ML12339A607
| ML12339A607 | |
| Person / Time | |
|---|---|
| Site: | Indian Point  |
| Issue date: | 12/31/2011 |
| From: | US Environmental Protection Agency, Office of Air Quality Planning & Standards |
| To: | Atomic Safety and Licensing Board Panel |
| SECY RAS | |
| References | |
| RAS 22161, 50-247-LR, 50-286-LR, ASLBP 07-858-03-LR-BD01 EPA-452/R-11-011 | |
| Download: ML12339A607 (96) | |
Text
United States Nuclear Regulatory Commission Official Hearing Exhibit Entergy Nuclear Operations, Inc.
In the Matter of: ENT000496 (Indian Point Nuclear Generating Units 2 and 3)
Submitted: March 30, 2012 ASLBP #: 07-858-03-LR-BD01 Docket #: 05000247 l 05000286 Exhibit #: ENT000496-00-BD01 Identified: 10/15/2012 Admitted: 10/15/2012 Withdrawn:
Rejected: Stricken:
Other:
Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards
EPA-452/R-11-011 December 2011 Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards U.S. Environmental Protection Agency Office of Air Quality Planning and Standards Health and Environmental Impacts Division Research Triangle Park, NC
CHAPTER 3 COST, ECONOMIC, AND ENERGY IMPACTS This chapter reports the compliance cost, economic, and energy impact analysis performed for the Mercury and Air Toxics Standards (MATS). EPA used the Integrated Planning Model (IPM), developed by ICF Consulting, to conduct its analysis. IPM is a dynamic linear programming model that can be used to examine air pollution control policies for SO2, NOx, Hg, HCl, and other air pollutants throughout the United States for the entire power system.
Documentation for IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa-ipm, and updates specific to the MATS modeling are in the Documentation Supplement for EPA Base Case v.4.10_MATS - Updates for Final Mercury and Air Toxics Standards (MATS) Rule (hereafter IPM 4.10 Supplemental Documentation for MATS).
3.1 Background Over the last decade, EPA has on several occasions used IPM to consider pollution control options for reducing power-sector emissions.1 Most recently EPA used IPM extensively in the development and analysis of the impacts of the Cross-State Air Pollution Rule (CSAPR).2 As discussed in Chapter 2, MATS coincides with a period when many new pollution controls are being installed. Many are needed for compliance with NSR settlements and state rules, while others may have been planned in expectation of CAIR and its replacement, the CSAPR.
The emissions scenarios for the RIA reflects the Cross-State Air Pollution Rule (CSAPR) as finalized in July 2011 and the emissions reductions of SOX, NOX, directly emitted PM, and CO2 are consistent with application of federal rules, state rules and statutes, and other binding, enforceable commitments in place by December 2010 for the analysis timeframe.3 1
Many EPA analyses with IPM have focused on legislative proposals with national scope, such as EPAs IPM analyses of the Clean Air Planning Act (S.843 in 108th Congress), the Clean Power Act (S.150 in 109th Congress),
the Clear Skies Act of 2005 (S.131 in 109th Congress), the Clear Skies Act of 2003 (S.485 in 108th Congress), and the Clear Skies Manager's Mark (of S.131). These analyses are available at EPAs website:
(http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). EPA also analyzed several multi-pollutant reduction scenarios in July 2009 at the request of Senator Tom Carper to illustrate the costs and benefits of multiple levels of SO2 and NOX control in the power sector.
2 Additionally, IPM has been used to develop the NOX Budget Trading Program, the Clean Air Interstate Rule programs, the Clean Air Visibility Programs, and other EPA regulatory programs for the last 15 years.
3 Consistent with the mercury risk deposition modeling for MATS, EPA did not model non-federally enforceable mercury-specific emissions reduction rules in the base case or MATS policy case (see preamble section III.A).
Note that this approach does not significantly affect SO2 and NOX projections underlying the cost and benefit results presented in this RIA 3-1
EPA has made these base case assumptions recognizing that the power sector will install a significant amount of pollution controls in response to several requirements. The inclusion of CSAPR and other regulatory actions (including federal, state, and local actions) in the base case is necessary in order to reflect the level of controls that are likely to be in place in response to other requirements apart from MATS. This base case will provide meaningful projections of how the power sector will respond to the cumulative regulatory requirements for air emissions in totality, while isolating the incremental impacts of MATS relative to a base case with other air emission reduction requirements separate from todays action.
The models base case features an updated Title IV SO2 allowance bank assumption and incorporates updates related to the Energy Independence and Security Act of 2007. Some modeling assumptions, most notably the projected demand for electricity, are based on the 2010 Annual Energy Outlook from the Energy Information Administration (EIA). In addition, the model includes existing policies affecting emissions from the power sector: the Title IV of the Clean Air Act (the Acid Rain Program); the NOx SIP Call; various New Source Review (NSR) settlements 4; and several state rules5 affecting emissions of SO2, NOx, and CO2 that were finalized through June of 2011. IPM includes state rules that have been finalized and/or approved by a states legislature or environmental agency, with the exception of non-federal mercury-specific rules. The IPM 4.10 Supplemental Documentation for MATS contains details on all of these other legally binding and enforceable commitments for installation and operation of pollution controls. This chapter focuses on results of EPAs analysis with IPM for the models 2015 run-year in connection with the compliance date for MATS.
MATS establishes National Emissions Standards for Hazardous Air Pollutants (NESHAPS) for the electric utility steam generating unit source category, which includes those units that combust coal or oil for the purpose of generating electricity for sale and distribution through the national electric grid to the public.
4 The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Company (Vectren),
Public Service Enterprise Group, Tampa Electric Company, We Energies (WEPCO), Virginia Electric & Power Company (Dominion), Santee Cooper, Minnkota Power Coop, American Electric Power (AEP), East Kentucky Power Cooperative (EKPC), Nevada Power Company, Illinois Power, Mirant, Ohio Edison, Kentucky Utilities, Hoosier Energy, Salt River Project, Westar, Puerto Rico Power Authority, Duke Energy, American Municipal Power, and Dayton Power and Light. These agreements lay out specific NOx, SO2, and other emissions controls for the fleets of these major Eastern companies by specified dates. Many of the pollution controls are required between 2010 and 2015.
5 These include current and future state programs in Alabama, Arizona, California, Colorado, Connecticut, Delaware, Georgia, Illinois, Kansas, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, New Hampshire, New Jersey, New York, North Carolina, Oregon, Pennsylvania, Tennessee, Texas, Utah, Washington, West Virginia, and Wisconsin the cover certain emissions from the power sector.
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Coal-fired electric utility steam generating units include electric utility steam generating units that burn coal, coal refuse, or a synthetic gas derived from coal either exclusively, in any combination together, or in any combination with other supplemental fuels. Examples of supplemental fuels include petroleum coke and tire-derived fuels. The NESHAP establishes standards for HAP emissions from both coal- and oil-fired EGUs and will apply to any existing, new, or reconstructed units located at major or area sources of HAP. Although all HAP are pollutants of interest, those of particular concern are hydrogen fluoride (HF), hydrogen chloride (HCl), dioxins/furans, and HAP metals, including antimony, arsenic, beryllium, cadmium, chromium, cobalt, mercury, manganese, nickel, lead, and selenium.
This rule affects any fossil fuel fired combustion unit of more than 25 megawatts electric (MWe) that serves a generator that produces electricity for sale. A unit that cogenerates steam and electricity and supplies more than one-third of its potential electric output capacity and more than 25 MWe output to any utility power distribution system for sale is also considered an electric utility steam generating unit. The rule affects roughly 1,400 EGUs: approximately 1,100 existing coal-fired generating units and 300 oil-fired steam units, should those units combust oil. Of the 600 power plants potentially covered by this rule, about 430 have coal-fired units only, 30 have both coal- and oil- or gas-fired steam units, and 130 have oil- or gas-fired steam units only. Note that only steam electric units combusting coal or oil are covered by this rule.
EPA analyzed for the RIA the input-based (lbs/MMBtu) MATS control requirements shown in Table 3-1. In this analysis, EPA does not model an alternative SO2 standard. Coal steam units with access to lignite in the modeling are subjected to the Existing coal-fired unit low Btu virgin coal standard. For further discussion about the scope and requirements of MATS, see the preamble or Chapter 1 of this RIA.
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Table 3-1. Emissions Limitations for Coal-Fired and Solid Oil-Derived Fuel-Fired Electric Utility Steam Generating Units Subcategory Filterable Hydrogen Chloride Mercury Particulate Matter Existing coal-fired unit not low Btu 0.030 lb/MMBtu 0.0020 lb/MMBtu 1.2 lb/TBtu virgin coal (0.30 lb/MWh) (0.020 lb/MWh) (0.020 lb/GWh) 11.0 lb/TBtu Existing coal-fired unit low Btu 0.030 lb/MMBtu 0.0020 lb/MMBtu (0.20 lb/GWh) a virgin coal (0.30 lb/MWh) (0.020 lb/MWh) 4.0 lb/TBtu a
(0.040 lb/GWh )
0.040 lb/MMBtu 0.00050 lb/MMBtu 2.5 lb/TBtu Existing - IGCC (0.40 lb/MWh) (0.0050 lb/MWh) (0.030 lb/GWh) 0.0080 lb/MMBtu 0.0050 lb/MMBtu 0.20 lb/TBtu Existing - Solid oil-derived (0.090 lb/MWh) (0.080 lb/MWh) (0.0020 lb/GWh)
New coal-fired unit not low Btu 0.0070 lb/MWh 0.40 lb/GWh 0.00020 lb/GWh virgin coal New coal-fired unit low Btu virgin 0.040 lb/GWh 0.0070 lb/MWh 0.40 lb/GWh coal b
0.070 lb/MWh d e New - IGCC c 0.0020 lb/MWh 0.0030 lb/GWh 0.090 lb/MWh New - Solid oil-derived 0.020 lb/MWh 0.00040 lb/MWh 0.0020 lb/GWh Note: lb/MMBtu = pounds pollutant per million British thermal units fuel input lb/TBtu = pounds pollutant per trillion British thermal units fuel input lb/MWh = pounds pollutant per megawatt-hour electric output (gross) lb/GWh = pounds pollutant per gigawatt-hour electric output (gross) a Beyond-the-floor limit as discussed elsewhere b
Duct burners on syngas; based on permit levels in comments received c
Duct burners on natural gas; based on permit levels in comments received d
Based on best-performing similar source e
Based on permit levels in comments received 3-4
Table 3-2. Emissions Limitations for Liquid Oil-Fired Electric Utility Steam Generating Units Subcategory Filterable PM Hydrogen Chloride Hydrogen Fluoride Existing - Liquid oil-0.030 lb/MMBtu 0.0020 lb/MMBtu 0.00040 lb/MMBtu continental (0.30 lb/MWh) (0.010 lb/MWh) (0.0040 lb/MWh)
Existing - Liquid oil-0.030 lb/MMBtu 0.00020 lb/MMBtu 0.000060 lb/MMBtu non-continental (0.30 lb/MWh) (0.0020 lb/MWh) (0.00050 lb/MWh)
New - Liquid oil -
continental 0.070 lb/MWh 0.00040 lb/MWh 0.00040 lb/MWh New - Liquid oil -
non-continental 0.20 lb/MWh 0.0020 lb/MWh 0.00050 lb/MWh EPA used the Integrated Planning Model (IPM) v.4.10 to assess the impacts of the MATS emission limitations for coal-fired electricity generating units (EGU) in the contiguous United States. IPM modeling did not subject oil-fired units to policy criteria.6 Furthermore, IPM modeling did not include generation outside the contiguous U.S., where EPA is aware of only 2 facilities that would be subject to the coal-fired requirements of the final rule. Given the limited number of potentially impacted facilities, limited availability of input data to inform the modeling, and limited connection to the continental grid, EPA did not model the impacts of the rule beyond the contiguous U.S.
Mercury emissions are modeled as a function of mercury content of the fuel type(s) consumed at each plant in concert with that plants pollutant control configuration. HCl emissions are projected in a similar fashion using the chlorine content of the fuel(s). For both mercury and HCl, EGUs in the model must emit at or below the final mercury and HCl emission rate standards in order to operate from 2015 onwards. EGUs may change fuels and/or install additional control technology to meet the standard, or they may choose to retire if it is more economic for the power sector to meet electricity demand with other sources of generation.
See IPM 4.10 documentation and IPM 4.10 Supplemental Documentation for MATS for more details.
Total PM emissions are calculated exogenously to IPM, using EPAs Source Classification Code (SCC) and control-based emissions factors. SCC is a classification system that describes a generating units characteristics.
6 EPA did not model the impacts of MATS on oil-fired units using IPM. Rather, EPA performed an analysis of impacts on oil-fired units for the final rule. The results are summarized in Appendix 3A.
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Instead of emission limitations for the organic HAP, EPA is proposing that if requested, owners or operators of EGUs submit to the delegated authority or EPA, as appropriate, documentation showing that an annual performance test meeting the requirements of the rule was conducted. IPM modeling of the MATS policy assumes compliance with these work practice standards.
Electricity demand is anticipated to grow by roughly 1 percent per year, and total electricity demand is projected to be 4,103 billion kWh by 2015. Table 3-3 shows current electricity generation alongside EPAs base case projection for 2015 generation using IPM. EPAs IPM modeling for this rule relies on EIAs Annual Energy Outlook for 2010s electric demand forecast for the US and employs a set of EPA assumptions regarding fuel supplies and the performance and cost of electric generation technologies as well as pollution controls.7 The base case includes CSAPR as well as other existing state and federal programs for air emissions control from electric generating units, with the exception of state mercury rules.
7 Note that projected electricity demand in AEO 2010 is about 2% higher than the AEO 2011 projection in 2015.
Since this RIA assumes higher electricity demand in 2015 than is shown in the latest AEO projection, it is possible that the model may be taking compliance actions to meet incremental electricity demand that may not actually occur, and projected compliance costs may therefore be somewhat overstated in this analysis.
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Table 3-3. 2009 U.S. Electricity Net Generation and EPA Base Case Projections for 2015-2030 (Billion kWh)
Historical Base Case 2009 2015 2020 2030 Coal 1,741 1,982 2,002 2,027 Oil 36 0.11 0.13 0.21 Natural Gas 841 710 847 1,185 Nuclear 799 828 837 817 Hydroelectric 267 286 286 286 Non-hydro Renewables 116 252 289 333 Other 10 45 45 55 Total 3,810 4,103 4,307 4,702 Source: 2009 data from AEO Annual Energy Review, Table 8.2c Electricity Net Generation: Electric Power Sector by Plant Type, 1989-2010; Projections from Integrated Planning Model run by EPA, 2011.
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Figure 3-1. Geographic Distribution of Affected Units, by Facility, Size and Fuel Source in 2012 Source/Notes: National Electric Energy Data System (NEEDS 4.10 MATS) (EPA, December 2011) and EPAs Information Collection Request (ICR) for New and Existing Coal- And Oil-Fired Electric Utility Stream Generation Units (2010). This map displays facilities that are included in the NEEDS 4.10 MATS data base and that contain at least one oil-fired steam generating unit or one coal-fired steam generating unit that generates more than 25 megawatts of power. This includes coal-fired units that burn petroleum coke and that turn coal into gas before burning (using integrated gasification combined cycle or IGCC). NEEDS reflects available capacity on-line by the end of 2011; this includes committed new builds and committed retirements of old units. Only coal and oil-fired units are covered by this rule. Some of the oil units displayed on the map are capable of burning oil and/or gas. If a unit burns only gas, it will not be covered in the rule. In areas with a dense concentration of facilities, the facilities on the map may overlap and some may be impossible to see. IPM modeling did not include generation outside the contiguous U.S., where EPA is aware of only two facilities that would be subject to the coal-fired requirements of the final rule. Given the limited number of potentially impacted facilities, limited availability of input data to inform the modeling, and limited connection to the continental grid, EPA did not model the impacts of the rule beyond the contiguous U.S. Facilities outside the contiguous U.S. are displayed based on data from EPAs 2010 ICR for the rule.
As noted above, IPM has been used for evaluating the economic and emission impacts of environmental policies for over two decades. The economic modeling presented in this chapter has been developed for specific analyses of the power sector. Thus, the model has been designed to reflect the industry as accurately as possible. To that end, EPA uses a series of capital charge factors in IPM that embody financial terms for the various types of investments that the power sector considers for meeting future generation and environmental constraints.
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The model applies a discount rate of 6.15% for optimizing the sectors decision-making over time. IPMs discount rate, designed to represent a broad range of private-sector decisions for power generation, rates differs from discount rates used in other analyses in this RIA, such as the benefits analysis which each assume alternative social discount rates of 3% and 7%. These discount rates represent social rates of time preference, whereas the discount rate in IPM represents an empirically-informed price of raising capital for the power sector. Like all other assumed price inputs in IPM, EPA uses the best available information from utilities, financial institutions, debt rating agencies, and government statistics as the basis for the capital charge rates and the discount rate used for power sector modeling in IPM.
More detail on IPM can be found in the model documentation, which provides additional information on the assumptions discussed here as well as all other assumptions and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epa-ipm). Updates specific to MATS modeling are also in the IPM 4.10 Supplemental Documentation for MATS.
3.2 Projected Emissions MATS is anticipated to achieve substantial emissions reductions from the power sector.
Since the technologies available to meet the emission reduction requirements of the rule reduce multiple air pollutants, EPA expects the rule to yield a broad array of pollutant reductions from the power sector. The primary pollutants of concern under MATS from the power sector are mercury, acid gases such as hydrogen chloride (HCl), and HAP metals, including antimony, arsenic, beryllium, cadmium, chromium, cobalt, mercury, manganese, nickel, lead, and selenium. EPA has extensively analyzed mercury emissions from the power sector, and IPM modeling assesses the mercury contents in all coals and the removal efficiencies of relevant emission control technologies (e.g., ACI). EPA also models emissions and the pollution control technologies associated with HCl (as a surrogate for acid gas emissions).
Like SO2, HCl is removed by both scrubbers and DSI (dry sorbent injection). Projected emissions are based on both control technology and detailed coal supply curves used in the model that reflect the chlorine content of coals, which corresponds with the supply region, coal grade, and sulfur, mercury, and ash content of each coal type. This information is critical for accurately projecting future HCl emissions, and for understanding how the power sector will respond to a policy requiring reductions of multiple HAPs.
Generally, existing pollution control technologies reduce emissions across a range of pollutants. For example, both FGD and SCR can achieve notable reductions in mercury in addition to their primary targets of SO2 and NOX reductions. DSI will reduce HCl emissions while 3-9
also yielding substantial SO2 emission reductions, but is not assumed in EPA modeling to result in mercury reductions. Since there are many avenues to reduce emissions, and because the power sector is a highly complex and dynamic industry, EPA employs IPM in order to reflect the relevant components of the power sector accurately, while also providing a sophisticated view of how the industry could respond to particular policies to reduce emissions. For more detail on how EPA models emissions from the power sector, including recent updates to include acid gases, see IPM 4.10 Supplemental Documentation for MATS.
Under MATS, EPA projects annual HCl emissions reductions of 88 percent in 2015, Hg emissions reductions of 75 percent in 2015, and PM2.5 emissions reductions of 19 percent in 2015 from coal-fired EGUs greater than 25 MW. In addition, EPA projects SO2 emission reductions of 41 percent, and annual CO2 reductions of 1 percent from coal-fired EGUs greater than 25 MW by 2015, relative to the base case (see Table 3-4). 8 Mercury emission projections in EPAs base case are affected by the incidental capture in other pollution control technologies (such as FGD and SCR) as described above.
Table 3-4. Projected Emissions of SO2, NOX, Mercury, Hydrogen Chloride, PM, and CO2 with the Base Case and with MATS, 2015 Million Tons Thousand Tons CO2 Mercury (Million Metric SO2 NOX (Tons) HCl PM2.5 Tonnes)
Base All EGUs 3.4 1.9 28.7 48.7 277 2,230 Covered EGUs 3.3 1.7 26.6 45.3 270 1,906 MATS All EGUs 2.1 1.9 8.8 9.0 227 2,215 Covered EGUs 1.9 1.7 6.6 5.5 218 1,882 Source: Integrated Planning Model run by EPA, 2011 8
The CO2 emissions reported from IPM account for the direct CO2 emissions from fuel combustion and CO2 created from chemical reactions in pollution controls to reduced sulfur.
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APPENDIX 5A IMPACT OF THE INTERIM POLICY SCENARIO ON EMISSIONS 5A.1 Introduction This section summarizes the emissions inventories that are used to create emissions inputs to the air quality modeling performed for this rule. A summary of the emissions reductions that were modeled for this rule is provided. Note that the emissions processing and corresponding air quality modeling were used to develop BPT scaling factors for the benefits calculation as described in this RIA. More information on this approach can be found in Appendix 5C. The emissions inventories were processed into the form required by the Community Multi-scale Air Quality (CMAQ) model. CMAQ simulates the numerous physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and air toxics.
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 and visibility impairment. Model predictions of PM2.5 and ozone are used in a relative sense to estimate scenario-specific, future-year design values of PM2.5 and ozone. These are combined with monitoring data to estimate population-level exposures to changes in ambient concentrations for use in estimating health and welfare effects. In the remainder of this section we provide an overview of (1) the emissions components of the modeling platform, (2) the development of the 2005 base year emissions, (3) the development of the future year baseline emissions, and (4) the development of the future year control case emissions.
