September 2010 

Regulatory Impact Analysis: 

Standards of Performance for New 

Stationary Sources and Emission 

Guidelines for Existing Sources: 

Sewage Sludge Incineration Units 

Prepared for 

Tom Walton 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709

RTI Project Number 0209897.004.074 
_________________________________ 

RTI International is a trade name of Research Triangle Institute. 

RTI Project Number 

0209897.004.074 

Regulatory Impact Analysis: 

Standards of Performance for New 

Stationary Sources and Emission 

Guidelines for Existing Sources: 

Sewage Sludge Incineration Units 

September 2010

Prepared for 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Prepared by 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709
iii 

CONTENTS 

Section Page

1 Introduction ..............................................................................................................1-1

1.1 Executive Summary..........................................................................................1-1

1.2 Organization of this Report...............................................................................1-2

2 Description of Sewage Sludge Incineration................................................................2-1

2.1 Relation to Publicly Owned Treatment Works (POTWs) ..................................2-1

2.2 Alternative Disposal Options ............................................................................2-2

2.2.1 Surface Disposal: Landfills....................................................................2-2

2.2.2 Other Land Application.........................................................................2-2

2.3 Ownership ........................................................................................................2-3

3 Engineering Cost Analysis.........................................................................................3-1

3.1 Calculation of Costs and Emissions Reductions of the Maximum 

Achievable Control Technology (MACT) Floor................................................3-1

3.1.1 Compliance Costs..................................................................................3-2

3.1.2 Emission Reductions.............................................................................3-8

3.2 Analysis of Beyond the MACT Floor Controls for Existing SSI Units ............3-11

3.2.1 Selection of More Stringent Controls...................................................3-11

3.2.2 Methodology Used to Estimate Cost and Emission Reductions............3-13

3.2.3 Selection of Regulatory Options..........................................................3-14

3.3 Estimation of Impacts for New Units Constructed within 5 Years After 

Promulgation of the SSI NSPS........................................................................3-25

3.3.1 Estimation of New Sources .................................................................3-25

3.3.2 Methodology Used to Estimate Cost and Emission Reductions of 

the MACT Floor Level of Control.......................................................3-26

3.3.3 Analysis of Beyond the Floor Options.................................................3-31
iv 

4 Economic Impact Analysis........................................................................................4-1

4.1 Social Cost Estimates........................................................................................4-1

4.2 Small Entity Analysis .......................................................................................4-2

4.2.1 Identify Affected Small Entities ............................................................4-3

4.2.2 Screening Analysis: Revenue Test.........................................................4-3

5 Human Health Benefits of Emissions Reductions......................................................5-1

5.1 Synopsis...........................................................................................................5-1

5.2 Calculation of PM2.5 Human Health Benefits....................................................5-1

5.3 Energy Disbenefits..........................................................................................5-15

5.3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits ......................5-16

5.4 Unquantified Benefits.....................................................................................5-19

5.4.1 Carbon Monoxide Benefits..................................................................5-19

5.4.2 Other SO2 Benefits..............................................................................5-20

5.4.3 HAP Benefits......................................................................................5-21

5.5 Characterization of Uncertainty in the Monetized PM2.5 Benefits....................5-28

5.6 Comparison of Benefits and Costs ..................................................................5-32

Appendixes 

A Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-Related Mortality.................................A-1 

B Lowest Measured Level (LML) Assessment for Rules without Policy-Specific 

Air Quality Data Available: Technical Support Document (TSD).............................B-1 

C Additional Engineering Cost Analysis Data..............................................................C-1 
v 

LIST OF FIGURES 

Number Page

5-1. Breakdown of Monetized PM2.5 Health Benefits using Mortality Function 

from Pope et al. (2002)...........................................................................................5-7

5-2. Total Monetized PM2.5 Benefits for the Proposed SSI NSPS and EG in 2015........5-13

5-3. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by 

PM2.5 Precursor Pollutant and Source....................................................................5-14

5-4. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by 

Subcategory..........................................................................................................5-15

5-5. Percentage of Adult Population by Annual Mean PM2.5 Exposure (pre- and 

post-policy policy)................................................................................................5-30

5-6. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy policy).................................................................................5-30

5-7. Net Benefits for the Proposed SSI NSPS and EG at 3% Discount Rate ................5-34

5-8. Net Benefits for the Proposed SSI NSPS and EG at 7% Discount Rate ................5-35
vi 

LIST OF TABLES 

Number Page

1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

SSI NSPS and EG in 2015 (millions of 2008$) .......................................................1-3

3-1. Summary of Average CO Emissions Collected from MH Units............................3-13

3-2. Emissions Reductions and Costs If All Units Comply...........................................3-16

3-3. Emissions Reductions and Costs If Small Entities Landfill ...................................3-18

3-4. Emissions Reductions and Costs If All Units Comply – Per Unit Basis.................3-20

3-5. Emissions Reductions and Costs If Small Entities Landfill – Per Unit Basis.........3-22

3-6. Emissions Reductions and Costs If All Units Comply...........................................3-24

3-7. Emissions Reductions and Costs If Large Entities Comply and Small Entities 

Landfill.................................................................................................................3-24

3-8. Control Device Distribution for Fluidized Bed Incinerators..................................3-27

3-9. Cost and Emission Reduction Calculation Inputs..................................................3-29

3-10. Summary of Emission Reductions for New SSI Units...........................................3-30

3-11. MACT Costs Associated with Model FB Unit ......................................................3-31

4-1. Annual Social Cost Estimates by Option and Disposal Choices ($ million, 

2008$)....................................................................................................................4-2

4-2. Calculated Municipal and Township Per Capita Revenues by Population Size........4-3

4-3. Option 1 Revenue Tests for Government Entities: All Entities Comply ..................4-4

4-4. Option 1 Revenue Tests for Government Entities: Large Entities Comply and 

Small Entities Landfill............................................................................................4-4

4-5. Option 2 Revenue Tests for Government Entities: All Entities Comply ..................4-5

4-6. Option 2 Revenue Tests for Government Entities: Large Entities Comply and 

Small Entities Landfill............................................................................................4-5

4-7. Option 3 Revenue Tests for Government Entities: All Entities Comply ..................4-5

4-8. Option 3 Revenue Tests for Government Entities: Large Entities Comply and 

Small Entities Landfill............................................................................................4-6

5-1. Human Health and Welfare Effects of PM2.5...........................................................5-2

5-2. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in 

2015 (2008$) (large entities comply and small entities landfill)...............................5-8

5-3. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in 

2015 (2008$) (all units comply)............................................................................5-10

5-4. Summary of Reductions in Health Incidences from PM2.5 Benefits for the 

Proposed SSI NSPS and EG in 2015.....................................................................5-11
vii 

5-5. All PM2.5 Benefits Estimates for the Proposed SSI NSPS and EG at Discount 

Rates of 3% and 7% in 2015 (in millions of 2008$)..............................................5-12

5-6. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2015 ...................5-18

5-7. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015 (large 

entities comply and small entities landfill, millions of 2008$)...............................5-18

5-8. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015 (large 

entities comply and small entities landfill, millions of 2008$)...............................5-18

5-9. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

SSI NSPS and EG in 2015 (millions of 2008$) .....................................................5-33
1-1 

SECTION 1

INTRODUCTION 

Section 129 of the Clean Air Act (CAA) requires that performance standards for new 

units and emission guidelines (EG) for existing units be established for each category of solid 

waste incineration units. In previous actions, the U.S. Environmental Protection Agency (EPA) 

has promulgated rules and EG for hospital medical and infectious waste incinerators (HMIWI), 

commercial and industrial solid waste incinerators (CISWI), and other solid waste incineration 

(OSWI) units. These actions did not apply to sewage sludge incinerators (SSI). EPA is proposing 

new source performance standards (NSPS) and EG for SSI units. As part of the regulatory 

process, EPA is required to develop a regulatory impact analysis (RIA). The RIA includes an 

economic impact analysis (EIA) and a small entity impacts analysis and documents the RIA 

methods and results. 

1.1 Executive Summary 

The key results of the RIA are as follows: 

§ Options Analyzed: EPA analyzed the following options and selected Option 2: 

– Option 1 is the MACT floor level of control for the two subcategories developed 

for existing sewage sludge incineration (SSI) units, multiple hearth (MH) units, 

and fluidized bed (FB) units. 

– Option 2 is the same as Option 1, with the addition of activated carbon injection 

for additional mercury (Hg) emissions reduction from MH units. 

– Option 3 is the same as Option 2, with the addition of an afterburner on all MH 

units for additional carbon monoxide (CO) emissions reduction. 

§ Engineering Cost Analysis: EPA estimates the proposed rule’s total annualized costs 

will be $92 million (2008$). 

§ Social Cost Analysis: Because the proposed regulatory option affects governmental 

entities (96 of the 97 owners are governmental entities) providing services not 

provided in a market, the Office of Air Quality Planning and Standards (OAQPS) has 

used the direct compliance cost method as a measure of social costs. The social cost is 

approximately $92 million (2008$). 

§ Small Entity Analyses: EPA performed a screening analysis for impacts for 18 small 

government entities by comparing compliance costs to revenues (e.g., revenue tests). 

EPA’s analysis found the tests were below 1% for small entities. 

§ Benefits Analysis: In the year of full implementation (2015), EPA estimates the 

monetized PM2.5 benefits of the proposed NSPS and EG are $130 million to $320 

million and $120 million to $290 million, at 3% and 7% discount rates, respectively. 
1-2 

All estimates are in 2008$ for the year 2015. Using alternate relationships between 

PM2.5 and premature mortality supplied by experts, higher and lower benefits 

estimates are plausible, but most of the expert-based estimates fall between these 

estimates. The benefits from reducing other air pollutants have not been monetized in 

this analysis, including reducing 2,900 tons of CO, 96 tons of HCl, 3.0 tons of Pb, 1.6 

tons of Cd, 5,500 pounds of mercury (Hg), and 90 grams of total dioxins/furans 

(CDD/CDF) each year. In addition, ecosystem benefits and visibility benefits have 

not been monetized in this analysis. 

§ Net Benefits: The net benefits for the NSPS and EG are $37 million to $220 million 

and $26 million to $190 million, at 3% and 7% discount rates, respectively (Table 1-

1). All estimates are in 2008$ for the year 2015. 

1.2 Organization of this Report 

The remainder of this report supports and details the methodology and the results of the 

EIA: 

§ Section 2 describes the SSI process, alternative disposal methods, and affected 

entities. 

§ Section 3 describes the engineering cost analysis. 

§ Section 4 describes the economic impact and small entity analyses. 

§ Section 5 presents the benefits estimates. 
1-3 

Table 1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the SSI 

NSPS and EG in 2015 (millions of 2008$)a

3% Discount Rate 7% Discount Rate 

Proposed: Option 2 

Total Monetized Benefitsb

$130 to $320 $120 to $290 

Total Social Costsc

$92 $92 

Net Benefits $37 to $220 $26 to $190 

26,000 tons of carbon monoxide 

96 tons of HCl 

5,500 pounds of mercury 

1.6 tons of cadmium 

3.0 tons of lead 

90 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Non-monetized Benefits 

Visibility impairment 

Option 1 

Total Monetized Benefitsb

$130 to $320 $120 to $290 

Total Social Costsc

$63 $63 

Net Benefits $66 to $250 $55 to $220 

2,900 tons of carbon monoxide 

96tons of HCl 

820 pounds of mercury 

1.6 tons of cadmium 

3.0 tons of lead 

74 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Non-monetized Benefits 

Visibility impairment 

Option 3 

Total Monetized Benefitsb

$130 to $310 $120 to $290 

Total Social Costsc

$132 $132 

Net Benefits -$5.4 to $180 -$14 to $150 

26,000 tons of carbon monoxide 

96 tons of HCl 

5,500 pounds of mercury 

1.6 tons of cadmium 

3.0 tons of lead 

90 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Non-monetized Benefits 

Visibility impairment 

a All estimates are for the implementation year (2015), and are rounded to two significant figures. These results include 2 new FB 

incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill assumption. 

b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of 

directly emitted PM2.5 and PM2.5 precursors such as NOx and SO2. It is important to note that the monetized benefits include 

many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden 

et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality because there is no clear scientific evidence that would support the development of differential effects 

estimates by particle type. These estimates include energy disbenefits valued at $0.5 million at a 3% discount rate from CO2

emissions. 

c The annual compliances costs serve as a proxy for the annual social costs of this rule given the lack of difference between the two. 
2-1 

SECTION 2

DESCRIPTION OF SEWAGE SLUDGE INCINERATION1

Sewage sludge incinerators combust the organic and inorganic solids and dissolved 

materials resulting from the wastewater treatment process. Incineration greatly reduces the 

sludge volume, and post-incineration sludge ash can be disposed of more easily. Sludge ash is 

generally disposed of in landfills but can also be used in construction materials. In addition to 

disposal functions, some facilities capture the heat from sewage sludge incineration operations 

and use the heat as an energy source. 

The incineration process releases several pollutants, some of which were present in the 

sewage sludge and some of which are created as a result of combustion. Pollutants emitted from 

SSI include particulate matter (PM), hydrocarbons, CO, nitrogen oxides, sulfur dioxide, 

hydrogen chloride, dioxins and dibenzofurans, and a number of metals. The amount of these 

pollutants released during incineration depends on the content of the sludge, the type of 

incinerator used, and the level of PM control. 

The majority of incineration facilities (163, or 75%) are multiple hearth (MH) 

incinerators. These incinerators consist of a cylinder around a series of hearths with a rotating 

shaft through the center. Rabble arms with teeth in each hearth rake the sludge while air is ducted 

into the shaft and circulated. The incinerator consists of the upper drying zone, the middle sludge 

combustion zone, and the lower cooling zone. 

Although MH incinerators have been in use since the 1930s and remain in the majority, 

fluidized bed (FB) incinerators have begun to replace them.2 Of the 218 incineration units in 

operation 55 (25%) are FB incinerators. In a FB incinerator, a steel shell holds a refractory-lined 

grid beneath a bed of sand. Air is injected into the incinerator, fluidizing the sand and sludge. FB 

incinerators work efficiently to transfer heat from the sand to the sludge, using less excess air 

than MH incinerators. Emissions for most pollutants are, therefore, lower for FB incinerators. 

2.1 Relation to Publicly Owned Treatment Works (POTWs) 

Publicly owned treatment works (POTWs) are wastewater treatment systems owned by 

states, municipalities, or other public entities. POTWs receive sewage from homes and 

 

1

Portions of this section rely on information provided by EPA (2007 and 2009). 

2Other types of sewage sludge incinerators, such as electric arc furnaces, are no longer used in the United States. 
2-2 

businesses, runoff, and sometimes industrial wastewater. After the wastewater treatment process, 

POTWs are responsible for disposing of the sewage sludge. 

POTWs treat sewage in three steps: primary, secondary, and tertiary treatment. In the 

primary stage, heavy solids settle to the bottom while oil and light solids are skimmed from the 

top. The sludge removed during this step is known as primary sludge. During secondary 

treatment, biological treatment creates secondary sludge. Some plants may continue with a 

tertiary treatment of chemical disinfection, which produces a tertiary or chemical sludge (EPA, 

2009). The three sludge types are then generally combined and disposed of or sent for further 

treatment. 

2.2 Alternative Disposal Options 

Incineration continues to be utilized to dispose of sewage sludge but is increasingly 

becoming less common. Additional pollution controls will increase costs for facilities that 

continue to use the incineration disposal method. If the additional costs are high enough, many 

POTWs may choose to adopt alternative disposals methods (e.g., surface disposal in landfills or 

other beneficial land applications). However, the use of alternative disposal methods may be of 

limited in some areas because of landfill capacity constraints, local geography, or other legal or 

economic constraints. 

2.2.1 Surface Disposal: Landfills 

Landfilling, in some cases, provides a simple and low-cost option for sewage sludge 

disposal. Sewage sludge may be placed in landfills used for other municipal solid waste or in 

landfills constructed specifically for sewage sludge. The landfill disposal option is attractive for 

low-volume incinerators; landfill capacity constraints limit disposal opportunities for large 

sludge volumes. 

Sewage sludge may also be useful for landfills. For example, sludge can be used in place 

of a daily soil cover for odor and blowing litter control or as a final cover for closed landfills to 

aid growth of a vegetative layer. The sewage sludge’s high organic content also helps break 

down other landfill waste. 

2.2.2 Other Land Application 

Sewage sludge that has undergone treatment to make it safe for use on other land 

application (e.g., fertilizer) is commonly referred to as biosolids. Biosolids can be sold to 

agricultural or landscaping entities for land application, so the organic material in biosolids is 
2-3 

reused to contribute to crop production. Land application has also been used in mine reclamation 

to reestablish vegetation. 

Biosolids must meet federal and state regulations to ensure their safety; meeting these 

standards may make other land applications a less attractive disposal option. In addition, land 

application may not be suitable in some areas, based on factors such as proximity of water 

sources and slope. Rules vary based on the quality of the biosolid: Class A biosolids meet strict 

standards, while Class B biosolids are treated but still contain detectable pathogen levels and 

face greater restrictions on usage (EPA, 2007). Actions must also be taken to reduce the vector 

attraction of biosolids, either through additional treatment or by preventing contact with vectors. 

2.3 Ownership 

Sewage sludge incinerators can be operated by municipalities or other entities. There is 

no specific North American Industry Classification System (NAICS) code for these units. 

Applicable NAICS codes include 562213 (solid waste combustors and incinerators) and 221320 

(sewage treatment facilities). Most sludge incinerators are located in the eastern United States. 

The United States has 97 operators that own 112 facilities with a total of 218 affected 

incinerator units; the typical (e.g., median) operator owns one facility. Almost all operators are 

towns, cities, and their utility authorities; the exception is one operator that is a large publicly 

owned company. Among owner municipalities whose exact population is known, the average 

(median) population is 336,305 (108,213). Out of the 94 owners with population information 

available, 18 (or 19%) are small entities that serve a population under 50,000. 
3-1 

SECTION 3

ENGINEERING COST ANALYSIS 

This section documents the calculation of costs and emissions reductions associated with 

existing and new sources complying with the MACT floor level of control, the selection of 

control options more stringent than the MACT floor level of control, and summarizes cost and 

emissions reductions of each control option. The costs and emission reductions of each option 

are then used in the economic analysis (Section 4) and human health benefits analysis (Section 

5). Costs and emissions reductions were calculated for two scenarios: 

§ Control options were applied to all SSI units, and 

§ Control options were applied to only larger entities. Larger entities mean wastewater 

treatment facilities that are owned by municipalities or authorities with more than 

50,000 people. Entities with fewer than 50,000 people are likely to dispose of sewage 

sludge by landfilling rather than continuing to operate their incineration unit. 

3.1 Calculation of Costs and Emissions Reductions of the Maximum Achievable Control 

Technology (MACT) Floor 

A significant portion of the total cost for industry compliance comes from the cost of 

installing new pollution control devices or improving existing pollution control devices for units 

not currently meeting the proposed limits. In order to determine the control costs, it was 

necessary to evaluate, for each SSI unit, how much improvement for each pollutant would be 

needed to meet the proposed emissions limits. 

The average pollutant concentration values used to calculate baseline annual emissions

(Estimation of Baseline Emissions, 2010) for each unit were compared with the proposed 

emissions limits, and percentages were calculated to quantify the amount of improvement needed 

for the unit to meet the proposed limits. Tables C-1a and C-1b in Appendix C contain the 

baseline pollutant concentration values used for each unit in each subcategory and the percentage 

improvement required to meet the proposed emissions limits for each unit for each pollutant. The 

existing SSI units are subcategorized into two main groups: multiple hearth (MH) units and 

fluidized bed (FB) units. The pollutant- and subcategory-specific limits are shown in each header 

row of these tables. 

Control methods and cost algorithms utilized in a recent rulemaking for another waste 

combustion source category, Hospital, Medical and Infectious Waste Incinerators (HMIWI) were 

updated and utilized generally for the SSI source category, since most of these algorithms can be 

tailored to the combustion units found in the SSI source category with slight modifications. 
3-2 

Based on these required improvements, pollutant-specific control methods were chosen as 

follows for units requiring more than 10 percent improvement to meet the proposed limits. It was 

assumed that units within 10 percent of the limit would be able to meet the limit by making 

minor adjustments to the unit and/or controls already in place. 

Metals (cadmium and lead) and PM: Adding fabric filters (FF). 

Mercury and dioxins/furans (CDD/CDF): Adding activated carbon injection (ACI) and 

adjusting the carbon addition rate to meet the amount of reduction required. 

Hydrogen chloride (HCl): Adding packed bed scrubbers (PBS). 

Carbon monoxide (CO): No further improvement was needed for units to meet the 

MACT floor limit. However, the beyond-the-floor limit required the use of afterburner retrofits 

for units not already having similar control. The costs and emission reductions associated with 

the proposed CO limit are discussed in the memorandum “Analysis of Beyond the Maximum 

Achievable Control Technology (MACT) Floor Controls for Existing SSI Units.”2

Nitrogen oxides (NOx): No more than 10 percent improvement in NOx control was 

needed for any units. Minor adjustments were considered sufficient for those needing 

improvement to meet the NOx limit. 

Sulfur dioxide (SO2): Adding packed bed scrubbers. 

Further descriptions of these controls and their associated costs are listed below in 

Section 3.1.1. 

3.1.1 Compliance Costs 

This section presents the methodology used to estimate costs for existing SSI for (A) the 

emission controls used to comply with the proposed limits; (B) the monitoring, testing, 

recordkeeping, and reporting activities used to demonstrate compliance; and (C) the alternatives 

to compliance. 

3.1.1.1 Emission Control Costs 

Emission control technologies and other control measures that can be used to comply 

with the MACT floor options for existing SSI units include PBS, FF, and activated ACI. This 

section presents the costs that were estimated for each of these control measures. 
3-3 

The retrofit factors for the capital costs were assumed to be 40 percent for packed bed 

scrubbers, fabric filters, and 20 percent for and ACI.5, 6 Downtime costs for the retrofits were 

assumed to be negligible. Most SSI are expected to have adequate space to install an emission 

control system without shutting down the incinerator for an extended period. It was also 

expected that connecting the ductwork could be performed during a scheduled downtime for 

maintenance, thereby minimizing expected downtime.7 

The capital and annual costs for the emission controls were estimated in units of dollars 

($) and $/flow. The $/flow costs were calculated by dividing the capital/annual control cost 

estimate for each unit by the average gas flow rate assigned to that unit. 

Costs are on a 2008 basis, and annualized costs assumed an interest rate of 7 percent. 

Tables C-2a to C-2c in Appendix C present a summary of the parameters and equations used in 

the cost algorithms for each emission control and alternative to compliance where applicable. 

Table C-3 in Appendix C lists of the unit-specific inputs used in the algorithms (e.g., incinerator 

charge rate, stack gas flow rate, incinerator operating hours, and concentrations) 

a. Adding a fabric filter.

Fabric filters can be installed either alone or with other add-on controls. The cost 

algorithm for installing a fabric filter is presented in Table C-2a in Appendix C and is based on 

algorithms in the Model Plant Description and Control Cost Report for HMIWI.6The fabric 

filter capital costs range from approximately $893,000 to $4.2 million, and annualized costs 

range from approximately $209,000/yr to $1.2 million/yr. Sources for specific cost data are noted 

below Table C-2a in Appendix C. 

b. Adding a packed bed scrubber. 

Wet scrubbers can be installed alone or after a dry scrubber/fabric filter. The cost 

algorithm for installing a packed-bed wet scrubber is presented in Table C-2b of Appendix C and 

is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI.8

The packed-bed wet scrubber capital costs range from approximately $366,000 to $8.7 million, 

and annualized costs range from approximately $103,000/yr to $1.8 million/yr. Sources for 

specific cost data are noted below Table C-2b in Appendix C. 

c. Adding an activated carbon injection (ACI) system. 

Injecting activated carbon before the fabric filter has been demonstrated to improve the 

removal efficiency of both Hg and CDD/CDF from SSI. The cost algorithm for installing an ACI 
3-4 

system is presented in Table C-2c in Appendix C and is based on algorithms in the Model Plant 

Description and Control Cost Report for HMIWI.8 Adjustments to the carbon injection rate were 

made to account for how much reduction was required to meet the proposed limit, and whether a 

packed-bed scrubber was being added, since those may also assist in reducing Hg emissions. The 

packed-bed scrubber adjustment is a ten percent Hg reduction, and is based on input from the 

boiler NESHAP development. The ACI factor compares the carbon grain loading originally 

assumed to achieve 90 percent control of mercury or 98 percent control of CDD/CDF to the 

amount of reduction the unit will need to meet the proposed emission limits. The highest factor 

(Hg or CDD/CDF) is then used to adjust the carbon injection rate calculation of the algorithm. 

ACI capital costs range from approximately $8,400 to $37,000, and annualized costs range from 

approximately $9,300/yr to $210,000/yr. Sources for specific cost data are noted below Table C2c in Appendix C. 

d. Additional Control Options. 

Minor adjustments, such as air handling and distribution adjustments in the firebox, can 

be made to certain units to improve NOx control. It was assumed these adjustments could be 

made at no additional cost. 

3.1.1.2 Stack Testing, Monitoring, and Recordkeeping Costs 

Monitoring Costs. Initial and continuous compliance provisions for SSI units were 

selected to be as consistent as possible with proposed commercial and industrial solid waste 

incinerator (CISWI) and current HMIWI provisions. This section presents the costs that were 

estimated for each of these requirements. 

The total capital cost for stack testing, monitoring, and recordkeeping and reporting for 

all subcategories is estimated at approximately $14.9 million, and the total annualized cost is 

about $16.9 million per year. Cost estimates were based on algorithms recently utilized in the 

HMIWI regulatory development. Costs were updated to a 2008 basis, and annualized costs 

assumed an interest rate of 7 percent. Tables C-4a to C-4e in Appendix C present a summary of 

the parameters and equations used in the cost algorithms for each monitoring component, where 

applicable. 

Inspections. Consistent with HMIWI regulations, it was assumed that annual control 

device inspections will be required for any units having control devices in place or requiring 

further controls to meet the proposed emission limits. In this context, control devices include 
3-5 

fabric filters, afterburners, wet scrubbers, or ACI systems. The cost was estimated at a flat rate of 

$1000 per year. See Table C-4a in Appendix C for further details and sources. 

Parameter monitors. Monitoring of operating parameters can be used to indicate whether 

air pollution control equipment and practices are functioning properly to minimize air pollution. 

Based on the existing CISWI regulations and HMIWI regulations, it was assumed that parameter 

monitoring will be mandatory for all units required to add fabric filters, packed bed scrubbers, or 

ACI systems. Costs for each monitoring system were estimated as follows: 

• For a fabric filter bag leak detection system, capital cost was estimated at $25,500 

and annualized cost at $9,700/yr. 

• For a wet scrubber monitoring system, capital cost was estimated at $24,300 and 

annualized cost at $5,600/yr. 

• The cost for ACI monitoring depends on a unit’s annual operational hours. There 

are no capital costs for ACI monitoring. Annual costs ranged from $500 to $9,800. 

For default parameters and equations used for monitoring costs, see Table C-4b. Sources 

for specific cost data are noted below the table. 

a. Testing Costs

1. Initial Stack Testing. It was assumed that initial stack testing will be required for each 

pollutant that the ICR testing showed did not meet the proposed emission limit. Any 

unit having no test data for certain pollutants will also be required to perform an 

initial emissions test for those pollutants. Costs for each required stack test were 

summed and multiplied by 2/3 to adjust for economies of scale when multiple 

pollutant tests were being performed on a unit. The annualized costs were calculated 

assuming a capital recovery factor of 0.10979 (15 years at 7 percent). The basis of 

these cost estimates for each stack test is summarized in Table C-4c in Appendix C. 

2. Annual Stack Testing. It was assumed that all units, to some extent, will be required to 

demonstrate ongoing compliance with the emissions limits for all pollutants. It was 

assumed that all units will be required to conduct annual stack tests for all pollutants. 

The cost for this annual testing was estimated to be approximately $61,000/yr. The 

basis of these cost estimates for each stack test is summarized in Table C-4c in 

Appendix C. 

3. Visible emissions testing. All SSI units will likely have ash handling operations. 

Therefore, these units would be required to demonstrate compliance to a 5 percent 

visible emissions limit for fugitive emissions generated during ash handling (similar 

to HMIWI). We are proposing that units will be required to conduct annual 

performance tests for fugitive emissions from ash handling using EPA Method 22. 

Costs for this annual test include a capital cost of $250 and an annual cost of $200, 

based on the Revised Compliance Costs and Economic Inputs for Existing HMIWI
3-6 

memo.8Further details regarding this cost estimate are included in Table C-4d in 

Appendix C. 

b. Recordkeeping and Reporting Costs

For all units, a flat rate of $2,989 per year was estimated as the annual cost for 

recordkeeping and reporting. Further details regarding this cost estimate, including hourly labor 

assumptions, labor rates, and associated sources, are included in Table C-4e in Appendix C. 

3.1.1.3 Alternative Disposal Costs 

Certain SSI units may have waste disposal alternatives other than combustion available to 

them, and these alternatives may prove to be less costly than the controls and monitoring 

required for compliance with the proposed SSI standards. To determine if landfilling would be 

an affordable option for facilities even in the absence of the proposed standards, both the annual 

cost to landfill and the annual unit operating cost were estimated. Then, the overall cost for the 

landfilling option was calculated using the following equation: 

Annual Cost for Landfilling Option = Annual Cost to Landfill – Annual Cost to Operate 

SSI Unit 

The methodology for determining annual landfilling costs and annual unit operational 

costs is described below. 

a. Cost to Haul to Landfill

The cost to haul waste to a landfill is the sum of additional sludge storage costs, landfill 

tipping fees, and transportation costs, which depend on the amount of waste to be hauled and the 

distance traveled per haul. 

If choosing to landfill, it was assumed that a facility would need adequate storage 

capacity to store a minimum of 2 to 4 days worth of dried sludge, to account for occasional 

multi-day landfill closures (e.g. weekends and holidays). Facilities may already have such 

storage on-site to account for non-continuous operation of the incineration unit. For this analysis, 

to provide a conservative estimate of costs of the landfilling option, a cost for storing dewatered 

sludge was calculated. It was assumed that a concrete pad with metal railing would be sufficient 

for storage of dried or dewatered sludge at small entities. The smaller entities have a lower 

average dry sludge capacity than large entities. Sewage sludge incineration capacity was known 

for 4 of the 21 units owned by small entities. An average capacity of 1.90 dry tons per hour was 

applied to the other 17 units, and these capacities were used to estimate the maximum volume of 

dry sludge that would accumulate over 4 days. Costs were then estimated for the concrete and 
3-7 

aluminum required to accommodate these volumes. Table C-5c in Appendix C presents these 

costs estimates by unit. For large entities, a different type of storage would likely be required 

(such as a concrete basin capable of storing large quantities of sludge); storage costs for these 

facilities were not estimated because it was assumed large entities would comply rather than shut 

down their units and landfill. 

Tipping fees used in the analysis were specific to each state where state data were 

available9; where state data were not available, landfill tipping fees were based on regional 

tipping fees.10 All fees were in units of $/ton waste and were converted to 2008 dollars. The 

annual tonnage of waste being diverted was calculated based on the dry sludge feed rate of each 

unit and the number of hours it operates per year. Operational hours and sludge feed rates are 

discussed in further detail in the SSI inventory and baseline emissions memos. Discussion with 

landfill experts indicated that landfills may accept wet sewage sludge as well. However, because 

landfills might have a wet sludge capacity limit and SSI units are already dewatering their 

sludge, it’s likely they would continue to do this. The cost analysis therefore focuses on 

landfilling dry sludge rather than wet sludge. 

Transportation costs were based on an estimated $0.266 per ton-mile11. It was assumed 

that a landfill could be found within 50 miles of each facility, yielding a roundtrip distance of 

100 miles. However, a review of state regulations for states where small entities are located 

revealed that Connecticut and New Jersey do not allow sewage sludge to be landfilled. To adjust 

for this, round trip distances for facilities in these states were increased to 200 miles, assuming a 

landfill could be found in another state within 100 miles from the facility. 

Annual landfilling costs varied widely, ranging from $13,000/yr to $5.1 million/yr. Table 

C-5a in Appendix C summarizes the parameters and equations used to calculate the annual cost 

for each facility to landfill the waste it would otherwise incinerate in an SSI. 

b. Cost to Operate Incinerator

Annual incinerator operational costs were based on data provided from the ICR survey 

and known unit capacities. The survey specifically requested that respondents provide annual 

costs to operate each incinerator in 2006, 2007 and 2008. Costs were then confirmed or revised 

based on follow-up contact with the survey recipients. Several steps were taken and assumptions 

made to standardize the data: (1) total costs provided were assumed to be for operating only the 

incinerator (i.e. did not include dewatering or other aspects of plant operation); (2) total costs 
3-8 

listed for multiple units were divided evenly among each unit; and (3) individual cost 

components (e.g. electricity, labor, fuel) were summed if a total cost was not explicitly provided. 

Because cost information was only available for the 9 surveyed entities, an annual cost 

factor, in $/dry ton, was developed using the available data and multiplied by the average 

capacities of all other units. Both an average factor ($113.80/dry ton for FB units and 

$329.22/dry ton for MH units) and a minimum factor ($55.50/dry ton for FB units and 

$79.43/dry ton for MH units) were calculated and applied. The minimum factor is the most 

conservative estimate (i.e. would yield the lowest unit operational cost and thus the highest net 

cost for the landfilling option) and was used for the economic analysis.

12

Table C-5b in Appendix C summarizes the information provided, assumptions made, and 

cost factors used to estimate costs for all units not having cost data. 

3.1.2 Emission Reductions 

Emissions reductions were calculated for each of the nine pollutants for two scenarios: 

(1) assuming each existing unit complied with the proposed emissions limits; and (2) assuming 

that all large entities would comply with the proposed emission limits and small entities would 

cease using their incinerators and landfill the dewatered sludge instead. Emission reductions 

were calculated by estimating the emissions resulting from each scenario and subtracting the 

baseline emissions previously calculated. Baseline emission calculations are discussed in a 

separate memorandum.3 The baseline memorandum indicates that emissions and flow rate 

information was collected from only 25 of the 218 SSI units. Sludge capacity information was 

collected from 105 of 218 units. As described in the baseline memorandum, default factors for 

emissions, flow rate, and sludge capacity were developed and applied to units without data. 

3.1.2.1 Emission Reductions if All Entities Comply With MACT Floor Limits 

Emission reductions were calculated using the following equation: 

Reduction = Baseline – MACT Floor Emission 

The calculation of baseline emissions are described in detail in a separate memo.3 The 

MACT floor emission values, resulting from all entities meeting the proposed limits, were 

calculated as follows: 
3-9 

a. Units already meeting the proposed limits. 

If a unit was already meeting the MACT floor for a given pollutant, then the MACT floor 

emission value was assumed to equal the baseline value (i.e., no backsliding or emissions 

increases would occur), yielding zero reduction. 

b. Units not currently meeting the proposed limits.

For units not already meeting the MACT floor for a given pollutant, it was assumed that 

with the proposed limits in place the unit would reduce its pollutant concentration to at least that 

of the floor. Thus, the reduction would be the difference between the baseline and the proposed 

limit. 

3.1.2.2 Emission Reductions if Large Entities Comply With MACT Floor Limits and Small 

Entities Landfill 

For large entities, reductions are calculated as described in Section 4.1. For small entities, 

however, the emissions resulting from hauling the diverted waste, landfilling the waste, and 

flaring the landfill gas generated from the waste need to be considered. Emission reductions for 

small entities were calculated using the following equations: 

Reduction = Baseline – (MACT Floor Emission + Emissions from Landfilling) 

Emissions from Landfilling = Vehicle Emissions + Direct Landfill Emissions + Flare 

Emissions 

a. Vehicle Emissions

To determine the vehicle emissions resulting from the trucks that would haul the 

dewatered sewage sludge to a nearby landfill, assumptions regarding sludge density, truck 

capacity, and vehicle emission factors were made: 

1. A dewatered sludge density of 1,215 pounds per cubic yard13was used in 

conjunction with each unit’s capacity to determine the approximate volume of 

sludge to be hauled. 

2. It was assumed that, since most facilities would need to move at least 50 cubic 

yards per day, a maximum capacity hauling vehicle (36 yd3) would be the most 

likely vehicle used.14

3. The following emission factors for CO, NOx, Filterable PM, PM2.5, and SO2 were 

derived from EPA’s Office of Transportation and Air Quality (OTAQ) Motor 

Vehicle Emission Simulator (MOVES),15using national defaults for parameters 

and refuse trucks as the source type : 
3-10 

CO 2.99 grams emitted per mile 

NOx 10.8 grams emitted per mile 

Filterable PM 0.65 grams emitted per mile 

PM2.5 0.56 grams emitted per mile 

SO2 0.03 grams emitted per mile 

Table C-6a shows the inputs and resulting emissions calculated for each unit choosing the 

landfill option. 

b. Direct Landfill Emissions

Landfill gas generated by the decomposition of waste is a source of Hg, HCl, and SO2. 

Emissions of these three criteria pollutants due to landfilled, dewatered sewage sludge, were 

estimated using EPA’s LandGEM16 model in conjunction with default landfill gas sulfur and 

chlorine concentrations, as reported in the AP-42.17 As a conservative estimate, it was assumed 

that landfill gas collection systems would collect 50 percent of the landfill gas generated. Unit 

capacities and operational hours were used to determine the amount of waste diverted annually 

from all units. Instead of running LandGEM for each individual unit, a total estimate of landfill 

gas generated by running the model once using the total annual waste diverted for all units. Unitspecific estimates for landfill emissions were not calculated. Raw LandGEM outputs and default 

assumptions are presented in Table C-6b in Appendix C. Resulting total emissions over 20 years 

for these three pollutants are presented in Table C-6c in Appendix C. These values were divided 

by 20 to obtain annual emissions directly emitted from landfills as a result of landfilling 

dewatered sewage sludge. 

c. Emissions from Landfill Gas Flaring

Additional emissions of PM, NOx, and CO will result from flaring landfill gas generated 

by the landfilling of dewatered sewage sludge. A landfill gas collection efficiency of 50 percent 

was assumed, meaning that 50 percent of the landfill gas generated from landfilled sewage 

sludge would be collected and combusted. AP-42 emission factors, representing the mass of 

pollutant emitted per volume of methane combusted, were applied in conjunction with the 

methane output calculated in the LandGEM model. Again, LandGEM outputs are presented in 

Table C-6b in Appendix C, and resulting total emissions over 20 years for these three pollutants 

are presented in Table C-6c in Appendix C. Values were divided by 20 to obtain annual 

emissions resulting from landfill gas flaring. 
3-11 

3.2 Analysis of Beyond the MACT Floor Controls for Existing SSI Units 

The MACT floor analysis for existing sources results in emission levels that each existing 

SSI unit is required to meet. The costs and emission reductions of the MACT floor requirements 

were estimated using the following assumptions: (1) units that needed to meet the MACT floor 

for Cd, Pb, and PM would add a FF, (2) units that needed to meet the MACT floor for HCl and 

SO2 would add a packed bed scrubber (PBS), and (3) units that needed to meet the MACT floor 

for Hg and CDD/CDF would use activated carbon injection (ACI) (Cost and Emissions 

Reduction, 2010). All FB and MH units were determined to meet the floor level of control for 

NOx and CO, and no additional control was necessary. 

