From:      |  |Keith Barnett/RTP/USEPA/US                                                    

| To:         |Cortney_Higgins@omb.eop.gov                                                   

| Date:       |08/06/2010 01:12 PM                                                           

| Subject:    |Fw: Cement RIA                                                               

Keith W. Barnett

USEPA/OAQPS/SPPD/MMG

Mail Code D243‐02

Research Triangle Park, NC  27711

919‐541‐5605

barnett.keith@epa.gov

‐‐‐‐‐ Forwarded by Keith Barnett/RTP/USEPA/US on 08/06/2010 01:11 PM

| From:      |  |Tom Walton/RTP/USEPA/US                                                     

| To:        |  |Ron Evans/RTP/USEPA/US@EPA                                                  

| Cc:        |Keith Barnett/RTP/USEPA/US@EPA, Amy Lamson/RTP/USEPA/US@EPA, Alison

Davis/RTP/USEPA/US@EPA, bmd@rti.org                               |

| Date:      |  |08/06/2010 12:49 PM                                                         

| Subject:   | |Cement RIA                                                                   

|

Tom Walton

Economist, Air Benefit & Cost Group

HEID/OAQPS/OAR/EPA

Mail Drop C‐339‐01

919‐541‐5311

919‐541‐0839 fax

(See attached file: RIA_Cement_8‐6‐10.doc)
August 2010 

Regulatory Impact Analysis: 

Amendments to the National 

Emission Standards for Hazardous 

Air Pollutants and New Source 

Performance Standards (NSPS) for 

the Portland Cement 

Manufacturing Industry

Final Report

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 
 

Regulatory Impact Analysis: 

Amendments to the National 

Emission Standards for Hazardous 

Air Pollutants and New Source 

Performance Standards (NSPS) for 

the Portland Cement 

Manufacturing Industry

Final Report

August 2010

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards (OAQPS) 

Air Benefit and Cost Group 

(MD-C439-02) 

Research Triangle Park, NC 27711 
iii 

CONTENTS 

Section Page

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

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

1.2 Organization of this Report .............................................................................................. 1-3

2 Industry Profile ............................................................................................................. 2-1

2.1 The Supply Side ...............................................................................................................2-1

2.1.1 Production Process .............................................................................................. 2-1

2.1.2 Types of Portland Cement .................................................................................. 2-3

2.1.3 Production Costs ................................................................................................. 2-3

2.2 The Demand Side ............................................................................................................. 2-8

2.3 Industry Organization .................................................................................................... 2-10

2.3.1 Market Structure ............................................................................................... 2-10

2.3.2 Manufacturing Plants ........................................................................................ 2-11

2.3.3 Firm Characteristics .......................................................................................... 2-16

2.4 Markets .......................................................................................................................... 2-20

2.4.1 Market Volumes ............................................................................................... 2-21

2.4.2 Market Prices .................................................................................................... 2-22

2.4.3 Future Projections ............................................................................................. 2-22

3 Economic Impact Analysis ........................................................................................... 3-1

3.1 Regulatory Program Costs ............................................................................................... 3-2

3.2 Partial-Equilibrium Analysis ........................................................................................... 3-5

3.2.1 Regional Structure and Baseline Data ................................................................ 3-6

3.2.2 Near-Term Cement Plant Production Decisions ................................................. 3-6

3.2.3 Economic Impact Model Results ........................................................................ 3-9

3.3 Other Economic Analyses: Direct Compliance Cost Methods ...................................... 3-18

3.4 Social Cost Estimates ..................................................................................................... 3-19

3.5 Energy Impacts .............................................................................................................. 3-21
iv 

3.6 Assessment.....................................................................................................................3-23

4 Small Business Impact Analysis ................................................................................... 4-1

4.1 Identify Affected Small Entities ...................................................................................... 4-1

4.2 Sales and Revenue Test Screening Analysis.................................................................... 4-1

4.3 Additional Market Analysis ............................................................................................. 4-3

4.4 Assessment....................................................................................................................... 4-3

5 Air Quality Modeling of Emission Reductions ............................................................ 5-1

5.1 Synopsis ........................................................................................................................... 5-1

5.2 Photochemical Model Background .................................................................................. 5-1

5.3 Model Domain and Grid Resolution ................................................................................ 5-2

5.4 Emissions Input Data ....................................................................................................... 5-2

5.5 Model Results: Air Quality Impacts ................................................................................ 5-5 

5.5 Limitations (Uncertainties) Associated with the Air Quality Modeling .......................... 5-5 

6 Benefits of Emissions Reductions ................................................................................ 6-1

6.1 Synopsis ........................................................................................................................... 6-1

6.2 Calculation of PM2.5 Human Health Benefits .................................................................. 6-2

6.2.1 Methodology Improvements since Proposal ....................................................... 6-2

6.2.2 Benefits Analysis Approach ............................................................................... 6-3

6.2.3 Health Impact Analysis (HIA) ............................................................................ 6-4

6.2.4 Estimating PM2.5-related premature mortality .................................................... 6-8

6.2.5 Economic valuation of health impacts .............................................................. 6-11

6.3 Health Benefits Results .................................................................................................. 6-13

6.4 Energy Disbenefits ......................................................................................................... 6-20

6.4.1 PM2.5 Disbenefits .............................................................................................. 6-25

6.4.2 Social Cost of Carbon and Greenhouse Gas Disbenefits .................................. 6-27 

6.4.2 Total Disbenefits ............................................................................................... 6-27

6.5 Unquantified or Nonmonetized Benefits ....................................................................... 6-20

6.5.1 Other SO2 and PM Benefits .............................................................................. 6-25
v 

6.5.2 HAP Benefits .................................................................................................... 6-27

6.6 Limitations and Uncertainties ........................................................................................ 6-41

6.6.1 Monte Carlo Analysis ....................................................................................... 6-41

6.6.2 Alternate Concentration-Response Functions for PM Mortality ...................... 6-42

6.6.3 LML Assessment .............................................................................................. 6-42

6.6.4 Qualitative Assessment of Uncertainty and Other Analysis Limitations ......... 6-43

6.7 Comparison of Benefits and Costs ................................................................................. 6-44

7 References ..................................................................................................................... 7-1 

Appendixes

A Short-Run Regional Portland Cement Economic Model ................................................ A-1 

B The Cement Plant’s Production Decision: A Mathematical Representation .................. B-1 

C Social Cost Methodology ................................................................................................ C-1 

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

Concentration-Response Function for PM2.5-related Mortality ...................................... D-1 
vi 

LIST OF FIGURES 

Number Page

2-1. Simplified Flow Sheet of Clinker and Cement Manufacture........................................ 2-2 

2-2. Labor Costs per Metric Ton of Cement ($2005) .......................................................... 2-7 

2-3. Distribution of Energy Consumption ............................................................................ 2-8 

2-4. End Uses of Cement: 1975 to 2003 .............................................................................. 2-9 

2-5. Producer Price Indices for Competitive Building Materials: 2003 to 2008................ 2-10 

2-6. Distribution of Cement Kilns in the United States ..................................................... 2-13 

2-7. Historical U.S. Cement Price ...................................................................................... 2-23 

2-8. Deviation from National Average Cement Price per Metric Ton by Region: 

2005............................................................................................................................. 2-24 

3-1. Range of Per-Ton Total Annualized Compliance Costs (2005$) ................................. 3-4 

5-1. Map of the Photochemical Modeling Domain .............................................................. 5-3 

6-1. Total Monetized Benefits for the Final Cement NESHAP and NSPS in 2013 ............ 6-2 

6-2. Illustration of BenMAP Approach ................................................................................ 6-5 

6-3. Data inputs and outputs for the BenMAP model .......................................................... 6-6 

6-4. Breakdown of Monetized PM2.5 Health Benefits using Mortality Function from 

Pope et al. (2002) ........................................................................................................ 6-16 

6-5. Total Monetized PM2.5 Benefits for the Final Cement NESHAP and NSPS in 

2013............................................................................................................................. 6-18 

6-6. Percentage of Total PM-Related Mortalities Avoided by Baseline Air Quality 

Level for Final Portland Cement NESHAP and NSPS ............................................... 6-19 

6-7. Cumulative Percentage of Total PM-related Mortalities Avoided by Baseline 

Air Quality Level for Final Portland Cement NESHAP and NSPS ........................... 6-20 

6-8. Estimated County Level Carcinogenic Risk from HAP exposure from outdoor 

sources (NATA, 2002) ................................................................................................ 6-28 

6-9. Estimated County Level Noncancer (Respiratory) Risk from HAP exposure 

from outdoor sources (NATA, 2002) .......................................................................... 6-29 

6-10. Reductions in Total Mercury Deposition (μg/m2) in the Eastern U.S. ...................... 6-33 

6-11. Reductions in Total Mercury Deposition (μg/m2) in the Western U.S. ..................... 6-34 

6-12. Net Benefits for the Final Portland Cement NESHAP and NSPS at 3% Discount 

Rate ............................................................................................................................. 6-47 

6-13. Net Benefits for the Final Portland Cement NESHAP and NSPS at 7% Discount 

Rate ............................................................................................................................. 6-48 
vii 

LIST OF TABLES 

Number Page

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

Final Portland Cement NESHAP in 2013 (millions of 2005$) ..................................... 1-4

2-1. Portland Cement Shipped from Plants in the United States to Domestic 

Customers, by Type ...................................................................................................... 2-4

2-2. Raw Material Input Ratios for the U.S. Cement Industry: 2000 to 2005 ..................... 2-5

2-3. Raw Material Costs by Market and State: 2005 ........................................................... 2-6

2-4. Labor Productivity Measures for the U.S. Cement Industry by Process Type: 

2000 to 2005 (employee hours per metric ton) ............................................................. 2-6

