7 - 1 **CHAPTER 7 STATUTORY AND EXECUT**  **IVE 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 ana lysis 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 Enforcemen t 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 t ribal 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 Su pply, 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: Re** **gulatory P lanning and 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 Ma nagement and Budget (OMB) for review under EO 12866 and 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 a ssociated with this action. **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 infor mation collection requirements are not enforceable until OMB approves them.7 - 2

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 repo rting 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 notificatio ns or reports beyond those required by the General Provisions. The recordkeeping provisions require only the specific information needed to determine compliance. The annual monitoring, reporting, and record keeping burden for this collection ( averaged over the first 3 years after the effective date of the standards) is estimated to be $15 8 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 record keeping. Initial capital expenses to purchase monitoring equipment for affected units are estimated a t 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 bur den 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 currentl y valid OMB control number. The OMB control numbers for the EPA’s 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 rul e 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 enti ties include small businesses, small organizations, and small governmental jurisdictions.7 - 3

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 bill ion 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 -forprofit enterprise which is independently owned and operated and is not dominant in its field. 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 Panel’s advice and recommendations is found in the Panel Report (EPA- HQ- OAR - 2009 - 0234 - 2921). A summary of the Panel’s recommendations is presented at 76 FR 24975. As required by section 604 of the RFA, we also prep ared a final regulatory flexibility analysis (FRFA) for today’s 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 s ection 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 conju nction 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. Cou rt 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 EPA’s 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 regulatory7 -4

determination. CAA section 112(n)(1)(A) a nd 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. **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 t he docket.

**Comment:** Several commenters expressed concern with the SBAR panel. Some believe S mall 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 present ed with an opportunity to respond. One commenter believes the EPA’s formal SBAR Panel notification and subsequent information provided by the EPA to the P anel did not include information on the potential impacts of the rule as required by section 609 (b)(1). Additional commenters suggested that the EPA’s 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 propos ed rules or discussions of regulatory alternatives, to the SBAR P anel 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 c ourt 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 SER s’7 - 5

input is very important because more than 90 percent of public power utility systems meet the definition and qualify as small businesses under the SBREF A. **Response:** The RFA requires that SBAR P anels 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;

 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 P anel members review any material the c overed 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 enabl e an informed P anel 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 coll ect 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 Administrat or 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.7 - 6

**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 acqu ire 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 a ble to make more money serving larger utilities. **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 pursuan t 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 critic al 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 gover nments or small entities that are operating obsolete units, particularly given the7 - 7

current market situation and applicable equitable factors. The commenter suggests both the EPA’s and SBA’s 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 imp act of the proposed action on small entities, including municipal units. A similar assessment 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 resulti ng 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, tr ibal, or municipal governments or small entities that are operating obsolete units . **Comment:** O ne 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 requi rements. 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 “Michigan’s Mercury Electric Utility Workgroup, Final Report on Mercury Emissions from Coal - Fired Power Pla nts,” (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 th an larger, investor- owned systems, due to statutory inability to borrow from the private7 - 8

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, al ternative compliance options, and emissions averaging as a means of demonstrating compliance with the standards for existing EGUs. **Comment:** One commenter believes that the EPA should develop more limited monitoring requirements for small EGUs. The commente r 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 th e 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 source s, 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 engage d in meaningful o utreach 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 EPA’s 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 comment er believes it does not provide sufficient support for its decisions to understand on what basis the7 - 9

EPA rejected alternatives that may or may not have reduced burden on small entities while meeting the stated objectives of the rule. Additionally, the comm enter notes that the EPA did not evaluate the economic or environmental impacts of significant alternatives to the proposed rule. One commenter believes that the EPA’s stated reasons for declining to specify or analyze an area source standard are inadequat e 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 s pecify 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 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. Th e 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 EPA’s response. Another commenter is concerne d 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 anticipate d 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 com plied 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 con vene 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, t he 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 rule making 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 which7 - 10

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 t he 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 tha t the SBAR Panel can be reconvened and a new IRFA can be 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 Ass’n 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 requiremen ts of the modification provision of the Consent Decree. If plaintiffs in the American Nurses litigation objected to an extension request, which the EPA believe s 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 th e deadline provided in the Consent Decree. See Sierra Club v. Jackson , Civil Action No. 01 - 1537 (D.D.C.) (Opinion of the Court denying EPA’s motion to extend a consent decree deadline). The EPA negotiated a 30 - day extension and was able to complete the rul e 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 com ment 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 t esting 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:7 - 11

( 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 ki lowatt 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 enterpr ise that is independently owned and operated and is not dominant in its field. 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 technolo gies 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 electric ity price increases on industries and households. The EPA used Velocity Suite’s 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 corpor ate 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 t he 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, additio nal 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 EPA’s 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 the7 - 12

rule. After omitting entities for the reasons above, the EPA identified a to tal of 82 potentially affected small entities that are affiliated with 10 2 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

 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 estimate d compliance costs of MATS as follows:

C Compliance = Δ C Operating+Capital + Δ C Fuel - Δ R where C represents a component of cost as labeled, and Δ R represents the value of change in 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 (whic h 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 complia nce 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 w ith 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 .

1 This methodology for estimating small en tity impacts has been used in recent EPA rulemakings such as the CSAPR promulgated by EPA in July, 2011.7 - 13

For th is analysis, EPA used unit- level IPM parsed output s – from modeling runs conducted with EPA’s 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 change in electricity generation revenues under MATS relative to the base cas e. 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 technolog ies under the rule. The equations for calculating operating and capital costs were adopted from technology assumptions used in EPA’s 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 EPA’s modeling with IPM. The incremental f uel 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 EP A’s 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 becaus e 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 200 7 dollars. EPA estimated the annualized net compliance cost to small entities to be approximatel y $106 million in 2015.

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 i ndividual 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 data base.7 - 14

**Table 7 -**  **1. Projected Impact of**  **MATS on Small Entities**  **in 201 5** 

**EGU Ownership Type**  **Number of**

**Potentially Affected**  **Entities Number of** 

**Entities Projected to**  **Withdraw all Affected Units**  **as Uneconomic Total Net** 

**Compliance Costs (2007$**  **mi llions)**  **Number of Small**

**Entities with Compliance Cost >**  **1% of Generation Revenues**  **Number of**

**Small Entities with**  **Compliance Cost > 3% of**  **Generation 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 Priva te 4 3 8.4 4 4 **Total 8**  **2 3**  **106 4** **0 3**  **5** Note s: T he 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 Ventyx’s 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 p olitical subdivision that is not classified as a municipality under state law. Private: Similar to investor- owned, but ownership s hares are not openly traded on the stock markets. Source: ICF International analysis based on IPM modeling results EPA as sessed 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 percen t. Of the 8 2 small entities identified, EPA’s 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, al l 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 3 5 of the 8 2 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: “EPA’s Action Development Process : Final Guidance for EPA Rulewriters: Regulatory Flexibility Act as amended by the Small Business Regulatory Enforcement Fairness Act.” OPEI R egulatory Development Series. November 2006. This can be found on the Internet at http://www.epa.gov/sbrefa/documents/rfaguidance11
- 00 - 06.pdf.7 - 15

concluded that it cannot certify that there will be no SISNOSE for this rule. Results for small entities discussed her e , 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 opera te 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, EPA’s 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 beha vior 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).7 - 16

