1

PM2.5 refers to particulate matter with an aerodynamic diameter equal to or less than

2.5 micrometers.

Date: December 8, 2003

To: Barrett Parker, U.S. Environmental Protection Agency

From: Leigh Barr and Karen Schaffner, RTI International

Subject: Impact of Improved Monitoring on PM2.5 Emissions

I. Introduction

This memorandum discusses the impacts associated with conducting improved

monitoring for PM2.5 emissions at stationary sources in six metropolitan statistical areas (MSA)

in Indiana.1

 Improved monitoring may include more frequent monitoring, an improved

monitoring technique, or a combination of the two. The impacts include emissions reductions of

PM2.5 and the cost impacts related to the improved monitoring. These impacts are based on four

scenarios to reduce PM2.5 emissions. Scenario 1 considers the impact of increasing the

monitoring frequency of electrostatic precipitators (ESPs), scrubbers, and fabric filters from once

per day to four times per hour, with no change in monitoring technique. This scenario is

discussed in Section IV. Scenario 2 considers the impacts of improving or upgrading the

monitoring technique (and the corollary impact of increasing the monitoring frequency). This

second scenario consists of the following two examples: (2a) upgrading the monitoring

equipment to a particulate matter continuous emissions monitoring system (PM CEMS) for

emissions units/processes that are currently utilizing ESPs, scrubbers, and fabric filters, and (2b)

upgrading the monitoring equipment to a bag leak detector (BLD) system for all fabric filters. 

Scenario 2 is discussed in Sections V and VI. A comparison of Scenarios 1 and 2 is provided in

Section VII.

The third scenario, discussed in Section VIII, addresses the impact of adding air pollution

control devices (APCDs) to two uncontrolled emissions units/processes at stationary sources

located within the MSAs. One of the selected uncontrolled emissions units/processes emits large

quantities of PM2.5, and the other uncontrolled unit is a smaller unit that operates and emits PM2.5

only for part of the year. Each unit is capable of being controlled with an APCD, such as a

fabric filter. The emissions reductions and costs are calculated for controlling PM2.5 emissions

with a fabric filter.
2

2

Primary PM2.5 emissions include both filterable and condensible PM.

In the fourth scenario, we determined the impacts of applying improved monitoring

specifically to visible emissions (VE) techniques. The frequency of conducting VE observations

was increased in one example, and the monitoring technique was upgraded to a BLD system in

another example. These two examples are based on model units rather than specific units or a

subset of emissions units from the six MSAs. The emissions reductions and costs for these

examples are discussed in Section IX.

II. Background on Improved Monitoring

An improved monitoring control measure requires an increased monitoring frequency or

an improved monitoring technique other than that currently required in existing rules. 

Incumbent with a requirement for increased monitoring frequency is the assumption that faster

detection of a problem leads to faster correction and, in result, a decrease in excess emissions to

the atmosphere. Increased frequency of monitoring helps source owners and operators maintain

the effectiveness of emissions controls by identifying excursions early and repairing or adjusting

the control device immediately. The level of excess emissions from a source is directly related

to the length of time that an emissions unit experiences excess emissions. The excess emissions

rate, referred to in this memorandum as Pee, is the percentage of operating time that a unit

operates in excess emissions mode. The excess emissions rate can decrease to a value closer to

zero with more frequent monitoring. The increased monitoring frequency reduces the amount of

time the emissions unit operates in the excess emissions mode thereby reducing the units, actual

emissions to the atmosphere.

An improved monitoring technique provides more certainty that the presence of a

problem will be detected, and subsequently corrected. With a previous technique, the presence

of a problem may go unnoticed, allowing an emissions unit/process to operate in excess

emissions mode for long periods of time. With the improved technique, these modes are

detected and corrections/repairs are made to the process or APCD.

III. Basis for PM2.5 Emissions Reductions

A. 1999 National Emissions Inventory Database

In order to determine the emissions reductions associated with improved monitoring,

emissions data from the 1999 National Emissions Inventory (NEI) database version 3 (v3) were

used. We selected six MSAs in Indiana, including Evansville, Fort Wayne, Gary, Indianapolis,

Lafayette, and Terre Haute. In these six MSAs, the total PM2.5 primary emissions from

stationary sources were reported to be 36,000 tons/yr.2 The controlled emissions units/processes

in the six MSAs emitted approximately 16,000 tons/yr of PM2.5 (primary), and the uncontrolled

emissions units/processes emitted 20,000 tons/yr.

All controlled emissions units/processes that utilize ESPs, scrubbers, and fabric filters to

control PM2.5 emissions at stationary sources are included in a data subset to determine the
3

3

 “Emissions Reductions of PM2.5 due to Improved Monitoring,” notes from B. Parker,

U.S. Environmental Protection Agency. July 2003.

impacts. This data subset includes 689 emissions units/processes at 128 facilities, with PM2.5

emissions totaling 6,400 tons/yr (filterable and some condensible). The data subset used in this

analysis is included in Appendix A.

In addition to the controlled emissions units/processes, we also selected two uncontrolled

emissions units/processes from the six MSAs. An add-on APCD was applied to each of these

emissions units, and the impacts were determined.

B. Emissions Reduction Equations

The impacts for controlled stationary sources have been determined for each scenario: 

increased monitoring frequency and improved monitoring technique. One method used to

calculate the emissions reductions is discussed in this section, and the equations used to calculate

the emissions reductions are shown below.3

In this calculation method, the initial PM2.5 emissions rate is represented by Equation 1. 

The first step in calculating emissions reductions with this method is to solve Equation 1 for E

(average PM2.5 emissions rate without controls) for each emissions unit/process controlled by an

ESP, scrubber, and fabric filter.

PM T P E T P n E 2 5.( ) initial ee ee = ( * * ) ( *( )*( )* ) + 1 1 − − (Eq. 1)

where

PM2.5(initial) = initial annual emissions rate of PM2.5, tons/yr 

T = initial operating hours of the emissions unit, hours/yr

Pee = initial percentage of excess emissions per year, fraction

E = average, without controls emissions rate of PM2.5, tons/hr

n = initial control efficiency of the add-on control device, fraction

The initial values of PM2.5(initial) (annual emissions rate), T (operating hours) and n (control

efficiency) are obtained from the 1999 NEI v3 database. The initial excess emissions rate, Pee, is

assumed to be 5 percent. Although some facilities may experience excess emissions greater than

5 percent, it is assumed that sources are limiting their excess emissions rate to avoid triggering

additional reporting requirements for national rules, such as the New Source Performance

Standards (NSPS). An excess emissions period represents a noncompliance period, where

emissions are most likely above the allowable emissions rate.

After calculating E, the values of Eu (annual uncontrolled emissions rate) and Ec (annual

controlled emissions rate) are calculated using Equations 2 and 3, respectively.

E T u ee = * * P E (Eq. 2)
4

where

Eu = annual uncontrolled emissions rate of PM2.5, tons/yr

The annual uncontrolled emissions rate, Eu, represents the portion of the emissions to the

atmosphere that occurs due to excess emissions periods. The Eu is based on the assumption that

the control efficiency is zero during excess emissions periods.