2-5. Energy Consumption by Type of U.S. Cement Plant (million BTU per metric 

ton) ................................................................................................................................ 2-8

2-6. Number of Kilns and Clinker Capacity by State: 2005 .............................................. 2-12

2-7. Number of Kilns and Clinker Capacity by Age and Process Type ............................. 2-14

2-8. Clinker Capacity, Production, and Capacity Utilization in the United States: 

2000 to 2005 ............................................................................................................... 2-15

2-9. Capacity Utilization Rates by State: 2005 .................................................................. 2-17

2-10. Cement Manufacturing Employment (NAICS 327310): 2000 to 2005 ...................... 2-18

2-11. Ultimate Parent Company Summary Data: 2005 ........................................................ 2-19

2-12. Historical U.S. Cement Statistics (106 metric tons) .................................................... 2-21

2-13. U.S. Cement Trade Data: 2000 to 2007 ...................................................................... 2-22

3-1. Summary of Direct Total Annualized Compliance Costs (million, 2005$) ................. 3-3

3-2. Range of Per-ton Total Annualized Compliance Costs by State (2005$) .................... 3-5

3-3. Portland Cement Prices by Market ($/metric tons): 2005 ............................................ 3-7

3-4. Portland Cement Markets (106 metric tons): 2005 ....................................................... 3-8

3-5. National-Level Market Impacts: 2005 .......................................................................... 3-9

3-6. Regional Compliance Costs and Market Price Changes ($/metric ton of 

cement): 2005.............................................................................................................. 3-10

3-7. Summary of Regional Market Impacts: 2005 ............................................................. 3-12

3-8. Distribution of Industry Impacts: 2005 ....................................................................... 3-13

3-9. Cement Plants with Significant Utilization Changes: 2005 ........................................ 3-14

3-10. Job Losses/Gains Associated with the Final Rule ...................................................... 3-16

3-11. Distribution of Social Costs ($106): 2005 ................................................................... 3-20

3-12. U.S. Cement Sector Energy Consumption (Trillion BTUs): 2013 ............................. 3-23
viii 

4-1. Small Entity Analysis ................................................................................................... 4-2

5-1. Geographic Elements of Domains Used in Photochemical Modeling .......................... 5-3 

5-2. Cement Kiln Emissions in 2005 Base and Estimated Future Year (2013) in tons 

per year .......................................................................................................................... 5-3 

6-1. Human Health and Welfare Effects of Pollutants Affected .......................................... 6-7 

6-2. Summary of Monetized Benefits Estimates for Final Cement NESHAP and 

NSPS in 2013 (millions of 2005$) .............................................................................. 6-14

6-3. Summary of Reductions in Health Incidences and Monetized Benefits from 

PM2.5 Benefits for the Final Cement NESHAP and NSPS in 2013 (95th

percentile confidence interval) .................................................................................... 6-15

6-4. Comparison of Monetized Benefits and Emission Reductions for Final Cement 

NESHAP and NSPS in 2013 (2005$) ......................................................................... 6-17 

6-5. Summary of Monetized PM2.5 Energy Disbenefits for the Final Portland Cement 

NSPS and NESHAP in 2013 (2005$) ......................................................................... 6-17

6-6. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2013 ........................ 6-17

6-7. Monetized Disbenefits of CO2 Emission Increases in 2013 ....................................... 6-17

6-8. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the final 

Portland Cement NESHAP in 2013 (millions of 2005$) ............................................ 6-46
1-1 

SECTION 1

INTRODUCTION 

The U.S. Environmental Protection Agency (EPA) is finalizing amendments to the 

National Emission Standards for Hazardous Air Pollutants (NESHAP) from the Portland cement 

manufacturing industry and New Source Performance Standards (NSPS) for Portland cement 

plants. The final amendments to the NESHAP add or revise, as applicable, emission limits for 

mercury (Hg), total hydrocarbons (THC), and particulate matter (PM) from kilns located at a 

major or an area sources, and hydrochloric acid (HCl) from kilns and located at major sources. 

EPA is also adopting separate standards for these pollutants that apply during startup, shutdown, 

and operating modes. Finally, EPA is adopting performance specifications for use of Hg 

continuous emission monitors (CEMS) and updating recordkeeping and testing requirements. 

The final amendments to the NSPS add or revise, as applicable, emission limits for particulate 

matter (PM), opacity, nitrogen oxides (NOx), and sulfur dioxide (SO2) for facilities that 

commence construction, modification, or reconstruction after June 16, 2008. The final rule also 

includes additional testing and monitoring requirements for affected sources. As part of the 

regulatory process, EPA is required to develop a regulatory impact analysis (RIA). The RIA 

includes an economic impact analysis (EIA) and a small entity impacts analysis and documents 

the RIA methods and results. 

1.1 Executive Summary 

The key results of the RIA are as follows: 

 Options Analyzed: EPA’s analysis focuses on the results of the final NESHAP and 

NSPS. We also present additional information on different combinations of the 

regulatory programs to help stakeholders better understand the size and scope of each. 

These include 

– final NSPS only, 

– final NESHAP only, and 

– alternative: more stringent NSPS and final NESHAP. 

The rest of this summary addresses the results of analyzing the final NESHAP and NSPS. 

 Engineering Cost Analysis: EPA estimates that total annualized costs with the final 

NESHAP and NSPS will be $466 million (2005$). 

 Market Analysis: The partial-equilibrium economic model suggests the average 

national price for Portland cement could be 5% higher with the NESHAP, or $4.50 

per metric ton, while annual domestic production may fall by 11%, or 10 million tons 
1-2 

per year. Because of higher domestic prices, imports rise by 10%, or 3 million metric 

tons per year. 

 Industry Analysis: Net industry operating profits fall by $241 million; EPA also 

identified 10 domestic plants with negative operating profits and significant 

utilization changes that could temporarily idle until market demand conditions 

improve. The plants have unit compliance costs close to $8 per ton of clinker capacity 

and $116 million total change in operating profits. Since these plants account for 

approximately 8% of domestic capacity, a decision to permanently shut down these 

plants would reduce domestic supply and could lead to additional projected market 

price increases and reductions in pollution control costs. 

 Employment Changes: EPA uses two methods for estimating employment impacts. 

A simplistic, limited assessment narrowly focused on output changes in the Portland 

cement industry indicates that the final rule’s gross impact on employment is 1,500 

job losses. However, this approach inherently overstates job losses, as it is based on 

the assumption that employment is proportional to output, and because it ignores 

offsetting general equilibrium and other effects as discussed in detail in Chapter 3. A 

more sophisticated analytical approach that includes other types of employment 

effects estimates changes in net employment could range from a loss of 600 to a net 

gain of 1,300 jobs. 

 Social Cost Analysis: The estimated social cost is $926 to $950 million (2005$). The 

range represents the estimated difference in surplus if ten facilities with low estimated 

post regulation capacity utilization choose to idle or close rather than operate at a low 

capacity utilization. The social cost estimates are significantly higher than the 

engineering analysis estimates, which estimated annualized costs of $466 million. 

This is a direct consequence of EPA’s assumptions about existing market structure 

discussed extensively in previous cement industry rulemakings and Section 2, 

Appendix A, and Appendix B of this RIA. Under baseline conditions without 

regulation, the existing domestic cement plants are assumed to choose a production 

level that is less than the level produced under perfect competition. As a result, a 

preexisting market distortion exists in the markets covered by the final rule (i.e., the 

observed baseline market price is higher than the [unobserved] market price that a 

model of perfect competition would predict). The imposition of additional regulatory 

costs tends to widen the gap between price and marginal cost in these markets and 

contributes to additional social costs. 

 Energy Impacts: EPA concludes that the rule when implemented will not have a 

significant adverse effect on the supply, distribution, or use of energy. The cement 

industry accounts for less than 0.4% of the U.S. total energy use. EPA estimates the 

additional add-on controls may increase national electrical demand by 780 million 

kWh per year and the natural gas use to be 1.0 million MMBTU per year for existing 

kilns. For new kilns, assuming that of the 16 new kilns to start up by 2013 all 16will 

add alkaline scrubbers and ACI systems, the electrical demand is estimated to be 199 

million kWh per year. This is less than 0.1% of AEO 2010 forecasts of total 

electricity and natural gas consumption. 
1-3 

 Small Business Analysis: Only 4 of the over 40 cement parent companies are small 

entities. EPA performed a screening analysis for impacts on the 4 small entities by 

comparing compliance costs to average company revenues. EPA’s analysis found that 

the ratio of compliance cost to company revenue falls below 1% for two of the four 

small entities (includes a Tribal government). Two small entities would have an 

annualized cost of between 1% and 3% of sales. No small businesses would have an 

annualized cost greater than 3% of sales. 

 Benefits Analysis: In the year of full implementation (2013), EPA estimates that the 

total monetized benefits of the final NESHAP and NSPS are $7.4 billion to $18 

billion and $6.7 billion to $16 billion, at 3% and 7% discount rates, respectively 

(Table 1-1). All estimates are in 2005 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. Due to data, methodology, and resource limitations, the 

benefits from reducing other air pollutants have not been monetized in this analysis, 

including reducing 4,400 tons of NOx, 5,200 tons of organic hazardous air pollutants 

(HAPs), 5,900 tons of HCl, and 16,400 pounds of Hg each year. In addition, 

ecosystem benefits and visibility benefits have not been monetized in this analysis. 

These estimates include the energy disbenefits associated with increased electricity 

usage by the control devices. 

 Net Benefits: In the year of full implementation (2013), EPA estimates the net 

benefits of the final NESHAP and NSPS are $6.5 billion to $17 billion and $5.8 

billion to $16 billion, at 3% and 7% discount rates, respectively. All estimates are in 

2005 dollars for the year 2013. 