Section 3.2.1 discusses the selection of more stringent controls or emission levels than 

the floor level reviewed for this analysis. Section 3.2.2 discusses the methodology used to 

estimate costs and emission reductions of the more stringent controls, and Section 3.2.3 

summarizes regulatory options selected for the BTF analysis. Baseline emissions and emission 

reductions of PM2.5 were calculated from emissions data collected by EPA and assuming that 

controls applicable for PM would also reduce PM2.5. 

3.2.1 Selection of More Stringent Controls 

The control technologies that were costed to achieve the MACT floor levels for PM, Cd, 

Pb, HCl, SO2, Hg, and CDD/CDF are the most effective controls available to reduce these 

pollutants. Consequently, no additional technologies were considered to control these pollutants 

for this analysis. Since not every SSI unit was determined to need FF, PBS, or ACI to achieve 

the MACT floor level of control or operated them currently (i.e., the baseline level of control), 

more stringent controls to be analyzed for the entire SSI source category would be requiring all 

units that did not have these controls at baseline or for meeting the MACT floors to add these 

controls. Consequently, more stringent controls applied to SSI units that were analyzed include 

adding a FF for all SSI units (if the units did not already have one at baseline or to meet the 

MACT floor) to control PM, Cd, and Pb; adding a PBS for all SSI units (if the units did not 

already have one at baseline or to meet the MACT floor) to control HCl and SO2; and adding 

ACI (if the units did not already have one at baseline or to meet the MACT floor) to control Hg 

and CDD/CDF. Emission reductions of PM2.5 were calculated assuming that controls applicable 

for PM would also reduce PM2.5. 

Potential add-on control technologies that achieve NOx reduction at other combustion 

sources are selective catalytic reduction (SCR), selective noncatalytic reduction (SNCR), and 

flue gas recirculation (FGR). However, none of these technologies were evaluated to be 
3-12 

appropriate for SSI units. SSI units do not use SCR or SNCR (Inventory Database, 2010). 

Additionally, there are no successful applications of SCR technology to waste-combustion units 

possibly because of the difficulties operating SCRs in operations where there is significant PM or 

sulfur loading in the gas stream. Application of SNCR also may not be technically feasible 

considering the combustion mechanisms of MH and FB units (U.S. EPA, 2003). Application of 

SNCR requires installation of a reagent injection system that is unlikely to work for existing SSI 

units. Additionally, SNCR is optimal for combustion units with high residence time and exit 

incinerator temperature, and less effective for lower uncontrolled NOx pollutant loadings (e.g., 

less than 200 ppm). Existing SSI units are not good matches for these considerations. FGR has 

been used on combustion devices to reduce NOx emissions. However, the amount of NOx

reduced varies widely, ranging from 20% to 80%, and site-specific factors often affect the 

performance. To support regulations for SSI units, EPA collected emissions information on the 

nine Section 129 pollutants. One unit providing emission test data operates a MH unit with FGR. 

However, its emission levels are similar to units without FGR. Therefore, no conclusion could be 

made on FGR performance. Additionally, no FB units use any add-on NOx control because FB 

units can achieve low NOx emission levels, below 100 ppmv and many achieve below 70 ppmv. 

For control of CO, an add-on combustion device, such as an afterburner or thermal 

oxidizer, was analyzed as a more stringent control device that could be applied to SSI units. CO 

emissions data were collected from nine MH SSI units as part of the data collection efforts 

supporting the development of emission standards for SSI units. Table 3-1 summarizes the 

average CO concentration levels from these units (Facility, Unit, and Emissions Test Database, 

2010). The table is grouped into three classes of SSIs: (1) units that do not use any combustion 

controls, (2) units that use an on-hearth afterburner, and (3) units that use either a detached 

afterburner or thermal oxidizer or use FGR in combination with an on-hearth afterburner. 

Afterburner, or secondary chamber, retrofits include retrofitting an incinerator with a 

larger secondary chamber (with a longer gas residence time, for example, 2 seconds) and 

operating it at a higher temperature (e.g., 1,800°F). On-hearth afterburners are the top hearth of a 

MH unit that has been redesigned so that sludge is rerouted to the second hearth. Retrofitting the 

MH unit with an on-hearth afterburner may require modifications to downstream air pollution 

control systems because of higher temperatures and larger volumes of exhaust gases (Dangtran, 

Mullen, and Mayrose, 2000). Although there will be reductions in CO and total hydrocarbon 

(THC) emissions, the reductions may be limited because of low temperature and limited 

residence time of the gas in the afterburner stage. The use of FGR in combination with an onhearth afterburner shows significantly lower emissions levels than just using an on-hearth 
3-13 

Table 3-1. Summary of Average CO Emissions Collected from MH Units 

Classes Facility Location Unit ID 

Average CO Emission Level 

(ppmvd @ 7% O2) 

Uncontrolled Boat Harbor VA 1 3,761 

1 1,323 

Seneca MN 

2 853 

1 905 

On-hearth afterburner 

Central Contra Costa CA 

2 752 

Columbia Metro SC 1 63 

Mountain View NJ 2 39 

1 28 

Detached afterburner, 

thermal oxidizer, or onhearth afterburner with 

flue gas recirculation Upper Blackstone MA 

3 59 

afterburner. However, this may be a generalization because only one data point for this control 

combination was reviewed. Additionally, performance of FGR is often influenced by sitespecific parameters that may not be generalized to the entire subcategory. 

Table 3-1 shows that MH units using an add-on afterburner or thermal oxidizer can 

achieve CO emission levels less than 100 ppmv. The Clean Water Acts “503 Rule” [40CFR Part 

503] limits sewage sludge incinerators to 100 ppm THC as propane, dry basis, corrected to 7% 

oxygen, averaged for 30 days. The 503 Rule allows substitution of 100 ppm CO dry basis, 

corrected to 7% oxygen for the THC originally required. This allows the use of a lower cost, 

easier to maintain CO monitor in place of the THC monitor, which is difficult to keep online. To 

be consistent with the 503 regulations for disposal of sewage sludge, a value of 100 ppmv was 

used as the emission level that a MH unit with an afterburner could achieve. Because CO levels 

for FB units are below 100 ppmv, no afterburners were costed for this subcategory. 

3.2.2 Methodology Used to Estimate Cost and Emission Reductions 

The methodology used to calculate costs and emission reductions from applying the more 

stringent controls followed the procedures discussed in Section 3.1 and in the SSI cost 

memorandum (Cost and Emissions Reduction, 2010). As described above, if a unit already had a 

FF or needed one to meet the MACT floor limits, no additional costs for FF were calculated. 

Otherwise, a FF was costed out for the unit. Similar procedures were followed for PBS and ACI. 

The cost algorithms; inputs to the algorithms; and testing, monitoring, recordkeeping, and 
3-14 

reporting costs calculations are the same as conducted for the MACT floor and are discussed in 

detail in the MACT floor cost and emission reductions memorandum. 

Emission reductions from applying the controls relative to the MACT floor limits were 

calculated using the following procedure. First, the reduction efficiency of the control for each 

pollutant was applied to the uncontrolled concentration to determine the total reduction the 

control would achieve. The reduction from uncontrolled levels to the MACT floor limits was 

previously calculated for the MACT floor cost and emission reduction analysis discussed in 

Section 3.1 and in a supporting memorandum (Cost and Emissions Reduction, 2010). For each 

pollutant, the incremental reduction between the more stringent control application and the 

MACT floor was calculated by subtracting the MACT floor concentration from the reduction 

achieved by the more stringent control. Reduction of PM2.5 was calculated assuming that controls 

applicable for PM would also reduce PM2.5. 

3.2.3 Selection of Regulatory Options 

Tables 3-2 and 3-3 summarize the costs, emission reductions, and incremental cost 

effectiveness of the controls analyzed in the BTF analysis, for the case where all entities comply 

(Table 3-2) and the case where small entities choose to landfill (Table 3-3). Tables 3-4 and 3-5 

present the results from Tables 3-2 and 3-3 on a per unit basis. The number of Fluidized Bed 

units requiring some sort of control to meet the MACT floors can be broken down as follows: 

Fabric Filter, 41 units; Afterburner Retrofit, 0 units; Packed Bed Scrubber, 7 units; and ACI, 51 

units. The number of Multiple Hearth units requiring some sort of control to meet the MACT 

floors can be broken down as follows: Fabric Filter, 25 units; Afterburner Retrofit, 0 units; 

Packed Bed Scrubber, 11 units; and ACI, 2 units. The total number of SSIs requiring some sort 

of control to meet the MACT floors can be broken down as follows: Fabric Filter, 66 units; 

Afterburner Retrofit, 0 units; Packed Bed Scrubber, 18 units; and ACI, 53 units. The per unit 

values were calculated by dividing the costs and emissions reduction for each option by the 

number of SSI units that would require control for the option. The tables indicate that except for 

the afterburner, all of the controls applied result in a high incremental cost-effectiveness, greater 

than $70,000/ton. Consequently, these controls, with the exception of activation carbon injection 

for Hg control, were considered infeasible. Activated carbon injection was determined to provide 

significant reduction in Hg emissions at MH units. Therefore, the following control options were 

selected for further analysis: 

§ Option 1 is the MACT floor level of control for the two subcategories developed for 

existing SSI units, MH units and FB units. 
3-15 

§ Option 2 is the same as Option 1, with the addition of activated carbon injection for 

additional Hg emissions reduction from MH units. 

§ Option 3 is the same as Option 2, with the addition of an afterburner on all MH units 

for additional CO emissions reduction. 

Tables 3-6 and 3-7 summarize the costs, total emission reductions, and incremental costeffectiveness of the three options. Detailed costs and emission reductions for each SSI unit for 

the each option are presented in supporting memoranda 
3-16

Table 3-2. Emissions Reductions and Costs If All Units Comply 

Cost (2008$)f Baseline Emissions and Incremental Emission Reductions (tons/year) 

Fluidized Bed Incinerators 

# of units 

requiring 

additional 

control 

Total Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.0103 119.6 2.99 0.0531 0.0758 327.4 56.60 54.22 134.1 0.000082 0.0000068 - - 

MACT Floor Total Cost and 

Emission Reductions

a 

51 $86,696,269 $32,313,699 0.0010 0 1.53 0.0053 0.0579 0 41.00 38.88 59.7 0.000079 0.0000065 141.1 - 

Fabric Filter 14 $32,663,593 $8,402,116 0.0066 0 0 0.0343 0 0 0.00 0.00 0 0 0 0.041 $205,482,746 

Afterburner 

Retrofitb 

52 $31,532,870 $10,384,276 0 0 0 0 0 0 0 0 0 0 0 0.000 - 

Packed Bed 

Scrubber 

46 $48,701,933 $10,854,865 0 0 1.01 0 0 0 0 0 54.2 0 0 55.240 $196,505 

BTF Costs 

and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection

c 

0 $0 $0 0 0 0 0 0.0074 0 0 0 0 0.000001 0.0000002 0.007 $0 

Cost (2008$)f Baseline Emissions and Incremental Emission Reductions (tons/year) 

Multiple Hearth Incinerators 

# of units 

requiring 

additional 

control 

Total Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) 

Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 2.83 29024 122.59 6.0595 3.0536 7358.5 1101.46 666.13 3078.8 0.000020 0.0000013 - - 

MACT Floor Total Cost and 

Emission Reductions

a 

41 $131,764,712 $40,327,113 1.41 0 91.51 2.6237 0.0315 4.3051 277.90 167.21 2132.6 0.000000 0.0000000 2,677.5 $15,061 

Fabric Filter 138 $478,373,914 $115,254,825 1.15 0 0 2.8750 0 0 614.00 372.00 0 0 0 990.0 $116,416 

Afterburner 

Retrofit 

128 $145,514,140 $43,193,966 0 25691 0 0 0 0 0 0 0 0 0 25,691 $1,681 

Packed Bed 

Scrubber 

148 $258,596,495 $54,863,534 0 0 19.60 0 0 0 0 0 659.2 0 0 678.8 $80,820 

BTF Costs 

and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

161 $6,230,844 $32,335,212 0 0 0 0 2.6235 0 0 0 0 0.000020 0.0000013 2.624 $12,324,974d 

Cost (2008$)f Baseline Emissions and Incremental Emission Reductions (tons/year) 

All Incinerators 

# of units 

requiring 

additional 

control 

Total Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 2.84 29144 125.58 6.1126 3.1294 7685.9 1158.05 720.36 3212.9 0.000102 0.0000081 - - 

MACT Floor Total Cost and 

Emission Reductions

a 

92 $218,460,981 $72,640,812 1.41 0 93.04 2.6291 0.0894 4.3051 318.90 206.09 2192.2 0.000079 0.0000065 2,818.7 $25,771 

Fabric Filter 152 $511,037,506 $123,656,941 1.16 0 0 2.9093 0 0 614.00 372.00 0 0 0 990.1 $124,897 

Afterburner 

Retrofit 

180 $177,047,010 $53,578,242 0 25691 0 0 0 0 0 0 0 0 0 25,691 $2,086 

Packed Bed 

Scrubber 

194 $307,298,429 $65,718,399 0 0 20.61 0 0 0 0 0 713.5 0 0 734.1 $89,525 

BTF Costs 

and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

161 $6,230,844 $32,335,212 0 0 0 0 2.6310 0 0 0 0 0.000021 0.0000015 2.631 $12,290,076e 
3-17

a. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 41 units; Afterburner Retrofit, 0 units; Packed Bed Scrubber, 7 units; and ACI, 51 units. 

The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 25 units; Afterburner Retrofit, 0 units; Packed Bed Scrubber, 11 units; and ACI, 2 units. 

The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 66 units; Afterburner Retrofit, 0 units; Packed Bed Scrubber, 18 units; and ACI, 53 units. 

b. Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls. For other pollutants, reductions resulted from the installation of controls where 

improvement was needed in order to meet the proposed limit. For any case where the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would be able to at 

least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit. 

c. Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for the BTF scenario, the maximum control efficiency (98%) was assumed. For the MACT 

floor scenario, only the percent reduction required to meet the floor limits were incorporated as the control efficiencies. 

d. The cost-effectiveness of ACI control for MH units is equivalent to $6,160 per pound of Hg reduced. 

e. The cost-effectiveness of ACI control for all units is equivalent to $6,150 per pound of Hg reduced. 

f. Costs were annualized using a discount rate of 7 percent

.
3-18

Table 3-3. Emissions Reductions and Costs If Small Entities Landfill 

Cost (2008$)a Baseline Emissions and Incremental Emission Reductions (tons/year)b

Fluidized Bed Incinerators 

# of units 

requiring 

additional 

control 

Total Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.0103

2 

119.6 2.99 0.0531 0.0758 327.4 56.60 54.22 134.1 0.000082 0.0000068 - - 

MACT Floor Total Cost and 

Emission Reductions

c 

46 $69,952,757 $26,163,050 0.0028

2 

18.89 1.81 0.0147 0.0612 53.05 43.47 41.23 76.8 0.000080 0.0000065 235.3 $111,194 

Fabric Filter 13 $30,642,201 $7,926,815 0.0052

4 

0 0 0.0271 0 0 0.00 0.00 0 0 0 0.032 $245,034,357 

Afterburner 

Retrofitd 

43 $26,571,102 $8,659,394 0 0 0 0 0 0 0 0 0 0 0 0.000 - 

Packed Bed 

Scrubber 

39 $41,683,343 $9,277,850 0 0 0.80 0 0 0 0 0 40.6 0 0 41.437 $223,903 

Additional 

Costs and 

Emission 

Reductions by 

Control 

Activated 

Carbon 

Injection

e 

0 $0 $0 0 0 0 0 0.0060 0 0 0 0 0.000001 0.0000001 0.006 $0 

Cost (2008$)a Baseline Emissions and Incremental Emission Reductions (tons/year)b

Multiple Hearth Incinerators 

# of units 

requiring 

additional 

control 

Total Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 2.8277

9 

29024.

5 

122.59 6.0595 3.0536 7358.

5 

1101.46 666.13 3078.

8 

0.000020 0.0000013 - - 

MACT Floor Total Cost and 

Emission Reductions

c 

38 $125,327,287 $33,647,893 1.5459

0 

3080.1

6 

94.72 2.9497 0.3536 793.8

1 

348.85 210.41 2221.

0 

0.000002 0.0000001 6,753.8 $4,982 

Fabric Filter 127 $440,670,924 $105,196,529 1.0471

0 

0 0 2.6084 0 0 469.00 284.00 0 0 0 756.655 $139,028 

Afterburner 

Retrofit 

122 $137,648,283 $40,428,804 0 22971.

28 

0 0 0 0 0 0 0 0 0 22,971.284 $1,760 

Packed Bed 

Scrubber 

137 $237,426,572 $50,085,972 0 0 17.62 0 0 0 0 0 601.5 0 0 619.149 $80,895 

Additional 

Costs and 

Emission 

Reductions by 

Control 

Activated 

Carbon 

Injection 

149 $5,744,514 $28,913,350 0 0 0 0 2.3440 0 0 0 0 0.000017 0.0000012 2.344 $12,334,707f 

Cost (2008$)a Baseline Emissions and Incremental Emission Reductions (tons/year)b

All Incinerators 

# of units 

requiring 

additional 

control 

Total Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 2.8381

1 

29144.

0 

125.58 6.1126 3.1294 7685.

9 

1158.05 720.36 3212.9 0.000102 0.0000081 - - 

MACT Floor Total Cost and 

Emission Reductions

c 

84 $195,280,044 $59,810,943 1.5487

2 

3099.0

5 

96.53 2.9644 0.4147 846.8

6 

392.32 251.64 2297.8 0.000082 0.0000067 6,989.1 $8,558 

Fabric Filter 140 $471,313,125 $113,123,344 1.0523

4 

0 0 2.6355 0 0 469.00 284.00 0 0 0 756.688 $149,498 

Afterburner 

Retrofit 

165 $164,219,385 $49,088,198 0 22971.

28 

0 0 0 0 0 0 0 0 0 22,971.284 $2,137 

Packed Bed 

Scrubber 

176 $279,109,916 $59,363,822 0 0 18.43 0 0 0 0 0 642.2 0 0 660.586 $89,865 

Additional 

Costs and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

149 $5,744,514 $28,913,350 0 0 0 0 2.3500 0 0 0 0 0.000019 0.0000013 2.350 $12,303,406g 

a. Costs were annualized using a discount rate of 7 percent. 
3-19

b. Emissions from landfilling activities are not included in this table. 

c. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 33 units; Afterburner 

Retrofit, 0 units; Packed Bed Scrubber, 5 units; and ACI, 46 units. 

The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 24 units; 

Afterburner Retrofit, 0 units; Packed Bed Scrubber, 10 units; and ACI, 2 units. 

The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 57 units; Afterburner Retrofit, 

0 units; Packed Bed Scrubber, 15 units; and ACI, 48 units. 

d. Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls. 

For other pollutants, reductions resulted from the installation of controls where improvement was needed in order to meet the proposed limit. For any case 

where the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would be 

able to at least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit. 

e. Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for the 

BTF scenario, the maximum control efficiency (98%) was assumed. For the MACT floor scenario, only the percent reduction required to meet the floor limits 

were incorporated as the control efficiencies. 

f. The cost-effectiveness of ACI control for MH units is equivalent to $6,170 per pound of Hg reduced. 

g. The cost-effectiveness of ACI control for all units is equivalent to $6,150 per pound of Hg reduced. 
3-20

Table 3-4. Emissions Reductions and Costs If All Units Comply – Per Unit Basis 

Cost (2008$)b Baseline Emissions and Incremental Emission Reductions (tons/year) 

Fluidized Bed Incinerators 

# of units 

requiring 

additional 

control 

Total 

Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.0002 2.2 0.05 0.0010 0.0014 6.0 1.03 0.99 2.4 0.000001 0.0000001 - - 

MACT Floor Total Cost and 

Emission Reductions

a 

51 $1,699,927 $633,602 0.0000 0 0.03 0.0001 0.0011 0 0.80 0.76 1.2 0.000002 0.0000001 2.8 $228,934 

Fabric Filter 14 $2,333,114 $600,151 0.0005 0 0 0.0024 0 0 0.00 0.00 0 0 0 0.003 $205,482,746 

Afterburner 

Retrofit 

52 $606,401 $199,698 0 0 0 0 0 0 0 0 0 0 0 0.000 - 

Packed Bed 

Scrubber 

46 $1,058,738 $235,975 0 0 0.02 0 0 0 0 0 1.2 0 0 1.201 $196,505 

BTF Costs 

and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

0 - - - - - - - - - - - - - - - 

Cost (2008$)b Baseline Emissions and Incremental Emission Reductions (tons/year) 

Multiple Hearth Incinerators 

# of units 

requiring 

additional 

control 

Total 

Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.02 178 0.75 0.0372 0.0187 45.1 6.76 4.09 18.9 0.000000 0.0000000 - - 

MACT Floor Total Cost and 

Emission Reductions

a 

41 $3,213,773 $983,588 0.03 0 2.23 0.0640 0.0008 0.105 6.78 4.08 52.0 0.000000 0.0000000 65.3 $15,061 

Fabric Filter 138 $3,466,478 $835,180 0.01 0 0 0.0208 0 0 4.45 2.70 0 0 0 7.2 $116,416 

Afterburner 

Retrofit 

128 $1,136,829 $337,453 0 201 0 0 0 0 0 0 0 0 0 201 $1,681 

Packed Bed 

Scrubber 

148 $1,747,274 $370,700 0 0 0.13 0 0 0 0 0 4.5 0 0 4.6 $80,820 

Additional 

Costs and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

161 $38,701 $200,840 0 0 0 0 0.0163 0 0 0 0 0.000000 0.0000000 0.016 $12,324,974 

Cost (2008$)b Baseline Emissions and Incremental Emission Reductions (tons/year) 

All Incinerators 

# of units 

requiring 

additional 

control 

Total 

Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.02 180 0.81 0.0381 0.0201 51.1 7.79 5.07 21.3 0.000002 0.0000001 - - 

MACT Floor Total Cost and 

Emission Reductions

a 

92 $4,913,700 $1,617,190 0.03 0 2.26 0.0641 0.0019 0.105 7.58 4.84 53.2 0.000002 0.0000001 30.6 $52,784 

Fabric Filter 152 $5,799,591 $1,435,331 0.01 0 0 0.0233 0 0 4.45 2.70 0 0 0 6.5 $220,359 

Afterburner 

Retrofit 

180 $1,743,231 $537,150 0 201 0 0 0 0 0 0 0 0 0 143 $3,763 

Packed Bed 

Scrubber 

194 $2,806,011 $606,675 0 0 0.15 0 0 0 0 0 5.6 0 0 3.8 $160,330 

Additional 

Costs and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

161 $38,701 $200,840 0 0 0 0 0.0163 0 0 0 0 0.000000 0.0000000 0.016 $12,290,076 

a. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 41 units; Afterburner 

Retrofit, 0 units; Packed Bed Scrubber, 7 units; and ACI, 51 units. 
3-21

The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 25 units; 

Afterburner Retrofit, 0 units; Packed Bed Scrubber, 11 units; and ACI, 2 units. 

The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 66 units; Afterburner Retrofit, 

0 units; Packed Bed Scrubber, 18 units; and ACI, 53 units. 

b. Costs were annualized using a discount rate of 7 percent. 
3-22

Table 3-5. Emissions Reductions and Costs If Small Entities Landfill – Per Unit Basis 

Cost (2008$)a Baseline Emissions and Incremental Emission Reductions (tons/year)b

Fluidized Bed Incinerators 

# of units 

requiring 

additional 

control 

Total 

Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.00019 2.2 0.05 0.0010 0.0014 6.0 1.03 0.99 2.4 0.000001 0.0000001 - - 

MACT Floor Total Cost and 

Emission Reductions

c 

46 $1,520,712 $568,762 0.00006 0.41 0.04 0.0003 0.0013 1.15 0.95 0.90 1.7 0.000002 0.0000001 5.1 $111,194 

Fabric Filter 13 $2,357,092 $609,755 0.00040 0 0 0.0021 0 0 0.00 0.00 0 0 0 0.002 $245,034,357 

Afterburner 

Retrofitd 

43 $617,933 $201,381 0 0 0 0 0 0 0 0 0 0 0 0.000 - 

Packed Bed 

Scrubber 

39 $1,068,804 $237,894 0 0 0.02 0 0 0 0 0 1.0 0 0 1.062 $223,903 

Additional 

Costs and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection

e 

0 - - - - - - - - - - - - - - - 

Cost (2008$)a Baseline Emissions and Incremental Emission Reductions (tons/year)b

Multiple Hearth Incinerators 

# of units 

requiring 

additional 

control 

Total 

Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.01735 178.1 0.75 0.0372 0.0187 45.1 6.76 4.09 18.9 0.000000 0.0000000 - - 

MACT Floor Total Cost and 

Emission Reductions

c 

38 $3,298,086 $885,471 0.04068 81.06 2.49 0.0776 0.0093 20.89 9.18 5.54 58.4 0.000000 0.0000000 177.7 $4,982 

Fabric Filter 127 $3,469,850 $828,319 0.00824 0 0 0.0205 0 0 3.69 2.24 0 0 0 5.958 $139,028 

Afterburner 

Retrofit 

122 $1,128,265 $331,384 0 188.2

892 

0 0 0 0 0 0 0 0 0 188.289 $1,760 

Packed Bed 

Scrubber 

137 $1,733,041 $365,591 0 0 0.13 0 0 0 0 0 4.4 0 0 4.519 $80,895 

Additional 

Costs and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

149 $38,554 $194,049 0 0 0 0 0.0157 0 0 0 0 0.000000 0.0000000 0.016 $12,334,707 

Cost (2008$)a Baseline Emissions and Incremental Emission Reductions (tons/year)b

All Incinerators 

# of units 

requiring 

additional 

control 

Total 

Capital 

Investment 

($) 

Total 

Annualized 

Cost ($/yr) Cd CO HCl Pb Hg NOx PM Filt PM 2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions 

(tons/yr) 

Incremental 

Costeffectiveness 

($/ton) 

Baseline Emissions - - 0.01754 180.2 0.81 0.0381 0.0201 51.1 7.79 5.07 21.3 0.000002 0.0000001 - - 

MACT Floor Total Cost and 

Emission Reductions

c 

84 $4,818,799 $1,454,233 0.04074 81.47 2.53 0.0779 0.0106 22.04 10.13 6.43 60.1 0.000002 0.0000001 182.8 $7,953 

Fabric Filter 140 $5,826,942 $1,438,074 0.00865 0 0 0.0226 0 0 3.69 2.24 0 0 0 5.960 $241,271 

Afterburner 

Retrofit 

165 $1,746,197 $532,765 0 188.2

892 

0 0 0 0 0 0 0 0 0 188.289 $2,830 

Packed Bed 

Scrubber 

176 $2,801,844 $603,485 0 0 0.15 0 0 0 0 0 5.4 0 0 5.582 $108,116 

Additional 

Costs and 

Emission 

Reductions 

by Control 

Activated 

Carbon 

Injection 

149 $38,554 $194,049 0 0 0 0 0.0157 0 0 0 0 0.000000 0.0000000 0.016 $12,334,707 
3-23

a. Costs were annualized using a discount rate of 7 percent. 

b. Emissions from landfilling activities are not included in this table. 

c. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 33 units; Afterburner 

Retrofit, 0 units; Packed Bed Scrubber, 5 units; and ACI, 46 units. 

The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 24 units; 

Afterburner Retrofit, 0 units; Packed Bed Scrubber, 10 units; and ACI, 2 units. 

The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 57 units; Afterburner Retrofit, 

0 units; Packed Bed Scrubber, 15 units; and ACI, 48 units. 

d. Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls. 

For other pollutants, reductions resulted from the installation of controls where improvement was needed in order to meet the proposed limit. For any case 

where the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would be 

able to at least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit. 

e. Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for the 

BTF scenario, the maximum control efficiency (98%) was assumed. For the MACT floor scenario, only the percent reduction required to meet the floor limits 

were incorporated as the control efficiencies. 
3-24

Table 3-6. Emissions Reductions and Costs If All Units Complya

Cost (2008$) Baseline Emissions and Incremental Emission Reductions (tons/year) 

Option Total Capital 

Investment 

($million) 

Total 

Annualized Cost

($million/yr) Cd CO HCl Pb Hg NOx PM Filt PM2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions of 

129 Pollutants

Baseline Emissions — — 2.84 29,100 126 6.11 3.13 7,700 1,160 720 3,210 0.000102 0.0000081 — 

Option 1 (MACT Floor) $220 $73 1.41 0 93.0 2.63 0.09 4.31 319 206 2,190 0.000079 0.0000065 2,819

Option 2 (MACT Floor 

+ Activated carbon 

injection for MH units) $225 $105 1.41 0 93.0 2.63 2.71 4.31 319 206 2,190 0.000098 0.0000078 2,821

Costs and 

Emission 

Reductions 

Option 3 (Option 2 + 

Afterburners for MH 

Units) $370 $148 1.41 25,700 93.0 2.63 2.71 4.31 319 206 2,190 0.000098 0.0000078 28,500

Table 3-7. Emissions Reductions and Costs If Large Entities Comply and Small Entities Landfilla

Cost (2008$) Baseline Emissions and Incremental Emission Reductions (tons/year) 

Option Total Capital 

Investment 

($million) 

Total 

Annualized Cost

($million/yr) Cd CO HCl Pb Hg NOx PM Filt PM2.5 SO2 D/F Total D/F TEQ 

Total 

Emission 

Reductions of 

129 Pollutants

Baseline Emissions — — 2.84 29,100 126 6.11 3.13 7,700 1,160 720 3,210 0.000102 0.0000081 — 

Option 1 (MACT Floor) $195 $59.8 1.55 2,850 96.2 2.96 0.41 823 390 251 2,300 0.000082 0.0000067 6,714

Option 2 (MACT Floor 

+ Activated carbon 

injection for MH units) $201 $89 1.55 2,850 96.2 2.96 2.76 823 390 251 2,300 0.000099 0.0000078 6,717

Costs and 

Emission 

Reductions 

Option 3 (Option 2 + 

Afterburner for MH 

Units) $338 $129 1.55 25,800 96.2 2.96 2.76 823 390 251 2,300 0.000099 0.0000078 29,690

aAnnualized costs were calculated using a discount rate of 7 percent.
3-25 

3.3 Estimation of Impacts for New Units Constructed within 5 Years After 

Promulgation of the SSI NSPS 

3.3.1 Estimation of New Sources 

Several significant changes have occurred to SSI units in the past 20 years. EPA’s Office 

of Water (OW) set emission and discharge standards for sewage sludge disposal methods 

(including incineration) in 1993 (40 CFR part 503). As a result of the CWA part 503 Rule, many 

wastewater treatment facilities chose to use alternative methods for disposing of sewage sludge, 

such as landfilling or land application, rather than try to meet the incineration requirements. 

Many of the closed incinerators had been operated by municipalities or agencies serving smaller 

populations (i.e., fewer than 50,000 people) (Summary of Telephone Contacts, 2010). 

The general trend has also been for facilities still incinerating sewage sludge to replace 

older MH units with newer FB units because of better emissions performance, savings in fuel 

cost, and flexibility in operation. Since 1988, over 40 new FB systems have been installed, with 

11 replacing existing MH units (Dangtran, Mullen, and Mayrose, 2000). Discussions with the 

National Association of Clean Water Agencies (NACWA), the industry trade group, indicated 

that only FB units are likely to be constructed in the future (U.S. EPA, 2009b). Consequently, it 

was assumed that any new units that would be built after promulgation of the NSPS would be a 

FB design. 

To estimate the number of new sources that might be constructed in the 5 years following 

promulgation of the NSPS, the number of sources being constructed 5 years prior to proposal of 

the rule was reviewed to determine if there was a trend. Under EPA’s New Source Review 

(NSR) program, if a company is planning to build a new plant or modify an existing plant such 

that air pollution emissions will increase by a large amount, then the company must obtain an 

NSR permit. The NSR permit is a construction permit that requires the company to minimize air 

pollution emissions by changing the process to prevent air pollution and/or installing air 

pollution control equipment. The NSR program defines control levels based on the type of 

program the source is subject to: reasonably available control technology (RACT), best available 

control technology (BACT), or lowest achievable emissions reduction (LAER). Information 

from the EPA’s RACT/BACT/LAER database contains case-specific information on the “best 

available” air pollution technologies that have been required to reduce the emission of air 

pollutants from stationary sources. This information has been provided by state and local 

permitting agencies. The database was searched for SSI units permitted or constructed since 

2005. The search results showed two FB units at the R.L. Sutton Water Reclamation facility in 

Georgia were permitted in 2005, and completed construction in 2008 and are currently in 
3-26 

operation. Additional information collected from state environmental agencies and permits 

indicated an additional three units at the Mill Creek Wastewater Treatment Plant in Ohio were 

expected to finish construction and be in operation in 2010 (Oommen and Allen, 2010a). All of 

these new FB units were replacements for MH units. 

Based on the data collected and assuming the trend in construction continues, five 

additional FB units will be permitted to be constructed in 5 years after the NSPS is proposed. 

However, given the time necessary to review and assess the requirements of the NSPS and plan, 

permit, and construct incineration units, it is unlikely that all five would be in operation in the 5 

years. For this analysis, it was assumed at least two new FB units would be constructed and in 

operation in this time period. 

3.3.2 Methodology Used to Estimate Cost and Emission Reductions of the MACT Floor 

Level of Control 

Cost and emission reductions for new units complying with the NSPS were calculated by 

(1) determining the controls that these units would most likely apply if the NSPS were not in 

place (referred to as the baseline level of control), (2) calculating the cost of complying with the 

NSPS emission levels, and (3) estimating the emissions reduction from complying with the 

NSPS emissions levels. Each of these steps is discussed in more detail. 

3.3.2.1 Determining Baseline Controls 

The baseline level of control that new units would likely implement (in the absence of the 

NSPS) was determined from reviewing the most common controls used at existing FB units, as 

shown in the SSI inventory memorandum (Inventory Database, 2010). Table 3-8 shows the 

distribution of controls. Based on this information, the baseline controls assumed for the new 

units are a combination of venturi scrubbers and impingement scrubbers. Data gathered on the 

controls currently used at FB units indicate that few FB units operate an afterburner, because 

their CO emissions are already low. However, to meet the new source floor limit, the analysis 

costs out an afterburner to reach the limit. In reality, new FB units that are constructed are likely 

to be designed to meet the CO level. Costing an afterburner provides a conservative estimate of 

costs. 

3.3.2.2 Calculating Baseline Emissions 

The SSI baseline emissions memorandum (Estimation of Baseline Emissions, 2010) 

documents the calculation of baseline emissions from existing FB SSI units. Baseline emissions 

were calculated on a mass basis by multiplying the concentration of the pollutant in the emission 

stream, flow rate of the emission stream, and the hours of operation of the SSI unit. For units 
3-27 

Table 3-8. Control Device Distribution for Fluidized Bed Incineratorsa

Existing Control Devices Number of Units Percent 

Distribution of Individual Controls 

Venturi scrubber (vs, vs(ad)) 49 89 

Impingement scrubber (imp) 38 69 

Wet ESP (wesp) 14 25 

Cyclone separator (cs) 4 7 

Activated carbon (ac inject or ac polish) 4 7 

Afterburner (abo or abd) 4 7 

Packed bed scrubber (ccpt, pbs, pbt) 2 4 

Distribution of Control Combinations 

abd – mc – vs – imp 2 3.64 

abd – vs – imp – hss – cs 1 1.82 

abo – imp – wesp 1 1.82 

ac inject. – vs(ad) – wesp 3 5.45 

ccpt 1 1.82 

cs – vs – pbt 2 3.64 

unknown 4 7.27 

vs 5 9.09 

vs – cs 1 1.82 

vs – imp 25 45.45 

vs – imp – wesp 8 14.55 

vs – imp – wesp – ac polish. 1 1.82 

vs(ad) – wesp 1 1.82 

Total 55 100.00 

a Dominak, Robert, Co-Chair NACWA Biosolids Management Committee, e-mail to Amy Hambrick, U.S. EPA. 

August 5, 2009. “SSI Inventory Updated Information.” 

where no emissions test data were collected, baseline emissions were estimated using an average 

uncontrolled concentration and applying reduction efficiencies associated with the control 

devices located at each SSI unit for each pollutant. 

An average flue gas flow rate factor was also developed for FB units relating the flue gas 

flow rate to the dry sludge feed rate from units providing emission test data. For units where 

sludge feed rates were not collected, unit capacities were multiplied by a capacity utilization 
3-28 

factor of 75%, which was the median of the capacity utilizations reported in the ICR survey 

responses. More information about how unit capacity values were obtained can be found in the 

SSI inventory database memorandum (Inventory Database, 2010). The flow rate of the flue gas 

stream was calculated by multiplying the dry sludge feed rate by the average flue gas flow rate 

factor. 

Based on the information gathered from RACT/BACT/LAER and permits, it is likely that 

new FB units constructed will be replacements for existing units. However, it cannot be 

determined how many units will be replaced at a facility or the total number of units that will be 

in operation at a facility. For this analysis, the simplest and most conservative assumption was 

used—that only one FB unit would be constructed replacing one older MH unit. The operating 

hours for facilities operating one unit were assumed to be 8,400 hours per year (incorporating 

two weeks’ downtime). 

Table 3-9 shows the average concentration factors, average dry sludge capacity, and 

operating hours, as well as other default parameters necessary for the costs. These factors were 

applied to each new unit estimated to be constructed within the next 5 years. Table 3-10 shows 

the estimated baseline concentrations for new units. 

Calculating Costs and Emission Reductions 

Costs were calculated using the procedures and algorithms discussed in the memorandum 

“Cost and Emissions Reduction of Complying with the MACT Floor for Existing SSI Units” 

(Oommen and Allen, 2010b). Control devices costed out were those that would be necessary to 

meet the MACT floor level of control for new sources. It is possible for some units with wet 

scrubbers to comply with the NSPS limits for SO2 by adding caustic. However, it is uncertain if 

all units could do this. Therefore, this analysis assumed a PBS would be used, which would 

provide a more conservative estimate of costs. Similarly, wet electrostatic precipitators can be 

used for PM control; a FF was costed in this analysis to provide a conservative estimate of costs. 

Table 3-11 shows the comparison of baseline emissions levels to MACT floor levels to 

determine the amount of pollutant reduction needed and the types of control devices that would 

be used to meet the levels. Emission reductions from applying the MACT floor requirements to 

the baseline emission levels are presented in Table 3-10. The inputs to the cost algorithm are 

presented in Table 3-9. For this analysis, it was assumed that controls applicable for PM would 

also reduce PM2.5. 
3-29 

Table 3-9. Cost and Emission Reduction Calculation Inputs 

Parameter 

Default (Average of known data for FB 

subcategory) 

Capacity (dtph) 2.26 

Capacity 

(dry lb/hr) 

4,516.36 

Sludge feed rate 

(dry tons/hr) 

1.69 

Sludge feed rate (dry lb/hr) 3,387.27 

Operating hours (hr/yr)a8,400 

Stack gas flow rate (dscfm) 9,239.97 

Stack gas temperature (°F)b1,050 

ACI adjustment factorc1.03 

Sludge heating value (BTU/lb)d7740 

NOX, lb/MMBTU 0.07 

PM 

(gr/dscf) 

0.0054 

HCl (ppmvd) 0.124 

a Conservatively assumed new unit would operate 350 days per year (2 weeks’ downtime). 

b Assumed average gas temperature used for commercial and industrial solid waste incinerators (CISWI). 

c ACI algorithm is based on 90% Hg reduction efficiency and 98% CDD/CDF reduction efficiency. This 

adjustment factor will be used to adjust total annual costs to the estimated reduction efficiency needed to meet the 

floor. 

d Converted to BTU/lb from 18 MJ/kg dried, undigested sludge 

(http://www.aseanenvironment.info/Abstract/41015799.pdf). 

