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CHAPTER 7

STATUTORY AND EXECUTIVE ORDER ANALYSES

7.1 Introduction

This chapter presents discussion and analyses relating to Executive Orders and statutory 

requirements relevant for the final Mercury and Air Toxics Standards (MATS). We discuss the 

analysis conducted to comply with Executive Order (EO) 12866 and the Paperwork Reduction 

Act (PRA) as well as potential impacts to affected small entities required by the Regulatory 

Flexibility Act (RFA), as amended by the Small Business Regulatory Enforcement Fairness Act 

(SBREFA). We also describe the analysis conducted to meet the requirements of the Unfunded 

Mandates Reform Act of 1995 (UMRA) assessing the impact of the final rule on state, local and 

tribal governments and the private sector. In addition, we address the requirements of EO 

13132: Federalism; EO 13175: Consultation and Coordination with Indian Tribal Governments; 

EO 13045: Protection of Children from Environmental Health and Safety Risks; EO 13211: 

Actions that Significantly Affect Energy Supply, Distribution, or Use; the National Technology 

Transfer and Advancement Act; EO 12898: Federal Actions to Address Environmental Justice in 

Minority Populations and Low-Income Populations; and the Congressional Review Act.

7.2 Executive Order 12866: Regulatory Planning and Review

Under Executive Order (EO) 12866 (58 FR 51,735, October 4, 1993), this action is an 

“economically significant regulatory action” because it is likely to have an annual effect on the 

economy of $100 million or more or adversely affect in a material way the economy, a sector of 

the economy, productivity, competition, jobs, the environment, public health or safety, or 

state, local, or tribal governments or communities. Accordingly, the EPA submitted this action 

to the Office of Management and Budget (OMB) for review under 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 

associated 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 information collection requirements are not enforceable until OMB approves 

them.
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The information requirements are based on notification, recordkeeping, and reporting 

requirements in the NESHAP General Provisions (40 CFR part 63, subpart A), which are 

mandatory for all owners and operators subject to national emission standards. These 

recordkeeping and reporting requirements are specifically authorized by CAA section 114 (42 

U.S.C. 7414). All information submitted to the EPA pursuant to the recordkeeping and reporting 

requirements for which a claim of confidentiality is made is safeguarded according to Agency 

policies set forth in 40 CFR Part 2, subpart B. This final rule requires maintenance inspections of 

the control devices but would not require any notifications or reports beyond those required by 

the General Provisions. The recordkeeping provisions require only the specific information 

needed to determine compliance.

The annual monitoring, reporting, and recordkeeping burden for this collection 

(averaged over the first 3 years after the effective date of the standards) is estimated to be 

$158 million. This includes 698,907 labor hours per year at a total labor cost of $49 million per 

year, and total non-labor capital costs of $108 million per year. This estimate includes initial and 

annual performance tests, semiannual excess emission reports, developing a monitoring plan, 

notifications, and recordkeeping. Initial capital expenses to purchase monitoring equipment for 

affected units are estimated at a cost of $231 million. This includes 504,629 labor hours at a 

total labor cost of $35 million for planning, selection, purchase, installation, configuration, and 

certification of the new systems and total non-labor capital costs of $196 million. All burden 

estimates are in 2007 dollars and represent the most cost effective monitoring approach for 

affected facilities.

An Agency may not conduct or sponsor, and a person is not required to respond to, a 

collection of information unless it displays a currently valid OMB control number. The OMB 

control numbers for the 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 rule subject to notice and comment rulemaking requirements under 

the Administrative Procedure Act or any other statute unless the agency certifies that the rule 

will not have a significant economic impact on a substantial number of small entities. Small 

entities include small businesses, small organizations, and small governmental jurisdictions.
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For purposes of assessing the impacts of today's rule on small entities, small entity is 

defined as: (1) a small business that is an electric utility producing 4 billion kilowatt-hours or 

less as defined by NAICS codes 221122 (fossil fuel-fired electric utility steam generating units) 

and 921150 (fossil fuel-fired electric utility steam generating units in Indian country); (2) a small 

governmental jurisdiction that is a government of a city, county, town, school district or special 

district with a population of less than 50,000; and (3) a small organization that is any not-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 prepared 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 coaland oil-fired electric utility steam generating units (EGUs) under Clean Air Act (CAA) section 112 

and listed EGUs pursuant to CAA section 112(c). On March 29, 2005 (70 FR 15,994), the EPA 

published a final rule (2005 Action) that removed EGUs from the list of sources for which 

regulation under CAA section 112 was required. That rule was published in conjunction with a 

rule requiring reductions in emissions of mercury from EGUs pursuant to CAA section 111, i.e., 

CAMR, May 18, 2005, 70 FR 28606). The Section 112(n) Revision Rule was vacated on February 

8, 2008, by the U.S. Court of Appeals for the District of Columbia Circuit. As a result of that 

vacatur, CAMR was also vacated and EGUs remain on the list of sources that must be regulated 

under CAA section 112. This action provides the 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 regulatory 
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determination. CAA section 112(n)(1)(A) and the 2000 determination do not differentiate 

between EGUs located at major versus area sources of HAP. Thus, the NESHAP for EGUs will 

regulate units at both major and area sources. Major sources of HAP are those that have the 

potential to emit at least 10 tons per year (tpy) of any one HAP or at least 25 tpy of any 

combination of HAP. Area sources are any stationary sources of HAP that are not major 

sources.

7.4.3 Summary of Issues Raised during the Public Comment Process on the IRFA

The EPA received a number of comments related to the Regulatory Flexibility Act during 

the public comment process. A consolidated version of the comments received is reproduced 

below. These comments can also be found in their entirety in the response to comment 

document in the docket.

Comment: Several commenters expressed concern with the SBAR panel. Some believe Small 

Entity Representatives (SERs) were not provided with regulatory alternatives including 

descriptions of significant regulatory options, differing timetables, or simplifications of 

compliance and reporting requirements, and subsequently were not presented with an 

opportunity to respond. One commenter believes the EPA’s formal SBAR Panel notification and 

subsequent information provided by the EPA to the Panel did not include information on the 

potential impacts of the rule as required by section 609(b)(1). Additional 

commenters suggested that the 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 proposed rules or discussions of regulatory alternatives, to the SBAR 

Panel members. One commenter stated the SBAR Panel Report does not meet the statutory 

obligation to recommend less burdensome alternatives. The commenter suggested the EPA 

panel members declined to make recommendations that went further than consideration or 

investigation of broad regulatory alternatives, with the exception of those recommendations in 

which the EPA rejected alternative interpretations of the CAA section 112 and relevant court 

cases. Two stated that the EPA did not respond to the concerns of the small business 

community, the SBA, or OMB, ignoring concerns expressed by the SER panelists. One 

commenter believes the EPA failed to convene required meetings and hearings with affected 

parties as required by law for small business entities. One commenter stated that the SERs’ 
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input is very important because more than 90 percent of public power utility systems meet the 

definition and qualify as small businesses under the SBREFA.

Response: The RFA requires that SBAR Panels collect advice and recommendations from SERs 

on the issues related to: 

 the number and description of the small entities to which the proposed rule will 

apply; 

 the projected reporting, record keeping and other compliance requirements of the 

proposed rule; 

 duplication, overlap or conflict between the proposed rule and other federal rules; 

and 

 alternatives to the proposed rule that accomplish the stated statutory objectives and 

minimize any significant economic impact on small entities.

The RFA does not require a covered agency to create or assemble information for SERs or for 

the government panel members. While section 609(b)(4) requires that the government Panel 

members review any material the covered agency has prepared in connection with the RFA, the 

law does not prescribe the materials to be reviewed. The EPA's policy, as reflected in its RFA 

guidance, is to provide as much information as possible, given time and resource constraints, to 

enable an informed Panel discussion. In this rulemaking, because of a court-ordered deadline, 

the EPA was unable to hold a pre-panel meeting but still provided SERs with the 

information available at the time, held a standard Panel Outreach meeting to collect verbal 

advice and recommendations from SERs, and provided the standard 14-day written comment 

period to SERs. The EPA received substantial input from the SERs, and the Panel report 

describes recommendations made by the Panel on measures the Administrator should consider 

that would minimize the economic impact of the proposed rule on small entities. The EPA 

complied with the RFA.

Comment: One commenter requested that the EPA work with utilities such that new 

regulations are as flexible and cost efficient as possible.

Response: In developing the final rule, the EPA has considered all information provided prior to, 

as well as in response to, the proposed rule. The EPA has endeavored to make the final 

regulations flexible and cost efficient while adhering to the requirements of the CAA.
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Comment: One commenter was concerned about the ability of small entities or nonprofit 

utilities such as those owned and/or operated by rural electric co-op utilities, and municipal 

utilities to comply with the proposed standards within three years. The commenter believes 

that the EPA disregarded the SER panelists who explained that under these current economic 

conditions they have constraints on their ability to raise capital for the construction of control 

projects and to acquire the necessary resources in order to meet a three-year compliance 

deadline. Two commenters expressed concern that smaller utilities and those in rural areas will 

be unable to get vendors to respond to their requests for proposals, because they will be able 

to make more money serving larger utilities.

Response: The preamble to the proposed rule (76FR 25054, May 3, 2011) provides a detailed 

discussion of how the EPA determined compliance times for the proposed (and final) rule. The 

EPA has provided pursuant to section 112(i)(3)(A) the maximum three-year period for sources 

to come into compliance. Sources may also seek a one-year extension of the compliance period 

from their title V permitting authority if the source needs that time to install controls. CAA 

section 112(i)(3)(B). If the situation described by commenters (i.e., where small entities or 

nonprofit utilities constraints on ability to raise capital for construction of control projects and 

to acquire necessary resources) results in the source needing additional time to install controls, 

they would be in a position to request the one-year extension. The EPA discusses in more detail 

in section VII of this preamble how the agency plans to address those units that are still unable 

to comply within the statutorily mandated period.

Comment: Several commenters believe the EPA did not adequately consider the 

disproportionately large impact on smaller generating units. The commenters note the 

diseconomies in scale for pollution controls for such units. One commenter noted the rule will 

create a more serious compliance hurdle for small communities that depend on coal-fired 

generation to meet their base load demand. The commenter notes that by not subcategorizing 

units, the EPA is dictating a fuel switch due to the disproportionately high cost on small 

communities. The other commenter believes the MACT and NSPS standards are unachievable 

by going too far without really considering the impacts on small municipal units, as public 

powers is critical to communities, jobs, economic viability and electric reliability. A generating 

and transmissions electric cooperative which qualifies as a small entity believes the rule will 

ultimately result in increased electricity costs to its members and will negatively impact the 

economies of the primarily rural areas that they serve. Another commenter believes there is no 

legal or factual basis for creating subcategories or weaker standards for state, tribal, or 

municipal governments or small entities that are operating obsolete units, particularly given the 
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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 

impact 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 resulting from compliance, but also the impact of higher 

electricity prices. The EPA evaluated suggestions from SERs, including subcategorization 

recommendations. In the preamble to the proposed rule, the EPA explains that, normally, any 

basis for subcategorizing must be related to an effect on emissions, rather than some 

difference which does not affect emissions performance. The EPA does not see a distinction 

between emissions from smaller generating units versus larger units. The EPA acknowledges 

the comment that there is no legal or factual basis for creating subcategories or weaker 

standards for state, tribal, or municipal governments or small entities that are operating 

obsolete units.

