55
801
                                      56
0
                                      57
0
                                      58
905
                                      59
288
                                      60
329
                                      64
0
                                                                    (continued)
Table 5-4.	Estimated Affected Facilities Using 13 State Boiler Inspector Inventory: Area Sources (continued)
SIC
Total Number of Affected Facilities in SIC Code
                                      65
2,878
                                      70
4,893
                                      72
2,138
                                      73
165
                                      75
1,606
                                      76
0
                                      79
1,151
                                      80
15,293
                                      81
0
                                      82
33,303
                                      83
0
                                      84
165
                                      86
3,330
                                      87
666
                                   91 to 98
5,098
                                    Unknown
576

Description of the Projected Reporting, Record keeping and Other Compliance Requirements of the Rule
The information collection activities in this ICR include initial and annual stack tests, fuel analyses, operating parameter monitoring, continuous O2 monitoring for all coal-fired area source boilers greater than 10 MMBtu/h, certified energy assessments for area source facilities having a boiler greater than 10 MMBtu/h, biennial tune-ups, preparation of a startup, shutdown, malfunction plan (SSMP), preparation of a site-specific monitoring plan and a site-specific fuel monitoring plan, one-time and periodic reports, and the maintenance of records. Based on 13 states' inventories of boilers, there are an estimated 92,000 existing facilities with affected boilers. It is estimated that 53 percent are located in the private sector and the remaining 47 percent are located in the public sector. Of these, only about 0.3 percent of the area source facilities are subject to emission limits and the testing and monitoring requirements in the final rule. A table included in the FRFA summarizes the types and number of each type of small entities expected to be affected by the area source rule.
The Agency expects that persons with knowledge of .pdf software, spreadsheet and relational database programs will be necessary in order to prepare the report or record. Based on experience with previous emission stack testing, we expect most facilities to contract out preparation of the reports associated with emission stack testing, including creation of the Electronic Reporting Tool submittal which will minimize the need for in depth knowledge of databases or spreadsheet software at the source. We also expect affected sources will need to work with web-based applicability tools and flowcharts to determine the requirements applicable to them, knowledge of the heat input capacity and fuel use of the combustion units at each facility will be necessary in order to develop the reports and determine initial applicability to the rule. Affected facilities will also need skills associated with vendor selection in order to identify service providers that can help them complete their compliance requirements, as necessary.
Description of the steps the Agency has Taken to Minimize the Significant Economic Impact on Small Entities
While EPA did make significant changes based on public comment, EPA is maintaining, but clarifying, the energy assessment requirement. Some changes to the energy assessment requirement that will reduce costs for small entities include a the following provisions: the energy assessment for facilities with affected boilers using less than 0.3 trillion Btu per year heat input will be one day in length maximum. The boiler system and energy use system accounting for at least 50 percent of the energy output will be evaluated to identify energy savings opportunities, within the limit of performing a one-day energy assessment; and the energy assessment for facilities with affected boilers using 0.3 to 1.0 trillion Btu per year will be 3 days in length maximum. The boiler system and any energy use system accounting for at least 33 percent of the energy output will be evaluated to identify energy savings opportunities, within the limit of performing a 3-day energy assessment. In addition, the final rule allows facilities to use a previously completed energy assessment to satisfy the energy assessment requirement.
As required by section 212 of SBREFA, EPA also is preparing a Small Entity Compliance Guide to help small entities comply with this rule. Small entities will be able to obtain a copy of the Small Entity Compliance guide at the following web site: http://www.epa.gov/ttn/atw/boiler/boilerpg.html.
Section 5 References
U.S. Bureau of Economic Analysis. 2010. "Implicit Price Deflators for Gross Domestic Product" (Table 1.1.9). <http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N>.
U.S. Census Bureau. 2005a. 2002 Census of Governments, Volume 4, Number 3, Finances of County Governments: 2002 GC02(4)-3 (Table 12).Washington, DC: U.S. Government Printing Office.
U.S. Census Bureau. 2005b. 2002 Census of Governments, Volume 4, Number 4, Finances of Municipal and Township Governments: 2002 GC02(4)-4 (Table 13). Washington, DC: U.S. Government Printing Office.
U.S. Census Bureau. 2008. "Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2002." <http://www.census.gov/csd/susb/download_susb02.htm>.
U.S. Small Business Administration. 2008. "Table of Small Business Size Standards Matched to North American Industry Classification System Codes." <http://www.sba.gov/services/contractingopportunities/sizestandardstopics/
size/index.html>.

Air Quality Modeling of Emissions Reductions
Synopsis
This section describes the air quality modeling performed by EPA in support of the final boiler MACT rule. A national scale air quality modeling analysis was performed to estimate the impact of the sector emissions changes on future year: annual and 24-hour PM2.5 concentrations, 8-hr maximum ozone, total mercury deposition, as well as visibility impairment. Air quality benefits are estimated with the Comprehensive Air Quality Model with Extensions (CAMx) model. CAMx simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. In addition to the CAMx model, the modeling platform includes the emissions, meteorology, and initial and boundary condition data which are inputs to this model.
Emissions and air quality modeling decisions are made early in the analytical process. For this reason, it is important to note that the inventories used in the air quality modeling and the benefits modeling are different than the final boiler sector inventories presented in the RIA. At the time of proposal, we did not have the results of the ICR; those results have been incorporated into this final analysis as explained below. The boiler ICR does not have the emissions release point information such as stack height, exit velocity, exit temperature, etc. necessary for photochemical modeling so the ICR emissions data was matched with the National Emissions Inventory (NEI). Since States and local agencies have different criteria for emissions that get inventoried as a point source with stack information and area sources (no specific stack information) the ability to match ICR emissions to NEI varies from State to State depending on the level of information provided in the ICR and NEI.
Photochemical grid models use state of the science numerical algorithms to estimate pollutant formation, transport, and deposition over a variety of spatial scales that range from urban to continental. Emissions of precursor species are injected into the model where they react to form secondary species such as ozone and then transport around the modeling domain before ultimately being removed by deposition or chemical reaction. Photochemical model source apportionment tracks the formation and transport of primarily and secondarily formed pollutants from emissions sources and allows the estimation of contributions at receptors. This type of emissions apportionment is useful to understand what types of sources or regions are contributing to pollutants estimated by photochemical grid models.
The 2005-based CAMx modeling platform was used for the air quality modeling for this rule. This platform represents a structured system of connected modeling-related tools and data that provide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used to construct this platform includes emissions and meteorology for 2005. The platform is intended to support a variety of regulatory and research model applications and analyses. This modeling platform and analysis is described below. Additional information about the photochemical modeling is available as part of the modeling technical assessment document to support this source sector rule (USEPA, 2010).
Photochemical Model Background
CAMx version 5.20 is a freely available computer model that simulates the formation and fate of photochemical oxidants, ozone, primary and secondary PM concentrations, and air toxics, over regional and urban spatial scales for given input sets of meteorological conditions and emissions. CAMx includes numerous science modules that simulate the emission, production, decay, deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the atmosphere (Baker and Scheff, 2007; Nobel et al., 2001; Russell, 2008). CAMx is applied with ISORROPIA inorganic chemistry (Nenes et al., 1999), a semi-volatile equilibrium scheme to partition condensable organic gases between gas and particle phase (Strader et al., 1999), Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987), and Carbon Bond 05 (CB05) gas-phase chemistry module (ENVIRON, 2008; Gery et al., 1989). Mercury oxidation pathways are represented for both the gas and aqueous phases in addition to aqueous phase reduction reactions (ENVIRON, 2008).
CAMx contains a variety of ozone source apportionment tools, including the original ozone source apportionment tool (OSAT) and the anthropogenic pre-cursor culpability assessment (APCA) tool (ENVIRON, 2008). Ozone source apportionment tracers are treated using the standard model algorithms for vertical advection, vertical diffusion, and horizontal diffusion. Horizontal advective fluxes for each of the regular model species that make up nitrogen oxides (NOx) and volatile organic compounds (VOC) are combined and normalized by a concentration based weighted mean. Separate ozone tracers are used in CAMx to track ozone formation that happens under NOx and VOC limited conditions.
Particulate matter source apportionment technology (PSAT) implemented in CAMx estimates the contribution from specific emissions source groups to PM2.5 and all forms of mercury using reactive tracers (ENVIRON, 2008; Wagstrom et al., 2008). The tracer species are estimated with source apportionment algorithms rather than by the host model routines. PSAT tracks contribution to PM2.5 sulfate, nitrate, ammonium, secondary organic aerosol, and inert primarily emitted species. Non-linear processes like gas and aqueous phase chemistry are solved for bulk species and then apportioned to the tagged species. Emissions of nitrogen oxides are tracked through all intermediate nitrogen species to particulate nitrate ion. Ammonia emissions are tracked to particulate ammonium ion. This modeling assessment used the PSAT approach to estimate source contribution to PM2.5 species and mercury and the APCA method to estimate source contribution to modeled ozone.
Model Domain and Grid Resolution
The modeling analyses were performed for a domain covering the continental United States, as shown in Figure 6-1. This domain has a parent horizontal grid of 36 km with two finer-scale 12 km grids over portions of the eastern and western U.S. The model extends vertically from the surface to 100 millibars (approximately 15 km) using a sigma-pressure coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken from a global model and vary in time and space. The 36 km grid was only used to establish the incoming air quality concentrations along the boundaries of the 12 km grids. Only the finer grid data were used in determining the impacts of the emission standard program changes. Table 6-1 provides some basic geographic information regarding the photochemical model domains.

Figure 6-1.	Map of the photochemical modeling domains. The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km western U.S. grid; and the blue inner box is the 12 km eastern U.S. grid.