 
3-30

Table 3-10. Summary of Emission Reductions for New SSI Units 

Pollutant 

Concentration 

Units 

Additional 

Control 

Needed for 

MACT 

Baseline 

Concentration NSPS Limit

MACT 

Emission 

Concentration 

Emission 

Reduction 

(concentration) 

Emission 

Reduction 

(tpy) 

Annual Emission 

Reductions: 

Year 5 

(Assuming 2 new 

units come online 

in 5 years) 

Cadmium (Cd) mg/dscm Add FF 0.002 0.00051 0.00051 0.002 2.36E-04 4.73E-04 

Carbon monoxide (CO) ppmvd Add ABD 16.331 7.4 7.4 8.931 1.51E+00 3.02E+00 

Hydrogen chloride 

(HCl) 

ppmvd nonea0.124 0.13 0.050 0.074 1.64E-02 3.27E-02 

Lead (Pb) mg/dscm Add FF 0.011 0.00053 0.00053 0.011 1.53E-03 3.06E-03 

Mercury (Hg) mg/dscm Add ACI 0.014 0.001 0.001 0.013 1.82E-03 3.64E-03 

Nitrogen oxides (NOx) ppmvd noneb27.926 26 26 1.926 5.35E-01 1.07E+00 

Particulate matter 

(filterable) 

mg/dscm Add FF 12.443 4.1 4.1 8.343 1.21E+00 2.43E+00 

Particulate matter 

(PM2.5) 

mg/dscm Add FF 11.801 2.3 2.3 9.501 1.38E+00 2.76E+00 

Sulfur dioxide (SO2) ppmvd Add PBS 3.303 2.0 2.0 1.303 5.04E-01 1.01E+00 

Total dioxin/furans ng/dscm Add ACI 15.962 0.94 0.94 15.022 2.18E-06 4.37E-06 

Total dioxin/furans 

(TEQ) 

ng/dscm Add ACI 1.312 0.023 0.023 1.289 1.87E-07 3.75E-07 

a Assumed scrubber (installed for SO2 control) has 98% efficiency for HCl control. 

b Assumed units could meet limit by making minor adjustments rather than installing add-on control. 
3-31 

Table 3-11 shows the estimated total capital investment (TCI) and total annual costs 

(TAC) calculated for a single unit using the cost algorithms previously discussed. The table also 

shows the monitoring, testing, reporting, and recordkeeping costs. The table shows the TCI and 

TAC for the two new FB units that are assumed to be constructed and in operation in the 5 years 

after proposal of the NSPS. 

Table 3-11. MACT Costs Associated with Model FB Unit 

Parameter TCI TAC 

Add FF $1,995,892 $580,670 

Add PBS $1,013,167 $233,832 

Add ACI $25,786 $163,338 

Add ABD $625,106 $233,589 

Controls 

Subtotal: $3,659,952 $1,211,429 

Initial Stack Test $61,000 

Annual Stack Test $61,000 

Bag Leak Detection System $25,500 $9,700 

Wet Scrubber Monitoring $24,300 $5,600 

ACI Monitoring $0 $9,800 

Annual Control Device Inspection $1,000 

CO CEMS $134,000 $41,400 

Annual Visual Emissions Test of Ash Handling $250 $740 

Reporting and Recordkeeping $2,989 

Monitoring, Testing, 

Reporting and 

Recordkeeping 

Subtotal: $245,050 $132,229 

TOTAL: $3,905,002 $1,343,657 

3.3.3 Analysis of Beyond the Floor Options 

The control technologies costed to achieve the MACT floor levels are generally the most 

effective controls available: FFs for PM, Cd, Pb; ACI for Hg and CDD/CDF; afterburners for 

CO; and PBSs for HCl and SO2. In addition, incremental additions of activated carbon have not 

been proven to achieve further reductions above the projected flue gas concentration estimated to 

achieve the limits for new sources. Data gathered do not indicate that any FB units operate NOx

controls, such as SNCR, SCR, or flue gas recirculation because the NOx emissions are already 

low. Therefore, no BTF options were analyzed for this analysis because we are not aware of any 

technologies or methods to achieve emission limits more stringent than the MACT floor limits 

for new units, which are based on the lowest emitting FB units. 
4-1 

SECTION 4

ECONOMIC IMPACT ANALYSIS 

EPA has prepared an EIA to provide decision makers with a measure of the social costs 

of using resources to comply with the proposed greenhouse gas (GHG) reporting requirements. 

As noted in EPA’s (2000) Guidelines for Preparing Economic Analyses, several tools are 

available to estimate social costs and range from simple direct compliance cost methods to the 

development of a more complex market analysis that estimates market changes (e.g., price and 

consumption) and economic welfare changes (e.g., changes in consumer and producer surplus). 

Because the proposed regulatory option affects governmental entities (96 of the 97 owners are 

governmental entities) providing services not provided in a market, the Office of Air Quality 

Planning and Standards (OAQPS) has used the direct compliance cost method as a measure of 

social costs. Since no market impacts are anticipated, the economic analysis focused on the 

comparison of control cost to total governmental revenue. 

The EIA evaluates three options discussed in Section 3: 

§ Option 1 is the MACT floor level of control for the two subcategories developed for 

existing SSI units, MH units and FB units. 

§ Option 2 is the same as Option 1, with the addition of activated carbon injection for 

additional Hg emissions reduction from MH units. 

§ Option 3 is the same as Option 2, with the addition of an afterburner on all MH units 

for additional CO emissions reduction. 

Within each option, EPA presents the results of the cost analysis using two assumptions: 

§ Large government entities comply and incinerate while small government entities 

choose to landfill. EPA anticipates this is the most likely response to the regulation 

based on analysis of landfilling costs and interviews with a sample of small 

government entities. 

§ All government entities (small and large) comply and incinerate. EPA anticipates this 

assumption significantly overstates the rule’s costs because it assumes small entities 

do not consider other disposal options. 

4.1 Social Cost Estimates 

EPA has estimated compliance costs for all existing units to add the necessary controls, 

monitoring equipment, inspections, and recordkeeping and reporting requirements to comply 

with the proposed SSI standards. Based on the engineering cost analysis, we anticipate the 
4-2 

overall total annual social cost to be approximately $92 million. The lowest cost option is the 

MACT floor where large entities comply and small entities landfill ($63 million). The highest 

cost option is the MACT floor with afterburners and fabric filters for MH units and all entities 

comply ($151 million). All cost options are displayed in Table 4-1. 

Table 4-1. Annual Social Cost Estimates by Option and Disposal Choices ($ million, 

2008$) 

Large Entities Comply and 

Small Entities Landfill All Units Comply 

Existing Sources 

Option 1 

(MACT Floor) 

$60 $73 

Option 2 

(MACT Floor + Afterburner 

for MH Units) 

$89 $105 

Option 3 

(Option 2 + Fabric Filters for 

MH Units) 

$129 $148 

New Sources (Fluidized Bed)a $3 $3 

a Two new FB units that are assumed to be constructed and in operation in the 5 years after proposal of the NSPS. 

4.2 Small Entity Analysis 

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute unless the agency 

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities (SISNOSE). The first step in this assessment was to determine whether the rule 

will have SISNOSE. To make this determination, EPA used a screening analysis to indicate 

whether EPA can certify the rule as not having a SISNOSE. The elements of this analysis 

included 

§ identifying affected small entities, 

§ selecting and describing the measures and economic impact thresholds used in the 

analysis, and 

§ completing the assessment and determining the SISNOSE certification category. 
4-3 

4.2.1 Identify Affected Small Entities 

For the purposes of assessing the impacts of the proposed rule on small entities, small 

entity is defined as (1) a small business as defined by the Small Business Administration’s 

regulations at 13 CFR 121.20; (2) a small governmental jurisdiction that is a government of a 

city, county, town, school district, or special district with a population of less than 50,000; and 

(3) a small organization that is any not-for-profit enterprise that is independently owned and 

operated and is not dominant in its field. As reported in Section 2, EPA has identified 18 small 

entities that have a population of fewer than 50,000. There are no small businesses or 

organizations affected by the proposed rule. 

4.2.2 Screening Analysis: Revenue Test 

In the next step of the analysis, EPA compared each regulatory option’s control costs to 

total government revenues (i.e., a “revenue” test). To estimate government revenues, we 

collected U.S. Census financial information for municipal governments by population ranges, 

computed average per capita revenues for each population range, and multiplied the per capita 

revenue figure by the population served by small and large government entities (Table 4-2). 

Table 4-2. Calculated Municipal and Township Per Capita Revenues by Population Size 

Population Size 

Fewer than 10K 10 to 25K 25 to 50K >50K 

Number of municipalities/townships 16,745 1,436 643 605 

Population 28,750,200 22,588,957 22,576,240 100,966,557 

Revenue (thousand 2002$) 34,944,647 32,010,988 31,630,676 238,846,095 

Per capita (2002$) $1,215 $1,417 $1,401 $2,366 

Per capita (2008$) $1,455 $1,696 $1,677 $2,831 

Source: U.S. Census. 2005. Finances of Municipal and Township Governments: 2002. Table 13, accessed June 8, 

2010 at http://www.census.gov/prod/2005pubs/gc024x4.pdf. 

Each option’s screening results under two disposal assumptions are presented in Tables 

4-3 through Table 4-8. As noted above, EPA anticipates small government entities will most 

likely switch from incineration to landfilling (Tables 4-4, 4-6, and 4-8). EPA has also presented 

small entity results where small entities comply and incinerate (Tables 4-3, 4-5, and 4-7). 

However, EPA anticipates this assumption would significantly overstate the rule’s small entity 

impacts because it assumes small entities continue to incinerate and do not consider other less 

expensive disposal options. 
4-4 

Based on the engineering cost analysis, EPA anticipates the overall total annualized cost 

for the selection option will be $92 million (Option 2: MACT floor with activated carbon 

injection for MH, large entities comply and small entities landfill); under this option and set of 

disposal choices, all small entities are affected at less than 1% revenues (Table 4-6). 

For the lowest cost Option 1, the MACT floor where large entities comply and small 

entities landfill (total annualized costs = $63 million), all small entities are affected at less than 

1% revenue (Table 4-4). 

For the highest cost Option 3, the MACT floor with activated carbon injection and 

afterburners for MH units and small entities landfill (total annualized cost = $132 million). All 

small entities are still affected at less than 1% revenue (Table 4-8). 

Table 4-3. Option 1 Revenue Tests for Government Entities: All Entities Comply 

Sample Statistic Small Large 

Cost-Revenue-Ratios 

Mean 1.1% 0.1% 

Median 0.9% 0.1% 

Minimum 0.1% 0.0% 

Maximum 3.4% 1.0% 

Number of Entities 18 69 

Number of Entities > 1% 9 0 

Number of Entities > 3% 2 0 

Table 4-4. Option 1 Revenue Tests for Government Entities: Large Entities Comply and 

Small Entities Landfill 

Sample Statistic Small Large 

Cost-Revenue-Ratios 

Mean −0.6% 0.1% 

Median −0.2% 0.1% 

Minimum −2.6% 0.0% 

Maximum 0.7% 1.0% 

Number of Entities 18 69 

Number of Entities > 1% 0 0 

Number of Entities > 3% 0 0 
4-5 

Table 4-5. Option 2 Revenue Tests for Government Entities: All Entities Comply 

Sample Statistic Small Large 

Cost-Revenue-Ratios 

Mean 1.6% 0.2% 

Median 1.2% 0.1% 

Minimum 0.5% 0.0% 

Maximum 4.4% 1.2% 

Number of Entities 18 69 

Number of Entities > 1% 13 2 

Number of Entities > 3% 2 0 

Table 4-6. Option 2 Revenue Tests for Government Entities: Large Entities Comply and 

Small Entities Landfill 

Sample Statistic Small Large 

Cost-Revenue-Ratios 

Mean −0.6% 0.2% 

Median −0.2% 0.1% 

Minimum −2.6% 0.0% 

Maximum 0.7% 1.2% 

Number of Entities 18 69 

Number of Entities > 1% 0 2 

Number of Entities > 3% 0 0 

Table 4-7. Option 3 Revenue Tests for Government Entities: All Entities Comply 

Sample Statistic Small Large 

Cost-Revenue-Ratios 

Mean 1.9% 0.3% 

Median 1.3% 0.2% 

Minimum 0.6% 0.0% 

Maximum 6.0% 1.2% 

Number of Entities 18 69 

Number of Entities > 1% 16 2 

Number of Entities > 3% 3 0 
4-6 

Table 4-8. Option 3 Revenue Tests for Government Entities: Large Entities Comply and 

Small Entities Landfill 

Sample Statistic Small Large 

Cost-Revenue-Ratios 

Mean −0.6% 0.3% 

Median −0.2% 0.2% 

Minimum −2.6% 0.0% 

Maximum 0.7% 1.2% 

Number of Entities 18 69 

Number of Entities > 1% 0 2 

Number of Entities > 3% 0 0 

 
5-1 

SECTION 5

HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS 

5.1 Synopsis 

In this section, we provide an estimate of the monetized benefits associated with reducing 

particulate matter (PM) for the proposed Sewage Sludge Incinerator (SSI) New Source 

Performance Standard (NSPS) and Emissions Guidelines (EG). For this rule, the PM reductions 

are the result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and 

SO2, as well as emission limits on other pollutants. The total PM2.5 reductions are the 

consequence of the technologies installed or waste diversion to meet these multiple limits. These 

estimates reflect the monetized human health benefits of reducing cases of morbidity and 

premature mortality among populations exposed to the PM2.5 precursors reduced by this 

rulemaking. Using a 3% discount rate, we estimate the total monetized benefits including energy 

disbenefits of the proposed SSI NSPS and EG to be $130 million to $320 million in the 

implementation year (2015). Using a 7% discount rate, we estimate the total monetized benefits 

including energy disbenefits of the proposed SSI NSPS and EG to be $120 million to $290 

million in the implementation year. All estimates are in 2008$. 

These estimates reflect EPA’s most current interpretation of the scientific literature. 

Higher or lower estimates of benefits are possible using other assumptions; examples of this are 

provided in Figure 5-2. Data, resource, and methodological limitations prevented EPA from 

monetizing the benefits from several important benefit categories, including benefits from 

reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The benefits 

from reducing other air pollutants have not been monetized in this analysis, including reducing 

2,900 tons of CO, 96 tons of HCl, 3.0 tons of Pb, 1.6 tons of Cd, 5,500 pounds of mercury (Hg), 

and 90 grams of total dioxins/furans (CDD/CDF) each year. 

5.2 Calculation of PM2.5 Human Health Benefits 

This rulemaking would reduce emissions of PM2.5, SO2, and NO2. Because SOx and NO2

are also precursors to PM2.5, reducing these emissions would also reduce PM2.5 formation, human 

exposure, and the incidence of PM2.5-related health effects. For this rule, the PM reductions are 

the result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and SO2, as 

well as emission limits on other pollutants. The total PM2.5 reductions are the consequence of the 

technologies installed or waste diversion to meet these multiple limits. Due to analytical 

limitations, it was not possible to provide a comprehensive estimate of PM2.5-related benefits. 

Instead, we used the “benefit-per-ton” approach to estimate these benefits based on the 
5-2 

methodology described in Fann, Fulcher, and Hubbell (2009). The key assumptions are 

described in detail below. These PM2.5 benefit-per-ton estimates provide the total monetized 

human health benefits (the sum of premature mortality and premature morbidity) of reducing one 

ton of PM2.5 from a specified source. EPA has used the benefit per-ton technique in several 

previous RIAs, including the recent NO2 NAAQS RIA (U.S. EPA, 2010b). Table 5-1 shows the 

quantified and unquantified benefits captured in those benefit-per-ton estimates. 

Table 5-1. Human Health and Welfare Effects of PM2.5 

Pollutant / 

Effect 

Quantified and Monetized 

in Primary Estimates 

Unquantified Effects 

Changes in: 

PM2.5 Adult premature mortality 

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic 

bronchitis 

Non-asthma respiratory emergency room visits 

Visibility 

Household soiling 

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM2.5 benefits 

estimates utilize the concentration-response functions as reported in the epidemiology literature, 

as well as the 12 functions obtained in EPA’s expert elicitation study as a sensitivity analysis. 

§ One estimate is based on the concentration-response (C-R) function developed from 

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope 

et al. (2002), a study that EPA has previously used to generate its primary benefits 

estimate. When calculating the estimate, EPA applied the effect coefficient as 

reported in the study without an adjustment for assumed concentration threshold of 10 

μg/m3as was done in recent (2006-2009) Office of Air and Radiation RIAs. 

§ One estimate is based on the C-R function developed from the extended analysis of 

the Harvard Six Cities cohort, as reported by Laden et al. (2006). This study, 

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has 

been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 benefits 

estimates in RIAs completed since the PM2.5 NAAQS. When calculating the estimate, 

EPA applied the effect coefficient as reported in the study without an adjustment for 

assumed concentration threshold of 10 μg/m3as was done in recent (2006-2009) 

RIAs. 
5-3 

§ Twelve estimates are based on the C-R functions from EPA’s expert elicitation study 

(IEc, 2006; Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted 

for benefits analysis in EPA’s final RIA for the PM2.5 NAAQS. For that study, twelve 

experts (labeled A through L) provided independent estimates of the PM2.5 -mortality 

concentration-response function. EPA practice has been to develop independent 

estimates of PM2.5 -mortality estimates corresponding to the concentration-response 

function provided by each of the twelve experts, to better characterize the degree of 

variability in the expert responses. 

The effect coefficients are drawn from epidemiology studies examining two large 

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six 

Cities cohort (Laden et al., 2006).1 These are logical choices for anchor points in our presentation 

because, while both studies are well designed and peer reviewed, there are strengths and 

weaknesses inherent in each, which we believe argues for using both studies to generate benefits 

estimates. Previously, EPA had calculated benefits based on these two empirical studies, but 

derived the range of benefits, including the minimum and maximum results, from an expert 

elicitation of the relationship between exposure to PM2.5 and premature mortality (Roman et al., 

2008).2 Within this assessment, we include the benefits estimates derived from the concentrationresponse function provided by each of the twelve experts to better characterize the uncertainty in 

the concentration-response function for mortality and the degree of variability in the expert 

responses. Because the experts used these cohort studies to inform their concentration-response 

functions, benefits estimates using these functions generally fall between results using these 

epidemiology studies (see Figure 5-2). In general, the expert elicitation results support the 

conclusion that the benefits of PM2.5 control are very likely to be substantial. 

Readers interested in reviewing the general methodology for creating the benefit-per-ton 

estimates used in this analysis should consult Fann, Fulcher, and Hubbell (2009). As described in 

Fann, Fulcher, and Hubbell (2009), benefit-per-ton estimates are developed for selected 

pollutant/source category combinations. The per-ton values calculated therefore apply only to 

tons reduced from those specific pollutant/source combinations (e.g., SO2 emitted from electric 

generating units; NO2 emitted from mobile sources). In this analysis, we apply the national 

average benefit-per-ton estimate for a 2015 analysis year and multiply it by the corresponding 

emission reductions of directly emitted PM2.5, SO2, and NOx to quantify the benefits of this rule. 

The benefit-per-ton estimates found in Fann, Fulcher, and Hubbell (2009) reflect a specifc set of 

key assumptions and input data. As we update these underlying assumptions to reflect the 

 

1These two studies specify multi-pollutant models that control for SO2, among other pollutants. 

2

Please see the Section 5.2 of the Portland Cement proposal RIA in Appendix 5A for more information regarding the 

change in the presentation of benefits estimates. 
5-4 

scientific literature, we re-estimate the benefit-per-ton estimates and post the updated estimates 

at http://www.epa.gov/air/benmap/bpt.html. In addition, we adjust these estimates to match the 

currency year for the costs in this analysis. 

These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because there is no clear scientific evidence that 

would support the development of differential effects estimates by particle type. Directly emitted 

PM, SO2, and NOx are the primary PM2.5 precursors affected by this rule. Even though we 

assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary 

between precursors because each ton of precursor reduced has a different propensity to form 

PM2.5. For example, NOX has a lower benefit-per-ton estimate than direct PM2.5 because it does 

not form as much PM2.5, thus the exposure would be lower, and the monetized health co-benefits 

would be lower. 

The benefit-per-ton coefficients in this analysis were derived using modified versions of 

the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, 

this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA 

(U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded 

geographic scope of the benefit-per-ton calculation, and the functions directly from the 

epidemiology studies without an adjustment for an assumed threshold.3 Removing the threshold 

assumption is a key difference between the method used in this analysis of PM benefits and the 

methods used in RIAs prior to Portland Cement, and we now calculate incremental benefits 

down to the lowest modeled PM2.5 air quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. Based on our review of the current body of scientific literature, EPA now estimates 

PM-related mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by 

EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear 

model most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function. 

Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council 

 

3The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as 

discussed on the next page. 
5-5 

concluded, “The HES fully supports EPA’s decision to use a no-threshold model to estimate 

mortality reductions. This decision is supported by the data, which are quite consistent in 

showing effects down to the lowest measured levels. Analyses of cohorts using data from more 

recent years, during which time PM concentrations have fallen, continue to report strong 

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” 

In conjunction with the underlying scientific literature, this document provided a basis for 

reconsidering the application of thresholds in PM2.5 concentration-response functions used in 

EPA’s RIAs. For a summary of these scientific review statements and the panel members 

commenting on thresholds since 2002, please consult the Technical Support Document (TSD) 

Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010c), which is 

provided as an appendix to this RIA. 

Consistent with this recent scientific advice, we are replacing the previous threshold 

sensitivity analysis with a new “Lowest Measured Level” (LML) assessment. This information 

allows readers to determine the portion of population exposed to annual mean PM2.5 levels at or 

above the LML of each study; in general, our confidence in the estimated PM mortality 

decreases as we consider air quality levels further below the LML in major cohort studies that 

estimate PM-related mortality. While an LML assessment provides some insight into the level of 

uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold 

and continues to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations. It is important to emphasize that we have high confidence in PM2.5-related effects 

down to the lowest LML of the major cohort studies. Just because we have greater confidence in 

the benefits above the LML, this does not mean that we have no confidence that benefits occur 

below the LML. 

For this analysis, policy-specific air quality data is not available due to time or resource 

limitations. For these rules, we are unable to estimate the percentage of premature mortality 

associated with this specific rule’s emission reductions at each PM2.5 level. However, we believe 

that it is still important to characterize the distribution of exposure to baseline air quality levels. 

As a surrogate measure of mortality impacts, we provide the percentage of the population 

exposed at each PM2.5 level using the most recent modeling available from the recently proposed 

Transport Rule (U.S. EPA, 2010e). It is important to note that baseline exposure is only one 

parameter in the health impact function, along with baseline incidence rates population, and 

change in air quality. In other words, the percentage of the population exposed to air pollution 

below the LML is not the same as the percentage of the population experiencing health impacts 

as a result of a specific emission reduction policy. The most important aspect, which we are 
5-6 

unable to quantify for rules without air quality modeling, is the shift in exposure associated with 

this specific rule. Therefore, caution is warranted when interpreting the LML assessment. For 

more information on the data and conclusions in the LML assessment for rules without policyspecific air quality modeling, please consult the LML TSD (U.S. EPA, 2010d), which is 

provided as an appendix to this RIA. The results of this analysis are provided in Section 5.4. 

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods 

used to estimate air quality benefits evolve over time to reflect the Agency’s most current 

interpretation of the scientific and economic literature. For a period of time (2004–2008), the 

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical 

life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived 

at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of 

the wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$)4 was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected 

the interim estimate to a scientific peer-review process through the Science Advisory Board 

(SAB) or other peer-review group. 

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by EPA and the SAB on combining estimates from the various 

data sources. In addition, the Agency consulted several times with the Science Advisory Board 

Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the 

meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, 

appropriate meta-analytic techniques to combine estimates from unique data sources and 

different studies, including those using different methodologies (i.e., wage-risk and stated 

preference) (U.S. EPA-SAB, 2007). 

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the 

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

 

4 After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to 

2015, the $5.5 million VSL is $7.9 million. 
5-7 

for Preparing Economic Analyses (U.S. EPA, 2000)5 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$).6 The Agency is committed to 

using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions 

and has made significant progress in responding to the SAB-EEAC’s specific recommendations. 

Figure 5-1 illustrates the relative breakdown of the monetized PM2.5 health benefits. 

Adult Mortality - Pope et 

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms 

0.5%

InfantMortality0.4%

Work Loss Days0.2%

HospitalAdmissions,Cardio 

0.2%

HospitalAdmissions, Resp 

0.04%

AsthmaExacerbation0.01%

Acute Bronchitis0.01%

UpperRespSymp0.00%

LowerRespSymp 0.00%

ERVisits, Resp0.00%

Other1%

Figure 5-1. Breakdown of Monetized PM2.5 Health Benefits using Mortality Function from 

Pope et al. (2002)a

a This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the 

Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult 

mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be 

similar if a 7% discount rate was used. 

Tables 5-2 and 5-3 provide a general summary of the all units comply assumption and 

large entities comply and small entities landfill assumption results by pollutant, including the 

 

5

In the (draft) update of the Economic Guidelines (U.S. EPA, 2008), EPA retained the VSL endorsed by the SAB 

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the 

near future. Therefore, this report does not represent final agency policy.

6

In this analysis, we adjust the VSL to account for a different currency year (2008$) and to account for income 

growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1 million.
5-8 

emission reductions and monetized benefits-per-ton at discount rates of 3% and 7%.7 Table 5-4 

provides a summary of the reductions in health incidences as a result of the pollution reductions 

for the large entities comply and small entities landfill results. In Table 5-5, we provide the 

benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the 

expert 

Table 5-2. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in 

2015 (2008$) (large entities comply and small entities landfill)a

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit per 

ton (Pope, 

3%) 

Benefit per 

ton (Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit per 

ton 

(Laden, 

7%) 

Total 

Monetized 

Benefits 

(millions 

2008$ at 3%)

Total 

Monetized 

Benefits 

(millions 

2008$ at 7%)

Direct PM2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

PM2.5 Precursors 

SO2 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

O

NO2 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8

ptio

n 1

 Total $130 to $320 $120 to $290

Direct PM2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

PM2.5 Precursors 

SO2 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

NO2 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8

Pr

o

p

osed: O

ptio

n 2

 Total $130 to $320 $120 to $290

Direct PM2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

PM2.5 Precursors 

SO2 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

O

NO2 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8

ptio

n 3

 Total $130 to $320 $120 to $290

 

7To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). 

These benefits are estimated for a specific analysis year (i.e., 2015), and most of the PM benefits occur within that 

year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 

years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a “cessation” 

lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag 

is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30% 

of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction 

in PM2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total number of estimated 

deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis 

year and the valuation of premature mortalities that occur after the analysis year. As such, the monetized benefits 

using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate. 
5-9 

a All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may 

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton 

estimates vary because each ton of precursor reduced has a different propensity to form PM2.5. The monetized 

benefits incorporate the conversion from precursor emissions to ambient fine particles. These results include 2 

new FB incinerators anticipated to come online by 2015. These estimates do not include energy disbenefits valued 

at $0.5 million at a 3% discount rate for CO2 emissions. 

elicitation on PM mortality. Figures 5-2 through 5-4 provide a visual representation of the range 

of benefits estimates and the pollutant breakdown of the monetized benefits. 
5-10 

Table 5-3. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in 

2015 (2008$) (all units comply)a

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope, 

3%) 

Benefit per 

ton 

(Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit 

per ton 

(Laden, 

7%) 

Total 

Monetized 

Benefits 

(millions 2008$ 

at 3%) 

Total 

Monetized 

Benefits 

(millions 2008$ 

at 7%) 

Direct PM2.5 209 $230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0

PM2.5 Precursors 

SO2 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140 

O

NO2 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05 

ptio

n 1

 Total $110 to $270 $100 to $250 

Direct PM2.5 209 $230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0

PM2.5 Precursors 

SO2 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140 

NO2 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05 

Pr

o

p

osed: O

ptio

n 2

 Total $110 to $270 $100 to $250 

Direct PM2.5 209 $230,000 $560,000 $210,000 $500,000 $48 to $120 $43 to $110 

PM2.5 Precursors 

SO2 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140 

O

NO2 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05 

ptio

n 3

 Total $110 to $270 $100 to $250 

a All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may 

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton 

estimates vary because each ton of precursor reduced has a different propensity to form PM2.5. The monetized 

benefits incorporate the conversion from precursor emissions to ambient fine particles. These results include 2 

new FB incinerators anticipated to come online by 2015. These estimates do not include energy disbenefits valued 

at $0.5 million at a 3% discount rate for CO2 emissions. 
5-11 

Table 5-4. Summary of Reductions in Health Incidences from PM2.5 Benefits for the 

Proposed SSI NSPS and EG in 2015a

 Option 1 Proposed: Option 2 Option 3 

Avoided Premature Mortality 

Pope et al. 14 14 14 

Laden et al. 36 36 36 

Avoided Morbidity 

Chronic Bronchitis 10 10 10 

Acute Myocardial Infarction 23 23 23 

Hospital Admissions, Respiratory 3 3 3 

Hospital Admissions, Cardiovascular 7 7 7 

Emergency Room Visits, Respiratory 14 14 14 

Acute Bronchitis 23 23 23 

Work Loss Days 1,900 1,900 1,900 

Asthma Exacerbation 250 250 250 

Minor Respiratory Symptoms 11,000 11,000 11,000 

Lower Respiratory Symptoms 270 270 270 

Upper Respiratory Symptoms 210 210 210 

a All estimates are for the analysis year (2015) and are rounded to whole numbers with two significant figures. All 

fine particles are assumed to have equivalent health effects, but each PM2.5 precursor pollutant has a different 

propensity to form PM2.5. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. These results include 2 new FB incinerators anticipated to come online by 2015 and the large 

entities comply and small entities landfill assumption. 
5-12 

Table 5-5. All PM2.5 Benefits Estimates for the Proposed SSI NSPS and EG at Discount 

Rates of 3% and 7% in 2015 (in millions of 2008$)a

Option 1 

Proposed: 

Option 2 

Option 3 

 3% 7% 3% 7% 3% 7% 

Benefit-per-ton Coefficients derived from Epidemiology Literature

Pope et al. $130 $120 $130 $120 $130 $120 

Laden et al. $320 $290 $320 $290 $320 $290 

Benefit-per-ton Coefficients Derived from Expert Elicitation 

Expert A $340 $300 $340 $300 $340 $300 

Expert B $260 $230 $260 $230 $260 $230 

Expert C $260 $230 $260 $230 $260 $230 

Expert D $180 $160 $180 $160 $180 $160 

Expert E $420 $380 $420 $380 $420 $380 

Expert F $230 $210 $230 $210 $230 $210 

Expert G $150 $140 $150 $140 $153 $139 

Expert H $190 $170 $190 $170 $190 $170 

Expert I $250 $230 $250 $230 $250 $230 

Expert J $210 $190 $210 $190 $210 $190 

Expert K $51 $46 $51 $46 $51 $46 

Expert L $190 $170 $190 $170 $190 $170 

a All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they 

were derived through the benefit-per-ton technique described above. The benefits estimates from the Expert 

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated 

with the concentration-response function. These results include 2 new FB incinerators anticipated to come online 

by 2015 and the large entities comply and small entities landfill assumption. These estimates do not include 

energy disbenefits valued at $0.5 million at a 3% discount rate for CO2 emissions. 
5-13 

$0

$50

$100

$150

$200

$250

$300

$350

$400

Pope et al.

Laden et al.

Millions(2008$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

2015*

3% DR

7% DR

Figure 5-2. Total Monetized PM2.5 Benefits for the Proposed SSI NSPS and EG in 2015 

a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the 

Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived from EPA’s expert elicitation 

on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the 

estimates are based in part on the concentration-response function provided in those studies. These results include 

2 new FB incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill 

assumption. These estimates do not include energy disbenefits valued at $0.5 million at a 3% discount rate for 

CO2 emissions. 
5-14 

Direct PM2.5 NonEGU

45%

SO2 NonEGU

52%

NO2

3%

Figure 5-3. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by 

PM2.5 Precursor Pollutant and Source 
5-15 

Fluidized Bed

10%

Multiple Hearth

90%

Figure 5-4. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by 

Subcategory 

5.3 Energy Disbenefits 

Electricity usage associated with the operation of control devices is anticipated to 

increase emissions of pollutants from utility boilers that supply electricity to the sewage sludge 

incinerators. For example, increased scrubber pump horsepower and sorbent injection controls 

may cause slight increases in electricity consumption. We estimate that the increased electricity 

consumption associated with the proposed option would be 12 million kWh if all entities 

comply, and 12 million kWh if the small entities landfill. Using national emission factors from 

eGRID for electrical generating units (EGUs), we estimate the increased emissions to be 19,000 

tpy of CO2 for the proposed option assuming that small entities landfill.8 Since NOx and SO2 are 

covered by capped emissions trading programs, we are only estimating the CO2 emission 

increases from the increased electricity demand. The methodology used to calculate these 

 

8 Option 3 has additional energy disbenefits associated with the supplemental fuel required to run the afterburners, 

which results in additional emissions of CO2, CO, and NOx. The CO2 energy disbenefits for Option 3 are 

shown in Tables 5-7 and 5-8. The additional NOx disbenefits (as a precursor to PM2.5 using the methodolology 

described in Section 5.2) for Option 3 are $0.4 million to $0.9 million, which do not affect the rounded benefits 

results. 
5-16 

emission increases is described “Secondary Impacts of Control Options for the Sewage Sludge 

Incineration Source Category”, which is available in the docket. 

5.3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits 

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using 

recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized 

damages associated with an incremental increase in carbon emissions in a given year. It is 

intended to include (but is not limited to) changes in net agricultural productivity, human health, 

property damages from increased flood risk, and the value of ecosystem services due to climate 

change. The SCC estimates used in this analysis were developed through an interagency process 

that included EPA and other executive branch entities, and concluded in February 2010. EPA 

first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking 

to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average 

Fuel Economy Standards; see the rule’s preamble for discussion about application of SCC (75 

FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete 

discussion of the methods used to develop these SCC estimates.9 

The interagency group selected four SCC values for use in regulatory analyses, which we 

have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions10 in 2010, in 

2007 dollars. The first three values are based on the average SCC from three integrated 

assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several 

discount rates are included because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all 

three models at a 3 percent discount rate. It is included to represent higher-than-expected impacts 

from temperature change further out in the tails of the SCC distribution. Low probability, high 

impact events are incorporated into all of the SCC values through explicit consideration of their 

effects in two of the three models as well as the use of a probability density function for 

 

9 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also 

available at http://www.epa.gov/otaq/climate/regulations.htm

10 The interagency group decided that these estimates apply only to CO2 emissions. Given that warming profiles and 

impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded 

“transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, 

would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13). 
5-17 

equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high 

temperature outcomes, which in turn lead to higher projections of damages. 

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the SCC 

directly using the three integrated assessment models rather than assuming a constant annual 

growth rate. This helps to ensure that the estimates are internally consistent with other modeling 

assumptions. The SCC estimates for the analysis years of 2015, in 2008$ are provided in Table 

5-6. 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC, 2008) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

The interagency group noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. The limited amount of research linking climate impacts to economic damages makes 

the interagency modeling exercise even more difficult. The interagency group hopes that over 

time researchers and modelers will work to fill these gaps and that the SCC estimates used for 

regulatory analysis by the Federal government will continue to evolve with improvements in 

modeling. Additional details on these limitations are discussed in the SCC TSD. 

In light of these limitations, the interagency group has committed to updating the current 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, the interagency group has set a preliminary goal of 

revisiting the SCC values within two years or at such time as substantially updated models 

become available, and to continue to support research in this area. 
5-18 

Applying the global SCC estimates to the estimated increases in CO2 emissions for the 

range of policy scenarios, we estimate the dollar value of the climate-related disbenefits captured 

by the models for each analysis year. For internal consistency, the annual disbenefits are 

discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. 5%, 3%, 

and 2.5%) rather than 3% and 7%.11 These estimates are provided in Tables 5-7 and 5-8. 

Table 5-6. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2015 a

Discount Rate and Statistic SCC estimate (2008$) 

5% Average $5.9 

3% Average $24.7 

2.5% Average $39.9 

3% 95%ile $75.6 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

Table 5-7. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015 

(all units comply, millions of 2008$)a

Discount Rate and Statistic Option 1 

Proposed: 

Option 2 Option 3 

24,900 tpy CO2 24,900 tpy CO2 126,000 tpy CO2

5% Average $0.1 $0.1 $0.7 

3% Average $0.6 $0.6 $3.1 

2.5% Average $1.0 $1.0 $5.0 

3% 95%ile $1.9 $1.9 $9.5 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. These results include 2 new FB incinerators anticipated to come online by 2015. 

Table 5-8. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015 

(large entities comply and small entities landfill, millions of 2008$)a

Discount Rate and Statistic Option 1 

Proposed: 

Option 2 Option 3 

21,782 tpy CO2 21,782 tpy CO2 114,784tpy CO2

5% Average $0.1 $0.1 $0.7 

3% Average $0.5 $0.5 $2.8 

2.5% Average $0.9 $0.9 $4.6 

 

11 It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be discounted at 

rates that differ from those used to develop the SCC estimates. 
5-19 

3% 95%ile $1.6 $1.6 $8.7 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. These results include 2 new FB incinerators anticipated to come online by 2015. 

5.4 Unquantified Benefits 

The monetized benefits estimated in this RIA only reflect the portion of benefits 

attributable to the health effect reductions associated with ambient fine particles. Data, resource, 

and methodological limitations prevented EPA from quantifying or monetizing the benefits from 

several important benefit categories, including benefits from reducing toxic emissions, 

ecosystem effects, and visibility impairment. The health benefits from reducing hazardous air 

pollutants (HAPs) and carbon monoxide have not been monetized in this analysis. In addition to 

being a PM2.5 precursor, SO2 emissions also contribute to adverse effects from acidic deposition 

in aquatic and terrestrial ecosystems, increased mercury methylation, as well as visibility 

impairment. The benefits from reducing other air pollutants that have not been monetized in this 

analysis including 2,900 tons of carbon monoxide, 96 tons of HCl, 3.0 tons of lead, 1.6 tons of 

cadmium, 5,500 pounds of mercury, and 90 grams of total dioxins/furans each year. 

5.4.1 Carbon Monoxide Benefits 

Carbon monoxide (CO) exposure is associated with a variety of health effects. Without 

knowing the location of the emission reductions and the resulting ambient concentrations using 

fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby 

populations. Due to data, resource, and methodological limitations, we were unable to estimate 

the benefits associated with the reductions of CO emissions that would occur as a result of this 

rule. 

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO 

emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the 

combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon 

inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia 

(reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple 

tissues and organ systems that are dependent upon concentration and duration of exposure. 

The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded 

that short-term exposure to CO is “likely to have a causal relationship” with cardiovascular 

morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies 

associate short-term CO exposure with increased risk of emergency department visits and 
5-20 

hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac 

chest pain), as well as those who have experienced a heart attack. Other subpopulations 

potentially at risk include individuals with diseases such as chronic obstructive pulmonary 

disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as 

older adults or the developing young. The evidence is suggestive of a causal relationship 

between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also 

suggestive of a causal relationship for birth outcomes and developmental effects following longterm exposure to CO, and for central nervous system effects linked to short- and long-term 

exposure to CO. 