Comment: One commenter notes that the EPA recognizes LEEs in the rule such that they should 

receive less onerous monitoring requirements; however, the EPA does not recognize that small 

and LEEs also need and merit more flexible and achievable pollution control requirements. The 

commenter notes that the capital costs for emissions control at small utility units is 

disproportionately high due to inefficiencies in Hg removal, space constraints for control 

technology retrofits, and the fact that small units have fewer rate base customers across which 

to spread these costs. The commenter cites the Michigan Department of Environmental Quality 

report titled “Michigan’s Mercury Electric Utility Workgroup, Final Report on Mercury Emissions 

from Coal-Fired Power Plants,” (June 2005). The commenter notes that the EPA has addressed 

such concerns previously, citing the RIA for the 1997 8-hour ozone standard. The commenter 

also suggests smaller utility systems generally have less capital to invest in pollution control 

than larger, investor-owned systems, due to statutory inability to borrow from the private 
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capital markets, statutory debt ceilings, limited bonding capacity, borrowing limitations related 

to fiscal strain posed by other, non-environmental factors, and other limitations.

Response: The EPA acknowledges that the rule contains reduced monitoring requirements for 

existing units that qualify as LEEs. Although the EPA does not believe that reduced pollution 

control requirements are warranted for LEEs, including small entity LEEs, we believe that 

flexible and achievable pollution control requirements are promoted through alternative 

standards, alternative compliance options, and emissions averaging as a means of 

demonstrating compliance with the standards for existing EGUs.

Comment: One commenter believes that the EPA should develop more limited monitoring 

requirements for small EGUs. The commenter notes small entities do not possess the monetary 

resources, manpower, or technical expertise needed to operate cutting-edge monitoring 

techniques such as Hg CEMS and PM CEMs. The commenter notes the EPA could have 

identified monitoring alternatives to the SER panel for consideration.

Response: The EPA provided monitoring alternatives to using PM CEMS, HCl CEMS, and Hg 

CEMS in its proposed standards and in this final rule. The continuous compliance alternatives 

are available to all affected sources, including small entities. As alternatives to the use of PM 

CEMS and HCl CEMS, sources are allowed to conduct additional performance testing. Sorbent 

trap monitoring is allowed in lieu of Hg CEMS.

Comment: Several commenters believe the EPA has not sufficiently complied with the 

requirements of the RFA or adequately considered the impact this rulemaking would have on 

small entities. One commenter believes the EPA has not engaged in meaningful outreach and 

consultation with small entities and therefore recommends that the EPA seek to revise the 

court-ordered deadlines to which this rulemaking is subject, re-convene the SBAR panel, 

prepare a new initial regulatory flexibility analysis (IRFA), and issue it for additional public 

comment prior to final rulemaking. The commenter believes the IRFA does not sufficiently 

consider impacts on small entities as identified in the SBAR Panel Report. The commenter 

believes it is not apparent that the EPA considered the recommendations of the Panel. The 

commenter believes the description of significant alternatives in the IRFA is almost entirely 

quoted from the SBAR Panel Report, which the commenter does not believe is an adequate 

substitute for the 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 commenter 

believes it does not provide sufficient support for its decisions to understand on what basis the 
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EPA rejected alternatives that may or may not have reduced burden on small entities while 

meeting the stated objectives of the rule. Additionally, the commenter notes that the EPA did 

not evaluate the economic or environmental impacts of significant alternatives to the proposed 

rule. One commenter believes that the EPA’s stated reasons for declining to specify or analyze 

an area source standard are inadequate under the RFA. The commenter believes the EPA must 

give serious consideration to regulatory alternatives that accomplish the stated objectives of 

the CAA while minimizing any significant economic impacts on small entities and that the EPA 

has a duty to specify and analyze this option or to more clearly state its policy reasons for 

excluding serious consideration of a separate standard for area sources. A commenter believes 

the EPA did not fully consider the subcategorization of sources such as boilers designed to burn 

lignite coals versus other fossil fuels, especially in regard to non-Hg metal and acid gas 

emissions. The commenter references the SBAR Panel Report suggestion provided in the 

preamble of the proposed rule that the EPA consider developing an area source vs. major 

source distinction for the source category and the EPA’s response. Another commenter is 

concerned that the recommendations made by the SER participants were ignored and not 

discussed in the rulemaking. Specifically, the commenter notes the EPA did not discuss 

subcategorizing by age, type of plant, fuel, physical space constraints or useful anticipated life 

of the plant. Nor did the EPA establish GACT for smaller emitters to alleviate regulatory costs 

and operational difficulties. A commenter believes it is likely that different numerical or work 

practice standards are appropriate for area sources of HAP.

Response: The EPA disagrees with one commenter's assertion that the agency has not complied 

with the requirements of the RFA. The EPA complied with both the letter and spirit of the RFA, 

notwithstanding the constraints of the court-ordered deadline. For example, the EPA notified 

the Chief Counsel for Advocacy of the SBA of its intent to convene a Panel; compiled a list of 

SERs for the Panel to consult with; and convened the Panel. The Panel met with SERs to collect 

their advice and recommendations; reviewed the EPA materials; and drafted a report of Panel 

findings. The EPA further disagrees with the commenter's assertion that the EPA's IRFA does 

not sufficiently consider impacts on small entities. The EPA's IRFA, which is included in chapter 

10 of the RIA for the proposed rule, addresses the statutorily required elements of an IRFA such 

as, the economic impact of the proposed rule on small entities and the Panel's findings. 

The EPA disagrees with the comment that recommendations made by the SERs and not 

considered or discussed in the proposed rulemaking such as recommendations regarding 

subcategorization and separate GACT standards for area sources. The preamble to the 

proposed standards includes a detailed discussion of how the EPA determined which 
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subcategories and sources would be regulated (76 FR 25036-25037, May 3, 2011). In that 

discussion, the EPA explains the rationale for its proposed subcategories based on five unit 

design types. In addition, the EPA acknowledges the subcategorization suggestions from the 

SERs and explains its reasons for not subcategorizing on those bases. The preamble to the 

proposed standards also includes a discussion of the SERs' suggestion that area source EGUs be 

distinguished from major-source EGUs and the EPA's reasons for not making that distinction (76 

FR 25020-25021, May 3, 2011).

The EPA also disagrees with the suggestion that the Agency pursue an extension of the timeline 

for final rulemaking such that the SBAR Panel can be reconvened and a new IRFA can be 

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 requirements of the 

modification provision of the Consent Decree. If plaintiffs in the American Nurses litigation 

objected to an extension request, which the EPA believes would have been likely based on their 

comments on the proposed rule, the Agency would have had to file a motion with the Court 

seeking an extension of the deadline. Consistent with governing case law, the Agency would 

have been required to demonstrate in its motion for extension that it was impossible to finalize 

the rule by the deadline provided in the Consent Decree. See Sierra Club v. Jackson, Civil Action 

No. 01-1537 (D.D.C.) (Opinion of the Court denying EPA’s motion to extend a consent decree 

deadline). The EPA negotiated a 30-day extension and was able to complete the rule by 

December 16, 2011; accordingly, the Agency had no basis for seeking a further extension of 

time.

A detailed description of the changes made to the rule since proposal, including those made as 

a result of feedback received during the public comment process can be found in sections VI 

(NESHAP) and X (NSPS) in the preamble. Changes explained in the identified sections include 

those related to applicability; subcategorization; work practices; periods of startup, shutdown, 

and malfunction; initial testing and compliance; continuous compliance; and notification, 

recordkeeping, and reporting.

7.4.4 Description and Estimate of the Affected Small Entities

For the purposes of assessing the impacts of MATS on small entities, a small entity is 

defined as:
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(1) A small business according to the Small Business Administration size standards by 

the North American Industry Classification System (NAICS) category of the owning 

entity. The range of small business size standards for electric utilities is 4 billion kilowatt 

hours (kWh) of production or less;

(2) A small government jurisdiction that is a government of a city, county, town, district, 

or special district with a population of less than 50,000; and

(3) A small organization that is any not for profit enterprise that is independently owned 

and operated and is not dominant in its field.

The EPA examined the potential economic impacts to small entities associated with this 

rulemaking based on assumptions of how the affected entities will install control technologies 

in compliance with MATS. The SBREFA analysis does not examine potential indirect economic 

impacts associated with this rule, such as employment effects in industries providing fuel and 

pollution control equipment, or the potential effects of electricity price increases on industries 

and households.

The EPA used Velocity 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 corporate affiliation information. The analysis focused only on those EGUs 

affected by the rule, which includes units burning coal, oil, petroleum coke, or coal refuse as 

the primary fuel, and excludes any combustion turbine units or EGUs burning natural gas. Also, 

because the rule does not affect combustion units with an equivalent electricity generating 

capacity up to 25 megawatts (MW), small entities that do not own at least one combustion unit 

with a capacity greater than 25 MW were removed from the dataset. For the affected units 

remaining, boiler and generator capacity, heat input, generation, and emissions data were 

aggregated by owner and then by parent company. Entities with more than 4 billion kWh of 

annual electricity generation were removed from the list, as were municipal owned entities 

serving a population greater than 50,000. For cooperatives, investor-owned utilities, and 

subdivisions that generate less than 4 billion kWh of electricity annually but which may be part 

of a large entity, additional research on power sales, operating revenues, and other business 

activities was performed to make a final determination regarding size. Finally, small entities for 

which the 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 the 
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rule. After omitting entities for the reasons above, the EPA identified a total of 82 potentially 

affected small entities that are affiliated with 102 electric generating units.

7.4.5 Compliance Cost Impacts

This section presents the methodology and results for estimating the impact of MATS on 

small entities in 2015 based on the following endpoints:

 annual economic impacts of MATS on small entities and

 ratio of small entity compliance cost impacts to revenues from electricity 

generation.1

7.4.5.1 Methodology for Estimating Impacts of MATS on Small Entities

EPA estimated compliance costs of MATS as follows:

CCompliance = Δ COperating+Capital + Δ CFuel - 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 (which raises their revenues from the remainder of their sales). On 

the other hand, some units may increase electricity generation, and coupled with the increase 

in electricity prices, will see an increase in electricity revenues resulting in lower net compliance 

costs. If entities are able to increase revenue more than an increase in retrofit and fuel costs, 

ultimately they will have negative net compliance costs (or savings). Because this analysis 

evaluates the total costs as a sum of the costs associated with compliance choices as well as 

changes in electricity revenues, it captures savings or gains such as those described. As a result, 

what EPA describes as a cost is really more of a measure of the net economic impact of the rule 

on small entities.

 

1

This methodology for estimating small entity impacts has been used in recent EPA rulemakings such as the CSAPR 

promulgated by EPA in July, 2011. 
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For this analysis, EPA used unit-level IPM parsed outputs – 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 case. These 

individual components of compliance cost were estimated as follows:

(1) Operating and capital costs: Using the IPM parsed outputs for the base case and 

MATS policy case, EPA identified units that installed one or more pollution 

control technologies under the rule. The equations for calculating operating and 

capital costs were adopted from technology assumptions used in 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 fuel expenditures under MATS were then 

estimated by taking the difference in fuel costs between MATS and the base 

case.