E T P nE c ee = *( )*( )* 1 1 − − (Eq. 3)

where

Ec = annual controlled emissions rate of PM2.5, tons/yr

The annual controlled emissions rate, Ec, represents the portion of the emissions to the

atmosphere that results from APCDs with control efficiencies less than 100 percent.

The revised PM2.5 emissions following implementation of improved monitoring are

determined by using Equation 4. In Equation 4, the ratio of revised to initial variables is used to

determine the emissions after the improved monitoring has been applied. Before calculating the

revised PM2.5 emissions, one must determine the values of the initial CT, CF, and the revised Pee,

T, CT variables. 

PM EP T C

P TC C

EP nT

P n TC

REV

u ee REV REV T REV

ee T F

c ee REV REV REV

ee F

2 5

1 1

1 1

.( )

* ** () () () () () ()

** *

*( )*( )*

( )*( )* *

= + − −

− − (Eq. 4)

where

PM2.5(REV) = revised annual emissions rate of PM2.5, tons/yr

Pee(REV) = revised percentage of excess emissions, fraction

T(REV) = revised operating hours of the emissions unit, hours/yr

CT(REV) = revised correction time (based on monitoring frequency of the APCD)

CT = correction time (based on monitoring frequency of the APCD)

n(REV) = revised control efficiency of the APCD, fraction

CF = certainty factor (dependent on type of improved monitoring performed)

The emissions reductions are determined in this study for a range of revised excess

emissions rates, Pee(REV), including 5 percent (nominal excess emissions rate), 2.5 percent (half of

the nominal rate), 0.46 percent (based on one week per year of excess emissions) and

zero percent (no excess emissions). As the excess emissions rate decreases in each of these

cases, the actual emissions to the atmosphere decrease as well.

In each of the four scenarios, no changes to the operating time for the emissions units are

included in the emissions reduction calculations. Therefore, the revised operating time, T(REV), is

the same as the initial operating time. There are also no changes to the control efficiencies, so

the revised control efficiency, n(rev), remains the same as the initial control efficiency.
5

The correction time, CT, accounts for the amount of time required to detect a problem

with the APCD, the amount of time required to determine what, if any, corrective action is

needed, and the amount of time required to complete the repair. Detection time, the first factor

in the correction time, is dependent on the monitoring frequency. For example, with a

monitoring frequency of once per hour, a facility knows within 2 hours that the APCD or process

needs adjustment or repair (where two hours represents the time period between the first out-ofrange reading to the third out-of-range reading). This determination is based on the assumption

that it requires three consecutive readings outside of the allowable range to detect and verify that

a trend, rather than an isolated event, exists and poses a potential problem. The corrective action

time, the second factor in the correction time, is the time spent by the source owner/operator

determining what, if any, corrective action is needed and making adjustments to the process or

APCD to bring the operation within normal ranges. This factor does not fluctuate with

monitoring frequency and depends only on the type of APCD used. The repair time, the third

factor in determining the correction time, is also dependent on the type of APCD used to control

emissions from the unit and does not change with monitoring frequency.

To be consistent with Compliance Assurance Monitoring (CAM) rule requirements, we

assumed that facilities are currently required to monitor their APCD at least once per day. While

not all controlled facilities in the six MSAs are subject to the CAM rule, this assumption is

conservative and ensures that emissions reductions achieved by the CAM rule are not doublecounted in this study. The correction times for monitoring at a frequency of once per day are

shown in Table 1, including the detection time, the corrective action time and the repair time, for

fabric filters, ESPs, and scrubbers.

The correction times, CTs, are also determined for other monitoring frequencies. Under

another increased frequency scenario, a facility is required to increase its monitoring frequency

to four times per hour, or every 15 minutes. With this monitoring frequency and the assumption

that it requires three consecutive readings to detect a problem, a facility would know within

30 minutes (the time period between the first out-of-range reading to the third out-of-range

reading) that the APCD or process needs adjustment or repair. The corrective action time and

the repair time remain the same, regardless of the monitoring frequency. The correction times

for monitoring four times per hour are shown in Table 2.

The correction times, CTs, are also estimated for monitoring every 7.5 minutes (for

PM CEMS) and for monitoring once per minute (for BLDs). These correction times are shown

in Table 3 (PM CEMS) and Table 4 (BLDs).

A certainty factor, CF, is included in Equation 4 to account for the confidence in the

monitoring technique. The certainty factor is a reflection of how certain one is that the APCD

and process are operating normally or, when an exceedance occurs, the monitoring equipment

correctly detects it as such. The certainty factor is also a measure of how certain one is of the

emissions levels and that the emissions are below the allowable limit. When a facility uses a

CEMS to directly measure the pollutant of concern, there is more certainty with respect to the

emissions level and whether the unit is meeting the allowable emissions limit. The certainty

factor for CEMS, including PM CEMS, is assigned a value of 1.0. Parametric monitoring (e.g.,
6

pressure drop for a scrubber), on the other hand, provides an indication of the condition or

operating level of the APCD and process and gives an indirect indication of the emissions level

of the pollutant of concern. With this type of monitoring, the quantification of the emissions

levels is expected to be less certain. In addition, there is less certainty that an exceedance would

be correctly detected. The certainty factor for parametric monitoring (including opacity

monitors used to monitor PM) is assigned a value of 0.9. For a BLD system, the unit directly

measures the pollutant of concern, which provides an indication of the emissions levels. The

BLD is assigned a certainty factor of 0.95. The certainty factors are summarized in Table 5.

Following calculation of the revised PM2.5 emissions, the emissions reduction from the

initial PM2.5 emissions level (PM2.5 emissions reported in the 1999 NEI v3) to the revised level

(Equation 4) is calculated with Equation 5.

Emission reduction PM PM = 25 25 .( ) .( ) initial REV − (Eq. 5)

where

Emissions reduction = annual reduction in PM2.5 emissions, tons/yr

C. Alternative Emissions Reduction Equations

The equations presented above in Section III.B assume that the emissions provided in the

1999 NEI v3 database rely on some periods of excess emissions. An alternative calculation

method assumes that the NEI data do not include emissions during excess emissions periods. In

this alternative calculation method, emissions due to excess emissions periods are added to the

NEI data to better account for these periods. The alternative equations for calculating the

emissions reductions due to increased monitoring frequency and improved monitoring technique

for stationary sources are shown below. The alternative equations are used to calculate

emissions reductions for each emissions unit/process controlled by an ESP, scrubber, and fabric

filter.

The PM2.5 total initial emissions rate is represented by Equation 1a.

PM E E 2 5.( ) total u c = + (Eq. 1a)

where

PM2.5(total) = initial calculated total annual emissions rate of PM2.5, tons/yr

Eu = annual uncontrolled emissions rate of PM2.5, tons/yr

Ec = annual controlled emissions rate of PM2.5 after controls, tons/yr

The annual controlled emissions, Ec, is obtained from the 1999 NEI v3 database. The annual

controlled emissions, Ec, represents the portion of the emissions to the atmosphere that results

from APCDs with control efficiencies less than 100 percent. In this alternative calculation, the

total amount of emissions from the NEI v3 database are assumed to be 100 percent compliant,

without any excess emissions periods.
7

The annual uncontrolled emissions, Eu, represents the portion of emissions to the

atmosphere that occur due to excess emissions periods. The initial excess emissions rate, Pee, is

assumed to be 5 percent. In this alternative method, Eu is comprised of excess emissions, which

occur during the 5 percent excess emissions period. During the excess emissions period, we

assume that the APCD’s control efficiency is achieving only 80 percent of its control efficiency

value. For example, an APCD with a 90 percent control efficiency achieves approximately 72

percent control during the excess emissions period due to fluctuations in control device

performance. For our calculation purposes, the control efficiency during the excess emissions

period is determined by multiplying the control efficiency, n, of the APCD by 0.80. The Eu is

calculated using Equation 2a.