5A.2 Overview of Modeling Platform and Emissions Processing Performed A modeling platform is the collection of the inputs to an air quality model, including the settings and data used for the model, including emissions data, meteorology, initial conditions, and boundary conditions. The 2005-based air quality modeling platform used for this RIA includes 2005 base year emissions and 2005 meteorology for modeling ozone and PM2.5 with CMAQ. In support of this rule, EPA modeled the air quality in the Eastern and the Western United States using two separate model runs, each with a horizontal grid resolution of 12 km x 12 km. These 12 km modeling domains were nested within a modeling domain covering the remainder of the lower 48 states and surrounding areas using a grid resolution of 36 x 36 km.
The results from the 36-km modeling were used to provide incoming boundary for the 12km grids. Additional details on the non-emissions portion of the 2005v4.3 modeling platform used for this RIA are described in the air quality modeling section (Appendix 5B).
The 2005-based air quality modeling platform used in support of this RIA is version 4.3 and is referred to as the 2005v4.3 platform. It is an update to the 2005-based platform, version 4.1 (i.e., 2005v4.1) used for the proposal modeling and for the appropriate and necessary finding. The Technical Support Document Preparation of Emissions Inventories for the Version 4.1, 2005-based Platform (see http://www.epa.gov/ttn/chief/emch/index.html#toxics) provides information on the platform used for the proposed version of this rule and for the appropriate and necessary finding. The 2005v4.3 platform builds upon the 2005-based platform, version 4.2 which was the version of the platform used for the final Cross-State Air Pollution Rule and incorporated changes made in response to public comments on the proposed version of that rule. Detailed documentation about the 2005v4.3 platform emissions inventories used for this rule can be found in the Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document.
5A.3 Development of 2005 Base Year Emissions Emissions inventory inputs representing the year 2005 were developed to provide a base year for forecasting future air quality. The emission source sectors and the basis for current and future-year inventories include Electric Generating Utility point sources, non-EGU point sources, and the following types of sources with inventories primarily at the county level:
onroad mobile, nonroad mobile, nonpoint, and fires. The specific sectors used for modeling are listed and defined in detail in the Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document. The inventories used include emissions of criteria pollutants, and for some sectors the pollutants benzene, formaldehyde, acetaldehyde and methanol are used to speciate VOC into the chemical species needed by CMAQ.
The 2005v4 platform was the initial starting point for the 2005v4.3 platform used for this modeling. There were two intermediate versions: the version used for the MATS proposal modeling (2005v4.1), and the version used for the final Cross-State Air Pollution Rule modeling (2005v4.2). The basis of the 2005v4 platform and subsequent versions is the U.S. inventory is the 2005 National Emission Inventory (NEI), version 2 from October 6, 2008 (http://www.epa.gov/ttn/chief/net/2005inventory.html). The 2005 NEI v2 includes 2005-specific data for point and mobile sources, while most nonpoint data were carried forward from version 3 of the 2002 NEI.
Emissions for point sources were primarily from the 2005 NEI v2 inventory, consisting mostly of 2005 values with some 2002 emissions values used where 2005 data were not available. The point sources are split into EGU (aka ptipm) and Non-EGU (aka 5A-2
ptnonipm) sectors for modeling purposes based on the matching of the unit level data in the NEI units in the National Electric Energy Database System (NEEDS) version 4.10 database. All units that matched NEEDS were included in the EGU sector so that the future year emissions could easily be taken from the Integrated Planning Model (IPM) as its outputs are also based on the NEEDS units. Efforts made to ensure the quality of the 2005 EGU inventory included ensuring that there were not duplicate emissions (e.g., data pulled forward from earlier inventories), accounting for plants or units that shutdown prior to 2005, adding estimates for ethanol plants, and accounting for installed emissions control devices.
The 2005 annual NOX and SO2 emissions for sources in the EGU sector are based primarily on data from EPAs Clean Air Markets Divisions Continuous Emissions Monitoring (CEM) program, with other pollutants estimated using emission factors and the CEM annual heat input. For EGUs without CEMs, emissions were obtained from the state-submitted data in the NEI. For the 2005 base year, the annual EGU NEI emissions were allocated to hourly emissions values needed for modeling based on the 2004, 2005, and 2006 CEM data. The NOX CEM data were used to create NOX-specific profiles, the SO2 data were used to create SO2-specific profiles, and the heat input data were used to allocate all other pollutants. The three years of data were used to create monthly profiles by state, while the 2005 data were used to create state-averaged profiles for allocating monthly emissions to daily. These daily values were input into SMOKE, which utilized state-averaged 2005-based hourly profiles to allocate to hourly values. This approach to temporal allocation was used for all base and control cases modeled to provide a temporal consistency between the years modeled without tying the temporalization to the events of a single year.
For nonpoint sources, the 2002 NEI v2 inventory was augmented with updated oil and gas exploration emissions for Texas and Oklahoma (for CO, NOX, PM, SO2, VOC). These oil and gas exploration emissions were in addition to oil and gas data previously available in the 2005 v4 platform that includes emissions within the following states: Arizona, Colorado, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, and Wyoming.
The commercial marine category 3 (C3) vessel emissions portion of the nonroad sector used point-based gridded 2005 emissions that reflect the final projections developed for the category 3 commercial marine Emissions Control Area (ECA) proposal to the International Maritime Organization (EPA-420-F-10-041, August 2010). These emissions include Canada as part of the ECA, and were updated using region-specific growth rates and thus contain Canadian province codes. The state/federal water boundaries were based on a file available 5A-3
from the Mineral Management Service (MMS) that specifies boundaries ranging from three to ten nautical miles from the coast.
The onroad emissions were primarily based on the version of the Motor Vehicle Emissions Simulator (MOVES) (http://www.epa.gov/otaq/models/moves/) used for the Tier 3 proposed rule. The first step was to run MOVES to output emission factors for a set of counties with characteristics representative of the counties within the continental United States. Data for each representative county included county-specific fuels, vehicle age distribution, inspection and maintenance programs, temperatures and relative humidity. The emission factors produced by MOVES were then combined by SMOKE with county-based activity data (vehicle miles traveled, speed data, and vehicle population) and gridded temperature data to create hourly, gridded emissions. Additional information on this approach is available in the Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document.
The nonroad emissions utilized the National Mobile Inventory Model (NMIM) to run the NONROAD model for all states to create county/month emissions, updated from the annual emissions in the 2005 NEI v2 with some improvements. For this case, NMIM was run using representing county mode, with improved fuels, an improved toxics emission factor (1,3-butadiene for 2-stroke snowmobiles), and a small coding change that enabled NONROAD to process 15% ethanol (E15) fuels.
Other emissions inventories used included average-year county-based inventories for emissions from wildfires and prescribed burning. These emissions are intended to be representative of both base and future years and are held constant for each. As a result, post-processing techniques minimize their impact on the modeling results. The 2005v4.3 platform utilizes the same 2006 Canadian inventory and a 1999 Mexican inventory as were used since the v4 platform, as these were the latest available data from these countries.
Once developed, the emissions inventories were processed to provide the hourly, gridded emissions for the model-species needed by CMAQ. Details on this processing are further described in the Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document. Table 5A-1 provides summaries of the 2005 U.S. emissions inventories modeled for this rule by sector. Tables 5A-2 through 5A-3 provide state-level summaries of SO2, and PM2.5 by sector. Note that the nonroad columns include emissions from traditional nonroad sources that are found on-land, along with commercial marine sources.
The nonpoint columns include area fugitive dust, agriculture, and other nonpoint emissions.
Table 5A-1. 2005 US Emissions by Sector 2005 NOX 2005 SO2 2005 PM2.5 2005 PM10 2005 NH3 2005 CO 2005 VOC Emissions Sector [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]
Agriculture 3,251,990 Area fugitive dust 1,030,391 8,858,992 Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992 Commercial marine 130,164 97,485 10,673 11,628 11,862 4,570 Category 3 (US)
EGU 3,729,161 10,380,883 496,877 602,236 21,995 603,788 41,089 Locomotive/marine 1,922,723 153,068 56,666 59,342 773 270,007 67,690 Non-EGU point 2,213,471 2,030,759 433,346 647,873 158,342 3,201,418 1,279,308 Nonpoint 1,696,902 1,216,362 1,079,906 1,349,639 133,962 7,410,946 7,560,061 Nonroad 2,031,527 196,277 201,406 210,767 1,971 20,742,873 2,806,422 Onroad 8,235,002 168,480 301,073 369,911 144,409 41,117,658 3,267,931 US Total 20,148,378 14,292,410 4,294,373 12,906,616 3,750,218 81,913,104 16,986,064 Table 5A-2. 2005 Base Year SO2 Emissions (tons/year) for States by Sector State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Alabama 460,123 66,373 52,325 5,622 3,554 983 588,980 Arizona 52,733 23,966 2,571 6,151 3,622 2,888 91,931 Arkansas 66,384 13,039 27,260 5,678 1,918 728 115,008 California 601 33,097 77,672 40,222 4,526 6,735 162,852 Colorado 64,174 1,550 6,810 4,897 2,948 1,719 82,098 (continued) 5A-5
Table 5A-2. 2005 Base Year SO2 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Connecticut 10,356 1,831 18,455 2,557 1,337 4 34,540 Delaware 32,378 34,859 1,030 2,657 486 6 71,416 District of Columbia 1,082 686 1,559 414 205 0 3,947 Florida 417,321 57,429 70,490 31,190 12,388 7,018 595,836 Georgia 616,063 52,827 56,829 9,224 6,939 2,010 743,893 Idaho 0 17,151 2,915 2,304 902 3,845 27,117 Illinois 330,382 131,357 5,395 19,305 6,881 20 493,339 Indiana 878,979 86,337 59,775 9,437 4,641 24 1,039,194 Iowa 130,264 41,010 19,832 8,838 2,036 25 202,004 Kansas 136,520 12,926 36,381 8,035 1,978 103 195,943 Kentucky 502,731 25,808 34,229 6,943 3,240 364 573,315 Louisiana 109,875 165,705 2,378 25,451 2,902 892 307,202 Maine 3,887 18,512 9,969 1,625 963 150 35,106 Maryland 283,205 34,988 40,864 9,353 3,016 32 371,458 Massachusetts 84,234 19,620 25,261 6,524 2,669 93 138,402 Michigan 349,877 76,510 42,066 14,626 8,253 91 491,423 Minnesota 101,678 24,603 14,747 10,409 2,934 631 155,002 Mississippi 75,047 29,892 6,796 5,930 2,590 1,051 121,306 Missouri 284,384 78,308 44,573 10,464 4,901 186 422,816 Montana 19,715 11,056 2,600 3,813 874 1,422 39,480 Nebraska 74,955 7,910 7,659 9,199 1,510 105 101,337 Nevada 53,363 2,253 12,477 2,880 656 1,346 72,975 New Hampshire 51,445 3,155 7,408 789 746 38 63,580 New Jersey 57,044 7,639 10,726 13,321 3,038 61 91,830 New Mexico 30,628 7,831 3,193 3,541 1,801 3,450 50,445 New York 180,847 58,426 125,158 15,666 6,258 113 386,468 North Carolina 512,231 59,433 22,020 8,766 6,287 696 609,433 North Dakota 137,371 9,582 6,455 5,986 533 66 159,994 Ohio 1,116,095 115,155 19,810 15,425 7,336 22 1,273,843 (continued) 5A-6
Table 5A-2. 2005 Base Year SO2 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Oklahoma 110,081 40,482 8,556 5,015 3,039 469 167,642 Oregon 12,304 9,825 9,845 5,697 1,790 4,896 44,357 Pennsylvania 1,002,203 83,375 68,349 11,999 6,266 32 1,172,224 Rhode Island 176 2,743 3,365 816 254 1 7,354 South Carolina 218,781 31,495 13,489 7,719 3,589 646 275,719 South Dakota 12,215 1,702 10,347 3,412 623 498 28,797 Tennessee 266,148 65,693 32,714 6,288 5,538 277 376,659 Texas 534,949 223,625 115,192 34,944 16,592 1,178 926,480 Tribal 3 1,511 0 0 0 0 1,515 Utah 34,813 9,132 3,577 2,439 1,890 1,934 53,784 Vermont 9 902 5,385 385 342 49 7,073 Virginia 220,287 69,401 32,923 10,095 4,600 399 337,705 Washington 3,409 24,211 7,254 18,810 3,343 407 57,433 West Virginia 469,456 46,710 14,589 2,133 1,378 215 534,481 Wisconsin 180,200 66,807 6,369 7,163 3,647 70 264,256 Wyoming 89,874 22,321 6,721 2,674 721 1,106 123,417 Total 10,380,883 2,030,759 1,216,362 446,831 168,480 49,094 14,292,410 Table 5A-3. 2005 Base Year PM2.5 Emissions (tons/year) for States by Sector State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Alabama 23,366 19,498 35,555 4,142 5,775 13,938 102,273 Arizona 7,418 3,940 21,402 4,486 6,920 37,151 81,316 Arkansas 1,688 10,820 34,744 3,803 3,102 10,315 64,472 California 347 21,517 94,200 22,815 26,501 97,302 262,682 Colorado 4,342 7,116 25,340 3,960 4,377 24,054 69,189 Connecticut 562 224 11,460 1,740 2,544 56 16,586 Delaware 2,169 1,810 1,590 818 922 87 7,397 District of Columbia 17 172 589 277 367 0 1,421 (continued) 5A-7
Table 5A-3. 2005 Base Year PM2.5 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Florida 24,217 25,193 52,955 15,035 16,241 99,484 233,125 Georgia 28,057 12,666 63,133 6,504 12,449 24,082 146,892 Idaho 0 2,072 41,492 2,140 1,402 52,808 99,914 Illinois 16,585 15,155 74,045 12,880 12,574 277 131,516 Indiana 34,439 14,124 74,443 6,515 7,585 344 137,450 Iowa 8,898 6,439 54,312 6,969 3,468 349 80,436 Kansas 5,549 7,387 138,437 5,719 3,109 1,468 161,669 Kentucky 19,830 10,453 31,245 4,762 5,566 5,155 77,010 Louisiana 5,599 32,201 28,164 9,440 4,288 12,647 92,339 Maine 52 3,783 15,037 1,363 1,759 2,127 24,120 Maryland 15,417 6,768 23,323 3,410 5,504 531 54,952 Massachusetts 3,110 2,245 31,116 3,293 5,913 1,324 47,001 Michigan 11,022 12,926 47,722 8,561 13,006 1,283 94,520 Minnesota 3,262 10,538 73,990 8,541 6,842 8,943 112,116 Mississippi 2,029 10,602 34,217 4,133 4,195 14,897 70,074 Missouri 6,471 6,966 76,419 7,230 7,665 2,636 107,388 Montana 2,398 2,729 30,096 2,654 1,347 17,311 56,536 Nebraska 1,246 2,340 45,661 5,848 2,620 1,483 59,198 Nevada 3,341 4,095 9,920 2,212 1,290 19,018 39,876 New Hampshire 2,586 568 13,316 907 1,512 534 19,423 New Jersey 4,625 2,588 13,623 5,042 5,963 865 32,707 New Mexico 5,583 1,460 50,698 1,959 2,861 48,662 111,224 New York 9,648 4,994 48,540 8,607 11,139 1,601 84,529 North Carolina 16,967 12,665 49,551 6,272 8,939 9,870 104,264 North Dakota 6,397 598 41,504 4,552 976 934 54,962 Ohio 53,572 12,847 52,348 9,847 11,785 316 140,715 Oklahoma 1,411 6,246 90,047 3,765 4,559 6,644 112,672 Oregon 412 8,852 58,145 3,741 3,375 65,350 139,874 Pennsylvania 55,547 16,263 44,607 7,565 11,058 454 135,494 (continued) 5A-8
Table 5A-3. 2005 Base Year PM2.5 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Rhode Island 10 256 1,289 394 577 14 2,540 South Carolina 14,455 4,779 26,598 3,491 5,061 9,163 63,548 South Dakota 390 2,982 33,678 2,910 1,056 7,062 48,079 Tennessee 12,856 21,912 32,563 5,072 8,514 3,934 84,851 Texas 21,464 37,563 194,036 21,361 29,859 21,578 325,861 Tribal 0 1,569 0 0 0 0 1,569 Utah 5,055 3,595 14,761 1,627 2,703 27,412 55,153 Vermont 37 337 6,943 479 605 696 9,098 Virginia 12,357 11,455 38,140 5,968 6,661 5,659 80,241 Washington 2,396 4,618 45,599 6,697 6,721 4,487 70,519 West Virginia 26,377 5,154 14,778 1,702 1,930 3,050 52,991 Wisconsin 5,233 7,967 37,277 6,083 6,783 994 64,337 Wyoming 8,068 10,298 31,645 1,455 1,103 15,686 68,254 Total 496,877 433,346 2,110,298 268,745 301,073 684,035 4,294,373 5A.4 Development of Future year baseline Emissions The future year baseline scenario, also known as the reference case, represents predicted emissions including adjustments for known promulgated federal measures for all sectors as of the year 2017, which is assumed to be representative of 2016. The EGU and mobile sectors reflect projected economic and fuel usage changes. Emissions from non-EGU stationary sectors have previously been shown to not be well correlated with economic forecasts, and therefore economic impacts were not included for non-EGU stationary sources.
Like the 2005 base case, these emissions cases include criteria pollutants and for some sectors, benzene, formaldehyde, acetaldehyde and methanol from the inventory is used in VOC speciation. The future year baseline scenario represents predicted emissions in the absence of any further controls beyond those Federal measures already promulgated. For EGUs, all state and other programs available at the time of modeling have been included. For mobile sources, all national measures promulgated at the time of modeling have been included. Additional details on the future year baseline (i.e., reference case) emissions modeling can be found in the 5A-9
Emissions Modeling for the Final Mercury and Air Toxics Standards Technical Support Document.
The future year baseline EGU emissions were obtained using version 4.10 Final of the Integrated Planning Model (IPM) (http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector. Version 4.10 Final reflects state rules and consent decrees through December 1, 2010, information obtained from the 2010 Information Collection Request (ICR), and information from comments received on the IPM-related Notice of Data Availability (NODA) published on September 1, 2010. Notably, IPM 4.1 Final included the addition of over 20 GW of existing Activated Carbon Injection (ACI) for coal-fired EGUs reported to EPA via the ICR. Additional unit-level updates that identified existing pollution controls (such as scrubbers) were also made based on the ICR and on comments from the IPM NODA. Units with SO2 or NOX advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV, New Source Review (NSR), state settlements, or state-specific rules were modeled by IPM to either operate those controls or not based on economic efficiency parameters. The IPM run for future year baseline case modeled with CMAQ assumed that 100%
of the HCl found in the coal was emitted into the atmosphere. However, in the final IPM results for the rule, neutralization of 75% of the available HCl was included based on recent findings.
Further details on the future year baseline EGU emissions inventory used for this rule can be found in the IPM v.4.10 Documentation, available at http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html. The future year baseline modeled in IPM for this rule includes estimates of emissions reductions that will result from the Cross-State Air Pollution Rule. However, reductions from the Boiler MACT rule were not represented this modeling because the rule was stayed at the time the modeling was performed. A complete list of state regulations, NSR settlements, and state settlements included in the IPM modeling is given in Appendices 3-2, 3-3, and 3-4 beginning on p. 68 of http://www.epa.gov/airmarkets/progsregs/epa-ipm/CSAPR/docs/DocSuppv410_FTransport.pdf. For the future year baseline EGU emissions, the IPM outputs for 2020, which are also representative of the year 2017, were used as part of the 2017 reference case modeling. These emissions were very similar to the year 2015 emissions output from the same IPM modeling case.
Inventories of onroad mobile emissions for the future year baseline and control cases were created using the MOVES model with an approach consistent with the 2005 base year. As with the 2005 emissions, the future year onroad emissions were based on emission factors 5A-10
developed using the Tier 3 version of MOVES processed through the SMOKE-MOVES interface.
Future-year vehicle miles travelled (VMT) were projected from the 2005 NEI v2 VMT using growth rates from the 2009 Annual Energy Outlook (AEO) data. The VMT for heavy duty diesel vehicles class 8a and 8b was updated based on data from Oak Ridge National Laboratory. The future year onroad emissions reflect control program implementation through 2017 and include the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, the Mobile Source Air Toxics (MSAT) final rule, and the Renewable Fuel Standard version 2 (RFS2).
Future year nonroad mobile emissions were created using NMIM to run NONROAD in a consistent manner as was done for 2005, but with future-year equipment population estimates, fuels, and control programs. The fuels in 2017 are assumed to be E10. Emissions for locomotives and category 1 and 2 (C1 and C2) commercial marine vessels were derived based on emissions published in the Final Locomotive Marine Rule, Regulatory Impact Assessment, Chapter 3 (see http://www.epa.gov/otaq/locomotives.htm#2008final). The future year baseline nonroad mobile emissions reductions include emissions reductions to locomotives, various nonroad engines including diesel engines and various marine engine types, fuel sulfur content, and evaporative emissions standards, including the category 3 marine residual and diesel fuelled engines and International Maritime Organization standards which include the establishment of emission control areas for these ships. A summary of the onroad and nonroad mobile source control programs included in the projected future year baseline is shown in Table 5A-4.