(3) Value of electricity generated: EPA estimated the value of electricity generated 

by multiplying the electricity generation from EPA’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 because most of the 

private entities are independent power producers (IPP). IPPs sell their electricity 

to wholesale purchasers and do not own transmission facilities and, thus, their 

revenue was estimated based on wholesale electricity prices.

7.4.5.2 Results

The number of potentially affected small entities by ownership type and potential 

impacts of MATS are summarized in Table 7-1. All costs are presented in 2007 dollars. EPA 

estimated the annualized net compliance cost to small entities to be approximately $106 

million in 2015.

 

2

Unit-level cost impacts are adjusted for ownership shares for individual small entities, so as not to overestimate 

burden on each company. If an individual unit is owned by multiple small entities, total costs for that unit to 

meet MATS obligations are distributed across all owners based on the percentage of the unit owned by each 

company. Ownership percentage was estimated based on the Ventyx database.
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Table 7-1. Projected Impact of MATS on Small Entities in 2015

EGU Ownership 

Type

Number of 

Potentially 

Affected 

Entities

Number of 

Entities 

Projected to 

Withdraw all 

Affected Units 

as Uneconomic

Total Net 

Compliance 

Costs (2007$ 

millions)

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

Private 4 3 8.4 4 4

Total 82 3 106 40 35

Notes: The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only 

entities experiencing positive costs. About 23 of the 82 total potentially affected small entities are estimated to 

have cost savings under MATS (see text above for an explanation).

Definitions of ownership types are based on those provided by 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 

political subdivision that is not classified as a municipality under state law.

Private: Similar to investor-owned, but ownership shares are not openly traded on the stock markets.

Source: ICF International analysis based on IPM modeling results

EPA assessed the economic and financial impacts of the final rule using the ratio of 

compliance costs to the value of revenues from electricity generation, and our results focus on 

those entities for which this measure could be greater than 1 percent or 3 percent. Of the 82

small entities identified, 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, all generating capacity at 3 

small entities is projected to be uneconomic to maintain. In this analysis, the cost of 

withdrawing a unit as uneconomic is estimated as the base case profit that is forgone by not 

operating under the policy case. Because 35 of the 82 total entities, or more than 40 percent, 

are estimated to incur compliance cost greater than 3 percent of base revenues, EPA has 

 

3

One percent and three percent of generation revenue criteria based on: “EPA’s Action Development Process: 

Final Guidance for EPA Rulewriters: Regulatory Flexibility Act as amended by the Small Business Regulatory 

Enforcement Fairness Act.” OPEI Regulatory Development Series. November 2006. This can be found on the 

Internet at http://www.epa.gov/sbrefa/documents/rfaguidance11-00-06.pdf. 
7-15

concluded that it cannot certify that there will be no SISNOSE for this rule. Results for small 

entities discussed here, however, do not account for the reality that electricity markets are 

regulated in parts of the country. Entities operating in regulated or cost-of-service markets 

should be able to recover all of their costs of compliance through rate adjustments.

Note that the estimated costs for small entities are significantly lower than those 

estimated by EPA for the MATS proposal (which were $379 million). This is driven by a small 

group of units (less than 6 percent) which were projected to be uneconomic to operate under 

the proposal (and hence incurred lost profits due to lost electricity revenues), but are now 

projected to continue their operations under MATS. In addition, 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 behavior for this small group of units, in particular the one unit 

which operates at a higher capacity factor, has a substantial impact on total costs for the entire 

group as their increased generation revenues offsets a large portion of the compliance costs.

The separate components of annualized costs to small entities under MATS are 

summarized in Table 7-2. The most significant components of incremental costs to these 

entities are increased capital and operating costs for retrofits, followed by changes in electricity 

revenues (i.e., cost savings).
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Table 7-2. Incremental Annualized Costs under MATS Summarized by Ownership Group and 

Cost Category in 2015 (2007$ millions)

EGU Ownership 

Type

Capital+ 

Operating 

Costs ($MM)

Fuel Costs 

($MM)

Change in 

Electricity 

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 International analysis based on IPM modeling results

Capital and operating costs increase across all ownership types, but the direction of 

changes in electricity revenues vary among ownership types. All ownership types, with the 

exception of private entities, experience a net gain in electricity revenues under the MATS, 

unlike projections from 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 operate under the policy case, 

resulting in a net loss of $13 million (in 2007$) in profits. On the other hand, many operating 

units actually increase their electricity revenue due to higher electricity prices under MATS. In 

addition, as mentioned above, 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 

Consistent with the requirements of the RFA and SBREFA, the EPA has taken steps to 

minimize the significant economic impact on small entities. Because this rule does not affect 

units with a generating capacity of less than 25 MW, small entities that do not own at least one 

generating unit with a capacity greater than 25 MW are not subject to the rule. According to 

the EPA's analysis, among the coal- and oil-fired EGUs (i.e., excluding combined cycle gas 

turbines and gas combustion turbines) about 26 potentially small entities only own EGUs with a 
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capacity less than or equal to 25 MW, and none of those entities are subject to the final rule 

based on the statutory definition of potentially regulated units. 

For units affected by the proposed rule, the EPA considered a number of comments 

received, both during the Small Business Advocacy Review (SBAR) Panel and the public 

comment period. While none of the alternatives adopted are specifically applied to small 

entities, the EPA believes these modifications will make compliance less onerous for all 

regulated units, including those owned by small entities.

7.4.6.1 Work Practice Standards

Consistent with Sierra Club v. EPA, the EPA proposed numerical emission standards that 

would apply at all times, including during periods of startup and shutdown. After reviewing 

comments and other data regarding the nature of these periods of operation, the EPA is 

finalizing a work practice standard for periods of start up and shut down. The EPA is also 

finalizing work practice standards for organic HAP from all subcategories of EGUs. The EPA has 

chosen to finalize work practice standards because the significant majority of data for 

measured organic HAP emissions from EGUs are below the detection levels of the EPA test 

methods, and, as such, the Agency considers it impracticable to reliably measure emissions 

from these units. Descriptions of the work practice requirements for startup and shutdown, as 

well as organic HAP, can be found in Section VI.D-E. of the preamble.

7.4.6.2 Continuous Compliance and Notification, Record-keeping, and Reporting

The final rule greatly simplifies the continuous compliance requirements and provides 

two basic approaches for most situations: use of continuous monitoring and periodic testing. 

The frequency of periodic testing has been decreased from monthly in the proposal to quarterly 

in the final rule. In addition to simplifying compliance, the EPA believes these changes 

considerably reduce the overall burden associated with recordkeeping and reporting. These 

changes to the final rule are described in more detail in Section VI.G-H. of the preamble.

7.4.6.3 Subcategorization

The Small Entity Representatives on the SBAR Panel were generally supportive of 

subcategorization and suggested a number of additional subcategories the EPA should consider 

when developing the final rule. While it was not practicable to adopt the proposed 

subcategories, the EPA maintained the existing subcategories and split the “liquid oil-fired 

units” subcategory into two individual subcategories – continental and non-continental units.
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7.4.6.4 MACT Floor Calculations

As recommended by the EPA SBAR Panel representative, the EPA established the MACT 

floors using all the available ICR data that was received to the maximum extent possible 

consistent with the CAA requirements. The Agency believes this approach reasonably ensures 

that the emission limits selected as the MACT floors adequately represent the level of emissions 

actually achieved by the average of the units in the top 12 percent, considering operational 

variability of those units. Additionally, following proposal, the EPA reviewed and revised the 

procedure intended to account for the contribution of measurement imprecision to data 

variability in establishing effective emissions limits.

7.4.6.5 Alternatives Not Adopted

The EPA chose not to adopt several of the suggestions posed either during the SBAR 

Panel or public comment period. The EPA did not propose a percent reduction standard as an 

alternative to the concentration-based MACT floor. The percent reduction format for Hg and 

other HAP emissions would not have addressed the 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 preparation practices, sources would be required to track the HAP concentrations in 

coal from the mine to the stack, and not just before and after the control device(s), and such an 

approach would be difficult to implement and enforce. Furthermore, the EPA does not believe 

the percent reduction standard is in line with the 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 establish percent reduction 

standards for HAP, as explained further in the response to comments document.

The EPA chose not to establish GACT standards for area sources for a number of 

reasons. The data show that similar HAP emissions and control technologies are found on both 

major and area sources greater than 25 MWe, and some large units are synthetic area sources. 

In fact, because of the significant number of well-controlled EGUs of all sizes, we believe it 

would be difficult to make a distinction between MACT and GACT. Moreover, the EPA believes 

the standards for area source EGUs should reflect MACT, rather than GACT, because there is no 

essential difference between area source and major source EGUs with respect to emissions of 

HAP. 

The EPA chose not to exercise its discretionary authority to establish health-based 

emission standards for HCL and other HAP acid gases. Given the limitations of the currently 

available information (e.g., the HAP mix where EGUs are located, and the cumulative impacts of 
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respiratory irritants from nearby sources), the environmental effects of HCl and the other acid 

gas HAP, and the significant co-benefits from reductions in criteria pollutants the EPA 

determined that setting a conventional MACT standard for HCl and the other acid gas HAP was 

the appropriate course of action.

As required by section 212 of SBREFA, the EPA also is preparing a Small Entity 

Compliance Guide to help small entities comply with this rule. Small entities will be able to 

obtain a copy of the Small Entity Compliance guide at the following Web site: 

http://www.epa.gov/airquality/powerplanttoxics/actions.html.

7.5 Unfunded Mandates Reform Act (UMRA) Analysis

Title II of the UMRA of 1995 (Public Law 104-4)(UMRA) establishes requirements for 

federal agencies to assess the effects of their regulatory actions on state, local, and tribal 

governments and the private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, EPA 

generally must prepare a written statement, including a cost-benefit analysis, for any proposed 

or final rule that “includes any Federal mandate that may result in the expenditure by State, 

local, and tribal governments, in the aggregate, or by the private sector, of $100,000,000 or 

more ... in any one year.” A “Federal mandate” is defined under Section 421(6), 2 U.S.C. 658(6), 

to include a “Federal intergovernmental mandate” and a “Federal private sector mandate.” A 

“Federal intergovernmental mandate,” in turn, is defined to include a regulation that “would 

impose an enforceable duty upon State, Local, or tribal governments,” Section 421(5)(A)(i), 2 

U.S.C. 658(5)(A)(i), except for, among other things, a duty that is “a condition of Federal 

assistance,” Section 421(5)(A)(i)(I). A “Federal private sector mandate” includes a regulation 

that “would impose an enforceable duty upon the private sector,” with certain exceptions, 

Section 421(7)(A), 2 U.S.C. 658(7)(A).

Before promulgating an EPA rule for which a written statement is needed under Section 

202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires EPA to identify 

and consider a reasonable number of regulatory alternatives and adopt the least costly, most 

cost-effective, or least burdensome alternative that achieves the objectives of the rule. 

Moreover, section 205 allows EPA to adopt an alternative other than the least costly, most costeffective or least burdensome alternative if the Administrator publishes an explanation why 

that alternative was not adopted.

In a manner consistent with the intergovernmental consultation provisions of Section 

204 of the UMRA, EPA carried out consultations with the governmental entities affected by this 

rule. EPA held meetings with states and tribal representatives in which the Agency presented its 
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plan to develop a proposal and provided opportunities for participants to provide input as part 

of the rulemaking process. EPA has also analyzed the economic impacts of MATS on 

government entities and this section presents the results of that analysis. The UMRA analysis 

does not examine potential indirect economic impacts associated with the rule, such as 

employment effects in industries providing fuel and pollution control equipment, or the 

potential effects of electricity price increases on industries and households.