Table 6-1.	Geographic Elements of Domains Used in Photochemical Modeling

Photochemical Modeling Configuration

National Grid
Western U.S. Fine Grid
Eastern U.S. Fine Grid
Map Projection
                         Lambert Conformal Projection
Grid Resolution
                                     36 km
                                     12 km
                                     12 km
Coordinate Center
                              97 deg W, 40 deg N
True Latitudes
                             33 deg N and 45 deg N
Dimensions
                                148 x 112 x 14
                                213 x 192 x 14
                                279 x 240 x 14
Vertical extent
           14 Layers: Surface to 100 millibar level (see Table II-3)

Emissions Input Data
2005 Baseline Emissions
The emissions data used in the 2005 base year are from the EPA's 2005-based v4.1 modeling platform. This platform is based on the 2005 National Emissions Inventory (NEI), version 2. Emissions were processed to photochemical model inputs with the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system (Houyoux et al., 2000).
This platform includes criteria pollutants and precursors: particulate matter less than 10 microns (PM10), PM2.5, nitrogen oxides (NOX), sulfur dioxide (SO2), carbon monoxide (CO), volatile organic compounds (VOC), ammonia (NH3) and hazardous air pollutants (HAP): hydrogen chloride, chlorine and mercury. Additionally, for some sectors, HAP emissions of benzene, formaldehyde, acetaldehyde and methanol are used from the inventory for chemical speciation VOC. For this rule, mercury emissions were added to the v4.1 platform to reflect the needs for the rule development will not be used for other rules. The mercury emissions included in this platform are primarily from the 2005 National Air Toxics Assessment (NATA) inventory, which was updated from the 2005 NEI v2 in order to incorporate updated data for particular source categories such as cement and hazardous waste incineration, and also revised from comments from state and local inventory providers as a result of NATA review.
This inventory was further modified to remove sources that were found to have shut down prior to 2005 and to update the gold mine emissions per information collected during the Gold Mine Ore and Production NESHAP. In addition, mercury emissions were revised for the boiler sector to allow for greater consistency with the Information Collection Request (ICR) data collected for this rule. In particular, we used the unit-specific ICR mercury emissions for all ICR facilities that could be mapped to the NATA Inventory. We used the NEI to add important emissions release point information necessary for photochemical grid modeling such as geographic coordinates, stack coordinates, stack release height, exit temperature, exit velocity because the ICR lacks this information.
The replacement of the NATA mercury with the ICR mercury is described in more detail in the air quality modeling TSD. ICR emissions were not used directly for any other pollutants used in the v4.1 platform; however they informed numerous corrections and updates to the inventory including the removal of duplicates and facilities that had shut down prior to 2005, and the inclusion of control information.
The stationary inventory used in the v4.1 platform is separated into modeling sectors such as EGU point (ptipm), non-EGU point (ptnonipm), and stationary emissions not included in the point source inventories (nonpt). This nonpoint category is generally referred to as the "area" source inventory, and this category is not a direct representation of sources classified as area sources for the purposes of National Emissions Standards for Hazardous Air Pollutants (NESHAP) and Maximum Available Control Technology (MACT) standards.
Future Year Baseline Emissions
The 2016 baseline emissions are intended to represent the emissions associated with growth and controls in 2016. The projections used for this effort are unique to this project and are not associated with a particular modeling platform.
The EGU point source (ptipm) emissions estimates for the future year reference were created by the Integrated Planning Model (IPM) version 3.02 for criteria pollutants, hydrochloric acid, and mercury in 2015. For the non-EGU point (ptnonipm) and nonpoint (nonpt) sectors, both control and growth factors were applied to a subset of the 2005 v4.1 platform data to create the 2016 reference case. The 2014 projection factors developed for the Transport Rule proposal (see http://www.epa.gov/ttn/chief/emch/index.html#transport) were further enhanced and updated for these 2016 baseline projections.
The projected inventory incorporates emissions projections for the proposed Transport Rule, cement kiln NESHAP, RICE NESHAP, gold mine NESHAP, changes to boiler emissions based on the ICR database developed for this rule, and known consent decrees. A complete list of rules included in the future point source baseline inventory is shown in Table 6-2.
Table 6-2.	Control Strategies and/or Growth Assumptions Included in the 2016 Projection
Projections Carried Forward from the proposed Transport Rule[a][,][b]
Description
Pollutants
MACT rules, national, VOC: national applied by SCC, MACT
VOC
Consent Decrees and Settlements, including refinery consent decrees, and settlements for: (1) Alcoa, TX and (2) Premcor (formerly MOTIVA), DE
All
Municipal Waste Combustor Reductions -- plant level
PM
Hazardous Waste Combustion
PM
Hospital/Medical/Infectious Waste Incinerator Regulations under Section 129d/11 1d
NOX, PM, SO2
Large Municipal Waste Combustors -- growth applied to specific plants
All
MACT rules, plant-level, VOC: Auto Plants
VOC
MACT rules, plant-level, PM & SO2: Lime Manufacturing
PM, SO2
MACT rules, plant-level, PM: Taconite Ore
PM
Municipal Waste Landfills: project factor of 0.25 applied
All
Livestock Emissions Growth from year 2002 to 2016
NH3, PM
Residential Wood Combustion Growth and Changeouts from year 2005 to year 2016
All
Gasoline Stage II growth and control from year 2005 to year 2016
VOC
Portable Fuel Container MSAT2 inventory growth and control from year 2005 to year 2016
VOC
            Additional Projections Used In Boiler MACT modeling[c]

Emission Reductions resulting from controls put on specific boiler units (not due to MACT) after 2005, identified through analysis of the control data gathered from the ICR from the ICI Boiler NESHAP.
NOX, SO2, HCL
NESHAP: Portland Cement (09/09/10) -- plant level based on Industrial Sector Integrated Solutions (ISIS) policy emissions in 2013. The ISIS results are from the ISIS-Cement model runs for the NESHAP and NSPS analysis of July 28, 2010.
HG, NOX, SO2, PM, HCL
NESHAP: Gold Mine Ore Processing and Production Area Source Category (based on proposed rule 04-15-10) -- finalized 12/2010
HG
                                                                    (continued)
Table 6-2.	Control Strategies and/or Growth Assumptions Included in the 2016 Projection (continued)
Projections Carried Forward from the proposed Transport Rule[a,b]
New York SIP reductions
VOC, NOX
Additional plant and unit closures
All
NESHAP: Reciprocating Internal Combustion Engines[d]
NOX, CO, PM
[a]	They were only changed in that the projection year was 2015 or 2016, rather than 2012 / 2014.
[b]	We inadvertently did not apply closures that had been applied for the Transport Rule proposal; emissions from these plants sum to 3300 tons VOC, 178 tons PM2.5, 1982 tons SO2, 1639 tons NOX, 6 tons NH3 and 379 tons CO. At the state level, the largest impact is in West Virginia (717 tons NOX, which is 2% of emissions in ptnonipm) and 1604 tons SO2, which is 7% of the ptnonipm sector. When considering emissions from other sectors, the percentages will be much smaller. All other errors are under 500 tons (less than 1% of the ptnonipm sector).
[c]	We inadvertently did not apply LaFarge and SaintGobain consent decrees, since one of the LaFarge facilities was already covered in the cement ISIS projections, the reductions missed were lower than estimated by the consent decree were on the order of 20,000 tons SO2, 15,000 tons NOX, 400 tons HCL, 200 tons PM2.5.
[d]	Note that SO2 reductions are expected to occur to due fuel sulfur limits but were excluded from the projection. They were expected to reduce SO2 by 27,000 tons, nationwide.
The 2016 onroad emissions reflect control program implementation through 2016 and include the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics (MSAT) final rule and the category 3 marine diesel engines Clean Air Act and International Maritime Organization standards which includes the establishment of emission control areas for these ships. Emission reductions and increases from the Renewable Fuel Standard version 2 (RFS2) are not included. The future baseline case nonroad mobile emissions reductions for these years include reductions to locomotives, various nonroad engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative emissions standards.
Future Year Sector Contribution Approach
The 2016 reference scenario for the boilers affected by the rule includes 2005 emissions estimates with some emissions removed from the inventory because of shut downs. The length of time required to conduct emissions and photochemical modeling precluded the use of the final facility-specific emissions estimates based on controls implemented for this rule. A 2016 "control" or emissions adjustment scenario was developed by tracking the total contribution from potentially controllable boiler sector emissions from the 2016 baseline inventory. This total contribution estimate, essentially a "zero-out," of the sector creates a policy space where potential control impacts would be maximized at all locations. Since emissions reductions at controllable sources are not 100% (100% ~ total contribution), the boiler sector air quality contribution estimates from the 2016 source apportionment model simulation are adjusted based on nation-wide estimates of control percentages by pollutant to create a final 2016 "control" emissions scenario.
The 2016 estimated controllable emissions for the boiler sector are shown in Table 6-3. Boiler sector emissions are contained in several different general classes of emissions used for emissions modeling: non-point or area (nonpt), point EGU (ptipm), and point non-EGU (ptnonipm). These totals are the sum of emissions in the eastern and western U.S. modeling domains so the non-point (area) category contains some double-counting of emissions where the model domains overlap in the central United States (see Figure 6-2).
Table 6-3.	Estimated Future Year (2016) Controllable Boiler Sector Emissions

Sector
VOC
NOX
SO2
PM2.5
NH3
HG0
HG2
PM2.5 HG
Total (TPY)
Non-point -- Boiler (nonpt)
                                                                         14,107
                                                                        394,459
                                                                      1,149,402
                                                                        108,386
                                                                          8,489
0
                                       0
0
Total (TPY)
EGU Point -- Boiler (ptipm)
                                                                             57
                                                                              0
                                                                         54,337
                                                                            826
                                                                              0
0
                                       0
0
Total (TPY)
Non-EGU Point -- Boiler (ptnonipm)
                                                                         15,451
                                                                        215,809
                                                                        492,676
                                                                         28,838
                                                                            706
1
                                       2
1
Pct of Sector
Non-point -- Boiler (nonpt)
                                                                              0
                                                                             19
                                                                             82
                                                                              9
                                                                              5

Pct of Sector
EGU Point -- Boiler (ptipm)
                                                                              0
                                                                              0
                                                                              1
                                                                              0
                                                                              0

Pct of Sector
Non-EGU Point -- Boiler (ptnonipm)
                                                                              1
                                                                              9
                                                                             26
                                                                              6
                                                                              0
10
                                       7
12

Figure 6-2 shows the locations of boilers in the NEI point source inventory used in the modeling analysis. Non-point boilers in the NEI do not have stack location information and are not shown. These boilers are spatially distributed in the modeling domain using spatial surrogates appropriate for this sector.
Boiler emissions in the non-point/area (nonpt) modeling sector are used as the basis of estimating air quality impacts and health benefits for the area source rule. These emissions are based on SCC codes shown in Table II-5. Boiler emissions in the point non-EGU (ptnonipm) and point EGU (ptipm) are used to estimate impacts of the major source rule. Facilities were identified for the major source rule based on meeting several criteria: (1) NEI facility ID and process level fuel type matched to a facility in the boiler ICR database, (2) NEI source had a boiler SCC code, and (3) unit design capacity was less than 25 MW.