1.2 Organization of this Report 

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

EIA: 

 Section 2 presents a profile of the affected industry. 

 Section 3 describes the economic impact analysis and energy impacts. 

 Section 4 describes the small business impact analysis. 

 Section 5 presents the air quality modeling of emission reductions. 

 Section 6 presents the benefits analysis. 

 Appendix A provides an overview of the economic impact model. 

 Appendix B discusses the model of the cement plant’s production decision. 

 Appendix C presents the social cost methodology. 
1-4 

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

Final Portland Cement NESHAP in 2013 (millions of 2005$)a

Final NESHAP and NSPS 

3% Discount Rate 7% Discount Rate 

Total Monetized Benefitsb $7,400 to $18,000 $6,700 to $16,000 

Total Social Costsc $926 to $950 $926 to $950 

Net Benefits $6,500 to $17,000 $5,800 to $16,000 

Nonmonetized Benefitsd

4,400 tons of NOx (includes energy disbenefits)

5,200 tons of organic HAPs

5,900 tons of HCl 

16,400 pounds of mercury 

Health effects from HAPs, NO2, and SO2 exposure

Ecosystem effects 

Visibility impairment

Final NSPS only 

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefitsb $510 to $1,300 $460 to $1,100 

Total Social Costsc $72 $72 

Net Benefits $440 to $1,200 $390 to $1,000 

Nonmonetized Benefitsd

6,600 tons of NOx

520 tons of HCl 

Health effects from HAPs, NO2, and SO2 exposure

Ecosystem effects 

Visibility impairment

Final NESHAP only 

3% Discount Rate 7% Discount Rate 

Total Monetized Benefitsb $7,400 to $18,000 $6,700 to $16,000 

Total Social Costsc $904 to $930 $904 to $930 

Net Benefits $6,500 to $17,000 $5,800 to $16,000 

Nonmonetized Benefitsd

5,200 tons of organic HAPs

5,900 tons of HCl 

16,400 pounds of mercury 

Health effects from HAPs, SO2 exposure

Ecosystem effects 

Visibility impairment

Alternative: More Stringent NSPS and Final NESHAP 

3% Discount Rate 7% Discount Rate 

Total Monetized Benefitsb $7,400 to $18,000 $6,700 to $16,000 

Total Social Costsc $955 to $979 $955 to $979 

Net Benefits $6,500 to $17,000 $5,700 to $15,000 

Nonmonetized Benefitsd

7,800 tons of NOx (includes energy disbenefits)

5,200 tons of organic HAPs

5,900 tons of HCl 

16,400 pounds of mercury 

Health effects from HAPs, NO2, and SO2 exposure

Ecosystem effects 

Visibility impairment a All estimates are for the implementation year (2013) and are rounded to two significant figures. b The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of 

directly emitted PM2.5 and PM2.5 precursors such as SO2. 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. 

The total monetized benefits include the energy disbenefits. 

c The methodology used to estimate social costs for 1 year in the multimarket model using surplus changes results in the same social 

costs for both discount rates. 

d Due to data, methodology, and resource limitations, we were unable to monetize the benefits associated with these categories of

benefits. 
2-1 

SECTION 2

INDUSTRY PROFILE 

Hydraulic cement (primarily Portland cement) is a key component of an important 

construction material: concrete. Concrete is used in a wide variety of applications (e.g., 

residential and commercial buildings, public works projects), and cement demand is influenced 

by national and regional trends in these sectors. Recent data for 2007 show that the U.S. cement 

industry produced over 90 million metric tons of Portland cement (Department of Interior [DOI], 

U.S. Geological Survey [USGS], 2008b). The value of total U.S. sales, including imported 

cement, was about $11.8 billion, with an average value of approximately $100 per metric ton. 

The vast majority of cement sales went to ready-mixed concrete producers and concrete product 

manufacturers (88%). Since 2003, the United States has relied on cement imports to meet 

approximately 20% to 23% of its consumption needs. However, this share dropped to 

approximately 17% in 2007 as overall construction demand for cement fell (DOI, USGS, 2008b). 

The remainder of this section provides an introduction to the Portland cement industry. 

The purpose is to give the reader a general understanding of the technical and economic aspects 

of the industry that must be addressed in the economic impact analysis. Section 2.1 provides an 

overview of the production processes and costs data. Section 2.2 discusses the uses, consumers, 

and substitutes for cement. Section 2.3 summarizes the organization of the Portland cement 

industry. The industry profile concludes with a discussion of historical market data and the 

current industry outlook. 

2.1 The Supply Side 

2.1.1 Production Process 

As shown in Figure 2-1, the manufacturing process of an integrated cement plant includes 

 quarrying and crushing the raw materials, 

 grinding the carefully proportioned materials to a high degree of fineness, 

 firing the raw materials mixture in a rotary kiln to produce clinker, and 

 grinding the resulting clinker to a fine powder and mixing with gypsum to produce 

cement. 
2-2 

Figure 2-1. Simplified Flow Sheet of Clinker and Cement Manufacture 

There are two processes for manufacturing cement: the wet process and the dry process. 

In the wet process, water is added to the raw materials during the blending process and before 

feeding the mixture into the rotary kiln. In contrast, the dry process feeds the blended materials 

directly into the rotary kiln in a dry state. Newer dry process plants also use preheater and 

precalciner technologies that partially heat and calcine the blended raw materials before they 

enter the rotary kiln. These technologies can increase the overall energy efficiency of the cement 

plant and reduce production costs. 

The fuel efficiency differences between the wet and dry processes have led to a 

substantial decline in clinker capacity provided by the wet process over the last 3 decades. 

Historical data show capacity shares falling from 52% in 1980 to approximately 22% in 2000 

(Van Oss and Padovani, 2002). Data also show that the number of wet process plants fell from 

32 in 2000 to 23 in 2005 (DOI, USGS, 2007). 
2-3 

2.1.2 Types of Portland Cement 

Portland cement manufacturers produce a variety of types of cement in the United States 

designed to meet different requirements. The American Society for Testing Materials (ASTM) 

specification C-150 provides for eight types of Portland cement: five standard types (I, II, III, IV, 

V) and three additional types that include air-entraining properties (IA, IIA, IIIA) (PCA, 2008a). 

We describe these below. 

Types I and IA: These types are the usual product used in general concrete construction, 

most commonly known as gray cement because of its color. 

Types II and IIA: These types are intended for use when moderate heat of hydration is 

required or for general concrete construction exposed to moderate sulfate action. 

Type III and IIIA: These types are made from raw materials with a lime-to-silica ratio 

higher than that of Type I cement and are ground finer than Type I cements. They contain a 

higher proportion of tricalcium silicate than regular Portland cements. 

Type IV: This type contains a lower percentage of tricalcium silicate and tricalcium 

aluminate than Type I, thus lowering the heat evolution. Consequently, the percentage of 

tetracalcium aluminoferrite is increased. Type IV cements are produced to attain a low heat of 

hydration. 

Type V: This type resists sulfates better than the other four types. 

As shown in Table 2-1, the vast majority of Portland cement shipments1

 in 2005 were 

Types I and II grey cement. However, Type V (sulfate-resisting) is a growing market (DOI, 

USGS, 2007a); since 2000, Type V cement has increased its share of shipments from 4% to 

15%. Shipment shares for other types of cement remained constant during this period. 

2.1.3 Production Costs

Portland cement is produced using a combination of variable inputs such as raw 

materials, labor, electricity, and fuel. U.S. Census data for the cement industry (North American 

Industry Classification System [NAICS] 32731: cement manufacturing) provides an initial 

overview of aggregated industry expenditures on these inputs (Department of Commerce [DOC], 

Bureau of the Census, 2010). In 2007, the total value of shipments was $10.6 billion, and the 

industry spent approximately $1.7 billion on materials, parts, and packaging, or 16% of the value 

of shipments. Total compensation for all employees (includes payroll and fringe benefits) 

 

1

 USGS notes these shipment data include cement imports (primarily Types I, II, and V). 
2-4 

Table 2-1. Portland Cement Shipped from Plants in the United States to Domestic 

Customers, by Typea, b

Type 2000 Share 2005 Share 

General use and moderate heat (Types I and II) (gray)c 90,644 88% 93,900 77% 

High early strength (Type III) 3,815 4% 3,960 3% 

Sulfate resisting (Type V)c

 4,453 4% 18,100 15% 

Whited 894 1% 1,190 1% 

Blended 1,296 1% 3,160 3% 

Expansive and regulated fast setting 60 0% 6 0% 

Othere

 1,786 2% 1,997 2% 

Totalf

 102,947 100% 122,000 100% 

a Includes imported cement. 

b Data are rounded to no more than three significant digits; may not add to totals shown. 

c Cements classified as Type II/V hybrids are now commonly reported as Type V. 

d Mostly Types I and II but may include Types III through V and block varieties. 

e Includes block, oil well, low heat (Type IV), waterproof, and other Portland cements. 

f Data are based on an annual survey of plants and importers. 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Table 15. 

U.S. Department of the Interior, U.S. Geological Survey. 2002. 2001 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Table 15. 

amounted to $1.4 billion (13%).1 Fuels and electricity expenditures were approximately $1.7 

billion (16%). 

2.1.3.1 Raw Material Costs 

According to the USGS, approximately 159.7 million tons of raw materials were required 

to produce approximately 95.5 million tons of cement in 2005 or 1.67 tons of raw materials per 

ton of cement. Table 2-2 summarizes the amount of raw material inputs used per ton of cement 

produced in the United States between 2000 and 2005. As the data show, the amount of raw 

materials required to produce one ton of cement has remained essentially constant during this 

6-year period. 