**Table 7** **- 2.**  **Incremental Annualized Costs under MATS**  **Summarized by Ownership**  **Group and**

**Cost Category in 2015 (200**  **7$ millions)**  **EGU Ownership Type**  **Capital+**

**Operating Costs ($MM)**  **Fuel Costs**

**($MM) Change in** 

**Electricit y**  **Revenue ($MM) Total**  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 In ternational 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 entiti es, experience a net gain in electricity revenues under the MATS, unlike projections from EPA’s 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 EPA’s modeling, an estimated 274 MW of capacity owned by small entities is considered uneconomic to oper ate 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, EPA’s 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**  Co nsistent 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 o wn 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 potential ly small entities only own EGUs with a7 - 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 consid ered 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 pe riods 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 standa rds 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. Thes e 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 su bcategories 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 – contine ntal and non - continental units.7 - 18

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 possib le 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 perce nt, 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 M ACT floor. The percent reduction format for Hg and other HAP emissions would not have addressed the EPA’s desire to promote, and give credit for, coal preparation practices that remove Hg and other HAP before firing. Also, to account for the coal preparati on 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 no t believe the percent reduction standard is in line with the Court’s 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 establ ish 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 ar e 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 MA CT 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 e xercise 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 of7 - 19

respira tory 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 Co mpliance 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 a ssess the effects of their regulatory actions on state, local, and t ribal 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 propose d or final rule that “ includes any Federal mandate that may result in the expenditure by State, local, and t ribal 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 t ribal 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 pro visions of Section 204 of the UMRA, EPA carried out consultations with the governmental entities affected by this rule. EPA held meetings with states and t ribal representatives in which the Agency presented its7 - 20

plan to develop a proposal and provided oppo rtunities 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 i ndirect 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**  **Affec ted**  **Government Entities** Using Ventyx data, EPA identified state - and municipality- owned utilities and subdivisions that would be affected by th is rule. EPA then used IPM parsed outputs (ba sed 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 EPA’s 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 th is 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 est imated 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 estimate d compliance costs of MATS as follows:

C Compliance = Δ C Operating+Capital + Δ C Fuel - Δ R7 - 21

where C represents a component of cost as labeled, and Δ R represents the retail value of change in electricity generation, calculated as the diffe rence 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 electr icity 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, ultimat ely 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 EPA’s 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 c hange 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 EPA’s modeling results for the base case and the MATS policy case, EPA identif ied units that install control technology under this rule and the technologies installed. The equations for calculating operating and capital costs were adopted from EPA’s 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 EPA’s modeling. The change in fuel expenditures under MATS was then estimated by taking the difference in fuel costs between MATS and the base case .7 - 22

**(3) Value of electricity generated:**  EPA estimated the value of electricity generated by multiplying the estimated electricity generation from EPA’s IPM modeling results with the regional - adjusted retail electricity price s ($/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 v alue 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 201 5 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 annualize d 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 ( 200**  **7$ millions)**  **in 2015 EGU**  **Ownership Type**  **Capital Costs +**

**Operating Costs($MM)**  **Fuel Costs**

**($ MM) Change in** 

**Revenue ($ MM)**  **Total** 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 Ventyx’s Energy Velocity. Municipal: A municipal utility, responsible for power supply and distribution in a small region, such as a city. Sub - divisio n: 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 200 7$ millions . EPA estimated the annualized net compliance cost to government entities to be approximately $294 million in 2015.7 - 23

**Table 7** **- 4.**  **Summary of Potential Impacts on Government Entities under MATS**  **in 2015**
**EGU Ownership**  **Type Number of** 

**Potentially Affected**  **Entities Number of** 

**Entities Withdrawing all**  **Affected u nits**  **Total Net Costs**

**of MACT compliance ($**  **MM) Number of** 

**Government Entities with Compliance Cost**  **> 1% of Generation Revenues**  **Number of Government**

**Entities with Compliance Cost > 3% of Generation**  **Revenues** Sub - Division 11 0 72.3 5 4 State 5 0 34.4 4 3 Mu nicipal 80 0 187.4 33 2 5 **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 entitie s experiencing positive costs. About 3 0 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 r esults 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 analys is, 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 EPA’s 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 a ction 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 13132.7 - 24

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 com pliance 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 muni cipality 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 EPA’s 2010 ICR were used to estimate the nationwide numbe r 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/EIA’s “Annual Electric Generator Report,” and “Power Plant Operations Report.” T he 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 prov ide 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 t ribal implications. The EPA offered consultation with t ribal officials early in the regulation development process to permit them an opportunity to have meaningful and timely input . Consultation letters were sent to 584 t rib al leaders and provided information regarding the EPA’s development of this rule and offered consultation. Three consultation meetings were held : December 7, 2010, with the Upper Sioux Community of Minnesota; December 13, 201 0, 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 m eetings, 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 T ribes expressed interest in an expedited implementation of the rule. Other concerns expressed by T ribes rel ated to how the Agency would consider varia bility in setting the standards and the use of t ribal - specific fish7 - 25

consumption data from the Tribes in our assessments . T hey 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 t ribal communities. In addition to these consultations, the EPA also conducted outreach on this rule through presentations at the National Tribal Forum in Milwaukee, WI ; ph one 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 in dividual 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 T ribes expressed concerns about the impact of the rule on the N avajo Generating Station (NGS), the impact on the cost of the water allotted to the T ribes 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 130 45 (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, ” child ren 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. 1 6 of this RIA.7 - 26

**7. 1**  **0 National Technology Transfer and Advancement Act**  Section 12(d) of the National Tec hnology 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 impra ctical. 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 provid e 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 vol untary 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 - 10- 1981 is an acceptable alternative to Method 3B. The voluntary consensus standa rd 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 s everal 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 % ≥ R ≤ 130%. If the %R value does not meet this criterion for a target compound, the test data are not acceptable for that compound and the test must be rep eated for that analyte (i.e., the sampling and/or analytical procedure should be adjusted before a7 - 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 th at compound by using the following equation: Reported Result = (Measured Concentration in the Stack x 100) / % R. The voluntary consensus standard ASTM D6784 -0 2, “Standard Test Method for Elemental, Oxidized, Particle - Bound and Total Mercury in Flue Gas G enerated 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 fi nal 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 emi ssions 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 a lternatives 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 di scussed 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 b ecause 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 pres sure 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 defi ned, 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 EPA’s ability to make a definitive comparison of the method in these areas.7 - 28

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 E PA 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 co nsensus 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 m andatory 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 consen sus 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) sensitiv ity 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 volun tary 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 procedure s, 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 E PA 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 Emi ssions 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 de signed 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 pro s and con s of each option. The key specific differences between ASME PTC- 38 - 80 and the EPA methods are that the ASME standard: (1) allows in - st ack 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 bac kground 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 partic ulate loaded filters, sampling preparation and implementation guidance, sample recovery guidance, data reduction guidance, and particulate sample calculations input.7 - 30

