### January 2011



# Cost and Benefit Changes Since Proposal for Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Sewage Sludge Incineration Units

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 2771112/29/10

FINAL RIA FOR SSI NSPS AND EG

A Regulatory Impact Analysis (RIA) was prepared for the proposal for the Standards of Performance for New Stationary Sources and Emi ssion Guidelines for Existing Sources: Sewage Sludge Incineration Units in September of 2010. For the promulgation package, we are providing this update of the RIA ra ther than completely revising th e RIA. Within this update, we are providing updated costs a nd benefits of the options and have provided a comparison of the selected option with the alternatives. While the characteristics of the options have changed, we also have provided a comparison of the costs and benefits of the optio n preferred at proposal with the selected alternative. In summary,

 Options 2 and 3 changed since the proposal. Option 2 is now the MACT floor plus applying an afterburner to multiple hearth units to re duce CO emissions. Opti on 3 is now the MACT floor plus application of activ ated carbon injection with ex isting particulate control to reduce Hg emissions.

 Costs for the selected option for promulgati on are 80% lower and benefits are 81% lower than they were for the selected option for proposal.

 B ecause the regulated sewage sludge incineration is a government provided service that does not involve a market, no price, quantity, or employment im pacts were estimated for the proposal RIA. The economic impact analysis focused on the comparison of control cost to total governmental revenue. Because the cost s are 80% lower for the selected option for promulgation compared to the proposed option, the control costs are expected to be a smaller portion of government revenues for the selected option than they were for the proposed option.

 Because of insufficient information, employme nt changes due to the requirements for operating and maintaining control equipment were not estimat ed. Also, we did not have the information needed to estimate any labor ch anges related to governmental decisions to switch from incinera tion to landfilling.

Monetized benefits are greater than costs for selected option by $3 million to $34 million at 3% and $1 million to $29 million at 7%. The benefits from reducing exposure to hazardous air pollutants, direct exposure to NOx and SO 2 , ecosystem effects, and visibility impairment have not been monetized, including reducing 19 tons of HCl, 4 pounds of mercury, 2,400 pounds of lead, and 1,000 pounds of cadmium.12/29/10

Net Benefits for Final Sewage Sludge Inci nerators NSPS and EG (millions of $2008)

Option 1 : MACT Floor (Selected) 3 % Discount Rate 7% Discount Rate Monetized Benefits $21 to $52 $19 to $47 Costs $18 to $18 $18 to $18 Net Benefits $3 to $34 $1 to $29

Monetized Benefits for Final Sewage Sludge Incinerators NSPS and EG

Total Monetized Benefits for Final Options (millions of 2008$) 3 % Discount Rate 7% Discount Rate
Option 1: MACT Floor (Selected) $21 to $52 $19 to $47 Option 2: Beyond the Floor $20 to $50 $18 to $45 Option 3: Beyond the Floor $55 to $140 $50 to $130

Monetized Benefits Changes for MACT Floor (millions of 2008$) 3% discount rate 7% discount rate
Proposal (MACT Floor, all comply) $110 to $270 $100 to $250 Final (MACT Floor) $21 to $52 $19 to $47 % Change -81% -81%

Monetized Benefits Changes for Selected Options (millions of 2008$) 3% discount rate 7% discount rate Proposal (BTF Option 2, all comply) $110 to $270 $100 to $250 Final (MACT Floor) $21 to $52 $19 to $47 % Change -81% -81%12/29/10

Costs for Final Sewage Sludge Incinerators NSPS and EG

Total Costs for Final Options (millions of 2008$) 3% or 7% Discount Rate Option 1: MACT Floor (Selected) $18 Option 2: Beyond the Floor $46 Option 3: Beyond the Floor $138

Costs Changes for MACT Floor (millions of 2008$) 3% or 7% Discount Rate
Proposal (MACT Floor, all comply) $63 Final (MACT Floor) $18 % Change -71%

Cost Changes for Selected Options (millions of 2008$) 3% or 7% Discount Rate Proposal (BTF Option 2, all comply) $92 Final (MACT Floor) $18 % Change -80%

### September 2010 

# Regulatory Impact Analysis: Standards of Performance for New Stationary Sources and Emission Guidelines for Existing Sources: Sewage Sludge Incineration Units

Draft Report

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

Draft Report

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 27709iii 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 Sl udge Incineration .................................................................. 2-1

2.1 Relation to Publicly Owned Tr eatment 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 Emissi ons Reductions of the Maximum Achievable Control Tec hnology (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 C ontrols for Existing SSI Units ............ 3-11
3.2.1 Selection of More Stri ngent 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................................................... 3-31iv 4 Economic Impact Analysis ........................................................................................... 4-1

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

4.2 Small Entity An alysis .......................................................................................... 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 PM 2.5 Human Health Benefits ...................................................... 5-1

5.3 Energy Disbenefits ............................................................................................. 5-15
5.3.1 Social Cost of Carbon and Gr eenhouse Gas Disbenefits ....................... 5-16

5.4 Unquantified Benefits ........................................................................................ 5-19
5.4.1 Carbon Monoxide Benefits .................................................................... 5-19
5.4.2 Other SO 2 Benefits ................................................................................. 5-20
5.4.3 HAP Benefits ......................................................................................... 5-21

5.5 Characterization of Uncertainty in the Monetized PM 2.5 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 PM 2.5 -Related Mortality ................................. A-1 B Lowest Measured Level (LML) Assessment for Rules without Policy-Specific Air Quality Data Available: Tech nical Support Document (TSD) ...............................B-1 C Additional Engineering Cost Analysis Data .................................................................C-1v LIST OF FIGURES

Number Page

5-1. Breakdown of Monetized PM 2.5 Health Benefits using Mortality Function from Pope et al. (2002) .............................................................................................. 5-7 5-2. Total Monetized PM 2.5 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 PM 2.5 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 PM 2.5 Exposure (pre- and post-policy policy) ................................................................................................... 5-30 5-6. Cumulative Distribution of Adu lt Population at Annual Mean PM 2.5 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 ................. 5-35vi LIST OF TABLES

Number Page

1-1. Summary of the Monetized Benefits, Soci al 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 Fl uidized 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 Opti on 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 PM 2.5 ............................................................. 5-2 5-2. Summary of Monetized Benefits Estima tes for Proposed SSI NSPS and EG in 2015 (2008$) (large entities comply and small entities landfill) ............................... 5-8 5-3. Summary of Monetized Benefits Estima tes for Proposed SSI NSPS and EG in 2015 (2008$) (all units comply) .............................................................................. 5-10 5-4. Summary of Reductions in H ealth Incidences from PM 2.5 Benefits for the Proposed SSI NSPS and EG in 2015 ....................................................................... 5-11vii 5-5. All PM 2.5 Benefits Estimates for the Propos ed SSI NSPS and EG at Discount Rates of 3% and 7% in 2015 (in millions of 2008$)................................................ 5-12 5-6. Social Cost of Carbon (S CC) Estimates (per tonne of CO 2 ) for 2015 .................... 5-18 5-7. Monetized SCC-derived Disbenefits of CO 2 Emission Increases in 2015 (large entities comply and small entities landfill, millions of 2008$) ................................ 5-18 5-8. Monetized SCC-derived Disbenefits of CO 2 Emission Increases in 2015 (large entities comply and small entities landfill, millions of 2008$) ................................ 5-18 5-9. Summary of the Monetized Benefits, Soci al Costs, and Net Benefits for the SSI NSPS and EG in 2015 (millions of 2008$) ....................................................... 5-331-1 SECTION 1
INTRODUCTION

Section 129 of the Clean Air Act (CAA) requi res that performance standards for new units and emission guidelines (E G) for existing units be establ ished for each category of solid waste incineration units. In prev ious actions, the U.S. Environm ental 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 sewa ge 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 regulator y impact analysis (RIA). The RIA includes an economic impact analysis (EIA) and a small en tity 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 opti ons and selected Option 2:

– Option 1 is the MACT floor level of cont rol 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 activ ated carbon injection for additional mercury (Hg) emissions reduction from MH units.

– Option 3 is the same as Option 2, with th e 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 Qu ality Planning and St andards (OAQPS) has used the direct compliance cost method as a m easure 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 implemen tation (2015), EPA estimates the monetized PM 2.5 benefits of the proposed N SPS 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 re lationships between PM 2.5 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 reduc ing 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 a nd the results of the
EIA:

 Section 2 describes the SSI process, alternative dispos al methods, and affected entities.

 Section 3 describes the e ngineering cost analysis.

 Section 4 describes the economic im pact 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 Benefits b $130 to $320 $120 to $290 Total Social Costs c $92 $92 Net Benefits $37 to $220 $26 to $190

Non-monetized Benefits 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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment Option 1 Total Monetized Benefits b $130 to $320 $120 to $290 Total Social Costs c $63 $63 Net Benefits $66 to $250 $55 to $220

Non-monetized Benefits 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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment Option 3
Total Monetized Benefits b $130 to $310 $120 to $290 Total Social Costs c $132 $132 Net Benefits -$5.4 to $180 -$14 to $150

Non-monetized Benefits 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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementation year (2015), and are ro unded to two significant figures. These results include 2 new F B

incinerators anticipated to come online by 2015 and the larg e entities comply and small entities landfill assumption.
b The total monetized benefits reflect the human health benefits a ssociated with reducing exposure to PM 2.5 through reductions of

directly emitted PM 2.5 and PM 2.5 precursors such as NO x and SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a rang e from Pope et al. (2002) to Laden et al. (2006). These models assume that a ll fine particles, regardless of their chemical composition, are equally potent in cau sing premature mortality because there is no clear scientific evidence th at would support the developm ent of differential effects estimates by particle type. These estimates include energy disbenefits valued at $0.5 million at a 3% discount rate from CO 2 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 INCINERATION 1

Sewage sludge incinerators combust the organic and inorganic solids and dissolved materials resulting from the wastewater treatme nt process. Incineration greatly reduces the sludge volume, and post-incineratio n sludge ash can be disposed of more easily. Sludge ash is generally disposed of in landfills but can also be used in construction ma terials. 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 pollu tants, 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), hydrocar bons, CO, nitrogen oxide s, 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 th e sludge, the type of incinerator used, and the level of PM control.

The majority of incinera tion facilities (163, or 75%) are multiple hearth (MH)
incinerators. These incinerators consist of a cylinder around a seri es 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 c onsists of the upper dryi ng 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 in cinerator, a steel shell holds a refractory-lined grid beneath a bed of sand. Air is injected into the in cinerator, 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 pollutant s are, therefore, lower for FB incinerators.

2.1 Relation to Publicly Owned Treatment Works (POTWs)
Publicly owned treatment works (POTWs) ar e wastewater treatment systems owned by states, municipalities, or other public enti ties. POTWs receive sewage from homes and

1 Portions of this section rely on information provided by EPA (2007 and 2009).
2 Other types of sewage sludge incinerato rs, such as electric arc furnaces, are no longer used in the United States.2-2 businesses, runoff, and sometimes industrial wastew ater. After the wastewat er treatment process, POTWs are responsible for dis posing 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 seconda ry sludge. Some plants may continue with a tertiary treatment of chemical disinfection, which produces a tertia ry or chemical sludge (EPA, 2009). The three sludge types are th en generally combined and dis posed of or sent for further

treatment.

2.2 Alternative Disposal Options

Incineration continues to be ut ilized to dispose of sewage sludge but is increasingly becoming less common. Additional pollution controls will increase costs for facilities that
continue to use the incinerati on disposal method. If the addi tional costs are high enough, many POTWs may choose to adopt altern ative disposals methods (e.g., surf ace disposal in landfills or other beneficial land applications). However, the use of alternative dispos al 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 landfil ls used for other municipal solid waste or in landfills constructed specifically for sewage slud ge. The landfill disposal option is attractive for low-volume incinerators; landfill capacity constr aints limit disposal opportunities for large sludge volumes.

Sewage sludge may also be useful for landfill s. For example, sludge can be used in place of a daily soil cover for odor and blowing litter control or as a fi nal 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 treatm ent 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 is2-3 reused to contribute to crop pr oduction. Land application has also been used in mine reclamation to reestablish vegetation.

Biosolids must meet federal and state regula tions to ensure their safety; meeting these standards may make other land applications a le ss 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 biosolid s 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 th rough additional treatment or by prev enting contact with vectors.

2.3 Ownership
Sewage sludge incinerators can be operated by municipalities or other entities. There is no specific North American Industry Classifica tion System (NAICS) code for these units. Applicable NAICS codes include 562213 (solid waste combustors and incinerators) and 221320 (sewage treatment facilities). Most sludge incinerato rs are located in the ea stern United States.

The United States has 97 operators that ow n 112 facilities with a total of 218 affected incinerator units; the typical (e .g., median) operator owns one fac ility. Almost all operators are towns, cities, and their utility authorities; the ex ception 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 th e 94 owners with population information available, 18 (or 19%) are small en tities that serve a population under 50,000.3-1 SECTION 3
ENGINEERING COST ANALYSIS

This section documents the calculation of co sts and emissions reductions associated with existing and new sources complying with the MACT floor level of contro l, the selection of control options more stringent than the MACT fl oor 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 we re 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 muni cipalities or authorities with more than 50,000 people. Entities with fewer than 50,000 pe ople are likely to dispose of sewage sludge by landfilling rather than continuing to operate their incineration unit.

3.1 Calculation of Costs and Emissions Reducti ons of the Maximum Achievable Control Technology (MACT) Floor
A significant portion of the total cost for i ndustry compliance comes from the cost of installing new pollution control devices or impr oving 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 us ed 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. Tabl es C-1a and C-1b in Appendix C contain the baseline pollutant concentration values used for each unit in each subcat egory and the percentage improvement required to meet the proposed emissions limits for each unit for each pollutant. The existing SSI units are subcategorized into tw o main groups: multiple hearth (MH) units and fluidized bed (FB) units. The pollutant- and subcat egory-specific limits are shown in each header
row of these tables.

Control methods and cost algorithms utili zed 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 improve ments, pollutant-specific control methods were chosen as follows for units requiring more than 10 percent im provement 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 alre ady in place.

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

Mercury and dioxins/furans (CDD/CDF): Adding activated car bon 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. Th e costs and emission reductions associated with the proposed CO limit are discussed in th e memorandum “Analysis of Beyond the Maximum Achievable Control Technology (MACT) Fl oor Controls for Existing SSI Units.” 2

Nitrogen oxides (NO x ): No more than 10 percent improvement in NO x control was needed for any units. Minor adjustments were considered sufficient for those needing improvement to meet the NO x limit.

Sulfur dioxide (SO 2 ): 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 th e proposed limits; (B) th e monitoring, testing, recordkeeping, and reporting activities used to de monstrate compliance; a nd (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 inci nerator 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 c ontrols were estimated in units of dollars ($) and $/flow. The $/flow costs were calcula ted 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 Appendi x C present a summary of the parameters and equations used in the cost algorithms for each emission control a nd 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 insta lled either alone or with ot her add-on controls. The cost algorithm for installing a fabric filter is presen ted in Table C-2a in Appendix C and is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI. 6 The fabric filter capital costs range from approxima tely $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 installe d alone or after a dry scrubbe r/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 approximate ly $103,000/yr to $1.8 million/yr. Sources for specific cost data are noted be low Table C-2b in Appendix C.

c. Adding an activated car bon 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 ACI3-4 system is presented in Table C-2c in A ppendix C and is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI. 8 Adjustments to the car bon injection rate were made to account for how much reduction was require d to meet the proposed limit, and whether a packed-bed scrubber was being a dded, since those may also assist in reducing Hg emissions. The packed-bed scrubber adjustment is a ten percen t Hg reduction, and is based on input from the boiler NESHAP development. The ACI factor co mpares the carbon grain loading originally assumed to achieve 90 percent control of merc ury 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 car bon injection rate calcul ation 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 sp ecific cost data are noted below Table C2c in Appendix C.

d. Additional Control Options.

Minor adjustments, such as air handling a nd distribution adjustments in the firebox, can be made to certain units to improve NO x 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, m onitoring, 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 upd ated to a 2008 basis, and annualized costs assumed an interest rate of 7 pe rcent. 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 assume d 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 li mits. In this context, control devices include3-5 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 Appendi x C for further details and sources.

Parameter monitors. Monitoring of operating parameters can be used to indicate whether air pollution control e quipment and practices are functioni ng properly to minimize air pollution. Based on the existing CISWI regulations and HMIW I regulations, it was assumed that parameter monitoring will be mandatory for all units required to add fabric fi lters, 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 testi ng showed did not meet the proposed emission limit. Any unit having no test data for certain pollutant s 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 emi ssions 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 es timated to be approximately $61,000/yr. The basis of these cost estimates for each st ack 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 un its 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 cap ital cost of $250 and an annual cost of $200, based on the Revised Compliance Costs and Economic Inputs for Existing HMIWI3-6 memo. 8 Further 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 regard ing 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 alte rnatives 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 st andards. To determine if landfilling would be an affordable option for facilities even in the absence of the proposed st andards, both the annual
cost to landfill and the annual uni t 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 la ndfilling costs and a nnual 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 depe nd 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 wo rth of dried sludge, to account for occasional multi-day landfill closures (e.g. weekends and ho lidays). Facilities ma y already have such storage on-site to account for non-co ntinuous operation of the incine ration 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 assu med that a concrete pad with me tal railing would be sufficient for storage of dried or dewatered sludge at sm all 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, a nd these capacities were used to estimate the maximum volume of dry sludge that would accumulate over 4 days. Co sts were then estimated for the concrete and3-7 aluminum required to accommodate these volume s. Table C-5c in Appendix C presents these costs estimates by unit. For large entities, a diffe rent type of storage would likely be required (such as a concrete basin capable of storing la rge quantities of sludge); storage costs for these
facilities were not estimated because it was assume d large entities would comply rather than shut
down their units and landfill.

Tipping fees used in the analysis were sp ecific to each state where state data were
available 9 ; where state data were not available, landfill tipping fees were based on regional tipping fees. 10 All fees were in units of $/ton wast e and were converted to 2008 dollars. The annual tonnage of waste being diverted was calcu lated 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 em issions 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 ra ther than wet sludge.

Transportation costs were based on an estimated $0.266 per ton-mile 11 . 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 regulatio ns for states where small entities are located revealed that Connecticut and New Jersey do not a llow sewage sludge to be landfilled. To adjust for this, round trip distances for facilities in these stat es were increased to 200 miles, assuming a
landfill could be found in another state w ithin 100 miles from the facility.

Annual landfilling costs varied widely, ra nging from $13,000/yr to $5.1 million/yr. Table C-5a in Appendix C summarizes the parameters a nd 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 base d on data provided from the ICR survey and known unit capacities. The survey specificall y requested that respondents provide annual costs to operate each incinera tor in 2006, 2007 and 2008. Costs were then confirmed or revised based on follow-up contact with the survey recipi ents. Several steps were taken and assumptions made to standardize the data: (1) total cost s provided were assumed to be for operating only the incinerator (i.e. did not include dewatering or ot her aspects of plant ope ration); (2) total costs3-8 listed for multiple units were divided even ly 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 aver age factor ($113.80/dry ton for FB units and $329.22/dry ton for MH units) and a minimum fa ctor ($55.50/dry ton for FB units and $79.43/dry ton for MH units) were calculated a nd 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 in formation 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 th e proposed emissions limits; and (2) assuming that all large entities would comply with the proposed emission limits a nd small entities would cease using their incinerators and landfill th e dewatered sludge instead. Emission reductions were calculated by estimating the emissions resulting from each scenario and subtracting the baseline emissions previously calculated. Base line 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 th e 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 Co mply With MACT Floor Limits

Emission reductions were calculat ed 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 baseli ne 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 re duce 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 th e 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 Emis sion + Emissions from Landfilling)
Emissions from Landfilling = Vehicle Emissions + Direct Landfill Emissions + Flare Emissions

a. Vehicle Emissions

To determine the vehicle emissions resulti ng 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 yard 13 was 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 yd 3 ) would be the most likely vehicle used. 14
3. The following emission factors for CO, NO x , Filterable PM, PM 2.5 , and SO 2 were derived from EPA’s Office of Transpor tation and Air Quality (OTAQ) Motor Vehicle Emission Simulator (MOVES), 15 using national defaults for parameters and refuse trucks as the source type :3-10 CO 2.99 grams emitted per mile

NO x 10.8 grams emitted per mile
Filterable PM 0.65 grams emitted per mile

PM 2.5 0.56 grams emitted per mile

SO 2 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 SO 2
. Emissions of these three criteria pollutants du e to landfilled, dewatere d sewage sludge, were estimated using EPA’s LandGEM 16 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 de termine 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 mode l once using the total annual wast e 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 A ppendix 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 emitte d from landfills as a result of landfilling dewatered sewage sludge.

c. Emissions from Landfill Gas Flaring

Additional emissions of PM, NO x , and CO will result from flaring landfill gas generated by the landfilling of dewatered sewage sludge. A landfill gas collection effi ciency of 50 percent was assumed, meaning that 50 percent of the landfill gas generated from landfilled sewage sludge would be collected and combusted. AP-4 2 emission factors, representing the mass of pollutant emitted per volume of methane combusted, were applied in conjunction with the methane output calculated in th e LandGEM model. Again, LandGEM outputs are presented in Table C-6b in Appendix C, and resulting total em issions 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 emi ssion 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
SO 2 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 ca rbon injection (ACI) (Cost and Emissions Reduction, 2010). All FB and MH units were determ ined to meet the floor level of control for
NO x and CO, and no additional control was necessary.

Section 3.2.1 discusses the selec tion 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 contro ls, and Section 3.2.3 summarizes regulatory options selected for the BTF analysis. Baseline emissions and emission
reductions of PM 2.5 were calculated from emissions data collected by EPA and assuming that controls applicable for PM would also reduce PM 2.5
.
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, SO 2 , Hg, and CDD/CDF are the most effective controls available to reduce these pollutants. Consequently, no additional technologie s 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 base line level of control), more stringent controls to be analyzed for th e entire SSI source categor y would be requiring all
units that did not have these controls at base line or for meeting the MACT floors to add these controls. Consequently, more stri ngent controls applied to SSI un its 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 me et the MACT floor) to control HCl and SO 2 ; and adding ACI (if the units did not already have one at base line or to meet the MACT floor) to control Hg
and CDD/CDF. Emission reductions of PM 2.5 were calculated assuming that controls applicable
for PM would also reduce PM 2.5
.

Potential add-on control t echnologies that achieve NO x reduction at other combustion sources are selective catalytic reduction (SCR), selective noncatalytic reduction (SNCR), and flue gas recirculation (FGR). However, none of these technologies we re evaluated to be3-12 appropriate for SSI units. SSI units do not use SCR or SNCR (Invent ory Database, 2010). Additionally, there are no successf ul applications of SCR tec hnology 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 sy stem that is unlikely to work for existing SSI units. Additionally, SNCR is optimal for combus tion units with high residence time and exit incinerator temperature, and less effective for lower uncontrolled NO x 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 NO x emissions. However, the amount of NO x reduced varies widely, ranging from 20% to 80% , and site-specific factors often affect the performance. To support regulations for SSI un its, EPA collected emissions information on the nine Section 129 pollutants. One unit providing emi ssion 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. Additiona lly, no FB units use any add-on NO x control because FB
units can achieve low NO x emission levels, below 100 ppmv and many achieve below 70 ppmv.