E P PM n u ee wo = * *( . * ) 2 5.( ) 1 08 − (Eq. 2a)

where

Pee = initial percentage of excess emissions per year, fraction

PM2.5(wo) = emissions not considering the use of the control device, i.e., without controls,

tons/yr

n = initial control efficiency of the APCD, fraction

Before solving Equations 1a and 2a for the total PM2.5 emissions and Eu, the pre-control

device PM2.5(wo) emissions (i.e., uncontrolled emissions from the process) need to be calculated. 

The PM2.5(wo) emissions represent the emissions level prior to the control device and are

calculated using Equation 3a. 

PM E n 2 5.( ) wo c = /( ) 1− (Eq. 3a)

where

Ec = annual controlled emissions rate of PM2.5, tons/yr

The calculated PM2.5(wo) value is used in Equation 2a to calculate Eu, which is then used in

Equation 1a to solve for PM2.5(total).

The revised PM2.5 emissions, PM2.5(rev), following implementation of improved

monitoring, are calculated using Equation 4a. As explained in Section III.B, before calculating

the revised PM2.5 emissions, one must determine the values of the initial CT, CF, and revised Pee,

T, and CT variables.

PM EP T C

P TC C

EnT

n TC

REV

u ee REV REV T REV

ee T F

c REV REV

F

2 5

1

1

.( )

* ** () () () () ()

** *

*( )*

( )* *

= + −

− (Eq. 4a)

where

PM2.5(REV) = revised annual emissions rate of PM2.5, tons/yr

Pee(REV) = revised percentage of excess emissions, fraction
8

T(REV) = revised operating hours of the emissions unit, hours/yr

T = initial operating hours of the emissions unit, hours/yr

CT(REV) = revised correction time (based on monitoring frequency of the APCD)

CT = correction time (based on monitoring frequency of the APCD)

n(REV) = revised control efficiency of the APCD, fraction

CF = certainty factor (dependent on type of improved monitoring performed)

As discussed in Section III.B, the emissions reductions are calculated for a range of

revised excess emissions rates, Pee(rev) values of 5 percent, 2.5 percent, 0.46 percent, and

zero percent. The correction times, CT, and the certainty factors, CF, are the same values

discussed in Section III.B and are shown in Tables 1 through 5. The revised operating time,

T(rev), is the same as the initial operating time, T (obtained from the NEI). The revised control

efficiency, n(REV), is also the same as the initial control efficiency, n (obtained from the NEI).

Following calculation of the total PM2.5 emissions (Equation 1a) and the revised PM2.5

emissions, PM2.5(rev), (Equation 4a), the emissions reduction is calculated with Equation 5a.

Emission reduction PM PM = 25 25 .( ) .( ) total REV − (Eq. 5a)

where

Emissions reduction = annual reduction in PM2.5 emissions, tons/yr

IV. Increased Monitoring Frequency – Scenario 1

A. Emissions Reductions Due to Increased Monitoring Frequency

The first scenario examines the impact of increasing the monitoring frequency from once

per day to four times per hour for emissions units/processes using an ESP, scrubber, or fabric

filter. In this scenario, it is assumed that facilities are monitoring the control devices with

parametric monitors and that there is no change in the type of monitoring equipment used. There

are 128 facilities affected by this scenario. Emissions reductions of filterable PM2.5 are included

for APCDs, such as fabric filters and ESPs, and emissions reductions of primary PM2.5 (both

filterable and condensible) are included for scrubbers. The emissions reductions below include

filterable and some condensible PM2.5. 

The PM2.5 emissions levels and other operating information reported in the 1999 NEI v3

are used as the initial inputs to the original equations (Equations 1 through 5) and to the alternate

equations (Equations 1a through 5a) to calculate emissions reductions for increased monitoring

frequency. The determined CT(REV) and CF values are also used as inputs to the equations to

calculate the emissions reductions. The emissions reductions are calculated for each emissions

unit/process in the six MSAs for excess emissions rates, Pee(REV), of 5 percent, 2.5 percent,

0.46 percent, and zero percent. Table 6 presents a summary of the emissions reductions, and

Appendix B contains the calculation spreadsheets. 
9

4

“Employer Costs for Employee Compensation - June 2003,” Table 12. page 16, Bureau

of Labor Statistics. June 2003.

As shown in Table 6 using the original calculation method, increasing the monitoring

frequency from once per day to four times per hour at 5 percent excess emissions per year results

in PM2.5 emissions reductions of 90 tons/yr. If the excess emissions rate is decreased by half

(2.5 percent), an emissions reduction of 1,300 tons/yr can be achieved. Sources can obtain

further reductions by limiting excess emissions to 0.46 percent, which reduces emissions by

2,300 tons/yr, and to zero percent excess emissions, which reduces emissions by 2,600 tons/yr.

Emissions reductions are also determined using the alternative calculation method

(Equations 1a through 5a). Using the alternative method, the calculated PM2.5 emissions

reduction is 300 tons/yr for stationary sources increasing their monitoring frequency from once

per day to four times per hour, with an excess emissions rate of 5 percent and an emissions

control efficiency of 80 percent of design during the excess emissions periods. At an excess

emissions rate of 2.5 percent, the emissions reductions are 2,400 tons/yr. At excess emissions

rates of 0.46 percent and zero percent, the emissions reductions achieved are 4,200 tons/yr and

4,600 tons/yr, respectively.

B. Costs for Increased Monitoring Frequency

The total annual costs for an increased frequency of monitoring were calculated. These

costs included the incremental recordkeeping and reporting associated with the increased

monitoring frequency. In developing the costs, labor rates for 2003 were used that were

obtained from the Bureau of Labor Statistics (labor rates include 140 percent overhead).4

 The

incremental recordkeeping and reporting costs were determined in a manner similar to those

determined for the CAM rule. The incremental costs included a one-time cost for development

of the improved monitoring (i.e., man-hours required to decide on the approach and to describe

the approach and changes in detail), and recurring annual burden costs for incremental

recordkeeping, reporting, and certification activities (i.e., incremental man-hours related to

increased recordkeeping, increased reporting, and annual certifications). It was assumed that

each of the 128 facilities in the six MSAs had two APCDs that were affected by the increased

monitoring frequency (see Appendix B for the detailed cost calculations). Table 7 shows the

costs for the increased monitoring associated with the original and alternative calculation

methods.

