Regulatory Impact Analysis (RIA) for Existing 

Stationary Spark Ignition (SI) RICE 

NESHAP 

Final Report 
EPA 452/R-10-010 

August 2010 

Regulatory Impact Analysis (RIA) for Existing Stationary Spark Ignition RICE 

NESHAP 

By: 

Paramita Sinha 

Fern Braun 

Brooks Depro 

RTI International 

3040 Cornwallis Road 

Research Triangle Park, NC 27709 

Prepared for: 

Larry Sorrels 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group (ABCG) 

(MD-C439-02) 

Research Triangle Park, NC 27711 

Contract No. EP-D-06-003 

Work Assignment No. 4-82 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Air Benefit and Cost Group 

Research Triangle Park, North Carolina 
iii 

CONTENTS 

Section Page

1 Executive Summary ...................................................................................................... 1-1

2 Introduction ................................................................................................................... 2-1

2.1 Organization of this Report .................................................................................. 2-1

3 Industry Profile ............................................................................................................. 3-1

3.1 Electric Power Generation, Transmission, and Distribution ............................... 3-1

3.1.1 Overview .................................................................................................. 3-1

3.1.2 Goods and Services Used ......................................................................... 3-3

3.1.3 Business Statistics .................................................................................... 3-4

3.2 Oil and Gas Extraction ....................................................................................... 3-11

3.2.1 Overview ................................................................................................ 3-11

3.2.2 Goods and Services Used ....................................................................... 3-11

3.2.3 Business Statistics .................................................................................. 3-13

3.2.4 Case Study: Marginal Wells .................................................................. 3-17

3.3 Pipeline Transportation of Natural Gas ............................................................. 3-19

3.3.1 Overview ................................................................................................ 3-19

3.3.2 Goods and Services Used ....................................................................... 3-19

3.3.3 Business Statistics .................................................................................. 3-21

4 Regulatory Alternatives, Costs, and Emission Impacts ................................................ 4-1

4.1 Background .......................................................................................................... 4-1

4.2 Summary of the Final Rule .................................................................................. 4-1

4.2.1 What Is the Source Category Regulated by the Final Rule? .................... 4-1

4.2.2 What Are the Pollutants Regulated by the Rule? ..................................... 4-4

4.2.3 What Are the Final Requirements? .......................................................... 4-5

4.2.4 What Are the Operating Limitations? ...................................................... 4-9

4.2.5 What Are the Requirements for Demonstrating Compliance? ................ 4-9
iv 

4.2.6 What Are the Reporting and Recordkeeping Requirements? ................ 4-11

4.3 Summary of Significant Changes Since Proposal ............................................. 4-11

4.3.1 Applicability .......................................................................................... 4-11

4.3.2 Final Emission Standards ....................................................................... 4-12

4.3.3 Management Practices ........................................................................... 4-14

4.3.4 Startup, Shutdown, and Malfunction ..................................................... 4-15

4.3.5 Method 323 ............................................................................................ 4-16

4.4 Cost Impacts....................................................................................................... 4-17

4.4.1 Introduction ............................................................................................ 4-17

4.4.2 Control Cost Methodology .................................................................... 4-17 

4.4.3 Control Cost Equations .......................................................................... 4-20 

4.4.4 Summary ............................................................................................... 4-24 

4.4.4 Caveats and Uncertainties in the Cost Estimates ................................... 4-35 

4.5 Emissions and Emission Reductions ................................................................. 4-37

5 Economic Impact Analysis, Energy Impacts, and Social Costs ................................... 5-1

5.1 Compliance Costs of the Final Rule .................................................................... 5-1

5.2 How Might People and Firms Respond? A Partial Equilibrium Analysis .......... 5-4

5.2.1 Changes in Market Prices and Quantities ................................................ 5-5

5.2.2 Regulated Markets: The Electric Power Generation, Transmission, 

and Distribution Sector ............................................................................ 5-7

5.2.3 Partial Equilibrium Measures of Social Cost: Changes Consumer 

and Producer Surplus ............................................................................... 5-8

5.3 Social Cost Estimate ............................................................................................ 5-9

5.4 Energy Impacts .................................................................................................. 5-10

5.5 Unfunded Mandates ........................................................................................... 5-11

5.5.1 Future and Disproportionate Costs ........................................................ 5-11

5.5.2 Effects on the National Economy .......................................................... 5-11

6 Small Entity Screening Analysis .................................................................................. 6-1

6.1 Small Entity Data Set ........................................................................................... 6-1
v 

6.2 Small Entity Economic Impact Measures ............................................................ 6-2

6.2.1 Model Establishment Receipts and Annual Compliance Costs ............... 6-2

6.3 Small Government Entities ................................................................................ 6-10

7 Human Health Benefits of Emissions Reductions ........................................................ 7-1

7.1 Synopsis ............................................................................................................... 7-1

7.2 Calculation of PM2.5 Human Health Co-Benefits ................................................ 7-1

7.3 Unquantified Benefits ........................................................................................ 7-13

7.3.1 Carbon Monoxide Benefits .................................................................... 7-13

7.3.2 Other NOx Benefits ............................................................................... 7-14

7.3.3 Ozone Co-Benefits ................................................................................. 7-16

7.3.4 HAP Benefits ......................................................................................... 7-16

7.4 Characterization of Uncertainty in the Monetized PM2.5 Co-Benefits ............... 7-26

7.5 Comparison of Co-Benefits and Costs ............................................................... 7-31

8 References ..................................................................................................................... 8-1

Appendices

A Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality ................................... A-1 

B Lowest Measured Level (LML) Assessment for Rules without Policy-Specific 

Air Quality Data Available: Technical Support Document (TSD) ...............................B-1 
vi 

LIST OF FIGURES 

Number Page

3-1 Industrial Production Index (NAICS 2211) ................................................................ 3-3

3-2 Internal Combustion Generators by State: 2006 ......................................................... 3-4

3-3 2002 Regional Distribution of Establishments: Electric Power Generation, 

Transmission, and Distribution Industry (NAICS 2211) ............................................ 3-6

3-4 Industrial Production Index (NAICS 211) ................................................................ 3-12

3-5 2002 Regional Distribution of Establishments: Crude Petroleum and Natural 

Gas Extraction Industry (NAICS 211111) ................................................................ 3-15

3-6 2002 Regional Distribution of Establishments: Natural Gas Liquid Extraction 

Industry (NAICS 211112) ......................................................................................... 3-16

3-7 Trends in Marginal Oil and Gas Production: 1997 to 2006 ...................................... 3-19

3-8 Distribution of Establishments within Pipeline Transportation (NAICS 486) .......... 3-20

3-9 Distribution of Revenue within Pipeline Transportation (NAICS 486) .................... 3-21

3-10 2002 Regional Distribution of Establishments: Pipeline Transportation (NAICS 

486) ............................................................................................................................ 3-23

3-11 Share of Establishments by Legal Form of Organization in the Pipeline 

Transportation of Natural Gas Industry (NAICS 48621): 2002 ................................ 3-23

5-1 Distribution of Annualized Direct Compliance Costs by Industry ............................. 5-2

5-2 Average Annualized Cost per Engine by Horsepower Group ($2009 ....................... 5-3

5-3 Distribution of Engine Population by Horsepower Group .......................................... 5-4

5-4 Market Demand and Supply Model: With and Without Regulation ........................... 5-6

5-5 Electricity Restructuring by State ................................................................................ 5-9

6-1 Distribution of Engine Population by Size for All Industries ..................................... 6-7

6-2 Distribution of Compliance Costs by Engine Size for All Industries ......................... 6-8

7-1. Breakdown of Monetized PM2.5 Health Co- Benefits using Mortality Function 

from Pope et al. (2002) ................................................................................................ 7-7

7-2. Total Monetized PM2.5 Co-Benefits for the Final SI RICE NESHAP in 2013 ......... 7-12

7-3. Breakdown of Monetized Co-Benefits for the Final SI RICE NESHAP by 

PM2.5 Precursor and Source ....................................................................................... 7-13

7-4. Estimated County Level Carcinogenic Risk (NATA, 2002) ..................................... 7-18

7-5. Estimated County Level Noncancer (Respiratory) Risk (NATA, 2002) .................. 7-18

7-6. Percentage of Adult Population by Annual Mean PM2.5 Exposure (pre- and 

post-policy) ................................................................................................................ 7-28
vii 

7-7. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy) ................................................................................................ 7-28

7-8. Net Benefits for the Final SI RICE NESHAP at 3% Discount Rate ......................... 7-33

7-9. Net Benefits for the Final SI RICE NESHAP at 7% Discount Rate ......................... 7-34 
viii 

LIST OF TABLES 

Number Page

1-1 Summary of the Monetized Co-Benefits, Social Costs, and Net benefits for the 

Final SI RICE NESHAP in 2013 (millions of 2009$) ................................................ 1-2 

3-1 Key Statistics: Electric Power Generation, Transmission, and Distribution 

(NAICS 2211) ($2007) ................................................................................................ 3-2

3-2 Direct Requirements for Electric Power Generation, Transmission, and 

Distribution (NAICS 2211): 2002 ............................................................................... 3-5

3-3 Firm Concentration for Electric Power Generation, Transmission, and 

Distribution (NAICS 2211): 2002 ............................................................................... 3-7

3-4 United States Retail Electricity Sales Statistics: 2008 ................................................ 3-8

3-5 FY 2007 Financial Data for 70 U.S. Shareholder-Owned Electric Utilities ............... 3-9

3-6 Aggregate Tax Data for Accounting Period 7/07–6/08: NAICS 2211 ....................... 3-9

3-7 Key Enterprise Statistics by Receipt Size for Electric Power Generation, 

Transmission, and Distribution (NAICS 2211): 2002 ............................................... 3-10

3-8 Key Statistics: Crude Petroleum and Natural Gas Extraction (NAICS 211111): 

($2007) ...................................................................................................................... 3-12

3-9 Key Statistics: Natural Gas Liquid Extraction (NAICS 211112) ($2007) ................ 3-13

3-10 Direct Requirements for Oil and Gas Extraction (NAICS 211): 2002 ...................... 3-13

3-11 Key Enterprise Statistics by Employment Size for Crude Petroleum and 

Natural Gas Extraction (NAICS 211111): 2002 ....................................................... 3-17

3-12 Key Enterprise Statistics by Employment Size for Crude Natural Gas Liquid 

Extraction (NAICS 211112): 2002 ............................................................................ 3-17

3-13 Aggregate Tax Data for Accounting Period 7/07–6/08: NAICS 211 ....................... 3-18

3-14 Reported Gross Revenue Estimates from Marginal Wells: 2007 .............................. 3-18

3-15 Key Statistics: Pipeline Transportation of Natural Gas (NAICS 48621) ($2007) .... 3-20

3-16 Direct Requirements for Pipeline Transportation (NAICS 486): 2002 ..................... 3-22

3-17 Firm Concentration for Pipeline Transportation of Natural Gas (NAICS 

48621): 2002 .............................................................................................................. 3-24

3-18 Aggregate Tax Data for Accounting Period 7/07–6/08: NAICS 486 ....................... 3-24

3-19 Key Enterprise Statistics by Receipt Size for Pipeline Transportation of Natural 

Gas (NAICS 48621): 2002 ........................................................................................ 3-25

4-1 Emission Standards for Existing Stationary SI RICE ≤500 HP Located at 

Major Sources of HAP ................................................................................................ 4-5

4-2 Numerical Emission Standards for Existing Non-Emergency Stationary 4SLB 

and 4SRB SI RICE >500 HP Located at Area Sources of HAP ................................. 4-7
ix 

4-3 Summary of Annual and Capital Costs Equations for Existing Stationary SI 

Engines ...................................................................................................................... 4-24

4-4 Summary of Major Source and Area Source Costs for the SI RICE NESHAP ........ 4-25

4-5 Summary of Major Source and Area Source NAICS Costs for the SI RICE 

NESHAP ................................................................................................................... 4-27

4-6 Summary of Major Source and Area Source NAICS Costs for the SI RICE 

NESHAP, by Size ...................................................................................................... 4-28

4-7 Summary of Major Source and Area Source NAICS Costs for the SI RICE 

NESHAP, by Number of Engines ............................................................................. 4-32

4-8 Summary of Major Source and Area Source Baseline for the SI RICE 

NESHAP ................................................................................................................... 4-37

4-9 Emissions Factors ...................................................................................................... 4-37

4-10 Summary of Major Source and Area Source Emissions Reductions for the SI 

RICE NESHAP ......................................................................................................... 4-38

5-1 Selected Industry-Level Annualized Compliance Costs as a Fraction of Total 

Industry Revenue: 2009 ............................................................................................... 5-5

5-2 Hypothetical Price Increases for a 1% Increase in Unit Costs .................................... 5-7

5-3 Hypothetical Consumption Decreases for a 1% Increase in Unit Costs ..................... 5-8

5-4 U.S. Electric Powera Sector Energy Consumption (Quadrillion BTUs): 2013 ......... 5-10

6-1 Proposed NESHAP for Existing Stationary Reciprocating Internal Combustion 

Engines (RICE): Affected Sectors and SBA Small Business Size Standards ............. 6-3

6-2 Average Receipts for Affected Industry by Enterprise: 2002 ($2009 

Million/Establishment) ................................................................................................ 6-4

6-3 Average Receipts for Affected Industry by Enterprise Receipt Range: 2002 

($2009/Establishment) ................................................................................................. 6-4

6-4 Representative Establishment Costs Used for Small Entity Analysis ($2009) ........... 6-6

7-1 Human Health and Welfare Effects of PM2.5 .............................................................. 7-2

7-2. Summary of Monetized Co-Benefits Estimates for Final SI RICE NESHAP in 

2013 (millions of 2009$) ............................................................................................. 7-9

7-3. Summary of Reductions in Health Incidences from PM2.5 Benefits for the Final 

SI RICE NESHAP in 2013 ........................................................................................ 7-10

7-4. All Monetized PM2.5 Co-Benefits from PM2.5 Benefits for the Final SI RICE 

NESHAP in 2013 ...................................................................................................... 7-11

7-5. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

final SI RICE NESHAP in 2013 (millions of 2009$) ............................................... 7-31
1-1 

SECTION 1

EXECUTIVE SUMMARY 

This final action promulgates NESHAP for existing stationary SI RICE with a site rating 

of less than or equal to 500 HP located at major sources, and existing stationary SI RICE of any 

site rating located at area sources. EPA is finalizing these standards to meet its statutory 

obligation to address HAP emissions from these sources under sections 112(d), 112(c)(3) and 

112(k) of the CAA. The final NESHAP for stationary RICE will be promulgated under 40 CFR 

part 63, subpart ZZZZ, which already contains standards applicable to new and reconstructed 

stationary RICE and some existing stationary RICE. 

EPA estimates that complying with the final national emission standards for hazardous 

air pollutants (NESHAP) for stationary spark-ignition (SI) reciprocating internal combustion 

engines (RICE) will have an annualized cost of approximately $253 million per year (2009 

dollars) in the year of full implementation of the rule (2013). Using these costs, EPA estimates in 

its economic impact analysis that the NESHAP will have limited impacts on the industries 

affected and their consumers. Using sales data obtained for affected small entities in an analysis 

of the impacts of this rule on small entities, EPA expects that the NESHAP will not result in a 

SISNOSE (significant economic impacts for a substantial number of small entities). EPA also 

does not expect significant adverse energy impacts based on Executive Order 13211, an 

Executive Order that requires analysis of energy impacts for rules such as this one that are 

economically significant under Executive Order 12866. 

In the year of full implementation (2013), EPA estimates that the total monetized benefits 

of the final NESHAP are $510 million to $1.2 billion and $460 million to $1.1 billion, at 3% and 

7% discount rates, respectively (Table 1-1). All estimates are in 2009 dollars for the year 2013. 

Using alternate relationships between PM2.5 and premature mortality supplied by experts, higher 

and lower benefits estimates are plausible, but most of the expert-based estimates fall between 

these estimates. The benefits from reducing other air pollutants have not been monetized in this 

analysis, including reducing 109,000 tons of carbon monoxide and 6,000 tons of hazardous air 

pollutants (HAPs) each year. In addition, ecosystem benefits and visibility benefits have not been 

monetized in this analysis. 

In the year of full implementation (2013), EPA estimates the net benefits of the final 

NESHAP are $250 million to $980 million and $210 million to $860 million, at 3% and 7% 

discount rates, respectively (Table 1-1). All estimates are in 2009 dollars for the year 2013. The 

final NESHAP is the MACT floor level of control for all major SI RICE non-emergency sources 
1-2 

and the GACT level of control for area SI RICE sources. We also show results for an alternative 

(referred to as Alternative 2) which is more stringent than the final NESHAP for major sources. 

In Alternative 2, the MACT level of control is applied to all SI RICE major non-emergency 

sources except for four-stroke rich-burn (4SRB) engines of 300-500 horsepower (HP), where the 

required level of control is above the MACT floor, and the GACT level of control is applied for 

area SI RICE sources. 

 It should be noted that there is a difference between the annualized social costs 

estimated at 3% and 7%. We approximate the annualized social costs with the compliance costs 

of the rule for the RIA, as we mention later in Section 5. The annualized compliance costs of the 

rule are estimated to be $244 million (2009 dollars) using a 3% interest rate. Thus, the 

annualized social costs for a 3% rate are also $244 million using our approximation, and this 

estimate is very close to the annualized social cost estimate at a 7% rate. 
1-3 

Table 1-1. Summary of the Monetized Co-Benefits, Social Costs, and Net benefits for the 

Final SI RICE NESHAP in 2013 (millions of 2009$)1

 3% Discount Rate 7% Discount Rate 

Final NESHAP: Major4

Total Monetized Benefits2 $8.2 to $20 $7.4 to $18 

Total Social Costs3 $88 $88 

Net Benefits -$80 to -$68 -$81 to -$70 

Non-monetized Benefits 

12,500 tons of carbon monoxide 

1,300 tons of hazardous air pollutants (HAPs) 

Ecosystem effects 

Visibility impairment 

Alternative 2: Major 

Total Monetized Benefits2 $48 to $120 $43 to $110 

Total Social Costs3 $95 $95 

Net Benefits -$47 to $22 -$52 to $11 

Non-monetized Benefits 

17,800 tons of carbon monoxide 

1,400 tons of hazardous air pollutants (HAPs) 

Health effects from NO2 and ozone exposure 

Ecosystem effects 

Visibility impairment 

Final NESHAP: Area5

Total Monetized Benefits2 $500 to $1,200 $450 to $1,100 

Total Social Costs3 $166 $166 

Net Benefits $330 to $1,100 $290 to $930 

Non-monetized Benefits 

97,000 tons of carbon monoxide 

4,700 tons of hazardous air pollutants (HAPs) 

Health effects from NO2 and ozone exposure 

Ecosystem effects 

Visibility impairment 

Final Major and Area Source NESHAP 

Total Monetized Benefits2 $510 to $1,200 $460 to $1,100 

Total Social Costs3 $253 $253 

Net Benefits $250 to $980 $210 to $860 

Non-monetized Benefits 

109,000 tons of carbon monoxide 

6,000 tons of hazardous air pollutants (HAPs) 

Health effects from NO2 and ozone exposure 

Ecosystem effects 

Visibility impairment 1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 2

 The total monetized co-benefits reflect the human health co-benefits associated with reducing exposure to PM2.5

through reductions of PM2.5 precursors such as NOx and VOC. It is important to note that the monetized cobenefits include many but not all health effects associated with PM2.5 exposure. It is important to note that the 

monetized benefits include many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range 

from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality because there is no clear scientific evidence that would 

support the development of differential effects estimates by particle type. 3

 The annual compliance costs serve as a proxy for the annual social costs of this rule given the lack of difference 

between the two. 4 The final NESHAP is the MACT floor level of control for all major SI RICE non-emergency sources, and the GACT 

level of control for area SI RICE sources. 
1-4 

5 All of the benefits for area sources are attributable to reductions expected from 4SLB and 4SRB non-emergency 

engines above 500 HP. 
2-1 

SECTION 2

INTRODUCTION 

EPA is promulgating NESHAP for existing stationary SI RICE that either are located at 

area sources of hazardous air pollutant emissions or that have a site rating of less than or equal to 

500 horsepower and are located at major sources of hazardous air pollutant emissions. 

The rule is economically significant according to Executive Order 12866. As part of the 

regulatory process of preparing these standards, EPA has prepared a regulatory impact analysis 

(RIA). This analysis includes an analysis of impacts to small entities as part of compliance with 

the Small Business Regulatory Enforcement Fairness Act (SBREFA) and an analysis of impacts 

on energy consumption and production to comply with Executive Order 13211 (Statement of 

Energy Effects). 

2.1 Organization of this Report 

The remainder of this report supports and details the methodology and the results of the 

RIA: 

 Section 3 presents a profile of the affected industries. 

 Section 4 presents a summary of regulatory alternatives considered in the final rule, 

and provides the compliance costs of the rule. 

 Section 5 describes the estimated costs of the regulation and describes the economic 

impact analysis (EIA) methodology and reports market, welfare, and energy impacts. 

 Section 6 presents estimated impacts on small entities. 

 Section 7 presents the benefits estimates. 

 Appendices A and B present technical support documents related to the benefits 

estimates 
3-1 

SECTION 3

INDUSTRY PROFILE 

This section provides an introduction to the industries affected by the rule, i.e., industries 

in which the spark-ignition (SI) RICE being regulated are found. SI RICE generate electric 

power, pump gas or other fluids, or compress air for machinery. The primary non-utility 

application of internal combustion (IC) engines is in the natural gas industry to power 

compressors used for pipeline transportation, field gathering (collecting gas from wells), 

underground storage tanks, and in-gas processing plants. RICEs are separated into three design 

classes: 2 cycle (stroke) lean burn, 4-stroke lean burn, and 4-stroke rich burn. Each of these has 

design differences that affect both baseline emissions as well as the potential for emissions 

control. 

These industries include the following: 

 electric power generation, transmission, and distribution (NAICS 2211), 

 oil and gas extraction (including marginal wells) (NAICS 211), and 

 pipeline transportation of natural gas (NAICS 48621). 

These three industries incur over 80 percent of the annualized costs of the rule. The 

purpose is to give the reader a general understanding of the economic aspects of the industry; 

their relative size, relationships with other sectors in the economy, trends for the industries, and 

financial statistics. 

3.1 Electric Power Generation, Transmission, and Distribution 

3.1.1 Overview 

Electric power generation, transmission, and distribution (NAICS 2211) is an industry 

group within the utilities sector (NAICS 22). It includes establishments that produce electrical 

energy or facilitate its transmission to the final consumer. 

From 2002 to 2007, revenues from electric power generation grew about 18% to over 

$440 billion ($2007) (Table 3-1).1

 At the same time, payroll rose about 7% and the number of 

employees decreased by around 4%. The number of establishments rose by about 3%. Industrial 

production within NAICS 2211 has increased 26% since 1997 (Figure 3-1). 

 

1

 We provide revenues from electric power generation for the years 2002 and 2007 for these are years of the 

Economic Census. We reference data from these Economic Censuses frequently in this industry profile and 

show revenues from this industry over this time frame due to availability of such data. 
3-2 

Electric utility companies have traditionally been tightly regulated monopolies. Since 

1978, several laws and orders have been passed to encourage competition within the electricity 

market. In the late 1990s, many states began the process of restructuring their utility regulatory 

framework to support a competitive market. Following market manipulation in the early 2000s, 

however, several states have suspended their restructuring efforts. The majority (58%) of power 

generators controlled by combined heat and power (CHP) or independent power producers are 

located in states undergoing active restructuring (Figure 3-2). 

Table 3-1. Key Statistics: Electric Power Generation, Transmission, and Distribution 

(NAICS 2211) ($2007) 

2002 2007 

Revenue ($106) 373,309 440,355 

Payroll ($106) 40,842 43,792 

Employees 535,675 515,335 

Establishments 9,394 9,642 

Source: U.S. Census Bureau; American FactFinder; “Sector 22: EC0722I2: Utilities: Industry Series: Preliminary 

Comparative Statistics for the United States (2002 NAICS Basis): 2007 and 2002.” http://factfinder.census.gov
3-3 

80

85

90

95

100

105

110

115

1997‐011997‐061997‐111998‐041998‐091999‐021999‐071999‐122000‐052000‐102001‐032001‐082002‐012002‐062002‐112003‐042003‐092004‐022004‐072004‐122005‐052005‐102006‐032006‐082007‐012007‐062007‐112008‐042008‐092009‐022009‐072009‐12

Index Value (Jan 2002=100)

Figure 3-1. Industrial Production Index (NAICS 2211) 

Source: The Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Production” Series 

ID: G17/IP_MINING_AND_UTILITY_DETAIL/IP.G2211.S <http://www.federalreserve.gov/datadownload/>. 

(January 27, 2010). 

3.1.2 Goods and Services Used 

In Table 3-2, we use the latest detailed benchmark input-output data report by the Bureau 

of Economic Analysis (BEA) (2002) to identify the goods and services used in electric power 

generation. As shown, labor and tax requirements represent a significant share of the value of 

power generation. Extraction, transportation, refining, and equipment requirements potentially 
3-4 

Diesel and Natural Gas Internal

Combustion Generators By State

No Restructuring

Suspended Restructuring

Active Restructuring

100

50

10

1

Figure 3-2. Internal Combustion Generators by State: 2006 

Source: U.S. Department of Energy, Energy Information Administration. 2007. “2006 EIA-906/920 Monthly Time 

Series.” 

associated with reciprocating internal combustion engines (oil and gas extraction, pipeline 

transportation, petroleum refineries, and turbine manufacturing) represent around 10% of the 

value of services. 

3.1.3 Business Statistics 

The U.S. Economic Census and Statistics of U.S. Businesses (SUSB) programs provide 

national information on the distribution of economic variables by industry, location, and size of 

business. Throughout this section and report, we use the following definitions: 

 Establishment: An establishment is a single physical location where business is 

conducted or where services or industrial operations are performed. 
3-5 

Table 3-2. Direct Requirements for Electric Power Generation, Transmission, and 

Distribution (NAICS 2211): 2002 

Commodity Commodity Description 

Direct Requirements 

Coefficientsa

V00100 Compensation of employees 20.52% 

V00200 Taxes on production and imports, less subsidies 13.71% 

211000 Oil and gas extraction 6.16% 

212100 Coal mining 5.86% 

482000 Rail transportation 3.01% 

230301 Nonresidential maintenance and repair 2.83% 

486000 Pipeline transportation 1.70% 

722000 Food services and drinking places 1.40% 

52A000 Monetary authorities and depository credit intermediation 1.39% 

541100 Legal services 1.13% 

a

 These values show the amount of the commodity required to produce $1.00 of the industry’s output. The values 

are expressed in percentage terms (coefficient ×100). 

Source: U.S. Bureau of Economic Analysis. 2002. 2002 Benchmark Input-Output Accounts: Detailed Make Table, 

Use Table and Direct Requirements Table. Tables 4 and 5. 

 Receipts: Receipts (net of taxes) are defined as the revenue for goods produced, 

distributed, or services provided, including revenue earned from premiums, 

commissions and fees, rents, interest, dividends, and royalties. Receipts exclude all 

revenue collected for local, state, and federal taxes. 

 Firm: A firm is a business organization consisting of one or more domestic 

establishments in the same state and industry that were specified under common 

ownership or control. The firm and the establishment are the same for singleestablishment firms. For each multiestablishment firm, establishments in the same 

industry within a state are counted as one firm; the firm employment and annual 

payroll are summed from the associated establishments. 

 Enterprise: An enterprise is a business organization consisting of one or more 

domestic establishments that were specified under common ownership or control. The 

enterprise and the establishment are the same for single-establishment firms. Each 

multiestablishment company forms one enterprise; the enterprise employment and 

annual payroll are summed from the associated establishments. Enterprise size 

designations are determined by the summed employment of all associated 

establishments. 
3-6 

In 2002, Texas had almost 1,000 power establishments, while California, Georgia, and 

Ohio all had between 400 and 500 (Figure 3-3). Hawaii, Nebraska, and Rhode Island all had 

fewer than 20 establishments in their states. 

Establishments by State

Less than 100

100 - 199

200 - 349

350 - 500

More than 500

Figure 3-3. 2002 Regional Distribution of Establishments: Electric Power Generation, 

Transmission, and Distribution Industry (NAICS 2211) 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 22: Utilities: 

Geographic Area Series: Summary Statistics: 2002.” <http://factfinder.census.gov>; (November 10, 2008).. 

As shown in Table 3-3, the four largest firms owned over 1,200 establishments and 

accounted for about 16% of total industry receipts/revenue. The 50 largest firms accounted for 

almost 6,000 establishments and about 78% of total receipts/revenue. 

Investor-owned energy providers accounted for only 2% of retail electricity sold in the 

United States in 2008 (Table 3-4). In 2008, investor-owned energy provider companies with less 

than 50% of their assets regulated were unprofitable overall, while other companies in this 

category were profitable. (Table 3-5). In 2008, enterprises within NAICS 2211 had a pre-tax 

profit margin of 8.1% (Table 3-6). 

In 2002, about 82% of firms generating, transmitting, or distributing electric power had 

receipts of under $50 million (Table 3-7). However, these firms accounted for only 11% of 

employment, with 89% of employees working for firms with revenues in excess of $100 million. 
3-7 

Table 3-3. Firm Concentration for Electric Power Generation, Transmission, and 

Distribution (NAICS 2211): 2002 

Commodity Establishments 

Receipts/Revenue 

Number of 

Employees 

Employees per 

Amount ($10 Establishment 6) 

Percentage 

of Total 

All firms 9,394 $325,028 100.0% 535,675 57 

4 largest firms 1,260 $52,349 16.1% 68,432 54 

8 largest firms 2,566 $95,223 29.3% 151,575 59 

20 largest firms 3,942 $173,207 53.3% 271,393 69 

50 largest firms 5,887 $253,015 77.8% 408,021 69 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 22: Utilities: 

Subject Series—Estab & Firm Size: Concentration by Largest Firms for the United States: 2002.” 

<http://factfinder.census.gov>; (November 21, 2008). 
3-8 

Table 3-4. United States Retail Electricity Sales Statistics: 2008  Full-Service Providers Other ProviItem Investor-Owned Public Federal Cooperative Facility Energy DeNumber of entities 3 62 1 25 1 NA Number of retail customers 46,985 2,160,220 36 940,697 1 NA Retail sales (103 megawatthours) 2,257 70,303 9,625 21,868 117 NA Percentage of retail sales 2 67 9 21 0 — Revenue from retail sales ($106) 113 5,934 473 1,994 6 NA Percentage of revenue 1.33 69.65 5.55 23.41 0.07 — Average retail price (cents/kWh) 5.01 8.44 4.91 9.12 5.25 NA Source: U.S. Department of Energy, Energy Information Administration. 2009. “State Electricity Profiles 2008.” DOE/EIA-0348(01)/2

http://www.eia.doe.gov/cneaf/electricity/st_profiles/sep2008.pdf>. 
3-9 

Table 3-5. FY 2007 Financial Data for 70 U.S. Shareholder-Owned Electric Utilities 

 Profit Margin Net Income Operating Revenues 

Investor-Owned Utilities 4.81% $20,677 $430,037 

Regulateda 7.25% $12,129 $167,194 

Mostly regulatedb

 8.50% $17,704 $208,288 

Diversifiedc

 -16.78% -$9,156 $54,554 

a 80%+ of total assets are regulated. 

b 50% to 80% of total assets are regulated. 

c Less than 50% of total assets are regulated. 

Source: Edison Electric Institute. “Income Statement: Q4 2008 Financial Update. Quarterly Report of the U.S. 

Shareholder-Owned Electric Utility Industry.” <http://www.eei.org>. 

Table 3-6. Aggregate Tax Data for Accounting Period 7/07–6/08: NAICS 2211 

Number of enterprisesa 1,187 

Total receipts (103) $361,177,861 

Net sales(103

) $328,017,143 

Profit margin before tax 8.1% 

Profit margin after tax 5.4% 

a Includes corporations with and without net income. 

Source: Internal Revenue Service, U.S. Department of Treasury. 2010. “Corporation Source Book: Data Files 2004–

2007.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (May 2, 2010). 3.2.2 Goods and Services 

Used. 
3-10 

Table 3-7. Key Enterprise Statistics by Receipt Size for Electric Power Generation, Transmission, and D

2211): 2002 

 Owned by Enterprises with 

Variable 

All 

Enterprises 

0–99K 

Receipts 

100–

499.9K 

Receipts 

500–

999.9K 

Receipts 

1,000–

4,999.9K 

Receipts 

5,000,000–

9,999,999K 

Receipts 

<10,000K 

Receipts 

10,000–

49,999K 

Receipts 

Firms 1,756 129 250 80 232 205 896 538 Establishments 9,493 129 250 85 245 262 971 978 Employment 515,769 429 834 3,139 2,712 5,620 12,734 31,573 Receipts ($103) $320,502,670 $5,596 $63,339 $57,363 $627,414 $1,472,405 $2,226,117 $12,171,098 

Receipts/firm ($103) $182,519 $43 $253 $717 $2,704 $7,182 $2,485 $22,623 

Receipts/establishment ($103) $33,762 $43 $253 $675 $2,561 $5,620 $2,293 $12,445 

Receipts/employment ($) $621,407 $13,044 $75,946 $18,274 $231,347 $261,994 $174,817 $385,491 

Source: U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S. Businesses: U.S. All Industries Tabulated by Receipt Si

<http://www.census.gov/csd/susb/susb02.htm>. 
3-11 

3.2 Oil and Gas Extraction 

3.2.1 Overview 

Oil and gas extraction (NAICS 211) is an industry group within the mining sector 

(NAICS 21). It includes establishments that operate or develop oil and gas field properties 

through such activities as exploring for oil and gas, drilling and equipping wells, operating onsite equipment, and conducting other activities up to the point of shipment from the property. 

Oil and gas extraction consists of two industries: crude petroleum and natural gas 

extraction (NAICS 211111) and natural gas liquid extraction (NAICS 211112). Crude petroleum 

and natural gas extraction is the larger industry; in 2002, it accounted for 93% of establishments 

and 75% of oil and gas extraction revenues. 

Industrial production in this industry is particularly sensitive to hurricanes in the Gulf 

Coast. In September of both 2005 and 2008, production dropped 14% from the previous month. 

However, production is currently 3% higher than it was in 2002 (Figure 3-4). 

From 2002 to 2007, revenues from crude petroleum and natural gas extraction (NAICS 

211111) grew over 117% to almost $215 billion ($2007) (Table 3-8). At the same time, payroll 

grew 55% and the number of employees grew by 48%. The number of establishments dropped 

by over 17%; as a result, the average establishment revenue increased by 162%. Materials costs 

were approximately 18% of revenue over the period. 

From 2002 to 2007, revenue from natural gas liquid extraction (NAICS 211112) grew 

over 26% to about $42 billion (Table 3-9). At the same time, payroll dropped 18% and the 

number of employees dropped by 24%. The number of establishments dropped by 43%, resulting 

in an increase of revenue per establishment of about 122%. 

3.2.2 Goods and Services Used 

The oil and gas extraction industry has similar labor and tax requirements as the electric 

power generation sector. Extraction, support, power, and equipment requirements potentially 

associated with RICE (oil and gas extraction, support activities, electric power generation, 

machinery and equipment rental and leasing, and pipeline transportation) represent around 8% of 

the value of services (Table 3-10). 
3-12 

75

80

85

90

95

100

105

110

1997‐011997‐061997‐111998‐041998‐091999‐021999‐071999‐12

2000‐052000‐102001‐032001‐082002‐012002‐062002‐112003‐042003‐092004‐022004‐072004‐122005‐052005‐102006‐03

2006‐082007‐012007‐062007‐112008‐042008‐092009‐022009‐072009‐12

Index Value (Jan 2002=100)

Figure 3-4. Industrial Production Index (NAICS 211) 

Source: The Federal Reserve Board. “Industrial Production and Capacity Utilization: Industrial Production” Series 

ID: G17/IP_MINING_AND_UTILITY_DETAIL/IP.G211.S <http://www.federalreserve.gov/datadownload/>. 

(January 27, 2010). 

Table 3-8. Key Statistics: Crude Petroleum and Natural Gas Extraction (NAICS 211111): 

($2007) 

2002 2007 

Revenue ($106) $98,667 $214,198 

Payroll ($106) $5,785 $8,980 

Employees 94,886 140,160 

Establishments 7,178 5,956 

Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 21: Mining: 

Industry Series: Historical Statistics for the Industry: 2002 and 1997.” <http://factfinder.census.gov>; 

(November 26, 2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 21: EC0721I1: Mining: 

Industry Series: Detailed Statistics by Industry for the United States: 2007 ” <http://factfinder.census.gov>; 

(April 27, 2010). 
3-13 

Table 3-9. Key Statistics: Natural Gas Liquid Extraction (NAICS 211112) ($2007) 

2002 2007 

Revenue ($106) $33,579 $42,363 

Payroll ($106) $607 $501 

Employees 9,693 7,343 

Establishments 511 291 

Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 21: Mining: 

Industry Series: Historical Statistics for the Industry: 2002 and 1997.” <http://factfinder.census.gov>; 

(November 26, 2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 21: EC0721I1: Mining: 

Industry Series: Detailed Statistics by Industry for the United States: 2007 ” <http://factfinder.census.gov>; 

(April 27, 2010). 