For control of CO, an add-on combustion devi ce, such as an afterburner or thermal oxidizer, was analyzed as a more stringent contro l 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 standard s 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 afterbur ner, 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, retrofit s 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 temperat ure (e.g., 1,800°F). On-hearth afterbur ners are the top hearth of a MH unit that has been redesigned so that sludge is rerouted to the sec ond hearth. Retrofitting the MH unit with an on-hearth afterburner may re quire modifications to downstream air pollution control systems because of higher temperatures and larger volumes of exhaust gases (Dangtran, Mullen, and Mayrose, 2000). A lthough there will be reductions in CO and total hydrocarbon (THC) emissions, the reductions may be limite d because of low temperature and limited residence time of the gas in the afterburner stag e. The use of FGR in combination with an onhearth afterburner shows signifi cantly lower emissions levels th an just using an on-hearth3-13 Table 3-1. Summary of Average CO Em issions Collected from MH Units

Classes Facility Location Unit ID Average CO Emission Level (ppmvd @ 7% O 2 )
Uncontrolled Boat Harbor VA 1 3,761

On-hearth afterburner Seneca MN 1 1,323

2 853

Central Contra Costa CA 1 905

2 752

Detached afterburner,thermal oxidizer, or onhearth afterburner with flue gas recirculation Columbia Metro SC 1 63

Mountain View NJ 2 39

Upper Blackstone MA 1 28

3 59

afterburner. However, this may be a generalizat ion because only one data point for this controlcombination was reviewed. Additionally, performance of FGR is often influenced by sitespecific parameters that may not be ge neralized to the entire subcategory.

Table 3-1 shows that MH units using an a dd-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 basi s, 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 TH C originally required. This allo ws 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 re gulations for disposal of sewage sludge, a value of 100 ppmv was
used as the emission level that a MH unit with an afterburner could achie ve. Because CO levels for FB units are below 100 ppmv, no afterbur ners were costed fo r 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 lim its, no additional costs for FF were calculated. Otherwise, a FF was costed out for the unit. Simi lar procedures were followed for PBS and ACI. The cost algorithms; inputs to the algorithms ; and testing, monitoring, recordkeeping, and3-14 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 unc ontrolled concentration to determine the total reduction the
control would achieve. The reduction from uncontr olled 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 th e more stringent contro l application and the MACT floor was calculated by s ubtracting the MACT floor con centration from the reduction achieved by the more stringent control. Reduction of PM 2.5 was calculated assuming that controls applicable for PM would also reduce PM 2.5
.
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 ch oose 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 un its; Packed Bed Scrubber, 7 units; and ACI, 51 units. The number of Multiple Hearth units requi ring 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; a nd 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 Scr ubber, 18 units; and ACI, 53 units. The per unit values were calculated by divi ding the costs and emissions re duction 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, thes e controls, with the exception of activation carbon injection for Hg control, were considered infeasible. Activ ated 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 contro l 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 after burner on all MH units for additional CO emissions reduction.

Tables 3-6 and 3-7 summarize the costs, to tal 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 memoranda3-16Table 3-2. Emissions Reductions and Costs If All Units ComplyFluidized Bed Incinerators# of unitsrequiringadditionalcontrolCost (2008$)fBaseline Emissions and Incremental Emission Reductions (tons/year)TotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)Total CapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsa51 $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 -BTF CostsandEmissionReductionsby ControlFabric 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 $205,482,746AfterburnerRetrofitb52 $31,532,870 $10,384,276 0 0 0 0 0 0 0 0 0 0 0 0.000 -Packed BedScrubber46 $48,701,933 $10,854,865 0 0 1.01 0 0 0 0 0 54.2 0 0 55.240 $196,505ActivatedCarbonInjectionc0 $0 $0 0 0 0 0 0.0074 0 0 0 0 0.000001 0.0000002 0.007 $0Multiple Hearth Incinerators# of unitsrequiringadditionalcontrolCost (2008$)fBaseline Emissions and Incremental Emission Reductions (tons/year)TotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)Total CapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsa41 $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 $15,061BTF CostsandEmissionReductionsby ControlFabric 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 $116,416AfterburnerRetrofit128 $145,514,140 $43,193,966 0 25691 0 0 0 0 0 0 0 0 0 25,691 $1,681Packed BedScrubber148 $258,596,495 $54,863,534 0 0 19.60 0 0 0 0 0 659.2 0 0 678.8 $80,820ActivatedCarbonInjection161 $6,230,844 $32,335,212 0 0 0 0 2.6235 0 0 0 0 0.000020 0.0000013 2.624 $12,324,974dAll Incinerators# of unitsrequiringadditionalcontrolCost (2008$)fBaseline Emissions and Incremental Emission Reductions (tons/year)TotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)Total CapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsa92 $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 $25,771BTF CostsandEmissionReductionsby ControlFabric 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 $124,897AfterburnerRetrofit180 $177,047,010 $53,578,242 0 25691 0 0 0 0 0 0 0 0 0 25,691 $2,086Packed BedScrubber194 $307,298,429 $65,718,399 0 0 20.61 0 0 0 0 0 713.5 0 0 734.1 $89,525ActivatedCarbonInjection161 $6,230,844 $32,335,212 0 0 0 0 2.6310 0 0 0 0 0.000021 0.0000015 2.631 $12,290,076e3-17a. 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 whereimprovement 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 atleast 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 MACTfloor 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 $6,150 per pound of Hg reduced.f. Costs were annualized using a discount rate of 7 percent.3-18Table 3-3. Emissions Reductions andCosts If Small Entities LandfillFluidized Bed Incinerators# of unitsrequiringadditionalcontrolCost (2008$)aBaseline Emissions and Incremental Emission Reductions (tons/year)bTotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)Total CapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline Emissions - -0.01032119.6 2.99 0.0531 0.0758 327.4 56.60 54.22 134.1 0.000082 0.0000068 - -MACT Floor Total Cost andEmission Reductionsc46 $69,952,757 $26,163,0500.0028218.89 1.81 0.0147 0.0612 53.05 43.47 41.23 76.8 0.000080 0.0000065 235.3 $111,194AdditionalCosts andEmissionReductions byControlFabric Filter 13 $30,642,201 $7,926,8150.005240 0 0.0271 0 0 0.00 0.00 0 0 0 0.032 $245,034,357AfterburnerRetrofitd43 $26,571,102 $8,659,394 0 0 0 0 0 0 0 0 0 0 0 0.000 -Packed BedScrubber39 $41,683,343 $9,277,850 0 0 0.80 0 0 0 0 0 40.6 0 0 41.437 $223,903ActivatedCarbonInjectione0 $0 $0 0 0 0 0 0.0060 0 0 0 0 0.000001 0.0000001 0.006 $0Multiple Hearth Incinerators# of unitsrequiringadditionalcontrolCost (2008$)aBaseline Emissions and Incremental Emission Reductions (tons/year)bTotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)Total CapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline Emissions - -2.8277929024.5122.59 6.0595 3.05367358.51101.46 666.133078.80.000020 0.0000013 - -MACT Floor Total Cost andEmission Reductionsc38 $125,327,287 $33,647,8931.545903080.1694.72 2.9497 0.3536793.81348.85 210.412221.00.000002 0.0000001 6,753.8 $4,982AdditionalCosts andEmissionReductions byControlFabric Filter 127 $440,670,924 $105,196,5291.047100 0 2.6084 0 0 469.00 284.00 0 0 0 756.655 $139,028AfterburnerRetrofit122 $137,648,283 $40,428,804 022971.280 0 0 0 0 0 0 0 0 22,971.284 $1,760Packed BedScrubber137 $237,426,572 $50,085,972 0 0 17.62 0 0 0 0 0 601.5 0 0 619.149 $80,895ActivatedCarbonInjection149 $5,744,514 $28,913,350 0 0 0 0 2.3440 0 0 0 0 0.000017 0.0000012 2.344 $12,334,707fAll Incinerators# of unitsrequiringadditionalcontrolCost (2008$)aBaseline Emissions and Incremental Emission Reductions (tons/year)bTotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)Total CapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline Emissions - -2.8381129144.0125.58 6.1126 3.12947685.91158.05 720.36 3212.9 0.000102 0.0000081 - -MACT Floor Total Cost andEmission Reductionsc84 $195,280,044 $59,810,9431.548723099.0596.53 2.9644 0.4147846.86392.32 251.64 2297.8 0.000082 0.0000067 6,989.1 $8,558AdditionalCosts andEmissionReductionsby ControlFabric Filter 140 $471,313,125 $113,123,3441.052340 0 2.6355 0 0 469.00 284.00 0 0 0 756.688 $149,498AfterburnerRetrofit165 $164,219,385 $49,088,198 022971.280 0 0 0 0 0 0 0 0 22,971.284 $2,137Packed BedScrubber176 $279,109,916 $59,363,822 0 0 18.43 0 0 0 0 0 642.2 0 0 660.586 $89,865ActivatedCarbonInjection149 $5,744,514 $28,913,350 0 0 0 0 2.3500 0 0 0 0 0.000019 0.0000013 2.350 $12,303,406g3-19a. 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; AfterburnerRetrofit, 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 meetthe MACT floors can be brokendown 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 ofcontrols where improvement was needed in order to meet the proposed limit. For any casewhere 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 beable 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 theBTF scenario, the maximum control efficiency (98%) was assumed. For the MACT floor scenario, only the percent reduction required to meet the floor limitswere 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 $6,150 per pound of Hg reduced.3-20Table 3-4. Emissions Reductions and CostsIf All Units Comply – Per Unit BasisFluidized Bed Incinerators# of unitsrequiringadditionalcontrolCost (2008$)bBaseline Emissions and Incremental Emission Reductions (tons/year)TotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)TotalCapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsa51 $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 $228,934BTF CostsandEmissionReductionsby ControlFabric Filter 14 $2,333,114 $600,151 0.0005 0 0 0.0024 0 0 0.00 0.00 0 0 0 0.003 $205,482,746AfterburnerRetrofit52 $606,401 $199,698 0 0 0 0 0 0 0 0 0 0 0 0.000 -Packed BedScrubber46 $1,058,738 $235,975 0 0 0.02 0 0 0 0 0 1.2 0 0 1.201 $196,505ActivatedCarbonInjection0 - - - - - - - - - - - - - - -Multiple Hearth Incinerators# of unitsrequiringadditionalcontrolCost (2008$)bBaseline Emissions and Incremental Emission Reductions (tons/year)TotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)TotalCapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsa41 $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 $15,061AdditionalCosts andEmissionReductionsby ControlFabric Filter 138 $3,466,478 $835,180 0.01 0 0 0.0208 0 0 4.45 2.70 0 0 0 7.2 $116,416AfterburnerRetrofit128 $1,136,829 $337,453 0 201 0 0 0 0 0 0 0 0 0 201 $1,681Packed BedScrubber148 $1,747,274 $370,700 0 0 0.13 0 0 0 0 0 4.5 0 0 4.6 $80,820ActivatedCarbonInjection161 $38,701 $200,840 0 0 0 0 0.0163 0 0 0 0 0.000000 0.0000000 0.016 $12,324,974All Incinerators# of unitsrequiringadditionalcontrolCost (2008$)bBaseline Emissions and Incremental Emission Reductions (tons/year)TotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)TotalCapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsa92 $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 $52,784AdditionalCosts andEmissionReductionsby ControlFabric 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 $220,359AfterburnerRetrofit180 $1,743,231 $537,150 0 201 0 0 0 0 0 0 0 0 0 143 $3,763Packed BedScrubber194 $2,806,011 $606,675 0 0 0.15 0 0 0 0 0 5.6 0 0 3.8 $160,330ActivatedCarbonInjection161 $38,701 $200,840 0 0 0 0 0.0163 0 0 0 0 0.000000 0.0000000 0.016 $12,290,0763-21a. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 41 units; AfterburnerRetrofit, 0 units; Packed Bed Scrubber, 7 units; and ACI, 51 units.The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 25 units;Afterburner Retrofit, 0 units; Packed Bed Scrubber, 11 units; and ACI, 2 units.The total number of SSIs requiring some sort of control to meetthe MACT floors can be brokendown as follows: Fabric Filter,66 units; Afterburner Retrofit,0 units; Packed Bed Scrubber, 18 units; and ACI, 53 units.b. Costs were annualized using a discount rate of 7 percent.3-22Table 3-5. Emissions Reductionsand Costs If Small Entities Landfill – Per Unit BasisFluidized Bed Incinerators# of unitsrequiringadditionalcontrolCost (2008$)aBaseline Emissions and Incremental Emission Reductions (tons/year)bTotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)TotalCapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsc46 $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 $111,194AdditionalCosts andEmissionReductionsby ControlFabric Filter 13 $2,357,092 $609,755 0.00040 0 0 0.0021 0 0 0.00 0.00 0 0 0 0.002 $245,034,357AfterburnerRetrofitd43 $617,933 $201,381 0 0 0 0 0 0 0 0 0 0 0 0.000 -Packed BedScrubber39 $1,068,804 $237,894 0 0 0.02 0 0 0 0 0 1.0 0 0 1.062 $223,903ActivatedCarbonInjectione0 - - - - - - - - - - - - - - -Multiple Hearth Incinerators# of unitsrequiringadditionalcontrolCost (2008$)aBaseline Emissions and Incremental Emission Reductions (tons/year)bTotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)TotalCapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsc38 $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 $4,982AdditionalCosts andEmissionReductionsby ControlFabric Filter 127 $3,469,850 $828,319 0.00824 0 0 0.0205 0 0 3.69 2.24 0 0 0 5.958 $139,028AfterburnerRetrofit122 $1,128,265 $331,384 0188.28920 0 0 0 0 0 0 0 0 188.289 $1,760Packed BedScrubber137 $1,733,041 $365,591 0 0 0.13 0 0 0 0 0 4.4 0 0 4.519 $80,895ActivatedCarbonInjection149 $38,554 $194,049 0 0 0 0 0.0157 0 0 0 0 0.000000 0.0000000 0.016 $12,334,707All Incinerators# of unitsrequiringadditionalcontrolCost (2008$)aBaseline Emissions and Incremental Emission Reductions (tons/year)bTotalEmissionReductions(tons/yr)IncrementalCosteffectiveness($/ton)TotalCapitalInvestment($)TotalAnnualizedCost ($/yr)CdCOHClPbHgNOxPM FiltPM 2.5SO2D/F TotalD/F TEQBaseline 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 andEmission Reductionsc84 $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 $7,953AdditionalCosts andEmissionReductionsby ControlFabric 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 $241,271AfterburnerRetrofit165 $1,746,197 $532,765 0188.28920 0 0 0 0 0 0 0 0 188.289 $2,830Packed BedScrubber176 $2,801,844 $603,485 0 0 0.15 0 0 0 0 0 5.4 0 0 5.582 $108,116ActivatedCarbonInjection149 $38,554 $194,049 0 0 0 0 0.0157 0 0 0 0 0.000000 0.0000000 0.016 $12,334,7073-23a. Costs were annualized using a discount rate of 7 percent.b. Emissions from landfilling activities are not included in this table.c. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 33 units; AfterburnerRetrofit, 0 units; Packed Bed Scrubber, 5 units; and ACI, 46 units.The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 24 units;Afterburner Retrofit, 0 units; Packed Bed Scrubber, 10 units; and ACI, 2 units.The total number of SSIs requiring some sort of control to meetthe MACT floors can be brokendown as follows: Fabric Filter,57 units; Afterburner Retrofit,0 units; Packed Bed Scrubber, 15 units; and ACI, 48 units.d. Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls.For other pollutants, reductions resulted from the installation ofcontrols where improvement was needed in order to meet the proposed limit. For any casewhere the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would beable to at least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit.e. Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for theBTF scenario, the maximum control efficiency (98%) was assumed. For the MACT floor scenario, only the percent reduction required to meet the floor limitswere incorporated as the control efficiencies.3-24Table 3-6. Emissions Reductionsand Costs If All Units ComplyaOptionCost (2008$)Baseline Emissions and IncrementalEmission Reductions (tons/year)TotalEmissionReductions of129 PollutantsTotal CapitalInvestment($million)TotalAnnualized Cost($million/yr)CdCOHClPbHgNOxPM FiltPM2.5SO2D/F TotalD/F TEQBaseline Emissions— — 2.84 29,100 126 6.11 3.13 7,700 1,160 720 3,210 0.000102 0.0000081 —Costs andEmissionReductionsOption 1 (MACT Floor)$220 $73 1.41 0 93.0 2.63 0.09 4.31 319 206 2,190 0.000079 0.0000065 2,819Option 2 (MACT Floor+ Activated carboninjection for MH units)$225 $105 1.41 0 93.0 2.63 2.71 4.31 319 206 2,190 0.000098 0.0000078 2,821Option 3 (Option 2 +Afterburners for MHUnits)$370 $148 1.41 25,700 93.0 2.63 2.71 4.31 319 206 2,190 0.000098 0.0000078 28,500Table 3-7. Emissions Reductions and Costs If Large Entities Comply and Small Entities LandfillaOptionCost (2008$)Baseline Emissions and IncrementalEmission Reductions (tons/year)TotalEmissionReductions of129 PollutantsTotal CapitalInvestment($million)TotalAnnualized Cost($million/yr)CdCOHClPbHgNOxPM FiltPM2.5SO2D/F TotalD/F TEQBaseline Emissions— — 2.84 29,100 126 6.11 3.13 7,700 1,160 720 3,210 0.000102 0.0000081 —Costs andEmissionReductionsOption 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 6,714Option 2 (MACT Floor+ Activated carboninjection for MH units)$201 $89 1.55 2,850 96.2 2.96 2.76 823 390 251 2,300 0.000099 0.0000078 6,717Option 3 (Option 2 +Afterburner for MHUnits)$338 $129 1.55 25,800 96.2 2.96 2.76 823 390 251 2,300 0.000099 0.0000078 29,690aAnnualized 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 th e past 20 years. EPA’s Office of Water (OW) set emission and discharge st andards 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 c hose 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 municipalitie s 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 incinera ting 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, Mull en, 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 mi ght 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 de termine if there was a trend. Under EPA’s New Source Review (NSR) program, if a company is pl anning 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 perm it 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 de fines control levels based on the type of program the source is subject to : reasonably available control t echnology (RACT), best available control technology (BACT), or lowest achievable emissions reduction (LAER). Information
from the EPA’s RACT/BACT/LAER database c ontains case-specific information on the “best available” air pollution technologi es that have been required to reduce the emission of air pollutants from stationary sources. This inform ation 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 in3-26 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 constructi on and be in operation in 2010 (O ommen and Allen, 2010a). All of these new FB units were replacements for MH units.

Based on the data collected and assuming th e trend in construction continues, five additional FB units will be permitted to be c onstructed in 5 years after the NSPS is proposed. However, given the time necessary to review an d assess the requirements of the NSPS and plan, permit, and construct incineration units, it is unlik ely that all five would be in operation in the 5 years. For this analysis, it was assumed at leas t 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 co mplying with the NSPS were calculated by (1) determining the controls that these units w ould 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) es timating the emissions reducti on 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 (I nventory Database, 2010). Table 3-8 shows the
distribution of controls. Based on this informa tion, the baseline contro ls assumed for the new
units are a combination of venturi scrubbers a nd 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 (E stimation 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 c oncentration of the pollutant in the emission stream, flow rate of the emission stream, and th e hours of operation of the SSI unit. For units3-27 Table 3-8. Control Device Distributi on for Fluidized Bed Incinerators a

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 in ject 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, base line emissions were estimated using an average uncontrolled concentration and applying reduction efficiencies associated with the control
devices located at each SS I 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 capac ities were multiplied by a capacity utilization3-28 factor of 75%, which was the median of the capac ity utilizations reported in the ICR survey responses. More information about how unit capaci ty 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 permit s, 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 tota l number of units that will be in operation at a facilit y. For this analysis, the simplest and most conservative assumption was used—that only one FB unit would be construc ted 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, aver age dry sludge capacity, and operating hours, as well as other default paramete rs necessary for the costs. These factors were applied to each new unit estimated to be construc ted 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 w ith the MACT Floor for Existing SSI Units” ( Oommen and Allen , 2010b). Control devices costed out were t hose that would be necessary to
meet the MACT floor level of control for new s ources. It is possible for some units with wet scrubbers to comply with the NSPS limits for SO 2 by adding caustic. However, it is uncertain if all units could do this. Therefore, this analys is assumed a PBS would be used, which would provide a more conservative estimate of costs. Si milarly, wet electrostatic precipitators can be used for PM control; a FF was costed in this analys is to provide a conservativ e estimate of costs.

Table 3-11 shows the comparison of baseline em issions 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 Ta ble 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 PM 2.5
.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) a 8,400 Stack gas flow rate (dscfm) 9,239.97 Stack gas temperature (°F) b 1,050 ACI adjustment factor c 1.03 Sludge heating value (BTU/lb) d 7740

NO X , 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 effici ency and 98% CDD/CDF redu ction 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.in fo/Abstract/41015799.pdf).3-30Table 3-10. Summary of EmissionReductions for New SSI UnitsPollutantConcentrationUnitsAdditionalControlNeeded forMACTBaselineConcentrationNSPS LimitMACTEmissionConcentrationEmissionReduction(concentration)EmissionReduction(tpy)Annual EmissionReductions:Year 5(Assuming 2 newunits come onlinein 5 years)Cadmium (Cd) mg/dscm Add FF 0.002 0.00051 0.00051 0.002 2.36E-04 4.73E-04Carbon monoxide (CO) ppmvd Add ABD 16.331 7.4 7.4 8.931 1.51E+00 3.02E+00Hydrogen chloride(HCl)ppmvd nonea0.124 0.13 0.050 0.074 1.64E-02 3.27E-02Lead (Pb) mg/dscm Add FF 0.011 0.00053 0.00053 0.011 1.53E-03 3.06E-03Mercury (Hg) mg/dscm Add ACI 0.014 0.001 0.001 0.013 1.82E-03 3.64E-03Nitrogen oxides (NOx) ppmvd noneb27.926 26 26 1.926 5.35E-01 1.07E+00Particulate matter(filterable)mg/dscm Add FF 12.443 4.1 4.1 8.343 1.21E+00 2.43E+00Particulate matter(PM2.5)mg/dscm Add FF 11.801 2.3 2.3 9.501 1.38E+00 2.76E+00Sulfur dioxide (SO2) ppmvd Add PBS 3.303 2.0 2.0 1.303 5.04E-01 1.01E+00Total dioxin/furans ng/dscm Add ACI 15.962 0.94 0.94 15.022 2.18E-06 4.37E-06Total dioxin/furans(TEQ)ng/dscm Add ACI 1.312 0.023 0.023 1.289 1.87E-07 3.75E-07aAssumed scrubber (installed for SO2control) has 98% efficiency for HCl control.bAssumed 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 previ ously discussed. The table also shows the monitoring, testing, re porting, 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

Controls 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 Subtotal: $3,659,952 $1,211,429

Monitoring, Testing, Reporting and Recordkeeping 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 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 le vels 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 SO 2 . In addition, incremental additions of activated carbon have not been proven to achieve further reductions above th e projected flue gas concentration estimated to achieve the limits for new sources. Data gather ed do not indicate that any FB units operate NO x controls, such as SNCR, SCR, or fl ue gas recirculation because the NO x 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 limit s 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 ga s (GHG) reporting requirements. As noted in EPA’s (2000) Guidelines for Preparing Economic Analyses , several tools are
available to estimate social cost s 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 g overnmental entities (96 of the 97 owners are governmental entities) providing services not pr ovided in a market, the Office of Air Quality Planning and Standards (OAQPS) has used the di rect compliance cost method as a measure of social costs. Since no market impacts are an ticipated, 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 contro l for the two subcategories developed for existing SSI units, MH units and FB units.