The voluntary consensus standard ISO 9096:1992, “Determination of Concentration and Mass F low 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 t o 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 hours 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 Z 223.1 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 Emis sions 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 alt ernative 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 absorption7 - 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 samp ling 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 p ossibly 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 i n 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, an d 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 meth ods, performance specifications, or procedures specified.7 - 32

**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 provisio n 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 minori ty, low income, or indigenous populations because it increases the level of environmental protection for all affected populations without having any disproportionately high and adverse human health or environmental effects on any population, including any minority, low income, or indigenous 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 th is 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 P CB), 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 transforme d into fine PM (PM 2.5 ) in the atmosphere. Reducing direct emissions of PM 2.5 and SO2 will, as a result, reduce concentrations of PM 2.5 in the atmosphere. These reductions in PM 2.5 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 PM 2.5, please refer to Chap ters 4 and 5 of this RIA .) This final rule will also have a small effect on7 - 33

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 . Today’s 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 PM 2.5, as well as to other pollutants, from EGUs and othe r 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 th e 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 many, if not most, of these actions from EJ communities, among 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 o f 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 st ate 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. Thes e 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) act ive 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, we7 - 34

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 fundamental to the EPA’s assessment of both the underlying ris ks 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 EPA’s analysis of risks from consumption of Hg - contaminated fish is contained in Chapter 4 o f 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 seve ral 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 PM 2.5 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 t he 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 charact eristics 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 thes e 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 n earby communities. In addition, the EPA’s analysis indicates that there are localized areas with elevated levels of Hg deposition around most U.S. EGUs. 4

4 See Excess Local Deposition TSD fo r more detail.7 - 35

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 w ere 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 leve l. 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 li ne, 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 thos e 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). Wh ile 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 subpo pulation 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 EPA’s demographic analysis for coal fired EGUs are shown in Ta ble 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%. These7 - 36

results are presented in more detail in the ‘ ‘Review of Proximity Analysis,’’ February 2011, a copy of which is available in the docket. **PM 2.5**  **(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 reduc e emissions of PM and SO2. As a result, this final rule will reduce concentrations of PM 2.5 in the atmosphere. Exposure to PM 2.5 can cause or contribute to adverse health effects, such as asthma and heart disease, that significantly affect many minor ity, low- income, and tribal individuals and their communities. Fine PM (PM 2.5 ) 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 illn ess 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 particula rly susceptible to the health effects of PM 2.5 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 mortaliti es, 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 fewe r cases of upper and lower respiratory illness. We also examined the level of PM 2.5 mortality risks prior to the implementation of the rule according to race, income, and educational attainment. We then estimated the change in PM 2.5 mortality risk as a result of this final rule among people living in the counties with the highest (top 5 percent) PM 2.5 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 c ounties. In 2005, people living in the highest risk counties and in the poorest counties were estimated to be at substantially higher risk of PM 2.5 - related death than people living in the7 - 37

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 ed ucation were predicted to have significantly greater risk from PM 2.5 mortality than people with a greater than high school education. This was true both for the highest - risk counties and for the other counties. In summary, the analysis indicates t hat in 2005, educational status, living in one of the poorest counties, and living in a high - risk county are associated with higher estimated PM 2.5 mortality risk while race is not. Our analysis predicts that this final rule will likely significa ntly reduce the risk of PM 2.5 - 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 th e highest rates (top 5 percent) of PM 2.5 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 PM 2.5 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 race s, income levels, and level s 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 be tween those in the highest - risk counties and the other 95 percent of counties for all races and educational levels.7-38 **Table 7** **- 5**  **. Comparative Summary of the Demographics within 5 Kilometers (3 Miles) of the Affected Sources (**  **population in millions**  **) a** 
**Population White**  **African American**  **Native**

**A merican**  **Othe r or** 

**Multiracial Minority**  **b Hispanic**  **o r**  **Latino c**  **Age**

**0 –**  **17 Age** 

**65+ No High**  **School Diploma**  **Below**

**Poverty 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% 1 6% 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 Ra cial 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 rac ial category, Hispanics or Latinos may belong to any race.7 - 39

7. 11 .1.3 Meaningful Public Participation The EPA defines ‘‘ e nvironmental j ustice’’ to include meaningful involvement of all people regardless of race, color, national origin, or income with resp ect to the development, implementation, and enforcement of environmental laws, regulations, and polic i es. To promote meaningful involvement, the EPA publicized the rulemaki ng via newsletters, EJ listserve s, and the internet, including the Office of Policy’ s (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 emissi on 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 rene wed , 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 re quirements. 7. 11 .1. 4 Summary This final rule strictly limits the emissions rate of Hg and other HAP from every affected EGU . T he EPA’s analysis indicates substantial health benefits, including for vulnerable populations, from reductions in PM 2 .5 . The EPA’s 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, th e 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 lea rn about and comment on this rule and welcomes their participation. **7. 11**  **.2 Analysis of High Risk Sub** **- Populations**  7. 11 .2.1 Introduction In addition to the previously described assessment of EJ impacts, EPA conducted an analysis at proposal of sub - populatio ns with particularly high potential risks of mercury7 -40

exposure due to high rates of fish consumption using the basic methodology developed in Chapter 4 of this document with consumption rates specific to these subpopulations . 5 The consumption rates used her e add additional uncertainty to the analysis above that already highlighted in Chapter4. These populations overlap in many cases with traditional EJ populations and would benefit from mercury reductions resulting from this rule. Based on a det ailed 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 rec reational/subsistence fishers in the Southeast region 7

 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 Sportsmen’s Classic in Columbia, SC. O f 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. The black and white fisher populations were extrapolated to cover all watersheds modeled for risk

5 This analysis was not updated based on the final policy case. For the remainder of this section “proposed MATS” refers to the proposal policy used to conduct the analysis. 7The 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.7 -41

in the Southeastern states. The rationale for this was that fishing activity by these two groups could be generali zed in this region of the country. Note, however that these scenarios were only assessed for watersheds in the Southeast located within US Census tracts with at least 25 individuals from that ethnic group below the poverty line. Given the focus of the risk assessment on consumption by women (in considering risk to pregnant women in particular), we extrapolated the female consumption rates to all watersheds in the continental US with at least 25 individuals below the poverty line. Note that this analysis is based on scenarios representing individuals within population subgroups who engage in higher levels of fishing activity. We do not have information for specific subgroups on the proportion of those populations that actually engage in those levels of fish ing activity. As a result there is additional uncertainty about the actual distributions of risk represented by each exposure scenario. Evidence for the Hispanic and Laotian groups is based on a study by Shilling et al. (2010). This study looks at subsist ence 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 pr actice 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. The fish consumptio n rates for Hispanics and Laotians were extrapolated to cover US Census tracts with at least 25 poor members of the ethnic populations. 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 cove red 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 indiv iduals 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. To assess the distribution of in dividual risks from mercury exposure in these specific subpopulations, we modified the methodology in the following ways:7 -42