Table 3-10. Direct Requirements for Oil and Gas Extraction (NAICS 211): 2002 

Commodity Commodity Description 

Direct Requirements 

Coefficientsa

V00200 Taxes on production and imports, less subsidies 8.93% 

V00100 Compensation of employees 6.67% 

230301 Nonresidential maintenance and repair 6.36% 

211000 Oil and gas extraction 1.91% 

213112 Support activities for oil and gas operations 1.51% 

221100 Electric power generation, transmission, and distribution 1.47% 

541300 Architectural, engineering, and related services 1.24% 

532400 Commercial and industrial machinery and equipment rental and leasing 1.20% 

33291A Valve and fittings other than plumbing 1.10% 

541511 Custom computer programming services 0.99% 

a

 These values show the amount of the commodity required to produce $1.00 of the industry’s output. The values 

are expressed in percentage terms (coefficient ×100). 

Source: U.S. Bureau of Economic Analysis. 2002. 2002 Benchmark Input-Output Accounts: Detailed Make Table, 

Use Table and Direct Requirements Table. Tables 4 and 5. 

3.2.3 Business Statistics 

The U.S. Economic Census and SUSB programs provide national information on the 

distribution of economic variables by industry, location, and size of business. Throughout this 

section and report, we use the following definitions: 

 Establishment: An establishment is a single physical location where business is 

conducted or where services or industrial operations are performed. 
3-14 

 Receipts: Receipts (net of taxes) are defined as the revenue for goods produced, 

distributed, or services provided, including revenue earned from premiums, 

commissions and fees, rents, interest, dividends, and royalties. Receipts exclude all 

revenue collected for local, state, and federal taxes. 

 Firm: A firm is a business organization consisting of one or more domestic 

establishments in the same state and industry that were specified under common 

ownership or control. The firm and the establishment are the same for singleestablishment firms. For each multiestablishment firm, establishments in the same 

industry within a state are counted as one firm; the firm employment and annual 

payroll are summed from the associated establishments. 

 Enterprise: An enterprise is a business organization consisting of one or more 

domestic establishments that were specified under common ownership or control. The 

enterprise and the establishment are the same for single-establishment firms. Each 

multiestablishment company forms one enterprise; the enterprise employment and 

annual payroll are summed from the associated establishments. Enterprise size 

designations are determined by the summed employment of all associated 

establishments. 

In 2002, Texas had almost 2,500 crude petroleum and natural gas extraction 

establishments, Oklahoma had about 900, and every other state had under 400 (Figure 3-5). 

Twenty-two states had fewer than 10 establishments. Similarly, Texas had 830 natural gas liquid 

extraction establishments, Oklahoma had 41, Louisiana had 37, and every other state had under 

25 (Figure 3-6). Only seven states had 10 or more establishments, and 24 had no establishments. 

According to the SUSB, 89% of crude petroleum and natural gas extraction firms had 

fewer than 500 employees in 2002 (Table 3-11). Sixty-three percent of natural gas liquid 

extraction firms had fewer than 500 employees in 2002 (Table 3-12). 

Enterprises within this industry generated $193 billion in total receipts in 2008. Including 

those enterprises without net income, the industry averaged an after-tax profit margin of 8.5% 

(Table 3-13). 
3-15 

Establishments by State

Less than 100

100 - 249

250 - 499

500 - 1,000

More than 1,000

Figure 3-5. 2002 Regional Distribution of Establishments: Crude Petroleum and Natural 

Gas Extraction Industry (NAICS 211111) 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 21: Mining: 

Geographic Area Series: Industry Statistics for the State or Offshore Areas: 2007.” <http://factfinder.census.gov>; 

(January 27, 2010). 
3-16 

Establishments by State

Less than 5

5 - 9

10 - 19

20 - 40

More than 40

Figure 3-6. 2002 Regional Distribution of Establishments: Natural Gas Liquid 

Extraction Industry (NAICS 211112) 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 21: Mining: 

Geographic Area Series: Industry Statistics for the State or Offshore Areas: 2007.” <http://factfinder.census.gov>; 

(January 27, 2010). 
3-17 

Table 3-11. Key Enterprise Statistics by Employment Size for Crude Petroleum and 

Natural Gas Extraction (NAICS 211111): 2002 

 Owned by Enterprises with 

Variable 

All 

Enterprises 

1–20 

Employees

20–99 

Employees

100–499 

Employees

500–749 

Employees 

750–999 

Employees 

1,000–1,499 

Employees 

Firms 6,238 5,130 348 85 11 11 5

Establishments 7,135 5,185 449 254 37 63 25

Employment 76,794 5,825 5,171 2,757 Not disclosed Not disclosed Not disclosed

Receipts ($103

) $88,388,300 $2,353,181 $2,559,239 $2,051,860 Not disclosed Not disclosed Not disclosed

Receipts/firm ($103) $14,169 $459 $7,354 $24,140 Not disclosed Not disclosed Not disclosed

Receipts/establishment 

($103

) 

$12,388 $454 $5,700 $8,078 Not disclosed Not disclosed Not disclosed

Receipts/employment ($) $1,150,979 $403,980 $494,921 $744,236 Not disclosed Not disclosed Not disclosed

Source: U.S. Census Bureau. 2008a. Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment 

Sizes: 2002. http://www2.census.gov/csd/susb/2002/02us_detailed%20sizes_6digitnaics.txt. 

Table 3-12. Key Enterprise Statistics by Employment Size for Crude Natural Gas Liquid 

Extraction (NAICS 211112): 2002 

 Owned by Enterprises with 

Variable 

All 

Enterprises 

1–20 

Employees 

20–99 

Employees 

100–499 

Employees 

500–749 

Employees 

750–999 

Employees 

1,000–1,499 

Employees 

Firms 113 54 7 10 2 1 2 

Establishments 494 54 7 38 23 1 6 

Employment 11,486 65 Not disclosed 241 Not disclosed Not disclosed Not disclosed 

Receipts ($103

) $72,490,930 $13,862 Not disclosed $383,496 Not disclosed Not disclosed Not disclosed 

Receipts/firm ($103

) $641,513 $257 Not disclosed $38,350 Not disclosed Not disclosed Not disclosed 

Receipts/establishment 

($103

) 

$146,743 $257 Not disclosed $10,092 Not disclosed Not disclosed Not disclosed 

Receipts/employment ($) $6,311,242 $213,262 Not disclosed $1,591,270 Not disclosed Not disclosed Not disclosed 

Source: U.S. Census Bureau. 2008a. Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment 

Sizes: 2002. <http://www2.census.gov/csd/susb/2002/02us_detailed%20sizes_6digitnaics.txt>. 

3.2.4 Case Study: Marginal Wells 

To provide additional context for understanding energy sectors that use reciprocating 

internal combustion engines, we examine one segment of the oil and gas sector: marginal wells. 

This industry includes small-volume wells that are mature in age, are more difficult to extract oil 

or natural gas from than other types of wells, and generally operate at very low levels of 
3-18 

Table 3-13. Aggregate Tax Data for Accounting Period 7/07–6/08: NAICS 211 

Number of enterprisesa 19,441 

Total receipts (103) $193,230,241 

Net sales(103

) $166,989,539 

Profit margin before tax 12.9% 

Profit margin after tax 8.5% 

a Includes corporations with and without net income. 

Source: Internal Revenue Service, U.S. Department of Treasury. 2010. “Corporation Source Book: Data Files 2004-

2007.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (May 2, 2010). 

profitability. As a result, well operations can be quite responsive to small changes in the benefits 

and costs of their operation. 

In 2007, there were approximately 400,000 marginal oil wells and 320,000 marginal gas 

wells (Interstate Oil and Gas Compact Commission [IOGCC], 2008). These wells provide the 

United States with 4% of all oil and 8% of all natural gas consumed (IOGCC, 2008). Data for 

2007 show that revenue from the over 700,000 wells was approximately $30.6 billion 

(Table 3-14). 

Table 3-14. Reported Gross Revenue Estimates from Marginal Wells: 2007 

Well Type Number of Wells 

Production from 

Marginal Wells 

Estimated Gross 

Revenue ($109

) 

Oil 396,537 291.067592 MMbbls $18.6 

Natural gas 322,160 1763.592746 MCF $12.0 

Total 718,697 $30.6 

Source: Interstate Oil & Gas Compact Commission. 2008. “Marginal Wells: Fuel for Economic Growth.” Available 

at <http://iogcc.publishpath.com/Websites/iogcc/pdfs/2007-Marginal-Well-Report.pdf>. 

Historical data show marginal oil production fluctuated between 1997 and 2007, 

reflecting the industry’s sensitivity to changes in economic conditions of fuel markets (see 

Figure 3-7). In contrast, the number of marginal gas wells has continually increased during the 

past decade; the IOGCC estimates that daily production levels from these wells reached a 

10-year high in 2005. Although we have been unable to find data on what fraction of these 

marginal wells are operated by small businesses, the IOGCC states that many are run by “mom 

and pop operators” (IOGCC, 2007). 
3-19 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

260

270

280

290

300

310

320

330

340

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Million Barrels

Marginal Oil Production

Marginal Gas Production

Figure 3-7. Trends in Marginal Oil and Gas Production: 1997 to 2006 

Source: Interstate Oil & Gas Compact Commission. 2008. “Marginal Wells: Fuel for Economic Growth.” Available 

at < http://iogcc.publishpath.com/Websites/iogcc/pdfs/2008-Marginal-Well-Report.pdf>. 

3.3 Pipeline Transportation of Natural Gas 

3.3.1 Overview 

Pipeline transportation of natural gas (NAICS 48621) is an industry group within the 

transportation and warehousing sector (NAICS 48-49), but more specifically in the pipeline 

transportation subsector (486). It includes the transmission of natural gas as well as the 

distribution of the gas through a local network to participating businesses. 

From 2002 to 2007, natural gas transportation revenues fell by 29% to just over $16 

billion ($2007) (Table 3-15). At the same time, payroll decreased by 14%, while the number of 

paid employees decreased by nearly 25%. The number of establishments also fell by 8% from 

1,701 establishments in 2002 to 1,560 in 2007. 

3.3.2 Goods and Services Used 

The BEA reports pipeline transportation of natural gas only for total pipeline 

transportation (3-digit NAICS 486). In addition to pipeline transportation of natural gas (NAICS 

4862), this industry includes pipeline transportation of crude oil (NAICS 4861) and other 

pipeline transportation (NAICS 4869). However, the BEA data are likely representative of the 

affected sector since pipeline transportation of natural gas accounts for 60% of NAICS 486 

establishments and 66% of revenues (Figures 1-8 and 1-9). 
3-20 

Table 3-15. Key Statistics: Pipeline Transportation of Natural Gas (NAICS 48621) ($2007) 

Year 1997 2002 

Revenue ($106) 22,964 16,368 

Payroll ($106) 2,438 2,086 

Employees 32,542 24,519 

Establishments 1,701 1,560 

Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Industry Series: Comparative Statistics for the United States (1997 NAICS 

Basis): 2002 and 1997. <http://factfinder.census.gov>; (December 12, 2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: EC0748I1: 

Transportation and Warehousing: Industry Series: Preliminary Summary Statistics for the United States: 2007.” 

http://factfinder.census.gov (January 27, 2010). 

60%

25%

15%

0%

10%

20%

30%

40%

50%

60%

70%

4862 Pipeline

Transportation of

Natural Gas

4869 Other Pipeline

Transportation

4861 Pipeline

Transportation of Crude

Oil

Figure 3-8. Distribution of Establishments within Pipeline Transportation (NAICS 486) 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Industry Series: Summary Statistics for the United States: 2002 

<http://factfinder.census.gov>; (January 27, 2010). 

In Table 3-16, we use the latest detailed benchmark input-output data report by the BEA 

(2002) to identify the goods and services used by pipeline transportation (NAICS 486). As 

shown, labor, refineries, and maintenance requirements represent significant share of the cost 

associated with pipeline transportation. Power and equipment requirements potentially associated 
3-21 

with reciprocating internal combustion engines (electric power generation and distribution) 

represent less than 2% of the value of services. 

66%

19%

15%

0%

10%

20%

30%

40%

50%

60%

70%

4862 Pipeline

Transportation of

Natural Gas

4869 Other Pipeline

Transportation

4861 Pipeline

Transportation of Crude

Oil

Figure 3-9. Distribution of Revenue within Pipeline Transportation (NAICS 486) 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Industry Series: Summary Statistics for the United States: 2002” 

<http://factfinder.census.gov>; (January 27, 2010). 

3.3.3 Business Statistics 

The pipeline transportation of natural gas is clearly concentrated in the two states closest 

to the refineries in the Gulf of Mexico. In 2002, Texas and Louisiana contributed to 31% of all 

pipeline transportation establishments in the United States (Figure 3-10) and 41% of all U.S. 

revenues. Other larger contributors with over 50 establishments in their states include Oklahoma, 

Pennsylvania, Kansas, Mississippi, and West Virginia. 

According to 2002 U.S. Census data, about 86% of transportation of natural gas 

establishments were owned by corporations and about 8% were owned by individual 

proprietorships. About 6% were owned by partnerships (Figure 3-11). As shown in Table 3-17, 

the four largest firms accounted for nearly half of the establishments, and just over half, 51%, of 

total revenue. The 50 largest firms accounted for over 1,354 establishments and about 99% of 

total revenue. The average number of employees per establishment was approximately 17 across 

all groups of firms. 
3-22 

Enterprises within pipeline transportation (NAICS 486) generated $11.1 billion in total 

receipts in 2008. Including those enterprises without net income, the industry averaged an aftertax profit margin of 9.6% (Table 3-18). 

Table 3-16. Direct Requirements for Pipeline Transportation (NAICS 486): 2002 

Commodity Commodity Description 

Direct 

Requirements 

Coefficientsa

V00100 Compensation of employees 14.78% 

324110 Petroleum refineries 13.55% 

230301 Nonresidential maintenance and repair 6.07% 

211000 Oil and gas extraction 4.94% 

333415 Air conditioning, refrigeration, and warm air heating equipment 

manufacturing 

4.40% 

561300 Employment services 4.26% 

5416A0 Environmental and other technical consulting services 3.04% 

541300 Architectural, engineering, and related services 3.04% 

420000 Wholesale trade 2.79% 

332310 Plate work and fabricated structural product manufacturing 2.72% 

5419A0 All other miscellaneous professional, scientific, and technical services 2.48% 

524100 Insurance carriers 2.38% 

531000 Real estate 2.33% 

52A000 Monetary authorities and depository credit intermediation 1.76% 

V00200 Taxes on production and imports, less subsidies 1.41% 

541100 Legal services 1.19% 

221100 Electric power generation, transmission, and distribution 1.13% 

a These values show the amount of the commodity required to produce $1.00 of the industry’s output. The values 

are expressed in percentage terms (coefficient ×100). 

Source: U.S. Bureau of Economic Analysis. 2002. 2002 Benchmark Input-Output Accounts: Detailed Make Table, 

Use Table and Direct Requirements Table. Tables 4 and 5. 

The 2002 SUSB shows that 47% of all firms in this industry made under $5 million in 

revenue. Enterprises with revenue over $100 million provided an overwhelming share of 

employment in this industry (98%) (Table 3-19). 
3-23 

Establishments by State

Less than 15

15 - 39

40 - 79

80 - 149

More than 150

Figure 3-10. 2002 Regional Distribution of Establishments: Pipeline Transportation 

(NAICS 486) 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48-49: 

Geographic Distribution—Pipeline transportation of natural gas: 2002. <http://factfinder.census.gov>; (November 

10, 2008). 

86%

8% 6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Corporations Individual Proprietorships Partnerships

Figure 3-11. Share of Establishments by Legal Form of Organization in the Pipeline 

Transportation of Natural Gas Industry (NAICS 48621): 2002 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48-49: 

Transportation and Warehousing: Subject Series—Estab & Firm Size: Legal Form of Organization for the United 

States: 2002. <http://factfinder.census.gov>; (December 12, 2008). 
3-24 

Table 3-17. Firm Concentration for Pipeline Transportation of Natural Gas (NAICS 

48621): 2002

Commodity Establishments 

Receipts/Revenue 

Number of 

Employees 

Employees per 

Amount ($10 Establishment 6) 

Percentage of 

Total 

All firms 1,431 $14,797 100% 23,677 16.5 

4 largest firms 698 $7,551 51% 11,814 16.9 

8 largest firms 912 $10,059 68% 15,296 16.8 

20 largest firms 1,283 $13,730 93% 21,792 17.0 

50 largest firms 1,354 $14,718 99% 23,346 17.2 

Source: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Subject Series—Estab & Firm Size: Concentration by Largest Firms for the 

United States: 2002” <http://factfinder.census.gov>; (December 12, 2008). 

Table 3-18. Aggregate Tax Data for Accounting Period 7/07–6/08: NAICS 486 

Number of enterprisesa

321 

Total receipts (103) $11,062,608 

Net sales (103

) $10,210,083 

Profit margin before tax 13.2% 

Profit margin after tax 9.6% 

a Includes corporations with and without net income. 

Source: Internal Revenue Service, U.S. Department of Treasury. 2010. “Corporation Source Book: Data Files 2004-

2007.” <http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (May 2, 2010). 
3-25 

Table 3-19. Key Enterprise Statistics by Receipt Size for Pipeline Transportation of Natural Gas (NAICS  Owned by Enterprises with

Variable 

All 

Enterprises 

0–99K 

Receipts 

100–

499.9K 

Receipts 

500–999.9K 

Receipts 

1,000–

4,999.9K 

Receipts 

5,000,000–

9,999,999K 

Receipts 

<10,000K 

Receipts 

10,000–

49,999K 

Receipts 

Firms 154 8 32 10 22 6 78 1Establishments 1,936 8 32 10 22 7 79 2Employment 37,450 15 58 69 138 88 368 216 Receipts ($103) $35,896,535 $524 $8,681 $7,451 $46,429 $40,967 $104,052 $188,424 

Receipts/firm ($103) $233,094 $66 $271 $745 $2,110 $6,828 $1,334 $17,129 

Receipts/establishment ($103) 

$18,542 $66 $271 $745 $2,110 $5,852 $1,317 $8,973 

Receipts/employment ($) 

$958,519 $34,933 $149,672 $107,986 $336,442 $465,534 $282,750 $872,333 

Source: U.S. Census Bureau. 2008b. Firm Size Data from the Statistics of U.S. Businesses, U.S. All Industries Tabulated by Receipt Si

http://www2.census.gov/csd/susb/2002/usalli_r02.xls. 
4-1 

SECTION 4

REGULATORY ALTERNATIVES, COSTS, AND EMISSION IMPACTS 

4.1 Background 

This action promulgates NESHAP for existing stationary SI RICE with a site rating of 

less than or equal to 500 HP located at major sources, and existing stationary SI RICE of any site 

rating located at area sources. EPA is finalizing these standards to meet its statutory obligation to 

address HAP emissions from these sources under sections 112(d), 112(c)(3) and 112(k) of the 

CAA. The final NESHAP for stationary RICE will be promulgated under 40 CFR part 63, 

subpart ZZZZ, which already contains standards applicable to new and reconstructed stationary 

RICE and some existing stationary RICE. 

EPA promulgated NESHAP for existing, new, and reconstructed stationary RICE greater 

than 500 HP located at major sources on June 15, 2004 (69 FR 33474). EPA promulgated 

NESHAP for new and reconstructed stationary RICE that are located at area sources of HAP 

emissions and for new and reconstructed stationary RICE that have a site rating of less than or 

equal to 500 HP that are located at major sources of HAP emissions on January 18, 2008 (73 FR 

3568). On March 3, 2010, EPA promulgated NESHAP for existing stationary compression 

ignition (CI) RICE with a site rating of less than or equal to 500 HP located at major sources, 

existing non-emergency CI engines with a site rating greater than 500 HP at major sources, and 

existing stationary CI RICE of any site rating located at area sources (75 FR 9674). 

4.2 Summary of the Final Rule 

4.2.1 What Is the Source Category Regulated by the Final Rule? 

The final rule addresses emissions from existing stationary SI engines less than or equal 

to 500 HP located at major sources and all existing stationary SI engines located at area sources. 

A major source of HAP emissions is generally a stationary source that emits or has the potential 

to emit 10 tons per year or more of any single HAP or 25 tons per year or more of any 

combination of HAP. An area source of HAP emissions is a stationary source that is not a major 

source. 

This action revises the regulations at 40 CFR part 63, subpart ZZZZ. Through this action, 

we are adding to 40 CFR part 63, subpart ZZZZ requirements for: existing SI stationary RICE 

less than or equal to 500 HP located at major sources of HAP and existing SI stationary RICE 

located at area sources of HAP. 
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4.2.1.1 Existing Stationary SI RICE ≤500 HP at Major Sources of HAP 

This action revises 40 CFR part 63, subpart ZZZZ, to address HAP emissions from 

existing stationary SI RICE less than or equal to 500 HP located at major sources of HAP. For 

stationary engines less than or equal to 500 HP at major sources, EPA must determine what is the 

appropriate maximum achievable control technology (MACT) for those engines under sections 

112(d)(2) and (d)(3) of the CAA. 

EPA has divided stationary SI RICE less than or equal to 500 HP located at major 

sources of HAP into the following subcategories: 

 Non-emergency 2-stroke lean burn (2SLB) stationary SI RICE 100-500 HP; 

 Non-emergency 4-stroke lean burn (4SLB) stationary SI RICE 100-500 HP; 

 Non-emergency 4-stroke rich burn (4SRB) stationary SI RICE 100-500 HP; 

 Non-emergency landfill and digester gas stationary SI RICE 100-500 HP; 

 Non-emergency stationary SI RICE <100 HP; and 

 Emergency stationary SI RICE. 

4.2.1.2 Existing Stationary SI RICE at Area Sources of HAP 

This action revises 40 CFR part 63, subpart ZZZZ, in order to address HAP emissions 

from existing stationary SI RICE located at area sources of HAP. Section 112(d) of the CAA 

requires EPA to establish NESHAP for both major and area sources of HAP that are listed for 

regulation under CAA section 112(c). As noted above, an area source is a stationary source that 

is not a major source. 

Section 112(k)(3)(B) of the CAA calls for EPA to identify at least 30 HAP that, as a 

result of emissions of area sources, pose the greatest threat to public health in the largest number 

of urban areas. EPA implemented this provision in 1999 in the Integrated Urban Air Toxics 

Strategy (64 FR 38715, July 19, 1999). Specifically, in the Strategy, EPA identified 30 HAP that 

pose the greatest potential health threat in urban areas, and these HAP are referred to as the “30 

urban HAP.” Section 112(c)(3) of the CAA requires EPA to list sufficient categories or 

subcategories of area sources to ensure that area sources representing 90 percent of the emissions 

of the 30 urban HAP are subject to regulation. EPA implemented these requirements through the 

Integrated Urban Air Toxics Strategy (64 FR 38715, July 19, 1999). The area source stationary 

engine source category was one of the listed categories. A primary goal of the Strategy is to 
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achieve a 75 percent reduction in cancer incidence attributable to HAP emitted from stationary 

sources. 

Under CAA section 112(d)(5), EPA may elect to promulgate standards or requirements 

for area sources “which provide for the use of generally available control technologies or 

management practices by such sources to reduce emissions of hazardous air pollutants.” 

Additional information on generally available control technologies (GACT) and management 

practices is found in the Senate report on the legislation (Senate report Number 101-228, 

December 20, 1989), which describes GACT as: 

. . . methods, practices and techniques which are commercially available and appropriate 

for application by the sources in the category considering economic impacts and the 

technical capabilities of the firms to operate and maintain the emissions control systems. 

Consistent with the legislative history, EPA can consider costs and economic impacts in 

determining GACT, which is particularly important when developing regulations for source 

categories, like this one, that have many small businesses. 

Determining what constitutes GACT involves considering the control technologies and 

management practices that are generally available to the area sources in the source category. 

EPA also considers the standards applicable to major sources in the same industrial sector to 

determine if the control technologies and management practices are transferable and generally 

available to area sources. In appropriate circumstances, EPA may also consider technologies and 

practices at area and major sources in similar categories to determine whether such technologies 

and practices could be considered generally available for the area source category at issue. 

Finally, as EPA has already noted, in determining GACT for a particular area source category, 

EPA considers the costs and economic impacts of available control technologies and 

management practices on that category. 

The urban HAP that must be regulated from stationary SI RICE to achieve the CAA 

section 112(c)(3) requirement to regulate categories accounting for 90 percent of the urban HAP 

are: 7 polycyclic aromatic hydrocarbons (PAH), formaldehyde, and acetaldehyde. 

Similar to existing stationary SI RICE at major sources, EPA has also divided the 

existing stationary SI RICE at area sources into subcategories in order to properly take into 

account the differences between these engines. The subcategories for stationary SI RICE at area 

sources are as follows: 

 Non-emergency 2SLB stationary SI RICE 
4-4 

 Non-emergency 4SLB stationary SI RICE 

– ≤500 HP 

– >500 HP 

 Non-emergency 4SRB stationary SI RICE 

– ≤500 HP 

– >500 HP 

 Non-emergency landfill and digester gas stationary SI RICE 

 Emergency stationary SI RICE. 

4.2.2 What Are the Pollutants Regulated by the Rule? 

The final rule regulates emissions of HAP. Available emissions data show that several 

HAP, which are formed during the combustion process or which are contained within the fuel 

burned, are emitted from stationary engines. The HAP which have been measured in emission 

tests conducted on SI stationary RICE include: formaldehyde, acetaldehyde, acrolein, methanol, 

benzene, toluene, 1,3-butadiene, 2,2,4-trimethylpentane, hexane, xylene, naphthalene, PAH, 

methylene chloride, and ethylbenzene. EPA described the health effects of these HAP and other 

HAP emitted from the operation of stationary RICE in the preamble to 40 CFR part 63, subpart 

ZZZZ, published on June 15, 2004 (69 FR 33474). These HAP emissions are known to cause, or 

contribute significantly to air pollution, which may reasonably be anticipated to endanger public 

health or welfare. 

For the standards being finalized in this action, EPA believes that previous 

determinations regarding the appropriateness of using formaldehyde and carbon monoxide (CO) 

both in concentration (parts per million [ppm]) levels as surrogates for HAP for stationary RICE 

are still valid. Consequently, EPA is promulgating CO or formaldehyde standards in order to 

regulate HAP emissions. 

In addition to reducing HAP, the emission control technologies that will be installed on 

stationary RICE to reduce HAP will also reduce CO and VOC, and for rich burn engines will 

also reduce NOx. 
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4.2.3 What Are the Final Requirements? 

4.2.3.1 Existing Stationary SI RICE ≤500 HP at Major Sources 

The numerical emission standards that are being finalized for existing stationary nonemergency SI RICE less than or equal to 500 HP located at major sources of HAP are shown in 

Table 4-1. The emission standards are in units of ppm by volume, dry basis (ppmvd). 

Table 4-1. Emission Standards for Existing Stationary SI RICE ≤500 HP Located at 

Major Sources of HAP 

Subcategory Except during Periods of Startup 

2SLB Non-Emergency 100≤HP≤500 225 ppmvd CO at 15% O2 

4SLB Non-Emergency 100≤HP≤500 47 ppmvd CO at 15% O2 

4SRB Non-Emergency 100≤HP≤500 10.3 ppmvd formaldehyde at 15% O2 

Landfill/Digester Gas Non-Emergency 100≤HP≤500 177 ppmvd CO at 15% O2

EPA is finalizing work practice standards for existing emergency stationary SI RICE less 

than or equal to 500 HP located at major sources of HAP and existing non-emergency stationary 

SI RICE less than 100 HP located at major sources of HAP. Existing stationary emergency SI 

RICE less than or equal to 500 HP located at major sources of HAP are subject to the following 

work practices: 

 Change oil and filter every 500 hours of operation or annually, whichever comes first, 

except that sources can extend the period for changing the oil if the oil is part of an oil 

analysis program as discussed below and none of the condemning limits are 

exceeded; 

 Inspect spark plugs every 1000 hours of operation or annually, whichever comes first, 

and replace as necessary; and 

 Inspect all hoses and belts every 500 hours of operation or annually, whichever comes 

first, and replace as necessary. 

Existing stationary SI RICE less than 100 HP located at major sources of HAP that are 

not 2SLB stationary RICE are subject to the following work practices: 

 Change oil and filter every 1,440 hours of operation or annually, whichever comes 

first, except that sources can extend the period for changing the oil if the oil is part of 
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an oil analysis program as discussed below and none of the condemning limits are 

exceeded; 

 Inspect spark plugs every 1,440 hours of operation or annually, whichever comes 

first, and replace as necessary; and 

 Inspect all hoses and belts every 1,440 hours of operation or annually, whichever 

comes first, and replace as necessary. 

Existing 2SLB stationary SI RICE less than 100 HP located at major sources of HAP are subject 

to the following work practices: 

 Change oil and filter every 4,320 hours of operation or annually, whichever comes 

first, except that sources can extend the period for changing the oil if the oil is part of 

an oil analysis program as discussed below and none of the condemning limits are 

exceeded; 

 Inspect spark plugs every 4,320 hours of operation or annually, whichever comes 

first, and replace as necessary; and 

 Inspect all hoses and belts every 4,320 hours of operation or annually, whichever 

comes first, and replace as necessary. 

Sources also have the option to use an oil change analysis program to extend the oil 

change frequencies specified above. The analysis program must at a minimum analyze the 

following three parameters: Total Acid Number, viscosity, and percent water content. The 

analysis must be conducted at the same frequencies specified for changing the engine oil. If the 

condemning limits provided below are not exceeded, the engine owner or operator is not 

required to change the oil. If any of the condemning limits are exceeded, the engine owner or 

operator must change the oil within two days of receiving the results of the analysis; if the engine 

is not in operation when the results of the analysis are received, the engine owner or operator 

must change the oil within two days or before commencing operation, whichever is later. The 

condemning limits are as follows: 

 Total Acid Number increases by more than 3.0 milligrams (mg) potassium hydroxide 

per gram (KOH/g) from Total Acid Number of the oil when new; or 

 viscosity of the oil changes by more than 20 percent from the viscosity of the oil 

when new; or 

 percent water content (by volume) is greater than 0.5. 

Pursuant to the provisions of 40 CFR 63.6(g), sources can also request that the 

Administrator approve alternative work practices. 
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4.2.3.2 Existing Stationary SI RICE at Area Sources of HAP. 

The numerical emission standards that EPA is finalizing for non-emergency 4SLB 

stationary SI RICE and non-emergency 4SRB stationary SI RICE located at area sources of HAP 

are shown in Table 4-2. 

Table 4-2. Numerical Emission Standards for Existing Non-Emergency Stationary 4SLB 

and 4SRB SI RICE >500 HP Located at Area Sources of HAP 

Subcategory Except during Periods of Startup 

4SLB Non-Emergency >500 HP that operate more than 

24 hours per calendar year 

47 ppmvd CO at 15% O2 or 93% CO reduction 

4SRB Non-Emergency >500 HP that operate more than 

24 hours per calendar year 

2.7 ppmvd formaldehyde at 15% O2 or 76% 

formaldehyde reduction 

EPA is finalizing management practices for existing non-emergency 4SLB stationary SI 

RICE less than or equal to 500 HP located at area sources of HAP, existing non-emergency 

4SLB stationary SI RICE greater than 500 HP located at area sources of HAP that operate 24 

hours or less per calendar year, existing non-emergency 4SRB stationary SI RICE less than or 

equal to 500 HP located at area sources of HAP, existing non-emergency 4SRB stationary SI 

RICE greater than 500 HP located at area sources of HAP that operate 24 hours or less per 

calendar year, existing 2SLB non-emergency stationary SI RICE located at area sources of HAP, 

existing non-emergency landfill and digester gas stationary RICE located at area sources of 

HAP, and existing emergency stationary SI RICE located at area sources of HAP. 

Existing non-emergency 4SLB and 4SRB stationary SI RICE less than or equal to 500 

HP located at area sources of HAP and existing landfill or digester gas non-emergency stationary 

SI RICE located at area sources of HAP are subject to the following management practices: 

 Change oil and filter every 1,440 hours of operation or annually, whichever comes 

first, except that sources can extend the period for changing the oil if the oil is part of 

an oil analysis program as discussed below and none of the condemning limits are 

exceeded; 

 Inspect spark plugs every 1,440 hours of operation or annually, whichever comes 

first, and replace as necessary; and 

 Inspect all hoses and belts every 1,440 hours of operation or annually, whichever 

comes first, and replace as necessary. 
4-8 

Existing stationary 2SLB non-emergency engines located at area sources of HAP are 

subject to the following work practices: 

 Change oil and filter every 4,320 hours of operation or annually, whichever comes 

first, except that sources can extend the period for changing the oil if the oil is part of 

an oil analysis program as discussed below and none of the condemning limits are 

exceeded; 

 Inspect spark plugs every 4,320 hours of operation or annually, whichever comes 

first, and replace as necessary; and 

 Inspect all hoses and belts every 4,320 hours of operation or annually, whichever 

comes first, and replace as necessary. 

Existing stationary emergency SI RICE located at area sources of HAP and existing nonemergency 4SLB and 4SRB stationary SI RICE greater than 500 HP located at area sources of 

HAP that operate 24 hours or less per calendar year are subject to the following work practices: 

 Change oil and filter every 500 hours of operation or annually, whichever comes first, 

except that sources can extend the period for changing the oil if the oil is part of an oil 

analysis program as discussed below and none of the condemning limits are 

exceeded; 

 Inspect spark plugs every 1,000 hours of operation or annually, whichever comes 

first, and replace as necessary; and 

 Inspect all hoses and belts every 500 hours of operation or annually, whichever comes 

first, and replace as necessary. 

As discussed above for major sources, these sources may utilize an oil analysis program 

in order to extend the specified oil change requirement specified above. Also, sources have the 

option to work with state permitting authorities pursuant to EPA’s regulations at 40 CFR subpart 

E (“Approval of State Programs and Delegation of Federal Authorities”) for approval of 

alternative management practices. 40 CFR subpart E implements section 112(l) of the CAA, 

which authorizes EPA to approve alternative state/local/tribal HAP standards or programs when 

such requirements are demonstrated to be no less stringent than EPA promulgated standards. 

4.2.3.3 Startup Requirements 

Existing stationary SI RICE less than or equal to 500 HP located at major sources of HAP 

and existing stationary SI RICE located at area sources of HAP must meet specific operational 

standards during engine startup. Engine startup is defined as the time from initial start until 

applied load and engine and associated equipment reaches steady state or normal operation. For 
4-9 

stationary engines with catalytic controls, engine startup means the time from initial start until 

applied load and engine and associated equipment reaches steady state, or normal operation, 

including the catalyst. Owners and operators must minimize the engine’s time spent at idle and 

minimize the engine’s startup to a period needed for appropriate and safe loading of the engine, 

not to exceed 30 minutes, after which time the engine must meet the otherwise applicable 

emission standards. These requirements will limit the HAP emissions during periods of engine 

startup. Pursuant to the provisions of 40 CFR 63.6(g), engines at major sources may petition the 

Administrator for an alternative work practice. An owner or operator of an engine at an area 

source can work with its State permitting authority pursuant to EPA’s regulations at 40 CFR 

subpart E for approval of an alternative management practice. See 40 CFR subpart E (setting 

forth requirements for, among other things, equivalency by permit, rule substitution). 

4.2.4 What Are the Operating Limitations? 

In addition to the standards discussed above, EPA is finalizing operating limitations for 

stationary non-emergency 4SLB and 4SRB RICE that are greater than 500 HP and located at an 

area source of HAP and operated more than 24 hours per calendar year. Owners and operators of 

engines that are equipped with oxidation catalyst or non-selective catalytic reduction (NSCR) 

must maintain the catalyst so that the pressure drop across the catalyst does not change by more 

than 2 inches of water from the pressure drop across the catalyst that was measured during the 

initial performance test. If the engine is equipped with oxidation catalyst, owners and operators 

must also maintain the temperature of the stationary RICE exhaust so that the catalyst inlet 

temperature is between 450 and 1,350 degrees Fahrenheit (°F). If the engine is equipped with 

NSCR, owners and operators must maintain the temperature of the stationary RICE exhaust so 

that the NSCR inlet temperature is between 750 and 1,250 °F. Owners and operators may 

petition for a different temperature range; the petition must demonstrate why it is operationally 

necessary and appropriate to operate below the temperature range specified in the final rule (see 

40 CFR 63.8(f)). Owners and operators of engines that are not using oxidation catalyst or NSCR 

must comply with any operating limitations approved by the Administrator. 