 

1

 Wages paid to production workers were $0.8 billion (8% of the value of shipments) at an average hourly rate of 

$27. 
2-5 

Table 2-2. Raw Material Input Ratios for the U.S. Cement Industry: 2000 to 2005 

 2000 2001 2002 2003 2004 2005 

Raw material input (103 metric tons) 144,949 147,300 153,100 150,500 158,200 159,700 

Cement production (103 metric tons) 85,178 86,000 86,817 89,592 94,014 95,488 

Metric tons of raw material input per 

ton of cement 

1.70 1.71 1.76 1.68 1.68 1.67 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2002–2007a. 2001–2005 Minerals Yearbook, 

Cement. Table 6. Washington, DC: U.S. Department of the Interior. 

U.S. Department of the Interior, U.S. Geological Survey. 2002–2007a. 2001–2005 Minerals Yearbook, 

Cement. Table 3. Washington, DC: U.S. Department of the Interior. 

The price of these raw materials varies across regions. Table 2-3 lists the average price of 

raw materials per metric ton by state. In 2005, the prices of raw materials were highest in Hawaii 

where they sold for an average of $13.34 per metric ton. The prices of raw materials were lowest 

in Michigan, where they sold for an average of $3.89 per metric ton. 

2.1.3.2 Labor Costs 

In 2005, the Portland Cement Association (PCA) reported labor productivity measures (in 

terms of metric tons of cement per employee hour)1

 for 2000 to 2005 in its U.S. and Canadian 

Labor-Energy Input Survey. Using these data, we computed a measure of labor hour 

requirements to produce cement (see Table 2-4). As these data show, wet process plants are 

typically more labor intensive, requiring approximately 45% more labor hours to produce a 

metric ton of cement than dry process plants. 

In addition, labor productivity has been improving more quickly in dry process plants 

than in those using a wet manufacturing process. Between 2000 and 2005, labor requirements 

decreased by 15% in dry process plants, while in wet process plants labor requirements remained 

constant. As a result, the wet process labor costs relative to dry process plants labor costs have 

risen in recent years (Figure 2-2).2

 

1

 Throughout this report, we use PCA’s method to calculate labor and energy efficiency. This measure is a weighted 

sum of clinker and finished cement production. Weights for labor are 85% clinker and 15% finished cement 

production. Weights for energy are 92% clinker and 8% finished cement production (PCA, 2005). 2

 The labor costs reported in Figure 2-3 were calculated by first multiplying the number of employee hours per 

metric ton of cement reported in Table 2-4 by the average hourly earnings of production workers for each year 

(BLS, 2007a and 2007b). Next, these cost estimates were adjusted for inflation and expressed in 2005 dollars by 

using the consumer price index (CPI) (BLS, 2008). 
2-6 

Table 2-3. Raw Material Costs by Market and State: 2005 

State(s) 

Price of Raw Materials 

($/metric ton)a State(s) 

Price of Raw Materials

($/metric ton)a

AK 6.60 MT $4.76 

AL 6.57 NC $8.59 

AR $6.29 ND $4.45 

AZ $5.75 NE $7.10 

CA $8.37 NH $8.02 

CO $6.85 NJ $7.04 

CT $9.19 NM $6.67 

DE $6.89 NV $7.17 

FL $8.67 NY $8.44 

GA $7.63 OH $5.82 

HI $13.34 OK $5.67 

IA $7.27 OR $6.01 

ID $5.37 PA $6.67 

IL $7.16 RI $7.74 

IN $5.40 SC $7.61 

KS $7.20 SD $4.60 

KY $7.24 TN $7.55 

LA $8.18 TX $6.15 

MA $9.19 UT $5.58 

MD $8.28 VA $9.03 

ME $6.85 VT $6.75 

MI $3.89 WA $6.92 

MN $8.30 WI $5.83 

MO $7.37 WV $6.86 

MS $11.90 WY $5.68 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007b. 2005 Minerals Yearbook, Crushed Stone.

Table 4. Washington, DC: U.S. Department of the Interior. 

Table 2-4. Labor Productivity Measures for the U.S. Cement Industry by Process Type: 

2000 to 2005 (employee hours per metric ton) 

Year 2000 2001 2002 2003 2004 2005 

All plants 0.394 0.388 0.360 0.347 0.338 0.338 

Wet process 0.469 0.457 0.450 0.465 0.452 0.463 

Dry process 0.376 0.375 0.342 0.328 0.318 0.318 

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 
2-7 

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

$7.00

$8.00

$9.00

$10.00

2000 2001 2002 2003 2004 2005

All Plants Wet Process Dry Process

Figure 2-2. Labor Costs per Metric Ton of Cement ($2005) 

Sources: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2007a. “Current Employment Statistics 

(National): Customizable Data Tables” Available at <http://www.bls.gov/ces/>. As obtained on March 14, 

2008. 

U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2008. “Consumer Price Index All Items – 

U.S. City Average Data: Customizable Data Tables.” Available at <http://www.bls.gov/cpi/>. As obtained 

on March 14, 2008. 

2.1.3.3 Energy Costs 

Figure 2-3 provides a detailed breakdown of U.S. energy consumption in 2005. As this 

figure shows, the vast majority of energy in U.S. cement plants is derived from coal and coke 

(75%). The remaining 25% of energy consumption is derived from electricity, waste, natural gas, 

and petroleum products. 

PCA also reported energy consumption data by type of U.S. cement plant (in terms of 

millions of BTUs per metric ton of cement) (see Table 2-5). As these data show, wet process 

plants are typically more energy intensive, consuming approximately 44% more energy per ton 

of cement than dry process plants. In addition, the trends in energy consumption continue to 

show that dry plants have become more energy efficient than wet process plants. Between 2000 

and 2005, energy consumption per ton of cement in dry process plants decreased by 5%; in 

contrast, wet process plants’ energy consumption increased slightly during this period. 
2-8 

Coal & Coke

75.4%

Petroleum 

Products

0.8%

Electricity

11.0%

Natural Gas

3.6%

Waste

9.2%

Figure 2-3. Distribution of Energy Consumption 

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

Table 2-5. Energy Consumption by Type of U.S. Cement Plant (million BTU per metric 

ton) 

Year 2000 2001 2002 2003 2004 2005 

All plants 4.982 4.93 4.858 4.762 4.755 4.699 

Wet process 6.25 6.442 6.676 6.647 6.807 6.387 

Dry process 4.673 4.655 4.498 4.433 4.407 4.433 

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 

2.2 The Demand Side 

The demand for Portland cement is considered a “derived” demand because it depends on 

the construction demands for its end product—concrete. A recent study by the U.S. International 

Trade Commission suggests that 0.192 metric tons of grey Portland cement were used per $1,000 

of construction in 1998 (USITC, 2006). Given cement prices at this time (approximately $75 per 

metric ton), Portland cement costs represented only a small share of the total value of 

construction expenditures (less than 2%). 

Concrete is used in a wide variety of construction applications, including residential and 

commercial buildings, and public works projects such as the national highway system. As shown 

in Figure 2-4, ready-mixed concrete producers have historically accounted for over half of the 

Portland cement consumption. Although government and miscellaneous expenditures saw 

substantial increases in the early 1990s, their consumption share returned to pre-1990s levels 

after 1996. The latest USGS use data show that ready-mixed concrete producers accounted for 
2-9 

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

1975 1980 1985 1990 1995 2000

Metric tons

Ready-mix concrete Concrete products Contractors

Building material dealers Oil well, mining, and waste stabilization Government and miscellaneous

Masonry cement

Figure 2-4. End Uses of Cement: 1975 to 2003 

Source: Kelly, T. and G. Matos. 2007a. “Historical Statistics for Mineral and Material Commodities in the United 

States: Cement End Use Statistics.” U.S. Geological Survey Data Series 140, Version 1.2. Available at 

http://minerals.usgs.gov/ds/2005/140/. 

74% of cement sales in 2005, followed by concrete product manufacturers (14%), contractors 

(6%), and other (6%) (Kelly and Matos, 2007a). 

Cement competes with other construction materials such as steel, asphalt, and lumber. 

Lumber is the primary substitute in the residential construction market, while steel is the primary 

substitute in commercial applications. Asphalt is a key substitute in transportation projects such 

as road and parking lot surfacing. However, concrete has advantages over these substitutes 

because it tends to be available locally and has lower long-term maintenance costs (Van Oss and 

Padovani, 2002). 

The PCA regularly reports price trends for these competing building materials (PCA, 

2008b). As shown in Figure 2-5, steel and asphalt have risen sharply relative to cement since 

2003 while lumber has declined. 
2-10 

Figure 2-5. Producer Price Indices for Competitive Building Materials: 2003 to 2008 

Source: Portland Cement Association. 2008b. “Market Research: Producer Price Indices—Competitive Building 

Materials.” Available at <http://www.cement.org/market/>. 

2.3 Industry Organization 

2.3.1 Market Structure 

A review and description of market characteristics (i.e., degree of concentration, entry 

barriers, and product differentiation) can enhance our understanding of how U.S. cement markets 

operate. These characteristics provide indicators of a firm’s ability to influence market prices by 

varying the quantity of cement it sells. For example, in markets with large numbers of sellers and 

identical products, firms are unlikely to be able to influence market prices via their production 

decisions (i.e., they are “price takers”). However, in markets with few firms, significant barriers 

to entry (e.g., licenses, legal restrictions, or high fixed costs), or products that are similar but can 

be differentiated, the firm may have some degree of market power (i.e., set or significantly 

influence market prices). 

Cement sales are often concentrated locally among a small number of firms for two 

reasons: high transportation costs and production economies of scale.1

 Transportation costs 

significantly influence where cement is ultimately sold; high transportation costs relative to unit 

value provide incentives to produce and sell cement locally in regional markets (USITC, 2006). 