5.4.2 Other SO2 Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of respiratory health effects. Unfortunately, we were unable to estimate the health benefits 

associated with reduced SO2 exposure in this analysis because we do not have air quality 

modeling data available. Without knowing the location of the emission reductions and the 

resulting ambient concentrations, we were unable to estimate the exposure to SO2 for nearby 

populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated 

with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the U.S. EPA has concluded that there is a causal relationship between respiratory health 

effects and short-term exposure to SO2 (U.S. EPA, 2008). According to summary of the ISA in 

EPA’s risk and exposure assessment (REA) for the SO2 NAAQS, “the immediate effect of SO2 

on the respiratory system in humans is bronchoconstriction” (U.S. EPA, 2009c). In addition, the 

REA summarized from the ISA that “asthmatics are more sensitive to the effects of SO2 likely 

resulting from preexisting inflammation associated with this disease.” A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO2 at 

concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and 

percentage of asthmatics adversely affected (U.S. EPA, 2009c). Based on our review of this 

information, we identified four short-term morbidity endpoints that the SO2 ISA identified as a 

“causal relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also 

concluded that the relationship between short-term SO2 exposure and premature mortality was 

“suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to 

SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a 
5-21 

relationship between SO2 exposure and mortality, there was a lack of robustness of the observed 

associations to adjustment for pollutants. 

SO2 emissions also contribute to adverse welfare effects from acidic deposition, visibility 

impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which 

can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern United States, the 

surface waters affected by acidification are a source of food for some recreational and 

subsistence fishermen and for other consumers and support several cultural services, including 

aesthetic and educational services and recreational fishing. Biological effects of acidification in 

terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root 

growth, which restricts the ability of the plant to take up water and nutrients. These direct effects 

can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold 

temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial 

acidification affects several important ecological services, including declines in habitat for 

threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in 

forest productivity (provisioning), and increases in forest soil erosion and reductions in water 

retention (cultural and regulating) (U.S. EPA, 2008d). 

Reducing SO2 emissions and the secondary formation of PM2.5 would improve the level 

of visibility throughout the United States. Fine particles with significant light-extinction 

efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). 

These suspended particles and gases degrade visibility by scattering and absorbing light. Higher 

visibility impairment levels in the East are due to generally higher concentrations of fine 

particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate 

sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually 

and 75% during summer). In the western U.S., particulate sulfate contributes to 20-50% of 

regional haze (U.S. EPA, 2009c). Visibility has direct significance to people’s enjoyment of 

daily activities and their overall sense of wellbeing. Good visibility increases the quality of life 

where individuals live and work, and where they engage in recreational activities. 

5.4.3 HAP Benefits 

Due to data, resource, and methodology limitations, we were unable to estimate the 

benefits associated with the hazardous air pollutants that would be reduced as a result of this 

rule. Available emissions data show that several different HAPs are emitted from SSI. This rule 
5-22 

is anticipated to reduce 96 tons of HCl, 3.0 tons of lead, 1.6 tons of cadmium, 5,500 pounds of 

mercury, and 90 grams of total dioxins/furans each year. In the absence of air quality modeling 

and/or concentration-response functions, we are unable to quantify the magnitude of the 

reduction in human exposure to these pollutants associated with the emission reductions from 

this rule. 

5.4.3.1 Mercury 

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated 

compound, methylmercury (U.S. EPA, 2008d). The contaminant is concentrated in higher 

trophic levels, including fish eaten by humans. Experimental evidence has established that only 

inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 

2008d). Current evidence indicates that in watersheds where mercury is present, increased sulfate 

deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; 

Munthe et al, 2007). The SO2 ISA concluded that evidence is sufficient to infer a casual 

relationship between sulfur deposition and increased mercury methylation in wetlands and 

aquatic environments (U.S. EPA, 2008d). 

In addition to the role of sulfate deposition on methylation, this proposed rule would also 

reduce mercury emissions. Mercury is emitted to the air from various man-made and natural 

sources. These emissions transport through the atmosphere and eventually deposit to land or 

water bodies. This deposition can occur locally, regionally, or globally, depending on the form of 

mercury emitted and other factors such as the weather. The form of mercury emitted varies 

depending on the source type and other factors. Available data indicate that the mercury 

emissions from these sources are a mixture of gaseous elemental mercury, inorganic ionic 

mercury, and particulate bound mercury. Gaseous elemental mercury can be transported very 

long distances, even globally, to regions far from the emissions source (becoming part of the 

global “pool”) before deposition occurs. Inorganic ionic and particulate bound mercury have a 

shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions 

source. Furthermore, elemental mercury in the atmosphere can undergo transformation into ionic 

mercury, providing a significant pathway for deposition of emitted elemental mercury. 

This source category emitted about 3.1 tons of mercury in the air in 2008 in the U.S. 

Based on the EPA’s National Emission Inventory, about 103 tons of mercury were emitted from 

all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that 

about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We 

believe that total mercury emissions in the U.S. and globally in 2008 were about the same 
5-23 

magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 3% of the 

total anthropogenic mercury emissions in the U.S. and about 0.15% of the global emissions. 

Overall, this rule would reduce mercury emissions by about 5,500 pounds per year from current 

levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to 

time and resource limitations, we were unable to model mercury dispersion, deposition, 

methylation, bioaccumulation in fish tissue, and human consumption of mercury-contaminated 

fish that would be needed in order to estimate the human health benefits from reducing mercury 

emissions. 

Potential exposure routes to mercury emissions include both direct inhalation and 

consumption of fish containing methylmercury. In the U.S., the primary route of human 

exposure to mercury emissions from industrial sources is generally indirectly through the 

consumption of fish containing methylmercury. As described above, mercury that has been 

emitted to the air eventually settles into water bodies or onto land where it can either move 

directly or be leached into waterbodies. Once deposited, certain microorganisms can change it 

into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish. 

Consumption of fish and shellfish are the main sources of methylmercury exposure to humans. 

Methylmercury builds up more in some types of fish and shellfish than in others. The levels of 

methylmercury in fish and shellfish vary widely depending on what they eat, how long they live, 

and how high they are in the food chain. Most fish, including ocean species and local freshwater 

fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. 

Geological Survey (USGS) of fish tissues, every fish sampled from 291 streams across the 

country contained some methylmercury (Scudder, 2009). 

The majority of fish consumed in the U.S. are ocean species. The methylmercury 

concentrations in ocean fish species are primarily influenced by the global mercury pool. 

However, the methylmercury found in local fish can be due, at least partly, to mercury emissions 

from local sources. Research shows that most people’s fish consumption does not cause a 

mercury-related health concern. However, certain people may be at higher risk because of their 

routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families 

who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high 

levels of methylmercury in the bloodstream of unborn babies and young children may harm the 

developing nervous system, making the child less able to think and learn. Moreover, mercury 

exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people 

of all ages. 
5-24 

Several studies suggest that the methylmercury content of fish may reduce these cardioprotective effects of fish consumption. Some of these studies also suggest that methylmercury 

may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of 

the literature concerning methylmercury health effects took note of two epidemiological studies 

that found an association between dietary exposure to methylmercury and adverse cardiovascular 

effects.12 Moreover, in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. 

(1995) observed a relationship between methylmercury exposure via fish consumption and acute 

myocardial infarction (AMI or heart attacks), coronary heart disease, cardiovascular disease, and 

all-cause mortality.13 The NRC also noted a study of 917 seven year old children in the Faroe 

Islands, whose initial exposure to methylmercury was in utero although post natal exposures may 

have occurred as well. At seven years of age, these children exhibited an increase in blood 

pressure and a decrease in heart rate variability.14 Based on these and other studies, NRC 

concluded in 2000 that, while “the data base is not as extensive for cardiovascular effects as it is 

for other end points (i.e. neurologic effects) the cardiovascular system appears to be a target for 

methylmercury toxicity.”15 

Since publication of the NRC report there have been some 30 published papers 

presenting the findings of studies that have examined the possible cardiovascular effects of 

methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic 

investigations. Over a dozen review papers have also been published. If there is a causal 

relationship between methylmercury exposure and adverse cardiovascular effects, then reducing 

exposure to methylmercury would result in public health benefits from reduced cardiovascular 

effects. 

In early 2010, EPA sponsored a workshop in which a group of experts were asked to 

assess the plausibility of a causal relationship between methylmercury exposure and 

cardiovascular health effects and to advise EPA on methodologies for estimating population 

 

12 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies 

Press. Washington, DC. pp. 168-173. 

13Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of 

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 

(3):645-655.

14Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P., 1999. “Prenatal Methylmercury 

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375.

15National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies 

Press. Washington, DC. p. 229.
5-25 

level cardiovascular health impacts of reduced methylmercury exposure. The report from that 

workshop is in preparation. 

5.4.3.2 Cadmium 

Breathing air with very high levels of cadmium can severely damage the lungs and may 

cause death. In the United States, where proper industrial hygiene is generally practiced, inhaling 

very high levels of cadmium at work is expected to be rare and accidental. Breathing air with 

lower levels of cadmium over long periods of time (for years) results in a build-up of cadmium 

in the kidney, and if sufficiently high, may result in kidney disease. Lung cancer has been found 

in some studies of workers exposed to cadmium in the air and studies of rats that breathed in 

cadmium. The U.S. Department of Health and Human Services (DHHS) has determined that 

cadmium and cadmium compounds are known human carcinogens. The International Agency for 

Research on Cancer (IARC) has determined that cadmium is carcinogenic to humans. The EPA 

has determined that cadmium is a probable human carcinogen.16

5.4.3.3 Lead 

The main target for lead toxicity is the nervous system, both in adults and children. Longterm exposure of adults to lead at work has resulted in decreased performance in some tests that 

measure functions of the nervous system. Lead exposure may also cause weakness in fingers, 

wrists, or ankles. Lead exposure also causes small increases in blood pressure, particularly in 

middle-aged and older people. Lead exposure may also cause anemia. At high levels of 

exposure, lead can severely damage the brain and kidneys in adults or children and ultimately 

cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the organs responsible for sperm production. 

We have no conclusive proof that lead causes cancer (is carcinogenic) in humans. Kidney 

tumors have developed in rats and mice that had been given large doses of some kind of lead 

compounds. The Department of Health and Human Services (DHHS) has determined that lead 

and lead compounds are reasonably anticipated to be human carcinogens based on limited 

evidence from studies in humans and sufficient evidence from animal studies, and the EPA has 

determined that lead is a probable human carcinogen. The International Agency for Research on 

Cancer (IARC) has determined that inorganic lead is probably carcinogenic to humans. IARC 

determined that organic lead compounds are not classifiable as to their carcinogenicity in 

humans based on inadequate evidence from studies in humans and in animals. 

 

16 Agency for Toxic Substances and Disease Registry (ATSDR). 2008. Public Health Statement for Cadmium. 

CAS# 1306-19-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. 

Available on the Internet at <http://www.atsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15>. 
5-26 

Children are more sensitive to the health effects of lead than adults. No safe blood lead 

level in children has been determined. Lead affects children in different ways depending on how 

much lead a child swallows. A child who swallows large amounts of lead may develop anemia, 

kidney damage, colic (severe “stomach ache”), muscle weakness, and brain damage, which 

ultimately can kill the child. In some cases, the amount of lead in the child’s body can be 

lowered by giving the child certain drugs that help eliminate lead from the body. If a child 

swallows smaller amounts of lead, such as dust containing lead from paint, much less severe but 

still important effects on blood, development, and behavior may occur. In this case, recovery is 

likely once the child is removed from the source of lead exposure, but there is no guarantee that 

the child will completely avoid all long-term consequences of lead exposure. At still lower levels 

of exposure, lead can affect a child’s mental and physical growth. Fetuses exposed to lead in the 

womb, because their mothers had a lot of lead in their bodies, may be born prematurely and have 

lower weights at birth. Exposure in the womb, in infancy, or in early childhood also may slow 

mental development and cause lower intelligence later in childhood. There is evidence that these 

effects may persist beyond childhood.17

5.4.3.4 Hydrogen Chloride (HCl) 

Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, 

throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 

100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; 

exposure to high concentrations can rapidly lead to swelling and spasm of the throat and 

suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue 

coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures 

may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to 

Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-induced type of 

asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly 

when exposed. Hydrogen chloride has not been classified for carcinogenic effects.18

 

17 Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Public Health Statement for Lead. CAS#: 

7439-92-1. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on 

the Internet at < http://www.atsdr.cdc.gov/ToxProfiles/phs13.html>. 

18 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen 

Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health 

Service. Available on the Internet at <http://www.atsdr.cdc.gov/Mhmi/mmg173.html>. 
5-27 

5.4.3.5 Dioxins (Chlorinated dibenzodioxins (CDDs) 

A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that 

are at least 10 times higher than background levels. The most obvious health effect in people 

exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin 

disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects 

noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and 

excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in 

people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, 

sugar, and protein have been reported in people exposed to relatively high concentrations of 

2,3,7,8-TCDD. Most of the effects are considered mild and were reversible. However, in some 

people these effects may last for many years. Slight increases in the risk of diabetes and 

abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-

TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result 

in reproductive or developmental effects in people, but animal studies suggest that this is a 

potential health concern. 

In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a 

single exposure. Exposure to lower levels can cause a variety of effects in animals, such as 

weight loss, liver damage, and disruption of the endocrine system. In many species of animals, 

2,3,7,8-TCDD weakens the immune system and causes a decrease in the system’s ability to fight 

bacteria and viruses at relatively low levels (approximately 10 times higher than human 

background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused 

reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy 

had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often 

had severe birth defects including skeletal deformities, kidney defects, and weakened immune 

responses. In some studies, effects were observed at body burdens 10 times higher than human 

background levels.19

5.4.3.6 Furans (Chlorinated dibenzofurans (CDFs)) 

Most of the information on the adverse health effects comes from studies in people who 

were accidentally exposed to food contaminated with CDFs. The amounts that these people were 

exposed to were much higher than are likely from environmental exposures or from a normal 

diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids 

 

19 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxins 

(CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). Atlanta, GA: U.S. Department of Health and Human Services, Public 

Health Service. Available on the Internet at http://www.atsdr.cdc.gov/tfacts104.html.
5-28 

with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also 

caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the 

nervous system, and mild changes in the liver. Children born to exposed mothers had skin 

irritation and more difficulty learning, but it is unknown if this effect was permanent or caused 

by CDFs alone or CDFs and polychlorinated biphenyls in combination. 

Many of the same effects that occurred in people accidentally exposed also occurred in 

laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, 

livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and 

testicular damage, and in severe cases, some animals died. These effects in animals were seen 

when they were fed large amounts of CDFs over a short time, or small amounts over several 

weeks or months. Nothing is known about the possible health effects in animals from eating 

CDFs over a lifetime.20 

5.5 Characterization of Uncertainty in the Monetized PM2.5 Benefits 

In any complex analysis, there are likely to be many sources of uncertainty. Many inputs 

are used to derive the final estimate of economic benefits, including emission inventories, air 

quality models (with their associated parameters and inputs), epidemiological estimates of 

concentration-response (C-R) functions, estimates of values, population estimates, income 

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human 

behavior). For some parameters or inputs it may be possible to provide a statistical representation 

of the underlying uncertainty distribution. For other parameters or inputs, the necessary 

information is not available. 

The annual benefit estimates presented in this analysis are also inherently variable due to 

the processes that govern pollutant emissions and ambient air quality in a given year. Factors 

such as hours of equipment use and weather are constantly variable, regardless of our ability to 

measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 5-5) (U.S. EPA, 2006), 

there are a variety of uncertainties associated with these PM benefits. Therefore, the estimates of 

annual benefits should be viewed as representative of the magnitude of benefits expected, rather 

than the actual benefits that would occur every year. 

It is important to note that the monetized benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and benefits 

 

20 Agency for Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans 

(CDFs). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the 

Internet at <http://www.atsdr.cdc.gov/tfacts32.html>. 
5-29 

modeling assumptions. For example, these estimates do not reflect local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of 

these $/ton values to estimate benefits associated with different emission control programs (e.g., 

for reducing emissions from large stationary sources like EGUs) may lead to higher or lower 

benefit estimates than if benefits were calculated based on direct air quality modeling. Great care 

should be taken in applying these estimates to emission reductions occurring in any specific 

location, as these are all based on national or broad regional emission reduction programs and 

therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton 

for emission reductions in specific locations may be very different than the estimates presented 

here. 

PM2.5 mortality benefits are the largest benefit category that we monetized in this 

analysis. To better characterize the uncertainty associated with mortality impacts that are 

estimated to occur in areas with low baseline levels of PM2.5, we included the LML assessment. 

Without policy-specific air quality modeling, we are unable to quantify the shift in exposure 

associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we 

provide the percentage of the population exposed at each PM2.5 level using the most recent 

modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). A very large 

proportion of the population is exposed at or above the lowest LML of the cohort studies 

(Figures 5-5 and 5-6), increasing our confidence in the PM mortality analysis. Figure 5-5 shows 

a bar chart of the percentage of the population exposed to various air quality levels in the preand post-policy policy. Figure 5-6 shows a cumulative distribution function of the same data. 

Both figures identify the LML for each of the major cohort studies. As the policy shifts the 

distribution of air quality levels, fewer people are exposed to PM2.5 levels at or above the LML. 

Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM2.5

levels at or above the LML of 7.5 μg/m3. Using the Laden et al. (2006) study, 40% of the 

population is exposed above the LML of 10 μg/m3. As we model mortality impacts among 

populations exposed to levels of PM2.5 that are successively lower than the LML of the lowest 

cohort study, our confidence in the results diminishes. However, the analysis above confirms that 

the great majority of the impacts occur at or above the lowest cohort study’s LML. It is important 

to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of 

the major cohort studies. Just because we have greater confidence in the benefits above the LML, 

this does not mean that we have no confidence that benefits occur below the LML. 
5-30 

0%

5%

10%

15%

20%

25%

1 2 3 4 5 5.8 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Post-control Baseline

The control strategy lowers PM2.5

levels substantially, particularly 

among highly exposed 

populations. In the baseline, 85% 

of the population lived in areas 

where PM2.5 levels above the 

lowest measured levels of the 

Pope study, increasing our 

confidence in the estimated 

mortality reductions for this rule.

Pope et al. 2002 Laden et al. 2006

Figure 5-5. Percentage of Adult Population by Annual Mean PM2.5 Exposure 

(pre- and post-policy policy) 

Figure 5-6. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy policy) 
5-31 

Above we present the estimates of the total monetized benefits, based on our 

interpretation of the best available scientific literature and methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions 

and uncertainties. For example, for key assumptions underlying the estimates for premature 

mortality, which typically account for at least 90% of the total monetized benefits, we were able 

to quantify include the following: 

1. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect 

local variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual benefits of controlling directly emitted fine particulates. 

2. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 

sources, but no clear scientific grounds exist for supporting differential effects 

estimates by particle type. 

3. We assume that the health impact function for fine particles is linear down to the 

lowest air quality levels modeled in this analysis. Thus, the estimates include health 

benefits from reducing fine particles in areas with varied concentrations of PM2.5,

including both regions that are in attainment with fine particle standard and those that 

do not meet the standard down to the lowest modeled concentrations. 

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized benefits), we 

include a set of twelve estimates based on results of the expert elicitation study in 

addition to our core estimates. Even these multiple characterizations omit the 

uncertainty in air quality estimates, baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the 

overall uncertainty in the PM2.5 estimates. This information should be interpreted 

within the context of the larger uncertainty surrounding the entire analysis. For more 

information on the uncertainties associated with PM2.5 benefits, please consult the 

PM2.5 NAAQS RIA (Table 5-5). 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA because we lack the necessary air quality input and monitoring data to run the 

benefits model. In addition, we have not conducted any air quality modeling for this rule. 

Moreover, it was not possible to develop benefit-per-ton metrics and associated estimates of 

uncertainty using the benefits estimates from the PM RIA because of the significant differences 

between the sources affected in that rule and those regulated here. However, the results of the 
5-32 

Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA 

can provide some evidence of the uncertainty surrounding the benefits results presented in this 

analysis. 

5.6 Comparison of Benefits and Costs 

Using a 3% discount rate, we estimate the total monetized benefits of the proposed SSI 

NSPS and EG including energy disbenefits to be $130 million to $320 million in the 

implementation year (2015). Using a 7% discount rate, we estimate the total monetized benefits 

of the SSI NSPS and EG including energy disbenefits to be $120 million to $290 million. The 

annualized costs are $92 million at a 7% interest rate.21 Thus, net benefits are $37 million to $220 

million at a 3% discount rate for the benefits and $26 million to $190 million at a 7% discount 

rate. All estimates are in 2008$. 

Table 5-9 shows a summary of the monetized co-benefits, social costs, and net benefits 

for the SSI NSPS and EG, respectively. Figures 5-7 and 5-8 show the full range of net benefits 

estimates (i.e., annual co-benefits minus annualized costs) utilizing the 14 different PM2.5 

mortality functions at discount rates of 3% and 7%. In addition, the benefits from reducing 2,900 

tons of carbon monoxide, 96 tons of HCl, 3.0 tons of lead, 1.6 tons of cadmium, 5,500 pounds of 

mercury, and 90 grams of total dioxins/furans each year have not been included in these 

estimates. 

 

21 For more information on the annualized costs, please refer to Section 4 of this RIA. There are no estimates of 

costs available at a 3% discount rate. 
5-33 

Table 5-9. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the SSI 

NSPS and EG in 2015 (millions of 2008$)a

3% Discount Rate 7% Discount Rate 

Proposed: Option 2 

Total Monetized Benefitsb

$130 to $320 $120 to $290 

Total Social Costsc

$92 $92 

Net Benefits $37 to $220 $26 to $190 

26,000 tons of carbon monoxide 

96 tons of HCl 

5,500 pounds of mercury 

1.6 tons of cadmium 

3.0 tons of lead 

90 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Non-monetized Benefits 

Visibility impairment 

Option 1 

Total Monetized Benefitsb

$130 to $320 $120 to $290 

Total Social Costsc

$63 $63 

Net Benefits $66 to $250 $55 to $220 

2,900 tons of carbon monoxide 

96tons of HCl 

820 pounds of mercury 

1.6 tons of cadmium 

3.0 tons of lead 

74 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Non-monetized Benefits 

Visibility impairment 

Option 3 

Total Monetized Benefitsb

$130 to $310 $120 to $290 

Total Social Costsc

$132 $132 

Net Benefits -$5.4 to $180 -$14 to $150 

26,000 tons of carbon monoxide 

96 tons of HCl 

5,500 pounds of mercury 

1.6 tons of cadmium 

3.0 tons of lead 

90 grams of dioxins/furans 

Health effects from NO2 and SO2 exposure 

Ecosystem effects 

Non-monetized Benefits 

Visibility impairment 

a All estimates are for the implementation year (2015), and are rounded to two significant figures. These results include 2 new FB 

incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill assumption. 

b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of 

directly emitted PM2.5 and PM2.5 precursors such as NOx and SO2. It is important to note that the monetized benefits include 

many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden 

et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing 

premature mortality because there is no clear scientific evidence that would support the development of differential effects 

estimates by particle type. These estimates include energy disbenefits valued at $0.5 million at a 3% discount rate for CO2

emissions. 

c The annual compliances costs serve as a proxy for the annual social costs of this rule given the lack of difference between the two. 
5-34 

-$50

$0

$50

$100

$150

$200

$250

$300

$350

Pope et al.

Laden et al.

Millions (2008$)

Cost estimate combined with total monetized benefits estimates derived from 2

epidemiology functions and 12 expert functions

Figure 5-7. Net Benefits for the Proposed SSI NSPS and EG at 3% Discount Rate a

a Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2015). This graph shows 14 benefits 

estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All 

fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton 

of precursor reduced has a different propensity to become PM2.5. The monetized benefits incorporate the 

conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at 

$0.5 million at a 3% discount rate for CO2 emissions. 
5-35 

-$50

$0

$50

$100

$150

$200

$250

$300

Pope et al.

Laden et al.

Millions (2008$)

Cost estimate combined with total monetized benefits estimates derived from2 

epidemiology functions and 12 expert functions

Figure 5-8. Net Benefits for the Proposed SSI NSPS and EG at 7% Discount Rate a

a Net Benefits are quantified in terms of PM2.5 benefits for implementation year (2015). This graph shows 14 benefits 

estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All 

fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton 

of precursor reduced has a different propensity to become PM2.5. The monetized benefits incorporate the 

conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at 

$0.5 million at a 3% discount rate for CO2 emissions. 
R-1 

REFERENCES 

“Analysis of Beyond the Maximum Achievable Control Technology (MACT) Floor Controls for 

Existing SSI Units” Memorandum from Roy Oommen, Eastern Research Group, Inc. to 

Amy Hambrick, U.S. Environmental Protection Agency. August, 2010. 

Clean Air Technology Center, Technology Transfer Network. 

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APPENDIX A 

SUMMARY OF EXPERT OPINIONS ON THE EXISTENCE OF A THRESHOLD IN 

THE CONCENTRATION-RESPONSE FUNCTION FOR PM2.5-RELATED 

MORTALITY 
A-1 

Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality 

Technical Support Document (TSD) 

June 2010 

Compiled by: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impact Division 

Air Benefit-Cost Group 

Research Triangle Park, North Carolina

Contents: 

A. HES comments on 812 Analysis (2010) 

B. American Heart Association Scientific Statement (2010) 

C. Integrated Science Assessment for Particulate Matter (2009) 

D. CASAC comments on PM ISA and REA (2009) 

E. Krewski et al. (2009) 

F. Schwartz et al. (2008) 

G. Expert Elicitation on PM Mortality (2006, 2008) 

H. CASAC comments on PM Staff Paper (2005) 

I. HES comments on 812 Analysis (2004) 

J. NRC (2002) 
A-2 

A. HES Comments on 812 Analysis (2010) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010. 

Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of 

the Clean Air Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500738776/$

File/EPA-COUNCIL-10-001-unsigned.pdf>. 

Pg 2: “The HES generally agrees with other decisions made by the EPA project team with 

respect to PM, in particular, the PM mortality effect threshold model, the cessation lag model, 

the inclusion of infant mortality estimation, and differential toxicity of PM.” 

Pg 2: “Further, the HES fully supports EPA’s use of a no-threshold model to estimate the 

mortality reductions associated with reduced PM exposure.” 

Pg 6: “The HES also supports the Agency’s choice of a no-threshold model for PM-related 

effects.” 

Pg 13: “The HES fully supports EPA’s decision to use a no-threshold model to estimate mortality 

reductions. This decision is supported by the data, which are quite consistent in showing effects down to 

the lowest measured levels. Analyses of cohorts using data from more recent years, during which time 

PM concentrations have fallen, continue to report strong associations with mortality. Therefore, there is 

no evidence to support a truncation of the CRF.” 

HES Panel Members

Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in Residence, The National 

Academies, Washington, DC 

Dr. Michelle Bell, Associate Professor, School of Forestry and Environmental Studies, Yale 

University, New Haven, CT 

Dr. James K. Hammitt, Professor, Department of Health Policy and Management, Harvard 

School of Public Health, Boston, MA 

Dr. Jonathan Levy, Associate Professor, Department of Environmental Health, Harvard School 

of Public Health, Boston, MA 

Dr. C. Arden Pope, III Professor, Department of Economics, Brigham Young University, 

Provo, UT 

Mr. John Fintan Hurley, Research Director, Institute of Occupational Medicine (IOM), 

Edinburgh, United Kingdom, UK 

Dr. Patrick Kinney, Professor, Department of Environmental Health Sciences, Mailman School 

of Public Health, Columbia University, New York, NY 

Dr. Michael T. Kleinman, Professor, Department of Medicine, Division of Occupational and 

Environmental Medicine, University of California, Irvine, Irvine, CA 
A-3 

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency, Oakland, CA 

Dr. Rebecca Parkin, Professor and Associate Dean, Environmental and Occupational Health, 

School of Public Health and Health Services, The George Washington University Medical 

Center, Washington, DC 
A-4 

B. Scientific Statement from American Heart Association (2010) 

Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin 

F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L, 

Kaufman JD; on behalf of the American Heart Association Council on Epidemiology and 

Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, 

Physical Activity and Metabolism. (2010). “Particulate matter air pollution and 

cardiovascular disease: an update to the scientific statement from the American Heart 

Association.” Circulation. 121: 2331-2378. 

Pg 2338: “Finally, there appeared to be no lower-limit threshold below which PM10 was not 

associated with excess mortality across all regions.” 

Pg 2350: “There also appears to be a monotonic (e.g., linear or log-linear) concentrationresponse relationship between PM2.5 and mortality risk observed in cohort studies that extends 

below present-day regulations of 15 μg/m3for mean annual levels, without a discernable “safe” 

threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008)

Pg 2364: “The PM2.5 concentration– cardiovascular risk relationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m3 (the 2006 annual NAAQS 

level) without a discernable “safe” threshold.” 

Pg 2365: “This updated review by the AHA writing group corroborates and strengthens the 

conclusions of the initial scientific statement. In this context, we agree with the concept and 

continue to support measures based on scientific evidence, such as the US EPA NAAQS, that 

seek to control PM levels to protect the public health. Because the evidence reviewed supports 

that there is no safe threshold, it appears that public health benefits would accrue from lowering 

PM2.5 concentrations even below present-day annual (15 μg/m3) and 24-hour (35 μg/m3) 

NAAQS, if feasible, to optimally protect the most susceptible populations.” 

Pg 2366: “Although numerous insights have greatly enhanced our understanding of the PMcardiovascular relationship since the first AHA statement was published, the following list 

represents broad strategic avenues for future investigation: ... Determine whether any “safe” PM 

threshold concentration exists that eliminates both acute and chronic cardiovascular effects in 

healthy and susceptible individuals and at a population level.” 

Scientific Statement Authors

Dr. Robert D. Brook, MD 

Dr. Sanjay Rajagopalan, MD 

Dr. C. Arden Pope, PhD 

Dr. Jeffrey R. Brook, PhD 

Dr. Aruni Bhatnagar, PhD, FAHA 

Dr. Ana V. Diez-Roux, MD, PhD, MPH 
A-5 

Dr. Fernando Holguin, MD 

Dr. Yuling Hong, MD, PhD, FAHA 

Dr. Russell V. Luepker, MD, MS, FAHA 

Dr. Murray A. Mittleman, MD, DrPH, FAHA 

Dr. Annette Peters, PhD 

Dr. David Siscovick, MD, MPH, FAHA 

Dr. Sidney C. Smith, Jr, MD, FAHA 

Dr. Laurie Whitsel, PhD 

Dr. Joel D. Kaufman, MD, MPH 
A-6 

C. Integrated Science Assessment for Particulate Matter (2009) 

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment 

for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment – RTP Division. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 

Pg 1-22: “An important consideration in characterizing the public health impacts associated with 

exposure to a pollutant is whether the concentration-response relationship is linear across the full 

concentration range encountered, or if nonlinear relationships exist along any part of this range. 

Of particular interest is the shape of the concentration-response curve at and below the level of 

the current standards. The shape of the concentration-response curve varies, depending on the 

type of health outcome, underlying biological mechanisms and dose. At the human population 

level, however, various sources of variability and uncertainty tend to smooth and “linearize” the 

concentration-response function (such as the low data density in the lower concentration range, 

possible influence of measurement error, and individual differences in susceptibility to air 

pollution health effects). In addition, many chemicals and agents may act by perturbing naturally 

occurring background processes that lead to disease, which also linearizes population 

concentration-response relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976, 

003192; Hoel, 1980, 156555). These attributes of population dose-response may explain why the 

available human data at ambient concentrations for some environmental pollutants (e.g., PM, O3, 

lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely 

mechanisms include nonlinear processes for some key events. These attributes of human 

population dose-response relationships have been extensively discussed in the broader 

epidemiologic literature (Rothman and Greenland, 1998, 086599).” 

Pg 2-16: “In addition, cardiovascular hospital admission and mortality studies that examined the 

PM10 concentration-response relationship found evidence of a log-linear no-threshold 

relationship between PM exposure and cardiovascular-related morbidity (Section 6.2) and 

mortality (Section 6.5).” 

Pg 2-25: “2.4.3. PM Concentration-Response Relationship 

An important consideration in characterizing the PM-morbidity and mortality association is 

whether the concentration-response relationship is linear across the full concentration range that 

is encountered or if there are concentration ranges where there are departures from linearity (i.e., 

nonlinearity). In this ISA studies have been identified that attempt to characterize the shape of 

the concentration-response curve along with possible PM “thresholds” (i.e., levels which PM 

concentrations must exceed in order to elicit a health response). The epidemiologic studies 

evaluated that examined the shape of the concentration-response curve and the potential presence 

of a threshold have focused on cardiovascular hospital admissions and ED visits and mortality 

associated with short-term exposure to PM10 and mortality associated with long-term exposure to 

PM2.5. 

“A limited number of studies have been identified that examined the shape of the PM 

cardiovascular hospital admission and ED visit concentration-response relationship. Of these 
A-7 

studies, some conducted an exploratory analysis during model selection to determine if a linear 

curve most adequately represented the concentration-response relationship; whereas, only one 

study conducted an extensive analysis to examine the shape of the concentration-response curve 

at different concentrations (Section 6.2.10.10). Overall, the limited evidence from the studies 

evaluated supports the use of a no-threshold, log-linear model, which is consistent with the 

observations made in studies that examined the PM-mortality relationship. 

“Although multiple studies have previously examined the PM-mortality concentration-response 

relationship and whether a threshold exists, more complex statistical analyses continue to be 

developed to analyze this association. Using a variety of methods and models, most of the 

studies evaluated support the use of a no-threshold, log-linear model; however, one study did 

observe heterogeneity in the shape of the concentration-response curve across cities (Section 

6.5). Overall, the studies evaluated further support the use of a no-threshold log-linear model, but 

additional issues such as the influence of heterogeneity in estimates between cities, and the effect 

of seasonal and regional differences in PM on the concentration-response relationship still 

require further investigation. 

“In addition to examining the concentration-response relationship between short-term exposure 

to PM and mortality, Schwartz et al. (2008, 156963) conducted an analysis of the shape of the 

concentration-response relationship associated with long-term exposure to PM. Using a variety 

of statistical methods, the concentration-response curve was found to be indistinguishable from 

linear, and, therefore, little evidence was observed to suggest that a threshold exists in the 

association between long-term exposure to PM2.5 and the risk of death (Section 7.6).” 

Pg 6-75: “6.2.10.10. Concentration Response 

The concentration-response relationship has been extensively analyzed primarily through studies 

that examined the relationship between PM and mortality. These studies, which have focused on 

short- and long-term exposures to PM have consistently found no evidence for deviations from 

linearity or a safe threshold (Daniels et al., 2004, 087343; Samoli et al., 2005, 087436; Schwartz, 

2004, 078998; Schwartz et al., 2008, 156963) (Sections 6.5.2.7 and 7.1.4). Although on a more 

limited basis, studies that have examined PM effects on cardiovascular hospital admissions and 

ED visits have also analyzed the PM concentration-response relationship, and contributed to the 

overall body of evidence which suggests a log-linear, no-threshold PM concentration-response 

relationship. 

“The results from the three multicity studies discussed above support no-threshold log-linear 

models, but issues such as the possible influence of exposure error and heterogeneity of shapes 

across cities remain to be resolved. Also, given the pattern of seasonal and regional differences 

in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463), 

the very concept of a concentration-response relationship estimated across cities and for all-year 

data may not be very informative.” 

Pg 6-197: “6.5.2.7. Investigation of Concentration-Response Relationship 

The results from large multicity studies reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 

056905) suggested that strong evidence did not exist for a clear threshold for PM mortality 

effects. However, as discussed in the 2004 PM AQCD (U.S. EPA, 2004, 056905), there are 
A-8 

several challenges in determining and interpreting the shape of PM-mortality concentrationresponse functions and the presence of a threshold, including: (1) limited range of available 

concentration levels (i.e., sparse data at the low and high end); (2) heterogeneity of susceptible 

populations; and (3) investigate the PM-mortality concentration-response relationship. 

“Daniels et al. (2004, 087343) evaluated three concentration-response models: (1) log-linear 

models (i.e., the most commonly used approach, from which the majority of risk estimates are 

derived); (2) spline models that allow data to fit possibly non-linear relationship; and (3) 

threshold models, using PM10 data in 20 cities from the 1987-1994 NMMAPS data. They 

reported that the spline model, combined across the cities, showed a linear relation without 

indicating a threshold for the relative risks of death for all-causes and for cardiovascularrespiratory causes in relation to PM10, but “the other cause” deaths (i.e., all cause minus 

cardiovascular-respiratory) showed an apparent threshold at around 50 μg/m3PM10, as shown in 

Figure 6-35. For all-cause and cardio-respiratory deaths, based on the Akaike’s Information 

Criterion (AIC), a log-linear model without threshold was preferred to the threshold model and 

to the spline model. 

“The HEI review committee commented that interpretation of these results required caution, 

because (1) the measurement error could obscure any threshold; (2) the city-specific 

concentration-response curves exhibited a variety of shapes; and (3) the use of AIC to choose 

among the models might not be appropriate due to the fact it was not designed to assess scientific 

theories of etiology. Note, however, that there has been no etiologically credible reason 

suggested thus far to choose one model over others for aggregate outcomes. Thus, at least 

statistically, the result of Daniels et al. (2004, 087343) suggests that the log-linear model is 

appropriate in describing the relationship between PM10 and mortality. 

“The Schwartz (2004, 078998) analysis of PM10 and mortality in 14 U.S. cities, described in 

Section 6.5.2.1, also examined the shape of the concentration-response relationship by including 

indicator variables for days when concentrations were between 15 and 25 μg/m3, between 25 and 

34 μg/m3, between 35 and 44 μg/m3, and 45 μg/m3and above. In the model, days with 

concentrations below 15 μg/m3served as the reference level. This model was fit using the single 

stage method, combining strata across all cities in the case-crossover design. Figure 6-36 shows 

the resulting relationship, which does not provide sufficient evidence to suggest that a threshold 

exists. The authors did not examine city-to-city variation in the concentration-response 

relationship in this study. 

“PM10 and mortality in 22 European cities (and BS in 15 of the cities) participating in the 

APHEA project. In nine of the 22 cities, PM10 levels were estimated using a regression model 

relating co-located PM10 to BS or TSP. They used regression spline models with two knots (30 

and 50 μg/m3) and then combined the individual city estimates of the splines across cities. The 

investigators concluded that the association between PM and mortality in these cities could be 

adequately estimated using the log-linear model. However, in an ancillary analysis of the 

concentration-response curves for the largest cities in each of the three distinct geographic areas 

(western, southern, and eastern European cities): London, England; Athens, Greece; and Cracow, 

Poland, Samoli et al. (2005, 087436) observed a difference in the shape of the concentrationresponse curve across cities. Thus, while the combined curves (Figure 6-37) appear to support 
A-9 

no-threshold relationships between PM10 and mortality, the heterogeneity of the shapes across 

cities makes it difficult to interpret the biological relevance of the shape of the combined curves. 

“The results from the three multicity studies discussed above support no-threshold log-linear 

models, but issues such as the possible influence of exposure error and heterogeneity of shapes 

across cities remain to be resolved. Also, given the pattern of seasonal and regional differences 

in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463), 

the very concept of a concentration-response relationship estimated across cities and for all-year 

data may not be very informative.” 

