1

Source Evaluation Society 

March 28, 2013

Cooper Environmental Services LLC

Cooper Environmental Services LLC

John A. Cooper

Portland, Oregon
Cooper Environmental Services 2

Outline

 HAP Metal Emissions

 Uncertainties in Metal Emissions

– Actual emissions

– Measured emissions

 Session 9: Uncertainty Details

Applicable Sources:

Incinerators Coal-fired Boilers Smelters

Mills Mineral Processing Kilns

Plating Portland Cement Welding
Key HAP Metals are Different

 They typically occur at trace levels in feed, fuel, 

additives and emissions

 Concentrations and potency are highly variable 

 May partition between vapor and particulate 

phases to varying degrees depending on source 

chemistry

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EPA-Designated HAP Metals

Element Cancer Toxic PM2.5* NAAQS

Transition 

Metal**

Antimony √

Arsenic √ √ √

Beryllium √ √

Cadmium √ √

Chromium √ √ √ √

Cobalt √ √

Lead √ √ √

Manganese √ √

Mercury √

Nickel √ √ √ √

Selenium √ √

Other Non-HAP Metals of Concern

Thallium √ √

Vanadium √ √ √

Iron √ √ √

Copper √ √ √

Molybdenum √ √

Zinc √

*Identified as a significant contributor to observed PM health effects.

**Generate reactive oxygen species, which cause damage to cell structures.
Data Quality Objectives of 

Emissions Monitoring for Metals

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Congress

EPA

§112 of 

CAAA (1990)

Protect Public Health 

with Adequate Margin of Safety MACT

Residual Risk

PM Health Effects

Metals are a significant contributor

to PM Health Effects
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EPA Has Said:

 Monitoring is part of the emissions reduction and 

control process

Uncertainty minimization is best achieved with 

more measurements of emissions

 Reducing uncertainty in compliance 

measurements produces quantifiable benefits

Knowing sources of uncertainty and their 

general magnitude is important to 

reducing those uncertainties.
Examples of Uncertainties

 Typically Small (<10%)

CEMS single point sampling

FRM sample recovery

Deviations from Isokinetic sampling 

Analytical sensor/procedure

 Potentially Large (~100+%)

Feed/fuel composition variability

Process/aerosol variability

Probe loss variability

Surrogacy assumptions

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Actual Metal Emissions Variability 

and Measurement Uncertainty

Process

Feed

Fuel

Emissions Controls

Additives

Stack

Emissions

Metals 

Emissions

Measurements

Differences between 

measured (PM or Metals) and 

actual metals emissions
Cooper Environmental Services 9

Example: HWI Compliance 

Demonstration

Initial Tests – Performance or “Trial Burn”

– Determine transmission efficiency (T)

Metal spiking (Fs) 

Method 29 measurements (C=M/U)

– Define plant operating parameters

Continued compliance demonstration

– Maintain plant operating parameters

– Monitor Feed Rate (FCi)

Periodic “Stack Tests” - years between tests
Cooper Environmental Services 10

HWI Stack Emissions Model

Emissions Feed Rate

Transmission 

Efficiency

Assumptions:

-Transmission efficiency is constant and independent

-Metal feed rate is accurately known

E  TF  CVA

Stack 

Concentration

Velocity

Area
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Assumed/Calculated

Concentration of ith Metal

C F T V A

o o

i

o

i

o

i



Metal Feed Rate i

th Metal Transmission 

Efficiency (Performance tests)

Current Operating Conditions (°)

Stack Velocity & Area
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Calculated Concentration Based on 

Feed and Performance Test Data 

Ci

o



F

oCif

Fs

tCis













V

t

V

o













Mi

t

U

t











S

g

S

f Re

Measured

Assumed = 1.0

Gas Sampling Bias

Feed Sampling Bias

Efficiency Representation Bias

Method 29
Cooper Environmental Services 13

Uncertainty (σ) in Current Calculated Gas 

Concentration for ith Element

2

1

2 2 2 2 2

2 2 2 2 2

C

2

 

σ σ σ σ

C

σ σ























































































































































































































e

R

f

S

g

S

t

U

t

i

M

o

V

t

V

s

i

C

t

s

F

f

o i

o F

C i

R

σ

S

σ

S

σ

U

σ

M

σ

F F C V V

C

t g f e t

i

s

t o

i

t

s

f

o i

o

i



1 to 10% ~100+%

1 to 10%
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HWI Uncertainties

 Measured Parameters (<10%, small)

 Bias – Sampling Stack Gas (<10%, small)

 Bias – Sampling feed, fuel, etc. (~100+%, large)

 Bias - Representation of process, controls, etc. 

during trial burn testing (~100+%, large)

 Deviations from Assumptions (~100+%, large)

– PM surrogate for metals emissions

Small

Large

~99% of Uncertainty in Actual Metals 

Emissions is Due to These Large Sources
Estimating 

Magnitudes of 

Variability and 

Metals Emissions 

Uncertainties

-EXAMPLESCooper Environmental Services 15
Cooper Environmental Services LLC 16

N

W E

S

E. St. 

Louis 

Monitor

3 AM 

5 AM 

(0.65)

7 AM 

(0.58)

9 AM 

(0.76)

11 AM 

(2,345)

1 PM 

(173)

3 PM 

(11)

5 PM 

(2.4)

An Example

Wind Direction 

(Concentration, ng/m3)