 

1

 The 2002 Economic Census reports that the national Herfindahl-Hirschman Index (HHI) for cement (North 

American Industry Classification System [NAICS] 32731) is 568. However, this measure is likely not 

representative of actual concentration that exists in regional markets. 
2-11 

To support this claim, the empirical literature has typically pointed to Census of Transportation 

data showing over 80% of cement shipments were made within a 200-mile radius (Jans and 

Rosenbaum, 1997)1

 and reported evidence of high transportation costs per dollar of product 

value from case studies (Ryan, 2006). The cement industry is also very capital intensive and 

entry requires substantial investments. In additional, large plants are typically more economical 

because they can produce cement at lower unit costs; this reduces entry incentives for smallsized cement plants. Using recent data for planned capacity expansions between 2008 and 2012, 

the PCA reports these expansions will cost $5.9 billion and add 25 million metric tons (PCA, 

2007), or $240 per metric ton, of new capacity. 

For a given construction application, consumers are likely to view cement produced by 

different firms as very good substitutes. American Society for Testing and Materials (ASTM) 

specifications tend to ensure uniform quality, and recent industry reviews (USITC, 2006) suggest 

that there is little or no brand loyalty that allows firms to differentiate their products. 

2.3.2 Manufacturing Plants 

During 2005, 107 cement manufacturing plants with 186 cement kilns were operating in 

the United States. This section describes the location, age, production capacity, and employment 

of these manufacturing facilities. Section 2.3.2 concludes with a discussion of future trends. 

Section 2.3.3 provides a detailed discussion of the characteristics of the firms owning these 

facilities. 

2.3.2.1 Location 

Table 2-6 summarizes the geographic location of cement kilns in the United States and 

clinker capacity. The top five states in order of clinker capacity are California, Texas, 

Pennsylvania, Florida, and Alabama. Together these states account for 75 (40%) of the kilns in 

the United States and 41 million metric tons (44%) of clinker capacity. Figure 2-6 provides a 

graphical depiction of the number of kilns distributed by state. 

Fourteen states (Alaska, Hawaii, Connecticut, Louisiana, New Hampshire, North Dakota, 

Wisconsin, Delaware, Massachusetts, New Jersey, Rhode Island, Minnesota, North Carolina, and 

Vermont) and the District of Columbia had no clinker-producing facilities in 2005. 

 

1

 A recent USITC study of California cement markets found more than 75% of gray Portland cement shipments in 

the state were shipped to customers within 200 miles of the cement producer (USITC, 2006). 
2-12 

Table 2-6. Number of Kilns and Clinker Capacity by State: 2005 

 No. Kilns Clinker Capacity (103

 metric tons per year) 

AK 0 

AL 5 5,375 

AR 3 831 

AZ 8 2,809 

CA 20 12,392 

CO 2 2,117 

CT 0 

DE 0 

FL 7 5,489 

GA 2 1,020 

HI 0 

IA 4 2,672 

ID 2 260 

IL 8 2,770 

IN 8 3,191 

KS 9 2,835 

KY 1 1,365 

LA 0 

MA 0 

MD 4 2,538 

ME 1 392 

MI 8 4,243 

MN 0 

MO 6 5,169 

MS 1 419 

MT 2 573 

NC 0 

ND 

NE 2 845 

NH 0 

NJ 0 

NM 2 432 

NV 2 452 

NY 4 2,886 

OH 3 1,115 

OK 7 1,869 

OR 1 816 

(continued) 
2-13 

Table 2-6. Number of Kilns and Clinker Capacity by State: 2005 (continued) 

 No. Kilns Clinker Capacity (103

 metric tons per year) 

PA 21 6,414 

RI 0 

SC 6 3,480 

SD 3 851 

TN 2 1,438 

TX 22 11,688 

UT 2 1,514 

VA 1 1,120 

VT 0 

WA 2 1,100 

WI 0 

WV 3 708 

WY 2 597 

Total 186 93,785 

Source: Portland Cement Association (PCA). 2004. U.S. and Canadian Portland Cement Industry: Plant 

Information Summary. Skokie, IL: PCA’s Economic Research Department. 

Figure 2-6. Distribution of Cement Kilns in the United States 

Source: Portland Cement Association (PCA). December 2004. U.S. and Canadian Portland Cement Industry: Plant 

Information Summary. Skokie, IL: Portland Cement Association Economic Research Department. 

2.3.2.2 Age 

In 2005, 72% (134) of all kilns in the United States used the dry manufacturing process, 

and it accounted for 83% (78 million metric tons) of national clinker capacity. The growing 

prevalence of the dry process among cement manufacturers is part of a long-term trend. As the 

data in Table 2-7 indicate, no new wet clinker capacity has been added within the past 30 years. 
2-14 

Table 2-7. Number of Kilns and Clinker Capacity by Age and Process Type 

 No. Kilns 

Clinker Capacity (103

 metric 

tons per year) Average Annual Capacity per Kiln 

Total 

0–10 26 28,144 1,082.5 

11–15 3 2,176 725.3 

16–20 5 3,345 669.0 

21–25 16 14,982 936.4 

26–30 18 11,843 657.9 

31–35 16 5,786 361.6 

36–40 21 9,285 442.1 

41–45 29 8,971 309.3 

46–50 32 6,564 205.1 

51–55 6 991 165.2 

56–60 6 800 133.3 

60+ 8 898 112.3 

Total 186 93,785 504.2 

Dry Process 

0–10 26 28,144 1,082.5 

11–15 3 2,176 725.3 

16–20 5 3,345 669.0 

21–25 16 14,982 936.4 

26–30 18 11,843 657.9 

31–35 10 3,962 396.2 

36–40 12 5,498 458.2 

41–45 14 3,800 271.4 

46–50 16 2,651 165.7 

51–55 4 682 170.5 

56–60 6 800 133.3 

60+ 4 328 82.0 

Total 134 78,211 583.7 

Wet Process 

0–10 0 

11–15 0 

16–20 0 

21–25 0 

26–30 0 

31–35 6 1,824 304.0 

36–40 9 3,787 420.8 

41–45 15 5,171 344.7 

(continued) 
2-15 

Table 2-7. Number of Kilns and Clinker Capacity by Age and Process Type (continued) 

 No. Kilns 

Clinker Capacity (103

 metric 

tons per year) Average Annual Capacity per Kiln 

Wet Process (cont.) 

46–50 16 3,913 244.6 

51–55 2 309 154.5 

56–60 0 

60+ 4 570 142.5 

Total 52 15,574 299.5 

Source: Portland Cement Association (PCA). 2004. U.S. and Canadian Portland Cement Industry: Plant 

Information Summary. Skokie, IL: PCA’s Economic Research Department. 

All 68 kilns that have become operational within the past 30 years use the dry manufacturing 

process. These new kilns account for 64% (60 million metric tons) of national clinker capacity. 

2.3.2.3 Production Capacity and Utilization 

Between 2000 and 2005, apparent annual clinker capacity grew approximately 17%, 

while clinker production grew by approximately 14% (Table 2-8). Because capacity tends to 

grow more rapidly than production, total capacity utilization decreased slightly in this period 

from 87.5% in 2000 to 85.4% in 2005. 

Table 2-8. Clinker Capacity, Production, and Capacity Utilization in the United States: 

2000 to 2005 

2000 2001 2002 2003 2004 2005 

Apparent annual capacity (103 

metric tons) 

89,264 100,360 101,000 102,000 105,000 104,000 

Production (103 metric tons) 78,138 79,979 82,959 83,315 88,190 88,783 

Capacity utilization (%) 87.5% 79.7% 82.1% 81.7% 84.0% 85.4% 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2000–2005. Minerals Yearbook, Cement. Table 5. 

Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/pubs/ 

commodity/cement/>. As obtained on March 14, 2008. 

Much of the vast majority of the growth in clinker capacity came in 2001 when existing 

Portland cement plants completed major capacity upgrade projects, resulting in a 12% increase in 

clinker capacity over the previous year (USGS, 2002). As a result, capacity utilization fell to 

79.7% that year. After 2001, clinker capacity grew an average of 1% each year, while production 

grew an average of 2%. As a result, capacity utilization has risen slowly since 2001. However, 
2-16 

throughout these movements in clinker capacity and production, capacity utilization tended to 

remain between 80% and 85%. 

Capacity utilization often varies by geographic region as a result of fluctuations in 

regional construction activity. For example, 2005 data show that Idaho, Montana, and Nevada 

shared a capacity utilization rate of 95.5%—well above the national average. In contrast, South 

Carolina used only 64.5% of its clinker capacity. Table 2-9 provides a complete listing of 

capacity utilization rates by state in 2005. 

2.3.2.4 Employment 

Each year, the Annual Survey of Manufactures (ASM) collects employment, payroll, 

sales, and other data for all manufacturing establishments. Table 2-10 summarizes the 

employment data collected by the ASM for the cement manufacturing industry (NAICS 327310) 

from 2000 to 2005. As these data indicate, total employment fell approximately 2% over this 

6-year period, from approximately 17,000 employees in 2000 to 16,900 in 2005. 

2.3.2.5 Trends 

As previously discussed, clinker capacity has been increasing at a slower pace since 

2001. However, according to the PCA, the cement industry has announced that it will increase 

clinker capacity by nearly 25 million metric tons between 2007 and 2012. This represents a 27% 

increase over U.S. 2006 clinker capacity and amounts to a $5.9 billion investment (PCA, 2007). 

In addition to these expected capacity expansions, likely changes in U.S. specifications 

allowing the use of limestone in Portland cement could also increase production capacity. 

According to the PCA, domestic cement supply could increase by as much as 2 million 

additional tons by 2012. Increases in EPA production variances could also add another 1.1 

million metric tons of domestic supply (PCA, 2007). 