Table 5A-4. Summary of Mobile Source Control Programs Included in the Future Year Baseline National Onroad Rules:
Tier 2 rule (Signature date: February 28, 2000)
Onroad heavy-duty rule (February 24, 2009)
Final mobile source air toxics rule (MSAT2) (February 9, 2007)
Renewable fuel standard Version 2 (March 26, 2010)
Light duty greenhouse gas standards (May, 2010)
Corporate Average Fuel Economy (CAFE) standards for 2008-2011 Local Onroad Programs:
National low emission vehicle program (NLEV) (March 2, 1998)
Ozone transport commission (OTC) LEV Program (January, 1995)
(continued) 5A-11
Table 5A-4. Summary of Mobile Source Control Programs Included in the Future Year Baseline (continued)
National Nonroad Controls:
Tier 1 nonroad diesel rule (June 17, 2004)
Phase 1 nonroad SI rule (July 3, 1995)
Marine SI rule (October 4, 1996)
Nonroad large SI and recreational engine rule (November 8, 2002)
Clean Air Nonroad Diesel RuleTier 4 (June 29, 2004)
Locomotive and marine rule (May 6, 2008)
Nonroad SI rule (October 8, 2008)
Aircraft:
Itinerant (ITN) operations at airports adjusted to year 2017 Locomotives:
Locomotive Emissions Final Rulemaking (December 17, 1997)
Clean Air nonroad diesel final ruleTier 4 (June 29, 2004)
Locomotive rule (April 16, 2008)
Locomotive and marine rule (May 6, 2008)
Commercial Marine:
Locomotive and marine rule (May 6, 2008)
EIA fuel consumption projections for diesel-fueled vessels Clean Air Nonroad Diesel Final Rule - Tier 4 Emissions Standards for Commercial Marine Diesel Engines (December 29,1999)
Tier 1 Marine Diesel Engines (February 28, 2003)
Category 3 marine diesel engines Clean Air Act and International Maritime Organization standards (April, 30, 2010)
For non-EGU point sources, emissions were projected by including emissions reductions and increases from a variety of source data. Other than for certain large municipal waste combustors and airports, non-EGU point source emissions were not grown using economic growth projections, but rather were held constant at the emissions levels in 2005. Emissions reductions were applied to non-EGU point source to reflect final federal measures, known plant closures, and consent decrees. The starting inventories for this rule were the projected 5A-12
emission inventories developed for the 2005v4.2 platform for the final Cross-State Air Pollution Rule (see http://www.epa.gov/ttn/chief/emch/index.html#final). The most significant updates to the emission projections for this rule are the addition of future year ethanol, biodiesel and cellulosic plants that account for increased ethanol production from the Renewable Fuel Standard Rule that is incorporated into the base case for 2017.
Since aircraft at airports were treated as point emissions sources in the 2005 NEI v2, we developed future year baseline emissions for airports by applying projection factors based on activity growth projected by the Federal Aviation Administration Terminal Area Forecast (TAF) system, published in January 2010 for these sources.
Emissions from stationary nonpoint sources were projected using procedures specific to individual source categories. Refueling emissions were projected using refueling emissions from MOVES inventory mode runs. Portable fuel container emissions were projected using estimates from previous rulemaking inventories compiled by the Office of Transportation and Air Quality (OTAQ). Emissions of ammonia and dust from animal operations were projected based on animal population data from the Department of Agriculture and EPA. Residential wood combustion emissions were projected by replacement of obsolete woodstoves with new woodstoves and a 1 percent annual increase in fireplaces. Landfill emissions were projected using MACT controls. Other nonpoint sources were held constant between the 2005 and future year scenarios.
A summary of all rules and growth assumptions impacting non-EGU stationary sources is provided in Table 5A-5, along with the affected pollutants. Note that reductions associated with the Boiler MACT are not included in the future year baseline.
Table 5A-5. Control Strategies and/or Growth Assumptions Included in the Future Year Baseline for Non-EGU Stationary Sources MACT rules, national, VOC: national applied by SCC, MACT VOC Consent decrees and settlements, including refinery consent decrees, and settlements All for: Alcoa, TX and Premcor (formerly MOTIVA), DE Municipal waste combustor reductionsplant level PM Hazardous waste combustion PM Hospital/medical/infectious waste incinerator regulations NOx, PM, SO2 Large municipal waste combustorsgrowth applied to specific plants All (continued) 5A-13
Table 5A-5. Control Strategies and/or Growth Assumptions Included in the Future Year Baseline for Non-EGU Stationary Sources (continued)
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: projection factor of 0.25 applied All Livestock emissions growth from year 2002 to year 2017 NH3, PM Residential wood combustion growth and change-outs from years 2005 to year 2017 All Gasoline Stage II growth and control via MOVES from year 2005 to year 2017 VOC Portable fuel container mobile source air toxics rule 2: inventory growth and control VOC from year 2005 to year 2017 NESHAP: Portland Cement (09/09/10)plant level based on industrial sector Hg, NOX, SO2, PM, HCl 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 and include closures.
New York ozone SIP standards VOC, HAP VOC, NOX Additional plant and unit closures provided by state, regional, and EPA agencies All Emission reductions resulting from controls put on specific boiler units (not due to NOX, SO2, HCL MACT) after 2005, identified through analysis of the control data gathered from the ICR from the ICI boiler NESHAP.
NESHAP: Reciprocating Internal Combustion Engines (RICE). NOX, CO, PM, SO2 RICE controls applied to Phase II WRAP 2018 oil and gas emissions VOC, SO2, NOX, CO RICE controls applied to 2008 Oklahoma and Texas Oil and gas emissions VOC, SO2, NOX, CO, PM Ethanol plants that account for increased ethanol due to RFS2 All State fuel sulfur content rules for fuel oileffective in 2017, only in Maine, New Jersey, SO2 and New York In all future year cases, the same emissions were used for Canada and Mexico as were used in the 2005 base case because appropriate future year emissions for sources in these countries were not available. Future year emissions need to reflect expected percent reductions or increases between the base year and the future year to be considered appropriate for this type of modeling and such emissions were not available.
Table 5A-6 shows a summary of the 2005 and modeled future year baseline emissions for the lower 48 states. Tables 5A-7 and 5A-8 below provide summaries of SO2 and PM2.5 in the 5A-14
2017 baseline for each sector by state. Table 5A-9 shows the future year baseline EGU emissions by state for all criteria air pollutants.
Table 5A-6. Summary of Modeled Base Case Annual Emissions (tons/year) for 48 States by Sector: SO2 and PM2.5 Source Sector SO2 Emissions 2005 2017 EGU point 10,380,883 3,281,364 Non-EGU point 2,030,759 1,534,991 Nonpoint 1,216,362 1,125,985 Nonroad 446,831 15,759 On-road 168,480 29,288 Average fire 49,094 49,094 Total SO2, all sources 14,292,410 6,036,480 Source Sector PM2.5 Emissions 2005 2017 EGU point 496,877 276,430 Non-EGU point 433,346 411,437 Nonpoint 2,110,298 1,912,757 Nonroad 268,745 150,221 On-road 301,073 129,416 Average fire 684,035 684,035 Total PM2.5, all sources 4,294,373 3,564,296 Table 5A-7. Future Year Baseline SO2 Emissions (tons/year) for States by Sector State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Alabama 186,084 63,053 52,341 146 569 983 303,177 Arizona 36,996 24,191 2,467 59 724 2,888 67,324 Arkansas 92,804 12,160 26,801 123 314 728 132,929 California 5,346 21,046 67,846 3,311 2,087 6,735 106,370 Colorado 74,255 1,415 6,210 50 532 1,719 84,181 Connecticut 3,581 1,833 18,149 100 311 4 23,978 (continued) 5A-15
Table 5A-7. Future Year Baseline SO2 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Delaware 2,835 4,770 1,018 500 91 6 9,220 District of Columbia 5 686 1,505 3 38 0 2,237 Florida 117,702 49,082 70,073 1,255 2,111 7,018 247,241 Georgia 96,712 44,248 55,946 192 1,158 2,010 200,266 Idaho 182 17,133 2,894 23 162 3,845 24,240 Illinois 118,217 81,683 5,650 295 1,107 20 206,971 Indiana 200,969 73,930 59,771 150 760 24 335,604 Iowa 85,178 22,865 19,929 86 324 25 128,407 Kansas 45,740 10,288 36,140 57 294 103 92,622 Kentucky 116,927 23,530 33,852 215 463 364 175,350 Louisiana 142,447 129,730 2,669 1,449 447 892 277,634 Maine 2,564 14,285 2,007 72 149 150 19,226 Maryland 29,786 33,562 40,642 494 593 32 105,110 Massachusetts 15,133 17,077 24,907 266 565 93 58,041 Michigan 163,168 48,697 42,185 448 995 91 255,584 Minnesota 52,380 24,742 14,635 220 558 631 93,164 Mississippi 34,865 24,284 6,635 208 396 1,051 67,440 Missouri 178,143 33,757 44,680 191 722 186 257,679 Montana 24,018 7,212 1,875 25 106 1,422 34,657 Nebraska 70,910 6,885 7,899 58 202 105 86,058 Nevada 14,140 2,132 12,028 27 200 1,346 29,873 New Hampshire 6,719 2,471 7,284 21 137 38 16,671 New Jersey 9,042 6,700 9,528 686 757 61 26,774 New Mexico 10,211 7,813 2,719 26 262 3,450 24,480 New York 14,653 45,222 71,060 659 1,466 113 133,173 North Carolina 71,113 58,517 21,713 197 890 696 153,125 North Dakota 105,344 9,915 5,559 36 71 66 120,991 Ohio 180,935 93,600 19,777 373 1,093 22 295,799 (continued) 5A-16
Table 5A-7. Future Year Baseline SO2 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Oklahoma 141,433 27,873 7,731 49 501 469 178,056 Oregon 13,211 9,790 9,508 218 361 4,896 37,985 Pennsylvania 126,316 64,697 67,650 427 1,060 32 260,183 Rhode Island 0 2,745 3,338 33 85 1 6,202 South Carolina 103,694 28,536 13,310 294 500 646 146,980 South Dakota 29,711 1,655 10,301 23 86 498 42,273 Tennessee 33,080 59,145 32,624 154 757 277 126,037 Texas 249,748 129,667 108,633 1,146 2,483 1,178 492,855 Tribal 0 676 0 0 0 0 676 Utah 34,912 6,599 3,365 27 291 1,934 47,128 Vermont 264 902 5,283 8 129 49 6,634 Virginia 51,004 50,387 32,439 275 849 399 135,353 Washington 5,569 19,780 6,885 881 633 407 34,156 West Virginia 84,344 32,458 14,322 64 178 215 131,582 Wisconsin 50,777 61,080 6,260 122 633 70 118,941 Wyoming 48,198 20,491 5,944 18 87 1,106 75,844 Total 3,281,364 1,534,991 1,125,985 15,759 29,288 49,094 6,036,480 Table 5A-8. Future Year Baseline PM2.5 Emissions (tons/year) for States by Sector State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Alabama 13,154 17,052 33,235 2,403 2,217 13,938 81,999 Arizona 3,889 3,809 20,214 2,674 2,762 37,151 70,498 Arkansas 2,838 10,527 33,486 2,042 1,242 10,315 60,450 California 475 20,693 73,607 14,875 13,492 97,302 220,443 Colorado 3,845 7,037 19,868 2,350 2,387 24,054 59,540 Connecticut 400 222 6,838 1,038 1,414 56 9,968 Delaware 434 772 1,207 383 375 87 3,259 (continued) 5A-17
Table 5A-8. Future Year Baseline PM2.5 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total District of Columbia 1 172 536 139 154 0 1,002 Florida 12,723 24,620 50,472 8,100 7,652 99,484 203,050 Georgia 13,445 12,105 59,412 3,803 4,863 24,082 117,711 Idaho 36 2,076 40,288 1,186 714 52,808 97,108 Illinois 8,587 13,471 70,775 6,885 4,926 277 104,922 Indiana 22,354 13,570 72,501 3,491 3,380 344 115,640 Iowa 4,298 7,000 51,684 3,348 1,519 349 68,198 Kansas 3,199 6,895 136,633 2,872 1,268 1,468 152,335 Kentucky 12,078 10,353 26,811 2,717 2,059 5,155 59,173 Louisiana 3,093 30,865 27,082 5,107 1,673 12,647 80,467 Maine 355 3,543 8,213 881 750 2,127 15,869 Maryland 3,969 6,382 18,960 1,975 2,492 531 34,310 Massachusetts 1,465 2,123 23,729 1,914 2,590 1,324 33,145 Michigan 8,102 11,688 43,055 4,696 4,949 1,283 73,773 Minnesota 2,598 9,867 68,121 4,483 2,882 8,943 96,893 Mississippi 2,201 10,492 31,474 2,337 1,525 14,897 62,926 Missouri 7,061 6,384 69,722 3,954 3,059 2,636 92,816 Montana 3,870 2,562 28,479 1,332 492 17,311 54,048 Nebraska 2,358 2,834 44,904 2,967 919 1,483 55,465 Nevada 2,505 4,032 9,351 1,319 857 19,018 37,083 New Hampshire 1,130 464 8,981 576 663 534 12,348 New Jersey 2,452 2,520 8,559 2,929 3,244 865 20,569 New Mexico 3,153 1,442 49,789 1,148 1,103 48,662 105,298 New York 2,331 4,859 44,334 5,032 6,723 1,601 64,879 North Carolina 9,983 12,656 43,398 3,583 3,521 9,870 83,011 North Dakota 5,870 795 40,802 2,126 383 934 50,910 Ohio 18,920 12,353 47,811 5,302 5,013 316 89,715 Oklahoma 3,530 5,695 88,862 2,029 2,006 6,644 108,767 (continued) 5A-18
Table 5A-8. Future Year Baseline PM2.5 Emissions (tons/year) for States by Sector (continued)
State EGU Non-EGU Nonpoint Nonroad Onroad Fires Total Oregon 381 8,869 39,503 2,148 1,627 65,350 117,877 Pennsylvania 16,727 14,874 38,523 4,582 4,854 454 80,014 Rhode Island 4 256 1,070 222 383 14 1,949 South Carolina 9,997 4,527 23,430 1,932 1,929 9,163 50,978 South Dakota 737 2,399 32,697 1,339 416 7,062 44,650 Tennessee 5,053 21,553 28,449 2,939 3,057 3,934 64,985 Texas 21,677 34,648 187,604 11,901 9,289 21,578 286,698 Tribal 1 1,568 0 0 0 0 1,569 Utah 4,524 3,530 13,978 963 1,318 27,412 51,724 Vermont 67 336 4,930 307 653 696 6,989 Virginia 4,529 10,165 32,254 3,507 3,446 5,659 59,561 Washington 1,444 4,421 35,706 3,328 2,874 4,487 52,259 West Virginia 13,602 4,281 12,951 1,048 762 3,050 35,695 Wisconsin 5,323 7,853 27,656 3,161 3,148 994 48,135 Wyoming 5,662 10,225 30,812 850 392 15,686 63,626 Total 276,430 411,437 1,912,757 150,221 129,416 684,035 3,564,296 Table 5A-9. Future Year Baseline EGU CAP Emissions (tons/year) by State State CO NOX VOC SO2 NH3 PM10 PM2.5 Alabama 27,024 64,064 1,524 186,084 1,472 16,686 13,154 Arizona 16,797 36,971 825 36,996 1,163 5,038 3,889 Arkansas 9,925 36,297 658 92,804 560 3,507 2,838 California 45,388 20,910 1,031 5,346 2,519 580 475 Colorado 9,006 50,879 636 74,255 398 4,605 3,845 Connecticut 9,180 2,738 139 3,581 313 431 400 Delaware 4,256 2,452 132 2,835 119 580 434 District of Columbia 67 11 2 5 3 1 1 (continued) 5A-19
Table 5A-9. Future Year Baseline EGU CAP Emissions (tons/year) by State (continued)
State CO NOX VOC SO2 NH3 PM10 PM2.5 Florida 72,915 83,174 2,253 117,702 3,997 19,098 12,723 Georgia 16,537 43,778 1,293 96,712 903 18,668 13,445 Idaho 1,532 613 41 182 57 38 36 Illinois 51,862 56,128 3,091 118,217 1,437 9,926 8,587 Indiana 30,587 106,881 2,295 200,969 1,317 33,816 22,354 Iowa 8,316 42,698 791 85,178 452 5,735 4,298 Kansas 5,066 25,163 683 45,740 305 3,996 3,199 Kentucky 37,287 71,259 1,604 116,927 928 16,279 12,078 Louisiana 32,626 33,509 852 142,447 1,427 3,677 3,093 Maine 12,789 6,121 306 2,564 269 366 355 Maryland 13,446 17,933 533 29,786 301 5,322 3,969 Massachusetts 7,128 7,991 279 15,133 395 1,915 1,465 Michigan 25,856 66,846 1,497 163,168 874 11,056 8,102 Minnesota 9,365 36,867 746 52,380 460 3,034 2,598 Mississippi 9,704 27,319 440 34,865 469 3,113 2,201 Missouri 16,499 52,464 1,714 178,143 740 9,093 7,061 Montana 5,266 20,946 338 24,018 198 6,117 3,870 Nebraska 4,691 28,898 542 70,910 292 2,948 2,358 Nevada 9,677 15,627 438 14,140 953 3,095 2,505 New Hampshire 5,667 4,908 206 6,719 207 1,234 1,130 New Jersey 25,831 11,178 823 9,042 747 2,948 2,452 New Mexico 9,079 65,189 574 10,211 570 3,833 3,153 New York 19,731 21,172 731 14,653 1,076 3,248 2,331 North Carolina 17,367 44,141 1,076 71,113 654 13,368 9,983 North Dakota 7,437 53,778 867 105,344 383 6,757 5,870 Ohio 33,481 93,150 2,005 180,935 1,317 25,688 18,920 Oklahoma 26,165 47,454 957 141,433 1,073 4,457 3,530 Oregon 5,905 10,828 203 13,211 381 446 381 (continued) 5A-20
Table 5A-9. Future Year Baseline EGU CAP Emissions (tons/year) by State (continued)
State CO NOX VOC SO2 NH3 PM10 PM2.5 Pennsylvania 38,767 123,501 2,023 126,316 1,522 22,117 16,727 Rhode Island 1,748 456 44 0 136 7 4 South Carolina 10,305 37,516 726 103,694 515 14,469 9,997 South Dakota 742 14,293 129 29,711 48 764 737 Tennessee 10,693 16,982 862 33,080 406 6,313 5,053 Texas 78,317 145,182 4,975 249,748 5,304 31,404 21,677 Tribal 601 73 15 0 47 2 1 Utah 5,632 67,476 526 34,912 279 5,843 4,524 Vermont 1,868 458 52 264 25 69 67 Virginia 30,205 39,408 821 51,004 1,115 5,404 4,529 Washington 7,183 14,284 326 5,569 346 1,706 1,444 West Virginia 15,496 54,247 1,320 84,344 658 18,415 13,602 Wisconsin 19,247 35,179 1,137 50,777 649 6,503 5,323 Wyoming 9,087 71,380 970 48,198 481 7,385 5,662 Total 873,344 1,930,769 46,050 3,281,364 40,259 371,101 276,430 Note: Emission estimates apply to all fossil Electric Generating Units, including those with capacity < 25MW.
5A.5 Development of Future Year Control Case Emissions for Air Quality Modeling For the future year control case (i.e., policy case) air quality modeling, the emissions for all sectors were unchanged from the base case modeling except for those from EGUs. The IPM model was used to prepare the future year policy case for EGU emissions. The air quality modeling for MATS relied on EGU emission projections from an interim IPM platform based on the Cross-state Air Pollution Rule version 4.10_FTransport, and was subsequently updated during the rulemaking process. The updates made include: updated assumptions regarding the removal of HCl by alkaline fly ash in subbituminous and lignite coals; an update to the fuel-based mercury emission factor for petroleum coke, which was corrected based on re-examination of the 1999 ICR data; updated capital cost for new nuclear capacity and nuclear life extension costs; corrected variable operating and maintenance cost (VOM) for ACI retrofits; adjusted coal rank availability for some units, consistent with EIA From 923 (2008); updated state rules in Washington and Colorado; and numerous unit-level revisions based on comments received through the notice and comment process. In particular, the policy case modeled with 5A-21
CMAQ did not include the neutralization of 75% of HCl as did the final policy case. Additional details on the version of IPM used to develop the control case are available in Chapter 3.
The changes in EGU SO2, and PM2.5 emissions as a result of the policy case for the lower 48 states are summarized in Table 5A-10. Table 5A-11 shows the CAP emissions for the modeled MATS control case by State. State-specific difference summaries of EGU SO2 and PM2.5 for the sum of the lower 48 states are shown in Tables 5A-12 and 5A-13, respectively.
Table 5A-10. Summary of Emissions Changes for the MATS AQ Modeling in the Lower 48 States Future Year EGU Emissions SO2 PM2.5 Base case EGU emissions (tons) 3,281,364 276,430 Control case EGU emissions (tons) 1,866,247 223,320 Reductions to base case in control case (tons) 1,415,117 53,110 Percentage reduction of base EGU emissions 43% 19%
a Total Man-Made Emissions Total base case emissions (tons) 6,036,480 3,564,296 Total control case emissions (tons) 4,621,363 3,511,186 Percentage reduction of all man-made emissions 23% 1%
a In this table, man-made emissions includes average fires.