1. We limited the analysis to only include census tracts with at least 25 residents (100 residents for the Chippewa group) in the defi ned demographic group (based on the 2000 Census). Tracts with fewer individuals were assumed to be less representative of the location and conditions of the subpopulation of interest. 2. Rather than using four distance intervals around each census tract, w e limited the analysis to one distance interval (0 to 20 miles). This interval was selected to better reflect the likely shorter distances traveled by low- income and/or subsistence fishers who fish with relatively high frequency. As a result, the populatio ns were not subdivided according to income or urban classification; however, the separation between river and lake anglers was preserved using the same methodology described above to define c r and c l . 3. Due to data limitations, rather than specifically se lecting and estimating populations of pregnant women in angler households for the high - risk demographic subgroup of interest, we used the entire population of the defined demographic subgroup in each selected census tract. This approach was used because th e purpose of this part of the analysis is not to estimate the total size of the exposed population or the aggregate impacts on this demographic subgroup. Instead, the objective is to examine the potential distribution of risks within the group. Using the e ntire subgroup population to represent the risk distribution in the exposed population relies on the key assumption that the spatial distribution of the entire subgroup provides a reasonable approximation fo r the distribution of pregnant women in high - cons uming angler households. In other words, it wa s assumed that the expected proportion of the subgroup ’ s population in each Census tract that consists of pregnant women in fishing households is the same across the selected census tracts. The main limitation of this assumption is that it does not allow or account for spatial variation in freshwater angler participation rates for the subgroups of interest. Unfortunately, data to address these limitations are not readily available. 4. Population size projections for future years (beyond 2000) in each selected tract were based on county- level growth projections for the full population (all ages and both sexes) in the most closely corresponding race category (Woods and Poole, 2008 ). For example, the Asian and Native American categories were used for the Laotian and Chippewa population projections, respectively. 5. Rather than assuming a single fish consumption rate (C) for all exposed individuals, the analysis assumed and applied a different distribution of consumpt ion rates for each subgroup, based on evidence from existing empirical studies . Using the consumption rate information reported in Table 7 - 6 , we fit a separate log- normal distribution of consumption rates for each of the six subpopulati ons. We then estimat e d a specific mercury ingestion rate (HgI) for each subpopulation member in the selected census tracts. To specify the average fish tissue mercury concentration (CHg), residents were divided into river and lake fishers according to the state - level7 -43 percenta ges (as described in Section 4. 7.2 ) and assigned the corresponding average mercury concentration within the 20 - mile interval from the tract centroid. To specify the consumption rate (C) for each individual, we randomly drew a separate value from the specific log- normal consumption rate distribution developed for the subpopulation. We then estimate d the corresponding IQ loss for each individual. **Table 7 -**  **6 .**  **Reported Distributions of Self -**  **Caught Freshwater Fish Consumption Rates**

**Among Selected Potentially High**  **- Risk Subpopulations** 

**Population Self** **- Caught Freshwater Fish Consumption Rate**  **(g/day) Study**  **Sample Size**  **Mean**

**(Median) 90** **th (95**  **th )** 

**Percentile** Low- income African -American recreational/subsistence fishers in Southeast 39 171 (137) 446 (557) Burger (2002)
Low- income white recreational/ subsistence fishers in Southeast 415 38.8 (15.3) 93 (129) Burger (2002) Low- income female recreational/ subsistence fishers 149 39.1 (11.6) 123 (173) Burger (2002) Hispanic subsistence fishers 45 25.8 (19.1) 98 a (155.9) Shi lling et al. (2010) Laotian subsistence fishers 54 47.2 (17) 144.8 a (265.8) Shilling et al. (2010) Great Lakes tribal groups 822 60 (113 b ) 136.2 a (213.1) a Dellinger (2004)

a Derived values using a log - normal distribution, based on the median and the 95 t h percentile or standard deviation reported in study. b Standard deviation in parentheses, rather than median. 7.1 1 .2.2 Primary Results for Potentially High - Risk Subpopulations The methodology described in above, used to estimate the aggregate benefits o f mercury emission reductions, was also adapted and applied to assess effects on the distribution of individual - level risks among specific potentially high - risk subpopulations in the United States. The analysis of these subpopulations focuses on the distri bution of individual mercury exposures risks (i.e., expected IQ loss) within these groups, particularly in the high end of the risk distributions. It also examines the distribution of reductions in IQ loss in these groups as a result of EGU mercury emissio n reductions. **Low -**  **income African**  **- American**  **recreational/subsistence fishers in the Southeast**  **region.** The analysis of low- income African -Americans in the Southeast United States focuses on census tracts that have (1) at least one HUC- 12 within 20 miles with a mercury fish tissue7 -44

concentration estimate and (2) at least 25 African -American inhabitants living below the poverty level. Using county- level growth projections, there were an estimated 3.09 million lowincome African Americans in these areas in 2005, and 3.56 million are expected to reside in these areas in 2016. 8 The geographic distribution of the expected 2016 population is shown in Figure 7 - 1 . The spatial distribution of these population estimates for all low- income African Americans in these areas was used to model the distribution of risk among pregnant women in recreational/subsistence fishing households in this group. This approach was used because population estimates for this latter group are not readily available and difficult to generate. Th is inability to define the population of subsistence fishers within the African American population adds uncertainty to the analysis in addition to the uncertainty in the consumption rates. The approach assumes that the spatial distribution of these pregna nt women is the same as for the total low- income African American population in these areas. Populations were linked at the tract level to average mercury fish tissue concentrations within 20 miles using the methods described in Section 7.11.2.1. The distr ibution of mercury ingestion and IQ loss resulting from these fish tissue levels was then simulated using the fish consumption rate distribution for this subpopulation summarized in Table 7 - 6 . Table 7 - 7 summarizes the estimated distribution of IQ loss amon g this subpopulation due to mercury exposure through consumption of self- caught freshwater fish. These distribution estimates are reported for populations in two time periods (2005 and 2016) under base case conditions and under alternative emission control scenarios. In the 2016 base case, the median IQ loss is estimated to be 1.87 points and the 95th percentile is 11.56 points per exposed individual. Under proposed MATS, the median declines to 1.82 points and the 95th percentile to 11.33 points in 2016 .

8A rough approximation of the size of the annual exposed population — low- income African American pregnant women i n angler households in the Southeast in 2016 — can be estimated by multiplying the total population estimate (3.56 million) by (1) the percent of the total U.S. population that is female and age 15 –44 (21%; Census, 2000) (2) the fertility rate for African Am erican women of childbearing age (6.9%; U.S. Vital Statistics, 2005), and (3) the percent of the low- income African American population that are freshwater fishers (6–8%; Henderson, 2004). The resulting estimated exposed population is between 9,400 and 12, 600 individuals.7 -45

**Figure 7 -**  **1 .**  **Modeled African -** **American Population Below the Poverty Level by Census Tract in the Southeast for 2016**  **Table 7 -**  **7 .**  **Simulated Distribution of IQ Loss (per Exposed Person) for Low** **- Income African**  ****

**American Recreational/Subsistence Fishers in the Sou theast Region** 

**Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 base case 0.930 1.980 4.173 8.104 12.023 3.601 2005 EGU zero - out 0.835 1.805 3.854 7.555 11.271 3.342 2016 base case 0.868 1.868 3.974 7.771 11.556 3.437 2016 EGU zero - out 0.832 1 .801 3.848 7.551 11.246 3.335 2016 Proposed MATS 0.843 1.820 3.884 7.611 11.328 3.3637 -46