2.3.3 Firm Characteristics 

EPA has reviewed industry information and publicly available sales and employment 

databases to identify the chain of ownership by accounting for subsidiaries, divisions, and joint 

ventures to appropriately group companies by size. Table 2-11 provides sales and employment 

data for 27 ultimate parent companies operating Portland cement manufacturing plants in 2005. 

 
2-17 

Table 2-9. Capacity Utilization Rates by State: 2005 

State USGS Geographic Area 

Utilization Rate 

(percent) 

AL Alabama 86.7 

AR Arkansas and Oklahoma 90.9 

AZ Arizona and New Mexico 87 

CA California, northern and southern 88.8 

CO Colorado and Wyoming 79.5 

FL Florida 85.9 

GA Georgia, Virginia, West Virginia 78.4 

IA Iowa, Nebraska, South Dakota 85.5 

ID Idaho, Montana, Nevada, Utah 95.5 

IL Illinois 91.4 

IN Indiana 86.8 

KS Kansas 89.1 

KY Kentucky, Mississippi, Tennessee 87.4 

MD Maryland 89.1 

ME Maine and New York 83.6 

MI Michigan 85.5 

MO Missouri 90.3 

MS Kentucky, Mississippi, Tennessee 87.4 

MT Idaho, Montana, Nevada, Utah 95.5 

NE Iowa, Nebraska, South Dakota 85.5 

NM Arizona and New Mexico 87 

NV Idaho, Montana, Nevada, Utah 95.5 

NY Maine and New York 83.6 

OH Ohio 84.7 

OK Arkansas and Oklahoma 90.9 

OR Oregon and Washington 83.3 

PA Pennsylvania, eastern and western 83.7 

SC South Carolina 64.5 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007b. 2005 Minerals Yearbook, Cement. 

Table 5. Washington, DC: U.S. Department of the Interior. 
2-18 

Table 2-10. Cement Manufacturing Employment (NAICS 327310): 2000 to 2005 

Year Number of Employees 

2000 17,175 

2001 17,220 

2002 17,660 

2003 17,352 

2004 16,883 

2005 16,877 

Sources: U.S. Department of Commerce, Bureau of the Census. 2006. 2005 Annual Survey of Manufactures. 

M05(AS)-1. Washington, DC: Government Printing Office. Available at 

<http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March 14, 2008. 

 U.S. Department of Commerce, Bureau of the Census. 2003. 2001 Annual Survey of Manufactures. 

M05(AS)-1. Washington, DC: Government Printing Office. Available at 

<http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March 14, 2008. 

2.3.3.1 Distribution of Small and Large Companies 

Firms are grouped into small and large categories using Small Business Administration 

(SBA) general size standard definitions for NAICS codes. These size standards are presented 

either by number of employees or by annual receipt levels, depending on the NAICS code. The 

manufacture of Portland cement is covered by NAICS code 327310 for cement manufacturing. 

Thus, according to SBA size standards, firms owning Portland cement manufacturing plants are 

categorized as small if the total number of employees at the firm is less than 750; otherwise, the 

firm is classified as large. As shown in Table 2-11, potentially affected firms range in size from 

160 to 71,000 employees. A total of 4 firms, or 15%, are categorized as small, while the 

remaining 23 firms, or 75%, are large.1

2.3.3.2 Capacity Share 

As shown in Table 2-11, the leading companies in terms of capacity at the end of 2005 

were Holcim (U.S.) Inc.; CEMEX, Inc.; Lafarge North America, Inc.; Buzzi Unicem USA, Inc.; 

HeidelbergCement AG (owner of Lehigh Cement Co.); Ash Grove Cement Co.; Texas 

Industries, Inc.; Italcementi S.p.A.; Taiheiyo Cement Corporation; Titan Cement; and VICAT. 

The top 5 had about 57% of total U.S. clinker capacity, and the top 10 accounted for 83% of total 

capacity. Small companies accounted for less than 5% of clinker capacity.

 

1

 In cases where no employment data were available, we used information from previous EPA analyses to determine 

firm size. 
2-19 

Table 2-11. Ultimate Parent Company Summary Data: 2005 

Ultimate Parent 

Name 

Annual 

Sales 

($106

) 

Employment Type Small 

Business Plants Kilns 

Clinker 

Capacity 

(103

 metric 

tons per 

year) 

Capacity 

Share 

Holcim, Inc $14,034 59,901 Public No 14 17 13,089 14.0% 

CEMEX, S.A. de 

C.V. 

$18,290 26,679 Public No 13 21 12,447 13.3% 

Lafarge S.A. $22,325 71,000 Public No 13 23 12,281 13.1% 

BUZZI UNICEM 

SpA 

$3,495 11,815 Private No 10 19 8,129 8.7% 

HeidelbergCement 

AG 

$12,182 45,958 Public No 10 13 7,786 8.3% 

Ash Grove Cement 

Company 

$1,190 2,600 Private No 9 15 6,687 7.1% 

Texas Industries, 

Inc. 

$944 2,680 Public No 4 15 5,075 5.4% 

Italcementi S.p.A. $5,921 20,313 Public No 6 16 4,442 4.7% 

Taiheiyo Cement 

Corporation 

$7,710 2,061 Private No 3 7 3,375 3.6% 

Titan Cement $1,589 1,834 Public No 2 2 2,612 2.8% 

VICAT $2,137 6,015 Public No 2 2 1,933 2.1% 

Eagle Materials $922 1,600 Public No 3 5 1,651 1.8% 

Mitsubishi Cement 

Corporation 

$1,134 NA Joint 

venture 

No 1 1 1,543 1.6% 

Rinker Materials $4,140 11,193 Private No 2 2 1,533 1.6% 

Hanson America 

Holdings 

$3,000 14,872 Private No 1 1 1,497 1.6% 

Salt River Materials 

Group a

$150b

 <750 Tribal 

Govern

ment 

Yes 1 4 1,477 1.6% 

Grupo Cementos de 

Chihuahua, S.A. de 

C.V. 

$663 2,591 Public No 2 5 1,283 1.4% 

Cementos Portland 

Valderrivas, S.A. 

$1,159 2,674 Public No 2 6 1,257 1.3% 

Zachary 

Construction 

$152 1,200 Private No 1 2 868 0.9% 

RMC Pacific 

Materials 

$160 800 Private No 1 1 812 0.9% 

(continued) 
2-20 

Table 2-11. Ultimate Parent Company Summary Data: 2005 (continued) 

Ultimate Parent 

Name 

Annual 

Sales 

($106

) 

Employment Type Small 

Business Plants Kilns 

Clinker 

Capacity 

(103

 metric 

tons per 

year) 

Capacity 

Share 

Monarch Cement 

Company 

$154 600 Public Yes 1 2 787 0.8% 

Florida Rock 

Industries 

$1,368 3,464 Public No 1 1 726 0.8% 

Votorantim Group 

and Anderson 

Columbia Company 

$9,518 30,572 Joint 

venture 

No 1 1 682 0.7% 

Dyckerhoff AG $1,876 6,958 Public No 1 1 586 0.6% 

Continental Cement 

Company, LLC 

$50b

 <750 Private Yes 1 1 549 0.6% 

Cementos Del 

Norte 

NA NA Private No 1 1 392 0.4% 

Snyder Associate 

Companies 

$29 350 Private Yes 1 2 286 0.3% 

a

 Enterprise is owned by Salt River Pima-Maricopa Indian Community. b

 EPA estimate. 

Sources: Dun & Bradstreet, Inc. 2007. D&B million dollar directory. Bethlehem, PA. 

LexisNexis. LexisNexis Academic [electronic resource]. Dayton, OH: LexisNexis. 

2.3.3.3 Company Revenue and Ownership Type 

Cement manufacturing is a capital-intensive industry. The vast majority of stakeholders 

are large global companies with sales exceeding $1 billion. In 2005, ultimate parent company 

sales ranged from $30 million to $22.3 billion (Table 2-11), with average (median) sales of 

$4,565 ($1,589) million. Small companies accounted for 0.3% share by sales. Ultimate parent 

companies were either privately or publicly owned or jointly operated by several companies. A 

majority of the companies (52%) were publicly owned. Private companies had a slightly smaller 

share (41%), and only two (or 7%) were joint ventures. 

2.4 Markets 

Portland cement is produced and consumed domestically as well as traded internationally. 

The United States meets a substantial fraction of its cement needs through imports; in contrast, it 

exports only a small fraction of domestically produced cement to other countries. We provide 

value, quantity, and price trends over the past decade for Portland cement when detailed statistics 
2-21 

are available. In the case of international trade, we can report data only for hydraulic cement, 

which includes Portland and masonry cement. 

2.4.1 Market Volumes 

2.4.1.1 Domestic Production 

In 2007, the domestic shipments of Portland cement were 90.6 million metric tons, 

reflecting an 8.5% increase from 2000 and, more recently, a 3% decrease from 2006 (see 

Table 2-12). Year-end stocks remained relatively level during this period at 7.4 million metric 

tons. Stocks fell slightly by 5% since 2006 and equaled 8.9 million tons in 2007. As Table 2-12 

shows, shipments to customers increased steadily since 2000, reaching 128 million tons in 2006. 

However, affected by declines in the housing market, the shipments fell by 9% in 2007. 

Table 2-12. Historical U.S. Cement Statistics (106 metric tons) 

 2000 2001 2002 2003 2004 2005 2006 2007 

Production 

Clinker 78.1 78.5 82.0 81.9 86.7 87.4 88.6 87.2 

Portland cement 83.5 84.5 85.3 88.1 92.4 93.9 93.2 90.6 

Masonry cement 4.3 4.5 4.4 4.7 5.0 5.4 5.0 4.9 

Total cement 87.8 88.9 89.7 92.8 97.4 99.3 98.2 95.5 

Shipments to customers 110.0 113.1 110.0 112.9 120.7 127.4 127.9 116.0 

Stocks, cement, year end 7.6 6.6 7.6 6.6 6.7 7.4 9.4 8.9 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2008b. Minerals Commodity Summaries, Cement 

2008. Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/ 

pubs/commodity/cement/mcs-2008-cemen.pdf>. 