4.2.5 What Are the Requirements for Demonstrating Compliance? 

The following sections describe the requirements for demonstrating compliance under the 

final rule. 

4.2.5.1 Existing Stationary SI RICE ≤500 at Major Sources of HAP. 

Owners and operators of existing stationary non-emergency SI RICE located at major 

sources that are less than 100 HP and existing stationary emergency SI RICE located at major 
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sources must operate and maintain their stationary RICE and aftertreatment control device (if 

any) according to the manufacturer’s emission-related written instructions or develop their own 

maintenance plan. The maintenance plan must specify how the work practices will be met and 

provide to the extent practicable for the maintenance and operation of the engine in a manner 

consistent with good air pollution control practices for minimizing emissions. Owners and 

operators of existing stationary non-emergency SI RICE located at major sources that are less 

than 100 HP and existing stationary emergency SI RICE located at major sources do not have to 

conduct any performance testing because they are not subject to numerical emission standards. 

Owners and operators of existing stationary non-emergency SI RICE located at major 

sources that are greater than or equal to 100 HP and less than or equal to 500 HP must conduct 

an initial performance test to demonstrate that they are achieving the required emission 

standards. 

4.2.5.2 Existing Stationary SI RICE at Area Sources of HAP 

Owners and operators of existing stationary RICE located at area sources of HAP that are 

subject to management practices do not have to conduct any performance testing; they must 

develop a maintenance plan that specifies how the management practices will be met and 

provides to the extent practicable for the maintenance and operation of the engine in a manner 

consistent with good air pollution control practices for minimizing emissions. Owners and 

operators of existing 4SLB and 4SRB non-emergency stationary SI RICE that are greater than 

500 HP and located at an area source of HAP , and operated more than 24 hours per calendar 

year must conduct an initial performance test to demonstrate compliance with the applicable 

emission limitations and must conduct subsequent performance testing every 8,760 hours of 

operation or 3 years, whichever comes first. Owners and operators of existing 4SLB and 4SRB 

non-emergency stationary SI RICE that are greater than 500 HP and located at an area source of 

HAP , and operated more than 24 hours per calendar year must continuously monitor and record 

the inlet temperature of the oxidation catalyst or NSCR and also take monthly measurements of 

the pressure drop across the oxidation catalyst or NSCR. If an oxidation catalyst or NSCR is not 

being used on the engine, the owner or operator must continuously monitor and record the 

operating parameters (if any) approved by the Administrator. As discussed in the March 3, 2010, 

final NESHAP for existing stationary CI RICE (75 FR 9648) and in section V.E. of the 

preamble, EPA is finalizing performance specification requirements in 40 CFR part 63, subpart 

ZZZZ for the continuous parametric monitoring systems used for continuous catalyst inlet 

temperature monitoring. 
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4.2.6 What Are the Reporting and Recordkeeping Requirements? 

The following sections describe the reporting and recordkeeping requirements that are 

required under the final rule. 

Owners and operators of existing stationary emergency SI RICE that do not meet the 

requirements for non-emergency engines are required to keep records of their hours of operation. 

Owners and operators of existing stationary emergency SI RICE must install a non-resettable 

hour meter on their engines to record the hours of operation of the engine. 

Owners and operators of existing stationary SI RICE located at major sources that are 

subject to work practices and existing stationary SI RICE located at area sources that are subject 

to management practices are required to keep records that show that the work or management 

practices that are required are being met. These records must include, at a minimum: oil and 

filter change dates and engine hours of operation; inspection and replacement dates for spark 

plugs, hoses, and belts; and records of other emission-related repairs and maintenance 

performed. 

In terms of reporting requirements, owners and operators of existing non-emergency 

stationary SI RICE greater than or equal to 100 HP and less than or equal to 500 HP located at 

major sources of HAP and existing non-emergency 4SLB and 4SRB stationary RICE greater 

than 500 HP located at area sources of HAP that are operated more than 24 hours per calendar 

year must submit the notifications required in Table 8 of 40 CFR part 63, subpart ZZZZ, which 

lists the NESHAP General Provisions applicable to this rule. (40 CFR part 63, subpart A) These 

notifications include an initial notification, notification of performance test, and a notification of 

compliance for each stationary RICE which must comply with the specified emission limitations. 

Owners and operators of existing stationary non-emergency SI RICE greater than or equal to 100 

HP and less than or equal to 500 HP located at major sources of HAP and existing stationary 

4SLB and 4SRB non-emergency SI RICE greater than 500 HP located at area sources of HAP 

that are operated more than 24 hours per calendar year must submit semiannual compliance 

reports. 

4.3 Summary of Significant Changes Since Proposal 

4.3.1 Applicability 

A change from the proposal is that the final rule is not applicable to existing stationary 

emergency engines at area sources that are located at residential, commercial, or institutional 

facilities. These engines are not subject to any requirements under the final rule because they are 
4-12 

not part of the regulated source category. EPA has found that existing stationary emergency 

engines located at residential, commercial, and institutional facilities that are area sources were 

not included in the original Urban Air Toxics Strategy inventory and were not included in the 

listing of urban area sources. More information on this issue can be found in the memorandum 

titled, “Analysis of the Types of Engines Used to Estimate the CAA Section 112(k) Area Source 

Inventory for Stationary Reciprocating Internal Combustion Engines,” available from the 

rulemaking docket. In the March 3, 2010, final NESHAP for existing stationary CI RICE (75 FR 

9648), EPA included a definition for residential/commercial/institutional emergency stationary 

RICE. After the final rule was promulgated, EPA received numerous questions regarding the 

definition and whether certain types of facilities would meet the definition. In the final rule, EPA 

is separating the definition into individual definitions for residential emergency stationary RICE, 

commercial emergency stationary RICE, and institutional emergency stationary RICE, and is 

also providing additional examples of the types of facilities that would be included under those 

categories in the definitions. EPA has also prepared a memorandum to provide further guidance 

regarding the types of facilities that would or would not be considered residential, commercial, 

or institutional facilities. The memorandum is titled, “Guidance Regarding Definition of 

Residential, Commercial, and Institutional Emergency Stationary RICE in the NESHAP for 

Stationary RICE,” and is available in the rulemaking docket. 

4.3.2 Final Emission Standards 

4.3.2.1 Existing Stationary SI Engines ≤500 HP Located at Major Sources of HAP 

EPA is revising the emission standards that it proposed for the subcategories of stationary 

SI engines less than or equal to 500 HP located at major sources. As discussed in section V.B. of 

the preamble, numerous commenters indicated that EPA’s dataset used to establish the proposed 

emission limits was insufficient and urged EPA to gather more data to obtain a more complete 

representation of emissions from existing stationary SI engines. Commenters also questioned the 

emission standard setting approach that EPA used at proposal and claimed that the proposed 

standards did not take into account emissions variability. For the final rule, EPA has obtained 

additional test data for existing stationary SI engines and has included this additional data in the 

MACT floor analysis. EPA is also using an approach that better considers emissions variability, 

as discussed below. EPA is also not using the Population Database to determine a percentage of 

engines that have emission controls installed, as it did at proposal. The Population Database has 

not been updated since 2000. It contains information regarding whether or not an engine has 

emission controls, but does not generally contain other types of emission-related information, 

like engine-out emissions or operational controls, and it does not include any emissions 
4-13 

concentration data, which is necessary to determine the MACT floor. EPA determined that it 

would be more appropriate and more defensible to base the MACT floor analysis directly on the 

emissions data that EPA has for stationary SI engines. 

For 2SLB non-emergency engines, EPA proposed a limit of 85 ppmvd CO for engines 

from 50 to 249 HP and 8 ppmvd CO or 90 percent CO reduction for engines greater than or 

equal to 250 HP. EPA is finalizing an emission limit of 225 ppmvd CO for 2SLB non-emergency 

engines from 100 to 500 HP. For 4SLB non-emergency engines, EPA proposed a limit of 95 

ppmvd CO for engines from 50 to 249 HP and 9 ppmvd CO or 90 percent CO reduction for 

engines greater than or equal to 250 HP. EPA is finalizing an emission limit of 47 ppmvd CO for 

4SLB non-emergency engines from 100 to 500 HP. For 4SRB non-emergency engines from 50 

to 500 HP, EPA proposed an emission limit of 200 ppbvd (parts per billion by volume, dry basis) 

formaldehyde or 90 percent formaldehyde reduction. EPA is finalizing an emission limit of 10.3 

ppmvd formaldehyde for 4SRB non-emergency engines from 100 to 500 HP. For landfill and 

digester gas engines, EPA proposed an emission limit of 177 ppmvd CO; EPA is finalizing an 

emission limit of 177 ppmvd CO. 

For the proposed rule, EPA required existing stationary engines less than 50 HP that are 

located at major sources to meet a formaldehyde emission standard. As discussed in the final rule 

published on March 3, 2010, for existing stationary CI RICE (75 FR 9674), EPA is not finalizing 

a formaldehyde emission standard for stationary SI engines less than 50 HP, but is instead 

requiring compliance with work practices. In addition, in light of several comments asserting that 

the level at which EPA subcategorized small engines at major sources was inappropriate, EPA is 

finalizing a work practice standard for engines less than 100 HP. These work practices are 

described in section III.C. of the preamble to the final rule. EPA believes that work practices are 

appropriate and justified for this group of stationary engines because the application of 

measurement methodology is not practicable due to technological and economic limitations. 

Further information on EPA’s decision can be found in the memorandum titled, “MACT Floor 

and MACT Determination for Existing Stationary Non-Emergency SI RICE <100 HP and 

Existing Stationary Emergency SI RICE Located at Major Sources and GACT for Existing 

Stationary SI RICE Located at Area Sources,” which is available from the rulemaking docket. 

For existing stationary emergency engines located at major sources, EPA proposed that 

these engines be subject to a 2 ppmvd formaldehyde emission standard. In the final rule, existing 

stationary emergency SI engines located at major sources of HAP must meet work practices. 

These work practices are described in section III.C. of the preamble to the final rule. EPA 

believes that work practices are appropriate and justified for this group of stationary engines 
4-14 

because the application of measurement methodology is not practicable due to technological and 

economic limitations. Further information on EPA’s decision can be found in the memorandum 

titled, “MACT Floor and MACT Determination for Existing Stationary Non-Emergency SI 

RICE <100 HP and Existing Stationary Emergency SI RICE Located at Major Sources and 

GACT for Existing Stationary SI RICE Located at Area Sources,” which is available from the 

rulemaking docket. 

4.3.2.2 Existing Stationary SI Engines Located at Area Sources of HAP 

EPA proposed numerical emission standards for the following stationary SI engines 

located at area sources of HAP: non-emergency 2SLB and 4SLB greater than or equal to 250 

HP, non-emergency 4SRB greater than or equal to 50 HP, landfill and digester gas fired greater 

than 500 HP, and emergency greater than 500 HP. For the remaining engines at area sources, 

EPA proposed management practice standards. 

In the final rule, EPA is promulgating numerical emission standards for non-emergency 

4SLB and 4SRB stationary SI RICE larger than 500 HP located at area sources of HAP 

emissions that operate more than 24 hours per calendar year. For non-emergency 4SLB engines 

greater than 500 HP located at area sources of HAP, EPA proposed an emission limit of 9 ppmvd 

CO or 90 percent CO reduction; EPA is finalizing an emission limit of 47 ppmvd CO or 93 

percent CO reduction. For non-emergency 4SRB engines greater than 500 HP located at area 

sources of HAP, EPA proposed an emission limit of 200 ppbvd formaldehyde or 90 percent 

formaldehyde reduction and is finalizing an emission limit of 2.7 ppmvd formaldehyde or 76 

percent formaldehyde reduction. For stationary SI RICE located at area sources of HAP that are 

non-emergency 2SLB stationary SI RICE greater than or equal to 250 HP, non-emergency 4SLB 

stationary SI RICE between 250 and 500 HP, non-emergency 4SRB stationary SI RICE between 

50 and 500 HP, landfill/digester gas stationary SI RICE greater than 500 HP, or emergency 

stationary SI RICE greater than 500 HP, EPA is finalizing management practices rather than 

numeric emission limitations as proposed. EPA is also finalizing management practices for nonemergency 4SLB and 4SRB stationary SI RICE that are greater than 500 HP, located at area 

sources of HAP, and operated 24 hours or less per calendar year. 

4.3.3 Management Practices 

EPA proposed management practices for several subcategories of engines located at area 

sources. EPA explained that the proposed management practices would be expected to ensure 

that emission control systems are working properly and would help minimize HAP emissions 

from the engines. EPA proposed specific maintenance practices and asked for comments on the 
4-15 

need and appropriateness for those procedures. Based on feedback received during the public 

comment period, which included information submitted in comment letters and additional 

information EPA received following the close of the comment period from different industry 

groups, EPA is finalizing management practices for existing stationary 2SLB non-emergency SI 

engines located at area sources of HAP, existing stationary 4SLB and 4SRB non-emergency SI 

engines less than or equal to 500 HP located at area sources of HAP; existing stationary landfill 

and digester gas non-emergency engines located at area sources of HAP; and all existing 

emergency stationary SI engines located at area sources of HAP. 

Based on the comments on the proposal and additional information received from 

stakeholders, EPA made changes to the intervals for the management practices from the 

proposal. EPA is also adding an option for sources to use an oil change analysis program to 

extend the oil change frequencies specified above. The analysis program must at a minimum 

analyze the following three parameters: Total Acid Number, viscosity, and percent water 

content. If the condemning limits for these parameters are not exceeded, the engine owner or 

operator is not required to change the oil. If any of the limits are exceeded, the engine owner or 

operator must change the oil within two days of receiving the results of the analysis; if the engine 

is not in operation when the results of the analysis are received, the engine owner or operator 

must change the oil within two days or before commencing operation, whichever is later. Owners 

and operators of all engines subject to management practices also have the option to work with 

State permitting authorities pursuant to EPA’s regulations at 40 CFR subpart E for alternative 

management practices to be used instead of the specific management practices promulgated in 

the final rule. The management practices must be at least as stringent as those specified in the 

final rule. 

4.3.4 Startup, Shutdown, and Malfunction 

EPA proposed formaldehyde and CO emission standards for existing stationary engines 

at major sources to apply during periods of startup and malfunction. EPA also proposed certain 

standards for existing stationary engines at area sources that would apply during startup and 

malfunction. EPA did not propose distinct standards for periods of shutdown. EPA proposed that 

engines would be subject to the same standards during shutdown as are applicable during other 

periods of operation. 

Based on various comments and concerns with the proposed emission standards for 

periods of startup, EPA has determined that it is not feasible to finalize numerical emission 

standards that would apply during startup because the application of measurement methodology 
4-16 

to this operation is not practicable due to technological and economic limitations. This issue is 

discussed in detail in the final rule published on March 3, 2010 (75 FR 9674), and as discussed in 

the Response to Comments for this rule, the analysis is the same for the engines regulated in this 

final rule. 

As a result, EPA is extending the operational standards during startup it promulgated in 

the March 3, 2010, final rule (75 FR 9674), which specify that owners and operators must limit 

the engine startup time to no more than 30 minutes and must minimize the engine’s time spent at 

idle during startup, to the engines newly subject to regulation in this rule. 

With respect to malfunctions, EPA proposed two options for subcategories where the 

proposed emission standard was based on the use of catalytic controls. The first proposed option 

was to have the same standards apply during normal operation and malfunctions. The second 

proposed option was that standards during malfunctions be based on emissions expected from 

the best controlled sources prior to the full warm-up of the catalytic control. For subcategories 

where the proposed emission standard was not based on the use of catalytic controls, we 

proposed the same emission limitations apply during malfunctions and periods of normal 

operations. EPA is finalizing the first option described above, which is that the same standards 

apply during normal operation and malfunctions. In the proposed rule, EPA expressed the view 

that there are different modes of operation for any stationary source, and that these modes 

generally include startup, normal operations, shutdown, and malfunctions. However, as 

discussed in detail in the final rule published on March 3, 2010 (75 FR 9674), and as discussed in 

the Response to Comments for this rule, after considering the issue of malfunctions more 

carefully, EPA has determined that malfunctions should not be viewed as a distinct operating 

mode and, therefore, any emissions that occur at such times do not need to be factored into 

development of CAA section 112(d) standards, which, once promulgated, apply at all times. In 

addition, as discussed in detail in the final rule published on March 3, 2010 (75 FR 9674), and as 

discussed in the Response to Comments for this rule, EPA believes that malfunctions will not 

cause stationary engines to violate the standard that applies during normal operations. Therefore, 

the standards that apply during normal operation also apply during malfunction. 

4.3.5 Method 323 

EPA proposed to remove Method 323 as an option for determining compliance with 

formaldehyde emission limitations in 40 CFR part 63, subpart ZZZZ. EPA Method 323 was first 

proposed as part of the NESHAP for Stationary Combustion Turbines published January 14, 

2003, (68 FR 1888) for measuring formaldehyde emissions from natural gas-fired sources. 
4-17 

However, the method was not included in the final Stationary Combustion Turbines NESHAP 

due to reliability concerns and EPA never promulgated EPA Method 323 as a final standard in 

40 CFR part 63, appendix A. Due to unresolved technical issues with the method affecting 

engine test results, EPA found it appropriate to propose to remove the method from 40 CFR part 

63, subpart ZZZZ. As discussed in greater detail in section V.D. of the preamble, after EPA 

proposed to remove Method 323 as a compliance test Method, the Agency received test data 

comparing Method 323 to EPA Method 320. The results of this comparison testing showed good 

agreement between the two methods and there was no evidence of bias in the results from 

Method 323. Therefore, EPA has determined that it is appropriate to promulgate Method 323 and 

to allow it as an option for measuring formaldehyde in 40 CFR part 63 subpart ZZZZ. 

4.4 Cost Impacts 

4.4.1 Introduction 

EPA has determined that oxidation catalysts for two-stroke lean burn (2SLB) and fourstroke lean burn (4SLB) engines, and non-selective catalytic reduction (NSCR) for four-stroke 

rich burn (4SRB) engines are applicable controls for HAP reduction from existing stationary SI 

RICE. To determine the capital and annual costs for these control technologies, equipment cost 

information was obtained from industry groups2

 and vendors and manufacturers of SI engine 

control technology. In some cases, the industry groups provided a breakdown of the capital and 

annual cost components for each of the retrofit options. Using this cost data, annualized cost and 

capital cost equations for oxidation catalysts and NSCR were developed. 

4.4.2 Control Cost Methodology 

The following sections describe the methodology used to derive the total capital and total 

annual costs for each of the control technology options. These methodologies were used to 

calculate total capital and total annual costs when only purchased equipment costs were available 

(e.g., vendor equipment costs). The methodologies were not used for cost data provided by 

industry groups because they included a breakdown of the actual total capital and total annual 

costs. A summary of the methodologies, equations, and assumptions used to estimate the total 

capital and total annual costs for some of the cost data are described in the following sections. 

 

2

 Reciprocating Internal Combustion Engine National Emission Standards for Hazardous Air Pollutants (RICE 

NESHAP) Proposed Revisions – Emission Control Costs Analysis Background for “Above the Floor” Emission 

Controls for Natural Gas-Fired RICE, Innovative Environmental Solutions Inc., October 2009. (EPA-HQ-OAR2008-0708-0279). 
4-18 

4.4.2.1 Total Capital Costs 

The total capital cost includes the direct and indirect costs of purchasing and installing 

the control equipment. The direct cost includes the cost of purchasing the equipment and 

instrumentation, cost of shipping, and the cost of installing the control equipment. The indirect 

cost includes the costs for engineering, contractor fees, testing costs, and also includes costs for 

contingencies, such as additional modifications, or delays in startup. The total capital cost 

equation can be summarized as follows: 

Total Capital Cost (TCC) = Direct Costs (DC) + Indirect Costs (IC) 

The direct costs include the costs of purchasing and installing the control equipment and can be 

summarized using the following equation; 

DC = Purchased Equipment Cost (PEC) + Direct Installation Costs (DIC). 

A summary of the cost assumptions for PEC includes the following: 

 Control Device and Auxiliary Equipment (EC); 

 Instrumentation (10% of EC); 

 Sales Tax (3% of EC); 

 Freight (5% of EC); 

and can be summarized as: 

PEC = 118% EC. 

A summary of the cost assumptions for DIC includes the following: 

 Foundations and Supports (8% of PEC); 

 Handling and Erection (14% of PEC); 

 Electrical (4% of PEC); 

 Piping (2% of PEC); 

 Insulation for Ductwork (1% of PEC); 

 Painting (1% of PEC); 

and can be summarized as: 
4-19 

DIC = 30% PEC = 0.3 PEC. 

Therefore, the direct costs can be simplified using the following equation: 

DC = PEC + 0.3 PEC = 1.3 PEC. 

The indirect costs include the costs of engineering and contractor fees and contingencies and can 

be summarized using the following equation: 

IC = Indirect Installation Costs (ICC) + Contingencies (C). 

A summary of the cost assumptions for ICC includes the following: 

 Engineering (10% of PEC); 

 Construction and Field Expenses (5% of PEC); 

 Contractor Fees (10% of PEC); 

 Startup (2% of PEC); 

 Performance Test (1% of PEC); 

and can be summarized as: 

IIC = 28% PEC = 0.28 PEC. 

A summary of the cost assumptions for C includes the following: 

 Equipment Redesign and Modifications; 

 Cost Escalations; 

 Delays in Startup; 

and is assumed to be: 

C = 3% PEC = 0.03 PEC. 

Therefore, the IC can be summarized using the following equation: 

IC = 0.28 PEC + 0.03 PEC = 0.31 PEC, 

and the simplified TCC equation can be expressed as: 
4-20 

TCC = 1.3 PEC + 0.31 PEC = 1.61 PEC = 1.61 (1.18 EC) = 1.9 EC 

4.4.2.2 Total Annual Costs 

The total annual cost includes the direct and indirect annual costs of operating and 

maintaining the control equipment. The direct annual cost includes the cost of the utilities, 

operating labor, and control device cleaning and maintenance. The indirect annual cost includes 

the overhead costs such as spare parts for the control equipment, administrative charges, and the 

capital recovery of the control technology. The total annual cost equation can be summarized as 

follows: 

Total Annual Cost (TAC) = Direct Annual Costs (DAC) + Indirect Annual Costs (IAC). 

The DAC includes the following parameters: 

 Utilities; 

 Operating Labor; 

 Maintenance; 

 Annual Compliance Test; 

 Catalyst Cleaning; 

 Catalyst Replacement; 

 Catalyst Disposal. 

The IAC includes the following parameters: 

 Overhead; 

 Fuel Penalty; 

 Property Tax; 

 Insurance; 

 Administrative Charges; 

 Capital Recovery = {I(1+I)n

/((1+I)n-1)*TCC} where I is the interest rate, and n is the 

equipment life. 

To calculate DAC, the costs were broken up into three separate costs: operation and 

maintenance materials cost, operation and maintenance labor cost, and the cost for annual 
4-21 

performance testing or downtime or allowance for catalyst washing. Actual annual cost data 

from the industry groups were used to estimate the DAC for each of the control technologies. 

The IAC was broken up into three separate costs: administrative, fuel penalty, and capital 

recovery. Again, cost data from the industry groups was used to estimate these costs for each of 

the control technologies. No fuel penalty was estimated for the oxidation catalyst control 

technologies, because this control technology does not increase the fuel usage of the SI engine. 

4.4.3 Control Cost Equations 

Control cost equations were developed for 2SLB oxidation catalyst, 4SLB oxidation 

catalyst, and a NSCR for 4SRB engines using the total capital cost and total annual cost data for 

each control technology. Control cost equations for 2SLB and 4SLB oxidation catalysts were 

developed separately because the 2SLB oxidation catalyst requires a premium catalyst to reduce 

the HAP compounds because of the low exhaust temperature of 2SLB engines. 

4.4.3.1 2SLB Oxidation Catalyst 

The 2SLB oxidation catalyst is an effective control technology that reduces HAP 

emissions from a 2SLB SI engine by oxidizing organic compounds using a catalyst. The 

oxidation catalyst unit contains a honeycomb-like structure or substrate with a large surface area 

that is coated with a premium active catalyst layer such as platinum or palladium. The oxidation 

catalyst works by oxidizing carbon monoxide (CO) and gaseous hydrocarbons (HAP) in the 

exhaust gas to carbon dioxide (CO2) and water. The reduction of CO and HAP varies depending 

on the type of catalyst used and the exhaust temperature of the pollutant stream. 

The cost of retrofitting an oxidation catalyst to an existing 2SLB engine was estimated 

using cost data obtained from vendors and industry groups covering engines ranging from 58 

horsepower (HP) to 4,670 HP. An equipment life of 10 years and an interest rate of 7 percent 

were used to estimate the capital recovery of the control technology and the fuel penalty was 

assumed to be negligible. The cost equations are presented in 2009 dollars. 

The total annualized cost equation for retrofitting an oxidation catalyst on a 2SLB engine 

was estimated to be: 

2SLB Oxidation Catalyst Total Annual Cost = $11.4 x HP + $13,928 

where 

HP = engine size in HP. 
4-22 

The linear equation has a correlation coefficient of 0.8046, which shows the data fits the 

equation closely. Therefore, this equation was used to estimate annualized cost for an oxidation 

catalyst on a 2SLB engine. 

The total capital cost equation for retrofitting an oxidation catalyst on a 2SLB engine was 

estimated to be: 

2SLB Oxidation Catalyst Total Capital Cost = $47.1 x HP + $41,603 

where 

HP = engine size in HP. 

4.4.3.2 4SLB Oxidation Catalyst 

The 4SLB oxidation catalyst is an effective control technology that reduces HAP 

emissions from a 4SLB SI engine by oxidizing organic compounds using a catalyst. The 

oxidation catalyst unit contains a honeycomb-like structure or substrate with a large surface area 

that is coated with a premium active catalyst layer such as platinum or palladium. The oxidation 

catalyst works by oxidizing CO and gaseous hydrocarbons (HAP) in the exhaust gas to CO2 and 

water. The reductions of CO and HAP vary depending on the type of catalyst used and the 

exhaust temperature of the pollutant stream. 

The cost of retrofitting an oxidation catalyst to an existing 4SLB engine was estimated 

using cost data obtained from vendors and industry groups covering engines ranging from 400 

HP to 8,000 HP. Again, an equipment life of 10 years and an interest rate of 7 percent were used 

to estimate the capital recovery of the control technology and the fuel penalty was assumed to be 

negligible. The cost equations are presented in 2009 dollars. 

The total annualized cost equation for retrofitting an oxidation catalyst on a 4SLB engine 

was estimated to be: 

4SLB Oxidation Catalyst Total Annual Cost = $1.81 x HP + $3,442 

where 

HP = engine size in HP. 
4-23 

The linear equation has a correlation coefficient of 0.9779, which shows the data fits the 

equation very closely. Therefore, this equation was used to estimate annualized cost for an 

oxidation catalyst on a 4SLB engine. 

The total capital cost equation for retrofitting an oxidation catalyst on a 4SLB SI engine was 

estimated to be: 

4SLB Oxidation Catalyst Total Capital Cost = $12.8 x HP + $3,069 

where 

HP = engine size in HP. 

A summary of the cost calculations, regression analyses, and graphical representations of the 

annual and capital cost data are presented in Appendix A of the cost memo that is the basis for 

the cost data presented in this RIA.3

4.4.3.3 Non-Selective Catalytic Reduction 

The NSCR or three-way catalyst is used to control HAP emissions from 4SRB engines. 

In addition to HAP reductions, NSCR also reduces the emissions of nitrogen oxides (NOx), CO, 

and other hydrocarbons (HC). The reduction of HAP and CO takes place through an oxidation 

reaction that converts HAP to CO2 and water and converts CO to CO2. The conversion of NOx

takes place through a reduction of the NOx to nitrogen gas and oxygen. 

The cost of retrofitting an NSCR on an existing 4SRB engine was estimated based on 

cost data received from vendors and industry groups. A linear regression analysis was done on 

the data set and the linear equation for annualized cost was; 

NSCR Annual Cost = $4.77 x HP + $5,679 

where 

HP = engine size in HP. 

The linear equation has a correlation coefficient of 0.7987, which shows an acceptable 

representation of the cost data. Therefore, this equation was used to estimate annualized cost for 

retrofitting the NSCR control technology on 4SRB engines. 

 

3

 Memorandum from Bradley Nelson, EC/R to Melanie King, EPA. OAQPS/SPPD/ESG. Impacts Associated with 

NESHAP for Existing Stationary SI RICE. June 29, 2010. 
4-24 

The capital cost equation for retrofitting an air-to-fuel ratio (AFR) controller and NSCR on a 

4SRB engine was estimated to be: 

NSCR Capital Cost = $24.9 x HP + $13,118 

where 

HP = engine size in HP. 

4.4.4 Summary 

Table 4-3 presents a summary of the annual and capital control costs as a function of 

engine size for the control technologies applicable to existing stationary SI engines, as discussed 

in this memorandum. 

Table 4-3. Summary of Annual and Capital Costs Equations for Existing Stationary SI 

Engines 

HAP Control Device Annual Cost ($2009) Capital Cost ($2009) 

2SLB Oxidation Catalyst $11.4 x HP + $13,928 $47.1 x HP + $41,603 

4SLB Oxidation Catalyst $1.81 x HP + $3,442 $12.8 x HP + $3,069 

NSCR $4.77 x HP + $5,679 $24.9 x HP + $13,118 

A summary of the annual and capital costs associated with the rule and obtained using the 

methodology described above are presented in Tables 4-4 to 4-7 below.4 These costs are used in 

the economic impact as well as the small entity analysis. 

 

4

 Memorandum from Bradley Nelson, EC/R to Melanie King, EPA. OAQPS/SPPD/ESG. Impacts Associated with 

NESHAP for Existing Stationary SI RICE. June 29, 2010. 
4-25 

Table 4-4. Summary of Major Source and Area Source Costs for the SI RICE NESHAPa,b

Size Range (HP) 

Capital 

Control Cost 

Annual 

Control Cost Initial Test 

Recordkeeping Reporting Monitoring— Capital Cost Monitoring— Annual Cost 

Major Sources 

25–50 $0 $0 $0 $4,060,795 $0 $0 $0 50–100 $0 $0 $0 $1,087,540 $0 $0 $0 100–175 $48,502,361 $37,071,061 $15,971,384 $1,721,899 $5,725,314 $0 $0 175–300 $13,225,919 $8,382,568 $3,442,648 $371,157 $1,234,097 $0 $0 300–500 $10,934,795 $5,562,872 $2,123,326 $228,919 $761,155 $0 $0 500–600 $0 $0 $0 $0 $0 $0 $0 600–750 $0 $0 $0 $0 $0 $0 $0 >750 $0 $0 $0 $0 $0 $0 $0 

Total $72,663,076 $51,016,500 $21,537,358 $7,470,310 $7,720,566 $0 $0 

Area Sources 

25–50 $0 $0 $0 $6,668,944 $0 $0 $0 50–100 $0 $0 $0 $2,868,511 $0 $0 $0 100–175 $0 $0 $0 $3,529,711 $0 $0 $0 175–300 $0 $0 $0 $1,264,799 $0 $0 $0 300–500 $0 $0 $0 $908,913 $0 $0 $0 500–600 $75,474,331 $26,628,053 $3,655,719 $454,493 $1,264,260 $2,821,013 $13,003,822 

600–750 $15,222,363 $5,052,207 $652,400 $77,882 $225,620 $503,438 $2,320,662 >750 $210,754,181 $62,143,967 $6,951,011 $829,795 $2,403,874 $5,363,896 $24,725,562 

Total $301,450,875 $93,824,227 $11,259,129 $16,603,048 $3,893,754 $8,688,347 $40,050,046 Grand Total Total $374,113,951 $144,840,727 $32,796,487 $24,073,358 $11,614,321 $8,688,347 $40,050,046 

a Costs are presented in 2009 dollars. 
4-26

b For some HP ranges, the annual compliance cost is greater than the capital compliance cost because not all of the engines in those HP 

incur capital costs for controls, but all of the engines in those HP ranges are expected to incur annual costs of testing, monitoring, rec
4-27

Table 4-5. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAPa,b

Major Source Area Source To

NAICS Capital Cost Annual Cost Capital Cost Annual Cost Capital 2211 Electric Power Generation $52,905,258 $63,062,494 $120,301,416 $65,334,028 $173,248621 Natural Gas Transmission $1,484,494 $1,462,530 $140,977,276 $67,467,484 $142,4211111 Crude Petroleum & NG 

Production $4,561,236 $6,138,383 

$732,943 $1,258,072 

$5,2

211112 Natural Gas Liquid Producers $4,561,236 $6,138,383 $732,943 $1,258,072 $5,292811 National Security $5,878,362 $7,006,944 $13,366,824 $7,259,336 $19,2335312 Hydro Power Units $0 $25,248 $0 $37,872 335312 Irrigation Sets $3,025,050 $3,230,856 $34,027,819 $22,445,211 $37,0333992 Welders $247,440 $679,896 $0 $570,130 $2 Total $72,663,076 $87,744,734 $310,139,222 $165,630,205 $382,8

a Costs are presented in 2009 dollars. b For some HP ranges, the annual compliance cost is greater than the capital compliance cost because not all of the engines in those HP 

incur capital costs for controls, but all of the engines in those HP ranges are expected to incur annual costs of testing, monitoring, rec
4-28

Table 4-6. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Siz

 Major Source Area Source ToNAICS Capital Cost Annual Cost Capital Cost Annual Cost CapitaElectric Power Generation (2211) 

25–50 hp $0 $2,758,459 $0 $4,137,688 50–100 hp $0 $606,144 $0 $909,215 100–175 hp $33,868,173 $42,238,648 $0 $1,803,548 $33,8175–300 hp $10,603,849 $10,767,847 $0 $446,361 $10,6300–500 hp $8,433,236 $6,691,397 $0 $264,820 $8,4500–600 hp $0 $0 $26,390,293 $15,169,876 $26,3600–750 hp $0 $0 $5,325,628 $2,820,584 $5,3>750 hp $0 $0 $88,585,495 $39,781,934 $88,5Total Electric Power Generation 2211 $52,905,258 $63,062,494 $120,301,416 $65,334,028 $173,2

Natural Gas Transmission (48621) 

25–50 hp $0 $102 $0 $1,934 50–100 hp $0 $4,872 $0 $92,571 100–175 hp $301,721 $376,291 $0 $203,518 $3175–300 hp $643,157 $653,104 $0 $342,928 $6300–500 hp $539,617 $428,162 $0 $214,637 $5500–600 hp $0 $0 $19,975,323 $11,482,372 $19,9600–750 hp $0 $0 $9,808,436 $5,194,789 $9,8>750 hp $0 $0 $111,193,518 $49,934,735 $111,Total Natural Gas Transmission (48621) $1,484,494 $1,462,530 $140,977,276 $67,467,484 $142,4

Crude Petroleum & NG Production (211111) 

25–50 hp $0 $388,115 $0 $582,173 50–100 hp $0 $66,698 $0 $100,047 100–175 hp $4,549,775 $5,674,246 $0 $242,285 $4,5
4-29

Table 4-6. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Size

 Major Source Area Source ToNAICS Capital Cost Annual Cost Capital Cost Annual Cost Capita

175–300 hp $1,037 $1,053 $0 $44 300–500 hp $10,424 $8,271 $0 $327 $500–600 hp $0 $0 $32,184 $18,500 $600–750 hp $0 $0 $0 $0 >750 hp $0 $0 $700,760 $314,697 $7Total Crude Petroleum & NG Production (211111) $4,561,236 $6,138,383 $732,943 $1,258,072 $5,2

Natural Gas Liquid Producers (211112) 

25–50 hp $0 $388,115 $0 $582,173 50–100 hp $0 $66,698 $0 $100,047 100–175 hp $4,549,775 $5,674,246 $0 $242,285 $4,5175–300 hp $1,037 $1,053 $0 $44 300–500 hp $10,424 $8,271 $0 $327 $500–600 hp $0 $0 $32,184 $18,500 $600–750 hp $0 $0 $0 $0 >750 hp $0 $0 $700,760 $314,697 $7Total Natural Gas Liquid Producers (211112) $4,561,236 $6,138,383 $732,943 $1,258,072 $5,2

National Security (92811) 

25–50 hp $0 $306,495 $0 $459,743 50–100 hp $0 $67,349 $0 $101,024 100–175 hp $3,763,130 $4,693,183 $0 $200,394 $3,7175–300 hp $1,178,205 $1,196,427 $0 $49,596 $1,300–500 hp $937,026 $743,489 $0 $29,424 $9500–600 hp $0 $0 $2,932,255 $1,685,542 $2,9
4-30

Table 4-6. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Size

 Major Source Area Source ToNAICS Capital Cost Annual Cost Capital Cost Annual Cost Capita