As indicated in Table 7, the increased monitoring frequency for the controlled stationary

sources costs approximately $1.5 million (annual costs for the six MSAs). The cost

effectiveness is based on PM2.5 filterable and some condensible reductions shown in Table 6 and

varies depending on the calculation method used. The original calculation method shows a cost

effectiveness at an excess emissions rate, Pee(REV), of 5 percent of $16,000 per ton PM2.5 reduced

($/ton). If the Pee(REV) is 2.5 percent, the cost effectiveness is $1,100/ton. Costs associated with a

Pee(REV) of 0.46 percent and zero percent are approximately $620/ton and $560/ton, respectively.
10

Table 7 also shows the cost effectiveness for the emissions reductions determined by the

alternative calculation method for the increased frequency scenario. The cost effectiveness

associated with a revised Pee of 5 percent is $4,800/ton. A revised Pee of 2.5 percent yields a cost

effectiveness of $600/ton. A Pee of 0.46 percent yields a cost effectiveness of $350/ton, whereas

a Pee of zero percent yields a cost effectiveness of $320/ton.

V. PM CEMS Upgrade (and Increased Monitoring Frequency) – Scenario 2a

A. Emissions Reductions Due to PM CEMS Upgrade (and Increased

Monitoring Frequency)

The second scenario examines the impacts of improving the monitoring technique at

emissions units/processes currently using an ESP, scrubber, or fabric filter. In this improved

technique scenario, the monitoring technique is upgraded to a PM CEMS. This improved

monitoring technique also results in an increase to the monitoring frequency because a PM

CEMS can make a measurement every 7.5 minutes. The monitoring frequency increases from

once per day to eight times per hour. There are 128 facilities affected by the PM CEMS

upgrade.

The PM2.5 emissions levels and other operating information obtained from the 1999 NEI

v3 are used as initial inputs to Equations 1 through 5 (original method) and to Equations 1a

through 5a (alternative method), along with the revised CT and CF values used to determine

emissions reductions. The revised correction times, CTs, and the certainty factor, CF, for a PM

CEMS are shown in Tables 3 and 5, respectively. Appendix C contains the calculation

spreadsheets for both calculation methods.

Emissions reductions associated with the original method are calculated using Equations

1 through 5 for excess emissions rates, Pee(REV), of 5 percent, 2.5 percent, 0.46 percent and zero

percent. Table 8 displays a summary of the calculated emissions reductions. As shown in the

table, if all 128 facilities upgrade their monitoring equipment for ESPs, scrubbers, and fabric

filters to a PM CEMS, they can achieve a PM2.5 emissions reduction of 800 tons/yr at 5 percent

excess emissions. If the facilities limit their excess emissions to 2.5 percent, they can achieve a

PM2.5 reduction of 1,900 tons/yr. At an excess emissions rate of 0.46 percent, the PM2.5

emissions reduction is 2,800 tons/yr, and at zero percent, the emissions reduction is 3,000

tons/yr.

Emissions reductions associated with the alternative method are calculated using

Equations 1a through 5a, with results presented in Table 8. At a percent excess emissions rate,

Pee(REV), of 5 percent, facilities can achieve a PM2.5 emissions reduction of 1,600 tons/yr. A

Pee(REV) of 2.5 percent yields a PM2.5 emissions reduction of 3,400 tons/yr. A Pee(REV) of

0.46 percent yields an emissions reduction of 4,900 tons/yr, and a Pee(REV) of zero percent yields

an emissions reduction of 5,300 tons/yr of PM2.5.
11

5

EPA CEMS Cost Model, Version 3.0, U.S. Environmental Protection Agency.

6

“Current Knowledge of Particulate Matter (PM) Continuous Emissions Monitoring,”

Chapter 9, PM CEMS Cost, U.S. Environmental Protection Agency. September 8, 2000.

B. Costs for PM CEMS Upgrade (and Increased Monitoring Frequency)

The total capital and annual operating costs for implementing an improved monitoring

technique are calculated. The total capital investment (TCI) costs for a PM CEMS upgrade are

calculated based on data from the EPA CEMS Cost Model5

 and the PM CEMS Knowledge

Document.6 Labor rates in the EPA CEMS Cost Model are scaled to reflect 2003 labor rates

(including 140 percent overhead) provided by the Bureau of Labor Statistics. It is assumed that

there are two APCDs per facility and that 128 facilities are affected by the PM CEMS upgrade. 

Total annual costs (TAC) are also based on data from the EPA CEMS Cost Model and

the PM CEMS Knowledge Document. Appendix C includes detailed costs for the PM CEMS

upgrade. Table 9 shows TCI for the six MSAs at approximately $34 million and a TAC at

$14 million. The original method shows a cost effectiveness of $18,000 per ton of PM2.5 at a

percent excess emissions rate, Pee(REV), of 5 percent. At a Pee(REV) of 2.5 percent, the cost

effectiveness is $7,700/ton. The cost effectiveness associated with a Pee(REV) of 0.46 percent is

$5,200/ton, and a Pee(REV) of zero percent yields a cost effectiveness of $4,900/ton.

As shown in Table 9 using the alternative emissions calculation method, a Pee(REV) of

5 percent yields a cost effectiveness of $9,000 per ton of PM2.5 reduced. At an excess emissions

rate of 2.5 percent, the cost effectiveness is $4,200/ton. For an excess emissions rate of 

0.46 percent, the cost effectiveness is $2,900/ton, and the cost effectiveness associated with a

Pee(REV) of zero percent is $2,700/ton. 

VI. BLD System Upgrade (and Increased Monitoring Frequency) – Scenario 2b

A. Emissions Reductions Due to BLD System Upgrade (and Increased

Monitoring Frequency)

In this monitoring scenario, the monitoring technique at facilities in the six MSAs using a

fabric filter is upgraded to a BLD system. This improved monitoring technique also results in an

increase to the monitoring frequency, because a BLD can make a measurement every 5 to

10 seconds (rounded to 1 minute). The monitoring frequency increases from once per day to

once per minute. There are 102 facilities affected by the BLD system upgrade.

Emissions reductions are determined for this monitoring technique scenario using the

original method (Equations 1 through 5) and the alternative method (Equations 1a through 5a). 

The PM2.5 emissions levels and other operating information obtained from the 1999 NEI v3 are

used as initial inputs to the original and alternative equations, along with the revised CT value

and the CF value to determine the emissions reductions. The revised CT for the BLD system

upgrade is shown in Table 4, and the CF for a BLD system is shown in Table 5. 
12

Table 10 shows a summary of the emissions reductions for excess emissions rates,

Pee(REV), of 5 percent, 2.5 percent, 0.46 percent, and zero percent. Appendix D contains the

calculation spreadsheets. As presented in Table 10 using the original method, if all 102 facilities

upgrade their fabric filters with a BLD system, the PM2.5 emissions reduction is 280 tons/yr at

5 percent excess emissions. If the excess emissions are limited to 2.5 percent, the PM2.5

reduction is 620 tons/yr. At an excess emissions rate of 0.46 percent, the PM2.5 emissions

reduction is 900 tons/yr, and at zero percent the emissions reduction is 960 tons/yr.

The alternative emissions calculation method shows an emissions reduction of

1,200 tons/yr of PM2.5 at a Pee of 5 percent. A Pee of 2.5 percent results in an emissions reduction

of 2,700 tons/yr. For a Pee of 0.46 percent, the emissions reduction is 3,900 tons/yr, and Pee of

zero percent results in an emissions reduction of 4,100 tons/yr.