Figure 6-2.	Locations of boilers in the NEI point inventory for the future baseline (2016)

Major source mercury emissions were identified as the units added from the boiler ICR database plus units in the NATA Hg inventory that were not in the ICR but were considered part of the Boiler MACT Universe. A percent emissions reduction was estimated based on the rule proposal unit-specific facility-fuel combination in the ICR database. Each facility/process matching the facility-fuel combinations that had emission reductions larger than 1% based on the proposed rule were tracked for source contribution.
Model Results
As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM2.5 concentrations, 8-hr maximum ozone, annual total mercury deposition levels and visibility impairment. Model predictions are used to estimate future-year design values of PM2.5 and ozone. The annual PM2.5 design value determines whether a monitor location is attaining the annual PM2.5 NAAQS. Specifically, we compare a 2016 reference scenario, a scenario without the boiler sector controls, to a 2016 control scenario which includes the adjustments to the boiler sector. This is done by calculating the simulated air quality ratios between any particular future year simulation and the 2005 base. These predicted ratios are then applied to ambient base year design values. The design value projection methodology used here followed EPA guidance for such analyses (USEPA, 2007). Additionally, the raw model outputs are also used in a relative sense as inputs to the health and welfare impact functions of the benefits analysis. Only model predictions for mercury deposition were analyzed using absolute model changes, although percent changes between the control case and two future baselines are also estimated.
The 36 km and both 12 km modeling domains were modeled for the entire year of 2005 and projected year 2016. Data from the entire year were utilized when looking at the estimation of PM2.5, total mercury deposition, and visibility impacts from the regulation. Data from April through October is used to estimate ozone impacts. All air quality impacts are based on improvements in future year pollution based on emissions changes from this source sector.
Impacts of Sector on Total Mercury Deposition
This section summarizes the results of our modeling of total mercury deposition impacts in the future based on changes to boiler emissions. Available data indicate that the mercury emissions from these sources are a mixture of gaseous elemental mercury (25%), inorganic divalent mercury (reactive gas phase mercury) (50%), and particulate bound mercury (25%). Model results for the eastern and central United States indicate that estimated total mercury deposition (wet and dry forms) reductions from this sector would be 1,393 ug/m[2] (1.5% of total mercury deposition from all sources); approximately 51% in dry form and 49% in wet form. The chemical composition of this estimated reduction is approximately 60% reactive gas phase (HG2) and 40% particulate. A reduction of 75 ug/m[2] (0.5% of total mercury deposition from all sources) is estimated for the western United States; approximately 56% in dry form and 44% in wet form. The chemical composition of the reductions in the western model domain is approximately 63% reactive gas phase (HG2) and 37% particulate. These reductions are related to changes in emissions at major boiler sources using the ptnonipm sector as a surrogate.
Impacts of Sector on Future Annual PM2.5 Levels
This section summarizes the results of our modeling of annual average PM2.5 air quality impacts in the future due to estimated reductions in emissions from this sector. Specifically, we compare a 2016 reference scenario to a 2016 control scenario. The modeling assessment indicates a decrease up to 0.81 ug/m[3] in annual PM2.5 design values is possible given an area's proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions from major sources under the recommended solid fuel category option. The estimated mean reduction over all monitor locations is 0.15 ug/m[3] for major source reductions under the recommended solid fuel category. A decrease up to 0.66 ug/m[3] in annual PM2.5 design values is possible given an area's proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions from major sources under the alternative option. The estimated mean reduction over all monitor locations is 0.12 ug/m[3] for major source reductions under the alternative option. Area source reductions show a decrease up to 0.01 ug/m[3] in annual PM2.5 design values is possible given an area's proximity to controlled area sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions.
Impacts of Sector on Future 24-hour PM2.5 Levels
This section summarizes the results of our modeling of 24-hr average PM2.5 air quality impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 2016 reference scenario to a 2016 control scenario. The modeling assessment indicates an estimated average decrease of 0.50 ug/m[3] in 24-hr average PM2.5 design values over all monitor locations in the United States is possible given the amount of reduced sulfur dioxide and primary PM2.5 emissions from major sources under the recommended solid fuel category option. An estimated average decrease of 0.39 ug/m[3] in 24-hr average PM2.5 design values over all monitor locations in the United States is possible given the amount of reduced sulfur dioxide and primary PM2.5 emissions from major sources under the alternative option. An estimated decrease up to 0.03 ug/m[3] in 24-hr average PM2.5 design values at monitor locations in the United States is possible given an area's proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions from area sources.
Impacts of Sector on Future Visibility Levels
Air quality modeling conducted for this final rule was used to project visibility conditions in 138 mandatory Class I federal areas across the U.S. in 2016 (USEPA, 2007). The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility index, called a "deciview," which is used in the valuation of visibility. The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the average person can generally perceive a change of one deciview. Higher deciview values are indicative of worse visibility. Thus, an improvement in visibility is a decrease in deciview value.
The modeling assessment indicates an estimated average visibility improvement of 0.51 deciviews in annual 20% worst visibility days over all Class I area monitors based on controls for major sources under the recommended solid fuel category option. An estimated average visibility improvement of 0.42 deciviews in annual 20% worst visibility days over all Class I area monitors based on controls for major sources under the alternative option. An improvement in visibility up to 0.03 deciviews at Class I monitor locations in the United States is possible given an area's proximity to controlled sources and the amount of reduced sulfur dioxide and primary PM2.5 emissions from area sources.
Impacts of Sector on Future Ozone Levels
This section summarizes the results of our modeling of 8-hr maximum ozone air quality impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 2016 reference scenario to a 2016 control scenario. The modeling assessment indicates a decrease of less than 0.01 ppb in ozone design values is possible given an area's proximity to controlled sources and the amount of reduced VOC emissions under all major and area source options. The full details involved in calculating design value are given in appendix P of 40 CFR part 50. Projected air quality benefits are estimated using procedures outlined by United States Environmental Protection Agency modeling guidance (USEPA, 2007).
Section 6 References
Baker, K., Scheff, P., 2007. Photochemical model performance for PM2.5 sulfate, nitrate, ammonium, and precursor species SO2, HNO3, and NH3 at background monitor locations in the central and eastern United States. Atmospheric Environment 41, 6185-6195.
Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P., Stockwell, W.R., Walcek, C.J. 1987. A 3-dimensional eulerian acid deposition model -- physical concepts and formulation. J. Geophys. Res.-Atmos. 92, 14681-14700.
ENVIRON, 2008. User's Guide Comprehensive Air Quality Model with Extensions. ENVIRON International Corporation, Novato.
Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A photochemical kinetics mechanism for urban and regional scale computer modeling. J. Geophys. Res.-Atmos. 94, 12925-12956.
Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla, P.S., 2000. Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of Geophysical Research-Atmospheres 105, 9079-9090.
Nenes, A., Pandis, S.N., Pilinis, C., 1999. Continued development and testing of a new thermodynamic aerosol module for urban and regional air quality models. Atmospheric Environment 33, 1553-1560.
Nobel, C.E., McDonald-Buller, E.C., Kimura, Y., Allen, D.T., 2001. Accounting for spatial variation of ozone productivity in NOx emission trading. Environmental Science & Technology 35, 4397-4407.
Russell, A.G., 2008. EPA Supersites Program-related emissions-based particulate matter modeling: Initial applications and advances. J. Air Waste Manage. Assoc. 58, 289-302.
Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary organic aerosol formation in winter. Atmospheric Environment 33, 4849-4863.USEPA, 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze, RTP.
USEPA, 2010. Air Quality Modeling Technical Support Document: Boiler Source Sector Rules (EPA-454/R-10-006), Research Triangle Park, North Carolina.
Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E., 2008. Development and application of a computationally efficient particulate matter apportionment algorithm in a three-dimensional chemical transport model. Atmospheric Environment 42, 5650-5659.