600–750 hp $0 $0 $591,736 $313,398 $5>750 hp $0 $0 $9,842,833 $4,420,215 $9,8Total Natural Gas Liquid Producers (211112) $5,878,362 $7,006,944 $13,366,824 $7,259,336 $19,2

Hydro Power Units (335312) 

25–50 hp $0 $22,688 $0 $34,032 50–100 hp $0 $2,560 $0 $3,840 100–175 hp $0 $0 $0 $0 175–300 hp $0 $0 $0 $0 300–500 hp $0 $0 $0 $0 500–600 hp $0 $0 $0 $0 600–750 hp $0 $0 $0 $0 >750 hp $0 $0 $0 $0 Total Hydro Power Units (335312) $0 $25,248 $0 $37,872 

Irrigation Sets (335312) 

25–50 hp $0 $32,913 $0 $625,338 50–100 hp $0 $65,825 $0 $1,250,677 100–175 hp $1,222,348 $1,524,449 $0 $824,505 $1,2175–300 hp $798,634 $810,986 $0 $425,827 $7300–500 hp $1,004,068 $796,683 $0 $399,376 $1,0500–600 hp $0 $0 $28,933,107 $16,631,556 $28,9600–750 hp $0 $0 $0 $0 >750 hp $0 $0 $5,094,712 $2,287,931 $5,0Total Irrigation Sets (335312) $3,025,050 $3,230,856 $34,027,819 $22,445,211 $37,0
4-31

Table 4-6. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Size

 Major Source Area Source ToNAICS Capital Cost Annual Cost Capital Cost Annual Cost CapitaWelders (333992) 

25–50 hp $0 $163,908 $0 $245,862 50–100 hp $0 $207,394 $0 $311,091 100–175 hp $247,440 $308,594 $0 $13,177 $2175–300 hp $0 $0 $0 $0 300–500 hp $0 $0 $0 $0 500–600 hp $0 $0 $0 $0 600–750 hp $0 $0 $0 $0 >750 hp $0 $0 $0 $0 Total Welders (333992) $247,440 $679,896 $0 $570,130 $2

Total $72,663,076 $87,744,734 $310,139,222 $165,630,205 $382,8

a Costs are presented in 2009 dollars. b For some HP ranges, the annual compliance cost is greater than the capital compliance cost because not all of the engines in those HP 

incur capital costs for controls, but all of the engines in those HP ranges are expected to incur annual costs of testing, monitoring, rec
4-32

Table 4-7. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Num

Number of Engines To

NAICS Major Area Total CapitaElectric Power Generation (2211) 

25–50 hp 37,933 56,900 94,833 50–100 hp 8,336 12,503 20,839 100–175 hp 16,534 24,802 41,336 $33,8175–300 hp 4,092 6,138 10,230 $10,6300–500 hp 2,428 3,642 6,070 $8,4500–600 hp 0 2,107 2,107 $26,3600–750 hp 0 363 363 $5,3>750 hp 0 4,677 4,677 $88,5Total Electric Power Generation 2211 69,323 111,132 180,455 $173,2

Natural Gas Transmission (48621) 

25–50 hp 1 27 28 50–100 hp 67 1,273 1,340 100–175 hp 147 2,799 2,946 $3175–300 hp 248 4,716 4,964 $6300–500 hp 155 2,952 3,107 $5500–600 hp 0 1,595 1,595 $19,9600–750 hp 0 668 668 $9,8>750 hp 0 5,871 5,871 $111,1Total Natural Gas Transmission (48621) 619 19,899 20,519 $142,4

Crude Petroleum & NG Production (211111) 

25–50 hp 5,337 8,006 13,343 50–100 hp 917 1,376 2,293 100–175 hp 2,221 3,332 5,553 $4,5
4-33

Table 4-7. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Num

(continued) 

Number of Engines To

NAICS Major Area Total Capita

175–300 hp 0 1 1 300–500 hp 3 5 8 $500–600 hp 0 3 3 $600–750 hp 0 0 0 >750 hp 0 37 37 $7Total Crude Petroleum & NG Production (211111) 8,479 12,758 21,237 $5,2

Natural Gas Liquid Producers (211112) 

25–50 hp 5,337 8,006 13,343 50–100 hp 917 1,376 2,293 100–175 hp 2,221 3,332 5,553 $4,5175–300 hp 0 1 1 300–500 hp 3 5 8 $500–600 hp 0 3 3 $600–750 hp 0 0 0 >750 hp 0 37 37 $7Total Natural Gas Liquid Producers (211112) 8,479 12,758 21,237 $5,2

National Security (92811) 

25–50 hp 4,215 6,322 10,537 50–100 hp 926 1,389 2,315 100–175 hp 1,837 2,756 4,593 $3,7175–300 hp 455 682 1,137 $1,1300–500 hp 270 404 674 $9500–600 hp 0 234 234 $2,9
4-34

Table 4-7. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Num

(continued) 

Number of Engines To

NAICS Major Area Total Capita

600–750 hp 0 40 40 $5>750 hp 0 520 520 $9,8Total Natural Gas Liquid Producers (211112) 7,702 12,347 20,050 $19,2

Hydro Power Units (335312) 

25–50 hp 312 468 780 50–100 hp 35 53 88 100–175 hp 0 0 0 175–300 hp 0 0 0 300–500 hp 0 0 0 500–600 hp 0 0 0 600–750 hp 0 0 0 >750 hp 0 0 0 Total Hydro Power Units (335312) 347 521 868 

Irrigation Sets (335312) 

25–50 hp 453 8,599 9,052 50–100 hp 905 17,199 18,104 100–175 hp 597 11,338 11,935 $1,2175–300 hp 308 5,856 6,164 $7300–500 hp 289 5,492 5,781 $1,0500–600 hp 0 2,310 2,310 $28,9600–750 hp 0 0 0 >750 hp 0 269 269 $5,0Total Irrigation Sets (335312) 2,552 51,063 53,615 $37,0
4-35

Table 4-7. Summary of Major Source and Area Source NAICS Costs for the SI RICE NESHAP, by Num

(continued) 

Number of Engines To

NAICS Major Area Total CapitaWelders (333992) 

25–50 hp 2,254 3,381 5,635 50–100 hp 2,852 4,278 7,130 100–175 hp 121 181 302 $2175–300 hp 0 0 0 300–500 hp 0 0 0 500–600 hp 0 0 0 600–750 hp 0 0 0 >750 hp 0 0 0 Total Welders (333992) 5,227 7,840 13,067 $2

Total 102,729 228,319 331,047 $382,8

a Costs are presented in 2009 dollars. b For some HP ranges, the annual compliance cost is greater than the capital compliance cost because not all of the engines in those 

to incur capital costs for controls, but all of the engines in those HP ranges are expected to incur annual costs of testing, monitoring

reporting. 
4-36 

4.4.5 Caveats and Uncertainties in the Cost Estimates 

* Current knowledge about NOx control techniques and costs is applied in this 

study. Advances such as alternative catalyst formulations may occur between 

now and when sources comply with this rulemaking that may lower costs. Scale 

economies can also lower per unit production costs as the market for these NOx 

control techniques expands. 

* The alternative control techniques and corresponding emission reductions and 

costs may not apply to every unit within the source category. Many factors 

influence the performance and cost of any control technique. Because control 

technology references typically evaluate average retrofit situations, costs may 

be underestimated for the fraction of the source population with difficult to 

retrofit conditions. Difficult to retrofit conditions may be less of an issue for 

RICEs than for other point sources, however. 

* NOx control efficiency and cost estimates associated with source category-control 

strategy combinations are represented as point estimates. In practice, control 

effectiveness and costs will vary by engine.
4-37 

4.5 Emissions and Emission Reductions 

The baseline emissions, emissions factors and emissions reductions associated with the 

final rule are provided in the tables below. Emissions are in tons per year. 

Table 4-8. Summary of Major Source and Area Source Baseline Emissions for the SI 

RICE NESHAP 

 Baseline Emissions (TPY) 

Size Range (HP) HAP CO NOx VOC 

Major Sources 

25–50 hp 1,107 28,557 41,751 5,696 

50–100 hp 593 15,296 22,363 3,051 

100–175 hp 1,721 44,399 64,913 8,855 

175–300 hp 641 16,530 24,168 3,297 

300–500 hp 666 17,171 25,105 3,425 

500–600 hp 0 0 0 0 

600–750 hp 0 0 0 0 

>750 hp 0 0 0 0 

Total 4,728 121,953 178,301 24,323 

Area Sources 

25–50 hp 1,818 46,898 68,566 9,354 

50–100 hp 1,564 40,344 58,985 8,047 

100–175 hp 3,529 91,013 133,065 18,153 

175–300 hp 2,184 56,331 82,359 11,235 

300–500 hp 2,643 68,178 99,679 13,598 

500–600 hp 1,830 47,273 69,094 9,415 

600–750 hp 383 9,876 14,438 1,969 

>750 hp 6,041 155,890 227,890 31,076 

Total 19,993 515,803 754,077 102,846 

Major + Area 

Total 

24,721 637,756 932,378 127,169 

Table 4-9. Emissions Factors 

Engine 

HAP 

(lb/hp-hr) 

CO 

(lb/hp-hr) 

NOx 

(lb/hp-hr) 

VOC 

(lb/hp-hr) 

Formaldehyde 

(lb/hp-hr) 

2SLB 5.96×10-4 1.06×10-2 4.18×10-2 3.07×10-3 4.29×10-4

4SLB 5.41×10-4 3.92×10-3 1.15×10-2 2.78×10-3 3.96×10-4

4SRB 2.43×10-4 1.93×10-2 1.47×10-2 1.25×10-3 1.75×10-4
4-38 

Table 4-10. Summary of Major Source and Area Source Emissions Reductions for the SI 

RICE NESHAP 

 Emission Reductions (TPY) 

Size Range (HP) HAP CO NOx VOC 

Major Sources 

25–50 hp 0 0 0 0 

50–100 hp 0 0 0 0 

100–175 hp 744 7,124 0 3,826 

175–300 hp 277 2,653 0 1,424 

300–500 hp 288 2,755 0 1,480 

500–600 hp 0 0 0 0 

600–750 hp 0 0 0 0 

>750 hp 0 0 0 0 

Total 1,308 12,532 0 6,730 

Area Sources 

25–50 hp 0 0 0 0 

50–100 hp 0 0 0 0 

100–175 hp 0 0 0 0 

175–300 hp 0 0 0 0 

300–500 hp 0 0 0 0 

500–600 hp 1,005 20,698 20,632 5,170 

600–750 hp 220 4,533 4,519 1,132 

>750 hp 3,475 71,557 71,328 17,874, 

Total 4,700 96,789 96,479 24,177 

Major + Area Total 6,008 109,321 96,479 30,907 
5-1 

SECTION 5

ECONOMIC IMPACT ANALYSIS, ENERGY IMPACTS, AND SOCIAL COSTS 

The EIA provides decision makers with social cost estimates and enhances understanding 

of how the costs may be distributed across stakeholders (EPA, 2000). Although several 

economic frameworks can be used to estimate social costs for regulations of this size and sector 

scope, OAQPS has typically used partial equilibrium market models. However, the current data 

do not provide sufficient details to develop a market model; the data that are available have little 

or no sector/firm detail and are reported at the national level. In addition, some sectors have 

unique market characteristics that make developing partial equilibrium models difficult. Given 

these constraints, we used the direct compliance costs as a measure of total social costs. In 

addition, we also provide a qualitative analysis of the final rule’s impact on stakeholder 

decisions, a qualitative discussion on if unfunded mandates occur as a result of this final rule, 

and the potential distribution of social costs between consumers and producers. 

5.1 Compliance Costs of the Final Rule 

EPA’s engineering cost analysis estimates the total annualized costs of the final rule are 

$253 million (in 2009 dollars) (Nelson, 2010). 

As shown in Figure 5-1, the majority of the costs fall on the electric power sector (51%), 

followed by natural gas transmission (27%). The remaining industries each account for less than 

15% of the total annualized cost. The industrial classification for each engine is taken from the 

Power Systems Research (PSR) database, which is the major source of data for the engines 

affected by the final rule. The PSR database used as a basis for the analyses in this RIA contains 

information on both mobile and stationary engines, among other data, and does so not only for 

the U.S. but worldwide. PSR has collected such data for more than 30 years. The Office of 

Transportation and Air Quality (OTAQ) uses this database frequently in the development of their 

mobile source rules. 

The annualized compliance costs per engine vary by the engine size (see Figure 5-2). For 

500 hp engines or less, the annualized per-engine costs are below $1,200 per engine. Per-engine 

costs for higher horsepower (hp) engines range between $7,200 and $8,500. 

The final rule will affect approximately 331,000 existing stationary SI engines. As shown 

in Figure 5-3, most of the affected engines fall within the 25 to 50 hp category (45%). The next 

highest categories are 100 to 175 hp (22%) and 50 to 100 hp (16%). 
5-2 

51%

27%

3% 3%

6%

0%

10%

0%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Electric Power Generation (2211)

Natural Gas Transmission (48621)

Crude Petroleum & NG Production (211111)

Natural Gas Liquid Producers (211112)

National Security (92811)

Hydro Power Units (335312)

Irrigation Sets (335312)

Welders (333992)

Figure 5-1. Distribution of Annualized Direct Compliance Costs by Industry 
5-3 

$73 $73

$886

$653

$1,100

$7,201

$7,777

$8,505

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

$9,000

25-50 hp 50-100 hp 100-175 hp 175-300 hp 300-500 hp 500-600 hp 600-750 hp >750 hp

Figure 5-2. Average Annualized Cost per Engine by Horsepower Group ($2009) 
5-4 

45%

16%

22%

7% 5%

2% 0%

3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

25-50 hp 50-100 hp 100-175 hp 175-300 hp 300-500 hp 500-600 hp 600-750 hp >750 hp

Figure 5-3. Distribution of Engine Population by Horsepower Group 

To assess the size of the compliance relative to the value of the goods and services for 

industries using affected engines, we collected Census data for selected industries. At the 

industry level, the annualized costs represent a very small fraction of revenue (less than 1%), for 

all affected industries. Results for affected industries can be found in Table 5-1. These industry 

level cost-to-sales ratios can be interpreted as an average impact on potentially affected firms in 

these industries. Based on the cost-to-sales ratios, we can conclude that the annualized cost of 

this rule should be no higher than 1% of the sales on average for a firm in each of these 

industries, excluding natural gas transmission and natural gas liquid producers, which face 

slightly higher costs to sales ratios. 

5.2 How Might People and Firms Respond? A Partial Equilibrium Analysis 

Markets are composed of people as consumers and producers trying to do the best they 

can given their economic circumstances. One way economists illustrate behavioral responses to 

pollution control costs is by using market supply and demand diagrams. The market supply curve 

describes how much of a good or service firms are willing and able to sell to people at a 
5-5 

Table 5-1. Selected Industry-Level Annualized Compliance Costs as a Fraction of Total 

Industry Revenue: 2009 

 

Total 

Annualized 

Costs 

Sales, Shipments, Receipt, 

or Revenue ($ Billion) 

Industry 

(NAICS) Industry Name ($ million)a

 ($2007) ($2009) 

Cost-toSales Ratio 

2211 Electric Power Generation $128.4 $440.4 $453.7 0.004% 

48621 Natural Gas Transmission $68.9 $16.4 $16.9 0.41% 

211111 Crude Petroleum & NG Production $7.4 $214.2 $220.5 0.001% 

211112 Natural Gas Liquid Producers $7.4 $42.4 $43.6 0.005% 

92811 National Security $14.3 #N/A #N/A #N/A 

333992 Welders $1.3 $5.2 $5.5 0.025% 

111 and 112 Agriculture using irrigation systemsa $25.7 $27.9 $28.8 0.09% 

a Irrigation engine costs assumed to be passed on to agricultural sectors that use irrigation systems. 

N/A: receipts are Not Available for National Security 

Sources: U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 00: All sectors: 

Geographic Area Series: Economy-Wide Key Statistics: 2007” <http://factfinder.census.gov>; (July 7th , 

2010). 

 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). 2009. “2008 

Farm and Ranch Irrigation Survey.” Washington, DC: USDA-NASS. 

 Costs from Existing SI RICE NESHAP Impacts 6-24-2010.xls received from EPA 6/24/10 

particular price; we often draw this curve as upward sloping because some production resources 

are fixed. As a result, the cost of producing an additional unit typically rises as more units are 

made. The market demand curve describes how much of a good or service consumers are willing 

and able to buy at some price. Holding other factors constant, the quantity demand is assumed to 

fall when prices rise. In a perfectly competitive market, equilibrium price (P0) and quantity (Q0) 

is determined by the intersection of the supply and demand curves (see Figure 5-4). 

5.2.1 Changes in Market Prices and Quantities 

To qualitatively assess how the regulation may influence the equilibrium price and 

quantity in the affected markets, we assumed the market supply function shifts up by the 

additional cost of producing the good or service; the unit cost increase is typically calculated by 

dividing the annual compliance cost estimate by the baseline quantity (Q0) (see Figure 5-4). As 

shown, this model makes two predictions: the price of the affected goods and services are likely 

to rise and the consumption/production levels are likely to fall. 
5-6 

consumer surplus = –[fghd + dhc] 

producer surplus = [fghd – aehb] – bdc 

total surplus = consumer surplus + producer surplus = 

–[aehb + dhc + bdc] 

Figure 5-4. Market Demand and Supply Model: With and Without Regulation 

The size of these changes depends on two factors: the size of the unit cost increase 

(supply shift) and differences in how each side of the market (supply and demand) responds to 

changes in price. Economists measure responses using the concept of price elasticity, which 

represents the percentage change in quantity divided by the percentage change in price. This 

dependence has been expressed in the following formula:1

( ) Price Elasticity of Supply - Price Elasticity of Demand)

Price Elasticity of Supply Share of per-unit cost =

As a general rule, a higher share of the per-unit cost increases will be passed on to 

consumers in markets where 

 goods and services are necessities and people do not have good substitutes that they 

can switch to easily (demand is inelastic) and 

 

1

For examples of similar mathematical models in the public finance literature, see Nicholson (1998), pages 444–447, 

or Fullerton and Metcalf (2002). 
5-7 

 suppliers have excess capacity and can easily adjust production levels at minimal 

costs, or the time period of analysis is long enough that suppliers can change their 

fixed resources; supply is more elastic over longer periods. 

Short-run demand elasticities for energy goods (electricity and natural gas), agricultural 

products, and construction are often inelastic. Specific estimates of short-run demand elasticities 

for these products can be obtained from existing literature. For the short-run demand of energy 

products, the National Energy Modeling System (NEMS) buildings module uses values between 

0.1 and 0.3; a 1% increase in price leads to a 0.1 to 0.3% decrease in energy demand (Wade, 

2003). For the short-run demand of agriculture and construction, the EPA has estimated 

elasticities to be 0.2 for agriculture and approximately 1 for construction (U.S. EPA, 2004). As a 

result, a 1% increase in the prices of agriculture products would lead to a 0.2% decrease in 

demand for those products, while a 1% increase in construction prices would lead to 

approximately a 1% decrease in demand for construction. Given these demand elasticity 

scenarios (shaded in gray), approximately a 1% increase unit costs would result in a price 

increase of 0.1 to 1% (Table 5-2). As a result, 10 to 100% of the unit cost increase could be 

passed on to consumers in the form of higher goods/services prices. This price increase would 

correspond to a 0.1 to 0.8% decline in consumption in these markets (Table 5-3). 

Table 5-2. Hypothetical Price Increases for a 1% Increase in Unit Costs 

Market Demand 

Elasticity 

Market Supply Elasticity 

0.1 0.3 0.5 0.7 1 1.5 3 

−0.1 0.5% 0.8% 0.8% 0.9% 0.9% 0.9% 1.0% 

−0.3 0.3% 0.5% 0.6% 0.7% 0.8% 0.8% 0.9% 

−0.5 0.2% 0.4% 0.5% 0.6% 0.7% 0.8% 0.9% 

−0.7 0.1% 0.3% 0.4% 0.5% 0.6% 0.7% 0.8% 

−1.0 0.1% 0.2% 0.3% 0.4% 0.5% 0.6% 0.8% 

−1.5 0.1% 0.2% 0.3% 0.3% 0.4% 0.5% 0.7% 

−3.0 0.0% 0.1% 0.1% 0.2% 0.3% 0.3% 0.5% 

5.2.2 Regulated Markets: The Electric Power Generation, Transmission, and Distribution 

Sector 

Given that the electric power sector bears majority of the estimated compliance costs 

(Figure 5-1) and the industry is also among the last major regulated energy industries in the 

United States (EIA, 2000), the competitive model is not necessarily applicable for this industry. 
5-8 

Table 5-3. Hypothetical Consumption Decreases for a 1% Increase in Unit Costs 

Market Demand 

Elasticity 

Market Supply Elasticity 

0.1 0.3 0.5 0.7 1 1.5 3 

−0.1 −0.1% −0.1% −0.1% −0.1% −0.1% −0.1% −0.1% 

−0.3 −0.1% −0.2% −0.2% −0.2% −0.2% −0.3% −0.3% 

−0.5 −0.1% −0.2% −0.3% −0.3% −0.3% −0.4% −0.4% 

−0.7 −0.1% −0.2% −0.3% −0.4% −0.4% −0.5% −0.6% 

−1.0 −0.1% −0.2% −0.3% −0.4% −0.5% −0.6% −0.8% 

−1.5 −0.1% −0.3% −0.4% −0.5% −0.6% −0.8% −1.0% 

−3.0 −0.1% −0.3% −0.4% −0.6% −0.8% −1.0% −1.5% 

Although the electricity industry continues to go through a process of restructuring, whereby the 

industry is moving toward a more competitive framework (see Figure 5-5 for the status of 

restructuring by state),2 in many states, electricity prices continue to be fully regulated by Public 

Service Commissions. As a result, the rules and processes outlined by these agencies would 

ultimately determine how these additional regulatory costs would be recovered by affected 

entities. 

5.2.3 Partial Equilibrium Measures of Social Cost: Changes Consumer and Producer 

Surplus 

In partial equilibrium analysis, the social costs are estimated by measuring the changes in 

consumer and producer surplus, and these values can be determined using the market supply and 

demand model (Figure 5-4). The change in consumer surplus is measured as follows: 

ΔCS = – [ΔQ1 × Δp] + [0.5 × ΔQ × Δp]. (5.1) 

Higher market prices and lower quantities lead to consumer welfare losses. Similarly, the change 

in producer surplus is measured as follows: 

ΔPS = [ΔQ1 × Δp] – [ΔQ1 × t] – [0.5 × ΔQ × (Δp – t)]. (5.2) 

Higher unit costs and lower production level reduce producer surplus because the net 

price change (Δp – t) is negative. However, these losses are mitigated because market prices tend 

to rise. 

 

2

http://tonto.eia.doe.gov/energy_in_brief/print_pages/electricity.pdf. 
5-9 

Figure 5-5. Electricity Restructuring by State 

Source. U.S. Energy Information Administration. 2008a. 

<http://www.eia.doe.gov/cneaf/electricity/page/restructuring/restructure_elect.html>. Last updated September 

2008. 

5.3 Social Cost Estimate 

As shown in Table 5-1 the compliance costs are only a small fraction of the affected 

product value; this suggests that shift of the supply curve may also be small and result in small 

changes in market prices and consumption. EPA believes the national annualized compliance 

cost estimates provide a reasonable approximation of the social cost of this final rule. EPA 

believes this approximation is better for industries whose markets are well characterized as 

perfectly competitive. This approximation is less well understood for industries where the 

characterization of markets is not always perfectly competitive such as electric power generation 

whose legal incidence of this rule is approximately 50 percent of the annualized compliance cost. 

However, given the data limitation noted earlier, EPA believes the accounting for compliance 

cost is a reasonable approximation to inform policy discussion in this rulemaking. To shed more 

light on this issue, EPA ran hypothetical analyses and the results are in Tables 5-2 and 5-3. 
5-10 

5.4 Energy Impacts 

Executive Order 13211 (66 FR 28355, May 22, 2001) provides that agencies will prepare 

and submit to the Administrator of the Office of Information and Regulatory Affairs, Office of 

Management and Budget, a Statement of Energy Effects for certain actions identified as 

“significant energy actions.” Section 4(b) of Executive Order 13211 defines “significant energy 

actions” as any action by an agency (normally published in the Federal Register) that 

promulgates or is expected to lead to the promulgation of a final rule or regulation, including 

notices of inquiry, advance notices of proposed rulemaking, and notices of proposed rulemaking: 

(1) (i) that is a significant regulatory action under Executive Order 12866 or any successor order, 

and (ii) is likely to have a significant adverse effect on the supply, distribution, or use of energy; 

or (2) that is designated by the Administrator of the Office of Information and Regulatory Affairs 

as a significant energy action. 

This rule is not a significant energy action as designated by the Administrator of the 

Office of Information and Regulatory Affairs because it is not likely to have a significant adverse 

impact on the supply, distribution, or use of energy. EPA has prepared an analysis of energy 

impacts that explains this conclusion as follows below. 

With respect to energy supply and prices, the analysis in Table 5-1 suggests at the 

industry level, the annualized costs represent a very small fraction of revenue (all industries are 

impacted under 1%). As a result, we can conclude supply and price impacts should be small. 

To enhance understanding regarding the regulation’s influence on energy consumption, 

we examined publicly available data describing energy consumption for the electric power sector 

that will be affected by this rule. The Annual Energy Outlook 2010 (EIA, 2009) provides energy 

consumption data. As shown in Table 5-4, this industry account for less than 0.5% of the U.S. 

total liquid fuels and less than 5.2% of natural gas. As a result, any energy consumption changes 

attributable to the regulatory program should not significantly influence the supply, distribution, 

or use of energy.

Table 5-4. U.S. Electric Powera Sector Energy Consumption (Quadrillion BTUs): 2013 

Quantity Share of Total Energy Use 

Distillate fuel oil 0.12 0.1% 

Residual fuel oil 0.34 0.3% 

Liquid fuels subtotal 0.45 0.5% 

Natural gas 5.17 5.1% 
5-11 

Steam coal 20.69 20.6% 

Nuclear power 8.59 8.5% 

Renewable energyb

 6.06 6.0% 

Electricity Imports 0.09 0.1% 

Total Electric Power Energy Consumptionc

 41.18 40.9% 

Delivered Energy Use 72.41 72.0% 

Total Energy Use 100.59 100.0% 

a

Includes consumption of energy by electricity-only and combined heat and power plants whose primary business is 

to sell electricity, or electricity and heat, to the public. Includes small power producers and exempt wholesale 

generators. 

b

Includes conventional hydroelectric, geothermal, wood and wood waste, biogenic municipal solid waste, other 

biomass, petroleum coke, wind, photovoltaic and solar thermal sources. Excludes net electricity imports. 

c

Includes non-biogenic municipal waste not included above. 

Source: U.S. Energy Information Administration. 2009a. Supplemental Tables to the Annual Energy Outlook 2010. 

Table 2. Available at: <http://www.eia.doe.gov/oiaf/aeo/aeoref_tab.html>. 

5.5 Unfunded Mandates 

The UMRA requires that we estimate, where accurate estimation is reasonably feasible, 

future compliance costs imposed by the rule and any disproportionate budgetary effects. Our 

estimates of the future compliance costs of the final rule are discussed previously in Chapter 4 of 

this RIA. We do not believe that there will be any disproportionate budgetary effects of the final 

rule on any particular areas of the country, State or local governments, types of communities 

(e.g., urban, rural), or particular industry segments. 

5.5.1 Future and Disproportionate Costs 

The UMRA requires that we estimate, where accurate estimation is reasonably feasible, 

future compliance costs imposed by the rule and any disproportionate budgetary effects. Our 

estimates of the future compliance costs of the final rule are discussed previously in Chapter 4 of 

this RIA. We do not believe that there will be any disproportionate budgetary effects of the final 

rule on any particular areas of the country, State or local governments, types of communities 

(e.g., urban, rural), or particular industry segments. 

5.5.2 Effects on the National Economy 

The UMRA requires that we estimate the effect of the final rule on the national economy. 

To the extent feasible, we must estimate the effect on productivity, economic growth, full 

employment, creation of productive jobs, and international competitiveness of the U.S. goods 

and services if we determine that accurate estimates are reasonably feasible and that such effect 

is relevant and material. The nationwide economic impact of the final rule is presented earlier in 
5-12 

this RIA chapter. This analysis provides estimates of the effect of the final rule on most of the 

categories mentioned above, and these estimates are presented earlier in this RIA chapter. In 

addition, we have determined that the final rule contains no regulatory requirements that might 

significantly or uniquely affect small governments. Therefore, today’s rule is not subject to the 

requirements of section 203 of the UMRA. 
6-1 

SECTION 6

SMALL ENTITY SCREENING ANALYSIS 

The Regulatory Flexibility Act as amended by the Small Business Regulatory 

Enforcement Fairness Act (SBREFA) 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 governmental jurisdictions, and small not-for-profit enterprises. 

After considering the economic impact of the final rule on small entities, the screening 

analysis indicates that this final rule will not have a significant economic impact on a substantial 

number of small entities (or “SISNOSE”). Under the analyses EPA considered, sales and 

revenue tests for establishments owned by model small entities are less than 1% except electric 

power generation (NAICS 2211 with receipts less than $100,000 per year) and crop and animal 

production (NAICS 111 and 112 with receipts less than $25,000 per year). 

6.1 Small Entity Data Set 

The industry sectors covered by the final rule were identified during the development of 

the cost analysis (Nelson, 2010). The SUSB provides national information on the distribution of 

economic variables by industry and enterprise size (U.S. Census, 2006a, b).1

 The Census Bureau 

and the Office of Advocacy of the SBA supported and developed these files for use in a broad 

range of economic analyses.2 Statistics include the total number of establishments and receipts 

for all entities in an industry; however, many of these entities may not necessarily be covered by 

the final rule. SUSB also provides statistics by enterprise employment and receipt size. 

The Census Bureau’s definitions used in the SUSB are as follows: 

 Establishment: An establishment is a single physical location where business is 

conducted or where services or industrial operations are performed. 

 Receipts: Receipts (net of taxes) are defined as the revenue for goods produced, 

distributed, or services provided, including revenue earned from premiums, 

commissions and fees, rents, interest, dividends, and royalties. Receipts exclude all 

revenue collected for local, state, and federal taxes. 

 

1

The SUSB data do not provide establishment information for the national security NAICS code (92811) or irrigated 

farms. Since most national security installations are owned by the federal government (e.g., military bases), EPA 

assumes these entities would not be considered small. For irrigated farms, we relied on receipt data provided in 

the 2008 Farm and Irrigation Survey (USDA, 2009). 2

See http://www.census.gov/csd/susb/ and http://www.sba.gov/advo/research/data.html for additional details. 
6-2 

 Enterprise: An enterprise is a business organization consisting of one or more 

domestic establishments that were specified under common ownership or control. The 

enterprise and the establishment are the same for single-establishment firms. Each 

multiestablishment company forms one enterprise—the enterprise employment and 

annual payroll are summed from the associated establishments. Enterprise size 

designations are determined by the summed employment of all associated 

establishments. 

Because the SBA’s business size definitions (SBA, 2008) apply to an establishment’s 

“ultimate parent company,” we assumed in this analysis that the “enterprise” definition above is 

consistent with the concept of ultimate parent company that is typically used for SBREFA 

screening analyses and the terms are used interchangeably. 

6.2 Small Entity Economic Impact Measures 

The analysis generated a set of establishment sales tests (represented as cost-to-receipt 

ratios)3

 for NAICS codes associated with sectors listed in Table 6-1. Although the appropriate 

SBA size definition should be applied at the parent company (enterprise) level, we can only 

compute and compare ratios for a model establishment owned by an enterprise within an SUSB 

size range (employment or receipts). Using the SUSB size range helps us account for receipt 

differences between establishments owned by large and small enterprises and also allows us to 

consider the variation in small business definitions across affected industries. Using 

establishment receipts is also a conservative approach, because an establishment’s parent 

company (the “enterprise”) may have other economic resources that could be used to cover the 

costs of the final rule. 

6.2.1 Model Establishment Receipts and Annual Compliance Costs 

The sales test compares a representative establishment’s total annual engine costs to the 

average establishment receipts for enterprises in several size categories.4

 For industries with SBA 

employment size standards, we calculated average establishment receipts for each enterprise 

employment range (Table 6-2).5

 For industries with SBA receipt size standards, we calculated 

 

3

The following metrics for other small entity economic impact measures (if applicable) would potentially include 

• small governments (if applicable): “revenue” test; annualized compliance cost as a percentage of annual 

government revenues and 

• small nonprofits (if applicable): “expenditure” test; annualized compliance cost as a percentage of annual 

operating expenses, 4

For the 1 to 20 employee category, we excluded SUSB data for enterprises with zero employees. These enterprises 

did not operate the entire year. 5

We use 2002 Economic Census data in estimating number of establishments by industry instead of using 2007 

Economic Census since this data was not available in time for use in our analysis. The release schedules for 

different types of 2007 Economic Census data are at 

http://www.census.gov/econ/census07/pdf/EconCensusScheduleByDate.pdf. 
6-3 

Table 6-1. SI NESHAP for Existing Stationary Reciprocating Internal Combustion 

Engines (RICE): Affected Sectors and SBA Small Business Size Standards 

Industry Description 

Corresponding 

NAICS 

SBA Size Standard for 

Businesses (August 22nd, 

2008) 

Type of Small 

Entity 

Electric Power Generation 2211 a Business and 

government 

Natural Gas Transmission 48621 $7.0 million in annual receipts Business 

Crude Petroleum & NG Production 211111 500 employees Business 

Natural Gas Liquid Producers 211112 500 employees Business 

National Security 92811 NA Government 

Hydro Power Units See NAICS 2211 1,000 employees Business and 

government 

Irrigation Sets Affects NAICS 111 and 

112 

Generally $750,000 or less in 

annual receipts 

Business 

Welders Affects industries that 

use heavy equipment 

such as construction, 

mining, farming 

Varies by 6-digit NAICS code; Business 

Example industry: 

NAICS 238 = $14 million in 

annual receipts 

a

NAICS codes 221111, 221112, 221113, 221119, 221121, 221122: A firm is small if, including its affiliates, it is 

primarily engaged in the generation, transmission, and/or distribution of electric energy for sale and its total 

electric output for the preceding fiscal year did not exceed 4 million megawatt hours. 

average establishment receipts for each enterprise receipt range (Table 6-3). We included the 

utility sector in the second group, although the SBA size standard for this industry is defined in 

terms of physical units (megawatt hours) versus receipts. Crop and animal production (NAICS 

111 and 112) also have an SBA receipt size standard that defines a small business as receiving 

$750,000 or less in receipts per year. However, SUSB data were not available for these 

industries. Therefore, we conducted the sales test using the following range of establishment 

receipts: farms with annual receipts of $25,000 or less, farms with annual receipts of $100,000 or 

less, farms with annual receipts of $500,000 or less, and farms with annual receipts of $750,000 

or less. 
6-4

Table 6-2. Average Receipts for Affected Industry by Enterprise: 2002 ($2009 Million/Establishment) 

SBA Size Standard 

for Businesses 

(effective August 22, 2008)

Owned By Enterprises 

NAICS NAICS Description 

All 

Enterprises 

1-20 

employees 

20 to 99 

employees 

100 to 499 

employees 

211111 Crude Petroleum & NG Production 500 employees $14.76 $0.54 $6.79 $9.63 211112 Natural Gas Liquid Producers 500 employees $174.86 $0.31 NA $12.03 335312 Motor & generator mfg 1,000 employees $18.80 $1.38 $6.22 $16.15 333992 Welding & soldering equipment mfg 500 employees $18.73 $1.58 $6.67 $33.64 NA = Not available. Table 6-3. Average Receipts for Affected Industry by Enterprise Receipt Range: 2002 ($2009/Establish

Owned By Enterprises with Receipt 

NAICS NAICS Description 

SBA Size 

Standard 

for Businesses 

(effective August 

22nd, 2008) 

All 

Enterprises 

0-99K 

Receipts

100– 

499.9K 

Receipts 

500–

999.9K 

Receipts 

1,000–

4,999.9K 

Receipts 

5,000,000–

9,999,999K 

Receipts 

<10,000

K 

Receipts 

1

49

R

2211 Electric Power 

Generation 

a $40.23 $0.1 $0.3 $0.8 $3.1 $6.7 $2.7 $

48621 Natural Gas 

Transmission 

$7.0 million in 

annual receipts $22.09 $0.08 $0.32 $0.89 $2.51 $6.97 $1.57 

92811 National Security NA NA NA NA NA NA NA NA 

a NAICS codes 221111, 221112, 221113, 221119, 221121, 221122 – A firm is small if, including its affiliates, it is primarily engaged 

transmission, and/or distribution of electric energy for sale and its total electric output for the preceding fiscal year did not exceed 4 m

NA = Not available. SUSB did not report this data disclosure or other reasons. 
6-5 

Annual entity compliance costs vary depending on the size of the SI engines used at the 

affected establishment. Absent facility-specific information, we computed per-entity compliance 

costs based for three different cases based on representative establishments—Cases 1, 2, and 3 

(see Table 6-4). Each representative establishment differs based on the size and number of SI 

engines being used. Compliance costs are calculated by summing the total annualized 

compliance costs for the relevant engine categories, dividing the sum by the total existing 

population of those engines, and multiplying the average engine cost by the number of engines 

assumed to be at the establishment. Since NAICS 2211 and 48621 are fundamentally different 

than other industries considered in this analysis due to the number of engines affected and 

amount of cost incurred resulting from this final rule, we used different assumptions about what 

constitutes the representative establishment and report these assumptions separately. 