B. Costs for BLD System Upgrade (and Increased Monitoring Frequency)

The TCI and TAC for a BLD system upgrade were calculated. Based on a 2001 cost

quote from Auburn International, the costs were scaled to reflect 2003 costs. Labor rates for

2003 (including 140 percent overhead) obtained from the Bureau of Labor Statistics were also

used to calculate costs. It was assumed that there were two fabric filters per facility and that 102

facilities were affected by the BLD system upgrade. It was also assumed that each fabric filter is

comprised of 10 compartments and that each compartment would be outfitted with a BLD probe

(1 probe per compartment). 

TAC includes costs for equipment cleaning and calibration, as suggested by the

manufacturer, as well as recordkeeping and reporting costs (consistent with those determined

from the EPA CEMS Cost Model for the PM CEMS). Appendix D includes a detailed cost

estimate for the BLD system upgrade. Table 11 shows TCI for the six MSAs at approximately

$13 million and TAC at $4.6 million. Table 11 also presents the cost effectiveness values

associated with excess emissions rates of 5 percent, 2.5 percent, 0.46 percent, and zero percent

for the original and alternative methods. The original method shows the cost effectiveness at a

Pee(REV) of 5 percent to be $16,000/ton of PM2.5. At a Pee(REV) of 2.5 percent, the cost effectiveness

is $7,400/ton. The cost effectiveness associated with a Pee(REV) of 0.46 percent is $5,100/ton, and

a Pee(REV) of zero percent yields a cost effectiveness of $4,800/ton.

The alternative method shows a cost effectiveness of $3,800/ton at a Pee(REV) of 5 percent. 

At a Pee(REV) of 2.5 percent, the cost effectiveness is $1,700/ton. For a Pee(REV) of 0.46 percent, the

cost effectiveness is $1,200/ton, and a Pee(REV) of zero percent results in a cost effectiveness of

$1,100/ton.

VII. Comparison of Costs

As previously outlined, original and alternative calculation methods are used to examine

the impacts of Scenarios 1 and 2. The first scenario looks at only increasing the monitoring

frequency, whereas the second scenario considers an improved monitoring technique. Table 12

shows a comparison, based on the original calculation method, of the impacts associated with
13

each scenario, and Table 13 presents a comparison, based on the alternative calculation method,

of the impacts associated with each scenario. Not surprisingly, both Tables 12 and 13 show that

the first scenario (increased monitoring frequency only) has the lowest TAC. There are no

capital costs associated with this scenario because no change is made to monitoring equipment. 

The second scenario shows an improved monitoring technique, where existing parametric

monitoring equipment is replaced with upgraded monitoring equipment, including a PM CEMS

and a BLD system. The capital costs for the PM CEMS upgrade are $34 million (based on

calculations for 128 facilities with 2 APCDs each) and the capital costs for the BLD system

upgrade are $13 million (based on calculations for 102 facilities with two fabric filters each). 

The annual costs associated with the improved monitoring technique in Scenario 2 (PM CEMS

at $14 million and BLD system at $4.6 million) are higher than the costs associated with

increased monitoring frequency in Scenario 1 ($1.5 million), due in large part to the cost of

monitoring equipment operation and maintenance.

Table 12 shows a cost comparison based on the original method, and Table 13 shows a

cost comparison based on the alternative method. The cost effectiveness values displayed in

Table 12 are much higher than the values presented in Table 13. This difference is attributed to

the higher emissions reductions achieved using the alternative calculation method. This method

assumes that the NEI data are based on 100 percent compliance for 100 percent of the time,

which does not include excess emissions periods. The alternative calculation method assumes

that excess emissions periods do occur for the majority of sources, but are not taken into account

in the inventory data; consequently, the excess emissions due to excess emissions periods are

added to the NEI data for the purposes of this method.

VIII. Air Pollution Control for an Uncontrolled Emissions Unit – Scenario 3

A. Emissions Reductions Due to Control with a Fabric Filter

Uncontrolled emissions units/processes in the six MSAs emit approximately 20,000 tons

of primary PM2.5 per year. If the State required add-on APCDs for these units, a significant

emissions reduction would likely be achieved. Examples 1 and 2 demonstrate the potential to

reduce PM2.5 emissions by adding an APCD to an uncontrolled emissions unit/process. In

Example 1, a large uncontrolled emissions unit/process emitting 132 tons/yr of PM2.5 (filterable)

is required to add an APCD such as a fabric filter. With a fabric filter control efficiency of

99 percent, the potential emissions reduction achieved is 131 tons/yr. In Example 2, a small

uncontrolled emissions unit/process emitting 9.0 tons/yr of PM2.5 (filterable) is also required to

add a fabric filter. At the same control efficiency (99 percent), the expected emissions reduction

for Example 2 is 8.9 tons/yr. It is assumed in both examples that the new fabric filter is

monitored continuously (at least once per minute) with a BLD system.

B. Fabric Filter and Monitoring Costs

The TCI for adding a fabric filter and BLD system to the uncontrolled emissions

unit/process presented in Examples 1 and 2 is based on EPA’s Control Cost Manual, a 2001 cost
14

7

“The EPA Air Pollution Control Cost Manual,” U.S. Environmental Protection Agency.

January 2002.

8

PM10 refers to particulate matter with an aerodynamic diameter equal to or less than

10 micrometers. PM10 emission rates include PM2.5 emissions.

quote from Auburn International (scaled to reflect 2003 costs), and labor rates for 2003

(including 140 percent overhead) obtained from the Bureau of Labor Statistics.7

 The actual costs

associated with the large uncontrolled emissions unit/process (Example 1) are potentially higher

than those shown in Table 14 and Appendix E, given the large size of the emissions unit and the

low concentration of the emissions. Total annual costs are also based on the EPA’s Control Cost

Manual and include costs for BLD equipment cleaning and calibration, as suggested by the

manufacturer, as well as recordkeeping and reporting costs (determined from the EPA CEMS

Cost Model). Table 14 shows a TCI of $6.3 million and TAC of $2.4 million for Example 1 and

a TCI of $0.31 million and TAC of $0.10 million for Example 2. The cost effectiveness to

control and monitor the large emissions unit/process from Example 1 is $18,200/ton of PM2.5. 

The cost effectiveness to control and monitor the small emissions unit/process from Example 2 is

$11,800/ton of PM2.5. Appendix E includes a detailed cost estimate of both examples for the

addition of a fabric filter and BLD system. (If co-pollutants are also considered, the cost

effectiveness for each unit may be lower. In Example 1, the emissions reductions for PM10

filterable are also shown in Table 14.8 For Example 2, the PM2.5 emissions are the same as the

PM10 emissions.)

IX. Improved Monitoring Applied to Visual Emissions (VE) Techniques – Scenario 4

The fourth scenario examines the impacts of applying increased monitoring frequency to

VE techniques and applying improved monitoring techniques in place of the VE technique. In

one example, the monitoring frequency of VE observations is increased; in a second example, an

improved monitoring technique, a BLD system, is used. The emissions units used in these

analyses are model units: one small emissions unit with 0.61 tons/yr PM2.5 filterable emissions

and one large emissions unit with 15 tons/yr PM2.5 filterable emissions. Both of the units are

controlled with a fabric filter achieving 99 percent control efficiency, and the base case

monitoring is a VE observation once per week on the fabric filter emissions point. Each model

unit operates 8760 hr/yr. The CT and CF values are determined, and the emissions reductions

achieved for excess emissions rates, Pee(REV), of 5 percent, 2.5 percent, 0.46 percent, and

zero percent are calculated. Appendix F contains the emission reduction and cost calculation

sheets for both the small and large model units.

