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
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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 27709
RTI International is a trade name of Research Triangle Institute.
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CONTENTS
Section Page
1 Introduction 1-1
1.1 Executive Summary 1-1
1.2 Organization of this Report 1-2
2 Description of Sewage Sludge Incineration 2-1
2.1 Relation to Publicly Owned Treatment Works (POTWs) 2-1
2.2 Alternative Disposal Options 2-2
2.2.1 Surface Di sposal: Landfill s 2-2
2.2.2 Other Land Application 2-2
2.3 Ownership 2-3
3 Engineering Cost Analysis 3-1
3.1 Calculation of Costs and Emissions Reductions of the Maximum
Achievable Control Technology (MACT) Floor 3-1
3.1.1 Compliance Costs 3-2
3.1.2 Emission Reductions 3-8
3.2 Analysis of Beyond the MACT Floor Controls for Existing SSI Units 3-11
3.2.1 Selection of More Stringent Controls 3-11
3.2.2 Methodology Used to Estimate Cost and Emission Reductions 3-13
3.2.3 Selection of Regulatory Options 3-14
3.3 Estimation of Impacts for New Units Constructed within 5 Years After
Promulgation of the SSI NSPS 3-25
3.3.1 Estimation of New Sources 3-25
3.3.2 Methodology Used to Estimate Cost and Emission Reductions of
the MACT Floor Level of Control 3-26
3.3.3 Analysis of Beyond the Floor Options 3-31
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4 Economic Impact Analysis 4-1
4.1 Social Cost Estimates 4-1
4.2 Small Entity Analysis 4-2
4.2.1 Identify Affected Small Entities 4-3
4.2.2 Screening Analysis: Revenue Test 4-3
5 Human Health Benefits of Emissions Reductions 5-1
5.1 Synopsis 5-1
5.2 Calculation of PM2.5 Human Health Benefits 5-1
5.3 Energy Di sb enefits 5-15
5.3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits 5-16
5.4 Unquantified Benefits 5-19
5.4.1 Carbon Monoxide Benefits 5-19
5.4.2 Other SO2 Benefits 5-20
5.4.3 HAP Benefits 5-21
5.5 Characterization of Uncertainty in the Monetized PM2.5 Benefits 5-28
5.6 Comparison of Benefits and Costs 5-32
Appendixes
A Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.5-Related Mortality A-l
B Lowest Measured Level (LML) Assessment for Rules without Policy-Specific
Air Quality Data Available: Technical Support Document (TSD) B-l
C Additional Engineering Cost Analysis Data C-l
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LIST OF FIGURES
Number Page
5-1. Breakdown of Monetized PM2.5 Health Benefits using Mortality Function
from Pope et al. (2002) 5-7
5-2. Total Monetized PM2.5 Benefits for the Proposed SSI NSPS and EG in 2015 5-13
5-3. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by
PM2.5 Precursor Pollutant and Source 5-14
5-4. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by
Subcategory 5-15
5-5. Percentage of Adult Population by Annual Mean PM2.5 Exposure (pre- and
post-policy policy) 5-30
5-6. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels
(pre- and post-policy policy) 5-30
5-7. Net Benefits for the Proposed SSI NSPS and EG at 3% Discount Rate 5-34
5-8. Net Benefits for the Proposed SSI NSPS and EG at 7% Discount Rate 5-35
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LIST OF TABLES
Number Page
1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the
SSINSPS and EG in 2015 (millions of 2008$) 1-3
3-1. Summary of Average CO Emissions Collected from MH Units 3-13
3-2. Emissions Reductions and Costs If All Units Comply 3-16
3-3. Emissions Reductions and Costs If Small Entities Landfill 3-18
3-4. Emissions Reductions and Costs If All Units Comply - Per Unit Basis 3-20
3-5. Emissions Reductions and Costs If Small Entities Landfill - Per Unit Basis 3-22
3-6. Emissions Reductions and Costs If All Units Comply 3-24
3-7. Emissions Reductions and Costs If Large Entities Comply and Small Entities
Landfill 3-24
3-8. Control Device Distribution for Fluidized Bed Incinerators 3-27
3-9. Cost and Emission Reduction Calculation Inputs 3-29
3-10. Summary of Emission Reductions for New SSI Units 3-30
3-11. MACT Costs Associated with Model FB Unit 3-31
4-1. Annual Social Cost Estimates by Option and Disposal Choices ($ million,
2008$) 4-2
4-2. Calculated Municipal and Township Per Capita Revenues by Population Size 4-3
4-3. Option 1 Revenue Tests for Government Entities: All Entities Comply 4-4
4-4. Option 1 Revenue Tests for Government Entities: Large Entities Comply and
Small Entities Landfill 4-4
4-5. Option 2 Revenue Tests for Government Entities: All Entities Comply 4-5
4-6. Option 2 Revenue Tests for Government Entities: Large Entities Comply and
Small Entities Landfill 4-5
4-7. Option 3 Revenue Tests for Government Entities: All Entities Comply 4-5
4-8. Option 3 Revenue Tests for Government Entities: Large Entities Comply and
Small Entities Landfill 4-6
5-1. Human Health and Welfare Effects of PM2.5 5-2
5-2. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in
2015 (2008$) (large entities comply and small entities landfill) 5-8
5-3. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in
2015 (2008$) (all units comply) 5-10
5-4. Summary of Reductions in Health Incidences from PM2.5 Benefits for the
Proposed SSI NSPS and EG in 2015 5-11
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5-5. All PM2.5 Benefits Estimates for the Proposed SSI NSPS and EG at Discount
Rates of 3% and 7% in 2015 (in millions of 2008$) 5-12
5-6. Social Cost of Carbon (SCC) Estimates (per tonne of C02) for 2015 5-18
5-7. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015 (large
entities comply and small entities landfill, millions of 2008$) 5-18
5-8. Monetized SCC-derived Disbenefits of C02 Emission Increases in 2015 (large
entities comply and small entities landfill, millions of 2008$) 5-18
5-9. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the
SSI NSPS and EG in 2015 (millions of 2008$) 5-33
vii
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SECTION 1
INTRODUCTION
Section 129 of the Clean Air Act (CAA) requires that performance standards for new
units and emission guidelines (EG) for existing units be established for each category of solid
waste incineration units. In previous actions, the U.S. Environmental Protection Agency (EPA)
has promulgated rules and EG for hospital medical and infectious waste incinerators (HMIWI),
commercial and industrial solid waste incinerators (CISWI), and other solid waste incineration
(OSWI) units. These actions did not apply to sewage sludge incinerators (SSI). EPA is proposing
new source performance standards (NSPS) and EG for SSI units. As part of the regulatory
process, EPA is required to develop a regulatory impact analysis (RIA). The RIA includes an
economic impact analysis (EIA) and a small entity impacts analysis and documents the RIA
methods and results.
1.1 Executive Summary
The key results of the RIA are as follows:
Options Analyzed: EPA analyzed the following options and selected Option 2:
- Option 1 is the MACT floor level of control for the two subcategories developed
for existing sewage sludge incineration (SSI) units, multiple hearth (MH) units,
and fluidized bed (FB) units.
- Option 2 is the same as Option 1, with the addition of activated carbon injection
for additional mercury (Hg) emissions reduction from MH units.
- Option 3 is the same as Option 2, with the addition of an afterburner on all MH
units for additional carbon monoxide (CO) emissions reduction.
¦ Engineering Cost Analysis: EPA estimates the proposed rule's total annualized costs
will be $92 million (2008$).
Social Cost Analysis: Because the proposed regulatory option affects governmental
entities (96 of the 97 owners are governmental entities) providing services not
provided in a market, the Office of Air Quality Planning and Standards (OAQPS) has
used the direct compliance cost method as a measure of social costs. The social cost is
approximately $92 million (2008$).
Small Entity Analyses: EPA performed a screening analysis for impacts for 18 small
government entities by comparing compliance costs to revenues (e.g., revenue tests).
EPA's analysis found the tests were below 1% for small entities.
¦ Benefits Analysis: In the year of full implementation (2015), EPA estimates the
monetized PM2.5 benefits of the proposed NSPS and EG are $130 million to $320
million and $120 million to $290 million, at 3% and 7% discount rates, respectively.
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All estimates are in 2008$ for the year 2015. Using alternate relationships between
PM2.5 and premature mortality supplied by experts, higher and lower benefits
estimates are plausible, but most of the expert-based estimates fall between these
estimates. The benefits from reducing other air pollutants have not been monetized in
this analysis, including reducing 2,900 tons of CO, 96 tons of HC1, 3.0 tons of Pb, 1.6
tons of Cd, 5,500 pounds of mercury (Hg), and 90 grams of total dioxins/furans
(CDD/CDF) each year. In addition, ecosystem benefits and visibility benefits have
not been monetized in this analysis.
¦ Net Benefits: The net benefits for the NSPS and EG are $37 million to $220 million
and $26 million to $190 million, at 3% and 7% discount rates, respectively (Table 1-
1). All estimates are in 2008$ for the year 2015.
1.2 Organization of this Report
The remainder of this report supports and details the methodology and the results of the
EI A:
Section 2 describes the SSI process, alternative disposal methods, and affected
entities.
Section 3 describes the engineering cost analysis.
Section 4 describes the economic impact and small entity analyses.
Section 5 presents the benefits estimates.
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Table 1-1. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the SSI
NSPS and EG in 2015 (millions of 2008$)a
3% Discount Rate 7% Discount Rate
Proposed: Option 2
Total Monetized Benefitsb
$130 to $320 $120
to
$290
Total Social Costs0
$92
$92
Net Benefits
$37 to $220 $26
26,000 tons of carbon monoxide
96 tons of HC1
5,500 pounds of mercury
1.6 tons of cadmium
to
$190
Non-monetized Benefits
3.0 tons of lead
90 grams of dioxins/furans
Health effects from N02 and S02 exposure
Ecosystem effects
Visibility impairment
Option 1
Total Monetized Benefitsb
$130 to $320 $120
to
$290
Total Social Costs0
$63
$63
Net Benefits
$66 to $250 $55
2,900 tons of carbon monoxide
96tons of HC1
820 pounds of mercury
1.6 tons of cadmium
to
$220
Non-monetized Benefits
3.0 tons of lead
74 grams of dioxins/furans
Health effects from N02 and S02 exposure
Ecosystem effects
Visibility impairment
Option 3
Total Monetized Benefits'3
$130 to $310 $120
to
$290
Total Social Costs0
$132
$132
Net Benefits
-$5.4 to $180 -$14
26,000 tons of carbon monoxide
96 tons of HC1
5,500 pounds of mercury
1.6 tons of cadmium
to
$150
Non-monetized Benefits
3.0 tons of lead
90 grams of dioxins/furans
Health effects from N02 and S02 exposure
Ecosystem effects
Visibility impairment
a All estimates are for the implementation year (2015), and are rounded to two significant figures. These results include 2 new FB
incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill assumption.
b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2 5 through reductions of
directly emitted PM2 5 and PM2 5 precursors such as NOx and S02. It is important to note that the monetized benefits include
many but not all health effects associated with PM2 5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden
et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing
premature mortality because there is no clear scientific evidence that would support the development of differential effects
estimates by particle type. These estimates include energy disbenefits valued at $0.5 million at a 3% discount rate from C02
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.
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SECTION 2
DESCRIPTION OF SEWAGE SLUDGE INCINERATION1
Sewage sludge incinerators combust the organic and inorganic solids and dissolved
materials resulting from the wastewater treatment process. Incineration greatly reduces the
sludge volume, and post-incineration sludge ash can be disposed of more easily. Sludge ash is
generally disposed of in landfills but can also be used in construction materials. In addition to
disposal functions, some facilities capture the heat from sewage sludge incineration operations
and use the heat as an energy source.
The incineration process releases several pollutants, some of which were present in the
sewage sludge and some of which are created as a result of combustion. Pollutants emitted from
SSI include particulate matter (PM), hydrocarbons, CO, nitrogen oxides, sulfur dioxide,
hydrogen chloride, dioxins and dibenzofurans, and a number of metals. The amount of these
pollutants released during incineration depends on the content of the sludge, the type of
incinerator used, and the level of PM control.
The majority of incineration facilities (163, or 75%) are multiple hearth (MH)
incinerators. These incinerators consist of a cylinder around a series of hearths with a rotating
shaft through the center. Rabble arms with teeth in each hearth rake the sludge while air is ducted
into the shaft and circulated. The incinerator consists of the upper drying zone, the middle sludge
combustion zone, and the lower cooling zone.
Although MH incinerators have been in use since the 1930s and remain in the majority,
fluidized bed (FB) incinerators have begun to replace them.2 Of the 218 incineration units in
operation 55 (25%) are FB incinerators. In a FB incinerator, a steel shell holds a refractory-lined
grid beneath a bed of sand. Air is injected into the incinerator, fluidizing the sand and sludge. FB
incinerators work efficiently to transfer heat from the sand to the sludge, using less excess air
than MH incinerators. Emissions for most pollutants are, therefore, lower for FB incinerators.
2.1 Relation to Publicly Owned Treatment Works (POTWs)
Publicly owned treatment works (POTWs) are wastewater treatment systems owned by
states, municipalities, or other public entities. POTWs receive sewage from homes and
Portions of this section rely on information provided by EPA (2007 and 2009).
2Other types of sewage sludge incinerators, such as electric arc furnaces, are no longer used in the United States.
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businesses, runoff, and sometimes industrial wastewater. After the wastewater treatment process,
POTWs are responsible for disposing of the sewage sludge.
POTWs treat sewage in three steps: primary, secondary, and tertiary treatment. In the
primary stage, heavy solids settle to the bottom while oil and light solids are skimmed from the
top. The sludge removed during this step is known as primary sludge. During secondary
treatment, biological treatment creates secondary sludge. Some plants may continue with a
tertiary treatment of chemical disinfection, which produces a tertiary or chemical sludge (EPA,
2009). The three sludge types are then generally combined and disposed of or sent for further
treatment.
2.2 Alternative Disposal Options
Incineration continues to be utilized to dispose of sewage sludge but is increasingly
becoming less common. Additional pollution controls will increase costs for facilities that
continue to use the incineration disposal method. If the additional costs are high enough, many
POTWs may choose to adopt alternative disposals methods (e.g., surface disposal in landfills or
other beneficial land applications). However, the use of alternative disposal methods may be of
limited in some areas because of landfill capacity constraints, local geography, or other legal or
economic constraints.
2.2.1 Surface Disposal: Landfills
Landfilling, in some cases, provides a simple and low-cost option for sewage sludge
disposal. Sewage sludge may be placed in landfills used for other municipal solid waste or in
landfills constructed specifically for sewage sludge. The landfill disposal option is attractive for
low-volume incinerators; landfill capacity constraints limit disposal opportunities for large
sludge volumes.
Sewage sludge may also be useful for landfills. For example, sludge can be used in place
of a daily soil cover for odor and blowing litter control or as a final cover for closed landfills to
aid growth of a vegetative layer. The sewage sludge's high organic content also helps break
down other landfill waste.
2.2.2 Other Land Application
Sewage sludge that has undergone treatment to make it safe for use on other land
application (e.g., fertilizer) is commonly referred to as biosolids. Biosolids can be sold to
agricultural or landscaping entities for land application, so the organic material in biosolids is
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reused to contribute to crop production. Land application has also been used in mine reclamation
to reestablish vegetation.
Biosolids must meet federal and state regulations to ensure their safety; meeting these
standards may make other land applications a less attractive disposal option. In addition, land
application may not be suitable in some areas, based on factors such as proximity of water
sources and slope. Rules vary based on the quality of the biosolid: Class A biosolids meet strict
standards, while Class B biosolids are treated but still contain detectable pathogen levels and
face greater restrictions on usage (EPA, 2007). Actions must also be taken to reduce the vector
attraction of biosolids, either through additional treatment or by preventing contact with vectors.
2.3 Ownership
Sewage sludge incinerators can be operated by municipalities or other entities. There is
no specific North American Industry Classification System (NAICS) code for these units.
Applicable NAICS codes include 562213 (solid waste combustors and incinerators) and 221320
(sewage treatment facilities). Most sludge incinerators are located in the eastern United States.
The United States has 97 operators that own 112 facilities with a total of 218 affected
incinerator units; the typical (e.g., median) operator owns one facility. Almost all operators are
towns, cities, and their utility authorities; the exception is one operator that is a large publicly
owned company. Among owner municipalities whose exact population is known, the average
(median) population is 336,305 (108,213). Out of the 94 owners with population information
available, 18 (or 19%) are small entities that serve a population under 50,000.
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SECTION 3
ENGINEERING COST ANALYSIS
This section documents the calculation of costs and emissions reductions associated with
existing and new sources complying with the MACT floor level of control, the selection of
control options more stringent than the MACT floor level of control, and summarizes cost and
emissions reductions of each control option. The costs and emission reductions of each option
are then used in the economic analysis (Section 4) and human health benefits analysis (Section
5). Costs and emissions reductions were calculated for two scenarios:
Control options were applied to all SSI units, and
Control options were applied to only larger entities. Larger entities mean wastewater
treatment facilities that are owned by municipalities or authorities with more than
50,000 people. Entities with fewer than 50,000 people are likely to dispose of sewage
sludge by landfilling rather than continuing to operate their incineration unit.
3.1 Calculation of Costs and Emissions Reductions of the Maximum Achievable Control
Technology (MACT) Floor
A significant portion of the total cost for industry compliance comes from the cost of
installing new pollution control devices or improving existing pollution control devices for units
not currently meeting the proposed limits. In order to determine the control costs, it was
necessary to evaluate, for each SSI unit, how much improvement for each pollutant would be
needed to meet the proposed emissions limits.
The average pollutant concentration values used to calculate baseline annual emissions
(Estimation of Baseline Emissions, 2010) for each unit were compared with the proposed
emissions limits, and percentages were calculated to quantify the amount of improvement needed
for the unit to meet the proposed limits. Tables C-la and C-lb in Appendix C contain the
baseline pollutant concentration values used for each unit in each subcategory and the percentage
improvement required to meet the proposed emissions limits for each unit for each pollutant. The
existing SSI units are subcategorized into two main groups: multiple hearth (MH) units and
fluidized bed (FB) units. The pollutant- and subcategory-specific limits are shown in each header
row of these tables.
Control methods and cost algorithms utilized in a recent rulemaking for another waste
combustion source category, Hospital, Medical and Infectious Waste Incinerators (HMIWI) were
updated and utilized generally for the SSI source category, since most of these algorithms can be
tailored to the combustion units found in the SSI source category with slight modifications.
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Based on these required improvements, pollutant-specific control methods were chosen as
follows for units requiring more than 10 percent improvement to meet the proposed limits. It was
assumed that units within 10 percent of the limit would be able to meet the limit by making
minor adjustments to the unit and/or controls already in place.
Metals (cadmium and lead) andPM: Adding fabric filters (FF).
Mercury anddioxins/furans (CDD/CDF): Adding activated carbon injection (ACI) and
adjusting the carbon addition rate to meet the amount of reduction required.
Hydrogen chloride (HCl): Adding packed bed scrubbers (PBS).
Carbon monoxide (CO): No further improvement was needed for units to meet the
MACT floor limit. However, the beyond-the-floor limit required the use of afterburner retrofits
for units not already having similar control. The costs and emission reductions associated with
the proposed CO limit are discussed in the memorandum "Analysis of Beyond the Maximum
Achievable Control Technology (MACT) Floor Controls for Existing SSI Units."2
Nitrogen oxides (NOx): No more than 10 percent improvement in NOx control was
needed for any units. Minor adjustments were considered sufficient for those needing
improvement to meet the NOx limit.
Sulfur dioxide (SO2): Adding packed bed scrubbers.
Further descriptions of these controls and their associated costs are listed below in
Section 3.1.1.
3.1.1 Compliance Costs
This section presents the methodology used to estimate costs for existing SSI for (A) the
emission controls used to comply with the proposed limits; (B) the monitoring, testing,
recordkeeping, and reporting activities used to demonstrate compliance; and (C) the alternatives
to compliance.
3.1.1.1 Emission Control Costs
Emission control technologies and other control measures that can be used to comply
with the MACT floor options for existing SSI units include PBS, FF, and activated ACI. This
section presents the costs that were estimated for each of these control measures.
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The retrofit factors for the capital costs were assumed to be 40 percent for packed bed
scrubbers, fabric filters, and 20 percent for and ACI.5'6 Downtime costs for the retrofits were
assumed to be negligible. Most SSI are expected to have adequate space to install an emission
control system without shutting down the incinerator for an extended period. It was also
expected that connecting the ductwork could be performed during a scheduled downtime for
maintenance, thereby minimizing expected downtime.7
The capital and annual costs for the emission controls were estimated in units of dollars
($) and $/flow. The $/flow costs were calculated by dividing the capital/annual control cost
estimate for each unit by the average gas flow rate assigned to that unit.
Costs are on a 2008 basis, and annualized costs assumed an interest rate of 7 percent.
Tables C-2a to C-2c in Appendix C present a summary of the parameters and equations used in
the cost algorithms for each emission control and alternative to compliance where applicable.
Table C-3 in Appendix C lists of the unit-specific inputs used in the algorithms (e.g., incinerator
charge rate, stack gas flow rate, incinerator operating hours, and concentrations)
a . Adding a fabric filter.
Fabric filters can be installed either alone or with other add-on controls. The cost
algorithm for installing a fabric filter is presented in Table C-2a in Appendix C and is based on
algorithms in the Model Plant Description and Control Cost Report for HMIWI.6 The fabric
filter capital costs range from approximately $893,000 to $4.2 million, and annualized costs
range from approximately $209,000/yr to $1.2 million/yr. Sources for specific cost data are noted
below Table C-2a in Appendix C.
b. Adding a packed bed scrubber.
Wet scrubbers can be installed alone or after a dry scrubber/fabric filter. The cost
algorithm for installing a packed-bed wet scrubber is presented in Table C-2b of Appendix C and
is based on algorithms in the Model Plant Description and Control Cost Report for HMIWI.8
The packed-bed wet scrubber capital costs range from approximately $366,000 to $8.7 million,
and annualized costs range from approximately $103,000/yr to $1.8 million/yr. Sources for
specific cost data are noted below Table C-2b in Appendix C.
c . Adding an activated carbon injection (ACI) system.
Injecting activated carbon before the fabric filter has been demonstrated to improve the
removal efficiency of both Hg and CDD/CDF from SSI. The cost algorithm for installing an ACI
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system is presented in Table C-2c in Appendix C and is based on algorithms in the Model Plant
Description and Control Cost Report for HMIWI.8 Adjustments to the carbon injection rate were
made to account for how much reduction was required to meet the proposed limit, and whether a
packed-bed scrubber was being added, since those may also assist in reducing Hg emissions. The
packed-bed scrubber adjustment is a ten percent Hg reduction, and is based on input from the
boiler NESHAP development. The ACI factor compares the carbon grain loading originally
assumed to achieve 90 percent control of mercury or 98 percent control of CDD/CDF to the
amount of reduction the unit will need to meet the proposed emission limits. The highest factor
(Hg or CDD/CDF) is then used to adjust the carbon injection rate calculation of the algorithm.
ACI capital costs range from approximately $8,400 to $37,000, and annualized costs range from
approximately $9,300/yr to $210,000/yr. Sources for specific cost data are noted below Table C-
2c in Appendix C.
d. Additional Control Options.
Minor adjustments, such as air handling and distribution adjustments in the firebox, can
be made to certain units to improve NOx control. It was assumed these adjustments could be
made at no additional cost.
3.1.1.2 Stack Testing, Monitoring, and Recordkeeping Costs
Monitoring Costs. Initial and continuous compliance provisions for SSI units were
selected to be as consistent as possible with proposed commercial and industrial solid waste
incinerator (CISWI) and current HMIWI provisions. This section presents the costs that were
estimated for each of these requirements.
The total capital cost for stack testing, monitoring, and recordkeeping and reporting for
all subcategories is estimated at approximately $14.9 million, and the total annualized cost is
about $16.9 million per year. Cost estimates were based on algorithms recently utilized in the
HMIWI regulatory development. Costs were updated to a 2008 basis, and annualized costs
assumed an interest rate of 7 percent. Tables C-4a to C-4e in Appendix C present a summary of
the parameters and equations used in the cost algorithms for each monitoring component, where
applicable.
Inspections. Consistent with HMIWI regulations, it was assumed that annual control
device inspections will be required for any units having control devices in place or requiring
further controls to meet the proposed emission limits. In this context, control devices include
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fabric filters, afterburners, wet scrubbers, or ACI systems. The cost was estimated at a flat rate of
$1000 per year. See Table C-4a in Appendix C for further details and sources.
Parameter monitors. Monitoring of operating parameters can be used to indicate whether
air pollution control equipment and practices are functioning properly to minimize air pollution.
Based on the existing CISWI regulations and HMIWI regulations, it was assumed that parameter
monitoring will be mandatory for all units required to add fabric filters, packed bed scrubbers, or
ACI systems. Costs for each monitoring system were estimated as follows:
For a fabric filter bag leak detection system, capital cost was estimated at $25,500
and annualized cost at $9,700/yr.
For a wet scrubber monitoring system, capital cost was estimated at $24,300 and
annualized cost at $5,600/yr.
The cost for ACI monitoring depends on a unit's annual operational hours. There
are no capital costs for ACI monitoring. Annual costs ranged from $500 to $9,800.
For default parameters and equations used for monitoring costs, see Table C-4b. Sources
for specific cost data are noted below the table.
a. Testing Costs
1. Initial Stack Testing. It was assumed that initial stack testing will be required for each
pollutant that the ICR testing showed did not meet the proposed emission limit. Any
unit having no test data for certain pollutants will also be required to perform an
initial emissions test for those pollutants. Costs for each required stack test were
summed and multiplied by 2/3 to adjust for economies of scale when multiple
pollutant tests were being performed on a unit. The annualized costs were calculated
assuming a capital recovery factor of 0.10979 (15 years at 7 percent). The basis of
these cost estimates for each stack test is summarized in Table C-4c in Appendix C.
2. Annual Stack Testing. It was assumed that all units, to some extent, will be required to
demonstrate ongoing compliance with the emissions limits for all pollutants. It was
assumed that all units will be required to conduct annual stack tests for all pollutants.
The cost for this annual testing was estimated to be approximately $61,000/yr. The
basis of these cost estimates for each stack test is summarized in Table C-4c in
Appendix C.
3. Visible emissions testing. All SSI units will likely have ash handling operations.
Therefore, these units would be required to demonstrate compliance to a 5 percent
visible emissions limit for fugitive emissions generated during ash handling (similar
to HMIWI). We are proposing that units will be required to conduct annual
performance tests for fugitive emissions from ash handling using EPA Method 22.
Costs for this annual test include a capital cost of $250 and an annual cost of $200,
based on the Revised Compliance Costs and Economic Inputs for Existing HMIWI
3-5
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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 regarding this cost estimate, including hourly labor
assumptions, labor rates, and associated sources, are included in Table C-4e in Appendix C.
3.1.1.3 Alternative Disposal Costs
Certain SSI units may have waste disposal alternatives other than combustion available to
them, and these alternatives may prove to be less costly than the controls and monitoring
required for compliance with the proposed SSI standards. To determine if landfilling would be
an affordable option for facilities even in the absence of the proposed standards, both the annual
cost to landfill and the annual unit operating cost were estimated. Then, the overall cost for the
landfilling option was calculated using the following equation:
Annual Cost for Landfilling Option = Annual Cost to Landfill - Annual Cost to Operate
SSI Unit
The methodology for determining annual landfilling costs and annual unit operational
costs is described below.
a. Cost to Haul to Landfill
The cost to haul waste to a landfill is the sum of additional sludge storage costs, landfill
tipping fees, and transportation costs, which depend on the amount of waste to be hauled and the
distance traveled per haul.
If choosing to landfill, it was assumed that a facility would need adequate storage
capacity to store a minimum of 2 to 4 days worth of dried sludge, to account for occasional
multi-day landfill closures (e.g. weekends and holidays). Facilities may already have such
storage on-site to account for non-continuous operation of the incineration unit. For this analysis,
to provide a conservative estimate of costs of the landfilling option, a cost for storing dewatered
sludge was calculated. It was assumed that a concrete pad with metal railing would be sufficient
for storage of dried or dewatered sludge at small entities. The smaller entities have a lower
average dry sludge capacity than large entities. Sewage sludge incineration capacity was known
for 4 of the 21 units owned by small entities. An average capacity of 1.90 dry tons per hour was
applied to the other 17 units, and these capacities were used to estimate the maximum volume of
dry sludge that would accumulate over 4 days. Costs were then estimated for the concrete and
3-6
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aluminum required to accommodate these volumes. Table C-5c in Appendix C presents these
costs estimates by unit. For large entities, a different type of storage would likely be required
(such as a concrete basin capable of storing large quantities of sludge); storage costs for these
facilities were not estimated because it was assumed large entities would comply rather than shut
down their units and landfill.
Tipping fees used in the analysis were specific to each state where state data were
available9; where state data were not available, landfill tipping fees were based on regional
tipping fees.10 All fees were in units of $/ton waste and were converted to 2008 dollars. The
annual tonnage of waste being diverted was calculated based on the dry sludge feed rate of each
unit and the number of hours it operates per year. Operational hours and sludge feed rates are
discussed in further detail in the SSI inventory and baseline emissions memos. Discussion with
landfill experts indicated that landfills may accept wet sewage sludge as well. However, because
landfills might have a wet sludge capacity limit and SSI units are already dewatering their
sludge, it's likely they would continue to do this. The cost analysis therefore focuses on
landfilling dry sludge rather than wet sludge.
Transportation costs were based on an estimated $0,266 per ton-mile11. It was assumed
that a landfill could be found within 50 miles of each facility, yielding a roundtrip distance of
100 miles. However, a review of state regulations for states where small entities are located
revealed that Connecticut and New Jersey do not allow sewage sludge to be landfilled. To adjust
for this, round trip distances for facilities in these states were increased to 200 miles, assuming a
landfill could be found in another state within 100 miles from the facility.
Annual landfilling costs varied widely, ranging from $13,000/yr to $5.1 million/yr. Table
C-5a in Appendix C summarizes the parameters and equations used to calculate the annual cost
for each facility to landfill the waste it would otherwise incinerate in an SSI.
b. Cost to Operate Incinerator
Annual incinerator operational costs were based on data provided from the ICR survey
and known unit capacities. The survey specifically requested that respondents provide annual
costs to operate each incinerator in 2006, 2007 and 2008. Costs were then confirmed or revised
based on follow-up contact with the survey recipients. Several steps were taken and assumptions
made to standardize the data: (1) total costs provided were assumed to be for operating only the
incinerator (i.e. did not include dewatering or other aspects of plant operation); (2) total costs
3-7
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listed for multiple units were divided evenly among each unit; and (3) individual cost
components (e.g. electricity, labor, fuel) were summed if a total cost was not explicitly provided.
Because cost information was only available for the 9 surveyed entities, an annual cost
factor, in $/dry ton, was developed using the available data and multiplied by the average
capacities of all other units. Both an average factor ($113.80/dry ton for FB units and
$329.22/dry ton for MH units) and a minimum factor ($55.50/dry ton for FB units and
$79.43/dry ton for MH units) were calculated and applied. The minimum factor is the most
conservative estimate (i.e. would yield the lowest unit operational cost and thus the highest net
cost for the landfilling option) and was used for the economic analysis.12
Table C-5b in Appendix C summarizes the information provided, assumptions made, and
cost factors used to estimate costs for all units not having cost data.
3.1.2 Emission Reductions
Emissions reductions were calculated for each of the nine pollutants for two scenarios:
(1) assuming each existing unit complied with the proposed emissions limits; and (2) assuming
that all large entities would comply with the proposed emission limits and small entities would
cease using their incinerators and landfill the dewatered sludge instead. Emission reductions
were calculated by estimating the emissions resulting from each scenario and subtracting the
baseline emissions previously calculated. Baseline emission calculations are discussed in a
separate memorandum.3 The baseline memorandum indicates that emissions and flow rate
information was collected from only 25 of the 218 SSI units. Sludge capacity information was
collected from 105 of 218 units. As described in the baseline memorandum, default factors for
emissions, flow rate, and sludge capacity were developed and applied to units without data.
3.1.2.1 Emission Reductions if All Entities Comply With MACT Floor Limits
Emission reductions were calculated using the following equation:
Reduction = Baseline - MACT Floor Emission
The calculation of baseline emissions are described in detail in a separate memo.3 The
MACT floor emission values, resulting from all entities meeting the proposed limits, were
calculated as follows:
3-8
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a. Units already meeting the proposed limits.
If a unit was already meeting the MACT floor for a given pollutant, then the MACT floor
emission value was assumed to equal the baseline value (i.e., no backsliding or emissions
increases would occur), yielding zero reduction.
b. Units not currently meeting the proposed limits.
For units not already meeting the MACT floor for a given pollutant, it was assumed that
with the proposed limits in place the unit would reduce its pollutant concentration to at least that
of the floor. Thus, the reduction would be the difference between the baseline and the proposed
limit.
3.1.2.2 Emission Reductions if Large Entities Comply With MACT Floor Limits and Small
Entities Landfill
For large entities, reductions are calculated as described in Section 4.1. For small entities,
however, the emissions resulting from hauling the diverted waste, landfilling the waste, and
flaring the landfill gas generated from the waste need to be considered. Emission reductions for
small entities were calculated using the following equations:
Reduction = Baseline - (MACT Floor Emission + Emissions from Landfilling)
Emissions from Landfilling = Vehicle Emissions + Direct Landfill Emissions + Flare
Emissions
a. Vehicle Emissions
To determine the vehicle emissions resulting from the trucks that would haul the
dewatered sewage sludge to a nearby landfill, assumptions regarding sludge density, truck
capacity, and vehicle emission factors were made:
1. A dewatered sludge density of 1,215 pounds per cubic yard13 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 yd3) would be the most
likely vehicle used.14
3. The following emission factors for CO, NOx, Filterable PM, PM2.5, and SO2 were
derived from EPA's Office of Transportation and Air Quality (OTAQ) Motor
Vehicle Emission Simulator (MOVES),15 using national defaults for parameters
and refuse trucks as the source type :
3-9
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CO 2.99 grams emitted per mile
NOx 10.8 grams emitted per mile
Filterable PM 0.65 grams emitted per mile
PM2.5 0.56 grams emitted per mile
S02 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, HC1, and S02.
Emissions of these three criteria pollutants due to landfilled, dewatered sewage sludge, were
estimated using EPA's LandGEM16 model in conjunction with default landfill gas sulfur and
chlorine concentrations, as reported in the AP-42.17 As a conservative estimate, it was assumed
that landfill gas collection systems would collect 50 percent of the landfill gas generated. Unit
capacities and operational hours were used to determine the amount of waste diverted annually
from all units. Instead of running LandGEM for each individual unit, a total estimate of landfill
gas generated by running the model once using the total annual waste diverted for all units. Unit-
specific estimates for landfill emissions were not calculated. Raw LandGEM outputs and default
assumptions are presented in Table C-6b in Appendix C. Resulting total emissions over 20 years
for these three pollutants are presented in Table C-6c in Appendix C. These values were divided
by 20 to obtain annual emissions directly emitted from landfills as a result of landfilling
dewatered sewage sludge.
c. Emissions from Landfill Gas Flaring
Additional emissions of PM, NOx, and CO will result from flaring landfill gas generated
by the landfilling of dewatered sewage sludge. A landfill gas collection efficiency of 50 percent
was assumed, meaning that 50 percent of the landfill gas generated from landfilled sewage
sludge would be collected and combusted. AP-42 emission factors, representing the mass of
pollutant emitted per volume of methane combusted, were applied in conjunction with the
methane output calculated in the LandGEM model. Again, LandGEM outputs are presented in
Table C-6b in Appendix C, and resulting total emissions over 20 years for these three pollutants
are presented in Table C-6c in Appendix C. Values were divided by 20 to obtain annual
emissions resulting from landfill gas flaring.
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3.2 Analysis of Beyond the MACT Floor Controls for Existing SSI Units
The MACT floor analysis for existing sources results in emission levels that each existing
SSI unit is required to meet. The costs and emission reductions of the MACT floor requirements
were estimated using the following assumptions: (1) units that needed to meet the MACT floor
for Cd, Pb, and PM would add a FF, (2) units that needed to meet the MACT floor for HC1 and
SO2 would add a packed bed scrubber (PBS), and (3) units that needed to meet the MACT floor
for Hg and CDD/CDF would use activated carbon injection (ACI) (Cost and Emissions
Reduction, 2010). All FB and MH units were determined to meet the floor level of control for
NOx and CO, and no additional control was necessary.
Section 3.2.1 discusses the selection of more stringent controls or emission levels than
the floor level reviewed for this analysis. Section 3.2.2 discusses the methodology used to
estimate costs and emission reductions of the more stringent controls, and Section 3.2.3
summarizes regulatory options selected for the BTF analysis. Baseline emissions and emission
reductions of PM2.5 were calculated from emissions data collected by EPA and assuming that
controls applicable for PM would also reduce PM25.
3.2.1 Selection of More Stringent Controls
The control technologies that were costed to achieve the MACT floor levels for PM, Cd,
Pb, HC1, SO2, Hg, and CDD/CDF are the most effective controls available to reduce these
pollutants. Consequently, no additional technologies were considered to control these pollutants
for this analysis. Since not every SSI unit was determined to need FF, PBS, or ACI to achieve
the MACT floor level of control or operated them currently (i.e., the baseline level of control),
more stringent controls to be analyzed for the entire SSI source category would be requiring all
units that did not have these controls at baseline or for meeting the MACT floors to add these
controls. Consequently, more stringent controls applied to SSI units that were analyzed include
adding a FF for all SSI units (if the units did not already have one at baseline or to meet the
MACT floor) to control PM, Cd, and Pb; adding a PBS for all SSI units (if the units did not
already have one at baseline or to meet the MACT floor) to control HC1 and SO2; and adding
ACI (if the units did not already have one at baseline or to meet the MACT floor) to control Hg
and CDD/CDF. Emission reductions of PM2.5 were calculated assuming that controls applicable
for PM would also reduce PM2.5.
Potential add-on control technologies that achieve NOx reduction at other combustion
sources are selective catalytic reduction (SCR), selective noncatalytic reduction (SNCR), and
flue gas recirculation (FGR). However, none of these technologies were evaluated to be
3-11
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appropriate for SSI units. SSI units do not use SCR or SNCR (Inventory Database, 2010).
Additionally, there are no successful applications of SCR technology to waste-combustion units
possibly because of the difficulties operating SCRs in operations where there is significant PM or
sulfur loading in the gas stream. Application of SNCR also may not be technically feasible
considering the combustion mechanisms of MH and FB units (U.S. EPA, 2003). Application of
SNCR requires installation of a reagent injection system that is unlikely to work for existing SSI
units. Additionally, SNCR is optimal for combustion units with high residence time and exit
incinerator temperature, and less effective for lower uncontrolled NOx pollutant loadings (e.g.,
less than 200 ppm). Existing SSI units are not good matches for these considerations. FGR has
been used on combustion devices to reduce NOx emissions. However, the amount of NOx
reduced varies widely, ranging from 20% to 80%, and site-specific factors often affect the
performance. To support regulations for SSI units, EPA collected emissions information on the
nine Section 129 pollutants. One unit providing emission test data operates a MH unit with FGR.