Table 7 - 8 summarizes the estimated distribution of reductions in IQ loss associated with alternative emission reduction scenarios. 9 T he median reduction in IQ loss re sulting from proposed MATS in 2016 (relative to the 2016 base case) scenario is 0.032 points, and the 95th percentile is 0.274 points. **Table 7 -** **8 .**  **Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for Low** ****

**Income African -** **American Recreation al/Subsistence Fishers in the Southeast**  **Region**

**Emission Reduction Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 EGU zero - out (relative to 2005 base case ) 0.049 0.123 0.290 0.607 0.935 0.260 2016 EGU zero - out (relative to 2016 base case) 0. 019 0.050 0.118 0.240 0.363 0.103 2016 Proposed MATS (relative to 2016 base case) 0.011 0.032 0.082 0.176 0.274 0.074

**Low -**  **in come White**  **recreational/subsistence fishers in the Southeast**  **region.** The analysis of the low- income white population in the Sout heast United States uses the same methodology as for the low- income African -American subpopulation, except that it uses tract level population estimates for all whites living below the poverty level in the southeast United States, and it uses the different fish consumption rate distribution for this population described in Table 7 - 6 . For this group, the spatial distribution of all low- income whites in these areas was used to simulate the distribution of IQ loss es resulting from exposures via pregnant women in recreational/subsistence fishing households. We are unable to define the population of subsistence fishers within the low – income white population which adds uncertainty to the analysis in addition to the uncertainty in the consumption rates. The total low- income White population in the southeastern states w as 3.26 million for 2005 and was projected to be 3.58 million in 2016 . 10 The geographic distribution of this population for 2016 is shown in Figure 7 - 2 .

9The scenarios comparing different years (i.e., 2016 base case [relative to 2005 base case] and 2016 MATS [compared to 2005 base case]) were not evaluated for the reduction in individual exposure analysis because the exposed populations ar e different in the two periods. 10A rough approximation of the size of the annual exposed population — low- income white pregnant women in angler households in the Southeast in 2016 — can be estimated by multiplying the total population estimate (3.58 million) b y (1) the percent of the total U.S. population that is female and age 15 –44 (21%; Census, 2000) (2) the fertility rate for white women of childbearing age (6.6%; U.S. Vital Statistics, 2005), and (3) the percent7 -47

**Figure 7 -**  **2 .**  **Modeled White Population Below the**  **Poverty Level b y Census Tract**  **in the Southeast for 2016**  Table 7 - 9 summarizes the estimated distribution of IQ loss among this subpopulation. In the 2016 base case, the median IQ loss is estimated to be 0.188 points, and the 95th percentile is 2.459 point s p er exposed individual. Under proposed MATS, the median declines to 0.184 points and the 95th percentile to 2.415 points in 2016 . **Table 7 -**  **9 .**  **Simulated Distribution of IQ Loss (per Exposed Person) for Low -**  **Income White**

**Recreational/Subsistence Fishers in t he Southeast Region** 

**Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 base case 0.071 0.203 0.580 1.480 2.586 0.670 2005 EGU zero - out 0.063 0.182 0.527 1.362 2.396 0.618 2016 base case 0.065 0.188 0.541 1.396 2.459 0.633
of the low- income white population that are freshwater fishers (10– 11%; Henderson, 2004). The resulting estimated exposed population is between 15,800 and 17,400 individuals.7 -48

2016 EGU zero - out 0.06 2 0.182 0.525 1.358 2.396 0.617 2016 Proposed MATS 0.063 0.184 0.530 1.371 2.415 0.622 Table 7 - 10 summarizes the estimated distribution of reductions in IQ loss associated with alternative emission reduction scenarios. T he median reduction in IQ loss re sulting from proposed MATS in 2016 (relative to the 2016 base case) scenario is 0.002 points, and the 95th percentile is 0.045 points. **Table 7 -**  **10 .**  **Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for Low******

**Income White Recreational/Subsist ence Fishers in the Southeast Region** 

**Emission Reduction Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 EGU zero - out (relative to 2005 base case ) 0.004 0.013 0.042 0.113 0.204 0.051 2016 EGU zero - out (relative to 2016 base case) 0.001 0.004 0.013 0.036 0.066 0.017 2016 Proposed MATS (relative to 2016 base case) 0.001 0.002 0.008 0.024 0.045 0.011

**Low -**  **income female recreational/subsistence fisher.** The analysis of the low- income female population uses tract - level female population and poverty estimates for the entire continental United States, and it uses the specific fish consumption rate distribution for this population described in Table 7 - 6 . We are unable to define the population of subsistence fishers within the low- income female popula tion which adds uncertainty to the analysis in addition to the uncertainty in the consumption rates. For this group, the total modeled populations used to simulate the distribution of IQ loss were 18.4 million for 2005 and 20.1 million for 2016 . 11 The geog raphic distribution of the population modeled for 2016 is shown in Figure 7 - 3 . Table 7 - 11 summarizes the estimated distribution of IQ loss among this subpopulation. In the 2016 base case, the median IQ loss is estimated to be 0.111 points, and the 95th

11A rough approximation of the size of the annual exposed population — low- income pregnant women in angler households in 2016 — ca n be estimated by multiplying the total population estimate (20.1 million) by (1) the percent of the total female U.S. population that is age 15 -44 (42%; Census 2000) (2) the fertility rate for all women of childbearing age (6.7%; U.S. Vital Statistics 200 5), and (3) the percent of the low- income women that are freshwater fishers (7%; Henderson 2004). The resulting estimated exposed population is roughly 62,100 individuals.7 -49

percentile is 3.122 points per exposed individual. Under proposed MATS, the median declines to 0.108 points and the 95th percentile to 3.064 points in 2016 .

**Figure 7 -**  **3 .**  **Modeled Female Population Below t**  **he Poverty Le vel b** **y Census Tract for 2016**7 - 50

**Table 7 -**  **11 .**  **S imulated Distribution of IQ Loss (per Exposed Person) for Low** **- Income Female** 

**Recreational/Subsistence Fishers in the United States**

**Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 base case 0.030 0.122 0.480 1.629 3.373 0.932 2005 EGU zero - out 0 .027 0.109 0.431 1.477 3.073 0.855 2016 base case 0.027 0.111 0.439 1.499 3.122 0.868 2016 EGU zero - out 0.026 0.107 0.425 1.457 3.038 0.846 2016 Proposed MATS 0.026 0.108 0.429 1.470 3.064 0.853 Table 7 - 12 summarizes the estimated distribution of red uctions in IQ loss associated with alternative emission reduction scenarios. T he median reduction in IQ loss resulting from proposed MATS in 2016 ( relative to the 2016 base case) scenario is 0.001 points, and the 95th percentile is 0.053 points. **Table 7 -**  **12 .**  **Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for Low******

**Income Female Recreational/Subsistence Fishers in the United States**

**Emission Reduction Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 EGU zero - out (relative to 2005 base case ) 0.001 0.007 0.034 0.127 0.274 0.077 2016 EGU zero - out (relative to 2016 base case) 0.000 0.002 0.010 0.037 0.078 0.021 2016 Proposed MATS (relative to 2016 base case) 0.000 0.001 0.006 0.024 0.053 0.015
**Hispanic subsistence fisher.**  The an alysis of the Hispanic population uses tract - level population estimates for this group for the entire continental United States, and it uses the specific fish consumption rate distribution for this population described in Table 7 - 6 . We are unable to define the population of subsistence fishers within the Hispanic population which adds uncertainty to the analysis in addition to the uncertainty in the consumption rates. For this group, the total modeled populations used to simulate the distribution of IQ los s were 19.67 - 51

million for 2005 and 27.2 million for 2016 . 12 The geographic distribution of the population modeled for 2016 is shown in Figure 7 -4 . Table 7 - 13 summarizes the estimated distribution of IQ loss among this subpopulation. In the 2016 base case, the median IQ loss is estimated to be 0.184 points, and the 95th percentile is 2.494 points per exposed individual. Under proposed MATS, the median declines to 0.180 points and the 95th percentile to 2.468 points in 2016 .