Authors of ISA

Dr. Lindsay Wichers Stanek (PM Team Leader)—National Center for Environmental 

Assessment (NCEA), U.S. Environmental Protection Agency (U.S. EPA), Research Triangle 

Park, NC 

Dr. Jeffrey Arnold—NCEA, U.S. EPA, Research Triangle Park, NC (now at Institute for Water 

Resources, U.S. Army Corps of Engineers, Washington, D.C) 

Dr. Christal Bowman—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. James S. Brown—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Barbara Buckley—NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Allen Davis—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Jean-Jacques Dubois—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Steven J. Dutton—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Tara Greaver—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Erin Hines—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Douglas Johns—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Ellen Kirrane—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Dennis Kotchmar—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Thomas Long—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Thomas Luben—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research 

Fellow to NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Kristopher Novak—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Joseph Pinto—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Jennifer Richmond-Bryant—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Mary Ross—NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Jason Sacks—NCEA, U.S. EPA, Research Triangle Park, NC 
A-10 

Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis, OR 

Dr. David Svendsgaard—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Lisa Vinikoor—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. William Wilson—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Lori White— NCEA, U.S. EPA, Research Triangle Park, NC (now at National Institute for 

Environmental Health Sciences, Research Triangle Park, NC) 

Dr. Christy Avery—University of North Carolina, Chapel Hill, NC 

Dr. Kathleen Belanger —Center for Perinatal, Pediatric and Environmental Epidemiology, 

Yale University, New Haven, CT 

Dr. Michelle Bell—School of Forestry & Environmental Studies, Yale University, New Haven, 

CT 

Dr. William D. Bennett—Center for Environmental Medicine, Asthma and Lung Biology, 

University of North Carolina, Chapel Hill, NC 

Dr. Matthew J. Campen—Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC 

Dr. Janneane F. Gent—Center for Perinatal, Pediatric and Environmental Epidemiology, Yale 

University, New Haven, CT 

Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary Medicine, Duke 

University Medical Center, Durham, NC 

Dr. Kazuhiko Ito—Nelson Institute of Environmental Medicine, NYU School of Medicine, 

Tuxedo, NY 

Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research Triangle Park, NC 

Dr. Michael Kleinman—Department of Community and Environmental Medicine, University 

of California, Irvine 

Dr. Sergey Napelenok—National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Dr. Marc Pitchford—National Oceanic and Atmospheric Administration, Las Vegas, NV 

Dr. Les Recio—Genetic Toxicology Division, Integrated Laboratory Systems, Inc., Research 

Triangle Park, NC 

Dr. David Quincy Rich—Department of Epidemiology, University of Medicine and Dentistry 

of New Jersey, Piscataway, NJ 

Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR 

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY 

Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess 

Medical Center, Boston, MA 
A-11 

Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University of North Carolina, 

Chapel Hill, NC 

Peer Reviewers

Dr. Sara Dubowsky Adar, Department of Epidemiology, University of Washington, Seattle, 

WA 

Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI 

Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann Arbor, MI 

Dr. Prakash Bhave, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Mr. George Bowker, Office of Atmospheric Programs, U.S. EPA, Washington, D.C. 

Dr. Judith Chow, Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 

Dr. Dan Costa, U.S. EPA, Research Triangle Park, NC 

Dr. Ila Cote, NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Robert Devlin, National Health and Environmental Effects Research Laboratory, U.S. EPA, 

Research Triangle Park, NC 

Dr. David DeMarini, National Health and Environmental Effects Research Laboratory, U.S. 

EPA, Research Triangle Park, NC 

Dr. Neil Donahue, Department of Chemical Engineering, Carnegie Mellon University, 

Pittsburgh, PA 

Dr. Aimen Farraj, National Health and Environmental Effects Research Laboratory, U.S. EPA, 

Research Triangle Park, NC 

Dr. Mark Frampton, Department of Environmental Medicine, University of Rochester Medical 

Center, Rochester, NY 

Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jim Gauderman, Department of Environmental Medicine, Department of Preventive 

Medicine, University of Southern California, Los Angeles, CA 

Dr. Barbara Glenn, National Center for Environmental Research, U.S. EPA, Washington, D.C. 

Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY 

Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jack Harkema, Department of Pathobiology and Diagnostic Investigation, Michigan State 

University, East Lansing, MI 

Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 
A-12 

Dr. Amy Herring, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 

Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical University, Prescott, 

AZ 

Dr. Mike Kleeman, Department of Civil and Environmental Engineering, University of 

California, Davis, CA 

Dr. Petros Koutrakis, Exposure, Epidemiology and Risk Program, Harvard School of Public 

Health, Boston, MA 

Dr. Sagar Krupa, Department of Plant Pathology, University of Minnesota, St. Paul, MN 

Mr. John Langstaff, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Dr. Meredith Lassiter, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Ms. Connie Meacham, NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jennifer Peel, Department of Environmental and Radiological Health Sciences, College of 

Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 

Dr. Zackary Pekar, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Rob Pinder, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC 

Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Sanjay Rajagopalan, Division of Cardiovascular Medicine, Ohio State University, 

Columbus, OH 

Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Venkatesh Rao, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 
A-13 

Ms. Victoria Sandiford, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Dr. Stefanie Sarnat, Department of Environmental and Occupational Health, Emory University, 

Atlanta, GA 

Dr. Frances Silverman, Gage Occupational and Environmental Health, University of Toronto, 

Toronto, ON 

Mr. Steven Silverman, Office of General Council, U.S. EPA, Washington, D.C. 

Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers University, New 

Brunswick, NJ 

Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Dr. John Vandenberg (Director)—NCEA-RTP Division, U.S. EPA, Research Triangle Park, 

NC 

Dr. Alan Vette, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC 

Ms. Debra Walsh (Deputy Director)—NCEA-RTP Division, U.S. EPA, Research Triangle 

Park, NC 

Mr. Tim Watkins, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC 

Dr. Christopher Weaver, NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Ms. Karen Wesson, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jason West, Department of Environmental Sciences and Engineering, University of North 

Carolina, Chapel Hill, NC 

Mr. Ronald Williams, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Dr. George Woodall, NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Antonella Zanobetti, Department of Environmental Health, Harvard School of Public 

Health, Boston, MA 
A-14 

D. CASAC comments on PM ISA and REA (2009) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. 

Review of EPA’s Integrated Science Assessment for Particulate Matter (First External 

Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet 

at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/7

3ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>. 

Pg 9: “There is an appropriate discussion of the time-series studies, but this section needs to have 

an explicit finding that the evidence supports a relationship between PM and mortality that is 

seen in these studies. This conclusion should be followed by the discussion of statistical 

methodology and the identification of any threshold that may exist.” 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009. 

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: 

Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-

009. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/7

23FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>. 

Pg 6: “On the issue of cut-points raised on 3-18, the authors should be prepared to offer a 

scientifically cogent reason for selection of a specific cut-point, and not simply try different cutpoints to see what effect this has on the analysis. The draft ISA was clear that there is little 

evidence for a population threshold in the C-R function.” 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. Review of 

Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009). 

EPA-CASAC-10-001. November. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F8

3B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. 

Pg 2: “The paragraph on lines 22-30 of page 2-37 is not clearly written. Twice in succession it 

states that the use of a no-threshold log-linear model is supported, but then cites other studies 

that suggest otherwise. It would be good to revise this paragraph to more clearly state – well, I’m 

not sure what. Probably that more research is needed.” 

CASAC Panel Members

Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive Medicine, University of 

Southern California, Los Angeles, CA 

Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology, Department of Environmental 

Health, Harvard School of Public Health, Harvard University, Boston, MA 

Dr. Ellis B. Cowling, University Distinguished Professor At-Large Emeritus, Colleges of Natural 

Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC 
A-15 

Dr. James Crapo, Professor of Medicine, Department of Medicine, National Jewish Medical and 

Research Center, Denver, CO 

Dr. H. Christopher Frey, Professor, Department of Civil, Construction and Environmental Engineering, 

College of Engineering, North Carolina State University, Raleigh, NC 

Dr. Armistead (Ted) Russell, Professor, Department of Civil and Environmental Engineering, Georgia 

Institute of Technology, Atlanta, GA 

Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab, University of California, 

Davis, Davis, CA 

Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern 

California, Los Angeles, CA 

Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina 

University, Greenville, NC 

Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution Research Center, Riverside 

Campus and Kearney Agricultural Center, University of California, Parlier, CA 

Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering, Dartmouth College, Hanover, 

NH 

Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, 

Clarkson University, Potsdam, NY 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University 

School of Medicine, Tuxedo, NY 

Dr. Helen Suh MacIntosh, Associate Professor, Environmental Health, School of Public Health, 

Harvard University, Boston, MA 

Dr. William Malm, Research Physicist, National Park Service Air Resources Division, Cooperative 

Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 

Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager, Western Governors’ 

Association, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort 

Collins, CO 

Dr. Robert F. Phalen, Professor, Department of Community & Environmental Medicine; Director, Air 

Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, Center for 

Occupation & Environment Health, College of Medicine, University of California Irvine, Irvine, CA 

Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the 

Environment, University of California, Davis, CA 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical School, 

Boston, MA 

Dr. Sverre Vedal, Professor, Department of Environmental and Occupational Health Sciences, School of 

Public Health and Community Medicine, University of Washington, Seattle, WA 

Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, IL 
A-16 

Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY 
A-17 

E. Krewski et al. (2009) 

Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli 

Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, and 

Michael J. Thun with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. 

Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara 

Tempalski. (2009). Extended follow-up and spatial analysis of the American Cancer 

Society study linking particulate air pollution and mortality. HEI Research Report, 140, 

Health Effects Institute, Boston, MA. 

Pg 119: [About Pope et al. (2002)] “Each 10-μg/m3increase in long-term average ambient PM2.5

concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death 

from all causes, cardiopulmonary disease, and lung cancer, respectively. There was no evidence 

of a threshold exposure level within the range of observed PM2.5 concentrations.” 

Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams (dated July 7, 

2009) regarding “EPA queries regarding HEI Report 140”. Dr. Adams then forwarded 

the letter on July 10, 2009 to EPA’s Beth Hassett-Sipple. (letter placed in docket #EPAHQ-OAR-2007-0492). 

Pg 4: “6. The Health Review Committee commented that the Updated Analysis completed by 

Pope et al. 2002 reported “no evidence of a threshold exposure level within the range of 

observed PM2.5 concentrations” (p. 119). In the Extended Follow-Up study, did the analyses 

provide continued support for a no-threshold response or was there evidence of a threshold? 

“Response: As noted above, the HEI Health Review Committee commented on the lack of 

evidence for a threshold exposure level in Pope et al. (2002) with follow-up through the year 

1998. The present report, which included follow-up through the year 2000, also does not appear 

to demonstrate the existence of a threshold in the exposure-response function within the range of 

observed PM2.5 concentrations.” 

HEI Health Review Committee Members

Dr. Homer A. Boushey, MD, Chair, Professor of Medicine, Department of Medicine, 

University of California–San Francisco 

Dr. Ben Armstrong, Reader, in Epidemiological Statistics, Department of Public Health and 

Policy, London School of Hygiene and Tropical Medicine, United Kingdom 

Dr. Michael Brauer, ScD, Professor, School of Environmental Health, University of British 

Columbia, Canada 

Dr. Bert Brunekreef, PhD, Professor of Environmental Epidemiology, Institute of Risk 

Assessment Sciences, University of Utrecht, The Netherlands 

Dr. Mark W. Frampton, MD, Professor of Medicine & Environmental Medicine, University of 

Rochester Medical Center, Rochester, NY 
A-18 

Dr. Stephanie London, MD, PhD, Senior Investigator, Epidemiology Branch, National Institute 

of Environmental Health Sciences 

Dr. William N. Rom, MD, MPH, Sol and Judith Bergstein Professor of Medicine and 

Environmental Medicine and Director of Pulmonary and Critical Care Medicine, New York 

University Medical Center 

Dr. Armistead Russell, Georgia Power Distinguished Professor of Environmental Engineering, 

School of Civil and Environmental Engineering, Georgia Institute of Technology 

Dr. Lianne Sheppard, PhD, Professor, Department of Biostatistics, University of Washington 
A-19 

F. Schwartz et al. (2008) 

Schwartz J, Coull B, Laden F. (2008). The Effect of Dose and Timing of Dose on the 

Association between Airborne Particles and Survival. Environmental Health Perspectives. 

116: 64-69. 

Pg 67: “A key finding of this study is that there is little evidence for a threshold in the 

association between exposure to fine particles and the risk of death on follow-up, which 

continues well below the U.S. EPA standard of 15 μg/m3.” 

Pg 68: “In conclusion, penalized spline smoothing and model averaging represent reasonable, 

feasible approaches to addressing questions of the shape of the exposure–response curve, and can 

provide valuable information to decisionmakers. In this example, both approaches are consistent, 

and suggest that the association of particles with mortality has no threshold down to close to 

background levels.” 
A-20 

G. Expert Elicitation on PM-Mortality (2006, 2008) 

Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

the U.S.EPA, Office of Air Quality Planning and Standards, September. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>. 

Pg v: “Each expert was given the option to integrate their judgments about the likelihood of a 

causal relationship and/or threshold in the C-R function into his distribution or to provide a 

distribution "conditional on" one or both of these factors.” 

Pg vii: “Only one of 12 experts explicitly incorporated a threshold into his C-R function.3 The 

rest believed there was a lack of empirical and/or theoretical support for a population threshold. 

However, three other experts gave differing effect estimate distributions above and below some 

cut-off concentration. The adjustments these experts made to median estimates and/or 

uncertainty at lower PM2.5 concentrations were modest.” 

“

3

 Expert K indicated that he was 50 percent sure that a threshold existed. If there 

were a threshold, he thought that there was an 80 percent chance that it would be 

less than or equal to 5 μg/m3, and a 20 percent chance that it would fall between 5 

and 10 μg/m3.” 

Pg ix: “Compared to the pilot study, experts in this study were in general more confident in a 

causal relationship, less likely to incorporate thresholds, and reported higher mortality effect 

estimates. The differences in results compared with the pilot appear to reflect the influence of 

new research on the interpretation of the key epidemiological studies that were the focus of both 

elicitation studies, more than the influence of changes to the structure of the protocol.” 

Pg 3-25: “3.1.8 THRESHOLDS 

The protocol asked experts for their judgments regarding whether a threshold exists in the PM2.5 

mortality C-R function. The protocol focused on assessing expert judgments regarding theory 

and evidential support for a population threshold (i.e., the concentration below which no member 

of the study population would experience an increased risk of death).32 If an expert wished to 

incorporate a threshold in his characterization of the concentration-response relationship, the 

team then asked the expert to specify the threshold PM2.5 concentration probabilistically, 

incorporating his uncertainty about the true threshold level. 

“From a theoretical and conceptual standpoint, all experts generally believed that individuals 

exhibit thresholds for PM-related mortality. However, 11 of them discounted the idea of a 

population threshold in the C-R function on a theoretical and/or empirical basis. Seven of these 

experts noted that theoretically one would be unlikely to observe a population threshold due to 

the variation in susceptibility at any given time in the study population resulting from 

combinations of genetic, environmental, and socioeconomic factors.33 All 11 thought that there 

was insufficient empirical support for a population threshold in the C-R function. In addition, 

two experts (E and L) cited analyses of the ACS cohort data in Pope et al. (2002) and another (J) 

cited Krewski et al. (2000a & b) as supportive of a linear relationship in the study range. 
A-21 

“Seven of the experts favored epidemiological studies as ideally the best means of addressing the 

population threshold issue, because they are best able to evaluate the full range of susceptible 

individuals at environmentally relevant exposure levels. However, those who favored 

epidemiologic studies generally acknowledged that definitive studies addressing thresholds 

would be difficult or impossible to conduct, because they would need to include a very large and 

diverse population with wide variation in exposure and a long follow-up period. Furthermore, 

two experts (B and I) cited studies documenting difficulties in detecting a threshold using 

epidemiological studies (Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts 

generally thought that clinical and toxicological studies are best suited for researching 

mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B, 

thought that a better understanding of the detailed biological mechanism is critical to addressing 

the question of a threshold. 

“One expert, K, believed it was possible to make a conceptual argument for a population 

threshold. He drew an analogy with smoking, indicating that among heavy smokers, only a 

proportion of them gets lung cancer or demonstrates an accelerated decline in lung function. He 

thought that the idea that there is no level that is biologically safe is fundamentally at odds with 

toxicological theory. He did not think that a population threshold was detectable in the currently 

available epidemiologic studies. He indicated that some of the cohort studies showed greater 

uncertainty in the shape of the C-R function at lower levels, which could be indicative of a 

threshold. 

“Expert K chose to incorporate a threshold into his C-R function. He indicated that he was 50 

percent sure that a threshold existed. If there were a threshold, he thought that there was an 80 

percent chance that it would be less than or equal to 5 μg/m3, and a 20 percent chance that it 

would fall between 5 and 10 μg/m3.” 

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. 

Richmond, Bryan J. Hubbell, and Patrick L. Kinney. (2008). “Expert Judgment 

Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in 

the U.S.” Environ. Sci. Technol., 42(7):2268-2274. 

Pg 2271: “Eight experts thought the true C-R function relating mortality to changes in annual 

average PM2.5 was log-linear across the entire study range (ln(mortality) ) β × PM). Four experts 

(B, F, K, and L) specified a “piecewise” log-linear function, with different β coefficients for PM 

concentrations above and below an expert-specified break point. This approach allowed them to 

express increased uncertainty in mortality effects seen at lower concentrations in major 

epidemiological studies. Expert K thought the relationship would be log-linear above a 

threshold.” 

Pg 2271: “Expert K also applied a threshold, T, to his function, which he described 

probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m3) = 0.8 

and P(5 μg/m3< T ≤ 10 μg/m3) = 0.2. Figure 3 does not include the impact of applying expert 

K’s threshold, as the size of the reduction in benefits will depend on the distribution of baseline 

PM levels in a benefits analysis.” 
A-22 

Experts:

Dr. Doug W. Dockery, Harvard School of Public Health 

Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School of Medicine, 

Tuxedo, NY 

Dr. Dan Krewski, University of Ottawa 

Dr. Nino Künzli, University of Southern California Keck School of Medicine 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University 

School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Lovelace Respiratory Research Institute 

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency, Oakland, CA 

Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT 

Dr. Richard Schlesinger, Pace University 

Dr. Joel Schwartz, Harvard School of Public Health 

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY 

Dr. Mark Utell, University of Rochester School of Medicine and Dentistry 
A-23 

H. CASAC comments on PM Staff Paper (2005) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2005. 

EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter 

(Second Draft PM Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June. Available 

on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E523DD36175EB5AD8525701B007332AE/$Fil

e/SAB-CASAC-05-007_unsigned.pdf>. 

Pg 6: “A second concern is with methodological issues. The issue of the selection of 

concentration-response (C-R) relationships based on locally-derived coefficients needs more 

discussion. The Panel did not agree with EPA staff in calculating the burden of associated 

incidence in their risk assessment using either the predicted background or the lowest measured 

level (LML) in the utilized epidemiological analysis. The available epidemiological database on 

daily mortality and morbidity does not establish either the presence or absence of threshold 

concentrations for adverse health effects. Thus, in order to avoid emphasizing an approach that 

assumes effects that extend to either predicted background concentrations or LML, and to 

standardize the approach across cities, for the purpose of estimating public health impacts, the 

Panel favored the primary use of an assumed threshold of 10 μg/m3. The original approach of 

using background or LML, as well as the other postulated thresholds, could still be used in a 

sensitivity analysis of threshold assumptions. 

“The analyses in this chapter highlight the impact of assumptions regarding thresholds, or lack of 

threshold, on the estimates of risk. The uncertainty associated with threshold or nonlinear models 

needs more thorough discussion. A major research need is for more work to determine the 

existence and level of any thresholds that may exist or the shape of nonlinear concentrationresponse curves at low levels of exposure that may exist, and to reduce uncertainty in estimated 

risks at the lowest PM concentrations.” 

CASAC Panel Members

Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

Dr. Ellis Cowling, University Distinguished Professor-at-Large, North Carolina State 

University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina 

State University, Raleigh, NC 

Dr. James D. Crapo, Professor, Department of Medicine, Biomedical Research and 

PatientCare, National Jewish Medical and Research Center, Denver, CO 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical 

Engineering, Clarkson University, Potsdam, NY 

Dr. Jane Q. Koenig, Professor, Department of Environmental Health, School of Public Health 

and Community Medicine, University of Washington, Seattle, WA 
A-24 

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental Health , School of 

Public Health, Harvard University (HSPH), Boston, MA 

Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta 

Dr. Paul J. Lioy, Associate Director and Professor, Environmental and Occupational Health 

Sciences Institute, UMDNJ - Robert Wood Johnson Medical School, NJ 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York 

University School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Vice President, Senior Scientist, and Director, National Environmental 

Respiratory Center, Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Roger O. McClellan, Consultant, Albuquerque, NM 

Dr. Frederick J. Miller, Consultant, Cary, NC 

Dr. Gunter Oberdorster, Professor of Toxicology, Department of Environmental Medicine, 

School of Medicine and Dentistry, University of Rochester, Rochester, NY 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO 

Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology, Bloomberg School 

of Public Health, Johns Hopkins University, Baltimore, MD 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard 

Medical School, Boston, MA 

Dr. Sverre Vedal, Professor of Medicine, School of Public Health and Community 

Medicine University of Washington, Seattle, WA 

Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of Public Health, 

Johns Hopkins University, Baltimore, MD 

Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory, University of California 

-Davis, Davis, CA 

Dr. George T. Wolff, Principal Scientist, General Motors Corporation, Detroit, MI 

Dr. Barbara Zielinska, Research Professor, Division of Atmospheric Science, Desert Research 

Institute, Reno, NV 
A-25 

I. HES Comments on 812 Analysis (2004) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2004. 

Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second 

Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990-2020. Advisory by 

the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance 

Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400

433D5D/$File/council_adv_04002.pdf>. 

Pg 20: “The Subcommittee agrees that the whole range of uncertainties, such as the questions of 

causality, shape of C-R functions and thresholds, relative toxicity, years of life lost, cessation lag 

structure, cause of death, biologic pathways, or susceptibilities may be viewed differently for 

acute effects versus long-term effects. 

“For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted 

the most careful work on this issue. They report that the associations between PM2.5 and both allcause and cardiopulmonary mortality were near linear within the relevant ranges, with no 

apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, Figure 3 and 

Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. 

Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end 

of the concentrations reported in the studies.” 

HES Panel Members

Dr. Bart Ostro, California Office of Environmental Health Hazard Assessment (OEHHA), 

Oakland, CA 

Mr. John Fintan Hurley, Institute of Occupational Medicine (IOM), Edinburgh, Scotland 

Dr. Patrick Kinney, Columbia University, New York, NY 

Dr. Michael Kleinman, University of California, Irvine, CA 

Dr. Nino Künzli, University of Southern California, Los Angeles, CA 

Dr. Morton Lippmann, New York University School of Medicine, Tuxedo, NY Dr. Rebecca 

Parkin, The George Washington University, Washington, DC 

Dr. Trudy Cameron, University of Oregon, Eugene, OR 

Dr. David T. Allen, University of Texas, Austin, TX 

Ms. Lauraine Chestnut, Stratus Consulting Inc., Boulder, CO 

Dr. Lawrence Goulder, Stanford University, Stanford, CA 

Dr. James Hammitt, Harvard University, Boston, MA 

Dr. F. Reed Johnson, Research Triangle Institute, Research Triangle Park, NC 

Dr. Charles Kolstad, University of California, Santa Barbara, CA 
A-26 

Dr. Lester B. Lave, Carnegie Mellon University, Pittsburgh, PA 

Dr. Virginia McConnell, Resources for the Future, Washington, DC 

Dr. V. Kerry Smith, North Carolina State University, Raleigh, NC 

Other Panel Members

Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis, Clark University, 

Worcester, MA Dr. D. Warner North, NorthWorks Inc., Belmont, CA Dr. Thomas S. Wallsten, 

University of Maryland, College Park, MD 
A-27 

J. NRC – Committee on Estimating the Health Risk Reduction Benefits of Proposed Air 

Pollution Regulations (2002) 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed 

Air Pollution Regulations. Washington, DC: The National Academies Press. 

Pg 109: “Linearity and Thresholds 

“The shape of the concentration-response functions may influence the overall estimate of 

benefits. The shape is particularly important for lower ambient air pollution concentrations to 

which a large portion of the population is exposed. For this reason, the impact of the existence of 

a threshold may be considerable. 

“In epidemiological studies, air pollution concentrations are usually measured and modeled as 

continuous variables. Thus, it may be feasible to test linearity and the existence of thresholds, 

depending on the study design. In time-series studies with the large number of repeated 

measurements, linearity and thresholds have been formally addressed with reasonable statistical 

power. For pollutants such as PM10 and PM2.5, there is no evidence for any departure of linearity 

in the observed range of exposure, nor any indication of a threshold. For example, examination 

of the mortality effects of short-term exposure to PM10 in 88 cities indicates that the 

concentration-response functions are not due to the high concentrations and that the slopes of 

these functions do not appear to increase at higher concentrations (Samet et al. 2000). Many 

other mortality studies have examined the shape of the concentration-response function and 

indicated that a linear (nonthreshold) model fit the data well (Pope 2000). Furthermore, studies 

conducted in cities with very low ambient pollution concentrations have similar effects per unit 

change in concentration as those studies conducted in cities with higher concentrations. Again, 

this finding suggests a fairly linear concentration-response function over the observed range of 

exposures. 

“Regarding the studies of long-term exposure, Krewski et al. (2000) found that the assumption of 

a linear concentration-response function for mortality outcomes was not unreasonable. However, 

the statistical power to assess the shape of these functions is weakest at the upper and lower end 

of the observed exposure ranges. Most of the studies examining the effects of long-term 

exposure on morbidity compare subjects living in a small number of communities (Dockery et al. 

1996; Ackermmann-Liebrich 1997; Braun-Fahrländer et al. 1997). Because the number of longterm effects studies are few and the number of communities studied is relatively small (8 to 24), 

the ability to test formally the absence or existence of a no-effect threshold is not feasible. 

However, even if thresholds exist, they may not be at the same concentration for all health 

outcomes. 

“A review of the time-series and cohort studies may lead to the conclusion that although a 

threshold is not apparent at commonly observed concentrations, one may exist at lower levels. 

An important point to acknowledge regarding thresholds is that for health benefits analysis a key 

threshold is the population threshold (the lowest of the individual thresholds). However, the 

population threshold would be very difficult to observe empirically through epidemiology, 
A-28 

because epidemiology integrates information from very large groups of people (thousands). Air 

pollution regulations affect even larger groups of people (millions). It is reasonable to assume 

that among such large groups susceptibility to air pollution health effects varies considerably 

across individuals and depends on a large set of underlying factors, including genetic makeup, 

age, exposure measurement error, preexisting disease, and simultaneous exposures from smoking 

and occupational hazards. This variation in individual susceptibilities and the resulting 

distribution of individual thresholds underlies the concentration-response function observed in 

epidemiology. Thus, until biologically based models of the distribution of individual thresholds 

are developed, it may be productive to assume that the population concentration-response 

function is continuous and to focus on finding evidence of changes in its slope as one approaches 

lower concentrations. 

EPA’s Use of Thresholds 

“In EPA’s benefits analyses, threshold issues were discussed and interpreted. For the PM and 

ozone National Ambient Air Quality Standards (NAAQS), EPA investigated the effects of a 

potential threshold or reference value below which health consequences were assumed to be zero 

(EPA 1997). Specifically, the high-end benefits estimate assumed a 12-microgram per cubic 

meter (μg/m3) mean threshold for mortality associated with long-term exposure to PM2.5. The 

low-end benefits estimate assumed a 15-μg/m3threshold for all PM-related health effects. The 

studies, however, included concentrations as low as 7.5 μg/m3. For the Tier 2 rule and the HD 

engine and diesel-fuel rule, no threshold was assumed (EPA 1999, 2000). EPA in these analyses 

acknowledged that there was no evidence for a threshold for PM. 

“Several points should be noted regarding the threshold assumptions. If a threshold is assumed 

where one was not apparent in the original study, then the data should be refit and a new curve 

generated with the assumption of a zero slope over a segment of the concentration-response 

function that was originally found to be positively sloped. The assumption of a zero slope over a 

portion of the curve will force the slope in the remaining segment of the positively sloped 

concentration-response function to be greater than was indicated in the original study. A new 

concentration-response function was not generated for EPA’s benefits analysis for the PM and 

ozone NAAQS for which threshold assumptions were made. The generation of the steeper slope 

in the remaining portion of the concentration-response function may fully offset the effect of 

assuming a threshold. These aspects of assuming a threshold in a benefits analysis where one 

was not indicated in the original study should be conveyed to the reader. The committee notes 

that the treatment of thresholds should be evaluated in a consistent and transparent framework by 

using different explicit assumptions in the formal uncertainty analyses (see Chapter 5).” 

Pg 117: “Although the assumption of no thresholds in the most recent EPA benefits analyses was 

appropriate, EPA should evaluate threshold assumptions in a consistent and transparent 

framework using several alternative assumptions in the formal uncertainty analysis.” 

Pg 136: “Two additional illustrative examples are thresholds for adverse effects and lag 

structures.

2

EPA considers implausible any threshold for mortality in the particulate matter (PM) 

exposure ranges under consideration (EPA 1999a, p. 3-8). Although the agency conducts 

sensitivity analyses incorporating thresholds, it provides no judgment as to their relative 
A-29 

plausibility. In a probabilistic uncertainty analysis, EPA could assign appropriate weights to 

various threshold models. For PM-related mortality in the Tier 2 analysis, the committee expects 

that this approach would have resulted in only a slight widening of the probability distribution 

for avoided mortality and a slight reduction in the mean of that distribution, thus reflecting 

EPA’s views about the implausibility of thresholds. The committee finds that such formal 

incorporation of EPA’s expert judgments about the plausibility of thresholds into its primary 

analysis would have been an improvement. 

“Uncertainty about thresholds is a special aspect of uncertainty about the shape of concentrationresponse functions. Typically, EPA and authors of epidemiological studies assume that these 

functions are linear on some scale. Often, the scale is a logarithmic transformation of the risk or 

rate of the health outcome, but when a rate or risk is low, a linear function on the logarithmic 

scale is approximately linear on the scale of the rate or risk itself. Increasingly, epidemiological 

investigators are employing analytic methods that permit the estimation of nonlinear shapes for 

concentration-response functions (Greenland et al. 1999). As a consequence, EPA will need to be 

prepared to incorporate nonlinear concentration-response functions from epidemiological studies 

into the agency’s health benefits analyses. Any source of error or bias that can distort an 

epidemiological association can also distort the shape of an estimated concentration -response 

function, as can variation in individual susceptibility (Hattis and Burmaster 1994; Hattis et al. 

2001).” 

Pg 137: “In principle, many components of the health benefits model need realistic probabilistic 

models (see Table 5-1 for a listing of such components), in addition to concentration-response 

thresholds and time lags between exposure and response. For example, additional features of the 

concentration-response function—such as projection of the results from the study population to 

the target populations (which may have etiologically relevant characteristics outside the range 

seen in the study population) and the projection of baseline frequencies of morbidity and 

mortality into the future—must be characterized probabilistically. Other uncertainties that might 

affect the probability distributions are the estimations of population exposure (or even 

concentration) from emissions, estimates of emissions themselves, and the relative toxicity of 

various classes of particles. Similarly, many aspects of the analysis of the impact of regulation on 

ambient concentrations and on population exposure involve considerable uncertainty and, 

therefore, may be beneficially modeled in this way. Depending on the analytic approach used, 

joint probability distributions will have to be specified to incorporate correlations between model 

components that are structurally dependent upon each other, or the analysis will have to be 

conducted in a sequential fashion that follows the model for the data-generating process. 

“EPA should explore alternative options for incorporating expert judgment into its probabilistic 

uncertainty analyses. The agency possesses considerable internal expertise, which should be 

employed as fully as possible. Outside experts should also be consulted as needed, individually 

or in panels. In all cases, when expert judgment is used in the construction of a model 

component, the experts should be identified and the rationales and empirical bases for their 

judgments should be made available.” 
A-30 

NRC members

Dr. JOHN C. BAILAR, III (Chair), (emeritus) University of Chicago, Chicago, Illinois 

Dr. HUGH ROSS ANDERSON, University of London, London, England 

Dr. MAUREEN L. CROPPER, University of Maryland, College Park 

Dr. JOHN S. EVANS, Harvard University, Boston, Massachusetts 

Dr. DALE B. HATTIS, Clark University, Worcester, Massachusetts 

Dr. ROGENE F. HENDERSON, Lovelace Respiratory Research Institute, Albuquerque, New 

Mexico 

Dr. PATRICK L. KINNEY, Columbia University, New York, New York 

Dr. NINO KÜNZLI, University of Basel, Basel, Switzerland; as of September 2002, University 

of Southern California, Los Angeles 

Dr. BART D. OSTRO, California Environmental Protection Agency, Oakland 

Dr. CHARLES POOLE, University of North Carolina, Chapel Hill 

Dr. KIRK R. SMITH, University of California, Berkeley 

Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts 

Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland
APPENDIX B 

LOWEST MEASURED LEVEL (LML) ASSESSMENT FOR RULES WITHOUT 

POLICY-SPECIFIC AIR QUALITY DATA AVAILABLE: TECHNICAL SUPPORT 

DOCUMENT (TSD) 
B-1 

Lowest Measured Level (LML) Assessment for Rules 

without Policy-Specific Air Quality Data Available 

Technical Support Document (TSD) 

June 2010 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impact Division 

Air Benefit-Cost Group 

Research Triangle Park, North Carolina
B-2 

Inherent in any complex Regulatory Impact Analysis (RIA) are multiple sources of 

uncertainty. Health benefits analysis relies on an array of data inputs—including air quality 

modeling, health impact functions and valuation estimates among others—which are themselves 

subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. 

There are a variety of methods to characterizing the uncertainty associated with the human health 

benefits of air pollution, including quantitative and qualitative methods. When evaluated within 

the context of these uncertainties, the health impact and monetized benefits estimates in an RIA 

can provide useful information regarding the magnitude of the public health impacts attributable 

to reducing air pollution. 

Reductions in premature mortality typically dominate the size of the overall monetized 

benefits. Therefore, most of the uncertainty characterization generally focuses on the mortalityrelated benefits. Typically, EPA employs two primary techniques for quantifying this 

uncertainty. First, because this characterization of random statistical error may omit important 

sources of uncertainty, we employ the results of an expert elicitation on the relationship between 

premature mortality and ambient PM2.5 concentration (Roman et al., 2008); this provides 

additional insight into the likelihood of different outcomes and about the state of knowledge 

regarding the benefits estimates. Second, when we have air quality modeling specific to the 

policy we are evaluating and it can be used as an input to the health impact and economic 

analysis, we use Monte Carlo methods for characterizing random sampling error associated with 

the concentration response functions from epidemiological studies and economic valuation 

functions.1 Both approaches have different strengths and weaknesses, which are fully described 

in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). 

In addition, some RIAs, including the PM NAAQS RIA (2006d) and Ozone NAAQS 

RIA (2008a), also contain a suite of sensitivity analyses that evaluate the sensitivity of the 

monetized benefits to the specification of alternate mortality cessation lags and income growth 

adjustment factors. Cessation lags and income growth adjustments are simply multipliers applied 

to the valuation function, which generally affect monetized benefits estimates in the same 

manner. Thus, it is possible for readers to infer the sensitivity of these parameters by referring to 

those previous analyses.2 Other RIAs contain unique sensitivity analyses that are specific to the 

 

1 Currently, we are unable to characterize the random sampling error from the underlying studies when applying 

national average benefit-per-ton estimates. 

2

For example, in the PM NAAQS RIA, the use of an alternate lag structure would change the PM2.5-related 

mortality benefits discounted at 3% discounted by between 10.4% and –27%; when discounted at 7%, these 
B-3 

input parameters of that analysis, such as blood lead level (U.S. EPA, 2008b) or rollback method 

(U.S. EPA, 2010a). Other sources of uncertainty, including the projection of atmospheric 

conditions and source-level emissions, the projection of baseline morbidity rates, incomes and 

technological development are typically unquantified in our RIAs. For these sources, we 

typically provide a qualitative uncertainty characterization associated with these input 

parameters. 

One particular aspect of uncertainty has received extensive quantitative and qualitative 

attention in recent RIAs: the existence of a threshold in the concentration-response function for 

PM2.5-related mortality. A threshold is a specific type of discontinuity in the concentrationresponse function where there are no benefits associated with reducing PM2.5 levels in areas 

where the baseline air quality is less than the threshold. Previously, EPA had included a 

sensitivity analysis with an arbitrary assumed threshold at 10 μg/m3in the PM-mortality health 

impact function in the RIA to illustrate that the fraction of benefits that occur at lower air 

pollution concentration levels are inherently more uncertain. A threshold of 10 μg/m3does not 

necessarily have any stronger technical basis than any other threshold, and we could have instead 

assumed a threshold at 4, 7.5, or 12 μg/m3for the sensitivity analysis. In addition to identifying 

the most support for a non-threshold model, the underlying scientific evidence does not support 

any specific “bright line”. 

Based on our review of the current body of scientific literature, EPA now estimates PMrelated mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by 

EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear 

model most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function.1

Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council 

concluded, “The HES fully supports EPA’s decision to use a no-threshold model to estimate 

mortality reductions. This decision is supported by the data, which are quite consistent in 

 

benefits change by between 31% and -49%. When applying higher and lower income growth adjustments, the 

monetary value of PM2.5 and ozone-related premature changes between 30% and -10%; the value of chronic 

endpoints change between 5% and -2% and the value of acute endpoints change between 6% and -7%. (U.S. EPA, 

2006) 

1

It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually 

distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a 

discontinuity, which is a specific example of non-linearity. 
B-4 

showing effects down to the lowest measured levels. Analyses of cohorts using data from more 

recent years, during which time PM concentrations have fallen, continue to report strong 

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” 

For a summary of these scientific review statements and the panel members please consult the 

Technical Support Document (TSD) Summary of Expert Opinions on the Existence of a 

Threshold (U.S. EPA, 2010c). 

Consistent with this finding, we have conformed the previous threshold sensitivity 

analysis to the current state of the PM science by incorporating a new “Lowest Measured Level” 

(LML) assessment. While an LML assessment provides some insight into the level of uncertainty 

in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues 

to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations. Unlike an assumed threshold, which is a modeling assumption that reduces the 

magnitude of the estimated health impacts, the LML is a characterization of the fraction of 

benefits that are more uncertain. It is important to emphasize that just because we have greater 

confidence in the benefits above the LML, this does not mean that we have no confidence that 

benefits occur below the LML. 

While the LML of each study is important to consider when characterizing and 

interpreting the overall level PM-related benefits, EPA believes that large cohort-based mortality 

estimates are suitable for use in air pollution health impact analyses. When estimating PM 

mortality impacts using risk coefficients drawn from the Harvard Six Cities and the American 

Cancer Society cohorts there are innumerable other attributes that may affect the size of the 

reported risk estimates—including differences in population demographics, the size of the 

cohort, activity patterns and particle composition among others. The LML assessment provides a 

limited representation of one key difference between the two studies. For the purpose of 

estimating the benefits associated with reducing PM2.5 levels, we utilize the effect coefficients 

from Pope et al. (2002) for the American Cancer Society cohort and from Laden et al. (2006) for 

the Harvard Six Cities cohort. 

Analyses of these cohorts using data from more recent years, during which time PM 

concentrations have fallen, continue to report strong associations with mortality. For example, 

the Krewski et al. (2009) follow-up study of the American Cancer Society cohort had an LML of 

5.8 μg/m3. As we model mortality impacts among populations exposed to levels of PM2.5 that are 

successively lower than the LML of each study, our confidence in the results diminishes. As air 

pollution emissions continue to decrease over time, there will be more people in areas where we 

do not have published epidemiology studies. However, each successive cohort study has shown 
B-5 

evidence of effects at successively lower levels of PM2.5. As more large cohort studies follow 

populations over time, we will likely have more studies with lower LML as air quality levels 

continue to improve. Even in the absence of a definable threshold, we have more confidence in 

the benefits estimates above the LML of the large cohort studies. To account for the uncertainty 

in each of the studies that we base our mortality estimates on, we provide the LML for each of 

the cohort studies. However, the finding of effects at the lowest LML from the recent Krewski et 

al (2009) study indicates that confidence in PM2.5-related mortality effects down to at least 5.8 

μg/m3 is high. 