However, its emission levels are similar to units without FGR. Therefore, no conclusion could be
made on FGR performance. Additionally, no FB units use any add-on NOx control because FB
units can achieve lowNOx emission levels, below 100 ppmv and many achieve below 70 ppmv.
For control of CO, an add-on combustion device, such as an afterburner or thermal
oxidizer, was analyzed as a more stringent control device that could be applied to SSI units. CO
emissions data were collected from nine MH SSI units as part of the data collection efforts
supporting the development of emission standards for SSI units. Table 3-1 summarizes the
average CO concentration levels from these units (Facility, Unit, and Emissions Test Database,
2010). The table is grouped into three classes of SSIs: (1) units that do not use any combustion
controls, (2) units that use an on-hearth afterburner, and (3) units that use either a detached
afterburner or thermal oxidizer or use FGR in combination with an on-hearth afterburner.
Afterburner, or secondary chamber, retrofits include retrofitting an incinerator with a
larger secondary chamber (with a longer gas residence time, for example, 2 seconds) and
operating it at a higher temperature (e.g., 1,800°F). On-hearth afterburners are the top hearth of a
MH unit that has been redesigned so that sludge is rerouted to the second hearth. Retrofitting the
MH unit with an on-hearth afterburner may require modifications to downstream air pollution
control systems because of higher temperatures and larger volumes of exhaust gases (Dangtran,
Mullen, and Mayrose, 2000). Although there will be reductions in CO and total hydrocarbon
(THC) emissions, the reductions may be limited because of low temperature and limited
residence time of the gas in the afterburner stage. The use of FGR in combination with an on-
hearth afterburner shows significantly lower emissions levels than just using an on-hearth
3-12
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Table 3-1. Summary of Average CO Emissions Collected from MH Units
Classes
Facility
Location
Unit ID
Average CO Emission Level
(ppmvd @ 7% 02)
Uncontrolled
Boat Harbor
VA
1
3,761
Seneca
MN
1
1,323
On-hearth afterburner
2
853
Central Contra Costa
CA
1
2
905
752
Detached afterburner,
thermal oxidizer, or on-
Columbia Metro
SC
1
63
Mountain View
NJ
2
39
hearth afterburner with
flue gas recirculation
Upper Blackstone
MA
1
28
3
59
afterburner. However, this may be a generalization because only one data point for this control
combination was reviewed. Additionally, performance of FGR is often influenced by site-
specific parameters that may not be generalized to the entire subcategory.
Table 3-1 shows that MH units using an add-on afterburner or thermal oxidizer can
achieve CO emission levels less than 100 ppmv. The Clean Water Acts "503 Rule" [40CFR Part
503] limits sewage sludge incinerators to 100 ppm THC as propane, dry basis, corrected to 7%
oxygen, averaged for 30 days. The 503 Rule allows substitution of 100 ppm CO dry basis,
corrected to 7% oxygen for the THC originally required. This allows the use of a lower cost,
easier to maintain CO monitor in place of the THC monitor, which is difficult to keep online. To
be consistent with the 503 regulations for disposal of sewage sludge, a value of 100 ppmv was
used as the emission level that a MH unit with an afterburner could achieve. Because CO levels
for FB units are below 100 ppmv, no afterburners were costed for this subcategory.
3.2.2 Methodology Used to Estimate Cost and Emission Reductions
The methodology used to calculate costs and emission reductions from applying the more
stringent controls followed the procedures discussed in Section 3.1 and in the SSI cost
memorandum (Cost and Emissions Reduction, 2010). As described above, if a unit already had a
FF or needed one to meet the MACT floor limits, no additional costs for FF were calculated.
Otherwise, a FF was costed out for the unit. Similar procedures were followed for PBS and ACI.
The cost algorithms; inputs to the algorithms; and testing, monitoring, recordkeeping, and
3-13
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reporting costs calculations are the same as conducted for the MACT floor and are discussed in
detail in the MACT floor cost and emission reductions memorandum.
Emission reductions from applying the controls relative to the MACT floor limits were
calculated using the following procedure. First, the reduction efficiency of the control for each
pollutant was applied to the uncontrolled concentration to determine the total reduction the
control would achieve. The reduction from uncontrolled levels to the MACT floor limits was
previously calculated for the MACT floor cost and emission reduction analysis discussed in
Section 3.1 and in a supporting memorandum (Cost and Emissions Reduction, 2010). For each
pollutant, the incremental reduction between the more stringent control application and the
MACT floor was calculated by subtracting the MACT floor concentration from the reduction
achieved by the more stringent control. Reduction of PM2.5 was calculated assuming that controls
applicable for PM would also reduce PM2.5.
3.2.3 Selection of Regulatory Options
Tables 3-2 and 3-3 summarize the costs, emission reductions, and incremental cost
effectiveness of the controls analyzed in the BTF analysis, for the case where all entities comply
(Table 3-2) and the case where small entities choose to landfill (Table 3-3). Tables 3-4 and 3-5
present the results from Tables 3-2 and 3-3 on a per unit basis. The number of Fluidized Bed
units requiring some sort of control to meet the MACT floors can be broken down as follows:
Fabric Filter, 41 units; Afterburner Retrofit, 0 units; Packed Bed Scrubber, 7 units; and ACI, 51
units. The number of Multiple Hearth units requiring some sort of control to meet the MACT
floors can be broken down as follows: Fabric Filter, 25 units; Afterburner Retrofit, 0 units;
Packed Bed Scrubber, 11 units; and ACI, 2 units. The total number of SSIs requiring some sort
of control to meet the MACT floors can be broken down as follows: Fabric Filter, 66 units;
Afterburner Retrofit, 0 units; Packed Bed Scrubber, 18 units; and ACI, 53 units. The per unit
values were calculated by dividing the costs and emissions reduction for each option by the
number of SSI units that would require control for the option. The tables indicate that except for
the afterburner, all of the controls applied result in a high incremental cost-effectiveness, greater
than $70,000/ton. Consequently, these controls, with the exception of activation carbon injection
for Hg control, were considered infeasible. Activated carbon injection was determined to provide
significant reduction in Hg emissions at MH units. Therefore, the following control options were
selected for further analysis:
Option 1 is the MACT floor level of control for the two subcategories developed for
existing SSI units, MH units and FB units.
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Option 2 is the same as Option 1, with the addition of activated carbon injection for
additional Hg emissions reduction from MH units.
Option 3 is the same as Option 2, with the addition of an afterburner on all MH units
for additional CO emissions reduction.
Tables 3-6 and 3-7 summarize the costs, total emission reductions, and incremental cost-
effectiveness of the three options. Detailed costs and emission reductions for each SSI unit for
the each option are presented in supporting memoranda
3-15
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Table 3-2. Emissions Reductions and Costs If All Units Comply
Fluidized Bed Incinerators
# of units
requiring
additional
control
Cost (2008$)'
Baseline Emissions and Incremental Emission Reductions (tons/year)
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1 Pb Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.0103
119.6
2.99
0.0531
0.0758
327.4
56.60
54.22
134.1
0.000082
0.0000068
-
-
MACT Floor Total Cost and
Emission Reductions21
51
$86,696,269
$32,313,699
0.0010
0
1.53
0.0053
0.0579
0
41.00
38.88
59.7
0.000079
0.0000065
141.1
-
BTF Costs
and
Emission
Reductions
by Control
Fabric Filter
14
$32,663,593
$8,402,116
0.0066
0
0
0.0343
0
0
0.00
0.00
0
0
0
0.041
$205,482,746
Afterburner
Retrofit15
52
$31,532,870
$10,384,276
0
0
0
0
0
0
0
0
0
0
0
0.000
-
Packed Bed
Scrubber
46
$48,701,933
$10,854,865
0
0
1.01
0
0
0
0
0
54.2
0
0
55.240
$196,505
Activated
Carbon
Injection1
0
$0
$0
0
0
0
0
0.0074
0
0
0
0
0.000001
0.0000002
0.007
$0
Multiple Hearth Incinerators
# of units
requiring
additional
control
Cost (2008$)'
Baseline Emissions and Incremental Emission Reductions (tons/year)
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
2.83
29024
122.59
6.0595
3.0536
7358.5
1101.46
666.13
3078.8
0.000020
0.0000013
-
-
MACT Floor Total Cost and
Emission Reductions21
41
$131,764,712
$40,327,113
1.41
0
91.51
2.6237
0.0315
4.3051
277.90
167.21
2132.6
0.000000
0.0000000
2,677.5
$15,061
BTF Costs
and
Emission
Reductions
by Control
Fabric Filter
138
$478,373,914
$115,254,825
1.15
0
0
2.8750
0
0
614.00
372.00
0
0
0
990.0
$116,416
Afterburner
Retrofit
128
$145,514,140
$43,193,966
0
25691
0
0
0
0
0
0
0
0
0
25,691
$1,681
Packed Bed
Scrubber
148
$258,596,495
$54,863,534
0
0
19.60
0
0
0
0
0
659.2
0
0
678.8
$80,820
Activated
Carbon
Injection
161
$6,230,844
$32,335,212
0
0
0
0
2.6235
0
0
0
0
0.000020
0.0000013
2.624
$12,324,97 4d
All Incinerators
# of units
requiring
additional
control
Cost (2008$)'
Baseline Emissions and Incremental Emission Reductions (tons/year)
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
2.84
29144
125.58
6.1126
3.1294
7685.9
1158.05
720.36
3212.9
0.000102
0.0000081
-
-
MACT Floor Total Cost and
Emission Reductions21
92
$218,460,981
$72,640,812
1.41
0
93.04
2.6291
0.0894
4.3051
318.90
206.09
2192.2
0.000079
0.0000065
2,818.7
$25,771
BTF Costs
and
Emission
Reductions
by Control
Fabric Filter
152
$511,037,506
$123,656,941
1.16
0
0
2.9093
0
0
614.00
372.00
0
0
0
990.1
$124,897
Afterburner
Retrofit
180
$177,047,010
$53,578,242
0
25691
0
0
0
0
0
0
0
0
0
25,691
$2,086
Packed Bed
Scrubber
194
$307,298,429
$65,718,399
0
0
20.61
0
0
0
0
0
713.5
0
0
734.1
$89,525
Activated
Carbon
Injection
161
$6,230,844
$32,335,212
0
0
0
0
2.6310
0
0
0
0
0.000021
0.0000015
2.631
$12,290,076e
-------
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.
Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls. For other pollutants, reductions resulted from the installation of controls where
improvement was needed in order to meet the proposed limit. For any case where the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would be able to at
least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit.
Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for the BTF scenario, the maximum control efficiency (98%) was assumed. For the MACT
floor scenario, only the percent reduction required to meet the floor limits were incorporated as the control efficiencies.
The cost-effectiveness of ACI control for MH units is equivalent to $6,160 per pound of Hg reduced.
The cost-effectiveness of ACI control for all units is equivalent to $6,150 per pound of Hg reduced.
Costs were annualized using a discount rate of 7 percent
-------
Table 3-3.
Emissions Reductions and Costs If Small Entities Landfill
Fluidized Bed Incinerators
# of units
requiring
additional
control
Cost (2008$)a
Baseline Emissions and Incremental Emission Reductions (tons/year)b
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.0103
2
119.6
2.99
0.0531
0.0758
327.4
56.60
54.22
134.1
0.000082
0.0000068
-
-
MACT Floor Total Cost and
Emission Reductions1
46
$69,952,757
$26,163,050
0.0028
2
18.89
1.81
0.0147
0.0612
53.05
43.47
41.23
76.8
0.000080
0.0000065
235.3
$111,194
Additional
Costs and
Emission
Reductions by
Control
Fabric Filter
13
$30,642,201
$7,926,815
0.0052
4
0
0
0.0271
0
0
0.00
0.00
0
0
0
0.032
$245,034,357
Afterburner
Retrofit13
43
$26,571,102
$8,659,394
0
0
0
0
0
0
0
0
0
0
0
0.000
-
Packed Bed
Scrubber
39
$41,683,343
$9,277,850
0
0
0.80
0
0
0
0
0
40.6
0
0
41.437
$223,903
Activated
Carbon
Injection6
0
$0
$0
0
0
0
0
0.0060
0
0
0
0
0.000001
0.0000001
0.006
$0
Multiple Hearth Incinerators
# of units
requiring
additional
control
Cost (2008$)a
Baseline Emissions and Incremental Emission Reductions (tons/year)b
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
2.8277
9
29024.
5
122.59
6.0595
3.0536
7358.
5
1101.46
666.13
3078.
8
0.000020
0.0000013
-
-
MACT Floor Total Cost and
Emission Reductions1
38
$125,327,287
$33,647,893
1.5459
0
3080.1
6
94.72
2.9497
0.3536
793.8
1
348.85
210.41
2221.
0
0.000002
0.0000001
6,753.8
$4,982
Additional
Costs and
Emission
Reductions by
Control
Fabric Filter
127
$440,670,924
$105,196,529
1.0471
0
0
0
2.6084
0
0
469.00
284.00
0
0
0
756.655
$139,028
Afterburner
Retrofit
122
$137,648,283
$40,428,804
0
22971.
28
0
0
0
0
0
0
0
0
0
22,971.284
$1,760
Packed Bed
Scrubber
137
$237,426,572
$50,085,972
0
0
17.62
0
0
0
0
0
601.5
0
0
619.149
$80,895
Activated
Carbon
Injection
149
$5,744,514
$28,913,350
0
0
0
0
2.3440
0
0
0
0
0.000017
0.0000012
2.344
$12,334,707'
All Incinerators
# of units
requiring
additional
control
Cost (2008S)a
Baseline Emissions and Incremental Emission Reductions (tons/year)b
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
2.8381
1
29144.
0
125.58
6.1126
3.1294
7685.
9
1158.05
720.36
3212.9
0.000102
0.0000081
-
-
MACT Floor Total Cost and
Emission Reductions1
84
$195,280,044
$59,810,943
1.5487
2
3099.0
5
96.53
2.9644
0.4147
846.8
6
392.32
251.64
2297.8
0.000082
0.0000067
6,989.1
$8,558
Additional
Costs and
Emission
Reductions
by Control
Fabric Filter
140
$471,313,125
$113,123,344
1.0523
4
0
0
2.6355
0
0
469.00
284.00
0
0
0
756.688
$149,498
Afterburner
Retrofit
165
$164,219,385
$49,088,198
0
22971.
28
0
0
0
0
0
0
0
0
0
22,971.284
$2,137
Packed Bed
Scrubber
176
$279,109,916
$59,363,822
0
0
18.43
0
0
0
0
0
642.2
0
0
660.586
$89,865
Activated
Carbon
Injection
149
$5,744,514
$28,913,350
0
0
0
0
2.3500
0
0
0
0
0.000019
0.0000013
2.350
$12,303,406g
-------
a. Costs were annualized using a discount rate of 7 percent.
b. Emissions from landfilling activities are not included in this table.
c. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 33 units; Afterburner
Retrofit, 0 units; Packed Bed Scrubber, 5 units; and ACI, 46 units.
The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 24 units;
Afterburner Retrofit, 0 units; Packed Bed Scrubber, 10 units; and ACI, 2 units.
The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 57 units; Afterburner Retrofit,
0 units; Packed Bed Scrubber, 15 units; and ACI, 48 units.
d. Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls.
For other pollutants, reductions resulted from the installation of controls where improvement was needed in order to meet the proposed limit. For any case
where the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would be
able to at least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit.
e. Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for the
BTF scenario, the maximum control efficiency (98%) was assumed. For the MACT floor scenario, only the percent reduction required to meet the floor limits
were incorporated as the control efficiencies.
f. The cost-effectiveness of ACI control for MH units is equivalent to $6,170 per pound of Hg reduced.
g. The cost-effectiveness of ACI control for all units is equivalent to $6,150 per pound of Hg reduced.
-------
Table 3-4. Emissions Reductions and Costs If All Units Comply - Per Unit Basis
Fluidized Bed Incinerators
# of units
requiring
additional
control
Cost (2008$)"
Baseline Emissions and Incremental Emission Reductions (tons/year)
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total
Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.0002
2.2
0.05
0.0010
0.0014
6.0
1.03
0.99
2.4
0.000001
0.0000001
-
-
MACT Floor Total Cost and
Emission Reductions21
51
$1,699,927
$633,602
0.0000
0
0.03
0.0001
0.0011
0
0.80
0.76
1.2
0.000002
0.0000001
2.8
$228,934
BTF Costs
and
Emission
Reductions
by Control
Fabric Filter
14
$2,333,114
$600,151
0.0005
0
0
0.0024
0
0
0.00
0.00
0
0
0
0.003
$205,482,746
Afterburner
Retrofit
52
$606,401
$199,698
0
0
0
0
0
0
0
0
0
0
0
0.000
-
Packed Bed
Scrubber
46
$1,058,738
$235,975
0
0
0.02
0
0
0
0
0
1.2
0
0
1.201
$196,505
Activated
Carbon
Injection
0
Multiple Hearth Incinerators
# of units
requiring
additional
control
Cost (2008$)"
Baseline Emissions and Incremental Emission Reductions (tons/year)
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total
Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.02
178
0.75
0.0372
0.0187
45.1
6.76
4.09
18.9
0.000000
0.0000000
-
-
MACT Floor Total Cost and
Emission Reductions21
41
$3,213,773
$983,588
0.03
0
2.23
0.0640
0.0008
0.105
6.78
4.08
52.0
0.000000
0.0000000
65.3
$15,061
Additional
Costs and
Emission
Reductions
by Control
Fabric Filter
138
$3,466,478
$835,180
0.01
0
0
0.0208
0
0
4.45
2.70
0
0
0
7.2
$116,416
Afterburner
Retrofit
128
$1,136,829
$337,453
0
201
0
0
0
0
0
0
0
0
0
201
$1,681
Packed Bed
Scrubber
148
$1,747,274
$370,700
0
0
0.13
0
0
0
0
0
4.5
0
0
4.6
$80,820
Activated
Carbon
Injection
161
$38,701
$200,840
0
0
0
0
0.0163
0
0
0
0
0.000000
0.0000000
0.016
$12,324,974
All Incinerators
# of units
requiring
additional
control
Cost (2008$)"
Baseline Emissions and Incremental Emission Reductions (tons/year)
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total
Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.02
180
0.81
0.0381
0.0201
51.1
7.79
5.07
21.3
0.000002
0.0000001
-
-
MACT Floor Total Cost and
Emission Reductions21
92
$4,913,700
$1,617,190
0.03
0
2.26
0.0641
0.0019
0.105
7.58
4.84
53.2
0.000002
0.0000001
30.6
$52,784
Additional
Costs and
Emission
Reductions
by Control
Fabric Filter
152
$5,799,591
$1,435,331
0.01
0
0
0.0233
0
0
4.45
2.70
0
0
0
6.5
$220,359
Afterburner
Retrofit
180
$1,743,231
$537,150
0
201
0
0
0
0
0
0
0
0
0
143
$3,763
Packed Bed
Scrubber
194
$2,806,011
$606,675
0
0
0.15
0
0
0
0
0
5.6
0
0
3.8
$160,330
Activated
Carbon
Injection
161
$38,701
$200,840
0
0
0
0
0.0163
0
0
0
0
0.000000
0.0000000
0.016
$12,290,076
-------
. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 41 units; Afterburner
Retrofit, 0 units; Packed Bed Scrubber, 7 units; and ACI, 51 units.
The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 25 units;
Afterburner Retrofit, 0 units; Packed Bed Scrubber, 11 units; and ACI, 2 units.
The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 66 units; Afterburner Retrofit,
0 units; Packed Bed Scrubber, 18 units; and ACI, 53 units.
. Costs were annualized using a discount rate of 7 percent.
-------
Table 3-5. Emissions Reductions and Costs If Small Entities Landfill - Per Unit Basis
Fluidized Bed Incinerators
# of units
requiring
additional
control
Cost (2008$)a
Baseline Emissions and Incremental Emission Reductions (tons/year)b
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total
Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.00019
2.2
0.05
0.0010
0.0014
6.0
1.03
0.99
2.4
0.000001
0.0000001
-
-
MACT Floor Total Cost and
Emission Reductions1
46
$1,520,712
$568,762
0.00006
0.41
0.04
0.0003
0.0013
1.15
0.95
0.90
1.7
0.000002
0.0000001
5.1
$111,194
Additional
Costs and
Emission
Reductions
by Control
Fabric Filter
13
$2,357,092
$609,755
0.00040
0
0
0.0021
0
0
0.00
0.00
0
0
0
0.002
$245,034,357
Afterburner
Retrofit13
43
$617,933
$201,381
0
0
0
0
0
0
0
0
0
0
0
0.000
-
Packed Bed
Scrubber
39
$1,068,804
$237,894
0
0
0.02
0
0
0
0
0
1.0
0
0
1.062
$223,903
Activated
Carbon
Injection®
0
Multiple Hearth Incinerators
# of units
requiring
additional
control
Cost (2008$)a
Baseline Emissions and Incremental Emission Reductions (tons/year)b
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total
Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.01735
178.1
0.75
0.0372
0.0187
45.1
6.76
4.09
18.9
0.000000
0.0000000
-
-
MACT Floor Total Cost and
Emission Reductions1
38
$3,298,086
$885,471
0.04068
81.06
2.49
0.0776
0.0093
20.89
9.18
5.54
58.4
0.000000
0.0000000
177.7
$4,982
Additional
Costs and
Emission
Reductions
by Control
Fabric Filter
127
$3,469,850
$828,319
0.00824
0
0
0.0205
0
0
3.69
2.24
0
0
0
5.958
$139,028
Afterburner
Retrofit
122
$1,128,265
$331,384
0
188.2
892
0
0
0
0
0
0
0
0
0
188.289
$1,760
Packed Bed
Scrubber
137
$1,733,041
$365,591
0
0
0.13
0
0
0
0
0
4.4
0
0
4.519
$80,895
Activated
Carbon
Injection
149
$38,554
$194,049
0
0
0
0
0.0157
0
0
0
0
0.000000
0.0000000
0.016
$12,334,707
All Incinerators
# of units
requiring
additional
control
Cost (2008S)a
Baseline Emissions and Incremental Emission Reductions (tons/year)b
Total
Emission
Reductions
(tons/yr)
Incremental
Cost-
effectiveness
(S/ton)
Total
Capital
Investment
(S)
Total
Annualized
Cost (S/yr)
Cd
CO
HC1
Pb
Hg
NOx
PM Filt
PM 2.5
S02
D/F Total
D/F TEQ
Baseline Emissions
-
-
0.01754
180.2
0.81
0.0381
0.0201
51.1
7.79
5.07
21.3
0.000002
0.0000001
-
-
MACT Floor Total Cost and
Emission Reductions1
84
$4,818,799
$1,454,233
0.04074
81.47
2.53
0.0779
0.0106
22.04
10.13
6.43
60.1
0.000002
0.0000001
182.8
$7,953
Additional
Costs and
Emission
Reductions
by Control
Fabric Filter
140
$5,826,942
$1,438,074
0.00865
0
0
0.0226
0
0
3.69
2.24
0
0
0
5.960
$241,271
Afterburner
Retrofit
165
$1,746,197
$532,765
0
188.2
892
0
0
0
0
0
0
0
0
0
188.289
$2,830
Packed Bed
Scrubber
176
$2,801,844
$603,485
0
0
0.15
0
0
0
0
0
5.4
0
0
5.582
$108,116
Activated
Carbon
Injection
149
$38,554
$194,049
0
0
0
0
0.0157
0
0
0
0
0.000000
0.0000000
0.016
$12,334,707
-------
a. Costs were annualized using a discount rate of 7 percent.
b. Emissions from landfilling activities are not included in this table.
c. The number of Fluidized Bed units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 33 units; Afterburner
Retrofit, 0 units; Packed Bed Scrubber, 5 units; and ACI, 46 units.
The number of Multiple Hearth units requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 24 units;
Afterburner Retrofit, 0 units; Packed Bed Scrubber, 10 units; and ACI, 2 units.
The total number of SSIs requiring some sort of control to meet the MACT floors can be broken down as follows: Fabric Filter, 57 units; Afterburner Retrofit,
0 units; Packed Bed Scrubber, 15 units; and ACI, 48 units.
d. Emission reductions of zero are an artifact of the methodology used to conservatively estimate reductions, which was kept consistent for all pollutant controls.
For other pollutants, reductions resulted from the installation of controls where improvement was needed in order to meet the proposed limit. For any case
where the unit already met a pollutant limit, that MACT pollutant concentration was set equal to the baseline, based on the assumption that the unit would be
able to at least achieve the limit. For CO, all FB units already met the limit, yielding a calculated reduction of zero for each unit.
e. Although no additional ACI is required for beyond-the-floor control for FB units (hence no incremental cost), small reductions are calculated because for the
BTF scenario, the maximum control efficiency (98%) was assumed. For the MACT floor scenario, only the percent reduction required to meet the floor limits
were incorporated as the control efficiencies.
LtJ
to
LtJ
-------
Table 3-6. Emissions Reductions and Costs If All Units Comply3
Option
Cost (2008$)
Baseline Emissions and Incremental Emission Reductions (tons/vear)
Total
Emission
Reductions of
129 Pollutants
Total Capital
Investment
(Smillion)
Total
Annualized Cost
($million/yr)
Cd
CO
HC1
Pb
NOx
PM Filt
pm25
so2
D/F Total
D/F TEQ
Baseline Emissions
_
_
2.84
29,100
126
6.11
3.13
7,700
1,160
720
3,210
0.000102
0.0000081
_
Costs and
Emission
Reductions
Option 1 (MACT Floor)
$220
$73
1.41
0
93.0
2.63
0.09
4.31
319
206
2,190
0.000079
0.0000065
2,819
Option 2 (MACT Floor
+ Activated carbon
injection for MH units)
$225
$105
1.41
0
93.0
2.63
2.71
4.31
319
206
2,190
0.000098
0.0000078
2,821
Option 3 (Option 2 +
Afterburners for MH
Units)
$370
$148
1.41
25,700
93.0
2.63
2.71
4.31
319
206
2,190
0.000098
0.0000078
28,500
Table 3-7. Emissions Reductions and Costs If Large Entities Comply and Small Entities Landfill3
Option
Cost (2008$)
Baseline Emissions and Incremental Emission Reductions (tons/vear)
Total
Emission
Reductions of
129 Pollutants
Total Capital
Investment
($million)
Total
Annualized Cost
($million/yr)
Cd
CO
HC1
Pb
Kg
NOx
PM Filt
PM25
so2
D/F Total
D/F TEQ
Baseline Emissions
_
_
2.84
29,100
126
6.11
3.13
7,700
1,160
720
3,210
0.000102
0.0000081
_
Costs and
Emission
Reductions
Option 1 (MACT Floor)
$195
$59.8
1.55
2,850
96.2
2.96
0.41
823
390
251
2,300
0.000082
0.0000067
6,714
Option 2 (MACT Floor
+ Activated carbon
injection for MH units)
$201
$89
1.55
2,850
96.2
2.96
2.76
823
390
251
2,300
0.000099
0.0000078
6,717
Option 3 (Option 2 +
Afterburner for MH
Units)
$338
$129
1.55
25,800
96.2
2.96
2.76
823
390
251
2,300
0.000099
0.0000078
29,690
"Annualized costs were calculated using a discount rate of 7 percent.
-------
3.3 Estimation of Impacts for New Units Constructed within 5 Years After
Promulgation of the SSI NSPS
3.3.1 Estimation of New Sources
Several significant changes have occurred to SSI units in the past 20 years. EPA's Office
of Water (OW) set emission and discharge standards for sewage sludge disposal methods
(including incineration) in 1993 (40 CFR part 503). As a result of the CWA part 503 Rule, many
wastewater treatment facilities chose to use alternative methods for disposing of sewage sludge,
such as landfilling or land application, rather than try to meet the incineration requirements.
Many of the closed incinerators had been operated by municipalities or agencies serving smaller
populations (i.e., fewer than 50,000 people) (Summary of Telephone Contacts, 2010).
The general trend has also been for facilities still incinerating sewage sludge to replace
older MH units with newer FB units because of better emissions performance, savings in fuel
cost, and flexibility in operation. Since 1988, over 40 new FB systems have been installed, with
11 replacing existing MH units (Dangtran, Mullen, and Mayrose, 2000). Discussions with the
National Association of Clean Water Agencies (NACWA), the industry trade group, indicated
that only FB units are likely to be constructed in the future (U.S. EPA, 2009b). Consequently, it
was assumed that any new units that would be built after promulgation of the NSPS would be a
FB design.
To estimate the number of new sources that might be constructed in the 5 years following
promulgation of the NSPS, the number of sources being constructed 5 years prior to proposal of
the rule was reviewed to determine if there was a trend. Under EPA's New Source Review
(NSR) program, if a company is planning to build a new plant or modify an existing plant such
that air pollution emissions will increase by a large amount, then the company must obtain an
NSR permit. The NSR permit is a construction permit that requires the company to minimize air
pollution emissions by changing the process to prevent air pollution and/or installing air
pollution control equipment. The NSR program defines control levels based on the type of
program the source is subject to: reasonably available control technology (RACT), best available
control technology (BACT), or lowest achievable emissions reduction (LAER). Information
from the EPA's RACT/BACT/LAER database contains case-specific information on the "best
available" air pollution technologies that have been required to reduce the emission of air
pollutants from stationary sources. This information has been provided by state and local
permitting agencies. The database was searched for SSI units permitted or constructed since
2005. The search results showed two FB units at the R.L. Sutton Water Reclamation facility in
Georgia were permitted in 2005, and completed construction in 2008 and are currently in
3-25
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operation. Additional information collected from state environmental agencies and permits
indicated an additional three units at the Mill Creek Wastewater Treatment Plant in Ohio were
expected to finish construction and be in operation in 2010 (Oommen and Allen, 2010a). All of
these new FB units were replacements for MH units.
Based on the data collected and assuming the trend in construction continues, five
additional FB units will be permitted to be constructed in 5 years after the NSPS is proposed.
However, given the time necessary to review and assess the requirements of the NSPS and plan,
permit, and construct incineration units, it is unlikely that all five would be in operation in the 5
years. For this analysis, it was assumed at least two new FB units would be constructed and in
operation in this time period.
3.3.2 Methodology Used to Estimate Cost and Emission Reductions of the MACT Floor
Level of Control
Cost and emission reductions for new units complying with the NSPS were calculated by
(1) determining the controls that these units would most likely apply if the NSPS were not in
place (referred to as the baseline level of control), (2) calculating the cost of complying with the
NSPS emission levels, and (3) estimating the emissions reduction from complying with the
NSPS emissions levels. Each of these steps is discussed in more detail.
3.3.2.1 Determining Baseline Controls
The baseline level of control that new units would likely implement (in the absence of the
NSPS) was determined from reviewing the most common controls used at existing FB units, as
shown in the SSI inventory memorandum (Inventory Database, 2010). Table 3-8 shows the
distribution of controls. Based on this information, the baseline controls assumed for the new
units are a combination of venturi scrubbers and impingement scrubbers. Data gathered on the
controls currently used at FB units indicate that few FB units operate an afterburner, because
their CO emissions are already low. However, to meet the new source floor limit, the analysis
costs out an afterburner to reach the limit. In reality, new FB units that are constructed are likely
to be designed to meet the CO level. Costing an afterburner provides a conservative estimate of
costs.
3.3.2.2 Calculating Baseline Emissions
The SSI baseline emissions memorandum (Estimation of Baseline Emissions, 2010)
documents the calculation of baseline emissions from existing FB SSI units. Baseline emissions
were calculated on a mass basis by multiplying the concentration of the pollutant in the emission
stream, flow rate of the emission stream, and the hours of operation of the SSI unit. For units
3-26
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Table 3-8. Control Device Distribution for Fluidized Bed Incinerators3
Existing Control Devices Number of Units Percent
Distribution of Individual Controls
Venturi scrubber (vs, vs(ad))
Impingement scrubber (imp)
Wet ESP (wesp)
Cyclone separator (cs)
Activated carbon (ac inject or ac polish)
Afterburner (abo or abd)
Packed bed scrubber (ccpt, pbs, pbt)
Distribution of Control Combinations
abd - mc - vs - imp
2
3.64
abd - vs - imp - hss - cs
1
1.82
abo - imp - wesp
1
1.82
ac inject. - vs(ad) - wesp
3
5.45
ccpt
1
1.82
cs - vs - pbt
2
3.64
unknown
4
7.27
vs
5
9.09
vs - cs
1
1.82
vs - imp
25
45.45
vs - imp - wesp
8
14.55
vs - imp - wesp - ac polish.
1
1.82
vs(ad) - wesp
1
1.82
Total
55
100.00
a Dominak, Robert, Co-Chair NACWA Biosolids Management Committee, e-mail to Amy Hambrick, U.S. EPA.
August 5, 2009. "SSI Inventory Updated Information."
where no emissions test data were collected, baseline emissions were estimated using an average
uncontrolled concentration and applying reduction efficiencies associated with the control
devices located at each SSI unit for each pollutant.
An average flue gas flow rate factor was also developed for FB units relating the flue gas
flow rate to the dry sludge feed rate from units providing emission test data. For units where
sludge feed rates were not collected, unit capacities were multiplied by a capacity utilization
49
38
14
4
4
4
2
89
69
25
7
7
7
4
3-27
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factor of 75%, which was the median of the capacity utilizations reported in the ICR survey
responses. More information about how unit capacity values were obtained can be found in the
SSI inventory database memorandum (Inventory Database, 2010). The flow rate of the flue gas
stream was calculated by multiplying the dry sludge feed rate by the average flue gas flow rate
factor.
Based on the information gathered from RACT/BACT/LAER and permits, it is likely that
new FB units constructed will be replacements for existing units. However, it cannot be
determined how many units will be replaced at a facility or the total number of units that will be
in operation at a facility. For this analysis, the simplest and most conservative assumption was
used—that only one FB unit would be constructed replacing one older MH unit. The operating
hours for facilities operating one unit were assumed to be 8,400 hours per year (incorporating
two weeks' downtime).
Table 3-9 shows the average concentration factors, average dry sludge capacity, and
operating hours, as well as other default parameters necessary for the costs. These factors were
applied to each new unit estimated to be constructed within the next 5 years. Table 3-10 shows
the estimated baseline concentrations for new units.
Calculating Costs and Emission Reductions
Costs were calculated using the procedures and algorithms discussed in the memorandum
"Cost and Emissions Reduction of Complying with the MACT Floor for Existing SSI Units"
(Oommen and Allen, 2010b). Control devices costed out were those that would be necessary to
meet the MACT floor level of control for new sources. It is possible for some units with wet
scrubbers to comply with the NSPS limits for SO2 by adding caustic. However, it is uncertain if
all units could do this. Therefore, this analysis assumed a PBS would be used, which would
provide a more conservative estimate of costs. Similarly, wet electrostatic precipitators can be
used for PM control; a FF was costed in this analysis to provide a conservative estimate of costs.
Table 3-11 shows the comparison of baseline emissions levels to MACT floor levels to
determine the amount of pollutant reduction needed and the types of control devices that would
be used to meet the levels. Emission reductions from applying the MACT floor requirements to
the baseline emission levels are presented in Table 3-10. The inputs to the cost algorithm are
presented in Table 3-9. For this analysis, it was assumed that controls applicable for PM would
also reduce PM2.5
3-28
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Table 3-9. Cost and Emission Reduction Calculation Inputs
Default (Average of known data for FB
Parameter subcategory)
Capacity (dtph) 2.26
Capacity 4,516.36
(dry lb/hr)
Sludge feed rate 1.69
(dry tons/hr)
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 factor0 1.03
Sludge heating value (BTU/lb)d 7740
NOx, lb/MMBTU 0.07
PM 0.0054
(gr/dscf)
HC1 (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).
0 ACI algorithm is based on 90% Hg reduction efficiency and 98% CDD/CDF reduction efficiency. This
adjustment factor will be used to adjust total annual costs to the estimated reduction efficiency needed to meet the
floor.
d Converted to BTU/lb from 18 MJ/kg dried, undigested sludge
(http://www.aseanenvironment.info/Abstract/41015799.pdf).
3-29
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Table 3-10. Summary of Emission Reductions for New SSI Units
Pollutant
Concentration
Units
Additional
Control
Needed for
MACT
Baseline
Concentration
NSPS Limit
MACT
Emission
Concentration
Emission
Reduction
(concentration)
Emission
Reduction
(tpy)
Annual Emission
Reductions:
Year 5
(Assuming 2 new
units come online
in 5 years)
Cadmium (Cd)
mg/dscm
AddFF
0.002
0.00051
0.00051
0.002
2.36E-04
4.73E-04
Carbon monoxide (CO)
ppmvd
Add ABD
16.331
7.4
7.4
8.931
1.51E+00
3.02E+00
Hydrogen chloride
(HC1)
ppmvd
none3
0.124
0.13
0.050
0.074
1.64E-02
3.27E-02
Lead (Pb)
mg/dscm
AddFF
0.011
0.00053
0.00053
0.011
1.53E-03
3.06E-03
Mercury (Hg)
mg/dscm
Add ACI
0.014
0.001
0.001
0.013
1.82E-03
3.64E-03
Nitrogen oxides (NOx)
ppmvd
noneb
27.926
26
26
1.926
5.35E-01
1.07E+00
Particulate matter
(filterable)
mg/dscm
AddFF
12.443
4.1
4.1
8.343
1.21E+00
2.43E+00
Particulate matter
(PM25)
mg/dscm
AddFF
11.801
2.3
2.3
9.501
1.38E+00
2.76E+00
Sulfur dioxide (S02)
ppmvd
Add PBS
3.303
2.0
2.0
1.303
5.04E-01
1.01E+00
Total dioxin/furans
ng/dscm
Add ACI
15.962
0.94
0.94
15.022
2.18E-06
4.37E-06
Total dioxin/furans
(TEQ)
ng/dscm
Add ACI
1.312
0.023
0.023
1.289
1.87E-07
3.75E-07
a Assumed scrubber (installed for S02 control) has 98% efficiency for HC1 control.
b Assumed units could meet limit by making minor adjustments rather than installing add-on control.
-------
Table 3-11 shows the estimated total capital investment (TCI) and total annual costs
(TAC) calculated for a single unit using the cost algorithms previously discussed. The table also
shows the monitoring, testing, reporting, and recordkeeping costs. The table shows the TCI and
TAC for the two new FB units that are assumed to be constructed and in operation in the 5 years
after proposal of the NSPS.