Benefits of Emissions Reductions
Synopsis
In this section, we provide an estimate of the monetized benefits associated with reducing exposure to particulate matter (PM) and ozone for the final Boiler MACT and Boiler Area Source Rule. The PM2.5 reductions are the result of emission limits on PM as well as emission limits on other pollutants, including hazardous air pollutants (HAPs). The total PM2.5 reductions are the consequence of the technologies installed to meet these multiple limits. The latter are often referred to as "co-benefits." Ozone reductions are the result of reductions in emissions of VOCs, which are precursors to ozone formation. These benefit estimates include the number of cases of avoided morbidity and premature mortality among populations exposed to PM2.5 and ozone, as well as the monetized value of those avoided cases. Because we were unable to monetize the direct benefits associated with reducing HAPs, the monetized benefits estimate is an underestimate of the total benefits. The extent of this underestimate, whether small or large, is unknown. Using a 3% discount rate, we estimate the total combined monetized benefits of the final Boiler MACT and Boiler Area Source Rule to be $22 billion to $54 billion in the implementation year (2014). Using a 7% discount rate, we estimate the total combined monetized benefits of the final Boiler MACT and Boiler Area Source Rule to be $20 billion to $49 billion in the implementation year. Higher or lower estimates of benefits are possible using other assumptions; examples of this are provided in Figure 7-1. All estimates are in 2008$ and include any energy disbenefits associated with the increased emissions from additional energy usage.
These monetized estimates reflect EPA's current interpretation of the scientific literature (U.S. EPA, 2009c). In addition, these estimates incorporate an array of improvements introduced since the proposal, including boiler sector-specific air quality modeling data, lowest measured level (LML) assessment, mercury deposition maps, ozone benefits, and energy disbenefits. Methodological and time limitations under the court-ordered schedule prevented EPA from monetizing the benefits from several important benefit categories, including direct benefits from reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The direct benefits from reducing other air pollutants have not been monetized in this analysis, including reducing a combined 113,000 tons of carbon monoxide, 30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of other metals, and 23 grams of dioxins/furans (TEQ) each year. We assess the direct benefits of these emission reductions qualitatively in this analysis.

Figure 7-1.	Total Monetized PM2.5 and Ozone Benefits for the Final Boiler MACT and Boiler Area Source Rule in 2014[a]
[a]	This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. (2002) study and the Laden et al. (2006) 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 estimates do not include the direct benefits from reducing HAP emissions, but they do include the energy disbenefits associated with the increased emissions from additional energy usage. Due to methodology and time limitations under the court-ordered schedule, we were unable to monetize the benefits associated with several categories of benefits, including direct exposure to HAPs and SO2, as well as ecosystem effects, and visibility effects. These benefits reflect existing boilers and new boilers anticipated to come online by 2014.
Calculating Benefits
The benefit categories associated with the emission reduction anticipated for these rules can be broadly categorized as those benefits directly attributable to reduced exposure to HAPs, the PM co-benefits associated with reducing HAPs (i.e., PM reduced by introducing technology designed to reduce HAPs), and those attributable to exposure to other pollutants. Several of the HAPs that would be reduced by these rules have been classified as known or probable human carcinogens. As a result, one benefit of the proposed regulation is a reduction in the risk of cancer. Other benefit categories include potential reduced incidence of neurological effects and irritations of the lungs and skin, reduced mortality and other morbidity effects associated with PM and SO2. In addition to health impacts occurring as a result of reductions in HAPs and other pollutant emissions, there are welfare impacts which can also be identified. In general, welfare impacts include effects on vegetation, visibility impairment, and acidification of water bodies. We were unable to monetize the direct benefits associated with reducing HAPs in this analysis. In Section 7.5.5 of this RIA, we provide a full qualitative discussion of the direct health benefits associated with the reductions in emissions of HAPs anticipated by these rules, including a full discussion of the complexity associated with monetizing HAP benefits. We also provide maps of reduced mercury deposition in that section. Therefore, all monetized benefits provided in this analysis only reflect improvements in ambient PM2.5 and ozone concentrations. Thus, the monetized benefits estimate is an underestimate of the total benefits. The extent of this underestimate, whether small or large, is unknown. In addition to pollutants we do not directly monetize, these rulemakings are expected to reduce emissions of PM2.5, SO2, and VOCs. Because SO2 is also a precursor to PM2.5, reducing SO2 emissions will also reduce PM2.5 formation, human exposure, and therefore reduce estimated incidence of PM2.5-related health effects. The estimated PM reductions are the result of emission limits on PM as well as emission limits on other pollutants, including hazardous air pollutants for these rules. The total PM2.5 reductions are the consequence of the technologies installed to meet these multiple limits. In addition, these rules are expected to result in reductions in VOCs, which will result in changes in ambient ozone concentrations.
In implementing these rules, emission controls may lead to reductions in ambient PM2.5 below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air quality standard nationwide based on an array of emission control strategies for different sources. In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. However, some costs and benefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM2.5 NAAQS RIA.
By contrast, the emission reductions for this rule are from a specific class of well-characterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for these rules. It is important to note that emission reductions anticipated from these rules do not result in emission increases elsewhere (other than potential energy disbenefits). Emission reductions achieved under these and other promulgated rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the incremental costs and benefits associated with attaining the NAAQS. EPA remains forward looking towards the next iteration of the 5-year review cycle for the NAAQS, and as a result does not issue updated RIAs for existing NAAQS that retroactively update the baseline for NAAQS implementation. For more information on the relationship between the NAAQS and rules such as analyzed here, please see Section 1.2.4 of the SO2 NAAQS RIA (U.S. EPA, 2010h).
Methodology Improvements since Proposal
This benefits analysis for the final Boiler MACT and Area Source Rule incorporates an array of policy and technical improvements since the proposal analysis in 2010 (U.S. EPA, 2010c), including:
         1.          Boiler sector-specific air quality modeling data. The benefits estimates for this final analysis are derived from air quality data modeled using CAMx that reflect the emissions from the boiler sector and their contribution to ambient PM2.5 and ozone levels. These data provide a different and likely superior representation of the geographic distribution of emissions and associated ambient concentrations specifically from boilers than the generic benefit-per-ton estimates used in the proposal that reflected emissions for the entire non-EGU (non-electrical generating unit) category. For more information regarding the modeling inputs and assumptions, please see Section 6 of this RIA. For more information regarding the derivation of boiler sector-specific benefit-per-ton estimates and how they are superior to the estimates used in the proposal, please see Section 7.2.6 of this RIA.
         2.          Lowest Measured Level (LML) assessment. Consistent with the rationale outlined in the Cement NESHAP proposal RIA (U.S. EPA, 2009a), EPA has recently changed its approach to characterizing the uncertainty associated with benefits estimated at lower air quality levels. Specifically, EPA now estimates PM-related mortality without assuming an threshold at 10 ug/m[3] in the concentration-response function. Consistent with recent scientific advice, we are replacing the previous threshold sensitivity analysis with a new LML assessment. We discuss this assessment in more detail in Section 7.2.4 and provide the results of this LML assessment in Section 7.3.
         3.          Mercury deposition. The air quality modeling data provide an estimate of the reduction in mercury deposition associated with the mercury emission reductions anticipated as a result of these rules. We provide maps of the reduced mercury deposition in Section <IP_ADDRESS>. EPA did not model mercury 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.
         4.          Ozone benefits. The air quality modeling data provide an estimate of the change in ambient ozone concentrations associated with the VOC emission reductions anticipated as a result of the Boiler MACT. We provide a table of the ozone-related health benefits in Section 7.3.
         5.          Energy disbenefits. For this final analysis, we include an estimate of the increased CO2 emissions associated with the additional electricity required to operate control devices. We provide the results of this analysis in Section 7.4.
         6.          Improved characterization of uncertainty. For this final analysis, we characterize uncertainty using four methods (Monte Carlo methods, LML assessment, alternate concentration-response functions, and qualitative descriptions). While the proposal incorporated some of these approaches, the Monte Carlo methods and LML assessment are an improvement for the final analysis. Confidence intervals reflecting the standard errors in the underlying epidemiology and economics literature are provided for incidence and valuation results in Tables 7-4 to 7-8.
Benefits Analysis Approach for PM2.5 and Ozone
We follow a "damage-function" approach in calculating total benefits of the modeled changes in environmental quality. This approach estimates changes in individual health and welfare endpoints and assigns values to those changes assuming independence of the individual values. Total benefits are calculated simply as the sum of the values for all non-overlapping health and welfare endpoints. The "damage-function" approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et al., 2009).
To assess economic value in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value. For changes in PM2.5 or ozone, a health impact analysis (HIA) must first be conducted to convert air quality changes into effects that can be assigned dollar values. For this RIA, the health impacts analysis is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to PM2.5 and ozone. We also provide qualitative discussions of the impact of changes in other environmental and ecological effects, including the benefits associated with decreasing deposition of sulfur to terrestrial and aquatic ecosystems, but we are unable to place an economic value on these changes due to time limitations under the court-ordered schedule.
We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2001) and other recent health impact analyses, our estimates are based on the best available methods of benefits transfer. Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis. Adjustments are made for the level of environmental quality change, the socio-demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates.
Health Impact Analysis (HIA)
The HIA quantifies the potential changes in the incidence of adverse health impacts resulting from estimated changes in human exposure to air pollution. HIAs are a well-established approach for estimating the retrospective or prospective change in adverse health impacts resulting from population-level changes in exposure to pollutants (Levy et al., 2009). Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Hubbell et al., 2005; Davidson et al., 2007; Tagaris et al., 2009). For this analysis, we used the environmental Benefits Mapping and Analysis Program (BenMAP), which is a PC-based tool that can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections.
The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMx-generated projections of PM2.5 and ozone air quality and estimating the change in the spatial distribution of the ambient air quality; (2) determining the subsequent change in population-level exposure; (3) calculating health impacts by applying concentration-response relationships drawn from the epidemiological literature (Hubbell et al., 2009) to this change in population exposure.
A typical health impact function might look as follows:
	∆y=yo ∙eβ∙∆x- 1∙Pop
where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the population affected by the change in air quality; x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. For this analysis, we systematize the HIA calculation process using BenMAP's library of existing air quality monitoring data, population data and health impact functions. Figure 7-2 provides a simplified overview of this approach, and Figure 7-3 identifies the data inputs and outputs for the BenMAP model for a PM2.5 analysis.
Baseline Air QualityPost-Policy Scenario  Air QualityIncremental Air QualityImprovementPM2.5 ReductionPopulationAges 18-65BackgroundIncidenceRateEffectEstimateMortality Reduction
Figure 7-2.	Illustration of BenMAP Approach