Arsenic
Cooper Environmental Services LLC 17

E. St. Louis Monitoring Site

223°

Incinerator

Time WD WS As ng/m3

9 118 0.9 0.76

11 223 2.4 2,345.00

13 275 3.7 173.00

Schools

Monitor

WIND DIRECTION 

DURING MAXIMUM 

ARSENIC IMPACT

If you can see or 

measure it, you 

can fix it.
Cooper Environmental Services 18

0

50

100

150

200

250

300

350

0

5

10

15

20

25

30

35

40

45

1:0

0

2:0

0

3:0

0

4:0

0

5:0

0

6:0

0

7:0

0

8:0

0

9:0

0

1

0:0

0

1

1:0

0

1

2:0

0

1

3:0

0

1

4:0

0

1

5:0

0

1

6:0

0

1

7:0

0

1

8:0

0

1

9:0

0

2

0:0

0

2

1:0

0

2

2:0

0

2

3:0

0

0:0

0

L

e

a

d

C

o

n

c (μ

g

/

m

3)

Time of Day

Win

d

Dir

e

c

tio

n

Hourly Lead (11/23/09)

Two thirds of

total lead

24-hour average = 4.6 μg/m3

Wind 

Direction
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Pb Mass

No. Samples

16% of samples represent 

87% of Pb Mass

One 1-hour sample 

represents >11% of 

Pb mass
Probe Losses And BAM-Based PM 

CEMS Correlations

20

y = 1.07x - 2.96

R2 = 0.985

0.0

20.0

40.0

60.0

80.0

0.00 20.00 40.00 60.00 80.00

Q

A

G

C

o

n

c (m

g/wsc

m)

Beta # 1 Conc (mg/wscm)

QAG Correlation

y = 1.05x - 1.35

R2 = 0.994

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

0.00 20.00 40.00 60.00 80.00

Q

A

G

C

o

n

c (m

g/wsc

m)

Beta # 2 Conc (mg/wscm)

QAG Correlation

y = 1.06x - 0.364

R2 = 0.993

0.0

20.0

40.0

60.0

80.0

0.00 20.00 40.00 60.00 80.00

Q

A

G

C

o

n

c (m

g/wsc

m)

Beta # 3 Conc (mg/wscm)

QAG Correlation Slope

CEMS QAG PS-11 R2

1 1.07 1.17 >0.98

2 1.05 1.19 >0.98

1 2

3

3 1.06 1.63 >0.98
QAG Calibration of BAM PM CEMS

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C5 = (1+K)Cc+B K = Cp/Cc

Proportional Bias Factor Constant Bias Term Probe Loss Conc.

M5 Measured Conc. CEMS Measured Conc.

PS-11 CALIBRATION
Light Scattering PM CEMS on HWI

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Slope = - 0.27

Method 5 vs Sigrist CEMS

QAG vs Sigrist CEMS
Comparison of Light Scattering PM CEMS 

(PS-11 Calib.) with Series of M5B 

Measurements (Wet Stack)

CEMS Slope Intercept R2

1 +0.19 +2.7 0.74

2 +0.24 +1.8 0.80

M5B 0.36 +6.2 0.38

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% M5B Probe Deposit = 95%
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PM May Be Dominated by Sources 

Independent of HAP Metal Sources

Process

Feed

Fuel

Controls

Additives

Stack

Emissions

FGD

Scrubber Slurry

PM can be dominated 

by slurry blow-by
Coal-Fired Boiler – Wet Stack

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1

0

0
Hypothetical Variability of PM and HAP Metal 

Emissions from CFPP

Time (months)

P

M

o

r

M

e

t

al C

o

n

c. (

μ

g

/

m

3

)

105

104

103

102

101

100

10-1

10-2

10-3 Coal Blend 

Changed

Coal from 

different seam

Additive

PM

Se

Co, 

Cr
95% CONFIDENCE INERVALS FOR METALS

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10-1 100 101 102 103 104 105

Confidence Interval (μg/m3)

HWI PM

HWI Metals

CFB PM

CFB Metals

Data Quality Objectives: Process Optimization, Compliance or Health?

PM2.5
Estimated Metals Uncertainties for Compliance 

Demonstration Options

Options

Small 

Sources

Probe 

Deposit*

Process 

Variability

Metals 

Surrogacy

Total 

Uncertainty

MM CEMS < 5% < 20% < 10% ------------- ~30%

PM CPMS

(with M29)

<5% <20% > 100% ~300% > 400%

Periodic M29 15% ------------

-

> 300% ------------- > 300%

PM CEMS

--BAM < 5% < 20% < 10% ~1000% > 1000%

--L. Scat < 5% < 20% ~ 50% ~1000% > 1000%

PM CPMS

(with M5)

<5% <20% >50% ~1000% > 1000%

Periodic M5 15% ------------

-

>300% ~1000% > 1000%

* Due to Probe deposit variability.
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Conclusions and Recommendations

 Focus on obtaining a more representative sample by 

developing a probe with minimal PM loss – <5% in either 

wet or dry stacks

 Uncertainty minimization is best achieved with more, 

direct measurements – Multi-Metals CEMS

 If the first two can be achieved, concern for process 

variability is minimized

Focus on largest sources of uncertainty

• Process variability

• Probe loss variability

• Surrogacy assumption
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Clear Improvements

A century of improvements – Pittsburg, PA, USA

1925

1957

1998

From AWMA files

If you can 

see or 

measure it, 

you can fix 

it.
Cooper Environmental Services 31

QUESTIONS

?