 Case 1: The representative establishment for all industries uses three 750+ hp engines 

with an average compliance cost of $8,500 per engine, resulting in a total annualized 

compliance cost of approximately $25,500 for this representative establishment. 

 Case 2: The representative establishment in NACIS 2211 and 48621 uses two 25 to 

750+ hp engines with an average compliance cost of $1,013 per engine, resulting in a 

total annualized compliance cost of $2,026 for this representative establishment. For 

all other industries, the representative establishment uses two 25 to 300 hp engines 

with an average compliance cost of $245 per engine, resulting in a total compliance 

cost of $490 for this representative establishment. 

 Case 3: The representative establishment for all industries uses two 50 to 100 hp 

engines with an average compliance cost of $73 per engine, resulting in a total 

compliance cost of $145 for this representative establishment. 

EPA believes that small entities are most likely to face costs similar to Case 2 (columns 

shaded in gray in Table 6-4) because most of the engines to be affected by this final rule in 

NAICS 335312, 333992, 211111, and 211112 are under 300 hp capacity, and most small entities 

in these industries will own engines of this size or smaller. This is corroborated by Figure 6-1 

and 6-2 which shows the distribution of engine population and compliance costs by engine size 

for all industries. However, it is difficult to make a similar claim for NAICS 2211 and 48621 

based on the existing distribution of engines in these industries.6

 

6

This claim also cannot be made for NAICS 92811: National Security. However, since most national security 

installations are owned by the federal government (e.g., military bases), EPA assumes these entities would not be 

considered small. 
6-6 

For the sales test, we divided the representative establishment compliance costs reported 

in Table 6-4 by the representative establishment receipts reported in Tables 6-2 and 6-3. This is 

known as the cost-to-receipt (i.e., sales) ratio, or the “sales test.” The “sales test” is the impact 

Table 6-4. Representative Establishment Costs Used for Small Entity Analysis ($2009) 

 Case 1 Case 2 Case 3 

 

NAICS 

2211, 48621 

(+750 hp 

only) 

All Other 

NAICS 

(+750 hp 

only) 

NAICS 

2211,48621 

(25-750+ hp) 

All Other 

NAICS 

(25–300 

hp) 

NAICS 

2211, 

48621 

(25–100 

hp only) 

All Other 

NAICS 

(25–100 

hp only) 

Total Annualized Costs ($) $89,716,669 $7,337,540 $203,582,405 $28,057,197 $8,510,985 $6,174,805 

Engine Population 10,548 863 200,974 114,517 117,040 84,913 

Average Engine Cost 

($/engine) $8,506 $8,502 $1,013 $245 $73 $73 

Assumed Engines Per 

Establishment 3 3 2 2 2 2 

Total Annualized Costs per 

Establishment $25,517 $25,507 $2,026 $490 $145 $145 
6-7 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Electric Power

Generation

(2211)

Natural Gas

Transmission

(48621)

Crude Petroleum

&NG Production

(211111)

Natural Gas

Liquid Producers

(211112)

National Security

(92811)

Hydro Power

Units (335312)

Irrigation Sets

(335312)

Welders

(333992)

>750 hp

600‐750 hp

500‐600 hp

300‐500 hp

175‐300 hp

100‐175 hp

50‐100 hp

25‐50 hp

Figure 6-1. Distribution of Engine Population by Size for All Industries 
6-8 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Electric Power

Generation

(2211)

Natural Gas

Transmission

(48621)

Crude Petroleum

&NG Production

(211111)

Natural Gas

Liquid Producers

(211112)

National Security

(92811)

Hydro Power

Units (335312)

Irrigation Sets

(335312)

Welders

(333992)

>750 hp

600‐750 hp

500‐600 hp

300‐500 hp

175‐300 hp

100‐175 hp

50‐100 hp

25‐50 hp

Figure 6-2. Distribution of Compliance Costs by Engine Size for All Industries 

methodology EPA employs in analyzing small entity impacts as opposed to a “profits test,” in 

which annualized compliance costs are calculated as a share of profits. 

This is because revenues or sales data are commonly available data for entities normally 

impacted by EPA regulations and profits data normally made available are often not the true 

profit earned by firms because of accounting and tax considerations. Revenues as typically 

published are usually correct figures and are more reliably reported when compared to profit 

data. The use of a “sales test” for estimating small business impacts for a rulemaking such as this 

one is consistent with guidance offered by EPA on compliance with SBREFA7

 and is consistent 

with guidance published by the U.S. SBA’s Office of Advocacy that suggests that cost as a 

percentage of total revenues is a metric for evaluating cost increases on small entities in relation 

to increases on large entities.8

 

7

The SBREFA compliance guidance to EPA rulewriters regarding the types of small business analysis that should be 

considered can be found at http://www.epa.gov/sbrefa/documents/rfafinalguidance06.pdf, pp. 24-25. 8

U.S. SBA, Office of Advocacy. A Guide for Government Agencies, How to Comply with the Regulatory 

Flexibility Act, Implementing the President’s Small Business Agenda and Executive Order 13272, May 2003. 
6-9 

If the cost-to-receipt ratio is less than 1%, then we consider the final rule to not have a 

significant impact on the establishment company in question. We summarize the industries with 

cost-to-receipt ratios exceeding 1% below: 

Primary Analysis: 

 Case 2: NAICS 2211 with receipts less than $100,000 per year and NAICS 111 and 

112 with receipts less than $25,000 per year 

 Case 3: No industries 

Sensitivity Analysis (unlikely): 

 Case 1: NAICS 2211 with receipts less than $100,000 per year 

In the Case 2 primary analysis, only establishments in NAICS 2211 with receipts less 

than $100,000 per year (less than 5 percent of the total), and establishments in NAICS 111 and 

112 with receipts less than $25,000 per year (around 30 percent of the total) have cost-to-receipt 

ratios above 1%. However, establishments earning this level of receipts are likely to be using 

smaller engines than those assumed in Case 2, such as 25 to 300 hp engines. The results of our 

Case 3 analysis demonstrate that these establishments are not significantly impacted when taking 

this engine size into account. 

After considering the economic impacts of this final rule on small entities, we certify that 

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

This certification is based on the economic impact of this final action to all affected small entities 

across all industries affected. We estimate that all small entities will have annualized costs of less 

than 1 percent of their sales in all industries except NAICS 2211 (electric power generation, 

transmission, and distribution) and NAICS 111 and 112 (Crop and Animal Production). The 

number of small entities in NAICS 2211 having annualized costs of greater than 1 percent of 

their sales is less than 5 percent, and the number of small entities in NAICS 111 and 112 having 

annualized costs of greater than 1 percent of their sales (but less than 2 percent of sales) is 30 

percent. We thus conclude that there is no significant economic impact on a substantial number 

of small entities (SISNOSE) for this rule. 

Although the final rule would not have a significant economic impact on a substantial 

number of small entities, EPA nonetheless tried to reduce the impact of the final rule on small 

entities. When developing the revised standards, EPA took special steps to ensure that the 

burdens imposed on small entities were minimal. EPA conducted several meetings with industry 
6-10 

trade associations to discuss regulatory options and the corresponding burden on industry, such 

as recordkeeping and reporting. In this rule, we are applying the minimum level of control (i.e., 

the MACT floor) to small non-emergency engines (below 300 HP) and emergency engines 

located at major HAP sources and the minimum level of testing, monitoring, recordkeeping, and 

reporting to affected RICE sources, both major and area, allowed by the CAA. Other alternatives 

considered that provided more than the minimum level of control were deemed as not technically 

feasible or cost-effective for EPA to implement for small non-emergency engines and emergency 

engines as explained earlier. 

6.3 Small Government Entities 

The rule also covers sectors that include entities owned by small and large governments. 

However, given the uncertainty and data limitations associated with identifying and 

appropriately classifying these entities, we computed a “revenue” test for a model small 

government, where the annualized compliance cost is a percentage of annual government 

revenues (U.S. Census, 2005a, b). The use of a “revenue test” for estimating impacts to small 

governments for a rulemaking such as this one is consistent with guidance offered by EPA on 

compliance with SBREFA,9

 and is consistent with guidance published by the US SBA’s Office 

of Advocacy.10 For example, from the 2002 Census (in 2008 dollars), the average revenue for 

small governments (counties and municipalities) with populations fewer than 10,000 is $3 

million per entity, and the average revenue for local governments with populations fewer than 

50,000 is $8 million per entity (U.S. Census Bureau, 2005a; U.S. Census Bureau, 2005b). For the 

smallest group of local governments (<10,000 people), the cost-to-revenue ratio would be 0.2% 

or less under each case. For the larger group of governments (<50,000 people), the cost-torevenue ratio is 0.1% or less under all cases. 

 

9

The SBREFA compliance guidance to EPA rule writers regarding the types of small business analysis that should 

be considered can be found at http://www.epa.gov/sbrefa/documents/rfafinalguidance06.pdf, pp. 24-25. 10U.S. SBA, Office of Advocacy. A Guide for Government Agencies, How to Comply with the Regulatory 

Flexibility Act, Implementing the President’s Small Business Agenda and Executive Order 13272, May 2003. 
7-1 

SECTION 7

HUMAN HEALTH BENEFITS OF EMISSIONS REDUCTIONS 

7.1 Synopsis 

In this section, we provide an estimate of the monetized co-benefits associated with 

reducing particulate matter (PM) for the final NESHAP for spark ignition reciprocating internal 

combustion engines (SI RICE). Specifically, we calculated the co-benefits of this rule in terms of 

the co-benefits associated with reducing PM rather than calculating the co-benefits associated 

with reducing hazardous air pollutants (HAPs). These PM reductions are a consequence of the 

technologies installed to reduce HAP emissions from SI RICE. These estimates reflect the 

monetized human health co-benefits of reducing cases of morbidity and premature mortality 

among populations exposed to the PM2.5 precursors reduced by this rulemaking. Using a 3% 

discount rate, we estimate the total monetized co-benefits of the final NESHAP to be $510 

million to $1.2 billion in the implementation year (2013). Using a 7% discount rate, we estimate 

the total monetized co-benefits of the final NESHAP to be $460 million to $1.1 billion in the 

implementation year. All estimates are in 2009$. 

These estimates reflect EPA’s most current interpretation of the scientific literature. 

Higher or lower estimates of benefits are possible using other assumptions; examples of this are 

provided in Figure 7-2. Data, resource, and methodological limitations prevented EPA from 

monetizing the benefits from several important benefit categories, including benefits from 

reducing hazardous air pollutants, ecosystem effects, and visibility impairment. The benefits 

from reducing other air pollutants have not been monetized in this analysis, including reducing 

109,000 tons of carbon monoxide and 6,000 tons of HAPs each year. 

7.2 Calculation of PM2.5 Human Health Co-Benefits 

This rulemaking would reduce emissions of NOx and VOCs. Because NOx and VOCs 

are precursors to PM2.5, reducing these emissions would also reduce PM2.5 formation, human 

exposure, and the incidence of PM2.5-related health effects. These PM reductions are a 

consequence of the technologies installed to reduce HAP emissions from SI RICE. Due to 

analytical limitations, it was not possible to provide a comprehensive estimate of PM2.5-related 

co-benefits. Instead, we used the “benefit-per-ton” approach to estimate these co-benefits based 

on the methodology described in Fann, Fulcher, and Hubbell (2009). The key assumptions are 

described in detail below. These PM2.5 benefit-per-ton estimates provide the total monetized 

human health co-benefits (the sum of premature mortality and premature morbidity) of reducing 

one ton of PM2.5 from a specified source. EPA has used the benefit per-ton technique in several 
7-2 

previous RIAs, including the recent NO2 NAAQS RIA (U.S. EPA, 2010b). Table 7-1 shows the 

quantified and unquantified co-benefits captured in those benefit-per-ton estimates. 

Table 7-1. Human Health and Welfare Effects of PM2.5 

Pollutant / 

Effect 

Quantified and Monetized in Primary 

Estimates Unquantified Effects Changes in: 

PM2.5 Adult premature mortality 

Bronchitis: chronic and acute 

Hospital admissions: respiratory and 

cardiovascular 

Emergency room visits for asthma 

Nonfatal heart attacks (myocardial infarction) 

Lower and upper respiratory illness 

Minor restricted-activity days 

Work loss days 

Asthma exacerbations (asthmatic population) 

Infant mortality 

Subchronic bronchitis cases 

Low birth weight 

Pulmonary function 

Chronic respiratory diseases other than chronic 

bronchitis 

Non-asthma respiratory emergency room visits 

Visibility 

Household soiling 

Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the PM2.5 co-benefits 

estimates utilize the concentration-response functions as reported in the epidemiology literature, 

as well as the 12 functions obtained in EPA’s expert elicitation study as a sensitivity analysis. 

 One estimate is based on the concentration-response (C-R) function developed from 

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope 

et al. (2002), a study that EPA has previously used to generate its primary benefits 

estimate. When calculating the estimate, EPA applied the effect coefficient as 

reported in the study without an adjustment for assumed concentration threshold of 10 

μg/m3 as was done in recent (2006-2009) Office of Air and Radiation RIAs. 

 One estimate is based on the C-R function developed from the extended analysis of 

the Harvard Six Cities cohort, as reported by Laden et al. (2006). This study, 

published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has 

been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 benefits 

estimates in RIAs completed since the PM2.5 NAAQS. When calculating the estimate, 

EPA applied the effect coefficient as reported in the study without an adjustment for 

assumed concentration threshold of 10 μg/m3 as was done in recent (2006-2009) 

RIAs. 

 Twelve estimates are based on the C-R functions from EPA’s expert elicitation study 

(IEc, 2006; Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted 

for benefits analysis in EPA’s final RIA for the PM2.5 NAAQS. For that study, twelve 
7-3 

experts (labeled A through L) provided independent estimates of the PM2.5 -mortality 

concentration-response function. EPA practice has been to develop independent 

estimates of PM2.5 -mortality estimates corresponding to the concentration-response 

function provided by each of the twelve experts, to better characterize the degree of 

variability in the expert responses. 

The effect coefficients are drawn from epidemiology studies examining two large 

population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six 

Cities cohort (Laden et al., 2006).1

 These are logical choices for anchor points in our presentation 

because, while both studies are well designed and peer reviewed, there are strengths and 

weaknesses inherent in each, which we believe argues for using both studies to generate cobenefits estimates. Previously, EPA had calculated co-benefits based on these two empirical 

studies, but derived the range of co-benefits, including the minimum and maximum results, from 

an expert elicitation of the relationship between exposure to PM2.5 and premature mortality 

(Roman et al., 2008).2 Within this assessment, we include the co-benefits estimates derived from 

the concentration-response function provided by each of the twelve experts to better characterize 

the uncertainty in the concentration-response function for mortality and the degree of variability 

in the expert responses. Because the experts used these cohort studies to inform their 

concentration-response functions, co-benefits estimates using these functions generally fall 

between results using these epidemiology studies (see Figure 7-2). In general, the expert 

elicitation results support the conclusion that the co-benefits of PM2.5 control are very likely to be 

substantial. 

Readers interested in reviewing the methodology for creating the benefit-per-ton 

estimates used in this analysis should consult Fann, Fulcher, and Hubbell (2009). As described in 

the documentation for the benefit per-ton estimates cited above, national per-ton estimates are 

developed for selected pollutant/source category combinations. The per-ton values calculated 

therefore apply only to tons reduced from those specific pollutant/source combinations (e.g., 

NOx emitted from electric generating units; NO2 emitted from mobile sources). Our estimate of 

PM2.5 control co-benefits is therefore based on the total NOx and VOC emissions controlled by 

sector and multiplied by this per-ton value. 

These models assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality because there is no clear scientific evidence that 

would support the development of differential effects estimates by particle type. NOx and VOCs 

 

1

 These two studies specify multi-pollutant models that control for NOx, among other pollutants. 

2

 Please see the Section 5.2 of the Portland Cement proposal RIA in Appendix 5A for more information regarding 

the change in the presentation of co-benefits estimates. 
7-4 

are the primary PM2.5 precursors affected by this rule. Even though we assume that all fine 

particles have equivalent health effects, the benefit-per-ton estimates vary between precursors 

because each ton of precursor reduced has a different propensity to form PM2.5. For example, 

NOX has a lower benefit-per-ton estimate than direct PM2.5 because it does not form as much 

PM2.5, thus the exposure would be lower, and the monetized health co-benefits would be lower. 

The benefit-per-ton coefficients in this analysis were derived using modified versions of 

the health impact functions used in the PM NAAQS Regulatory Impact Analysis. Specifically, 

this analysis uses the benefit-per-ton method first applied in the Portland Cement NESHAP RIA 

(U.S. EPA, 2009a), which incorporated three updates: a new population dataset, an expanded 

geographic scope of the benefit-per-ton calculation, and the functions directly from the 

epidemiology studies without an adjustment for an assumed threshold.3

 Removing the threshold 

assumption is a key difference between the method used in this analysis of PM co-benefits and 

the methods used in RIAs prior to Portland Cement, and we now calculate incremental cobenefits down to the lowest modeled PM2.5 air quality levels. 

EPA strives to use the best available science to support our benefits analyses, and we 

recognize that interpretation of the science regarding air pollution and health is dynamic and 

evolving. Based on our review of the current body of scientific literature, EPA now estimates 

PM-related mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by 

EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear 

model most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function. 

Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council 

concluded, “The HES fully supports EPA’s decision to use a no-threshold model to estimate 

mortality reductions. This decision is supported by the data, which are quite consistent in 

showing effects down to the lowest measured levels. Analyses of cohorts using data from more 

recent years, during which time PM concentrations have fallen, continue to report strong 

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” 

In conjunction with the underlying scientific literature, this document provided a basis for 

reconsidering the application of thresholds in PM2.5 concentration-response functions used in 

EPA’s RIAs. For a summary of these scientific review statements and the panel members 

 

3

 The benefit-per-ton estimates have also been updated since the Cement RIA to incorporate a revised VSL, as 

discussed on the next page. 
7-5 

commenting on thresholds since 2002, please consult the Technical Support Document (TSD) 

Summary of Expert Opinions on the Existence of a Threshold (U.S. EPA, 2010c), which is 

provided as an appendix to this RIA. 

Consistent with this recent scientific advice, we are replacing the previous threshold 

sensitivity analysis with a new “Lowest Measured Level” (LML) assessment. This information 

allows readers to determine the portion of population exposed to annual mean PM2.5 levels at or 

above the LML of each study; in general, our confidence in the estimated PM mortality 

decreases as we consider air quality levels further below the LML in major cohort studies that 

estimate PM-related mortality. While an LML assessment provides some insight into the level of 

uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold 

and continues to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations. It is important to emphasize that we have high confidence in PM2.5-related effects 

down to the lowest LML of the major cohort studies. Just because we have greater confidence in 

the benefits above the LML, this does not mean that we have no confidence that benefits occur 

below the LML. 

For this analysis, policy-specific air quality data is not available due to time or resource 

limitations. For these rules, we are unable to estimate the percentage of premature mortality 

associated with this specific rule’s emission reductions at each PM2.5 level. However, we believe 

that it is still important to characterize the distribution of exposure to baseline air quality levels. 

As a surrogate measure of mortality impacts, we provide the percentage of the population 

exposed at each PM2.5 level using the most recent modeling available from the recently proposed 

Transport Rule (U.S. EPA, 2010e). It is important to note that baseline exposure is only one 

parameter in the health impact function, along with baseline incidence rates population, and 

change in air quality. In other words, the percentage of the population exposed to air pollution 

below the LML is not the same as the percentage of the population experiencing health impacts 

as a result of a specific emission reduction policy. The most important aspect, which we are 

unable to quantify for rules without air quality modeling, is the shift in exposure associated with 

this specific rule. Therefore, caution is warranted when interpreting the LML assessment. For 

more information on the data and conclusions in the LML assessment for rules without policyspecific air quality modeling, please consult the LML TSD (U.S. EPA, 2010d), which is 

provided as an appendix to this RIA. The results of this analysis are provided in Section 7.4. 

As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods 

used to estimate air quality co-benefits evolve over time to reflect the Agency’s most current 

interpretation of the scientific and economic literature. For a period of time (2004-2008), the 
7-6 

Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical 

life (VSL) estimate derived from a limited analysis of some of the available studies. OAR arrived 

at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of 

the wage-risk literature. The $1 million value represented the lower end of the interquartile range 

from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 million value 

represented the upper end of the interquartile range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 million (2000$)4

 was also consistent with the 

mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis. However, the 

Agency neither changed its official guidance on the use of VSL in rule-makings nor subjected 

the interim estimate to a scientific peer-review process through the Science Advisory Board 

(SAB) or other peer-review group. 

During this time, the Agency continued work to update its guidance on valuing mortality 

risk reductions, including commissioning a report from meta-analytic experts to evaluate 

methodological questions raised by EPA and the SAB on combining estimates from the various 

data sources. In addition, the Agency consulted several times with the Science Advisory Board 

Environmental Economics Advisory Committee (SAB-EEAC) on the issue. With input from the 

meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, 

appropriate meta-analytic techniques to combine estimates from unique data sources and 

different studies, including those using different methodologies (i.e., wage-risk and stated 

preference) (U.S. EPA-SAB, 2007). 

Until updated guidance is available, the Agency determined that a single, peer-reviewed 

estimate applied consistently best reflects the SAB-EEAC advice it has received. Therefore, the 

Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines 

for Preparing Economic Analyses (U.S. EPA, 2000)5

 while the Agency continues its efforts to 

update its guidance on this issue. This approach calculates a mean value across VSL estimates 

derived from 26 labor market and contingent valuation studies published between 1974 and 

1991. The mean VSL across these studies is $6.3 million (2000$).6 The Agency is committed to 

using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions 

and has made significant progress in responding to the SAB-EEAC’s specific recommendations. 

 

4

. After adjusting the VSL for a different currency year (2009$) and to account for income growth to 2015 to the 

$5.5 million value, the VSL is $7.9 million. 5

 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008), EPA retained the VSL endorsed by the SAB 

with the understanding that further updates to the mortality risk valuation guidance would be forthcoming in the 

near future. Therefore, this report does not represent final agency policy. 6

 In this analysis, we adjust the VSL to account for a different currency year (2009$) and to account for income 

growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $9.1 million. 
7-7 

Figure 7-1 illustrates the relative breakdown of the monetized PM2.5 health co-benefits by 

health endpoint. 

Adult Mortality ‐ Pope et

al. 93%

ChronicBronchitis 4%

AMI 2%

Acute Respiratory Symptoms

0.5%

InfantMortality 0.4%

Work Loss Days 0.2%

HospitalAdmissions, Cardio

0.2%

HospitalAdmissions, Resp

0.04%

AsthmaExacerbation 0.01%

Acute Bronchitis 0.01%

UpperResp Symp 0.00%

LowerResp Symp 0.00%

ERVisits, Resp 0.00%

Other1%

Figure 7-1. Breakdown of Monetized PM2.5 Health Co-Benefits using Mortality Function 

from Pope et al. (2002)a

a

 This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example. Using the 

Laden et al. (2006) function for premature mortality, the percentage of total monetized co-benefits due to adult 

mortality would be 97%. This chart shows the breakdown using a 3% discount rate, and the results would be 

similar if a 7% discount rate was used. 

Table 7-2 provides a general summary of the monetized co-benefits results by pollutant, 

including the emission reductions and benefits-per-ton estimates at discount rates of 3% and 7%.7

Table 7-3 provides a summary of the reductions in health incidences anticipated as a result of the 

pollution reductions. In Table 7-4, we provide the monetized co-benefits using our anchor points 

of Pope et al. and Laden et al. as well as the results from the expert elicitation on PM mortality. 

 

7

 To comply with Circular A-4, EPA provides monetized co-benefits using discount rates of 3% and 7% (OMB, 

2003). These co-benefits are estimated for a specific analysis year (i.e., 2013), and most of the PM co-benefits 

occur within that year with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For 

AMIs, we assume 5 years of follow-up medical costs and lost wages. For premature mortality, we assume that 

there is a “cessation” lag between PM exposures and the total realization of changes in health effects. Although 

the structure of the lag is uncertain, EPA follows the advice of the SAB-HES to assume a segmented lag 

structure characterized by 30% of mortality reductions in the first year, 50% over years 2 to 5, and 20% over the 

years 6 to 20 after the reduction in PM2.5 (U.S. EPA-SAB, 2004). Changes in the lag assumptions do not change 

the total number of estimated deaths but rather the timing of those deaths. Therefore, discounting only affects the 

AMI costs after the analysis year and the valuation of premature mortalities that occur after the analysis year. As 

such, the monetized co-benefits using a 7% discount rate are only approximately 10% less than the monetized 

co-benefits using a 3% discount rate. 
7-8 

Figures 7-2 and 7-3 provide a visual representation of the range of monetized co-benefits 

estimates and the pollutant breakdown of the monetized co-benefits of the proposed option. The 

final NESHAP is the MACT floor level of control for all major SI RICE sources except for fourstroke rich-burn (4SRB) engines of 300-500 horsepower (HP), where the required level of 

control is above the MACT floor, and the GACT level of control for area SI RICE sources. We 

also show results for an alternative in which only the MACT level of control is applied to all SI 

RICE major sources. 
7-9 

Table 7-2. Summary of Monetized Co-Benefits Estimates for the Final Spark Ignition 

NESHAP in 2013 (2009$)a

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope, 

3%) 

Benefit 

per ton 

(Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit 

per ton 

(Laden, 

7%) 

Total 

Monetized 

Benefits 

(millions 2008$ 

at 3%) 

Total 

Monetized 

Benefits 

(millions 

2008$ at 7%)

Final NESHAP: 

Majorb

PM2.5 Precursors 

VOC 6,730 $1,200 $3,000 $1,100 $2,700 $8.2 to $20 $7.4 to $18 

NOX 0 $4,900 $12,000 $4,400 $11,000 $0 $0 

 Total $12to $30 $11to$27 

Alternative 1: 

Major 

PM2.5 Precursors 

VOC 7,265 $1,200 $3,000 $1,100 $2,700 $8.8 to $22 $8.0 to $20 

NOX 8,040 $4,900 $12,000 $4,400 $11,000 $39 to $95 $35 to $86 

 Total $48to $120 $43to$110 

Final: Area 

Source onlyc 

PM2.5 Precursors 

VOC 24,177 $1,200 $3,000 $1,100 $2,700 $29 to $72 $27 to $65 

NOX 96,479 $4,900 $12,000 $4,400 $11,000 $470 to $1,100 $420 to $1,000

 Total $500to $1,200 $450to$1,100

Final: Major and 

Area Total 

PM2.5 Precursors 

VOC 30,907 $1,200 $3,000 $1,100 $2,700 $38 to $92 $34 to $83 

NOX 96,479 $4,900 $12,000 $4,400 $11,000 $470 to $1,100 $420 to $1,000

 Total $510to $1,200 $460to$1,100

 a All estimates are for the implementation year (2013), and are rounded to two significant figures so numbers may 

not sum across columns. All fine particles are assumed to have equivalent health effects, but the benefit per ton 

estimates vary because each ton of precursor reduced has a different propensity to become PM2.5. The monetized 

co-benefits incorporate the conversion from precursor emissions to ambient fine particles. Confidence intervals 

are unavailable for this analysis because of the benefit-per-ton methodology. 

b The final NESHAP is the MACT floor level of control for all major SI RICE sources, and the GACT level of 

control for area SI RICE sources. We also show results for an alternative (referred to as Alternative 2) in which 

the MACT level of control is applied to all SI RICE major sources except for four-stroke rich-burn (4SRB) 

engines of 300-500 horsepower (HP), where the required level of control is above the MACT floor and the GACT 

level of control is applied to all SI RICE area sources. 

c All of the benefits for area sources are attributable to reductions expected from 4SLB and 4SRB non-emergency 

engines above 500 HP. 
7-10 

Table 7-3. Summary of Reductions in Health Incidences from PM2.5 Co-Benefits for Final 

SI RICE NESHAP in 2013a

 Final NESHAP: 

Majorb

Alternative 2: 

Major 

Final: 

Area Source 

onlyc

Final: Major 

and Area 

Sources TOTAL

Avoided Premature Mortality 

Pope et al. 1 2 16 17 

Laden et al. 2 5 42 44 

Avoided Morbidity 

Chronic Bronchitis 1 2 12 12 

Acute Myocardial Infarction 2 4 31 33 

Hospital Admissions, Respiratory 0 1 4 4 

Hospital Admissions, Cardiovascular 0 1 8 9 

Emergency Room Visits, Respiratory 1 2 12 13 

Acute Bronchitis 2 3 27 29 

Work Loss Days 130 280 2,200 2,400 

Asthma Exacerbation 16 37 300 310 

Minor Restricted Activity Days 740 1,700 13,000 14,000 

Lower Respiratory Symptoms 18 41 320 340 

Upper Respiratory Symptoms 14 31 240 260 

a

 All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures. All 

fine particles are assumed to have equivalent health effects, but each PM2.5 precursor pollutant has a different 

propensity to form PM2.5. Confidence intervals are unavailable for this analysis because of the benefit-per-ton 

methodology. 

b

The final NESHAP is the MACT floor level of control for all major SI RICE sources and the GACT level of 

control for area SI RICE sources. We also show results for an alternative (referred to as Alternative 2) in which 

the MACT level of control is applied to all SI RICE major sources except for four-stroke rich-burn (4SRB) 

engines of 300-500 horsepower (HP), where the level of control is above the MACT floor, and the GACT level of 

control is applied to all area SI RICE sources. 

c All of the benefits for area sources are attributable to reductions expected from 4SLB and 4SRB non-emergency 

engines above 500 HP. 
7-11 

Table 7-4. All Monetized PM2.5 Co-Benefits Estimates for the Final SI RICE NESHAP at 

discount rates of 3% and 7% in 2013 (in millions of 2009$)a

Final NESHAP: 

Majorb

Alternative 2: 

Major Final: Area Source onlyc Final: Major and Area 

Sources Total 

 3% 7% 3% 7% 3% 7% 3% 7% 

Benefit-per-ton Coefficients derived from Epidemiology Literature

Pope et al. $8.2 $7.4 $48 $43 $500 $450 $510 $460 

Laden et al. $20 $18 $120 $110 $1,200 $1,100 $1,200 $1,100 

Benefit-per-ton Coefficients Derived from Expert Elicitation

Expert A $21 $19 $124 $112 $1,300 $1,160 $1,300 $1,200 

Expert B $16 $15 $95 $86 $1,000 $900 $1,000 $910 

Expert C $16 $15 $95 $85 $1,000 $900 $1,000 $900 

Expert D $11 $10 $67 $61 $700 $600 $710 $640 

Expert E $26 $24 $153 $139 $1,600 $1,400 $1,600 $1,500 

Expert F $15 $13 $86 $78 $900 $800 $910 $820 

Expert G $10 $9 $57 $51 $590 $530 $600 $540 

Expert H $12 $11 $71 $65 $700 $700 $750 $680 

Expert I $16 $14 $94 $85 $1,000 $900 $990 $890 

Expert J $13 $12 $76 $69 $800 $700 $810 $730 

Expert K $3.3 $3.0 $19 $18 $200 $190 $210 $190 

Expert L $12 $11 $70 $63 $700 $700 $740 $670 

a

 All estimates are rounded to two significant figures. Estimates do not include confidence intervals because they 

were derived through the benefit-per-ton technique described above. The co-benefits estimates from the Expert 

Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated 

with the concentration-response function. Confidence intervals are unavailable for this analysis because of the 

benefit-per-ton methodology. 

b The final NESHAP is the MACT floor level of control for all major SI RICE and the GACT level of control for 

area SI RICE sources. We also show results for an alternative (referred to as Alternative 2) in which the MACT 

level of control is applied to all SI RICE major sources except for four-stroke rich-burn (4SRB) engines of 300-

500 horsepower (HP), where the required level of control is above the MACT floor, and the GACT level of 

controls is applied to all area SI RICE sources. 

c All of the benefits for area sources are attributable to reductions expected from 4SLB and 4SRB non-emergency 

engines above 500 HP. 
7-12 

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

$1,600

Pope et al.

Laden et al.

Millions(2009$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

3% DR

7% DR

Figure 7-2. Total Monetized PM2.5 Co-Benefits for the Final SI RICE NESHAP in 2013 

a

 This graph shows the estimated co-benefits at discount rates of 3% and 7% using effect coefficients derived from 

the Pope et al. study and the Laden et al. study, as well as 12 effect coefficients derived from EPA’s expert 

elicitation on PM mortality. The results shown are not the direct results from the studies or expert elicitation; 

rather, the estimates are based in part on the concentration-response function provided in those studies. 
7-13 

VOC (major)

2%

VOC (area)

6%

NOx (major)

0%

NOx (area)

92%

Figure 7-3. Breakdown of Monetized Co-Benefits for the Final SI RICE NESHAP by 

PM2.5 Precursor Pollutant and Source 

7.3 Unquantified Benefits 

The monetized co-benefits estimated in this RIA only reflect the portion of co-benefits 

attributable to the health effect reductions associated with ambient fine particles. Data, resource, 

and methodological limitations prevented EPA from quantifying or monetizing the benefits from 

several important benefit categories, including benefits from reducing toxic emissions, 

ecosystem effects, and visibility impairment. The health co-benefits from reducing hazardous air 

pollutants (HAPs) and carbon monoxide each year have not been monetized in this analysis. In 

addition to being a PM2.5 precursor, NOx emissions also contribute to adverse effects from acidic 

deposition in aquatic and terrestrial ecosystems, increase mercury methylation, as well as 

visibility impairment. 

7.3.1 Carbon Monoxide Benefits 

Carbon monoxide (CO) exposure is associated with a variety of health effects. Without 

knowing the location of the emission reductions and the resulting ambient concentrations using 

fine-scale air quality modeling, we were unable to estimate the exposure to CO for nearby 

populations. Due to data, resource, and methodological limitations, we were unable to estimate 
7-14 

the benefits associated with the reductions in CO emissions that would occur as a result of this 

rule. 

Carbon monoxide in ambient air is formed primarily by the incomplete combustion of 

carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO 

emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the 

combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon 

inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia 

(reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple 

tissues and organ systems that are dependent upon concentration and duration of exposure. 

The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded 

that short-term exposure to CO is “likely to have a causal relationship” with cardiovascular 

morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies 

associate short-term CO exposure with increased risk of emergency department visits and 

hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac 

chest pain), as well as those who have experienced a heart attack. Other subpopulations 

potentially at risk include individuals with diseases such as chronic obstructive pulmonary 

disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as 

older adults or the developing young. The evidence is suggestive of a causal relationship 

between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also 

suggestive of a causal relationship for birth outcomes and developmental effects following longterm exposure to CO, and for central nervous system effects linked to short- and long-term 

exposure to CO. 

7.3.2 Other NOx Benefits 

In addition to being a precursor to PM2.5, NOx emissions are also associated with a 

variety of respiratory health effects. Unfortunately, we were unable to estimate the health 

benefits associated with reduced NOx exposure in this analysis because we do not have air 

quality modeling data available. Without knowing the location of the emission reductions and the 

resulting ambient concentrations, we were unable to estimate the exposure to NOx for nearby 

populations. Therefore, this analysis only quantifies and monetizes the PM2.5 co-benefits 

associated with the reductions in NOx emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment (ISA) for Nitrogen Dioxide concluded that there is a 

likely causal relationship between respiratory health effects and short-term exposure to NO2 
7-15 

(U.S. EPA, 2008a). Persons with preexisting respiratory disease, children, and older adults may 

be more susceptible to the effects of NO2 exposure. Based on our review of this information, we 

identified four short-term morbidity endpoints that the NO2 ISA identified as a “likely causal 

relationship”: asthma exacerbation, respiratory-related emergency department visits, and 

respiratory-related hospitalizations. The differing evidence and associated strength of the 

evidence for these different effects is described in detail in the NO2 ISA. The NO2 ISA also 

concluded that the relationship between short-term NO2 exposure and premature mortality was 

“suggestive but not sufficient to infer a causal relationship” because it is difficult to attribute the 

mortality risk effects to NO2 alone. Although the NO2 ISA stated that studies consistently 

reported a relationship between NO2 exposure and mortality, the effect was generally smaller 

than that for other pollutants such as PM. 