A. Increased Frequency of VE Monitoring

1. Emissions Reductions. In these examples, the VE monitoring frequency is increased

from once per week to once per day. The correction times, CT(REV), are calculated for parametric

monitoring of a fabric filter at a frequency of once per day. For these analyses, the certainty

factor, CF, used for parametric monitoring also is used for VE (i.e., CF = 0.9).
15

Small model unit. The emissions reductions calculated for the small model unit using the

original calculation method are 0.43 tons/yr PM2.5 filterable at a 5 percent excess emissions rate

and 0.50 tons/yr at a zero percent excess emissions rate. The emissions reductions are shown in

Table 15. If co-pollutant control is also considered in the analysis, the emissions reduction for

PM10 filterable ranges from 0.68 to 0.79 tons/yr at excess emissions rates of 5 percent and

zero percent, respectively. The emissions reductions for the small unit are also calculated using

the alternative calculation method given in Section III.C. These emissions reductions are also

shown in Table 15.

Large model unit. The emissions reductions calculated for the large model unit using the

original calculation method are 11 tons/yr PM2.5 filterable at a 5 percent excess emissions rate

and 12 tons/yr at a zero percent excess emissions rate. The emissions reductions are shown in

Table 16. The co-pollutant emissions reductions for PM10 are also determined, and the emissions

reductions for PM10 filterable range from 17 to 20 tons/yr at excess emissions rates of 5 percent

and zero percent, respectively. The emissions reductions for the large unit are also calculated

using the alternative method. These emissions reductions are also shown in Table 16.

2. Costs. The total annual costs for increased monitoring frequency are calculated in a

manner similar to that described in Section IV.B. The labor cost to conduct VE observations

once per week are determined, as well as the incremental recordkeeping and reporting associated

with the increased monitoring frequency (daily) and the additional labor cost to conduct daily

VE observations. The incremental recordkeeping and reporting costs include a one-time cost to

develop the improved monitoring, as well as the recurring annual burden costs for incremental

recordkeeping, reporting, and training activities. The TAC, due to the increased monitoirng

frequency, is calculated by subtracting the cost to conduct weekly monitoring from the cost to

conduct daily monitoring (i.e., the increment to increase the monitoring from 52 times per year

to 365 times per year). The cost to conduct VE observations on a daily basis is the same for both

the small and large unit. Table 17 shows the costs associated with the increase in monitoring

frequency. There are no capital costs associated with the increase in monitoring frequency. The

calculated TAC for increasing the monitoring frequency to daily monitoring is $15,000. The

emissions reductions and cost effectiveness values for the small and large model units are

summarized in Table 17 for each excess emissions rate level.

B. Upgrade to Instrumental Monitoring (BLD)

1. Emissions Reductions. In these examples, the monitoring technique is improved from

daily VE observations to instrumental monitoring using a BLD system. The correction times,

CT(REV), are calculated for BLD monitoring of a fabric filter, and the certainty factor, CF, for a

BLD system are used.

Small model unit. The emissions reductions calculated for the small model unit using the

original calculation method are 0.46 tons/yr PM2.5 filterable at a 5 percent excess emissions rate

and 0.50 tons/yr at a zero percent excess emissions rate. The emissions reductions are shown in

Table 18. If co-pollutant control is also considered in the analysis, the emissions reduction for

PM10 filterable ranges from 0.74 to 0.80 tons/yr at excess emissions rates of 5 percent and
16

zero percent, respectively. The emissions reductions are also calculated using the alternative

calculation method given in Section III.C. These emissions reductions are also shown in

Table 18. 

Large model unit. The emissions reductions calculated for the large model unit using the

original calculation method are 11 tons/yr PM2.5 filterable at an excess emissions rate of

5 percent and 12 tons/yr at a zero percent excess emissions rate. The emissions reductions are

shown in Table 19. The co-pollutant emissions reductions for PM10 are also determined, and the

emissions reductions for PM10 filterable range from 18 to 20 tons/yr for excess emissions rates of

5 percent and zero percent, respectively. The emissions reductions are also calculated using the

alternative calculation method. These reductions are also shown in Table 19.

2. Costs. In this example, the monitoring technique is upgraded from weekly VE

observations to instrumental monitoring using a BLD system. The cost to conduct the VE

observations once per week is the same for both the small and large emissions units. The TCI

and TAC for a BLD system are calculated in a manner similar to that described in Section VI.B. 

The calculated TCI and TAC are different for the two sized units and are discussed below.

Small model unit. We assume the fabric filter used to control PM2.5 emissions from the

small model emissions unit would be outfitted with a BLD system with one probe. Table 20

summarizes the cost and cost effectiveness for the BLD system. The TCI for the small unit is

$27,000 and the TAC is $7,900. The cost effectiveness for PM2.5 filterable reductions for excess

emissions rates of 5 percent to zero percent, and the cost effectiveness of co-pollutant reductions

for PM10 filterable are shown in Table 20. The cost effectiveness calculated using the alternative

calculation method for the PM2.5 filterable and PM10 filterable emissions reductions is also shown

in Table 20.

Large model unit. We assume the fabric filter on the large model unit would be outfitted

with a BLD system with 10 probes. Table 20 presents the costs and cost effectiveness for the

BLD system. The TCI for the large unit is $65,000; the TAC is $20,000. The cost effectiveness

for PM2.5 filterable reductions for excess emissions rates of 5 percent to zero percent, as well as

the cost effectiveness of co-pollutant reductions for PM10 filterable are shown in Table 20. The

cost effectiveness calculated using the alternative calculation method is also shown for the PM2.5

filterable and PM10 filterable emissions reductions.
17

Table 1. Correction Times for Increased Monitoring Frequency, Once per Day

Parameter ESP Scrubber Fabric Filter

Detection time due to monitoring

frequency, hours

688

Time to determine and take corrective

action, hours

240

Repair time, hours 16 24 16

CT, hours 24 36 24

Table 2. Correction Times for Increased Monitoring Frequency,

Four Times per Hour

Parameter ESP Scrubber Fabric Filter

Detection time due to monitoring

frequency, hours

0.5 0.5 0.5

Time to determine and take corrective

action, hours

240

Repair time, hours 16 24 16

CT (rounded), hours 19 29 17

Table 3. Correction Times for Improved Monitoring Technique,

PM CEMS Upgrade

Parameter ESP Scrubber Fabric Filter

Detection time due to monitoring

frequency, hours

0.25 0.25 0.25

Time to determine and take corrective

action, hours

240

Repair time, hours 16 24 16

CT, hours 18.25 28.25 16.25
18

Table 4. Correction Time for Improved Monitoring Technique,

BLD System Upgrade

Parameter Fabric Filter

Detection time due to monitoring frequency,

hours

0.033

Time to determine and take corrective action,

hours

0

Repair time, hours 16

CT, hours 16.033

Table 5. Certainty Factors for Improved Monitoring Techniques

Monitoring Technique Certainty Factor (CF)

Parametric monitoring (including COMS for PM) 0.90

BLD System 0.95

PM CEMS 1.0
19

Table 6. Summary of Emissions Reductions due to Increased Monitoring Frequency

Pee(rev)

Initial PM2.5

emissions,

tons/yr

Revised PM2.5

emissions,

tons/yr

Emissions

reductions,

tons/yr

Percent

emissions

reduction, % a

Emissions reductions using original equations

5% 6,400 6,300 90 0.25%

2.5% 6,400 5,100 1,300 3.7%

0.46% 6,400 4,100 2,300 6.5%

0% 6,400 3,800 2,600 7.2%

Emissions reductions using alternative equations

5% 11,700 11,400 300 0.84%

2.5% 11,700 9,300 2,400 6.8%

0.46% 11,700 7,500 4,200 12%

0% 11,700 7,100 4,600 13%

a The percent emissions reduction is based on the total PM2.5 primary emissions level for the

six MSAs, 36,000 ton/yr.