7.5.1 Identification of Affected Government Entities

Using Ventyx data, EPA identified state- and municipality-owned utilities and 

subdivisions that would be affected by this rule. EPA then used IPM parsed outputs (based on 

EPA modeling assumptions) to associate these entities with individual generating units. The 

analysis focused only on EGUs affected by MATS, which includes units burning coal, oil, 

petroleum coke, or waste coal as the primary fuel, and excludes any combustion turbine units. 

Entities that did not own at least one unit with a generating capacity of greater than 25 MW 

were also removed from the dataset because of their exemption from the rule. Finally, 

government entities for which 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 this rule. Based on this, EPA identified 

96 state, municipal, and sub-divisions affiliated with 172 electric generating units that are 

potentially affected by MATS.

7.5.2 Compliance Cost Impacts

After identifying the potentially affected government entities, EPA estimated the impact 

of MATS in 2015 based on the following:

 total impacts of compliance on government entities and

 ratio of government entity impacts to revenues from electricity generation.

7.5.2.1 Methodology for Estimating Impacts MATS on Government Entities

EPA estimated compliance costs of MATS as follows:

CCompliance = Δ COperating+Capital + Δ CFuel - Δ R
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where C represents a component of cost as labeled, and Δ R represents the retail value of 

change in electricity generation, calculated as the difference in projected revenues between the 

base case and MATS. 

Based on this formula, compliance costs for a given government entity could either be 

positive or negative (i.e., cost savings) based on their compliance choices and market 

conditions. Under MATS, some units will forgo some level of electricity generation (and thus 

revenues) to comply and this impact will be lessened on those entities by the projected 

increase in electricity prices under MATS. On the other hand, some units may increase 

electricity generation, and coupled with the increase in electricity prices, will see an increase in 

electricity revenues resulting in lower net compliance costs. If entities are able to increase 

revenue more than an increase in retrofit and fuel costs, ultimately they will have negative net 

compliance costs (or savings). Because this analysis evaluates the total costs as a sum of the 

costs associated with compliance choices as well as changes in electricity revenues, it captures 

savings or gains such as those described. As a result, what EPA describes as a cost is really more 

of a measure of the net economic impact of the rule on government entities.

For this analysis, EPA used unit-level data from IPM runs conducted with 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 change in electricity generation revenues under MATS relative to the base case. 

These components of compliance cost were estimated as follows:

(1) Capital and operating costs: Using EPA’s modeling results for the base case and the 

MATS policy case, EPA identified units that install control technology under this rule and 

the technologies installed. The equations for calculating operating and capital costs 

were adopted from 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. 
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(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 prices ($/MWh). 

7.5.2.2 Results

As was done for the small entities analysis, EPA assessed the economic and financial 

impacts of the rule using the ratio of compliance costs to the value of revenues from electricity 

generation, and our results focus on those entities for which this measure could be greater than 

1 percent or 3 percent of base revenues. EPA projects that 42 government entities will have 

compliance costs greater than 1 percent of base generation revenue in 2015 and 32 may 

experience compliance costs greater than 3 percent of base revenues. Overall, 6 units owned 

by government entities are projected to be uneconomic to maintain. 

The separate components of the annualized costs to government entities under MATS

are summarized in Table 7-3 below. The most significant components of incremental costs to 

these entities are the increased capital and operating costs, followed by increases in electricity 

revenues (i.e., a cost saving). 

Table 7-3. Incremental Annualized Costs under MATS Summarized by Ownership Group and 

Cost Category (2007$ millions) in 2015

EGU 

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-division: Political Subdivision Utility is a county, municipality, school district, hospital district, or any other 

political subdivision that is not classified as a municipality under state law.

Source: ICF International analysis based on IPM modeling results

The number of potentially affected government entities by ownership type and 

potential impacts of MATS are summarized in Table 7-4. All costs are reported in 2007$ 

millions. EPA estimated the annualized net compliance cost to government entities to be 

approximately $294 million in 2015.
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Table 7-4. Summary of Potential Impacts on Government Entities under MATS in 2015

EGU 

Ownership 

Type

Number of 

Potentially 

Affected 

Entities

Number of 

Entities 

Withdrawing all 

Affected units

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

Municipal 80 0 187.4 33 25

Total 96 0 294 42 32

Note: The total number of entities with costs greater than 1 percent or 3 percent of revenues includes only entities 

experiencing positive costs. About 30 of the 96 total potentially affected government entities are estimated to 

have cost savings under the MACT policy case (see text above for an explanation).

Source: ICF International analysis based on IPM modeling results

Capital and operating costs increase over all ownership types. All ownership types, 

however, also experience a net gain in electricity revenue, mainly due to higher electricity 

prices under the policy case. As described in the small entity analysis, the change in electricity 

revenue takes into account both the profit lost from units that do not operate under the policy 

case and the difference in revenue for operating units under the policy case. According to 

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 action may have federalism implications, because it 

may impose substantial direct compliance costs on state or local governments, and the Federal 

government will not provide the funds necessary to pay those costs. Accordingly, the EPA 

provides the following federalism summary impact statement as required by section 6(b) of EO 

13132.
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Based on estimates in the RIA, provided in the docket, the final rule may have 

federalism implications because the rule may impose approximately $294 million in annual 

direct compliance costs on an estimated 96 state or local governments. Specifically, we 

estimate that there are 80 municipalities, 5 states, and 11 political subdivisions (i.e., a public 

district with territorial boundaries embracing an area wider than a single municipality and 

frequently covering more than one county for the purpose of generating, transmitting and 

distributing electric energy) that may be directly impacted by this final rule. Responses to the 

EPA’s 2010 ICR were used to estimate the nationwide number of potentially impacted state or 

local governments. As previously explained, this 2010 survey was submitted to all coal- and oilfired EGUs listed in the 2007 version of DOE/EIA’s “Annual Electric Generator Report,” and 

“Power Plant Operations Report.”

The EPA consulted with state and local officials in the process of developing the rule to 

permit them to have meaningful and timely input into its development. The EPA met with 10 

national organizations representing state and local elected officials to provide general 

background on the rule, answer questions, and solicit input.

7.7 Executive Order 13175, Consultation and Coordination with Indian Tribal

Governments

EPA has concluded that this action may have tribal implications. The EPA offered 

consultation with tribal officials early in the regulation development process to permit them an 

opportunity to have meaningful and timely input. Consultation letters were sent to 584 tribal 

leaders and provided information regarding the 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, 2010, with the Moapa Band of Paiutes, Forest County 

Potawatomi, Standing Rock Sioux Tribal Council, and Fond du Lac Band of Chippewa; January 5, 

2011, with the Forest County Potawatomi and a representative from the National Tribal Air 

Association. In these meetings, the EPA presented the authority under the CAA used to develop 

these rules and an overview of the industry and the industrial processes that have the potential 

for regulation. Tribes expressed concerns about the impact of EGUs on Indian country. 

Specifically, they were concerned about potential Hg deposition and the impact on the water 

resources of the Tribes, with particular concern about the impact on subsistence lifestyles for 

fishing communities, the cultural impact of impaired water quality for ceremonial purposes, 

and the economic impact on tourism. In light of these concerns, the Tribes expressed interest in 

an expedited implementation of the rule. Other concerns expressed by Tribes related to how 

the Agency would consider variability in setting the standards and the use of tribal-specific fish 
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consumption data from the Tribes in our assessments. They were not supportive of using work 

practice standards as part of the rule and asked the Agency to consider going beyond the MACT 

floor to offer more protection for the tribal communities.

In addition to these consultations, the EPA also conducted outreach on this rule through 

presentations at the National Tribal Forum in Milwaukee, WI; phone calls with the National 

Tribal Air Association; and a webinar for Tribes on the proposed rule. The EPA specifically 

requested tribal data that could support the appropriate and necessary analyses and the RIA for 

this rule. In addition, the EPA held individual consultations with the Navajo Nation on October 

12, 2011; as well as the Gila River Indian Community, Ak-Chin Indian Community, and the Hopi 

Nation on October 14, 2011. These Tribes expressed concerns about the impact of the rule on 

the Navajo Generating Station (NGS), the impact on the cost of the water allotted to the Tribes 

from the Central Arizona Project (CAP), the impact on tribal revenues from the coal mining 

operations (i.e., assumptions about reduced mining if NGS were to retire one or more units), 

and the impacts on employment of tribal members at both the NGS and the mine. More 

specific comments can be found in the docket.

7.8 Protection of Children from Environmental Health and Safety Risks

This final rule is subject to EO 13045 (62 FR 19885, April 23, 1997) because it is an 

economically significant regulatory action as defined by EO 12866, and the EPA believes that 

the environmental health or safety risk addressed by this action may have a disproportionate 

effect on children. Accordingly, we have evaluated the environmental health or safety effects of 

the standards on children.

Although this final rule is based on technology performance, the standards are designed 

to protect against hazards to public health with an adequate margin of safety as described in 

the preamble. The protection offered by this rule may be particularly important for children, 

especially the developing fetus. As referenced in Chapter 4 of this RIA, “Mercury and Other HAP 

Benefits Analysis,” children are more vulnerable than adults to many HAP emitted by EGUs due 

to differential behavior patterns and physiology. These unique susceptibilities were carefully 

considered in a number of different ways in the analyses associated with this rulemaking, and

are summarized in the RIA.

7.9 Statement of Energy Effects

Our analysis to comply with EO 13211 (Statement of Energy Effects) can be found in 

Section 3.16 of this RIA.
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7.10 National Technology Transfer and Advancement Act

Section 12(d) of the National Technology Transfer and Advancement Act (NTTAA) of 

1995 (Public Law No. 104-113; 15 U.S.C. 272 note) directs the EPA to use voluntary consensus 

standards in its regulatory activities unless to do so would be inconsistent with applicable law 

or otherwise impractical. Voluntary consensus standards are technical standards (e.g., materials 

specifications, test methods, sampling procedures, business practices) that are developed or 

adopted by voluntary consensus standards bodies. The NTTAA directs the EPA to provide 

Congress, through OMB, explanations when the Agency decides not to use available and 

applicable voluntary consensus standards.

This rulemaking involves technical standards. The EPA cites the following standards in 

the final rule: EPA Methods 1, 2, 2A, 2C, 2F, 2G, 3A, 3B, 4, 5, 5D, 17, 19, 23, 26, 26A, 29, 30B of 

40 CFR Part 60 and Method 320 of 40 CFR Part 63. Consistent with the NTTAA, the EPA 

conducted searches to identify voluntary consensus standards in addition to these EPA 

methods. No applicable voluntary consensus standards were identified for EPA Methods 2F, 2G, 

5D, and 19. The search and review results have been documented and are placed in the docket 

for the proposed rule.

The three voluntary consensus standards described below were identified as acceptable 

alternatives to EPA test methods for the purposes of the final rule.

The voluntary consensus standard American National Standards Institute (ANSI) / 

American Society of Mechanical Engineers (ASME) PTC 19-10-1981, “Flue and Exhaust Gas 

Analyses [Part 10, Instruments and Apparatus]” is cited in the final rule for its manual method 

for measuring the O2, CO2, and CO content of exhaust gas. This part of ANSI/ASME PTC 19-10-

1981 is an acceptable alternative to Method 3B.