Table 3-11. MACT Costs Associated with Model FB Unit
Parameter
TCI
TAC
AddFF
$1,995,892
$580,670
Add PBS
$1,013,167
$233,832
Controls
Add ACI
$25,786
$163,338
Add ABD
$625,106
$233,589
Subtotal:
$3,659,952
$1,211,429
Initial Stack Test
$61,000
Annual Stack Test
$61,000
Bag Leak Detection System
$25,500
$9,700
Wet Scrubber Monitoring
$24,300
$5,600
Monitoring, Testing,
ACI Monitoring
$0
$9,800
Reporting and
$1,000
Recordkeeping
Annual Control Device Inspection
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 levels are generally the most
effective controls available: FFs for PM, Cd, Pb; ACI for Hg and CDD/CDF; afterburners for
CO; and PBSs for HC1 and SO2. In addition, incremental additions of activated carbon have not
been proven to achieve further reductions above the projected flue gas concentration estimated to
achieve the limits for new sources. Data gathered do not indicate that any FB units operate NOx
controls, such as SNCR, SCR, or flue gas recirculation because the NOx emissions are already
low. Therefore, no BTF options were analyzed for this analysis because we are not aware of any
technologies or methods to achieve emission limits more stringent than the MACT floor limits
for new units, which are based on the lowest emitting FB units.
3-31
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SECTION 4
ECONOMIC IMPACT ANALYSIS
EPA has prepared an EIA to provide decision makers with a measure of the social costs
of using resources to comply with the proposed greenhouse gas (GHG) reporting requirements.
As noted in EPA's (2000) Guidelines for Preparing Economic Analyses, several tools are
available to estimate social costs and range from simple direct compliance cost methods to the
development of a more complex market analysis that estimates market changes (e.g., price and
consumption) and economic welfare changes (e.g., changes in consumer and producer surplus).
Because the proposed regulatory option affects governmental entities (96 of the 97 owners are
governmental entities) providing services not provided in a market, the Office of Air Quality
Planning and Standards (OAQPS) has used the direct compliance cost method as a measure of
social costs. Since no market impacts are anticipated, the economic analysis focused on the
comparison of control cost to total governmental revenue.
The EIA evaluates three options discussed in Section 3:
Option 1 is the MACT floor level of control for the two subcategories developed for
existing SSI units, MH units and FB units.
Option 2 is the same as Option 1, with the addition of activated carbon injection for
additional Hg emissions reduction from MH units.
Option 3 is the same as Option 2, with the addition of an afterburner on all MH units
for additional CO emissions reduction.
Within each option, EPA presents the results of the cost analysis using two assumptions:
¦ Large government entities comply and incinerate while small government entities
choose to landfill. EPA anticipates this is the most likely response to the regulation
based on analysis of landfilling costs and interviews with a sample of small
government entities.
¦ All government entities (small and large) comply and incinerate. EPA anticipates this
assumption significantly overstates the rule's costs because it assumes small entities
do not consider other disposal options.
4.1 Social Cost Estimates
EPA has estimated compliance costs for all existing units to add the necessary controls,
monitoring equipment, inspections, and recordkeeping and reporting requirements to comply
with the proposed SSI standards. Based on the engineering cost analysis, we anticipate the
4-1
-------
overall total annual social cost to be approximately $92 million. The lowest cost option is the
MACT floor where large entities comply and small entities landfill ($63 million). The highest
cost option is the MACT floor with afterburners and fabric filters for MH units and all entities
comply ($151 million). All cost options are displayed in Table 4-1.
Table 4-1. Annual Social Cost Estimates by Option and Disposal Choices ($ million,
2008$)
Large Entities Comply and
Small Entities Landfill All Units Comply
Existing Sources
Option 1 $60 $73
(MACT Floor)
Option 2 $89 $105
(MACT Floor + Afterburner
for MH Units)
Option 3 $129 $148
(Option 2 + Fabric Filters for
MH Units)
New Sources (Fluidized Bed)3 $3 $3
a Two new FB units that are assumed to be constructed and in operation in the 5 years after proposal of the NSPS.
4.2 Small Entity Analysis
The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a
regulatory flexibility analysis of any rule subject to notice and comment rulemaking
requirements under the Administrative Procedure Act or any other statute unless the agency
certifies that the rule will not have a significant economic impact on a substantial number of
small entities (SISNOSE). The first step in this assessment was to determine whether the rule
will have SISNOSE. To make this determination, EPA used a screening analysis to indicate
whether EPA can certify the rule as not having a SISNOSE. The elements of this analysis
included
¦ identifying affected small entities,
selecting and describing the measures and economic impact thresholds used in the
analysis, and
completing the assessment and determining the SISNOSE certification category.
4-2
-------
4.2.1 Identify Affected Small Entities
For the purposes of assessing the impacts of the proposed rule on small entities, small
entity is defined as (1) a small business as defined by the Small Business Administration's
regulations at 13 CFR 121.20; (2) a small governmental jurisdiction that is a government of a
city, county, town, school district, or special district with a population of less than 50,000; and
(3) a small organization that is any not-for-profit enterprise that is independently owned and
operated and is not dominant in its field. As reported in Section 2, EPA has identified 18 small
entities that have a population of fewer than 50,000. There are no small businesses or
organizations affected by the proposed rule.
4.2.2 Screening Analysis: Revenue Test
In the next step of the analysis, EPA compared each regulatory option's control costs to
total government revenues (i.e., a "revenue" test). To estimate government revenues, we
collected U.S. Census financial information for municipal governments by population ranges,
computed average per capita revenues for each population range, and multiplied the per capita
revenue figure by the population served by small and large government entities (Table 4-2).
Table 4-2. Calculated Municipal and Township Per Capita Revenues by Population Size
Population Size
Fewer than 10K
10 to 25K
25 to 50K
>50K
Number of municipalities/townships
16,745
1,436
643
605
Population
28,750,200
22,588,957
22,576,240
100,966,557
Revenue (thousand 2002$)
34,944,647
32,010,988
31,630,676
238,846,095
Per capita (2002$)
$1,215
$1,417
$1,401
$2,366
Per capita (2008$)
$1,455
$1,696
$1,677
$2,831
Source: U.S. Census. 2005. Finances of Municipal and Township Governments: 2002. Table 13, accessed June 8,
2010 at http://www.census.gov/prod/2005pubs/gc024x4.pdf.
Each option's screening results under two disposal assumptions are presented in Tables
4-3 through Table 4-8. As noted above, EPA anticipates small government entities will most
likely switch from incineration to landfilling (Tables 4-4, 4-6, and 4-8). EPA has also presented
small entity results where small entities comply and incinerate (Tables 4-3, 4-5, and 4-7).
However, EPA anticipates this assumption would significantly overstate the rule's small entity
impacts because it assumes small entities continue to incinerate and do not consider other less
expensive disposal options.
4-3
-------
Based on the engineering cost analysis, EPA anticipates the overall total annualized cost
for the selection option will be $92 million (Option 2: MACT floor with activated carbon
injection for MH, large entities comply and small entities landfill); under this option and set of
disposal choices, all small entities are affected at less than 1% revenues (Table 4-6).
For the lowest cost Option 1, the MACT floor where large entities comply and small
entities landfill (total annualized costs = $63 million), all small entities are affected at less than
1% revenue (Table 4-4).
For the highest cost Option 3, the MACT floor with activated carbon injection and
afterburners for MH units and small entities landfill (total annualized cost = $132 million). All
small entities are still affected at less than 1% revenue (Table 4-8).
Table 4-3. Option 1 Revenue Tests for Government Entities: All Entities Comply
Sample Statistic
Small
Large
Cost-Revenue-Ratios
Mean
1.1%
0.1%
Median
0.9%
0.1%
Minimum
0.1%
0.0%
Maximum
3.4%
1.0%
Number of Entities
18
69
Number of Entities > 1%
9
0
Number of Entities > 3%
2
0
Table 4-4. Option 1 Revenue Tests for Government Entities: Large Entities Comply and
Small Entities Landfill
Sample Statistic
Small
Large
Cost-Revenue-Ratios
Mean
-0.6%
0.1%
Median
-0.2%
0.1%
Minimum
-2.6%
0.0%
Maximum
0.7%
1.0%
Number of Entities
18
69
Number of Entities > 1%
0
0
Number of Entities > 3%
0
0
4-4
-------
Table 4-5. Option 2 Revenue Tests for Government Entities: All Entities Comply
Sample Statistic
Small
Large
Cost-Revenue-Ratios
Mean
1.6%
0.2%
Median
1.2%
0.1%
Minimum
0.5%
0.0%
Maximum
4.4%
1.2%
Number of Entities
18
69
Number of Entities > 1%
13
2
Number of Entities > 3%
2
0
Table 4-6. Option 2 Revenue Tests for Government Entities: Large Entities Comply and
Small Entities Landfill
Sample Statistic
Small
Large
Cost-Revenue-Ratios
Mean
-0.6%
0.2%
Median
-0.2%
0.1%
Minimum
-2.6%
0.0%
Maximum
0.7%
1.2%
Number of Entities
18
69
Number of Entities > 1%
0
2
Number of Entities > 3%
0
0
Table 4-7. Option 3 Revenue Tests for Government Entities: All Entities Comply
Sample Statistic
Small
Large
Cost-Revenue-Ratios
Mean
1.9%
0.3%
Median
1.3%
0.2%
Minimum
0.6%
0.0%
Maximum
6.0%
1.2%
Number of Entities
18
69
Number of Entities > 1%
16
2
Number of Entities > 3%
3
0
4-5
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Table 4-8. Option 3 Revenue Tests for Government Entities: Large Entities Comply and
Small Entities Landfill
Sample Statistic
Small
Large
Cost-Revenue-Ratios
Mean
-0.6%
0.3%
Median
-0.2%
0.2%
Minimum
-2.6%
0.0%
Maximum
0.7%
1.2%
Number of Entities
18
69
Number of Entities > 1%
0
2
Number of Entities > 3%
0
0
4-6
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SECTION 5
HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS
5.1 Synopsis
In this section, we provide an estimate of the monetized benefits associated with reducing
particulate matter (PM) for the proposed Sewage Sludge Incinerator (SSI) New Source
Performance Standard (NSPS) and Emissions Guidelines (EG). For this rule, the PM reductions
are the result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and
SO2, as well as emission limits on other pollutants. The total PM2.5 reductions are the
consequence of the technologies installed or waste diversion to meet these multiple limits. These
estimates reflect the monetized human health benefits of reducing cases of morbidity and
premature mortality among populations exposed to the PM2.5 precursors reduced by this
rulemaking. Using a 3% discount rate, we estimate the total monetized benefits including energy
disbenefits of the proposed SSI NSPS and EG to be $130 million to $320 million in the
implementation year (2015). Using a 7% discount rate, we estimate the total monetized benefits
including energy disbenefits of the proposed SSI NSPS and EG to be $120 million to $290
million in the implementation year. All estimates are in 2008$.
These estimates reflect EPA's most current interpretation of the scientific literature.
Higher or lower estimates of benefits are possible using other assumptions; examples of this are
provided in Figure 5-2. Data, resource, and methodological limitations prevented EPA from
monetizing the benefits from several important benefit categories, including benefits from
reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The benefits
from reducing other air pollutants have not been monetized in this analysis, including reducing
2,900 tons of CO, 96 tons of HC1, 3.0 tons of Pb, 1.6 tons of Cd, 5,500 pounds of mercury (Hg),
and 90 grams of total dioxins/furans (CDD/CDF) each year.
5.2 Calculation of PM2.5 Human Health Benefits
This rulemaking would reduce emissions of PM2.5, SO2, and NO2. Because SOx and NO2
are also precursors to PM2.5, reducing these emissions would also reduce PM2.5 formation, human
exposure, and the incidence of PM2.5-related health effects. For this rule, the PM reductions are
the result of emission limits on PM, emission limits on PM2.5 precursors such as NOx and SO2, as
well as emission limits on other pollutants. The total PM2.5 reductions are the consequence of the
technologies installed or waste diversion to meet these multiple limits. Due to analytical
limitations, it was not possible to provide a comprehensive estimate of PM2.5-related benefits.
Instead, we used the "benefit-per-ton" approach to estimate these benefits based on the
5-1
-------
methodology described in Fann, Fulcher, and Hubbell (2009). The key assumptions are
described in detail below. These PM^.sbenefit-per-ton estimates provide the total monetized
human health benefits (the sum of premature mortality and premature morbidity) of reducing one
ton of PM2.5 from a specified source. EPA has used the benefit per-ton technique in several
previous RIAs, including the recent NO2 NAAQS RIA (U.S. EPA, 2010b). Table 5-1 shows the
quantified and unquantified benefits captured in those benefit-per-ton estimates.
Table 5-1. Human Health and Welfare Effects of PM2i5
Pollutant /
Quantified and Monetized
Unquantified Effects
Effect
in Primary Estimates
Changes in:
pm25
Adult premature mortality
Subchronic bronchitis cases
Bronchitis: chronic and acute
Low birth weight
Hospital admissions: respiratory and
Pulmonary function
cardiovascular
Chronic respiratory diseases other than chronic
Emergency room visits for asthma
bronchitis
Nonfatal heart attacks (myocardial infarction)
Non-asthma respiratory emergency room visits
Lower and upper respiratory illness
Visibility
Minor restricted-activity days
Household soiling
Work loss days
Asthma exacerbations (asthmatic population)
Infant mortality
Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM2.5 benefits
estimates utilize the concentration-response functions as reported in the epidemiology literature,
as well as the 12 functions obtained in EPA's expert elicitation study as a sensitivity analysis.
One estimate is based on the concentration-response (C-R) function developed from
the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope
et al. (2002), a study that EPA has previously used to generate its primary benefits
estimate. When calculating the estimate, EPA applied the effect coefficient as
reported in the study without an adjustment for assumed concentration threshold of 10
|ig/m3 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 reported by Laden et al. (2006). This study,
published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has
been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 benefits
estimates in RIAs completed since the PM2.5 NAAQS. When calculating the estimate,
EPA applied the effect coefficient as reported in the study without an adjustment for
assumed concentration threshold of 10 |ig/m3 as was done in recent (2006-2009)
RIAs.
5-2
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Twelve estimates are based on the C-R functions from EPA's expert elicitation study
(IEc, 2006; Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted
for benefits analysis in EPA's final RIA for the PM2.5NAAQS. For that study, twelve
experts (labeled A through L) provided independent estimates of the PM2.5 -mortality
concentration-response function. EPA practice has been to develop independent
estimates of PM2.5 -mortality estimates corresponding to the concentration-response
function provided by each of the twelve experts, to better characterize the degree of
variability in the expert responses.
The effect coefficients are drawn from epidemiology studies examining two large
population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six
Cities cohort (Laden et al., 2006).1 These are logical choices for anchor points in our presentation
because, while both studies are well designed and peer reviewed, there are strengths and
weaknesses inherent in each, which we believe argues for using both studies to generate benefits
estimates. Previously, EPA had calculated benefits based on these two empirical studies, but
derived the range of benefits, including the minimum and maximum results, from an expert
elicitation of the relationship between exposure to PM2.5 and premature mortality (Roman et al.,
2008).2 Within this assessment, we include the benefits estimates derived from the concentration-
response function provided by each of the twelve experts to better characterize the uncertainty in
the concentration-response function for mortality and the degree of variability in the expert
responses. Because the experts used these cohort studies to inform their concentration-response
functions, benefits estimates using these functions generally fall between results using these
epidemiology studies (see Figure 5-2). In general, the expert elicitation results support the
conclusion that the benefits of PM2.5 control are very likely to be substantial.
Readers interested in reviewing the general methodology for creating the benefit-per-ton
estimates used in this analysis should consult Fann, Fulcher, and Hubbell (2009). As described in
Fann, Fulcher, and Hubbell (2009), benefit-per-ton estimates are developed for selected
pollutant/source category combinations. The per-ton values calculated therefore apply only to
tons reduced from those specific pollutant/source combinations (e.g., SO2 emitted from electric
generating units; NO2 emitted from mobile sources). In this analysis, we apply the national
average benefit-per-ton estimate for a 2015 analysis year and multiply it by the corresponding
emission reductions of directly emitted PM2.5, SO2, and NOx to quantify the benefits of this rule.
The benefit-per-ton estimates found in Fann, Fulcher, and Hubbell (2009) reflect a specifc set of
key assumptions and input data. As we update these underlying assumptions to reflect the
1 These two studies specify multi-pollutant models that control for S02, among other pollutants.
2Please 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.
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scientific literature, we re-estimate the benefit-per-ton estimates and post the updated estimates
at http://www.epa.gov/air/benmap/bpt.html. In addition, we adjust these estimates to match the
currency year for the costs in this analysis.
These models assume that all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality because there is no clear scientific evidence that
would support the development of differential effects estimates by particle type. Directly emitted
PM, SO2, and NOx are the primary PM2.5 precursors affected by this rule. Even though we
assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary
between precursors because each ton of precursor reduced has a different propensity to form
PM2.5. For example, NOx has a lower benefit-per-ton estimate than direct PM2.5 because it does
not form as much PM2.5, thus the exposure would be lower, and the monetized health co-benefits
would be lower.
The benefit-per-ton coefficients in this analysis were derived using modified versions of
the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically,
this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA
(U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded
geographic scope of the benefit-per-ton calculation, and the functions directly from the
epidemiology studies without an adjustment for an assumed threshold.3 Removing the threshold
assumption is a key difference between the method used in this analysis of PM benefits and the
methods used in RIAs prior to Portland Cement, and we now calculate incremental benefits
down to the lowest modeled PM2.5 air quality levels.
EPA strives to use the best available science to support our benefits analyses, and we
recognize that interpretation of the science regarding air pollution and health is dynamic and
evolving. Based on our review of the current body of scientific literature, EPA now estimates
PM-related mortality without applying an assumed concentration threshold. EPA's Integrated
Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by
EPA's Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB,
2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear
model most adequately portrays the PM-mortality concentration-response relationship while
recognizing potential uncertainty about the exact shape of the concentration-response function.
Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA's Council
3The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as
discussed on the next page.
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concluded, "The HES fully supports EPA's decision to use a no-threshold model to estimate
mortality reductions. This decision is supported by the data, which are quite consistent in
showing effects down to the lowest measured levels. Analyses of cohorts using data from more
recent years, during which time PM concentrations have fallen, continue to report strong
associations with mortality. Therefore, there is no evidence to support a truncation of the CRF."
In conjunction with the underlying scientific literature, this document provided a basis for
reconsidering the application of thresholds in PM2.5 concentration-response functions used in
EPA's RIAs. For a summary of these scientific review statements and the panel members
commenting on thresholds since 2002, please consult the Technical Support Document (TSD)
Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010c), which is
provided as an appendix to this RIA.
Consistent with this recent scientific advice, we are replacing the previous threshold
sensitivity analysis with a new "Lowest Measured Level" (LML) assessment. This information
allows readers to determine the portion of population exposed to annual mean PM2.5 levels at or
above the LML of each study; in general, our confidence in the estimated PM mortality
decreases as we consider air quality levels further below the LML in major cohort studies that
estimate PM-related mortality. While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold
and continues to quantify PM-related mortality impacts using a full range of modeled air quality
concentrations. It is important to emphasize that we have high confidence in PM2.5-related effects
down to the lowest LML of the major cohort studies. Just because we have greater confidence in
the benefits above the LML, this does not mean that we have no confidence that benefits occur
below the LML.
For this analysis, policy-specific air quality data is not available due to time or resource
limitations. For these rules, we are unable to estimate the percentage of premature mortality
associated with this specific rule's emission reductions at each PM2.5 level. However, we believe
that it is still important to characterize the distribution of exposure to baseline air quality levels.
As a surrogate measure of mortality impacts, we provide the percentage of the population
exposed at each PM2.5 level using the most recent modeling available from the recently proposed
Transport Rule (U.S. EPA, 2010e). It is important to note that baseline exposure is only one
parameter in the health impact function, along with baseline incidence rates population, and
change in air quality. In other words, the percentage of the population exposed to air pollution
below the LML is not the same as the percentage of the population experiencing health impacts
as a result of a specific emission reduction policy. The most important aspect, which we are
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unable to quantify for rules without air quality modeling, is the shift in exposure associated with
this specific rule. Therefore, caution is warranted when interpreting the LML assessment. For
more information on the data and conclusions in the LML assessment for rules without policy-
specific air quality modeling, please consult the LML TSD (U.S. EPA, 2010d), which is
provided as an appendix to this RIA. The results of this analysis are provided in Section 5.4.
As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods
used to estimate air quality benefits evolve over time to reflect the Agency's most current
interpretation of the scientific and economic literature. For a period of time (2004-2008), the
Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical
life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived
at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of
the wage-risk literature. The $1 million value represented the lower end of the interquartile range
from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value
represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-
analysis of 43 studies. The mean estimate of $5.5 million (2000$)4 was also consistent with the
mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the
Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected
the interim estimate to a scientific peer-review process through the Science Advisory Board
(SAB) or other peer-review group.
During this time, the Agency continued work to update its guidance on valuing mortality
risk reductions, including commissioning a report from meta-analytic experts to evaluate
methodological questions raised by EPA and the SAB on combining estimates from the various
data sources. In addition, the Agency consulted several times with the Science Advisory Board
Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the
meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific,
appropriate meta-analytic techniques to combine estimates from unique data sources and
different studies, including those using different methodologies (i.e., wage-risk and stated
preference) (U.S. EPA-SAB, 2007).
Until updated guidance is available, the Agency determined that a single, peer-reviewed
estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the
Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines
4 After adjusting the VSL to account for a different currency year (2008$) and to account for income growth to
2015, the $5.5 million VSL is $7.9 million.
5-6
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for Preparing Economic Analyses (U.S. EPA, 2000)5 while the Agency continues its efforts to
update its guidance on this issue. This approach calculates a mean value across VSL estimates
derived from 26 labor market and contingent valuation studies published between 1974 and
1991. The mean VSL across these studies is $6.3 million (2000$).6 The Agency is committed to
using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions
and has made significant progress in responding to the SAB-EEAC's specific recommendations.
Figure 5-1 illustrates the relative breakdown of the monetized PM2.5 health benefits.
ER Visits, Resp0.00%
Figure 5-1. Breakdown of Monetized PM2.5 Health Benefits using Mortality Function from
Pope et al. (2002)a
a This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the
Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult
mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be
similar if a 7% discount rate was used.
Tables 5-2 and 5-3 provide a general summary of the all units comply assumption and
large entities comply and small entities landfill assumption results by pollutant, including the
5In 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.
6In 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 $9.1 million.
5-7
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emission reductions and monetized benefits-per-ton at discount rates of 3% and 7%.7 Table 5-4
provides a summary of the reductions in health incidences as a result of the pollution reductions
for the large entities comply and small entities landfill results. In Table 5-5, we provide the
benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the
expert
Table 5-2. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in
2015 (2008$) (large entities comply and small entities landfill)3
Emissions Benefit per Benefit per
Pollutant Reductions ton (Pope, ton (Laden,
(tons) 3%) 3%)
Benefit Benefit per
per ton ton
(Pope, (Laden,
7%) 7%)
Total
Monetized
Benefits
(millions
Total
Monetized
Benefits
(millions
2008$ at 3%) 2008$ at 7%)
s
_o
o.
o
Direct PM2 5
PM2 5 Precursors
S02
N02
254
2,298
824
$230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130
$29,000 $72,000
$4,900 $12,000
$27,000 $65,000 $68 to $170 $61 to $150
$4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8
Total $130 to $320 $120 to $290
s
_o
o.
o
o
s.
o
•-
a.
Direct PM2 5 254
PM2 5 Precursors
S02 2,298
N02 824
$230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130
$29,000 $72,000 $27,000
$4,900 $12,000 $4,400
$65,000 $68 to $170 $61 to $150
$11,000 $4.0 to $9.8 $3.6 to $8.8
Total $130 to $320 $120 to $290
s
_o
o.
o
Direct PM2 5 254
PM2 5 Precursors
S02 2,298
N02 824
$230,000 $560,000 $210,000 $500,000 $58 to $140 $52 to $130
$29,000 $72,000
$4,900 $12,000
$27,000 $65,000 $68 to $170 $61 to $150
$4,400 $11,000 $4.0 to $9.8 $3.6 to $8.8
Total $130 to $320 $120 to $290
7To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003).
These benefits are estimated for a specific analysis year (i.e., 2015), and most of the PM benefits occur within that
year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5
years of follow-up medical costs and lost wages. For premature mortality, we assume that there is a "cessation"
lag between PM exposures and the total realization of changes in health effects. Although the structure of the lag
is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag structure characterized by 30%
of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the years 6 to 20 after the reduction
in PM2 5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change the total number of estimated
deaths but rather the timing of those deaths. Therefore, discounting only affects the AMI costs after the analysis
year and the valuation of premature mortalities that occur after the analysis year. As such, the monetized benefits
using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate.
5-8
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a All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may
not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton
estimates vary because each ton of precursor reduced has a different propensity to form PM2 5. The monetized
benefits incorporate the conversion from precursor emissions to ambient fine particles. These results include 2
new FB incinerators anticipated to come online by 2015. These estimates do not include energy disbenefits valued
at $0.5 million at a 3% discount rate for C02 emissions.
elicitation on PM mortality. Figures 5-2 through 5-4 provide a visual representation of the range
of benefits estimates and the pollutant breakdown of the monetized benefits.
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Table 5-3. Summary of Monetized Benefits Estimates for Proposed SSI NSPS and EG in
2015 (2008$) (all units comply)3
Pollutant
Benefit Benefit
Emissions per ton Benefit per per ton
Reductions (Pope, ton (Laden, (Pope,
(tons) 3%) 3%) 7%)
Total Total
Benefit Monetized Monetized
per ton Benefits Benefits
(Laden, (millions 2008$ (millions 2008$
7%) at 3%) at 7%)
s
_o
o.
o
Direct PM2 5 209
PM2 5 Precursors
S02 2,193
N02 5
$230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0
$29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140
$4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05
Total $110 to $270 $100 to $250
$230,000 $560,000 $210,000 $500,000 $48.0 to $120.0 $43.0 to $110.0
$29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140
$4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05
^ Direct PM2 5
s
_o
PM2 5 Precursors
O
« so2
o
s.
o
•-
a.
N02
209
2,193
5
Total $110 to $270 $100 to $250
Direct PM2 5 209
PM2 5 Precursors
S02 2,193
N02 5
s
_o
o.
o
$230,000 $560,000 $210,000 $500,000 $48 to $120 $43 to $110
$29,000 $72,000 $27,000 $65,000 $65 to $160 $59 to $140
$4,900 $12,000 $4,400 $11,000 $.02 to $.06 $.02 to $.05
Total $110 to $270 $100 to $250
All estimates are for the implementation year (2015), and are rounded to two significant figures so numbers may
not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton
estimates vary because each ton of precursor reduced has a different propensity to form PM2 5. The monetized
benefits incorporate the conversion from precursor emissions to ambient fine particles. These results include 2
new FB incinerators anticipated to come online by 2015. These estimates do not include energy disbenefits valued
at $0.5 million at a 3% discount rate for C02 emissions.
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Table 5-4. Summary of Reductions in Health Incidences from PM2.s Benefits for the
Proposed SSI NSPS and EG in 2015a
Option 1 Proposed: Option 2 Option 3
Avoided Premature Mortality
Pope et al. 14 14 14
Laden etal. 36 36 36
Avoided Morbidity
Chronic Bronchitis 10 10 10
Acute Myocardial Infarction 23 23 23
Hospital Admissions, Respiratory 3 3 3
Hospital Admissions, Cardiovascular 7 7 7
Emergency Room Visits, Respiratory 14 14 14
Acute Bronchitis 23 23 23
Work Loss Days 1,900 1,900 1,900
Asthma Exacerbation 250 250 250
Minor Respiratory Symptoms 11,000 11,000 11,000
Lower Respiratory Symptoms 270 270 270
Upper Respiratory Symptoms 210 210 210
a All estimates are for the analysis year (2015) and are rounded to whole numbers with two significant figures. All
fine particles are assumed to have equivalent health effects, but each PM2 5 precursor pollutant has a different
propensity to form PM2 5. Confidence intervals are unavailable for this analysis because of the benefit-per-ton
methodology. These results include 2 new FB incinerators anticipated to come online by 2015 and the large
entities comply and small entities landfill assumption.
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Table 5-5. All PM2.5 Benefits Estimates for the Proposed SSI NSPS and EG at Discount
Rates of 3% and 7% in 2015 (in millions of 2008$)a
Proposed:
Option 1 Option 3
Option 2
3% 7% 3% 7% 3% 7%
Benefit-per-ton Coefficients derived from Epidemiology Literature
Pope et al.
$130
$120
$130
$120
$130
$120
Laden et al.
$320
$290
$320
$290
$320
$290
enefit-per-ton Coefficients Derived from Expert Elicitation
Expert A
$340
$300
$340
$300
$340
$300
Expert B
$260
$230
$260
$230
$260
$230
Expert C
$260
$230
$260
$230
$260
$230
Expert D
$180
$160
$180
$160
$180
$160
Expert E
$420
$380
$420
$380
$420
$380
Expert F
$230
$210
$230
$210
$230
$210
Expert G
$150
$140
$150
$140
$153
$139
Expert H
$190
$170
$190
$170
$190
$170
Expert I
$250
$230
$250
$230
$250
$230
Expert J
$210
$190
$210
$190
$210
$190
Expert K
$51
$46
$51
$46
$51
$46
Expert L
$190
$170
$190
$170
$190
$170
a All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they
were derived through the benefit-per-ton technique described above. The benefits estimates from the Expert
Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated
with the concentration-response function. These results include 2 new FB incinerators anticipated to come online
by 2015 and the large entities comply and small entities landfill assumption. These estimates do not include
energy disbenefits valued at $0.5 million at a 3% discount rate for C02 emissions.
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$400
$350
$300
$250
$200
$150
$100
$50
$0
3%DR
I 7% DR
Laden et al.
Ml
inn
Benefits estimates derived from 2 epidemiology functions and 12 expert functions
Figure 5-2. Total Monetized PM2.5 Benefits for the Proposed SSI NSPS and EG in 2015
a This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the
Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived from EPA's expert elicitation
on PM mortality. The results shown are not the direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function provided in those studies. These results include
2 new FB incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill
assumption. These estimates do not include energy disbenefits valued at $0.5 million at a 3% discount rate for
C02 emissions.
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S02 NonEGU
52%
Figure 5-3. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by
PM2.5 Precursor Pollutant and Source
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Multiple Hearth
Figure 5-4. Breakdown of Monetized Benefits for the Proposed SSI NSPS and EG by
Subcategory
5.3 Energy Disbenefits
Electricity usage associated with the operation of control devices is anticipated to
increase emissions of pollutants from utility boilers that supply electricity to the sewage sludge
incinerators. For example, increased scrubber pump horsepower and sorbent injection controls
may cause slight increases in electricity consumption. We estimate that the increased electricity
consumption associated with the proposed option would be 12 million kWh if all entities
comply, and 12 million kWh if the small entities landfill. Using national emission factors from
eGRID for electrical generating units (EGUs), we estimate the increased emissions to be 19,000
tpy of CO2 for the proposed option assuming that small entities landfill.8 Since NOx and SO2 are
covered by capped emissions trading programs, we are only estimating the CO2 emission
increases from the increased electricity demand. The methodology used to calculate these
8 Option 3 has additional energy disbenefits associated with the supplemental fuel required to run the afterburners,
which results in additional emissions of C02. CO, and NOx. The C02 energy disbenefits for Option 3 are
shown in Tables 5-7 and 5-8. The additional NOx disbenefits (as a precursor to PM2 5 using the methodolology
described in Section 5.2) for Option 3 are $0.4 million to $0.9 million, which do not affect the rounded benefits
results.
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emission increases is described "Secondary Impacts of Control Options for the Sewage Sludge
Incineration Source Category", which is available in the docket.
5.3.1 Social Cost of Carbon and Greenhouse Gas Disbenefits
EPA has assigned a dollar value to reductions in carbon dioxide (C02) emissions using
recent estimates of the "social cost of carbon" (SCC). The SCC is an estimate of the monetized
damages associated with an incremental increase in carbon emissions in a given year. It is
intended to include (but is not limited to) changes in net agricultural productivity, human health,
property damages from increased flood risk, and the value of ecosystem services due to climate
change. The SCC estimates used in this analysis were developed through an interagency process
that included EPA and other executive branch entities, and concluded in February 2010. EPA
first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking
to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average
Fuel Economy Standards; see the rule's preamble for discussion about application of SCC (75
FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete
discussion of the methods used to develop these SCC estimates.9
The interagency group selected four SCC values for use in regulatory analyses, which we
have applied in this analysis: $5, $21, $35, and $65 per metric ton of C02 emissions10 in 2010, in
2007 dollars. The first three values are based on the average SCC from three integrated
assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several
discount rates are included because the literature shows that the SCC is quite sensitive to
assumptions about the discount rate, and because no consensus exists on the appropriate rate to
use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all
three models at a 3 percent discount rate. It is included to represent higher-than-expected impacts
from temperature change further out in the tails of the SCC distribution. Low probability, high
impact events are incorporated into all of the SCC values through explicit consideration of their
effects in two of the three models as well as the use of a probability density function for
9 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for
Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of
Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of
Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental
Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management
and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also
available at http://www.epa.gov/otaa/climate/regulations.htm
10 The interagency group decided that these estimates apply only to C02 emissions. Given that warming profiles and
impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded
"transforming gases into C02-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC,
would not result in accurate estimates of the social costs of non-C02 gases" (SCC TSD, pg 13).
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equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high
temperature outcomes, which in turn lead to higher projections of damages.
The SCC increases over time because future emissions are expected to produce larger
incremental damages as physical and economic systems become more stressed in response to
greater climatic change. Note that the interagency group estimated the growth rate of the SCC
directly using the three integrated assessment models rather than assuming a constant annual
growth rate. This helps to ensure that the estimates are internally consistent with other modeling
assumptions. The SCC estimates for the analysis years of 2015, in 2008$ are provided in Table
5-6.
When attempting to assess the incremental economic impacts of carbon dioxide
emissions, the analyst faces a number of serious challenges. A recent report from the National
Academies of Science (NRC, 2008) points out that any assessment will suffer from uncertainty,
speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the
effects of past and future emissions on the climate system, (3) the impact of changes in climate
on the physical and biological environment, and (4) the translation of these environmental
impacts into economic damages. As a result, any effort to quantify and monetize the harms
associated with climate change will raise serious questions of science, economics, and ethics and
should be viewed as provisional.
The interagency group noted a number of limitations to the SCC analysis, including the
incomplete way in which the integrated assessment models capture catastrophic and non-
catastrophic impacts, their incomplete treatment of adaptation and technological change,
uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk
aversion. The limited amount of research linking climate impacts to economic damages makes
the interagency modeling exercise even more difficult. The interagency group hopes that over
time researchers and modelers will work to fill these gaps and that the SCC estimates used for
regulatory analysis by the Federal government will continue to evolve with improvements in
modeling. Additional details on these limitations are discussed in the SCC TSD.
In light of these limitations, the interagency group has committed to updating the current
estimates as the science and economic understanding of climate change and its impacts on
society improves over time. Specifically, the interagency group has set a preliminary goal of
revisiting the SCC values within two years or at such time as substantially updated models
become available, and to continue to support research in this area.
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Applying the global SCC estimates to the estimated increases in CO2 emissions for the
range of policy scenarios, we estimate the dollar value of the climate-related disbenefits captured
by the models for each analysis year. For internal consistency, the annual disbenefits are
discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. 5%, 3%,
and 2.5%) rather than 3% and 7%.11 These estimates are provided in Tables 5-7 and 5-8.
Table 5-6. Social Cost of Carbon (SCC) Estimates (per tonne of C02) 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 $75.6
aThe SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of
climate impacts.
Table 5-7. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015
(all units comply, millions of 2008$)a
Proposed:
Discount Rate and Statistic Option 1 Option 2 Option 3
24,900 tpy C02 24,900 tpy C02 126,000 tpy C02
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 $9.5
aThe SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of
climate impacts. These results include 2 new FB incinerators anticipated to come online by 2015.
Table 5-8. Monetized SCC-derived Disbenefits of CO2 Emission Increases in 2015
(large entities comply and small entities landfill, millions of 2008$)a
Proposed:
Discount Rate and Statistic
Option 1
Option 2
Option 3
21,782 tpy C02
21,782 tpy C02
114,784tpy C02
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 C02 emissions will be discounted at
rates that differ from those used to develop the SCC estimates.
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3% 95%ile
$1.6
$1.6
$8.7
aThe SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of
climate impacts. These results include 2 new FB incinerators anticipated to come online by 2015.
5.4 Unquantified Benefits
The monetized benefits estimated in this RIA only reflect the portion of benefits
attributable to the health effect reductions associated with ambient fine particles. Data, resource,
and methodological limitations prevented EPA from quantifying or monetizing the benefits from
several important benefit categories, including benefits from reducing toxic emissions,
ecosystem effects, and visibility impairment. The health benefits from reducing hazardous air
pollutants (HAPs) and carbon monoxide have not been monetized in this analysis. In addition to
being a PM2.5 precursor, S02 emissions also contribute to adverse effects from acidic deposition
in aquatic and terrestrial ecosystems, increased mercury methylation, as well as visibility
impairment. The benefits from reducing other air pollutants that have not been monetized in this
analysis including 2,900 tons of carbon monoxide, 96 tons of HC1, 3.0 tons of lead, 1.6 tons of
cadmium, 5,500 pounds of mercury, and 90 grams of total dioxins/furans each year.
5.4.1 Carbon Monoxide Benefits
Carbon monoxide (CO) exposure is associated with a variety of health effects. Without
knowing the location of the emission reductions and the resulting ambient concentrations using
fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby
populations. Due to data, resource, and methodological limitations, we were unable to estimate
the benefits associated with the reductions of CO emissions that would occur as a result of this
rule.
Carbon monoxide in ambient air is formed primarily by the incomplete combustion of
carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO
emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the
combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon
inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia
(reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple
tissues and organ systems that are dependent upon concentration and duration of exposure.
The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded
that short-term exposure to CO is "likely to have a causal relationship" with cardiovascular
morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies
associate short-term CO exposure with increased risk of emergency department visits and
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hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac
chest pain), as well as those who have experienced a heart attack. Other subpopulations
potentially at risk include individuals with diseases such as chronic obstructive pulmonary
disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as
older adults or the developing young. The evidence is suggestive of a causal relationship
between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also
suggestive of a causal relationship for birth outcomes and developmental effects following long-
term exposure to CO, and for central nervous system effects linked to short- and long-term
exposure to CO.
5.4.2 Other SO2 Benefits
In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety
of respiratory health effects. Unfortunately, we were unable to estimate the health benefits
associated with reduced SO2 exposure in this analysis because we do not have air quality
modeling data available. Without knowing the location of the emission reductions and the
resulting ambient concentrations, we were unable to estimate the exposure to SO2 for nearby
populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated
with the reductions in SO2 emissions.