In the recently proposed Transport Rule RIA (U.S. EPA, 2010b), we included the new 

LML assessment in which we binned the estimated number of avoided PM2.5-related premature 

mortalities resulting from the implementation of the Transport Rule according to the projected 

2014 baseline PM2.5 air quality levels. This presentation is consistent with our approach to 

applying PM2.5 mortality risk coefficients that have not been adjusted to incorporate an assumed 

threshold. A very large proportion of the avoided PM-related impacts occurred among 

populations initially exposed at or above the LML of each study, which gave us a high level of 

confidence in the PM mortality estimates. This assessment summarized the distribution of 

avoided PM mortality impacts according to the baseline PM2.5 levels experienced by the 

population receiving the PM2.5 mortality benefit. Approximately 80% of the avoided impacts 

occurred at or above a baseline annual mean PM2.5 level of 10 μg/m3(the LML of the Laden et 

al. 2006 study); about 97% occur at or above an annual mean PM2.5 level of 7.5 μg/m3 (the LML 

of the Pope et al. 2002 study). This assessment confirmed that the great majority of the impacts 

associated with the Transport Rule occurred at or above each study’s LML. 

For the Transport Rule, policy-specific air quality modeling data for the year 2014 was 

available as an input into the benefits analysis. For some rules, especially New Source 

Performance Standards (NSPS) or National Emissions Standards for Hazardous Air Pollutant 

(NESHAP) rules, policy-specific air quality data is not available due to time or resource 

limitations. For these rules, we provide the following LML assessment as a characterization of 

the baseline exposure to PM2.5 levels in the U.S. Many of the upcoming NSPS and NESHAP 

rules have compliance dates between 2013 and 2016 and represent marginal improvements in air 

quality levels. Although it the data is not a perfect match, we believe that the air quality data 
B-6 

from the Transport Rule is a reasonable approximation of the baseline exposure in the U.S. for 

upcoming NSPS and NESHAP rules.1 

For rules without air quality modeling, we generally estimate the monetized benefits and 

health impacts using benefit-per-ton estimates (Fann, Fulcher and Hubbell, 2009). Using this 

method, we are unable to estimate the percentage of premature mortality associated with the 

specific rules’ emission reductions at each PM2.5 level. However, we believe that it is still 

important to characterize the uncertainty associated with the distribution of the baseline air 

quality. As a surrogate measure of mortality impacts, we provide the percentage of baseline 

exposure at each PM2.5 level. If air quality levels in the baseline are above the LML, the marginal 

changes anticipated from these rules would likely also lead to post-policy air quality levels above 

the LML. Therefore, we have high confidence that the magnitude of the benefits estimated for 

these rules, as the marginal changes would also be above the LML. 

It is important to note that baseline exposure is only one parameter in the health impact 

function, along with baseline incidence rates population, and change in air quality. In other 

words, the percentage of the population exposed to air pollution below the LML is not the same 

as the percentage of the population experiencing health impacts as a result of a specific emission 

reduction policy. The most important aspect, which we are unable to quantify for rules without 

air quality modeling, is the shift in exposure associated with the specific rule. Therefore, caution 

is warranted when interpreting the following assessment. 

A very large proportion of the population is exposed at or above the lowest LML of the 

cohort studies (Figures 1 and 2), increasing our confidence in the PM mortality analysis. Figure 1 

shows a bar chart of the percentage of the population exposed to various air quality levels in the 

pre- and post-policy policy. Figure 2 shows a cumulative distribution function of the same data. 

In addition, Figure 2 also demonstrates that policy had a greater impact on reducing exposure to 

the portion of the population in areas with high PM2.5 levels relative to the portion of the 

population at low PM2.5 levels. Both figures identify the LML for each of the major cohort 

studies. As the policy shifts the distribution of air quality levels, fewer people are exposed to 

PM2.5 levels above the LML. Under baseline conditions, about 96 percent of the population is 

 

1 Because the Transport Rule is not yet promulgated, the baseline exposure obtained from this modeling data would 

slightly overestimate the fraction of the population exposed to air quality levels below the LML. As additional 

rules continue to reduce the ambient PM2.5 levels over time, a larger fraction of the population would be exposed 

to air quality levels below the LML. However, the emission reductions anticipated from the rules without air 

quality modeling available are comparatively small and represent marginal changes. We intend to update this 

LML assessment as necessary to correspond with the successively lower baseline air quality levels anticipated as 

the result of promulgating significant upcoming rules. 
B-7 

exposed to annual mean PM2.5 levels of at least 5.8 μg/m3, which is the lowest air quality level 

considered in the most recent study of the American Cancer Society cohort by Krewski et al. 

(2009). Using the Pope et al. (2002) study, the 85% of the population is exposed at or above the 

LML of 7.5 μg/m3. Using the Laden et al. (2006) study, 40% of the population is exposed above 

the LML of 10 μg/m3. As we model mortality impacts among populations exposed to levels of 

PM2.5 that are successively lower than the LML of the lowest cohort study, our confidence in the 

results diminishes. However, the analysis above confirms that the great majority of the impacts 

occur at or above the lowest cohort study’s LML. It is important to emphasize that we have high 

confidence in PM2.5-related effects down to the lowest LML of the major cohort studies, which is 

5.8 μg/m3. Just because we have greater confidence in the benefits above the LML, this does not 

mean that we have no confidence that benefits occur below the LML. 

Figure 1: Percentage of Adult Population by Annual Mean PM2.5 Exposure 

(pre- and post- policy) 
B-8 

Figure 2: Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy) 

There are several important differences between the assessment conducted for the 

Transport Rule and the assessment presented here. If you compare the graphics in the Transport 

Rule to those provided here, you will notice that these graphs show a larger percentage of the 

population below the LML. It is imperative to point out that the Transport Rule graphics 

represented mortality impacts attributable to the Transport Rule, whereas these graphics 

represent exposure. Mortality impacts are the result of the incremental change in exposure 

between the baseline and control. However, the baseline population exposure at lower air quality 

levels is so much larger than the impacts among these same populations. In other words, the 

population exposed to lower PM2.5 levels are not receiving very much of the air quality benefit 

between the base and the control case. 
B-9 

References 

Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. The influence of location, source, and emission type 

in estimates of the human health benefits of reducing a ton of air pollution. Air Qual Atmos 

Health (2009) 2:169-176. 

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and 

spatial analysis of the American Cancer Society study linking particulate air pollution and 

mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. 

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air 

Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine 

173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

Washington, DC: The National Academies Press. 

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 

2002. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate 

Air Pollution. Journal of the American Medical Association 287:1132-1141. 

Roman, H.A., K.D. Walker, T.L. Walsh, L. Conner, H.M. Richmond, .J. Hubbell, and P.L. 

Kinney. 2008. “Expert Judgment Assessment of the Mortality Impact of Changes in Ambient 

Fine Particulate Matter in the U.S.” Environ. Sci. Technol. 42(7):2268-2274. 

U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 

National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. October. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%205--Benefits.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 

National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneriachapter6.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008b. Regulatory Impact Analysis of the 

Proposed Revisions to the National Ambient Air Quality Standards for Lead. Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. March. Available at <http 

http://www.epa.gov/ttn/ecas/ria.html >. 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Final Regulatory Impact Analysis 

(RIA) for the SO2 National Ambient Air Quality Standards (NAAQS). Office of Air Quality 

Planning and Standards, Research Triangle Park, NC. June. Available at 

<http://www.epa.gov/ttn/ecas/ria.html>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Regulatory Impact Analysis for the 

Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

June. Available at <http://www.epa.gov/ttn/ecas/ria.html>. 
B-10 

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Summary of Expert Opinions on the 

Existence of a Threshold in the Concentration-Response Function for PM2.5-related 

Mortality: Technical Support Document. Compiled by Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. July. Available at 

<http://www.epa.gov/ttn/ecas/benefits.html>. 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010. 

Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of 

the Clean Air Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500738776/$F

ile/EPA-COUNCIL-10-001-unsigned.pdf>. 
APPENDIX C 

ADDITIONAL ENGINEERING COST ANALYSIS DATA 
C-1

Table C-1a. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Fluidized Bed 

Incinerators 

Part 1 Red cells indicate where additional control is needed 

Cadmium (Cd)

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg) 

EG Limit (mg/dscm): 

0.0019 EG Limit (ppmvd): 56 EG Limit (ppmvd): 0.49 

EG Limit (mg/dscm): 

0.0098 

EG Limit (mg/dscm): 

0.0033 

WS or PB 

Adjustment1 

Facility ID

Unit 

ID

Existing 

Control 

Devices 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

AKJuneau 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

CTMattabassett 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

CTSynagroWaterbury 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

CTWestHaven 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

GANoondayCreek 1 unknown 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

IADubuque 1 cs - vs - pbt 0.00214 11 16.33 -243 0.050 -889 0.01105 11 0.01354 76 66 0.73 

IADubuque 2 cs - vs - pbt 0.00214 11 16.33 -243 0.050 -889 0.01105 11 0.01354 76 66 0.73 

KSKawPoint 1 vs 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

KSKawPoint 2 vs 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

LANewOrleansEastBan

k 

1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

MALynnRegional 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

MALynnRegional 2 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

MIYpsilanti EUFBSS

I 

vs- imp - wesp - 

ac polish. 

0.00047 -308 2.64 -2020 0.282 -73 0.00618 -59 0.00057 -482 -492 -5.47 

MNStPaulMetro FBR1 ac inject. - bag - 

vs(ad) - wesp 

0.00043 -344 23.46 -139 0.167 -193 0.00288 -241 0.00170 -95 -95 -1.06 

MNStPaulMetro FBR2 ac inject. - bag - 

vs(ad) - wesp 

0.00075 -154 23.71 -136 0.156 -215 0.00262 -273 0.00089 -271 -271 -3.01 

MNStPaulMetro FBR3 ac inject. - bag - 

vs(ad) - wesp 

0.00069 -174 20.46 -174 0.200 -144 0.00240 -309 0.00039 -753 -753 -8.37 

MOLittleBlueValley 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

MORockCreek 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NCBuncombeAshville 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NCTZOsborne ES-1 abd - vs - imp - 

hss - cs 

0.00017 -988 11.39 -392 0.044 -1008 0.00031 -3028 0.04113 92 82 0.91 

NHManchester 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NJBayshoreRegional 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NJBayshoreRegional 2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NJCamden 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NJGloucester 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NJGloucester 2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NJNorthwestBergen 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 
C-2

Cadmium (Cd)

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg) 

EG Limit (mg/dscm): 

0.0019 EG Limit (ppmvd): 56 EG Limit (ppmvd): 0.49 

EG Limit (mg/dscm): 

0.0098 

EG Limit (mg/dscm): 

0.0033 

WS or PB 

Adjustment1 

Facility ID

Unit 

ID

Existing 

Control 

Devices 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

NJNorthwestBergen 2 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NJPequannockLincolnF

airfield 

1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NJPequannockLincolnF

airfield 

2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NJSomersetRaritan 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NJSomersetRaritan 2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

NYArlington 1 unknown 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

NYErieCounty 1 vs 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

NYErieCounty 2 vs 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

NYGlensFalls 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NYOneidaCounty 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NYOneidaCounty 2 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NYOneidaCounty 3 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NYPortChester 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NYPortChester 2 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

NYSaratogaCounty 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

OHLittleMiami 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

OHNEORSDEasterly 1 abo - imp - 

wesp 

0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

PAAlleghenyCounty 001 abd - mc - vs - 

imp 

0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

PAAlleghenyCounty 002 abd - mc - vs - 

imp 

0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

PAWyomingValley 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 76 66 0.73 

PRPuertoNuevo 1 vs(ad) - wesp 0.00085 -122 16.33 -243 0.050 -889 0.00442 -122 0.01354 76 76 0.84 

SCFelixCDavis 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

VABlacksburg 1 unknown 0.00214 11 16.33 -243 2.478 80 0.01105 11 0.01504 78 68 0.76 

VAHLMooney 2 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

WAAnacortes 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

WAEdmonds 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

WALynnwood 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 

WAWestside 1 vs - imp 0.00214 11 16.33 -243 0.124 -295 0.01105 11 0.01354 76 66 0.73 
C-3

Table C-1a. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Fluidized Bed 

Incinerators 

Part 2 Red cells indicate where additional control is needed 

Nitrogen Oxides (NOx)

Particulate Matter 

(filterable)

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans

Total Dioxin/Furans 

(TEQ)

EG Limit (ppmvd): 63 EG Limit (mg/dscm): 12 EG Limit (mg/dscm): 11 EG Limit (ppmvd): 22 EG Limit (ng/dscm): 0.61 

EG Limit (ng/dscm): 

0.056 

Facility ID

Unit 

ID

Existing 

Control 

Devices

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improve

ment 

Needed 

Average 

(ng/dscm) 

% 

Improve

ment 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis)

Average 

(ng/dscm) 

% 

Improvement 

Needed 

AKJuneau 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

CTMattabassett 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

CTSynagroWaterbury 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

CTWestHaven 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

GANoondayCreek 1 unknown 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

IADubuque 1 cs - vs - pbt 27.93 -126 12.44 4 11.80 7 1.32 -1565 15.9621 96 0.98 1.3121 96 

IADubuque 2 cs - vs - pbt 27.93 -126 12.44 4 11.80 7 1.32 -1565 15.9621 96 0.98 1.3121 96 

KSKawPoint 1 vs 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

KSKawPoint 2 vs 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

LANewOrleansEastBank 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

MALynnRegional 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

MALynnRegional 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

MIYpsilanti EUFBS

SI 

vs- imp - wesp - 

ac polish. 

29.76 -112 2.87 -317 4.83 -128 3.30 -566 0.1469 -315 -3.21 0.0064 -772 

MNStPaulMetro FBR

1 

ac inject. - bag - 

vs(ad) - wesp 

31.00 -103 2.26 -432 1.72 -538 0.62 -3468 0.4048 -51 -0.52 0.0356 -57 

MNStPaulMetro FBR

2 

ac inject. - bag - 

vs(ad) - wesp 

41.44 -52 1.41 -750 1.57 -601 1.65 -1235 0.4060 -50 -0.51 0.0367 -52 

MNStPaulMetro FBR

3 

ac inject. - bag - 

vs(ad) - wesp 

22.53 -180 5.38 -123 1.45 -659 1.13 -1843 0.4054 -50 -0.51 0.0362 -55 

MOLittleBlueValley 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

MORockCreek 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NCBuncombeAshville 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NCTZOsborne ES-1 abd - vs - imp - 

hss - cs 

14.90 -323 2.58 -366 11.16 1 7.64 -188 15.9621 96 0.98 1.3121 96 

NHManchester 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NJBayshoreRegional 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NJBayshoreRegional 2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NJCamden 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NJGloucester 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NJGloucester 2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NJNorthwestBergen 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NJNorthwestBergen 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 
C-4

Nitrogen Oxides (NOx)

Particulate Matter 

(filterable)

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans

Total Dioxin/Furans 

(TEQ)

EG Limit (ppmvd): 63 EG Limit (mg/dscm): 12 EG Limit (mg/dscm): 11 EG Limit (ppmvd): 22 EG Limit (ng/dscm): 0.61 

EG Limit (ng/dscm): 

0.056 

Facility ID

Unit 

ID

Existing 

Control 

Devices

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improve

ment 

Needed 

Average 

(ng/dscm) 

% 

Improve

ment 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis)

Average 

(ng/dscm) 

% 

Improvement 

Needed 

NJPequannockLincolnFa

irfield 

1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NJPequannockLincolnFa

irfield 

2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NJSomersetRaritan 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NJSomersetRaritan 2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

NYArlington 1 unknown 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

NYErieCounty 1 vs 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

NYErieCounty 2 vs 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

NYGlensFalls 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NYOneidaCounty 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NYOneidaCounty 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NYOneidaCounty 3 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NYPortChester 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NYPortChester 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

NYSaratogaCounty 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

OHLittleMiami 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

OHNEORSDEasterly 1 abo - imp - 

wesp 

27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

PAAlleghenyCounty 001 abd - mc - vs - 

imp 

27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

PAAlleghenyCounty 002 abd - mc - vs - 

imp 

27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

PAWyomingValley 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.9621 96 0.98 1.3121 96 

PRPuertoNuevo 1 vs(ad) - wesp 27.93 -126 2.49 -382 2.36 -366 1.32 -1565 15.9621 96 0.98 1.3121 96 

SCFelixCDavis 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

VABlacksburg 1 unknown 27.93 -126 12.44 4 11.80 7 66.05 67 15.9621 96 0.98 1.3121 96 

VAHLMooney 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

WAAnacortes 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

WAEdmonds 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

WALynnwood 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 

WAWestside 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.9621 96 0.98 1.3121 96 
C-5

Table C-1a. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Fluidized Bed 

Incinerators 

Part 3 Red cells indicate where additional control is needed 

MACT Floor Control Needs 

(if % improvement >10, add control)

Facility ID Unit ID Existing Control Devices

ACI Performance 

Adjustment Factor

2 

(CDD/CDF TEQ basis)

Max ACI 

Adjustment 

Factor FF Scrubber ACI

AKJuneau 1 vs - imp 0.98 0.98 add FF add ACI 

CTMattabassett 1 vs - imp 0.98 0.98 add FF add ACI 

CTSynagroWaterbury 1 vs - imp 0.98 0.98 add FF add ACI 

CTWestHaven 1 vs - imp 0.98 0.98 add FF add ACI 

GANoondayCreek 1 unknown 0.98 0.98 add FF add PBS add ACI 

IADubuque 1 cs - vs - pbt 0.98 0.98 add FF add ACI 

IADubuque 2 cs - vs - pbt 0.98 0.98 add FF add ACI 

KSKawPoint 1 vs 0.98 0.98 add FF add PBS add ACI 

KSKawPoint 2 vs 0.98 0.98 add FF add PBS add ACI 

LANewOrleansEastBank 1 vs - imp 0.98 0.98 add FF add ACI 

MALynnRegional 1 vs - imp 0.98 0.98 add FF add ACI 

MALynnRegional 2 vs - imp 0.98 0.98 add FF add ACI 

MIYpsilanti EU-FBSSI vs- imp - wesp - ac polish. -7.88 -3.21 

MNStPaulMetro FBR1 ac inject. - bag - vs(ad) - 

wesp 

-0.58 -0.52 

MNStPaulMetro FBR2 ac inject. - bag - vs(ad) - 

wesp 

-0.53 -0.51 

MNStPaulMetro FBR3 ac inject. - bag - vs(ad) - 

wesp 

-0.56 -0.51 

MOLittleBlueValley 1 vs - imp 0.98 0.98 add FF add ACI 

MORockCreek 1 vs - imp 0.98 0.98 add FF add ACI 

NCBuncombeAshville 1 vs - imp 0.98 0.98 add FF add ACI 

NCTZOsborne ES-1 abd - vs - imp - hss - cs 0.98 0.98 add ACI 

NHManchester 1 vs - imp 0.98 0.98 add FF add ACI 

NJBayshoreRegional 1 vs - imp - wesp 0.98 0.98 add ACI 

NJBayshoreRegional 2 vs - imp - wesp 0.98 0.98 add ACI 

NJCamden 1 vs - imp 0.98 0.98 add FF add ACI 

NJGloucester 1 vs - imp - wesp 0.98 0.98 add ACI 

NJGloucester 2 vs - imp - wesp 0.98 0.98 add ACI 

NJNorthwestBergen 1 vs - imp 0.98 0.98 add FF add ACI 

NJNorthwestBergen 2 vs - imp 0.98 0.98 add FF add ACI 

NJPequannockLincolnFairfield 1 vs - imp 0.98 0.98 add FF add ACI 

NJPequannockLincolnFairfield 2 vs - imp - wesp 0.98 0.98 add ACI 

NJSomersetRaritan 1 vs - imp - wesp 0.98 0.98 add ACI 

NJSomersetRaritan 2 vs - imp - wesp 0.98 0.98 add ACI 

NYArlington 1 unknown 0.98 0.98 add FF add PBS add ACI 

NYErieCounty 1 vs 0.98 0.98 add FF add PBS add ACI 
C-6

MACT Floor Control Needs 

(if % improvement >10, add control)

Facility ID Unit ID Existing Control Devices

ACI Performance 

Adjustment Factor

2 

(CDD/CDF TEQ basis)

Max ACI 

Adjustment 

Factor FF Scrubber ACI

NYErieCounty 2 vs 0.98 0.98 add FF add PBS add ACI 

NYGlensFalls 1 vs - imp 0.98 0.98 add FF add ACI 

NYOneidaCounty 1 vs - imp 0.98 0.98 add FF add ACI 

NYOneidaCounty 2 vs - imp 0.98 0.98 add FF add ACI 

NYOneidaCounty 3 vs - imp 0.98 0.98 add FF add ACI 

NYPortChester 1 vs - imp 0.98 0.98 add FF add ACI 

NYPortChester 2 vs - imp 0.98 0.98 add FF add ACI 

NYSaratogaCounty 1 vs - imp 0.98 0.98 add FF add ACI 

OHLittleMiami 1 vs - imp 0.98 0.98 add FF add ACI 

OHNEORSDEasterly 1 abo - imp - wesp 0.98 0.98 add FF add ACI 

PAAlleghenyCounty 001 abd - mc - vs - imp 0.98 0.98 add FF add ACI 

PAAlleghenyCounty 002 abd - mc - vs - imp 0.98 0.98 add FF add ACI 

PAWyomingValley 1 vs - imp - wesp 0.98 0.98 add ACI 

PRPuertoNuevo 1 vs(ad) - wesp 0.98 0.98 add ACI 

SCFelixCDavis 1 vs - imp 0.98 0.98 add FF add ACI 

VABlacksburg 1 unknown 0.98 0.98 add FF add PBS add ACI 

VAHLMooney 2 vs - imp 0.98 0.98 add FF add ACI 

WAAnacortes 1 vs - imp 0.98 0.98 add FF add ACI 

WAEdmonds 1 vs - imp 0.98 0.98 add FF add ACI 

WALynnwood 1 vs - imp 0.98 0.98 add FF add ACI 

WAWestside 1 vs - imp 0.98 0.98 add FF add ACI 

NOTE: Data gaps in pollutant concentrations were filled using values found for similar units or using the average concentration over the entire subcategory. For 

Dioxin/Furan TEQ concentrations, no data was available for the subcategory, so TEQ concentrations were assumed to be 57% of the TMB values. 

1. Assumes that units with a packed bed scrubber or installing a packed bed scrubber will get a 10% Hg reduction. 

2. ACI algorithm is based on 90% Hg reduction efficiency and 98% CDD/CDF reduction efficiency. This adjustment factor will be used to adjust total annual 

costs to the estimated reduction efficiency needed to meet the floor. 
C-7

Table C-1b. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Multiple Hearth 

Incinerators 

Part 1 Red cells indicate where additional control is needed 

Cadmium (Cd) 

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg)

Nitrogen Oxides 

(NOx) 

EG Limit (mg/dscm): 

0.095 

EG Limit (ppmvd): 

3900 EG Limit (ppmvd): 1.0 

EG Limit (mg/dscm): 

0.30 

EG Limit (mg/dscm): 

0.17 

WS or PB 

Adjustment

1 

EG Limit (ppmvd): 

210 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

Average 

(ppmvd) 

% 

Improve

-ment 

Needed 

AKJohnMAsplund 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

CACentralContraCos

ta 

MHF 1 abo - cs - vs - 

imp 

0.0150 -534 836.4 -366 0.79 -26 0.060 -398 0.051 -231 -241 -2.68 127.7 -64 

CACentralContraCos

ta 

MHF 2 abo - cs - vs - 

imp 

0.0176 -439 752.1 -419 0.79 -26 0.036 -740 0.065 -160 -170 -1.89 172.3 -22 

CAPaloAlto 1 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

CAPaloAlto 2 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

CTHartford 001 abo - fgr - vs - 

imp 

0.0053 -1701 853.9 -357 0.65 -53 0.015 -1873 0.124 -37 -47 -0.52 106.6 -97 

CTHartford 002 abo - fgr - vs - 

imp 

0.0062 -1422 853.9 -357 0.65 -53 0.024 -1144 0.118 -44 -54 -0.60 106.6 -97 

CTHartford 3 abo - fgr - vs - 

imp 

0.0058 -1550 853.9 -357 0.65 -53 0.020 -1426 0.121 -40 -50 -0.56 106.6 -97 

CTNaugatuck 1 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

CTNaugatuck 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

CTSynagroNewHave

n 

1 vs - imp - wesp - 

rto 

0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58 

GAPresidentStreet 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

GAPresidentStreet 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

GARLSutton 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

GARLSutton 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

GARMClayton 1 imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

GARMClayton 2 imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

GAUtoyCreek 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

GAUtoyCreek 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

GAWeyerhaeuser 1 unknown 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

IACedarRapids 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

INBelmontNorth 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.113 -50 -60 -0.67 133.3 -58 

INBelmontNorth 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -63 -73 -0.81 133.3 -58 

INBelmontNorth 3 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.091 -86 -96 -1.07 133.3 -58 

INBelmontNorth 4 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.106 -60 -70 -0.78 133.3 -58 

INBelmontNorth 5 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58 

INBelmontNorth 6 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58 

INBelmontNorth 7 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58 
C-8

Cadmium (Cd) 

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg)

Nitrogen Oxides 

(NOx) 

EG Limit (mg/dscm): 

0.095 

EG Limit (ppmvd): 

3900 EG Limit (ppmvd): 1.0 

EG Limit (mg/dscm): 

0.30 

EG Limit (mg/dscm): 

0.17 

WS or PB 

Adjustment

1 

EG Limit (ppmvd): 

210 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

Average 

(ppmvd) 

% 

Improve

-ment 

Needed 

INBelmontNorth 8 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58 

LANewOrleansEastB

ank 

2 unknown 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

MAFitchburgEast 1 vs - wesp - rto 0.0178 -433 853.9 -357 13.09 92 0.038 -684 0.114 -49 -59 -0.66 133.3 -58 

MAUpperBlackstone 1 agr - vs - imp - 

wesp - rto 

0.0042 -2153 27.6 -14042 0.34 -196 0.002 -14243 0.090 -89 -99 -1.10 76.4 -175 

MAUpperBlackstone Inciner

ator 3 

agr - vs - imp - 

wesp - rto 

0.0041 -2231 59.4 -6470 0.31 -218 0.005 -5870 0.065 -160 -170 -1.89 68.5 -206 

MDWesternBranch 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MDWesternBranch 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIAnnArbor 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIBattleCreek 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIBattleCreek 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex1 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex1 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex1 3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex1 4 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex1 5 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex1 6 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 1 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 2 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 3 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 4 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 5 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 6 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 7 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIDetroitComplex2 8 hjs - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIFlint 1 abd - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIFlint 2 abd - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIFlint 3 abd - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIFlint 4 abd - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MIPontiacAuburn 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MIWarren 1 imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

MNSeneca Inciner

ator 1 

abo - vs 0.2509 62 1323.3 -195 0.42 -136 0.072 -318 0.301 43 43 0.48 219.3 4 

MNSeneca Inciner

ator 2 

abo - vs 0.2509 62 823.7 -373 0.42 -136 0.072 -318 0.301 43 43 0.48 219.3 4 

MOBigBlueRiver 1 abo - vs 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

MOBigBlueRiver 2 abo - vs 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

MOBigBlueRiver 3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOBissellPoint 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 
C-9

Cadmium (Cd) 

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg)

Nitrogen Oxides 

(NOx) 

EG Limit (mg/dscm): 

0.095 

EG Limit (ppmvd): 

3900 EG Limit (ppmvd): 1.0 

EG Limit (mg/dscm): 

0.30 

EG Limit (mg/dscm): 

0.17 

WS or PB 

Adjustment

1 

EG Limit (ppmvd): 

210 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

Average 

(ppmvd) 

% 

Improve

-ment 

Needed 

MOBissellPoint 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOBissellPoint 3 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOBissellPoint 4 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOBissellPoint 5 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOBissellPoint 6 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOLemay 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOLemay 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOLemay 3 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

MOLemay 4 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NCRockyRiver 1 abd - vs - imp - 

wesp 

0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58 

NJAtlanticCounty 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NJAtlanticCounty 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NJMountainView #1 vs - imp - wesp - 

rto 

0.0003 -28547 38.6 -9995 0.86 -16 0.001 -30629 0.099 -72 -82 -0.91 142.0 -48 

NJMountainView #2 vs - imp - wesp - 

rto 

0.0003 -28547 38.6 -9995 0.86 -16 0.001 -30629 0.099 -72 -82 -0.91 142.0 -48 

NJParsippanyTroyHi

lls 

1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NJParsippanyTroyHi

lls 

2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NJStonyBrook 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NJStonyBrook 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYAlbanyCountyNo

rth 

1 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

NYAlbanyCountyNo

rth 

2 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

NYAlbanyCountySo

uth 

1 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

NYAlbanyCountySo

uth 

2 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

NYAuburn 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYBirdIsland 1 abd - vs 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

NYBirdIsland 2 abd - vs 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

NYBirdIsland 3 abd - vs 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

NYFrankEVanLare 1 abo - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

NYFrankEVanLare 2 abo - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

NYFrankEVanLare 3 abo - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

NYNewRochelle 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYNewRochelle 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYNorthwestQuadra

nt 

1 abo - imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 
C-10

Cadmium (Cd) 

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg)

Nitrogen Oxides 

(NOx) 

EG Limit (mg/dscm): 

0.095 

EG Limit (ppmvd): 

3900 EG Limit (ppmvd): 1.0 

EG Limit (mg/dscm): 

0.30 

EG Limit (mg/dscm): 

0.17 

WS or PB 

Adjustment

1 

EG Limit (ppmvd): 

210 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

Average 

(ppmvd) 

% 

Improve

-ment 

Needed 

NYOrangetown 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYOssining 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYOssining 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYSchenectady 1 imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 

NYSouthwestBergen

Point 

1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYSouthwestBergen

Point 

2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

NYTonawanda 1 unknown 0.0446 -113 853.9 -357 13.09 92 0.096 -213 0.114 -49 -59 -0.66 133.3 -58 

OHCanton 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHCanton 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHColumbusSouther

ly 

1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHColumbusSouther

ly 

2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHColumbusSouther

ly 

3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHColumbusSouther

ly 

4 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHEuclid 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHEuclid 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHJacksonPike 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHJacksonPike 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHMillCreek 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHMillCreek 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHMillCreek 3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHMillCreek 4 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHMillCreek 5 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHMillCreek 6 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHNEORSDSouther

ly 

1 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHNEORSDSouther

ly 

2 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHNEORSDSouther

ly 

3 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHNEORSDSouther

ly 

4 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHNEORSDWesterl

y 

1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHNEORSDWesterl

y 

2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHWilloughbyEastla

ke 

1 imp 0.4456 79 853.9 -357 0.65 -53 0.957 69 0.103 -65 -75 -0.83 133.3 -58 
C-11

Cadmium (Cd) 

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg)

Nitrogen Oxides 

(NOx) 

EG Limit (mg/dscm): 

0.095 

EG Limit (ppmvd): 

3900 EG Limit (ppmvd): 1.0 

EG Limit (mg/dscm): 

0.30 

EG Limit (mg/dscm): 

0.17 

WS or PB 

Adjustment

1 

EG Limit (ppmvd): 

210 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

Average 

(ppmvd) 

% 

Improve

-ment 

Needed 

OHYoungstown 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

OHYoungstown 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

PADelawareCounty

Western 

1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

PADelawareCounty

Western 

2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

PAEastNorritonPlym

outhWhitpain 

1 cs - vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

PAErie 1 vs - wesp 0.0178 -433 853.9 -357 13.09 92 0.038 -684 0.114 -49 -59 -0.66 133.3 -58 

PAErie 2 vs - wesp 0.0178 -433 853.9 -357 13.09 92 0.038 -684 0.114 -49 -59 -0.66 133.3 -58 

PAHatfield 1 vs - imp - wesp - 

rto 

0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58 

PAKiskiValley 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

PAUpperMorelandH

atboro 

1 vs - imp - rto 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

RICranston 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

RICranston 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

RINewEngland 1 vs - imp - wesp 0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58 

SCColumbiaMetro 1 abo/fgr - pbs - vs 

- imp 

0.0025 -3700 63.5 -6045 0.20 -398 0.004 -7886 0.074 -130 -140 -1.56 84.4 -149 

SCColumbiaMetro 2 abo/fgr - pbs - vs 

- imp 

0.0025 -3700 63.5 -6045 0.20 -398 0.004 -7886 0.077 -121 -131 -1.46 84.4 -149 

SCPlumIsland 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VAArmyBaseNorfol

k 

1 ws - vs - imp 0.1122 15 853.9 -357 0.65 -53 0.398 25 0.130 -31 -41 -0.46 133.3 -58 

VAArmyBaseNorfol

k 

2 ws - vs - imp 0.1122 15 853.9 -357 0.65 -53 0.398 25 0.130 -31 -41 -0.46 133.3 -58 

VABoatHarbor 1 ws - vs - pbs - 

vs(ad) 

0.0537 -77 3761.0 -4 0.70 -42 0.069 -337 0.107 -59 -59 -0.66 154.5 -36 

VABoatHarbor 2 ws - vs - pbs - 

vs(ad) 

0.0537 -77 3761.0 -4 0.70 -42 0.069 -337 0.107 -59 -59 -0.66 154.5 -36 

VAChesapeakeEliza

beth 

1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VAChesapeakeEliza

beth 

2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VAHopewell 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VANomanCole 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VANomanCole 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VANomanCole 3 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VANomanCole 4 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VANomanCole 5 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VANomanCole 6 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58 

VAVirginiaInitiative 1 ws - vs - imp 0.0745 -28 853.9 -357 0.65 -53 0.461 35 0.064 -166 -176 -1.96 133.3 -58 
C-12

Cadmium (Cd) 

Carbon Monoxide 

(CO) 

Hydrogen Chloride 

(HCl) Lead (Pb) Mercury (Hg)

Nitrogen Oxides 

(NOx) 

EG Limit (mg/dscm): 

0.095 

EG Limit (ppmvd): 

3900 EG Limit (ppmvd): 1.0 

EG Limit (mg/dscm): 

0.30 

EG Limit (mg/dscm): 

0.17 

WS or PB 

Adjustment

1 

EG Limit (ppmvd): 

210 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

% Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(Hg Basis) 

Average 

(ppmvd) 

% 

Improve

-ment 

Needed 

VAVirginiaInitiative 2 ws - vs - imp 0.0745 -28 853.9 -357 0.65 -53 0.461 35 0.064 -166 -176 -1.96 133.3 -58 

VAWilliamsburg 1 vs - imp 0.0323 -194 853.9 -357 0.65 -53 0.097 -210 0.103 -65 -75 -0.83 133.3 -58 

VAWilliamsburg 2 vs - imp 0.0323 -194 853.9 -357 0.65 -53 0.097 -210 0.103 -65 -75 -0.83 133.3 -58 

WABellinghamPostP

oint 

1 vs - imp - wesp 0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58 

WABellinghamPostP

oint 

2 vs - imp - wesp 0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58 

WIGreenBayMetro 1 vs(a) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 

WIGreenBayMetro 2 vs(a) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58 
C-13

Table C-1b. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Multiple Hearth 

Incinerators 

Part 2 Red cells indicate where additional control is needed 

Particulate Matter 

(filterable) 

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans 

Total Dioxin/Furans 

(TEQ) 

EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5 

EG Limit (ng/dscm): 

0.32 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ng/dscm) 

% 

Improv

ement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis) 

Average 

(ng/dscm) 

% 

Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TEQ basis) 

AKJohnMAsplund 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CACentralContraCosta MHF 1 abo - cs - vs - 

imp 

31.42 -155 21.07 -175 7.27 -258 0.01 -52670 -537.45 0.001 -33797 -344.87 

CACentralContraCosta MHF 2 abo - cs - vs - 

imp 

25.79 -210 21.07 -175 3.41 -663 0.01 -52670 -537.45 0.001 -33797 -344.87 

CAPaloAlto 1 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CAPaloAlto 2 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CTHartford 001 abo - fgr - vs - 

imp 

36.09 -122 8.57 -577 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CTHartford 002 abo - fgr - vs - 

imp 

36.09 -122 13.77 -321 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CTHartford 3 abo - fgr - vs - 

imp 

36.09 -122 11.17 -419 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CTNaugatuck 1 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CTNaugatuck 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

CTSynagroNewHaven 1 vs - imp - wesp - 

rto 

7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GAPresidentStreet 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GAPresidentStreet 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GARLSutton 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GARLSutton 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GARMClayton 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GARMClayton 2 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GAUtoyCreek 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GAUtoyCreek 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

GAWeyerhaeuser 1 unknown 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

IACedarRapids 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 1 abo - vs - imp 39.79 -101 21.97 -164 26.57 2 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 2 abo - vs - imp 40.31 -98 21.97 -164 23.90 -9 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 3 abo - vs - imp 33.85 -136 21.97 -164 5.51 -372 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 4 abo - vs - imp 17.55 -356 21.97 -164 1.75 -1382 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 5 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 6 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96 

INBelmontNorth 7 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96 
C-14

Particulate Matter 

(filterable) 

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans 

Total Dioxin/Furans 

(TEQ) 

EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5 

EG Limit (ng/dscm): 

0.32 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ng/dscm) 

% 

Improv

ement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis) 

Average 

(ng/dscm) 

% 

Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TEQ basis) 

INBelmontNorth 8 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96 

LANewOrleansEastBank 2 unknown 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

MAFitchburgEast 1 vs - wesp - rto 7.22 -1008 4.39 -1220 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

MAUpperBlackstone 1 agr - vs - imp - 

wesp - rto 

1.75 -4464 2.62 -2111 1.20 -2067 0.12 -4036 -41.18 0.006 -5068 -51.71 

MAUpperBlackstone Inciner

ator 3 

agr - vs - imp - 

wesp - rto 

1.21 -6528 2.62 -2111 2.70 -864 0.12 -4036 -41.18 0.006 -5068 -51.71 

MDWesternBranch 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MDWesternBranch 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIAnnArbor 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIBattleCreek 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIBattleCreek 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex1 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex1 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex1 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex1 4 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex1 5 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex1 6 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 1 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 2 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 3 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 4 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 5 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 6 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 7 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIDetroitComplex2 8 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIFlint 1 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIFlint 2 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIFlint 3 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIFlint 4 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIPontiacAuburn 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MIWarren 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MNSeneca Inciner

ator 1 

abo - vs 78.76 -2 51.13 -13 18.23 -43 0.76 -559 -5.70 0.069 -362 -3.69 

MNSeneca Inciner

ator 2 

abo - vs 76.16 -5 51.13 -13 18.23 -43 0.76 -559 -5.70 0.069 -362 -3.69 

MOBigBlueRiver 1 abo - vs 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

MOBigBlueRiver 2 abo - vs 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

MOBigBlueRiver 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOBissellPoint 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 
C-15

Particulate Matter 

(filterable) 

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans 

Total Dioxin/Furans 

(TEQ) 

EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5 

EG Limit (ng/dscm): 

0.32 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ng/dscm) 

% 

Improv

ement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis) 

Average 

(ng/dscm) 

% 

Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TEQ basis) 

MOBissellPoint 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOBissellPoint 3 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOBissellPoint 4 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOBissellPoint 5 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOBissellPoint 6 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOLemay 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOLemay 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOLemay 3 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