U.S. Department of the Interior, U.S. Geological Survey. 2003. 2002 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/ 

commodity/cement/>. 

2.4.1.2 International Trade 

Cement imports are a significant share of domestic consumption (approximately 20%); 

they also grew by 30% from 2000 to 2006 (see Table 2-13). Major importing countries in 2007 

included Canada (18% of total imports in 2006), China (16%), and Thailand (11%) (DOI, USGS, 

2008b). In 2007, the falling value of the dollar and construction activity declines in the housing 

market tempered the quantity of import demanded. As a result, the share of U.S. consumption 

met by imports fell to its lowest level in 10 years. 
2-22 

Table 2-13. U.S. Cement Trade Data: 2000 to 2007 

 2000 2001 2002 2003 2004 2005 2006 2007 

Exports (106 metric tons) 0.7 0.7 0.9 0.8 0.7 0.8 1.5 1.9 

Imports (106 metric tons) 24.6 23.6 22.5 21.0 25.4 30.4 32.1 21.3 

Net import share of apparent 

consumption (%) 

20.0 21.0 19.0 20.0 21.0 23.0 23.0 17.0 

Sources: U.S. Department of the Interior, U.S. Geological Survey. 2008b. Minerals Commodity Summaries, Cement 

2008. Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/ 

pubs/commodity/cement/mcs-2008-cemen.pdf>. 

U.S. Department of the Interior, U.S. Geological Survey. 2003. 2002 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Available at 

<http://minerals.er.usgs.gov/minerals/pubs/>. 

During the period from 2000 to 2005, U.S. exports remained relatively constant at about 

800,000 tons and typically did not exceed 1% of production. However, the level of U.S. exports 

has increased during the last 2 years. In 2007, U.S. exports totaled 1.9 million metric tons. The 

vast majority of U.S. exports of hydraulic cement are supplied to Canada: U.S. producers 

shipped a total of 650,000 tons to Canada in 2005, or 85% of total U.S. exports. The remaining 

fraction of U.S. exports in 2005 went to the Bahamas, Mexico, and 33 other countries around the 

world (DOI, USGS, 2008b). 

2.4.2 Market Prices 

Correcting for the effects of inflation, we find that the real price of cement per metric ton 

(2005 dollars) has typically ranged between $75 and $95 since 1990 (see Figure 2-7). However, 

data for the last 2 years suggest the average price of cement is at its highest level in over 2 

decades (approximately $100). Because of transportation constraints, there are regional 

differences in the price of cement across states. For example, remote locations such as Alaska 

and Hawaii had the highest deviation from the national average ($48 in 2005) (see Figure 2-8). 

In the contiguous states, prices in Arizona, New Mexico, and California were higher than the 

national averages, while prices in Texas, Indiana, and South Carolina were among the lowest. 

2.4.3 Future Projections 

Although estimates of future cement demand are not publicly available, the Energy 

Information Administration provides projections for the real value of shipments for the stone, 

clay, and glass industry in its AEO (DOE, 2007). The forecasted annual average growth rate for 

2005 to 2030 is approximately 1.7%. 
2-23 

$0

$10

$20

$30

$40

$50

$60

$70

$80

$90

$100

$110

F.O.B. Cement Price ($/metric ton)

Year

Current U.S. Dollars

Constant 2005 U.S. Dollars

Figure 2-7. Historical U.S. Cement Price 

Sources: 1990–2003: Kelly, T. and G. Matos. 2007b. “Historical Statistics for Mineral and Material Commodities in 

the United States: Cement Supply and Demand Statistics.” U.S. Geological Survey Data Series 140, 

Version 1.2. Available at <http://minerals.usgs.gov/ds/2005/140/>. Last modified April 11, 2006. 

2004–2007: U.S. Department of the Interior, U.S. Geological Survey. 2008b. Minerals Commodity 

Summaries, Cement 2008. Washington, DC: U.S. Department of the Interior. Available at 

<http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2008-cemen.pdf>. 
2-24 

-$20 -$15 -$10 -$5 $0 $5 $10 $15 $20 $25 $30 $35 $40 $45 $50 $55

South Carolina

Indiana

Texas, southern

Alabama

Maryland

Arkansas and Oklahoma

Texas, northern

Kansas

Georgia, Virginia, West Virginia

Kentucky, Mississippi, Tennessee

Missouri

Pennsylvania, eastern

Colorado and Wyoming

Oregon and Washington

Illinois

Maine and New York

Pennsylvania, western

Ohio

Florida

Iowa, Nebraska, South Dakota

Michigan and Wisconsin

Idaho, Montana, Nevada, Utah

California, southern

California, northern

Arizona and New Mexico

Alaska and Hawaii

Dollar Difference From National Average 

F.O.B. Cement Price ($90 per metric ton): 2005

Figure 2-8. Deviation from National Average Cement Price per Metric Ton by Region: 

2005 

Source: U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement. 

Washington, DC: U.S. Department of the Interior. Table 11. Available at 

<http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. 
3-1 

SECTION 3

ECONOMIC IMPACT ANALYSIS 

EPA prepares an EIA to provide decision makers with a measure of the social costs of 

using resources to comply with a program (EPA, 2000). The social costs can then be compared 

with estimated social benefits (as presented in Section 5). As noted in EPA’s (2000) Guidelines 

for Preparing Economic Analyses, several tools are available to estimate social costs and range 

from simple direct compliance cost methods to the development of a more complex market 

analysis that estimates market changes (e.g., price and consumption) and economic welfare 

changes (e.g., changes in consumer and producer surplus). 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standard 

industry-level analysis described in the Office’s resource manual (EPA, 1999a). This approach is 

consistent with previous EPA analyses of the Portland cement industry (EPA, 1998; EPA, 1999b, 

and 2009a) and uses a single-period static partial-equilibrium model to compare pre-policy 

cement market baselines with expected post-policy outcomes in these markets. The benchmark 

time horizon for the analysis is the intermediate run where producers have some constraints on 

their flexibility to adjust factors of production. This time horizon allows us to capture important 

transitory impacts of the program on existing producers. Key measures in this analysis include 

 market-level effects (market prices, changes in domestic production and 

consumption, and international trade), 

 industry-level effects (changes in (i.e. operating profits) and employment), 

 facility-level effects (plant utilization changes), and 

 social costs (changes in producer and consumer surplus). 

Absent forecasts and the uncertainties of future economic baselines, the partialequilibrium market analysis can only cover a subset of plants presumed to be operating in 

conditions similar to 2005. Thus, this analysis does not reflect changes in the state of the US 

economy which may occur by the analysis year of 2013 which could significantly influence the 

quantity of cement needed. As shown in the following sections, the market analysis covers $378 

million of the total $466 million in regulatory program costs, or 81%; simulated post policy 

outcomes described throughout Section 3.2 should be interpreted in light of this modeling 

choice. EPA analyzed the remaining $88 million in NESHAP and NSPS regulatory program 

costs “outside” of the partial equilibrium market analyses using direct compliance costs methods 

(see Section 3.3). EPA provides complete social cost accounting in the section describing the 
3-2 

social cost estimates (Section 3.4) and provides a discussion of its overall assessment (Section 

3.5). 

3.1 Regulatory Program Costs 

EPA is finalizing amendments to the NESHAP from the Portland cement manufacturing 

industry and (NSPS for Portland cement plants. The final amendments to the NESHAP add or 

revise, as applicable, emission limits for Hg, THC, and PM from kilns located at a major or an 

area sources, and HCl from kilns and located at major sources. EPA is also adopting separate 

standards for these pollutants which apply during startup, shutdown, and operating modes. 

Finally, EPA is adopting performance specifications for use of mercury CEMS and updating 

recordkeeping and testing requirements. The final amendments to the NSPS add or revise, as 

applicable, emission limits for particulate matter (PM), opacity, nitrogen oxides (NOx), and 

sulfur dioxide (SO2) for facilities that commence construction, modification, or reconstruction 

after June 16, 2008. The final rule also includes additional testing and monitoring requirements 

for affected sources. Although EPA’s analysis focuses on the final NESHAP and NSPS 

engineering cost estimates, EPA also presents additional information on different combinations 

of the regulatory programs. This information helps stakeholders better understand the size and 

scope of the each. These include 

 final NSPS only, 

 final NESHAP only, and 

 alternative: more stringent NSPS and final NESHAP. 

For the year 2013, EPA’s engineering cost analysis estimates the total annualized costs of 

the final NESHAP and NSPS are $466 million (in 2005 dollars) (see Table 3-1). These costs 

include a variety of pollution control expenditures: equipment installation, operating and 

maintenance, recordkeeping, and performance-testing activities. Capital costs are annualized 

over the expected life of the control equipment which is 20 years for all devices except RTOs 

which are 15 years. The majority of the costs ($455 million, or 98%, are associated with the 

final NESHAP. The remaining costs ($11 million) are associated with the final NSPS limits for 

SO2 and NOx. Figure 3-1 illustrates the distribution of annualized compliance costs per metric 

ton of clinker capacity by different combinations of the regulatory programs. In Table 3-2, we 

report state-level summary statistics for total annualized compliance costs per metric ton of 

clinker capacity for the final NESHAP and NSPS to highlight any regional differences in control 

costs. 
3-3 

Table 3-1. Summary of Direct Total Annualized Compliance Costs (million, 2005$) 

 Total Annualized Compliance Costs 

Description Final NSPS Only More Stringent NSPS Only 

Total: $40a $56a

Final NESHAP Only 

Partial Equilibrium Analysis 

(136 Kilns)

$378

NSPS kilns (7 kilns) $29

Other kilns (13 kilns) $48

Total: $455

 Final NESHAP and NSPS 

Final NESHAP and More 

Stringent NSPS 

136 Kilns $378 $378

20 Kilns $88 $104

Total: $466 $482

a The final NSPS only also includes the $29 million in NESHAP costs for 7 kilns. The 7 kilns will also incur an 

additional $11 in compliance costs to meet the final NSPS limits for SO2 and NOx

.