 Option 2 is the same as Option 1, with th e 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 after burner 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 inci nerate while small government entities choose to landfill. EPA an ticipates 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) co mply 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 ex isting units to add the necessary controls, monitoring equipment, inspections, and recordk eeping and reporting requirements to comply with the proposed SSI standards. Based on the engineering cost analysis, we anticipate the4-2 overall total annual social cost to be approxi mately $92 million. The lowest cost option is the MACT floor where large entities comply and sm all 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 opti ons 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) gene rally 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 determinati on, 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 sm all business as defined by the Small Business Administration’s regulations at 13 CFR 121.20; (2) a small governmental jurisdicti on that is a government of a city, county, town, school district , or special district with a po pulation of less than 50,000; and (3) a small organization that is any not-for-profit enterprise th at is independently owned and operated and is not dominant in its field. As re ported in Section 2, EP A has identified 18 small entities that have a population of fewer th an 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 comp ared each regulatory opti on’s control costs to total government revenues (i.e., a “revenue” te st). To estimate government revenues, we
collected U.S. Census financial information for municipal governme nts 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 en tities (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$) 34,944,6 47 32,010,988 31,630,676 238,846,095 Per capita (2002$) $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 dis posal assumptions are presented in Tables 4-3 through Table 4-8. As noted above, EPA an ticipates small government entities will most likely switch from incineration to landfilling (Table s 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 an ticipates 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 sma ll entities landfill); under this option and set of disposal choices, all small entit ies 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 fl oor with activated carbon injection and
afterburners for MH units and small entities landfill (total an nualized 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 Gov ernment 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 Govern ment 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 04-5 Table 4-5. Option 2 Revenue Tests for Gov ernment 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 Govern ment 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 Gov ernment 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 04-6 Table 4-8. Option 3 Revenue Tests for Govern ment 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 05-1 SECTION 5
HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS

5.1 Synopsis
In this section, we provide an estimate of th e monetized benefits as sociated with reducing particulate matter (PM) for the proposed Sewage Sludge Incinerator (SSI) New Source Performance Standard (NSPS) and Emissions Guidel ines (EG). For this rule, the PM reductions are the result of emission limits on PM, emission limits on PM 2.5 precursors such as NO x and
SO 2 , as well as emission limits on other pollutants. The total PM 2.5 reductions are the consequence of the technologies installed or wa ste diversion to meet these multiple limits. These
estimates reflect the monetized human health benefits of reducing cas es of morbidity and premature mortality among populations exposed to the PM 2.5 precursors reduced by this rulemaking. Using a 3% discount ra te, we estimate the total moneti zed 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 ra te, 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 in terpretation of the scientific literature. Higher or lower estimates of benefits are possibl e 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, in cluding benefits from reducing hazardous air pollutants, ecosystem effect s, 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 PM 2.5 Human Health Benefits
This rulemaking would reduce emissions of PM 2.5 , SO 2, and NO 2 . Because SO x and NO 2 are also precursors to PM 2.5, reducing these emissions would also reduce PM 2.5 formation, human exposure, and the incidence of PM 2.5 -related health effects. For th is rule, the PM reductions are the result of emission limits on PM, emission limits on PM 2.5 precursors such as NO x and SO 2 , as well as emission limits on other pollutants. The total PM 2.5 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 pr ovide a comprehensive estimate of PM 2.5 -related benefits. Instead, we used the “benefit- per-ton” approach to estimate these benefits based on the5-2 methodology described in Fann, Fulcher, a nd Hubbell (2009). The key assumptions are
described in detail below. These PM 2.5 benefit-per-ton estimates pr ovide the total monetized human health benefits (the sum of premature mo rtality and premature mo rbidity) of reducing one
ton of PM 2.5 from a specified source. EPA has used the benefit per-ton technique in several previous RIAs, including the recent NO 2 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 PM 2.5

Pollutant /

Effect Quantified and Monetized in Primary Estimates Unquantified Effects Changes in:

PM 2.5 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 (a sthmatic 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 PM 2.5 benefits
estimates utilize the concentratio n-response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expe rt elicitation study as a sensitivity analysis.

 One estimate is based on the concentrati on-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/m 3 as 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 report ed by Laden et al. (2006). This study, published after the completion of the Staff Paper for the 2006 PM 2.5 NAAQS, has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 benefits estimates in RIAs completed since the PM 2.5 NAAQS. When calculating the estimate, EPA applied the effect coefficient as repor ted in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 as was done in recent (2006-2009) RIAs.5-3  Twelve estimates are based on the C-R func tions from EPA’s e xpert elicitation study (IEc, 2006; Roman et al., 2008) on the PM 2.5 -mortality relationship and interpreted for benefits analysis in EPA’s final RIA for the PM 2.5 NAAQS. For that study, twelve experts (labeled A through L) provi ded independent estimates of the PM 2.5 -mortality concentration-response function. EPA pract ice has been to develop independent estimates of PM 2.5 -mortality estimates corresponding to the concentration-response function provided by each of the twelve expe rts, 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 an chor points in our presentation because, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, whic h we believe argues for using both studies to generate benefits estimates. Previously, EPA had calculated benefi ts based on these two empirical studies, but derived the range of benefits, including the mi nimum and maximum results, from an expert
elicitation of the relationsh ip between exposure to PM 2.5 and premature mortality (Roman et al., 2008). 2 Within this assessment, we include the bene fits estimates derived from the concentrationresponse function provided by each of the twelve e xperts to better character ize 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 us ing these functions generally fall between results using these epidemiology studies (see Figure 5-2). In genera l, the expert elicitation results support the
conclusion that the benefits of PM 2.5 control are very likely to be substantial.

Readers interested in reviewing the genera l 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-t on values calculated therefore apply only to tons reduced from those specific po llutant/source combinations (e.g., SO 2 emitted from electric generating units; NO 2 emitted from mobile sources). In this analysis, we apply the national average benefit-per-ton estimate for a 2015 analys is year and multiply it by the corresponding emission reductions of directly emitted PM 2.5, SO 2, and NOx to quantify the benefits of this rule. The benefit-per-ton estimates found in Fann, Fulche r, and Hubbell (2009) refl ect a specifc set of key assumptions and input data. As we update these underlying assumptions to reflect the

1 These two studies specify multi-pollutant models that control for SO 2 , 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 bene fit-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 premat ure mortality because there is no clear scientific evidence that would support the development of differential eff ects estimates by particle type. Directly emitted PM, SO 2 , and NOx are the primary PM 2.5 precursors affected by th is 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

PM 2.5 . For example, NO X has a lower benefit-per-ton estimate than direct PM 2.5 because it does
not form as much PM 2.5 , thus the exposure would be lower, and the monetized h ealth co-benefits
would be lower.

The benefit-per-ton coefficients in this analys is 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 up dates: a new population dataset, an expanded geographic scope of the benef it-per-ton calculation, and the f unctions directly from the epidemiology studies without an adjustment for an assumed threshold. 3 Removing the threshold assumption is a key difference between the method us ed 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 PM 2.5 air quality levels.

EPA strives to use the best available science to support our benefits analyses, and we recognize that interpreta tion of the science rega rding air pollution and h ealth is dynamic and evolving. Based on our review of the current body of scientific literatu re, EPA now estimates PM-related mortality without ap plying 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 Comm ittee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientif ic literature consistently finds that a no-thr eshold log-linear model most adequately portrays the PM-morta lity concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function. Since then, the Health Effects Subcommitt ee (U.S. EPA-SAB, 2010) of EPA’s Council

3 The 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 deci sion to use a no-threshold model to estimate mortality reductions. This deci sion is supported by the data, wh ich are quite consistent in showing effects down to the lowest measured leve ls. Analyses of cohorts using data from more recent years, during which time PM concentratio ns have fallen, continue to report strong associations with mortality. Ther efore, 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 applica tion of thresholds in PM 2.5 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 co nsult 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 advi ce, we are replacing the previous threshold sensitivity analysis with a new “Lowest Measur ed Level” (LML) assessment. This information allows readers to determine the portion of population exposed to annual mean PM 2.5 levels at or above the LML of each study; in general, ou r confidence in the estimated PM mortality decreases as we consider air quality levels fu rther below the LML in major cohort studies that estimate PM-related mortality. While an LML assessm ent provides some insight into the level of uncertainty in the estimated PM mortality bene fits, EPA does not view the LML as a threshold and continues to quantify PM-related mortality imp acts using a full range of modeled air quality concentrations. It is important to emphasi ze that we have high confidence in PM 2.5 -related effects
down to the lowest LML of the ma jor 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-specifi c air quality data is not avai lable due to time or resource limitations. For these rules, we are unable to estimate the percentage of premature mortality associated with this specific ru le’s emission reductions at each PM 2.5 level. However, we believe that it is still important to characterize the dist ribution of exposure to ba seline air quality levels. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM 2.5 level using the most recent modeling av ailable 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 inciden ce rates population, and change in air quality. In other words, the per centage of the population exposed to air pollution below the LML is not the same as the percenta ge of the population experiencing health impacts as a result of a specific emission reduction polic y. The most important aspect, which we are5-6 unable to quantify for rules without air quality modeling, is the shif t 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 anal ysis are provided in Section 5.4.

As is the nature of Regulatory Impact An alyses (RIAs), the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency’s most current interpretation of the scientif ic 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 availabl e 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-a nalysis 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 pe er-review group.

During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a repo rt 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 Ag ency consulted several times w ith the Science Advisory Board Environmental Economics Advisory Committee (SAB-EEAC) on the i ssue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic tech niques to combine estimates from unique data sources and different studies, including those using differe nt methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).

Until updated guidance is availabl e, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has re ceived. 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 curre ncy 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 st udies 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 progr ess in responding to the SAB-EEA C’s specific recommendations.

Figure 5-1 illustrates the relativ e breakdown of the monetized PM 2.5 health benefits.

Adult Mortality ‐ Pope et al. 93% Chronic Bronchitis 4%

AMI 2% Acute Respiratory Symptoms 0.5%

Infant Mortality 0.4%

Work Loss Days 0.2%

Hospital Admissions, Cardio 0.2% Hospital Admissions, Resp 0.04% Asthma Exacerbation 0.01%
Acute Bronchitis 0.01% Upper Resp Symp 0.00% Lower Resp Symp 0.00%
ER Visits, Resp 0.00% Other 1%

Figure 5-1. Breakdown of Monetized PM 2.5 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 premat ure 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 $9.1 million.5-8 emission reductions and monetized benefits-p er-ton at discount rates of 3% and 7%. 7 Table 5-4 provides a summary of the reductions in health in cidences as a result of the pollution reductions for the large entities comply and small entities landfill results. In Table 5-5, we provide the benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the
expert
Table 5-2. Summary of Monetized Benefits Es timates for Proposed SSI NSPS and EG in 2015 (2008$) (large entities comply a nd 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%)Option 1 Direct PM 2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

PM 2.5 Precursors

SO 2 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

NO 2 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8
Total $130 to $320 $120 to $290Proposed: Option 2 Direct PM 2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

PM 2.5 Precursors

SO 2 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

NO 2 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8
Total $130 to $320 $120 to $290Option 3 Direct PM 2.5 254 $230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130

PM 2.5 Precursors

SO 2 2,298 $29,000 $72,000 $27,000 $65,000 $68 to $170 $61 to $150

NO 2 824 $4,900 $12,000 $4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8
Total $130 to $320 $120 to $290

7 To 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 lo st 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 PM 2.5 (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 aff ects the AMI costs after the analysis year and the valuation of premature mort alities 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 implementati on 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 re duced has a different propensity to form PM 2.5 . 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 CO 2 emissions.

elicitation on PM mortality. Figur es 5-2 through 5-4 provide a visu al representation of the range of benefits estimates and the pollutant breakdown of the monetized benefits.5-10 Table 5-3. Summary of Monetized Benefits Es timates 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%)Option 1 Direct PM 2.5 209 $230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0

PM 2.5 Precursors

SO 2 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140

NO 2 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05
Total $110 to $270 $100 to $250Proposed: Option 2 Direct PM 2.5 209 $230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0

PM 2.5 Precursors

SO 2 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140

NO 2 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05
Total $110 to $270 $100 to $250Option 3 Direct PM 2.5 209 $230,000 $560,000 $210,000 $500,000 $48 to $120 $43 to $110

PM 2.5 Precursors

SO 2 2,193 $29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140

NO 2 5 $4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05
Total $110 to $270 $100 to $250

a All estimates are for the implementati on 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 re duced has a different propensity to form PM 2.5 . 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 CO 2 emissions.5-11 Table 5-4. Summary of Reductions in Health Incidences from PM 2.5 Benefits for the Proposed SSI NSPS and EG in 2015 a

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 Exacerbatio n 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 eq uivalent health effects, but each PM 2.5 precursor pollutant has a different propensity to form PM 2.5 . Confidence intervals are unavailable for th is analysis because of the benefit-per-ton methodology. These results include 2 new FB incinerato rs anticipated to come online by 2015 and the large entities comply and small entities landfill assumption.5-12 Table 5-5. All PM 2.5 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 deri ved 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 D erived 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. Es timates 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 mort ality 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 CO 2 emissions.5-13 $0 $50 $100 $150 $200 $250 $300 $350 $400

Pope et al. Laden et al.Millions(2008$)

Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR
7% DR

Figure 5-2. Total Monetized PM 2.5 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 CO 2 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 PM 2.5 Precursor Pollutant and Source5-15 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 fr om utility boilers that supply electricity to the sewage sludge incinerators. For example, increased scrubber pu mp horsepower and sorben t injection controls may cause slight increases in el ectricity consumption. We estimate that the increased electricity consumption associated with the proposed op tion 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 CO 2 for the proposed option assumi ng that small entities landfill. 8 Since NOx and SO 2 are covered by capped emissions trading progr ams, we are only estimating the CO 2 emission increases from the increased electricity dema nd. 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 CO 2 , CO, and NOx. The CO 2 energy disbenefits for Option 3 are shown in Tables 5-7 and 5-8. The additio nal NOx disbenefits (as a precursor to PM 2.5 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 Imp acts of Control Options for the Sewage Sludge Incineration Source Category”, which is available in the docket.

5.3.1 Social Cost of Carbon an d Greenhouse Gas Disbenefits
EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 ) emissions using recent estimates of the “social cost of carbon” (S CC). The SCC is an estimate of the monetized damages associated with an in cremental increase in carbon emi ssions in a given year. It is intended to include (but is not limited to) change s in net agricultural pr oductivity, human health, property damages from increased flood risk, and the valu e 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 en tities, 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 Vehi cle Greenhouse Gas Emission Sta ndards and Corporate Average Fuel Economy Standards; see the rule’s preamb le 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 CO 2 emissions 10 in 2010, in
2007 dollars. The first three valu es are based on the average SCC from three integrated assessment models, at discount rates of 2.5, 3, a nd 5 percent, respectivel y. SCCs at several
discount rates are included because the literatu re 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 percen tile of the SCC from all three models at a 3 percent disc ount rate. It is included to repr esent higher-than-expected impacts from temperature change further out in the ta ils of the SCC distribution. Low probability, high impact events are incorporated in to 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 Advi sers, 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 CO 2 emissions. Given that warming profiles and

impacts other than temperature cha nge (e.g. ocean acidifica tion) vary across GHGs, the group concluded “transforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, would not result in accurate estimate s of the social costs of non-CO 2 gases” (SCC TSD, pg 13).5-17 equilibrium climate sensitivity. Treating climate sens itivity 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 sy stems become more stressed in response to greater climatic change. Note th at the interagency group estimated the growth rate of the SCC directly using the three integr ated 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 incremen tal 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 climat e 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 limita tions to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion 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 th ese gaps and that the SCC estimates used for regulatory analysis by the Federal government w ill continue to evolve with improvements in modeling. Additional details on these limita tions are discussed in the SCC TSD.

In light of these limitations , the interagency group has co mmitted to updating the current
estimates as the science and economic understa nding of climate change and its impacts on society improves over time. Specifically, the inte ragency 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 CO 2 emissions for the range of policy scenarios, we estimate the dollar va lue of the climate-relate d disbenefits captured by the models for each analysis year. For inte rnal consistency, the annual disbenefits are discounted back to NPV terms using the same di scount 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 CO 2 ) 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 specifi c. SCC values represent only a partial accounting of climate impacts.

Table 5-7. Monetized SCC-d erived Disbenefits of CO 2 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 CO 2 24,900 tpy CO 2 126,000 tpy CO 2 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 specifi c. 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-d erived Disbenefits of CO 2 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 CO 2 21,782 tpy CO 2 114,784tpy CO 2 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 CO 2 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 specifi c. 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 fro m quantifying or monetizing the benefits from several important benefit categories, includ ing benefits from reducing toxic emissions, ecosystem effects, and visibility impairment. Th e health benefits from reducing hazardous air pollutants (HAPs) and carbon monoxide have not been monetized in this an alysis. In addition to being a PM 2.5 precursor, SO 2 emissions also contribute to adve rse effects from acidic deposition in aquatic and terrestrial ecosystems, increa sed mercury methylation, as well as visibility impairment. The benefits from reducing other air po llutants that have not b een 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 gram s 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 a nd the resulting ambient concentrations using fine-scale air quality modeling, we were unabl e to estimate the exposure to CO for nearby populations. Due to data, resour ce, 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 photoc hemical reactions in the atmosphere. The amount of CO emitted from these reactions, re lative to carbon dioxide (CO 2 ), 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 dur ation of exposure.

The Integrated Science Assessment for Ca rbon 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 co ronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency de partment visits and5-20 hospital admissions. Coronary hear t disease includes those who ha ve angina pectoris (cardiac chest pain), as well as those who have e xperienced a heart att ack. Other subpopulations potentially at risk include i ndividuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabet es, and individuals in very early or late life stages, such as older adults or the developing young. The evid ence is suggestive of a causal relationship between short-term exposure to CO and respirator y morbidity and mortality. The evidence is also suggestive of a causal relationship for birth ou tcomes and developmental effects following longterm exposure to CO, and for central nervous sy stem effects linked to short- and long-term exposure to CO.

5.4.2 Other SO 2 Benefits
In addition to being a precursor to PM 2.5 , SO 2 emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits
associated with reduced SO 2 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 concentra tions, we were unable to es timate the exposure to SO 2 for nearby populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 benefits associated
with the reductions in SO 2 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 betw een respiratory health effects and short-term exposure to SO 2 (U.S. EPA, 2008). According to summary of the ISA in EPA’s risk and exposure assessment (REA) for the SO 2 NAAQS, “the immediate effect of SO 2 on the respiratory system in humans is bronchocon striction” (U.S. EPA, 2009c). In addition, the
REA summarized from the ISA that “asthmatic s are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associat ed with this disease.” A clear concentrationresponse relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in te rms 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 SO 2 ISA identified as a “causal relationship”: asthma exacer bation, respiratory-related emer gency department visits, and respiratory-related hospitalizations. The differi ng evidence and associated strength of the
evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also
concluded that the relations hip between short-term SO 2 exposure and premature mortality was “suggestive of a causal relationship” because it is difficult to attribut e the mortality risk effects to
SO 2 alone. Although the SO 2 ISA stated that studies are gene rally consistent in reporting a5-21 relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants.

SO 2 emissions also contribute to adverse welf are effects from acidi c deposition, visibility impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, z ooplankton, 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. Bi ological effects of acidification in terrestrial ecosystems are generally linked to al uminum toxicity, which can cause reduced root growth, which restricts the ability of the plant to take up water a nd nutrients. These direct effects can, in turn, increase the sensit ivity of these plants to stre sses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial
acidification affects several im portant ecological services, incl uding declines in habitat for threatened and endangered species (c ultural), declines in forest aesthetics (cultural), declines in forest productivity (provi sioning), and increases in forest so il erosion and reductions in water retention (cultural and regul ating) (U.S. EPA, 2008d).

Reducing SO 2 emissions and the secondary formation of PM 2.5 would improve the level of visibility throughout the Un ited States. Fine particles wi th significant li ght-extinction efficiencies include sulfates, nitrates, organic car bon, elemental carbon, and soil (Sisler, 1996). These suspended particles and gases degrade visi bility by scattering and absorbing light. Higher visibility impairment levels in the East are due to generally higher c oncentrations of fine particles, particularly sulfates , and higher average relative humid ity levels. In fact, particulate sulfate is the largest contributor to regional haze in th e eastern U.S. (i.e., 40% or more annually and 75% during summer). In the western U.S., particulate sulfate cont ributes 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 wellbei ng. Good visibility increa ses 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 limita tions, we were unable to estimate the benefits associated with the hazardous air polluta nts that would be reduced as a result of this
rule. Available emissions data s how that several different HAPs ar e emitted from SSI. This rule5-22 is anticipated to reduce 96 tons of HCl, 3.0 t ons of lead, 1.6 tons of cadmium, 5,500 pounds of mercury, and 90 grams of total dioxins/furans eac h year. In the absence of air quality modeling and/or concentration-response functions, we are unable to qu antify the magnitude of the reduction in human exposure to th ese pollutants associated with the emission reductions from
this rule.

5.4.3.1 Mercury
Mercury is a highly neurotoxic contaminant th at enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008d). The contaminant is concentrated in higher trophic levels, including fish eat en by humans. Experimental ev idence 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 watershe ds where mercury is present, increased sulfate deposition very likely results in methylmerc ury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007). The SO 2 ISA concluded that evidence is sufficient to infer a casual relationship between sulfur de position and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008d).

In addition to the role of su lfate deposition on methylation, th is 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 a tmosphere and eventually deposit to land or water bodies. This deposition can occur locally, re gionally, or globally, de pending on the form of mercury emitted and other factors such as the weather. The form of mercury emitted varies depending on the source type a nd 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 el emental 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. Inorgani c ionic and particulat e 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, a bout 103 tons of mercury were emitted from all anthropogenic sources in th e U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were emitted wo rldwide by anthropogenic sources in 2005. We believe that total mercury emissions in the U.S. and globally in 2008 were about the same5-23 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 emi ssions 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 unabl e to model mercury dispersion, deposition, methylation, bioaccumulation in fish tissue, a nd human consumption of mercury-contaminated
fish that would be needed in order to estimat e the human health benef its from reducing mercury
emissions.

Potential exposure routes to mercury emi ssions include both direct inhalation and consumption of fish containing methylmercur y. 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 se ttles 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 so me 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 fis h, including ocean specie s and local freshwater fish, contain some methylmercury. For exampl e, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, ev ery 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 pr imarily 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 subs tantial 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 consum ption. Some of these studies al so suggest that methylmercury may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of the literature concerning methylme rcury health effects took note of two epidemiological studies
that found an association between dietary exposure to methylmerc ury and adverse cardiovascular
effects. 12 Moreover, in a study of 1,833 males in Finl and aged 42 to 60 years, Solonen et al. (1995) observed a relationship between methylmerc ury exposure via fish consumption and acute myocardial infarction (AMI or he art attacks), coronary heart disease, cardiovascular disease, and all-cause mortality. 13 The NRC also noted a study of 917 seve n 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 da ta base is not as extensive fo r cardiovascular effects as it is for other end points (i.e. neurologic effects) the cardiovascular syst em 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 possi ble cardiovascular effects of methylmercury exposure. These studies include ep idemiological, toxicologi cal, and toxicokinetic investigations. Over a dozen review papers ha ve also been published. If there is a causal relationship between methylmercury exposure and a dverse 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 methodol ogies 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.
13 Salonen, 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.
14 Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. We ihe, and Grandjean, P., 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375.
15 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. p. 229.5-25 level cardiovascular health imp acts of reduced methylmercury exposure. The report from that workshop is in preparation.