Following an extensive evaluation of health evidence from epidemiologic and laboratory
studies, the U.S. EPA has concluded that there is a causal relationship between respiratory health
effects and short-term exposure to SO2 (U.S. EPA, 2008). According to summary of the ISA in
EPA's risk and exposure assessment (REA) for the SO2 NAAQS, "the immediate effect of SO2
on the respiratory system in humans is bronchoconstriction" (U.S. EPA, 2009c). In addition, the
REA summarized from the ISA that "asthmatics are more sensitive to the effects of SO2 likely
resulting from preexisting inflammation associated with this disease." A clear concentration-
response relationship has been demonstrated in laboratory studies following exposures to SO2 at
concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and
percentage of asthmatics adversely affected (U.S. EPA, 2009c). Based on our review of this
information, we identified four short-term morbidity endpoints that the SO2 ISA identified as a
"causal relationship": asthma exacerbation, respiratory-related emergency department visits, and
respiratory-related hospitalizations. The differing evidence and associated strength of the
evidence for these different effects is described in detail in the SO2 ISA. The SO2 ISA also
concluded that the relationship between short-term SO2 exposure and premature mortality was
"suggestive of a causal relationship" because it is difficult to attribute the mortality risk effects to
SO2 alone. Although the SO2 ISA stated that studies are generally consistent in reporting a
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relationship between SO2 exposure and mortality, there was a lack of robustness of the observed
associations to adjustment for pollutants.
SO2 emissions also contribute to adverse welfare effects from acidic deposition, visibility
impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which
can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic
ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and
sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern United States, the
surface waters affected by acidification are a source of food for some recreational and
subsistence fishermen and for other consumers and support several cultural services, including
aesthetic and educational services and recreational fishing. Biological effects of acidification in
terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root
growth, which restricts the ability of the plant to take up water and nutrients. These direct effects
can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold
temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial
acidification affects several important ecological services, including declines in habitat for
threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in
forest productivity (provisioning), and increases in forest soil erosion and reductions in water
retention (cultural and regulating) (U.S. EPA, 2008d).
Reducing SO2 emissions and the secondary formation of PM2 5 would improve the level
of visibility throughout the United States. Fine particles with significant light-extinction
efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996).
These suspended particles and gases degrade visibility by scattering and absorbing light. Higher
visibility impairment levels in the East are due to generally higher concentrations of fine
particles, particularly sulfates, and higher average relative humidity levels. In fact, particulate
sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annually
and 75% during summer). In the western U.S., particulate sulfate contributes to 20-50% of
regional haze (U.S. EPA, 2009c). Visibility has direct significance to people's enjoyment of
daily activities and their overall sense of wellbeing. Good visibility increases the quality of life
where individuals live and work, and where they engage in recreational activities.
5.4.3 HAP Benefits
Due to data, resource, and methodology limitations, we were unable to estimate the
benefits associated with the hazardous air pollutants that would be reduced as a result of this
rule. Available emissions data show that several different HAPs are emitted from SSI. This rule
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is anticipated to reduce 96 tons of HC1, 3.0 tons of lead, 1.6 tons of cadmium, 5,500 pounds of
mercury, and 90 grams of total dioxins/furans each year. In the absence of air quality modeling
and/or concentration-response functions, we are unable to quantify the magnitude of the
reduction in human exposure to these pollutants associated with the emission reductions from
this rule.
5.4.3.1 Mercury
Mercury is a highly neurotoxic contaminant that enters the food web as a methylated
compound, methylmercury (U.S. EPA, 2008d). The contaminant is concentrated in higher
trophic levels, including fish eaten by humans. Experimental evidence has established that only
inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA,
2008d). Current evidence indicates that in watersheds where mercury is present, increased sulfate
deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007;
Munthe et al, 2007). The SO2 ISA concluded that evidence is sufficient to infer a casual
relationship between sulfur deposition and increased mercury methylation in wetlands and
aquatic environments (U.S. EPA, 2008d).
In addition to the role of sulfate deposition on methylation, this proposed rule would also
reduce mercury emissions. Mercury is emitted to the air from various man-made and natural
sources. These emissions transport through the atmosphere and eventually deposit to land or
water bodies. This deposition can occur locally, regionally, or globally, depending on the form of
mercury emitted and other factors such as the weather. The form of mercury emitted varies
depending on the source type and other factors. Available data indicate that the mercury
emissions from these sources are a mixture of gaseous elemental mercury, inorganic ionic
mercury, and particulate bound mercury. Gaseous elemental mercury can be transported very
long distances, even globally, to regions far from the emissions source (becoming part of the
global "pool") before deposition occurs. Inorganic ionic and particulate bound mercury have a
shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions
source. Furthermore, elemental mercury in the atmosphere can undergo transformation into ionic
mercury, providing a significant pathway for deposition of emitted elemental mercury.
This source category emitted about 3.1 tons of mercury in the air in 2008 in the U.S.
Based on the EPA's National Emission Inventory, about 103 tons of mercury were emitted from
all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that
about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We
believe that total mercury emissions in the U.S. and globally in 2008 were about the same
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magnitude in 2005. Therefore, we estimate that in 2008, these sources emitted about 3% of the
total anthropogenic mercury emissions in the U.S. and about 0.15% of the global emissions.
Overall, this rule would reduce mercury emissions by about 5,500 pounds per year from current
levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to
time and resource limitations, we were unable to model mercury dispersion, deposition,
methylation, bioaccumulation in fish tissue, and human consumption of mercury-contaminated
fish that would be needed in order to estimate the human health benefits from reducing mercury
emissions.
Potential exposure routes to mercury emissions include both direct inhalation and
consumption of fish containing methylmercury. In the U.S., the primary route of human
exposure to mercury emissions from industrial sources is generally indirectly through the
consumption of fish containing methylmercury. As described above, mercury that has been
emitted to the air eventually settles into water bodies or onto land where it can either move
directly or be leached into waterbodies. Once deposited, certain microorganisms can change it
into methylmercury, a highly toxic form that builds up in fish, shellfish and animals that eat fish.
Consumption of fish and shellfish are the main sources of methylmercury exposure to humans.
Methylmercury builds up more in some types of fish and shellfish than in others. The levels of
methylmercury in fish and shellfish vary widely depending on what they eat, how long they live,
and how high they are in the food chain. Most fish, including ocean species and local freshwater
fish, contain some methylmercury. For example, in recent studies by EPA and the U.S.
Geological Survey (USGS) of fish tissues, every fish sampled from 291 streams across the
country contained some methylmercury (Scudder, 2009).
The majority of fish consumed in the U.S. are ocean species. The methylmercury
concentrations in ocean fish species are primarily influenced by the global mercury pool.
However, the methylmercury found in local fish can be due, at least partly, to mercury emissions
from local sources. Research shows that most people's fish consumption does not cause a
mercury-related health concern. However, certain people may be at higher risk because of their
routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families
who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high
levels of methylmercury in the bloodstream of unborn babies and young children may harm the
developing nervous system, making the child less able to think and learn. Moreover, mercury
exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people
of all ages.
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Several studies suggest that the methylmercury content of fish may reduce these cardio-
protective effects of fish consumption. Some of these studies also suggest that methylmercury
may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of
the literature concerning methylmercury health effects took note of two epidemiological studies
that found an association between dietary exposure to methylmercury and adverse cardiovascular
effects.12 Moreover, in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al.
(1995) observed a relationship between methylmercury exposure via fish consumption and acute
myocardial infarction (AMI or heart attacks), coronary heart disease, cardiovascular disease, and
all-cause mortality.13 The NRC also noted a study of 917 seven year old children in the Faroe
Islands, whose initial exposure to methylmercury was in utero although post natal exposures may
have occurred as well. At seven years of age, these children exhibited an increase in blood
pressure and a decrease in heart rate variability.14 Based on these and other studies, NRC
concluded in 2000 that, while "the data base is not as extensive for cardiovascular effects as it is
for other end points (i.e. neurologic effects) the cardiovascular system appears to be a target for
methylmercury toxicity."15
Since publication of the NRC report there have been some 30 published papers
presenting the findings of studies that have examined the possible cardiovascular effects of
methylmercury exposure. These studies include epidemiological, toxicological, and toxicokinetic
investigations. Over a dozen review papers have also been published. If there is a causal
relationship between methylmercury exposure and adverse cardiovascular effects, then reducing
exposure to methylmercury would result in public health benefits from reduced cardiovascular
effects.
In early 2010, EPA sponsored a workshop in which a group of experts were asked to
assess the plausibility of a causal relationship between methylmercury exposure and
cardiovascular health effects and to advise EPA on methodologies for estimating population
12National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the
Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies
Press. Washington, DC. pp. 168-173.
13Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. "Intake of mercury from fish lipid peroxidation, and the risk of
myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men." Circulation, 91
(3):645-655.
14Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P., 1999. "Prenatal Methylmercury
Exposure As A Cardiovascular Risk Factor At Seven Years of Age", Epidemiology, pp370-375.
15National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the
Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies
Press. Washington, DC. p. 229.
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level cardiovascular health impacts of reduced methylmercury exposure. The report from that
workshop is in preparation.
5.4.3.2 Cadmium
Breathing air with very high levels of cadmium can severely damage the lungs and may
cause death. In the United States, where proper industrial hygiene is generally practiced, inhaling
very high levels of cadmium at work is expected to be rare and accidental. Breathing air with
lower levels of cadmium over long periods of time (for years) results in a build-up of cadmium
in the kidney, and if sufficiently high, may result in kidney disease. Lung cancer has been found
in some studies of workers exposed to cadmium in the air and studies of rats that breathed in
cadmium. The U.S. Department of Health and Human Services (DHHS) has determined that
cadmium and cadmium compounds are known human carcinogens. The International Agency for
Research on Cancer (IARC) has determined that cadmium is carcinogenic to humans. The EPA
has determined that cadmium is a probable human carcinogen.16
5.4.3.3 Lead
The main target for lead toxicity is the nervous system, both in adults and children. Long-
term exposure of adults to lead at work has resulted in decreased performance in some tests that
measure functions of the nervous system. Lead exposure may also cause weakness in fingers,
wrists, or ankles. Lead exposure also causes small increases in blood pressure, particularly in
middle-aged and older people. Lead exposure may also cause anemia. At high levels of
exposure, lead can severely damage the brain and kidneys in adults or children and ultimately
cause death. In pregnant women, high levels of exposure to lead may cause miscarriage. High-
level exposure in men can damage the organs responsible for sperm production.
We have no conclusive proof that lead causes cancer (is carcinogenic) in humans. Kidney
tumors have developed in rats and mice that had been given large doses of some kind of lead
compounds. The Department of Health and Human Services (DHHS) has determined that lead
and lead compounds are reasonably anticipated to be human carcinogens based on limited
evidence from studies in humans and sufficient evidence from animal studies, and the EPA has
determined that lead is a probable human carcinogen. The International Agency for Research on
Cancer (IARC) has determined that inorganic lead is probably carcinogenic to humans. IARC
determined that organic lead compounds are not classifiable as to their carcinogenicity in
humans based on inadequate evidence from studies in humans and in animals.
16 Agency for Toxic Substances and Disease Registry (ATSDR). 2008. Public Health Statement for Cadmium.
CAS# 1306-19-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service.
Available on the Internet at .
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Children are more sensitive to the health effects of lead than adults. No safe blood lead
level in children has been determined. Lead affects children in different ways depending on how
much lead a child swallows. A child who swallows large amounts of lead may develop anemia,
kidney damage, colic (severe "stomach ache"), muscle weakness, and brain damage, which
ultimately can kill the child. In some cases, the amount of lead in the child's body can be
lowered by giving the child certain drugs that help eliminate lead from the body. If a child
swallows smaller amounts of lead, such as dust containing lead from paint, much less severe but
still important effects on blood, development, and behavior may occur. In this case, recovery is
likely once the child is removed from the source of lead exposure, but there is no guarantee that
the child will completely avoid all long-term consequences of lead exposure. At still lower levels
of exposure, lead can affect a child's mental and physical growth. Fetuses exposed to lead in the
womb, because their mothers had a lot of lead in their bodies, may be born prematurely and have
lower weights at birth. Exposure in the womb, in infancy, or in early childhood also may slow
mental development and cause lower intelligence later in childhood. There is evidence that these
effects may persist beyond childhood.17
5.4.3.4 Hydrogen Chloride (HCl)
Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose,
throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to
100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract;
exposure to high concentrations can rapidly lead to swelling and spasm of the throat and
suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue
coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures
may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to
Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-induced type of
asthma. Children may be more vulnerable to corrosive agents than adults because of the
relatively smaller diameter of their airways. Children may also be more vulnerable to gas
exposure because of increased minute ventilation per kg and failure to evacuate an area promptly
when exposed. Hydrogen chloride has not been classified for carcinogenic effects.18
17 Agency for Toxic Substances and Disease Registry (ATSDR). 2007. Public Health Statement for Lead. CAS#:
7439-92-1. Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on
the Internet at < http://www.atsdr.cdc.gov/ToxProfiles/phsl3.html>.
18 Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen
Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. Department of Health and Human Services, Public Health
Service. Available on the Internet at .
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5.4.3.5 Dioxins (Chlorinated dibenzodioxins (CDDs)
A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that
are at least 10 times higher than background levels. The most obvious health effect in people
exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin
disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects
noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and
excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in
people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids,
sugar, and protein have been reported in people exposed to relatively high concentrations of
2,3,7,8-TCDD. Most of the effects are considered mild and were reversible. However, in some
people these effects may last for many years. Slight increases in the risk of diabetes and
abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-
TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result
in reproductive or developmental effects in people, but animal studies suggest that this is a
potential health concern.
In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a
single exposure. Exposure to lower levels can cause a variety of effects in animals, such as
weight loss, liver damage, and disruption of the endocrine system. In many species of animals,
2,3,7,8-TCDD weakens the immune system and causes a decrease in the system's ability to fight
bacteria and viruses at relatively low levels (approximately 10 times higher than human
background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused
reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy
had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often
had severe birth defects including skeletal deformities, kidney defects, and weakened immune
responses. In some studies, effects were observed at body burdens 10 times higher than human
background levels.19
5.4.3.6 Furans (Chlorinated dibenzofurans (CDFs))
Most of the information on the adverse health effects comes from studies in people who
were accidentally exposed to food contaminated with CDFs. The amounts that these people were
exposed to were much higher than are likely from environmental exposures or from a normal
diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids
19 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxins
(CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). Atlanta, GA: U.S. Department of Health and Human Services, Public
Health Service. Available on the Internet at http://www.atsdr.cdc.gov/tfactsl04.html.
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with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also
caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the
nervous system, and mild changes in the liver. Children born to exposed mothers had skin
irritation and more difficulty learning, but it is unknown if this effect was permanent or caused
by CDFs alone or CDFs and poly chlorinated biphenyls in combination.
Many of the same effects that occurred in people accidentally exposed also occurred in
laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs,
livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and
testicular damage, and in severe cases, some animals died. These effects in animals were seen
when they were fed large amounts of CDFs over a short time, or small amounts over several
weeks or months. Nothing is known about the possible health effects in animals from eating
CDFs over a lifetime.20
5.5 Characterization of Uncertainty in the Monetized PM2.s Benefits
In any complex analysis, there are likely to be many sources of uncertainty. Many inputs
are used to derive the final estimate of economic benefits, including emission inventories, air
quality models (with their associated parameters and inputs), epidemiological estimates of
concentration-response (C-R) functions, estimates of values, population estimates, income
estimates, and estimates of the future state of the world (i.e., regulations, technology, and human
behavior). For some parameters or inputs it may be possible to provide a statistical representation
of the underlying uncertainty distribution. For other parameters or inputs, the necessary
information is not available.
The annual benefit estimates presented in this analysis are also inherently variable due to
the processes that govern pollutant emissions and ambient air quality in a given year. Factors
such as hours of equipment use and weather are constantly variable, regardless of our ability to
measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 5-5) (U.S. EPA, 2006),
there are a variety of uncertainties associated with these PM benefits. Therefore, the estimates of
annual benefits should be viewed as representative of the magnitude of benefits expected, rather
than the actual benefits that would occur every year.
It is important to note that the monetized benefit-per-ton estimates used here reflect
specific geographic patterns of emissions reductions and specific air quality and benefits
20 Agency for Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQs™ for Chlorodibenzofurans
(CDFs). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the
Internet at .
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modeling assumptions. For example, these estimates do not reflect local variability in population
density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of
these $/ton values to estimate benefits associated with different emission control programs (e.g.,
for reducing emissions from large stationary sources like EGUs) may lead to higher or lower
benefit estimates than if benefits were calculated based on direct air quality modeling. Great care
should be taken in applying these estimates to emission reductions occurring in any specific
location, as these are all based on national or broad regional emission reduction programs and
therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton
for emission reductions in specific locations may be very different than the estimates presented
here.
PM2.5 mortality benefits are the largest benefit category that we monetized in this
analysis. To better characterize the uncertainty associated with mortality impacts that are
estimated to occur in areas with low baseline levels of PM2 5, we included the LML assessment.
Without policy-specific air quality modeling, we are unable to quantify the shift in exposure
associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we
provide the percentage of the population exposed at each PM2.5 level using the most recent
modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). A very large
proportion of the population is exposed at or above the lowest LML of the cohort studies
(Figures 5-5 and 5-6), increasing our confidence in the PM mortality analysis. Figure 5-5 shows
a bar chart of the percentage of the population exposed to various air quality levels in the pre-
and post-policy policy. Figure 5-6 shows a cumulative distribution function of the same data.
Both figures identify the LML for each of the major cohort studies. As the policy shifts the
distribution of air quality levels, fewer people are exposed to PM2.5 levels at or above the LML.
Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM2.5
levels at or above the LML of 7.5 |ig/m3. Using the Laden et al. (2006) study, 40% of the
population is exposed above the LML of 10 |ig/m3. As we model mortality impacts among
populations exposed to levels of PM2.5 that are successively lower than the LML of the lowest
cohort study, our confidence in the results diminishes. However, the analysis above confirms that
the great majority of the impacts occur at or above the lowest cohort study's LML. It is important
to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of
the major cohort studies. Just because we have greater confidence in the benefits above the LML,
this does not mean that we have no confidence that benefits occur below the LML.
5-29
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25%
20%
15%
10%
5%
0% r
Pope et al. 2002
Laden et al. 2006
The control strategy lowers PM2 5
levels substantially, particularly
among highly exposed
populations. In the baseline, 85%
of the population lived in areas
where PM2 5 levels above the
lowest measured levels of the
Pope study, increasing our
confidence in the estimated
mortality reductions for this rule.
Figure 5-5. Percentage of Adult Population by Annual Mean PM2.5 Exposure
(pre- and post-policy policy)
100%
80%
60%
10%
Pops etal. 2002 Laden etal, 2006
The control strategy lowers PMj£ levels
substantially, particularly among highly
exposed populations. In the baseline, 89% of
the population lived in areas where PM2E
levels above the lowest measured levels of
the Pope study, increasing our confidence in
the estimated mortality reductions forthis
rule.
Post-control Baseline
Figure 5-6. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels
(pre- and post-policy policy)
5-30
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Above we present the estimates of the total monetized benefits, based on our
interpretation of the best available scientific literature and methods and supported by the SAB-
HES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions
and uncertainties. For example, for key assumptions underlying the estimates for premature
mortality, which typically account for at least 90% of the total monetized benefits, we were able
to quantify include the following:
1. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect
local variability in population density, meteorology, exposure, baseline health
incidence rates, or other local factors that might lead to an over-estimate or under-
estimate of the actual benefits of controlling directly emitted fine particulates.
2. We assume that all fine particles, regardless of their chemical composition, are
equally potent in causing premature mortality. This is an important assumption,
because PM25 produced via transported precursors emitted from EGUs may differ
significantly from direct PM2.5 released from diesel engines and other industrial
sources, but no clear scientific grounds exist for supporting differential effects
estimates by particle type.
3. We assume that the health impact function for fine particles is linear down to the
lowest air quality levels modeled in this analysis. Thus, the estimates include health
benefits from reducing fine particles in areas with varied concentrations of PM25j
including both regions that are in attainment with fine particle standard and those that
do not meet the standard down to the lowest modeled concentrations.
4. To characterize the uncertainty in the relationship between PM2.5 and premature
mortality (which typically accounts for 85% to 95% of total monetized benefits), we
include a set of twelve estimates based on results of the expert elicitation study in
addition to our core estimates. Even these multiple characterizations omit the
uncertainty in air quality estimates, baseline incidence rates, populations exposed and
transferability of the effect estimate to diverse locations. As a result, the reported
confidence intervals and range of estimates give an incomplete picture about the
overall uncertainty in the PM25 estimates. This information should be interpreted
within the context of the larger uncertainty surrounding the entire analysis. For more
information on the uncertainties associated with PM2 5 benefits, please consult the
PM2 5NAAQS RIA (Table 5-5).
This RIA does not include the type of detailed uncertainty assessment found in the PM
NAAQS RIA because we lack the necessary air quality input and monitoring data to run the
benefits model. In addition, we have not conducted any air quality modeling for this rule.
Moreover, it was not possible to develop benefit-per-ton metrics and associated estimates of
uncertainty using the benefits estimates from the PM RIA because of the significant differences
between the sources affected in that rule and those regulated here. However, the results of the
5-31
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Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA
can provide some evidence of the uncertainty surrounding the benefits results presented in this
analysis.
5.6 Comparison of Benefits and Costs
Using a 3% discount rate, we estimate the total monetized benefits of the proposed SSI
NSPS and EG including energy disbenefits to be $130 million to $320 million in the
implementation year (2015). Using a 7% discount rate, we estimate the total monetized benefits
of the SSI NSPS and EG including energy disbenefits to be $120 million to $290 million. The
annualized costs are $92 million at a 7% interest rate.21 Thus, net benefits are $37 million to $220
million at a 3% discount rate for the benefits and $26 million to $190 million at a 7% discount
rate. All estimates are in 2008$.
Table 5-9 shows a summary of the monetized co-benefits, social costs, and net benefits
for the SSI NSPS and EG, respectively. Figures 5-7 and 5-8 show the full range of net benefits
estimates (i.e., annual co-benefits minus annualized costs) utilizing the 14 different PM2.5
mortality functions at discount rates of 3% and 7%. In addition, the benefits from reducing 2,900
tons of carbon monoxide, 96 tons of HC1, 3.0 tons of lead, 1.6 tons of cadmium, 5,500 pounds of
mercury, and 90 grams of total dioxins/furans each year have not been included in these
estimates.
21 For more information on the annualized costs, please refer to Section 4 of this RIA. There are no estimates of
costs available at a 3% discount rate.
5-32
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Table 5-9. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the SSI
NSPS and EG in 2015 (millions of 2008$)a
3% Discount Rate 7% Discount Rate
Proposed: Option 2
Total Monetized Benefitsb
$130 to $320 $120
to
$290
Total Social Costs0
$92
$92
Net Benefits
$37 to $220 $26
26,000 tons of carbon monoxide
96 tons of HC1
5,500 pounds of mercury
1.6 tons of cadmium
to
$190
Non-monetized Benefits
3.0 tons of lead
90 grams of dioxins/furans
Health effects from N02 and S02 exposure
Ecosystem effects
Visibility impairment
Option 1
Total Monetized Benefitsb
$130 to $320 $120
to
$290
Total Social Costs0
$63
$63
Net Benefits
$66 to $250 $55
2,900 tons of carbon monoxide
96tons of HC1
820 pounds of mercury
1.6 tons of cadmium
to
$220
Non-monetized Benefits
3.0 tons of lead
74 grams of dioxins/furans
Health effects from N02 and S02 exposure
Ecosystem effects
Visibility impairment
Option 3
Total Monetized Benefits'3
$130 to $310 $120
to
$290
Total Social Costs0
$132
$132
Net Benefits
-$5.4 to $180 -$14
26,000 tons of carbon monoxide
96 tons of HC1
5,500 pounds of mercury
1.6 tons of cadmium
to
$150
Non-monetized Benefits
3.0 tons of lead
90 grams of dioxins/furans
Health effects from N02 and S02 exposure
Ecosystem effects
Visibility impairment
a All estimates are for the implementation year (2015), and are rounded to two significant figures. These results include 2 new FB
incinerators anticipated to come online by 2015 and the large entities comply and small entities landfill assumption.
b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2 5 through reductions of
directly emitted PM2 5 and PM2 5 precursors such as NOx and S02. It is important to note that the monetized benefits include
many but not all health effects associated with PM2 5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden
et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing
premature mortality because there is no clear scientific evidence that would support the development of differential effects
estimates by particle type. These estimates include energy disbenefits valued at $0.5 million at a 3% discount rate for C02
emissions.
c The annual compliances costs serve as a proxy for the annual social costs of this rule given the lack of difference between the two.
5-33
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$350
$300
Laden et al.
$250
o
$100
$200
$150
$50
$0
Popeet al.
I
I:: 1111
I I
-$50
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
aNet Benefits are quantified in terms of PM25 benefits for implementation year (2015). This graph shows 14 benefits
estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All
fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton
of precursor reduced has a different propensity to become PM2 5. The monetized benefits incorporate the
conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at
$0.5 million at a 3% discount rate for CO: emissions.
5-34
-------
Laden
|X|1
Pope etal.
$300
$250
$200
29 $150
$100
$50
$0
-$50
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
aNet Benefits are quantified in terms of PM25 benefits for implementation year (2015). This graph shows 14 benefits
estimates combined with the cost estimate. All combinations are treated as independent and equally probable. All
fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton
of precursor reduced has a different propensity to become PM2 5. The monetized benefits incorporate the
conversion from precursor emissions to ambient fine particles. These estimates include energy disbenefits valued at
$0.5 million at a 3% discount rate for CO: emissions.
5-35
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APPENDIX A
SUMMARY OF EXPERT OPINIONS ON THE EXISTENCE OF A THRESHOLD IN
THE CONCENTRATION-RESPONSE FUNCTION FOR PM2.5-RELATED
MORTALITY
-------
Summary of Expert Opinions on the Existence of a Threshold in the
Concentration-Response Function for PM2.s-related Mortality
Technical Support Document (TSD)
June 2010
Compiled by:
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impact Division
Air Benefit-Cost Group
Research Triangle Park, North Carolina
Contents:
A. HES comments on 812 Analysis (2010)
B. American Heart Association Scientific Statement (2010)
C. Integrated Science Assessment for Particulate Matter (2009)
D. CASAC comments on PM ISA and REA (2009)
E. Krewski et al. (2009)
F. Schwartz et al. (2008)
G. Expert Elicitation on PM Mortality (2006, 2008)
H. CASAC comments on PM Staff Paper (2005)
I. HES comments on 812 Analysis (2004)
J. NRC (2002)
A-l
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A. HES Comments on 812 Analysis (2010)
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010.
Review of EPA's DRAFT Health Benefits of the Second Section 812 Prospective Study of
the Clean Air Act. EPA-COUNCIL-10-001. June. Available on the Internet at
.
Pg 2: "The HES generally agrees with other decisions made by the EPA project team with
respect to PM, in particular, the PM mortality effect threshold model, the cessation lag model,
the inclusion of infant mortality estimation, and differential toxicity of PM."
Pg 2: "Further, the HES fully supports EPA's use of a no-threshold model to estimate the
mortality reductions associated with reduced PM exposure."
Pg 6: "The HES also supports the Agency's choice of a no-threshold model for PM-related
effects."
Pg 13: "The HES fully supports EPA's decision to use a no-threshold model to estimate mortality
reductions. This decision is supported by the data, which are quite consistent in showing effects down to
the lowest measured levels. Analyses of cohorts using data from more recent years, during which time
PM concentrations have fallen, continue to report strong associations with mortality. Therefore, there is
no evidence to support a truncation of the CRF."
HES Panel Members
Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in Residence, The National
Academies, Washington, DC
Dr. Michelle Bell, Associate Professor, School of Forestry and Environmental Studies, Yale
University, New Haven, CT
Dr. James K. Hammitt, Professor, Department of Health Policy and Management, Harvard
School of Public Health, Boston, MA
Dr. Jonathan Levy, Associate Professor, Department of Environmental Health, Harvard School
of Public Health, Boston, MA
Dr. C. Arden Pope, III Professor, Department of Economics, Brigham Young University,
Provo, UT
Mr. John Fintan Hurley, Research Director, Institute of Occupational Medicine (IOM),
Edinburgh, United Kingdom, UK
Dr. Patrick Kinney, Professor, Department of Environmental Health Sciences, Mailman School
of Public Health, Columbia University, New York, NY
Dr. Michael T. Kleinman, Professor, Department of Medicine, Division of Occupational and
Environmental Medicine, University of California, Irvine, Irvine, CA
A-2
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Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health
Hazard Assessment, California Environmental Protection Agency, Oakland, CA
Dr. Rebecca Parkin, Professor and Associate Dean, Environmental and Occupational Health,
School of Public Health and Health Services, The George Washington University Medical
Center, Washington, DC
A-3
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B. Scientific Statement from American Heart Association (2010)
Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin
F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L,
Kaufman JD; on behalf of the American Heart Association Council on Epidemiology and
Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition,
Physical Activity and Metabolism. (2010). "Particulate matter air pollution and
cardiovascular disease: an update to the scientific statement from the American Heart
Association." Circulation. 121: 2331-2378.
Pg 2338: "Finally, there appeared to be no lower-limit threshold below which PMio was not
associated with excess mortality across all regions."
Pg 2350: "There also appears to be a monotonic (e.g., linear or log-linear) concentration-
response relationship between PM2.5 and mortality risk observed in cohort studies that extends
below present-day regulations of 15 |ig/m3 for mean annual levels, without a discernable "safe"
threshold." (cites Pope 2004, Krewski 2009, and Schwartz 2008)
Pg 2364: "The PM25 concentration- cardiovascular risk relationships for both short- and long-
term exposures appear to be monotonic, extending below 15 |ig/m3 (the 2006 annual NAAQS
level) without a discernable "safe" threshold."
Pg 2365: "This updated review by the AHA writing group corroborates and strengthens the
conclusions of the initial scientific statement. In this context, we agree with the concept and
continue to support measures based on scientific evidence, such as the US EPA NAAQS, that
seek to control PM levels to protect the public health. Because the evidence reviewed supports
that there is no safe threshold, it appears that public health benefits would accrue from lowering
PM2.5 concentrations even below present-day annual (15 |ig/m3) and 24-hour (35 |ig/m3)
NAAQS, if feasible, to optimally protect the most susceptible populations."
Pg 2366: "Although numerous insights have greatly enhanced our understanding of the PM-
cardiovascular relationship since the first AHA statement was published, the following list
represents broad strategic avenues for future investigation: ... Determine whether any "safe" PM
threshold concentration exists that eliminates both acute and chronic cardiovascular effects in
healthy and susceptible individuals and at a population level."
Scientific Statement Authors
Dr. Robert D. Brook, MD
Dr. Sanjay Rajagopalan, MD
Dr. C. Arden Pope, PhD
Dr. Jeffrey R. Brook, PhD
Dr. Aruni Bhatnagar, PhD, FAHA
Dr. Ana V. Diez-Roux, MD, PhD, MPH
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Dr. Fernando Holguin, MD
Dr. Yuling Hong, MD, PhD, FAHA
Dr. Russell V. Luepker, MD, MS, FAHA
Dr. Murray A. Mittleman, MD, DrPH, FAHA
Dr. Annette Peters, PhD
Dr. David Siscovick, MD, MPH, FAHA
Dr. Sidney C. Smith, Jr, MD, FAHA
Dr. Laurie Whitsel, PhD
Dr. Joel D. Kaufman, MD, MPH
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C. Integrated Science Assessment for Particulate Matter (2009)
U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment
for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for
Environmental Assessment - RTP Division. December. Available on the Internet at
.
Pg 1-22: "An important consideration in characterizing the public health impacts associated with
exposure to a pollutant is whether the concentration-response relationship is linear across the full
concentration range encountered, or if nonlinear relationships exist along any part of this range.
Of particular interest is the shape of the concentration-response curve at and below the level of
the current standards. The shape of the concentration-response curve varies, depending on the
type of health outcome, underlying biological mechanisms and dose. At the human population
level, however, various sources of variability and uncertainty tend to smooth and "linearize" the
concentration-response function (such as the low data density in the lower concentration range,
possible influence of measurement error, and individual differences in susceptibility to air
pollution health effects). In addition, many chemicals and agents may act by perturbing naturally
occurring background processes that lead to disease, which also linearizes population
concentration-response relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976,
003192; Hoel, 1980, 156555). These attributes of population dose-response may explain why the
available human data at ambient concentrations for some environmental pollutants (e.g., PM, 03,
lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely
mechanisms include nonlinear processes for some key events. These attributes of human
population dose-response relationships have been extensively discussed in the broader
epidemiologic literature (Rothman and Greenland, 1998, 086599)."
Pg 2-16: "In addition, cardiovascular hospital admission and mortality studies that examined the
PMio concentration-response relationship found evidence of a log-linear no-threshold
relationship between PM exposure and cardiovascular-related morbidity (Section 6.2) and
mortality (Section 6.5)."
Pg 2-25: "2.4.3. PM Concentration-Response Relationship
An important consideration in characterizing the PM-morbidity and mortality association is
whether the concentration-response relationship is linear across the full concentration range that
is encountered or if there are concentration ranges where there are departures from linearity (i.e.,
nonlinearity). In this ISA studies have been identified that attempt to characterize the shape of
the concentration-response curve along with possible PM "thresholds" (i.e., levels which PM
concentrations must exceed in order to elicit a health response). The epidemiologic studies
evaluated that examined the shape of the concentration-response curve and the potential presence
of a threshold have focused on cardiovascular hospital admissions and ED visits and mortality
associated with short-term exposure to PMio and mortality associated with long-term exposure to
pm25.
"A limited number of studies have been identified that examined the shape of the PM
cardiovascular hospital admission and ED visit concentration-response relationship. Of these
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studies, some conducted an exploratory analysis during model selection to determine if a linear
curve most adequately represented the concentration-response relationship; whereas, only one
study conducted an extensive analysis to examine the shape of the concentration-response curve
at different concentrations (Section 6.2.10.10). Overall, the limited evidence from the studies
evaluated supports the use of a no-threshold, log-linear model, which is consistent with the
observations made in studies that examined the PM-mortality relationship.
"Although multiple studies have previously examined the PM-mortality concentration-response
relationship and whether a threshold exists, more complex statistical analyses continue to be
developed to analyze this association. Using a variety of methods and models, most of the
studies evaluated support the use of a no-threshold, log-linear model; however, one study did
observe heterogeneity in the shape of the concentration-response curve across cities (Section
6.5). Overall, the studies evaluated further support the use of a no-threshold log-linear model, but
additional issues such as the influence of heterogeneity in estimates between cities, and the effect
of seasonal and regional differences in PM on the concentration-response relationship still
require further investigation.
"In addition to examining the concentration-response relationship between short-term exposure
to PM and mortality, Schwartz et al. (2008, 156963) conducted an analysis of the shape of the
concentration-response relationship associated with long-term exposure to PM. Using a variety
of statistical methods, the concentration-response curve was found to be indistinguishable from
linear, and, therefore, little evidence was observed to suggest that a threshold exists in the
association between long-term exposure to PM2.5 and the risk of death (Section 7.6)."
Pg 6-75: "6.2.10.10. Concentration Response
The concentration-response relationship has been extensively analyzed primarily through studies
that examined the relationship between PM and mortality. These studies, which have focused on
short- and long-term exposures to PM have consistently found no evidence for deviations from
linearity or a safe threshold (Daniels et al., 2004, 087343; Samoli et al., 2005, 087436; Schwartz,
2004, 078998; Schwartz et al., 2008, 156963) (Sections 6.5.2.7 and 7.1.4). Although on a more
limited basis, studies that have examined PM effects on cardiovascular hospital admissions and
ED visits have also analyzed the PM concentration-response relationship, and contributed to the
overall body of evidence which suggests a log-linear, no-threshold PM concentration-response
relationship.
"The results from the three multicity studies discussed above support no-threshold log-linear
models, but issues such as the possible influence of exposure error and heterogeneity of shapes
across cities remain to be resolved. Also, given the pattern of seasonal and regional differences
in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463),
the very concept of a concentration-response relationship estimated across cities and for all-year
data may not be very informative."
Pg 6-197: "6.5.2.7. Investigation of Concentration-Response Relationship
The results from large multicity studies reviewed in the 2004 PM AQCD (U.S. EPA, 2004,
056905) suggested that strong evidence did not exist for a clear threshold for PM mortality
effects. However, as discussed in the 2004 PM AQCD (U.S. EPA, 2004, 056905), there are
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several challenges in determining and interpreting the shape of PM-mortality concentration-
response functions and the presence of a threshold, including: (1) limited range of available
concentration levels (i.e., sparse data at the low and high end); (2) heterogeneity of susceptible
populations; and (3) investigate the PM-mortality concentration-response relationship.
"Daniels et al. (2004, 087343) evaluated three concentration-response models: (1) log-linear
models (i.e., the most commonly used approach, from which the majority of risk estimates are
derived); (2) spline models that allow data to fit possibly non-linear relationship; and (3)
threshold models, using PMio data in 20 cities from the 1987-1994 NMMAPS data. They
reported that the spline model, combined across the cities, showed a linear relation without
indicating a threshold for the relative risks of death for all-causes and for cardiovascular-
respiratory causes in relation to PMi0, but "the other cause" deaths (i.e., all cause minus
cardiovascular-respiratory) showed an apparent threshold at around 50 |ig/m3 PM10, as shown in
Figure 6-35. For all-cause and cardio-respiratory deaths, based on the Akaike's Information
Criterion (AIC), a log-linear model without threshold was preferred to the threshold model and
to the spline model.
"The HEI review committee commented that interpretation of these results required caution,
because (1) the measurement error could obscure any threshold; (2) the city-specific
concentration-response curves exhibited a variety of shapes; and (3) the use of AIC to choose
among the models might not be appropriate due to the fact it was not designed to assess scientific
theories of etiology. Note, however, that there has been no etiologically credible reason
suggested thus far to choose one model over others for aggregate outcomes. Thus, at least
statistically, the result of Daniels et al. (2004, 087343) suggests that the log-linear model is
appropriate in describing the relationship between PM10 and mortality.
"The Schwartz (2004, 078998) analysis of PMio and mortality in 14 U.S. cities, described in
Section 6.5.2.1, also examined the shape of the concentration-response relationship by including
indicator variables for days when concentrations were between 15 and 25 (J,g/m3, between 25 and
34 |ig/m3, between 35 and 44 |ig/m3, and 45 (J,g/m3 and above. In the model, days with
concentrations below 15 (j,g/m3 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 evidence to suggest that a threshold
exists. The authors did not examine city-to-city variation in the concentration-response
relationship in this study.