MOLemay 4 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NCRockyRiver 1 abd - vs - imp - 

wesp 

7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NJAtlanticCounty 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NJAtlanticCounty 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NJMountainView #1 vs - imp - wesp - 

rto 

3.93 -1934 4.83 -1101 9.28 -180 0.22 -2156 -22.00 0.014 -2160 -22.04 

NJMountainView #2 vs - imp - wesp - 

rto 

3.93 -1934 4.83 -1101 9.28 -180 0.22 -2156 -22.00 0.014 -2160 -22.04 

NJParsippanyTroyHills 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NJParsippanyTroyHills 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NJStonyBrook 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NJStonyBrook 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYAlbanyCountyNorth 1 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYAlbanyCountyNorth 2 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYAlbanyCountySouth 1 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYAlbanyCountySouth 2 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYAuburn 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYBirdIsland 1 abd - vs 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

NYBirdIsland 2 abd - vs 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

NYBirdIsland 3 abd - vs 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

NYFrankEVanLare 1 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYFrankEVanLare 2 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYFrankEVanLare 3 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYNewRochelle 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYNewRochelle 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYNorthwestQuadrant 1 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYOrangetown 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYOssining 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYOssining 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYSchenectady 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYSouthwestBergenPoint 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 
C-16

Particulate Matter 

(filterable) 

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans 

Total Dioxin/Furans 

(TEQ) 

EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5 

EG Limit (ng/dscm): 

0.32 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ng/dscm) 

% 

Improv

ement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis) 

Average 

(ng/dscm) 

% 

Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TEQ basis) 

NYSouthwestBergenPoint 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

NYTonawanda 1 unknown 36.09 -122 21.97 -164 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

OHCanton 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHCanton 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHColumbusSoutherly 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHColumbusSoutherly 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHColumbusSoutherly 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHColumbusSoutherly 4 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHEuclid 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHEuclid 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHJacksonPike 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHJacksonPike 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHMillCreek 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHMillCreek 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHMillCreek 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHMillCreek 4 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHMillCreek 5 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHMillCreek 6 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHNEORSDSoutherly 1 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHNEORSDSoutherly 2 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHNEORSDSoutherly 3 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHNEORSDSoutherly 4 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHNEORSDWesterly 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHNEORSDWesterly 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHWilloughbyEastlake 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHYoungstown 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

OHYoungstown 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

PADelawareCountyWeste

rn 

1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

PADelawareCountyWeste

rn 

2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

PAEastNorritonPlymouth

Whitpain 

1 cs - vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

PAErie 1 vs - wesp 7.22 -1008 4.39 -1220 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

PAErie 2 vs - wesp 7.22 -1008 4.39 -1220 186.34 86 0.69 -619 -6.32 0.047 -584 -5.96 

PAHatfield 1 vs - imp - wesp - 

rto 

7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

PAKiskiValley 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

PAUpperMorelandHatbor

o 

1 vs - imp - rto 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

RICranston 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 
C-17

Particulate Matter 

(filterable) 

Particulate Matter 

(PM 2.5) Sulfur Dioxide (SO2) Total Dioxin/Furans 

Total Dioxin/Furans 

(TEQ) 

EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5 

EG Limit (ng/dscm): 

0.32 

Facility ID

Unit 

ID

Existing 

Control Devices

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(mg/dscm) 

% 

Improvement 

Needed 

Average 

(ppmvd) 

% 

Improvement 

Needed 

Average 

(ng/dscm) 

% 

Improv

ement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TMB basis) 

Average 

(ng/dscm) 

% 

Improvement 

Needed 

ACI 

Performance 

Adjustment 

Factor

2 

(CDD/CDF 

TEQ basis) 

RICranston 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

RINewEngland 1 vs - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

SCColumbiaMetro 1 abo/fgr - pbs - vs 

- imp 

11.11 -620 21.97 -164 7.99 -225 2.36 -112 -1.14 0.143 -123 -1.26 

SCColumbiaMetro 2 abo/fgr - pbs - vs 

- imp 

14.63 -447 21.97 -164 7.99 -225 2.36 -112 -1.14 0.143 -123 -1.26 

SCPlumIsland 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAArmyBaseNorfolk 1 ws - vs - imp 72.18 -11 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAArmyBaseNorfolk 2 ws - vs - imp 72.18 -11 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VABoatHarbor 1 ws - vs - pbs - 

vs(ad) 

57.89 -38 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VABoatHarbor 2 ws - vs - pbs - 

vs(ad) 

57.89 -38 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAChesapeakeElizabeth 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAChesapeakeElizabeth 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAHopewell 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VANomanCole 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VANomanCole 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VANomanCole 3 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VANomanCole 4 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VANomanCole 5 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VANomanCole 6 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAVirginiaInitiative 1 ws - vs - imp 39.44 -103 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAVirginiaInitiative 2 ws - vs - imp 39.44 -103 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAWilliamsburg 1 vs - imp 40.25 -99 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

VAWilliamsburg 2 vs - imp 40.25 -99 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

WABellinghamPostPoint 1 vs - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

WABellinghamPostPoint 2 vs - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

WIGreenBayMetro 1 vs(a) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 

WIGreenBayMetro 2 vs(a) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96 
C-18 

Table C-1b. Percent Improvement Needed to Meet MACT floor and Additional Controls 

Required: Multiple Hearth Incinerators 

Part 3 Red cells indicate where additional control is needed 

MACT Floor Control Needs

(if %improvement >10, add control) 

Facility ID Unit ID Existing Control Devices

Max 

ACI 

Adjustment 

Factor FF Scrubber ACI 

AKJohnMAsplund 1 vs - imp -0.83 

CACentralContraCosta MHF 1 abo - cs - vs - imp -2.68 

CACentralContraCosta MHF 2 abo - cs - vs - imp -1.89 

CAPaloAlto 1 vs(ad) -0.72 

CAPaloAlto 2 vs(ad) -0.72 

CTHartford 001 abo - fgr - vs - imp -0.52 

CTHartford 002 abo - fgr - vs - imp -0.60 

CTHartford 3 abo - fgr - vs - imp -0.56 

CTNaugatuck 1 abo - imp - wesp -0.83 

CTNaugatuck 2 vs - imp -0.83 

CTSynagroNewHaven 1 vs - imp - wesp - rto -0.83 

GAPresidentStreet 1 vs - imp -0.83 

GAPresidentStreet 2 vs - imp -0.83 

GARLSutton 1 abo - vs - imp -0.83 

GARLSutton 2 abo - vs - imp -0.83 

GARMClayton 1 imp -0.83 add FF 

GARMClayton 2 imp -0.83 add FF 

GAUtoyCreek 1 vs - imp -0.83 

GAUtoyCreek 2 vs - imp -0.83 

GAWeyerhaeuser 1 unknown -0.66 add PBS 

IACedarRapids 1 vs - imp -0.83 

INBelmontNorth 1 abo - vs - imp -0.67 

INBelmontNorth 2 abo - vs - imp -0.81 

INBelmontNorth 3 abo - vs - imp -1.07 

INBelmontNorth 4 abo - vs - imp -0.78 

INBelmontNorth 5 abo - vs - imp -0.82 

INBelmontNorth 6 abo - vs - imp -0.82 

INBelmontNorth 7 abo - vs - imp -0.82 

INBelmontNorth 8 abo - vs - imp -0.82 

LANewOrleansEastBank 2 unknown -0.66 add PBS 

MAFitchburgEast 1 vs - wesp - rto -0.66 add PBS 

MAUpperBlackstone 1 agr - vs - imp - wesp - rto -1.10 

MAUpperBlackstone Incinerat

or 3 

agr - vs - imp - wesp - rto -1.89 

MDWesternBranch 1 abo - vs - imp -0.83 

MDWesternBranch 2 abo - vs - imp -0.83 

MIAnnArbor 1 abd - vs - imp -0.83 

MIBattleCreek 1 abd - vs - imp -0.83 

MIBattleCreek 2 abd - vs - imp -0.83 

MIDetroitComplex1 1 vs - imp -0.83 

MIDetroitComplex1 2 vs - imp -0.83 

MIDetroitComplex1 3 vs - imp -0.83 

MIDetroitComplex1 4 vs - imp -0.83 

MIDetroitComplex1 5 vs - imp -0.83 

MIDetroitComplex1 6 vs - imp -0.83 

MIDetroitComplex2 1 hjs - imp -0.83 add FF 

MIDetroitComplex2 2 hjs - imp -0.83 add FF 

MIDetroitComplex2 3 hjs - imp -0.83 add FF 

MIDetroitComplex2 4 hjs - imp -0.83 add FF 

MIDetroitComplex2 5 hjs - imp -0.83 add FF 

MIDetroitComplex2 6 hjs - imp -0.83 add FF 

MIDetroitComplex2 7 hjs - imp -0.83 add FF 

MIDetroitComplex2 8 hjs - imp -0.83 add FF 

MIFlint 1 abd - imp -0.83 add FF 
C-19 

MACT Floor Control Needs

(if %improvement >10, add control) 

Facility ID Unit ID Existing Control Devices

Max 

ACI 

Adjustment 

Factor FF Scrubber ACI 

MIFlint 2 abd - imp -0.83 add FF 

MIFlint 3 abd - imp -0.83 add FF 

MIFlint 4 abd - imp -0.83 add FF 

MIPontiacAuburn 1 vs - imp -0.83 

MIWarren 1 imp -0.83 add FF 

MNSeneca Incinerat

or 1 

abo - vs 0.48 add FF add ACI 

MNSeneca Incinerat

or 2 

abo - vs 0.48 add FF add ACI 

MOBigBlueRiver 1 abo - vs -0.66 add PBS 

MOBigBlueRiver 2 abo - vs -0.66 add PBS 

MOBigBlueRiver 3 vs - imp -0.83 

MOBissellPoint 1 abo - vs - imp -0.83 

MOBissellPoint 2 abo - vs - imp -0.83 

MOBissellPoint 3 abo - vs - imp -0.83 

MOBissellPoint 4 abo - vs - imp -0.83 

MOBissellPoint 5 abo - vs - imp -0.83 

MOBissellPoint 6 abo - vs - imp -0.83 

MOLemay 1 abo - vs - imp -0.83 

MOLemay 2 abo - vs - imp -0.83 

MOLemay 3 abo - vs - imp -0.83 

MOLemay 4 abo - vs - imp -0.83 

NCRockyRiver 1 abd - vs - imp - wesp -0.83 

NJAtlanticCounty 1 abo - vs - imp -0.83 

NJAtlanticCounty 2 abo - vs - imp -0.83 

NJMountainView #1 vs - imp - wesp - rto -0.91 

NJMountainView #2 vs - imp - wesp - rto -0.91 

NJParsippanyTroyHills 1 vs - imp -0.83 

NJParsippanyTroyHills 2 vs - imp -0.83 

NJStonyBrook 1 abd - vs - imp -0.83 

NJStonyBrook 2 abd - vs - imp -0.83 

NYAlbanyCountyNorth 1 vs(ad) -0.72 

NYAlbanyCountyNorth 2 vs(ad) -0.72 

NYAlbanyCountySouth 1 vs(ad) -0.72 

NYAlbanyCountySouth 2 vs(ad) -0.72 

NYAuburn 1 abd - vs - imp -0.83 

NYBirdIsland 1 abd - vs -0.66 add PBS 

NYBirdIsland 2 abd - vs -0.66 add PBS 

NYBirdIsland 3 abd - vs -0.66 add PBS 

NYFrankEVanLare 1 abo - imp -0.83 add FF 

NYFrankEVanLare 2 abo - imp -0.83 add FF 

NYFrankEVanLare 3 abo - imp -0.83 add FF 

NYNewRochelle 1 abo - vs - imp -0.83 

NYNewRochelle 2 abo - vs - imp -0.83 

NYNorthwestQuadrant 1 abo - imp -0.83 add FF 

NYOrangetown 1 vs - imp -0.83 

NYOssining 1 vs - imp -0.83 

NYOssining 2 vs - imp -0.83 

NYSchenectady 1 imp -0.83 add FF 

NYSouthwestBergenPoint 1 abd - vs - imp -0.83 

NYSouthwestBergenPoint 2 abd - vs - imp -0.83 

NYTonawanda 1 unknown -0.66 add PBS 

OHCanton 1 vs - imp -0.83 

OHCanton 2 vs - imp -0.83 

OHColumbusSoutherly 1 vs - imp -0.83 

OHColumbusSoutherly 2 vs - imp -0.83 

OHColumbusSoutherly 3 vs - imp -0.83 

OHColumbusSoutherly 4 vs - imp -0.83 

OHEuclid 1 abd - vs - imp -0.83 

OHEuclid 2 abd - vs - imp -0.83 

OHJacksonPike 1 vs - imp -0.83 
C-20 

MACT Floor Control Needs

(if %improvement >10, add control) 

Facility ID Unit ID Existing Control Devices

Max 

ACI 

Adjustment 

Factor FF Scrubber ACI 

OHJacksonPike 2 vs - imp -0.83 

OHMillCreek 1 vs - imp -0.83 

OHMillCreek 2 vs - imp -0.83 

OHMillCreek 3 vs - imp -0.83 

OHMillCreek 4 vs - imp -0.83 

OHMillCreek 5 vs - imp -0.83 

OHMillCreek 6 vs - imp -0.83 

OHNEORSDSoutherly 1 abo - imp - wesp -0.83 

OHNEORSDSoutherly 2 abo - imp - wesp -0.83 

OHNEORSDSoutherly 3 abo - imp - wesp -0.83 

OHNEORSDSoutherly 4 abo - imp - wesp -0.83 

OHNEORSDWesterly 1 abo - vs - imp -0.83 

OHNEORSDWesterly 2 abo - vs - imp -0.83 

OHWilloughbyEastlake 1 imp -0.83 add FF 

OHYoungstown 1 abo - vs - imp -0.83 

OHYoungstown 2 abo - vs - imp -0.83 

PADelawareCountyWestern 1 vs - imp -0.83 

PADelawareCountyWestern 2 vs - imp -0.83 

PAEastNorritonPlymouthWhi

tpain 

1 cs - vs(ad) -0.72 

PAErie 1 vs - wesp -0.66 add PBS 

PAErie 2 vs - wesp -0.66 add PBS 

PAHatfield 1 vs - imp - wesp - rto -0.83 

PAKiskiValley 1 vs - imp -0.83 

PAUpperMorelandHatboro 1 vs - imp - rto -0.83 

RICranston 1 abd - vs - imp -0.83 

RICranston 2 abd - vs - imp -0.83 

RINewEngland 1 vs - imp - wesp -0.83 

SCColumbiaMetro 1 abo/fgr - pbs - vs - imp -1.14 

SCColumbiaMetro 2 abo/fgr - pbs - vs - imp -1.14 

SCPlumIsland 1 vs - imp -0.83 

VAArmyBaseNorfolk 1 ws - vs - imp -0.46 add FF 

VAArmyBaseNorfolk 2 ws - vs - imp -0.46 add FF 

VABoatHarbor 1 ws - vs - pbs - vs(ad) -0.66 

VABoatHarbor 2 ws - vs - pbs - vs(ad) -0.66 

VAChesapeakeElizabeth 1 vs - imp -0.83 

VAChesapeakeElizabeth 2 vs - imp -0.83 

VAHopewell 1 abd - vs - imp -0.83 

VANomanCole 1 abd - vs - imp -0.83 

VANomanCole 2 abd - vs - imp -0.83 

VANomanCole 3 abd - vs - imp -0.83 

VANomanCole 4 abd - vs - imp -0.83 

VANomanCole 5 abd - vs - imp -0.83 

VANomanCole 6 abd - vs - imp -0.83 

VAVirginiaInitiative 1 ws - vs - imp -1.96 

VAVirginiaInitiative 2 ws - vs - imp -1.96 

VAWilliamsburg 1 vs - imp -0.83 

VAWilliamsburg 2 vs - imp -0.83 

WABellinghamPostPoint 1 vs - imp - wesp -0.83 

WABellinghamPostPoint 2 vs - imp - wesp -0.83 

WIGreenBayMetro 1 vs(a) -0.72 

WIGreenBayMetro 2 vs(a) -0.72 

NOTE: Data gaps in pollutant concentrations were filled using values found for similar units or using the average 

concentration over the entire subcategory. For Dioxin/Furan TEQ concentrations, no data was available for the 

subcategory, so TEQ concentrations were assumed to be 57% of the TMB values. 

1. Assumes that units with a packed bed scrubber or installing a packed bed scrubber will get a 10% Hg reduction. 

2. ACI algorithm is based on 90% Hg reduction efficiency and 98% CDD/CDF reduction efficiency. This 

adjustment factor will be used to adjust total annual costs to the estimated reduction efficiency needed to meet the 

floor. 
C-21 

Table C-2a. Control Costs: Fabric Filter Cost Algorithm 

Parameters/Costs Equation/Defaults 

A. Parameters 

1. Incinerator capacity, lb/hr (C) 

2. Annual operating hours, hr/yr (H) 

3. Exhaust gas flow rate, dscfm (Q) 

4. PM concentration, gr/dscf (PM) 

5. Water vapor in gas from incinerator (10% by weight) 

 a. lb/min = Q / (385 ft3/lb-mol) x (29 lb/lb-mol) x moisture content 

(0.10) 

 b. scfm = (lb/min) / (18 lb/lb-mol) x (385 ft3/lb-mol) 

6. Enthalpy change in quench (1800°F to 300°F) 

 a. Dry gas from incinerator, Btu/lb air = [7.010 x (300°F - 77°F) - 7.554 x (1800°F - 77°F)] / (29 

lb/lb-mol) 

 b. Water vapor from incinerator, Btu/lb water vapor = [8.154 x (300°F - 77°F) - 9.215 x (1800°F - 77°F)] / (18 

lb/lb-mol) 

 c. Total gas stream, Btu/yr = [(Btu/lb air) x Q / (385 ft3/lb-mol) x (29 lb/lb-mol) x (60 

min/hr) x H] + [(Btu/lb water vapor) x Q x (0.00753 lb water 

vapor/ft3) x (60 min/hr) x H] 

 d. Cooling water 

 i. Heat of vaporization at 77°F, Btu/lb 1,050 

 ii. Sensible heat for vapor, Btu/lb 85 

 iii. Total, Btu/lb water 1,135 

7. Cooling water evaporated, lb/yr 

 a. lb/yr = [enthalpy change (total gas stream, Btu/yr)] / [enthalpy 

change (cooling water, Btu/lb)] 

 b. scfm = [cooling water evaporated (lb/yr)] / (18 lb/lb-mol) x (385 

ft3/lb-mol) / (H * 60 min/hr) 

8. Actual gas flow into fabric filter, acfm (AQ) = [Q + (water vapor in gas from incinerator, scfm) + (water 

vapor added in quench, i.e., cooling water evaporated, scfm)] x 

[(300°F + 460°F)/528°R] 

9. Operating labor rate, $/hr (LR) $34.60 

10. Electricity cost, $/kWh (EC) $0.07 

11. Water cost, $/1,000 gal (WC) $0.20 

12. Compressed air cost, $/1,000 ft3(CAC) $0.24 

13. Dust disposal cost, $/ton (DDC) $34.29 

14. Capital recovery factors = [i x (1 + i)a] / [(1 + i)a - 1], where i = interest rate, a = 

equipment life 

 a. Bag CRF, 2-yr life, 7% interest 0.55309 

 b. Cage CRF, 4-yr life, 7% interest 0.29523 

 c. Equipment CRF, 20-yr life, 7% interest 0.09439 

15. Cost index 

 a. 2008 575.4 

 b. 1989 357.5 

B. Total Capital Investment 

1. $ = (47.0 x Q + 306,720) x (1.4 retrofit cost factor) x 

(525.4/357.5) 

2. $/dscfm = $ / Q 

C. Direct Annual Operating Costs, $/yr 

1. Electricity = (0.746 kW/hp) x hp (0.0072 x Q + 3.20) x H x EC 

3. Evaporative cooler water = (0.1007 x Q + 23.1506) gal/min x (60 min/hr) x H x WC 
C-22 

Parameters/Costs Equation/Defaults 

4. Operating labor = (1 hr/shift) x (1 shift/8 hr) x H x LR 

5. Supervisory labor = 0.15 x (operating labor) 

6. Maintenance labor = (0.5 hr/shift) x (1 shift/8 hr) x H x (LR x 1.1) 

7. Maintenance materials = 0.02 x TCI 

8. Compressed air = AQ x (2 ft3air/1,000 ft3filtered) x (60 min/hr) x H x CAC 

9. Dust disposal = (PM gr/dscf x Q x 60 min/hr x 1 lb/7,000 gr) x (1 ton/2,000 

lb) x H x DDC 

10. Bag replacement 

 a. Bag cost = AQ x ($2.5/ft2) x (525.4/317.4) x (1.08 taxes and freight 

ratio)/(3.5 ft/min G/C ratio) 

 b. Bag replacement labor cost = AQ x (0.15 hr/bag)/(18 ft2bag area)/(3.5 ft/min G/C ratio) x 

LR 

 c. Bag replacement cost = Bag CRF x [(total bag cost) + (bag replacement labor cost)] 

11. Cage replacement 

 a. Number of bags = AQ/(3.5 ft/min G/C ratio)/(18 ft2bag area) 

 b. Cage replacement labor cost = bag replacement labor cost 

 c. Cage replacement cost = Cage CRF x [single-cage cost (4.941+ 0.163 x 18 ft2bag 

area) x (number of bags) x (525.4/317.4) + (cage replacement 

labor cost)] 

D. Indirect Annual Costs, $/yr 

1. Overhead = 0.6 x (labor + maintenance materials) 

2. Property taxes, insurance, and administration = 0.04 x TCI 

3. Capital recovery = Equipment CRF x (TCI - bag replacement cost - cage 

replacement cost) 

E. Total Annual Cost 

1. $/yr = Direct Annual Costs + Indirect Annual Costs 

2. ($/yr) / dscfm = ($/yr) / Q 

Sources: 

1. Cost equations: Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534] Model 

Plant Description and Cost Report (II-A-112); and Dry Injection Fabric Filter Cost Memorandum (IV-B-32). 

2. Operating labor rate: Bureau of Labor Statistics, Occupational Employment Statistics, May 2008 National 

Industry-Specific Occupational Employment and Wage Estimates 

3. Electricity cost: Energy Information Administration. Average Industrial Retail Price of Electricity: October 

2009. 

4. Water cost: Air Compliance Advisor, version 7.5. 

5. Compressed air cost: P2Pays.org. Energy Tips – Compressed Air. Compressed Air Tip Sheet #1. August 2004. 

6. Dust disposal cost: NSWMA’s 2005 Tip Fee Survey 
C-23 

Table C-2b. Control Costs: Packed-Bed Scrubber Cost Algorithm 

Parameters/Costs Equations/Defaults 

A. Parameters 

1. Incinerator capacity, lb/hr (C) 

2. Temperature into quench, F (T1) 130 

3. Temperature out of PB to ID fan, F (T2) 

4. Annual operating hours, hr/yr (H) 

5. Exhaust gas flow rate, dscfm (Qd) 

6. Assumed moisture content in gas entering quench, % 

(M) 

10 

7. Exhaust gas flow rate, scfm (Qw) = (Qd) / (1 - M/100) 

8. Water added in quench, scfm (Qh) = ((7.010 x (T1 - 77°F) - 6.958 x (T2 - 77°F)) x 0.9 + (8.154 x 

(T1 - 77°F) - 8.064 x (T2 - 77°F)) x 0.1) x (lb-mole/385 scf) x 

Qw / (1,160 Btu/lb) / (18 lb/lb-mole) x (0.7302 ft3-atm/lb-mol-

°R) x 528°R / 1 atm 

9. Actual flow out of PB, acfm (Qa) = (Qw + Qh) x (460°F + T2)/(528°R) 

10. HCl concentration, ppmvd (HCl) 

11. Operating labor rate, $/hr (LR) $34.60 

12. Electricity cost, $/kWh (EC) $0.07 

13. Caustic cost, $/ton (CC) $357 

14. Sewage disposal cost, $/1,000 gal (SDC) $0.00 

15. Water cost, $/1,000 gal (WC) $0.20 

16. Assumed pressure drop through control system, 

inches of water (ΔP) 

15 

17. Surface area-to-volume ratio for 1" dia. Ceramic 

Raschig rings, ft2/ft3(SAV) 

58 

18. Minimum packing wetting rate, ft2/hr (WR) 1 

19. Water density, lb/ft3(Wd) 62.4 

20. Water circulation flow rate, lb/hr-ft2(Gs) = SAV x Wd x WR 

21. Estimated column cross-sectional area from separate 

analysis, ft2(A) 

19.2 

22. Water circulation rate, gpm (GPM) = Gs x A x (1 hr/60 min) x (1 gal/8.33 lb) 

23. Water head, ft of water (Head) 

24. Wastewater (blowdown) flow, gpm (B) = (HCl/1000000) x (Qd) x (lb-mole/385 ft3) x (1 lb-mole 

NaCl/1 lb-mole HCl) x (58.2 lb NaCl/lb-mole NaCl) x (1 lb 

wastewater/0.1 lb NaCl) x (1 gal/8.33 lb) 

25. Capital recovery factor, 15-yr equipment life, 7% 

interest (CRF) 

= [i x (1 + i)a] / [(1 + i)a - 1], where i = interest rate, a = 

equipment life 

26. Chemical Engineering plant cost index 

 a. 2008 575.4 

 b. 1989 357.5 

B. Total Capital Investment 

1. $ = (27.6 x Qd + 109,603) x (525.4/357.5) x (1.4 retrofit factor) 

2. $/dscfm = $ / Qd 

C. Direct Annual Costs, $/yr 

1. Operating labor = (if Qa < 20,000, then 0, otherwise 0.5 hr/shift) x H x LR 

2. Supervisory labor = 0.15 x (operating labor) 

3. Maintenance labor = (0.5 hr/8-hr shift) x H x (LR x 1.1) 

4. Maintenance materials = 0.02 x TCI 

5. Electricity = (0.000181 x Qa x ΔP x H x EC) + (0.000289 x GPM x Head 

x H x EC) 
C-24 

Parameters/Costs Equations/Defaults 

6. Caustic = HCl x (3.117E-9) x Q x H x CC 

7. Sewage disposal = B x (60 min/hr) x H x SDC 

8. Makeup water = (B + Qh x (lb-mole/385 scf) x (18 lb/lb-mole) x (gal/8.33 

lb)) x (60 min/hr) x H x WC 

D. Indirect Annual Costs, $/yr 

1. Overhead = 0.6 x (labor + maintenance materials) 

2. Property taxes, insurance, and administration = 0.04 x TCI 

3. Capital recovery = CRF x TCI 

E. Total Annual Cost 

1. $/yr = Direct Annual Costs + Indirect Annual Costs 

2. ($/yr) /dscfm = ($/yr) / Qd 

Sources: 

1. Cost equations: Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534]-Model 

Plant Description and Cost Report (II-A-112); and Wet Scrubber Cost Memorandum (IV-B-30). 

2. Operating labor rate: Bureau of Labor Statistics, Occupational Employment Statistics, May 2008 National 

Industry-Specific Occupational Employment and Wage Estimates. 

3. Electricity cost: Energy Information Administration. Average Industrial Retail Price of Electricity: October 2009. 

4. Caustic cost: Purchasing.com. Caustic soda price hike is on the horizon. August 29, 2007. 
C-25 

Table C-2c. Control Costs: Activated Carbon Injection (ACI) Cost Algorithm 

Parameters/Costs Equations/Defaults 

A. Parameters 

1. Incinerator capacity, lb/hr (C) 

2. Annual operating hours, hr/yr (H) 

3. Exhaust gas flow rate, dscfm (Q) 

4. Operating labor rate, $/hr (LR) $34.60 

5. Activated carbon cost, $/lb (ACC) $1.38 

6. Dust disposal cost, $/ton (DDC) $34.29 

7. Capital recovery factor, 20-yr equipment life, 7% 

interest (CRF) 

= [i x (1 + i)a] / [(1 + i)a - 1], where i = interest rate, a = 

equipment life 

8. Cost index 

 a. 2008 575.4 

 b. 1990 361.3 

9. ACI Adjustment Factor (AF) 

B. Total Capital Investment 

1. $ = 4,500 x (Q/1,976)0.6 x (1.2 retrofit factor) x (575.4/361.3) 

2. $/dscfm = $ / Q 

C. Direct Annual Costs, $/yr 

1. Operating labor = (0.25 hr/8-hr shift) x H x LR 

2. Supervisory labor = 0.15 x (operating labor) 

3. Maintenance = 0.2 x TCI 

4. Activated carbon = 0.00127 x Q x H x ACC x AF 

5. Dust disposal = 0.00127 x Q x (1 ton/2,000 lb) x H x DDC x AF 

D. Indirect Annual Costs, $/yr 

1. Overhead = 0.6 x (labor + maintenance materials) 

2. Property taxes, insurance, and administration = 0.04 x TCI 

3. Capital recovery = CRF x TCI 

E. Total Annual Cost 

1. $/yr = Direct Annual Costs + Indirect Annual Costs 

2. ($/yr) / dscfm = ($/yr) / Q 

Sources: 

1. Cost equations: Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534] Model 

Plant Description and Cost Report (II-A-112). 

2. Operating labor rate: Bureau of Labor Statistics, Occupational Employment Statistics, May 2007 National 

Industry-Specific Occupational Employment and Wage Estimates. 

3. Activated carbon cost: The Innovation Group. Chemical Profiles: Carbon, Activated. 2002. Assumed 20% price 

increase based on online information from Norit, an activated carbon vendor. 

4. Dust disposal cost: NSWMA’s 2005 Tip Fee Survey. 
C-26

Table C-3. Unit-Specific Inputs Used in Cost Algorithms 

Facility ID 

Unit 

ID 

Unit 

Type 

Capacity 

(dtph) 

Capacity 

(dry lb/hr) 

Sludge Feed 

Rate 

(dry tons/hr) 

Sludge Feed 

Rate 

(dry lb/hr) 

Operating 

Hours 

(hr/yr) 

Stack Gas 

Flow Rate 

(dscfm) 

Stack 

Gas 

Temp1

(°F) 

ACI 

Adjustment 

Factor 

Landfill 

Tipping 

Fee 

PM 

(gr/dscf) 

HCl 

(ppmvd) 

AKJuneau 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 43.02 0.0054 0.12 

CTMattabassett 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 80.40 0.0054 0.12 

CTSynagroWaterbury 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 80.40 0.0054 0.12 

CTWestHaven 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 80.40 0.0054 0.12 

GANoondayCreek 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 35.31 0.0054 2.48 

IADubuque 1 FB 1.70 3400 1.28 2550 4200 6956 1050 0.98 39.85 0.0054 0.05 

IADubuque 2 FB 1.70 3400 1.28 2550 4200 6956 1050 0.98 39.85 0.0054 0.05 

KSKawPoint 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 27.51 0.0054 2.48 

KSKawPoint 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 27.51 0.0054 2.48 

LANewOrleansEastBank 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 34.61 0.0054 0.12 

MALynnRegional 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 84.53 0.0054 0.12 

MALynnRegional 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 84.53 0.0054 0.12 

MIYpsilanti EUFBSSI FB 3.46 6920 2.85 5692 3240 14465 1050 -1.86 39.85 0.0013 0.28 

MNStPaulMetro FBR1 FB 5.42 10840 4.19 8372 7270 21898 1050 -0.47 39.85 0.0010 0.17 

MNStPaulMetro FBR2 FB 5.42 10840 3.94 7876 7270 20984 1050 -0.49 39.85 0.0006 0.16 

MNStPaulMetro FBR3 FB 5.42 10840 3.76 7523 7270 19859 1050 -0.48 39.85 0.0024 0.20 

MOLittleBlueValley 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 39.85 0.0054 0.12 

MORockCreek 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 39.85 0.0054 0.12 

NCBuncombeAshville 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 37.45 0.0054 0.12 

NCTZOzborne ES-1 FB 3.25 6500 2.42 4840 8400 10281 1050 1.02 37.45 0.0011 0.04 

NHManchester 1 FB 2.00 4000 1.50 3000 8400 8184 1050 0.98 80.40 0.0054 0.12 

NJBayshoreRegional 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12 

NJBayshoreRegional 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12 

NJCamden 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 77.04 0.0054 0.12 

NJGloucester 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12 

NJGloucester 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12 

NJNorthwestBergen 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0054 0.12 

NJNorthwestBergen 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0054 0.12 

NJPequannockLincolnFairfi

eld 

1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0054 0.12 

NJPequannockLincolnFairfi

eld 

2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12 

NJSomersetRaritan 1 FB 0.65 1300 0.49 975 4200 2660 1050 0.98 77.04 0.0011 0.12 

NJSomersetRaritan 2 FB 1.33 2660 1.00 1994 4200 5439 1050 0.98 77.04 0.0011 0.12 

NYArlington 1 FB 0.35 700 0.26 525 8400 1432 1050 0.98 59.92 0.0054 2.48 

NYErieCounty 1 FB 0.78 1560 0.59 1172 4200 3197 1050 0.98 59.92 0.0054 2.48 

NYErieCounty 2 FB 0.78 1560 0.59 1172 4200 3197 1050 0.98 59.92 0.0054 2.48 

NYGlensFalls 1 FB 1.54 3080 1.16 2310 8400 6301 1050 0.98 59.92 0.0054 0.12 
C-27

Facility ID 

Unit 

ID 

Unit 

Type 

Capacity 

(dtph) 

Capacity 

(dry lb/hr) 

Sludge Feed 

Rate 

(dry tons/hr) 

Sludge Feed 

Rate 

(dry lb/hr) 

Operating 

Hours 

(hr/yr) 

Stack Gas 

Flow Rate 

(dscfm) 

Stack 

Gas 

Temp1

(°F) 

ACI 

Adjustment 

Factor 

Landfill 

Tipping 

Fee 

PM 

(gr/dscf) 

HCl 

(ppmvd) 

NYOneidaCounty 1 FB 0.84 1680 0.63 1253 8400 3417 1050 0.98 59.92 0.0054 0.12 

NYOneidaCounty 2 FB 0.84 1680 0.63 1253 8400 3417 1050 0.98 59.92 0.0054 0.12 

NYOneidaCounty 3 FB 0.84 1680 0.63 1253 360 3417 1050 0.98 59.92 0.0054 0.12 

NYPortChester 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 59.92 0.0054 0.12 

NYPortChester 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 59.92 0.0054 0.12 

NYSaratogaCounty 1 FB 1.44 2880 1.08 2156 8400 5882 1050 0.98 59.92 0.0054 0.12 

OHLittleMiami 1 FB 3.00 6000 2.25 4500 8400 12275 1050 0.98 34.24 0.0054 0.12 

OHNEORSDEasterly 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 34.24 0.0011 0.12 

PAAlleghenyCounty 001 FB 3.25 6500 1.88 3760 8400 10257 1050 0.98 52.77 0.0054 0.12 

PAAlleghenyCounty 002 FB 3.25 6500 1.88 3760 8400 10257 1050 0.98 52.77 0.0054 0.12 

PAWyomingValley 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 52.77 0.0011 0.12 

PRPuertoNuevo 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 36.69 0.0011 0.05 

SCFelixCDavis 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 38.52 0.0054 0.12 

VABlacksburg 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 52.77 0.0054 2.48 

VAHLMooney 2 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 52.77 0.0054 0.12 

WAAnacortes 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 48.47 0.0054 0.12 

WAEdmonds 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 48.47 0.0054 0.12 

WALynnwood 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 48.47 0.0054 0.12 

WAWestside 1 FB 2.42 4840 1.81 3625 8400 9888 1050 0.98 48.47 0.0054 0.12 

AKJohnMAsplund 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 43.02 0.0158 0.65 

CACentralContraCosta MHF 1 MH 2.50 5000 1.95 3900 4200 23132 1050 -2.61 43.02 0.0137 0.79 

CACentralContraCosta MHF 2 MH 2.50 5000 1.54 3085 4200 22925 1050 -1.78 43.02 0.0113 0.79 

CAPaloAlto 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 43.02 0.0158 0.65 

CAPaloAlto 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 43.02 0.0158 0.65 

CTHartford 001 MH 2.50 5000 2.38 4759 6016 17217 1050 -0.41 80.40 0.0158 0.65 

CTHartford 002 MH 2.50 5000 2.30 4603 6016 16360 1050 -0.49 80.40 0.0158 0.65 

CTHartford 3 MH 2.50 5000 1.88 3750 360 18080 1050 -0.44 80.40 0.0158 0.65 

CTNaugatuck 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 80.40 0.0032 0.65 

CTNaugatuck 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 80.40 0.0158 0.65 

CTSynagroNewHaven 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 80.40 0.0032 0.65 

GAPresidentStreet 1 MH 0.38 760 0.29 576 4200 2777 1050 -0.72 35.31 0.0158 0.65 

GAPresidentStreet 2 MH 0.38 760 0.29 576 4200 2777 1050 -0.72 35.31 0.0158 0.65 

GARLSutton 1 MH 0.25 500 0.19 375 4200 1808 1050 -0.72 35.31 0.0158 0.65 

GARLSutton 2 MH 0.25 500 0.19 375 4200 1808 1050 -0.72 35.31 0.0158 0.65 

GARMClayton 1 MH 1.25 2500 0.94 1875 4200 9040 1050 -0.72 35.31 0.0315 0.65 

GARMClayton 2 MH 1.25 2500 0.94 1875 4200 9040 1050 -0.72 35.31 0.0315 0.65 

GAUtoyCreek 1 MH 1.75 3500 1.31 2625 4200 12656 1050 -0.72 35.31 0.0158 0.65 

GAUtoyCreek 2 MH 1.75 3500 1.31 2625 4200 12656 1050 -0.72 35.31 0.0158 0.65 

GAWeyerhaeuser 1 MH 3.52 7040 2.64 5279 8400 25454 1050 -0.66 35.31 0.0158 13.09 

IACedarRapids 1 MH 3.92 7840 2.94 5880 8400 28349 1050 -0.72 39.85 0.0158 0.65 
C-28

Facility ID 

Unit 

ID 

Unit 

Type 

Capacity 

(dtph) 

Capacity 

(dry lb/hr) 

Sludge Feed 

Rate 

(dry tons/hr) 

Sludge Feed 

Rate 

(dry lb/hr) 

Operating 

Hours 

(hr/yr) 

Stack Gas 

Flow Rate 

(dscfm) 

Stack 

Gas 

Temp1

(°F) 

ACI 

Adjustment 

Factor 

Landfill 

Tipping 

Fee 

PM 

(gr/dscf) 

HCl 

(ppmvd) 

INBelmontNorth 1 MH 2.60 5200 2.03 4060 4200 7085 1050 -0.67 31.64 0.0174 0.65 

INBelmontNorth 2 MH 2.60 5200 2.15 4293 4200 19574 1050 -0.70 31.64 0.0176 0.65 

INBelmontNorth 3 MH 2.60 5200 2.12 4233 4200 7888 1050 -0.96 31.64 0.0148 0.65 

INBelmontNorth 4 MH 2.60 5200 2.09 4187 4200 20699 1050 -0.67 31.64 0.0077 0.65 

INBelmontNorth 5 MH 2.00 4000 1.50 3000 4200 7662 1050 -0.71 31.64 0.0144 0.65 

INBelmontNorth 6 MH 2.00 4000 1.50 3000 4200 20410 1050 -0.71 31.64 0.0144 0.65 

INBelmontNorth 7 MH 2.00 4000 1.50 3000 4200 7413 1050 -0.71 31.64 0.0144 0.65 

INBelmontNorth 8 MH 2.00 4000 1.50 3000 4200 20185 1050 -0.71 31.64 0.0144 0.65 

LANewOrleansEastBank 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 34.61 0.0158 13.09 