5.4.3.2 Cadmium
Breathing air with very high le vels of cadmium can severely damage the lungs and may cause death. In the United States, where proper industrial hygiene is gene rally 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 sufficien tly high, may result in kidney di sease. 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 cad mium is a probable human carcinogen. 16

5.4.3.3 Lead
The main target for lead toxi city is the nervous system, bot h in adults and children. Longterm exposure of adults to lead at work has resu lted 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 sma ll 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 an d kidneys in adults or children and ultimately cause death. In pregnant wome n, high levels of exposure to lead may cause miscarriage. Highlevel exposure in men can damage the or gans responsible for sperm production.

We have no conclusive proof that lead cause s cancer (is carcinogeni c) in humans. Kidney tumors have developed in rats and mice that ha d 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 reason ably anticipated to be hum an 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 carci nogen. The International Agency for Research on Cancer (IARC) has determined that inorgani c 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 fr om studies in humans and in animals.

16 Agency for Toxic Substances and Disease Registry (A TSDR). 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.a tsdr.cdc.gov/PHS/PHS.asp?id=46&tid=15>.5-26 Children are more sensitive to the health eff ects of lead than adults. No safe blood lead
level in children has been determined. Lead a ffects children in differe nt 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 certai n drugs that help eliminate lead from the body. If a child swallows smaller amounts of lead, such as dust c ontaining lead from paint, much less severe but still important effects on blood, de velopment, and behavior may occu r. 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 conse quences of lead exposure. At still lower levels of exposure, lead can affect a ch ild’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 persis t beyond childhood. 17

5.4.3.4 Hydrogen Chloride (HCl)
Hydrogen chloride gas is intensely irritati ng to the mucous membranes of the nose, throat, and respiratory tract. Brie f exposure to 35 ppm causes throat irritation, and levels of 50 to 100 ppm are barely tolerable for 1 hour. The grea test impact is on the upper respir atory tract; exposure to high concentrations can rapidly lead to swelling and spasm of th e throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronc hioles. Patients who have massive exposures may develop an accumulation of fluid in the lung s. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irri tant-induced type of asthma. Children may be more vulnerable to co rrosive agents than a dults because of the relatively smaller diameter of their airways. Children may al so be more vulnerable to gas exposure because of increased minute ventilation pe r 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 (A TSDR). 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 (A TSDR). 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 leve ls. 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 th at 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 th at 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 re ported in people exposed to rela tively high concentrations of 2,3,7,8-TCDD. Most of the effects ar e considered mild and were reversible. However, in some people these effects may last for many years. Sl ight increases in the risk of diabetes and abnormal glucose tolerance have been observe d 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 peopl e, but animal studies suggest that this is a potential health concern.

In certain animal species, 2,3,7,8-TCDD is especi ally 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 rela tively 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 anim al species exposed to CDDs during pregnancy had miscarriages and the offspring of animal s exposed to 2,3,7,8-TCDD during pregnancy often
had severe birth defects includi ng skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were obser ved 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, es pecially 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. Availabl e 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 fre quent lung infections, numbness, effects on the nervous system, and mild changes in the live r. Children born to exposed mothers had skin irritation and more difficulty lear ning, but it is unknown if this effect was permanent or caused by CDFs alone or CDFs and polychlor inated 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 serious ly injured. Some animals had birth defects and testicular damage, and in severe cases, some an imals died. These effects in animals were seen when they were fed large amounts of CDFs ove r a short time, or small amounts over several weeks or months. Nothing is known about the pos sible health effects in animals from eating
CDFs over a lifetime. 20

5.5 Characterization of Uncertainty in the Monetized PM 2.5 Benefits
In any complex analysis, there are likely to be many sources of uncertainty. Many inputs
are used to derive the final estimate of econo mic benefits, including emission inventories, air quality models (with their associated paramete rs and inputs), epidemio logical estimates of concentration-response (C-R) f unctions, estimates of values , population estimates, income estimates, and estimates of the future state of the world (i.e., regula tions, technology, and human behavior). For some parameters or inputs it may be possible to provide a st atistical representation of the underlying uncertainty distribution. For other parameters or inputs, the necessary
information is not available.

The annual benefit estimates presen ted in this analysis are also inherently variable due to the processes that govern pollutant emissions a nd ambient air quality in a given year. Factors such as hours of equipment use a nd weather are constantly variable , regardless of our ability to measure them accurately. As discussed in the PM 2.5 NAAQS RIA (Table 5-5) (U.S. EPA, 2006), there are a variety of uncertainties associated with these PM benef its. 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 redu ctions 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 Hu man 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 va riability in population density, meteorology, exposure, baseline health inci dence rates, or other local factors. Use of these $/ton values to estimate benefits associat ed with different emissi on control programs (e.g., for reducing emissions from large stationary s ources 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 bene fits-per-ton over the entire Unite d States. The benefits-per-ton for emission reductions in specific locations may be very different than the estimates presented
here.

PM 2.5 mortality benefits are the largest benef it category that we monetized in this analysis. To better characterize the uncertainty associated with mort ality impacts that are
estimated to occur in areas with low baseline levels of PM 2.5 , 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 th e population exposed at each PM 2.5 level using the most recent modeling available from the recently proposed Tr ansport 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 distributi on 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 PM 2.5 levels at or above the LML. Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM 2.5 levels at or above the LML of 7.5 μg/m 3 . Using the Laden et al. (2006) study, 40% of the population is exposed above the LML of 10 μg/m 3

. As we model mortality impacts among populations exposed to levels of PM 2.5 that are successively lower than the LML of the lowest cohort study, our confidence in the results diminish es. However, the analysis above confirms that the great majority of the impacts occur at or abov e the lowest cohort study’s LML. It is important to emphasize that we have high confidence in PM 2.5 -related effects down to the lowest LML of the major cohort studies. Just because we have gr eater confidence in the benefits above the LML,
this does not mean that we have no confid ence 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 PM 2.5 levels substantially, particularly among highly exposed populations. In the baseline, 85% of the population lived in areas where PM 2.5 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 Adul t Population by Annual Mean PM 2.5 Exposure (pre- and post-policy policy)

Figure 5-6. Cumulative Distribution of Adult Population at Annual Mean PM 2.5 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 avai lable scientific literature a nd methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estima tes are subject to a number of assumptions and uncertainties. For example, for key assu mptions underlying the estimates for premature mortality, which typically account fo r at least 90% of the total mone tized benefits, we were able to quantify include the following:

1. PM 2.5 benefits were derived through benefit per-ton estimates, which do not reflect local variability in popula tion density, meteorology, exposure, baseline health incidence rates, or other local factors thatmight lead to an over-estimate or underestimate of the actual benefits of control ling directly emitted fine particulates.

2. We assume that all fine particles, re gardless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 released from diesel engines and other industrial sources, but no clear scien tific grounds exist for s upporting differential effects estimates by particle type.

3. We assume that the health impact functi on 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 PM 2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard down to th e lowest modeled concentrations.

4. To characterize the uncertainty in the relationship between PM 2.5 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 PM 2.5 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 PM 2.5 benefits, please consult the PM 2.5 NAAQS RIA (Table 5-5).

This RIA does not include the type of detail ed 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 c onducted 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 th e PM RIA because of the significant differences
between the sources affected in that rule and th ose regulated here. Howeve r, the results of the5-32 Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty su rrounding the benefits results presented in this analysis.

5.6 Comparison of Benefits and Costs
Using a 3% discount rate, we estimate the to tal 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 ra te, 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 annua lized costs) utilizi ng the 14 different PM 2.5 mortality functions at discount rates of 3% and 7%. In additi on, the benefits from reducing 2,900 tons of carbon monoxide, 96 tons of HCl, 3.0 t ons 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 Benefits b $130 to $320 $120 to $290 Total Social Costs c $92 $92 Net Benefits $37 to $220 $26 to $190

Non-monetized Benefits 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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment Option 1 Total Monetized Benefits b $130 to $320 $120 to $290 Total Social Costs c $63 $63 Net Benefits $66 to $250 $55 to $220

Non-monetized Benefits 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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment Option 3
Total Monetized Benefits b $130 to $310 $120 to $290 Total Social Costs c $132 $132 Net Benefits -$5.4 to $180 -$14 to $150

Non-monetized Benefits 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 NO 2 and SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementation year (2015), and are ro unded to two significant figures. These results include 2 new F B

incinerators anticipated to come online by 2015 and the larg e entities comply and small entities landfill assumption.
b The total monetized benef its reflect the human health benefits associated with reducing exposure to PM 2.5 through reductions of directly emitted PM 2.5 and PM 2.5 precursors such as NO x and SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a rang e from Pope et al. (2002) to Laden et al. (2006). These models assume that a ll fine particles, regardless of their chemical composition, are equally potent in cau sing premature mortality because there is no clear scientific evidence th at would support the developm ent of differential effects estimates by particle type. These estimat es include energy disbenefits valued at $0.5 million at a 3% discount rate for CO 2 emissions.

c The annual compliances costs serve as a proxy for the annual soci al costs of this rule given the lack of difference between th e 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 PM 2.5 benefits for implementation year (2015). This graph shows 14 benefits

estimates combined with the cost estimate. All combinatio ns are treated as independent and equally probable. All fine particles are assumed to have equivalent health eff ects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . 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 CO 2 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 from 2 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 PM 2.5 benefits for implementation year (2015). This graph shows 14 benefits

estimates combined with the cost estimate. All combinatio ns are treated as independent and equally probable. All fine particles are assumed to have equivalent health eff ects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . 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 CO 2 emissions.R-1 REFERENCES

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Dangtran, Ky, John Mullen, and Dale Mayrose. “A Comparison of Fluid Bed and Multiple Hearth Biosolids Incineration.” Paper presented at the 14 th Annual Residuals and Sludge Management Conference. Februa ry 27–March 1, 2000, Boston, MA.

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SUMMARY OF EXPERT OPINIONS ON THE EXISTENCE OF A THRESHOLD IN
THE CONCENTRATION-RESPONSE FUNCTION FOR PM 2.5 -RELATED
MORTALITYA-1 

## Summary of Expert Opinions on th e Existence of a Threshold in the Concentration-Response Function for PM 2.5 

## -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 Associati on 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 - Sc ience 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/sabpro duct.nsf/0/72D4EFA39E48CDB28525774500738776/$ File/EPA-COUNCIL-10-001-unsigned.pdf>.

Pg 2: “The HES generally agrees with other decisions made by the EP A project team with respect to PM, in particular, th e PM mortality effect threshold model, the cessation lag model, the inclusion of infant mortality estima tion, 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, whic h are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts us ing data from more recent years, during which time PM concentrations have fallen, con tinue 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 Eff ects Subcommittee, Scholar in Residence, The National Academies, Washington, DC

Dr. Michelle Bell , Associate Professor, School of Forest ry and Environmental Studies, Yale University, New Haven, CT

Dr. James K. Hammitt , Professor, Department of Hea lth 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 Ec onomics, 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 Environm ental Health Sciences, Mailman School

of Public Health, Columbia University, New York, NY

Dr. Michael T. Kleinman , Professor, Department of Medi cine, Division of Occupational and

Environmental Medicine, University of California, Irvine, Irvine, CAA-3 Dr. Bart Ostro , Chief, Air Pollution Epidemiology Unit, Office of Environmental Health

Hazard Assessment, California Environm ental Protection Agency, Oakland, CA

Dr. Rebecca Parkin , Professor and Associate Dean, Envi ronmental and Occupational Health,

School of Public Health and Health Services, The George Washington University Medical Center, Washington, DCA-4 B. Scientific Statement from Am erican Heart Association (2010)

Brook RD, Rajagopalan S, Pope CA 3rd, Br ook 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 He art 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 sc ientific statement fr om the American Heart Association.” Circulation . 121: 2331-2378.

Pg 2338: “Finally, there appeared to be no lower-limit threshold below which PM 10 was not associated with excess mortality across all regions.”

Pg 2350: “There also appears to be a monot onic (e.g., linear or log-linear) concentrationresponse relationship between PM 2.5 and mortality risk observed in cohort studies that extends below present-day regulations of 15 μg/m 3 for mean annual levels, wi thout a discernable “safe” threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008)

Pg 2364: “The PM 2.5 concentration– cardiovascular risk re lationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m 3 (the 2006 annual NAAQS level) without a discer nable “safe” threshold.”

Pg 2365: “This updated review by the AHA writi ng 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 scientif ic 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 bene fits would accrue from lowering PM 2.5 concentrations even below present-day annual (15 μg/m 3 ) and 24-hour (35 μg/m 3 ) NAAQS, if feasible, to optimally prot ect the most susceptible populations.”

Pg 2366: “Although numerous insights have great ly enhanced our understanding of the PMcardiovascular relationship since the first AHA statement was published, the following list represents broad strategic avenues for future in vestigation: ... Determine whether any “safe” PM threshold concentration exists that eliminates both acute and chronic car diovascular effects in healthy and susceptible individua ls 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, MPHA-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, MPHA-6 C. Integrated Science Assessmen t 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 character izing the public health impacts associated with exposure to a pollutant is whether the concentrat ion-response relationship is linear across the full concentration range encountered, or if nonlinear relationships exis t along any part of this range. Of particular interest is the shape of the con centration-response curve at and below the level of the current standards. The shap e of the concentration-respons e curve varies, depending on the type of health outcome, underl ying 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 (s uch as the low data density in the lower concentration range, possible influence of measurement error, and in dividual differences in susceptibility to air pollution health effects). In addition, many chemi cals and agents may act by perturbing naturally occurring background processes that lead to disease, which also linearizes population concentration-response relations hips (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, O 3 , lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely mechanisms include nonlinear processes for so me key events. These attributes of human population dose-response relationships have be en extensively discussed in the broader epidemiologic literature (Rothman and Greenland, 1998, 086599 ).”

Pg 2-16: “In addition, cardiovascular hospital ad mission and mortality studies that examined the PM 10 concentration-response relationship f ound evidence of a log-linear no-threshold relationship between PM expos ure and cardiovascular-relate d morbidity (Section 6.2) and mortality (Section 6.5).”

Pg 2-25: “2.4.3. PM Concentr ation-Response Relationship An important consideration in characterizing th e PM-morbidity and mortality association is whether the concentration-respons e relationship is linear across th e full concentration range that is encountered or if there are concentration ranges where there ar e departures from linearity (i.e., nonlinearity). In this ISA studies have been identified that atte mpt to characterize the shape of the concentration-response curve along with pos sible 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 concen tration-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 PM 10 and mortality associated with long-term exposure to PM 2.5
. “A limited number of studies have been identified that examined the shape of the PM cardiovascular hospital admission and ED visit concen tration-response rela tionship. Of theseA-7 studies, some conducted an explor atory analysis during model sele ction to determine if a linear curve most adequately represented the concentr ation-response relationship; whereas, only one study conducted an extensive anal ysis to examine the shape of the concentr ation-response curve at different concentrations (Section 6.2.10.10). Ov erall, the limited eviden ce from the studies evaluated supports the us e 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 exam ined 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 con centration-response curve across cities (Section 6.5). Overall, the studies evaluate d further support the use of a no-th reshold log-linear model, but additional issues such as the infl uence of heterogeneity in estimate s 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 c oncentration-response relationshi p between short-term exposure to PM and mortality, Schwartz et al. (2008, 156963) conducted an analysis of the shape of the concentration-response relationshi p associated with long-term e xposure to PM. Using a variety of statistical methods, the concentration-respons e curve was found to be indistinguishable from linear, and, therefore, little evidence was observe d to suggest that a th reshold exists in the association between long-term exposure to PM 2.5 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 cons istently 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 rela tionship, 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 erro r and heterogeneity of shapes across cities remain to be resolved. Also, give n the pattern of seasonal and regional differences in PM risk estimates depicted in rece nt multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a co ncentration-response relationship estima ted across cities and for all-year data may not be very informative.”

Pg 6-197: “6.5.2.7. Investigation of C oncentration-Response Relationship The results from large multicity studies re viewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) suggested that strong evidence did not exis t for a clear threshold for PM mortality effects. However, as discussed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ), there areA-8 several challenges in determining and interpre ting the shape of PM-mortality concentrationresponse functions and the presence of a threshol d, including: (1) limited range of available concentration levels (i.e., sparse data at the low and high end); (2) hete rogeneity 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 PM 10 data in 20 cities from the 1987-1994 NMMAPS data. They reported that the spline model, combined acro ss the cities, showed a linear relation without indicating a threshold for the relative risks ofdeath for all-causes and for cardiovascularrespiratory causes in relation to PM 10 , but “the other cause” deaths (i.e., all cause minus cardiovascular-respiratory) showed an apparent threshold at around 50 μ g/m 3 PM 10 , as shown in Figure 6-35. For all-cause and cardio-respirator y deaths, based on the Akaike’s Information Criterion (AIC), a log-linear m odel without threshold was prefe rred to the threshold model and to the spline model.

“The HEI review committee commented that in terpretation of these re sults required caution, because (1) the measurement error could obscu re any threshold; (2) the city-specific concentration-response curves exhibited a variet y of shapes; and (3) the use of AIC to choose among the models might not be appr opriate due to the fact it was not designed to assess scientific theories of etiology. Note, however, that ther e has been no etiologically credible reason suggested thus far to choose one model over ot hers 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 relati onship between PM10 and mortality.

“The Schwartz (2004, 078998 ) analysis of PM 10 and mortality in 14 U.S. cities, described in Section 6.5.2.1, also examined the shape of the c oncentration-response rela tionship by including indicator variables for days when c oncentrations were between 15 and 25 μ g/m 3 , between 25 and 34 μ g/m 3 , between 35 and 44 μ g/m 3 , and 45 μ g/m 3 and above. In the model, days with concentrations below 15 μ g/m 3 served 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 eviden ce to suggest that a threshold exists. The authors did not examine city-to-c ity variation in the concentration-response relationship in this study.

“PM 10 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 estim ated using a regression model relating co-located PM10 to BS or TSP. They used regression spline models with two knots (30 and 50 μ g/m 3 ) and then combined the individual city es timates of the splines across cities. The investigators concluded that the association between PM and mortal ity 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 comb ined curves (Figure 6-37) appear to supportA-9 no-threshold relationships between PM 10 and mortality, the heterogene ity 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 erro r and heterogeneity of shapes across cities remain to be resolved. Also, give n the pattern of seasonal and regional differences in PM risk estimates depicted in rece nt multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a co ncentration-response relationship estima ted 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 Protec tion 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, Res earch 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, Res earch Triangle Park, NC

Dr. Tara Greaver —NCEA, U.S. EPA, Resear ch Triangle Park, NC

Dr. Erin Hines —NCEA, U.S. EPA, Research Triangle Park, NC Dr. Douglas Johns —NCEA, U.S. EPA, Resear ch 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, Resear ch Triangle Park, NC Dr. Qingyu Meng —Oak Ridge Institute for Science a nd Education, Postdoctoral Research

Fellow to NCEA, U.S. EPA, Re search 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, Res earch Triangle Park, NC Dr. Mary Ross —NCEA, U.S. EPA, Research Triangle Park, NC

Mr. Jason Sacks —NCEA, U.S. EPA, Research Triangle Park, NCA-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, Resear ch Triangle Park, NC

Dr. William Wilson —NCEA, U.S. EPA, Resear ch Triangle Park, NC

Dr. Lori White — NCEA, U.S. EPA, Research Triangle Pa rk, NC (now at National Institute for

Environmental Health Sciences, Research Triangle Park, NC) Dr. Christy Avery —University of North Caro lina, Chapel Hill, NC Dr. Kathleen Belanger —Center for Perinatal, Pediatri c and Environmental Epidemiology,

Yale University, New Haven, CT

Dr. Michelle Bell —School of Forestry & E nvironmental 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 a nd 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 Environmenta l 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 Envi ronmental 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 Atmospheri c 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, Univer sity 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 Resear ch Unit, Beth Israel Deaconess

Medical Center, Boston, MAA-11 Dr. Eric Whitsel —Departments of Epidemiology and Medi cine, University of North Carolina,

Chapel Hill, NC

Peer Reviewers Dr. Sara Dubowsky Adar , Department of Epidemiology, Univ ersity of Washington, Seattle,

WA Mr. Chad Bailey , Office of Transportation a nd Air Quality, Ann Arbor, MI

Mr. Richard Baldauf , Office of Transportation a nd 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, Resear ch Triangle Park, NC

Dr. Robert Devlin , National Health and Environmental Eff ects 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 Eff ects Research Laboratory, U.S. EPA, Research Triangle Park, NC Dr. Mark Frampton , Department of Environmental Medici ne, University of Rochester Medical

Center, Rochester, NY

Mr. Neil Frank , Office of Air Quality Planning and Sta ndards, U.S. EPA, Research Triangle

Park, NC Mr. Tyler Fox , Office of Air Quality Planning and Sta ndards, U.S. EPA, Research Triangle

Park, NC

Dr. Jim Gauderman , Department of Environmental Me dicine, Department of Preventive

Medicine, University of Southern California, Los Angeles, CA

Dr. Barbara Glenn , National Center for Environmental Re search, U.S. EPA, Washington, D.C. Dr. Terry Gordon , School of Medicine, New Yo rk 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 Dia gnostic Investigation, Michigan State

University, East Lansing, MI Ms. Beth Hassett-Sipple , Office of Air Quality Planning and Standards, U.S. EPA, Research

Triangle Park, NCA-12 Dr. Amy Herring , Department of Biostatistics, University of North Carolina, Chapel Hill, NC

Dr. Israel Jirak , Department of Meteorology, Embry-Riddl e Aeronautical University, Prescott,

AZ

Dr. Mike Kleeman , Department of Civil and Environm ental 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, Univ ersity 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 Sta ndards, U.S. EPA, Research Triangle

Park, NC

Dr. Karen Martin , Office of Air Quality Planning and Stan dards, U.S. EPA, Research Triangle

Park, NC

Ms. Connie Meacham , NCEA, U.S. EPA, Res earch Triangle Park, NC

Mr. Tom Pace , Office of Air Quality Planning and Sta ndards, U.S. EPA, Research Triangle

Park, NC

Dr. Jennifer Peel , Department of Environmental and Radi ological Health Sciences, College of

Veterinary Medicine and Biomed ical 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 St andards, U.S. EPA, Research Triangle

Park, NC Dr. Sanjay Rajagopalan , Division of Cardiovascular Me dicine, 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 Sta ndards, U.S. EPA, Research Triangle

Park, NC Mr. Harvey Richmond , Office of Air Quality Planning and Standards, U.S. EPA, Research

Triangle Park, NCA-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 O ccupational Health, Emory University,

Atlanta, GA

Dr. Frances Silverman , Gage Occupational and Environmenta l 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 Laborato ry, U.S. EPA, Research Triangle

Park, NC Dr. John Vandenberg (Director)— NCEA-RTP Division, U.S. EP A, 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 Divisi on, 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, Un iversity of North

Carolina, Chapel Hill, NC

Mr. Ronald Williams , National Exposure Research Laborat ory, U.S. EPA, Research Triangle

Park, NC Dr. George Woodall, NCEA, U.S. EPA, Resear ch Triangle Park, NC

Dr. Antonella Zanobetti , Department of Environmental He alth, Harvard School of Public

Health, Boston, MAA-14 D. CASAC comments on PM ISA and REA (2009)

U.S. Environmental Protection Agency - Sc ience Advisory Board (U.S. EPA-SAB). 2009.