The voluntary consensus standard ASTM D6348-03 (Reapproved 2010), “Standards Test 

Method for Determination of Gaseous Compounds by Extractive Direct Interface Fourier 

Transform (FTIR) Spectroscopy” is acceptable as an alternative to Method 320 and is cited in 

the final rule, but with several conditions: (1) The test plan preparation and implementation in 

the Annexes to ASTM D-6348-03, Sections A1 through A8 are mandatory; and (2) In ASTM 

D6348-03 Annex A5 (Analyte Spiking Technique), the percent (%) R must be determined for 

each target analyte (Equation A5.5). In order for the test data to be acceptable for a compound, 

%R must be 70 % ≥ 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 repeated 

for that analyte (i.e., the sampling and/or analytical procedure should be adjusted before a 
7-27

retest). The %R value for each compound must be reported in the test report, and all field 

measurements must be corrected with the calculated %R value for that compound by using the 

following equation: Reported Result = (Measured Concentration in the Stack x 100) / % R.

The voluntary consensus standard ASTM D6784-02, “Standard Test Method for 

Elemental, Oxidized, Particle-Bound and Total Mercury in Flue Gas Generated from Coal-Fired 

Stationary Sources (Ontario Hydro Method),” is an acceptable alternative to use of EPA Method 

29 for Hg only or Method 30B for the purpose of conducting relative accuracy tests of mercury 

continuous monitoring systems under this final rule. Because of the limitations of this method 

in terms of total sampling volume, it is not appropriate for use in performance testing under 

this rule. In addition to the voluntary consensus standards the EPA used in the final rule, the 

search for emissions measurement procedures identified 16 other voluntary consensus 

standards. The EPA determined that 14 of these 16 standards identified for measuring 

emissions of the HAP or surrogates subject to emission standards in the final rule were 

impractical alternatives to EPA test methods for the purposes of this final rule. Therefore, the 

EPA does not intend to adopt these standards for this purpose. The reasons for this 

determination for the 14 methods are discussed below, and the remaining 2 methods are 

discussed later in this section.

The voluntary consensus standard ASTM D3154-00, “Standard Method for Average 

Velocity in a Duct (Pitot Tube Method),” is impractical as an alternative to EPA Methods 1, 2, 

3B, and 4 for the purposes of this rulemaking because the standard appears to lack in quality 

control and quality assurance requirements. Specifically, ASTM D3154-00 does not include the 

following: (1) proof that openings of standard pitot tube have not plugged during the test; (2) if 

differential pressure gauges other than inclined manometers (e.g., magnehelic gauges) are 

used, their calibration must be checked after each test series; and (3) the frequency and validity 

range for calibration of the temperature sensors.

The voluntary consensus standard ASTM D3464-96 (Reapproved 2001), “Standard Test 

Method Average Velocity in a Duct Using a Thermal Anemometer,” is impractical as an 

alternative to EPA Method 2 for the purposes of this rule primarily because applicability 

specifications are not clearly defined, e.g., range of gas composition, temperature limits. Also, 

the lack of supporting quality assurance data for the calibration procedures and specifications, 

and certain variability issues that are not adequately addressed by the standard limit the 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 EPA Method 2 in this rule. The standard recommends the use of an L-shaped 

pitot, which historically has not been recommended by the EPA. The EPA specifies the S-type 

design which has large openings that are less likely to plug up with dust.

The voluntary consensus standard, CAN/CSA Z223.2-M86 (1999), “Method for the 

Continuous Measurement of Oxygen, Carbon Dioxide, Carbon Monoxide, Sulphur Dioxide, and 

Oxides of Nitrogen in Enclosed Combustion Flue Gas Streams,” is unacceptable as a substitute 

for EPA Method 3A because it does not include quantitative specifications for measurement 

system performance, most notably the calibration procedures and instrument performance 

characteristics. The instrument performance characteristics that are provided are nonmandatory and also do not provide the same level of quality assurance as the EPA methods. For 

example, the zero and span/calibration drift is only checked weekly, whereas the EPA methods 

require drift checks after each run.

Two very similar voluntary consensus standards, ASTM D5835-95 (Reapproved 2001), 

“Standard Practice for Sampling Stationary Source Emissions for Automated Determination of 

Gas Concentration,” and ISO 10396:1993, “Stationary Source Emissions: Sampling for the 

Automated Determination of Gas Concentrations,” are impractical alternatives to EPA Method 

3A for the purposes of this final rule because they lack in detail and quality assurance/quality 

control requirements. Specifically, these two standards do not include the following: (1) 

sensitivity of the method; (2) acceptable levels of analyzer calibration error; (3) acceptable 

levels of sampling system bias; (4) zero drift and calibration drift limits, time span, and required 

testing frequency; (5) a method to test the interference response of the analyzer; (6) 

procedures to determine the minimum sampling time per run and minimum measurement 

time; and (7) specifications for data recorders, in terms of resolution (all types) and recording 

intervals (digital and analog recorders, only).

The voluntary consensus standard ISO 12039:2001, “Stationary Source Emissions--

Determination of Carbon Monoxide, Carbon Dioxide, and Oxygen--Automated Methods,” is not 

acceptable as an alternative to EPA Method 3A. This ISO standard is similar to EPA Method 3A, 

but is missing some key features. In terms of sampling, the hardware required by ISO 

12039:2001 does not include a 3-way calibration valve assembly or equivalent to block the 

sample gas flow while calibration gases are introduced. In its calibration procedures, ISO 

12039:2001 only specifies a two-point calibration while EPA Method 3A specifies a three-point 

calibration. Also, ISO 12039:2001 does not specify performance criteria for calibration error, 
7-29

calibration drift, or sampling system bias tests as in the EPA method, although checks of these 

quality control features are required by the ISO standard.

The voluntary consensus standard ASTM D6522-00, “Standard Test Method for the 

Determination of Nitrogen Oxides, Carbon Monoxide, and Oxygen Concentrations in Emissions 

from Natural Gas-Fired Reciprocating Engines, Combustion Turbines, Boilers and Process 

Heaters Using Portable Analyzers” is not an acceptable alternative to EPA Method 3A for 

measuring CO and O2 concentrations for this final rule as the method is designed for 

application to sources firing natural gas. 

The voluntary consensus standard ASME PTC-38-80 R85 (1985), “Determination of the 

Concentration of Particulate Matter in Gas Streams,” is not acceptable as an alternative for EPA 

Method 5 because ASTM PTC-38-80 is not specific about equipment requirements, and instead 

presents the options available and the pros and cons of each option. The key specific 

differences between ASME PTC-38-80 and the EPA methods are that the ASME standard: (1) 

allows in-stack filter placement as compared to the out-of-stack filter placement in EPA 

Methods 5 and 17; (2) allows many different types of nozzles, pitots, and filtering equipment; 

(3) does not specify a filter weighing protocol or a minimum allowable filter weight fluctuation 

as in the EPA methods; and (4) allows filter paper to be only 99 percent efficient, as compared 

to the 99.95 percent efficiency required by the EPA methods.

The voluntary consensus standard ASTM D3685/D3685M-98, “Test Methods for 

Sampling and Determination of Particulate Matter in Stack Gases,” is similar to EPA Methods 5 

and 17, but is lacking in the following areas that are needed to produce quality, representative 

particulate data: (1) requirement that the filter holder temperature should be between 120oC 

and 134oC, and not just “above the acid dew-point;” (2) detailed specifications for measuring 

and monitoring the filter holder temperature during sampling; (3) procedures similar to EPA 

Methods 1, 2, 3, and 4, that are required by EPA Method 5; (4) technical guidance for 

performing the Method 5 sampling procedures, e.g., maintaining and monitoring sampling train 

operating temperatures, specific leak check guidelines and procedures, and use of reagent 

blanks for determining and subtracting background contamination; and (5) detailed equipment 

and/or operational requirements, e.g., component exchange leak checks, use of glass cyclones 

for heavy particulate loading and/or water droplets, operating under a negative stack pressure, 

exchanging particulate loaded filters, sampling preparation and implementation guidance, 

sample recovery guidance, data reduction guidance, and particulate sample calculations input.
7-30

The voluntary consensus standard ISO 9096:1992, “Determination of Concentration and 

Mass Flow Rate of Particulate Matter in Gas Carrying Ducts - Manual Gravimetric Method,” is 

not acceptable as an alternative for EPA Method 5. Although sections of ISO 9096 incorporate 

EPA Methods 1, 2, and 5 to some degree, this ISO standard is not equivalent to EPA Method 5 

for collection of PM. The standard ISO 9096 does not provide applicable technical guidance for 

performing many of the integral procedures specified in Methods 1, 2, and 5. Major 

performance and operational details are lacking or nonexistent and detailed quality 

assurance/quality control guidance for the sampling operations required to produce quality, 

representative particulate data (e.g., guidance for maintaining and monitoring train operating 

temperatures, specific leak check guidelines and procedures, and sample preparation and 

recovery procedures) are not provided by the standard, as in EPA Method 5. Also, details of 

equipment and/or operational requirements, such as those specified in EPA Method 5, are not 

included in the ISO standard, e.g., stack gas moisture measurements, data reduction guidance, 

and particulate sample calculations.

The voluntary consensus standard CAN/CSA Z223.1-M1977, “Method for the 

Determination of Particulate Mass Flows in Enclosed Gas Streams,” is not acceptable as an 

alternative for EPA Method 5. Detailed technical procedures and quality control measures that 

are required in EPA Methods 1, 2, 3, and 4 are not included in CAN/CSA Z223.1. Second, 

CAN/CSA Z223.1 does not include the EPA Method 5 filter weighing requirement to repeat 

weighing every 6 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 Z223.1 allows the use of a standard pitot 

for velocity measurement when plugging of the tube opening is not expected to be a problem. 

The EPA Method 5 requires an S-shaped pitot.

The voluntary consensus standard EN 1911-1,2,3 (1998), “Stationary Source EmissionsManual Method of Determination of HCl-Part 1: Sampling of Gases Ratified European Text-Part 

2: Gaseous Compounds Absorption Ratified European Text-Part 3: Adsorption Solutions Analysis 

and Calculation Ratified European Text,” is impractical as an alternative to EPA Methods 26 and 

26A. Part 3 of this standard cannot be considered equivalent to EPA Method 26 or 26A because 

the sample absorbing solution (water) would be expected to capture both HCl and chlorine gas, 

if present, without the ability to distinguish between the two. The EPA Methods 26 and 26A use 

an acidified absorbing solution to first separate HCl and chlorine gas so that they can be 

selectively absorbed, analyzed, and reported separately. In addition, in EN 1911 the absorption 
7-31

efficiency for chlorine gas would be expected to vary as the pH of the water changed during 

sampling.

The voluntary consensus standard EN 13211 (1998), is not acceptable as an alternative 

to the Hg portion of EPA Method 29 primarily because it is not validated for use with impingers, 

as in the EPA method, although the method describes procedures for the use of impingers. This 

European standard is validated for the use of fritted bubblers only and requires the use of a 

side (split) stream arrangement for isokinetic sampling because of the low sampling rate of the 

bubblers (up to 3 liters per minute, maximum). Also, only two bubblers (or impingers) are 

required by EN 13211, whereas EPA Method 29 require the use of six impingers. In addition, EN 

13211 does not include many of the quality control procedures of EPA Method 29, especially 

for the use and calibration of temperature sensors and controllers, sampling train assembly and 

disassembly, and filter weighing.