Table 7. Summary of Costs for Increased Monitoring Frequency

Pee(rev)

TAC,

$ millions

Emissions reduction,

tons/yr

Cost effectiveness,

$/ton

Cost effectiveness based on emissions reductions using original equations

5% $1.5 90 $16,000

2.5% $1.5 1,300 $1,100

0.46% $1.5 2,300 $620

0% $1.5 2,600 $560

Cost effectiveness based on emissions reductions using alternative equations

5% $1.5 300 $4,800

2.5% $1.5 2,400 $600

0.46% $1.5 4,200 $350

0% $1.5 4,600 $320
20

Table 8. Summary of Emissions Reductions due to Improved Monitoring Technique,

PM CEMS Upgrade (and Increased Monitoring Frequency)

Pee(rev)

Initial PM2.5

emissions,

tons/yr

Revised PM2.5

emissions,

tons/yr

Emissions

reductions,

tons/yr

Percent

emissions

reduction, % a

Emissions reductions using original equations

5% 6,400 5,600 800 2.3%

2.5% 6,400 4,500 1,900 5.2%

0.46% 6,400 3,700 2,800 7.7%

0% 6,400 3,500 3,000 8.2%

Emissions reductions using alternative equations

5% 11,700 10,000 1,600 4.5%

2.5% 11,700 8,300 3,400 9.6%

0.46% 11,700 6,800 4,900 14%

0% 11,700 6,400 5,300 15%

a The percent emissions reduction is based on the total PM2.5 primary emissions level for the

six MSAs, 36,000 tons/yr.

Table 9. Summary of Costs for Improved Monitoring Technique,

PM CEMS Upgrade (and Increased Monitoring Frequency)

Parameter

TCI,

$ millions

TAC,

$ millions

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Cost effectiveness based on emissions reductions using original equations

 5% $34 $14 800 $18,000

2.5% $34 $14 1,900 $7,700

 0.46% $34 $14 2,800 $5,200

0% $34 $14 3,000 $4,900

Cost effectiveness based on emissions reductions using alternative equations

 5% $34 $14 1,600 $9,000

2.5% $34 $14 3,400 $4,200

 0.46% $34 $14 4,900 $2,900

0% $34 $14 5,300 $2,700
21

Table 10. Summary of Emissions Reductions due to Improved Monitoring Technique,

BLD System Upgrade (and Increased Monitoring Frequency)

Pee(REV)

Initial PM2.5

emissions,

tons/yr

Revised PM2.5

emissions,

tons/yr

 Emissions

reductions,

tons/yr

Percent

emissions

reduction, % a

Emissions reductions using original equations

5% 1,200 870 280 0.78%

2.5% 1,200 530 620 1.7%

0.46% 1,200 260 900 2.5%

0% 1,200 200 960 2.7%

Emissions reductions using alternative equations

5% 5,400 4,200 1,200 3.3%

2.5% 5,400 2,700 2,700 7.4%

0.46% 5,400 1,500 3,900 11%

0% 5,400 1,200 4,100 12%

a The percent emissions reduction is based on the total PM2.5 primary emissions level for the

six MSAs, 36,000 tons/yr.

Table 11. Summary of Costs for Improved Monitoring Technique,

BLD System Upgrade (and Increased Monitoring Frequency)

Pee(rev)

TCI,

$ millions

TAC,

$ millions

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Cost effectiveness based on emissions reductions using original equations

5% $13 $4.6 280 $16,000

2.5% $13 $4.6 620 $7,400

0.46% $13 $4.6 900 $5,100

0% $13 $4.6 960 $4,800

Cost effectiveness based on emissions reductions using alternative equations

5% $13 $4.6 1,200 $3,800

2.5% $13 $4.6 2,700 $1,700

0.46% $13 $4.6 3,900 $1,200

0% $13 $4.6 4,100 $1,100
22

Table 12. Cost Comparison of Emissions Reductions for Controlled Stationary Sources

Using Original Equations

Impacts Scenario Pee(rev)

PM2.5 emissions

reductions,

tons/yr

TCI,

$ millions

TAC,

$ millions

Cost

effectiveness,

$/ton

(1) Increased

monitoring frequency

of ESPs, scrubbers,

and fabric filters from

once per day to four

times per hour

(128 facilities

affected)

5% 90 --- $1.5 $16,000

2.5% 1,300 --- $1.5 $1,100

0.46% 2,300 --- $1.5 $620

0% 2,600 --- $1.5 $560

(2a) Upgrading the

monitoring equipment

of ESPs, scrubbers,

and fabric filters to a

PM CEMs (128

facilities affected)

5% 800 $34 $14 $18,000

2.5% 1,900 $34 $14 $7,700

0.46% 2,800 $34 $14 $5,200

0% 3,000 $34 $14 $4,900

(2b) Upgrading the

monitoring equipment

of fabric filters to a

BLD system (102

Facilities Affected)

5% 280 $13 $4.6 $16,000

2.5% 620 $13 $4.6 $7,400

0.46% 900 $13 $4.6 $5,100

0% 960 $13 $4.6 $4,800
23

Table 13. Cost Comparison of Emissions Reductions for Controlled Stationary Sources

Using Alternative Equations

Impacts Scenario Pee(rev)

PM2.5 emissions

reductions,

tons/yr

TCI,

$ millions

TAC,

$ millions

Cost

effectiveness,

$/ton

(1) Increased

monitoring frequency

of ESPs, scrubbers,

and fabric filters from

once per day to four

times per hour

(128 facilities

affected)

5% 300 --- $1.5 $4,800

2.5% 2,400 --- $1.5 $600

0.46% 4,200 --- $1.5 $350

0% 4,600 --- $1.5 $320

(2a) Upgrading the

monitoring equipment

of ESPs, scrubbers,

and fabric filters to a

PM CEMs (128

facilities affected)

5% 1,600 $34 $14 $9,000

2.5% 3,400 $34 $14 $4,200

0.46% 4,900 $34 $14 $2,900

0% 5,300 $34 $14 $2,700

(2b) Upgrading the

monitoring equipment

of fabric filters to a

BLD system (102

Facilities Affected)

5% 1,200 $13 $4.6 $3,800

2.5% 2,700 $13 $4.6 $1,700

0.46% 3,900 $13 $4.6 $1,200

0% 4,100 $13 $4.6 $1,100
Table 14. Summary of Emissions Reductions and Costs for Controlling an Uncontrolled Source, Fabr