Review of EPA’s Integrated Science Assessm ent for Particulate Matter (First External Review Draft, December 2008). EPA-COUNCIL- 09-008. May. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRO DUCT.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 c onclusion should be followed by the discussion of statistical methodology and the identif ication of any thres hold that may exist.”

U.S. Environmental Protection Agency Sc ience 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/SABPRO DUCT.NSF/81e39f4c09954fcb85256ead006be86e/7 23FE644C5D758DF852575BD00763A32/$File/E PA-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 sp ecific 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 Ma tter (Second External Review Draft, July 2009)

. EPA-CASAC-10-001. November. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.N SF/81e39f4c09954fcb8525 6ead006be86e/151B1F8 3B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>.

Pg 2: “The paragraph on lines 22 -30 of page 2-37 is not clearl y written. Twice in succession it states that the use of a no-thre shold log-linear model is supporte d, 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 Hea lth, Harvard University, Boston, MA Dr. Ellis B. Cowling , University Distinguished Professor At-Large Emeritus, Colleges of Natural

Resources and Agriculture and Life Sciences, No rth Carolina State University, Raleigh, NCA-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, Cons truction and Environmental Engineering,

College of Engineering, North Carolin a 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, Un iversity of California, Parlier, CA Dr. Joseph Helble, Dean and Professor, Thayer School of E ngineering, Dartmouth College, Hanover,

NH Dr. Rogene Henderson , Senior Scientist Emeritus, Lovel ace 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 Environm ental 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 Se rvice Air Resources Division, Cooperative

Institute for Research in the Atmosphere, Co lorado 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, Co llege 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 a nd Occupational Health Sciences, School of

Public Health and Community Medicine, Un iversity of Washington, Seattle, WA Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, ILA-16 Dr. Kathy Weathers , Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NYA-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, Pa t 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 sp atial analysis of the American Cancer Society study linking particulat e air pollution and mortality. HEI Research Repor t, 140, Health Effects Institute, Boston, MA.

Pg 119: [About Pope et al. (2002)] “Each 10- μ g/m 3 increase in long-term average ambient PM 2.5 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 w ithin the range of observed PM 2.5 concentrations.”

Krewski (2009). Letter from Dr. Daniel Krewsk i to HEI’s Dr. Kate Adams (dated July 7,

2009) regarding “EPA queries regarding HE I Report 140”. Dr. Adams then forwarded the letter on July 10, 2009 to EPA’s Beth Hasset t-Sipple. (letter plac ed in docket #EPAHQ-OAR-2007-0492).

Pg 4: “6. The Health Review Committee comment ed that the Updated Analysis completed by Pope et al. 2002 reported “no evidence of a th reshold exposure level within the range of observed PM 2.5 concentrations” (p. 119). In the Extende d Follow-Up study, did the analyses provide continued support for a no-threshold res ponse 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 functi on within the range of observed PM 2.5 concentrations.”

HEI Health Review Committee Members Dr. Homer A. Boushey, MD , Chair, Professor of Medici ne, 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 Environmen tal 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 & Envir onmental Medicine, University of

Rochester Medical Center, Rochester, NYA-18 Dr. Stephanie London, MD, PhD , Senior Investigator, Epidemio logy Branch, National Institute

of Environmental Health Sciences Dr. William N. Rom, MD, MPH , Sol and Judith Bergstein Professor of Medicine and

Environmental Medicine and Dire ctor of Pulmonary and Criti cal Care Medicine, New York University Medical Center

Dr. Armistead Russell , Georgia Power Distinguished Profe ssor of Environmental Engineering,

School of Civil and Environmental Engin eering, Georgia Institute of Technology Dr. Lianne Sheppard, PhD , Professor, Department of Biostati stics, University of WashingtonA-19 F. Schwartz et al. (2008)

Schwartz J, Coull B, Laden F. (2008). The E ffect 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 ev idence for a threshold in the association between exposure to fine partic les and the risk of death on follow-up, which continues well below the U. S. EPA standard of 15 μ g/m 3 .”

Pg 68: “In conclusion, penalized spline smoothi ng and model averaging represent reasonable, feasible approaches to addressi ng questions of the sh ape 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 ha s no threshold down to close to background levels.”A-20 G. Expert Elicitation on PM-Mortality (2006, 2008)

Industrial Economics, Inc., 2006. Expanded Expert JudgmentAssessment of the

Concentration-Response Relationship Between PM 2.5 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 inte grate their judgments a bout the likelihood of a causal relationship and/or threshold in the C-R function into his distri bution or to provide a distribution "conditional on" one or both of these factors.”

Pg vii: “Only one of 12 experts explicitly in corporated a threshold into his C-R function. 3 The rest believed there was a lack of empirical and/or theoreti cal support for a p opulation 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 PM 2.5 concentrations were modest.”

“ 3 Expert K indicated that he was 50 percen t sure that a threshold existed. If there were a threshold, he thought that there wa s an 80 percent chance that it would be less than or equal to 5 μ g/m 3 , and a 20 percent chance that it would fall between 5 and 10 μ g/m 3 .”

Pg ix: “Compared to the pilot st udy, experts in this study were in general more confident in a causal relationship, less likely to incorporate th resholds, 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 epid emiological studies that were the focus of both elicitation studies, more than th e influence of changes to the structure of the protocol.”

Pg 3-25: “3.1.8 THRESHOLDS The protocol asked experts for their judgments regarding whethe r a threshold exists in the PM 2.5 mortality C-R function. The protocol focused on assessing expert judgmen ts regarding theory and evidential support for a popula tion threshold (i.e., the concen tration below which no member of the study population would experien ce an increased risk of death). 32 If an expert wished to incorporate a threshold in his characterization of the concentr ation-response relationship, the team then asked the expert to specify the threshold PM 2.5 concentration probabilistically, incorporating his uncertainty about the true threshold level.

“From a theoretical and conceptu al standpoint, all experts genera lly believed that individuals exhibit thresholds for PM-related mortality. Ho wever, 11 of them discounted the idea of a population threshold in the C-R function on a theore tical and/or empirical basis. Seven of these experts noted that theo retically 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, environm ental, and socioeconomic factors. 33 All 11 thought that there was insufficient empirical support for a populatio n threshold in the C-R function. In addition, two experts (E and L) cited analys es of the ACS cohort data in P ope et al. (2002) and another (J) cited Krewski et al. (2000a & b) as supportive of a linear rela tionship in the study range.A-21 “Seven of the experts favored epidemiological stud ies as ideally the best means of addressing the population threshold issue, because they are best able to evaluate the fu ll range of susceptible individuals at environmentall y relevant exposure levels. However, those who favored epidemiologic studies generally acknowledged th at 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 va riation 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 toxicologi cal studies are best su ited for researching mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B, thought that a better understanding of the detailed biological mechan ism 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 demonstrat es 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 wa s detectable in the currently available epidemiologic studies. He indicated th at some of the cohort studies showed greater uncertainty in the shape of the C-R function at lower levels, which c ould be indicative of a threshold.

“Expert K chose to incorporate a threshold into his C-R functi on. He indicated that he was 50 percent sure that a thre shold existed. If there we re a threshold, he thought that there was an 80 percent chance that it would be less than or equal to 5 μ g/m 3 , and a 20 percent chance that it would fall between 5 and 10 μ g/m 3 .”

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 Chan ges in Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol. , 42(7):2268-2274.

Pg 2271: “Eight experts thought th e true C-R function relating mo rtality to changes in annual average PM 2.5 was log-linear across the entire study range (ln(mortality) ) β × PM). Four experts (B, F, K, and L) specified a “piecewi se” log-linear functi on, 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 eff ects 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 threshol d, T, to his function, which he described probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μ g/m 3 ) = 0.8 and P(5 μ g/m 3 < T ≤ 10 μ g/m 3 ) = 0.2. Figure 3 does not include the impact of applying expert K’s threshold, as the size of the reduction in bene fits 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 E nvironmental Medicine, NYU School of Medicine,

Tuxedo, NY

Dr. Dan Krewski , University of Ottawa

Dr. Nino Künzli , University of Southern Califor nia Keck School of Medicine
Dr. Morton Lippmann , Professor, Nelson Institute of Environm ental 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 Environm ental 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. Mar k Utell , University of Rochester Schoo l of Medicine and DentistryA-23 H. CASAC comments on PM Staff Paper (2005) U.S. Environmental Protection Agency - Sc ience Advisory Board (U.S. EPA-SAB). 2005. EPA’s Review of the National Ambient Ai r 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/sabproduc t.nsf/E523DD36175EB5AD8525701B007332AE/$Fil e/SAB-CASAC-05-007_unsigned.pdf>.

Pg 6: “A second concern is with methodologi cal issues. The issue of the selection of concentration-response (C-R) rela tionships based on locally-deriv ed coefficients needs more discussion. The Panel did not agree with EPA st aff 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 anal ysis. The available epidemiological database on daily mortality and morbidity does not establis h 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 predic ted background concentrations or LML, and to standardize the approach across cities, for the pur pose of estimating public health impacts, the Panel favored the primary use of an assumed threshold of 10 μ g/m 3 . The original approach of using background or LML, as well as the other pos tulated thresholds, could still be used in a sensitivity analysis of threshold assumptions. “The analyses in this chapter hi ghlight 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 ma y exist or the shape ofnonlinear concentrationresponse curves at low levels of exposure that ma y exist, and to reduce uncertainty in estimated risks at the lowest PM concentrations.”

CASAC Panel Members
Dr. Rogene Henderson , Scientist Emeritus, Lovelace Re spiratory Research Institute,

Albuquerque, NM Dr. Ellis Cowling , University Distinguished Professo r-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 Medi cine, Biomedical Research and

PatientCare, National Jewish Medical and Research Center, Denver, CO Dr. Philip Hopke , Bayard D. Clarkson Distinguished Pr ofessor, Department of Chemical

Engineering, Clarkson Univ ersity, Potsdam, NY Dr. Jane Q. Koenig , Professor, Department of Environmen tal Health, School of Public Health

and Community Medicine, Universi ty of Washington, Seattle, WAA-24 Dr. Petros Koutrakis , Professor of Environmental Science, Environmental Health , School of

Public Health, Harvard University (HSPH), Boston, MA Dr. Allan Legge , President, Biosphere So lutions, 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 Respirator y Research Institute, Albuquerque, NM Dr. Roger O. McClellan , Consultant, Albuquerque, NM

Dr. Frederick J. Miller , Consultant, Cary, NC

Dr. Gunter Oberdorster , Professor of Toxicology, Departme nt of Environmental Medicine,

School of Medicine and Dentistry, Univer sity of Rochester, Rochester, NY

Mr. Richard L. Poirot , Environmental Analyst, Air Pollutio n 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 Medici ne, Channing Laboratory, Harvard

Medical School, Boston, MA

Dr. Sverre Vedal , Professor of Medicine, School of Public Health and Community

Medicine University of Wa shington, 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 Mo tors Corporation, Detroit, MI

Dr. Barbara Zielinska , Research Professor, Division of At mospheric Science, Desert Research

Institute, Reno, NVA-25 I. HES Comments on 812 Analysis (2004)

U.S. Environmental Protection Agency - Sc ience 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 Subcommi ttee 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%5CSA BPRODUCT.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, biol ogic 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 PM 2.5 and both allcause and cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. Graphical an alyses of these studies (Doc kery 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 re ported 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 Medi cine (IOM), Edinburgh, Scotland Dr. Patrick Kinney , Columbia University, New York, NY

Dr. Michael Kleinman , University of Calif ornia, 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 Washingt on 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 In c., 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, CAA-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, Portsm outh, NH Dr. Dale Hattis, Clark University, Worcester, MA Dr. D. Warner North, NorthWor ks Inc., Belmont, CA Dr. Thomas S. Wallsten, University of Maryland, College Park, MDA-27 J. NRC – Committee on Estimating the Health Ri sk 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 concentrati on-response functions may influe nce the overall estimate of benefits. The shape is particularly important fo r lower ambient air pollu tion concentrations to which a large portion of the population is exposed. Fo r this reason, the impact of the existence of a threshold may be considerable.

“In epidemiological studies, air pollution concen trations 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 st udies with the large number of repeated measurements, linearity and thresholds have been formally addressed with reasonable statistical power. For pollutants such as PM 10 and PM 2.5 , there is no evidence for any departure of linearity in the observed range of exposure, nor any indi cation of a threshold. For example, examination of the mortality effects of short-term exposure to PM 10 in 88 cities indicates that the concentration-response functions are not due to the high concentr ations and that the slopes of these functions do not appear to increase at hi gher concentrations (S amet et al. 2000). Many other mortality studies have examined the sh ape of the concentrati on-response function and indicated that a linear (nonthres hold) model fit the data well (Pope 2000). Furthermore, studies conducted in cities with very lo w ambient pollution concentrations have similar effects per unit change in concentration as thos e studies conducted in cities with higher concentrations. Again, this finding suggests a fairly lin ear concentration-response func tion over the observed range of
exposures.
“Regarding the studies of long-term exposure, Kr ewski et al. (2000) found that the assumption of a linear concentration-response f unction for mortality outcomes was not unreasonable. However, the statistical power to assess th e shape of these functions is w eakest 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 exis tence of a no-effect thre shold 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 stud ies may lead to the c onclusion that although a threshold is not apparent at commonly observed concentrations, one may exist at lower levels. An important point to acknowledge re garding thresholds is that for health benefits analysis a key threshold is the population thresh old (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 ai r pollution health effects varies considerably across individuals and depends on a large set of underlying factors, in cluding genetic makeup, age, exposure measurement error, preexisting di sease, and simultaneous exposures from smoking and occupational hazards. This variation in individual susceptibilities and the resulting distribution of individual thres holds 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 co ncentration-response function is continuous and to focus on finding eviden ce of changes in its sl ope 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 Qual ity Standards (NAAQS), EPA i nvestigated 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/m 3 ) mean threshold for mortality associ ated with long-term exposure to PM 2.5 . The low-end benefits estimate assumed a 15-μg/m 3 threshold for all PM-related health effects. The studies, however, included concentrations as low as 7.5 μg/m 3 . 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 ev idence for a threshold for PM.

“Several points should be noted regarding the th reshold 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 concentr ation-response function that was originally f ound to be positively sloped. The a ssumption 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 indicate d in the original study. A new concentration-response function wa s not generated for EPA’s benef its analysis for the PM and ozone NAAQS for which threshold assumptions we re 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 evalua ted 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 a ssumptions in a consistent and transparent framework using several altern ative 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 fo r mortality in the pa rticulate matter (PM) exposure ranges under consideration (EPA 1999a, p. 3-8). Although the agency conducts sensitivity analyses incorporat ing thresholds, it provides no j udgment as to their relativeA-29 plausibility. In a probabilistic uncertainty analysis, EPA coul d assign appropriate weights to various threshold models. For PM-related mortalit y 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 slig ht reduction in the mean of th at distribution, thus reflecting EPA’s views about the implausibility of thres holds. The committee finds that such formal incorporation of EPA’s expert j udgments 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 logarithmi c 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. In creasingly, epidemiological investigators are employing analyt ic methods that permit the estimation of nonlinear shapes for concentration-response functions (G reenland et al. 1999). As a consequence, EPA will need to be prepared to incorporate nonlinea r concentration-respons e functions from epidemiological studies into the agency’s health benefits analyses. A ny 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 susceptib ility (Hattis and Burmaster 1994; Hattis et al. 2001).”

Pg 137: “In principle, many components of the hea lth benefits model need realistic probabilistic models (see Table 5-1 for a listi ng of such components), in ad dition to concen tration-response thresholds and time lags between exposure and response. For example, additional features of the concentration-response function—s uch as projection of the results from the study population to the target populations (which may have etiologi cally relevant characteri stics outside the range seen in the study population) a nd the projection of baseline frequencies of morbidity and mortality into the future—must be characterized probabilistically. Other uncertainties that might affect the probability distri butions are the estimations of population exposure (or even concentration) from emissions, estimates of emi ssions themselves, and th e relative toxicity of various classes of particles. Similarly, many aspect s of the analysis of th e impact of regulation on ambient concentrations and on population exposu re involve considerable uncertainty and, therefore, may be beneficially modeled in th is way. Depending on the analytic approach used, joint probability distributions will have to be sp ecified 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 incor porating expert judgment into its probabilistic uncertainty analyses. The agency possesses consid erable internal expertise, which should be employed as fully as possible. Outside experts sh ould also be consulted as needed, individually or in panels. In all cases, when expert judgm ent is used in the c onstruction 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 Ma ryland, College Park

Dr. JOHN S. EVANS , Harvard University, Boston, Massachusetts

Dr. DALE B. HATTIS , Clark University, Wor cester, 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, Switzer land; 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, MarylandAPPENDIX 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 CarolinaB-2 Inherent in any complex Regulatory Impact Analysis (RIA) are multiple sources of uncertainty. Health benefits analysis relies on an array of data i nputs—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 overal l uncertainty in this analysis. There are a variety of methods to characterizing the uncertainty a ssociated with the human health benefits of air pollution, includi ng quantitative and qualitative methods. When evaluated within
the context of these uncer tainties, the health impact and monetized benefits estimates in an RIA can provide useful information regarding the magn itude 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 th e uncertainty characterization generally focuses on the mortalityrelated benefits. Typically, EPA employs two primary techniques for quantifying this uncertainty. First, because this characterizati on 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 PM 2.5 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 qua lity 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 charac terizing random sampling error associated with the concentration response f unctions from epidemiological studies and economic valuation
functions. 1 Both approaches have different strength s 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 altern ate mortality cessation lags and income growth adjustment factors. Cessation lags and income gr owth adjustments are simply multipliers applied to the valuation function, which generally aff ect monetized benefits estimates in the same manner. Thus, it is possible for readers to infer th e sensitivity of these pa rameters by referring to those previous analyses. 2 Other RIAs contain unique sensitivit y analyses that are specific to the

1 Currently, we are unable to characterize the random samp ling 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 PM 2.5 -related mortality benefits discounted at 3% discounted by between 10.4% and –27%; when discounted at 7%, theseB-3 input parameters of that analys is, such as blood lead level (U.S. EPA, 2008b) or rollback method (U.S. EPA, 2010a). Other sources of uncertain ty, including the projection of atmospheric conditions and source-level emissions, the projec tion of baseline morbidity rates, incomes and technological development are typically unquantified in our RI As. For these sources, we typically provide a qualitative uncertainty characterization as sociated with these input
parameters.

One particular aspect of uncertainty has rece ived extensive quantitative and qualitative
attention in recent RIAs: the exis tence of a threshold in the concentration-response function for

PM 2.5 -related mortality. A threshold is a specific type of discontinuity in the concentrationresponse function where there are no be nefits associated with reducing PM 2.5 levels in areas where the baseline air quality is less than th e threshold. Previousl y, EPA had included a sensitivity analysis with an arbi trary assumed threshold at 10 μg/m 3 in 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 inherent ly more uncertain. A threshold of 10 μg/m 3 does not necessarily have any stronger tec hnical basis than any other thres hold, and we could have instead assumed a threshold at 4, 7.5, or 12 μg/m 3 for the sensitivity analysis . In addition to identifying the most support for a non-threshold model, th e underlying scientific evidence does not support any specific “bright line”.

Based on our review of the current body of sc ientific literature, EPA now estimates PMrelated mortality without applying an assume d 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 Comm ittee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientif ic literature consistently finds that a no-thr eshold log-linear model most adequately portrays the PM-morta lity concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function. 1 Since then, the Health Effects Subcommitt ee (U.S. EPA-SAB, 2010) of EPA’s Council concluded, “The HES fully supports EPA’s deci sion to use a no-threshold model to estimate mortality reductions. This deci sion is supported by the data, wh ich are quite consistent in

benefits change by between 31% and -49%. When applying higher and lower income growth adjustments, the monetary value of PM 2.5 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 leve ls. Analyses of cohorts using data from more recent years, during which time PM concentratio ns have fallen, continue to report strong associations with mortality. Ther efore, there is no evidence to support a truncation of the CRF.” For a summary of these scientific review statem ents and the panel memb ers 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 c onformed 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 provide s 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 us ing a full range of modeled air quality concentrations. Unlike an assumed threshold, wh ich is a modeling assumption that reduces the magnitude of the estimated health impacts, th e LML is a characteriza tion 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-r elated benefits, EPA believes th at large cohort-based mortality estimates are suitable for use in air pollution health impact analyses. When estimating PM mortality impacts using risk coefficients draw n from the Harvard Six Cities and the American Cancer Society cohorts there are innumerable othe r 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 compos ition among others. The LML assessment provides a limited representation of one key difference be tween the two studies. For the purpose of estimating the benefits associated with reducing PM 2.5 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 st rong 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/m 3 . As we model mortality impacts among populations exposed to levels of PM 2.5 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. Ho wever, each successive cohort study has shownB-5 evidence of effects at succe ssively lower levels of PM 2.5 . As more large cohort studies follow populations over time, we will likely have more st udies with lower LML as air quality levels continue to improve. Even in the absence of a definable threshold, we ha ve more confidence in the benefits estimates above the LML of the larg e 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 effect s at the lowest LML from the recent Krewski et al (2009) study indicate s that confidence in PM 2.5 -related mortality effects down to at least 5.8 μg/m 3 is high.

In the recently proposed Transport Rule RI A (U.S. EPA, 2010b), we included the new
LML assessment in which we binned the estimated number of avoided PM 2.5 -related premature mortalities resulting from the implementation of the Transport Rule according to the projected
2014 baseline PM 2.5 air quality levels. This presentation is consistent with our approach to applying PM 2.5 mortality risk coefficients that have not been adjusted to incorporate an assumed threshold. A very large proportion of th e avoided PM-related impacts occurred among populations initially exposed at or above the LM L 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 PM 2.5 levels experienced by the population receiving the PM 2.5 mortality benefit. Approximately 80% of the avoided impacts
occurred at or above a baseline annual mean PM 2.5 level of 10 μg/m 3 (the LML of the Laden et al. 2006 study); about 97% occur at or above an annual mean PM 2.5 level of 7.5 μg/m 3 (the LML of the Pope et al. 2002 study). This assessment conf irmed that the great majority of the impacts associated with the Transport Rule oc curred at or above each study’s LML.

For the Transport Rule, policy-specific ai r quality modeling data for the year 2014 was available as an input into th e benefits analysis. For some rules, especially New Source Performance Standards (NSPS) or National Emi ssions Standards for Hazardous Air Pollutant (NESHAP) rules, policy-specific ai r quality data is not availabl e due to time or resource limitations. For these rules, we provide the fo llowing LML assessment as a characterization of the baseline exposure to PM 2.5 levels in the U.S. Many of the upcoming NSPS and NESHAP rules have compliance dates between 2013 and 2016 a nd represent marginal improvements in air quality levels. Although it the data is not a perfect match, we belie ve that the air quality dataB-6 from the Transport Rule is a reasonable approxim ation of the baseline exposure in the U.S. for upcoming NSPS and NESHAP rules. 1

For rules without air quality modeling, we ge nerally 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 PM 2.5 level. However, we believe that it is still important to characterize the uncertainty associ ated with the distribut ion of the baseline air quality. As a surrogate measure of mortality imp acts, we provide the percentage of baseline exposure at each PM 2.5 level. If air quality levels in the ba seline are above the LML, the marginal changes anticipated from these ru les would likely also lead to pos t-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 change s would also be above the LML.