NOx emissions also contribute to adverse welfare effects from acidic deposition, nutrient 

enrichment, and visibility impairment. Deposition of nitrogen causes acidification, which can 

cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic 

ecosystems, as well as a decline in sensitive tree species, such as red spruce (Picea rubens) and 

sugar maple (Acer saccharum) in terrestrial ecosystems. In the northeastern United States, the 

surface waters affected by acidification are a source of food for some recreational and 

subsistence fishermen and for other consumers and support several cultural services, including 

aesthetic and educational services and recreational fishing. Biological effects of acidification in 

terrestrial ecosystems are generally linked to aluminum toxicity, which can cause reduced root 

growth, which restricts the ability of the plant to take up water and nutrients. These direct effects 

can, in turn, increase the sensitivity of these plants to stresses, such as droughts, cold 

temperatures, insect pests, and disease leading to increased mortality of canopy trees. Terrestrial 

acidification affects several important ecological services, including declines in habitat for 

threatened and endangered species (cultural), declines in forest aesthetics (cultural), declines in 

forest productivity (provisioning), and increases in forest soil erosion and reductions in water 

retention (cultural and regulating). (U.S. EPA, 2008d) 

Deposition of is also associated with aquatic and terrestrial nutrient enrichment. In 

estuarine waters, excess nutrient enrichment can lead to eutrophication. Eutrophication of 

estuaries can disrupt an important source of food production, particularly fish and shellfish 

production, and a variety of cultural ecosystem services, including water-based recreational and 

aesthetic services. Terrestrial nutrient enrichment is associated with changes in the types and 

number of species and biodiversity in terrestrial systems. Excessive nitrogen deposition upsets 

the balance between native and nonnative plants, changing the ability of an area to support 
7-16 

biodiversity. When the composition of species changes, then fire frequency and intensity can 

also change, as nonnative grasses fuel more frequent and more intense wildfires. (U.S. EPA, 

2008d) 

Reducing NOX emissions and the secondary formation of PM2.5 would improve the level 

of visibility throughout the United States. Fine particles with significant light-extinction 

efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). 

These suspended particles and gases degrade visibility by scattering and absorbing light. Higher 

visibility impairment levels in the East are due to generally higher concentrations of fine 

particles, particularly sulfates, and higher average relative humidity levels. Visibility has direct 

significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good 

visibility increases the quality of life where individuals live and work, and where they engage in 

recreational activities. 

7.3.3 Ozone Co-Benefits 

In the presence of sunlight, NOx and VOCs can undergo a chemical reaction in the 

atmosphere to form ozone. Reducing ambient ozone concentrations is associated with significant 

human health benefits, including mortality and respiratory morbidity (U.S. EPA, 2008). 

Epidemiological researchers have associated ozone exposure with adverse health effects in 

numerous toxicological, clinical and epidemiological studies (U.S. EPA, 2006c). These health 

effects include respiratory morbidity such as fewer asthma attacks, hospital and ER visits, school 

loss days, as well as premature mortality. 

7.3.4 HAP Benefits 

Americans are exposed to ambient concentrations of air toxics at levels which have the 

potential to cause adverse health effects.8

 The levels of air toxics to which people are exposed 

vary depending on where people live and work and the kinds of activities in which they engage. 

In order to identify and prioritize air toxics, emission source types and locations which are of 

greatest potential concern, U.S. EPA conducts the National-Scale Air Toxics Assessment 

(NATA). The most recent NATA was conducted for calendar year 2002, and was released in 

June 2009.9 NATA for 2002 includes four steps: 

1) Compiling a national emissions inventory of air toxics emissions from outdoor sources 

 

8

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 9

 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 
7-17 

2) Estimating ambient concentrations of air toxics across the United States 

3) Estimating population exposures across the United States 

4) Characterizing potential public health risk due to inhalation of air toxics including both 

cancer and noncancer effects 

Noncancer health effects can result from chronic,10 subchronic,11 or acute12 inhalation 

exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory 

effects as well as effects on the immune and reproductive systems. According to the 2002 

NATA, nearly the entire U.S. population was exposed to an average concentration of air toxics 

that has the potential for adverse noncancer respiratory health effects. 13 Figures 7-4 and 7-5 

depict estimated county-level carcinogenic risk and noncancer respiratory hazard from the 

assessment. The respiratory hazard is dominated by a single pollutant, acrolein. 

 

10 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database 

(http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than 

approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used 

laboratory animal species). 11 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 12 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 

13 The NATA modeling framework has a number of limitations which prevent its use as the sole basis for setting 

regulatory standards. These limitations and uncertainties are discussed on the 2002 NATA website. Even so, this 

modeling framework is very useful in identifying air toxic pollutants and sources of greatest concern, setting 

regulatory priorities, and informing the decision making process. U.S. EPA. (2009) 2002 National-Scale Air 

Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 
7-18 

Figure 7-4. Estimated County Level Carcinogenic Risk from HAP exposure from 

outdoor sources (from 2002 NATA) 
7-19 

Figure 7-5. Estimated County Level Noncancer (Respiratory) Risk from HAP exposure 

from outdoor sources (from 2002 NATA)

Due to data, resource, and methodology limitations, we were unable to estimate the 

benefits associated with the thousands tons of hazardous air pollutants that would be reduced as a 

result of this rule. Available emissions data show that several different HAPs are emitted from SI 

RICE, either contained within the fuel burned or formed during the combustion process. 

Although numerous HAPs may be emitted from SI RICE, a few HAPs account for over 

90% of the total mass of HAPs emissions emitted. These HAPs are formaldehyde (72%), 

acetaldehyde (8%), acrolein (7%), methanol (3%), and benzene (3%). Although we do not have 

estimates of emission reductions for each HAP, this rule is anticipated to reduce 6,000 tons of 

HAPs each year. Below we describe the health effects associated with the top 5 HAPs by mass 

emitted from SI RICE. 

7.3.4.1 Formaldehyde 

 Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on 

evidence in humans and in rats, mice, hamsters, and monkeys.14 EPA is currently reviewing 

 

14 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to 

Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 
7-20 

recently published epidemiological data. For instance, research conducted by the National 

Cancer Institute (NCI) found an increased risk of nasopharyngeal cancer and 

lymphohematopoietic malignancies such as leukemia among workers exposed to 

formaldehyde.15,16 In an analysis of the lymphohematopoietic cancer mortality from an extended 

follow-up of these workers, NCI confirmed an association between lymphohematopoietic cancer 

risk and peak exposures.17 A recent National Institute of Occupational Safety and Health 

(NIOSH) study of garment workers also found increased risk of death due to leukemia among 

workers exposed to formaldehyde.18 Extended follow-up of a cohort of British chemical workers 

did not find evidence of an increase in nasopharyngeal or lymphohematopoietic cancers, but a 

continuing statistically significant excess in lung cancers was reported.19

 In the past 15 years there has been substantial research on the inhalation dosimetry for 

formaldehyde in rodents and primates by the CIIT Centers for Health Research (formerly the 

Chemical Industry Institute of Toxicology), with a focus on use of rodent data for refinement of 

the quantitative cancer dose-response assessment.20,21,22 CIIT’s risk assessment of formaldehyde 

incorporated mechanistic and dosimetric information on formaldehyde. However, it should be 

noted that recent research published by EPA indicates that when two-stage modeling 

assumptions are varied, resulting dose-response estimates can vary by several orders of 

magnitude.23,24,25,26 These findings are not supportive of interpreting the CIIT model results as 

 

15 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2003. Mortality from lymphohematopoetic 

malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615-1623. 16 Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among 

workers in formaldehyde industries. American Journal of Epidemiology 159: 1117-1130. 17 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. 

Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National 

Cancer Institute cohort. J. National Cancer Inst. 101: 751-761. 18 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. 

Occup. Environ. Med. 61: 193-200. 19 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow-up of a cohort of British chemical workers 

exposed to formaldehyde. J National Cancer Inst. 95:1608-1615. 20 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2003. Biologically 

motivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432-447. 21 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human respiratory 

tract cancer risks of inhaled formaldehyde: Dose-response predictions derived from biologically-motivated 

computational modeling of a combined rodent and human dataset. Tox Sci 82: 279-296. 22 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characterization and dose-response 

assessment for carcinogenicity by the route of inhalation. CIIT, September 28, 1999. Research Triangle Park, NC. 23 U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal Growth Models for Formaldehyde with 

Relevance to Other Biologically-Based Dose Response (BBDR) Models. U.S. Environmental Protection Agency, 

Washington, D.C., EPA/600/R-08/103, 2008 24 Subramaniam, R; Chen, C; Crump, K; .et .al. (2008) Uncertainties in biologically-based modeling of 

formaldehyde-induced cancer risk: identification of key issues. Risk Anal 28(4):907-923. 25 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 
7-21 

providing a conservative (health protective) estimate of human risk.27 EPA research also 

examined the contribution of the two-stage modeling for formaldehyde towards characterizing 

the relative weights of key events in the mode-of-action of a carcinogen. For example, the 

model-based inference in the published CIIT study that formaldehyde’s direct mutagenic action 

is not relevant to the compound’s tumorigenicity was found not to hold under variations of 

modeling assumptions.28

Based on the developments of the last decade, in 2004, the working group of the IARC 

concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient 

evidence in humans and sufficient evidence in experimental animals - a higher classification than 

previous IARC evaluations. After reviewing the currently available epidemiological evidence, 

the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as 

“sufficient,” based upon the data on nasopharyngeal cancers; the epidemiologic evidence on 

leukemia was characterized as “strong.”29 EPA is reviewing the recent work cited above from the 

NCI and NIOSH, as well as the analysis by the CIIT Centers for Health Research and other 

studies, as part of a reassessment of the human hazard and dose-response associated with 

formaldehyde. 

Formaldehyde exposure also causes a range of noncancer health effects, including 

irritation of the eyes (burning and watering of the eyes), nose and throat. Effects from repeated 

exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial 

lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also 

cause airway inflammation – including eosinophil infiltration into the airways. There are several 

studies that suggest that formaldehyde may increase the risk of asthma – particularly in the 

young.30,31

 26 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 27 Crump, K; Chen, C; Fox, J; .et .al. (2008) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 28 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2-stage model for formaldehydeinduced nasal cancer in the F344 rat: a limited sensitivity analysis-I. Risk Anal 27:1237 29 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2-

ol. Monographs Volume 88. World Health Organization, Lyon, France. 30 Agency for Toxic Substances and Disease Registry (ATSDR). 1999. Toxicological profile for Formaldehyde. 

Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. 

http://www.atsdr.cdc.gov/toxprofiles/tp111.html 31 WHO (2002) Concise International Chemical Assessment Document 40: Formaldehyde. Published under the joint 

sponsorship of the United Nations Environment Programme, the International Labour Organization, and the 

World Health Organization, and produced within the framework of the Inter-Organization Programme for the 

Sound Management of Chemicals. Geneva. 
7-22 

7.3.4.2 Acetaldehyde 

Acetaldehyde is classified in EPA’s IRIS database as a probable human carcinogen, 

based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous 

routes.32 Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. DHHS in 

the 11th Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) 

by the IARC.33,34 EPA is currently conducting a reassessment of cancer risk from inhalation 

exposure to acetaldehyde. 

The primary noncancer effects of exposure to acetaldehyde vapors include irritation of 

the eyes, skin, and respiratory tract.35 In short-term (4 week) rat studies, degeneration of olfactory 

epithelium was observed at various concentration levels of acetaldehyde exposure.36, 37 Data from 

these studies were used by EPA to develop an inhalation reference concentration. Some 

asthmatics have been shown to be a sensitive subpopulation to decrements in functional 

expiratory volume (FEV1 test) and bronchoconstriction upon acetaldehyde inhalation.38 The 

agency is currently conducting a reassessment of the health hazards from inhalation exposure to 

acetaldehyde. 

7.3.4.3 Acrolein 

 EPA determined in 2003 that the human carcinogenic potential of acrolein could not be 

determined because the available data were inadequate. No information was available on the 

carcinogenic effects of acrolein in humans and the animal data provided inadequate evidence of 

 

32 U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrated Risk Information System File of 

Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. 

This material is available electronically at http://www.epa.gov/iris/subst/0290.htm. 33 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

available at: http://ntp.niehs.nih.gov/go/16183. 34 International Agency for Research on Cancer (IARC). 1999. Re-evaluation of some organic chemicals, hydrazine, 

and hydrogen peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of Chemical to Humans, Vol 

71. Lyon, France. 35 U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrated Risk Information System File of 

Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. 

This material is available electronically at http://www.epa.gov/iris/subst/0290.htm. 36 U.S. Environmental Protection Agency (U.S. EPA). 1991. Integrated Risk Information System File of 

Acetaldehyde. Research and Development, National Center for Environmental Assessment, Washington, DC. 

This material is available electronically at http://www.epa.gov/iris/subst/0290.htm. 37 Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982). Inhalation toxicity of acetaldehyde in rats. I. Acute and 

subacute studies. Toxicology. 23: 293-297. 38 Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993) Aerosolized acetaldehyde induces 

histamine-mediated bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1): 940-943. 
7-23 

carcinogenicity.39 The IARC determined in 1995 that acrolein was not classifiable as to its 

carcinogenicity in humans.40

Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure 

resulting in upper respiratory tract irritation, mucus hypersecretion and congestion. The intense 

irritancy of this carbonyl has been demonstrated during controlled tests in human subjects, who 

suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure.41 These 

data and additional studies regarding acute effects of human exposure to acrolein are 

summarized in EPA’s 2003 IRIS Human Health Assessment for acrolein.42 Evidence available 

from studies in humans indicate that levels as low as 0.09 ppm (0.21 mg/m3) for five minutes 

may elicit subjective complaints of eye irritation with increasing concentrations leading to more 

extensive eye, nose and respiratory symptoms.43 Lesions to the lungs and upper respiratory tract 

of rats, rabbits, and hamsters have been observed after subchronic exposure to acrolein.44 Acute 

exposure effects in animal studies report bronchial hyper-responsiveness.45 In a recent study, the 

acute respiratory irritant effects of exposure to 1.1 ppm acrolein were more pronounced in mice 

with allergic airway disease by comparison to non-diseased mice which also showed decreases in 

respiratory rate.46 Based on these animal data and demonstration of similar effects in humans 

(i.e., reduction in respiratory rate), individuals with compromised respiratory function (e.g., 

emphysema, asthma) are expected to be at increased risk of developing adverse responses to 

strong respiratory irritants such as acrolein. 

 

39 U.S. Environmental Protection Agency (U.S. EPA). 2003. Integrated Risk Information System File of Acrolein. 

Research and Development, National Center for Environmental Assessment, Washington, DC. This material is 

available at http://www.epa.gov/iris/toxreviews/0364tr.pdf. 40 International Agency for Research on Cancer (IARC). 1995. Monographs on the evaluation of carcinogenic risk 

of chemicals to humans, Volume 63, Dry cleaning, some chlorinated solvents and other industrial chemicals, 

World Health Organization, Lyon, France. 41 U.S. Environmental Protection Agency (U.S. EPA). 2003. Integrated Risk Information System File of Acrolein. 

EPA/635/R-03/003. p. 10. Research and Development, National Center for Environmental Assessment, 

Washington, DC. This material is available at http://www.epa.gov/iris/toxreviews/0364tr.pdf. 42 U.S. Environmental Protection Agency (U.S. EPA). 2003. Integrated Risk Information System File of Acrolein. 

2003. Research and Development, National Center for Environmental Assessment, Washington, DC. 

EPA/635/R-03/003. This material is available at http://www.epa.gov/iris/toxreviews/0364tr.pdf. 43 U.S. Environmental Protection Agency (U.S. EPA). 2003. Integrated Risk Information System File of Acrolein. 

Research and Development, National Center for Environmental Assessment, Washington, DC. EPA/635/R03/003. p. 11. This material is available at http://www.epa.gov/iris/toxreviews/0364tr.pdf. 44 U.S. Environmental Protection Agency (U.S. EPA). 2003. Integrated Risk Information System File of Acrolein. 

Research and Development, National Center for Environmental Assessment, Washington, DC. EPA/635/R-03/003. 

This material is available at http://www.epa.gov/iris/toxreviews/0364tr.pdf. 45 U.S. Environmental Protection Agency (U.S. EPA). 2003. Integrated Risk Information System File of Acrolein. 

Research and Development, National Center for Environmental Assessment, Washington, DC. EPA/635/R-03/003. 

This material is available at http://www.epa.gov/iris/toxreviews/0364tr.pdf. 46 Morris JB, Symanowicz PT, Olsen JE, et al. 2003. Immediate sensory nerve-mediated respiratory responses to 

irritants in healthy and allergic airway-diseased mice. J Appl Physiol 94(4):1563-1571. 
7-24 

7.3.4.4 Methanol 

Exposure of humans to methanol by inhalation or ingestion may result in central nervous 

system depression and degenerative changes in the brain and visual systems. After inhaled or 

ingested, methanol is converted to formate, a highly toxic metabolite that within the course of a 

few hours can cause narcosis, metabolic acidosis, headaches, severe abdominal and leg pain and 

visual degeneration that can lead to blindness.47

Methanol has been demonstrated to cause developmental toxicity in rats and mice, and 

reproductive and developmental toxicity in monkeys. A number of studies have reported adverse 

effects in the offspring of rats and mice exposed to methanol by inhalation including reduced 

weight of brain pituitary gland, thymus, thyroid, reduced overall fetal body weight and increased 

incidence of extra ribs and cleft palate.48,49,50 Methanol inhalation studies using rhesus monkeys 

have reported a decrease in the length of pregnancy, and limited evidence of impaired learning 

ability in offspring.51,52,53,54 EPA has not classified methanol with respect to its carcinogenicity. 

7.3.4.5 Benzene 

The EPA’s IRIS database lists benzene as a known human carcinogen (causing leukemia) 

by all routes of exposure, and concludes that exposure is associated with additional health 

effects, including genetic changes in both humans and animals and increased proliferation of 

bone marrow cells in mice.55,56,57 EPA states in its IRIS database that data indicate a causal 

 

47 Rowe, VK and McCollister, SB. 1981. Alcohols. In: Patty's Industrial Hygiene and Toxicology, 3rd ed. Vol. 2C, 

GD Clayton, FE Clayton, Eds. John Wiley & Sons, New York, pp. 4528-4541. 48 New Energy Development Organization (NEDO). 1987. Toxicological research of methanol as a fuel for power 

station: summary report on tests with monkeys, rats and mice. Tokyo, Japan. 49 Nelson, BK; Brightwell, WS; MacKenzie, DR; Khan, A; Burg, JR; Weigel, WW; Goad, PT. 1985. Teratological 

assessment of methanol and ethanol at high inhalation levels in rats. Toxicol Sci, 5: 727-736. 50 Rogers, JM; Barbee, BD; Rehnberg, BF. 1993. Critical periods of sensitivity for the developmental toxicity of 

inhaled methanol. Teratology, 47: 395. 51 Burbacher, T; Grant, K; Shen, D; Damian, D; Ellis, S; Liberato, N. 1999. Reproductive and offspring 

developmental effects following maternal inhalation exposure to methanol in nonhuman primates Part II: 

developmental effects in infants exposed prenatally to methanol. Health Effects Institute. Cambridge, MA. 52 Burbacher, T; Shen, D; Grant, K; Sheppard, L; Damian, D; Ellis, S; Liberato, N. 1999. Reproductive and 

offspring developmental effects following maternal inhalation exposure to methanol in nonhuman primates Part I: 

methanol disposition and reproductive toxicity in adult females. Health Effects Institute. Cambridge, MA. 53 Burbacher, TM; Grant, KS; Shen, DD; Sheppard, L; Damian, D; Ellis, S; Liberato, N. 2004. Chronic maternal 

methanol inhalation in nonhuman primates (Macaca fascicularis): reproductive performance and birth outcome. 

Neurotoxicol Teratol, 26: 639-650. 54 Burbacher, TM; Shen, DD; Lalovic, B; Grant, KS; Sheppard, L; Damian, D; Ellis, S; Liberato, N. 2004. Chronic 

maternal methanol inhalation in nonhuman primates (Macaca fascicularis): exposure and toxicokinetics prior to 

and during pregnancy. Neurotoxicol Teratol, 26: 201-221. 55 U.S. Environmental Protection Agency (U.S. EPA). 2000. Integrated Risk Information System File for Benzene. 

Research and Development, National Center for Environmental Assessment, Washington, DC. This material is 
7-25 

relationship between benzene exposure and acute lymphocytic leukemia and suggest a 

relationship between benzene exposure and chronic non-lymphocytic leukemia and chronic 

lymphocytic leukemia. The International Agency for Research on Carcinogens (IARC) has 

determined that benzene is a human carcinogen and the U.S. Department of Health and Human 

Services (DHHS) has characterized benzene as a known human carcinogen.58,59

A number of adverse noncancer health effects including blood disorders, such as preleukemia 

and aplastic anemia, have also been associated with long-term exposure to benzene.60,61 The 

most sensitive noncancer effect observed in humans, based on current data, is the depression of 

the absolute lymphocyte count in blood.62,63 In addition, recent work, including studies sponsored 

by the Health Effects Institute (HEI), provides evidence that biochemical responses are occurring 

at lower levels of benzene exposure than previously known.64,65,66,67 EPA’s IRIS program has not 

yet evaluated these new data. 

 available electronically at: http://www.epa.gov/iris/subst/0276.htm. 56 International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic risk of 

chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs, International Agency for Research on 

Cancer, World Health Organization, Lyon, France, p. 345-389, 1982. 57 Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of the benzene metabolite 

hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony-stimulating factor in vitro, 

Proc. Natl. Acad. Sci. 89:3691-3695. 58 International Agency for Research on Cancer (IARC). 1987. Monographs on the evaluation of carcinogenic risk 

of chemicals to humans, Volume 29, Supplement 7, Some industrial chemicals and dyestuffs, World Health 

Organization, Lyon, France. 59 U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

available at: http://ntp.niehs.nih.gov/go/16183. 60 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193-197. 61 Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541-554. 

62 Rothman, N., G.L. Li, M. Dosemeci, W.E. Bechtold, G.E. Marti, Y.Z. Wang, M. Linet, L.Q. Xi, W. Lu, M.T. 

Smith, N. Titenko-Holland, L.P. Zhang, W. Blot, S.N. Yin, and R.B. Hayes (1996) Hematotoxicity among 

Chinese workers heavily exposed to benzene. Am. J. Ind. Med. 29: 236-246. 63 U.S. Environmental Protection Agency (U.S. EPA). 2000. Integrated Risk Information System File for Benzene 

(Noncancer Effects). Research and Development, National Center for Environmental Assessment, Washington, 

DC. This material is available electronically at: http://www.epa.gov/iris/subst/0276.htm. 64 Qu, O.; Shore, R.; Li, G.; Jin, X.; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport, S.; Li, H.; Rupa, 

D.; Suramaya, R.; Songnian, W.; Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.; 

Zhang, X.; Li, K. (2003). HEI Report 115, Validation & Evaluation of Biomarkers in Workers Exposed to 

Benzene in China. 

65 Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes among Chinese 

workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 275-285. 66 Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers Exposed to Low Levels 

of Benzene. Science 306: 1774-1776. 67 Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to human exposure from 

Urban Air. Research Reports Health Effect Inst. Report No.113.
7-26 

7.3.4.6 Other Air Toxics 

In addition to the compounds described above, other compounds from SI RICE would be 

affected by this rule. Information regarding the health effects of these compounds can be found 

in EPA’s IRIS database.68

7.4 Characterization of Uncertainty in the Monetized PM2.5 Co-Benefits 

In any complex analysis, there are likely to be many sources of uncertainty. Many inputs 

are used to derive the final estimate of economic co-benefits, including emission inventories, air 

quality models (with their associated parameters and inputs), epidemiological estimates of 

concentration-response (C-R) functions, estimates of values, population estimates, income 

estimates, and estimates of the future state of the world (i.e., regulations, technology, and human 

behavior). For some parameters or inputs it may be possible to provide a statistical representation 

of the underlying uncertainty distribution. For other parameters or inputs, the necessary 

information is not available. Because we used the benefit-per-ton approach for this analysis, 

confidence intervals are unavailable. 

The annual benefit estimates presented in this analysis are also inherently variable due to 

the processes that govern pollutant emissions and ambient air quality in a given year. Factors 

such as hours of equipment use and weather are constantly variable, regardless of our ability to 

measure them accurately. As discussed in the PM2.5 NAAQS RIA (Table 5.5) (U.S. EPA, 

2006a), there are a variety of uncertainties associated with these PM co-benefits. Therefore, the 

estimates of annual co-benefits should be viewed as representative of the magnitude of cobenefits expected, rather than the actual co-benefits that would occur every year. 

It is important to note that the monetized benefit-per-ton estimates used here reflect 

specific geographic patterns of emissions reductions and specific air quality and co-benefits 

modeling assumptions. For example, these estimates do not reflect local variability in population 

density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of 

these $/ton values to estimate co-benefits associated with different emission control programs 

(e.g., for reducing emissions from large stationary sources like EGUs) may lead to higher or 

lower benefit estimates than if co-benefits were calculated based on direct air quality modeling. 

Great care should be taken in applying these estimates to emission reductions occurring in any 

specific location, as these are all based on national or broad regional emission reduction 

programs and therefore represent average co-benefits-per-ton over the entire United States. The 

 

68 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 
7-27 

co-benefits- per-ton for emission reductions in specific locations may be very different than the 

estimates presented here. 

Understanding the transport of pollutants is a critical component for estimating exposure 

and the associated human health benefits from reducing air pollution emissions. The underlying 

emissions modeling and air quality modeling supporting the PM2.5 co-benefits analysis accounts 

for the current distribution of emissions sources, including both urban and rural sources. In 

addition, the air quality modeling included 14 vertical layers to simulate the differences between 

ground-level emissions and higher stack emissions (U.S. EPA, 2006b). The distance that HAPs 

travel away from the emission source depends on several factors. HAPs such as formaldehyde, 

acetaldehyde, acrolein, methanol, and benzene are emitted as gases. Regional photochemical 

model simulations, examining particular scenarios, have shown that gaseous HAPs like 

formaldehyde and acetaldehyde can be transported hundreds of kilometers from their emissions 

source in distinct plumes (U.S. EPA, 2010f). Further, these emissions can contribute to regional 

airmasses with elevated concentrations of gaseous HAPs. These polluted airmasses can be 

transported thousands of kilometers and affect locations well distant from the original emissions 

source. For the SI RICE examined in this rule, EPA does not have enough information to 

determine the extent of transport specific to the HAPs reduced. 

PM2.5 mortality benefits are the largest benefit category that we monetized in this 

analysis. To better characterize the uncertainty associated with mortality impacts that are 

estimated to occur in areas with low baseline levels of PM2.5, we included the LML assessment. 

Without policy-specific air quality modeling, we are unable to quantify the shift in exposure 

associated with this specific rule. For this rule, as a surrogate measure of mortality impacts, we 

provide the percentage of the population exposed at each PM2.5 level using the most recent 

modeling available from the recently proposed Transport Rule (U.S. EPA, 2010e). A very large 

proportion of the population is exposed at or above the lowest LML of the cohort studies 

(Figures 7-6 and 7-7), increasing our confidence in the PM mortality analysis. Figure 7-6 shows 

a bar chart of the percentage of the population exposed to various air quality levels in the preand post-policy policy. Figure 7-7 shows a cumulative distribution function of the same data. 

Both figures identify the LML for each of the major cohort studies. As the policy shifts the 

distribution of air quality levels, fewer people are exposed to PM2.5 levels at or above the LML. 

Using the Pope et al. (2002) study, the 85% of the population is exposed to annual mean PM2.5

levels at or above the LML of 7.5 μg/m3. Using the Laden et al. (2006) study, 40% of the 

population is exposed above the LML of 10 μg/m3. As we model mortality impacts among 

populations exposed to levels of PM2.5 that are successively lower than the LML of the lowest 
7-28 

cohort study, our confidence in the results diminishes. However, the analysis above confirms that 

the great majority of the impacts occur at or above the lowest cohort study’s LML. It is important 

to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of 

the major cohort studies. Just because we have greater confidence in the benefits above the LML, 

this does not mean that we have no confidence that benefits occur below the LML. 
7-29 

0%

5%

10%

15%

20%

25%

1 2 3 4 5 5.8 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Post‐control Baseline

The control strategy lowers PM2.5

levels substantially, particularly 

among highly exposed 

populations. In the baseline, 85% 

of the population lived in areas 

where PM2.5 levels above the 

lowest measured levels of the 

Pope study, increasing our 

confidence in the estimated 

mortality reductions for this rule.

Pope et al. 2002 Laden et al. 2006

Figure 7-6. Percentage of Adult Population by Annual Mean PM2.5 

Exposure (pre- and post-policy policy) 

Figure 7-7. Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels 

(pre- and post-policy policy) 
7-30 

Above we present the estimates of the total monetized co-benefits, based on our 

interpretation of the best available scientific literature and methods and supported by the SABHES and the NAS (NRC, 2002). The co-benefits estimates are subject to a number of 

assumptions and uncertainties. For example, for key assumptions underlying the estimates for 

premature mortality, which typically account for at least 90% of the total monetized co-benefits, 

we were able to quantify include the following: 

1. PM2.5 co-benefits were derived through benefit per-ton estimates, which do not reflect 

local variability in population density, meteorology, exposure, baseline health 

incidence rates, or other local factors that might lead to an over-estimate or underestimate of the actual co-benefits of controlling directly emitted fine particulates. 

2. We assume that all fine particles, regardless of their chemical composition, are 

equally potent in causing premature mortality. This is an important assumption, 

because PM2.5 produced via transported precursors emitted from EGUs may differ 

significantly from direct PM2.5 released from diesel engines and other industrial 

sources, but no clear scientific grounds exist for supporting differential effects 

estimates by particle type. 

3. We assume that the health impact function for fine particles is linear down to the 

lowest air quality levels modeled in this analysis. Thus, the estimates include health 

co-benefits from reducing fine particles in areas with varied concentrations of PM2.5,

including both regions that are in attainment with fine particle standard and those that 

do not meet the standard down to the lowest modeled concentrations. 

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized co-benefits), 

we include a set of twelve estimates based on results of the expert elicitation study in 

addition to our core estimates. Even these multiple characterizations omit the 

uncertainty in air quality estimates, baseline incidence rates, populations exposed and 

transferability of the effect estimate to diverse locations. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the 

overall uncertainty in the PM2.5 estimates. This information should be interpreted 

within the context of the larger uncertainty surrounding the entire analysis. For more 

information on the uncertainties associated with PM2.5 co-benefits, please consult the 

PM2.5 NAAQS RIA (Table 5-5). 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA because we lack the necessary air quality input and monitoring data to run the cobenefits model. Moreover, it was not possible to develop benefit-per-ton metrics and associated 

estimates of uncertainty using the co-benefits estimates from the PM RIA because of the 

significant differences between the sources affected in that rule and those regulated here. 

However, the results of the Monte Carlo analyses of the health and welfare co-benefits presented 
7-31 

in Chapter 5 of the PM NAAQS RIA can provide some evidence of the uncertainty surrounding 

the co-benefits results presented in this analysis. 

7.5 Comparison of Co-Benefits and Costs 

Using a 3% discount rate, we estimate the total combined monetized co-benefits of the 

final SI RICE NESHAP to be $510 million to $1.2 billion in the implementation year (2013). 

Using a 7% discount rate, we estimate the total monetized co-benefits of the final rule to be $460 

million to $1.1 billion. The annualized social costs of the final NESHAP are $253 million at a 

7% interest rate.69 Thus, the net benefits are $250 million to $980 million at a 3% discount rate 

and $210 million to $860 million at a 7% discount rate. All estimates are in 2009$ for the year 

2013. 

Table 7-5 shows a summary of the monetized co-benefits, social costs, and net benefits 

for the final SI RICE NESHAP, respectively. Figures 7-8 and 7-9 show the full range of net 

benefits estimates (i.e., annual co-benefits minus annualized costs) utilizing the 14 different 

PM2.5 mortality functions at discount rates of 3% and 7%. In addition, the benefits from reducing 

109,000 tons of carbon monoxide and 6,000 tons of HAPs each year from SI RICE have not 

been included in these estimates. EPA believes that the co-benefits are likely to exceed the costs 

under this rulemaking even when taking into account uncertainties in the cost and benefit 

estimates. As mentioned earlier in this RIA, the final NESHAP is the MACT floor level of 

control for all SI RICE major sources and the GACT level of control for all SI RICE area 

sources. We show results in Table 7-5 for an alternative (referred to as “Alternative 2”) which is 

a more stringent alternative than the final NESHAP for major sources. For this alternative, the 

MACT floor level of control is applied to all SI RICE major sources except for four-stroke richburn (4SRB) engines of 300-500 horsepower (HP), where the level of control is above the 

MACT floor, and the GACT level of control is applied to all area SI RICE sources. 

 

69 For more information on the annualized social costs, please refer to Section 5 of this RIA. 
7-32 

Table 7-5. Summary of the Monetized Co-Benefits, Social Costs, and Net benefits for the 

Final SI RICE NESHAP in 2013 (millions of 2009$)1

 3% Discount Rate 7% Discount Rate 

Final NESHAP: Major4

Total Monetized Benefits2 $8.2 to $20 $7.4 to $18 

Total Social Costs3 $88 $88 

Net Benefits -$80 to -$68 -$81 to -$70 

Non-monetized Benefits 

12,500 tons of carbon monoxide 

2,000 tons of hazardous air pollutants (HAPs) 

Ecosystem effects 

Visibility impairment 

Alternative 2: Major 

Total Monetized Benefits2 $48 to $120 $43 to $110 

Total Social Costs3 $95 $95 

Net Benefits -$47 to $22 -$52 to $11 

Non-monetized Benefits 

17,800 tons of carbon monoxide 

1,400 tons of hazardous air pollutants (HAPs) 

Health effects from NO2 and ozone exposure 

Ecosystem effects 

Visibility impairment 

Final NESHAP: Area5

Total Monetized Benefits2 $500 to $1,200 $450 to $1,100 

Total Social Costs3 $166 $166 

Net Benefits $330 to $1,100 $290 to $930 

Non-monetized Benefits 

97,000 tons of carbon monoxide 

4,700 tons of hazardous air pollutants (HAPs) 

Health effects from NO2 and ozone exposure 

Ecosystem effects 

Visibility impairment 

Final Major and Area Source NESHAP 

Total Monetized Benefits2 $510 to $1,200 $460 to $1,100 

Total Social Costs3 $253 $253 

Net Benefits $250 to $980 $210 to $860 

Non-monetized Benefits 

109,000 tons of carbon monoxide 

6,000 tons of hazardous air pollutants (HAPs) 

Health effects from NO2 and ozone exposure 

Ecosystem effects 

Visibility impairment 

1

All estimates are for the implementation year (2013), and are rounded to two significant figures. 

2

 The total monetized co-benefits reflect the human health co-benefits associated with reducing exposure to PM2.5

through reductions of PM2.5 precursors such as NOx and VOC. It is important to note that the monetized cobenefits include many but not all health effects associated with PM2.5 exposure. It is important to note that the 

monetized benefits include many but not all health effects associated with PM2.5 exposure. Benefits are shown as a 

range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of 

their chemical composition, are equally potent in causing premature mortality because there is no clear scientific 

evidence that would support the development of differential effects estimates by particle type. 

3

 The annual compliance costs serve as a proxy for the annual social costs of this rule given the lack of difference 

between the two. 
7-33 

4

The final NESHAP is the MACT floor level of control for all major SI RICE non-emergency sources. We also 

show results for Alternative 2, an more stringent alternative than the final NESHAP for major sources. In this 

alternative, the MACT level of control is applied to all SI RICE major non-emergency sources except for fourstroke rich-burn (4SRB) engines of 300-500 horsepower (HP), where the level of control is above the MACT 

floor, and the GACT level of control is applied to all area SI RICE sources. 

5 All of the benefits for area sources are attributable to reductions expected from 4SLB and 4SRB non-emergency 

engines above 500 HP. 
7-34 

‐$200

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Pope et al.

Laden et al.

Millions(2009$)

Cost estimate combined with total monetized benefits estimates derived from2

epidemiology functions and 12 expert functions

Figure 7-8. Net Benefits for the Final SI RICE NESHAP at 3% Discount Rate a

a

 Net Benefits are quantified in terms of PM2.5 co-benefits for implementation year (2013). This graph shows 14 

benefits estimates combined with the cost estimate. All combinations are treated as independent and equally 

probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary 

because each ton of precursor reduced has a different propensity to become PM2.5. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles. 
7-35 

‐$200

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Pope et al.

Laden et al.