Table 5A-11. EGU Emissions Totals for the Modeled MATS Control Case in the Lower 48 States State CO NOX VOC SO2 NH3 PM10 PM2.5 Alabama 20,873 61,863 1,313 68,517 1,235 9,734 7,844 Arizona 13,238 34,804 749 23,459 921 4,264 3,494 Arkansas 9,036 35,788 642 35,112 490 1,696 1,593 California 56,360 27,159 1,307 5,041 2,548 1,057 942 Colorado 8,219 44,409 582 19,564 358 3,492 2,859 Connecticut 8,017 2,800 136 1,400 313 439 412 Delaware 1,312 2,527 67 4,160 93 3,056 1,455 District of Columbia Florida 66,378 61,676 2,055 64,791 3,482 16,434 11,377 (continued) 5A-22
Table 5A-11. EGU Emissions Totals for the Modeled MATS Control Case in the Lower 48 States (continued)
State CO NOX VOC SO2 NH3 PM10 PM2.5 Georgia 14,217 41,006 1,197 78,197 790 11,165 9,742 Idaho 1,523 609 41 182 56 38 36 Illinois 24,365 50,655 2,353 103,867 1,050 7,309 6,588 Indiana 17,061 102,045 1,872 156,781 1,110 29,683 20,388 Iowa 7,340 41,247 747 48,030 410 3,318 2,947 Kansas 4,683 22,136 623 22,767 282 2,504 2,263 Kentucky 25,911 70,126 1,476 125,430 882 12,544 10,635 Louisiana 28,171 31,655 767 30,509 1,261 2,003 1,899 Maine 10,992 5,683 302 1,372 267 342 331 Maryland 4,283 16,554 400 18,091 211 3,851 3,143 Massachusetts 5,408 7,211 226 5,033 344 1,702 1,267 Michigan 18,792 60,982 1,215 82,834 718 8,261 6,893 Minnesota 8,699 34,942 709 33,214 430 3,332 2,936 Mississippi 8,782 20,749 410 15,975 397 1,949 1,720 Missouri 12,249 52,755 1,605 95,965 686 5,216 4,809 Montana 2,223 19,758 264 6,399 133 2,637 1,727 Nebraska 4,493 28,180 533 34,631 277 2,152 1,828 Nevada 7,178 14,382 336 6,372 725 2,626 2,073 New Hampshire 6,781 4,862 232 2,102 232 1,336 1,264 New Jersey 8,350 7,699 315 6,404 546 2,020 1,583 New Mexico 7,987 64,922 545 9,984 554 2,961 2,750 New York 18,725 20,863 699 28,174 1,086 3,123 2,350 North Carolina 15,195 35,309 1,033 59,551 602 8,885 7,988 North Dakota 7,266 53,267 858 23,889 371 5,940 5,051 Ohio 29,956 85,565 1,852 139,208 1,229 19,599 15,823 Oklahoma 26,687 44,725 892 44,602 970 2,293 2,056 Oregon 6,002 9,671 198 3,565 379 241 233 Pennsylvania 24,865 104,906 1,645 93,606 1,349 17,330 14,080 (continued) 5A-23
Table 5A-11. EGU Emissions Totals for the Modeled MATS Control Case in the Lower 48 States (continued)
State CO NOX VOC SO2 NH3 PM10 PM2.5 Rhode Island 1,721 443 43 0 134 7 4 South Carolina 9,826 37,849 725 40,901 459 9,627 6,963 South Dakota 641 14,290 117 2,483 41 260 245 Tennessee 5,551 16,931 723 42,666 334 6,721 5,272 Texas 71,475 138,086 4,444 105,958 4,774 25,359 17,601 Tribal 266 32 7 0 21 1 1 Utah 4,003 65,286 474 17,007 241 4,755 3,896 Vermont 1,868 458 52 264 25 69 67 Virginia 26,778 37,255 707 33,704 748 5,306 4,506 Washington 6,334 3,834 179 854 254 183 176 West Virginia 13,923 47,836 1,263 66,857 632 14,321 11,572 Wisconsin 16,124 32,865 1,012 28,322 578 4,725 3,969 Wyoming 7,516 71,135 932 28,456 467 5,946 4,671 Total 707,640 1,789,790 40,875 1,866,247 35,493 281,811 223,320 Table 5A-12. State Specific Changes in Annual EGU SO2 for the Lower 48 States Future Year Future Year Policy Baseline SO2 Case SO2 EGU SO2 Reduction EGU SO2 Reduction State (tons) (tons) (tons) (%)
Alabama 186,084 68,517 117,568 63%
Arizona 36,996 23,459 13,537 37%
Arkansas 92,804 35,112 57,692 62%
California 5,346 5,041 305 6%
Colorado 74,255 19,564 54,690 74%
Connecticut 3,581 1,400 2,181 61%
Delaware 2,835 4,160 -1,324 -47%
District of Columbia 5 0 5 100%
(continued) 5A-24
Table 5A-12. State Specific Changes in Annual EGU SO2 for the Lower 48 States (continued)
Future Year Future Year Policy Baseline SO2 Case SO2 EGU SO2 Reduction EGU SO2 Reduction State (tons) (tons) (tons) (%)
Florida 117,702 64,791 52,911 45%
Georgia 96,712 78,197 18,515 19%
Idaho 182 182 0 0%
Illinois 118,217 103,867 14,350 12%
Indiana 200,969 156,781 44,189 22%
Iowa 85,178 48,030 37,148 44%
Kansas 45,740 22,767 22,973 50%
Kentucky 116,927 125,430 -8,503 -7%
Louisiana 142,447 30,509 111,938 79%
Maine 2,564 1,372 1,191 46%
Maryland 29,786 18,091 11,695 39%
Massachusetts 15,133 5,033 10,100 67%
Michigan 163,168 82,834 80,334 49%
Minnesota 52,380 33,214 19,165 37%
Mississippi 34,865 15,975 18,890 54%
Missouri 178,143 95,965 82,177 46%
Montana 24,018 6,399 17,618 73%
Nebraska 70,910 34,631 36,279 51%
Nevada 14,140 6,372 7,768 55%
New Hampshire 6,719 2,102 4,618 69%
New Jersey 9,042 6,404 2,638 29%
New Mexico 10,211 9,984 228 2%
New York 14,653 28,174 -13,521 -92%
North Carolina 71,113 59,551 11,562 16%
North Dakota 105,344 23,889 81,455 77%
Ohio 180,935 139,208 41,727 23%
Oklahoma 141,433 44,602 96,831 68%
(continued) 5A-25
Table 5A-12. State Specific Changes in Annual EGU SO2 for the Lower 48 States (continued)
Future Year Future Year Policy Baseline SO2 Case SO2 EGU SO2 Reduction EGU SO2 Reduction State (tons) (tons) (tons) (%)
Oregon 13,211 3,565 9,646 73%
Pennsylvania 126,316 93,606 32,710 26%
Rhode Island 0 0 0 N/A South Carolina 103,694 40,901 62,793 61%
South Dakota 29,711 2,483 27,228 92%
Tennessee 33,080 42,666 -9,586 -29%
Texas 249,748 105,958 143,790 58%
Tribal 0 0 0 N/A Utah 34,912 17,007 17,905 51%
Vermont 264 264 0 0%
Virginia 51,004 33,704 17,300 34%
Washington 5,569 854 4,716 85%
West Virginia 84,344 66,857 17,488 21%
Wisconsin 50,777 28,322 22,454 44%
Wyoming 48,198 28,456 19,742 41%
Total 3,281,364 1,866,247 1,415,117 Table 5A-13. State Specific Changes in Annual EGU PM2.5 for the Lower 48 States Future Year Future Year Policy EGU PM2.5 EGU PM2.5 Baseline PM2.5 Case PM2.5 Reduction Reduction State (tons) (tons) (tons) (%)
Alabama 13,154 7,844 5,310 40%
Arizona 3,889 3,494 395 10%
Arkansas 2,838 1,593 1,246 44%
California 475 942 -467 -98%
Colorado 3,845 2,859 985 26%
Connecticut 400 412 -12 -3%
(continued) 5A-26
Table 5A-13. State Specific Changes in Annual EGU PM2.5 for the Lower 48 States (continued)
Future Year Future Year Policy EGU PM2.5 EGU PM2.5 Baseline PM2.5 Case PM2.5 Reduction Reduction State (tons) (tons) (tons) (%)
Delaware 434 1,455 -1,021 -235%
District of Columbia 1 0 1 100%
Florida 12,723 11,377 1,346 11%
Georgia 13,445 9,742 3,703 28%
Idaho 36 36 0 0%
Illinois 8,587 6,588 2,000 23%
Indiana 22,354 20,388 1,966 9%
Iowa 4,298 2,947 1,351 31%
Kansas 3,199 2,263 936 29%
Kentucky 12,078 10,635 1,443 12%
Louisiana 3,093 1,899 1,193 39%
Maine 355 331 24 7%
Maryland 3,969 3,143 826 21%
Massachusetts 1,465 1,267 198 14%
Michigan 8,102 6,893 1,210 15%
Minnesota 2,598 2,936 -339 -13%
Mississippi 2,201 1,720 481 22%
Missouri 7,061 4,809 2,252 32%
Montana 3,870 1,727 2,143 55%
Nebraska 2,358 1,828 530 22%
Nevada 2,505 2,073 432 17%
New Hampshire 1,130 1,264 -134 -12%
New Jersey 2,452 1,583 868 35%
New Mexico 3,153 2,750 403 13%
New York 2,331 2,350 -19 -1%
North Carolina 9,983 7,988 1,995 20%
North Dakota 5,870 5,051 819 14%
(continued) 5A-27
Table 5A-13. State Specific Changes in Annual EGU PM2.5 for the Lower 48 States (continued)
Future Year Future Year Policy EGU PM2.5 EGU PM2.5 Baseline PM2.5 Case PM2.5 Reduction Reduction State (tons) (tons) (tons) (%)
Ohio 18,920 15,823 3,097 16%
Oklahoma 3,530 2,056 1,474 42%
Oregon 381 233 148 39%
Pennsylvania 16,727 14,080 2,646 16%
Rhode Island 4 4 0 2%
South Carolina 9,997 6,963 3,033 30%
South Dakota 737 245 492 67%
Tennessee 5,053 5,272 -219 -4%
Texas 21,677 17,601 4,077 19%
Tribal 1 1 1 56%
Utah 4,524 3,896 627 14%
Vermont 67 67 0 0%
Virginia 4,529 4,506 24 1%
Washington 1,444 176 1,268 88%
West Virginia 13,602 11,572 2,031 15%
Wisconsin 5,323 3,969 1,354 25%
Wyoming 5,662 4,671 991 17%
Total 276,430 223,320 53,110 5A-28
CHAPTER 7 STATUTORY AND EXECUTIVE ORDER ANALYSES 7.1 Introduction This chapter presents discussion and analyses relating to Executive Orders and statutory requirements relevant for the final Mercury and Air Toxics Standards (MATS). We discuss the analysis conducted to comply with Executive Order (EO) 12866 and the Paperwork Reduction Act (PRA) as well as potential impacts to affected small entities required by the Regulatory Flexibility Act (RFA), as amended by the Small Business Regulatory Enforcement Fairness Act (SBREFA). We also describe the analysis conducted to meet the requirements of the Unfunded Mandates Reform Act of 1995 (UMRA) assessing the impact of the final rule on state, local and tribal governments and the private sector. In addition, we address the requirements of EO 13132: Federalism; EO 13175: Consultation and Coordination with Indian Tribal Governments; EO 13045: Protection of Children from Environmental Health and Safety Risks; EO 13211:
Actions that Significantly Affect Energy Supply, Distribution, or Use; the National Technology Transfer and Advancement Act; EO 12898: Federal Actions to Address Environmental Justice in Minority Populations and Low-Income Populations; and the Congressional Review Act.
7.2 Executive Order 12866: Regulatory Planning and Review and Executive Order 13563, Improving Regulation and Regulatory Review Under Executive Order (EO) 12866 (58 FR 51,735, October 4, 1993), this action is an economically significant regulatory action because it is likely to have an annual effect on the economy of $100 million or more or adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or state, local, or tribal governments or communities. Accordingly, the EPA submitted this action to the Office of Management and Budget (OMB) for review under Executive Orders 12866 and 13563 any changes in response to OMB recommendations have been documented in the docket for this action. In addition, EPA prepared this Regulatory Impact Analysis (RIA) of the potential costs and benefits associated with this action.
When estimating the human health benefits and compliance costs detailed in this RIA, the EPA applied methods and assumptions consistent with the state-of-the-science for human health impact assessment, economics and air quality analysis. The EPA applied its best professional judgment in performing this analysis and believes that these estimates provide a reasonable indication of the expected benefits and costs to the nation of this rulemaking. This RIA describes in detail the empirical basis for the EPAs assumptions and characterizes the 7-1
various sources of uncertainties affecting the estimates below. In doing what is laid out above in this paragraph, the EPA adheres to EO 13563, Improving Regulation and Regulatory Review, (76 FR 3821; January 18, 2011), which is a supplement to EO 12866.
In addition to estimating costs and benefits, EO 13563 focuses on the importance of a regulatory system [that]...promote[s] predictability and reduce[s] uncertainty and that identify[ies] and use[s] the best, most innovative, and least burdensome tools for achieving regulatory ends. In addition, EO 13563 states that [i]n developing regulatory actions and identifying appropriate approaches, each agency shall attempt to promote such coordination, simplification, and harmonization. Each agency shall also seek to identify, as appropriate, means to achieve regulatory goals that are designed to promote innovation. We recognize that the utility sector faces a variety of requirements, including ones under CAA section 110(a)(2)(D) dealing with the interstate transport of emissions contributing to ozone and PM air quality problems, with coal combustion wastes, and with the implementation of CWA section 316(b).
In developing todays final rule, the EPA recognizes that it needs to approach these rulemakings in ways that allow the industry to make practical investment decisions that minimize costs in complying with all of the final rules, while still achieving the fundamentally important environmental and public health benefits that underlie the rulemakings.
A summary of the monetized costs, benefits, and net benefits for the final rule at discount rates of 3 percent and 7 percent is the Executive Summary and Chapter 8 of this RIA.
7.3 Paperwork Reduction Act The information collection requirements in this rule have been submitted for approval to the Office of Management and Budget (OMB) under the Paperwork Reduction Act, 44 U.S.C.
3501 et seq. The information collection requirements are not enforceable until OMB approves them.
The information requirements are based on notification, recordkeeping, and reporting requirements in the NESHAP General Provisions (40 CFR part 63, subpart A), which are mandatory for all owners and operators subject to national emission standards. These recordkeeping and reporting requirements are specifically authorized by CAA section 114 (42 U.S.C. 7414). All information submitted to the EPA pursuant to the recordkeeping and reporting requirements for which a claim of confidentiality is made is safeguarded according to Agency policies set forth in 40 CFR Part 2, subpart B. This final rule requires maintenance inspections of the control devices but would not require any notifications or reports beyond those required by 7-2
the General Provisions. The recordkeeping provisions require only the specific information needed to determine compliance.
The annual monitoring, reporting, and recordkeeping burden for this collection (averaged over the first 3 years after the effective date of the standards) is estimated to be
$158 million. This includes 698,907 labor hours per year at a total labor cost of $49 million per year, and total non-labor capital costs of $108 million per year. This estimate includes initial and annual performance tests, semiannual excess emission reports, developing a monitoring plan, notifications, and recordkeeping. Initial capital expenses to purchase monitoring equipment for affected units are estimated at a cost of $231 million. This includes 504,629 labor hours at a total labor cost of $35 million for planning, selection, purchase, installation, configuration, and certification of the new systems and total non-labor capital costs of $196 million. All burden estimates are in 2007 dollars and represent the most cost effective monitoring approach for affected facilities.
An Agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB control number. The OMB control numbers for the EPAs regulations are listed in 40 CFR Part 9. When this ICR is approved by OMB, the Agency will publish a technical amendment to 40 CFR Part 9 in the Federal Register to display the OMB control number for the approved information collection requirements contained in this final rule.
7.4 Final Regulatory Flexibility Analysis The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. Small entities include small businesses, small organizations, and small governmental jurisdictions.
For purposes of assessing the impacts of today's rule on small entities, small entity is defined as: (1) a small business that is an electric utility producing 4 billion kilowatt-hours or less as defined by NAICS codes 221122 (fossil fuel-fired electric utility steam generating units) and 921150 (fossil fuel-fired electric utility steam generating units in Indian country); (2) a small governmental jurisdiction that is a government of a city, county, town, school district or special district with a population of less than 50,000; and (3) a small organization that is any not-for-profit enterprise which is independently owned and operated and is not dominant in its field.
7-3
Pursuant to section 603 of the RFA, the EPA prepared an initial regulatory flexibility analysis (IRFA) for the proposed rule and convened a Small Business Advocacy Review Panel to obtain advice and recommendations of representatives of the regulated small entities. A detailed discussion of the Panels advice and recommendations is found in the Panel Report (EPA-HQ-OAR-2009-0234-2921). A summary of the Panels recommendations is presented at 76 FR 24975.
As required by section 604 of the RFA, we also prepared a final regulatory flexibility analysis (FRFA) for todays final rule. The FRFA addresses the issues raised by public comments on the IRFA, which was part of the proposal of this rule. The FRFA is summarized below and in the preamble.
7.4.1 Reasons Why Action Is Being Taken In 2000, the EPA made a finding that it was appropriate and necessary to regulate coal-and oil-fired electric utility steam generating units (EGUs) under Clean Air Act (CAA) section 112 and listed EGUs pursuant to CAA section 112(c). On March 29, 2005 (70 FR 15,994), the EPA published a final rule (2005 Action) that removed EGUs from the list of sources for which regulation under CAA section 112 was required. That rule was published in conjunction with a rule requiring reductions in emissions of mercury from EGUs pursuant to CAA section 111, i.e.,
CAMR, May 18, 2005, 70 FR 28606). The Section 112(n) Revision Rule was vacated on February 8, 2008, by the U.S. Court of Appeals for the District of Columbia Circuit. As a result of that vacatur, CAMR was also vacated and EGUs remain on the list of sources that must be regulated under CAA section 112. This action provides the EPAs final NESHAP for EGUs.
7.4.2 Statement of Objectives and Legal Basis for Final Rules MATS will protect air quality and promote public health by reducing emissions of HAP.
In the December 2000 regulatory determination, the EPA made a finding that it was appropriate and necessary to regulate EGUs under CAA section 112. The February 2008 vacatur of the 2005 Action reverted the status the rule to the December 2000 regulatory determination. CAA section 112(n)(1)(A) and the 2000 determination do not differentiate between EGUs located at major versus area sources of HAP. Thus, the NESHAP for EGUs will regulate units at both major and area sources. Major sources of HAP are those that have the potential to emit at least 10 tons per year (tpy) of any one HAP or at least 25 tpy of any combination of HAP. Area sources are any stationary sources of HAP that are not major sources.
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7.4.3 Summary of Issues Raised during the Public Comment Process on the IRFA The EPA received a number of comments related to the Regulatory Flexibility Act during the public comment process. A consolidated version of the comments received is reproduced below. These comments can also be found in their entirety in the response to comment document in the docket.
Comment: Several commenters expressed concern with the SBAR panel. Some believe Small Entity Representatives (SERs) were not provided with regulatory alternatives including descriptions of significant regulatory options, differing timetables, or simplifications of compliance and reporting requirements, and subsequently were not presented with an opportunity to respond. One commenter believes the EPAs formal SBAR Panel notification and subsequent information provided by the EPA to the Panel did not include information on the potential impacts of the rule as required by section 609(b)(1). Additional commenters suggested that the EPAs rulemaking schedule put pressure on the SBAR Panel through the abbreviated preparation for the Panel. Commenters also expressed concerns that the EPA did not provide participants more than cursory background information on which to base their comments. One commenter stated that the EPA did not provide deliberative materials, including draft proposed rules or discussions of regulatory alternatives, to the SBAR Panel members. One commenter stated the SBAR Panel Report does not meet the statutory obligation to recommend less burdensome alternatives. The commenter suggested the EPA panel members declined to make recommendations that went further than consideration or investigation of broad regulatory alternatives, with the exception of those recommendations in which the EPA rejected alternative interpretations of the CAA section 112 and relevant court cases. Two stated that the EPA did not respond to the concerns of the small business community, the SBA, or OMB, ignoring concerns expressed by the SER panelists. One commenter believes the EPA failed to convene required meetings and hearings with affected parties as required by law for small business entities. One commenter stated that the SERs input is very important because more than 90 percent of public power utility systems meet the definition and qualify as small businesses under the SBREFA.
Response: The RFA requires that SBAR Panels collect advice and recommendations from SERs on the issues related to:
the number and description of the small entities to which the proposed rule will apply; 7-5
the projected reporting, record keeping and other compliance requirements of the proposed rule; duplication, overlap or conflict between the proposed rule and other federal rules; and alternatives to the proposed rule that accomplish the stated statutory objectives and minimize any significant economic impact on small entities.
The RFA does not require a covered agency to create or assemble information for SERs or for the government panel members. While section 609(b)(4) requires that the government Panel members review any material the covered agency has prepared in connection with the RFA, the law does not prescribe the materials to be reviewed. The EPA's policy, as reflected in its RFA guidance, is to provide as much information as possible, given time and resource constraints, to enable an informed Panel discussion. In this rulemaking, because of a court-ordered deadline, the EPA was unable to hold a pre-panel meeting but still provided SERs with the information available at the time, held a standard Panel Outreach meeting to collect verbal advice and recommendations from SERs, and provided the standard 14-day written comment period to SERs. The EPA received substantial input from the SERs, and the Panel report describes recommendations made by the Panel on measures the Administrator should consider that would minimize the economic impact of the proposed rule on small entities. The EPA complied with the RFA.
Comment: One commenter requested that the EPA work with utilities such that new regulations are as flexible and cost efficient as possible.
Response: In developing the final rule, the EPA has considered all information provided prior to, as well as in response to, the proposed rule. The EPA has endeavored to make the final regulations flexible and cost efficient while adhering to the requirements of the CAA.