MAFitchburgEast 1 MH 2.30 4600 1.72 3443 8400 16597 1050 -0.66 84.53 0.0032 13.09 

MAUpperBlackstone 1 MH 3.00 6000 1.79 3587 8544 6271 1050 -0.99 84.53 0.0008 0.34 

MAUpperBlackstone Inciner

ator 3 

MH 3.00 6000 1.96 3921 216 14421 1050 -1.67 84.53 0.0005 0.31 

MDWesternBranch 1 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 55.64 0.0158 0.65 

MDWesternBranch 2 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 55.64 0.0158 0.65 

MIAnnArbor 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 39.85 0.0158 0.65 

MIBattleCreek 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIBattleCreek 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex1 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex1 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex1 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex1 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex1 5 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex1 6 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MIDetroitComplex2 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 5 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 6 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 7 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIDetroitComplex2 8 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIFlint 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIFlint 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIFlint 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIFlint 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65 

MIPontiacAuburn 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 39.85 0.0158 0.65 

MIWarren 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 39.85 0.0315 0.65 

MNSeneca Inciner

ator 1 

MH 1.58 3160 1.34 2676 4000 16607 1050 0.48 39.85 0.0344 0.42 
C-29

Facility ID 

Unit 

ID 

Unit 

Type 

Capacity 

(dtph) 

Capacity 

(dry lb/hr) 

Sludge Feed 

Rate 

(dry tons/hr) 

Sludge Feed 

Rate 

(dry lb/hr) 

Operating 

Hours 

(hr/yr) 

Stack Gas 

Flow Rate 

(dscfm) 

Stack 

Gas 

Temp1

(°F) 

ACI 

Adjustment 

Factor 

Landfill 

Tipping 

Fee 

PM 

(gr/dscf) 

HCl 

(ppmvd) 

MNSeneca Inciner

ator 2 

MH 1.58 3160 1.42 2843 4000 15606 1050 0.37 39.85 0.0333 0.42 

MOBigBlueRiver 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 39.85 0.0158 13.09 

MOBigBlueRiver 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 39.85 0.0158 13.09 

MOBigBlueRiver 3 MH 2.69 5381 2.02 4036 360 19458 1050 -0.72 39.85 0.0158 0.65 

MOBissellPoint 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOBissellPoint 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOBissellPoint 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOBissellPoint 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOBissellPoint 5 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOBissellPoint 6 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOLemay 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOLemay 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOLemay 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

MOLemay 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65 

NCRockyRiver 1 MH 2.97 5940 2.23 4452 8400 21464 1050 -0.72 37.45 0.0032 0.65 

NJAtlanticCounty 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65 

NJAtlanticCounty 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65 

NJMountainView #1 MH 0.80 1600 0.80 1597 2715 7698 1050 -0.80 77.04 0.0017 0.86 

NJMountainView #2 MH 0.80 1600 0.80 1597 2715 9267 1050 -0.80 77.04 0.0017 0.86 

NJParsippanyTroyHills 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65 

NJParsippanyTroyHills 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65 

NJStonyBrook 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65 

NJStonyBrook 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65 

NYAlbanyCountyNorth 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYAlbanyCountyNorth 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYAlbanyCountySouth 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYAlbanyCountySouth 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYAuburn 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0158 0.65 

NYBirdIsland 1 MH 14.04 28080 10.53 21063 8400 101548 1050 -0.66 59.92 0.0158 13.09 

NYBirdIsland 2 MH 14.04 28080 10.53 21063 8400 101548 1050 -0.66 59.92 0.0158 13.09 

NYBirdIsland 3 MH 14.04 28080 10.53 21063 360 101548 1050 -0.66 59.92 0.0158 13.09 

NYFrankEVanLare 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65 

NYFrankEVanLare 2 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65 

NYFrankEVanLare 3 MH 2.69 5381 2.02 4036 360 19458 1050 -0.72 59.92 0.0315 0.65 

NYNewRochelle 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYNewRochelle 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYNorthwestQuadrant 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65 

NYOrangetown 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0158 0.65 

NYOssining 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 

NYOssining 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65 
C-30

Facility ID 

Unit 

ID 

Unit 

Type 

Capacity 

(dtph) 

Capacity 

(dry lb/hr) 

Sludge Feed 

Rate 

(dry tons/hr) 

Sludge Feed 

Rate 

(dry lb/hr) 

Operating 

Hours 

(hr/yr) 

Stack Gas 

Flow Rate 

(dscfm) 

Stack 

Gas 

Temp1

(°F) 

ACI 

Adjustment 

Factor 

Landfill 

Tipping 

Fee 

PM 

(gr/dscf) 

HCl 

(ppmvd) 

NYSchenectady 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65 

NYSouthwestBergenPoint 1 MH 4.92 9840 3.69 7375 4200 35557 1050 -0.72 59.92 0.0158 0.65 

NYSouthwestBergenPoint 2 MH 4.92 9840 3.69 7375 4200 35557 1050 -0.72 59.92 0.0158 0.65 

NYTonawanda 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.66 59.92 0.0158 13.09 

OHCanton 1 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 34.24 0.0158 0.65 

OHCanton 2 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 34.24 0.0158 0.65 

OHColumbusSoutherly 1 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65 

OHColumbusSoutherly 2 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65 

OHColumbusSoutherly 3 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65 

OHColumbusSoutherly 4 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65 

OHEuclid 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 34.24 0.0158 0.65 

OHEuclid 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 34.24 0.0158 0.65 

OHJacksonPike 1 MH 2.32 4640 1.74 3486 4200 16807 1050 -0.72 34.24 0.0158 0.65 

OHJacksonPike 2 MH 2.32 4640 1.74 3486 4200 16807 1050 -0.72 34.24 0.0158 0.65 

OHMillCreek 1 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65 

OHMillCreek 2 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65 

OHMillCreek 3 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65 

OHMillCreek 4 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65 

OHMillCreek 5 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65 

OHMillCreek 6 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65 

OHNEORSDSoutherly 1 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65 

OHNEORSDSoutherly 2 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65 

OHNEORSDSoutherly 3 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65 

OHNEORSDSoutherly 4 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65 

OHNEORSDWesterly 1 MH 1.79 3580 1.34 2685 4200 12945 1050 -0.72 34.24 0.0158 0.65 

OHNEORSDWesterly 2 MH 1.79 3580 1.34 2685 4200 12945 1050 -0.72 34.24 0.0158 0.65 

OHWilloughbyEastlake 1 MH 3.42 6840 2.57 5130 8400 24733 1050 -0.72 34.24 0.0315 0.65 

OHYoungstown 1 MH 2.00 4000 1.50 3000 4200 14464 1050 -0.72 34.24 0.0158 0.65 

OHYoungstown 2 MH 2.00 4000 1.50 3000 4200 14464 1050 -0.72 34.24 0.0158 0.65 

PADelawareCountyWestern 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 52.77 0.0158 0.65 

PADelawareCountyWestern 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 52.77 0.0158 0.65 

PAEastNorritonPlymouthW

hitpain 

1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65 

PAErie 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 52.77 0.0032 13.09 

PAErie 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 52.77 0.0032 13.09 

PAHatfield 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0032 0.65 

PAKiskiValley 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65 

PAUpperMorelandHatboro 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65 

RICranston 1 MH 0.95 1900 0.71 1424 4200 6863 1050 -0.72 48.48 0.0158 0.65 

RICranston 2 MH 1.98 3960 1.48 2968 4200 14311 1050 -0.72 48.48 0.0158 0.65 

RINewEngland 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 48.48 0.0032 0.65 
C-31

Facility ID 

Unit 

ID 

Unit 

Type 

Capacity 

(dtph) 

Capacity 

(dry lb/hr) 

Sludge Feed 

Rate 

(dry tons/hr) 

Sludge Feed 

Rate 

(dry lb/hr) 

Operating 

Hours 

(hr/yr) 

Stack Gas 

Flow Rate 

(dscfm) 

Stack 

Gas 

Temp1

(°F) 

ACI 

Adjustment 

Factor 

Landfill 

Tipping 

Fee 

PM 

(gr/dscf) 

HCl 

(ppmvd) 

SCColumbiaMetro 1 MH 1.08 2160 0.89 1773 7300 4620 1050 -1.44 38.52 0.0049 0.20 

SCColumbiaMetro 2 MH 1.08 2160 0.68 1351 7300 5145 1050 -1.34 38.52 0.0064 0.20 

SCPlumIsland 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 38.52 0.0158 0.65 

VAArmyBaseNorfolk 1 MH 1.50 3000 0.82 1648 4200 8455 1050 -0.34 52.77 0.0315 0.65 

VAArmyBaseNorfolk 2 MH 1.50 3000 1.13 2250 4200 10848 1050 -0.34 52.77 0.0315 0.65 

VABoatHarbor 1 MH 1.79 3580 1.64 3278 4200 11399 1050 -0.66 52.77 0.0253 0.70 

VABoatHarbor 2 MH 1.79 3580 1.34 2688 4200 12957 1050 -0.66 52.77 0.0253 0.70 

VAChesapeakeElizabeth 1 MH 1.50 3000 1.12 2236 4200 7908 1050 -0.72 52.77 0.0158 0.65 

VAChesapeakeElizabeth 2 MH 1.50 3000 1.13 2250 4200 10848 1050 -0.72 52.77 0.0158 0.65 

VAHopewell 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65 

VANomanCole 1 MH 1.88 3760 1.41 2813 4200 13560 1050 -0.72 52.77 0.0158 0.65 

VANomanCole 2 MH 1.88 3760 1.41 2813 4200 13560 1050 -0.72 52.77 0.0158 0.65 

VANomanCole 3 MH 3.83 7660 2.88 5750 4200 27722 1050 -0.72 52.77 0.0158 0.65 

VANomanCole 4 MH 3.83 7660 2.88 5750 4200 27722 1050 -0.72 52.77 0.0158 0.65 

VANomanCole 5 MH 1.58 3160 1.19 2375 4200 11450 1050 -0.72 52.77 0.0158 0.65 

VANomanCole 6 MH 1.58 3160 1.19 2375 4200 11450 1050 -0.72 52.77 0.0158 0.65 

VAVirginiaInitiative 1 MH 1.88 3760 2.08 4164 4200 16867 1050 -1.84 52.77 0.0172 0.65 

VAVirginiaInitiative 2 MH 1.88 3760 1.41 2813 4200 13560 1050 -1.84 52.77 0.0172 0.65 

VAWilliamsburg 1 MH 1.96 3920 1.55 3101 4200 6501 1050 -0.72 52.77 0.0176 0.65 

VAWilliamsburg 2 MH 1.96 3920 1.47 2938 4200 14162 1050 -0.72 52.77 0.0176 0.65 

WABellinghamPostPoint 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 48.47 0.0032 0.65 

WABellinghamPostPoint 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 48.47 0.0032 0.65 

WIGreenBayMetro 1 MH 1.23 2460 0.92 1848 4200 8910 1050 -0.72 39.59 0.0158 0.65 

WIGreenBayMetro 2 MH 1.23 2460 0.92 1848 4200 8910 1050 -0.72 39.59 0.0158 0.65 

1. Assumed average gas temperature used for incinerators (CISWI) 
C-32 

Table C-4b. Monitoring Costs 

Based on Default Parameters 

Based on Default Parameters 

and hr/yr 

Parameters/Costs Equation 

Bag Leak 

Detector 

Wet 

Scrubber 

Monitor ACI 

A. Parameters 

1. Recording lime/carbon flow, 

min/4-hr period 

5 

2. Annual operating hours, 

hr/yr (H) 

3. Cost index 

 a. 2008 575.4 575.4 575.4 

 b. 2006 499.6 499.6 499.6 

 c. 1997 386.5 386.5 386.5 

 d. 1993 359.2 359.2 359.2 

 e. 1992 358.2 358.2 358.2 

4. Operating labor wage rate, 

$/hr (LR) 

$34.60 $34.60 $34.60 

5. Capital recovery factor, 20-

yr equipment life, 7% interest 

(CRF) 

= [i x (1 + i)a] / [(1 + i)a - 1], 

where i = interest rate, a = 

equipment life 

0.09439 0.09439 0.09439 

B. Total Capital Investment, $ 

(TCI) 

1. Planning $800 $700 

2. Select type of equipment $4,500 $400 

3. Provide support facilities $500 $1,500 

4. Purchased equipment cost 

(PEC) 

$14,100 $19,300 

5. Install and check equipment $4,800 $1,000 

6. Perf. spec. tests (certif.) $0 $700 

7. Prepare QA/QC plan $800 $700 

8. Total capital cost = Planning + selecting 

equipment + support facilities 

+ PEC + installation + perf. 

spec. tests + QA/QC plan 

$25,500 $24,300 

C. Annual Costs, $/yr 

1. Operating labor = (5 min to record lime/carbon 

flow/4-hr period) x (1 hr/60 

min) x H x LR 

= (5 min to record lime/carbon 

flow/4-hr period) x (1 hr/60 

min) x H x LR 

2. Maintenance materials = 0.02 x TCI $500 

3. Operation & maintenance = Day-to-day activities + 

annual RATA + CGA + annual 

QA + O&M review and update 

$6,000 

4. Recordkeeping and reporting = $1,000 x (525.4/386.5) $200 $1,500 

5. Overhead = 0.6 x (labor + maintenance 

materials) 

$300 = 0.6 x (labor + maintenance 

materials) 

6. Property taxes, insurance, 

and administration 

= 0.04 x TCI $1,000 

7. Capital recovery = CRF x TCI $3,500 $2,300 

8. Total annual cost = Operating labor + 

maintenance materials + 

recordkeeping and reporting + 

overhead + property taxes, 

insurance, and administration + 

capital recovery 

$9,700 $5,600 = Operating labor + 

maintenance materials + 

recordkeeping and reporting + 

overhead + property taxes, 

insurance, and administration 

+ capital recovery 

Notes: 

1. Monitoring costs have been rounded to the nearest $100 to be consistent with level of rounding in original costs. 
C-33 

2. Costs to be replaced include: (a) bag leak detector replacing opacity test; (b) CO CEMS replacing CO test and 

secondary chamber temperature monitor;(c) HCl CEMS replacing HCl test, HCl sorbent monitor (dry scrubbers) 

and scrubber liquor pH monitor (wet 

Sources: 

1. Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534] Testing and Monitoring 

Options and Costs Memo (IV-B-66). 

2. E-mail and attachment from Peter Westlin, EPA, to Mary Johnson, EPA. August 19, 2008. Monitoring Options 

for SNCR on Medical Waste Incinerators. 

3. E-mail from Dan Bivins, EPA, to Mary Johnson, EPA. September 27, 2006. Cost of CO CEMS. 

4. E-mail from Dan Bivins, EPA, to Mary Johnson, EPA. July 28, 2006. Some Preliminary Thoughts on the HWI 

Monitoring. 

Table C-4c. Stack Testing Costs 

Parameters/Costs Equation Values 

A. Parameters 

1. Cost index 

 a. 2008 575.4 

 d. 1992 358.2 

B. Testing Costs, $ 

1. Method 5 (PM) = $8,000 x (575.4/358.2) $13,000 

2. Method 9 (opacity) = $1,000 x (575.4/358.2) + $1,500 $3,500 

3. Method 10 (CO) = $4,000 x (575.4/358.2) + $1,000 $7,000 

4. Method 26 (HCl) = $5,000 x (575.4/358.2) $8,000 

5. Method 29 (metals) = $8,000 x (575.4/358.2) + $2,000 $15,000 

6. Method 23 (CDD/CDF) = $21,000 x (575.4/358.2) - $5,000 $29,000 

7. Method 7E (NOX) = $5,000 x (575.4/358.2) $8,000 

8. Method 6C (SO2) = $5,000 x (575.4/358.2) $8,000 

Annual testing for all: 2/3*sum of costs $61,000 

CRF (15 yr, 7%): (0.07*(1+0.07)^15)/((1+0.07)^15-1) 0.10979 

Note: 

1. Initial testing costs to be annualized over 15 years at 7% interest. 

2. Testing costs have been rounded to the nearest $1,000 (except for opacity) to be consistent with level of rounding 

in original costs; costs also adjusted based on additional information from EPA. 

3. Multiple test costs adjusted by 2/3 in nationwide cost estimates to account for travel, accommodations, 

methods/sampling trains, etc. common to the tests. 

Sources: 

1. Memorandum from R. Segall, EPA/EMB, to R. Copland, EPA/SDB. October 14, 1992. Medical Waste 

Incinerator Study: Emission Measurement and Continuous Monitoring. (II-B-89) 

2. E-mail from Jason Dewees, EPA, to Peter Westlin, EPA. August 20, 2008. Monitoring Options for SNCR & 

Test Cost Questions. 

3. E-mail from Jason Dewees, EPA, to Mary Johnson, EPA. August 20, 2008. Re: Monitoring Options for SNCR 

& Test Cost Questions. 
C-34 

Table C-4d. Visible Emissions Testing Costs 

Parameters/Costs Equation Values 

A. Parameters 

1. Operating labor rate, $/hr (LR) $34.60 

2. Capital recovery factor, 5-yr 

equipment life, 7% interest (CRF) 

= [i x (1 + i)a] / [(1 + i)a - 1], where i = interest rate, a = 

equipment life 

0.24389 

B. Total Capital Investment, $ (TCI) = Combination light meter/anemometer ($200) + digital 

stopwatches (2 each at $25) 

$250 

C. Direct Annual Costs, $/yr 

1. Operating labor = (1 hr/reading) x (3 readings/test) x (1 test/yr) x LR $104 

D. Indirect Annual Costs, $/yr 

1. Overhead = 0.6 x (operating labor) $62 

2. Property taxes, insurance, and 

administration 

= 0.04 x TCI $10 

3. Capital recovery = CRF x TCI $61 

E. Total Annual Cost, $/yr (rounded) = Direct Annual Costs + Indirect Annual Costs $200 

Sources: 

1. Professional Equipment. 2008. Light Meters Industrial and Professional: Digital Light Meter. Website: 

http://www.professionalequipment.com. 

Accessed July 24, 2008. 

2. Cole-Parmer. 2008. Digital Stopwatches -Cole Parmer Instrument Catalog. Website: 

http://www.coleparmer.com. Accessed July 24, 2008. 
C-35

Table C-4e. Recordkeeping and Reporting Costs 

(A) (B) (C) (D) (E) (F) (G) 

Burden item 

Personhours per 

occurrence 

Number of 

occurrences 

per year 

Technical 

person-hours 

per year 

(C = A x B) 

Management 

person-hours 

per year 

(D = C x 0.05) 

Clerical personhours per year 

(E = C x 0.1) 

Total personhours per year 

(F = C + D + E) Cost, $ 

A. Applications N/A 

B. Surveys and Studies N/A 

C. Reporting Requirements 

1. Read instructions 1.0 1 1.0 0.05 0.1 1.2 $41 

2. Required activities 

 a. Perf. spec. tests (certif.) for CMS 17 1 17 0.9 1.7 20 $696 

3. Write report 

 a. Notification of initial performance test 

 i. Pollutants, fugitive ash emissions 2.0 1 2.0 0.1 0.2 2.3 $82 

 ii. Fugitive ash emissions 1.0 1 1.0 0.05 0.1 1.2 $41 

 b. Notification of initial CMS 

demonstration 

2.0 1 2.0 0.1 0.2 2.3 $82 

 c. Report of initial performance test 

 i. Pollutants, fugitive ash emissions 8.0 1 8.0 0.4 0.8 9.2 $328 

 ii. Fugitive ash emissions 2.0 1 2.0 0.1 0.2 2.3 $82 

 d. Report of initial CMS demonstration Incl. in C2 

 e. Annual report 

 i. Results of performance tests 

conducted during the year 

40 1 40 2.0 4.0 46 $1,638 

D. Recordkeeping Requirements 

1. Read instructions Incl. in C1 

2. Plan activities N/A 

3. Implement activities N/A 

4. Develop record system N/A 

5. Time to enter information 

 a. Records of initial performance test Incl. in C3 

 b. Records of annual and any subsequent 

compliance tests 

Incl. in C3 

E. Total Labor Burden and Cost 73 3.7 7.3 84 $2,989 

Notes: 

1. Industry costs are based on the following hourly rates: technical at $34.60, management at $82.23, and clerical at $22.32 (see table below). The composite 

hourly labor rate is ($34.60/hr) + (0.05 x $82.23/hr) + (0.1 x $22.32/hr) = $40.94/hr. Labor 
C-36

2. Person-hours per occurrence for CMS performance specification costs are based on the performance specification costs to certify CMS ($700) divided by the 

composite hourly labor rate ($40.94/hr). 

3. Control device inspection cost already accounted for under monitoring costs. 

4. Assume 8 hours for each facility to review the report of the initial performance test for pollutants and fugitive ash. 

5. Assume 2 hours for each facility to review the report of the initial performance test for fugitive ash. 

6. Assume 40 hours to review report of annual PM, CO, and HCl compliance reports. 

7. The average recurrent burden and cost in the first 3 years after promulgation for the sources with recurrent burden are equal to the person-hours added down 

each column for technical, management, and clerical and the sum of the cost column. 

Sources: 

1. Bureau of Labor Statistics, Occupational Employment Statistics, May 2008 National Industry-Specific Occupational Employment and Wage Estimates. 

2. Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR2006-0534] Testing and Monitoring Options and Costs Memo (IV-B-66). 

Labor Rates: 

Parameter 

Pulp, Paper and 

Paperboard Mills 

Pipeline 

Transportatio

n 

Cement and 

Concrete 

Product 

Manufacturing 

Pharma-ceutical & 

Medicine Manufacturing Total Loaded 

1. Technical - Stationary Engineers & Boiler Operators $19.14 $21.11 $18.88 $27.36 $21.62 $34.60 

2. Management - Engineering Managers $41.41 $58.22 $43.90 $62.05 $51.40 $82.23 

3. Clerical - Office Clerks, General $14.21 $14.15 $12.58 $14.85 $13.95 $22.32 

4. Composite labor rate $40.94 
C-37 

Table C-5a. Alternative Disposal Cost Option: Cost to Landfill 

Parameters/Costs Equation 

A. Parameters 

1. Incinerator feed rate, lb/hr (C) 

2. Annual operating hours, hr/yr (H) 

3. Landfill tip fee ($/ton)1 

B. Annual Costs, $/ton 

50 mile round trip = $0.266/ton-mile x 50 miles + landfill tip fee 

100 mile round trip = $0.266/ton-mile x 100 miles + landfill tip fee 

200 mile round trip = $0.266/ton-mile x 200 miles + landfill tip fee 

C. Annual Costs (with landfill tip fee), $/yr 

50 mile round trip = Total annual cost x (C x 0.67) x H 

100 mile round trip 

200 mile round trip 

Sources: 

1. State average tipping fees from BioCycle December 2008, Vol 49, No. 12, P. 22, Table 5. 

Where state data unavailable, NSWMA’s 2005 Tip Fee Survey regional averages were used. 

For Puerto Rico, NSWMA national U.S. average used. All values corrected to 2008 dollars using 

CPI data from Bureau of Labor Statistics: http://data.bls.gov/cgi-bin/cpicalc.pl

Unit-specific tipping fees are listed in Table 5. 

2. Hauling cost: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau 

of Transportation Statistics, Table 3-17: Average Freight Revenue Per Ton-mile. 

Assumed 50, 100, or 150 mile/trip to reach nearest landfill. 

Table C-5b. Reported Operating Costs and Calculated Cost Factors 

Facility ID Unit ID Unit Type 

Feedrate 

(dry tons/year) 

Total Annual Cost 

To Operate Unit 

Cost Factor 

($/dry ton) 

MIYpsilanti EU-FBSSI FB 9,221.27 

MNStPaulMetro FBR1 FB 30,433.03 2,633,334.00 86.53 

MNStPaulMetro FBR2 FB 28,630.10 2,633,334.00 91.98 

MNStPaulMetro FBR3 FB 27,346.28 2,633,334.00 96.30 

NCTZOsborne ES-1 FB 20,328.00 1,128,240.75 55.50 

PAAlleghenyCounty 001 FB 15,792.00 2,783,333.00 176.25 

PAAlleghenyCounty 002 FB 15,792.00 2,783,333.00 176.25 

CACentralContraCosta MHF 1 MH 8,190.00 4,760,351.00 581.24 

CACentralContraCosta MHF 2 MH 6,478.15 4,760,351.00 734.83 

CTHartford 001 MH 14,314.07 1,137,000.00 79.43 

CTHartford 002 MH 13,844.82 1,137,000.00 82.12 

MAUpperBlackstone 1 MH 15,322.20 1,513,370.00 98.77 

MNSeneca Incinerator 1 MH 5,352.73 950,000.00 177.48 

MNSeneca Incinerator 2 MH 5,685.93 950,000.00 167.08 

NJMountainView #1 MH 2,167.48 1,410,554.67 650.78 

NJMountainView #2 MH 2,167.48 1,410,554.67 650.78 

SCColumbiaMetro 1 MH 6,472.39 1,116,666.67 172.53 

SCColumbiaMetro 2 MH 4,932.37 1,116,666.67 226.40 
C-38 

Cost Factors* 

FB Minimum ($/dry ton) 55.50 

Average ($/dry ton) 113.80 

MH Minimum ($/dry ton) 79.43 

Average ($/dry ton) 329.22 

* Cost factors were multiplied with the average feedrates determined for each unit in order to estimate the annual 

cost to operate it. 
C-39

Table C-5c. Alternative Disposal Cost Option: Sludge Storage Cost 

FacilityID UnitID 

Unit 

Type ControlCategory 

Capacity 

(dtph) 

Assigned 

Capacity 

Assigned 

tons/day

a 

Assigned 

cubic 

yards/ 

day

b 

Assigned 

4-day 

Capacity 

(ft3) 

Pad 

size if 

6' deep 

storage 

(ft2) 

Rail 

Length

c

(ft) 

Aluminum 

Sheet Area

d

 

(ft2) 

# of 

4'x8' 

sheets 

required 

Rail 

Coste 

Concrete 

Costf

(at $6/ft2) 

Total 

Storage 

Cost ($) 

Annualized 

Storage 

Costg 

PAKiskiValley 1 MH vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

NYGlensFalls 1 FB vs - imp 1.54 1.54 36.96 61 6571 1095 33 794 25 $3,850 $6,571 $10,421 $1,144 

WAAnacortes 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

PAHatfield 1 MH vs - imp - wesp - rto 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

VAHopewell 1 MH abd - vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

OHWilloughbyEastlake 1 MH imp 3.42 3.42 82.08 135 14592 2432 49 1184 37 $5,698 $14,592 $20,290 $2,228 

NYAuburn 1 MH abd - vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

NYArlington 1 FB 0.35 0.35 8.4 14 1493 249 16 379 12 $1,848 $1,493 $3,341 $367 

AKJuneau 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

CTNaugatuck 1 MH abo - imp - wesp 1.90 91.32 150 16235 2706 52 1248 40 $6,160 $16,235 $11,197 $1,229 

CTNaugatuck 2 MH vs - imp 1.90 $11,197 $1,229 

WALynnwood 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

MAFitchburgEast 1 MH vs - wesp - rto 2.30 2.30 55.2 91 9813 1636 40 971 31 $4,774 $9,813 $14,587 $1,602 

NJPequannockLincolnFa

irfield 

1 FB vs - imp 1.90 91.32 150 16235 2706 52 1248 40 $6,160 $16,235 $11,197 $1,229 

NJPequannockLincolnFa

irfield 

2 FB vs - imp - wesp 1.90 $11,197 $1,229 

WAEdmonds 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

VABlacksburg 1 FB 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

RINewEngland 1 MH vs - imp - wesp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

NYOrangetown 1 MH vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365 

OHEuclid 1 MH abd - vs - imp 1.90 91.32 150 16235 2706 52 1248 40 $6,160 $16,235 $11,197 $1,229 

OHEuclid 2 MH abd - vs - imp 1.90 $11,197 $1,229 

a. Assumed unit operating 24 hrs per day. 

b. Volume based on sludge density of 1215 lbs/yd3 (Pocket Ref (ISBN 1-885071-00-0) page 435). 

c. Assumed square concrete pad. 

d. Rail height of 6 feet chosen for minimal concrete surface area requirement. 

e. Based on cost of $154 per 4' by 8' sheet of flattened aluminum, 0.125 inches thick (Metals Depot: 

http://www.metalsdepot.com/products/hrsteel2.phtml?page=expanded&LimAcc=$LimAcc) 

f. Researched concrete slab costs (including installation, materials, and labor) ranged from $3/ft2 to $10/ft2. For this analysis, an average of $6/ft2 was used. 

g. Capital Recovery Factor based on 7% interest and 15 year lifetime. 
C-40

Table C-6a. Emissions for Landfilling Option: Increased Emissions from Waste-Hauling Vehicles 

FacilityID UnitID 

Unit 

Type 

Maximum 

Charge Rate 

(ton waste/hr) 

Daily 

waste 

hauled 

(tons/day) 

Daily 

waste 

hauled 

(cu yd/day) 

Operating 

Hours 

(hr/yr) 

Annual 

Waste 

(tpy) 

Annual 

Waste 

(cu 

yd/yr) 

Number of 

truck trips 

per year 

Round 

Trip 

Miles mi/yr 

CO 

(tpy) 

NOx 

(tpy) 

PM10 

(tpy) 

PM 2.5 

(tpy) 

SO2 

(tpy) 

AKJuneau 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248 

NJPequannockLincolnFa

irfield 

1 FB 2.26 54 89 4200 9,484 15,612 434 200 86,731 0.28603 1.03549 0.06187 0.05315 0.00248 

NJPequannockLincolnFa

irfield 

2 FB 2.26 54 89 4200 9,484 15,612 434 200 86,731 0.28603 1.03549 0.06187 0.05315 0.00248 

NYArlington 1 FB 0.35 8 14 8400 2,940 4,840 134 100 13,443 0.04433 0.16050 0.00959 0.00824 0.00039 

NYGlensFalls 1 FB 1.54 37 61 8400 12,936 21,294 591 100 59,150 0.19507 0.70619 0.04219 0.03625 0.00169 

VABlacksburg 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248 

WAAnacortes 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248 

WAEdmonds 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248 

WALynnwood 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248 

CTNaugatuck 1 MH 2.69 65 106 4200 11,298 18,598 517 200 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

CTNaugatuck 2 MH 2.69 65 106 4200 11,298 18,598 517 200 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

MAFitchburgEast 1 MH 2.30 55 91 8400 19,320 31,802 883 100 88,340 0.29133 1.05471 0.06302 0.05413 0.00253 

NYAuburn 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

NYOrangetown 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

OHEuclid 1 MH 2.69 65 106 4200 11,298 18,598 517 100 51,660 0.17037 0.61677 0.03685 0.03166 0.00148 

OHEuclid 2 MH 2.69 65 106 4200 11,298 18,598 517 100 51,660 0.17037 0.61677 0.03685 0.03166 0.00148 

OHWilloughbyEastlake 1 MH 3.42 82 135 8400 28,728 47,289 1,314 100 131,358 0.43320 1.56830 0.09370 0.08049 0.00376 

PAHatfield 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

PAKiskiValley 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

RINewEngland 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

VAHopewell 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296 

6.03 21.84 1.30 1.12 0.05 

Notes: 

*assumed density of dewatered sludge is 1215 lbs/yd3 (Pocket Ref (ISBN 1-885071-00-0) page 435) 

*assumed maximum capacity of hauling vehicles (36 cu yd) for 50+ cu yd/day. (Land application of biosolids: process design manual. Center for Environmental 

Research Information (U.S.), 1997. Page 214.) 

*emission factors based on national average output from EPA’s Office of Transportation and Air Quality (OTAQ) MOtor Vehicle Emission Simulator 

(MOVES). See factors below: 

Pollutant g/mi lb/mi 

CO 2.99 0.0066 

NOx 10.8 0.0239 

PM10 0.65 0.0014 

PM2.5 0.56 0.0012 

SO2 0.03 0.0001 
C-41

Table C-6b. Emissions for Landfilling Option: LandGEM Output 

Waste Accepted Waste-In-Place Total landfill gas Methane Carbon monoxide Mercury (total) - HAP 

Year (Mg/year) 

(short 

tons/year) (Mg) 

(short 

tons) (Mg/year) (m3

/year) 

(short 

tons/year) (Mg/year) (m3

/year) 

(short 

tons/year) (Mg/year) (m3

/year) 

(short 

tons/year) (Mg/year) (m3

/year) 

(short 

tons/year) 

2011 325,914 358,505 0 0 0 0 0 0 0 0 0 0 0 0 0 0 

2012 325,914 358,505 325,914 358,505 1.997E+03 1.599E+06 1.074E+02 5.334E+02 7.995E+05 5.372E+01 1.464E+03 7.995E+05 5.372E+01 3.869E-06 4.637E-04 3.116E-08 

2013 325,914 358,505 651,827 717,010 3.878E+03 3.105E+06 2.086E+02 1.036E+03 1.553E+06 1.043E+02 2.842E+03 1.553E+06 1.043E+02 7.513E-06 9.005E-04 6.050E-08 

2014 325,914 358,505 977,741 1,075,515 5.649E+03 4.523E+06 3.039E+02 1.509E+03 2.262E+06 1.520E+02 4.140E+03 2.262E+06 1.520E+02 1.095E-05 1.312E-03 8.814E-08 

2015 325,914 358,505 1,303,655 1,434,020 7.317E+03 5.859E+06 3.937E+02 1.954E+03 2.929E+06 1.968E+02 5.362E+03 2.929E+06 1.968E+02 1.418E-05 1.699E-03 1.142E-07 

2016 325,914 358,505 1,629,568 1,792,525 8.888E+03 7.117E+06 4.782E+02 2.374E+03 3.558E+06 2.391E+02 6.514E+03 3.558E+06 2.391E+02 1.722E-05 2.064E-03 1.387E-07 

2017 325,914 358,505 1,955,482 2,151,030 1.037E+04 8.301E+06 5.578E+02 2.769E+03 4.151E+06 2.789E+02 7.598E+03 4.151E+06 2.789E+02 2.009E-05 2.407E-03 1.618E-07 

2018 325,914 358,505 2,281,395 2,509,535 1.176E+04 9.417E+06 6.327E+02 3.141E+03 4.709E+06 3.164E+02 8.619E+03 4.709E+06 3.164E+02 2.279E-05 2.731E-03 1.835E-07 

2019 325,914 358,505 2,607,309 2,868,040 1.307E+04 1.047E+07 7.033E+02 3.492E+03 5.234E+06 3.517E+02 9.581E+03 5.234E+06 3.517E+02 2.533E-05 3.036E-03 2.040E-07 

2020 325,914 358,505 2,933,223 3,226,545 1.431E+04 1.146E+07 7.698E+02 3.822E+03 5.729E+06 3.849E+02 1.049E+04 5.729E+06 3.849E+02 2.772E-05 3.323E-03 2.232E-07 

2021 325,914 358,505 3,259,136 3,585,050 1.547E+04 1.239E+07 8.324E+02 4.133E+03 6.195E+06 4.162E+02 1.134E+04 6.195E+06 4.162E+02 2.998E-05 3.593E-03 2.414E-07 

2022 325,914 358,505 3,585,050 3,943,555 1.657E+04 1.327E+07 8.914E+02 4.425E+03 6.633E+06 4.457E+02 1.214E+04 6.633E+06 4.457E+02 3.210E-05 3.847E-03 2.585E-07 

2023 325,914 358,505 3,910,964 4,302,060 1.760E+04 1.409E+07 9.469E+02 4.701E+03 7.047E+06 4.735E+02 1.290E+04 7.047E+06 4.735E+02 3.410E-05 4.087E-03 2.746E-07 

2024 325,914 358,505 4,236,877 4,660,565 1.857E+04 1.487E+07 9.992E+02 4.961E+03 7.436E+06 4.996E+02 1.361E+04 7.436E+06 4.996E+02 3.599E-05 4.313E-03 2.898E-07 

2025 325,914 358,505 4,562,791 5,019,070 1.949E+04 1.560E+07 1.048E+03 5.205E+03 7.802E+06 5.242E+02 1.428E+04 7.802E+06 5.242E+02 3.776E-05 4.525E-03 3.041E-07 

2026 325,914 358,505 4,888,705 5,377,575 2.035E+04 1.629E+07 1.095E+03 5.436E+03 8.147E+06 5.474E+02 1.491E+04 8.147E+06 5.474E+02 3.943E-05 4.726E-03 3.175E-07 

2027 325,914 358,505 5,214,618 5,736,080 2.116E+04 1.695E+07 1.139E+03 5.652E+03 8.473E+06 5.693E+02 1.551E+04 8.473E+06 5.693E+02 4.100E-05 4.914E-03 3.302E-07 

2028 325,914 358,505 5,540,532 6,094,585 2.193E+04 1.756E+07 1.180E+03 5.857E+03 8.779E+06 5.898E+02 1.607E+04 8.779E+06 5.898E+02 4.248E-05 5.092E-03 3.421E-07 

2029 325,914 358,505 5,866,445 6,453,090 2.265E+04 1.813E+07 1.218E+03 6.049E+03 9.067E+06 6.092E+02 1.660E+04 9.067E+06 6.092E+02 4.388E-05 5.259E-03 3.533E-07 

2030 325,914 358,505 6,192,359 6,811,595 2.332E+04 1.868E+07 1.255E+03 6.230E+03 9.339E+06 6.275E+02 1.709E+04 9.339E+06 6.275E+02 4.519E-05 5.416E-03 3.639E-07 

2031 0 0 6,518,273 7,170,100 2.396E+04 1.919E+07 1.289E+03 6.401E+03 9.594E+06 6.446E+02 1.756E+04 9.594E+06 6.446E+02 4.643E-05 5.565E-03 3.739E-07 

119,434,741 8,025 8,025 0.00000465 

Notes: 

Values derived from LandGEM V3.02, using the following defaults: 

k = 0.06 k value based on default IPCC value for sewage sludge in dry, temperate climate. 

Lo = 42 Inventory default Lo for MSW = 100 for conventional climate (dry, temperate); CAA default Lo for MSW = 170 for conventional climate (dry, 

temperate). 

Sewage sludge Lo value calculated based on IPCC equation using default degradable organic carbon (DOC) value of 0.05 for sewage sludge. 

IPCC values for other Lo parameters are consistent with inventory defaults, so multplied the result by 1.7 to be consistent with CAA defaults. 

Methane in landfill gas = 50% 
C-42 

Table C-6c. Emissions for Landfilling Option: Increased Emissions from Landfill and 

Flare 

Total Tons Emitted Over 20 Years 

Pollutant 358,505 tpy basis 

Tons 

per Year 

PM 17.92 0.90 

HCl 7.62 0.38 

SO2 14.94 0.75 

CO 4802.91 240.15 

NOx 42.16 2.11 

CDD/CDF - - 

Hg 4.65E-06 2.3E-07 

Pb - - 

Cd - - 

Notes: 

PM based on LandGEM methane output and default flare emission factor of 17 lb/MMdscf methane 

(AP-42 Table 4.2-5) 

HCl based on default landfill gas Cl content of 42 ppmv (AP-42 Section 2.4.4.2) 

SO2 based on LandGEM gas output and default landfillgas S content of 46.9 ppmv (AP-42 Section 2.4.4.2) 

CO based on LandGEM methane output and default flare emission factor of 750 lb/MMdscf methane 

(AP-42 Table 4.2-5) 

NOx based on LandGEM methane output and default flare emission factor of 40 lb/MMdscf methane 

(AP-42 Table 4.2-5) 

Hg based on LandGEM Hg output