Two of the 16 voluntary consensus standards identified in this search were not available 

at the time the review was conducted for the purposes of the final rule because they are under 

development by a voluntary consensus body: ASME/BSR MFC 13M, “Flow Measurement by 

Velocity Traverse,” for EPA Method 2 (and possibly 1); and ASME/BSR MFC 12M, “Flow in 

Closed Conduits Using Multiport Averaging Pitot Primary Flowmeters,” for EPA Method 2.

Finally, in addition to the three voluntary consensus standards identified as acceptable 

alternatives to EPA methods required in the final rule, the EPA is also specifying four voluntary 

consensus standards in the rule for use in sampling and analysis of liquid oil samples for 

moisture content. These standards are: ASTM D95-05 (Reapproved 2010), “Standard Test 

Method for Water in Petroleum Products and Bituminous Materials by Distillation”, ASTM 

D4006-11, “Standard Test Method for Water in Crude Oil by Distillation”, ASTM D4177-95

(Reapproved 2010), “Standard Practice for Automatic Sampling of Petroleum and Petroleum 

Products, and ASTM D4057-06 (Reapproved 2011), “Standard Practice for Manual Sampling of 

Petroleum and Petroleum Products.”

Table 5, section 4.1.1.5 of appendix A, and section 3.1.2 of appendix B to subpart 

UUUUU, 40 CFR Part 63, list the EPA testing methods included in the final rule. Under section 

63.7(f) and section 63.8(f) of subpart A of the General Provisions, a source may apply to the EPA 

for permission to use alternative test methods or alternative monitoring requirements in place 

of any of the EPA testing methods, performance specifications, or procedures specified.
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 provision directs Federal agencies, to the greatest 

extent practicable and permitted by law, to make environmental justice (EJ) part of their 

mission by identifying and addressing, as appropriate, disproportionately high and adverse 

human health or environmental effects of their programs, policies, and activities on minority 

populations and low-income populations in the U.S. 

The EPA has determined that this final rule will not have disproportionately high and 

adverse human health or environmental effects on minority, low income, or indigenous 

populations because it increases the level of environmental protection for all affected 

populations 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 this final rule.

This final rule will reduce emissions of all the listed HAP that come from EGUs. This 

includes metals (Hg, As, Be, Cd, Cr, Pb, Mn, Ni, and Se), organics (POM, acetaldehyde, acrolein, 

benzene, dioxins, ethylene dichloride, formaldehyde, and PCB), and acid gases (HCl and HF). At 

sufficient levels of exposure, these pollutants can cause a range of health effects including 

cancer; irritation of the lungs, skin, and mucous membranes; effects on the central nervous 

system such as memory and IQ loss and learning disabilities; damage to the kidneys; and other 

acute health disorders. 

The final rule will also result in substantial reductions of criteria pollutants such as CO, 

PM, and SO2. Sulfur dioxide is a precursor pollutant that is often transformed into fine PM 

(PM2.5) in the atmosphere. Reducing direct emissions of PM2.5 and SO2 will, as a result, reduce 

concentrations of PM2.5 in the atmosphere. These reductions in PM2.5 will provide large health 

benefits, such as reducing the risk of premature mortality for adults, chronic and acute 

bronchitis, childhood asthma attacks, and hospitalizations for other respiratory and 

cardiovascular diseases. (For more details on the health effects of metals, organics, and PM2.5, 

please refer to Chapters 4 and 5 of this RIA.) This final rule will also have a small effect on 
7-33

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 PM2.5, as well as to other pollutants, from EGUs and other sources. In addition, the 

EPA will pursue energy efficiency improvements throughout the economy, along with other 

Federal agencies, states and other groups. This will contribute to additional environmental and 

public health improvements while lowering the costs of realizing those improvements. 

Together, these rules and actions will have substantial and long-term effects on both the U.S. 

power industry and on communities currently breathing dirty air. Therefore, we anticipate 

significant interest in 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 of Hg and other HAP emitted from each 

affected EGU. As a result, emissions of Hg and other HAP such as HCl will be substantially 

reduced in the vast majority of states. In some states, however, there may be small increases in 

Hg and other HAP emissions due to shifts in electricity generation from EGUs with higher 

emission rates to EGUs with already low emission rates. Hydrogen chloride emissions are 

projected to increase at a small number of sources but that does not lead to any increased 

emissions at the state level. 

The primary risk analysis to support the finding that this final rule is both appropriate 

and necessary includes an analysis of the effects of Hg from EGUs on people who rely on 

freshwater fish they catch as a regular and frequent part of their diet. These groups are 

characterized as subsistence level fishing populations or fishers. A significant portion of the 

data in this analysis came from published studies of EJ communities where people frequently 

consume locally-caught freshwater fish. These communities included: (1) White and black 

populations (including female and poor strata) surveyed in South Carolina; (2) Hispanic, 

Vietnamese and Laotian populations surveyed in California; and (3) Great Lakes tribal 

populations (Chippewa and Ojibwe) active on ceded territories around the Great Lakes. These 

data were used to help estimate risks to similar populations beyond the areas where the study 

data was collected. For example, while the Vietnamese and Laotian survey data were collected 

in California, given the ethnic (heritage) nature of these high fish consumption rates, we 
7-34

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 risks that make this final rule 

appropriate and necessary, and of the analysis of the benefits of reducing exposure to Hg and 

the other hazardous air pollutants. 

The EPA’s analysis of risks from consumption of Hg-contaminated fish is contained in 

Chapter 4 of this RIA. The effects of this final rule on the health risks from Hg and other HAP are 

presented in the preamble and in the RIA for this rule.

7.11.1.2 Potential Environmental and Public Health Impacts to Vulnerable Populations

The EPA has conducted several analyses that provide additional insight on the potential 

effects of this rule on EJ communities. These include: (1) The socio-economic distribution of 

people living close to affected EGUs who may be exposed to pollution from these sources; and 

(2) an analysis of the distribution of health effects expected from the reductions in PM2.5 that 

will result from implementation of this final rule (‘‘co-benefits’’).

Socio-economic distribution. As part of the analysis for this final rule, the EPA reviewed 

the aggregate demographic makeup of the communities near EGUs covered by this final rule. 

Although this analysis gives some indication of populations that may be exposed to levels of 

pollution that cause concern, it does NOT identify the demographic characteristics of the most 

highly affected individuals or communities. EGUs usually have very tall emission stacks; this 

tends to disperse the pollutants emitted from these stacks fairly far from the source. In 

addition, several of the pollutants emitted by these sources, such as a common form of 

mercury and SO2, are known to travel long distances and contribute to adverse impacts on the 

environment and human health hundreds or even thousands of miles from where they were 

emitted (in the case of elemental mercury, globally). 

The proximity-to-the-source review is included in the analysis for this final rule because 

some EGUs emit enough hazardous air pollutants such as Nickel or Chromium (VI) to cause 

elevated lifetime cancer risks greater than 1 in a million in nearby communities. In addition, the 

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 for 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 were 

provided to the EPA by the US Census Bureau. Distributions by race are based on demographic 

information at the census block level, and all other demographic groups are based on the 

extrapolation of census block group level data to the census block level. The socio-demographic 

parameters used in the analysis included the following categories: Racial (White, African 

American, Native American, Other or Multiracial, and All Other Races); Ethnicity (Hispanic); and 

Other (Number of people below the poverty line, Number of people with ages between 0 and 

18, Number of people greater than or equal to 65, Number of people with no high school 

diploma). 

In determining the aggregate demographic makeup of the communities near affected 

sources, the EPA focused on those census blocks within three miles of affected sources and 

determined the demographic composition (e.g., race, income, etc.) of these census blocks and 

compared them to the corresponding compositions nationally. The radius of three miles (or 

approximately 5 kilometers) is consistent with other demographic analyses focused on areas 

around potential sources. In addition, air quality modeling experience has shown that the area 

within three miles of an individual source of emissions can generally be considered the area 

with the highest ambient air levels of the primary pollutants being emitted for most sources, 

both in absolute terms and relative to the contribution of other sources (assuming there are 

other sources in the area, as is typical in urban areas). While facility processes and fugitive 

emissions may have more localized impacts, the EPA acknowledges that because of various 

stack heights there is the potential for dispersion beyond 3 miles. To the extent that any 

minority, low income, or indigenous subpopulation is disproportionately impacted by the 

current emissions as a result of the proximity of their homes to these sources, that 

subpopulation also stands to see increased environmental and health benefit from the 

emissions reductions called for by this rule.

The results of EPA’s demographic analysis for coal fired EGUs are shown in Table 7-5.

The data indicate that affected sources are located in areas where the minority share of 

the population living within a three mile buffer is higher than the national average by 12 

percentage points or 48%. For these same areas, the percent of the population below the 

poverty line is also higher than the national average by 4 percentage points or 31%. These 
7-36

results are presented in more detail in the ‘‘Review of Proximity Analysis,’’ February 2011, a 

copy of which is available in the docket.

PM2.5 (co-benefits) analysis. As mentioned above, many of the steps EGUs take to 

reduce their emissions of air toxics as required by this final rule will also reduce emissions of 

PM and SO2. As a result, this final rule will reduce concentrations of PM2.5 in the atmosphere. 

Exposure to PM2.5 can cause or contribute to adverse health effects, such as asthma and heart 

disease, that significantly affect many minority, low-income, and tribal individuals and their 

communities. Fine PM (PM2.5) is particularly (but not exclusively) harmful to children, the 

elderly, and people with existing heart and lung diseases, including asthma. Exposure can cause 

premature death and trigger heart attacks, asthma attacks in children and adults with asthma, 

chronic and acute bronchitis, and emergency room visits and hospitalizations, as well as milder 

illnesses that keep children home from school and adults home from work. Missing work due to 

illness or the illness of a child is a particular problem for people who have jobs that do not 

provide paid sick days. Low-wage employees also risk losing their jobs if they are absent too 

often, even if it is due to their own illness or the illness of a child or other relative. Finally, many 

individuals in these communities lack access to high quality health care to treat these types of 

illnesses. Due to all these factors, many minority and low-income communities are particularly 

susceptible to the health effects of PM2.5 and receive a variety of benefits from reducing it. 

We estimate that in 2016 the annual PM related benefits of the final rule for adults 

include approximately 4,200 to 11,000 fewer premature mortalities, 2,800 fewer cases of 

chronic bronchitis, 4,800 fewer non-fatal heart attacks, 2,600 fewer hospitalizations (for 

respiratory and cardiovascular disease combined), 3.2 million fewer days of restricted activity 

due to respiratory illness and approximately 540,000 fewer lost work days. We also estimate 

substantial health improvements for children in the form of 130,000 fewer asthma attacks, 

3,100 fewer emergency room visits due to asthma, 6,300 fewer cases of acute bronchitis, and 

approximately 140,000 fewer cases of upper and lower respiratory illness.

We also examined the level of PM2.5 mortality risks prior to the implementation of the 

rule according to race, income, and educational attainment. We then estimated the change in 

PM2.5 mortality risk as a result of this final rule among people living in the counties with the 

highest (top 5 percent) PM2.5 mortality risk in 2005. We then compared the change in risk 

among the people living in these ‘‘high-risk’’ counties with people living in all other counties. 