Comment: One commenter was concerned about the ability of small entities or nonprofit utilities such as those owned and/or operated by rural electric co-op utilities, and municipal utilities to comply with the proposed standards within three years. The commenter believes that the EPA disregarded the SER panelists who explained that under these current economic conditions they have constraints on their ability to raise capital for the construction of control projects and to acquire the necessary resources in order to meet a three-year compliance deadline. Two commenters expressed concern that smaller utilities and those in rural areas will be unable to get vendors to respond to their requests for proposals, because they will be able to make more money serving larger utilities.
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Response: The preamble to the proposed rule (76FR 25054, May 3, 2011) provides a detailed discussion of how the EPA determined compliance times for the proposed (and final) rule. The EPA has provided pursuant to section 112(i)(3)(A) the maximum three-year period for sources to come into compliance. Sources may also seek a one-year extension of the compliance period from their title V permitting authority if the source needs that time to install controls. CAA section 112(i)(3)(B). If the situation described by commenters (i.e., where small entities or nonprofit utilities constraints on ability to raise capital for construction of control projects and to acquire necessary resources) results in the source needing additional time to install controls, they would be in a position to request the one-year extension. The EPA discusses in more detail in section VII of this preamble how the agency plans to address those units that are still unable to comply within the statutorily mandated period.
Comment: Several commenters believe the EPA did not adequately consider the disproportionately large impact on smaller generating units. The commenters note the diseconomies in scale for pollution controls for such units. One commenter noted the rule will create a more serious compliance hurdle for small communities that depend on coal-fired generation to meet their base load demand. The commenter notes that by not subcategorizing units, the EPA is dictating a fuel switch due to the disproportionately high cost on small communities. The other commenter believes the MACT and NSPS standards are unachievable by going too far without really considering the impacts on small municipal units, as public powers is critical to communities, jobs, economic viability and electric reliability. A generating and transmissions electric cooperative which qualifies as a small entity believes the rule will ultimately result in increased electricity costs to its members and will negatively impact the economies of the primarily rural areas that they serve. Another commenter believes there is no legal or factual basis for creating subcategories or weaker standards for state, tribal, or municipal governments or small entities that are operating obsolete units, particularly given the current market situation and applicable equitable factors. The commenter suggests both the EPAs and SBAs analyses focus exclusively on the effects on entities causing HAP emissions and primarily on those operating obsolete EGUs, and fail to consider either impacts on downwind businesses and governments or the positive impacts on small entities and governments owning and operating competing, clean and modern EGUs.
Response: The EPA disagrees with the commenters' belief that the impacts on smaller generating units were not adequately considered when developing the rule. The EPA determined the number of potentially impacted small entities and assessed the potential impact of the proposed action on small entities, including municipal units. A similar assessment 7-7
was conducted in support of the final action. Specifically, the EPA estimated the incremental net annualized compliance cost, which is a function of the change in capital and operating costs, fuel costs, and change in revenue. The projected compliance cost was considered relative to the projected revenue from generation. Thus, the EPA's analysis accounts not only for the additional costs these entities face resulting from compliance, but also the impact of higher electricity prices. The EPA evaluated suggestions from SERs, including subcategorization recommendations. In the preamble to the proposed rule, the EPA explains that, normally, any basis for subcategorizing must be related to an effect on emissions, rather than some difference which does not affect emissions performance. The EPA does not see a distinction between emissions from smaller generating units versus larger units. The EPA acknowledges the comment that there is no legal or factual basis for creating subcategories or weaker standards for state, tribal, or municipal governments or small entities that are operating obsolete units.
Comment: One commenter notes that the EPA recognizes LEEs in the rule such that they should receive less onerous monitoring requirements; however, the EPA does not recognize that small and LEEs also need and merit more flexible and achievable pollution control requirements. The commenter notes that the capital costs for emissions control at small utility units is disproportionately high due to inefficiencies in Hg removal, space constraints for control technology retrofits, and the fact that small units have fewer rate base customers across which to spread these costs. The commenter cites the Michigan Department of Environmental Quality report titled Michigans Mercury Electric Utility Workgroup, Final Report on Mercury Emissions from Coal-Fired Power Plants, (June 2005). The commenter notes that the EPA has addressed such concerns previously, citing the RIA for the 1997 8-hour ozone standard. The commenter also suggests smaller utility systems generally have less capital to invest in pollution control than larger, investor-owned systems, due to statutory inability to borrow from the private capital markets, statutory debt ceilings, limited bonding capacity, borrowing limitations related to fiscal strain posed by other, non-environmental factors, and other limitations.
Response: The EPA acknowledges that the rule contains reduced monitoring requirements for existing units that qualify as LEEs. Although the EPA does not believe that reduced pollution control requirements are warranted for LEEs, including small entity LEEs, we believe that flexible and achievable pollution control requirements are promoted through alternative standards, alternative compliance options, and emissions averaging as a means of demonstrating compliance with the standards for existing EGUs.
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Comment: One commenter believes that the EPA should develop more limited monitoring requirements for small EGUs. The commenter notes small entities do not possess the monetary resources, manpower, or technical expertise needed to operate cutting-edge monitoring techniques such as Hg CEMS and PM CEMs. The commenter notes the EPA could have identified monitoring alternatives to the SER panel for consideration.
Response: The EPA provided monitoring alternatives to using PM CEMS, HCl CEMS, and Hg CEMS in its proposed standards and in this final rule. The continuous compliance alternatives are available to all affected sources, including small entities. As alternatives to the use of PM CEMS and HCl CEMS, sources are allowed to conduct additional performance testing. Sorbent trap monitoring is allowed in lieu of Hg CEMS.
Comment: Several commenters believe the EPA has not sufficiently complied with the requirements of the RFA or adequately considered the impact this rulemaking would have on small entities. One commenter believes the EPA has not engaged in meaningful outreach and consultation with small entities and therefore recommends that the EPA seek to revise the court-ordered deadlines to which this rulemaking is subject, re-convene the SBAR panel, prepare a new initial regulatory flexibility analysis (IRFA), and issue it for additional public comment prior to final rulemaking. The commenter believes the IRFA does not sufficiently consider impacts on small entities as identified in the SBAR Panel Report. The commenter believes it is not apparent that the EPA considered the recommendations of the Panel. The commenter believes the description of significant alternatives in the IRFA is almost entirely quoted from the SBAR Panel Report, which the commenter does not believe is an adequate substitute for the EPAs own analysis of alternatives. The commenter also notes the EPA does not discuss the potential impacts of its decisions on small entities or the impacts of possible flexibilities. Where the EPA does consider regulatory alternatives in principle, the commenter believes it does not provide sufficient support for its decisions to understand on what basis the EPA rejected alternatives that may or may not have reduced burden on small entities while meeting the stated objectives of the rule. Additionally, the commenter notes that the EPA did not evaluate the economic or environmental impacts of significant alternatives to the proposed rule. One commenter believes that the EPAs stated reasons for declining to specify or analyze an area source standard are inadequate under the RFA. The commenter believes the EPA must give serious consideration to regulatory alternatives that accomplish the stated objectives of the CAA while minimizing any significant economic impacts on small entities and that the EPA has a duty to specify and analyze this option or to more clearly state its policy reasons for excluding serious consideration of a separate standard for area sources. A commenter believes 7-9
the EPA did not fully consider the subcategorization of sources such as boilers designed to burn lignite coals versus other fossil fuels, especially in regard to non-Hg metal and acid gas emissions. The commenter references the SBAR Panel Report suggestion provided in the preamble of the proposed rule that the EPA consider developing an area source vs. major source distinction for the source category and the EPAs response. Another commenter is concerned that the recommendations made by the SER participants were ignored and not discussed in the rulemaking. Specifically, the commenter notes the EPA did not discuss subcategorizing by age, type of plant, fuel, physical space constraints or useful anticipated life of the plant. Nor did the EPA establish GACT for smaller emitters to alleviate regulatory costs and operational difficulties. A commenter believes it is likely that different numerical or work practice standards are appropriate for area sources of HAP.
Response: The EPA disagrees with one commenter's assertion that the agency has not complied with the requirements of the RFA. The EPA complied with both the letter and spirit of the RFA, notwithstanding the constraints of the court-ordered deadline. For example, the EPA notified the Chief Counsel for Advocacy of the SBA of its intent to convene a Panel; compiled a list of SERs for the Panel to consult with; and convened the Panel. The Panel met with SERs to collect their advice and recommendations; reviewed the EPA materials; and drafted a report of Panel findings. The EPA further disagrees with the commenter's assertion that the EPA's IRFA does not sufficiently consider impacts on small entities. The EPA's IRFA, which is included in chapter 10 of the RIA for the proposed rule, addresses the statutorily required elements of an IRFA such as, the economic impact of the proposed rule on small entities and the Panel's findings.
The EPA disagrees with the comment that recommendations made by the SERs and not considered or discussed in the proposed rulemaking such as recommendations regarding subcategorization and separate GACT standards for area sources. The preamble to the proposed standards includes a detailed discussion of how the EPA determined which subcategories and sources would be regulated (76 FR 25036-25037, May 3, 2011). In that discussion, the EPA explains the rationale for its proposed subcategories based on five unit design types. In addition, the EPA acknowledges the subcategorization suggestions from the SERs and explains its reasons for not subcategorizing on those bases. The preamble to the proposed standards also includes a discussion of the SERs' suggestion that area source EGUs be distinguished from major-source EGUs and the EPA's reasons for not making that distinction (76 FR 25020-25021, May 3, 2011).
The EPA also disagrees with the suggestion that the Agency pursue an extension of the timeline for final rulemaking such that the SBAR Panel can be reconvened and a new IRFA can be 7-10
prepared and released for public comment prior to the final rulemaking. The EPA entered into a Consent Decree to resolve litigation alleging that the EPA failed to perform a non-discretionary duty to promulgate CAA section 112(d) standards for EGUs. American Nurses Assn v. EPA, 08-2198 (D.D.C.). That Decree required the EPA to sign the final MATS rule by November 16, 2011, unless the agency sought to extend the deadline consistent with the requirements of the modification provision of the Consent Decree. If plaintiffs in the American Nurses litigation objected to an extension request, which the EPA believes would have been likely based on their comments on the proposed rule, the Agency would have had to file a motion with the Court seeking an extension of the deadline. Consistent with governing case law, the Agency would have been required to demonstrate in its motion for extension that it was impossible to finalize the rule by the deadline provided in the Consent Decree. See Sierra Club v. Jackson, Civil Action No. 01-1537 (D.D.C.) (Opinion of the Court denying EPAs motion to extend a consent decree deadline). The EPA negotiated a 30-day extension and was able to complete the rule by December 16, 2011; accordingly, the Agency had no basis for seeking a further extension of time.
A detailed description of the changes made to the rule since proposal, including those made as a result of feedback received during the public comment process can be found in sections VI (NESHAP) and X (NSPS) in the preamble. Changes explained in the identified sections include those related to applicability; subcategorization; work practices; periods of startup, shutdown, and malfunction; initial testing and compliance; continuous compliance; and notification, recordkeeping, and reporting.
7.4.4 Description and Estimate of the Affected Small Entities For the purposes of assessing the impacts of MATS on small entities, a small entity is defined as:
(1) A small business according to the Small Business Administration size standards by the North American Industry Classification System (NAICS) category of the owning entity. The range of small business size standards for electric utilities is 4 billion kilowatt hours (kWh) of production or less; (2) A small government jurisdiction that is a government of a city, county, town, district, or special district with a population of less than 50,000; and (3) A small organization that is any not for profit enterprise that is independently owned and operated and is not dominant in its field.
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The EPA examined the potential economic impacts to small entities associated with this rulemaking based on assumptions of how the affected entities will install control technologies in compliance with MATS. The SBREFA analysis does not examine potential indirect economic impacts associated with this rule, such as employment effects in industries providing fuel and pollution control equipment, or the potential effects of electricity price increases on industries and households.
The EPA used Velocity Suites Ventyx data as a basis for identifying plant ownership and compiling the list of potentially affected small entities. The Ventyx dataset contains detailed ownership and corporate affiliation information. The analysis focused only on those EGUs affected by the rule, which includes units burning coal, oil, petroleum coke, or coal refuse as the primary fuel, and excludes any combustion turbine units or EGUs burning natural gas. Also, because the rule does not affect combustion units with an equivalent electricity generating capacity up to 25 megawatts (MW), small entities that do not own at least one combustion unit with a capacity greater than 25 MW were removed from the dataset. For the affected units remaining, boiler and generator capacity, heat input, generation, and emissions data were aggregated by owner and then by parent company. Entities with more than 4 billion kWh of annual electricity generation were removed from the list, as were municipal owned entities serving a population greater than 50,000. For cooperatives, investor-owned utilities, and subdivisions that generate less than 4 billion kWh of electricity annually but which may be part of a large entity, additional research on power sales, operating revenues, and other business activities was performed to make a final determination regarding size. Finally, small entities for which the EPAs modeling with the Integrated Planning Model (IPM) does not project generation in 2015 in the base case were omitted from the analysis because they are not projected to be operating and, thus, are not projected to face the costs of compliance with the rule. After omitting entities for the reasons above, the EPA identified a total of 82 potentially affected small entities that are affiliated with 102 electric generating units.
7.4.5 Compliance Cost Impacts This section presents the methodology and results for estimating the impact of MATS on small entities in 2015 based on the following endpoints:
annual economic impacts of MATS on small entities and 7-12
ratio of small entity compliance cost impacts to revenues from electricity generation.1 7.4.5.1 Methodology for Estimating Impacts of MATS on Small Entities EPA estimated compliance costs of MATS as follows:
CCompliance Operating+Capital Fuel -
electricity generation, calculated as the difference in revenues between the base case and MATS.
Based on this formula, compliance costs for a given small entity could either be positive or negative (i.e., cost savings) based on their compliance choices and market conditions. Under MATS, some units will forgo some level of electricity generation (and, thus, revenues) to comply and this impact will be lessened on those entities by the projected increase in electricity prices under the MATS scenario (which raises their revenues from the remainder of their sales). On the other hand, some units may increase electricity generation, and coupled with the increase in electricity prices, will see an increase in electricity revenues resulting in lower net compliance costs. If entities are able to increase revenue more than an increase in retrofit and fuel costs, ultimately they will have negative net compliance costs (or savings). Because this analysis evaluates the total costs as a sum of the costs associated with compliance choices as well as changes in electricity revenues, it captures savings or gains such as those described. As a result, what EPA describes as a cost is really more of a measure of the net economic impact of the rule on small entities.
For this analysis, EPA used unit-level IPM parsed outputs - from modeling runs conducted with EPAs base case v4.10_MATS assumptions - to estimate costs based on the parameters above. These impacts were then summed for each small entity, adjusting for ownership share. 2 Net impact estimates were based on the following: changes in operating and capital costs, driven mainly by retrofit installations or upgrades, change in fuel costs, and 1
This methodology for estimating small entity impacts has been used in recent EPA rulemakings such as the CSAPR promulgated by EPA in July, 2011.
2 Unit-level cost impacts are adjusted for ownership shares for individual small entities, so as not to overestimate burden on each company. If an individual unit is owned by multiple small entities, total costs for that unit to meet MATS obligations are distributed across all owners based on the percentage of the unit owned by each company. Ownership percentage was estimated based on the Ventyx database.
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change in electricity generation revenues under MATS relative to the base case. These individual components of compliance cost were estimated as follows:
(1) Operating and capital costs: Using the IPM parsed outputs for the base case and MATS policy case, EPA identified units that installed one or more pollution control technologies under the rule. The equations for calculating operating and capital costs were adopted from technology assumptions used in EPAs version of IPM (version 4.10). The model calculates the capital cost (in $/MW); the fixed operation and maintenance (O&M) cost (in $/MW-year); and the variable O&M cost (in $/MWh).
(2) Fuel costs: Fuel costs were estimated by multiplying fuel input (in million British thermal units, MMBtu) by region and fuel prices ($/MMBtu) from EPAs modeling with IPM. The incremental fuel expenditures under MATS were then estimated by taking the difference in fuel costs between MATS and the base case.
(3) Value of electricity generated: EPA estimated the value of electricity generated by multiplying the electricity generation from EPAs IPM modeling results with the regionally-adjusted retail electricity price ($/MWh), for all entities except those categorized as Private in Ventyx. For private entities, EPA used wholesale electricity price instead of retail electricity price because most of the private entities are independent power producers (IPP). IPPs sell their electricity to wholesale purchasers and do not own transmission facilities and, thus, their revenue was estimated based on wholesale electricity prices.
7.4.5.2 Results The number of potentially affected small entities by ownership type and potential impacts of MATS are summarized in Table 7-1. All costs are presented in 2007 dollars. EPA estimated the annualized net compliance cost to small entities to be approximately $106 million in 2015.
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Table 7-1. Projected Impact of MATS on Small Entities in 2015 Number of Number of Number of Small Small Entities Number of Entities Total Net Entities with with EGU Ownership Potentially Projected to Compliance Compliance Cost > Compliance Type Affected Withdraw all Costs (2007$
1% of Generation Cost > 3% of Entities Affected Units millions)
Revenues Generation as Uneconomic Revenues Co-Op 19 0 -29.7 9 8 IOU 8 0 33.0 7 5 Municipal 42 0 49.7 16 15 Sub-division 9 0 44.8 4 3 Private 4 3 8.4 4 4 Total 82 3 106 40 35 Notes: The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities experiencing positive costs. About 23 of the 82 total potentially affected small entities are estimated to have cost savings under MATS (see text above for an explanation).
Definitions of ownership types are based on those provided by Ventyxs Energy Velocity.
Co-op (Cooperative): non-profit, customer-owned electric companies that generate and/or distribute electric power.
IOU (Investor-Owned Utility): Includes Investor Owned assets (e.g., a marketer, independent power producer, financial entity) and electric companies owned by stockholders, etc.
Municipal: A municipal utility, responsible for power supply and distribution in a small region, such as a city.
Sub-division: Political Subdivision Utility is a county, municipality, school district, hospital district, or any other political subdivision that is not classified as a municipality under state law.
Private: Similar to investor-owned, but ownership shares are not openly traded on the stock markets.
Source: ICF International analysis based on IPM modeling results EPA assessed the economic and financial impacts of the final rule using the ratio of compliance costs to the value of revenues from electricity generation, and our results focus on those entities for which this measure could be greater than 1 percent or 3 percent. Of the 82 small entities identified, EPAs analysis shows 40 entities may experience compliance costs greater than 1 percent of base generation revenues in 2015, and 35 may experience compliance costs greater than 3 percent of base revenues.3 Also, all generating capacity at 3 small entities is projected to be uneconomic to maintain. In this analysis, the cost of withdrawing a unit as uneconomic is estimated as the base case profit that is forgone by not operating under the policy case. Because 35 of the 82 total entities, or more than 40 percent, are estimated to incur compliance cost greater than 3 percent of base revenues, EPA has 3
One percent and three percent of generation revenue criteria based on: EPAs Action Development Process:
Final Guidance for EPA Rulewriters: Regulatory Flexibility Act as amended by the Small Business Regulatory Enforcement Fairness Act. OPEI Regulatory Development Series. November 2006. This can be found on the Internet at http://www.epa.gov/sbrefa/documents/rfaguidance11-00-06.pdf.
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concluded that it cannot certify that there will be no SISNOSE for this rule. Results for small entities discussed here, however, do not account for the reality that electricity markets are regulated in parts of the country. Entities operating in regulated or cost-of-service markets should be able to recover all of their costs of compliance through rate adjustments.
Note that the estimated costs for small entities are significantly lower than those estimated by EPA for the MATS proposal (which were $379 million). This is driven by a small group of units (less than 6 percent) which were projected to be uneconomic to operate under the proposal (and hence incurred lost profits due to lost electricity revenues), but are now projected to continue their operations under MATS. In addition, EPAs modeling indicates one unit that would have operated at a low capacity factor under the base case would find it economical to increase its generation significantly under MATS to meet electricity demand in its region. Excluding this unit, the total cost impacts across all entities would be roughly $175 million. Changes in compliance behavior for this small group of units, in particular the one unit which operates at a higher capacity factor, has a substantial impact on total costs for the entire group as their increased generation revenues offsets a large portion of the compliance costs.
The separate components of annualized costs to small entities under MATS are summarized in Table 7-2. The most significant components of incremental costs to these entities are increased capital and operating costs for retrofits, followed by changes in electricity revenues (i.e., cost savings).
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Table 7-2. Incremental Annualized Costs under MATS Summarized by Ownership Group and Cost Category in 2015 (2007$ millions)
Capital+ Change in EGU Ownership Fuel Costs Operating Electricity Total Type ($MM)
Costs ($MM) Revenue ($MM)
A B C =A+B-C Co-Op 161.5 86.4 277.5 -29.7 IOU 39.3 0 6.3 33.0 Municipal 76.4 1.9 28.7 49.7 Sub-division 73.9 2.2 31.3 44.8 Private 5.5 0 -2.9 8.4 Total 356 91 341 106 Note: Totals may not add due to rounding.