"PMio and mortality in 22 European cities (and BS in 15 of the cities) participating in the
APHEA project. In nine of the 22 cities, PM10 levels were estimated using a regression model
relating co-located PM10 to BS or TSP. They used regression spline models with two knots (30
and 50 (J,g/m3) and then combined the individual city estimates of the splines across cities. The
investigators concluded that the association between PM and mortality in these cities could be
adequately estimated using the log-linear model. However, in an ancillary analysis of the
concentration-response curves for the largest cities in each of the three distinct geographic areas
(western, southern, and eastern European cities): London, England; Athens, Greece; and Cracow,
Poland, Samoli et al. (2005, 087436) observed a difference in the shape of the concentration-
response curve across cities. Thus, while the combined curves (Figure 6-37) appear to support
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no-threshold relationships between PMio and mortality, the heterogeneity of the shapes across
cities makes it difficult to interpret the biological relevance of the shape of the combined curves.
"The results from the three multicity studies discussed above support no-threshold log-linear
models, but issues such as the possible influence of exposure error and heterogeneity of shapes
across cities remain to be resolved. Also, given the pattern of seasonal and regional differences
in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463),
the very concept of a concentration-response relationship estimated across cities and for all-year
data may not be very informative."
Authors of ISA
Dr. Lindsay Wichers Stanek (PM Team Leader)—National Center for Environmental
Assessment (NCEA), U.S. Environmental Protection Agency (U.S. EPA), Research Triangle
Park, NC
Dr. Jeffrey Arnold—NCEA, U.S. EPA, Research Triangle Park, NC (now at Institute for Water
Resources, U.S. Army Corps of Engineers, Washington, D.C)
Dr. Christal Bowman—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. James S. Brown—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Barbara Buckley—NCEA, U.S. EPA, Research Triangle Park, NC
Mr. Allen Davis—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Jean-Jacques Dubois—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Steven J. Dutton—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Tara Greaver—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Erin Hines—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Douglas Johns—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Ellen Kirrane—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Dennis Kotchmar—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Thomas Long—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Thomas Luben—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research
Fellow to NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Kristopher Novak—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Joseph Pinto—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Jennifer Richmond-Bryant—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Mary Ross—NCEA, U.S. EPA, Research Triangle Park, NC
Mr. Jason Sacks—NCEA, U.S. EPA, Research Triangle Park, NC
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Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis, OR
Dr. David Svendsgaard—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Lisa Vinikoor—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. William Wilson—NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Lori White—NCEA, U.S. EPA, Research Triangle Park, NC (now at National Institute for
Environmental Health Sciences, Research Triangle Park, NC)
Dr. Christy Avery—University of North Carolina, Chapel Hill, NC
Dr. Kathleen Belanger —Center for Perinatal, Pediatric and Environmental Epidemiology,
Yale University, New Haven, CT
Dr. Michelle Bell—School of Forestry & Environmental Studies, Yale University, New Haven,
CT
Dr. William D. Bennett—Center for Environmental Medicine, Asthma and Lung Biology,
University of North Carolina, Chapel Hill, NC
Dr. Matthew J. Campen—Lovelace Respiratory Research Institute, Albuquerque, NM
Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC
Dr. Janneane F. Gent—Center for Perinatal, Pediatric and Environmental Epidemiology, Yale
University, New Haven, CT
Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary Medicine, Duke
University Medical Center, Durham, NC
Dr. Kazuhiko Ito—Nelson Institute of Environmental Medicine, NYU School of Medicine,
Tuxedo, NY
Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research Triangle Park, NC
Dr. Michael Kleinman—Department of Community and Environmental Medicine, University
of California, Irvine
Dr. Sergey Napelenok—National Exposure Research Laboratory, U.S. EPA, Research Triangle
Park, NC
Dr. Marc Pitchford—National Oceanic and Atmospheric Administration, Las Vegas, NV
Dr. Les Recio—Genetic Toxicology Division, Integrated Laboratory Systems, Inc., Research
Triangle Park, NC
Dr. David Quincy Rich—Department of Epidemiology, University of Medicine and Dentistry
of New Jersey, Piscataway, NJ
Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR
Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY
Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess
Medical Center, Boston, MA
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Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University of North Carolina,
Chapel Hill, NC
Peer Reviewers
Dr. Sara Dubowsky Adar, Department of Epidemiology, University of Washington, Seattle,
WA
Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI
Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann Arbor, MI
Dr. Prakash Bhave, National Exposure Research Laboratory, U.S. EPA, Research Triangle
Park, NC
Mr. George Bowker, Office of Atmospheric Programs, U.S. EPA, Washington, D.C.
Dr. Judith Chow, Division of Atmospheric Sciences, Desert Research Institute, Reno, NV
Dr. Dan Costa, U.S. EPA, Research Triangle Park, NC
Dr. Ila Cote, NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Robert Devlin, National Health and Environmental Effects Research Laboratory, U.S. EPA,
Research Triangle Park, NC
Dr. David DeMarini, National Health and Environmental Effects Research Laboratory, U.S.
EPA, Research Triangle Park, NC
Dr. Neil Donahue, Department of Chemical Engineering, Carnegie Mellon University,
Pittsburgh, PA
Dr. Aimen Farraj, National Health and Environmental Effects Research Laboratory, U.S. EPA,
Research Triangle Park, NC
Dr. Mark Frampton, Department of Environmental Medicine, University of Rochester Medical
Center, Rochester, NY
Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Dr. Jim Gauderman, Department of Environmental Medicine, Department of Preventive
Medicine, University of Southern California, Los Angeles, CA
Dr. Barbara Glenn, National Center for Environmental Research, U.S. EPA, Washington, D.C.
Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY
Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Dr. Jack Harkema, Department of Pathobiology and Diagnostic Investigation, Michigan State
University, East Lansing, MI
Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
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Dr. Amy Herring, Department of Biostatistics, University of North Carolina, Chapel Hill, NC
Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical University, Prescott,
AZ
Dr. Mike Kleeman, Department of Civil and Environmental Engineering, University of
California, Davis, CA
Dr. Petros Koutrakis, Exposure, Epidemiology and Risk Program, Harvard School of Public
Health, Boston, MA
Dr. Sagar Krupa, Department of Plant Pathology, University of Minnesota, St. Paul, MN
Mr. John Langstaff, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
Dr. Meredith Lassiter, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Ms. Connie Meacham, NCEA, U.S. EPA, Research Triangle Park, NC
Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Dr. Jennifer Peel, Department of Environmental and Radiological Health Sciences, College of
Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO
Dr. Zackary Pekar, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Mr. Rob Pinder, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park,
NC
Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Dr. Sanjay Rajagopalan, Division of Cardiovascular Medicine, Ohio State University,
Columbus, OH
Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Mr. Venkatesh Rao, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
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Ms. Victoria Sandiford, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
Dr. Stefanie Sarnat, Department of Environmental and Occupational Health, Emory University,
Atlanta, GA
Dr. Frances Silverman, Gage Occupational and Environmental Health, University of Toronto,
Toronto, ON
Mr. Steven Silverman, Office of General Council, U.S. EPA, Washington, D.C.
Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers University, New
Brunswick, NJ
Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. EPA, Research Triangle
Park, NC
Dr. John Vandenberg (Director)—NCEA-RTP Division, U.S. EPA, Research Triangle Park,
NC
Dr. Alan Vette, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park,
NC
Ms. Debra Walsh (Deputy Director)—NCEA-RTP Division, U.S. EPA, Research Triangle
Park, NC
Mr. Tim Watkins, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park,
NC
Dr. Christopher Weaver, NCEA, U.S. EPA, Research Triangle Park, NC
Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S. EPA, Research
Triangle Park, NC
Ms. Karen Wesson, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle
Park, NC
Dr. Jason West, Department of Environmental Sciences and Engineering, University of North
Carolina, Chapel Hill, NC
Mr. Ronald Williams, National Exposure Research Laboratory, U.S. EPA, Research Triangle
Park, NC
Dr. George Woodall, NCEA, U.S. EPA, Research Triangle Park, NC
Dr. Antonella Zanobetti, Department of Environmental Health, Harvard School of Public
Health, Boston, MA
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D. CASAC comments on PM ISA and REA (2009)
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009.
Review of EPA's Integrated Science Assessment for Particulate Matter (First External
Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet
at
.
Pg 9: "There is an appropriate discussion of the time-series studies, but this section needs to have
an explicit finding that the evidence supports a relationship between PM and mortality that is
seen in these studies. This conclusion should be followed by the discussion of statistical
methodology and the identification of any threshold that may exist."
U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009.
Consultation on EPA's Particulate Matter National Ambient Air Quality Standards:
Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-
009. May. Available on the Internet at
.
Pg 6: "On the issue of cut-points raised on 3-18, the authors should be prepared to offer a
scientifically cogent reason for selection of a specific cut-point, and not simply try different cut-
points to see what effect this has on the analysis. The draft ISA was clear that there is little
evidence for a population threshold in the C-R function."
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. Review of
Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009).
EPA-CASAC-10-001. November. Available on the Internet at
.
Pg 2: "The paragraph on lines 22-30 of page 2-37 is not clearly written. Twice in succession it
states that the use of a no-threshold log-linear model is supported, but then cites other studies
that suggest otherwise. It would be good to revise this paragraph to more clearly state - well, I'm
not sure what. Probably that more research is needed."
CASAC Panel Members
Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive Medicine, University of
Southern California, Los Angeles, CA
Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology, Department of Environmental
Health, Harvard School of Public Health, Harvard University, Boston, MA
Dr. Ellis B. Cowling, University Distinguished Professor At-Large Emeritus, Colleges of Natural
Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC
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Dr. James Crapo, Professor of Medicine, Department of Medicine, National Jewish Medical and
Research Center, Denver, CO
Dr. H. Christopher Frey, Professor, Department of Civil, Construction and Environmental Engineering,
College of Engineering, North Carolina State University, Raleigh, NC
Dr. Armistead (Ted) Russell, Professor, Department of Civil and Environmental Engineering, Georgia
Institute of Technology, Atlanta, GA
Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab, University of California,
Davis, Davis, CA
Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern
California, Los Angeles, CA
Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina
University, Greenville, NC
Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution Research Center, Riverside
Campus and Kearney Agricultural Center, University of California, Parlier, CA
Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering, Dartmouth College, Hanover,
NH
Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering,
Clarkson University, Potsdam, NY
Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University
School of Medicine, Tuxedo, NY
Dr. Helen Suh Macintosh, Associate Professor, Environmental Health, School of Public Health,
Harvard University, Boston, MA
Dr. William Malm, Research Physicist, National Park Service Air Resources Division, Cooperative
Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO
Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager, Western Governors'
Association, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort
Collins, CO
Dr. Robert F. Phalen, Professor, Department of Community & Environmental Medicine; Director, Air
Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, Center for
Occupation & Environment Health, College of Medicine, University of California Irvine, Irvine, CA
Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the
Environment, University of California, Davis, CA
Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical School,
Boston, MA
Dr. Sverre Vedal, Professor, Department of Environmental and Occupational Health Sciences, School of
Public Health and Community Medicine, University of Washington, Seattle, WA
Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, IL
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Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY
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E. Krewski et al. (2009)
Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli
Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, and
Michael J. Thun with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K.
Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara
Tempalski. (2009). Extended follow-up and spatial analysis of the American Cancer
Society study linking particulate air pollution and mortality. HEI Research Report, 140,
Health Effects Institute, Boston, MA.
Pg 119: [About Pope et al. (2002)] "Each 10-[j,g/m3 increase in long-term average ambient PM2.5
concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death
from all causes, cardiopulmonary disease, and lung cancer, respectively. There was no evidence
of a threshold exposure level within the range of observed PM2.5 concentrations."
Krewski (2009). Letter from Dr. Daniel Krewski to HEI's Dr. Kate Adams (dated July 7,
2009) regarding "EPA queries regarding HEI Report 140". Dr. Adams then forwarded
the letter on July 10, 2009 to EPA's Beth Hassett-Sipple. (letter placed in docket #EPA-
HQ-OAR-2007-0492).
Pg 4: "6. The Health Review Committee commented that the Updated Analysis completed by
Pope et al. 2002 reported "no evidence of a threshold exposure level within the range of
observed PM2.5 concentrations" (p. 119). In the Extended Follow-Up study, did the analyses
provide continued support for a no-threshold response or was there evidence of a threshold?
"Response: As noted above, the HEI Health Review Committee commented on the lack of
evidence for a threshold exposure level in Pope et al. (2002) with follow-up through the year
1998. The present report, which included follow-up through the year 2000, also does not appear
to demonstrate the existence of a threshold in the exposure-response function within the range of
observed PM2.5 concentrations."
HEI Health Review Committee Members
Dr. Homer A. Boushey, MD, Chair, Professor of Medicine, Department of Medicine,
University of California-San Francisco
Dr. Ben Armstrong, Reader, in Epidemiological Statistics, Department of Public Health and
Policy, London School of Hygiene and Tropical Medicine, United Kingdom
Dr. Michael Brauer, ScD, Professor, School of Environmental Health, University of British
Columbia, Canada
Dr. Bert Brunekreef, PhD, Professor of Environmental Epidemiology, Institute of Risk
Assessment Sciences, University of Utrecht, The Netherlands
Dr. Mark W. Frampton, MD, Professor of Medicine & Environmental Medicine, University of
Rochester Medical Center, Rochester, NY
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Dr. Stephanie London, MD, PhD, Senior Investigator, Epidemiology Branch, National Institute
of Environmental Health Sciences
Dr. William N. Rom, MD, MPH, Sol and Judith Bergstein Professor of Medicine and
Environmental Medicine and Director of Pulmonary and Critical Care Medicine, New York
University Medical Center
Dr. Armistead Russell, Georgia Power Distinguished Professor of Environmental Engineering,
School of Civil and Environmental Engineering, Georgia Institute of Technology
Dr. Lianne Sheppard, PhD, Professor, Department of Biostatistics, University of Washington
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F. Schwartz et al. (2008)
Schwartz J, Coull B, Laden F. (2008). The Effect of Dose and Timing of Dose on the
Association between Airborne Particles and Survival. Environmental Health Perspectives.
116: 64-69.
Pg 67: "A key finding of this study is that there is little evidence for a threshold in the
association between exposure to fine particles and the risk of death on follow-up, which
continues well below the U.S. EPA standard of 15 [j,g/m3."
Pg 68: "In conclusion, penalized spline smoothing and model averaging represent reasonable,
feasible approaches to addressing questions of the shape of the exposure-response curve, and can
provide valuable information to decisionmakers. In this example, both approaches are consistent,
and suggest that the association of particles with mortality has no threshold down to close to
background levels."
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G. Expert Elicitation on PM-Mortality (2006, 2008)
Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of the
Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for
the U.S.EPA, Office of Air Quality Planning and Standards, September. Available on the
Internet at .
Pg v: "Each expert was given the option to integrate their judgments about the likelihood of a
causal relationship and/or threshold in the C-R function into his distribution or to provide a
distribution "conditional on" one or both of these factors."
Pg vii: "Only one of 12 experts explicitly incorporated a threshold into his C-R function.3 The
rest believed there was a lack of empirical and/or theoretical support for a population threshold.
However, three other experts gave differing effect estimate distributions above and below some
cut-off concentration. The adjustments these experts made to median estimates and/or
uncertainty at lower PM2 5 concentrations were modest."
"3 Expert K indicated that he was 50 percent sure that a threshold existed. If there
were a threshold, he thought that there was an 80 percent chance that it would be
less than or equal to 5 (J,g/m3, and a 20 percent chance that it would fall between 5
and 10 (J,g/m3"
Pg ix: "Compared to the pilot study, experts in this study were in general more confident in a
causal relationship, less likely to incorporate thresholds, and reported higher mortality effect
estimates. The differences in results compared with the pilot appear to reflect the influence of
new research on the interpretation of the key epidemiological studies that were the focus of both
elicitation studies, more than the influence of changes to the structure of the protocol."
Pg 3-25: "3.1.8 THRESHOLDS
The protocol asked experts for their judgments regarding whether a threshold exists in the PM2.5
mortality C-R function. The protocol focused on assessing expert judgments regarding theory
and evidential support for a population threshold (i.e., the concentration below which no member
of the study population would experience an increased risk of death).32 If an expert wished to
incorporate a threshold in his characterization of the concentration-response relationship, the
team then asked the expert to specify the threshold PM2.5 concentration probabilistically,
incorporating his uncertainty about the true threshold level.
"From a theoretical and conceptual standpoint, all experts generally believed that individuals
exhibit thresholds for PM-related mortality. However, 11 of them discounted the idea of a
population threshold in the C-R function on a theoretical and/or empirical basis. Seven of these
experts noted that theoretically one would be unlikely to observe a population threshold due to
the variation in susceptibility at any given time in the study population resulting from
combinations of genetic, environmental, and socioeconomic factors.33 All 11 thought that there
was insufficient empirical support for a population threshold in the C-R function. In addition,
two experts (E and L) cited analyses of the ACS cohort data in Pope et al. (2002) and another (J)
cited Krewski et al. (2000a & b) as supportive of a linear relationship in the study range.
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"Seven of the experts favored epidemiological studies as ideally the best means of addressing the
population threshold issue, because they are best able to evaluate the full range of susceptible
individuals at environmentally relevant exposure levels. However, those who favored
epidemiologic studies generally acknowledged that definitive studies addressing thresholds
would be difficult or impossible to conduct, because they would need to include a very large and
diverse population with wide variation in exposure and a long follow-up period. Furthermore,
two experts (B and I) cited studies documenting difficulties in detecting a threshold using
epidemiological studies (Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts
generally thought that clinical and toxicological studies are best suited for researching
mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B,
thought that a better understanding of the detailed biological mechanism is critical to addressing
the question of a threshold.
"One expert, K, believed it was possible to make a conceptual argument for a population
threshold. He drew an analogy with smoking, indicating that among heavy smokers, only a
proportion of them gets lung cancer or demonstrates an accelerated decline in lung function. He
thought that the idea that there is no level that is biologically safe is fundamentally at odds with
toxicological theory. He did not think that a population threshold was detectable in the currently
available epidemiologic studies. He indicated that some of the cohort studies showed greater
uncertainty in the shape of the C-R function at lower levels, which could be indicative of a
threshold.
"Expert K chose to incorporate a threshold into his C-R function. He indicated that he was 50
percent sure that a threshold existed. If there were a threshold, he thought that there was an 80
percent chance that it would be less than or equal to 5 (J,g/m3, and a 20 percent chance that it
would fall between 5 and 10 [j,g/m3."
Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M.
Richmond, Bryan J. Hubbell, and Patrick L. Kinney. (2008). "Expert Judgment
Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in
the U.S." Environ. Sci. Technol., 42(7):2268-2274.
Pg 2271: "Eight experts thought the true C-R function relating mortality to changes in annual
average PM2.5 was log-linear across the entire study range (ln(mortality) ) P x PM). Four experts
(B, F, K, and L) specified a "piecewise" log-linear function, with different P coefficients for PM
concentrations above and below an expert-specified break point. This approach allowed them to
express increased uncertainty in mortality effects seen at lower concentrations in major
epidemiological studies. Expert K thought the relationship would be log-linear above a
threshold."
Pg 2271: "Expert K also applied a threshold, T, to his function, which he described
probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T < 5 (J,g/m3) = 0.8
and P(5 (J,g/m3 < T < 10 (^g/m3) = 0.2. Figure 3 does not include the impact of applying expert
K's threshold, as the size of the reduction in benefits will depend on the distribution of baseline
PM levels in a benefits analysis."
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Experts:
Dr. Doug W. Dockery, Harvard School of Public Health
Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School of Medicine,
Tuxedo, NY
Dr. Dan Krewski, University of Ottawa
Dr. Nino Kiinzli, University of Southern California Keck School of Medicine
Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University
School of Medicine, Tuxedo, NY
Dr. Joe Mauderly, Lovelace Respiratory Research Institute
Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health
Hazard Assessment, California Environmental Protection Agency, Oakland, CA
Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT
Dr. Richard Schlesinger, Pace University
Dr. Joel Schwartz, Harvard School of Public Health
Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY
Dr. Mark Utell, University of Rochester School of Medicine and Dentistry
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H. CASAC comments on PM Staff Paper (2005)
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2005.
EPA's Review of the National Ambient Air Quality Standards for Particulate Matter
(Second Draft PM Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June. Available
on the Internet at
.
Pg 6: "A second concern is with methodological issues. The issue of the selection of
concentration-response (C-R) relationships based on locally-derived coefficients needs more
discussion. The Panel did not agree with EPA staff in calculating the burden of associated
incidence in their risk assessment using either the predicted background or the lowest measured
level (LML) in the utilized epidemiological analysis. The available epidemiological database on
daily mortality and morbidity does not establish either the presence or absence of threshold
concentrations for adverse health effects. Thus, in order to avoid emphasizing an approach that
assumes effects that extend to either predicted background concentrations or LML, and to
standardize the approach across cities, for the purpose of estimating public health impacts, the
Panel favored the primary use of an assumed threshold of 10 (J,g/m3. The original approach of
using background or LML, as well as the other postulated thresholds, could still be used in a
sensitivity analysis of threshold assumptions.
"The analyses in this chapter highlight the impact of assumptions regarding thresholds, or lack of
threshold, on the estimates of risk. The uncertainty associated with threshold or nonlinear models
needs more thorough discussion. A major research need is for more work to determine the
existence and level of any thresholds that may exist or the shape of nonlinear concentration-
response curves at low levels of exposure that may exist, and to reduce uncertainty in estimated
risks at the lowest PM concentrations."
CASAC Panel Members
Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research Institute,
Albuquerque, NM
Dr. Ellis Cowling, University Distinguished Professor-at-Large, North Carolina State
University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina
State University, Raleigh, NC
Dr. James D. Crapo, Professor, Department of Medicine, Biomedical Research and
PatientCare, National Jewish Medical and Research Center, Denver, CO
Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical
Engineering, Clarkson University, Potsdam, NY
Dr. Jane Q. Koenig, Professor, Department of Environmental Health, School of Public Health
and Community Medicine, University of Washington, Seattle, WA
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Dr. Petros Koutrakis, Professor of Environmental Science, Environmental Health , School of
Public Health, Harvard University (HSPH), Boston, MA
Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta
Dr. Paul J. Lioy, Associate Director and Professor, Environmental and Occupational Health
Sciences Institute, UMDNJ - Robert Wood Johnson Medical School, NJ
Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York
University School of Medicine, Tuxedo, NY
Dr. Joe Mauderly, Vice President, Senior Scientist, and Director, National Environmental
Respiratory Center, Lovelace Respiratory Research Institute, Albuquerque, NM
Dr. Roger O. McClellan, Consultant, Albuquerque, NM
Dr. Frederick J. Miller, Consultant, Cary, NC
Dr. Gunter Oberdorster, Professor of Toxicology, Department of Environmental Medicine,
School of Medicine and Dentistry, University of Rochester, Rochester, NY
Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of
Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT
Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO
Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology, Bloomberg School
of Public Health, Johns Hopkins University, Baltimore, MD
Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard
Medical School, Boston, MA
Dr. Sverre Vedal, Professor of Medicine, School of Public Health and Community
Medicine University of Washington, Seattle, WA
Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of Public Health,
Johns Hopkins University, Baltimore, MD
Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory, University of California
-Davis, Davis, CA
Dr. George T. Wolff, Principal Scientist, General Motors Corporation, Detroit, MI
Dr. Barbara Zielinska, Research Professor, Division of Atmospheric Science, Desert Research
Institute, Reno, NV
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I. HES Comments on 812 Analysis (2004)
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2004.
Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA's Second
Prospective Analysis - Benefits and Costs of the Clean Air Act, 1990-2020. Advisory by
the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance
Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at
.
Pg 20: "The Subcommittee agrees that the whole range of uncertainties, such as the questions of
causality, shape of C-R functions and thresholds, relative toxicity, years of life lost, cessation lag
structure, cause of death, biologic pathways, or susceptibilities may be viewed differently for
acute effects versus long-term effects.
"For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted
the most careful work on this issue. They report that the associations between PM2.5 and both all-
cause and cardiopulmonary mortality were near linear within the relevant ranges, with no
apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, Figure 3 and
Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels.
Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end
of the concentrations reported in the studies."
HES Panel Members
Dr. Bart Ostro, California Office of Environmental Health Hazard Assessment (OEHHA),
Oakland, CA
Mr. John Fintan Hurley, Institute of Occupational Medicine (IOM), Edinburgh, Scotland
Dr. Patrick Kinney, Columbia University, New York, NY
Dr. Michael Kleinman, University of California, Irvine, CA
Dr. Nino Kiinzli, University of Southern California, Los Angeles, CA
Dr. Morton Lippmann, New York University School of Medicine, Tuxedo, NY Dr. Rebecca
Parkin, The George Washington University, Washington, DC
Dr. Trudy Cameron, University of Oregon, Eugene, OR
Dr. David T. Allen, University of Texas, Austin, TX
Ms. Lauraine Chestnut, Stratus Consulting Inc., Boulder, CO
Dr. Lawrence Goulder, Stanford University, Stanford, CA
Dr. James Hammitt, Harvard University, Boston, MA
Dr. F. Reed Johnson, Research Triangle Institute, Research Triangle Park, NC
Dr. Charles Kolstad, University of California, Santa Barbara, CA
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Dr. Lester B. Lave, Carnegie Mellon University, Pittsburgh, PA
Dr. Virginia McConnell, Resources for the Future, Washington, DC
Dr. V. Kerry Smith, North Carolina State University, Raleigh, NC
Other Panel Members
Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis, Clark University,
Worcester, MA Dr. D. Warner North, NorthWorks Inc., Belmont, CA Dr. Thomas S. Wallsten,
University of Maryland, College Park, MD
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J. NRC - Committee on Estimating the Health Risk Reduction Benefits of Proposed Air
Pollution Regulations (2002)
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed
Air Pollution Regulations. Washington, DC: The National Academies Press.
Pg 109: "Linearity and Thresholds
"The shape of the concentration-response functions may influence the overall estimate of
benefits. The shape is particularly important for lower ambient air pollution concentrations to
which a large portion of the population is exposed. For this reason, the impact of the existence of
a threshold may be considerable.
"In epidemiological studies, air pollution concentrations are usually measured and modeled as
continuous variables. Thus, it may be feasible to test linearity and the existence of thresholds,
depending on the study design. In time-series studies with the large number of repeated
measurements, linearity and thresholds have been formally addressed with reasonable statistical
power. For pollutants such as PMi0 and PM2.5, there is no evidence for any departure of linearity
in the observed range of exposure, nor any indication of a threshold. For example, examination
of the mortality effects of short-term exposure to PMi0 in 88 cities indicates that the
concentration-response functions are not due to the high concentrations and that the slopes of
these functions do not appear to increase at higher concentrations (Samet et al. 2000). Many
other mortality studies have examined the shape of the concentration-response function and
indicated that a linear (nonthreshold) model fit the data well (Pope 2000). Furthermore, studies
conducted in cities with very low ambient pollution concentrations have similar effects per unit
change in concentration as those studies conducted in cities with higher concentrations. Again,
this finding suggests a fairly linear concentration-response function over the observed range of
exposures.
"Regarding the studies of long-term exposure, Krewski et al. (2000) found that the assumption of
a linear concentration-response function for mortality outcomes was not unreasonable. However,
the statistical power to assess the shape of these functions is weakest at the upper and lower end
of the observed exposure ranges. Most of the studies examining the effects of long-term
exposure on morbidity compare subjects living in a small number of communities (Dockery et al.
1996; Ackermmann-Liebrich 1997; Braun-Fahrlander et al. 1997). Because the number of long-
term effects studies are few and the number of communities studied is relatively small (8 to 24),
the ability to test formally the absence or existence of a no-effect threshold is not feasible.
However, even if thresholds exist, they may not be at the same concentration for all health
outcomes.
"A review of the time-series and cohort studies may lead to the conclusion that although a
threshold is not apparent at commonly observed concentrations, one may exist at lower levels.
An important point to acknowledge regarding thresholds is that for health benefits analysis a key
threshold is the population threshold (the lowest of the individual thresholds). However, the
population threshold would be very difficult to observe empirically through epidemiology,
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because epidemiology integrates information from very large groups of people (thousands). Air
pollution regulations affect even larger groups of people (millions). It is reasonable to assume
that among such large groups susceptibility to air pollution health effects varies considerably
across individuals and depends on a large set of underlying factors, including genetic makeup,
age, exposure measurement error, preexisting disease, and simultaneous exposures from smoking
and occupational hazards. This variation in individual susceptibilities and the resulting
distribution of individual thresholds underlies the concentration-response function observed in
epidemiology. Thus, until biologically based models of the distribution of individual thresholds
are developed, it may be productive to assume that the population concentration-response
function is continuous and to focus on finding evidence of changes in its slope as one approaches
lower concentrations.
EPA 's Use of Thresholds
"In EPA's benefits analyses, threshold issues were discussed and interpreted. For the PM and
ozone National Ambient Air Quality Standards (NAAQS), EPA investigated the effects of a
potential threshold or reference value below which health consequences were assumed to be zero
(EPA 1997). Specifically, the high-end benefits estimate assumed a 12-microgram per cubic
meter (|ig/m3) mean threshold for mortality associated with long-term exposure to PM2.5. The
low-end benefits estimate assumed a 15-|ig/m3 threshold for all PM-related health effects. The
studies, however, included concentrations as low as 7.5 |ig/m3. For the Tier 2 rule and the HD
engine and diesel-fuel rule, no threshold was assumed (EPA 1999, 2000). EPA in these analyses
acknowledged that there was no evidence for a threshold for PM.
"Several points should be noted regarding the threshold assumptions. If a threshold is assumed
where one was not apparent in the original study, then the data should be refit and a new curve
generated with the assumption of a zero slope over a segment of the concentration-response
function that was originally found to be positively sloped. The assumption of a zero slope over a
portion of the curve will force the slope in the remaining segment of the positively sloped
concentration-response function to be greater than was indicated in the original study. A new
concentration-response function was not generated for EPA's benefits analysis for the PM and
ozone NAAQS for which threshold assumptions were made. The generation of the steeper slope
in the remaining portion of the concentration-response function may fully offset the effect of
assuming a threshold. These aspects of assuming a threshold in a benefits analysis where one
was not indicated in the original study should be conveyed to the reader. The committee notes
that the treatment of thresholds should be evaluated in a consistent and transparent framework by
using different explicit assumptions in the formal uncertainty analyses (see Chapter 5)."
Pg 117: "Although the assumption of no thresholds in the most recent EPA benefits analyses was
appropriate, EPA should evaluate threshold assumptions in a consistent and transparent
framework using several alternative assumptions in the formal uncertainty analysis."
Pg 136: "Two additional illustrative examples are thresholds for adverse effects and lag
structures.- EPA considers implausible any threshold for mortality in the particulate matter (PM)
exposure ranges under consideration (EPA 1999a, p. 3-8). Although the agency conducts
sensitivity analyses incorporating thresholds, it provides no judgment as to their relative
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plausibility. In a probabilistic uncertainty analysis, EPA could assign appropriate weights to
various threshold models. For PM-related mortality in the Tier 2 analysis, the committee expects
that this approach would have resulted in only a slight widening of the probability distribution
for avoided mortality and a slight reduction in the mean of that distribution, thus reflecting
EPA's views about the implausibility of thresholds. The committee finds that such formal
incorporation of EPA's expert judgments about the plausibility of thresholds into its primary
analysis would have been an improvement.
"Uncertainty about thresholds is a special aspect of uncertainty about the shape of concentration-
response functions. Typically, EPA and authors of epidemiological studies assume that these
functions are linear on some scale. Often, the scale is a logarithmic transformation of the risk or
rate of the health outcome, but when a rate or risk is low, a linear function on the logarithmic
scale is approximately linear on the scale of the rate or risk itself. Increasingly, epidemiological
investigators are employing analytic methods that permit the estimation of nonlinear shapes for
concentration-response functions (Greenland et al. 1999). As a consequence, EPA will need to be
prepared to incorporate nonlinear concentration-response functions from epidemiological studies
into the agency's health benefits analyses. Any source of error or bias that can distort an
epidemiological association can also distort the shape of an estimated concentration -response
function, as can variation in individual susceptibility (Hattis and Burmaster 1994; Hattis et al.
2001)."
Pg 137: "In principle, many components of the health benefits model need realistic probabilistic
models (see Table 5-1 for a listing of such components), in addition to concentration-response
thresholds and time lags between exposure and response. For example, additional features of the
concentration-response function—such as projection of the results from the study population to
the target populations (which may have etiologically relevant characteristics outside the range
seen in the study population) and the projection of baseline frequencies of morbidity and
mortality into the future—must be characterized probabilistically. Other uncertainties that might
affect the probability distributions are the estimations of population exposure (or even
concentration) from emissions, estimates of emissions themselves, and the relative toxicity of
various classes of particles. Similarly, many aspects of the analysis of the impact of regulation on
ambient concentrations and on population exposure involve considerable uncertainty and,
therefore, may be beneficially modeled in this way. Depending on the analytic approach used,
joint probability distributions will have to be specified to incorporate correlations between model
components that are structurally dependent upon each other, or the analysis will have to be
conducted in a sequential fashion that follows the model for the data-generating process.
"EPA should explore alternative options for incorporating expert judgment into its probabilistic
uncertainty analyses. The agency possesses considerable internal expertise, which should be
employed as fully as possible. Outside experts should also be consulted as needed, individually
or in panels. In all cases, when expert judgment is used in the construction of a model
component, the experts should be identified and the rationales and empirical bases for their
judgments should be made available."
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NRC members
Dr. JOHN C. BAILAR, III (ch ajr), (emeritus) University of Chicago, Chicago, Illinois
Dr. HUGH ROSS ANDERSON, University of London, London, England
Dr. MAUREEN L. CROPPER, University of Maryland, College Park
Dr. JOHN S. EVANS, Harvard University, Boston, Massachusetts
Dr. DALE B. HATTIS, Clark University, Worcester, Massachusetts
Dr. ROGENE F. HENDERSON, Lovelace Respiratory Research Institute, Albuquerque, New
Mexico
Dr. PATRICK L. KINNEY, Columbia University, New York, New York
Dr. NINO KUNZLI, University of Basel, Basel, Switzerland; as of September 2002, University
of Southern California, Los Angeles
Dr. BART D. OSTRO, California Environmental Protection Agency, Oakland
Dr. CHARLES POOLE, University of North Carolina, Chapel Hill
Dr. KIRK R. SMITH, University of California, Berkeley
Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts
Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland
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APPENDIX B
LOWEST MEASURED LEVEL (LML) ASSESSMENT FOR RULES WITHOUT
POLICY-SPECIFIC AIR QUALITY DATA AVAILABLE: TECHNICAL SUPPORT
DOCUMENT (TSD)
-------
Lowest Measured Level (LML) Assessment for Rules
without Policy-Specific Air Quality Data Available
Technical Support Document (TSD)
June 2010
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Health and Environmental Impact Division
Air Benefit-Cost Group
Research Triangle Park, North Carolina
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Inherent in any complex Regulatory Impact Analysis (RIA) are multiple sources of
uncertainty. Health benefits analysis relies on an array of data inputs—including air quality
modeling, health impact functions and valuation estimates among others—which are themselves
subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis.
There are a variety of methods to characterizing the uncertainty associated with the human health
benefits of air pollution, including quantitative and qualitative methods. When evaluated within
the context of these uncertainties, the health impact and monetized benefits estimates in an RIA
can provide useful information regarding the magnitude of the public health impacts attributable
to reducing air pollution.
Reductions in premature mortality typically dominate the size of the overall monetized
benefits. Therefore, most of the uncertainty characterization generally focuses on the mortality-
related benefits. Typically, EPA employs two primary techniques for quantifying this
uncertainty. First, because this characterization of random statistical error may omit important
sources of uncertainty, we employ the results of an expert elicitation on the relationship between
premature mortality and ambient PM2.5 concentration (Roman et al., 2008); this provides
additional insight into the likelihood of different outcomes and about the state of knowledge
regarding the benefits estimates. Second, when we have air quality modeling specific to the
policy we are evaluating and it can be used as an input to the health impact and economic
analysis, we use Monte Carlo methods for characterizing random sampling error associated with
the concentration response functions from epidemiological studies and economic valuation
functions.1 Both approaches have different strengths and weaknesses, which are fully described
in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006).
In addition, some RIAs, including the PM NAAQS RIA (2006d) and Ozone NAAQS
RIA (2008a), also contain a suite of sensitivity analyses that evaluate the sensitivity of the
monetized benefits to the specification of alternate mortality cessation lags and income growth
adjustment factors. Cessation lags and income growth adjustments are simply multipliers applied
to the valuation function, which generally affect monetized benefits estimates in the same
manner. Thus, it is possible for readers to infer the sensitivity of these parameters by referring to
those previous analyses.2 Other RIAs contain unique sensitivity analyses that are specific to the
1 Currently, we are unable to characterize the random sampling error from the underlying studies when applying
national average benefit-per-ton estimates.
2 For example, in the PM NAAQS RIA, the use of an alternate lag structure would change the PM2 5-related
mortality benefits discounted at 3% discounted by between 10.4% and -27%; when discounted at 7%, these
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input parameters of that analysis, such as blood lead level (U.S. EPA, 2008b) or rollback method
(U.S. EPA, 2010a). Other sources of uncertainty, including the projection of atmospheric
conditions and source-level emissions, the projection of baseline morbidity rates, incomes and
technological development are typically unquantified in our RIAs. For these sources, we
typically provide a qualitative uncertainty characterization associated with these input
parameters.
One particular aspect of uncertainty has received extensive quantitative and qualitative
attention in recent RIAs: the existence of a threshold in the concentration-response function for
PM2.5-related mortality. A threshold is a specific type of discontinuity in the concentration-
response function where there are no benefits associated with reducing PM2.5 levels in areas
where the baseline air quality is less than the threshold. Previously, EPA had included a
sensitivity analysis with an arbitrary assumed threshold at 10 |ig/m3 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 inherently more uncertain. A threshold of 10 |ig/m3 does not
necessarily have any stronger technical basis than any other threshold, and we could have instead
assumed a threshold at 4, 7.5, or 12 |ig/m3 for the sensitivity analysis. In addition to identifying
the most support for a non-threshold model, the underlying scientific evidence does not support
any specific "bright line".
Based on our review of the current body of scientific literature, EPA now estimates PM-
related mortality without applying an assumed concentration threshold. EPA's Integrated
Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by
EPA's Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB,
2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear
model most adequately portrays the PM-mortality concentration-response relationship while
recognizing potential uncertainty about the exact shape of the concentration-response function.1
Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA's Council
concluded, "The HES fully supports EPA's decision to use a no-threshold model to estimate
mortality reductions. This decision is supported by the data, which are quite consistent in
benefits change by between 31% and -49%. When applying higher and lower income growth adjustments, the
monetary value of PM2 5 and ozone-related premature changes between 30% and -10%; the value of chronic
endpoints change between 5% and -2% and the value of acute endpoints change between 6% and -7%. (U.S. EPA,
2006)
1 It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually
distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a
discontinuity, which is a specific example of non-linearity.
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showing effects down to the lowest measured levels. Analyses of cohorts using data from more
recent years, during which time PM concentrations have fallen, continue to report strong
associations with mortality. Therefore, there is no evidence to support a truncation of the CRF."