Millions(2009$)

Cost estimate combined with total monetized benefits estimates derived from2

epidemiology functions and 12 expert functions

Figure 7-9. Net Benefits for the Final SI RICE NESHAP at 7% Discount Rate a

a

 Net benefits are quantified in terms of PM2.5 co-benefits for implementation year (2013). This graph shows 14 cobenefits estimates combined with the cost estimate. All combinations are treated as independent and equally 

probable. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary 

because each ton of precursor reduced has a different propensity to become PM2.5. The monetized co-benefits 

incorporate the conversion from precursor emissions to ambient fine particles. 
8-1 

SECTION 8

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8-2 

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8-3 

Technical Report: RICE NESHAP Control Costs Background for "Above the Floor Analysis", 

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2002.” 
8-4 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 23: 

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(January 27, 2010). 

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States: 2007” 

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Manufacturing: Industry Series: Historical Statistics for the Industry: 2002 and Earlier 

Years” <http://factfinder.census.gov>; (November 25, 2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Industry Series: Comparative Statistics for the United 

States (1997 NAICS Basis): 2002 and 1997” <http://factfinder.census.gov>; 

(December 12, 2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Subject Series—Estab & Firm Size: Concentration by 

Largest Firms for the United States: 2002” <http://factfinder.census.gov>; (December 12, 

2008). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

Transportation and Warehousing: Industry Series: Summary Statistics for the United 

States: 2002” <http://factfinder.census.gov>; (January 27, 2010). 

U.S. Census Bureau; generated by RTI International; using American FactFinder; “Sector 48: 

EC0748I1: Transportation and Warehousing: Industry Series: Preliminary Summary 

Statistics for the United States: 2007.” http://factfinder.census.gov (January 27, 2010). 

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<http://factfinder.census.gov>; (November 10, 2008). 

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Organization for the United States: 2002” <http://factfinder.census.gov>; (December 12, 

2008). 

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Health Care and Social Assistance: Geographic Area Series: Summary Statistics: 2002.” 

<http://factfinder.census.gov>; (November 10, 2008). 

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Health Care and Social Assistance: Subject Series—Estab & Firm Size: Legal Form of 

Organization for the United States: 2002” <http://factfinder.census.gov>; (November 21, 

2008). 
8-5 

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Health Care and Social Assistance: Geographic Area Series: 2002 and 2007.” 

<http://factfinder.census.gov>; (January 27, 2010). 

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DC. Table 12. 

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Municipal and Township Governments: 2002 GC02(4)-4. U.S. Government Printing 

Office, Washington, DC. Table 13. 

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sectors: Geographic Area Series: Economy-Wide Key Statistics: 2007” 

<http://factfinder.census.gov>; (July 7, 2010). 

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Ranch Irrigation Survey.” Washington, DC: USDA-NASS. Table 1. 

U.S. Department of Agriculture, National Agricultural Statistics Service. 2009. “2008 Farm and 

Ranch Irrigation Survey.” Washington, DC: USDA-NASS. Table 20. 

U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). 2008. 

“2008 Farm and Ranch Irrigation Survey.” Washington, DC: USDA-NASS. Table 34. 

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<http://www.ers.usda.gov/Publications/OCE081/OCE20081c.pdf>. 

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Monthly Time Series.” 

U.S. Department of Energy, Energy Information Administration. 2009. “State Electricity Profiles 

2008.” DOE/EIA-0348(01)/2. p. 260. < 

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U.S. Energy Information Administration. 2008a. <http://www.eia.doe.gov/cneaf/electricity/page/ 

restructuring/restructure_elect.html>. Last updated September 2008. 

U.S. Energy Information Administration. 2009. Supplemental Tables to the Annual Energy 

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Control of Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Washington, 

DC: EPA. < http://www.epa.gov/nonroad-diesel/2004fr/420r04007.pdf>. 
8-6 

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National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Available at 

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U.S. Environmental Protection Agency (U.S. EPA). 2008a. Integrated Science Assessment for 

Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental 

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<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008c. Guidelines for Preparing Economic 

Analyses: External Review Draft. National Center for Environmental Economics, Office 

of Policy Economics and Innovation. Washington, DC. Available at 

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01.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008d. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report). National 

Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. 

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Manufacturing Industry. Office of Air Quality Planning and Standards, Research 

Triangle Park, NC. April. Available at 

<http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandcementria_4-20-09.pdf >. 
8-7 

U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available at 

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Assessment. Office of Air Quality Planning and Standards. June. Available at 

<http://www.epa.gov/ttn/atw/nata2002/> 

U.S. Environmental Protection Agency (U.S. EPA). 2010a. Integrated Science Assessment for 

Carbon Monoxide. National Center for Environmental Assessment, Research Triangle 

Park, NC. EPA/600/R-09/019F. January. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=218686>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010b. Final Regulatory Impact Analysis 

(RIA) for the NO2 National Ambient Air Quality Standards (NAAQS). Office of Air 

Quality Planning and Standards, Research Triangle Park, NC. January. Available at < 

http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf>. 

U.S. Environmental Protection Agency (U.S. EPA). 2010c. Summary of Expert Opinions on the 

Existence of a Threshold in the Concentration-Response Function for PM2.5-related 

Mortality: Technical Support Document. Compiled by Office of Air Quality Planning and 

Standards, Research Triangle Park, NC. July. Available at 

<http://www.epa.gov/ttn/ecas/benefits.html>. 

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Assessment for Rules without Policy-Specific Air Quality Data Available: Technical 

Support Document. Office of Air Quality Planning and Standards, Research Triangle 

Park, NC. July. Available at <http://www.epa.gov/ttn/ecas/benefits.html>. 

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Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

July. Available at <http://www.epa.gov/ttn/ecas/ria.html>. 

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Support Document: Changes to the Renewable Fuel Standard Program, Office of Air 

Quality Planning and Standards, EPA 454/R-10-001. February. Available at 

<http://www.epa.gov/OMS/renewablefuels/454r10001.pdf>. 

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Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990-2020. Advisory by 

the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance 

Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available at 

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0433D5D/$File/council_adv_04002.pdf>. 
8-8 

U.S. Environmental Protection Agency—Science Advisory Board (U.S. EPA-SAB). 2007. SAB 

Advisory on EPA’s Issues in Valuing Mortality Risk Reduction. EPA-SAB-08-001. 

October. Available at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F

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Review of EPA’s Integrated Science Assessment for Particulate Matter (First External 

Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

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Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: 

Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-

009. May. Available at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86

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Matched to North American Industry Classification System Codes.” Effective August 22, 

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tablepdf.pdf>. 

Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market 

Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. 

Wade, S.H. 2003. “Price Responsiveness in the AEO2003 NEMS Residential and Commercial 

Buildings Sector Models.” 

<http://www.eia.doe.gov/oiaf/analysispaper/elasticity/pdf/buildings.pdf>. 
APPENDIX A 

SUMMARY OF EXPERT OPINIONS ON THE EXISTENCE OF A THRESHOLD IN 

THE CONCENTRATION-RESPONSE FUNCTION FOR PM2.5-RELATED 

MORTALITY 
A-1 

Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM2.5-related Mortality 

Technical Support Document (TSD) 

June 2010 

Compiled by: 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impact Division 

Air Benefit-Cost Group 

Research Triangle Park, North Carolina

Contents: 

A. HES comments on 812 Analysis (2010) 

B. American Heart Association Scientific Statement (2010) 

C. Integrated Science Assessment for Particulate Matter (2009) 

D. CASAC comments on PM ISA and REA (2009) 

E. Krewski et al. (2009) 

F. Schwartz et al. (2008) 

G. Expert Elicitation on PM Mortality (2006, 2008) 

H. CASAC comments on PM Staff Paper (2005) 

I. HES comments on 812 Analysis (2004) 

J. NRC (2002) 
A-2 

A. HES Comments on 812 Analysis (2010) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2010. 

Review of EPA’s DRAFT Health Benefits of the Second Section 812 Prospective Study of 

the Clean Air Act. EPA-COUNCIL-10-001. June. Available on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB28525774500738776/$

File/EPA-COUNCIL-10-001-unsigned.pdf>. 

Pg 2: “The HES generally agrees with other decisions made by the EPA project team with 

respect to PM, in particular, the PM mortality effect threshold model, the cessation lag model, 

the inclusion of infant mortality estimation, and differential toxicity of PM.” 

Pg 2: “Further, the HES fully supports EPA’s use of a no-threshold model to estimate the 

mortality reductions associated with reduced PM exposure.” 

Pg 6: “The HES also supports the Agency’s choice of a no-threshold model for PM-related 

effects.” 

Pg 13: “The HES fully supports EPA’s decision to use a no-threshold model to estimate mortality 

reductions. This decision is supported by the data, which are quite consistent in showing effects down to 

the lowest measured levels. Analyses of cohorts using data from more recent years, during which time 

PM concentrations have fallen, continue to report strong associations with mortality. Therefore, there is 

no evidence to support a truncation of the CRF.” 

HES Panel Members

Dr. John Bailar, Chair of the Health Effects Subcommittee, Scholar in Residence, The National 

Academies, Washington, DC 

Dr. Michelle Bell, Associate Professor, School of Forestry and Environmental Studies, Yale 

University, New Haven, CT 

Dr. James K. Hammitt, Professor, Department of Health Policy and Management, Harvard 

School of Public Health, Boston, MA 

Dr. Jonathan Levy, Associate Professor, Department of Environmental Health, Harvard School 

of Public Health, Boston, MA 

Dr. C. Arden Pope, III Professor, Department of Economics, Brigham Young University, 

Provo, UT 

Mr. John Fintan Hurley, Research Director, Institute of Occupational Medicine (IOM), 

Edinburgh, United Kingdom, UK 

Dr. Patrick Kinney, Professor, Department of Environmental Health Sciences, Mailman School 

of Public Health, Columbia University, New York, NY 

Dr. Michael T. Kleinman, Professor, Department of Medicine, Division of Occupational and 

Environmental Medicine, University of California, Irvine, Irvine, CA 
A-3 

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency, Oakland, CA 

Dr. Rebecca Parkin, Professor and Associate Dean, Environmental and Occupational Health, 

School of Public Health and Health Services, The George Washington University Medical 

Center, Washington, DC 
A-4 

B. Scientific Statement from American Heart Association (2010) 

Brook RD, Rajagopalan S, Pope CA 3rd, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin 

F, Hong Y, Luepker RV, Mittleman MA, Peters A, Siscovick D, Smith SC Jr, Whitsel L, 

Kaufman JD; on behalf of the American Heart Association Council on Epidemiology and 

Prevention, Council on the Kidney in Cardiovascular Disease, and Council on Nutrition, 

Physical Activity and Metabolism. (2010). “Particulate matter air pollution and 

cardiovascular disease: an update to the scientific statement from the American Heart 

Association.” Circulation. 121: 2331-2378. 

Pg 2338: “Finally, there appeared to be no lower-limit threshold below which PM10 was not 

associated with excess mortality across all regions.” 

Pg 2350: “There also appears to be a monotonic (e.g., linear or log-linear) concentrationresponse relationship between PM2.5 and mortality risk observed in cohort studies that extends 

below present-day regulations of 15 μg/m3

 for mean annual levels, without a discernable “safe” 

threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008)

Pg 2364: “The PM2.5 concentration– cardiovascular risk relationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m3 (the 2006 annual NAAQS 

level) without a discernable “safe” threshold.” 

Pg 2365: “This updated review by the AHA writing group corroborates and strengthens the 

conclusions of the initial scientific statement. In this context, we agree with the concept and 

continue to support measures based on scientific evidence, such as the US EPA NAAQS, that 

seek to control PM levels to protect the public health. Because the evidence reviewed supports 

that there is no safe threshold, it appears that public health benefits would accrue from lowering 

PM2.5 concentrations even below present-day annual (15 μg/m3) and 24-hour (35 μg/m3) 

NAAQS, if feasible, to optimally protect the most susceptible populations.” 

Pg 2366: “Although numerous insights have greatly enhanced our understanding of the PMcardiovascular relationship since the first AHA statement was published, the following list 

represents broad strategic avenues for future investigation: ... Determine whether any “safe” PM 

threshold concentration exists that eliminates both acute and chronic cardiovascular effects in 

healthy and susceptible individuals and at a population level.” 

Scientific Statement Authors

Dr. Robert D. Brook, MD 

Dr. Sanjay Rajagopalan, MD 

Dr. C. Arden Pope, PhD 

Dr. Jeffrey R. Brook, PhD 

Dr. Aruni Bhatnagar, PhD, FAHA 

Dr. Ana V. Diez-Roux, MD, PhD, MPH 
A-5 

Dr. Fernando Holguin, MD 

Dr. Yuling Hong, MD, PhD, FAHA 

Dr. Russell V. Luepker, MD, MS, FAHA 

Dr. Murray A. Mittleman, MD, DrPH, FAHA 

Dr. Annette Peters, PhD 

Dr. David Siscovick, MD, MPH, FAHA 

Dr. Sidney C. Smith, Jr, MD, FAHA 

Dr. Laurie Whitsel, PhD 

Dr. Joel D. Kaufman, MD, MPH 
A-6 

C. Integrated Science Assessment for Particulate Matter (2009) 

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment 

for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment – RTP Division. December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>. 

Pg 1-22: “An important consideration in characterizing the public health impacts associated with 

exposure to a pollutant is whether the concentration-response relationship is linear across the full 

concentration range encountered, or if nonlinear relationships exist along any part of this range. 

Of particular interest is the shape of the concentration-response curve at and below the level of 

the current standards. The shape of the concentration-response curve varies, depending on the 

type of health outcome, underlying biological mechanisms and dose. At the human population 

level, however, various sources of variability and uncertainty tend to smooth and “linearize” the 

concentration-response function (such as the low data density in the lower concentration range, 

possible influence of measurement error, and individual differences in susceptibility to air 

pollution health effects). In addition, many chemicals and agents may act by perturbing naturally 

occurring background processes that lead to disease, which also linearizes population 

concentration-response relationships (Clewell and Crump, 2005, 156359; Crump et al., 1976, 

003192; Hoel, 1980, 156555). These attributes of population dose-response may explain why the 

available human data at ambient concentrations for some environmental pollutants (e.g., PM, O3, 

lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely 

mechanisms include nonlinear processes for some key events. These attributes of human 

population dose-response relationships have been extensively discussed in the broader 

epidemiologic literature (Rothman and Greenland, 1998, 086599).” 

Pg 2-16: “In addition, cardiovascular hospital admission and mortality studies that examined the 

PM10 concentration-response relationship found evidence of a log-linear no-threshold 

relationship between PM exposure and cardiovascular-related morbidity (Section 6.2) and 

mortality (Section 6.5).” 

Pg 2-25: “2.4.3. PM Concentration-Response Relationship 

An important consideration in characterizing the PM-morbidity and mortality association is 

whether the concentration-response relationship is linear across the full concentration range that 

is encountered or if there are concentration ranges where there are departures from linearity (i.e., 

nonlinearity). In this ISA studies have been identified that attempt to characterize the shape of 

the concentration-response curve along with possible PM “thresholds” (i.e., levels which PM 

concentrations must exceed in order to elicit a health response). The epidemiologic studies 

evaluated that examined the shape of the concentration-response curve and the potential presence 

of a threshold have focused on cardiovascular hospital admissions and ED visits and mortality 

associated with short-term exposure to PM10 and mortality associated with long-term exposure to 

PM2.5. 

“A limited number of studies have been identified that examined the shape of the PM 

cardiovascular hospital admission and ED visit concentration-response relationship. Of these 
A-7 

studies, some conducted an exploratory analysis during model selection to determine if a linear 

curve most adequately represented the concentration-response relationship; whereas, only one 

study conducted an extensive analysis to examine the shape of the concentration-response curve 

at different concentrations (Section 6.2.10.10). Overall, the limited evidence from the studies 

evaluated supports the use of a no-threshold, log-linear model, which is consistent with the 

observations made in studies that examined the PM-mortality relationship. 

“Although multiple studies have previously examined the PM-mortality concentration-response 

relationship and whether a threshold exists, more complex statistical analyses continue to be 

developed to analyze this association. Using a variety of methods and models, most of the 

studies evaluated support the use of a no-threshold, log-linear model; however, one study did 

observe heterogeneity in the shape of the concentration-response curve across cities (Section 

6.5). Overall, the studies evaluated further support the use of a no-threshold log-linear model, but 

additional issues such as the influence of heterogeneity in estimates between cities, and the effect 

of seasonal and regional differences in PM on the concentration-response relationship still 

require further investigation. 

“In addition to examining the concentration-response relationship between short-term exposure 

to PM and mortality, Schwartz et al. (2008, 156963) conducted an analysis of the shape of the 

concentration-response relationship associated with long-term exposure to PM. Using a variety 

of statistical methods, the concentration-response curve was found to be indistinguishable from 

linear, and, therefore, little evidence was observed to suggest that a threshold exists in the 

association between long-term exposure to PM2.5 and the risk of death (Section 7.6).” 

Pg 6-75: “6.2.10.10. Concentration Response 

The concentration-response relationship has been extensively analyzed primarily through studies 

that examined the relationship between PM and mortality. These studies, which have focused on 

short- and long-term exposures to PM have consistently found no evidence for deviations from 

linearity or a safe threshold (Daniels et al., 2004, 087343; Samoli et al., 2005, 087436; Schwartz, 

2004, 078998; Schwartz et al., 2008, 156963) (Sections 6.5.2.7 and 7.1.4). Although on a more 

limited basis, studies that have examined PM effects on cardiovascular hospital admissions and 

ED visits have also analyzed the PM concentration-response relationship, and contributed to the 

overall body of evidence which suggests a log-linear, no-threshold PM concentration-response 

relationship. 

“The results from the three multicity studies discussed above support no-threshold log-linear 

models, but issues such as the possible influence of exposure error and heterogeneity of shapes 

across cities remain to be resolved. Also, given the pattern of seasonal and regional differences 

in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463), 

the very concept of a concentration-response relationship estimated across cities and for all-year 

data may not be very informative.” 

Pg 6-197: “6.5.2.7. Investigation of Concentration-Response Relationship 

The results from large multicity studies reviewed in the 2004 PM AQCD (U.S. EPA, 2004, 

056905) suggested that strong evidence did not exist for a clear threshold for PM mortality 

effects. However, as discussed in the 2004 PM AQCD (U.S. EPA, 2004, 056905), there are 
A-8 

several challenges in determining and interpreting the shape of PM-mortality concentrationresponse functions and the presence of a threshold, including: (1) limited range of available 

concentration levels (i.e., sparse data at the low and high end); (2) heterogeneity of susceptible 

populations; and (3) investigate the PM-mortality concentration-response relationship. 

“Daniels et al. (2004, 087343) evaluated three concentration-response models: (1) log-linear 

models (i.e., the most commonly used approach, from which the majority of risk estimates are 

derived); (2) spline models that allow data to fit possibly non-linear relationship; and (3) 

threshold models, using PM10 data in 20 cities from the 1987-1994 NMMAPS data. They 

reported that the spline model, combined across the cities, showed a linear relation without 

indicating a threshold for the relative risks of death for all-causes and for cardiovascularrespiratory causes in relation to PM10, but “the other cause” deaths (i.e., all cause minus 

cardiovascular-respiratory) showed an apparent threshold at around 50 μg/m3 PM10, as shown in 

Figure 6-35. For all-cause and cardio-respiratory deaths, based on the Akaike’s Information 

Criterion (AIC), a log-linear model without threshold was preferred to the threshold model and 

to the spline model. 

“The HEI review committee commented that interpretation of these results required caution, 

because (1) the measurement error could obscure any threshold; (2) the city-specific 

concentration-response curves exhibited a variety of shapes; and (3) the use of AIC to choose 

among the models might not be appropriate due to the fact it was not designed to assess scientific 

theories of etiology. Note, however, that there has been no etiologically credible reason 

suggested thus far to choose one model over others for aggregate outcomes. Thus, at least 

statistically, the result of Daniels et al. (2004, 087343) suggests that the log-linear model is 

appropriate in describing the relationship between PM10 and mortality. 

“The Schwartz (2004, 078998) analysis of PM10 and mortality in 14 U.S. cities, described in 

Section 6.5.2.1, also examined the shape of the concentration-response relationship by including 

indicator variables for days when concentrations were between 15 and 25 μg/m3, between 25 and 

34 μg/m3

, between 35 and 44 μg/m3, and 45 μg/m3 and above. In the model, days with 

concentrations below 15 μg/m3

 served as the reference level. This model was fit using the single 

stage method, combining strata across all cities in the case-crossover design. Figure 6-36 shows 

the resulting relationship, which does not provide sufficient evidence to suggest that a threshold 

exists. The authors did not examine city-to-city variation in the concentration-response 

relationship in this study. 

“PM10 and mortality in 22 European cities (and BS in 15 of the cities) participating in the 

APHEA project. In nine of the 22 cities, PM10 levels were estimated using a regression model 

relating co-located PM10 to BS or TSP. They used regression spline models with two knots (30 

and 50 μg/m3

) and then combined the individual city estimates of the splines across cities. The 

investigators concluded that the association between PM and mortality in these cities could be 

adequately estimated using the log-linear model. However, in an ancillary analysis of the 

concentration-response curves for the largest cities in each of the three distinct geographic areas 

(western, southern, and eastern European cities): London, England; Athens, Greece; and Cracow, 

Poland, Samoli et al. (2005, 087436) observed a difference in the shape of the concentrationresponse curve across cities. Thus, while the combined curves (Figure 6-37) appear to support 
A-9 

no-threshold relationships between PM10 and mortality, the heterogeneity of the shapes across 

cities makes it difficult to interpret the biological relevance of the shape of the combined curves. 

“The results from the three multicity studies discussed above support no-threshold log-linear 

models, but issues such as the possible influence of exposure error and heterogeneity of shapes 

across cities remain to be resolved. Also, given the pattern of seasonal and regional differences 

in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463), 

the very concept of a concentration-response relationship estimated across cities and for all-year 

data may not be very informative.” 

Authors of ISA

Dr. Lindsay Wichers Stanek (PM Team Leader)—National Center for Environmental 

Assessment (NCEA), U.S. Environmental Protection Agency (U.S. EPA), Research Triangle 

Park, NC 

Dr. Jeffrey Arnold—NCEA, U.S. EPA, Research Triangle Park, NC (now at Institute for Water 

Resources, U.S. Army Corps of Engineers, Washington, D.C) 

Dr. Christal Bowman—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. James S. Brown—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Barbara Buckley—NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Allen Davis—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Jean-Jacques Dubois—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Steven J. Dutton—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Tara Greaver—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Erin Hines—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Douglas Johns—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Ellen Kirrane—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Dennis Kotchmar—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Thomas Long—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Thomas Luben—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Qingyu Meng—Oak Ridge Institute for Science and Education, Postdoctoral Research 

Fellow to NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Kristopher Novak—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Joseph Pinto—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Jennifer Richmond-Bryant—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Mary Ross—NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Jason Sacks—NCEA, U.S. EPA, Research Triangle Park, NC 
A-10 

Dr. Timothy J. Sullivan—E&S Environmental Chemistry, Inc., Corvallis, OR 

Dr. David Svendsgaard—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Lisa Vinikoor—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. William Wilson—NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Lori White— NCEA, U.S. EPA, Research Triangle Park, NC (now at National Institute for 

Environmental Health Sciences, Research Triangle Park, NC) 

Dr. Christy Avery—University of North Carolina, Chapel Hill, NC 

Dr. Kathleen Belanger —Center for Perinatal, Pediatric and Environmental Epidemiology, 

Yale University, New Haven, CT 

Dr. Michelle Bell—School of Forestry & Environmental Studies, Yale University, New Haven, 

CT 

Dr. William D. Bennett—Center for Environmental Medicine, Asthma and Lung Biology, 

University of North Carolina, Chapel Hill, NC 

Dr. Matthew J. Campen—Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Leland B. Deck— Stratus Consulting, Inc., Washington, DC 

Dr. Janneane F. Gent—Center for Perinatal, Pediatric and Environmental Epidemiology, Yale 

University, New Haven, CT 

Dr. Yuh-Chin Tony Huang—Department of Medicine, Division of Pulmonary Medicine, Duke 

University Medical Center, Durham, NC 

Dr. Kazuhiko Ito—Nelson Institute of Environmental Medicine, NYU School of Medicine, 

Tuxedo, NY 

Mr. Marc Jackson—Integrated Laboratory Systems, Inc., Research Triangle Park, NC 

Dr. Michael Kleinman—Department of Community and Environmental Medicine, University 

of California, Irvine 

Dr. Sergey Napelenok—National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Dr. Marc Pitchford—National Oceanic and Atmospheric Administration, Las Vegas, NV 

Dr. Les Recio—Genetic Toxicology Division, Integrated Laboratory Systems, Inc., Research 

Triangle Park, NC 

Dr. David Quincy Rich—Department of Epidemiology, University of Medicine and Dentistry 

of New Jersey, Piscataway, NJ 

Dr. Timothy Sullivan— E&S Environmental Chemistry, Inc., Corvallis, OR 

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY 

Dr. Gregory Wellenius—Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess 

Medical Center, Boston, MA 
A-11 

Dr. Eric Whitsel—Departments of Epidemiology and Medicine, University of North Carolina, 

Chapel Hill, NC 

Peer Reviewers

Dr. Sara Dubowsky Adar, Department of Epidemiology, University of Washington, Seattle, 

WA 

Mr. Chad Bailey, Office of Transportation and Air Quality, Ann Arbor, MI 

Mr. Richard Baldauf, Office of Transportation and Air Quality, Ann Arbor, MI 

Dr. Prakash Bhave, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Mr. George Bowker, Office of Atmospheric Programs, U.S. EPA, Washington, D.C. 

Dr. Judith Chow, Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 

Dr. Dan Costa, U.S. EPA, Research Triangle Park, NC 

Dr. Ila Cote, NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Robert Devlin, National Health and Environmental Effects Research Laboratory, U.S. EPA, 

Research Triangle Park, NC 

Dr. David DeMarini, National Health and Environmental Effects Research Laboratory, U.S. 

EPA, Research Triangle Park, NC 

Dr. Neil Donahue, Department of Chemical Engineering, Carnegie Mellon University, 

Pittsburgh, PA 

Dr. Aimen Farraj, National Health and Environmental Effects Research Laboratory, U.S. EPA, 

Research Triangle Park, NC 

Dr. Mark Frampton, Department of Environmental Medicine, University of Rochester Medical 

Center, Rochester, NY 

Mr. Neil Frank, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Tyler Fox, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jim Gauderman, Department of Environmental Medicine, Department of Preventive 

Medicine, University of Southern California, Los Angeles, CA 

Dr. Barbara Glenn, National Center for Environmental Research, U.S. EPA, Washington, D.C. 

Dr. Terry Gordon, School of Medicine, New York University, Tuxedo, NY 

Mr. Tim Hanley, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jack Harkema, Department of Pathobiology and Diagnostic Investigation, Michigan State 

University, East Lansing, MI 

Ms. Beth Hassett-Sipple, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 
A-12 

Dr. Amy Herring, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 

Dr. Israel Jirak, Department of Meteorology, Embry-Riddle Aeronautical University, Prescott, 

AZ 

Dr. Mike Kleeman, Department of Civil and Environmental Engineering, University of 

California, Davis, CA 

Dr. Petros Koutrakis, Exposure, Epidemiology and Risk Program, Harvard School of Public 

Health, Boston, MA 

Dr. Sagar Krupa, Department of Plant Pathology, University of Minnesota, St. Paul, MN 

Mr. John Langstaff, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Dr. Meredith Lassiter, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Mr. Phil Lorang, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Karen Martin, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Ms. Connie Meacham, NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Tom Pace, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jennifer Peel, Department of Environmental and Radiological Health Sciences, College of 

Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO 

Dr. Zackary Pekar, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Rob Pinder, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC 

Mr. Norm Possiel, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Sanjay Rajagopalan, Division of Cardiovascular Medicine, Ohio State University, 

Columbus, OH 

Dr. Pradeep Rajan, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Venkatesh Rao, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Ms. Joann Rice, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Mr. Harvey Richmond, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 
A-13 

Ms. Victoria Sandiford, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Dr. Stefanie Sarnat, Department of Environmental and Occupational Health, Emory University, 

Atlanta, GA 

Dr. Frances Silverman, Gage Occupational and Environmental Health, University of Toronto, 

Toronto, ON 

Mr. Steven Silverman, Office of General Council, U.S. EPA, Washington, D.C. 

Dr. Barbara Turpin, Department of Environmental Sciences, Rutgers University, New 

Brunswick, NJ 

Dr. Robert Vanderpool, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Dr. John Vandenberg (Director)—NCEA-RTP Division, U.S. EPA, Research Triangle Park, 

NC 

Dr. Alan Vette, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC 

Ms. Debra Walsh (Deputy Director)—NCEA-RTP Division, U.S. EPA, Research Triangle 

Park, NC 

Mr. Tim Watkins, National Exposure Research Laboratory, U.S. EPA, Research Triangle Park, 

NC 

Dr. Christopher Weaver, NCEA, U.S. EPA, Research Triangle Park, NC 

Mr. Lewis Weinstock, Office of Air Quality Planning and Standards, U.S. EPA, Research 

Triangle Park, NC 

Ms. Karen Wesson, Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle 

Park, NC 

Dr. Jason West, Department of Environmental Sciences and Engineering, University of North 

Carolina, Chapel Hill, NC 

Mr. Ronald Williams, National Exposure Research Laboratory, U.S. EPA, Research Triangle 

Park, NC 

Dr. George Woodall, NCEA, U.S. EPA, Research Triangle Park, NC 

Dr. Antonella Zanobetti, Department of Environmental Health, Harvard School of Public 

Health, Boston, MA 
A-14 

D. CASAC comments on PM ISA and REA (2009) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. 

Review of EPA’s Integrated Science Assessment for Particulate Matter (First External 

Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available on the Internet 

at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/7

3ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008-unsigned.pdf>. 

Pg 9: “There is an appropriate discussion of the time-series studies, but this section needs to have 

an explicit finding that the evidence supports a relationship between PM and mortality that is 

seen in these studies. This conclusion should be followed by the discussion of statistical 

methodology and the identification of any threshold that may exist.” 

U.S. Environmental Protection Agency Science Advisory Board (U.S. EPA-SAB). 2009. 

Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: 

Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-

009. May. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/7

23FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>. 

Pg 6: “On the issue of cut-points raised on 3-18, the authors should be prepared to offer a 

scientifically cogent reason for selection of a specific cut-point, and not simply try different cutpoints to see what effect this has on the analysis. The draft ISA was clear that there is little 

evidence for a population threshold in the C-R function.” 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2009. Review of 

Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009). 

EPA-CASAC-10-001. November. Available on the Internet at 

<http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86e/151B1F8

3B023145585257678006836B9/$File/EPA-CASAC-10-001-unsigned.pdf>. 

Pg 2: “The paragraph on lines 22-30 of page 2-37 is not clearly written. Twice in succession it 

states that the use of a no-threshold log-linear model is supported, but then cites other studies 

that suggest otherwise. It would be good to revise this paragraph to more clearly state – well, I’m 

not sure what. Probably that more research is needed.” 

CASAC Panel Members

Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive Medicine, University of 

Southern California, Los Angeles, CA 

Dr. Joseph Brain, Philip Drinker Professor of Environmental Physiology, Department of Environmental 

Health, Harvard School of Public Health, Harvard University, Boston, MA 

Dr. Ellis B. Cowling, University Distinguished Professor At-Large Emeritus, Colleges of Natural 

Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, NC 
A-15 

Dr. James Crapo, Professor of Medicine, Department of Medicine, National Jewish Medical and 

Research Center, Denver, CO 

Dr. H. Christopher Frey, Professor, Department of Civil, Construction and Environmental Engineering, 

College of Engineering, North Carolina State University, Raleigh, NC 

Dr. Armistead (Ted) Russell, Professor, Department of Civil and Environmental Engineering, Georgia 

Institute of Technology, Atlanta, GA 

Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab, University of California, 

Davis, Davis, CA 

Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern 

California, Los Angeles, CA 

Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina 

University, Greenville, NC 

Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution Research Center, Riverside 

Campus and Kearney Agricultural Center, University of California, Parlier, CA 

Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering, Dartmouth College, Hanover, 

NH 

Dr. Rogene Henderson, Senior Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, 

Clarkson University, Potsdam, NY 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University 

School of Medicine, Tuxedo, NY 

Dr. Helen Suh MacIntosh, Associate Professor, Environmental Health, School of Public Health, 

Harvard University, Boston, MA 

Dr. William Malm, Research Physicist, National Park Service Air Resources Division, Cooperative 

Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 

Mr. Charles Thomas (Tom) Moore, Jr., Air Quality Program Manager, Western Governors' 

Association, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort 

Collins, CO 

Dr. Robert F. Phalen, Professor, Department of Community & Environmental Medicine; Director, Air 

Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, Center for 

Occupation & Environment Health, College of Medicine, University of California Irvine, Irvine, CA 

Dr. Kent Pinkerton, Professor, Regents of the University of California, Center for Health and the 

Environment, University of California, Davis, CA 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard Medical School, 

Boston, MA 

Dr. Sverre Vedal, Professor, Department of Environmental and Occupational Health Sciences, School of 

Public Health and Community Medicine, University of Washington, Seattle, WA 

Dr. Donna Kenski, Data Analysis Director, Lake Michigan Air Directors Consortium, Rosemont, IL 
A-16 

Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY 
A-17 

E. Krewski et al. (2009) 

Krewski, Daniel, Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli 

Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, and 

Michael J. Thun with Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D.K. 

Moore, K. Bruce Newbold, Tim Ramsay, Zev Ross, Hwashin Shin, and Barbara 

Tempalski. (2009). Extended follow-up and spatial analysis of the American Cancer 

Society study linking particulate air pollution and mortality. HEI Research Report, 140, 

Health Effects Institute, Boston, MA. 

Pg 119: [About Pope et al. (2002)] “Each 10-μg/m3

 increase in long-term average ambient PM2.5

concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death 

from all causes, cardiopulmonary disease, and lung cancer, respectively. There was no evidence 

of a threshold exposure level within the range of observed PM2.5 concentrations.” 

Krewski (2009). Letter from Dr. Daniel Krewski to HEI’s Dr. Kate Adams (dated July 7, 

2009) regarding “EPA queries regarding HEI Report 140”. Dr. Adams then forwarded 

the letter on July 10, 2009 to EPA’s Beth Hassett-Sipple. (letter placed in docket #EPAHQ-OAR-2007-0492). 

Pg 4: “6. The Health Review Committee commented that the Updated Analysis completed by 

Pope et al. 2002 reported “no evidence of a threshold exposure level within the range of 

observed PM2.5 concentrations” (p. 119). In the Extended Follow-Up study, did the analyses 

provide continued support for a no-threshold response or was there evidence of a threshold? 

“Response: As noted above, the HEI Health Review Committee commented on the lack of 

evidence for a threshold exposure level in Pope et al. (2002) with follow-up through the year 

1998. The present report, which included follow-up through the year 2000, also does not appear 

to demonstrate the existence of a threshold in the exposure-response function within the range of 

observed PM2.5 concentrations.” 

HEI Health Review Committee Members

Dr. Homer A. Boushey, MD, Chair, Professor of Medicine, Department of Medicine, 

University of California–San Francisco 

Dr. Ben Armstrong, Reader, in Epidemiological Statistics, Department of Public Health and 

Policy, London School of Hygiene and Tropical Medicine, United Kingdom 

Dr. Michael Brauer, ScD, Professor, School of Environmental Health, University of British 

Columbia, Canada 

Dr. Bert Brunekreef, PhD, Professor of Environmental Epidemiology, Institute of Risk 

Assessment Sciences, University of Utrecht, The Netherlands 

Dr. Mark W. Frampton, MD, Professor of Medicine & Environmental Medicine, University of 

Rochester Medical Center, Rochester, NY 
A-18 

Dr. Stephanie London, MD, PhD, Senior Investigator, Epidemiology Branch, National Institute 

of Environmental Health Sciences 

Dr. William N. Rom, MD, MPH, Sol and Judith Bergstein Professor of Medicine and 

Environmental Medicine and Director of Pulmonary and Critical Care Medicine, New York 

University Medical Center 

Dr. Armistead Russell, Georgia Power Distinguished Professor of Environmental Engineering, 

School of Civil and Environmental Engineering, Georgia Institute of Technology 

Dr. Lianne Sheppard, PhD, Professor, Department of Biostatistics, University of Washington 
A-19 

F. Schwartz et al. (2008) 

Schwartz J, Coull B, Laden F. (2008). The Effect of Dose and Timing of Dose on the 

Association between Airborne Particles and Survival. Environmental Health Perspectives. 

116: 64-69. 

Pg 67: “A key finding of this study is that there is little evidence for a threshold in the 

association between exposure to fine particles and the risk of death on follow-up, which 

continues well below the U.S. EPA standard of 15 μg/m3.” 