It is important to note that baseline exposure is only one pa rameter in the health impact function, along with base line incidence rates popul ation, 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 populati on experiencing health impacts as a result of a specific emission reduction policy. The most important aspect, whic h we are unable to quantify for rules without air quality modeling, is th e shift in exposure associated with th e specific rule. Therefore, caution is warranted when interpreting the following assessment.

A very large proportion of the population is e xposed at or above the lowest LML of the cohort studies (Figures 1 and 2), increasing our c onfidence in the PM mort ality 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 cumu lative distribution function of the same data. In addition, Figure 2 also demonstrates that pol icy had a greater impact on reducing exposure to the portion of the population in areas with high PM 2.5 levels relative to the portion of the population at low PM 2.5 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

PM 2.5 levels above the LML. Under baseline cond itions, 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 expo sed to air quality levels below the LML. As additional rules continue to reduce the ambient PM 2.5 levels over time, a larger fraction of the population would be exposed to air quality levels below the LML. However, the emi ssion reductions anticipated from the rules without air quality modeling available are comparatively small and repr esent marginal changes. We intend to update this LML assessment as necessary to corresp ond with the successively lower baselin e air quality levels anticipated as the result of promulgating significant upcoming rules.B-7 exposed to annual mean PM 2.5 levels of at least 5.8 μg/m 3 , which is the lowest air quality level considered in the most recent study of the Amer ican 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/m 3 . Using the Laden et al. (2006) study, 40% of the popul ation is exposed above the LML of 10 μg/m 3

. As we model mortality impacts among populations exposed to levels of

PM 2.5 that are successively lower than the LML of the lowest cohort study, our confidence in the results diminishes. However, the analysis above c onfirms that the great majority of the impacts
occur at or above the lowest c ohort study’s LML. It is important to emphasize that we have high
confidence in PM 2.5 -related effects down to the lowest LML of the major cohort studies, which is 5.8 μg/m 3 . Just because we have greater confidence in the benefits above the LML, this does not
mean that we have no confidence th at benefits occur below the LML.

Figure 1: Percentage of Adult Population by Annual Mean PM 2.5 Exposure (pre- and post- policy)B-8 Figure 2: Cumulative Distribution of Adult Population at Annual Mean PM 2.5 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 gra phics in the Transport Rule to those provided here, you wi ll 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 re sult of the incremental change in exposure between the baseline and control. However, the baseline population exposur e at lower air quality levels is so much larger than the impacts among these same populations. In other words, the population exposed to lower PM 2.5 levels are not receiving very mu ch 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 in fluence 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, Hughe s E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of the American Cancer So ciety study linking partic ulate air pollution and mortality. HEI Research Report, 140, H ealth 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 Criti cal Care Medicine 173:667-672. Estimating the Public Health Bene fits of Proposed Air Pollution Regulations. Washington, DC: The Nati onal 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, a nd Long-term Exposure to Fine Particulate Air Pollution. Journal of the Amer ican Medical Association 287:1132-1141.

Roman, H.A., K.D. Walk er, 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/r egdata/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/R IAs/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 Tr iangle 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 SO 2 National Ambient Air Quality Standa rds (NAAQS). Office of Air Quality Planning and Standards, Research Tr iangle 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, Resear ch 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 C oncentration-Response Function for PM 2.5 -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 - Sc ience 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/sabproduc t.nsf/0/72D4EFA39E48CDB28525774500738776/$F ile/EPA-COUNCIL-10-001-unsigned.pdf>.APPENDIX C

ADDITIONAL ENGINEERING COST ANALYSIS DATAC-1Table C-1a. Percent Improvement Needed to Meet MACT floorand Additional ControlsRequired: Fluidized BedIncineratorsPart 1Red cells indicate where additional control is neededFacility IDUnitIDExistingControlDevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb) Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)EG Limit (mg/dscm):0.0019EG Limit (ppmvd): 56 EG Limit (ppmvd): 0.49EG Limit (mg/dscm):0.0098EG Limit (mg/dscm):0.0033Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% Improvement NeededAKJuneau 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73CTMattabassett 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73CTSynagroWaterbury 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73CTWestHaven 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73GANoondayCreek 1 unknown 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76IADubuque 1 cs - vs - pbt 0.0021411 16.33 -243 0.050 -889 0.0110511 0.01354 7666 0.73IADubuque 2 cs - vs - pbt 0.0021411 16.33 -243 0.050 -889 0.0110511 0.01354 7666 0.73KSKawPoint 1 vs 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76KSKawPoint 2 vs 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76LANewOrleansEastBank1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73MALynnRegional 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73MALynnRegional 2 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73MIYpsilantiEUFBSSIvs- imp - wesp -ac polish.0.00047 -308 2.64 -2020 0.282 -73 0.00618 -59 0.00057 -482 -492 -5.47MNStPaulMetro FBR1ac inject. - bag -vs(ad) - wesp0.00043 -344 23.46 -139 0.167 -193 0.00288 -241 0.00170 -95 -95 -1.06MNStPaulMetro FBR2ac inject. - bag -vs(ad) - wesp0.00075 -154 23.71 -136 0.156 -215 0.00262 -273 0.00089 -271 -271 -3.01MNStPaulMetro FBR3ac inject. - bag -vs(ad) - wesp0.00069 -174 20.46 -174 0.200 -144 0.00240 -309 0.00039 -753 -753 -8.37MOLittleBlueValley 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73MORockCreek 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NCBuncombeAshville 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NCTZOsborne ES-1abd - vs - imp -hss - cs0.00017 -988 11.39 -392 0.044 -1008 0.00031 -3028 0.04113 9282 0.91NHManchester 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NJBayshoreRegional 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NJBayshoreRegional 2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NJCamden 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NJGloucester 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NJGloucester 2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NJNorthwestBergen 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73C-2Facility IDUnitIDExistingControlDevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb) Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)EG Limit (mg/dscm):0.0019EG Limit (ppmvd): 56 EG Limit (ppmvd): 0.49EG Limit (mg/dscm):0.0098EG Limit (mg/dscm):0.0033Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% Improvement NeededNJNorthwestBergen 2 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NJPequannockLincolnFairfield1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NJPequannockLincolnFairfield2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NJSomersetRaritan 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NJSomersetRaritan 2 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73NYArlington 1 unknown 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76NYErieCounty 1 vs 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76NYErieCounty 2 vs 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76NYGlensFalls 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NYOneidaCounty 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NYOneidaCounty 2 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NYOneidaCounty 3 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NYPortChester 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NYPortChester 2 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73NYSaratogaCounty 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73OHLittleMiami 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73OHNEORSDEasterly 1abo - imp -wesp0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73PAAlleghenyCounty 001abd - mc - vs -imp0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73PAAlleghenyCounty 002abd - mc - vs -imp0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73PAWyomingValley 1 vs - imp - wesp 0.00085 -122 16.33 -243 0.124 -295 0.00442 -122 0.01354 7666 0.73PRPuertoNuevo 1 vs(ad) - wesp 0.00085 -122 16.33 -243 0.050 -889 0.00442 -122 0.01354 7676 0.84SCFelixCDavis 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73VABlacksburg 1 unknown 0.0021411 16.33 -243 2.47880 0.0110511 0.01504 7868 0.76VAHLMooney 2 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73WAAnacortes 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73WAEdmonds 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73WALynnwood 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73WAWestside 1 vs - imp 0.0021411 16.33 -243 0.124 -295 0.0110511 0.01354 7666 0.73C-3Table C-1a. Percent Improvement Needed to Meet MACT floorand Additional ControlsRequired: Fluidized BedIncineratorsPart 2Red cells indicate where additional control is neededFacility IDUnitIDExistingControlDevicesNitrogen Oxides (NOx)Particulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2)Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)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): 0.61EG Limit (ng/dscm):0.056Average(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAKJuneau 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196CTMattabassett 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196CTSynagroWaterbury 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196CTWestHaven 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196GANoondayCreek 1 unknown 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196IADubuque 1 cs - vs - pbt 27.93 -126 12.44 4 11.80 7 1.32 -1565 15.962196 0.98 1.312196IADubuque 2 cs - vs - pbt 27.93 -126 12.44 4 11.80 7 1.32 -1565 15.962196 0.98 1.312196KSKawPoint 1 vs 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196KSKawPoint 2 vs 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196LANewOrleansEastBank 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196MALynnRegional 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196MALynnRegional 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196MIYpsilantiEUFBSSIvs- imp - wesp -ac polish.29.76 -112 2.87 -317 4.83 -128 3.30 -566 0.1469 -315 -3.21 0.0064 -772MNStPaulMetroFBR1ac inject. - bag -vs(ad) - wesp31.00 -103 2.26 -432 1.72 -538 0.62 -3468 0.4048 -51 -0.52 0.0356 -57MNStPaulMetroFBR2ac inject. - bag -vs(ad) - wesp41.44 -52 1.41 -750 1.57 -601 1.65 -1235 0.4060 -50 -0.51 0.0367 -52MNStPaulMetroFBR3ac inject. - bag -vs(ad) - wesp22.53 -180 5.38 -123 1.45 -659 1.13 -1843 0.4054 -50 -0.51 0.0362 -55MOLittleBlueValley 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196MORockCreek 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NCBuncombeAshville 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NCTZOsborne ES-1abd - vs - imp -hss - cs14.90 -323 2.58 -366 11.16 1 7.64 -188 15.962196 0.98 1.312196NHManchester 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NJBayshoreRegional 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NJBayshoreRegional 2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NJCamden 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NJGloucester 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NJGloucester 2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NJNorthwestBergen 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NJNorthwestBergen 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196C-4Facility IDUnitIDExistingControlDevicesNitrogen Oxides (NOx)Particulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2)Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)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): 0.61EG Limit (ng/dscm):0.056Average(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededNJPequannockLincolnFairfield1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NJPequannockLincolnFairfield2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NJSomersetRaritan 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NJSomersetRaritan 2 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196NYArlington 1 unknown 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196NYErieCounty 1 vs 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196NYErieCounty 2 vs 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196NYGlensFalls 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NYOneidaCounty 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NYOneidaCounty 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NYOneidaCounty 3 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NYPortChester 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NYPortChester 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196NYSaratogaCounty 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196OHLittleMiami 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196OHNEORSDEasterly 1abo - imp -wesp27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196PAAlleghenyCounty 001abd - mc - vs -imp27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196PAAlleghenyCounty 002abd - mc - vs -imp27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196PAWyomingValley 1 vs - imp - wesp 27.93 -126 2.49 -382 2.36 -366 3.30 -566 15.962196 0.98 1.312196PRPuertoNuevo 1 vs(ad) - wesp 27.93 -126 2.49 -382 2.36 -366 1.32 -1565 15.962196 0.98 1.312196SCFelixCDavis 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196VABlacksburg 1 unknown 27.93 -126 12.44 4 11.80 7 66.0567 15.962196 0.98 1.312196VAHLMooney 2 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196WAAnacortes 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196WAEdmonds 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196WALynnwood 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196WAWestside 1 vs - imp 27.93 -126 12.44 4 11.80 7 3.30 -566 15.962196 0.98 1.312196C-5Table C-1a. Percent Improvement Needed to Meet MACT floorand Additional ControlsRequired: Fluidized BedIncineratorsPart 3Red cells indicate where additional control is neededFacility IDUnit IDExisting Control DevicesACI PerformanceAdjustment Factor2(CDD/CDF TEQ basis)Max ACIAdjustmentFactorMACT Floor Control Needs(if % improvement >10, add control)FFScrubberACIAKJuneau 1 vs - imp 0.98 0.98 add FF add ACICTMattabassett 1 vs - imp 0.98 0.98 add FF add ACICTSynagroWaterbury 1 vs - imp 0.98 0.98 add FF add ACICTWestHaven 1 vs - imp 0.98 0.98 add FF add ACIGANoondayCreek 1 unknown 0.98 0.98 add FF add PBS add ACIIADubuque 1 cs - vs - pbt 0.98 0.98 add FF add ACIIADubuque 2 cs - vs - pbt 0.98 0.98 add FF add ACIKSKawPoint 1 vs 0.98 0.98 add FF add PBS add ACIKSKawPoint 2 vs 0.98 0.98 add FF add PBS add ACILANewOrleansEastBank 1 vs - imp 0.98 0.98 add FF add ACIMALynnRegional 1 vs - imp 0.98 0.98 add FF add ACIMALynnRegional 2 vs - imp 0.98 0.98 add FF add ACIMIYpsilanti EU-FBSSI vs- imp - wesp - ac polish. -7.88 -3.21MNStPaulMetro FBR1ac inject. - bag - vs(ad) -wesp-0.58 -0.52MNStPaulMetro FBR2ac inject. - bag - vs(ad) -wesp-0.53 -0.51MNStPaulMetro FBR3ac inject. - bag - vs(ad) -wesp-0.56 -0.51MOLittleBlueValley 1 vs - imp 0.98 0.98 add FF add ACIMORockCreek 1 vs - imp 0.98 0.98 add FF add ACINCBuncombeAshville 1 vs - imp 0.98 0.98 add FF add ACINCTZOsborne ES-1 abd - vs - imp - hss - cs 0.98 0.98 add ACINHManchester 1 vs - imp 0.98 0.98 add FF add ACINJBayshoreRegional 1 vs - imp - wesp 0.98 0.98 add ACINJBayshoreRegional 2 vs - imp - wesp 0.98 0.98 add ACINJCamden 1 vs - imp 0.98 0.98 add FF add ACINJGloucester 1 vs - imp - wesp 0.98 0.98 add ACINJGloucester 2 vs - imp - wesp 0.98 0.98 add ACINJNorthwestBergen 1 vs - imp 0.98 0.98 add FF add ACINJNorthwestBergen 2 vs - imp 0.98 0.98 add FF add ACINJPequannockLincolnFairfield 1 vs - imp 0.98 0.98 add FF add ACINJPequannockLincolnFairfield 2 vs - imp - wesp 0.98 0.98 add ACINJSomersetRaritan 1 vs - imp - wesp 0.98 0.98 add ACINJSomersetRaritan 2 vs - imp - wesp 0.98 0.98 add ACINYArlington 1 unknown 0.98 0.98 add FF add PBS add ACINYErieCounty 1 vs 0.98 0.98 add FF add PBS add ACIC-6Facility IDUnit IDExisting Control DevicesACI PerformanceAdjustment Factor2(CDD/CDF TEQ basis)Max ACIAdjustmentFactorMACT Floor Control Needs(if % improvement >10, add control)FFScrubberACINYErieCounty 2 vs 0.98 0.98 add FF add PBS add ACINYGlensFalls 1 vs - imp 0.98 0.98 add FF add ACINYOneidaCounty 1 vs - imp 0.98 0.98 add FF add ACINYOneidaCounty 2 vs - imp 0.98 0.98 add FF add ACINYOneidaCounty 3 vs - imp 0.98 0.98 add FF add ACINYPortChester 1 vs - imp 0.98 0.98 add FF add ACINYPortChester 2 vs - imp 0.98 0.98 add FF add ACINYSaratogaCounty 1 vs - imp 0.98 0.98 add FF add ACIOHLittleMiami 1 vs - imp 0.98 0.98 add FF add ACIOHNEORSDEasterly 1 abo - imp - wesp 0.98 0.98 add FF add ACIPAAlleghenyCounty 001 abd - mc - vs - imp 0.98 0.98 add FF add ACIPAAlleghenyCounty 002 abd - mc - vs - imp 0.98 0.98 add FF add ACIPAWyomingValley 1 vs - imp - wesp 0.98 0.98 add ACIPRPuertoNuevo 1 vs(ad) - wesp 0.98 0.98 add ACISCFelixCDavis 1 vs - imp 0.98 0.98 add FF add ACIVABlacksburg 1 unknown 0.98 0.98 add FF add PBS add ACIVAHLMooney 2 vs - imp 0.98 0.98 add FF add ACIWAAnacortes 1 vs - imp 0.98 0.98 add FF add ACIWAEdmonds 1 vs - imp 0.98 0.98 add FF add ACIWALynnwood 1 vs - imp 0.98 0.98 add FF add ACIWAWestside 1 vs - imp 0.98 0.98 add FF add ACINOTE: Data gaps in pollutant concentrations were filled using values found for similar units or using the average concentrationover the entire subcategory. ForDioxin/Furan TEQ concentrations, no data was available for the subcategory, so TEQ concentrations were assumed to be 57% of theTMB 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 beused to adjust total annualcosts to the estimated reduction efficiency needed to meet the floor.C-7Table C-1b. Percent Improvement Needed to Meet MACT floorand Additional ControlsRequired: Multiple HearthIncineratorsPart 1Red cells indicate where additional control is neededFacility IDUnitIDExistingControl DevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb)Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)Nitrogen Oxides(NOx)EG Limit (mg/dscm):0.095EG Limit (ppmvd):3900EG Limit (ppmvd): 1.0EG Limit (mg/dscm):0.30EG Limit (mg/dscm):0.17EG Limit (ppmvd):210Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% ImprovementNeededAverage(ppmvd)%Improve-mentNeededAKJohnMAsplund 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58CACentralContraCostaMHF 1abo - cs - vs -imp0.0150 -534 836.4 -366 0.79 -26 0.060 -398 0.051 -231 -241 -2.68 127.7 -64CACentralContraCostaMHF 2abo - cs - vs -imp0.0176 -439 752.1 -419 0.79 -26 0.036 -740 0.065 -160 -170 -1.89 172.3 -22CAPaloAlto 1 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58CAPaloAlto 2 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58CTHartford 001abo - fgr - vs -imp0.0053 -1701 853.9 -357 0.65 -53 0.015 -1873 0.124 -37 -47 -0.52 106.6 -97CTHartford 002abo - fgr - vs -imp0.0062 -1422 853.9 -357 0.65 -53 0.024 -1144 0.118 -44 -54 -0.60 106.6 -97CTHartford 3abo - fgr - vs -imp0.0058 -1550 853.9 -357 0.65 -53 0.020 -1426 0.121 -40 -50 -0.56 106.6 -97CTNaugatuck 1 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58CTNaugatuck 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58CTSynagroNewHaven1vs - imp - wesp -rto0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58GAPresidentStreet 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58GAPresidentStreet 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58GARLSutton 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58GARLSutton 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58GARMClayton 1 imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58GARMClayton 2 imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58GAUtoyCreek 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58GAUtoyCreek 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58GAWeyerhaeuser 1 unknown 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58IACedarRapids 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58INBelmontNorth 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.113 -50 -60 -0.67 133.3 -58INBelmontNorth 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -63 -73 -0.81 133.3 -58INBelmontNorth 3 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.091 -86 -96 -1.07 133.3 -58INBelmontNorth 4 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.106 -60 -70 -0.78 133.3 -58INBelmontNorth 5 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58INBelmontNorth 6 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58INBelmontNorth 7 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58C-8Facility IDUnitIDExistingControl DevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb)Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)Nitrogen Oxides(NOx)EG Limit (mg/dscm):0.095EG Limit (ppmvd):3900EG Limit (ppmvd): 1.0EG Limit (mg/dscm):0.30EG Limit (mg/dscm):0.17EG Limit (ppmvd):210Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% ImprovementNeededAverage(ppmvd)%Improve-mentNeededINBelmontNorth 8 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.104 -64 -74 -0.82 133.3 -58LANewOrleansEastBank2 unknown 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58MAFitchburgEast 1 vs - wesp - rto 0.0178 -433 853.9 -357 13.0992 0.038 -684 0.114 -49 -59 -0.66 133.3 -58MAUpperBlackstone 1agr - vs - imp -wesp - rto0.0042 -2153 27.6 -14042 0.34 -196 0.002 -14243 0.090 -89 -99 -1.10 76.4 -175MAUpperBlackstoneIncinerator 3agr - vs - imp -wesp - rto0.0041 -2231 59.4 -6470 0.31 -218 0.005 -5870 0.065 -160 -170 -1.89 68.5 -206MDWesternBranch 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MDWesternBranch 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIAnnArbor 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIBattleCreek 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIBattleCreek 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex1 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex1 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex1 3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex1 4 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex1 5 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex1 6 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 1 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 2 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 3 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 4 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 5 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 6 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 7 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIDetroitComplex2 8 hjs - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIFlint 1 abd - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIFlint 2 abd - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIFlint 3 abd - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIFlint 4 abd - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MIPontiacAuburn 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MIWarren 1 imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58MNSenecaIncinerator 1abo - vs 0.250962 1323.3 -195 0.42 -136 0.072 -318 0.301 4343 0.48 219.3 4MNSenecaIncinerator 2abo - vs 0.250962 823.7 -373 0.42 -136 0.072 -318 0.301 4343 0.48 219.3 4MOBigBlueRiver 1 abo - vs 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58MOBigBlueRiver 2 abo - vs 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58MOBigBlueRiver 3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOBissellPoint 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58C-9Facility IDUnitIDExistingControl DevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb)Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)Nitrogen Oxides(NOx)EG Limit (mg/dscm):0.095EG Limit (ppmvd):3900EG Limit (ppmvd): 1.0EG Limit (mg/dscm):0.30EG Limit (mg/dscm):0.17EG Limit (ppmvd):210Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% ImprovementNeededAverage(ppmvd)%Improve-mentNeededMOBissellPoint 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOBissellPoint 3 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOBissellPoint 4 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOBissellPoint 5 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOBissellPoint 6 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOLemay 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOLemay 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOLemay 3 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58MOLemay 4 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NCRockyRiver 1abd - vs - imp -wesp0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58NJAtlanticCounty 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NJAtlanticCounty 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NJMountainView #1vs - imp - wesp -rto0.0003 -28547 38.6 -9995 0.86 -16 0.001 -30629 0.099 -72 -82 -0.91 142.0 -48NJMountainView #2vs - imp - wesp -rto0.0003 -28547 38.6 -9995 0.86 -16 0.001 -30629 0.099 -72 -82 -0.91 142.0 -48NJParsippanyTroyHills1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NJParsippanyTroyHills2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NJStonyBrook 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NJStonyBrook 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYAlbanyCountyNorth1 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58NYAlbanyCountyNorth2 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58NYAlbanyCountySouth1 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58NYAlbanyCountySouth2 vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58NYAuburn 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYBirdIsland 1 abd - vs 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58NYBirdIsland 2 abd - vs 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58NYBirdIsland 3 abd - vs 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58NYFrankEVanLare 1 abo - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58NYFrankEVanLare 2 abo - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58NYFrankEVanLare 3 abo - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58NYNewRochelle 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYNewRochelle 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYNorthwestQuadrant1 abo - imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58C-10Facility IDUnitIDExistingControl DevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb)Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)Nitrogen Oxides(NOx)EG Limit (mg/dscm):0.095EG Limit (ppmvd):3900EG Limit (ppmvd): 1.0EG Limit (mg/dscm):0.30EG Limit (mg/dscm):0.17EG Limit (ppmvd):210Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% ImprovementNeededAverage(ppmvd)%Improve-mentNeededNYOrangetown 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYOssining 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYOssining 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYSchenectady 1 imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58NYSouthwestBergenPoint1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYSouthwestBergenPoint2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58NYTonawanda 1 unknown 0.0446 -113 853.9 -357 13.0992 0.096 -213 0.114 -49 -59 -0.66 133.3 -58OHCanton 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHCanton 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHColumbusSoutherly1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHColumbusSoutherly2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHColumbusSoutherly3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHColumbusSoutherly4 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHEuclid 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHEuclid 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHJacksonPike 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHJacksonPike 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHMillCreek 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHMillCreek 2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHMillCreek 3 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHMillCreek 4 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHMillCreek 5 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHMillCreek 6 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHNEORSDSoutherly1 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHNEORSDSoutherly2 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHNEORSDSoutherly3 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHNEORSDSoutherly4 abo - imp - wesp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHNEORSDWesterly1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHNEORSDWesterly2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHWilloughbyEastlake1 imp 0.445679 853.9 -357 0.65 -53 0.95769 0.103 -65 -75 -0.83 133.3 -58C-11Facility IDUnitIDExistingControl DevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb)Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)Nitrogen Oxides(NOx)EG Limit (mg/dscm):0.095EG Limit (ppmvd):3900EG Limit (ppmvd): 1.0EG Limit (mg/dscm):0.30EG Limit (mg/dscm):0.17EG Limit (ppmvd):210Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% ImprovementNeededAverage(ppmvd)%Improve-mentNeededOHYoungstown 1 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58OHYoungstown 2 abo - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58PADelawareCountyWestern1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58PADelawareCountyWestern2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58PAEastNorritonPlymouthWhitpain1 cs - vs(ad) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58PAErie 1 vs - wesp 0.0178 -433 853.9 -357 13.0992 0.038 -684 0.114 -49 -59 -0.66 133.3 -58PAErie 2 vs - wesp 0.0178 -433 853.9 -357 13.0992 0.038 -684 0.114 -49 -59 -0.66 133.3 -58PAHatfield 1vs - imp - wesp -rto0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58PAKiskiValley 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58PAUpperMorelandHatboro1 vs - imp - rto 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58RICranston 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58RICranston 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58RINewEngland 1 vs - imp - wesp 0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58SCColumbiaMetro 1abo/fgr - pbs - vs- imp0.0025 -3700 63.5 -6045 0.20 -398 0.004 -7886 0.074 -130 -140 -1.56 84.4 -149SCColumbiaMetro 2abo/fgr - pbs - vs- imp0.0025 -3700 63.5 -6045 0.20 -398 0.004 -7886 0.077 -121 -131 -1.46 84.4 -149SCPlumIsland 1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VAArmyBaseNorfolk1 ws - vs - imp 0.112215 853.9 -357 0.65 -53 0.39825 0.130 -31 -41 -0.46 133.3 -58VAArmyBaseNorfolk2 ws - vs - imp 0.112215 853.9 -357 0.65 -53 0.39825 0.130 -31 -41 -0.46 133.3 -58VABoatHarbor 1ws - vs - pbs -vs(ad)0.0537 -77 3761.0 -4 0.70 -42 0.069 -337 0.107 -59 -59 -0.66 154.5 -36VABoatHarbor 2ws - vs - pbs -vs(ad)0.0537 -77 3761.0 -4 0.70 -42 0.069 -337 0.107 -59 -59 -0.66 154.5 -36VAChesapeakeElizabeth1 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VAChesapeakeElizabeth2 vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VAHopewell 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VANomanCole 1 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VANomanCole 2 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VANomanCole 3 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VANomanCole 4 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VANomanCole 5 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VANomanCole 6 abd - vs - imp 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -75 -0.83 133.3 -58VAVirginiaInitiative 1 ws - vs - imp 0.0745 -28 853.9 -357 0.65 -53 0.46135 0.064 -166 -176 -1.96 133.3 -58C-12Facility IDUnitIDExistingControl DevicesCadmium (Cd)Carbon Monoxide(CO)Hydrogen Chloride(HCl) Lead (Pb)Mercury (Hg)WS or PBAdjustment1ACIPerformanceAdjustmentFactor2(Hg Basis)Nitrogen Oxides(NOx)EG Limit (mg/dscm):0.095EG Limit (ppmvd):3900EG Limit (ppmvd): 1.0EG Limit (mg/dscm):0.30EG Limit (mg/dscm):0.17EG Limit (ppmvd):210Average(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeeded% ImprovementNeededAverage(ppmvd)%Improve-mentNeededVAVirginiaInitiative 2 ws - vs - imp 0.0745 -28 853.9 -357 0.65 -53 0.46135 0.064 -166 -176 -1.96 133.3 -58VAWilliamsburg 1 vs - imp 0.0323 -194 853.9 -357 0.65 -53 0.097 -210 0.103 -65 -75 -0.83 133.3 -58VAWilliamsburg 2 vs - imp 0.0323 -194 853.9 -357 0.65 -53 0.097 -210 0.103 -65 -75 -0.83 133.3 -58WABellinghamPostPoint1 vs - imp - wesp 0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58WABellinghamPostPoint2 vs - imp - wesp 0.0178 -433 853.9 -357 0.65 -53 0.038 -684 0.103 -65 -75 -0.83 133.3 -58WIGreenBayMetro 1 vs(a) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58WIGreenBayMetro 2 vs(a) 0.0446 -113 853.9 -357 0.65 -53 0.096 -213 0.103 -65 -65 -0.72 133.3 -58C-13Table C-1b. Percent Improvement Needed to Meet MACT floorand Additional ControlsRequired: Multiple HearthIncineratorsPart 2Red cells indicate where additional control is neededFacility IDUnitIDExistingControl DevicesParticulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2) Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)Total Dioxin/Furans(TEQ)ACIPerformanceAdjustmentFactor2(CDD/CDFTEQ basis)EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5EG Limit (ng/dscm):0.32Average(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAKJohnMAsplund 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CACentralContraCosta MHF 1abo - cs - vs -imp31.42 -155 21.07 -175 7.27 -258 0.01 -52670 -537.45 0.001 -33797 -344.87CACentralContraCosta MHF 2abo - cs - vs -imp25.79 -210 21.07 -175 3.41 -663 0.01 -52670 -537.45 0.001 -33797 -344.87CAPaloAlto 1 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CAPaloAlto 2 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CTHartford 001abo - fgr - vs -imp36.09 -122 8.57 -577 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CTHartford 002abo - fgr - vs -imp36.09 -122 13.77 -321 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CTHartford 3abo - fgr - vs -imp36.09 -122 11.17 -419 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CTNaugatuck 1 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CTNaugatuck 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96CTSynagroNewHaven 1vs - imp - wesp -rto7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GAPresidentStreet 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GAPresidentStreet 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GARLSutton 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GARLSutton 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GARMClayton 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GARMClayton 2 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GAUtoyCreek 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GAUtoyCreek 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96GAWeyerhaeuser 1 unknown 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96IACedarRapids 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 1 abo - vs - imp 39.79 -101 21.97 -164 26.57 2 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 2 abo - vs - imp 40.31 -98 21.97 -164 23.90 -9 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 3 abo - vs - imp 33.85 -136 21.97 -164 5.51 -372 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 4 abo - vs - imp 17.55 -356 21.97 -164 1.75 -1382 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 5 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 6 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96INBelmontNorth 7 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96C-14Facility IDUnitIDExistingControl DevicesParticulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2) Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)Total Dioxin/Furans(TEQ)ACIPerformanceAdjustmentFactor2(CDD/CDFTEQ basis)EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5EG Limit (ng/dscm):0.32Average(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededINBelmontNorth 8 abo - vs - imp 32.87 -143 21.97 -164 14.43 -80 0.69 -619 -6.32 0.047 -584 -5.96LANewOrleansEastBank 2 unknown 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96MAFitchburgEast 1 vs - wesp - rto 7.22 -1008 4.39 -1220 186.3486 0.69 -619 -6.32 0.047 -584 -5.96MAUpperBlackstone 1agr - vs - imp -wesp - rto1.75 -4464 2.62 -2111 1.20 -2067 0.12 -4036 -41.18 0.006 -5068 -51.71MAUpperBlackstoneIncinerator 3agr - vs - imp -wesp - rto1.21 -6528 2.62 -2111 2.70 -864 0.12 -4036 -41.18 0.006 -5068 -51.71MDWesternBranch 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MDWesternBranch 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIAnnArbor 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIBattleCreek 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIBattleCreek 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex1 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex1 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex1 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex1 4 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex1 5 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex1 6 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 1 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 2 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 3 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 4 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 5 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 6 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 7 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIDetroitComplex2 8 hjs - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIFlint 1 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIFlint 2 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIFlint 3 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIFlint 4 abd - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIPontiacAuburn 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MIWarren 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MNSenecaIncinerator 1abo - vs 78.76 -2 51.13 -13 18.23 -43 0.76 -559 -5.70 0.069 -362 -3.69MNSenecaIncinerator 2abo - vs 76.16 -5 51.13 -13 18.23 -43 0.76 -559 -5.70 0.069 -362 -3.69MOBigBlueRiver 1 abo - vs 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96MOBigBlueRiver 2 abo - vs 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96MOBigBlueRiver 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOBissellPoint 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96C-15Facility IDUnitIDExistingControl DevicesParticulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2) Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)Total Dioxin/Furans(TEQ)ACIPerformanceAdjustmentFactor2(CDD/CDFTEQ basis)EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5EG Limit (ng/dscm):0.32Average(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededMOBissellPoint 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOBissellPoint 3 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOBissellPoint 4 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOBissellPoint 5 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOBissellPoint 6 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOLemay 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOLemay 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOLemay 3 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96MOLemay 4 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NCRockyRiver 1abd - vs - imp -wesp7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NJAtlanticCounty 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NJAtlanticCounty 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NJMountainView #1vs - imp - wesp -rto3.93 -1934 4.83 -1101 9.28 -180 0.22 -2156 -22.00 0.014 -2160 -22.04NJMountainView #2vs - imp - wesp -rto3.93 -1934 4.83 -1101 9.28 -180 0.22 -2156 -22.00 0.014 -2160 -22.04NJParsippanyTroyHills 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NJParsippanyTroyHills 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NJStonyBrook 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NJStonyBrook 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYAlbanyCountyNorth 1 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYAlbanyCountyNorth 2 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYAlbanyCountySouth 1 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYAlbanyCountySouth 2 vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYAuburn 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYBirdIsland 1 abd - vs 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96NYBirdIsland 2 abd - vs 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96NYBirdIsland 3 abd - vs 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96NYFrankEVanLare 1 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYFrankEVanLare 2 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYFrankEVanLare 3 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYNewRochelle 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYNewRochelle 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYNorthwestQuadrant 1 abo - imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYOrangetown 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYOssining 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYOssining 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYSchenectady 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYSouthwestBergenPoint 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96C-16Facility IDUnitIDExistingControl DevicesParticulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2) Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)Total Dioxin/Furans(TEQ)ACIPerformanceAdjustmentFactor2(CDD/CDFTEQ basis)EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5EG Limit (ng/dscm):0.32Average(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededNYSouthwestBergenPoint 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96NYTonawanda 1 unknown 36.09 -122 21.97 -164 186.3486 0.69 -619 -6.32 0.047 -584 -5.96OHCanton 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHCanton 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHColumbusSoutherly 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHColumbusSoutherly 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHColumbusSoutherly 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHColumbusSoutherly 4 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHEuclid 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHEuclid 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHJacksonPike 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHJacksonPike 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHMillCreek 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHMillCreek 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHMillCreek 3 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHMillCreek 4 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHMillCreek 5 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHMillCreek 6 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHNEORSDSoutherly 1 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHNEORSDSoutherly 2 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHNEORSDSoutherly 3 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHNEORSDSoutherly 4 abo - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHNEORSDWesterly 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHNEORSDWesterly 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHWilloughbyEastlake 1 imp 72.19 -11 43.93 -32 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHYoungstown 1 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96OHYoungstown 2 abo - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96PADelawareCountyWestern1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96PADelawareCountyWestern2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96PAEastNorritonPlymouthWhitpain1 cs - vs(ad) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96PAErie 1 vs - wesp 7.22 -1008 4.39 -1220 186.3486 0.69 -619 -6.32 0.047 -584 -5.96PAErie 2 vs - wesp 7.22 -1008 4.39 -1220 186.3486 0.69 -619 -6.32 0.047 -584 -5.96PAHatfield 1vs - imp - wesp -rto7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96PAKiskiValley 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96PAUpperMorelandHatboro1 vs - imp - rto 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96RICranston 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96C-17Facility IDUnitIDExistingControl DevicesParticulate Matter(filterable)Particulate Matter(PM 2.5)Sulfur Dioxide (SO2) Total Dioxin/FuransACIPerformanceAdjustmentFactor2(CDD/CDFTMB basis)Total Dioxin/Furans(TEQ)ACIPerformanceAdjustmentFactor2(CDD/CDFTEQ basis)EG Limit (mg/dscm): 80 EG Limit (mg/dscm): 58 EG Limit (ppmvd): 26 EG Limit (ng/dscm): 5EG Limit (ng/dscm):0.32Average(mg/dscm)%ImprovementNeededAverage(mg/dscm)%ImprovementNeededAverage(ppmvd)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededAverage(ng/dscm)%ImprovementNeededRICranston 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96RINewEngland 1 vs - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96SCColumbiaMetro 1abo/fgr - pbs - vs- imp11.11 -620 21.97 -164 7.99 -225 2.36 -112 -1.14 0.143 -123 -1.26SCColumbiaMetro 2abo/fgr - pbs - vs- imp14.63 -447 21.97 -164 7.99 -225 2.36 -112 -1.14 0.143 -123 -1.26SCPlumIsland 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAArmyBaseNorfolk 1 ws - vs - imp 72.18 -11 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAArmyBaseNorfolk 2 ws - vs - imp 72.18 -11 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VABoatHarbor 1ws - vs - pbs -vs(ad)57.89 -38 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VABoatHarbor 2ws - vs - pbs -vs(ad)57.89 -38 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAChesapeakeElizabeth 1 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAChesapeakeElizabeth 2 vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAHopewell 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VANomanCole 1 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VANomanCole 2 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VANomanCole 3 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VANomanCole 4 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VANomanCole 5 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VANomanCole 6 abd - vs - imp 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAVirginiaInitiative 1 ws - vs - imp 39.44 -103 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAVirginiaInitiative 2 ws - vs - imp 39.44 -103 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAWilliamsburg 1 vs - imp 40.25 -99 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96VAWilliamsburg 2 vs - imp 40.25 -99 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96WABellinghamPostPoint 1 vs - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96WABellinghamPostPoint 2 vs - imp - wesp 7.22 -1008 4.39 -1220 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96WIGreenBayMetro 1 vs(a) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96WIGreenBayMetro 2 vs(a) 36.09 -122 21.97 -164 9.32 -179 0.69 -619 -6.32 0.047 -584 -5.96C-18 Table C-1b. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Multiple Hearth Incinerators