For a summary of these scientific review statements and the panel members please consult the
Technical Support Document (TSD) Summary of Expert Opinions on the Existence of a
Threshold (U.S. EPA, 2010c).
Consistent with this finding, we have conformed the previous threshold sensitivity
analysis to the current state of the PM science by incorporating a new "Lowest Measured Level"
(LML) assessment. While an LML assessment provides some insight into the level of uncertainty
in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues
to quantify PM-related mortality impacts using a full range of modeled air quality
concentrations. Unlike an assumed threshold, which is a modeling assumption that reduces the
magnitude of the estimated health impacts, the LML is a characterization of the fraction of
benefits that are more uncertain. It is important to emphasize that just because we have greater
confidence in the benefits above the LML, this does not mean that we have no confidence that
benefits occur below the LML.
While the LML of each study is important to consider when characterizing and
interpreting the overall level PM-related benefits, EPA believes that large cohort-based mortality
estimates are suitable for use in air pollution health impact analyses. When estimating PM
mortality impacts using risk coefficients drawn from the Harvard Six Cities and the American
Cancer Society cohorts there are innumerable other attributes that may affect the size of the
reported risk estimates—including differences in population demographics, the size of the
cohort, activity patterns and particle composition among others. The LML assessment provides a
limited representation of one key difference between the two studies. For the purpose of
estimating the benefits associated with reducing PM2.5 levels, we utilize the effect coefficients
from Pope et al. (2002) for the American Cancer Society cohort and from Laden et al. (2006) for
the Harvard Six Cities cohort.
Analyses of these cohorts using data from more recent years, during which time PM
concentrations have fallen, continue to report strong associations with mortality. For example,
the Krewski et al. (2009) follow-up study of the American Cancer Society cohort had an LML of
5.8 |ig/m3. As we model mortality impacts among populations exposed to levels of PM2.5 that are
successively lower than the LML of each study, our confidence in the results diminishes. As air
pollution emissions continue to decrease over time, there will be more people in areas where we
do not have published epidemiology studies. However, each successive cohort study has shown
B-4
-------
evidence of effects at successively lower levels of PM25. As more large cohort studies follow
populations over time, we will likely have more studies with lower LML as air quality levels
continue to improve. Even in the absence of a definable threshold, we have more confidence in
the benefits estimates above the LML of the large cohort studies. To account for the uncertainty
in each of the studies that we base our mortality estimates on, we provide the LML for each of
the cohort studies. However, the finding of effects at the lowest LML from the recent Krewski et
al (2009) study indicates that confidence in PM25-related mortality effects down to at least 5.8
|ig/m3 is high.
In the recently proposed Transport Rule RIA (U.S. EPA, 2010b), we included the new
LML assessment in which we binned the estimated number of avoided PM2.5-related premature
mortalities resulting from the implementation of the Transport Rule according to the projected
2014 baseline PM25 air quality levels. This presentation is consistent with our approach to
applying PM2 5 mortality risk coefficients that have not been adjusted to incorporate an assumed
threshold. A very large proportion of the avoided PM-related impacts occurred among
populations initially exposed at or above the LML of each study, which gave us a high level of
confidence in the PM mortality estimates. This assessment summarized the distribution of
avoided PM mortality impacts according to the baseline PM2 5 levels experienced by the
population receiving the PM2 5 mortality benefit. Approximately 80% of the avoided impacts
occurred at or above a baseline annual mean PM2 5 level of 10 |ig/m3 (the LML of the Laden et
al. 2006 study); about 97% occur at or above an annual mean PM2 5 level of 7.5 |ig/m3 (the LML
of the Pope et al. 2002 study). This assessment confirmed that the great majority of the impacts
associated with the Transport Rule occurred at or above each study's LML.
For the Transport Rule, policy-specific air quality modeling data for the year 2014 was
available as an input into the benefits analysis. For some rules, especially New Source
Performance Standards (NSPS) or National Emissions Standards for Hazardous Air Pollutant
(NESHAP) rules, policy-specific air quality data is not available due to time or resource
limitations. For these rules, we provide the following LML assessment as a characterization of
the baseline exposure to PM25 levels in the U.S. Many of the upcoming NSPS and NESHAP
rules have compliance dates between 2013 and 2016 and represent marginal improvements in air
quality levels. Although it the data is not a perfect match, we believe that the air quality data
B-5
-------
from the Transport Rule is a reasonable approximation of the baseline exposure in the U.S. for
upcoming NSPS and NESHAP rules.1
For rules without air quality modeling, we generally estimate the monetized benefits and
health impacts using benefit-per-ton estimates (Fann, Fulcher and Hubbell, 2009). Using this
method, we are unable to estimate the percentage of premature mortality associated with the
specific rules' emission reductions at each PM2.5 level. However, we believe that it is still
important to characterize the uncertainty associated with the distribution of the baseline air
quality. As a surrogate measure of mortality impacts, we provide the percentage of baseline
exposure at each PM2.5 level. If air quality levels in the baseline are above the LML, the marginal
changes anticipated from these rules would likely also lead to post-policy air quality levels above
the LML. Therefore, we have high confidence that the magnitude of the benefits estimated for
these rules, as the marginal changes would also be above the LML.
It is important to note that baseline exposure is only one parameter in the health impact
function, along with baseline incidence rates population, and change in air quality. In other
words, the percentage of the population exposed to air pollution below the LML is not the same
as the percentage of the population experiencing health impacts as a result of a specific emission
reduction policy. The most important aspect, which we are unable to quantify for rules without
air quality modeling, is the shift in exposure associated with the specific rule. Therefore, caution
is warranted when interpreting the following assessment.
A very large proportion of the population is exposed at or above the lowest LML of the
cohort studies (Figures 1 and 2), increasing our confidence in the PM mortality analysis. Figure 1
shows a bar chart of the percentage of the population exposed to various air quality levels in the
pre- and post-policy policy. Figure 2 shows a cumulative distribution function of the same data.
In addition, Figure 2 also demonstrates that policy had a greater impact on reducing exposure to
the portion of the population in areas with high PM2.5 levels relative to the portion of the
population at low PM2.5 levels. Both figures identify the LML for each of the major cohort
studies. As the policy shifts the distribution of air quality levels, fewer people are exposed to
PM2.5 levels above the LML. Under baseline conditions, about 96 percent of the population is
1 Because the Transport Rule is not yet promulgated, the baseline exposure obtained from this modeling data would
slightly overestimate the fraction of the population exposed to air quality levels below the LML. As additional
rules continue to reduce the ambient PM2 5 levels over time, a larger fraction of the population would be exposed
to air quality levels below the LML. However, the emission reductions anticipated from the rules without air
quality modeling available are comparatively small and represent marginal changes. We intend to update this
LML assessment as necessary to correspond with the successively lower baseline air quality levels anticipated as
the result of promulgating significant upcoming rules.
B-6
-------
exposed to annual mean PM2.5 levels of at least 5.8 |ig/m3, which is the lowest air quality level
considered in the most recent study of the American Cancer Society cohort by Krewski et al.
(2009). Using the Pope et al. (2002) study, the 85% of the population is exposed at or above the
LML of 7.5 |ig/m3. Using the Laden et al. (2006) study, 40% of the population is exposed above
the LML of 10 |ig/m3. As we model mortality impacts among populations exposed to levels of
PM2.5 that are successively lower than the LML of the lowest cohort study, our confidence in the
results diminishes. However, the analysis above confirms that the great majority of the impacts
occur at or above the lowest cohort study's LML. It is important to emphasize that we have high
confidence in PM2.5-related effects down to the lowest LML of the major cohort studies, which is
5.8 |ig/m3. Just because we have greater confidence in the benefits above the LML, this does not
mean that we have no confidence that benefits occur below the LML.
Figure 1: Percentage of Adult Population by Annual Mean PM2.5 Exposure
(pre- and post- policy)
25%
20%
15%
10%
5%
0%
Krewski et al. 2009 Pope et al. 2002
Laden et al. 2006
The control strategy lowers Pli^5
levels substantially, particularly
among highly exposed
populations. In the baseline, 96%
of the population lived in areas
where PM25 levels above the
lowest measured levels of the
Krewski study, increasing our
confidence in the estimated
mortality reductions for this rule.
B-7
-------
Figure 2: Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels
(pre- and post-policy)
100% -
90% -
80% -
70% -
60% -
50% -
40% -
30% -
20% -
10% -
0% r
1 2
There are several important differences between the assessment conducted for the
Transport Rule and the assessment presented here. If you compare the graphics in the Transport
Rule to those provided here, you will notice that these graphs show a larger percentage of the
population below the LML. It is imperative to point out that the Transport Rule graphics
represented mortality impacts attributable to the Transport Rule, whereas these graphics
represent exposure. Mortality impacts are the result of the incremental change in exposure
between the baseline and control. However, the baseline population exposure at lower air quality
levels is so much larger than the impacts among these same populations. In other words, the
population exposed to lower PM2.5 levels are not receiving very much of the air quality benefit
between the base and the control case.
The control strategy lowers PM^ levels
substantially, particularly among highly
exposed populations. In the baseline, 96% of
the population lived in areas where PM25
levels above the lowest measured levels of
the Krewski study, increasing our
confidence in the estimated mortality
reductions for this rule.
Krewski et al. 2009 Pope et al. 2002 Laden et al. 2006
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
B-8
-------
References
Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. The influence of location, source, and emission type
in estimates of the human health benefits of reducing a ton of air pollution. Air Qual Atmos
Health (2009) 2:169-176.
Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and
spatial analysis of the American Cancer Society study linking particulate air pollution and
mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.
Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air
Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine
173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regulations.
Washington, DC: The National Academies Press.
Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston.
2002. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate
Air Pollution. Journal of the American Medical Association 287:1132-1141.
Roman, H.A., K.D. Walker, T.L. Walsh, L. Conner, H.M. Richmond, .J. Hubbell, and P.L.
Kinney. 2008. "Expert Judgment Assessment of the Mortality Impact of Changes in Ambient
Fine Particulate Matter in the U.S." Environ. Sci. Technol. 42(7):2268-2274.
U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006
National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. October. Available on the
Internet at .
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
.
U.S. Environmental Protection Agency (U.S. EPA). 2008b. Regulatory Impact Analysis of the
Proposed Revisions to the National Ambient Air Quality Standards for Lead. Office of Air
Quality Planning and Standards, Research Triangle Park, NC. March. Available at .
U.S. Environmental Protection Agency (U.S. EPA). 2010a. Final Regulatory Impact Analysis
(RIA) for the SO2 National Ambient Air Quality Standards (NAAQS). Office of Air Quality
Planning and Standards, Research Triangle Park, NC. June. Available at
.
U.S. Environmental Protection Agency (U.S. EPA). 2010b. Regulatory Impact Analysis for the
Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
June. Available at .
B-9
-------
U.S. Environmental Protection Agency (U.S. EPA). 2010c. Summary of Expert Opinions on the
Existence of a Threshold in the Concentration-Response Function for PM2.s-related
Mortality: Technical Support Document. Compiled by Office of Air Quality Planning and
Standards, Research Triangle Park, NC. July. Available at
.
U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010.
Review of EPA's DRAFT Health Benefits of the Second Section 812 Prospective Study of
the Clean Air Act. EPA-COUNCIL-10-001. June. Available on the Internet at
-------
APPENDIX C
ADDITIONAL ENGINEERING COST ANALYSIS DATA
-------
Table C-la. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Fluidized Bed
Incinerators
Part 1 Red cells indicate where additional control is needed
Facility ID
Unit
ID
Existing
Control
Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment1
ACI
Performance
Adjustment
Factor2
(Hg Basis)
EG Limit (mg/dscm):
0.0019
EG Limit (ppmvd): 56
EG Limit (ppmvd): 0.49
EG Limit (mg/dscm):
0.0098
EG Limit (mg/dscm):
0.0033
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment Needed
AKJuneau
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
CTMattabassett
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
CT Synagro W aterbury
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
CTWestHaven
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
G AN oondayCreek
1
unknown
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
IADubuque
1
cs - vs - pbt
0.00214
11
16.33
-243
0.050
-889
0.01105
11
0.01354
76
66
0.73
IADubuque
2
cs - vs - pbt
0.00214
11
16.33
-243
0.050
-889
0.01105
11
0.01354
76
66
0.73
KSKawPoint
1
vs
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
KSKawPoint
2
vs
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
LAN ewOrleansEastBan
k
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
MALynnRegional
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
MALynnRegional
2
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
MEYpsilanti
EU-
FBSS
I
vs- imp - wesp -
ac polish.
0.00047
-308
2.64
-2020
0.282
-73
0.00618
-59
0.00057
-482
-492
-5.47
MN StPaulMetro
FBR1
ac inject. - bag -
vs(ad) - wesp
0.00043
-344
23.46
-139
0.167
-193
0.00288
-241
0.00170
-95
-95
-1.06
MN StPaulMetro
FBR2
ac inject. - bag -
vs(ad) - wesp
0.00075
-154
23.71
-136
0.156
-215
0.00262
-273
0.00089
-271
-271
-3.01
MN StPaulMetro
FBR3
ac inject. - bag -
vs(ad) - wesp
0.00069
-174
20.46
-174
0.200
-144
0.00240
-309
0.00039
-753
-753
-8.37
MOLittleBlue V alley
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
MORockCreek
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
N CBuncombe Ashville
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NCTZOsborne
ES-1
abd - vs - imp -
hss - cs
0.00017
-988
11.39
-392
0.044
-1008
0.00031
-3028
0.04113
92
82
0.91
NHManchester
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
N JBayshoreReg ional
1
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
N JBayshoreReg ional
2
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
NJCamden
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NJGloucester
1
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
NJGloucester
2
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
N JN orthwestBerg en
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
-------
Facility ID
Unit
ID
Existing
Control
Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment1
ACI
Performance
Adjustment
Factor2
(Hg Basis)
EG Limit (mg/dscm):
0.0019
EG Limit (ppmvd): 56
EG Limit (ppmvd): 0.49
EG Limit (mg/dscm):
0.0098
EG Limit (mg/dscm):
0.0033
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment Needed
N JNorthwestBergen
2
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NJPequannockLincolnF
airfield
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NJPequannockLincolnF
airfield
2
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
N J SomersetRaritan
1
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
N J SomersetRaritan
2
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
NYArlington
1
unknown
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
NYErieCounty
1
vs
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
NYErieCounty
2
vs
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
NYGlensFalls
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NY OneidaCounty
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NY OneidaCounty
2
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NY OneidaCounty
3
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NYPortChester
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NYPortChester
2
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
NY Saratog aCounty
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
OHL ittleMiami
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
OHNEORSDEasterly
1
abo - imp -
wesp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
PAAlleghenyCounty
001
abd - mc - vs -
imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
PAAlleghenyCounty
002
abd - mc - vs -
imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
PAW yoming Valley
1
vs - imp - wesp
0.00085
-122
16.33
-243
0.124
-295
0.00442
-122
0.01354
76
66
0.73
PRPuertoNuevo
1
vs(ad) - wesp
0.00085
-122
16.33
-243
0.050
-889
0.00442
-122
0.01354
76
76
0.84
SCFelixCDavis
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
VABlacksburg
1
unknown
0.00214
11
16.33
-243
2.478
80
0.01105
11
0.01504
78
68
0.76
V AHLMooney
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
WAAnacortes
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
WAEdmonds
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
WALynnwood
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
WAWestside
1
vs - imp
0.00214
11
16.33
-243
0.124
-295
0.01105
11
0.01354
76
66
0.73
-------
Table C-la. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Fluidized Bed
Incinerators
Part 2 Red cells indicate where additional control is needed
Facility ID
Unit
ID
Existing
Control
Devices
Nitrogen Oxides (NOx)
Particulate Matter
(filterable)
Particulate Matter
(PM 2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB 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): 0.61
EG Limit (ng/dscm):
0.056
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve
ment
Needed
Average
(ng/dscm)
%
Improve
ment
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
AKJuneau
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
CTMattabassett
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
CT Synagro W aterbury
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
CTWestHaven
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
GANoondayCreek
1
unknown
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
IADubuque
1
cs - vs - pbt
27.93
-126
12.44
4
11.80
7
1.32
-1565
15.9621
96
0.98
1.3121
96
IADubuque
2
cs - vs - pbt
27.93
-126
12.44
4
11.80
7
1.32
-1565
15.9621
96
0.98
1.3121
96
KSKawPoint
1
vs
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
KSKawPoint
2
vs
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
LANewOrleansEastBank
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
MALynnRegional
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
MALynnRegional
2
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
MEYpsilanti
EU-
FBS
SI
vs- imp - wesp -
ac polish.
29.76
-112
2.87
-317
4.83
-128
3.30
-566
0.1469
-315
-3.21
0.0064
-772
MN StPaulMetro
FBR
1
ac inject. - bag -
vs(ad) - wesp
31.00
-103
2.26
-432
1.72
-538
0.62
-3468
0.4048
-51
-0.52
0.0356
-57
MN StPaulMetro
FBR
2
ac inject. - bag -
vs(ad) - wesp
41.44
-52
1.41
-750
1.57
-601
1.65
-1235
0.4060
-50
-0.51
0.0367
-52
MN StPaulMetro
FBR
3
ac inject. - bag -
vs(ad) - wesp
22.53
-180
5.38
-123
1.45
-659
1.13
-1843
0.4054
-50
-0.51
0.0362
-55
MOL ittleBlue V alley
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
MORockCreek
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
N CBuncombe Ashville
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NCTZOsborne
ES-1
abd - vs - imp -
hss - cs
14.90
-323
2.58
-366
11.16
1
7.64
-188
15.9621
96
0.98
1.3121
96
NHManchester
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
N JBayshoreReg ional
1
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
N JBayshoreReg ional
2
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
NJCamden
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NJGloucester
1
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
NJGloucester
2
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
NJNorthwestBergen
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NJNorthwestBergen
2
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
-------
Facility ID
Unit
ID
Existing
Control
Devices
Nitrogen Oxides (NOx)
Particulate Matter
(filterable)
Particulate Matter
(PM 2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB 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): 0.61
EG Limit (ng/dscm):
0.056
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve
ment
Needed
Average
(ng/dscm)
%
Improve
ment
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
NJPequannockLincolnF a
irfield
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NJPequannockLincolnF a
irfield
2
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
N J Somers etRaritan
1
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
N J Somers etRaritan
2
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
NY Arlington
1
unknown
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
NYErieCounty
1
vs
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
NYErieCounty
2
vs
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
NYGlensFalls
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NY OneidaCounty
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NY OneidaCounty
2
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NY OneidaCounty
3
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NYPortChester
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NYPortChester
2
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
NY Saratog aCounty
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
OHL ittleMiami
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
OHNEORSDEasterly
1
abo - imp -
wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
PAAlleghenyCounty
001
abd - mc - vs -
imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
PAAlleghenyCounty
002
abd - mc - vs -
imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
PAW yoming Valley
1
vs - imp - wesp
27.93
-126
2.49
-382
2.36
-366
3.30
-566
15.9621
96
0.98
1.3121
96
PRPuertoNuevo
1
vs(ad) - wesp
27.93
-126
2.49
-382
2.36
-366
1.32
-1565
15.9621
96
0.98
1.3121
96
SCFelixCDavis
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
VABlacksburg
1
unknown
27.93
-126
12.44
4
11.80
7
66.05
67
15.9621
96
0.98
1.3121
96
VAHLMooney
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
WAAnacortes
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
WAEdmonds
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
WALynnwood
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
WAWestside
1
vs - imp
27.93
-126
12.44
4
11.80
7
3.30
-566
15.9621
96
0.98
1.3121
96
-------
Table C-la. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Fluidized Bed
Incinerators
Part 3 Red cells indicate where additional control is needed
Facility ID
Unit ID
Existing Control Devices
ACI Performance
Adjustment Factor2
(CDD/CDF TEQ basis)
Max ACI
Adjustment
Factor
MACT Floor Control Needs
(if % improvement >10, add control)
FF
Scrubber
ACI
AKJuneau
1
vs - imp
0.98
0.98
addFF
add ACI
CTMattabassett
1
vs - imp
0.98
0.98
addFF
add ACI
CT SynagroW aterbury
1
vs - imp
0.98
0.98
addFF
add ACI
CTWestHaven
1
vs - imp
0.98
0.98
addFF
add ACI
G AN oondayCreek
1
unknown
0.98
0.98
addFF
add PBS
add ACI
IADubuque
1
cs - vs - pbt
0.98
0.98
addFF
add ACI
IADubuque
2
cs - vs - pbt
0.98
0.98
addFF
add ACI
KSKawPoint
1
vs
0.98
0.98
addFF
add PBS
add ACI
KSKawPoint
2
vs
0.98
0.98
addFF
add PBS
add ACI
LAN ewOrle ansEastBank
1
vs - imp
0.98
0.98
addFF
add ACI
MALynnReg ional
1
vs - imp
0.98
0.98
addFF
add ACI
MALynnReg ional
2
vs - imp
0.98
0.98
addFF
add ACI
MEYpsilanti
EU-FBSSI
vs- imp - wesp - ac polish.
-7.88
-3.21
MN StPaulMetro
FBR1
ac inject - bag - vs(ad) -
wesp
-0.58
-0.52
MN StPaulMetro
FBR2
ac inject - bag - vs(ad) -
wesp
-0.53
-0.51
MN StPaulMetro
FBR3
ac inject - bag - vs(ad) -
wesp
-0.56
-0.51
MOLittleB lue V alley
1
vs - imp
0.98
0.98
addFF
add ACI
MORockCreek
1
vs - imp
0.98
0.98
addFF
add ACI
N CBuncombe Ashville
1
vs - imp
0.98
0.98
addFF
add ACI
NCTZOsborne
ES-1
abd - vs - imp - hss - cs
0.98
0.98
add ACI
NHManchester
1
vs - imp
0.98
0.98
addFF
add ACI
N JBayshoreReg ional
1
vs - imp - wesp
0.98
0.98
add ACI
N JBayshoreReg ional
2
vs - imp - wesp
0.98
0.98
add ACI
NJCamden
1
vs - imp
0.98
0.98
addFF
add ACI
NJGloucester
1
vs - imp - wesp
0.98
0.98
add ACI
NJGloucester
2
vs - imp - wesp
0.98
0.98
add ACI
NJNorthwestBergen
1
vs - imp
0.98
0.98
addFF
add ACI
NJNorthwestBergen
2
vs - imp
0.98
0.98
addFF
add ACI
N JPequannockL incolnFairfield
1
vs - imp
0.98
0.98
addFF
add ACI
N JPequannockL incolnFairfield
2
vs - imp - wesp
0.98
0.98
add ACI
N J SomersetRaritan
1
vs - imp - wesp
0.98
0.98
add ACI
N J SomersetRaritan
2
vs - imp - wesp
0.98
0.98
add ACI
NYArlington
1
unknown
0.98
0.98
addFF
add PBS
add ACI
NYErieCounty
1
vs
0.98
0.98
addFF
add PBS
add ACI
-------
Facility ID
Unit ID
Existing Control Devices
ACI Performance
Adjustment Factor2
(CDD/CDF TEQ basis)
Max ACI
Adjustment
Factor
MACT Floor Control Needs
(if % improvement >10, add control)
FF
Scrubber
ACI
NYErieCounty
2
vs
0.98
0.98
add FF
add PBS
add ACI
NYGlensFalls
1
vs - imp
0.98
0.98
add FF
add ACI
NYOneidaCounty
1
vs - imp
0.98
0.98
add FF
add ACI
NYOneidaCounty
2
vs - imp
0.98
0.98
add FF
add ACI
NYOneidaCounty
3
vs - imp
0.98
0.98
add FF
add ACI
NYPortChester
1
vs - imp
0.98
0.98
add FF
add ACI
NYPortChester
2
vs - imp
0.98
0.98
add FF
add ACI
NYSaratogaCounty
1
vs - imp
0.98
0.98
add FF
add ACI
OHLittleMiami
1
vs - imp
0.98
0.98
add FF
add ACI
OHNEORSD Easterly
1
abo - imp - wesp
0.98
0.98
add FF
add ACI
PAAlleghenyCounty
001
abd - mc - vs - imp
0.98
0.98
add FF
add ACI
PAAlleghenyCounty
002
abd - mc - vs - imp
0.98
0.98
add FF
add ACI
PAWyoming V alley
1
vs - imp - wesp
0.98
0.98
add ACI
PRPuertoNuev o
1
vs(ad) - wesp
0.98
0.98
add ACI
SCFelixCDavis
1
vs - imp
0.98
0.98
add FF
add ACI
VABlacksburg
1
unknown
0.98
0.98
add FF
add PBS
add ACI
VAHLMooney
2
vs - imp
0.98
0.98
add FF
add ACI
WAAnacortes
1
vs - imp
0.98
0.98
add FF
add ACI
WAEdmonds
1
vs - imp
0.98
0.98
add FF
add ACI
WALynnwood
1
vs - imp
0.98
0.98
add FF
add ACI
WAWestside
1
vs - imp
0.98
0.98
add FF
add ACI
NOTE: Data gaps in pollutant concentrations were filled using values found for similar units or using the average concentration over the entire subcategory. For
Dioxin/Furan TEQ concentrations, no data was available for the subcategory, so TEQ concentrations were assumed to be 57% of the TMB values.
1. Assumes that units with a packed bed scrubber or installing a packed bed scrubber will get a 10% Hg reduction.
2. ACI algorithm is based on 90% Hg reduction efficiency and 98% CDD/CDF reduction efficiency. This adjustment factor will be used to adjust total annual
costs to the estimated reduction efficiency needed to meet the floor.
-------
Table C-lb. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Multiple Hearth
Incinerators
Part 1 Red cells indicate where additional control is needed
Facility ID
Unit
ID
Existing
Control Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment
ACI
Performance
Adjustment
Factor2
(Hg Basis)
Nitrogen Oxides
(NOx)
EG Limit (mg/dscm):
0.095
EG Limit (ppmvd):
3900
EG Limit (ppmvd): 1.0
EG Limit (mg/dscm):
0.30
EG Limit (mg/dscm):
0.17
EG Limit (ppmvd):
210
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment
Needed
Average
(ppmvd)
%
Improve
-ment
Needed
AKJohnMAsplund
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
CACentralContraCos
ta
MHF1
abo - cs - vs -
imp
0.0150
-534
836.4
-366
0.79
-26
0.060
-398
0.051
-231
-241
-2.68
127.7
-64
CACentralContraCos
ta
MHF 2
abo - cs - vs -
imp
0.0176
-439
752.1
-419
0.79
-26
0.036
-740
0.065
-160
-170
-1.89
172.3
-22
CAPaloAlto
1
vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
CAPaloAlto
2
vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
CTHartford
001
abo - fgr - vs -
imp
0.0053
-1701
853.9
-357
0.65
-53
0.015
-1873
0.124
-37
-47
-0.52
106.6
-97
CTHartford
002
abo - fgr - vs -
imp
0.0062
-1422
853.9
-357
0.65
-53
0.024
-1144
0.118
-44
-54
-0.60
106.6
-97
CTHartford
3
abo - fgr - vs -
imp
0.0058
-1550
853.9
-357
0.65
-53
0.020
-1426
0.121
-40
-50
-0.56
106.6
-97
CTNaugatuck
1
abo - imp - wesp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
CTNaugatuck
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
CT SynagroNewHav e
n
1
vs - imp - wesp -
rto
0.0178
-433
853.9
-357
0.65
-53
0.038
-684
0.103
-65
-75
-0.83
133.3
-58
GAPresidentStreet
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
GAPresidentStreet
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
GARL Sutton
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
GARL Sutton
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
GARMClayton
1
imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
GARMClayton
2
imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
GAUtoyCreek
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
GAUtoyCreek
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
GA W eyerhaeus er
1
unknown
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
IACedarRapids
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
INBelmontNorth
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.113
-50
-60
-0.67
133.3
-58
INBelmontNorth
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.104
-63
-73
-0.81
133.3
-58
INBelmontNorth
3
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.091
-86
-96
-1.07
133.3
-58
INBelmontNorth
4
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.106
-60
-70
-0.78
133.3
-58
INBelmontNorth
5
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.104
-64
-74
-0.82
133.3
-58
INBelmontNorth
6
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.104
-64
-74
-0.82
133.3
-58
INBelmontNorth
7
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.104
-64
-74
-0.82
133.3
-58
-------
Facility ID
Unit
ID
Existing
Control Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment
ACI
Performance
Adjustment
Factor2
(Hg Basis)
Nitrogen Oxides
(NOx)
EG Limit (mg/dscm):
0.095
EG Limit (ppmvd):
3900
EG Limit (ppmvd): 1.0
EG Limit (mg/dscm):
0.30
EG Limit (mg/dscm):
0.17
EG Limit (ppmvd):
210
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment
Needed
Average
(ppmvd)
%
Improve
-ment
Needed
INBelmontNorth
8
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.104
-64
-74
-0.82
133.3
-58
LANewOrleansEastB
ank
2
unknown
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
MAFitchburgEast
1
vs - wesp - rto
0.0178
-433
853.9
-357
13.09
92
0.038
-684
0.114
-49
-59
-0.66
133.3
-58
MAUpperBlackstone
1
agr - vs - imp -
wesp - rto
0.0042
-2153
27.6
-14042
0.34
-196
0.002
-14243
0.090
-89
-99
-1.10
76.4
-175
MAUpperBlackstone
Inciner
ator 3
agr - vs - imp -
wesp - rto
0.0041
-2231
59.4
-6470
0.31
-218
0.005
-5870
0.065
-160
-170
-1.89
68.5
-206
MDWesternBranch
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MDWesternBranch
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MEAnnArbor
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIBattleCreek
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIBattleCreek
2
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MID etroitComplex 1
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex 1
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex 1
3
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex 1
4
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex 1
5
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex 1
6
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
1
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
2
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
3
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
4
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
5
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
6
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
7
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIDetroitC omplex2
8
hjs - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIFlint
1
abd - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIFlint
2
abd - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIFlint
3
abd - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIFlint
4
abd - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MIPontiacAuburn
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MlWarren
1
imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
MNSeneca
Inciner
ator 1
abo - vs
0.2509
62
1323.3
-195
0.42
-136
0.072
-318
0.301
43
43
0.48
219.3
4
MNSeneca
Inciner
ator 2
abo - vs
0.2509
62
823.7
-373
0.42
-136
0.072
-318
0.301
43
43
0.48
219.3
4
MOBigB lueRiv er
1
abo - vs
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
MOBigBlueRiv er
2
abo - vs
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
MOBigBlueRiv er
3
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOBissellPoint
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
-------
Facility ID
Unit
ID
Existing
Control Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment
ACI
Performance
Adjustment
Factor2
(Hg Basis)
Nitrogen Oxides
(NOx)
EG Limit (mg/dscm):
0.095
EG Limit (ppmvd):
3900
EG Limit (ppmvd): 1.0
EG Limit (mg/dscm):
0.30
EG Limit (mg/dscm):
0.17
EG Limit (ppmvd):
210
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment
Needed
Average
(ppmvd)
%
Improve
-ment
Needed
MOBissellPoint
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOBissellPoint
3
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOBissellPoint
4
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOBissellPoint
5
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOBissellPoint
6
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOLemay
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOLemay
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOLemay
3
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
MOLemay
4
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NCRockyRiver
1
abd - vs - imp -
wesp
0.0178
-433
853.9
-357
0.65
-53
0.038
-684
0.103
-65
-75
-0.83
133.3
-58
NJAtlanticCounty
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NJAtlanticCounty
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NJMountairiV iew
#1
vs - imp - wesp -
rto
0.0003
-28547
38.6
-9995
0.86
-16
0.001
-30629
0.099
-72
-82
-0.91
142.0
-48
NJMountairiV iew
#2
vs - imp - wesp -
rto
0.0003
-28547
38.6
-9995
0.86
-16
0.001
-30629
0.099
-72
-82
-0.91
142.0
-48
N JParsippanyT royHi
lis