Pg 68: “In conclusion, penalized spline smoothing and model averaging represent reasonable, 

feasible approaches to addressing questions of the shape of the exposure–response curve, and can 

provide valuable information to decisionmakers. In this example, both approaches are consistent, 

and suggest that the association of particles with mortality has no threshold down to close to 

background levels.” 
A-20 

G. Expert Elicitation on PM-Mortality (2006, 2008) 

Industrial Economics, Inc., 2006. Expanded Expert Judgment Assessment of the 

Concentration-Response Relationship Between PM2.5 Exposure and Mortality. Prepared for 

the U.S.EPA, Office of Air Quality Planning and Standards, September. Available on the 

Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>. 

Pg v: “Each expert was given the option to integrate their judgments about the likelihood of a 

causal relationship and/or threshold in the C-R function into his distribution or to provide a 

distribution "conditional on" one or both of these factors.” 

Pg vii: “Only one of 12 experts explicitly incorporated a threshold into his C-R function.3 The 

rest believed there was a lack of empirical and/or theoretical support for a population threshold. 

However, three other experts gave differing effect estimate distributions above and below some 

cut-off concentration. The adjustments these experts made to median estimates and/or 

uncertainty at lower PM2.5 concentrations were modest.” 

“

3

 Expert K indicated that he was 50 percent sure that a threshold existed. If there 

were a threshold, he thought that there was an 80 percent chance that it would be 

less than or equal to 5 μg/m3

, and a 20 percent chance that it would fall between 5 

and 10 μg/m3

.” 

Pg ix: “Compared to the pilot study, experts in this study were in general more confident in a 

causal relationship, less likely to incorporate thresholds, and reported higher mortality effect 

estimates. The differences in results compared with the pilot appear to reflect the influence of 

new research on the interpretation of the key epidemiological studies that were the focus of both 

elicitation studies, more than the influence of changes to the structure of the protocol.” 

Pg 3-25: “3.1.8 THRESHOLDS 

The protocol asked experts for their judgments regarding whether a threshold exists in the PM2.5 

mortality C-R function. The protocol focused on assessing expert judgments regarding theory 

and evidential support for a population threshold (i.e., the concentration below which no member 

of the study population would experience an increased risk of death).32 If an expert wished to 

incorporate a threshold in his characterization of the concentration-response relationship, the 

team then asked the expert to specify the threshold PM2.5 concentration probabilistically, 

incorporating his uncertainty about the true threshold level. 

“From a theoretical and conceptual standpoint, all experts generally believed that individuals 

exhibit thresholds for PM-related mortality. However, 11 of them discounted the idea of a 

population threshold in the C-R function on a theoretical and/or empirical basis. Seven of these 

experts noted that theoretically one would be unlikely to observe a population threshold due to 

the variation in susceptibility at any given time in the study population resulting from 

combinations of genetic, environmental, and socioeconomic factors.33 All 11 thought that there 

was insufficient empirical support for a population threshold in the C-R function. In addition, 

two experts (E and L) cited analyses of the ACS cohort data in Pope et al. (2002) and another (J) 

cited Krewski et al. (2000a & b) as supportive of a linear relationship in the study range. 
A-21 

“Seven of the experts favored epidemiological studies as ideally the best means of addressing the 

population threshold issue, because they are best able to evaluate the full range of susceptible 

individuals at environmentally relevant exposure levels. However, those who favored 

epidemiologic studies generally acknowledged that definitive studies addressing thresholds 

would be difficult or impossible to conduct, because they would need to include a very large and 

diverse population with wide variation in exposure and a long follow-up period. Furthermore, 

two experts (B and I) cited studies documenting difficulties in detecting a threshold using 

epidemiological studies (Cakmak et al. 1999, and Brauer et al., 2002, respectively). The experts 

generally thought that clinical and toxicological studies are best suited for researching 

mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B, 

thought that a better understanding of the detailed biological mechanism is critical to addressing 

the question of a threshold. 

“One expert, K, believed it was possible to make a conceptual argument for a population 

threshold. He drew an analogy with smoking, indicating that among heavy smokers, only a 

proportion of them gets lung cancer or demonstrates an accelerated decline in lung function. He 

thought that the idea that there is no level that is biologically safe is fundamentally at odds with 

toxicological theory. He did not think that a population threshold was detectable in the currently 

available epidemiologic studies. He indicated that some of the cohort studies showed greater 

uncertainty in the shape of the C-R function at lower levels, which could be indicative of a 

threshold. 

“Expert K chose to incorporate a threshold into his C-R function. He indicated that he was 50 

percent sure that a threshold existed. If there were a threshold, he thought that there was an 80 

percent chance that it would be less than or equal to 5 μg/m3

, and a 20 percent chance that it 

would fall between 5 and 10 μg/m3.” 

Roman, Henry A., Katherine D. Walker, Tyra L. Walsh, Lisa Conner, Harvey M. 

Richmond, Bryan J. Hubbell, and Patrick L. Kinney. (2008). “Expert Judgment 

Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in 

the U.S.” Environ. Sci. Technol., 42(7):2268-2274. 

Pg 2271: “Eight experts thought the true C-R function relating mortality to changes in annual 

average PM2.5 was log-linear across the entire study range (ln(mortality) ) β × PM). Four experts 

(B, F, K, and L) specified a “piecewise” log-linear function, with different β coefficients for PM 

concentrations above and below an expert-specified break point. This approach allowed them to 

express increased uncertainty in mortality effects seen at lower concentrations in major 

epidemiological studies. Expert K thought the relationship would be log-linear above a 

threshold.” 

Pg 2271: “Expert K also applied a threshold, T, to his function, which he described 

probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m3

) = 0.8 

and P(5 μg/m3

 < T ≤ 10 μg/m3) = 0.2. Figure 3 does not include the impact of applying expert 

K’s threshold, as the size of the reduction in benefits will depend on the distribution of baseline 

PM levels in a benefits analysis.” 
A-22 

Experts:

Dr. Doug W. Dockery, Harvard School of Public Health 

Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School of Medicine, 

Tuxedo, NY 

Dr. Dan Krewski, University of Ottawa 

Dr. Nino Künzli, University of Southern California Keck School of Medicine 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York University 

School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Lovelace Respiratory Research Institute 

Dr. Bart Ostro, Chief, Air Pollution Epidemiology Unit, Office of Environmental Health 

Hazard Assessment, California Environmental Protection Agency, Oakland, CA 

Dr. Arden Pope, Professor, Department of Economics, Brigham Young University, Provo, UT 

Dr. Richard Schlesinger, Pace University 

Dr. Joel Schwartz, Harvard School of Public Health 

Dr. George Thurston—Department of Environmental Medicine, NYU, Tuxedo, NY 

Dr. Mark Utell, University of Rochester School of Medicine and Dentistry 
A-23 

H. CASAC comments on PM Staff Paper (2005) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2005. 

EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter 

(Second Draft PM Staff Paper, January 2005). EPA-SAB-CASAC-05-007. June. Available 

on the Internet at 

<http://yosemite.epa.gov/sab/sabproduct.nsf/E523DD36175EB5AD8525701B007332AE/$Fil

e/SAB-CASAC-05-007_unsigned.pdf>. 

Pg 6: “A second concern is with methodological issues. The issue of the selection of 

concentration-response (C-R) relationships based on locally-derived coefficients needs more 

discussion. The Panel did not agree with EPA staff in calculating the burden of associated 

incidence in their risk assessment using either the predicted background or the lowest measured 

level (LML) in the utilized epidemiological analysis. The available epidemiological database on 

daily mortality and morbidity does not establish either the presence or absence of threshold 

concentrations for adverse health effects. Thus, in order to avoid emphasizing an approach that 

assumes effects that extend to either predicted background concentrations or LML, and to 

standardize the approach across cities, for the purpose of estimating public health impacts, the 

Panel favored the primary use of an assumed threshold of 10 μg/m3

. The original approach of 

using background or LML, as well as the other postulated thresholds, could still be used in a 

sensitivity analysis of threshold assumptions. 

“The analyses in this chapter highlight the impact of assumptions regarding thresholds, or lack of 

threshold, on the estimates of risk. The uncertainty associated with threshold or nonlinear models 

needs more thorough discussion. A major research need is for more work to determine the 

existence and level of any thresholds that may exist or the shape of nonlinear concentrationresponse curves at low levels of exposure that may exist, and to reduce uncertainty in estimated 

risks at the lowest PM concentrations.” 

CASAC Panel Members

Dr. Rogene Henderson, Scientist Emeritus, Lovelace Respiratory Research Institute, 

Albuquerque, NM 

Dr. Ellis Cowling, University Distinguished Professor-at-Large, North Carolina State 

University, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina 

State University, Raleigh, NC 

Dr. James D. Crapo, Professor, Department of Medicine, Biomedical Research and 

PatientCare, National Jewish Medical and Research Center, Denver, CO 

Dr. Philip Hopke, Bayard D. Clarkson Distinguished Professor, Department of Chemical 

Engineering, Clarkson University, Potsdam, NY 

Dr. Jane Q. Koenig, Professor, Department of Environmental Health, School of Public Health 

and Community Medicine, University of Washington, Seattle, WA 
A-24 

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental Health , School of 

Public Health, Harvard University (HSPH), Boston, MA 

Dr. Allan Legge, President, Biosphere Solutions, Calgary, Alberta 

Dr. Paul J. Lioy, Associate Director and Professor, Environmental and Occupational Health 

Sciences Institute, UMDNJ - Robert Wood Johnson Medical School, NJ 

Dr. Morton Lippmann, Professor, Nelson Institute of Environmental Medicine, New York 

University School of Medicine, Tuxedo, NY 

Dr. Joe Mauderly, Vice President, Senior Scientist, and Director, National Environmental 

Respiratory Center, Lovelace Respiratory Research Institute, Albuquerque, NM 

Dr. Roger O. McClellan, Consultant, Albuquerque, NM 

Dr. Frederick J. Miller, Consultant, Cary, NC 

Dr. Gunter Oberdorster, Professor of Toxicology, Department of Environmental Medicine, 

School of Medicine and Dentistry, University of Rochester, Rochester, NY 

Mr. Richard L. Poirot, Environmental Analyst, Air Pollution Control Division, Department of 

Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT 

Dr. Robert D. Rowe, President, Stratus Consulting, Inc., Boulder, CO 

Dr. Jonathan M. Samet, Professor and Chair, Department of Epidemiology, Bloomberg School 

of Public Health, Johns Hopkins University, Baltimore, MD 

Dr. Frank Speizer, Edward Kass Professor of Medicine, Channing Laboratory, Harvard 

Medical School, Boston, MA 

Dr. Sverre Vedal, Professor of Medicine, School of Public Health and Community 

Medicine University of Washington, Seattle, WA 

Mr. Ronald White, Research Scientist, Epidemiology, Bloomberg School of Public Health, 

Johns Hopkins University, Baltimore, MD 

Dr. Warren H. White, Visiting Professor, Crocker Nuclear Laboratory, University of California 

-Davis, Davis, CA 

Dr. George T. Wolff, Principal Scientist, General Motors Corporation, Detroit, MI 

Dr. Barbara Zielinska, Research Professor, Division of Atmospheric Science, Desert Research 

Institute, Reno, NV 
A-25 

I. HES Comments on 812 Analysis (2004) 

U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA-SAB). 2004. 

Advisory on Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second 

Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990-2020. Advisory by 

the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance 

Analysis. EPA-SAB-COUNCIL-ADV-04-002. March. Available on the Internet at 

<http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400

433D5D/$File/council_adv_04002.pdf>. 

Pg 20: “The Subcommittee agrees that the whole range of uncertainties, such as the questions of 

causality, shape of C-R functions and thresholds, relative toxicity, years of life lost, cessation lag 

structure, cause of death, biologic pathways, or susceptibilities may be viewed differently for 

acute effects versus long-term effects. 

“For the studies of long-term exposure, the HES notes that Krewski et al. (2000) have conducted 

the most careful work on this issue. They report that the associations between PM2.5 and both allcause and cardiopulmonary mortality were near linear within the relevant ranges, with no 

apparent threshold. Graphical analyses of these studies (Dockery et al., 1993, Figure 3 and 

Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. 

Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end 

of the concentrations reported in the studies.” 

HES Panel Members

Dr. Bart Ostro, California Office of Environmental Health Hazard Assessment (OEHHA), 

Oakland, CA 

Mr. John Fintan Hurley, Institute of Occupational Medicine (IOM), Edinburgh, Scotland 

Dr. Patrick Kinney, Columbia University, New York, NY 

Dr. Michael Kleinman, University of California, Irvine, CA 

Dr. Nino Künzli, University of Southern California, Los Angeles, CA 

Dr. Morton Lippmann, New York University School of Medicine, Tuxedo, NY Dr. Rebecca 

Parkin, The George Washington University, Washington, DC 

Dr. Trudy Cameron, University of Oregon, Eugene, OR 

Dr. David T. Allen, University of Texas, Austin, TX 

Ms. Lauraine Chestnut, Stratus Consulting Inc., Boulder, CO 

Dr. Lawrence Goulder, Stanford University, Stanford, CA 

Dr. James Hammitt, Harvard University, Boston, MA 

Dr. F. Reed Johnson, Research Triangle Institute, Research Triangle Park, NC 

Dr. Charles Kolstad, University of California, Santa Barbara, CA 
A-26 

Dr. Lester B. Lave, Carnegie Mellon University, Pittsburgh, PA 

Dr. Virginia McConnell, Resources for the Future, Washington, DC 

Dr. V. Kerry Smith, North Carolina State University, Raleigh, NC 

Other Panel Members

Dr. John Evans, Harvard University, Portsmouth, NH Dr. Dale Hattis, Clark University, 

Worcester, MA Dr. D. Warner North, NorthWorks Inc., Belmont, CA Dr. Thomas S. Wallsten, 

University of Maryland, College Park, MD 
A-27 

J. NRC – Committee on Estimating the Health Risk Reduction Benefits of Proposed Air 

Pollution Regulations (2002) 

National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed 

Air Pollution Regulations. Washington, DC: The National Academies Press. 

Pg 109: “Linearity and Thresholds 

“The shape of the concentration-response functions may influence the overall estimate of 

benefits. The shape is particularly important for lower ambient air pollution concentrations to 

which a large portion of the population is exposed. For this reason, the impact of the existence of 

a threshold may be considerable. 

“In epidemiological studies, air pollution concentrations are usually measured and modeled as 

continuous variables. Thus, it may be feasible to test linearity and the existence of thresholds, 

depending on the study design. In time-series studies with the large number of repeated 

measurements, linearity and thresholds have been formally addressed with reasonable statistical 

power. For pollutants such as PM10 and PM2.5, there is no evidence for any departure of linearity 

in the observed range of exposure, nor any indication of a threshold. For example, examination 

of the mortality effects of short-term exposure to PM10 in 88 cities indicates that the 

concentration-response functions are not due to the high concentrations and that the slopes of 

these functions do not appear to increase at higher concentrations (Samet et al. 2000). Many 

other mortality studies have examined the shape of the concentration-response function and 

indicated that a linear (nonthreshold) model fit the data well (Pope 2000). Furthermore, studies 

conducted in cities with very low ambient pollution concentrations have similar effects per unit 

change in concentration as those studies conducted in cities with higher concentrations. Again, 

this finding suggests a fairly linear concentration-response function over the observed range of 

exposures. 

“Regarding the studies of long-term exposure, Krewski et al. (2000) found that the assumption of 

a linear concentration-response function for mortality outcomes was not unreasonable. However, 

the statistical power to assess the shape of these functions is weakest at the upper and lower end 

of the observed exposure ranges. Most of the studies examining the effects of long-term 

exposure on morbidity compare subjects living in a small number of communities (Dockery et al. 

1996; Ackermmann-Liebrich 1997; Braun-Fahrländer et al. 1997). Because the number of longterm effects studies are few and the number of communities studied is relatively small (8 to 24), 

the ability to test formally the absence or existence of a no-effect threshold is not feasible. 

However, even if thresholds exist, they may not be at the same concentration for all health 

outcomes. 

“A review of the time-series and cohort studies may lead to the conclusion that although a 

threshold is not apparent at commonly observed concentrations, one may exist at lower levels. 

An important point to acknowledge regarding thresholds is that for health benefits analysis a key 

threshold is the population threshold (the lowest of the individual thresholds). However, the 

population threshold would be very difficult to observe empirically through epidemiology, 
A-28 

because epidemiology integrates information from very large groups of people (thousands). Air 

pollution regulations affect even larger groups of people (millions). It is reasonable to assume 

that among such large groups susceptibility to air pollution health effects varies considerably 

across individuals and depends on a large set of underlying factors, including genetic makeup, 

age, exposure measurement error, preexisting disease, and simultaneous exposures from smoking 

and occupational hazards. This variation in individual susceptibilities and the resulting 

distribution of individual thresholds underlies the concentration-response function observed in 

epidemiology. Thus, until biologically based models of the distribution of individual thresholds 

are developed, it may be productive to assume that the population concentration-response 

function is continuous and to focus on finding evidence of changes in its slope as one approaches 

lower concentrations. 

EPA’s Use of Thresholds 

“In EPA’s benefits analyses, threshold issues were discussed and interpreted. For the PM and 

ozone National Ambient Air Quality Standards (NAAQS), EPA investigated the effects of a 

potential threshold or reference value below which health consequences were assumed to be zero 

(EPA 1997). Specifically, the high-end benefits estimate assumed a 12-microgram per cubic 

meter (μg/m3

) mean threshold for mortality associated with long-term exposure to PM2.5. The 

low-end benefits estimate assumed a 15-μg/m3

 threshold for all PM-related health effects. The 

studies, however, included concentrations as low as 7.5 μg/m3. For the Tier 2 rule and the HD 

engine and diesel-fuel rule, no threshold was assumed (EPA 1999, 2000). EPA in these analyses 

acknowledged that there was no evidence for a threshold for PM. 

“Several points should be noted regarding the threshold assumptions. If a threshold is assumed 

where one was not apparent in the original study, then the data should be refit and a new curve 

generated with the assumption of a zero slope over a segment of the concentration-response 

function that was originally found to be positively sloped. The assumption of a zero slope over a 

portion of the curve will force the slope in the remaining segment of the positively sloped 

concentration-response function to be greater than was indicated in the original study. A new 

concentration-response function was not generated for EPA’s benefits analysis for the PM and 

ozone NAAQS for which threshold assumptions were made. The generation of the steeper slope 

in the remaining portion of the concentration-response function may fully offset the effect of 

assuming a threshold. These aspects of assuming a threshold in a benefits analysis where one 

was not indicated in the original study should be conveyed to the reader. The committee notes 

that the treatment of thresholds should be evaluated in a consistent and transparent framework by 

using different explicit assumptions in the formal uncertainty analyses (see Chapter 5).” 

Pg 117: “Although the assumption of no thresholds in the most recent EPA benefits analyses was 

appropriate, EPA should evaluate threshold assumptions in a consistent and transparent 

framework using several alternative assumptions in the formal uncertainty analysis.” 

Pg 136: “Two additional illustrative examples are thresholds for adverse effects and lag 

structures.2

 EPA considers implausible any threshold for mortality in the particulate matter (PM) 

exposure ranges under consideration (EPA 1999a, p. 3-8). Although the agency conducts 

sensitivity analyses incorporating thresholds, it provides no judgment as to their relative 
A-29 

plausibility. In a probabilistic uncertainty analysis, EPA could assign appropriate weights to 

various threshold models. For PM-related mortality in the Tier 2 analysis, the committee expects 

that this approach would have resulted in only a slight widening of the probability distribution 

for avoided mortality and a slight reduction in the mean of that distribution, thus reflecting 

EPA’s views about the implausibility of thresholds. The committee finds that such formal 

incorporation of EPA’s expert judgments about the plausibility of thresholds into its primary 

analysis would have been an improvement. 

“Uncertainty about thresholds is a special aspect of uncertainty about the shape of concentrationresponse functions. Typically, EPA and authors of epidemiological studies assume that these 

functions are linear on some scale. Often, the scale is a logarithmic transformation of the risk or 

rate of the health outcome, but when a rate or risk is low, a linear function on the logarithmic 

scale is approximately linear on the scale of the rate or risk itself. Increasingly, epidemiological 

investigators are employing analytic methods that permit the estimation of nonlinear shapes for 

concentration-response functions (Greenland et al. 1999). As a consequence, EPA will need to be 

prepared to incorporate nonlinear concentration-response functions from epidemiological studies 

into the agency’s health benefits analyses. Any source of error or bias that can distort an 

epidemiological association can also distort the shape of an estimated concentration -response 

function, as can variation in individual susceptibility (Hattis and Burmaster 1994; Hattis et al. 

2001).” 

Pg 137: “In principle, many components of the health benefits model need realistic probabilistic 

models (see Table 5-1 for a listing of such components), in addition to concentration-response 

thresholds and time lags between exposure and response. For example, additional features of the 

concentration-response function—such as projection of the results from the study population to 

the target populations (which may have etiologically relevant characteristics outside the range 

seen in the study population) and the projection of baseline frequencies of morbidity and 

mortality into the future—must be characterized probabilistically. Other uncertainties that might 

affect the probability distributions are the estimations of population exposure (or even 

concentration) from emissions, estimates of emissions themselves, and the relative toxicity of 

various classes of particles. Similarly, many aspects of the analysis of the impact of regulation on 

ambient concentrations and on population exposure involve considerable uncertainty and, 

therefore, may be beneficially modeled in this way. Depending on the analytic approach used, 

joint probability distributions will have to be specified to incorporate correlations between model 

components that are structurally dependent upon each other, or the analysis will have to be 

conducted in a sequential fashion that follows the model for the data-generating process. 

“EPA should explore alternative options for incorporating expert judgment into its probabilistic 

uncertainty analyses. The agency possesses considerable internal expertise, which should be 

employed as fully as possible. Outside experts should also be consulted as needed, individually 

or in panels. In all cases, when expert judgment is used in the construction of a model 

component, the experts should be identified and the rationales and empirical bases for their 

judgments should be made available.” 
A-30 

NRC members

Dr. JOHN C. BAILAR, III (Chair), (emeritus) University of Chicago, Chicago, Illinois 

Dr. HUGH ROSS ANDERSON, University of London, London, England 

Dr. MAUREEN L. CROPPER, University of Maryland, College Park 

Dr. JOHN S. EVANS, Harvard University, Boston, Massachusetts 

Dr. DALE B. HATTIS, Clark University, Worcester, Massachusetts 

Dr. ROGENE F. HENDERSON, Lovelace Respiratory Research Institute, Albuquerque, New 

Mexico 

Dr. PATRICK L. KINNEY, Columbia University, New York, New York 

Dr. NINO KÜNZLI, University of Basel, Basel, Switzerland; as of September 2002, University 

of Southern California, Los Angeles 

Dr. BART D. OSTRO, California Environmental Protection Agency, Oakland 

Dr. CHARLES POOLE, University of North Carolina, Chapel Hill 

Dr. KIRK R. SMITH, University of California, Berkeley 

Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts 

Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland
B-1 

APPENDIX B 

LOWEST MEASURED LEVEL (LML) ASSESSMENT FOR RULES WITHOUT 

POLICY-SPECIFIC AIR QUALITY DATA AVAILABLE: TECHNICAL SUPPORT 

DOCUMENT (TSD) 
B-2 

Lowest Measured Level (LML) Assessment for Rules 

without Policy-Specific Air Quality Data Available 

Technical Support Document (TSD) 

June 2010 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impact Division 

Air Benefit-Cost Group 

Research Triangle Park, North Carolina
B-3 

Inherent in any complex Regulatory Impact Analysis (RIA) are multiple sources of 

uncertainty. Health benefits analysis relies on an array of data inputs—including air quality 

modeling, health impact functions and valuation estimates among others—which are themselves 

subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. 

There are a variety of methods to characterizing the uncertainty associated with the human health 

benefits of air pollution, including quantitative and qualitative methods. When evaluated within 

the context of these uncertainties, the health impact and monetized benefits estimates in an RIA 

can provide useful information regarding the magnitude of the public health impacts attributable 

to reducing air pollution. 

Reductions in premature mortality typically dominate the size of the overall monetized 

benefits. Therefore, most of the uncertainty characterization generally focuses on the mortalityrelated benefits. Typically, EPA employs two primary techniques for quantifying this 

uncertainty. First, because this characterization of random statistical error may omit important 

sources of uncertainty, we employ the results of an expert elicitation on the relationship between 

premature mortality and ambient PM2.5 concentration (Roman et al., 2008); this provides 

additional insight into the likelihood of different outcomes and about the state of knowledge 

regarding the benefits estimates. Second, when we have air quality modeling specific to the 

policy we are evaluating and it can be used as an input to the health impact and economic 

analysis, we use Monte Carlo methods for characterizing random sampling error associated with 

the concentration response functions from epidemiological studies and economic valuation 

functions.81 Both approaches have different strengths and weaknesses, which are fully described 

in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). 

In addition, some RIAs, including the PM NAAQS RIA (2006d) and Ozone NAAQS 

RIA (2008a), also contain a suite of sensitivity analyses that evaluate the sensitivity of the 

monetized benefits to the specification of alternate mortality cessation lags and income growth 

adjustment factors. Cessation lags and income growth adjustments are simply multipliers applied 

to the valuation function, which generally affect monetized benefits estimates in the same 

manner. Thus, it is possible for readers to infer the sensitivity of these parameters by referring to 

those previous analyses.82 Other RIAs contain unique sensitivity analyses that are specific to the 

 

81 Currently, we are unable to characterize the random sampling error from the underlying studies when applying 

national average benefit-per-ton estimates. 

82 For example, in the PM NAAQS RIA, the use of an alternate lag structure would change the PM2.5-related 

mortality benefits discounted at 3% discounted by between 10.4% and –27%; when discounted at 7%, these 
B-4 

input parameters of that analysis, such as blood lead level (U.S. EPA, 2008b) or rollback method 

(U.S. EPA, 2010a). Other sources of uncertainty, including the projection of atmospheric 

conditions and source-level emissions, the projection of baseline morbidity rates, incomes and 

technological development are typically unquantified in our RIAs. For these sources, we 

typically provide a qualitative uncertainty characterization associated with these input 

parameters. 

One particular aspect of uncertainty has received extensive quantitative and qualitative 

attention in recent RIAs: the existence of a threshold in the concentration-response function for 

PM2.5-related mortality.A threshold is a specific type of discontinuity in the concentrationresponse function where there are no benefits associated with reducing PM2.5 levels in areas 

where the baseline air quality is less than the threshold. Previously, EPA had included a 

sensitivity analysis with an arbitrary assumed threshold at 10 μg/m3 in the PM-mortality health 

impact function in the RIA to illustrate that the fraction of benefits that occur at lower air 

pollution concentration levels are inherently more uncertain. A threshold of 10 μg/m3 does not 

necessarily have any stronger technical basis than any other threshold, and we could have instead 

assumed a threshold at 4, 7.5, or 12 μg/m3 for the sensitivity analysis. In addition to identifying 

the most support for a non-threshold model, the underlying scientific evidence does not support 

any specific “bright line”. 

Based on our review of the current body of scientific literature, EPA now estimates PMrelated mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed by 

EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear 

model most adequately portrays the PM-mortality concentration-response relationship while 

recognizing potential uncertainty about the exact shape of the concentration-response function.83

Since then, the Health Effects Subcommittee (U.S. EPA-SAB, 2010) of EPA’s Council 

concluded, “The HES fully supports EPA’s decision to use a no-threshold model to estimate 

mortality reductions. This decision is supported by the data, which are quite consistent in 

 benefits change by between 31% and -49%. When applying higher and lower income growth adjustments, the 

monetary value of PM2.5 and ozone-related premature changes between 30% and -10%; the value of chronic 

endpoints change between 5% and -2% and the value of acute endpoints change between 6% and -7%. (U.S. EPA, 

2006) 

83It is important to note that uncertainty regarding the shape of the concentration-response function is conceptually 

distinct from an assumed threshold. An assumed threshold (below which there are no health effects) is a 

discontinuity, which is a specific example of non-linearity. 
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showing effects down to the lowest measured levels. Analyses of cohorts using data from more 

recent years, during which time PM concentrations have fallen, continue to report strong 

associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” 

For a summary of these scientific review statements and the panel members please consult the 

Technical Support Document (TSD) Summary of Expert Opinions on the Existence of a 

Threshold (U.S. EPA, 2010c). 

Consistent with this finding, we have conformed the previous threshold sensitivity 

analysis to the current state of the PM science by incorporating a new “Lowest Measured Level” 

(LML) assessment. While an LML assessment provides some insight into the level of uncertainty 

in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues 

to quantify PM-related mortality impacts using a full range of modeled air quality 

concentrations. Unlike an assumed threshold, which is a modeling assumption that reduces the 

magnitude of the estimated health impacts, the LML is a characterization of the fraction of 

benefits that are more uncertain. It is important to emphasize that just because we have greater 

confidence in the benefits above the LML, this does not mean that we have no confidence that 

benefits occur below the LML. 

While the LML of each study is important to consider when characterizing and 

interpreting the overall level PM-related benefits, EPA believes that large cohort-based mortality 

estimates are suitable for use in air pollution health impact analyses. When estimating PM 

mortality impacts using risk coefficients drawn from the Harvard Six Cities and the American 

Cancer Society cohorts there are innumerable other attributes that may affect the size of the 

reported risk estimates—including differences in population demographics, the size of the 

cohort, activity patterns and particle composition among others. The LML assessment provides a 

limited representation of one key difference between the two studies. For the purpose of 

estimating the benefits associated with reducing PM2.5 levels, we utilize the effect coefficients 

from Pope et al. (2002) for the American Cancer Society cohort and from Laden et al. (2006) for 

the Harvard Six Cities cohort. 

Analyses of these cohorts using data from more recent years, during which time PM 

concentrations have fallen, continue to report strong associations with mortality. For example, 

the Krewski et al. (2009) follow-up study of the American Cancer Society cohort had an LML of 

5.8 μg/m3

. As we model mortality impacts among populations exposed to levels of PM2.5 that are 

successively lower than the LML of each study, our confidence in the results diminishes. As air 

pollution emissions continue to decrease over time, there will be more people in areas where we 

do not have published epidemiology studies. However, each successive cohort study has shown 
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evidence of effects at successively lower levels of PM2.5. As more large cohort studies follow 

populations over time, we will likely have more studies with lower LML as air quality levels 

continue to improve. Even in the absence of a definable threshold, we have more confidence in 

the benefits estimates above the LML of the large cohort studies. To account for the uncertainty 

in each of the studies that we base our mortality estimates on, we provide the LML for each of 

the cohort studies. However, the finding of effects at the lowest LML from the recent Krewski et 

al (2009) study indicates that confidence in PM2.5-related mortality effects down to at least 5.8 

μg/m3 is high. 

In the recently proposed Transport Rule RIA (U.S. EPA, 2010b), we included the new 

LML assessment in which we binned the estimated number of avoided PM2.5-related premature 

mortalities resulting from the implementation of the Transport Rule according to the projected 

2014 baseline PM2.5 air quality levels. This presentation is consistent with our approach to 

applying PM2.5 mortality risk coefficients that have not been adjusted to incorporate an assumed 

threshold. A very large proportion of the avoided PM-related impacts occurred among 

populations initially exposed at or above the LML of each study, which gave us a high level of 

confidence in the PM mortality estimates. This assessment summarized the distribution of 

avoided PM mortality impacts according to the baseline PM2.5 levels experienced by the 

population receiving the PM2.5 mortality benefit. Approximately 80% of the avoided impacts 

occurred at or above a baseline annual mean PM2.5 level of 10 μg/m3 (the LML of the Laden et 

al. 2006 study); about 97% occur at or above an annual mean PM2.5 level of 7.5 μg/m3 (the LML 

of the Pope et al. 2002 study). This assessment confirmed that the great majority of the impacts 

associated with the Transport Rule occurred at or above each study’s LML. 

 For the Transport Rule, policy-specific air quality modeling data for the year 2014 was 

available as an input into the benefits analysis. For some rules, especially New Source 

Performance Standards (NSPS) or National Emissions Standards for Hazardous Air Pollutant 

(NESHAP) rules, policy-specific air quality data is not available due to time or resource 

limitations. For these rules, we provide the following LML assessment as a characterization of 

the baseline exposure to PM2.5 levels in the U.S. Many of the upcoming NSPS and NESHAP 

rules have compliance dates between 2013 and 2016 and represent marginal improvements in air 

quality levels. Although it the data is not a perfect match, we believe that the air quality data 
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from the Transport Rule is a reasonable approximation of the baseline exposure in the U.S. for 

upcoming NSPS and NESHAP rules.84 

 For rules without air quality modeling, we generally estimate the monetized benefits and 

health impacts using benefit-per-ton estimates (Fann, Fulcher and Hubbell, 2009). Using this 

method, we are unable to estimate the percentage of premature mortality associated with the 

specific rules’ emission reductions at each PM2.5 level. However, we believe that it is still 

important to characterize the uncertainty associated with the distribution of the baseline air 

quality. As a surrogate measure of mortality impacts, we provide the percentage of baseline 

exposure at each PM2.5 level. If air quality levels in the baseline are above the LML, the marginal 

changes anticipated from these rules would likely also lead to post-policy air quality levels above 

the LML. Therefore, we have high confidence that the magnitude of the benefits estimated for 

these rules, as the marginal changes would also be above the LML. 

It is important to note that baseline exposure is only one parameter in the health impact 

function, along with baseline incidence rates population, and change in air quality. In other 

words, the percentage of the population exposed to air pollution below the LML is not the same 

as the percentage of the population experiencing health impacts as a result of a specific emission 

reduction policy. The most important aspect, which we are unable to quantify for rules without 

air quality modeling, is the shift in exposure associated with the specific rule. Therefore, caution 

is warranted when interpreting the following assessment. 

A very large proportion of the population is exposed at or above the lowest LML of the 

cohort studies (Figures 1 and 2), increasing our confidence in the PM mortality analysis. Figure 1 

shows a bar chart of the percentage of the population exposed to various air quality levels in the 

pre- and post-policy policy. Figure 2 shows a cumulative distribution function of the same data. 

In addition, Figure 2 also demonstrates that policy had a greater impact on reducing exposure to 

the portion of the population in areas with high PM2.5 levels relative to the portion of the 

population at low PM2.5 levels. Both figures identify the LML for each of the major cohort 

studies. As the policy shifts the distribution of air quality levels, fewer people are exposed to 

PM2.5 levels above the LML. Under baseline conditions, about 96 percent of the population is 

 

84 Because the Transport Rule is not yet promulgated, the baseline exposure obtained from this modeling data would 

slightly overestimate the fraction of the population exposed to air quality levels below the LML. As additional 

rules continue to reduce the ambient PM2.5 levels over time, a larger fraction of the population would be exposed 

to air quality levels below the LML. However, the emission reductions anticipated from the rules without air 

quality modeling available are comparatively small and represent marginal changes. We intend to update this 

LML assessment as necessary to correspond with the successively lower baseline air quality levels anticipated as 

the result of promulgating significant upcoming rules. 
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exposed to annual mean PM2.5 levels of at least 5.8 μg/m3, which is the lowest air quality level 

considered in the most recent study of the American Cancer Society cohort by Krewski et al. 

(2009). Using the Pope et al. (2002) study, the 85% of the population is exposed at or above the 

LML of 7.5 μg/m3

. Using the Laden et al. (2006) study, 40% of the population is exposed above 

the LML of 10 μg/m3

. As we model mortality impacts among populations exposed to levels of 

PM2.5 that are successively lower than the LML of the lowest cohort study, our confidence in the 

results diminishes. However, the analysis above confirms that the great majority of the impacts 

occur at or above the lowest cohort study’s LML. It is important to emphasize that we have high 

confidence in PM2.5-related effects down to the lowest LML of the major cohort studies, which is 

5.8 μg/m3

. Just because we have greater confidence in the benefits above the LML, this does not 

mean that we have no confidence that benefits occur below the LML. 

Figure 1: Percentage of Adult Population by Annual Mean PM2.5 Exposure (pre- and postpolicy) 
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Figure 2: Cumulative Distribution of Adult Population at Annual Mean PM2.5 levels (preand post-policy) 

There are several important differences between the assessment conducted for the 

Transport Rule and the assessment presented here. If you compare the graphics in the Transport 

Rule to those provided here, you will notice that these graphs show a larger percentage of the 

population below the LML. It is imperative to point out that the Transport Rule graphics 

represented mortality impacts attributable to the Transport Rule, whereas these graphics 

represent exposure. Mortality impacts are the result of the incremental change in exposure 

between the baseline and control. However, the baseline population exposure at lower air quality 

levels is so much larger than the impacts among these same populations. In other words, the 

population exposed to lower PM2.5 levels are not receiving very much of the air quality benefit 

between the base and the control case. 
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United States 

Environmental Protection 

Agency 

Office of Air Quality Planning and 

Standards 

Health and Environmental Impacts 

Division 

Research Triangle Park, NC 

Publication No. EPA 

452/R-10-010 

August 2010