Source: ICF International analysis based on IPM modeling results Capital and operating costs increase across all ownership types, but the direction of changes in electricity revenues vary among ownership types. All ownership types, with the exception of private entities, experience a net gain in electricity revenues under the MATS, unlike projections from EPAs modeling during the proposal, where only municipals benefitted from higher electricity revenues. The change in electricity revenue takes into account both the profit lost from units that do not operate under the policy case and the difference in revenue for operating units under the policy case. According to EPAs modeling, an estimated 274 MW of capacity owned by small entities is considered uneconomic to operate under the policy case, resulting in a net loss of $13 million (in 2007$) in profits. On the other hand, many operating units actually increase their electricity revenue due to higher electricity prices under MATS. In addition, as mentioned above, EPAs modeling indicates one unit finds it economical to increase its capacity factor significantly under the policy case which results in significantly higher revenues offsetting the costs.
7.4.6 Description of Steps to Minimize Impacts on Small Entities Consistent with the requirements of the RFA and SBREFA, the EPA has taken steps to minimize the significant economic impact on small entities. Because this rule does not affect units with a generating capacity of less than 25 MW, small entities that do not own at least one generating unit with a capacity greater than 25 MW are not subject to the rule. According to the EPA's analysis, among the coal- and oil-fired EGUs (i.e., excluding combined cycle gas turbines and gas combustion turbines) about 26 potentially small entities only own EGUs with a 7-17
capacity less than or equal to 25 MW, and none of those entities are subject to the final rule based on the statutory definition of potentially regulated units.
For units affected by the proposed rule, the EPA considered a number of comments received, both during the Small Business Advocacy Review (SBAR) Panel and the public comment period. While none of the alternatives adopted are specifically applied to small entities, the EPA believes these modifications will make compliance less onerous for all regulated units, including those owned by small entities.
7.4.6.1 Work Practice Standards Consistent with Sierra Club v. EPA, the EPA proposed numerical emission standards that would apply at all times, including during periods of startup and shutdown. After reviewing comments and other data regarding the nature of these periods of operation, the EPA is finalizing a work practice standard for periods of start up and shut down. The EPA is also finalizing work practice standards for organic HAP from all subcategories of EGUs. The EPA has chosen to finalize work practice standards because the significant majority of data for measured organic HAP emissions from EGUs are below the detection levels of the EPA test methods, and, as such, the Agency considers it impracticable to reliably measure emissions from these units. Descriptions of the work practice requirements for startup and shutdown, as well as organic HAP, can be found in Section VI.D-E. of the preamble.
7.4.6.2 Continuous Compliance and Notification, Record-keeping, and Reporting The final rule greatly simplifies the continuous compliance requirements and provides two basic approaches for most situations: use of continuous monitoring and periodic testing.
The frequency of periodic testing has been decreased from monthly in the proposal to quarterly in the final rule. In addition to simplifying compliance, the EPA believes these changes considerably reduce the overall burden associated with recordkeeping and reporting. These changes to the final rule are described in more detail in Section VI.G-H. of the preamble.
7.4.6.3 Subcategorization The Small Entity Representatives on the SBAR Panel were generally supportive of subcategorization and suggested a number of additional subcategories the EPA should consider when developing the final rule. While it was not practicable to adopt the proposed subcategories, the EPA maintained the existing subcategories and split the liquid oil-fired units subcategory into two individual subcategories - continental and non-continental units.
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7.4.6.4 MACT Floor Calculations As recommended by the EPA SBAR Panel representative, the EPA established the MACT floors using all the available ICR data that was received to the maximum extent possible consistent with the CAA requirements. The Agency believes this approach reasonably ensures that the emission limits selected as the MACT floors adequately represent the level of emissions actually achieved by the average of the units in the top 12 percent, considering operational variability of those units. Additionally, following proposal, the EPA reviewed and revised the procedure intended to account for the contribution of measurement imprecision to data variability in establishing effective emissions limits.
7.4.6.5 Alternatives Not Adopted The EPA chose not to adopt several of the suggestions posed either during the SBAR Panel or public comment period. The EPA did not propose a percent reduction standard as an alternative to the concentration-based MACT floor. The percent reduction format for Hg and other HAP emissions would not have addressed the EPAs desire to promote, and give credit for, coal preparation practices that remove Hg and other HAP before firing. Also, to account for the coal preparation practices, sources would be required to track the HAP concentrations in coal from the mine to the stack, and not just before and after the control device(s), and such an approach would be difficult to implement and enforce. Furthermore, the EPA does not believe the percent reduction standard is in line with the Courts interpretation of the Clean Air Act section 112 requirements. Even if we believed it was appropriate to establish a percent reduction standard, we do not have the data necessary to establish percent reduction standards for HAP, as explained further in the response to comments document.
The EPA chose not to establish GACT standards for area sources for a number of reasons. The data show that similar HAP emissions and control technologies are found on both major and area sources greater than 25 MWe, and some large units are synthetic area sources.
In fact, because of the significant number of well-controlled EGUs of all sizes, we believe it would be difficult to make a distinction between MACT and GACT. Moreover, the EPA believes the standards for area source EGUs should reflect MACT, rather than GACT, because there is no essential difference between area source and major source EGUs with respect to emissions of HAP.
The EPA chose not to exercise its discretionary authority to establish health-based emission standards for HCL and other HAP acid gases. Given the limitations of the currently available information (e.g., the HAP mix where EGUs are located, and the cumulative impacts of 7-19
respiratory irritants from nearby sources), the environmental effects of HCl and the other acid gas HAP, and the significant co-benefits from reductions in criteria pollutants the EPA determined that setting a conventional MACT standard for HCl and the other acid gas HAP was the appropriate course of action.
As required by section 212 of SBREFA, the 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/airquality/powerplanttoxics/actions.html.
7.5 Unfunded Mandates Reform Act (UMRA) Analysis Title II of the UMRA of 1995 (Public Law 104-4)(UMRA) establishes requirements for federal agencies to assess the effects of their regulatory actions on state, local, and tribal governments and the private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, EPA generally must prepare a written statement, including a cost-benefit analysis, for any proposed or final rule that includes any Federal mandate that may result in the expenditure by State, local, and tribal governments, in the aggregate, or by the private sector, of $100,000,000 or more ... in any one year. A Federal mandate is defined under Section 421(6), 2 U.S.C. 658(6),
to include a Federal intergovernmental mandate and a Federal private sector mandate. A Federal intergovernmental mandate, in turn, is defined to include a regulation that would impose an enforceable duty upon State, Local, or tribal governments, Section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other things, a duty that is a condition of Federal assistance, Section 421(5)(A)(i)(I). A Federal private sector mandate includes a regulation that would impose an enforceable duty upon the private sector, with certain exceptions, Section 421(7)(A), 2 U.S.C. 658(7)(A).
Before promulgating an EPA rule for which a written statement is needed under Section 202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires EPA to identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost-effective, or least burdensome alternative that achieves the objectives of the rule.
Moreover, section 205 allows EPA to adopt an alternative other than the least costly, most cost-effective or least burdensome alternative if the Administrator publishes an explanation why that alternative was not adopted.
In a manner consistent with the intergovernmental consultation provisions of Section 204 of the UMRA, EPA carried out consultations with the governmental entities affected by this rule. EPA held meetings with states and tribal representatives in which the Agency presented its 7-20
plan to develop a proposal and provided opportunities for participants to provide input as part of the rulemaking process. EPA has also analyzed the economic impacts of MATS on government entities and this section presents the results of that analysis. The UMRA analysis does not examine potential indirect economic impacts associated with the rule, such as employment effects in industries providing fuel and pollution control equipment, or the potential effects of electricity price increases on industries and households.
7.5.1 Identification of Affected Government Entities Using Ventyx data, EPA identified state- and municipality-owned utilities and subdivisions that would be affected by this rule. EPA then used IPM parsed outputs (based on EPA modeling assumptions) to associate these entities with individual generating units. The analysis focused only on EGUs affected by MATS, which includes units burning coal, oil, petroleum coke, or waste coal as the primary fuel, and excludes any combustion turbine units.
Entities that did not own at least one unit with a generating capacity of greater than 25 MW were also removed from the dataset because of their exemption from the rule. Finally, government entities for which EPAs modeling does not project generation in 2015 under the base case were also exempted from this analysis, because they are not projected to operate and are thus not projected to face compliance costs with this rule. Based on this, EPA identified 96 state, municipal, and sub-divisions affiliated with 172 electric generating units that are potentially affected by MATS.
7.5.2 Compliance Cost Impacts After identifying the potentially affected government entities, EPA estimated the impact of MATS in 2015 based on the following:
total impacts of compliance on government entities and ratio of government entity impacts to revenues from electricity generation.
7.5.2.1 Methodology for Estimating Impacts MATS on Government Entities EPA estimated compliance costs of MATS as follows:
CCompliance Operating+Capital Fuel -
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where
change in electricity generation, calculated as the difference in projected revenues between the base case and MATS.
Based on this formula, compliance costs for a given government entity could either be positive or negative (i.e., cost savings) based on their compliance choices and market conditions. Under MATS, some units will forgo some level of electricity generation (and thus revenues) to comply and this impact will be lessened on those entities by the projected increase in electricity prices under MATS. On the other hand, some units may increase electricity generation, and coupled with the increase in electricity prices, will see an increase in electricity revenues resulting in lower net compliance costs. If entities are able to increase revenue more than an increase in retrofit and fuel costs, ultimately they will have negative net compliance costs (or savings). Because this analysis evaluates the total costs as a sum of the costs associated with compliance choices as well as changes in electricity revenues, it captures savings or gains such as those described. As a result, what EPA describes as a cost is really more of a measure of the net economic impact of the rule on government entities.
For this analysis, EPA used unit-level data from IPM runs conducted with EPAs modeling assumptions to estimate costs based on the parameters above. These impacts were then aggregated for each government entity, adjusting for ownership share. Compliance cost estimates were based on the following: changes in capital and operating costs, change in fuel costs, and change in electricity generation revenues under MATS relative to the base case.
These components of compliance cost were estimated as follows:
(1) Capital and operating costs: Using EPAs modeling results for the base case and the MATS policy case, EPA identified units that install control technology under this rule and the technologies installed. The equations for calculating operating and capital costs were adopted from EPAs version of IPM (version 4.10_MATS). The model calculates the capital cost (in $/MW); the fixed operation and maintenance (O&M) cost (in
$/MW-year); and the variable O&M cost (in $/MWh)
(2) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by region and fuel prices ($/MMBtu) from EPAs modeling. The change in fuel expenditures under MATS was then estimated by taking the difference in fuel costs between MATS and the base case.
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(3) Value of electricity generated: EPA estimated the value of electricity generated by multiplying the estimated electricity generation from EPAs IPM modeling results with the regional-adjusted retail electricity prices ($/MWh).
7.5.2.2 Results As was done for the small entities analysis, EPA assessed the economic and financial impacts of the rule using the ratio of compliance costs to the value of revenues from electricity generation, and our results focus on those entities for which this measure could be greater than 1 percent or 3 percent of base revenues. EPA projects that 42 government entities will have compliance costs greater than 1 percent of base generation revenue in 2015 and 32 may experience compliance costs greater than 3 percent of base revenues. Overall, 6 units owned by government entities are projected to be uneconomic to maintain.
The separate components of the annualized costs to government entities under MATS are summarized in Table 7-3 below. The most significant components of incremental costs to these entities are the increased capital and operating costs, followed by increases in electricity revenues (i.e., a cost saving).
Table 7-3. Incremental Annualized Costs under MATS Summarized by Ownership Group and Cost Category (2007$ millions) in 2015 EGU Capital Costs + Change in Fuel Costs Ownership Operating Revenue Total
($ MM)
Type Costs($MM) ($ MM)
A B C =A+B-C Sub-Division 128.0 50.7 106.4 72.3 State 65.9 1.2 32.7 34.4 Municipal 516.3 45.4 374.3 187.4 Total 710 97 513 294 Note: Totals may not add due to rounding.
Definitions of ownership types are based on those provided by Ventyxs Energy Velocity.
Municipal: A municipal utility, responsible for power supply and distribution in a small region, such as a city.
Sub-division: Political Subdivision Utility is a county, municipality, school district, hospital district, or any other political subdivision that is not classified as a municipality under state law.
Source: ICF International analysis based on IPM modeling results The number of potentially affected government entities by ownership type and potential impacts of MATS are summarized in Table 7-4. All costs are reported in 2007$
millions. EPA estimated the annualized net compliance cost to government entities to be approximately $294 million in 2015.
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Table 7-4. Summary of Potential Impacts on Government Entities under MATS in 2015 Number of Number of Number of Total Net Costs Number of Government EGU Government Entities Potentially Entities of MACT Entities with Compliance Ownership with Compliance Cost Affected Withdrawing all compliance ($ Cost > 3% of Generation Type > 1% of Generation Entities Affected units MM) Revenues Revenues Sub-Division 11 0 72.3 5 4 State 5 0 34.4 4 3 Municipal 80 0 187.4 33 25 Total 96 0 294 42 32 Note: The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities experiencing positive costs. About 30 of the 96 total potentially affected government entities are estimated to have cost savings under the MACT policy case (see text above for an explanation).
Source: ICF International analysis based on IPM modeling results Capital and operating costs increase over all ownership types. All ownership types, however, also experience a net gain in electricity revenue, mainly due to higher electricity prices under the policy case. As described in the small entity analysis, the change in electricity revenue takes into account both the profit lost from units that do not operate under the policy case and the difference in revenue for operating units under the policy case. According to EPAs modeling, an estimated 757 MW of electricity generation is estimated to be uneconomic to operate under the policy case, accounting for about $20 million in lost profits. On the other hand, many operating units actually increase their electricity revenue due to higher electricity prices under the MATS policy scenario.
7.6 Executive Order 13132, Federalism Under EO 13132, the EPA may not issue an action that has federalism implications, that imposes substantial direct compliance costs, and that is not required by statute, unless the Federal government provides the funds necessary to pay the direct compliance costs incurred by state and local governments, or the EPA consults with state and local officials early in the process of developing the final action.
The EPA has concluded that this action may have federalism implications, because it may impose substantial direct compliance costs on state or local governments, and the Federal government will not provide the funds necessary to pay those costs. Accordingly, the EPA provides the following federalism summary impact statement as required by section 6(b) of EO 13132.
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Based on estimates in the RIA, provided in the docket, the final rule may have federalism implications because the rule may impose approximately $294 million in annual direct compliance costs on an estimated 96 state or local governments. Specifically, we estimate that there are 80 municipalities, 5 states, and 11 political subdivisions (i.e., a public district with territorial boundaries embracing an area wider than a single municipality and frequently covering more than one county for the purpose of generating, transmitting and distributing electric energy) that may be directly impacted by this final rule. Responses to the EPAs 2010 ICR were used to estimate the nationwide number of potentially impacted state or local governments. As previously explained, this 2010 survey was submitted to all coal- and oil-fired EGUs listed in the 2007 version of DOE/EIAs Annual Electric Generator Report, and Power Plant Operations Report.
The EPA consulted with state and local officials in the process of developing the rule to permit them to have meaningful and timely input into its development. The EPA met with 10 national organizations representing state and local elected officials to provide general background on the rule, answer questions, and solicit input.
7.7 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments EPA has concluded that this action may have tribal implications. The EPA offered consultation with tribal officials early in the regulation development process to permit them an opportunity to have meaningful and timely input. Consultation letters were sent to 584 tribal leaders and provided information regarding the EPAs development of this rule and offered consultation. Three consultation meetings were held: December 7, 2010, with the Upper Sioux Community of Minnesota; December 13, 2010, with the Moapa Band of Paiutes, Forest County Potawatomi, Standing Rock Sioux Tribal Council, and Fond du Lac Band of Chippewa; January 5, 2011, with the Forest County Potawatomi and a representative from the National Tribal Air Association. In these meetings, the EPA presented the authority under the CAA used to develop these rules and an overview of the industry and the industrial processes that have the potential for regulation. Tribes expressed concerns about the impact of EGUs on Indian country.
Specifically, they were concerned about potential Hg deposition and the impact on the water resources of the Tribes, with particular concern about the impact on subsistence lifestyles for fishing communities, the cultural impact of impaired water quality for ceremonial purposes, and the economic impact on tourism. In light of these concerns, the Tribes expressed interest in an expedited implementation of the rule. Other concerns expressed by Tribes related to how the Agency would consider variability in setting the standards and the use of tribal-specific fish 7-25
consumption data from the Tribes in our assessments. They were not supportive of using work practice standards as part of the rule and asked the Agency to consider going beyond the MACT floor to offer more protection for the tribal communities.
In addition to these consultations, the EPA also conducted outreach on this rule through presentations at the National Tribal Forum in Milwaukee, WI; phone calls with the National Tribal Air Association; and a webinar for Tribes on the proposed rule. The EPA specifically requested tribal data that could support the appropriate and necessary analyses and the RIA for this rule. In addition, the EPA held individual consultations with the Navajo Nation on October 12, 2011; as well as the Gila River Indian Community, Ak-Chin Indian Community, and the Hopi Nation on October 14, 2011. These Tribes expressed concerns about the impact of the rule on the Navajo Generating Station (NGS), the impact on the cost of the water allotted to the Tribes from the Central Arizona Project (CAP), the impact on tribal revenues from the coal mining operations (i.e., assumptions about reduced mining if NGS were to retire one or more units),
and the impacts on employment of tribal members at both the NGS and the mine. More specific comments can be found in the docket.
7.8 Protection of Children from Environmental Health and Safety Risks This final rule is subject to EO 13045 (62 FR 19885, April 23, 1997) because it is an economically significant regulatory action as defined by EO 12866, and the EPA believes that the environmental health or safety risk addressed by this action may have a disproportionate effect on children. Accordingly, we have evaluated the environmental health or safety effects of the standards on children.
Although this final rule is based on technology performance, the standards are designed to protect against hazards to public health with an adequate margin of safety as described in the preamble. The protection offered by this rule may be particularly important for children, especially the developing fetus. As referenced in Chapter 4 of this RIA, Mercury and Other HAP Benefits Analysis, children are more vulnerable than adults to many HAP emitted by EGUs due to differential behavior patterns and physiology. These unique susceptibilities were carefully considered in a number of different ways in the analyses associated with this rulemaking, and are summarized in the RIA.
7.9 Statement of Energy Effects Our analysis to comply with EO 13211 (Statement of Energy Effects) can be found in Section 3.16 of this RIA.
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7.10 National Technology Transfer and Advancement Act Section 12(d) of the National Technology Transfer and Advancement Act (NTTAA) of 1995 (Public Law No. 104-113; 15 U.S.C. 272 note) directs the EPA to use voluntary consensus standards in its regulatory activities unless to do so would be inconsistent with applicable law or otherwise impractical. Voluntary consensus standards are technical standards (e.g., materials specifications, test methods, sampling procedures, business practices) that are developed or adopted by voluntary consensus standards bodies. The NTTAA directs the EPA to provide Congress, through OMB, explanations when the Agency decides not to use available and applicable voluntary consensus standards.
This rulemaking involves technical standards. The EPA cites the following standards in the final rule: EPA Methods 1, 2, 2A, 2C, 2F, 2G, 3A, 3B, 4, 5, 5D, 17, 19, 23, 26, 26A, 29, 30B of 40 CFR Part 60 and Method 320 of 40 CFR Part 63. Consistent with the NTTAA, the EPA conducted searches to identify voluntary consensus standards in addition to these EPA methods. No applicable voluntary consensus standards were identified for EPA Methods 2F, 2G, 5D, and 19. The search and review results have been documented and are placed in the docket for the proposed rule.
The three voluntary consensus standards described below were identified as acceptable alternatives to EPA test methods for the purposes of the final rule.
The voluntary consensus standard American National Standards Institute (ANSI) /
American Society of Mechanical Engineers (ASME) PTC 19-10-1981, Flue and Exhaust Gas Analyses [Part 10, Instruments and Apparatus] is cited in the final rule for its manual method for measuring the O2, CO2, and CO content of exhaust gas. This part of ANSI/ASME PTC 19 1981 is an acceptable alternative to Method 3B.
The voluntary consensus standard ASTM D6348-03 (Reapproved 2010), Standards Test Method for Determination of Gaseous Compounds by Extractive Direct Interface Fourier Transform (FTIR) Spectroscopy is acceptable as an alternative to Method 320 and is cited in the final rule, but with several conditions: (1) The test plan preparation and implementation in the Annexes to ASTM D-6348-03, Sections A1 through A8 are mandatory; and (2) In ASTM D6348-03 Annex A5 (Analyte Spiking Technique), the percent (%) R must be determined for each target analyte (Equation A5.5). In order for the test data to be acceptable for a compound,
%R must be 70 % !"*>? *
compound, the test data are not acceptable for that compound and the test must be repeated for that analyte (i.e., the sampling and/or analytical procedure should be adjusted before a 7-27
retest). The %R value for each compound must be reported in the test report, and all field measurements must be corrected with the calculated %R value for that compound by using the following equation: Reported Result = (Measured Concentration in the Stack x 100) / % R.
The voluntary consensus standard ASTM D6784-02, Standard Test Method for Elemental, Oxidized, Particle-Bound and Total Mercury in Flue Gas Generated from Coal-Fired Stationary Sources (Ontario Hydro Method), is an acceptable alternative to use of EPA Method 29 for Hg only or Method 30B for the purpose of conducting relative accuracy tests of mercury continuous monitoring systems under this final rule. Because of the limitations of this method in terms of total sampling volume, it is not appropriate for use in performance testing under this rule. In addition to the voluntary consensus standards the EPA used in the final rule, the search for emissions measurement procedures identified 16 other voluntary consensus standards. The EPA determined that 14 of these 16 standards identified for measuring emissions of the HAP or surrogates subject to emission standards in the final rule were impractical alternatives to EPA test methods for the purposes of this final rule. Therefore, the EPA does not intend to adopt these standards for this purpose. The reasons for this determination for the 14 methods are discussed below, and the remaining 2 methods are discussed later in this section.