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
N JParsippanyT royHi
lis
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NJStonyBrook
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NJStonyBrook
2
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NY Albany C ountyN o
rth
1
vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
NY Albany C ountyN o
rth
2
vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
NY AlbanyC ounty So
uth
1
vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
NY AlbanyC ounty So
uth
2
vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
NY Auburn
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NYBirdlsland
1
abd - vs
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
NYBirdlsland
2
abd - vs
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
NYBirdlsland
3
abd - vs
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
NYFrankEVanLare
1
abo - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
NYFrankEVanLare
2
abo - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
NYFrankEVanLare
3
abo - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
NYNewRochelle
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NYNewRochelle
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NYNorthwestQuadra
nt
1
abo - imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
-------
Facility ID
Unit
ID
Existing
Control Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment
ACI
Performance
Adjustment
Factor2
(Hg Basis)
Nitrogen Oxides
(NOx)
EG Limit (mg/dscm):
0.095
EG Limit (ppmvd):
3900
EG Limit (ppmvd): 1.0
EG Limit (mg/dscm):
0.30
EG Limit (mg/dscm):
0.17
EG Limit (ppmvd):
210
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment
Needed
Average
(ppmvd)
%
Improve
-ment
Needed
NY Orangetown
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NYOssining
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NYOssining
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NY Schenectady
1
imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
NY SouthwestBergen
Point
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
NY SouthwestBergen
Point
2
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
N YT onawanda
1
unknown
0.0446
-113
853.9
-357
13.09
92
0.096
-213
0.114
-49
-59
-0.66
133.3
-58
OHCanton
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHCanton
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHColumbus Souther
ly
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHColumbus Souther
ly
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHColumbus Souther
ly
3
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHColumbus Souther
ly
4
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHEuclid
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHEuclid
2
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHJacksonPike
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHJacksonPike
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHMillCreek
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHMillCreek
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHMillCreek
3
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHMillCreek
4
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHMillCreek
5
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHMillCreek
6
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHNEORSDSouther
ly
1
abo - imp - wesp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHNEORSDSouther
ly
2
abo - imp - wesp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHNEORSDSouther
ly
3
abo - imp - wesp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHNEORSDSouther
ly
4
abo - imp - wesp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHNEORSDWesterl
y
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHNEORSDWesterl
y
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OHW illoughbyEastla
ke
1
imp
0.4456
79
853.9
-357
0.65
-53
0.957
69
0.103
-65
-75
-0.83
133.3
-58
-------
Facility ID
Unit
ID
Existing
Control Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment
ACI
Performance
Adjustment
Factor2
(Hg Basis)
Nitrogen Oxides
(NOx)
EG Limit (mg/dscm):
0.095
EG Limit (ppmvd):
3900
EG Limit (ppmvd): 1.0
EG Limit (mg/dscm):
0.30
EG Limit (mg/dscm):
0.17
EG Limit (ppmvd):
210
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment
Needed
Average
(ppmvd)
%
Improve
-ment
Needed
OH Y oung stown
1
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
OH Young stown
2
abo - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
PAD elawareCounty
Western
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
PAD elawareCounty
Western
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
PAEastNorritonPlym
outhWhitpain
1
cs - vs(ad)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
PAErie
1
vs - wesp
0.0178
-433
853.9
-357
13.09
92
0.038
-684
0.114
-49
-59
-0.66
133.3
-58
PAErie
2
vs - wesp
0.0178
-433
853.9
-357
13.09
92
0.038
-684
0.114
-49
-59
-0.66
133.3
-58
PAHatfield
1
vs - imp - wesp -
rto
0.0178
-433
853.9
-357
0.65
-53
0.038
-684
0.103
-65
-75
-0.83
133.3
-58
PAKiskiValley
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
PAUpperMorelandH
atboro
1
vs - imp - rto
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
RICranston
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
RICranston
2
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
RINewEngland
1
vs - imp - wesp
0.0178
-433
853.9
-357
0.65
-53
0.038
-684
0.103
-65
-75
-0.83
133.3
-58
SCColumbiaMetro
1
abo/fgr - pbs - vs
- imp
0.0025
-3700
63.5
-6045
0.20
-398
0.004
-7886
0.074
-130
-140
-1.56
84.4
-149
SCColumbiaMetro
2
abo/fgr - pbs - vs
- imp
0.0025
-3700
63.5
-6045
0.20
-398
0.004
-7886
0.077
-121
-131
-1.46
84.4
-149
SCPlumlsland
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VAArmyBaseNorfol
k
1
ws - vs - imp
0.1122
15
853.9
-357
0.65
-53
0.398
25
0.130
-31
-41
-0.46
133.3
-58
VAArmyBaseNorfol
k
2
ws - vs - imp
0.1122
15
853.9
-357
0.65
-53
0.398
25
0.130
-31
-41
-0.46
133.3
-58
VABoatHarbor
1
ws - vs - pbs -
vs(ad)
0.0537
-77
3761.0
-4
0.70
-42
0.069
-337
0.107
-59
-59
-0.66
154.5
-36
VABoatHarbor
2
ws - vs - pbs -
vs(ad)
0.0537
-77
3761.0
-4
0.70
-42
0.069
-337
0.107
-59
-59
-0.66
154.5
-36
VAChesapeakeEliza
beth
1
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VAChes ape akeEliza
beth
2
vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VAHopewell
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VANomanCole
1
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VANomanCole
2
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VANomanCole
3
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VANomanCole
4
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VANomanCole
5
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
VANomanCole
6
abd - vs - imp
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-75
-0.83
133.3
-58
V A V irginialnitiativ e
1
ws - vs - imp
0.0745
-28
853.9
-357
0.65
-53
0.461
35
0.064
-166
-176
-1.96
133.3
-58
-------
Facility ID
Unit
ID
Existing
Control Devices
Cadmium (Cd)
Carbon Monoxide
(CO)
Hydrogen Chloride
(HCl)
Lead (Pb)
Mercury (Hg)
WS or PB
Adjustment
ACI
Performance
Adjustment
Factor2
(Hg Basis)
Nitrogen Oxides
(NOx)
EG Limit (mg/dscm):
0.095
EG Limit (ppmvd):
3900
EG Limit (ppmvd): 1.0
EG Limit (mg/dscm):
0.30
EG Limit (mg/dscm):
0.17
EG Limit (ppmvd):
210
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
% Improve-
ment
Needed
Average
(ppmvd)
%
Improve
-ment
Needed
VA V irginialnitiativ e
2
ws - vs - imp
0.0745
-28
853.9
-357
0.65
-53
0.461
35
0.064
-166
-176
-1.96
133.3
-58
VA W illiamsburg
1
vs - imp
0.0323
-194
853.9
-357
0.65
-53
0.097
-210
0.103
-65
-75
-0.83
133.3
-58
VA W illiamsburg
2
vs - imp
0.0323
-194
853.9
-357
0.65
-53
0.097
-210
0.103
-65
-75
-0.83
133.3
-58
WABellinghamPostP
oint
1
vs - imp - wesp
0.0178
-433
853.9
-357
0.65
-53
0.038
-684
0.103
-65
-75
-0.83
133.3
-58
WABellinghamPostP
oint
2
vs - imp - wesp
0.0178
-433
853.9
-357
0.65
-53
0.038
-684
0.103
-65
-75
-0.83
133.3
-58
WIGreeriBayMetro
1
vs(a)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
WIGreeriBayMetro
2
vs(a)
0.0446
-113
853.9
-357
0.65
-53
0.096
-213
0.103
-65
-65
-0.72
133.3
-58
o
to
-------
Table C-lb. Percent Improvement Needed to Meet MACT floor and Additional Controls Required: Multiple Hearth
Incinerators
Part 2 Red cells indicate where additional control is needed
Facility ID
Unit
ID
Existing
Control Devices
Particulate Matter
(filterable)
Particulate Matter
(PM2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB basis)
Total Dioxin/Furans
(TEQ)
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TEQ basis)
EG Limit (mg/dscm): 80
EG Limit (mg/dscm): 58
EG Limit (ppmvd): 26
EG Limit (ng/dscm): 5
EG Limit (ng/dscm):
0.32
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ng/dscm)
%
Improv
ement
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
AK J ohnM Asplund
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CACentralContraCosta
MHF 1
abo - cs - vs -
imp
31.42
-155
21.07
-175
7.27
-258
0.01
-52670
-537.45
0.001
-33797
-344.87
CACentralContraCosta
MHF 2
abo - cs - vs -
imp
25.79
-210
21.07
-175
3.41
-663
0.01
-52670
-537.45
0.001
-33797
-344.87
CAPaloAlto
1
vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CAPaloAlto
2
vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CTHartford
001
abo - fgr - vs -
imp
36.09
-122
8.57
-577
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CTHartford
002
abo - fgr - vs -
imp
36.09
-122
13.77
-321
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CTHartford
3
abo - fgr - vs -
imp
36.09
-122
11.17
-419
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CTNaugatuck
1
abo - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CTNaugatuck
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
CT SynagroN ewHaven
1
vs - imp - wesp -
rto
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
G APres identStreet
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
G APres identStreet
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
GARL Sutton
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
GARL Sutton
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
GARMClayton
1
imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
GARMClayton
2
imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
GAUtoyCreek
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
GAUtoyCreek
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
G A W eyerhaeuser
1
unknown
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
IACedarRapids
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
1
abo - vs - imp
39.79
-101
21.97
-164
26.57
2
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
2
abo - vs - imp
40.31
-98
21.97
-164
23.90
-9
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
3
abo - vs - imp
33.85
-136
21.97
-164
5.51
-372
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
4
abo - vs - imp
17.55
-356
21.97
-164
1.75
-1382
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
5
abo - vs - imp
32.87
-143
21.97
-164
14.43
-80
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
6
abo - vs - imp
32.87
-143
21.97
-164
14.43
-80
0.69
-619
-6.32
0.047
-584
-5.96
INBelmontN orth
7
abo - vs - imp
32.87
-143
21.97
-164
14.43
-80
0.69
-619
-6.32
0.047
-584
-5.96
-------
Facility ID
Unit
ID
Existing
Control Devices
Particulate Matter
(filterable)
Particulate Matter
(PM2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB basis)
Total Dioxin/Furans
(TEQ)
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TEQ basis)
EG Limit (mg/dscm): 80
EG Limit (mg/dscm): 58
EG Limit (ppmvd): 26
EG Limit (ng/dscm): 5
EG Limit (ng/dscm):
0.32
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ng/dscm)
%
Improv
ement
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
INBelmontN orth
8
abo - vs - imp
32.87
-143
21.97
-164
14.43
-80
0.69
-619
-6.32
0.047
-584
-5.96
LAN ewOrle ansEastBank
2
unknown
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
MAFitchburgEast
1
vs - wesp - rto
7.22
-1008
4.39
-1220
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
MAUpperBlackstone
1
agr - vs - imp -
wesp - rto
1.75
-4464
2.62
-2111
1.20
-2067
0.12
-4036
-41.18
0.006
-5068
-51.71
MAUpperBlackstone
Inciner
ator 3
agr - vs - imp -
wesp - rto
1.21
-6528
2.62
-2111
2.70
-864
0.12
-4036
-41.18
0.006
-5068
-51.71
MDWesternBranch
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MDWesternBranch
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MEAnnArbor
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIBattleCreek
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIBattleCreek
2
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplexl
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplexl
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplexl
3
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplexl
4
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplexl
5
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplexl
6
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
1
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
2
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
3
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
4
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
5
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
6
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
7
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MID etroitComplex2
8
hjs - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIFlint
1
abd - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIFlint
2
abd - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIFlint
3
abd - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIFlint
4
abd - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MIPontiacAuburn
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MlWarren
1
imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MNSeneca
Inciner
ator 1
abo - vs
78.76
-2
51.13
-13
18.23
-43
0.76
-559
-5.70
0.069
-362
-3.69
MNSeneca
Inciner
ator 2
abo - vs
76.16
-5
51.13
-13
18.23
-43
0.76
-559
-5.70
0.069
-362
-3.69
MOBigBlueRiver
1
abo - vs
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
MOBigBlueRiver
2
abo - vs
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
MOBigBlueRiver
3
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOBissellPoint
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
-------
Facility ID
Unit
ID
Existing
Control Devices
Particulate Matter
(filterable)
Particulate Matter
(PM2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB basis)
Total Dioxin/Furans
(TEQ)
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TEQ basis)
EG Limit (mg/dscm): 80
EG Limit (mg/dscm): 58
EG Limit (ppmvd): 26
EG Limit (ng/dscm): 5
EG Limit (ng/dscm):
0.32
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ng/dscm)
%
Improv
ement
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
MOBissellPoint
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOBissellPoint
3
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOBissellPoint
4
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOBissellPoint
5
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOBissellPoint
6
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOLemay
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOLemay
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOLemay
3
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
MOLemay
4
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NCRockyRiver
1
abd - vs - imp -
wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N JAtlantic County
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N JAtlantic County
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N JMountainV iew
#1
vs - imp - wesp -
rto
3.93
-1934
4.83
-1101
9.28
-180
0.22
-2156
-22.00
0.014
-2160
-22.04
N JMountain V iew
#2
vs - imp - wesp -
rto
3.93
-1934
4.83
-1101
9.28
-180
0.22
-2156
-22.00
0.014
-2160
-22.04
N JP ars ippanyTroyHills
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N JP ars ippanyTroyHills
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NJStonyBrook
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NJStonyBrook
2
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYAlbanyCountyNorth
1
vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYAlbanyCountyNorth
2
vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYAlbanyCountySouth
1
vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYAlbanyCountySouth
2
vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYAuburn
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYBirdlsland
1
abd - vs
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
NYBirdlsland
2
abd - vs
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
NYBirdlsland
3
abd - vs
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
NYFrankEVanLare
1
abo - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYFrankEVanLare
2
abo - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYFrankEVanLare
3
abo - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N YN ewRochelle
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N YN ewRochelle
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N YN orthwestQuadrant
1
abo - imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NY Orang etown
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYOssining
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NYOssining
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NY Schenectady
1
imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
NY SouthwestBerg enP oint
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
-------
Facility ID
Unit
ID
Existing
Control Devices
Particulate Matter
(filterable)
Particulate Matter
(PM2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB basis)
Total Dioxin/Furans
(TEQ)
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TEQ basis)
EG Limit (mg/dscm): 80
EG Limit (mg/dscm): 58
EG Limit (ppmvd): 26
EG Limit (ng/dscm): 5
EG Limit (ng/dscm):
0.32
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ng/dscm)
%
Improv
ement
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
NY SouthwestBerg enP oint
2
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
N YT onawanda
1
unknown
36.09
-122
21.97
-164
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
OHCanton
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHCanton
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHColumbus Southerly
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHColumbus Southerly
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHColumbus Southerly
3
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHColumbus Southerly
4
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHEuclid
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHEuclid
2
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OH J acks onPike
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OH J acks onPike
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHMillCreek
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHMillCreek
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHMillCreek
3
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHMillCreek
4
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHMillCreek
5
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHMillCreek
6
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHNEORSD Southerly
1
abo - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHNEORSD Southerly
2
abo - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHNEORSD Southerly
3
abo - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHNEORSD Southerly
4
abo - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHNEORSD Westerly
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHNEORSD Westerly
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHWilloughbyEastlake
1
imp
72.19
-11
43.93
-32
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHY oungstown
1
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
OHY oungstown
2
abo - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
P ADelaware CountyW este
rn
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
P ADelaware County Weste
rn
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
PAEastN orritonPlymouth
Whitpain
1
cs - vs(ad)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
PAErie
1
vs - wesp
7.22
-1008
4.39
-1220
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
PAErie
2
vs - wesp
7.22
-1008
4.39
-1220
186.34
86
0.69
-619
-6.32
0.047
-584
-5.96
PAHatfield
1
vs - imp - wesp -
rto
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
PAKiskiValley
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
PAUpperMorelandHatbor
o
1
vs - imp - rto
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
RICranston
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
-------
Facility ID
Unit
ID
Existing
Control Devices
Particulate Matter
(filterable)
Particulate Matter
(PM 2.5)
Sulfur Dioxide (S02)
Total Dioxin/Furans
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TMB basis)
Total Dioxin/Furans
(TEQ)
ACI
Performance
Adjustment
Factor2
(CDD/CDF
TEQ basis)
EG Limit (mg/dscm): 80
EG Limit (mg/dscm): 58
EG Limit (ppmvd): 26
EG Limit (ng/dscm): 5
EG Limit (ng/dscm):
0.32
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(mg/dscm)
%
Improve-
ment
Needed
Average
(ppmvd)
%
Improve-
ment
Needed
Average
(ng/dscm)
%
Improv
ement
Needed
Average
(ng/dscm)
%
Improve-
ment
Needed
RICranston
2
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
RINewEngland
1
vs - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
SCColumbiaMetro
1
abo/fgr - pbs - vs
- imp
11.11
-620
21.97
-164
7.99
-225
2.36
-112
-1.14
0.143
-123
-1.26
SCColumbiaMetro
2
abo/fgr - pbs - vs
- imp
14.63
-447
21.97
-164
7.99
-225
2.36
-112
-1.14
0.143
-123
-1.26
SCPlumlsland
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
V AArmyB aseN orfolk
1
ws - vs - imp
72.18
-11
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
V AArmyB aseN orfolk
2
ws - vs - imp
72.18
-11
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VABoatH arbor
1
ws - vs - pbs -
vs(ad)
57.89
-38
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VABoatH arbor
2
ws - vs - pbs -
vs(ad)
57.89
-38
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VAChesapeakeElizabeth
1
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VAChesapeakeElizabeth
2
vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VAHopewell
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VANomanCole
1
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VANomanCole
2
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VANomanCole
3
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VANomanCole
4
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VANomanCole
5
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VANomanCole
6
abd - vs - imp
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
V A V irg inialnitiative
1
ws - vs - imp
39.44
-103
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
V A Virg inialnitiative
2
ws - vs - imp
39.44
-103
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VAWilliamsburg
1
vs - imp
40.25
-99
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
VAWilliamsburg
2
vs - imp
40.25
-99
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
WABellinghamPostPoint
1
vs - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
WABellinghamPostPoint
2
vs - imp - wesp
7.22
-1008
4.39
-1220
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
WIGreenBayMetro
1
vs(a)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
WIGreenBayMetro
2
vs(a)
36.09
-122
21.97
-164
9.32
-179
0.69
-619
-6.32
0.047
-584
-5.96
-------
Table C-lb. Percent Improvement Needed to Meet MACT floor and Additional Controls
Required: Multiple Hearth Incinerators
Part 3 Red cells indicate where additional control 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
AK J ohnMAsplund
1
vs - imp
-0.83
CACentralContraCosta
MHF1
abo - cs - vs - imp
-2.68
CACentralContraCosta
MHF 2
abo - cs - vs - imp
-1.89
CAP alo Alto
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
CT SynagroNewHaven
1
vs - imp - wesp - rto
-0.83
G APres identStreet
1
vs - imp
-0.83
G APres identStreet
2
vs - imp
-0.83
GARL Sutton
1
abo - vs - imp
-0.83
GARL Sutton
2
abo - vs - imp
-0.83
GARMClayton
1
imp
-0.83
addFF
GARMClayton
2
imp
-0.83
addFF
GAUtoyCreek
1
vs - imp
-0.83
GAUtoyCreek
2
vs - imp
-0.83
G AW ey erhaeus er
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
LAN ewOrleansEastBank
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
MEAnnArbor
1
abd - vs - imp
-0.83
MIBattleCreek
1
abd - vs - imp
-0.83
MIBattleCreek
2
abd - vs - imp
-0.83
MID etroitComplex 1
1
vs - imp
-0.83
MID etroitComplex 1
2
vs - imp
-0.83
MID etroitComplex 1
3
vs - imp
-0.83
MID etroitComplex 1
4
vs - imp
-0.83
MID etroitComplex 1
5
vs - imp
-0.83
MID etroitComplex 1
6
vs - imp
-0.83
MID etroitComplex2
1
hjs - imp
-0.83
addFF
MID etroitComplex2
2
hjs - imp
-0.83
addFF
MID etroitComplex2
3
hjs - imp
-0.83
addFF
MID etroitComplex2
4
hjs - imp
-0.83
addFF
MID etroitComplex2
5
hjs - imp
-0.83
addFF
MID etroitComplex2
6
hjs - imp
-0.83
addFF
MID etroitComplex2
7
hjs - imp
-0.83
addFF
MID etroitComplex2
8
hjs - imp
-0.83
addFF
MIFlint
1
abd - imp
-0.83
addFF
C-18
-------
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
MI Warren
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
N JMountainV ie w
#1
vs - imp - wesp - rto
-0.91
N JMountainV ie w
#2
vs - imp - wesp - rto
-0.91
N JPars ippany T royHills
1
vs - imp
-0.83
N JPars ippany T royHills
2
vs - imp
-0.83
NJStonyBrook
1
abd - vs - imp
-0.83
NJStonyBrook
2
abd - vs - imp
-0.83
NY Albany C ountyN orth
1
vs(ad)
-0.72
NY AlbanyC ountyN orth
2
vs(ad)
-0.72
NY Albany C ounty S outh
1
vs(ad)
-0.72
NY Albany C ounty S outh
2
vs(ad)
-0.72
NY Auburn
1
abd - vs - imp
-0.83
NYBirdlsland
1
abd - vs
-0.66
add PBS
NYBirdlsland
2
abd - vs
-0.66
add PBS
NYBirdlsland
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
addFF
NYSouthwestBergenPoint
1
abd - vs - imp
-0.83
NYSouthwestBergenPoint
2
abd - vs - imp
-0.83
N YT onawanda
1
unknown
-0.66
add PBS
OHCanton
1
vs - imp
-0.83
OHCanton
2
vs - imp
-0.83
OHColumbusSoutherly
1
vs - imp
-0.83
OHColumbusSoutherly
2
vs - imp
-0.83
OHColumbusSoutherly
3
vs - imp
-0.83
OHColumbusSoutherly
4
vs - imp
-0.83
OHEuclid
1
abd - vs - imp
-0.83
OHEuclid
2
abd - vs - imp
-0.83
OHJacksonPike
1
vs - imp
-0.83
C-19
-------
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
abo - vs - imp
-0.83
PADelawareCountyW estern
1
vs - imp
-0.83
PADelawareCountyWestern
vs - imp
-0.83
PAEastNorritonPlymouthWhi
tpain
1
cs - vs(ad)
-0.72
PAErie
1
vs - wesp
-0.66
add PBS
PAErie
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
abd - vs - imp
-0.83
RINewEngland
1
vs - imp - wesp
-0.83
SCColumbiaMetro
1
abo/fgr - pbs - vs - imp
-1.14
SCColumbiaMetro
abo/fgr - pbs - vs - imp
-1.14
SCPlumlsland
1
vs - imp
-0.83
V AArmyBas eN orfolk
1
ws - vs - imp
-0.46
addFF
VAArmyBaseNorfolk
ws - vs - imp
-0.46
addFF
VABoatH arbor
1
ws - vs - pbs - vs(ad)
-0.66
VABoatH arbor
ws - vs - pbs - vs(ad)
-0.66
VAChesapeakeElizabeth
1
vs - imp
-0.83
VAChesapeakeElizabeth
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
V A V irg inialnitiativ e
1
ws - vs - imp
-1.96
V A V irg inialnitiativ e
2
ws - vs - imp
-1.96
VAWilliamsburg
1
vs - imp
-0.83
VAWilliamsburg
2
vs - imp
-0.83
WABellinghamPostPoint
1
vs - imp - wesp
-0.83
WABellinghamPostPoint
2
vs - imp - wesp
-0.83
WIGreenBayMetro
1
vs(a)
-0.72
WIGreenBayMetro
2
vs(a)
-0.72
NOTE: Data gaps in pollutant concentrations were filled using values found for similar units or using the average
concentration over the entire subcategory. For Dioxin/Furan TEQ concentrations, no data was available for the
subcategory, so TEQ concentrations were assumed to be 57% of the TMB values.
1. Assumes that units with a packed bed scrubber or installing a packed bed scrubber will get a 10% Hg reduction.
2. ACI algorithm is based on 90% Hg reduction efficiency and 98% CDD/CDF reduction efficiency. This
adjustment factor will be used to adjust total annual costs to the estimated reduction efficiency needed to meet the
floor.
C-20
-------
Table C-2a. Control Costs: Fabric Filter Cost Algorithm
Parameters/Costs
Equation/Defaults
A. Parameters
1. Incinerator capacity, lb/hr (C)
2. Annual operating hours, hr/yr (H)
3. Exhaust gas flow rate, dscfm (Q)
4. PM concentration, gr/dscf (PM)
5. Water vapor in gas from incinerator (10% by weight)
a. lb/min
= Q / (385 ft3/lb-mol) x (29 lb/lb-mol) x moisture content
(0.10)
b. scfm
= (lb/min) / (18 lb/lb-mol) x (385 ft3/lb-mol)
6. Enthalpy change in quench (1800°F to 300°F)
a. Dry gas from incinerator, Btu/lb air
= [7.010 x (300°F - 77°F) - 7.554 x (1800°F - 77°F)] / (29
lb/lb-mol)
b. Water vapor from incinerator, Btu/lb water vapor
= [8.154 x (300°F - 77°F) - 9.215 x (1800°F - 77°F)] / (18
lb/lb-mol)
c. Total gas stream, Btu/yr
= [(Btu/lb air) x Q / (385 ft3/lb-mol) x (29 lb/lb-mol) x (60
min/hr) x H] + [(Btu/lb water vapor) x Q x (0.00753 lb water
vapor/ft3) x (60 min/hr) x H]
d. Cooling water
i. Heat of vaporization at 77°F, Btu/lb
1,050
ii. Sensible heat for vapor, Btu/lb
85
iii. Total, Btu/lb water
1,135
7. Cooling water evaporated, lb/yr
a. lb/yr
= [enthalpy change (total gas stream, Btu/yr)] / [enthalpy
change (cooling water, Btu/lb)]
b. scfm
= [cooling water evaporated (lb/yr)] / (18 lb/lb-mol) x (385
ft3/lb-mol) / (H * 60 min/hr)
8. Actual gas flow into fabric filter, acfm (AQ)
= [Q + (water vapor in gas from incinerator, scfm) + (water
vapor added in quench, i.e., cooling water evaporated, scfm)] x
[(300°F + 460°F)/528°R]
9. Operating labor rate, $/hr (LR)
$34.60
10. Electricity cost, $/kWh (EC)
$0.07
11. Water cost, $/l,000 gal (WC)
$0.20
12. Compressed air cost, $/l,000 ft3 (CAC)
$0.24
13. Dust disposal cost, $/ton (DDC)
$34.29
14. Capital recovery factors
= [i x (1 + i)a] / [(1 + i)a -1], where i = interest rate, a =
equipment life
a. Bag CRF, 2-yr life, 7% interest
0.55309
b. Cage CRF, 4-yr life, 7% interest
0.29523
c. Equipment CRF, 20-yr life, 7% interest
0.09439
15. Cost index
a. 2008
575.4
b. 1989
357.5
B. Total Capital Investment
1. $
= (47.0 x Q + 306,720) x (1.4 retrofit cost factor) x
(525.4/357.5)
2. $/dscfm
= $ / Q
C. Direct Annual Operating Costs, $/yr
1. Electricity
= (0.746 kW/hp) x hp (0.0072 x Q + 3.20) x H x EC
3. Evaporative cooler water
= (0.1007 x Q + 23.1506) gal/min x (60 min/hr) x H x WC
C-21
-------
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 ft3 air/1,000 ft3 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/ft2) x (525.4/317.4) x (1.08 taxes and freight
ratio)/(3.5 ft/min G/C ratio)
b. Bag replacement labor cost
= AQ x (0.15 hr/bag)/(18 ft2 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 ft2 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 ft2 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 (labor + maintenance materials)
2. Property taxes, insurance, and administration
= 0.04 x TCI
3. Capital recovery
= Equipment CRF x (TCI - bag replacement cost - cage
replacement cost)
E. Total Annual Cost
1. $/yr
= Direct Annual Costs + Indirect Annual Costs
2. ($/yr) / dscfm
= ($/yr) / Q
Sources:
1. Cost equations: Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534] Model
Plant Description and Cost Report (II-A-112); and Dry Injection Fabric Filter Cost Memorandum (IV-B-32).
2. Operating labor rate: Bureau of Labor Statistics, Occupational Employment Statistics, May 2008 National
Industry-Specific Occupational Employment and Wage Estimates
3. Electricity cost: Energy Information Administration. Average Industrial Retail Price of Electricity: October
2009.
4. Water cost: Air Compliance Advisor, version 7.5.
5. Compressed air cost: P2Pays.org. Energy Tips - Compressed Air. Compressed Air Tip Sheet #1. August 2004.
6. Dust disposal cost: NSWMA's 2005 Tip Fee Survey
C-22
-------
Table C-2b. Control Costs: Packed-Bed Scrubber Cost Algorithm
Parameters/Costs
Equations/Defaults
A. Parameters
1. Incinerator capacity, lb/hr (C)
2. Temperature into quench, F (Tl)
130
3. Temperature out of PB to ID fan, F (T2)
4. Annual operating hours, hr/yr (H)
5. Exhaust gas flow rate, dscfm (Qd)
6. Assumed moisture content in gas entering quench, %
(M)
10
7. Exhaust gas flow rate, scfm (Qw)
= (Qd) / (1 - M/100)
8. Water added in quench, scfm (Qh)
= ((7.010 x (Tl - 77°F) - 6.958 x (T2 - 77°F)) x 0.9 + (8.154 x
(Tl - 77°F) - 8.064 x (T2 - 77°F)) x 0.1) x (lb-mole/385 scf) x
Qw/ (1,160 Btu/lb) / (18 lb/lb-mole) x (0.7302 ft3-atm/lb-mol-
°R) x 528°R / 1 atm
9. Actual flow out of PB, acfm (Qa)
= (Qw + Qh) x (460°F + T2)/(528°R)
10. HC1 concentration, ppmvd (HC1)
11. Operating labor rate, $/hr (LR)
$34.60
12. Electricity cost, $/kWh (EC)
$0.07
13. Caustic cost, $/ton (CC)
$357
14. Sewage disposal cost, $/l,000 gal (SDC)
$0.00
15. Water cost, $/l,000 gal (WC)
$0.20
16. Assumed pressure drop through control system,
inches of water (AP)
15
17. Surface area-to-volume ratio for 1" dia. Ceramic
Raschig rings, ft2/ft3 (SAV)
58
18. Minimum packing wetting rate, ft2/hr (WR)
1
19. Water density, lb/ft3 (Wd)
62.4
20. Water circulation flow rate, lb/hr-ft2 (Gs)
= SAV x Wd x WR
21. Estimated column cross-sectional area from separate
analysis, ft2 (A)
19.2
22. Water circulation rate, gpm (GPM)
= Gs x A x (1 hr/60 min) x (1 gal/8.33 lb)
23. Water head, ft of water (Head)
24. Wastewater (blowdown) flow, gpm (B)
= (HC1/1000000) x (Qd) x (lb-mole/385 ft3) x (1 lb-mole
NaCl/1 lb-mole HC1) x (58.2 lb NaCl/lb-mole NaCl) x (1 lb
wastewater/0.1 lbNaCl)x(l 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 AP x H x EC) + (0.000289 x GPM x Head
x H x EC)
C-23
-------
Parameters/Costs
Equations/Defaults
6. Caustic
= HC1 x (3.117E-9) x Q x H x CC
7. Sewage disposal
= B x (60 min/hr) x H x SDC
8. Makeup water
= (B + Qh x (lb-mole/385 scf) x (18 lb/lb-mole) x (gal/8.33
lb)) x (60 min/hr) x H x WC
D. Indirect Annual Costs, $/yr
1. Overhead
= 0.6 x (labor + maintenance materials)
2. Property taxes, insurance, and administration
= 0.04 x TCI
3. Capital recovery
= CRF x TCI
E. Total Annual Cost
1. $/yr
= Direct Annual Costs + Indirect Annual Costs
2. ($/yr)/dscfm
= ($/yr) / Qd
Sources:
1. Cost equations: Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534]-Model
Plant Description and Cost Report (II-A-112); and Wet Scrubber Cost Memorandum (IV-B-30).
2. Operating labor rate: Bureau of Labor Statistics, Occupational Employment Statistics, May 2008 National
Industry-Specific Occupational Employment and Wage Estimates.
3. Electricity cost: Energy Information Administration. Average Industrial Retail Price of Electricity: October 2009.
4. Caustic cost: Purchasing.com. Caustic soda price hike is on the horizon. August 29, 2007.
C-24
-------
Table C-2c. Control Costs: Activated Carbon Injection (ACI) Cost Algorithm
Parameters/Costs
Equations/Defaults
A. Parameters
1. Incinerator capacity, lb/hr (C)
2. Annual operating hours, hr/yr (H)
3. Exhaust gas flow rate, dscfm (Q)
4. Operating labor rate, $/hr (LR)
$34.60
5. Activated carbon cost, $/lb (ACC)
$1.38
6. Dust disposal cost, $/ton (DDC)
$34.29
7. Capital recovery factor, 20-yr equipment life, 7%
interest (CRF)
= [i x (1 + i)a] / [(1 + i)a - 1], where i = interest rate, a =
equipment life
8. Cost index
a. 2008
575.4
b. 1990
361.3
9. ACI Adjustment Factor (AF)
B. Total Capital Investment
1. $
= 4,500 x (Q/l,976)0.6 x (1.2 retrofit factor) x (575.4/361.3)
2. $/dscfm
= $ / Q
C. Direct Annual Costs, $/yr
1. Operating labor
= (0.25 hr/8-hr shift) x H x LR
2. Supervisory labor
= 0.15 x (operating labor)
3. Maintenance
= 0.2 x TCI
4. Activated carbon
= 0.00127 x Q x H x ACC x AF
5. Dust disposal
= 0.00127 x Q x (1 ton/2,000 lb) x H x DDC x AF
D. Indirect Annual Costs, $/yr
1. Overhead
= 0.6 x (labor + maintenance materials)
2. Property taxes, insurance, and administration
= 0.04 x TCI
3. Capital recovery
= CRF x TCI
E. Total Annual Cost
1. $/yr
= Direct Annual Costs + Indirect Annual Costs
2. ($/yr) / dscfm
= ($/yr) / Q
Sources:
1. Cost equations: Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534] Model
Plant Description and Cost Report (II-A-112).
2. Operating labor rate: Bureau of Labor Statistics, Occupational Employment Statistics, May 2007 National
Industry-Specific Occupational Employment and Wage Estimates.
3. Activated carbon cost: The Innovation Group. Chemical Profiles: Carbon, Activated. 2002. Assumed 20% price
increase based on online information from Norit, an activated carbon vendor.