In 2005, people living in the highest risk counties and in the poorest counties were 

estimated to be at substantially higher risk of PM2.5- related death than people living in the 
7-37

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 

education were predicted to have significantly greater risk from PM2.5 mortality than people 

with a greater than high school education. This was true both for the highest-risk counties and 

for the other counties. In summary, the analysis indicates that in 2005, educational status, living 

in one of the poorest counties, and living in a high-risk county are associated with higher 

estimated PM2.5 mortality risk while race is not. 

Our analysis predicts that this final rule will likely significantly reduce the risk of PM2.5-

related premature mortality among all populations of different races living throughout the U.S. 

compared to both 2005 and 2016 pre-rule (i.e., base case) levels. The analysis indicates that 

people living in counties with the highest rates (top 5 percent) of PM2.5 mortality risk in 2005 

receive the largest reduction in mortality risk after this rule takes effect. We also estimate that 

people living in the poorest 5 percent of the counties will experience a larger reduction in PM2.5

mortality risk when compared to all other counties. More information can be found below in 

section 7.11.3. 

The EPA estimates that the benefits of the final rule are likely distributed among races, 

income levels, and levels of education fairly evenly, although there is insufficient data to 

generate different concentration response functions for each demographic group. However, 

the analysis does indicate that this final rule in conjunction with the implementation of existing 

or final rules (e.g., the Cross-State Air Pollution Rule) may help reduce the disparity in risk 

between those in the highest-risk counties and the other 95 percent of counties for all races 

and educational levels. 
7-3

8

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

American

Other or

Multiracial Minorityb

Hispanic

or

Latinoc

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% 16% 17%

National total 285 215 35.0 2.49 33.3 70.8 39.1 77.4 35.4 36.7 37.1

% of national total 75% 12% 1% 12% 25% 14% 27% 12% 13% 13%

Sources: The demographics are from the U.S. Census Bureau, 2000. Information on the facilities is from U.S. EPA.

a Racial and ethnic categories overlap and cannot be summed.

b The “Minority” population is the overall population (in the first row) minus white population (in the second row).

c The Census Bureau defines “Hispanic or Latino” as an ethnicity rather than a racial category, Hispanics or Latinos may belong to any race.
7-39

7.11.1.3 Meaningful Public Participation 

The EPA defines ‘‘environmental justice’’ to include meaningful involvement of all 

people regardless of race, color, national origin, or income with respect to the development, 

implementation, and enforcement of environmental laws, regulations, and policies. To promote 

meaningful involvement, the EPA publicized the rulemaking via newsletters, EJ listserves, and 

the internet, including the Office of 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 emission limits, any other new applicable requirements, and the 

associated monitoring and recordkeeping from this rule. The Title V permitting process provides 

that when most permits are reopened (for example, to incorporate new applicable 

requirements) or renewed, there must be opportunity for public review and comments. In 

addition, after the public review process, the EPA has an opportunity to review the proposed 

permit and object to its issuance if it does not meet CAA requirements.

7.11.1.4 Summary

This final rule strictly limits the emissions rate of Hg and other HAP from every affected 

EGU. The EPA’s analysis indicates substantial health benefits, including for vulnerable 

populations, from reductions in PM2.5.

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, the EPA has determined that 

this final rule will not have disproportionately high and adverse human health or environmental 

effects on minority, low income, or indigenous populations. The EPA is providing multiple 

opportunities for EJ communities to both learn about and comment on this rule and welcomes 

their participation.

7.11.2 Analysis of High Risk Sub-Populations

7.11.2.1 Introduction

In addition to the previously described assessment of EJ impacts, EPA conducted an 

analysis at proposal of sub-populations with particularly high potential risks of mercury 
7-40

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 here 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 detailed review of the literature, EPA identified several high-risk subpopulations (Moya, 2004; Burger, 2002, Shilling et al., 2010, Dellinger, 2004). The analysis of 

potentially high-risk groups focuses on six subpopulations:

 low-income African-American recreational/subsistence fishers in the Southeast 

region7

 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. Of 458 

respondents, 39 were black, 415 were white, and 149 were female. The sample size for the 

black population is relatively small, which increases uncertainty, particularly in higher percentile 

consumption rate values provided for this group. In this study, results are also split out for poor 

respondents (0–20K$ annual income). These consumption rates are relatively high, particularly 

for the higher percentiles. This observation forms the basis for our decision to assess a number 

of the subsistence populations only for watersheds located in US Census tracts containing 

members of source populations below the poverty line for the white and black populations. 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.

7

The low-income designation is based on Census 2000 estimates of populations living in poverty. The Southeast for 

purposes of this analysis comprises Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North 

Carolina, South Carolina, Tennessee, Virginia, and West Virginia.
7-41

in the Southeastern states. The rationale for this was that fishing activity by these two groups

could be generalized 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 fishing 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 subsistence fishing activity among ethnic groups associated with more 

urbanized areas near the Sacramento and San Joaquin rivers in the Central Valley in CA. The 

authors note that many of these ethnic groups relied on fishing in origin countries and bring 

that practice here (e.g., Cambodian, Vietnamese and Mexican). The authors also note that fish 

consumption rates reported here for specific ethnic groups (specifically Southeast Asian) are 

generally in-line with rates seen in WA and OR studies. The fish consumption 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 covered in the study (regions in the vicinity of the Great Lakes). The terms “subsistence” 

and “recreational” fishing are based on the terminology used in these published studies to 

describe the population of interest. In general, subsistence fishers are individuals whose 

primary objective in fishing is to acquire food for household consumption. For recreational 

fishers, the primary objective is to enjoy the outdoor activity; however, fish consumption is also 

often an objective.

To assess the distribution of individual 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 defined 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, we 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 populations 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 cr and cl.

3. Due to data limitations, rather than specifically selecting 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 the 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 entire 

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 for the distribution of pregnant women in 

high-consuming angler households. In other words, it was 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 consumption 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 subpopulations. We then 

estimated 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-level 
7-43

percentages (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 estimated 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)

90th (95th) 

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) 98a(155.9) Shilling et al. (2010)

Laotian subsistence fishers 54 47.2 (17) 144.8a(265.8) Shilling et al. (2010)

Great Lakes tribal groups 822 60 (113b) 136.2a(213.1)aDellinger (2004)

a

Derived values using a log-normal distribution, based on the median and the 95thpercentile or standard 

deviation reported in study. 

b

Standard deviation in parentheses, rather than median.

7.11.2.2 Primary Results for Potentially High-Risk Subpopulations

The methodology described in above, used to estimate the aggregate benefits of 

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 distribution 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 emission 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 tissue 
7-44

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 2016.8The 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. This 

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 pregnant 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 distribution 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 among 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.

 

8

A rough approximation of the size of the annual exposed population—low-income African American pregnant 

women in 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 American 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 AfricanAmerican Recreational/Subsistence Fishers in the Southeast Region

Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 3.363
7-46

Table 7-8 summarizes the estimated distribution of reductions in IQ loss associated with 

alternative emission reduction scenarios.9The median reduction in IQ loss resulting 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 LowIncome African-American Recreational/Subsistence Fishers in the Southeast 

Region

Emission Reduction Scenario

Percentile

25th 50th 75th 90th 95th Mean

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-income White recreational/subsistence fishers in the Southeast region. The 

analysis of the low-income white population in the Southeast United States uses the same 

methodology as for the low-income African-American subpopulation, except that it uses tractlevel 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 losses 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 was 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.

 

9

The 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 are 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) 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 white women of childbearing age (6.6%; U.S. Vital Statistics, 2005), and (3) the percent 
7-47

Figure 7-2. Modeled White Population Below the Poverty Level by 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 points per 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 the Southeast Region

Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 0.062 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. The median reduction in IQ loss resulting 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 LowIncome White Recreational/Subsistence Fishers in the Southeast Region

Emission Reduction Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 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 loss were 18.4 million for 2005 and 20.1 

million for 2016.

11 The geographic 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—can 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 2005), 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 the Poverty Level by Census Tract for 2016
7-50

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

Recreational/Subsistence Fishers in the United States

Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 reductions in IQ loss associated 

with alternative emission reduction scenarios. The 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 LowIncome Female Recreational/Subsistence Fishers in the United States

Emission Reduction Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 analysis 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 loss were 19.6 
7-51

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 Population by Census Tract for 2016

 

12A rough approximation of the size of the annual exposed population—Hispanic pregnant 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

25th 50th 75th 90th 95th Mean

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 7-14 summarizes the estimated distribution of reductions in IQ loss associated 

with alternative emission reduction scenarios. The 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 0.03 points.

Table 7-14. Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for Hispanic 

Recreational/Subsistence Fishers in the United States

Emission Reduction Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 subsistence 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 distribution of IQ loss were 80,000 
7-53

for 2005 and 137,500 for 2016.

13 The geographic distribution of the population modeled for 

2016 is shown in Figure 7-5.

Table 7-15 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 per 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 Laotian Population by Census Tract for 2016

 

13A rough approximation 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 7-15. Simulated Distribution of IQ Loss (per Exposed Person) for Laotian 

Recreational/Subsistence Fishers in the United States

Scenario

Percentile

25th 50th 75th 90th 95th Mean

2005 base case 0.061 0.212 0.744 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 summarizes the estimated distribution of reductions in IQ loss associated 

with alternative emission reduction scenarios. The 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 0.047 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

25th 50th 75th 90th 95th Mean

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

Chippewa Tribe 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 Tribe. 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 Chippewa tribal 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 loss were 23,900 
7-55

for 2005 and 29,500 for 2016.

14 The geographic distribution of the population modeled for 2016 

is shown in Figure 7-6.

Table 7-17 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 declines 

to 0.392 points and the 95th percentile to 3.241 points in 2016.

Figure 7-6. Modeled Chippewa Population by Census Tract in the Great Lakes Area 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 population 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 7-17. Simulated Distribution of IQ Loss (Per Exposed Person) for Chippewa in the Great 

Lakes Area

Scenario

Percentile

25th 50th 75th 90th 95th Mean

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 0.933 2.056 3.231 0.887

2016 Proposed MATS 0.163 0.392 0.937 2.062 3.241 0.890

Table 7-18 summarizes the estimated distribution of reductions in IQ loss associated 

with alternative emission reduction scenarios. The median reduction in IQ loss resulting from 

proposed MATS in 2016 (relative to the 2016 base case) scenario is 0.005 points, and the 95th 

percentile is 0.041 points.