                            Census Population Data
                Baseline and Post-Control PM2.5 Concentrations
                            PM2.5 Health Functions
                         Economic Valuation Functions
                            Population Projections
                     PM2.5 Incremental Air Quality Change
                         PM2.5 Related Health Impacts
                   Woods & Poole Population Projections
                   Background Incidence and Prevalence Rates
                       Monetized PM2.5-related Benefits
Blue identifies a user-selected input within the BenMAP model
Green identifies a data input generated outside of the BenMAP model
Figure 7-3.	Data Inputs and Outputs for the BenMAP Model for a PM2.5 Analysis
The benefits estimates in this analysis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis (RIA) (U.S. EPA, 2006) and the Ozone NAAQS RIA (U.S. EPA, 2010a). While many of the functions are identical to those used in the PM NAAQS RIA, we have updated a few of the underlying assumptions over the last few years. For a detailed description of the underlying functions, studies, baseline incidence rates, and population data used in this analysis, please refer to Chapter 5 of the recently proposed Transport Rule (U.S. EPA, 2010e). Table 7-1 identifies which human health and welfare endpoints are included in the monetized benefits and which endpoints remain unquantified. In summary, the estimate of monetized PM benefits includes premature mortality and eleven morbidity endpoints, and the estimate of monetized ozone benefits includes premature mortality and five morbidity endpoints.
Table 7-1.	Human Health and Welfare Effects of Air Pollutants Affected
Pollutant/Effect
Quantified and Monetized
Unquantified
PM: health [a]
Premature mortality based on cohort study estimates [b]
Low birth weight
Pre-term births

Premature mortality based on expert elicitation estimates
Pulmonary function
Nonfatal cardiovascular outcomes other than myocardial infarctions

Chronic bronchitis
Hospital admissions: respiratory and cardiovascular
Chronic respiratory diseases other than chronic bronchitis

Emergency room visits for asthma
Non-asthma respiratory emergency room visits

Nonfatal heart attacks (myocardial infarctions)
UVb exposure (+/−)[c]

Lower and upper respiratory illness

Minor restricted activity days

Work loss days

Asthma exacerbations (among asthmatic populations

Respiratory symptoms (among asthmatic populations)

Infant mortality

PM: welfare

Visibility in Class I areas
Household soiling

Visibility in residential and non-class I areas

UVb exposure (+/−)[c]
Global climate impacts [c]
Ozone: health
Premature mortality based on short-term study estimates
Chronic respiratory damage
Premature mortality due to long-term exposures

Hospital admissions: respiratory
Premature aging of the lungs

Emergency room visits for asthma
Non-asthma respiratory emergency room visits

Minor restricted activity days
UVb exposure (+/−)[c]

School loss days

Ozone: welfare
Decreased outdoor worker productivity
Yields for:
 -  Commercial forests
 -  Fruits and vegetables, and
 -  Other commercial and noncommercial crops

Damage to urban ornamental plants

Recreational demand from damaged forest aesthetics

Ecosystem functions

UVb exposure (+/−)[c]
SO2: health

Respiratory hospital admissions

Asthma emergency room visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function
                                                                    (continued)
Table 7-1.	Human Health and Welfare Effects of Air Pollutants Affected (continued)
Pollutant/Effect
Quantified and Monetized
Unquantified
SO2: welfare

Commercial fishing and forestry from acidic deposition

Recreation in terrestrial and aquatic ecosystems from acid deposition

Increased mercury methylation
Mercury: health

Incidence of neurological disorders

Incidence of learning disabilities

Incidences in developmental delays

Potential cardiovascular effects including:
 -  Altered blood pressure regulation
 -  Increased heart rate variability
 -  Incidences of heart attack
Potential reproductive effects
Mercury: environment

Impact on birds and mammals (e.g., reproductive effects, cardiovascular effects)
Mercury: welfare

Impacts to commercial., subsistence and recreational fishing
HAPs

Health effects associated with HAP exposure
[a]	In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints.
[b]	Cohort estimates are designed to examine the effects of long-term exposures to ambient pollution, but relative risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of this issue). While some of the effects of short-term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in the primary analysis.
[c]	May result in benefits or disbenefits.
Estimating PM2.5-related Premature Mortality
Consistent with all RIAs since the proposal RIA for 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 characterization of uncertainty.
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 ug/m[3] as was done in recent (2006 - 2009) Office of Air and Radiation RIAs.
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, 2009c), 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 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. 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 ug/m[3] as was done in recent (2006 - 2009) RIAs.
Twelve estimates are based on the C-R functions from EPA's expert elicitation study (IEc, 2006; Roman et al., 2008) on the PM2.5-mortality relationship and interpreted for benefits analysis in EPA's final RIA for the PM2.5 NAAQS. For that study, twelve experts (labeled A through L) provided independent estimates of the PM2.5 -mortality concentration-response function. EPA practice has been to develop independent estimates of PM2.5-mortality estimates corresponding to the concentration-response function provided by each of the twelve experts, to better characterize the degree of variability in the expert responses.
Therefore, there is no evidence to support a truncation of the CRF [concentration-response function]." 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, 2010d).
Consistent with the recent scientific advice summarized above, we are replacing the previous threshold sensitivity analysis with a new "Lowest Measured Level" (LML) assessment. This approach summarizes the distribution of avoided PM mortality impacts according to the baseline PM2.5 levels experienced by the population receiving the PM2.5 mortality benefit. In the results section, we identify on the figures the lowest air quality levels measured in each of the primary cohort studies that estimate PM-related mortality. This information allows readers to determine the portion of PM-related mortality benefits occurring above or below 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 the two epidemiological studies. 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.
Analyses of these cohorts using data from more recent years, during which time PM concentrations have fallen, continue to report strong associations with mortality. 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 evidence of effects at successively lower levels of PM2.5. As more cohort studies follow large 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 recent studies indicates that confidence in PM2.5-related mortality effects down to at least 7.5 ug/m[3] is high.
In implementing these rules, emission controls may lead to reductions in ambient PM2.5 below the PM NAAQS in some areas. While benefits occurring below the standard may be somewhat more uncertain than those occurring above the standard, EPA considers them to be legitimate components of the total benefits estimate. Furthermore, given that the epidemiological literature in most cases has not provided estimates based on threshold models, there would be additional uncertainties imposed by assuming thresholds or other non-linear concentration-functions for the purposes of benefits analysis.
Economic Valuation of Health Impacts
These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type (U.S. EPA, 2009c). Directly emitted PM, SO2, and VOC are the dominant PM2.5 precursors affected by this rule. Even though we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates (described in detail in Section 7.2.6) vary between precursors because each ton of precursor reduced has a different propensity to form PM2.5 and a different pattern of transport, resulting geographic distribution of exposure. When more people are exposed, the benefits per ton are greater. For example, SO2 tends to have a lower benefit-per-ton estimate than direct PM2.5 because sulfate particles formed from SO2 emissions can transport many miles, meaning that higher exposures may occur over areas with low populations. On the other hand, to the extent that direct PM2.5 emissions occur in high density population areas, exposures will tend to be higher there, leading to higher monetized health benefits for direct PM2.5 than for SO2 emissions. Economic Valuation of Health Impacts
After quantifying the change in adverse health impacts, the final step is to estimate the economic value of these avoided impacts. Please refer to Table 5-11 in the recently proposed Transport Rule (U.S. EPA, 2010e) for a detailed description of the underlying valuation functions and the monetized unit values for each endpoint incorporated into this analysis. The monetized mortality benefits dominate the total benefits estimates.
As is the nature of 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 - 2006), the Office of Air and Radiation (OAR) valued mortality risk reductions using a value-of-a-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 $5.5 million (2000$) 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 for Preparing Economic Analyses (U.S. EPA, 2000) 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 $6.3 million (2000$). 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.
Calculating Boiler Sector-specific Benefit-per-ton Estimates
Benefit per-ton (BPT) estimates can be used to quantify the health impacts and monetized human health benefits of an incremental change in air pollution precursor emissions. In situations when we are unable to specifically model a regulatory option because of data or resource constraints, this approach can provide a reliable estimate of the benefits of emission reduction scenarios. EPA has used the BPT technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 2010a) and Federal Transport Rule RIA (U.S. EPA, 2010e). For this analysis, the emissions inventories used in the air quality modeling and benefits modeling are slightly different from the final boiler sector emission inventories reflected in the rules' emission memos.  These discrepancies exist because emissions and air quality modeling decisions are made early in the analytical process, and the sheer number of boilers (over 180,000 area boilers and 13,000 major boilers) made it impossible to reconcile the data completely. Part of the discrepancy is because the emissions inventory categorizes boilers as "point" and "non-point" instead of "major source" or "area source." We assume that the boilers in the point source inventory are appropriate surrogates for the major source boilers and that the boilers in the non-point source inventory are appropriate surrogates for the area source boilers. Furthermore, the emission inventories used for air quality modeling do not reflect emissions from new sources anticipated to come online by the analysis year. To address concerns about the discrepancies between these inventories, we utilized the air quality modeling to derive boiler sector-specific BPT estimates, which are then multiplied by the tons of emissions reduced for each regulatory option.
To derive the BPT estimates for this analysis, we:
1.	Quantified the PM2.5 -related human health and monetized benefits of the SO2 emission reductions for major source boilers. We first quantified the health impacts and monetized benefits of total PM2.5 mass formed from the SO2 reductions, allowing us to isolate the PM air quality impacts from SO2 reductions alone. This procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-related benefits of incremental changes in SO2 emissions.
2.	Divided the health impacts and monetized benefits by the emission reduction. This calculation yields BPT estimates for PM-related SO2. The resulting BPT estimates were then multiplied by the projected SO2 emission reductions to produce an estimate of the PM-related health impacts and monetized benefits.
This process was repeated for directly emitted PM2.5 from major sources and for PM2.5 and SO2 emissions from area source boilers. This process was also repeated using the 2.5th percentile and 97.5[th] percentile benefits to provide confidence intervals for the BPT estimates. To calculate the total benefits, we simply multiply the BPT estimate by the emission reductions of directly emitted PM2.5 and SO2 for each regulatory option. Deriving boiler sector-specific BPT estimates provides equivalent benefits results as scaling the air quality data directly to correspond to the inventory differences, but it provides greater flexibility to examine alternate regulatory scenarios.
The BPT estimates used in this analysis are different from the BPT estimates used in the proposal analysis. While these boiler sector-specific BPT estimates are methodologically consistent with the BPT estimates in Fann, Fulcher, and Hubbell (2009) that were used in the proposal analysis, the BPT estimates applied in this final analysis provide a different and likely superior representation of the health impacts and monetized benefits from boilers compared to the proposal. This is because the BPT used in the proposal lumped emissions from major boilers together with other emission sources in the non-EGU category and area boilers together with other emission sources in the area category. For example, the non-EGU category includes emissions from many different large stationary sources, including boilers, metal production, mineral products, chemical manufacturing, petroleum industry, pulp and paper production, and oil and gas production, and waste disposal, which all have different geographic distributions across the country. Because the geographic distribution of boilers is not likely to exactly match the average distribution of all of these sources, ambient air quality and the associated benefits for reducing boiler emissions are going to be different than for the entire non-EGU category. In addition to the location of the emission sources, the geographic differences also affect the benefits to due to geographic differences in local air chemistry, meteorology, population density, and baseline health incidence.
In addition, the air quality modeling for these rules reflected higher spatial resolution (12 km by 12 km nationally) than the previous BPT estimates and reflects an updated baseline emissions inventory. We believe that these updates result in benefits estimates that are more accurate than the proposal. For comparison, the BPT for directly emitted PM2.5 in this analysis is lower for major and area sources than the proposal, whereas the BPT for SO2 is higher in this analysis than the proposal. In addition, we have updated the analysis year used in this final analysis to be consistent with the implementation timeline, but this revision would not have a significant impact on the benefits results. In the proposal, the BPT estimates reflected emissions inventories, income growth, and population growth for 2015 because we did not have BPT estimates available for the proposal analysis year (2013). For this final analysis, the emissions inventories are for 2016, but income growth and population growth are for the final analysis year (2014). Because the projected emissions inventory does not assume growth in the boiler sector, the boiler benefits are not likely to change due to a 2016 inventory instead of a 2014 inventory. We provide the boiler-specific BPT estimates in Section 7.3. We also provide maps of the total ambient PM2.5 impacts from the modeled point and non-point boilers in Figures 7-4 through 7-7.