4. Dust disposal cost: NSWMA's 2005 Tip Fee Survey.
C-25
-------
Table C-3. Unit-Specific Inputs Used in Cost Algorithms
Facility ID
Unit
ID
Unit
Type
Capacity
(dtph)
Capacity
(dry lb/hr)
Sludge Feed
Rate
(dry tons/hr)
Sludge Feed
Rate
(dry lb/hr)
Operating
Hours
(hr/yr)
Stack Gas
Flow Rate
(dscfm)
Stack
Gas
Temp1
(°F)
ACI
Adjustment
Factor
Landfill
Tipping
Fee
PM
(gr/dscf)
HC1
(ppmvd)
AKJuneau
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
43.02
0.0054
0.12
CTMattabassett
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
80.40
0.0054
0.12
CTSynagroWaterbury
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
80.40
0.0054
0.12
CTWestHaven
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
80.40
0.0054
0.12
GANoondayCreek
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
35.31
0.0054
2.48
IADubuque
1
FB
1.70
3400
1.28
2550
4200
6956
1050
0.98
39.85
0.0054
0.05
IADubuque
2
FB
1.70
3400
1.28
2550
4200
6956
1050
0.98
39.85
0.0054
0.05
KSKawPoint
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
27.51
0.0054
2.48
KSKawPoint
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
27.51
0.0054
2.48
LANewOrleansEastBank
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
34.61
0.0054
0.12
MALynnRegional
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
84.53
0.0054
0.12
MALynnRegional
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
84.53
0.0054
0.12
MIYpsilanti
EU-
FBSSI
FB
3.46
6920
2.85
5692
3240
14465
1050
-1.86
39.85
0.0013
0.28
MNStPaulMetro
FBR1
FB
5.42
10840
4.19
8372
7270
21898
1050
-0.47
39.85
0.0010
0.17
MNStPaulMetro
FBR2
FB
5.42
10840
3.94
7876
7270
20984
1050
-0.49
39.85
0.0006
0.16
MNStPaulMetro
FBR3
FB
5.42
10840
3.76
7523
7270
19859
1050
-0.48
39.85
0.0024
0.20
MOLittleBlueValley
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
39.85
0.0054
0.12
MORockCreek
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
39.85
0.0054
0.12
NCBuncombeAshville
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
37.45
0.0054
0.12
NCTZOzborne
ES-1
FB
3.25
6500
2.42
4840
8400
10281
1050
1.02
37.45
0.0011
0.04
NHManchester
1
FB
2.00
4000
1.50
3000
8400
8184
1050
0.98
80.40
0.0054
0.12
NJBayshoreRegional
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0011
0.12
NJB ay shoreRegional
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0011
0.12
NJCamden
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
77.04
0.0054
0.12
NJGloucester
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0011
0.12
NJGloucester
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0011
0.12
NJNorthwestBergen
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0054
0.12
NJNorthwestBergen
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0054
0.12
NJPequannockLincolnFairfi
eld
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0054
0.12
NJPequannockLincolnFairfi
eld
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
77.04
0.0011
0.12
NJSomersetRaritan
1
FB
0.65
1300
0.49
975
4200
2660
1050
0.98
77.04
0.0011
0.12
NJSomersetRaritan
2
FB
1.33
2660
1.00
1994
4200
5439
1050
0.98
77.04
0.0011
0.12
NYArlington
1
FB
0.35
700
0.26
525
8400
1432
1050
0.98
59.92
0.0054
2.48
NYErieCounty
1
FB
0.78
1560
0.59
1172
4200
3197
1050
0.98
59.92
0.0054
2.48
NYErieCounty
2
FB
0.78
1560
0.59
1172
4200
3197
1050
0.98
59.92
0.0054
2.48
NYGlensFalls
1
FB
1.54
3080
1.16
2310
8400
6301
1050
0.98
59.92
0.0054
0.12
-------
Facility ID
Unit
ID
Unit
Type
Capacity
(dtph)
Capacity
(dry lb/hr)
Sludge Feed
Rate
(dry tons/hr)
Sludge Feed
Rate
(dry lb/hr)
Operating
Hours
(hr/yr)
Stack Gas
Flow Rate
(dscfm)
Stack
Gas
Temp1
(°F)
ACI
Adjustment
Factor
Landfill
Tipping
Fee
PM
(gr/dscf)
HC1
(ppmvd)
NYOneidaCounty
1
FB
0.84
1680
0.63
1253
8400
3417
1050
0.98
59.92
0.0054
0.12
NYOneidaCounty
2
FB
0.84
1680
0.63
1253
8400
3417
1050
0.98
59.92
0.0054
0.12
NYOneidaCounty
3
FB
0.84
1680
0.63
1253
360
3417
1050
0.98
59.92
0.0054
0.12
NYPortChester
1
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
59.92
0.0054
0.12
NYPortChester
2
FB
2.26
4516
1.69
3387
4200
9240
1050
0.98
59.92
0.0054
0.12
NYSaratogaCounty
1
FB
1.44
2880
1.08
2156
8400
5882
1050
0.98
59.92
0.0054
0.12
OHLittleMiami
1
FB
3.00
6000
2.25
4500
8400
12275
1050
0.98
34.24
0.0054
0.12
OHNEORSDEasterly
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
34.24
0.0011
0.12
PAAllegheny County
001
FB
3.25
6500
1.88
3760
8400
10257
1050
0.98
52.77
0.0054
0.12
PAAllegheny County
002
FB
3.25
6500
1.88
3760
8400
10257
1050
0.98
52.77
0.0054
0.12
PAWyoming Valley
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
52.77
0.0011
0.12
PRPuertoNuevo
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
36.69
0.0011
0.05
SCFelixCDavis
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
38.52
0.0054
0.12
VABlacksburg
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
52.77
0.0054
2.48
VAHLMooney
2
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
52.77
0.0054
0.12
WAAnacortes
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
48.47
0.0054
0.12
WAEdmonds
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
48.47
0.0054
0.12
WALynnwood
1
FB
2.26
4516
1.69
3387
8400
9240
1050
0.98
48.47
0.0054
0.12
WAWestside
1
FB
2.42
4840
1.81
3625
8400
9888
1050
0.98
48.47
0.0054
0.12
AKJohnMAsplund
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
43.02
0.0158
0.65
CACentralContraCosta
MHF 1
MH
2.50
5000
1.95
3900
4200
23132
1050
-2.61
43.02
0.0137
0.79
CACentralContraCosta
MHF 2
MH
2.50
5000
1.54
3085
4200
22925
1050
-1.78
43.02
0.0113
0.79
CAPaloAlto
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
43.02
0.0158
0.65
CAPaloAlto
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
43.02
0.0158
0.65
CTHartford
001
MH
2.50
5000
2.38
4759
6016
17217
1050
-0.41
80.40
0.0158
0.65
CTHartford
002
MH
2.50
5000
2.30
4603
6016
16360
1050
-0.49
80.40
0.0158
0.65
CTHartford
3
MH
2.50
5000
1.88
3750
360
18080
1050
-0.44
80.40
0.0158
0.65
CTNaugatuck
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
80.40
0.0032
0.65
CTNaugatuck
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
80.40
0.0158
0.65
CTSynagroNewHaven
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
80.40
0.0032
0.65
GAPresidentStreet
1
MH
0.38
760
0.29
576
4200
2777
1050
-0.72
35.31
0.0158
0.65
GAPresidentStreet
2
MH
0.38
760
0.29
576
4200
2777
1050
-0.72
35.31
0.0158
0.65
GARL Sutton
1
MH
0.25
500
0.19
375
4200
1808
1050
-0.72
35.31
0.0158
0.65
GARL Sutton
2
MH
0.25
500
0.19
375
4200
1808
1050
-0.72
35.31
0.0158
0.65
GARMClayton
1
MH
1.25
2500
0.94
1875
4200
9040
1050
-0.72
35.31
0.0315
0.65
GARMClayton
2
MH
1.25
2500
0.94
1875
4200
9040
1050
-0.72
35.31
0.0315
0.65
GAUtoyCreek
1
MH
1.75
3500
1.31
2625
4200
12656
1050
-0.72
35.31
0.0158
0.65
GAUtoyCreek
2
MH
1.75
3500
1.31
2625
4200
12656
1050
-0.72
35.31
0.0158
0.65
GAWeyerhaeuser
1
MH
3.52
7040
2.64
5279
8400
25454
1050
-0.66
35.31
0.0158
13.09
IACedarRapids
1
MH
3.92
7840
2.94
5880
8400
28349
1050
-0.72
39.85
0.0158
0.65
-------
Facility ID
Unit
ID
Unit
Type
Capacity
(dtph)
Capacity
(dry lb/hr)
Sludge Feed
Rate
(dry tons/hr)
Sludge Feed
Rate
(dry lb/hr)
Operating
Hours
(hr/yr)
Stack Gas
Flow Rate
(dscfm)
Stack
Gas
Temp1
(°F)
ACI
Adjustment
Factor
Landfill
Tipping
Fee
PM
(gr/dscf)
HC1
(ppmvd)
INBelmontNorth
1
MH
2.60
5200
2.03
4060
4200
7085
1050
-0.67
31.64
0.0174
0.65
INBelmontNorth
2
MH
2.60
5200
2.15
4293
4200
19574
1050
-0.70
31.64
0.0176
0.65
INBelmontNorth
3
MH
2.60
5200
2.12
4233
4200
7888
1050
-0.96
31.64
0.0148
0.65
INBelmontNorth
4
MH
2.60
5200
2.09
4187
4200
20699
1050
-0.67
31.64
0.0077
0.65
INBelmontNorth
5
MH
2.00
4000
1.50
3000
4200
7662
1050
-0.71
31.64
0.0144
0.65
INBelmontNorth
6
MH
2.00
4000
1.50
3000
4200
20410
1050
-0.71
31.64
0.0144
0.65
INBelmontNorth
7
MH
2.00
4000
1.50
3000
4200
7413
1050
-0.71
31.64
0.0144
0.65
INBelmontNorth
8
MH
2.00
4000
1.50
3000
4200
20185
1050
-0.71
31.64
0.0144
0.65
LANewOrleansEastBank
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.66
34.61
0.0158
13.09
MAFitchburgEast
1
MH
2.30
4600
1.72
3443
8400
16597
1050
-0.66
84.53
0.0032
13.09
MAUpperBlackstone
1
MH
3.00
6000
1.79
3587
8544
6271
1050
-0.99
84.53
0.0008
0.34
MAUpperBlackstone
Inciner
ator 3
MH
3.00
6000
1.96
3921
216
14421
1050
-1.67
84.53
0.0005
0.31
MDWesternBranch
1
MH
1.08
2160
0.81
1620
4200
7810
1050
-0.72
55.64
0.0158
0.65
MDWesternBranch
2
MH
1.08
2160
0.81
1620
4200
7810
1050
-0.72
55.64
0.0158
0.65
MIAnnArbor
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
39.85
0.0158
0.65
MIBattleCreek
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIBattleCreek
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplexl
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplexl
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplexl
3
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplexl
4
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplexl
5
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplexl
6
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MIDetroitComplex2
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
3
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
4
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
5
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
6
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
7
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIDetroitComplex2
8
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIFlint
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIFlint
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIFlint
3
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIFlint
4
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0315
0.65
MIPontiacAuburn
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
39.85
0.0158
0.65
MlWarren
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
39.85
0.0315
0.65
MNSeneca
Inciner
ator 1
MH
1.58
3160
1.34
2676
4000
16607
1050
0.48
39.85
0.0344
0.42
-------
Facility ID
Unit
ID
Unit
Type
Capacity
(dtph)
Capacity
(dry lb/hr)
Sludge Feed
Rate
(dry tons/hr)
Sludge Feed
Rate
(dry lb/hr)
Operating
Hours
(hr/yr)
Stack Gas
Flow Rate
(dscfm)
Stack
Gas
Temp1
(°F)
ACI
Adjustment
Factor
Landfill
Tipping
Fee
PM
(gr/dscf)
HC1
(ppmvd)
MNSeneca
Inciner
ator 2
MH
1.58
3160
1.42
2843
4000
15606
1050
0.37
39.85
0.0333
0.42
MOBigBlueRiver
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.66
39.85
0.0158
13.09
MOBigBlueRiver
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.66
39.85
0.0158
13.09
MOBigBlueRiver
3
MH
2.69
5381
2.02
4036
360
19458
1050
-0.72
39.85
0.0158
0.65
MOBissellPoint
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOBissellPoint
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOBissellPoint
3
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOBissellPoint
4
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOBissellPoint
5
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOBissellPoint
6
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOLemay
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOLemay
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOLemay
3
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
MOLemay
4
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
39.85
0.0158
0.65
NCRockyRiver
1
MH
2.97
5940
2.23
4452
8400
21464
1050
-0.72
37.45
0.0032
0.65
NJAtlanticCounty
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
77.04
0.0158
0.65
N JAtl antic C ounty
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
77.04
0.0158
0.65
NJMountain V iew
#1
MH
0.80
1600
0.80
1597
2715
7698
1050
-0.80
77.04
0.0017
0.86
NJMountain V iew
#2
MH
0.80
1600
0.80
1597
2715
9267
1050
-0.80
77.04
0.0017
0.86
NJParsippanyTroyHills
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
77.04
0.0158
0.65
NJParsippanyTroyHills
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
77.04
0.0158
0.65
NJStonyBrook
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
77.04
0.0158
0.65
NJStonyBrook
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
77.04
0.0158
0.65
NYAlbanyCountyNorth
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYAlbanyCountyNorth
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYAlbanyCounty South
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYAlbanyCounty South
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYAubum
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
59.92
0.0158
0.65
NYBirdlsland
1
MH
14.04
28080
10.53
21063
8400
101548
1050
-0.66
59.92
0.0158
13.09
NYBirdlsland
2
MH
14.04
28080
10.53
21063
8400
101548
1050
-0.66
59.92
0.0158
13.09
NYBirdlsland
3
MH
14.04
28080
10.53
21063
360
101548
1050
-0.66
59.92
0.0158
13.09
N YFrankE V anLare
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
59.92
0.0315
0.65
N YFrankE V anLare
2
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
59.92
0.0315
0.65
N YFrankE V anLare
3
MH
2.69
5381
2.02
4036
360
19458
1050
-0.72
59.92
0.0315
0.65
NYNewRochelle
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYNewRochelle
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYNorthwestQuadrant
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
59.92
0.0315
0.65
NYOrangetown
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
59.92
0.0158
0.65
NYOssining
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
NYOssining
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
59.92
0.0158
0.65
-------
Facility ID
Unit
ID
Unit
Type
Capacity
(dtph)
Capacity
(dry lb/hr)
Sludge Feed
Rate
(dry tons/hr)
Sludge Feed
Rate
(dry lb/hr)
Operating
Hours
(hr/yr)
Stack Gas
Flow Rate
(dscfm)
Stack
Gas
Temp1
(°F)
ACI
Adjustment
Factor
Landfill
Tipping
Fee
PM
(gr/dscf)
HC1
(ppmvd)
NYSchenectady
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
59.92
0.0315
0.65
NYSouthwestBergenPoint
1
MH
4.92
9840
3.69
7375
4200
35557
1050
-0.72
59.92
0.0158
0.65
NYSouthwestBergenPoint
2
MH
4.92
9840
3.69
7375
4200
35557
1050
-0.72
59.92
0.0158
0.65
NYTonawanda
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.66
59.92
0.0158
13.09
OHCanton
1
MH
1.08
2160
0.81
1620
4200
7810
1050
-0.72
34.24
0.0158
0.65
OHCanton
2
MH
1.08
2160
0.81
1620
4200
7810
1050
-0.72
34.24
0.0158
0.65
OHColumbusSoutherly
1
MH
3.00
6000
2.25
4500
4200
21696
1050
-0.72
34.24
0.0158
0.65
OHColumbusSoutherly
2
MH
3.00
6000
2.25
4500
4200
21696
1050
-0.72
34.24
0.0158
0.65
OHColumbusSoutherly
3
MH
3.00
6000
2.25
4500
4200
21696
1050
-0.72
34.24
0.0158
0.65
OHColumbusSoutherly
4
MH
3.00
6000
2.25
4500
4200
21696
1050
-0.72
34.24
0.0158
0.65
OHEuclid
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
34.24
0.0158
0.65
OHEuclid
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
34.24
0.0158
0.65
OHJacksonPike
1
MH
2.32
4640
1.74
3486
4200
16807
1050
-0.72
34.24
0.0158
0.65
OHJacksonPike
2
MH
2.32
4640
1.74
3486
4200
16807
1050
-0.72
34.24
0.0158
0.65
OHMillCreek
1
MH
4.00
8000
3.00
6000
4200
28928
1050
-0.72
34.24
0.0158
0.65
OHMillCreek
2
MH
4.00
8000
3.00
6000
4200
28928
1050
-0.72
34.24
0.0158
0.65
OHMillCreek
3
MH
4.00
8000
3.00
6000
4200
28928
1050
-0.72
34.24
0.0158
0.65
OHMillCreek
4
MH
4.00
8000
3.00
6000
4200
28928
1050
-0.72
34.24
0.0158
0.65
OHMillCreek
5
MH
4.00
8000
3.00
6000
4200
28928
1050
-0.72
34.24
0.0158
0.65
OHMillCreek
6
MH
4.00
8000
3.00
6000
4200
28928
1050
-0.72
34.24
0.0158
0.65
OHNEORSDSoutherly
1
MH
3.60
7200
2.70
5400
4200
26035
1050
-0.72
34.24
0.0032
0.65
OHNEORSDSoutherly
2
MH
3.60
7200
2.70
5400
4200
26035
1050
-0.72
34.24
0.0032
0.65
OHNEORSDSoutherly
3
MH
3.60
7200
2.70
5400
4200
26035
1050
-0.72
34.24
0.0032
0.65
OHNEORSDSoutherly
4
MH
3.60
7200
2.70
5400
4200
26035
1050
-0.72
34.24
0.0032
0.65
OHNEORSDWesterly
1
MH
1.79
3580
1.34
2685
4200
12945
1050
-0.72
34.24
0.0158
0.65
OHNEORSDWesterly
2
MH
1.79
3580
1.34
2685
4200
12945
1050
-0.72
34.24
0.0158
0.65
OHWilloughbyEastlake
1
MH
3.42
6840
2.57
5130
8400
24733
1050
-0.72
34.24
0.0315
0.65
OHYoungstown
1
MH
2.00
4000
1.50
3000
4200
14464
1050
-0.72
34.24
0.0158
0.65
OHYoungstown
2
MH
2.00
4000
1.50
3000
4200
14464
1050
-0.72
34.24
0.0158
0.65
PADelawareCounty Western
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
52.77
0.0158
0.65
PADelawareCounty Western
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
52.77
0.0158
0.65
PAEastNorritonPlymouthW
hitpain
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
52.77
0.0158
0.65
PAErie
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.66
52.77
0.0032
13.09
PAErie
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.66
52.77
0.0032
13.09
PAHatfield
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
52.77
0.0032
0.65
PAKiskiValley
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
52.77
0.0158
0.65
PAUpperMorelandHatboro
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
52.77
0.0158
0.65
RICranston
1
MH
0.95
1900
0.71
1424
4200
6863
1050
-0.72
48.48
0.0158
0.65
RICranston
2
MH
1.98
3960
1.48
2968
4200
14311
1050
-0.72
48.48
0.0158
0.65
RINewEngland
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
48.48
0.0032
0.65
-------
Facility ID
Unit
ID
Unit
Type
Capacity
(dtph)
Capacity
(dry lb/hr)
Sludge Feed
Rate
(dry tons/hr)
Sludge Feed
Rate
(dry lb/hr)
Operating
Hours
(hr/yr)
Stack Gas
Flow Rate
(dscfm)
Stack
Gas
Temp1
(°F)
ACI
Adjustment
Factor
Landfill
Tipping
Fee
PM
(gr/dscf)
HC1
(ppmvd)
SCColumbiaMetro
1
MH
1.08
2160
0.89
1773
7300
4620
1050
-1.44
38.52
0.0049
0.20
SCColumbiaMetro
2
MH
1.08
2160
0.68
1351
7300
5145
1050
-1.34
38.52
0.0064
0.20
SCPlumlsland
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
38.52
0.0158
0.65
VAArmyBaseNorfolk
1
MH
1.50
3000
0.82
1648
4200
8455
1050
-0.34
52.77
0.0315
0.65
VAArmyBaseNorfolk
2
MH
1.50
3000
1.13
2250
4200
10848
1050
-0.34
52.77
0.0315
0.65
VABoatHarbor
1
MH
1.79
3580
1.64
3278
4200
11399
1050
-0.66
52.77
0.0253
0.70
VABoatHarbor
2
MH
1.79
3580
1.34
2688
4200
12957
1050
-0.66
52.77
0.0253
0.70
VAChesapeakeElizabeth
1
MH
1.50
3000
1.12
2236
4200
7908
1050
-0.72
52.77
0.0158
0.65
VAChesapeakeElizabeth
2
MH
1.50
3000
1.13
2250
4200
10848
1050
-0.72
52.77
0.0158
0.65
VAHopewell
1
MH
2.69
5381
2.02
4036
8400
19458
1050
-0.72
52.77
0.0158
0.65
VANomanCole
1
MH
1.88
3760
1.41
2813
4200
13560
1050
-0.72
52.77
0.0158
0.65
VANomanCole
2
MH
1.88
3760
1.41
2813
4200
13560
1050
-0.72
52.77
0.0158
0.65
VANomanCole
3
MH
3.83
7660
2.88
5750
4200
27722
1050
-0.72
52.77
0.0158
0.65
VANomanCole
4
MH
3.83
7660
2.88
5750
4200
27722
1050
-0.72
52.77
0.0158
0.65
VANomanCole
5
MH
1.58
3160
1.19
2375
4200
11450
1050
-0.72
52.77
0.0158
0.65
VANomanCole
6
MH
1.58
3160
1.19
2375
4200
11450
1050
-0.72
52.77
0.0158
0.65
VAVirginialnitiative
1
MH
1.88
3760
2.08
4164
4200
16867
1050
-1.84
52.77
0.0172
0.65
VAVirginialnitiative
2
MH
1.88
3760
1.41
2813
4200
13560
1050
-1.84
52.77
0.0172
0.65
VAWilliamsburg
1
MH
1.96
3920
1.55
3101
4200
6501
1050
-0.72
52.77
0.0176
0.65
VAWilliamsburg
2
MH
1.96
3920
1.47
2938
4200
14162
1050
-0.72
52.77
0.0176
0.65
WABellinghamPostPoint
1
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
48.47
0.0032
0.65
WABellinghamPostPoint
2
MH
2.69
5381
2.02
4036
4200
19458
1050
-0.72
48.47
0.0032
0.65
WIGreenB ay Metro
1
MH
1.23
2460
0.92
1848
4200
8910
1050
-0.72
39.59
0.0158
0.65
WIGreenB ay Metro
2
MH
1.23
2460
0.92
1848
4200
8910
1050
-0.72
39.59
0.0158
0.65
1. Assumed average gas temperature used for incinerators (CISWI)
-------
Table C-4b. Monitoring Costs
Parameters/Costs
Equation
Based on Default Parameters
Based on Default Parameters
and hr/yr
Wet
Bag Leak Scrubber
Detector 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)
= [ix(l+i)a]/[(l+i)a-l],
where i = interest rate, a =
equipment life
0.09439
0.09439
0.09439
B. Total Capital Investment, $
(TCI)
1. Planning
$800
$700
2. Select type of equipment
$4,500
$400
3. Provide support facilities
$500
$1,500
4. Purchased equipment cost
(PEC)
$14,100
$19,300
5. Install and check equipment
$4,800
$1,000
6. Perf. spec, tests (certif.)
$0
$700
7. Prepare QA/QC plan
$800
$700
8. Total capital cost
= Planning + selecting
equipment + support facilities
+ PEC + installation + perf.
spec, tests + QA/QC plan
$25,500
$24,300
C. Annual Costs, $/yr
1. Operating labor
= (5 min to record lime/carbon
flow/4-hr period) x (1 hr/60
min) x H x LR
= (5 min to record lime/carbon
flow/4-hr period) x (1 hr/60
min) x H x LR
2. Maintenance materials
= 0.02 x TCI
$500
3. Operation & maintenance
= Day-to-day activities +
annual RATA + CGA + annual
QA + O&M review and update
$6,000
4. Recordkeeping and reporting
= $1,000 x (525.4/386.5)
$200
$1,500
5. Overhead
= 0.6 x (labor + maintenance
materials)
$300
= 0.6 x (labor + maintenance
materials)
6. Property taxes, insurance,
and administration
= 0.04 x TCI
$1,000
7. Capital recovery
= CRF x TCI
$3,500
$2,300
8. Total annual cost
= Operating labor +
maintenance materials +
recordkeeping and reporting +
overhead + property taxes,
insurance, and administration +
capital recovery
$9,700
$5,600
= Operating labor +
maintenance materials +
recordkeeping and reporting +
overhead + property taxes,
insurance, and administration
+ capital recovery
Notes:
1. Monitoring costs have been rounded to the nearest $100 to be consistent with level of rounding in original costs.
C-32
-------
2. Costs to be replaced include: (a) bag leak detector replacing opacity test; (b) CO CEMS replacing CO test and
secondary chamber temperature monitor;(c) HC1 CEMS replacing HC1 test, HC1 sorbent monitor (dry scrubbers)
and scrubber liquor pH monitor (wet
Sources:
1. Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR-2006-0534] Testing and Monitoring
Options and Costs Memo (IV-B-66).
2. E-mail and attachment from Peter Westlin, EPA, to Mary Johnson, EPA. August 19, 2008. Monitoring Options
for SNCR on Medical Waste Incinerators.
3. E-mail from Dan Bivins, EPA, to Mary Johnson, EPA. September 27, 2006. Cost of CO CEMS.
4. E-mail from Dan Bivins, EPA, to Mary Johnson, EPA. July 28, 2006. Some Preliminary Thoughts on the HWI
Monitoring.
Table C-4c. Stack Testing Costs
Parameters/Costs
Equation
Values
A. Parameters
1. Cost index
a. 2008
575.4
d. 1992
358.2
B. Testing Costs, $
1. Method 5 (PM)
= $8,000 x (575.4/358.2)
$13,000
2. Method 9 (opacity)
= $1,000 x (575.4/358.2) + $1,500
$3,500
3. Method 10 (CO)
= $4,000 x (575.4/358.2) + $1,000
$7,000
4. Method 26 (HC1)
= $5,000 x (575.4/358.2)
$8,000
5. Method 29 (metals)
= $8,000 x (575.4/358.2) + $2,000
$15,000
6. Method 23 (CDD/CDF)
= $21,000 x (575.4/358.2) - $5,000
$29,000
7. Method 7E (NOx)
= $5,000 x (575.4/358.2)
$8,000
8. Method 6C (S02)
= $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)A15)/((1+0.07)A15-1)
0.10979
Note:
1. Initial testing costs to be annualized over 15 years at 7% interest.
2. Testing costs have been rounded to the nearest $1,000 (except for opacity) to be consistent with level of rounding
in original costs; costs also adjusted based on additional information from EPA.
3. Multiple test costs adjusted by 2/3 in nationwide cost estimates to account for travel, accommodations,
methods/sampling trains, etc. common to the tests.
Sources:
1. Memorandum from R. Segall, EPA/EMB, to R. Copland, EPA/SDB. October 14, 1992. Medical Waste
Incinerator Study: Emission Measurement and Continuous Monitoring. (II-B-89)
2. E-mail from Jason Dewees, EPA, to Peter Westlin, EPA. August 20, 2008. Monitoring Options for SNCR &
Test Cost Questions.
3. E-mail from Jason Dewees, EPA, to Mary Johnson, EPA. August 20, 2008. Re: Monitoring Options for SNCR
& Test Cost Questions.
C-33
-------
Table C-4d. Visible Emissions Testing Costs
Parameters/Costs
Equation
Values
A. Parameters
1. Operating labor rate, $/hr (LR)
$34.60
2. Capital recovery factor, 5-yr
equipment life, 7% interest (CRF)
= [i x (1 + i)a] / [(1 + i)a -1], where i = interest rate, a =
equipment life
0.24389
B. Total Capital Investment, $ (TCI)
= Combination light meter/anemometer ($200) + digital
stopwatches (2 each at $25)
$250
C. Direct Annual Costs, $/yr
1. Operating labor
= (1 hr/reading) x (3 readings/test) x (1 test/yr) x LR
$104
D. Indirect Annual Costs, $/yr
1. Overhead
= 0.6 x (operating labor)
$62
2. Property taxes, insurance, and
administration
= 0.04 x TCI
$10
3. Capital recovery
= CRF x TCI
$61
E. Total Annual Cost, $/yr (rounded)
= Direct Annual Costs + Indirect Annual Costs
$200
Sources:
1. Professional Equipment. 2008. Light Meters Industrial and Professional: Digital Light Meter. Website:
http://www.professionalequipment.com.
Accessed July 24, 2008.
2. Cole-Parmer. 2008. Digital Stopwatches-Cole Parmer Instrument Catalog. Website:
http://www.coleparmer.com. Accessed July 24, 2008.
C-34
-------
Table C-4e. Recordkeeping and Reporting Costs
Burden item
(A)
Person-
hours per
occurrence
(B)
Number of
occurrences
per year
(C)
Technical
person-hours
per year
(C = A x B)
(D)
Management
person-hours
per year
(D = C x 0.05)
(E)
Clerical person-
hours per year
(E = C x 0.1)
(F)
Total person-
hours per year
(F = C + D + E)
(G)
Cost, $
A. Applications
N/A
B. Surveys and Studies
N/A
C. Reporting Requirements
1. Read instructions
1.0
1
1.0
0.05
0.1
1.2
$41
2. Required activities
a. Perf. spec, tests (certif.) for CMS
17
1
17
0.9
1.7
20
$696
3. Write report
a. Notification of initial performance test
j Pollutants, fugitive ash emissions
2.0
1
2.0
0.1
0.2
2.3
$82
jj Fugitive ash emissions
1.0
1
1.0
0.05
0.1
1.2
$41
Notification of initial CMS
demonstration
2.0
1
2.0
0.1
0.2
2.3
$82
c. Report of initial performance test
i. Pollutants, fugitive ash emissions
8.0
1
8.0
0.4
0.8
9.2
$328
ii. Fugitive ash emissions
2.0
1
2.0
0.1
0.2
2.3
$82
Report of initial CMS demonstration
Incl. in C2
' e. Annual report
j Results of performance tests
conducted during the year
40
1
40
2.0
4.0
46
$1,638
D. Recordkeeping Requirements
1. Read instructions
Incl. in CI
2. Plan activities
N/A
3. Implement activities
N/A
4. Develop record system
N/A
5. Time to enter information
a. Records of initial performance test
Incl. in C3
Records of annual and any subsequent
(fempliance tests
Incl. in C3
E. Total Labor Burden and Cost
73
3.7
7.3
84
$2,989
Notes:
1. Industry costs are based on the following hourly rates: technical at $34.60, management at $82.23, and clerical at $22.32 (see table below). The composite
hourly labor rate is ($34.60/hr) + (0.05 x $82.23/hr) + (0.1 x $22.32/hr) = $40.94/hr. Labor
-------
2. Person-hours per occurrence for CMS performance specification costs are based on the performance specification costs to certify CMS ($700) divided by the
composite hourly labor rate ($40.94/hr).
3. Control device inspection cost already accounted for under monitoring costs.
4. Assume 8 hours for each facility to review the report of the initial performance test for pollutants and fugitive ash.
5. Assume 2 hours for each facility to review the report of the initial performance test for fugitive ash.
6. Assume 40 hours to review report of annual PM, CO, and HC1 compliance reports.
7. The average recurrent burden and cost in the first 3 years after promulgation for the sources with recurrent burden are equal to the person-hours added down
each column for technical, management, and clerical and the sum of the cost column.
Sources:
1. Bureau of Labor Statistics, Occupational Employment Statistics, May 2008 National Industry-Specific Occupational Employment and Wage Estimates.
2. Hospital/Medical/Infectious Waste Incinerators (HMIWI) [EPA-HQ-OAR2006-0534] Testing and Monitoring Options and Costs Memo (IV-B-66).
Labor Rates:
Parameter
Pulp, Paper and
Paperboard Mills
Pipeline
Transportatio
n
Cement and
Concrete
Product
Manufacturing
Pharma-ceutical &
Medicine Manufacturing
Total
Loaded
1. Technical - Stationary Engineers & Boiler Operators
$19.14
$21.11
$18.88
$27.36
$21.62
$34.60
2. Management - Engineering Managers
$41.41
$58.22
$43.90
$62.05
$51.40
$82.23
3. Clerical - Office Clerks, General
$14.21
$14.15
$12.58
$14.85
$13.95
$22.32
4. Composite labor rate
$40.94
-------
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 (S/ton)1
B. Annual Costs, $/ton
50 mile round trip
= $0.266/ton-mile x 50 miles + landfill tip fee
100 mile round trip
= $0.266/ton-mile x 100 miles + landfill tip fee
200 mile round trip
= $0.266/ton-mile x 200 miles + landfill tip fee
C. Annual Costs (with landfill tip fee), $/yr
50 mile round trip
= Total annual cost x (C x 0.67) x H
100 mile round trip
200 mile round trip
Sources:
1. State average tipping fees from BioCycle December 2008, Vol 49, No. 12, P. 22, Table 5.
Where state data unavailable, NSWMA's 2005 Tip Fee Survey regional averages were used.
For Puerto Rico, NSWMA national U.S. average used. All values corrected to 2008 dollars using
CPI data from Bureau of Labor Statistics: http://data.bls.gov/cgi-bin/cpicalc.pl
Unit-specific tipping fees are listed in Table 5.
2. Hauling cost: U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau
of Transportation Statistics, Table 3-17: Average Freight Revenue Per Ton-mile.
Assumed 50, 100, or 150 mile/trip to reach nearest landfill.
Table C-5b. Reported Operating Costs and Calculated Cost Factors
Feedrate
Total Annual Cost
Cost Factor
Facility ID
Unit ID
Unit Type
(dry tons/year)
To Operate Unit
($/dry ton)
MIYpsilanti
EU-FBSSI
FB
9,221.27
MNStPaulMetro
FBR1
FB
30,433.03
2,633,334.00
86.53
MNStPaulMetro
FBR2
FB
28,630.10
2,633,334.00
91.98
MNStPaulMetro
FBR3
FB
27,346.28
2,633,334.00
96.30
NCTZOsborne
ES-1
FB
20,328.00
1,128,240.75
55.50
PAAlleghenyCounty
001
FB
15,792.00
2,783,333.00
176.25
PAAlleghenyCounty
002
FB
15,792.00
2,783,333.00
176.25
CACentralContraCosta
MHF 1
MH
8,190.00
4,760,351.00
581.24
CACentralContraCosta
MHF 2
MH
6,478.15
4,760,351.00
734.83
CTHartford
001
MH
14,314.07
1,137,000.00
79.43
CTHartford
002
MH
13,844.82
1,137,000.00
82.12
MAUpperBlackstone
1
MH
15,322.20
1,513,370.00
98.77
MNSeneca
Incinerator 1
MH
5,352.73
950,000.00
177.48
MNSeneca
Incinerator 2
MH
5,685.93
950,000.00
167.08
NJMountainView
#1
MH
2,167.48
1,410,554.67
650.78
NJMountainView
#2
MH
2,167.48
1,410,554.67
650.78
SCColumbiaMetro
1
MH
6,472.39
1,116,666.67
172.53
SCColumbiaMetro
2
MH
4,932.37
1,116,666.67
226.40
C-37
-------
Cost Factors*
FB
Minimum ($/dry ton)
55.50
Average ($/dry ton)
113.80
MH
Minimum ($/dry ton)
79.43
Average ($/dry ton)
329.22
* Cost factors were multiplied with the average feedrates determined for each unit in order to estimate the annual
cost to operate it.
C-38
-------
Table C-5c. Alternative Disposal Cost Option: Sludge Storage Cost
FacilitylD
UnitID
Unit
Type
ControlCategory
Capacity
(dtph)
Assigned
Capacity
Assigned
tons/day8
Assigned
cubic
yards/
dayb
Assigned
4-day
Capacity
(ft3)
Pad
size if
6* deep
storage
(ft2)
Rail
Length
c(ft)
Aluminum
Sheet Aread
(ft2)
#of
4'x8'
sheets
required
Rail
Cost6
Concrete
Cost1
(at $6/ft2)
Total
Storage
Cost (S)
Annualized
Storage
Costg
PAKiskiValley
1
MH
vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
NYGlensFalls
1
FB
vs - imp
1.54
1.54
36.96
61
6571
1095
33
794
25
$3,850
$6,571
$10,421
$1,144
WAAnacortes
1
FB
vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
PAHatfield
1
MH
vs - imp - wesp - rto
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
VAHopewell
1
MH
abd - vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
OHWilloughbyEastlake
1
MH
imp
3.42
3.42
82.08
135
14592
2432
49
1184
37
$5,698
$14,592
$20,290
$2,228
NYAuburn
1
MH
abd - vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
NYArlington
1
FB
0.35
0.35
8.4
14
1493
249
16
379
12
$1,848
$1,493
$3,341
$367
AKJuneau
1
FB
vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
CTNaugatuck
1
MH
abo - imp - wesp
1.90
91.32
150
16235
2706
52
1248
40
$6,160
$16,235
$11,197
$1,229
CTNaugatuck
MH
vs - imp
1.90
$11,197
$1,229
WALynnwood
1
FB
vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
MAFitchburgEast
1
MH
vs - wesp - rto
2.30
2.30
55.2
91
9813
1636
40
971
31
$4,774
$9,813
$14,587
$1,602
NJPequannockLincolnFa
irfield
1
FB
vs - imp
1.90
91.32
150
16235
2706
52
1248
40
$6,160
$16,235
$11,197
$1,229
NJPequannockLincolnFa
irfield
2
FB
vs - imp - wesp
1.90
$11,197
$1,229
WAEdmonds
1
FB
vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
VABlacksburg
1
FB
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
RINewEngland
1
MH
vs - imp - wesp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
NYOrangetown
1
MH
vs - imp
1.90
45.66
75
8117
1353
37
883
28
$4,312
$8,117
$12,429
$1,365
OHEuclid
1
MH
abd - vs - imp
1.90
91.32
150
16235
2706
52
1248
40
$6,160
$16,235
$11,197
$1,229
OHEuclid
2
MH
abd - vs - imp
1.90
$11,197
$1,229
a. Assumed unit operating 24 hrs per day.
b. Volume based on sludge density of 1215 lbs/yd3 (Pocket Ref (ISBN 1-885071-00-0) page 435).
c. Assumed square concrete pad.
d. Rail height of 6 feet chosen for minimal concrete surface area requirement.
e. Based on cost of $154 per 4' by 8' sheet of flattened aluminum, 0.125 inches thick (Metals Depot:
http://www.metalsdepot.com/products/hrsteel2.phtml?page=expanded&LimAcc=$LimAcc)
f. Researched concrete slab costs (including installation, materials, and labor) ranged from $3/ft2 to $10/ft2. For this analysis, an average of $6/ft2 was used.
g. Capital Recovery Factor based on 7% interest and 15 year lifetime.
-------
Table C-6a. Emissions for Landfilling Option: Increased Emissions from Waste-Hauling Vehicles
Facility ID
UnitID
Unit
Type
Maximum
Charge Rate
(ton waste/hr)
Daily
waste
hauled
(tons/day)
Daily
waste
hauled
(cu yd/day)
Operating
Hours
(hr/yr)
Annual
Waste
(tpy)
Annual
Waste
(cu
yd/yr)
Number of
truck trips
per year
Round
Trip
Miles
mi/yr
CO
(tpy)
NOx
(tpy)
PM10
(tpy)
PM 2.5
(tpy)
S02
(tpy)
AKJuneau
1
FB
2.26
54
89
8400
18,969
31,225
867
100
86,735
0.28604
1.03554
0.06187
0.05315
0.00248
NJPequannockLincolnFa
irfield
1
FB
2.26
54
89
4200
9,484
15,612
434
200
86,731
0.28603
1.03549
0.06187
0.05315
0.00248
NJPequannockLincolnFa
irfield
2
FB
2.26
54
89
4200
9,484
15,612
434
200
86,731
0.28603
1.03549
0.06187
0.05315
0.00248
NYArlington
1
FB
0.35
8
14
8400
2,940
4,840
134
100
13,443
0.04433
0.16050
0.00959
0.00824
0.00039
NYGlensFalls
1
FB
1.54
37
61
8400
12,936
21,294
591
100
59,150
0.19507
0.70619
0.04219
0.03625
0.00169
VABlacksburg
1
FB
2.26
54
89
8400
18,969
31,225
867
100
86,735
0.28604
1.03554
0.06187
0.05315
0.00248
WAAnacortes
1
FB
2.26
54
89
8400
18,969
31,225
867
100
86,735
0.28604
1.03554
0.06187
0.05315
0.00248
WAEdmonds
1
FB
2.26
54
89
8400
18,969
31,225
867
100
86,735
0.28604
1.03554
0.06187
0.05315
0.00248
WALynnwood
1
FB
2.26
54
89
8400
18,969
31,225
867
100
86,735
0.28604
1.03554
0.06187
0.05315
0.00248
CTNaugatuck
1
MH
2.69
65
106
4200
11,298
18,598
517
200
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
CTNaugatuck
MH
2.69
65
106
4200
11,298
18,598
517
200
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
MAFitchburgEast
1
MH
2.30
55
91
8400
19,320
31,802
883
100
88,340
0.29133
1.05471
0.06302
0.05413
0.00253
NYAuburn
1
MH
2.69
65
106
8400
22,596
37,195
1,033
100
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
NYOrangetown
1
MH
2.69
65
106
8400
22,596
37,195
1,033
100
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
OHEuclid
1
MH
2.69
65
106
4200
11,298
18,598
517
100
51,660
0.17037
0.61677
0.03685
0.03166
0.00148
OHEuclid
MH
2.69
65
106
4200
11,298
18,598
517
100
51,660
0.17037
0.61677
0.03685
0.03166
0.00148
OHWilloughbyEastlake
1
MH
3.42
82
135
8400
28,728
47,289
1,314
100
131,358
0.43320
1.56830
0.09370
0.08049
0.00376
PAHatfield
1
MH
2.69
65
106
8400
22,596
37,195
1,033
100
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
PAKiskiValley
1
MH
2.69
65
106
8400
22,596
37,195
1,033
100
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
RINewEngland
1
MH
2.69
65
106
8400
22,596
37,195
1,033
100
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
VAHopewell
1
MH
2.69
65
106
8400
22,596
37,195
1,033
100
103,320
0.34073
1.23355
0.07370
0.06331
0.00296
6.03
21.84
1.30
1.12
0.05
Notes:
*assumed density of dewatered sludge is 1215 lbs/yd3 (Pocket Ref (ISBN 1-885071-00-0) page 435)
*assumed maximum capacity of hauling vehicles (36 cu yd) for 50+ cu yd/day. (Land application of biosolids: process design manual. Center for Environmental
Research Information (U.S.), 1997. Page 214.)
*emission factors based on national average output from EPA's Office of Transportation and Air Quality (OTAQ) MOtor Vehicle Emission Simulator
(MOVES). See factors below:
Pollutant
g/mi
lb/mi
CO
2.99
0.0066
NOx
10.8
0.0239
PM10
0.65
0.0014
PM2.5
0.56
0.0012
S02
0.03
0.0001
-------
Table C-6b. Emissions for Landfilling Option: LandGEM Output
Year
Waste Accepted
Waste-In-Place
Total landfill gas
Methane
Carbon monoxide
Mercury (total) - HAP
(Mg/year)
(short
tons/year)
(Mg)
(short
tons)
(Mg/year)
(m /year)
(short
tons/year)
(Mg/year)
(tn/year)
(short
tons/year)
(Mg/year)
(tn/year)
(short
tons/year)
(Mg/year)
(ni/year)
(short
tons/year)
2011
325,914
358,505
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2012
325,914
358,505
325,914
358,505
1.997E+03
1.599E+06
1.074E+02
5.334E+02
7.995E+05
5.372E+01
1.464E+03
7.995E+05
5.372E+01
3.869E-06
4.637E-04
3.116E-08
2013
325,914
358,505
651,827
717,010
3.878E+03
3.105E+06
2.086E+02
1.036E+03
1.553E+06
1.043E+02
2.842E+03
1.553E+06
1.043E+02
7.513E-06
9.005E-04
6.050E-08
2014
325,914
358,505
977,741
1,075,515
5.649E+03
4.523E+06
3.039E+02
1.509E+03
2.262E+06
1.520E+02
4.140E+03
2.262E+06
1.520E+02
1.095E-05
1.312E-03
8.814E-08
2015
325,914
358,505
1,303,655
1,434,020
7.317E+03
5.859E+06
3.937E+02
1.954E+03
2.929E+06
1.968E+02
5.362E+03
2.929E+06
1.968E+02
1.418E-05
1.699E-03
1.142E-07
2016
325,914
358,505
1,629,568
1,792,525
8.888E+03
7.117E+06
4.782E+02
2.374E+03
3.558E+06
2.391E+02
6.514E+03
3.558E+06
2.391E+02
1.722E-05
2.064E-03
1.387E-07
2017
325,914
358,505
1,955,482
2,151,030
1.037E+04
8.301E+06
5.578E+02
2.769E+03
4.151E+06
2.789E+02
7.598E+03
4.151E+06
2.789E+02
2.009E-05
2.407E-03
1.618E-07
2018
325,914
358,505
2,281,395
2,509,535
1.176E+04
9.417E+06
6.327E+02
3.141E+03
4.709E+06
3.164E+02
8.619E+03
4.709E+06
3.164E+02
2.279E-05
2.731E-03
1.835E-07
2019
325,914
358,505
2,607,309
2,868,040
1.307E+04
1.047E+07
7.033E+02
3.492E+03
5.234E+06
3.517E+02
9.581E+03
5.234E+06
3.517E+02
2.533E-05
3.036E-03
2.040E-07
2020
325,914
358,505
2,933,223
3,226,545
1.431E+04
1.146E+07
7.698E+02
3.822E+03
5.729E+06
3.849E+02
1.049E+04
5.729E+06
3.849E+02
2.772E-05
3.323E-03
2.232E-07
2021
325,914
358,505
3,259,136
3,585,050
1.547E+04
1.239E+07
8.324E+02
4.133E+03
6.195E+06
4.162E+02
1.134E+04
6.195E+06
4.162E+02
2.998E-05
3.593E-03
2.414E-07
2022
325,914
358,505
3,585,050
3,943,555
1.657E+04
1.327E+07
8.914E+02
4.425E+03
6.633E+06
4.457E+02
1.214E+04
6.633E+06
4.457E+02
3.210E-05
3.847E-03
2.585E-07
2023
325,914
358,505
3,910,964
4,302,060
1.760E+04
1.409E+07
9.469E+02
4.701E+03
7.047E+06
4.735E+02
1.290E+04
7.047E+06
4.735E+02
3.410E-05
4.087E-03
2.746E-07
2024
325,914
358,505
4,236,877
4,660,565
1.857E+04
1.487E+07
9.992E+02
4.961 E+03
7.436E+06
4.996E+02
1.361E+04
7.436E+06
4.996E+02
3.599E-05
4.313E-03
2.898E-07
2025
325,914
358,505
4,562,791
5,019,070
1.949E+04
1.560E+07
1.048E+03
5.205E+03
7.802E+06
5.242E+02
1.428E+04
7.802E+06
5.242E+02
3.776E-05
4.525E-03
3.041E-07
2026
325,914
358,505
4,888,705
5,377,575
2.035E+04
1.629E+07
1.095E+03
5.436E+03
8.147E+06
5.474E+02
1.491E+04
8.147E+06
5.474E+02
3.943E-05
4.726E-03
3.175E-07
2027
325,914
358,505
5,214,618
5,736,080
2.116E+04
1.695E+07
1.139E+03
5.652E+03
8.473E+06
5.693E+02
1.551E+04
8.473E+06
5.693E+02
4.100E-05
4.914E-03
3.302E-07
2028
325,914
358,505
5,540,532
6,094,585
2.193E+04
1.756E+07
1.180E+03
5.857E+03
8.779E+06
5.898E+02
1.607E+04
8.779E+06
5.898E+02
4.248E-05
5.092E-03
3.421E-07
2029
325,914
358,505
5,866,445
6,453,090
2.265E+04
1.813E+07
1.218E+03
6.049E+03
9.067E+06
6.092E+02
1.660E+04
9.067E+06
6.092E+02
4.388E-05
5.259E-03
3.533E-07
2030
325,914
358,505
6,192,359
6,811,595
2.332E+04
1.868E+07
1.255E+03
6.230E+03
9.339E+06
6.275E+02
1.709E+04
9.339E+06
6.275E+02
4.519E-05
5.416E-03
3.639E-07
2031
0
0
6,518,273
7,170,100
2.396E+04
1.919E+07
1.289E+03
6.401 E+03
9.594E+06
6.446E+02
1.756E+04
9.594E+06
6.446E+02
4.643E-05
5.565E-03
3.739E-07
119,434,741
8,025
8,025
0.00000465
Notes:
Values derived from LandGEM V3.02, using the following defaults:
k = 0.06 k value based on default IPCC value for sewage sludge in dry, temperate climate.
Lo = 42 Inventory default Lo for MSW = 100 for conventional climate (dry, temperate); CAA default Lo for MSW = 170 for conventional climate (dry,
temperate).
Sewage sludge Lo value calculated based on IPCC equation using default degradable organic carbon (DOC) value of 0.05 for sewage sludge.
IPCC values for other Lo parameters are consistent with inventory defaults, so multplied the result by 1.7 to be consistent with CAA defaults.
Methane in landfill gas = 50%
-------
Table C-6c. Emissions for Landfilling Option: Increased Emissions from Landfill and
Flare
Total Tons Emitted Over 20 Years
Tons
Pollutant
358,505 tpy basis
per Year
PM
17.92
0.90
HC1
7.62
0.38
S02
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)
HC1 based on default landfill gas CI content of 42 ppmv (AP-42 Section 2.4.4.2)
S02 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
C-42
------- |