Table 7-18. Simulated Distribution of Reduction in IQ Loss (per Exposed Person) for 

Chippewa in the Great Lakes Area

Emission Reduction Scenario

Percentile

25th 50th 75th 90th 95th Mean

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

(relative 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 subpopulations under the 2016 base case scenario. 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 prenatally 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. The 
7-57

group with the next highest estimated individual risk levels 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 2016 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

Percentile

IQ Loss (per exposed individual)

Low-income SE African-American Low-income SE White

Low-income U.S. Female U.S. Hispanic

U.S. Laotian Great Lakes Area Chippewa

Figure 7-7. Comparison of IQ Loss Distributions 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 HighRisk Subpopulations15

 

15We do note that overall confidence in IQ loss estimates above approximately 7 points decreases because we 

begin to apply the underlying IQ loss function at exposure levels (ppm hair levels) above those reflected in 
7-58

 Low-Income African-American Subsistence-Level Fishers in the Southeast

 In the 2016 base case scenario, the median IQ loss is estimated to be 1.87 IQ points 

and the 95th 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 95th 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

Percentile

Reduction in IQ Loss (per exposed individual)

Low-income SE African-American Low-income SE White

Low-income U.S. Female U.S. Hispanic

U.S. Laotian Great Lakes Area 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 Subsistence-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 95th 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 95th 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 95th percentile 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 95th 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 95th 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 scenario) is 0.001 IQ points, and the 95th 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 95th percentile is 4.26 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 95th percentile is 

0.047 IQ points.

 Chippewa Tribe 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 95th 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 95th percentile is 

0.041 IQ points.

7.11.3 Characterizing the Distribution of Health Impacts across Populations

EPA is developing new approaches and metrics to improve its characterization of the

impacts of EPA rules on different populations. This analysis reflects one such approach, which 

explores two principal questions regarding the distribution of PM2.5-related benefits resulting 

from the implementation of MATS:

1. What is the baseline distribution of PM2.5-related mortality risk for adults according 

to the race, income and education of the population?
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2. How does MATS change the distribution of PM2.5 mortality risk among populations of 

different races—particularly among those populations at greatest risk in the 

baseline?16

In this analysis we estimated that PM2.5 mortality risk from the modeled scenarios is not 

distributed equally throughout the U.S., or among populations of different levels of educational 

attainment—though the level of PM2.5 mortality risk appears to be shared fairly equally among 

populations of different races. We estimate that the air quality and PM2.5-related mortality risk

improvements achieved by MATS are relatively equally distributed among minority populations,

and that the rule reduces PM2.5 mortality risk the most among those populations at greatest 

risk in the 2005 baseline we selected for this analysis. We note that while the methods used for 

this analysis have been employed in recent EPA Regulatory Impact Assessments (EPA, 2011) 

and are drawn from techniques described in the peer reviewed literature (Fann et al. 2011b)

EPA will continue to modify these approaches as new scientific information becomes available.

7.11.3.1 Methodology

The methods used here to describe the distribution of PM2.5mortality impacts are 

consistent with the approach used in the proposed MATS RIA (U.S. EPA, 2011a) and the final 

CSAPR RIA (U.S. EPA, 2011b). As a first step, we estimate the level of PM2.5-related mortality risk 

in each county in the continental U.S. based on 2005 air quality levels, which provides a 

baseline distribution of risk which we use to identify populations with initial higher and lower 

baseline PM2.5-related mortality risk. This portion of the analysis follows an approach 

described elsewhere (Fann et al. 2011a, Fann et al. 2011b), wherein modeled 2005 PM2.5 levels 

are used to calculate the proportion of all-cause mortality risk attributable to total PM2.5 levels 

in each county in the Continental U.S. Within each county we estimate the level of all-cause 

PM2.5 mortality risks for adult populations as well as the level of PM2.5 mortality risk according 

to the race, income and educational attainment of the population. 

Our approach to calculating the distribution of PM2.5 mortality risk across the population 

is generally consistent with the benefits analysis conducted for the modeled scenario described 

in Appendix 5C with two exceptions: the PM2.5 mortality risk coefficients used to quantify 

impacts and the baseline mortality rates used to calculate mortality impacts (a detailed 

discussion of how both the mortality risk coefficients and baseline incidence rates are used to 

estimate the incidence of PM2.5-related deaths may be found in the Chapter 5 of the RIA). We 

 

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 PM2.5 mortality risk estimates.
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substitute risk estimates drawn from the Krewski et al. (2009) extended analysis of the ACS 

cohort. In particular, we applied the all-cause mortality risk estimate random effects Cox model 

that controls for 44 individual and 7 ecological covariates, using average exposure levels for 

1999-2000 over 116 U.S. cities (Krewski et al. 2009) (RR=1.06, 95% confidence intervals 1.04—

1.08 per 10μg/m3increase in PM2.5). This mean relative risk estimate is identical to the Pope et 

al. (2002) risk estimate applied for the benefits analysis (though the standard error around the 

mean RR estimate is slightly narrower).

Within both this and other analyses of the ACS cohort (see Krewski et al. 2000), 

educational attainment has been found to be inversely related to the risk of all-cause mortality. 

That is, populations with lower levels of education (in particular, < grade 12) are more 

vulnerable to PM2.5-related mortality. Krewski and colleagues note that “...the level of 

education attainment may likely indicate the effects of complex and multifactorial 

socioeconomic processes on mortality...,” factors that we would like to account for in this EJ 

assessment. When estimating PM mortality impacts among populations according to level of 

education, we applied PM2.5 mortality risk coefficients modified by educational attainment: less 

than grade 12 (RR = 1.082, 95% confidence intervals 1.024—1.144 per 10 μg/m3change), grade 

12 (RR = 1.072, 95% confidence intervals 1.020—1.127 per 10 μg/m3change), and greater than 

grade 12 (RR = 1.055, 95% confidence intervals 1.018—1.094 per 10 μg/m3change). The Pope 

et al. (2002) study does not provide education-stratified RR estimates. The principal reason we 

applied risk estimates from the Krewski et al. (2009) study was to ensure that the risk 

coefficients used to estimate all-cause mortality risk and education-modified mortality risk 

were drawn from a consistent modeling framework. 

The other key difference between this distributional analysis and the benefits analysis 

for this rule relates to the baseline mortality rates. As described in Chapter 5 of this RIA, we 

calculate PM2.5-related mortality risk relative to baseline mortality rates in each county. 

Traditionally, for benefits analysis, we have applied county-level age- and sex-stratified baseline 

mortality rates when calculating mortality impacts (Abt, 2010). To calculate PM2.5 impacts by 

race, we incorporated race-specific (stratified by White/Black/Asian/Native American) baseline 

mortality rates. This approach improves our ability to characterize the relationship between 

race and PM2.5-related mortality however, we do not have a differential concentrationresponse function as we do for education, and as a result, we are not able to capture the full 

impacts of race in modifying the benefits associated with reductions in PM2.5

The result of this analysis is a distribution of PM2.5 mortality risk estimates by county, 

stratified by each of the three population variables (race, income and educational attainment). 
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We have less confidence in county-level estimates of mortality than the national or even state 

estimates, however, the modeling down to the county level can be considered reasonable 

because the estimates are based on 12km air quality modeling estimates of PM2.5, county level 

baseline mortality rates, and a concentration-response function that is derived from county 

level data. We next identified the counties at or above the median and upper 95th percentile of 

the PM2.5 mortality risk distribution. We selected this percentile cut-off to capture the very 

highest levels of PM2.5 mortality risk. The second step of the analysis was to repeat the 

sequence above by estimating PM2.5 mortality risk in 2016 prior to, and after, the 

implementation of MATS.

7.11.3.2 Results

We estimated the change in PM2.5 mortality risk in 2016 among populations living in 

those counties at the upper 95th percentile of the mortality risk in 2005. We then compared 

the change in risk among these populations living in high-risk counties with populations living in 

all other counties (Tables 7-19 through 7-21). 

Table 7-19. Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before 

and After Implementation of MATS by 2016 for Each Populations, Stratified by Race

Year

Race

Asian Black

Native 

American White

Among populations at greater risk

2016 (pre-MATS Rule) 4.3% 4.4% 4.4% 4.5%

2016 (post-MATS Rule) 4.1% 4.1% 4.2% 4.3%

Among all other populations

2016 (pre-MATS Rule) 3.2% 3.1% 3.1% 3.3%

2016 (post-MATS Rule) 3% 2.9% 2.9% 3.1%

Table 7-20. Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before 

and After Implementation of MATS by 2016 for Each Population, Stratified by 

Race and Poverty Level

Year

Race

Asian Black Native American White

Among populations living in counties with the largest number of individuals living below the poverty line
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2016 (pre-MATS) 3.6% 3.5% 3.6% 3.6%

2016 (post-MATS) 3% 3.4% 3% 3.5%

Among all other populations

2016 (pre-MATS) 3.2% 3.2% 3.2% 3.3%

2016 (post-MATS) 3% 2.9% 3% 3.1%

Table 7-21. Estimated Change in the Percentage of All Deaths Attributable to PM2.5 Before 

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

Educational Attainment

Year

Race

< Grade 12 = Grade 12 > Grade 12

Among populations at greater risk

2016 (pre-MATS) 6.2% 5.5% 4.3%

2016 (post-MATS) 5.9% 5.3% 4.1%

Among all other populations

2016 (pre-MATS) 4.5% 4% 3.1%

2016 (post-MATS) 4.2% 3.8% 2.9%

Table 7-19, shows the estimated level of PM2.5 mortality risk among populations of 

different races according to whether those populations live in counties identified as “greater 

risk” counties or “all other counties.” As described above, we define “greater risk” counties as 

those at or above the 95th percentile of the estimated PM2.5 mortality risk in 2005, and “all 

other counties” as those with estimated PM2.5 mortality risk below this level. The results of this 

analysis suggest that the PM2.5 mortality risk among these populations at “greater risk” falls 

with implementation of the 2016 MATS. These results also suggest that all populations, 

irrespective of race, may receive an estimated reduction in PM2.5 mortality risk. However, limits 

to data resolution prevent us from delineating the PM2.5 mortality risk according to population 

race with confidence. 
7-64

Table 7-20 illustrates the estimated change in the level of PM2.5 mortality risk among 

populations living in those counties that meet two criteria: (1) the county is at the upper 95th

percentile of mortality risk in 2005; (2) the county is at the upper 95th percentile in terms of the 

number of individuals living below the poverty line. We also estimate the change in PM2.5 risk 

among all other counties. The analysis suggests that people living in the highest mortality risk 

and poorest counties may experience a larger improvement in PM2.5 mortality risk than those 

living in lower risk counties containing a smaller number of individuals living below the poverty 

line.

Table 7-21 summarizes the estimated change in PM2.5 mortality 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 PM2.5 mortality risk 

coefficients for this analysis. These results indicate that populations with less than a high school 

education are at higher risk of PM2.5 mortality, irrespective of whether these populations live in 

“greater risk” counties, according to the definition described above. We estimate that with the 

implementation of MATS, all populations see their PM2.5 mortality risk fall, regardless of 

educational attainment.

7.12 Congressional Review Act

The Congressional Review Act, 5 U.S.C. 801 et seq., as added by the Small Business 

Regulatory Enforcement Fairness Act of 1996, generally provides that before a rule may take 

effect, the agency promulgating the rule must submit a rule report, which includes a copy of the 

rule, to each House of the Congress and to the Comptroller General of the United States. EPA 

will submit a report containing this rule and other required information to the U.S. Senate, the 

U.S. House of Representatives, and the Comptroller General of the United States prior to 

publication of the rule in the Federal Register. A major rule cannot take effect until 60 days 

after it is published in the Federal Register. This action is a ‘‘major rule’’ as defined by 5 U.S.C. 

804(2). This rule will be effective 60 days after publication.

7.13 References

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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.
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Burger, J. (2002). Daily consumption of wild fish and game: Exposures of high end 

recreationalists, International Journal of Environmental Health Research, 12:4, p. 343-

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Poole Economics, Inc.

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for the Transport Rule. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. June.