Figure 7-4.	Change in Ambient PM2.5 Levels from SO2 Emissions from Point Source Boilers

Figure 7-5.	Change in Ambient PM2.5 Levels from PM Emissions from Point Source Boilers

Figure 7-6.	Change in Ambient PM2.5 Levels from SO2 Emissions from Non-point Source Boilers

Figure 7-7.	Change in Ambient PM2.5 Levels from PM Emissions from Non-point Source Boilers
Although VOCs are also precursors to PM2.5, we have not monetized their contribution to the PM2.5 benefits in this analysis for several reasons. Analysis of organic carbon measurements suggest only a fraction of secondarily formed organic carbon aerosols are of anthropogenic origin. The current state of the science of secondary organic carbon formation indicates that anthropogenic VOC contribution to secondary organic carbon aerosols is often lower than the biogenic (natural) contribution. Given that a fraction of secondarily formed organic carbon aerosols from anthropogenic VOC emissions and the extremely small amount of VOC emissions from this sector relative to the entire VOC inventory, it is unlikely this sector has a large contribution to ambient secondary organic carbon aerosols. Photochemical models typically estimate secondary organic carbon aerosols from anthropogenic VOC emissions to be less than 0.1 ug/m[3]. Given the resource requirements to apply source apportionment technology in the photochemical model for secondary organic carbon aerosols and that only a very small portion of secondary organic carbon aerosols (i.e., from anthropogenic sources) would be apportioned, this option was not employed to estimate the impact of this sector's VOC emissions on secondary organic carbon aerosols. Lastly, the contribution of VOC reductions to total monetized PM2.5 benefits would likely be very small for these rules. For example, while not an appropriate source for this application, if the BPT for VOC emissions from industrial point sources and EGUs from Fann, Fulcher, and Hubbell (2009) were applied to the boiler rule, the VOC-related benefits would be less than $6 million for the Boiler MACT.
The differences between the VOC emissions inventories are comparatively small, and VOCs are only reduced by 20% for both of the Boiler MACT options. In addition, the complex non-linear chemistry of ozone formation introduces uncertainty to the development and application of a BPT estimate. Therefore, we used the scaled air quality modeling results directly to estimate the ozone benefits associated with VOC reductions. As the ozone-related benefits are very small, we do not believe that the differences in the VOC emissions inventory contribute much uncertainty to the overall benefits results. Because the VOC emission reductions for the Boiler Area Source Rule are less than 1%, we do not include the ozone-related benefits associated with that rule, as the benefits would be very small.
Health Benefits Results
The health benefits have increased since the proposal analysis for two reasons: (1) emission reductions of SO2 have increased, and (2) the BPT for SO2 has increased. The decreases in directly emitted PM2.5 emissions and lower BPT for PM2.5 since the proposal do not offset the increase in benefits from SO2 emission reductions.
Tables 7-2 and 7-3 provide a summary of the monetized PM2.5 benefits (including the indirect PM co-benefits) using the anchor points of Pope et al. (2002) and Laden et al. (2006) at discount rates of 3% and 7% for the final Boiler MACT and Boiler Area Source Rule, respectively. Tables 7-4 and 7-5 provide the reductions in health incidences as a result of the reduction in ambient PM2.5 levels for the final Boiler MACT and Boiler Area Source Rule, respectively. Tables 7-6 and 7-7 provide the total monetized PM2.5 benefits derived from Pope et al. (2002) and Laden et al. (2006) as well as the expert elicitation for the final Boiler MACT and Boiler Area Source Rule, respectively. Table 7-8 provides the estimated reductions in health incidences and the monetized benefits associated with estimated reductions in ambient ozone concentrations.
Table 7-2.	Summary of Monetized Benefits Estimates for Final Boiler MACT in 2014 (millions of 2008$)[a]

                                   Pollutant
                          Emissions Reductions (tons)
                          Benefit per ton (Pope, 3%)
                          Benefit per ton (Laden, 3%)
                          Benefit per ton (Pope, 7%)
                          Benefit per ton (Laden, 7%)
              Total Monetized Benefits (millions of 2008$ at 3%)
              Total Monetized Benefits (millions of 2008$ at 7%)
                               Selected Option
Direct PM2.5
                                    29,007
                                    $72,000
                                   $180,000
                                    $65,000
                                   $160,000
                                                                         $2,100
                                      to
$5,100
                                                                         $1,900
to
$4,600

SO2
                                    439,901
                                    $46,000
                                   $110,000
                                    $42,000
                                   $100,000
                                                                        $20,000
                                      to
$49,000
                                                                        $18,000
                                      to
$45,000

                                     Total
                                                                        $22,000
                                                                             to
$54,000
                                                                        $20,000
                                                                             to
$49,000
                              Alternative Option
Direct PM2.5
                                    28,139
                                    $72,000
                                   $180,000
                                    $65,000
                                   $160,000
                                                                         $2,000
                                      to
$5,000
                                                                         $1,800
to
$4,500

SO2
                                    337,514
                                    $46,000
                                   $110,000
                                    $42,000
                                   $100,000
                                                                        $15,000
                                      to
$38,000
                                                                        $14,000
                                      to
$34,000

                                     Total
                                                                        $17,000
                                                                             to
$43,000
                                                                        $16,000
                                                                             to
$39,000
 [a]	All estimates are for the implementation year (2014), and are rounded to two significant figures so numbers may not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. The benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM2.5. The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles. These estimates do not include benefits from reducing HAP emissions, VOC emissions and ozone exposure, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.
Table 7-3.	Summary of Monetized Benefits Estimates for Final Boiler Area Source Rule in 2014 (millions of 2008$)[a]

                                   Pollutant
                          Emissions Reductions (tons)
                          Benefit per ton (Pope, 3%)
                          Benefit per ton (Laden, 3%)
                          Benefit per ton (Pope, 7%)
                          Benefit per ton (Laden, 7%)
              Total Monetized Benefits (millions of 2008$ at 3%)
              Total Monetized Benefits (millions of 2008$ at 7%)
                            Proposed MACT Approach
Direct PM2.5
                                      590
                                   $120,000
                                   $290,000
                                   $110,000
                                   $260,000
                                                                            $69
                                      to
$170
                                                                            $63
to
$150

SO2
                                     3,197
                                    $41,000
                                   $100,000
                                    $37,000
                                    $91,000
                                                                           $130
                                      to
$320
                                                                           $120
                                      to
$290

                                     Total
                                                                           $200
                                                                             to
$490
                                                                           $180
                                                                             to
$440
                           Final MACT/ GACT Approach
Direct PM2.5
                                      678
                                   $120,000
                                   $290,000
                                   $110,000
                                   $260,000
                                                                            $79
                                      to
$190
                                                                            $72
to
$180