The voluntary consensus standard ASTM D3154-00, Standard Method for Average Velocity in a Duct (Pitot Tube Method), is impractical as an alternative to EPA Methods 1, 2, 3B, and 4 for the purposes of this rulemaking because the standard appears to lack in quality control and quality assurance requirements. Specifically, ASTM D3154-00 does not include the following: (1) proof that openings of standard pitot tube have not plugged during the test; (2) if differential pressure gauges other than inclined manometers (e.g., magnehelic gauges) are used, their calibration must be checked after each test series; and (3) the frequency and validity range for calibration of the temperature sensors.
The voluntary consensus standard ASTM D3464-96 (Reapproved 2001), Standard Test Method Average Velocity in a Duct Using a Thermal Anemometer, is impractical as an alternative to EPA Method 2 for the purposes of this rule primarily because applicability specifications are not clearly defined, e.g., range of gas composition, temperature limits. Also, the lack of supporting quality assurance data for the calibration procedures and specifications, and certain variability issues that are not adequately addressed by the standard limit the EPAs ability to make a definitive comparison of the method in these areas.
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The voluntary consensus standard ISO 10780:1994, Stationary Source Emissions-Measurement of Velocity and Volume Flowrate of Gas Streams in Ducts, is impractical as an alternative to EPA Method 2 in this rule. The standard recommends the use of an L-shaped pitot, which historically has not been recommended by the EPA. The EPA specifies the S-type design which has large openings that are less likely to plug up with dust.
The voluntary consensus standard, CAN/CSA Z223.2-M86 (1999), Method for the Continuous Measurement of Oxygen, Carbon Dioxide, Carbon Monoxide, Sulphur Dioxide, and Oxides of Nitrogen in Enclosed Combustion Flue Gas Streams, is unacceptable as a substitute for EPA Method 3A because it does not include quantitative specifications for measurement system performance, most notably the calibration procedures and instrument performance characteristics. The instrument performance characteristics that are provided are non-mandatory and also do not provide the same level of quality assurance as the EPA methods. For example, the zero and span/calibration drift is only checked weekly, whereas the EPA methods require drift checks after each run.
Two very similar voluntary consensus standards, ASTM D5835-95 (Reapproved 2001),
Standard Practice for Sampling Stationary Source Emissions for Automated Determination of Gas Concentration, and ISO 10396:1993, Stationary Source Emissions: Sampling for the Automated Determination of Gas Concentrations, are impractical alternatives to EPA Method 3A for the purposes of this final rule because they lack in detail and quality assurance/quality control requirements. Specifically, these two standards do not include the following: (1) sensitivity of the method; (2) acceptable levels of analyzer calibration error; (3) acceptable levels of sampling system bias; (4) zero drift and calibration drift limits, time span, and required testing frequency; (5) a method to test the interference response of the analyzer; (6) procedures to determine the minimum sampling time per run and minimum measurement time; and (7) specifications for data recorders, in terms of resolution (all types) and recording intervals (digital and analog recorders, only).
The voluntary consensus standard ISO 12039:2001, Stationary Source Emissions--
Determination of Carbon Monoxide, Carbon Dioxide, and Oxygen--Automated Methods, is not acceptable as an alternative to EPA Method 3A. This ISO standard is similar to EPA Method 3A, but is missing some key features. In terms of sampling, the hardware required by ISO 12039:2001 does not include a 3-way calibration valve assembly or equivalent to block the sample gas flow while calibration gases are introduced. In its calibration procedures, ISO 12039:2001 only specifies a two-point calibration while EPA Method 3A specifies a three-point calibration. Also, ISO 12039:2001 does not specify performance criteria for calibration error, 7-29
calibration drift, or sampling system bias tests as in the EPA method, although checks of these quality control features are required by the ISO standard.
The voluntary consensus standard ASTM D6522-00, Standard Test Method for the Determination of Nitrogen Oxides, Carbon Monoxide, and Oxygen Concentrations in Emissions from Natural Gas-Fired Reciprocating Engines, Combustion Turbines, Boilers and Process Heaters Using Portable Analyzers is not an acceptable alternative to EPA Method 3A for measuring CO and O2 concentrations for this final rule as the method is designed for application to sources firing natural gas.
The voluntary consensus standard ASME PTC-38-80 R85 (1985), Determination of the Concentration of Particulate Matter in Gas Streams, is not acceptable as an alternative for EPA Method 5 because ASTM PTC-38-80 is not specific about equipment requirements, and instead presents the options available and the pros and cons of each option. The key specific differences between ASME PTC-38-80 and the EPA methods are that the ASME standard: (1) allows in-stack filter placement as compared to the out-of-stack filter placement in EPA Methods 5 and 17; (2) allows many different types of nozzles, pitots, and filtering equipment; (3) does not specify a filter weighing protocol or a minimum allowable filter weight fluctuation as in the EPA methods; and (4) allows filter paper to be only 99 percent efficient, as compared to the 99.95 percent efficiency required by the EPA methods.
The voluntary consensus standard ASTM D3685/D3685M-98, Test Methods for Sampling and Determination of Particulate Matter in Stack Gases, is similar to EPA Methods 5 and 17, but is lacking in the following areas that are needed to produce quality, representative particulate data: (1) requirement that the filter holder temperature should be between 120oC and 134oC, and not just above the acid dew-point; (2) detailed specifications for measuring and monitoring the filter holder temperature during sampling; (3) procedures similar to EPA Methods 1, 2, 3, and 4, that are required by EPA Method 5; (4) technical guidance for performing the Method 5 sampling procedures, e.g., maintaining and monitoring sampling train operating temperatures, specific leak check guidelines and procedures, and use of reagent blanks for determining and subtracting background contamination; and (5) detailed equipment and/or operational requirements, e.g., component exchange leak checks, use of glass cyclones for heavy particulate loading and/or water droplets, operating under a negative stack pressure, exchanging particulate loaded filters, sampling preparation and implementation guidance, sample recovery guidance, data reduction guidance, and particulate sample calculations input.
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The voluntary consensus standard ISO 9096:1992, Determination of Concentration and Mass Flow Rate of Particulate Matter in Gas Carrying Ducts - Manual Gravimetric Method, is not acceptable as an alternative for EPA Method 5. Although sections of ISO 9096 incorporate EPA Methods 1, 2, and 5 to some degree, this ISO standard is not equivalent to EPA Method 5 for collection of PM. The standard ISO 9096 does not provide applicable technical guidance for performing many of the integral procedures specified in Methods 1, 2, and 5. Major performance and operational details are lacking or nonexistent and detailed quality assurance/quality control guidance for the sampling operations required to produce quality, representative particulate data (e.g., guidance for maintaining and monitoring train operating temperatures, specific leak check guidelines and procedures, and sample preparation and recovery procedures) are not provided by the standard, as in EPA Method 5. Also, details of equipment and/or operational requirements, such as those specified in EPA Method 5, are not included in the ISO standard, e.g., stack gas moisture measurements, data reduction guidance, and particulate sample calculations.
The voluntary consensus standard CAN/CSA Z223.1-M1977, Method for the Determination of Particulate Mass Flows in Enclosed Gas Streams, is not acceptable as an alternative for EPA Method 5. Detailed technical procedures and quality control measures that are required in EPA Methods 1, 2, 3, and 4 are not included in CAN/CSA Z223.1. Second, CAN/CSA Z223.1 does not include the EPA Method 5 filter weighing requirement to repeat weighing every 6 hours6.944444e-5 days <br />0.00167 hours <br />9.920635e-6 weeks <br />2.283e-6 months <br /> until a constant weight is achieved. Third, EPA Method 5 requires the filter weight to be reported to the nearest 0.1 milligram (mg), while CAN/CSA Z223.1 requires reporting only to the nearest 0.5 mg. Also, CAN/CSA Z223.1 allows the use of a standard pitot for velocity measurement when plugging of the tube opening is not expected to be a problem.
The EPA Method 5 requires an S-shaped pitot.
The voluntary consensus standard EN 1911-1,2,3 (1998), Stationary Source Emissions-Manual Method of Determination of HCl-Part 1: Sampling of Gases Ratified European Text-Part 2: Gaseous Compounds Absorption Ratified European Text-Part 3: Adsorption Solutions Analysis and Calculation Ratified European Text, is impractical as an alternative to EPA Methods 26 and 26A. Part 3 of this standard cannot be considered equivalent to EPA Method 26 or 26A because the sample absorbing solution (water) would be expected to capture both HCl and chlorine gas, if present, without the ability to distinguish between the two. The EPA Methods 26 and 26A use an acidified absorbing solution to first separate HCl and chlorine gas so that they can be selectively absorbed, analyzed, and reported separately. In addition, in EN 1911 the absorption 7-31
efficiency for chlorine gas would be expected to vary as the pH of the water changed during sampling.
The voluntary consensus standard EN 13211 (1998), is not acceptable as an alternative to the Hg portion of EPA Method 29 primarily because it is not validated for use with impingers, as in the EPA method, although the method describes procedures for the use of impingers. This European standard is validated for the use of fritted bubblers only and requires the use of a side (split) stream arrangement for isokinetic sampling because of the low sampling rate of the bubblers (up to 3 liters per minute, maximum). Also, only two bubblers (or impingers) are required by EN 13211, whereas EPA Method 29 require the use of six impingers. In addition, EN 13211 does not include many of the quality control procedures of EPA Method 29, especially for the use and calibration of temperature sensors and controllers, sampling train assembly and disassembly, and filter weighing.
Two of the 16 voluntary consensus standards identified in this search were not available at the time the review was conducted for the purposes of the final rule because they are under development by a voluntary consensus body: ASME/BSR MFC 13M, Flow Measurement by Velocity Traverse, for EPA Method 2 (and possibly 1); and ASME/BSR MFC 12M, Flow in Closed Conduits Using Multiport Averaging Pitot Primary Flowmeters, for EPA Method 2.
Finally, in addition to the three voluntary consensus standards identified as acceptable alternatives to EPA methods required in the final rule, the EPA is also specifying four voluntary consensus standards in the rule for use in sampling and analysis of liquid oil samples for moisture content. These standards are: ASTM D95-05 (Reapproved 2010), Standard Test Method for Water in Petroleum Products and Bituminous Materials by Distillation, ASTM D4006-11, Standard Test Method for Water in Crude Oil by Distillation, ASTM D4177-95 (Reapproved 2010), Standard Practice for Automatic Sampling of Petroleum and Petroleum Products, and ASTM D4057-06 (Reapproved 2011), Standard Practice for Manual Sampling of Petroleum and Petroleum Products.
Table 5, section 4.1.1.5 of appendix A, and section 3.1.2 of appendix B to subpart UUUUU, 40 CFR Part 63, list the EPA testing methods included in the final rule. Under section 63.7(f) and section 63.8(f) of subpart A of the General Provisions, a source may apply to the EPA for permission to use alternative test methods or alternative monitoring requirements in place of any of the EPA testing methods, performance specifications, or procedures specified.
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7.11 Environmental Justice 7.11.1 Environmental Justice Impacts Executive Order 12898 (59 FR 7629, February 16, 1994) establishes Federal executive policy on environmental justice. Its main provision directs Federal agencies, to the greatest extent practicable and permitted by law, to make environmental justice (EJ) part of their mission by identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of their programs, policies, and activities on minority populations and low-income populations in the U.S.
The EPA has determined that this final rule will not have disproportionately high and adverse human health or environmental effects on minority, low income, or indigenous populations because it increases the level of environmental protection for all affected populations.
This final rule establishes national emission standards for new and existing EGUs that combust coal and oil. The EPA estimates that there are approximately 1,400 units located at 575 facilities covered by this final rule.
This final rule will reduce emissions of all the listed HAP that come from EGUs. This includes metals (Hg, As, Be, Cd, Cr, Pb, Mn, Ni, and Se), organics (POM, acetaldehyde, acrolein, benzene, dioxins, ethylene dichloride, formaldehyde, and PCB), and acid gases (HCl and HF). At sufficient levels of exposure, these pollutants can cause a range of health effects including cancer; irritation of the lungs, skin, and mucous membranes; effects on the central nervous system such as memory and IQ loss and learning disabilities; damage to the kidneys; and other acute health disorders.
The final rule will also result in substantial reductions of criteria pollutants such as CO, PM, and SO2. Sulfur dioxide is a precursor pollutant that is often transformed into fine PM (PM2.5) in the atmosphere. Reducing direct emissions of PM2.5 and SO2 will, as a result, reduce concentrations of PM2.5 in the atmosphere. These reductions in PM2.5 will provide large health benefits, such as reducing the risk of premature mortality for adults, chronic and acute bronchitis, childhood asthma attacks, and hospitalizations for other respiratory and cardiovascular diseases. (For more details on the health effects of metals, organics, and PM2.5, please refer to Chapters 4 and 5 of this RIA.) This final rule will also have a small effect on electricity and natural gas prices but has the potential to affect the cost structure of the utility industry and could lead to shifts in how and where electricity is generated.
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Todays final rule is one of a group of regulatory actions that the EPA will take over the next several years to respond to statutory and judicial mandates that will reduce exposure to HAP and PM2.5, as well as to other pollutants, from EGUs and other sources. In addition, the EPA will pursue energy efficiency improvements throughout the economy, along with other Federal agencies, states and other groups. This will contribute to additional environmental and public health improvements while lowering the costs of realizing those improvements.
Together, these rules and actions will have substantial and long-term effects on both the U.S.
power industry and on communities currently breathing dirty air. Therefore, we anticipate significant interest in these actions from EJ communities, as well as many others.
7.11.1.1 Key EJ Aspects of the Rule This is an air toxics rule; therefore, it does not permit emissions trading among sources.
Instead, this final rule will place a limit on the rates of Hg and other HAP emitted from each affected EGU. As a result, emissions of Hg and other HAP such as HCl will be substantially reduced in the vast majority of states. In some states, however, there may be small increases in Hg and other HAP emissions due to shifts in electricity generation from EGUs with higher emission rates to EGUs with already low emission rates. Hydrogen chloride emissions are projected to increase at a small number of sources but that does not lead to any increased emissions at the state level.
The primary risk analysis to support the finding that this final rule is both appropriate and necessary includes an analysis of the effects of Hg from EGUs on people who rely on freshwater fish they catch as a regular and frequent part of their diet. These groups are characterized as subsistence level fishing populations or fishers. A significant portion of the data in this analysis came from published studies of EJ communities where people frequently consume locally-caught freshwater fish. These communities included: (1) White and black populations (including female and poor strata) surveyed in South Carolina; (2) Hispanic, Vietnamese and Laotian populations surveyed in California; and (3) Great Lakes tribal populations (Chippewa and Ojibwe) active on ceded territories around the Great Lakes. These data were used to help estimate risks to similar populations beyond the areas where the study data was collected. For example, while the Vietnamese and Laotian survey data were collected in California, given the ethnic (heritage) nature of these high fish consumption rates, we assumed that they could also be associated with members of these ethnic groups living elsewhere in the U.S. Therefore, the high-end consumption rates referenced in the California study for these ethnic groups were used to model risk at watersheds elsewhere in the U.S. As a result of this approach, the specific fish consumption patterns of several different EJ groups are 7-34
fundamental to the EPAs assessment of both the underlying risks that make this final rule appropriate and necessary, and of the analysis of the benefits of reducing exposure to Hg and the other hazardous air pollutants.
The EPAs analysis of risks from consumption of Hg-contaminated fish is contained in Chapter 4 of this RIA. The effects of this final rule on the health risks from Hg and other HAP are presented in the preamble and in the RIA for this rule.
7.11.1.2 Potential Environmental and Public Health Impacts to Vulnerable Populations The EPA has conducted several analyses that provide additional insight on the potential effects of this rule on EJ communities. These include: (1) The socio-economic distribution of people living close to affected EGUs who may be exposed to pollution from these sources; and (2) an analysis of the distribution of health effects expected from the reductions in PM2.5 that will result from implementation of this final rule (co-benefits).
Socio-economic distribution. As part of the analysis for this final rule, the EPA reviewed the aggregate demographic makeup of the communities near EGUs covered by this final rule.
Although this analysis gives some indication of populations that may be exposed to levels of pollution that cause concern, it does NOT identify the demographic characteristics of the most highly affected individuals or communities. EGUs usually have very tall emission stacks; this tends to disperse the pollutants emitted from these stacks fairly far from the source. In addition, several of the pollutants emitted by these sources, such as a common form of mercury and SO2, are known to travel long distances and contribute to adverse impacts on the environment and human health hundreds or even thousands of miles from where they were emitted (in the case of elemental mercury, globally).
The proximity-to-the-source review is included in the analysis for this final rule because some EGUs emit enough hazardous air pollutants such as Nickel or Chromium (VI) to cause elevated lifetime cancer risks greater than 1 in a million in nearby communities. In addition, the EPAs analysis indicates that there are localized areas with elevated levels of Hg deposition around most U.S. EGUs.4 The analysis of demographic data used proximity-to-the-source as a surrogate for exposure to identify those populations considered to be living near affected sources, such that they have notable exposures to current hazardous air pollutant emissions from these sources.
The demographic data for this analysis were extracted from the 2000 census data which were 4
See Excess Local Deposition TSD for more detail.
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provided to the EPA by the US Census Bureau. Distributions by race are based on demographic information at the census block level, and all other demographic groups are based on the extrapolation of census block group level data to the census block level. The socio-demographic parameters used in the analysis included the following categories: Racial (White, African American, Native American, Other or Multiracial, and All Other Races); Ethnicity (Hispanic); and Other (Number of people below the poverty line, Number of people with ages between 0 and 18, Number of people greater than or equal to 65, Number of people with no high school diploma).
In determining the aggregate demographic makeup of the communities near affected sources, the EPA focused on those census blocks within three miles of affected sources and determined the demographic composition (e.g., race, income, etc.) of these census blocks and compared them to the corresponding compositions nationally. The radius of three miles (or approximately 5 kilometers) is consistent with other demographic analyses focused on areas around potential sources. In addition, air quality modeling experience has shown that the area within three miles of an individual source of emissions can generally be considered the area with the highest ambient air levels of the primary pollutants being emitted for most sources, both in absolute terms and relative to the contribution of other sources (assuming there are other sources in the area, as is typical in urban areas). While facility processes and fugitive emissions may have more localized impacts, the EPA acknowledges that because of various stack heights there is the potential for dispersion beyond 3 miles. To the extent that any minority, low income, or indigenous subpopulation is disproportionately impacted by the current emissions as a result of the proximity of their homes to these sources, that subpopulation also stands to see increased environmental and health benefit from the emissions reductions called for by this rule.
The results of EPAs demographic analysis for coal fired EGUs are shown in Table 7-5.
The data indicate that affected sources are located in areas where the minority share of the population living within a three mile buffer is higher than the national average by 12 percentage points or 48%. For these same areas, the percent of the population below the poverty line is also higher than the national average by 4 percentage points or 31%. These results are presented in more detail in the Review of Proximity Analysis, February 2011, a copy of which is available in the docket.
PM2.5 (co-benefits) analysis. As mentioned above, many of the steps EGUs take to reduce their emissions of air toxics as required by this final rule will also reduce emissions of 7-36
PM and SO2. As a result, this final rule will reduce concentrations of PM2.5 in the atmosphere.
Exposure to PM2.5 can cause or contribute to adverse health effects, such as asthma and heart disease, that significantly affect many minority, low-income, and tribal individuals and their communities. Fine PM (PM2.5) is particularly (but not exclusively) harmful to children, the elderly, and people with existing heart and lung diseases, including asthma. Exposure can cause premature death and trigger heart attacks, asthma attacks in children and adults with asthma, chronic and acute bronchitis, and emergency room visits and hospitalizations, as well as milder illnesses that keep children home from school and adults home from work. Missing work due to illness or the illness of a child is a particular problem for people who have jobs that do not provide paid sick days. Low-wage employees also risk losing their jobs if they are absent too often, even if it is due to their own illness or the illness of a child or other relative. Finally, many individuals in these communities lack access to high quality health care to treat these types of illnesses. Due to all these factors, many minority and low-income communities are particularly susceptible to the health effects of PM2.5 and receive a variety of benefits from reducing it.
We estimate that in 2016 the annual PM related benefits of the final rule for adults include approximately 4,200 to 11,000 fewer premature mortalities, 2,800 fewer cases of chronic bronchitis, 4,800 fewer non-fatal heart attacks, 2,600 fewer hospitalizations (for respiratory and cardiovascular disease combined), 3.2 million fewer days of restricted activity due to respiratory illness and approximately 540,000 fewer lost work days. We also estimate substantial health improvements for children in the form of 130,000 fewer asthma attacks, 3,100 fewer emergency room visits due to asthma, 6,300 fewer cases of acute bronchitis, and approximately 140,000 fewer cases of upper and lower respiratory illness.
We also examined the level of PM2.5 mortality risks prior to the implementation of the rule according to race, income, and educational attainment. We then estimated the change in PM2.5 mortality risk as a result of this final rule among people living in the counties with the highest (top 5 percent) PM2.5 mortality risk in 2005. We then compared the change in risk among the people living in these high-risk counties with people living in all other counties.
In 2005, people living in the highest risk counties and in the poorest counties were estimated to be at substantially higher risk of PM2.5- related death than people living in the other 95 percent of counties. This was true regardless of race; the difference between the groups of counties for each race was large while the differences among races in both groups of counties were very small. In contrast, the analysis found that people with less than high school education were predicted to have significantly greater risk from PM2.5 mortality than people with a greater than high school education. This was true both for the highest-risk counties and 7-37
for the other counties. In summary, the analysis indicates that in 2005, educational status, living in one of the poorest counties, and living in a high-risk county are associated with higher estimated PM2.5 mortality risk while race is not.