**Figure 7 -** **4 .**  **Modeled Hispanic Populat**  **ion b y Census Tract for 2016** 

12A rough approximation of the size of the annual exposed population — Hispanic pregnan t women in angler households in 2016 — can be estimated by multiplying the total population estimate (27.2 million) by (1) the percent of the total U.S. population that is female and age 15–44 (21%; Census, 2000) (2) the fertility rate for Hispanic women of childbearing age (9.9%; U.S. Vital Statistics, 2005), and (3) the percent of the Hispanic population that are freshwater fishers (5 –6%; Henderson, 2004). The resulting estimated exposed population is between 59,900 and 71,900 individuals.7 - 52

**Table 7 -**  **13 .**  **Simulated Distribution of IQ Loss (per Exposed Person) for Hispanic**

**Recreational/Subsistence Fishers in the United States**

**Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 base case 0.066 0.202 0.588 1.530 2.732 0.724 2005 EGU zero - out 0.060 0.186 0.546 1.435 2.578 0.686 2016 base case 0.059 0.184 0.538 1.402 2.494 0.657 2016 EGU zero - out 0.058 0.179 0.526 1.378 2.456 0.647 2016 Proposed MATS 0.058 0.180 0.530 1.385 2.468 0.650 Table 7 - 1 4 summarizes the estimated distribution of reductions in IQ loss associated with alternative emission reduction scenarios. T he median reductio n in IQ loss resulting from Proposed MATS in 2016 (relative to the 2016 base case) scenario is 0.001 points, and the 95th perc entile is 0.03 points. **Table 7 -**  **1 4**  **. Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for Hispanic** 

**Recreational/Subsistence Fishers in the United States**

**Emission Reduction Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 EGU zero - out (relative to 2005 base case ) 0.001 0.006 0.027 0.084 0.157 0.038 2016 EGU zero - out (relative to 2016 base case) 0.000 0.002 0.008 0.023 0.042 0.010 2016 Proposed MATS (relative to 2016 base case) 0.000 0.001 0.005 0.016 0.030 0.007

**Laotian sub**  **sistence fishers.** The analysis of the Laotian population uses tract - level population estimates for this group for the entire continental United States, and it uses the specific fish consumption rate distribution for this population described in Table 7 - 6 . We are unable to define the population of subsistence fishers within the Laotian population which adds uncertainty to the analysis in addition to the uncertainty in the consumption rates. For this group, the total modeled populations used to simulate the d istribution of IQ loss were 80,0007 - 53

for 2005 and 137,500 for 2016 . 13 The geographic distribution of the population modeled for 2016 is shown in Figure 7 - 5 . Table 7 - 1 5 summarizes the estimated distribution of IQ loss among this subpopulation. In the 2016 base case, the median IQ loss is estimated to be 0.196 points, and the 95th percentile is 4.260 points p er exposed individual. Under proposed MATS, the median declines to 0.192 points and the 95th percentile to 4.210 points in 2016 .

**Figure 7 -**  **5 .**  **Modeled Laotia n**  **Population b y Census Tract for 2016** 

13A rough approxim ation of the size of the annual exposed population — Laotian pregnant women in angler households in 2016 — can be estimated by multiplying the total population estimate (0.14 million) by (1) the percent of the total U.S. population that is female and age 15–44 (21%; Census 2000) (2) the fertility rate for Asian women of childbearing age (6.7%; U.S. Vital Statistics 2005), and (3) the percent of the Laotian population that are freshwater fishers (13–50%; Henderson, 2004; Hutchison and Kraft, 1994). The resulting estimated exposed population is between 600 and 3,000 individuals.7 - 54

**Table 7 -**  **1 5**  **. Simulated Distribution of IQ Loss (per Exposed Person) for Laotian** 

**Recreational/Subsistence Fishers in the United States**

**Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 base case 0.061 0.212 0.7 44 2.330 4.559 1.265 2005 EGU zero - out 0.057 0.198 0.692 2.197 4.335 1.201 2016 base case 0.056 0.196 0.688 2.142 4.260 1.145 2016 EGU zero - out 0.054 0.190 0.671 2.097 4.184 1.126 2016 Proposed MATS 0.055 0.192 0.676 2.111 4.210 1.133 Table 7 - 16 su mmarizes the estimated distribution of reductions in IQ loss associated with alternative emission reduction scenarios. T he median reduction in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case) scenario is 0.001 points, and the 9 5th percentile is 0.047 points. **Table 7 -**  **16 .**  **Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for Laotian**

**Recreational/Subsistence Fishers in the United States**

**Emission Reduction Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 EGU zero - out (relative to 2005 base case ) 0.000 0.003 0.022 0.093 0.209 0.064 2016 EGU Zero - out (relative to 2016 base case) 0.001 0.003 0.011 0.036 0.073 0.019 2016 Proposed MATS (relative to 2016 base case) 0.000 0.001 0.006 0.022 0.047 0.012

**Chi ppewa**  **T ribe members**  **in the Great Lakes area.**  The analysis of the Chippewa population uses tract - level population estimates for this group in Minnesota, Wisconsin, and Michigan, for tracts within 20 miles of the fishing area ceded to the T ribe. It uses th e specific fish consumption rate distribution for this population described in Table 7 - 6 . We are unable to define the population of subsistence fishers within the Chippewa tribal population which adds uncertainty to the analysis in addition to the uncertai nty in the consumption rates. For this group, the total modeled populations used to simulate the distribution of IQ loss were 23,9007 - 55

for 2005 and 29,500 for 2016 . 14 The geographic distribution of the population modeled for 2016 is shown in Figure 7 - 6 . Table 7 - 1 7 summarizes the estimated distribution of IQ loss among this subpopulation. In the 2016 base case, the median IQ loss is estimated to be 0.398 points and the 95th percentile is 3.284 points per exposed individual. Under proposed MATS, the median decli nes to 0.392 points and the 95th percentile to 3.241 points in 2016 .