SO2
                                     3,197
                                    $41,000
                                   $100,000
                                    $37,000
                                    $91,000
                                                                           $130
                                      to
$320
                                                                           $120
                                      to
$290

                                     Total
                                                                           $210
                                                                             to
$520
                                                                           $190
                                                                             to
$470
 [a]	All estimates are for the implementation year (2014), and are rounded to two significant figures so numbers may not sum across columns. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. 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 do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section.
Table 7-4.	Summary of Estimated Reductions in Health Incidences from PM2.5 for the Final Boiler MACT in 2014 (95[th] percentile confidence interval) a

                                Selected Option
                              Alternative Option
Avoided Premature Mortality

  Pope et al. (2002)
                                     2,500
                                     2,000

                               (1,600  -  8,100)
                               (1,400  -  7,100)
  Laden et al. (2006)
                                     6,500
                                     5,100

                              (6,200  -  19,000)
                              (5,500  -  16,000)
  Woodruff (Infant mortality)
                                      10
                                       8

                                  (0  -  58)
                                  (0  -  51)
Avoided Morbidity

  Chronic Bronchitis
                                     1,600
                                     1,300

                                (340  -  6,000)
                                (300  -  5,200)
  Acute Myocardial Infarction
                                     4,000
                                     3,100

                              (2,400  -  13,000)
                              (2,100  -  11,000)
  Hospital Admissions, Respiratory
                                      610
                                      480

                                (520  -  1,800)
                                (460  -  1,600)
  Hospital Admissions, Cardiovascular
                                     1,300
                                     1,000

                               (1,800  -  3,000)
                               (1,600  -  2,700)
  Emergency Room Visits, Respiratory
                                     2,400
                                     1,900

                               (2,600  -  6,800)
                               (2,300  -  6,000)
  Acute Bronchitis
                                     3,700
                                     2,900

                                (0  -  15,000)
                                (0  -  13,000)
  Work Loss Days
                                    310,000
                                    250,000

                             (520,000  -  690,000)
                             (460,000  -  600,000)
  Asthma Exacerbation
                                    41,000
                                    32,000

                              (5,800  -  250,000)
                              (5,000  -  220,000)
  Minor Restricted Activity Days
                                   1,900,000
                                   1,500,000

                           (3,000,000  -  4,200,000)
                           (2,600,000  -  3,700,000)
  Lower Respiratory Symptoms
                                    44,000
                                    35,000

                             (37,000  -  130,000)
                             (32,000  -  120,000)
  Upper Respiratory Symptoms
                                    34,000
                                    26,000

                             (16,000  -  110,000)
                              (14,000  -  99,000)
[a]	All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, VOC emissions and ozone exposure, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies, but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.
Table 7-5.	Summary of Estimated Reductions in Health Incidences from PM2.5 for the Final Boiler Area Source Rule in 2014 (95[th] percentile confidence interval)[a]

Proposed MACT Approach
Final MACT/GACT Approach
Avoided Premature Mortality

  Pope et al. (2002)
                                      23
                                      24

                                  (8  -  38)
                                  (8  -  40)
  Laden et al. (2006)
                                      58
                                      61

                                  (29  -  87)
                                  (31  -  91)
  Woodruff (Infant mortality)
                                       0
                                       0

                                   (0  -  0)
                                   (0  -  0)
Avoided Morbidity

  Chronic Bronchitis
                                      16
                                      17

                                  (2  -  30)
                                  (2  -  31)
  Acute Myocardial Infarction
                                      38
                                      40

                                  (12  -  64)
                                  (12  -  67)
  Hospital Admissions, Respiratory
                                       6
                                       6

                                   (3  -  9)
                                   (3  -  9)
  Hospital Admissions, Cardiovascular
                                      12
                                      13

                                  (8  -  14)
                                  (9  -  15)
  Emergency Room Visits, Respiratory
                                      20
                                      21

                                  (11  -  28)
                                  (11  -  30)
  Acute Bronchitis
                                      37
                                      38

                                  (0  -  77)
                                  (0  -  81)
  Work Loss Days
                                     3,100
                                     3,200

                               (2,600  -  3,500)
                               (2,800  -  3,700)
  Asthma Exacerbation
                                      400
                                      420

                                (29  -  1,200)
                                (31  -  1,300)
  Minor Restricted Activity Days
                                    18,000
                                    19,000

                              (15,000  -  21,000)
                              (16,000  -  22,000)
  Lower Respiratory Symptoms
                                      430
                                      460

                                 (190  -  680)
                                 (200  -  710)
  Upper Respiratory Symptoms
                                      330
                                      350

                                 (83  -  580)
                                 (87  -  610)
[a]	All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies, but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits reflect existing boilers and 6,779 new boilers anticipated to come online by 2014.
Table 7-6.	Summary of Monetized Benefits Estimates from PM2.5 for the Final Boiler MACT in 2014 (95[th] percentile confidence interval)[a]

Selected Option
Alternative Option

3%
7%
3%
7%
Based on Epidemiology Literature

  Pope et al. (2002)
                                    $22,000
                                    $20,000
                                    $17,000
                                    $16,000

                             ($1,900  -  $68,000)
                             ($1,600  -  $62,000)
                             ($1,500  -  $53,000)
                             ($1,300  -  $49,000)
  Laden et al. (2006)
                                    $54,000
                                    $49,000
                                    $43,000
                                    $39,000

                             ($4,900  -  $160,000)
                             ($4,300  -  $140,000)
                             ($3,800  -  $120,000)
                             ($3,400  -  $110,000)
Based on Expert Elicitation

Expert A
                                    $58,000
                                    $52,000
                                    $45,000
                                    $41,000

                             ($3,400  -  $190,000)
                             ($3,000  -  $170,000)
                             ($2,700  -  $150,000)
                             ($2,400  -  $140,000)
Expert B
                                    $44,000
                                    $39,000
                                    $34,000
                                    $31,000

                             ($1,700  -  $180,000)
                             ($1,500  -  $160,000)
                             ($1,300  -  $140,000)
                             ($1,200  -  $130,000)
Expert C
                                    $44,000
                                    $40,000
                                    $35,000
                                    $31,000

                             ($2,600  -  $170,000)
                             ($2,300  -  $150,000)
                             ($2,100  -  $130,000)
                             ($1,800  -  $120,000)
Expert D
                                    $31,000
                                    $28,000
                                    $24,000
                                    $22,000

                             ($2,100  -  $100,000)
                             ($1,800  -  $91,000)
                             ($1,600  -  $78,000)
                             ($1,400  -  $71,000)
Expert E
                                    $72,000
                                    $65,000
                                    $56,000
                                    $51,000

                             ($6,200  -  $210,000)
                             ($5,500  -  $190,000)
                             ($4,900  -  $170,000)
                             ($4,400  -  $150,000)
Expert F
                                    $40,000
                                    $36,000
                                    $31,000
                                    $28,000

                             ($3,900  -  $120,000)
                             ($3,500  -  $110,000)
                             ($3,100  -  $92,000)
                             ($2,700  -  $84,000)
Expert G
                                    $26,000
                                    $24,000
                                    $21,000
                                    $19,000

                              ($250  -  $95,000)
                              ($160  -  $86,000)
                              ($190  -  $74,000)
                              ($120  -  $68,000)
Expert H
                                    $33,000
                                    $30,000
                                    $26,000
                                    $24,000

                              ($300  -  $130,000)
                              ($210  -  $120,000)
                              ($240  -  $100,000)
                              ($160  -  $92,000)
Expert I
                                    $44,000
                                    $39,000
                                    $34,000
                                    $31,000

                             ($2,500  -  $140,000)
                             ($2,200  -  $130,000)
                             ($1,900  -  $110,000)
                             ($1,700  -  $100,000)
Expert J
                                    $36,000
                                    $32,000
                                    $28,000
                                    $25,000

                             ($2,700  -  $140,000)
                             ($2,400  -  $120,000)
                             ($2,100  -  $110,000)
                             ($1,900  -  $97,000)
Expert K
                                    $8,400
                                    $7,800
                                    $6,600
                                    $6,100

                              ($250  -  $54,000)
                              ($160  -  $50,000)
                              ($190  -  $43,000)
                              ($120  -  $39,000)
Expert L
                                    $29,000
                                    $27,000
                                    $23,000
                                    $21,000

($1,200  -  $110,000)
($1,000  -  $100,000)
($930  -  $89,000)
($790  -  $81,000)
[a]	All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.
Table 7-7.	Summary of Monetized Benefits Estimates from PM2.5 for the Final Boiler Area Source Rule in 2014 (95[th] percentile confidence interval)[a]

Proposed MACT Approach
Final MACT/GACT Approach

3%
7%
3%
7%
Based on Epidemiology Literature

  Pope et al. (2002)
                                     $200
                                     $180
                                     $210
                                     $190

                                ($17  -  $610)
                                ($15  -  $560)
                                ($18  -  $650)
                                ($16  -  $590)
  Laden et al. (2006)
                                     $490
                                     $440
                                     $520
                                     $470

                               ($44  -  $1,400)
                               ($39  -  $1,300)
                               ($46  -  $1,500)
                               ($41  -  $1,400)
Based on Expert Elicitation

Expert A
                                     $520
                                     $470
                                     $550
                                     $490

                               ($30  -  $1,700)
                               ($27  -  $1,500)
                               ($32  -  $1,800)
                               ($28  -  $1,600)
Expert B
                                     $390
                                     $360
                                     $410
                                     $370

                               ($15  -  $1,600)
                               ($14  -  $1,500)
                               ($16  -  $1,700)
                               ($14  -  $1,500)
Expert C
                                     $400
                                     $360
                                     $420
                                     $380