Uncontrolled example

TCI,

$ millions

TAC,

$ millions

PM2.5 filterable

emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

PM10 filterab

emissions

reduction,

tons/yr

Example 1(larger emissions unit/process)

$6.3 $2.4 130 $18,000 390 

Example 2(smaller emissions unit/process)

$0.31 $0.10 8.9 $12,000 8.9 
Table 15. Summary of Emissions Reductions due to Increased Frequency of VE Monitoring (Smal

Pee(rev)

PM2.5 PM10 

Initial 

emissions,

tons/yr

Revised

emissions,

tons/yr

Emissions

reductions,

tons/yr

Emissions

reductions,

percent 

Initial

emissions,

tons/yr

Revised 

emissions,

tons/yr

Emission

reduction

tons/yr

Emissions reductions using original equations

5% 0.61 0.18 0.43 70% 0.98 0.29 0.68 

2.5% 0.61 0.15 0.46 76% 0.98 0.24 0.74 

0.46% 0.61 0.12 0.49 80% 0.98 0.19 0.78 

0% 0.61 0.11 0.50 81% 0.98 0.18 0.79 

Emissions reductions using alternative equations

5% 1.2 0.77 0.45 37% 2.0 1.23 0.73 

2.5% 1.2 0.72 0.50 41% 2.0 1.16 0.80 

0.46% 1.2 0.69 0.53 44% 2.0 1.10 0.86 

0% 1.2 0.68 0.54 44% 2.0 1.08 0.87 
Table 16. Summary of Emissions Reductions due to Increased Frequency of VE Monitoring (Larg

Pee(rev)

PM2.5 PM10 

Initial 

emissions,

tons/yr

Revised

emissions,

tons/yr

Emissions

reductions,

tons/yr

Emissions

reductions,

percent 

Initial

emissions,

tons/yr

Revised 

emissions,

tons/yr

Emission

reduction

tons/yr

Emissions reductions using original equations

5% 15 4.5 10.5 70% 24 7.2 16.8 

2.5% 15 3.6 11.4 76% 24 5.8 18.2 

0.46% 15 3.0 12.0 80% 24 4.7 19.3 

0% 15 2.8 12.2 81% 24 4.5 19.5 

Emissions reductions using alternative equations

5% 30 18.8 11.2 37% 48 30.1 17.9 

2.5% 30 17.8 12.2 41% 48 28.4 19.6 

0.46% 30 16.9 13.1 44% 48 27.0 21.0 

0% 30 16.7 13.3 44% 48 26.7 21.3 
Table 17. Summary of Costs for Increased Frequency of VE Monitoring

Pee(rev)

TCI,

$

TAC,

$ 

Small Model Unit Large Model 

PM2.5 PM10 PM2.5 

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Em

red

to

Cost effectiveness based on emissions reductions using original equations

5% --- $15,000 0.43 $35,000 0.68 $22,000 10.5 $1,400 

2.5% --- $15,000 0.46 $33,000 0.74 $20,000 11.4 $1,300 

0.46% --- $15,000 0.49 $31,000 0.78 $19,000 12.0 $1,200 

0% --- $15,000 0.50 $30,000 0.79 $19,000 12.2 $1,200 

Cost effectiveness based on emissions reductions using alternative equations

5% --- $15,000 0.45 $33,000 0.73 $21,000 11.1 $1,300 

2.5% --- $15,000 0.50 $30,000 0.80 $19,000 12.3 $1,200 

0.46% --- $15,000 0.53 $28,000 0.85 $18,000 13.1 $1,100 

0% --- $15,000 0.54 $28,000 0.87 $17,000 13.3 $1,100 
Table 18. Summary of Emissions Reductions due to Improved Monitoring Technique

BLD System Upgrade and Increased Monitoring Frequency (Small Model Unit)

Pee(rev)

PM2.5 PM10 

Initial 

emissions,

tons/yr

Revised

emissions,

tons/yr

Emissions

reductions,

tons/yr

Emissions

reductions,

percent 

Initial

emissions,

tons/yr

Revised 

emissions,

tons/yr

Emission

reduction

tons/yr

Emissions reductions using original equations

5% 0.61 0.15 0.46 75% 0.98 0.24 0.74 

2.5% 0.61 0.13 0.48 79% 0.98 0.21 0.77 

0.46% 0.61 0.11 0.50 82% 0.98 0.18 0.80 

0% 0.61 0.11 0.50 82% 0.98 0.17 0.80 

Emissions reductions using alternative equations

5% 1.2 0.70 0.52 43% 2.0 1.12 0.84 

2.5% 1.2 0.67 0.55 45% 2.0 1.07 0.88 

0.46% 1.2 0.65 0.57 47% 2.0 1.04 0.92 

0% 1.2 0.64 0.58 47% 2.0 1.03 0.92 
Table 19. Summary of Emissions Reductions due to Improved Monitoring Technique

BLD System Upgrade and Increased Monitoring Frequency (Large Model Unit)

Pee(rev)

PM2.5 PM10 

Initial 

emissions,

tons/yr

Revised

emissions,

tons/yr

Emissions

reductions,

tons/yr

Emissions

reductions,

percent 

Initial

emissions,

tons/yr

Revised 

emissions,

tons/yr

Emission

reduction

tons/yr

Emissions reductions using original equations

5% 15 3.7 11.3 75% 24 5.9 18.1 

2.5% 15 3.2 11.8 79% 24 5.1 18.9 

0.46% 15 2.7 12.3 82% 24 4.4 19.6 

0% 15 2.7 12.3 82% 24 4.2 19.8 

Emissions reductions using alternative equations

5% 30 17.2 12.8 43% 48 27.5 20.5 

2.5% 30 16.5 13.5 45% 48 26.4 21.6 

0.46% 30 15.9 14.1 47% 48 25.5 22.5 

0% 30 15.8 14.2 47% 48 25.3 22.7 
Table 20. Summary of Costs for Improved Monitoring Technique, BLD System Upgrade and Increa

Pee(rev)

Small Model Unit Large Model Unit

TCI,

$

TAC,

$ 

PM2.5 PM10 

TCI,

$

TAC,

$ 

PM2.5 

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Emissions

reduction,

tons/yr

Cost

effectiveness,

$/ton

Cost effectiveness based on emissions reductions using original equations

5% $27,000 $7,900 0.46 $17,200 0.74 $11,000 $65,000 $20,000 11.3 $1,800 

2.5% $27,000 $7,900 0.48 $16,400 0.77 $10,000 $65,000 $20,000 11.8 $1,700 

0.46% $27,000 $7,900 0.50 $15,900 0.80 $9,900 $65,000 $20,000 12.3 $1,700 

0% $27,000 $7,900 0.50 $15,800 0.80 $9,800 $65,000 $20,000 12.3 $1,700 

Cost effectiveness based on emissions reductions using alternative equations

5% $27,000 $7,900 0.52 $15,100 0.84 $9,500 $65,000 $20,000 12.8 $1,600 

2.5% $27,000 $7,900 0.55 $14,400 0.88 $9,000 $65,000 $20,000 13.5 $1,500 

0.46% $27,000 $7,900 0.57 $13,800 0.92 $8,600 $65,000 $20,000 14.1 $1,500 

0% $27,000 $7,900 0.58 $13,700 0.92 $8,600 $65,000 $20,000 14.2 $1,400