**Figure 7 -**  **6 .**  **Modeled Chippewa Population b** **y Census Tract in the Great Lakes A**  **rea for 2016.** 

14A rough approximation of the size of the annual exposed population — Chippewa pregnant women in angler households in the Great Lakes are 2016 — can be estimated by multiplying the total popul ation estimate (29.5 thousand) by (1) the percent of the total U.S. population that is female and age 15 –44 (21%; Census 2000) (2) the fertility rate for American Indian women of childbearing age (6%; U.S. Vital Statistics 2005), and (3) the percent of the Chippewa population that are freshwater fishers (26%, assumed to be twice national average). The resulting estimated exposed population is roughly 300 individuals.7 - 56

**Table 7 -**  **1 7**  **. Simulated Distribution of IQ Loss (Per Exposed Person) for Chippewa in the Great** 

**La kes Area** 

**Scenario Percentile**  **Mean 25th**  **50th 75th**  **90th 95th**  2005 base case 0.175 0.410 0.989 2.125 3.341 0.940 2005 EGU zero - out 0.168 0.397 0.955 2.049 3.237 0.909 2016 base case 0.165 0.398 0.949 2.089 3.284 0.902 2016 EGU zero - out 0.162 0.391 0.9 33 2.056 3.231 0.887 2016 Proposed MATS 0.163 0.392 0.937 2.062 3.241 0.890 Table 7 - 1 8 summarizes the estimated distribution of reductions in IQ loss associated with alternative emission reduction scenarios. T he median reduction in IQ loss resulting fr om proposed MATS in 2016 (relative to the 2016 base case) scenario is 0.005 points, and the 95th percentile is 0.041 points. **Table 7 -**  **1 8**  **. Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for** 

**Chippewa in the Great Lakes Area**

**Emission Reduc tion Scenario**  **Percentile Mean**  **25th 50th**  **75th 90th**  **95th** 2005 EGU zero - out (relative to 2005 base case ) 0.005 0.012 0.030 0.068 0.112 0.030 2016 EGU zero - out (relative to 2016 base case) 0.002 0.006 0.014 0.033 0.053 0.014 2016 Proposed MATS (relat ive to 2016 base case) 0.002 0.005 0.011 0.026 0.041 0.011 **Comparison of risk distributions across high**  **- risk subpopulations.**  Using the results summarized above, Figure 7 - 7 compares the simulated IQ loss distributions for the six potential high - risk subpo pulations under the 2016 base case s cenario. The low- income African -American population in the Southeast United States stands out from the other groups, with relatively high individual risk levels at each percentile. It is estimated that 5% of the prenatal ly exposed population in this group in 2016 (the 95th and greater percentile) would experience IQ losses of more than 11.55 points because of mercury in freshwater fish under base case conditions. The7 - 57

group with the next highest estimated individual risk l evels at the 95th percentile and above is the Laotian subpopulation, with IQ losses of 4.26 points and greater. Figure 7 - 8 provides a similar comparison for the distribution of reductions in IQ loss associated with proposed MATS in 2016 (relative to the 20 16 base case) . Again, the low- income African -American subpopulation stands out from the others with the highest reductions at each percentile. It is estimated that 5% of the prenatally exposed population in this group in 2016 would have expected reductions in IQ losses of more than 0.274 points because of reductions in mercury emissions. At the 95th percentile, all of the other subpopulations have expected reductions in IQ loss between 0.03 and 0.053 IQ points.

0 10 20 30 40 50 60 70 80 90 100

0 2 4 6 8 10 12**P e** **r c** **e n** **t i** **l e** 

**IQ Loss**  **(pe r e**  **xposed individual)**  Low- income SEAfrican -Ame rican Low- incom e S E Wh ite Low- income U.S. Fe male U.S. Hispanic U. S.Laotian Gre atLakes Are a Chippewa **Figure 7 -**  **7 .**  **Comparison of IQ Loss Distributi ons for Selected High**  **- Risk Populations (2016**  **Base Case)** 7. 11 .2.3 Overall Conclusions Risk (IQ loss) Associated with Self- Caught Freshwater Fish Consumption among Selected High Risk Subpopulations 15

15 We do note that overall confidence in IQ loss estimates above approximately 7 points decre ases because we begin to apply the underlying IQ loss function at exposure levels (ppm hair levels) above those reflected in7 - 58

 Low- Income African -American Subsistence - Level Fishers in t he Southeast

 In the 2016 base case scenario, the median IQ loss is estimated to be 1.87 IQ points and the 95 th percentile is 11.56 IQ points per exposed individual.

 The median reduction in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case scenario) is 0.032 IQ points, and the 95 th percentile is 0.274 IQ points.

0 10 20 30 40 50 60 70 80 90 100

0 0.05 0.1 0. 15 0. 2 0.25 0. 3**P e** **r c** **e n** **t i** **l e** 

**Redu ction**  **in IQ**  **Loss (pe**  **r exp** **ose d**  **individual)** Low- incomeS E African -Am er ican Low- incom e S E Wh ite Low- incomeU .S. Fe ma le U. S. H ispanic U.S.Laotian Great Lak esA rea Chippewa **Figure 7 -** **8 .**  **Comparison of Reduction in IQ Loss Distributions for Selected High -**  **Risk Populations (2016**  **Proposed MATS**  **Relative to 2016 Base Case)**

 Low- Income White Subsist ence - Level Fishers in the Southeast

– In the 2016 base case scenario, the median IQ loss is estimated to be 0.19 IQ points and the 95 th percentile is 2.46 IQ points per exposed individual. epidemiological studies used to derive those functions. The 39.1 ppm was the highest measured ppm level in the Faroes Island study, while ~86 was the highest value in the New Zealand study (USEPA, 2005) (a 7 IQ points loss is approximately associated with a 40 ppm hair level given the concentration - response function we are using).7 - 59 – The median reduction in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case scenario) is 0.002 IQ points, and the 95 th percentile is 0.045 IQ points.
 Low- Income Female Subsistence - Level Fishers

– In the 2016 base case scenario, the median IQ loss is estimated to be 0.11 IQ points and the 95 th percenti le is 3.12 IQ points per exposed individual.

– The median reduction in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case scenario) is 0.001 IQ points, and the 95 th percentile is 0.053 IQ points.
 Hispanic Subsistence - Level Fisher

– In the 2016 base case scenario, the median IQ loss is estimated to be 0.18 IQ points and the 95 th percentile is 2.94 IQ points per exposed individual.

– The median reduction in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case scenar io) is 0.001 IQ points, and the 95 th percentile is 0.030 IQ points.
 Laotian Subsistence - Level Fishers

– In the 2016 base case scenario, the median IQ loss is estimated to be 0.20 IQ points and the 95 th percentile is 4.26 IQ points per exposed individual.

– The median reductio n in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case scenario) is 0.001 IQ points, and the 95 th percentile is 0.047 IQ points.
 Chippewa Trib e Members in the Great Lakes Area

– In the 2016 base case scenario, the median IQ loss is estimated to be 0.40 IQ points and the 95 th percentile is 3.28 IQ points per exposed individual.