                               ($24  -  $1,500)
                               ($21  -  $1,400)
                               ($25  -  $1,600)
                               ($22  -  $1,400)
Expert D
                                     $280
                                     $250
                                     $300
                                     $270

                                ($19  -  $900)
                                ($17  -  $820)
                                ($20  -  $950)
                                ($17  -  $860)
Expert E
                                     $640
                                     $580
                                     $680
                                     $610

                               ($56  -  $1,900)
                               ($50  -  $1,700)
                               ($59  -  $2,000)
                               ($52  -  $1,800)
Expert F
                                     $360
                                     $330
                                     $380
                                     $340

                               ($35  -  $1,100)
                                ($31  -  $960)
                               ($37  -  $1,100)
                               ($33  -  $1,000)
Expert G
                                     $240
                                     $220
                                     $250
                                     $230

                                ($2.1  -  $850)
                                ($1.6  -  $780)
                                ($2.2  -  $900)
                                ($1.6  -  $820)
Expert H
                                     $300
                                     $270
                                     $310
                                     $280

                               ($2.6  -  $1,200)
                               ($2.0  -  $1,000)
                               ($2.7  -  $1,200)
                               ($2.1  -  $1,100)
Expert I
                                     $390
                                     $350
                                     $410
                                     $370

                               ($22  -  $1,300)
                               ($20  -  $1,200)
                               ($23  -  $1,400)
                               ($21  -  $1,200)
Expert J
                                     $320
                                     $290
                                     $340
                                     $300

                               ($24  -  $1,200)
                               ($22  -  $1,100)
                               ($25  -  $1,300)
                               ($23  -  $1,200)
Expert K
                                      $77
                                      $71
                                      $81
                                      $75

                                ($2.1  -  $490)
                                ($1.6  -  $450)
                                ($2.2  -  $520)
                                ($1.6  -  $480)
Expert L
                                     $270
                                     $240
                                     $280
                                     $250

                               ($11  -  $1,000)
                                ($10  -  $930)
                               ($12  -  $1,100)
                                ($10  -  $970)
[a]	All estimates are for the analysis year (2014) and are rounded to whole numbers with two significant figures. These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effect estimates by particle type. These estimates do not include benefits from reducing HAP emissions, nor energy disbenefits associated with the increased emissions from additional energy usage described in the next section. These confidence intervals only reflect the standard errors within the epidemiology studies and valuation functions, but they do not reflect other sources of uncertainty inherent within the boiler-specific BPT estimates. These benefits reflect existing boilers and 6,779 new boilers anticipated to come online by 2014.
Table 7-8.	Summary of Monetized Benefits Estimates from Ozone for the Final Boiler MACT in 2014 (95[th] percentile confidence interval)[a]
Avoided Premature Mortality
Incidence
Valuation
(millions of 2008$)
NMMAPS
Bell et al. (2004)
                                       0
                                     $3.3

                                   (0  -  1)
                               ($0.27  -  $9.8)

Schwartz (2005)
                                       1
                                     $5.0

                                   (0  -  1)
                                ($0.41  -  $15)

Huang and Bell (2005)
                                       1
                                     $5.5

                                   (0  -  1)
                                ($0.46  -  $16)
Meta-analyses
Bell et al. (2005)
                                       1
                                      $11

                                   (1  -  2)
                                ($0.94  -  $31)

Ito et al. (2005)
                                       2
                                      $15

                                   (1  -  2)
                                ($1.4  -  $41)

Levy et al. (2005)
                                       2
                                      $15

                                   (1  -  2)
                                ($1.4  -  $41)
Avoided Morbidity

Adult Hospital Admissions, Respiratory
                                       3
                                     $0.07

                                   (0  -  5)
                               ($0.01  -  $0.12)
Infant Hospital Admissions, Respiratory
                                       2
                                     $0.03

                                   (1  -  4)
                               ($0.01  -  $0.04)
Emergency Room Visits, Respiratory
                                       2
                                  < $0.01

                                   (0  -  5)
                               ($0.00  -  $0.01)
School Loss Days
                                      810
                                     $0.08

                                (330  -  1,200)
                               ($0.03  -  $0.11)
Worker Productivity
                                      N/A
                                     $0.02

                               ($0.02  -  $0.02)
Minor Restricted Activity Days
                                     2,300
                                     $0.15

                               (1,100  -  3,600)
                               ($0.06  -  $0.28)
[a]	All estimates are for the analysis year (2014) and are rounded to two significant figures. Health effects associated with ozone exposure are assumed to occur within the analysis year. Therefore, the monetized benefits are the same for any discount rate. These confidence intervals reflect the standard errors within the epidemiology studies and valuation functions. These benefits reflect existing boilers and 47 new boilers anticipated to come online by 2014.
Figures 7-8 and 7-9 illustrate the relative breakdown of the estimated monetized health benefits associated with changes in PM2.5 and ozone, respectively. Figure 7-10 shows the total combined monetized benefits for the final Boiler MACT and Area Source Rule at discount rates of 3% and 7%. Figures 7-11 and 7-12 provide a breakdown of the estimated monetized PM2.5 benefits by precursor pollutant for the final Boiler MACT and Boiler Area Source Rule, respectively. Figures 7-13 and 7-14 provide a breakdown of the monetized PM2.5 benefits by subcategory for the final Boiler MACT and Boiler Area Source Rule, respectively.
This analysis shows that the majority of the population is exposed to PM levels at or above the lowest LML of the cohort studies (Figures 7-15 and 7-16), increasing our confidence in the PM mortality analysis. Because we used BPT estimates, we are unable to provide an estimate of the mortality impacts that occur at various PM levels. Figure 7-15 shows a bar chart of the percentage of the adult population exposed to each PM2.5 level in the baseline. Figure 7-16 shows a cumulative distribution function of the same data. Both figures identify the LML for each of the major cohort studies.
Using the Pope et al. (2002) study, approximately 79% of the population is exposed to annual mean PM2.5 levels at or above the LML of 7.5 ug/m[3]. Using the Laden et al. (2006) study, 34% of the population is exposed to annual mean PM2.5 levels at or above the LML of 10 ug/m[3]. 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.

Figure 7-8.	Breakdown of Monetized PM2.5 Health Benefits Estimates 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.

Figure 7-9.	Breakdown of Monetized Ozone Health Benefits Estimates using Mortality Function from Bell et al. (2004)[a]
[a]	This pie chart breakdown is illustrative, using the results based on Bell et al. (2004) as an example. Using the Levy et al. (2005) function for premature mortality, the percentage of total monetized benefits due to mortality would be 98%.

Figure 7-10.	Total Monetized PM2.5 and Ozone Benefits Estimates for the Final Boiler MACT and Boiler Area Source Rule in 2014[a]
[a]	This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. (2002) study and the Laden et al. (2006) 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 estimates do not include benefits from reducing HAP emissions, but they do include the energy disbenefits associated with the increased emissions from additional energy usage. Due to methodology and time limitations under the court-ordered schedule, we were unable to monetize the benefits associated with several categories of benefits, including direct exposure to HAPs and SO2, as well as ecosystem effects, and visibility effects. These benefits reflect existing boilers and new boilers anticipated to come online by 2014.

Figure 7-11.	Breakdown of Monetized PM2.5 Benefits Estimates by Precursor for the Final Boiler MACT in 2014[a]

Figure 7-12.	Breakdown of Monetized PM2.5 Benefits Estimates by Precursor for the Final Boiler Area Source Rule in 2014[a]

Figure 7-13.	Breakdown of Monetized PM2.5 Benefits Estimates by Subcategory for the Final Boiler MACT in 2014[a]

Figure 7-14.	Breakdown of Monetized PM2.5 Benefits Estimates by Subcategory for the Final Boiler Area Source Rule in 2014[a]

Figure 7-15.	Percentage of Population Exposed to Baseline Air Quality Levels for Final Boiler MACT and Boiler Area Source Rule[a]
[a]	Approximately 79% of the population is exposed to baseline exposure to annual mean PM2.5 levels at or above 7.5 ug/m[3], which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002).

Figure 7-16.	Cumulative Percentage of Population Exposed to Baseline Air Quality Levels for Final Boiler MACT and Boiler Area Source Rule[a]
[a]	Approximately 79% of the population is exposed baseline exposure to annual mean PM2.5 levels at or above 7.5 ug/m[3], which is the lowest air quality level considered in the ACS cohort study by Pope et al. (2002).
Energy Disbenefits
In this section, we provide an estimate of the energy disbenefits associated with the increased emissions from additional energy usage. Electricity usage associated with the operation of control devices is anticipated to increase emissions of pollutants from electric utility boilers (EGU boilers) that supply electricity to the non-EGU boiler facilities. We estimate emission increases of 910,000 tpy CO2 for major boilers and 22,000 tpy CO2 for area boilers.
Social Cost of Carbon and Greenhouse Gas Disbenefits
EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using recent estimates of the "social cost of carbon" (SCC). The SCC is an estimate of the monetized damages associated with an incremental increase in carbon emissions in a given year. It is intended to include (but is not limited to) changes in net agricultural productivity, human health, property damages from increased flood risk, and the value of ecosystem services due to climate change. The SCC estimates used in this analysis were developed through an interagency process that included EPA and other executive branch entities, and concluded in February, 2010. EPA first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; see the rule's preamble for discussion about application of SCC (75 FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete discussion of the methods used to develop these SCC estimates.
The interagency group selected four SCC values for use in regulatory analyses, which we have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions 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 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 2014, in 2005 dollars are provided in Table 7-9.
Table 7-9.	Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2014[a]
Discount Rate and Statistic

SCC estimate (2008$)
5% (Average)

                                     $5.7
3% (Average)

                                     $24.2
2.5% (Average)

                                     $39.1
3% (95[th] percentile)

                                     $73.9
[a]	The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of climate impacts.
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, 2009) 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.