Part 3 Red cells indicate where a dditional contro l is needed

Facility ID Unit ID Existing Control Devices Max ACI Adjustment

Factor MACT Floor Control Needs (if %improvement >10, add control)

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 FFC-19 Facility ID Unit ID Existing Control Devices Max ACI Adjustment

Factor MACT Floor Control Needs (if %improvement >10, add control)

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 -0.83C-20 Facility ID Unit ID Existing Control Devices Max ACI Adjustment

Factor MACT Floor Control Needs (if %improvement >10, add control)

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 inst alling 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: Fabr ic 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 fl ow rate, dscfm (Q) 4. PM concentration, gr/dscf (PM) 5. Water vapor in gas from incinerator (10% by weight)
a. lb/min = Q / (385 ft 3 /lb-mol) x (29 lb/lb-mol) x moisture content (0.10) b. scfm = (lb/min) / (18 lb/lb-mol) x (385 ft 3 /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., coo ling 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 ft 3 (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 WCC-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 ft 3 air/1,000 ft 3 filtered) 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/ft 2 ) 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 ft 2 bag 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 ft 2 bag 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 ft 2 bag area) x (number of bags) x ( 525.4/317.4) + (cage replacement labor cost)] D. Indirect Annual Costs, $/yr 1. Overhead = 0.6 x (la bor + 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 SurveyC-23 Table C-2b. Control Costs: Pack ed-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 conten t in gas entering quench, % (M) 10

7. Exhaust gas flow rate, sc fm (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 ft 3 -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 co st, $/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 ( Δ P) 15