– The median reduction in IQ loss resulting from proposed MATS in 2016 (relative to the 2016 base case scenario) is 0.005 IQ points, and the 95 th percentile is 0.041 IQ points. **7.1 1**  **.3 Characterizing the Distribution of Health Impacts across Population** **s** 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 PM 2.5 - related benefits resulting from the implementation of MATS: 1. What is the baseline distribution of PM 2.5 - related mortality risk for adults according to the race, income and education of the population?7 - 60

2. How does MATS change the distribution of PM 2.5 mortality risk among populations of different races — particularly among those populations at greatest risk in the baseline? 16 In this analysis we estimat ed that PM 2.5 mortality risk from the modeled scenarios is not distributed equally throughout the U.S., or among populations of different levels of educational attainment — though the level of PM 2.5 mortality risk appears to be shared fairly equally among po pulations of different races. We estimate that the air quality and PM 2.5 - related mortality risk improvements achieved by MATS are relatively equally distributed among minority populations , and that the rule reduces PM 2.5 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 draw n from techniques des cribed in the peer reviewed literature (Fann et al. 2011b) EPA will continue to modify these approaches as new scientific information becomes available. 7.1 1 .3.1 Methodology The methods used here to describe the distribution of PM 2.5 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 PM 2.5 - 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 PM 2.5 - related mortality risk . This portion of the analysis follows an approach described else where (Fann et al. 2011a, Fann et al. 2011b), wherein modeled 2005 PM 2.5 levels are used to calculate the proportion of all - cause mortality risk attributable to total PM 2.5 levels in each county in the Continental U.S. Within each county we estimate the le vel of all - cause PM 2.5 mortality risks for adult populations as well as the level of PM 2.5 mortality risk according to the race, income and educational attainment of the population. Our approach to calculating the distribution of PM 2.5 mortality risk acro ss the population is generally consistent with the benefits analysis conducted for the modeled scenario described in Appendix 5 C with two exceptions: the PM 2.5 mortality risk coefficients used to quantify impacts and the baseline mortality rates used to ca lculate mortality impacts (a detailed discussion of how both the mortality risk coefficients and baseline incidence rates are used to estimate the incidence of PM 2.5 - related deaths may be found in the Chapter 5 of the RIA ). We

16 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 PM 2.5 mortality risk estimates.7 - 61

substitute risk estimates dra wn 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 1 999 - 2000 over 116 U.S. cities (Krewski et al. 2009) (RR=1.06, 95% confidence intervals 1.04 — 1.08 per 10μg/m 3 increase in PM 2.5 )

. 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 PM 2.5 - related mortality. Krewski and colleagues note that “ ...the level of education attainment may likely indicate the effects of complex and multifact orial 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 PM 2.5 mortality risk coefficients modified by educational attainment: less than grade 12 (RR = 1.082, 95% confidence intervals 1.024 — 1.144 per 10 μg/m 3 change), grade 12 (RR = 1.072, 95% confidence intervals 1.020 — 1.127 per 10 μg/m 3 change), and greater than grade 12 (RR = 1.055, 95% confidence intervals 1.018 — 1.094 per 10 μg/m 3 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 esti mate 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 PM 2.5 - 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 PM 2.5 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 relationshi p between race and PM 2.5 - 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 assoc iated with reductions in PM2.5 The result of this analysis is a distribution of PM 2.5 mortality risk estimates by county, stratified by each of the three population variables (race, income and educational attainment).7 - 62

We have less confidence i n 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 PM 2.5, county level ba seline 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 95 th percentile of the PM 2.5 mortality risk distribution. We selected this percentile cu t- off to capture the very highest levels of PM 2.5 mortality risk. The second step of the analysis was to repeat the sequence above by estimating PM 2.5 mortality risk in 2016 prior to, and after, the implementation of MATS. 7.1 1 .3.2 Results We estimated th e change in PM 2.5 mortality risk in 2016 among populations living in those counties at the upper 95 th percentile of the mortality risk in 2005 . W e then compared the change in risk among these populations living in high - risk counties with populations livin g in all other counties (Tables 7 - 19 through 7 - 21 ) . **Table 7** **- 19**  **. Estimated**  **Change in the Percentage of All Deaths Attributable to PM 2.5**  **Before and After Implementation of MATS by 2016 for Each Populations, Stratified by Race** 

**Year Race**  **Asian Black**  **Native American**  **White Among populations at greater risk**  2016 (pre - MATS Rule) 4.3% 4.4% 4.4% 4.5% 201 6 (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% 201 6 (post - MATS Rule) 3% 2.9% 2.9% 3.1 % **Table 7** **- 20**  **. Estimated**  **Change in the Percentage of All Deaths Attributable to PM 2.5**  **Before**

**and After Implementation of MATS by 2016 for Each Population, Stratified by**  **Race and Poverty Level**

**Year Race**  **Asian Black**  **Native American White**  **Among population s living in counties with the largest number of individuals living below the poverty line** 7 - 63

2016 (pre - MATS) 3.6% 3.5% 3.6% 3.6% 201 6 (post - MATS) 3% 3.4% 3% 3.5% **Among all other populations** 2016 (pre - MATS) 3.2% 3 .2% 3.2% 3.3% 201 6 (post - MATS) 3% 2 .9% 3% 3.1%

**Table 7** **- 21**  **. Estimated**  **Change in the Percentage of All Deaths Attributable to PM 2.5**  **Before**

**and After the Implementation of MATS by 2016 for Each Population, Stratified by Educational Attainment** 

**Year Race**  **< Grade 12 = Grade 12**  **> Grade 12 Am**  **ong populations at greater risk** 2016 (pre - MATS) 6.2% 5.5% 4.3% 201 6 (post - MATS) 5.9% 5.3% 4.1% **Among all other populations** 2016 (pre - MATS) 4.5% 4% 3.1% 201 6 (post - MATS) 4.2% 3.8% 2.9% Table 7 - 19 , shows the estimated level of PM 2.5 mortality ri sk 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 95 th percentile of the estimated PM 2.5 mortality risk in 2005, and “ all other counties” as those with estimated PM 2.5 mortality risk below this level. The results of this analysis suggest that the PM 2.5 mortality risk among these populations at “greater risk” falls with imp lementation of the 2016 MATS. These results also suggest that all populations, irrespective of race, may receive a n estimated reduction in PM 2.5 mortality risk . However, limits to data resolution prevent us from delineating the PM 2.5 mortality risk accord ing to population race with confidence.7 - 64

Table 7 - 20 illustrate s the estimated change in the level of PM 2.5 mortality risk among populations living in those counties that meet two criteria: (1) the county is at the upper 95 th percentile of mortality risk i n 2005; (2) the county is at the upper 95 th percentile in terms of the number of individuals living below the poverty line. We also estimate the change in PM 2.5 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 PM 2.5 mortality risk than those living in lower risk counties containing a smaller number of individuals living below the poverty line. Table 7 - 21 summarize s the estimated change in PM 2.5 m ortality risk among populations who have attained three alternate levels of education — less than high school, high school and greater than high school. As described above, we apply education - stratified PM 2.5 mortality risk coefficients for this analysis. Th ese results indicate that populations with less than a high school education are at higher risk of PM 2.5 mortality, irrespective of whether these populations live in “greater risk” counties, according to the definition described above. We estimate that wit h the implementation of MATS, all populations see their PM 2.5 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 cann ot 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 . **7. 13**  **References** Abt Associates, Inc. 2010. Environmental Benefit s 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, 2007.7 - 65

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