ± 0.025 1.180 ± 0.060 0.9 97.9 -0.6

14 (2B)b1.194 ± 0.043 1.078 ± 0.079 9.7 94.4 8.6

15 (3A)b1.626 ± 0.054 1.321 ± 0.149 18.7 93.2 21.7

Avg. 7.9 3.7

StDev. 5.7 6.0

% RSD 72 164

a Example 3, which was presented in Tables 3 and 5, was omitted from this table because of excessive uncertainty in the slope (40%) 

and results were more than 5 SD from the mean. 

b

These examples are BetaGuard PM CEMS from the BMPS with individual off-line QAG measurements.

* Emission limit was estimated as 125% or highest PS-11 reading.
Draft EPRI Communication: PM-QAG Model Report January 10, 2013

Cooper Environmental Services, LLC Page 26 of 28

Table 6 compares the correlation slopes obtained with PS-11 to those obtained with PS-11B, as well as

the slopes’ uncertainties and percent uncertainties at each plant’s emission limits. The average percent 

error or difference between PS-11B- and PS-11-determined slopes was 8.3%, with a range of 0.1% to 

19.3%, and for all but one case, CEMS 3A, falls within the sum of one standard deviation of each 

parameter. The average percent error in the predicted concentration at the emission limit is only +5% 

and ranged from a low of -3.2% to a high of +21.7%, which places all of the slopes within the EPA’s P2 ±

25% limit. Although this data is far from definitive, it is encouraging and strongly supportive that PS-11B 

is a viable candidate for an alternative to PS-11.

The QAG-based PS-11B has the advantage that it would not require extrapolation to the emission limit 

and it would eliminate the need to modify normal plant operations and aerosol characteristics as is the 

case during performance of PS-11 and P-2. In addition, PS-11B is likely to have less pollution associated 

with its implementation and is expected to be easier and lower cost to implement.

4.5 Summary	of	Benefits	of	Proposed	Alternatives

In order to gain acceptance by the EPA, a newly proposed method should be:

 More stringent than the currently used method,

 Provide a stronger data set with higher statistical strength,

 Improve the “ease of use” for the plant to demonstrate compliance, and/or 

 Provide data of equal strength, but with improved and lower costs. 

We propose that the suggested alternative methods for PS-11 and P-2 using an off-line QAG calibration 

have strong potential to meet several, if not all, of these criteria. Firstly, the off-line QAG data is capable 

of challenging the PM CEMS over a much broader linear range than is possible under the current PS-11. 

In conjunction with the improved concentration range, the QAG method increases ease of use by not 

requiring plant modifications in order to meet linear range requirements. Due to the precise, accurate, 

and quantifiable aerosols produced by the QAG, as demonstrated in the field, the QAG method is 

anticipated to provide linear results that meet much more stringent acceptance limits than the +/- 25% 

that is currently accepted by the EPA in P-2. It is unclear at this time if the newly proposed method will 

provide cost savings; however, it would eliminate the need to modify or shut down normal operations 

during performance of PS-11 and P-2, which would translate to cost savings for the EGU.

Although our current knowledge of BAM-based PM CEMS functioning in EGUs suggests that an off-line 

certification with a QAG cannot be accomplished without knowledge of the probe and transport loss 

constant, the collection of appropriate data and calculation of the correction factor is not anticipated to 

be cumbersome. In addition, the calibration of the CEMS can be certified by minimal Method 5 runs, 

instead of the 12 or more runs required by the current procedures. 
Draft EPRI Communication: PM-QAG Model Report January 10, 2013

Cooper Environmental Services, LLC Page 27 of 28

5.0 		Conclusions and	Recommendations

A model has been developed that describes a linear relationship between BAM-based PM CEMS 

measured concentrations and 1) QAG-generated reference aerosol concentrations, and 2) M5 measured 

stack gas aerosol concentrations. Key components for this proposed linear model include 1) a slope 

whose deviation from 1.0 is due primarily to probe deposits and 2) in-stack BAM-based PM CEMS 

response functions that can be determined using off-line QAG-generated reference aerosols and instack measurements at typical stack conditions.

This report outlines alternatives to PS-11 and P-2 that do not require altering plant processes. These 

alternatives meet most, if not all, of the criteria for approval of alternative methods by the EPA.

Based on the encouraging results from QAG testing of the three BAM-based PM CEMS installed at BMPS

and the successful application of the proposed model to fourteen BAM-based M CEMS’ data sets, it is 

recommended that an alternative performance specification (PS-11B) and on-going performance 

procedure (P-2B) be further developed and formalized. Once formalized, it is recommended that these

alternatives be submitted to the EPA to solicit their comments and contributions to the development of 

a field demonstration plan. It is further recommended that the field demonstration tests be conducted 

to confirm the theories presented in this report. Finally, a report supporting a request for the EPA to 

approve the alternative PS-11B and P-2B should be submitted after further field demonstration tests are 

conducted.

6.0 References

Cooper Environmental Services, LLC, 2012. Draft Technical Update: QAG Modifications and Field Testing 

with BAM-based CEMS. Prepared for Electric Power Research Institute, Project EP-P44336/C19235. 

December 2012.

Environmental Protection Agency. Method 5 - Determination of Particulate Matter.

Environmental Protection Agency. Method 5B - Determination of Nonsulfuric Acid Particulate.

Environmental Protection Agency. Performance Specification 11—Specifications and Test Procedures for 

Particulate Matter Continuous Emission. 

Environmental Protection Agency. Procedure 2—Quality Assurance Requirements for Particulate Matter 

Continuous Emission Monitoring Systems. Web.

Environmental Protection Agency, 2006. Data Quality Assessment: Statistical Methods for Practitioners. 

EPA/240/B06/003. February 2006.

Environmental Protection Agency, Office of Air Quality, 2000. Current Knowledge of Particulate Matter 

Emission Monitoring. EPA-454/R-00-039. September 2000.
Draft EPRI Communication: PM-QAG Model Report January 10, 2013

Cooper Environmental Services, LLC Page 28 of 28

Hatch , Barbara. "Commonwealth of Pennsylvania Department of Environmental Protection." Review of 

Operating Permit Renewal Application First Energy Generation, LLC Bruce Mansfield Power Station 

Shippingport, Butler County. 24 May 2012. Print.

Jahnke, James A, 2000. Continuous Emission Monitoring, Second Edition. John Wiley & Sons, Inc. New 

York, NY.

MSI BetaGuard Data: Provided through email correspondence with Craig Clapsaddle, including examples 

of 13 BetaGuard PM CEMS PS-11 data and 2010 to 2012 P-2 data (RCA and RRA). Email correspondence, 

11/8/2012 and 11/29/2012