17. Surface area-to-volume ratio for 1" dia. Ceramic Raschig rings, ft 2 /ft 3 (SAV) 58

18. Minimum packing wetting rate, ft 2 /hr (WR) 1 19. Water density, lb/ft 3 (Wd) 62.4 20. Water circulation flow rate, lb/hr-ft 2 (Gs) = SAV x Wd x WR
21. Estimated column cross-s ectional area from separate analysis, ft 2 (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 ft 3 ) 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 Δ P 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 (la bor + 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 Wast e 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. Av erage 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 Ca rbon 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 fl ow 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 (la bor + 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 activat ed carbon vendor. 4. Dust disposal cost: NSWMA’s 2005 Tip Fee Survey.C-26Table C-3. Unit-Specific Inputs Used in Cost AlgorithmsFacility IDUnitIDUnitTypeCapacity(dtph)Capacity(dry lb/hr)Sludge FeedRate(dry tons/hr)Sludge FeedRate(dry lb/hr)OperatingHours(hr/yr)Stack GasFlow Rate(dscfm)StackGasTemp1(°F)ACIAdjustmentFactorLandfillTippingFeePM(gr/dscf)HCl(ppmvd)AKJuneau 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 43.02 0.0054 0.12CTMattabassett 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 80.40 0.0054 0.12CTSynagroWaterbury 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 80.40 0.0054 0.12CTWestHaven 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 80.40 0.0054 0.12GANoondayCreek 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 35.31 0.0054 2.48IADubuque 1 FB 1.70 3400 1.28 2550 4200 6956 1050 0.98 39.85 0.0054 0.05IADubuque 2 FB 1.70 3400 1.28 2550 4200 6956 1050 0.98 39.85 0.0054 0.05KSKawPoint 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 27.51 0.0054 2.48KSKawPoint 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 27.51 0.0054 2.48LANewOrleansEastBank 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 34.61 0.0054 0.12MALynnRegional 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 84.53 0.0054 0.12MALynnRegional 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 84.53 0.0054 0.12MIYpsilanti EUFBSSIFB 3.46 6920 2.85 5692 3240 14465 1050 -1.86 39.85 0.0013 0.28MNStPaulMetro FBR1 FB 5.42 10840 4.19 8372 7270 21898 1050 -0.47 39.85 0.0010 0.17MNStPaulMetro FBR2 FB 5.42 10840 3.94 7876 7270 20984 1050 -0.49 39.85 0.0006 0.16MNStPaulMetro FBR3 FB 5.42 10840 3.76 7523 7270 19859 1050 -0.48 39.85 0.0024 0.20MOLittleBlueValley 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 39.85 0.0054 0.12MORockCreek 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 39.85 0.0054 0.12NCBuncombeAshville 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 37.45 0.0054 0.12NCTZOzborne ES-1 FB 3.25 6500 2.42 4840 8400 10281 1050 1.02 37.45 0.0011 0.04NHManchester 1 FB 2.00 4000 1.50 3000 8400 8184 1050 0.98 80.40 0.0054 0.12NJBayshoreRegional 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12NJBayshoreRegional 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12NJCamden 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 77.04 0.0054 0.12NJGloucester 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12NJGloucester 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12NJNorthwestBergen 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0054 0.12NJNorthwestBergen 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0054 0.12NJPequannockLincolnFairfield1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0054 0.12NJPequannockLincolnFairfield2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 77.04 0.0011 0.12NJSomersetRaritan 1 FB 0.65 1300 0.49 975 4200 2660 1050 0.98 77.04 0.0011 0.12NJSomersetRaritan 2 FB 1.33 2660 1.00 1994 4200 5439 1050 0.98 77.04 0.0011 0.12NYArlington 1 FB 0.35 700 0.26 525 8400 1432 1050 0.98 59.92 0.0054 2.48NYErieCounty 1 FB 0.78 1560 0.59 1172 4200 3197 1050 0.98 59.92 0.0054 2.48NYErieCounty 2 FB 0.78 1560 0.59 1172 4200 3197 1050 0.98 59.92 0.0054 2.48NYGlensFalls 1 FB 1.54 3080 1.16 2310 8400 6301 1050 0.98 59.92 0.0054 0.12C-27Facility IDUnitIDUnitTypeCapacity(dtph)Capacity(dry lb/hr)Sludge FeedRate(dry tons/hr)Sludge FeedRate(dry lb/hr)OperatingHours(hr/yr)Stack GasFlow Rate(dscfm)StackGasTemp1(°F)ACIAdjustmentFactorLandfillTippingFeePM(gr/dscf)HCl(ppmvd)NYOneidaCounty 1 FB 0.84 1680 0.63 1253 8400 3417 1050 0.98 59.92 0.0054 0.12NYOneidaCounty 2 FB 0.84 1680 0.63 1253 8400 3417 1050 0.98 59.92 0.0054 0.12NYOneidaCounty 3 FB 0.84 1680 0.63 1253 360 3417 1050 0.98 59.92 0.0054 0.12NYPortChester 1 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 59.92 0.0054 0.12NYPortChester 2 FB 2.26 4516 1.69 3387 4200 9240 1050 0.98 59.92 0.0054 0.12NYSaratogaCounty 1 FB 1.44 2880 1.08 2156 8400 5882 1050 0.98 59.92 0.0054 0.12OHLittleMiami 1 FB 3.00 6000 2.25 4500 8400 12275 1050 0.98 34.24 0.0054 0.12OHNEORSDEasterly 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 34.24 0.0011 0.12PAAlleghenyCounty 001 FB 3.25 6500 1.88 3760 8400 10257 1050 0.98 52.77 0.0054 0.12PAAlleghenyCounty 002 FB 3.25 6500 1.88 3760 8400 10257 1050 0.98 52.77 0.0054 0.12PAWyomingValley 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 52.77 0.0011 0.12PRPuertoNuevo 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 36.69 0.0011 0.05SCFelixCDavis 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 38.52 0.0054 0.12VABlacksburg 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 52.77 0.0054 2.48VAHLMooney 2 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 52.77 0.0054 0.12WAAnacortes 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 48.47 0.0054 0.12WAEdmonds 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 48.47 0.0054 0.12WALynnwood 1 FB 2.26 4516 1.69 3387 8400 9240 1050 0.98 48.47 0.0054 0.12WAWestside 1 FB 2.42 4840 1.81 3625 8400 9888 1050 0.98 48.47 0.0054 0.12AKJohnMAsplund 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 43.02 0.0158 0.65CACentralContraCosta MHF 1 MH 2.50 5000 1.95 3900 4200 23132 1050 -2.61 43.02 0.0137 0.79CACentralContraCosta MHF 2 MH 2.50 5000 1.54 3085 4200 22925 1050 -1.78 43.02 0.0113 0.79CAPaloAlto 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 43.02 0.0158 0.65CAPaloAlto 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 43.02 0.0158 0.65CTHartford 001 MH 2.50 5000 2.38 4759 6016 17217 1050 -0.41 80.40 0.0158 0.65CTHartford 002 MH 2.50 5000 2.30 4603 6016 16360 1050 -0.49 80.40 0.0158 0.65CTHartford 3 MH 2.50 5000 1.88 3750 360 18080 1050 -0.44 80.40 0.0158 0.65CTNaugatuck 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 80.40 0.0032 0.65CTNaugatuck 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 80.40 0.0158 0.65CTSynagroNewHaven 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 80.40 0.0032 0.65GAPresidentStreet 1 MH 0.38 760 0.29 576 4200 2777 1050 -0.72 35.31 0.0158 0.65GAPresidentStreet 2 MH 0.38 760 0.29 576 4200 2777 1050 -0.72 35.31 0.0158 0.65GARLSutton 1 MH 0.25 500 0.19 375 4200 1808 1050 -0.72 35.31 0.0158 0.65GARLSutton 2 MH 0.25 500 0.19 375 4200 1808 1050 -0.72 35.31 0.0158 0.65GARMClayton 1 MH 1.25 2500 0.94 1875 4200 9040 1050 -0.72 35.31 0.0315 0.65GARMClayton 2 MH 1.25 2500 0.94 1875 4200 9040 1050 -0.72 35.31 0.0315 0.65GAUtoyCreek 1 MH 1.75 3500 1.31 2625 4200 12656 1050 -0.72 35.31 0.0158 0.65GAUtoyCreek 2 MH 1.75 3500 1.31 2625 4200 12656 1050 -0.72 35.31 0.0158 0.65GAWeyerhaeuser 1 MH 3.52 7040 2.64 5279 8400 25454 1050 -0.66 35.31 0.0158 13.09IACedarRapids 1 MH 3.92 7840 2.94 5880 8400 28349 1050 -0.72 39.85 0.0158 0.65C-28Facility IDUnitIDUnitTypeCapacity(dtph)Capacity(dry lb/hr)Sludge FeedRate(dry tons/hr)Sludge FeedRate(dry lb/hr)OperatingHours(hr/yr)Stack GasFlow Rate(dscfm)StackGasTemp1(°F)ACIAdjustmentFactorLandfillTippingFeePM(gr/dscf)HCl(ppmvd)INBelmontNorth 1 MH 2.60 5200 2.03 4060 4200 7085 1050 -0.67 31.64 0.0174 0.65INBelmontNorth 2 MH 2.60 5200 2.15 4293 4200 19574 1050 -0.70 31.64 0.0176 0.65INBelmontNorth 3 MH 2.60 5200 2.12 4233 4200 7888 1050 -0.96 31.64 0.0148 0.65INBelmontNorth 4 MH 2.60 5200 2.09 4187 4200 20699 1050 -0.67 31.64 0.0077 0.65INBelmontNorth 5 MH 2.00 4000 1.50 3000 4200 7662 1050 -0.71 31.64 0.0144 0.65INBelmontNorth 6 MH 2.00 4000 1.50 3000 4200 20410 1050 -0.71 31.64 0.0144 0.65INBelmontNorth 7 MH 2.00 4000 1.50 3000 4200 7413 1050 -0.71 31.64 0.0144 0.65INBelmontNorth 8 MH 2.00 4000 1.50 3000 4200 20185 1050 -0.71 31.64 0.0144 0.65LANewOrleansEastBank 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 34.61 0.0158 13.09MAFitchburgEast 1 MH 2.30 4600 1.72 3443 8400 16597 1050 -0.66 84.53 0.0032 13.09MAUpperBlackstone 1 MH 3.00 6000 1.79 3587 8544 6271 1050 -0.99 84.53 0.0008 0.34MAUpperBlackstone Incinerator 3MH 3.00 6000 1.96 3921 216 14421 1050 -1.67 84.53 0.0005 0.31MDWesternBranch 1 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 55.64 0.0158 0.65MDWesternBranch 2 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 55.64 0.0158 0.65MIAnnArbor 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 39.85 0.0158 0.65MIBattleCreek 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIBattleCreek 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex1 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex1 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex1 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex1 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex1 5 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex1 6 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MIDetroitComplex2 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 5 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 6 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 7 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIDetroitComplex2 8 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIFlint 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIFlint 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIFlint 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIFlint 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0315 0.65MIPontiacAuburn 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 39.85 0.0158 0.65MIWarren 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 39.85 0.0315 0.65MNSeneca Incinerator 1MH 1.58 3160 1.34 2676 4000 16607 1050 0.48 39.85 0.0344 0.42C-29Facility IDUnitIDUnitTypeCapacity(dtph)Capacity(dry lb/hr)Sludge FeedRate(dry tons/hr)Sludge FeedRate(dry lb/hr)OperatingHours(hr/yr)Stack GasFlow Rate(dscfm)StackGasTemp1(°F)ACIAdjustmentFactorLandfillTippingFeePM(gr/dscf)HCl(ppmvd)MNSeneca Incinerator 2MH 1.58 3160 1.42 2843 4000 15606 1050 0.37 39.85 0.0333 0.42MOBigBlueRiver 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 39.85 0.0158 13.09MOBigBlueRiver 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 39.85 0.0158 13.09MOBigBlueRiver 3 MH 2.69 5381 2.02 4036 360 19458 1050 -0.72 39.85 0.0158 0.65MOBissellPoint 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOBissellPoint 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOBissellPoint 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOBissellPoint 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOBissellPoint 5 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOBissellPoint 6 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOLemay 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOLemay 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOLemay 3 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65MOLemay 4 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 39.85 0.0158 0.65NCRockyRiver 1 MH 2.97 5940 2.23 4452 8400 21464 1050 -0.72 37.45 0.0032 0.65NJAtlanticCounty 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65NJAtlanticCounty 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65NJMountainView #1 MH 0.80 1600 0.80 1597 2715 7698 1050 -0.80 77.04 0.0017 0.86NJMountainView #2 MH 0.80 1600 0.80 1597 2715 9267 1050 -0.80 77.04 0.0017 0.86NJParsippanyTroyHills 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65NJParsippanyTroyHills 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65NJStonyBrook 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65NJStonyBrook 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 77.04 0.0158 0.65NYAlbanyCountyNorth 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYAlbanyCountyNorth 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYAlbanyCountySouth 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYAlbanyCountySouth 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYAuburn 1 MH 2.69 5381 2.02 4036 8400 194581050 -0.72 59.92 0.0158 0.65NYBirdIsland 1 MH 14.04 28080 10.53 21063 8400 101548 1050 -0.66 59.92 0.0158 13.09NYBirdIsland 2 MH 14.04 28080 10.53 21063 8400 101548 1050 -0.66 59.92 0.0158 13.09NYBirdIsland 3 MH 14.04 28080 10.53 21063 360 101548 1050 -0.66 59.92 0.0158 13.09NYFrankEVanLare 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65NYFrankEVanLare 2 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65NYFrankEVanLare 3 MH 2.69 5381 2.02 4036 360 19458 1050 -0.72 59.92 0.0315 0.65NYNewRochelle 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYNewRochelle 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYNorthwestQuadrant 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65NYOrangetown 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0158 0.65NYOssining 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65NYOssining 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 59.92 0.0158 0.65C-30Facility IDUnitIDUnitTypeCapacity(dtph)Capacity(dry lb/hr)Sludge FeedRate(dry tons/hr)Sludge FeedRate(dry lb/hr)OperatingHours(hr/yr)Stack GasFlow Rate(dscfm)StackGasTemp1(°F)ACIAdjustmentFactorLandfillTippingFeePM(gr/dscf)HCl(ppmvd)NYSchenectady 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 59.92 0.0315 0.65NYSouthwestBergenPoint 1 MH 4.92 9840 3.69 7375 4200 35557 1050 -0.72 59.92 0.0158 0.65NYSouthwestBergenPoint 2 MH 4.92 9840 3.69 7375 4200 35557 1050 -0.72 59.92 0.0158 0.65NYTonawanda 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.66 59.92 0.0158 13.09OHCanton 1 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 34.24 0.0158 0.65OHCanton 2 MH 1.08 2160 0.81 1620 4200 7810 1050 -0.72 34.24 0.0158 0.65OHColumbusSoutherly 1 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65OHColumbusSoutherly 2 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65OHColumbusSoutherly 3 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65OHColumbusSoutherly 4 MH 3.00 6000 2.25 4500 4200 21696 1050 -0.72 34.24 0.0158 0.65OHEuclid 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 34.24 0.0158 0.65OHEuclid 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 34.24 0.0158 0.65OHJacksonPike 1 MH 2.32 4640 1.74 3486 4200 16807 1050 -0.72 34.24 0.0158 0.65OHJacksonPike 2 MH 2.32 4640 1.74 3486 4200 16807 1050 -0.72 34.24 0.0158 0.65OHMillCreek 1 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65OHMillCreek 2 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65OHMillCreek 3 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65OHMillCreek 4 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65OHMillCreek 5 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65OHMillCreek 6 MH 4.00 8000 3.00 6000 4200 28928 1050 -0.72 34.24 0.0158 0.65OHNEORSDSoutherly 1 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65OHNEORSDSoutherly 2 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65OHNEORSDSoutherly 3 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65OHNEORSDSoutherly 4 MH 3.60 7200 2.70 5400 4200 26035 1050 -0.72 34.24 0.0032 0.65OHNEORSDWesterly 1 MH 1.79 3580 1.34 2685 4200 12945 1050 -0.72 34.24 0.0158 0.65OHNEORSDWesterly 2 MH 1.79 3580 1.34 2685 4200 12945 1050 -0.72 34.24 0.0158 0.65OHWilloughbyEastlake 1 MH 3.42 6840 2.57 5130 8400 24733 1050 -0.72 34.24 0.0315 0.65OHYoungstown 1 MH 2.00 4000 1.50 3000 4200 14464 1050 -0.72 34.24 0.0158 0.65OHYoungstown 2 MH 2.00 4000 1.50 3000 4200 14464 1050 -0.72 34.24 0.0158 0.65PADelawareCountyWestern 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 52.77 0.0158 0.65PADelawareCountyWestern 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 52.77 0.0158 0.65PAEastNorritonPlymouthWhitpain1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65PAErie 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 52.77 0.0032 13.09PAErie 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.66 52.77 0.0032 13.09PAHatfield 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0032 0.65PAKiskiValley 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65PAUpperMorelandHatboro 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65RICranston 1 MH 0.95 1900 0.71 1424 4200 6863 1050 -0.72 48.48 0.0158 0.65RICranston 2 MH 1.98 3960 1.48 2968 4200 14311 1050 -0.72 48.48 0.0158 0.65RINewEngland 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 48.48 0.0032 0.65C-31Facility IDUnitIDUnitTypeCapacity(dtph)Capacity(dry lb/hr)Sludge FeedRate(dry tons/hr)Sludge FeedRate(dry lb/hr)OperatingHours(hr/yr)Stack GasFlow Rate(dscfm)StackGasTemp1(°F)ACIAdjustmentFactorLandfillTippingFeePM(gr/dscf)HCl(ppmvd)SCColumbiaMetro 1 MH 1.08 2160 0.89 1773 7300 4620 1050 -1.44 38.52 0.0049 0.20SCColumbiaMetro 2 MH 1.08 2160 0.68 1351 7300 5145 1050 -1.34 38.52 0.0064 0.20SCPlumIsland 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 38.52 0.0158 0.65VAArmyBaseNorfolk 1 MH 1.50 3000 0.82 1648 4200 8455 1050 -0.34 52.77 0.0315 0.65VAArmyBaseNorfolk 2 MH 1.50 3000 1.13 2250 4200 10848 1050 -0.34 52.77 0.0315 0.65VABoatHarbor 1 MH 1.79 3580 1.64 3278 4200 11399 1050 -0.66 52.77 0.0253 0.70VABoatHarbor 2 MH 1.79 3580 1.34 2688 4200 12957 1050 -0.66 52.77 0.0253 0.70VAChesapeakeElizabeth 1 MH 1.50 3000 1.12 2236 4200 7908 1050 -0.72 52.77 0.0158 0.65VAChesapeakeElizabeth 2 MH 1.50 3000 1.13 2250 4200 10848 1050 -0.72 52.77 0.0158 0.65VAHopewell 1 MH 2.69 5381 2.02 4036 8400 19458 1050 -0.72 52.77 0.0158 0.65VANomanCole 1 MH 1.88 3760 1.41 2813 4200 13560 1050 -0.72 52.77 0.0158 0.65VANomanCole 2 MH 1.88 3760 1.41 2813 4200 13560 1050 -0.72 52.77 0.0158 0.65VANomanCole 3 MH 3.83 7660 2.88 5750 4200 27722 1050 -0.72 52.77 0.0158 0.65VANomanCole 4 MH 3.83 7660 2.88 5750 4200 27722 1050 -0.72 52.77 0.0158 0.65VANomanCole 5 MH 1.58 3160 1.19 2375 4200 11450 1050 -0.72 52.77 0.0158 0.65VANomanCole 6 MH 1.58 3160 1.19 2375 4200 11450 1050 -0.72 52.77 0.0158 0.65VAVirginiaInitiative 1 MH 1.88 3760 2.08 4164 4200 16867 1050 -1.84 52.77 0.0172 0.65VAVirginiaInitiative 2 MH 1.88 3760 1.41 2813 4200 13560 1050 -1.84 52.77 0.0172 0.65VAWilliamsburg 1 MH 1.96 3920 1.55 3101 4200 6501 1050 -0.72 52.77 0.0176 0.65VAWilliamsburg 2 MH 1.96 3920 1.47 2938 4200 14162 1050 -0.72 52.77 0.0176 0.65WABellinghamPostPoint 1 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 48.47 0.0032 0.65WABellinghamPostPoint 2 MH 2.69 5381 2.02 4036 4200 19458 1050 -0.72 48.47 0.0032 0.65WIGreenBayMetro 1 MH 1.23 2460 0.92 1848 4200 8910 1050 -0.72 39.59 0.0158 0.65WIGreenBayMetro 2 MH 1.23 2460 0.92 1848 4200 8910 1050 -0.72 39.59 0.0158 0.651. Assumed average gas temperature used for incinerators (CISWI)C-32 Table C-4b. Monitoring Costs

Parameters/Costs Equation Based on Default Parameters Based on Default Parameters and hr/yr

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 f acilities $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 re placing opacity test; (b) CO CEMS replacing CO test and

secondary chamber temperature monitor;(c) HCl CEMS repl acing HCl test, HCl sorbent monitor (dry scrubbers) and scrubber liquor pH monitor (wet

Sources: 1. Hospital/Medical/Infectious Waste Incinerators (HMI WI) [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 (NO X ) = $5,000 x (575.4/358.2) $8,000 8. Method 6C (SO 2 ) = $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 nationwid e 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 Em issions 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/an emometer ($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) = Di rect 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-35Table C-4e. Recordkeeping and Reporting CostsBurden item(A)(B)(C)(D)(E)(F)(G)Personhours peroccurrenceNumber ofoccurrencesper yearTechnicalperson-hoursper year(C = A x B)Managementperson-hoursper 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/AB. Surveys and Studies N/AC. Reporting Requirements1. Read instructions 1.0 1 1.0 0.05 0.1 1.2 $412. Required activitiesa. Perf. spec. tests (certif.) for CMS 17 1 17 0.9 1.7 20 $6963. Write reporta. Notification of initial performance testi. Pollutants, fugitive ash emissions 2.0 12.0 0.1 0.2 2.3 $82ii. Fugitive ash emissions 1.0 1 1.00.05 0.11.2 $41b. Notification of initial CMSdemonstration2.0 1 2.0 0.1 0.2 2.3 $82c. Report of initial performance testi. Pollutants, fugitive ash emissions 8.0 18.0 0.4 0.8 9.2 $328ii. Fugitive ashemissions 2.0 1 2.0 0.1 0.2 2.3 $82d. Report of initial CMS demonstration Incl. in C2e. Annual reporti. Results of performance testsconducted during the year40 1 40 2.0 4.0 46 $1,638D. Recordkeeping Requirements1. Read instructions Incl. in C12. Plan activities N/A3. Implement activities N/A4. Develop record system N/A5. Time to enter informationa. Records of initial performance test Incl. in C3b. Records of annual and any subsequentcompliance testsIncl. in C3E. Total Labor Burden and Cost 73 3.7 7.3 84 $2,989Notes: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 compositehourly labor rate is ($34.60/hr) + (0.05 x $82.23/hr) + (0.1 x $22.32/hr) = $40.94/hr. LaborC-362. Person-hours per occurrence for CMS performance specification costs are based onthe performance specification costs to certify CMS ($700) divided by thecomposite hourly labor rate ($40.94/hr).3. Control device inspection cost already accounted for under monitoring costs.4. Assume 8 hours for each facility toreview 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 downeach 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 Employmentand 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:ParameterPulp, Paper andPaperboard MillsPipelineTransportationCement andConcreteProductManufacturingPharma-ceutical &Medicine ManufacturingTotalLoaded1. Technical - Stationary Engineers & BoilerOperators $19.14 $21.11 $18.88 $27.36 $21.62 $34.602. Management - Engineering Managers $41.41 $58.22 $43.90 $62.05 $51.40 $82.233. Clerical - Office Clerks, General $14.21 $14.15 $12.58 $14.85 $13.95 $22.324. Composite labor rate $40.94C-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/tr ip to reach nearest landfill.

Table C-5b. Reported Operating Co sts 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 1,116,666.67 226.40C-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 de termined for each unit in order to estimate the annual

cost to operate it.C-39Table C-5c. Alternative DisposalCost Option: Sludge Storage CostFacilityID UnitIDUnitTypeControlCategoryCapacity(dtph)AssignedCapacityAssignedtons/dayaAssignedcubicyards/daybAssigned4-dayCapacity(ft3)Padsize if6' deepstorage(ft2)RailLengthc(ft)AluminumSheet Aread(ft2)# of4'x8'sheetsrequiredRailCosteConcreteCostf(at $6/ft2)TotalStorageCost ($)AnnualizedStorageCostgPAKiskiValley 1 MH vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365NYGlensFalls 1 FB vs - imp 1.54 1.54 36.96 61 6571 1095 33 794 25 $3,850 $6,571 $10,421 $1,144WAAnacortes 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365PAHatfield 1 MH vs - imp - wesp - rto 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365VAHopewell 1 MH abd - vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365OHWilloughbyEastlake 1 MH imp 3.42 3.42 82.08 135 14592 2432 49 1184 37 $5,698 $14,592 $20,290 $2,228NYAuburn 1 MH abd - vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365NYArlington 1 FB 0.35 0.35 8.4 14 1493 249 16 379 12 $1,848 $1,493 $3,341 $367AKJuneau 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365CTNaugatuck 1 MH abo - imp - wesp 1.90 91.32 150 16235 2706 52 1248 40 $6,160 $16,235 $11,197 $1,229CTNaugatuck 2 MH vs - imp 1.90 $11,197 $1,229WALynnwood 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365MAFitchburgEast 1 MH vs - wesp - rto 2.30 2.30 55.2 91 9813 1636 40 971 31 $4,774 $9,813 $14,587 $1,602NJPequannockLincolnFairfield1 FB vs - imp 1.90 91.32 150 16235 2706 52 1248 40 $6,160 $16,235 $11,197 $1,229NJPequannockLincolnFairfield2 FB vs - imp - wesp 1.90 $11,197 $1,229WAEdmonds 1 FB vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365VABlacksburg 1 FB 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365RINewEngland 1 MH vs - imp - wesp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365NYOrangetown 1 MH vs - imp 1.90 45.66 75 8117 1353 37 883 28 $4,312 $8,117 $12,429 $1,365OHEuclid 1 MH abd - vs - imp 1.90 91.32 150 16235 2706 52 1248 40 $6,160 $16,235 $11,197 $1,229OHEuclid 2 MH abd - vs - imp 1.90 $11,197 $1,229a. 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-40Table C-6a. Emissions for Landfilling Option: Increased Emissionsfrom Waste-Hauling VehiclesFacilityIDUnitIDUnitTypeMaximumCharge Rate(ton waste/hr)Dailywastehauled(tons/day)Dailywastehauled(cu yd/day)OperatingHours(hr/yr)AnnualWaste(tpy)AnnualWaste(cuyd/yr)Number oftruck tripsper yearRoundTripMilesmi/yrCO(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 0.00248NJPequannockLincolnFairfield1 FB 2.26 54 89 4200 9,484 15,612 434 200 86,731 0.28603 1.03549 0.06187 0.05315 0.00248NJPequannockLincolnFairfield2 FB 2.26 54 89 4200 9,484 15,612 434 200 86,731 0.28603 1.03549 0.06187 0.05315 0.00248NYArlington 1 FB 0.35 8 14 8400 2,940 4,840 134 100 13,443 0.04433 0.16050 0.00959 0.00824 0.00039NYGlensFalls 1 FB 1.54 37 61 8400 12,936 21,294 591 100 59,150 0.19507 0.70619 0.04219 0.03625 0.00169VABlacksburg 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248WAAnacortes 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248WAEdmonds 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248WALynnwood 1 FB 2.26 54 89 8400 18,969 31,225 867 100 86,735 0.28604 1.03554 0.06187 0.05315 0.00248CTNaugatuck 1 MH 2.69 65 106 4200 11,298 18,598 517 200 103,320 0.34073 1.23355 0.07370 0.06331 0.00296CTNaugatuck 2 MH 2.69 65 106 4200 11,298 18,598 517 200 103,320 0.34073 1.23355 0.07370 0.06331 0.00296MAFitchburgEast 1 MH 2.30 55 91 8400 19,320 31,802 883 100 88,340 0.29133 1.05471 0.06302 0.05413 0.00253NYAuburn 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296NYOrangetown 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296OHEuclid 1 MH 2.69 65 106 4200 11,298 18,598 517 100 51,660 0.17037 0.61677 0.03685 0.03166 0.00148OHEuclid 2 MH 2.69 65 106 4200 11,298 18,598 517 100 51,660 0.17037 0.61677 0.03685 0.03166 0.00148OHWilloughbyEastlake 1 MH 3.42 82 135 8400 28,728 47,289 1,314 100 131,358 0.43320 1.56830 0.09370 0.08049 0.00376PAHatfield 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296PAKiskiValley 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296RINewEngland 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.00296VAHopewell 1 MH 2.69 65 106 8400 22,596 37,195 1,033 100 103,320 0.34073 1.23355 0.07370 0.06331 0.002966.03 21.84 1.30 1.12 0.05Notes:*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 EnvironmentalResearch 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:Pollutantg/milb/miCO 2.99 0.0066NOx 10.8 0.0239PM10 0.65 0.0014PM2.5 0.56 0.0012SO2 0.03 0.0001C-41Table C-6b. Emissions for Landfilling Option: LandGEM OutputYearWaste AcceptedWaste-In-PlaceTotal landfill gasMethaneCarbon monoxideMercury (total) - HAP(Mg/year)(shorttons/year)(Mg)(shorttons)(Mg/year)(m3/year)(shorttons/year)(Mg/year)(m3/year)(shorttons/year)(Mg/year)(m3/year)(shorttons/year)(Mg/year)(m3/year)(shorttons/year)2011 325,914 358,505 0 0 0 0 0 0 0 0 0 0 0 0 0 02012 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+013.869E-06 4.637E-04 3.116E-082013 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+027.513E-06 9.005E-04 6.050E-082014 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-082015 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-072016 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-072017 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-072018 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-072019 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-072020 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-072021 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-072022 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-072023 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-072024 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-072025 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-072026 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-072027 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-072028 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-072029 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-072030 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-072031 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-07119,434,741 8,0258,0250.00000465Notes: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 La ndfilling Option: Increased Emissions from Landfill and Flare

Pollutant Total Tons Emitted Over 20 Years Tons per Year 358,505 tpy basis
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