Our analysis predicts that this final rule will likely significantly reduce the risk of PM2.5-related premature mortality among all populations of different races living throughout the U.S.
compared to both 2005 and 2016 pre-rule (i.e., base case) levels. The analysis indicates that people living in counties with the highest rates (top 5 percent) of PM2.5 mortality risk in 2005 receive the largest reduction in mortality risk after this rule takes effect. We also estimate that people living in the poorest 5 percent of the counties will experience a larger reduction in PM2.5 mortality risk when compared to all other counties. More information can be found below in section 7.11.3.
The EPA estimates that the benefits of the final rule are likely distributed among races, income levels, and levels of education fairly evenly, although there is insufficient data to generate different concentration response functions for each demographic group. However, the analysis does indicate that this final rule in conjunction with the implementation of existing or final rules (e.g., the Cross-State Air Pollution Rule) may help reduce the disparity in risk between those in the highest-risk counties and the other 95 percent of counties for all races and educational levels.
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Table 7-5. Comparative Summary of the Demographics within 5 Kilometers (3 Miles) of the Affected Sources (population in millions)a Hispanic No High Below African Native Other or or Age Age School Poverty b c Population White American American Multiracial Minority Latino 0-17 65+ Diploma Line Near source total (3 mi) 13.9 8.78 2.51 0.10 2.52 5.13 2.86 3.37 1.65 2.20 2.43
% of near source total 63% 18% 1% 18% 37% 21% 24% 12% 16% 17%
National total 285 215 35.0 2.49 33.3 70.8 39.1 77.4 35.4 36.7 37.1
% of national total 75% 12% 1% 12% 25% 14% 27% 12% 13% 13%
Sources: The demographics are from the U.S. Census Bureau, 2000. Information on the facilities is from U.S. EPA.
a Racial and ethnic categories overlap and cannot be summed.
b The Minority population is the overall population (in the first row) minus white population (in the second row).
c The Census Bureau defines Hispanic or Latino as an ethnicity rather than a racial category, Hispanics or Latinos may belong to any race.
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7.11.1.3 Meaningful Public Participation The EPA defines environmental justice to include meaningful involvement of all people regardless of race, color, national origin, or income with respect to the development, implementation, and enforcement of environmental laws, regulations, and policies. To promote meaningful involvement, the EPA publicized the rulemaking via newsletters, EJ listserves, and the internet, including the Office of Policys (OP) Rulemaking Gateway Web site (http://yosemite.epa.gov/opei/RuleGate.nsf/). During the comment period, the EPA discussed the proposed rule via a conference call with communities, conducted a community-oriented webinar on the proposed rule, and posted the webinar presentation on- line. The EPA also held three public hearings to receive additional input on the proposal.
Once this rule is finalized, affected EGUs will need to update their Title V operating permits to reflect their new emission limits, any other new applicable requirements, and the associated monitoring and recordkeeping from this rule. The Title V permitting process provides that when most permits are reopened (for example, to incorporate new applicable requirements) or renewed, there must be opportunity for public review and comments. In addition, after the public review process, the EPA has an opportunity to review the proposed permit and object to its issuance if it does not meet CAA requirements.
7.11.1.4 Summary This final rule strictly limits the emissions rate of Hg and other HAP from every affected EGU. The EPAs analysis indicates substantial health benefits, including for vulnerable populations, from reductions in PM2.5.
The EPAs analysis also indicates reductions in risks for individuals, including for members of minority populations, who eat fish frequently from U.S. lakes and rivers and who live near affected sources. Based on all the available information, the EPA has determined that this final rule will not have disproportionately high and adverse human health or environmental effects on minority, low income, or indigenous populations. The EPA is providing multiple opportunities for EJ communities to both learn about and comment on this rule and welcomes their participation.
7.11.2 Analysis of High Risk Sub-Populations In addition to the previously described assessment of EJ impacts, EPA is providing a qualitative assessment of sub-populations with particularly high potential risks of mercury exposure due to high rates of fish consumption. These populations overlap in many cases with 7-40
traditional EJ populations and would benefit from mercury reductions resulting from this rule.
This section describes the available information on consumption rates for subpopulations with high fish consumption, and shows their locations in the U.S. Because of their high rates of fish consumption, reductions in mercury occurring in waterbodies where these populations catch fish will have a larger IQ benefit for these populations relative to the general fish consuming population.
Based on a detailed review of the literature, EPA identified several high-risk sub-populations (Moya, 2004; Burger, 2002, Shilling et al., 2010, Dellinger, 2004). The analysis of potentially high-risk groups focuses on six subpopulations:
low-income African-American recreational/subsistence fishers in the Southeast region 5 low-income white recreational/subsistence fishers in the Southeast region low-income female recreational/subsistence fishers Hispanic subsistence fishers Laotian subsistence fishers Chippewa/Ojibwe Tribe members in the Great Lakes area These specific subpopulations were selected based on published empirical evidence of particularly high self-caught freshwater fish consumption rates among these groups. Evidence for the first three groups is based on a study by Burger (2002), which collected survey data from a random sample of participants in the Palmetto Sportsmens Classic in Columbia, SC. Of 458 respondents, 39 were black, 415 were white, and 149 were female. The sample size for the black population is relatively small, which increases uncertainty, particularly in higher percentile consumption rate values provided for this group. In this study, results are also split out for poor respondents (0-20K$ annual income). These consumption rates are relatively high, particularly for the higher percentiles. This observation forms the basis for our decision to assess a number of the subsistence populations only for watersheds located in US Census tracts containing members of source populations below the poverty line for the white and black populations.
5 The low-income designation is based on Census 2000 estimates of populations living in poverty. The Southeast for purposes of this analysis comprises Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia.
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Evidence for the Hispanic and Laotian groups is based on a study by Shilling et al. (2010).
This study looks at subsistence fishing activity among ethnic groups associated with more urbanized areas near the Sacramento and San Joaquin rivers in the Central Valley in CA. The authors note that many of these ethnic groups relied on fishing in origin countries and bring that practice here (e.g., Cambodian, Vietnamese and Mexican). The authors also note that fish consumption rates reported here for specific ethnic groups (specifically Southeast Asian) are generally in-line with rates seen in WA and OR studies. For the Chippewa population, we use results from a study by Dellinger (2004), which gathered data on self-reported fish consumption rates by Tribes in the Great Lakes area. Because fishing activity is highly variable across Tribes (and closely associated with heritage cultural practices) we have not extrapolated fishing behavior outside of the areas ceded to the Tribes covered in the study (regions in the vicinity of the Great Lakes). The terms subsistence and recreational fishing are based on the terminology used in these published studies to describe the population of interest. In general, subsistence fishers are individuals whose primary objective in fishing is to acquire food for household consumption. For recreational fishers, the primary objective is to enjoy the outdoor activity; however, fish consumption is also often an objective.
Table 7-6. Reported Distributions of Self-Caught Freshwater Fish Consumption Rates Among Selected Potentially High-Risk Subpopulations Self-Caught Freshwater Fish Consumption Rate (g/day) th th Sample Mean 90 (95 )
Population Size (Median) Percentile Study Low-income African-American 39 171 (137) 446 (557) Burger (2002) recreational/subsistence fishers in Southeast Low-income white recreational/ 415 38.8 (15.3) 93 (129) Burger (2002) subsistence fishers in Southeast Low-income female recreational/ 149 39.1 (11.6) 123 (173) Burger (2002) subsistence fishers a
Hispanic subsistence fishers 45 25.8 (19.1) 98 (155.9) Shilling et al. (2010) a Laotian subsistence fishers 54 47.2 (17) 144.8 (265.8) Shilling et al. (2010) b a a Great Lakes tribal groups 822 60 (113 ) 136.2 (213.1) Dellinger (2004) a th Derived values using a log-normal distribution, based on the median and the 95 percentile or standard deviation reported in study.
b Standard deviation in parentheses, rather than median.
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Using county-level growth projections, there were an estimated 3.09 million low-income African Americans in census tracts that have (1) at least one HUC-12 within 20 miles with a mercury fish tissue concentration estimate and (2) at least 25 African-American inhabitants living below the poverty level, and 3.56 million are projected to reside in these areas in 2016.
The geographic distribution of the expected 2016 population is shown in Figure 7-1. The total low-income (below the poverty level) White population in the southeastern states was 3.26 million for 2005 and is projected to be 3.58 million in 2016. The geographic distribution of this population for 2016 is shown in Figure 7-2. The total modeled low-income female population was 18.4 million for 2005 and is projected to be 20.1 million for 2016. The geographic distribution of the population modeled for 2016 is shown in Figure 7-3. The total modeled Hispanic population was 19.6 million for 2005 and is projected to be 27.2 million in 2016. The geographic distribution of the population modeled for 2016 is shown in Figure 7-4. The total modeled Laotian population was 80,000 for 2005 and projected to be 137,500 in 2016. The geographic distribution of the population modeled for 2016 is shown in Figure 7-5. The total modeled Chippewa population used to simulate the distribution of IQ loss was 23,900 for 2005 and is projected to be 29,500 for 2016. The geographic distribution of the population modeled for 2016 is shown in Figure 7-6.
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Figure 7-1. Projected African-American Population Below the Poverty Level by Census Tract in the Southeast for 2016 7-44
Figure 7-2. Projected White Population Below the Poverty Level by Census Tract in the Southeast for 2016 7-45
Figure 7-3. Projected Female Population Below the Poverty Level by Census Tract for 2016 7-46
Figure 7-4. Modeled Hispanic Population by Census Tract for 2016 7-47
Figure 7-5. Modeled Laotian Population by Census Tract for 2016 7-48
Figure 7-6. Modeled Chippewa Population by Census Tract in the Great Lakes Area for 2016.
7.11.3 Characterizing the Distribution of Health Impacts across Populations EPA is developing new approaches and metrics to improve its characterization of the impacts of EPA rules on different populations. This analysis reflects one such approach, which explores two principal questions regarding the distribution of PM2.5-related benefits resulting from the implementation of MATS:
- 1. What is the baseline distribution of PM2.5-related mortality risk for adults according to the race, income and education of the population?
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- 2. How does MATS change the distribution of PM2.5 mortality risk among populations of different racesparticularly among those populations at greatest risk in the baseline?6 In this analysis we estimated that PM2.5 mortality risk from the modeled scenarios is not distributed equally throughout the U.S., or among populations of different levels of educational attainmentthough the level of PM2.5 mortality risk appears to be shared fairly equally among populations of different races. We estimate that the air quality and PM2.5-related mortality risk improvements achieved by MATS are relatively equally distributed among minority populations, and that the rule reduces PM2.5 mortality risk the most among those populations at greatest risk in the 2005 baseline we selected for this analysis. We note that while the methods used for this analysis have been employed in recent EPA Regulatory Impact Assessments (EPA, 2011) and are drawn from techniques described in the peer reviewed literature (Fann et al. 2011b)
EPA will continue to modify these approaches based on evaluation of the methods.
7.11.3.1 Methodology The methods used here to describe the distribution of PM2.5 mortality impacts are consistent with the approach used in the proposed MATS RIA (U.S. EPA, 2011a) and the final CSAPR RIA (U.S. EPA, 2011b). As a first step, we estimate the level of PM2.5-related mortality risk in each county in the continental U.S. based on 2005 air quality levels, which provides a baseline distribution of risk which we use to identify populations with initial higher and lower baseline PM2.5-related mortality risk. This portion of the analysis follows an approach described elsewhere (Fann et al. 2011a, Fann et al. 2011b), wherein modeled 2005 PM2.5 levels are used to calculate the proportion of all-cause mortality risk attributable to total PM2.5 levels in each county in the Continental U.S. Within each county we estimate the level of all-cause PM2.5 mortality risks for adult populations as well as the level of PM2.5 mortality risk according to the race, income and educational attainment of the population.
Our approach to calculating the distribution of PM2.5 mortality risk across the population is generally consistent with the benefits analysis conducted for the modeled scenario described in Appendix 5C with two exceptions: the PM2.5 mortality risk coefficients used to quantify impacts and the baseline mortality rates used to calculate mortality impacts (a detailed discussion of how both the mortality risk coefficients and baseline incidence rates are used to estimate the incidence of PM2.5-related deaths may be found in the Chapter 5 of the RIA). We 6
In this analysis we assess the change in risk among populations of different race, income and educational attainment. As we discuss further in the methodology, we consider this last variable because of the availability of education-modified PM2.5 mortality risk estimates.
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substitute risk estimates drawn from the Krewski et al. (2009) extended analysis of the ACS cohort. In particular, we applied the all-cause mortality risk estimate random effects Cox model that controls for 44 individual and 7 ecological covariates, using average exposure levels for 1999-2000 over 116 U.S. cities (Krewski et al. 2009) (RR=1.06, 95% confidence intervals 1.04 1.08 per 10g/m3 increase in PM2.5). This mean relative risk estimate is identical to the Pope et al. (2002) risk estimate applied for the benefits analysis (though the standard error around the mean RR estimate is slightly narrower).
Within both this and other analyses of the ACS cohort (see Krewski et al. 2000),
educational attainment has been found to be inversely related to the risk of all-cause mortality.
That is, populations with lower levels of education (in particular, < grade 12) are more vulnerable to PM2.5-related mortality. Krewski and colleagues note that the level of education attainment may likely indicate the effects of complex and multifactorial socioeconomic processes on mortality, factors that we would like to account for in this EJ assessment. When estimating PM mortality impacts among populations according to level of education, we applied PM2.5 mortality risk coefficients modified by educational attainment: less than grade 12 (RR = 1.082, 95% confidence intervals 1.0241.144 per 10 g/m3 change), grade 12 (RR = 1.072, 95% confidence intervals 1.0201.127 per 10 g/m3 change), and greater than grade 12 (RR = 1.055, 95% confidence intervals 1.0181.094 per 10 g/m3 change). The Pope et al. (2002) study does not provide education-stratified RR estimates. The principal reason we applied risk estimates from the Krewski et al. (2009) study was to ensure that the risk coefficients used to estimate all-cause mortality risk and education-modified mortality risk were drawn from a consistent modeling framework.
The other key difference between this distributional analysis and the benefits analysis for this rule relates to the baseline mortality rates. As described in Chapter 5 of this RIA, we calculate PM2.5-related mortality risk relative to baseline mortality rates in each county.
Traditionally, for benefits analysis, we have applied county-level age- and sex-stratified baseline mortality rates when calculating mortality impacts (Abt, 2010). To calculate PM2.5 impacts by race, we incorporated race-specific (stratified by White/Black/Asian/Native American) baseline mortality rates. This approach improves our ability to characterize the relationship between race and PM2.5-related mortality however, we do not have a differential concentration-response function as we do for education, and as a result, we are not able to capture the full impacts of race in modifying the benefits associated with reductions in PM2.5 The result of this analysis is a distribution of PM2.5 mortality risk estimates by county, stratified by each of the three population variables (race, income and educational attainment).
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We have less confidence in county-level estimates of mortality than the national or even state estimates, however, the modeling down to the county level can be considered reasonable because the estimates are based on 12km air quality modeling estimates of PM2.5, county level baseline mortality rates, and a concentration-response function that is derived from county level data. We next identified the counties at or above the median and upper 95th percentile of the PM2.5 mortality risk distribution. We selected this percentile cut-off to capture the very highest levels of PM2.5 mortality risk. The second step of the analysis was to repeat the sequence above by estimating PM2.5 mortality risk in 2016 prior to, and after, the implementation of MATS.
7.11.3.2 Results We estimated the change in PM2.5 mortality risk in 2016 among populations living in those counties at the upper 95th percentile of the mortality risk in 2005. We then compared the change in risk among these populations living in high-risk counties with populations living in all other counties (Tables 7-17 through 7-9).
Table 7-17. Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before and After Implementation of MATS by 2016 for Each Populations, Stratified by Race Race Native Year Asian Black American White Among populations at greater risk 2016 (pre-MATS Rule) 4.3% 4.4% 4.4% 4.5%
2016 (post-MATS Rule) 4.1% 4.1% 4.2% 4.3%
Among all other populations 2016 (pre-MATS Rule) 3.2% 3.1% 3.1% 3.3%
2016 (post-MATS Rule) 3% 2.9% 2.9% 3.1%
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Table 7-8. Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before and After Implementation of MATS by 2016 for Each Population, Stratified by Race and Poverty Level Race Year Asian Black Native American White Among populations living in counties with the largest number of individuals living below the poverty line 2016 (pre-MATS) 3.6% 3.5% 3.6% 3.6%
2016 (post-MATS) 3% 3.4% 3% 3.5%
Among all other populations 2016 (pre-MATS) 3.2% 3.2% 3.2% 3.3%
2016 (post-MATS) 3% 2.9% 3% 3.1%
Table 7-9. Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before and After the Implementation of MATS by 2016 for Each Population, Stratified by Educational Attainment Race Year < Grade 12 = Grade 12 > Grade 12 Among populations at greater risk 2016 (pre-MATS) 6.2% 5.5% 4.3%
2016 (post-MATS) 5.9% 5.3% 4.1%
Among all other populations 2016 (pre-MATS) 4.5% 4% 3.1%
2016 (post-MATS) 4.2% 3.8% 2.9%
Table 7-7, shows the estimated level of PM2.5 mortality risk among populations of different races according to whether those populations live in counties identified as greater risk counties or all other counties. As described above, we define greater risk counties as those at or above the 95th percentile of the estimated PM2.5 mortality risk in 2005, and all other counties as those with estimated PM2.5 mortality risk below this level. The results of this 7-53
analysis suggest that the PM2.5 mortality risk among these populations at greater risk falls with implementation of the 2016 MATS. These results also suggest that all populations, irrespective of race, may receive an estimated reduction in PM2.5 mortality risk. However, limits to data resolution prevent us from delineating the PM2.5 mortality risk according to population race with confidence.
Table 7-8 illustrates the estimated change in the level of PM2.5 mortality risk among populations living in those counties that meet two criteria: (1) the county is at the upper 95th percentile of mortality risk in 2005; (2) the county is at the upper 95th percentile in terms of the number of individuals living below the poverty line. We also estimate the change in PM2.5 risk among all other counties. The analysis suggests that people living in the highest mortality risk and poorest counties may experience a larger improvement in PM2.5 mortality risk than those living in lower risk counties containing a smaller number of individuals living below the poverty line.
Table 7-9 summarizes the estimated change in PM2.5 mortality risk among populations who have attained three alternate levels of educationless than high school, high school and greater than high school. As described above, we apply education-stratified PM2.5 mortality risk coefficients for this analysis. These results indicate that populations with less than a high school education are at higher risk of PM2.5 mortality, irrespective of whether these populations live in greater risk counties, according to the definition described above. We estimate that with the implementation of MATS, all populations see their PM2.5 mortality risk fall, regardless of educational attainment.
7.12 Congressional Review Act The Congressional Review Act, 5 U.S.C. 801 et seq., as added by the Small Business Regulatory Enforcement Fairness Act of 1996, generally provides that before a rule may take effect, the agency promulgating the rule must submit a rule report, which includes a copy of the rule, to each House of the Congress and to the Comptroller General of the United States. EPA will submit a report containing this rule and other required information to the U.S. Senate, the U.S. House of Representatives, and the Comptroller General of the United States prior to publication of the rule in the Federal Register. A major rule cannot take effect until 60 days after it is published in the Federal Register. This action is a major rule as defined by 5 U.S.C.
804(2). This rule will be effective 60 days after publication.
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7.13 References Abt Associates, Inc. 2010. Environmental Benefits and Mapping Program (Version 4.0).
Bethesda, MD. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. Available on the Internet at
<http://www.epa.gov/air/benmap>.
Bullard RD, Mohai P, Wright B, Saha R, et al. Toxic Waste and Race at Twenty 1987-2007. United Church of Christ. March, 2007.
Burger, J. (2002). Daily consumption of wild fish and game: Exposures of high end recreationalists, International Journal of Environmental Health Research, 12:4, p. 343-354.
Dellinger, JA (2004). Exposure assessment and initial intervention regarding fish consumption of tribal members in the Upper Great Lakes Region in the United States. Environmental Research 95 (2004) p. 325-340.
Fann N, Lamson AD, Anenberg SC, Wesson K, Risley D, Hubbell B. 2011a. Estimating the national public health burden associated with exposure to ambient PM2.5 and ozone. Risk Analysis, in press.
Fann N, Roman HA, Fulcher CM, Gentile MA, Hubbell BJ, Wesson K, et al. 2011b. Maximizing Health Benefits and Minimizing Inequality: Incorporating Local-Scale Data in the Design and Evaluation of Air Quality Policies. Risk Analysis 31:908-922; doi:10.1111/j.1539-6924.2011.01629.x.
Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.
Krewski, D., R.T. Burnett, M.S. Goldbert, K. Hoover, J. Siemiatycki, M. Jerrett, M. Abrahamowicz, and W.H. White. 2000. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality. Special Report to the Health Effects Institute. Cambridge MA. July.
Mennis J. Using Geographic Information Systems to Create and Analyze Statistical Surfaces of Populations and Risk for Environmental Justice Analysis. Social Science Quarterly, 2002;83(1):281-297.
Mohai P, Saha R. Reassessing Racial and Socio-economic Disparities in Environmental Justice Research. Demography. 2006;43(2): 383-399.
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