 Alternatively, the 7 kilns 

would also incur an additional $27 in compliance costs to meet the stringent NSPS limits for SO2 and NOx. 
3-4 

0

10

20

30

40

50

60

<$0.10 $0.10 to

<$0.50

$0.50 to

<$1.00

$1.00 to

<$1.50

$1.50 to

<$2.00

$2.00 to

<$2.50

$2.50 to

<$3.00

$3.00 to

<$3.50

$3.50 to

<$4.00

$4.00 to

<$4.50

$4.50 to

<$5.00

>$5.00

Number of Kilns

Total Annualized Compliance Costs ($/metric ton of clinker capacity), 2005$

Final NESHAP only (136 Kilns included in Economic Impact Model)

Final NESHAP only (20 Kilns not included in Economic Impact Model)

Final NESHAP only (7 NSPS Kilns not included in Economic Impact Model)

Final NESHAP and Selected NSPS  (7 NSPS Kilns not included in Economic Impact Model)

Final NESHAP and Stringent NSPS  (7 NSPS Kilns not included in Economic Impact Model)

Figure 3-1. Range of Per-Ton Total Annualized Compliance Costs (2005$) 
3-5 

Table 3-2. Range of Per-ton Total Annualized Compliance Costs by State (2005$) 

ST

Data 

Average ($/ ton of clinker 

capacity) 

Minimum ($/ ton of clinker 

capacity) 

Maximum ($/ ton of clinker 

capacity) 

AL $3 $1 $5 

AZ $3 $1 $6 

CA $4 $3 $5 

CO $2 $1 $3 

FL $3 $1 $5 

GA $1 $1 $1 

IA $6 $4 $8 

ID $10 $9 $10 

IL $6 $1 $8 

IN $9 $5 $14 

KS $6 $6 $6

KY $4 $4 $4 

MD $6 $3 $9 

ME $1 $1 $1 

MI $5 $4 $6 

MO $5 $4 $5 

MT $2 $2 $2 

NE $6 $5 $6 

NM $2 $2 $2 

NV $2 $2 $2 

NY $3 $1 $4 

OH $5 $5 $5 

OK $8 $4 $13 

OR $4 $4 $4 

PA $5 $2 $7 

SC $4 $4 $4 

SD $2 $1 $2 

TN $3 $1 $5 

TX $5 $1 $8 

UT $5 $1 $9 

VA $4 $4 $4 

WA $1 $1 $2 

WV $7 $6 $8 

WY $7 $5 $8 

U.S. $5 $1 $14 

Note: Includes Final NESHAP only for 136 kilns included in economic impact model. 

3.2 Partial-Equilibrium Analysis 

The partial-equilibrium analysis develops a cement market model that simulates how 

stakeholders (consumers and firms) might respond to the additional regulatory program costs. In 

this section, we provide an overview of the economic model used during proposal (EPA, 2009). 

Appendix A provides additional details about economic model updates made since proposal, 

model equations, and parameters. 
3-6 

3.2.1 Regional Structure and Baseline Data 

Cement sales are often concentrated locally among a small number of firms for two 

reasons: high transportation costs and production economies of scale.1

 Transportation costs 

significantly influence where cement is ultimately sold; high transportation costs relative to unit 

value provide incentives to produce and sell cement locally in regional markets (USITC, 2006). 

To support this claim, the empirical literature has typically pointed to Census of Transportation 

data showing over 80% of cement shipments were made within a 200-mile radius (Jans and 

Rosenbaum, 1997)2

 and reported evidence of high transportation costs per dollar of product 

value from case studies (Ryan, 2006). Based on this literature, the Agency assumes that the U.S. 

Portland cement industry is divided into a number of independent regional markets with each 

having a single market-clearing price. 

The freight-on-board (f.o.b.) price of Portland cement for each regional market is derived 

as the production weighted average of the state level f.o.b. prices reported by the USGS for 

cement (see Table 3-3). The production of Portland cement within each market is the sum of 

estimated individual kiln production levels (EPA, 2009) and include adjustments described in 

Appendix A (see Table 3-4). We obtained estimates of Portland cement imports from the USGS 

and mapped them to each market based on the port of entry. 

3.2.2 Near-Term Cement Plant Production Decisions 

A cement company acts in the best interest of its shareholders and maximizes profits. 

When deciding whether to make another ton of cement, the company considers the production 

effect on profits by comparing the current market price of cement and the marginal production 

cost; if price is above marginal production cost, producing and selling the extra ton of cement 

increase profit. The company continues to produce additional cement until the profit from 

 

1

 The 2002 Economic Census reports that the national Herfindahl-Hirschman Index (HHI) for cement—North 

American Industry Classification System (NAICS) 32731—is 568. However, this measure is likely not 

representative of actual concentration that exists in regional markets. 2

 A recent USITC study of California cement markets found more than 75% of gray Portland cement shipments in 

the state were shipped to customers within 200 miles of the cement producer (USITC, 2006). 
3-7 

Table 3-3. Portland Cement Prices by Market ($/metric tons): 2005 

Market Price ($/metric ton) 

Atlanta $81 

Baltimore/Philadelphia $82

Birmingham $83 

Chicago $67 

Cincinnati $84 

Dallas $75 

Denver $89 

Detroit $93 

Florida $91 

Kansas City $86 

Los Angeles $78 

Minneapolis $92 

New York/Boston $89 

Phoenix $83 

Pittsburgh $88 

St. Louis $84

Salt Lake City $91 

San Antonio $82 

San Francisco $97 

Seattle $88 

producing an extra ton of cement is zero (price equals marginal cost) or capacity constraints are 

reached. The decision rule is consistent with the assumption of pure competition. 

Although perfect competition is widely accepted for modeling many industries regardless 

of the model time horizon (EPA, 2000), the cement industry has two characteristics that 

influenced EPA’s modeling choice relating to market structure. First, high transportation costs 

and other production economics tend to limit the number of sellers (particularly over a short time 

horizon), so each seller has a substantial regional market share. Timely market entry is also 

constrained by the high capital costs that involve purchases and construction of large rotary kilns 

that are not readily movable or transferable to other uses.3

 Second, cement producers offer 

similar or identical products. American Society for Testing and Materials (ASTM) specifications 

tend to ensure uniform quality, and recent industry reviews (USITC, 2006) suggest that there is 

little or no brand loyalty that allows firms to differentiate their products. 

 

3

 In addition, large plants are typically more economical because they can produce cement at lower unit costs; this 

reduces entry incentives for smaller capacity cement plants. 
3-8 

Table 3-4. Portland Cement Markets (106

 metric tons): 2005 

Market U.S. Production Imports Total 

Atlanta 5.8 2.3 8.1 

Baltimore/Philadelphia 7.8 0.6 8.5 

Birmingham 5.9 2.2 8.1

Chicago 4.7 0.2 4.9 

Cincinnati 3.7 0.0 3.7 

Dallas 8.1 2.4 10.5 

Denver 3.4 0.0 3.4 

Detroit 3.8 1.3 5.2 

Florida 5.5 5.8 11.4 

Kansas City 5.0 0.0 5.0 

Los Angeles 10.6 3.8 14.4 

Minneapolis 1.7 0.4 2.1 

New York/Boston 3.2 2.8 6.0 

Phoenix 4.3 0.0 4.3 

Pittsburgh 1.5 1.6 3.1 

St. Louis 6.0 0.0 6.0 

Salt Lake City 2.4 0.1 2.4 

San Antonio 5.5 4.6 10.0 

San Francisco 3.4 2.8 6.2 

Seattle 1.1 2.5 3.6

Given entry barriers, product characteristics, and the need to understand important nearterm/transitory stakeholder outcomes, EPA continued to use the economic impact model 

designed for previous analyses (EPA, 1998, 1999b, 2009). The model considers how regional 

markets may operate in near-term time horizons when 1) the number of companies is limited and 

2) the companies sell similar or identical products.4

 Under these circumstances, the short-run 

production decision rule that a cement company makes differs from pure competition. The 

company continues to consider the production effect described above; however, the company 

adds another dimension to the decision-making process by also considering the market price 

effect that is associated with producing an additional ton of cement. Given the small number of 

cement producers, adding an extra ton of cement to the regional market may lower the market 

 

4

 This economic model is formally known as a multi-firm Cournot oligopoly model. 
3-9 

cement price and reduce the profits on all the other cement sold. If the price effect is large 

enough, companies may find it more profitable to reduce production below the levels implied by 

pure competition. As a result, short-run regional market prices tend to be higher than marginal 

production costs (i.e., there may be a preexisting market distortion within cement markets prior

to regulation).5

 The size of the existing distortion depends on the seller’s market share and how 

responsive cement consumers are to changes in the cement price. Economic theory suggests the 

market distortion will typically be higher the smaller the number of sellers and when the quantity 

demanded is less sensitive to price (i.e., the demand elasticity is inelastic) (see Appendix A). 

3.2.3 Economic Impact Model Results 

3.2.3.1 Market-Level Results 

Market-level impacts include the regional price and quantity adjustments for Portland 

cement, including the changes in imports for the appropriate regions. As shown in Table 3-5, the 

average national price for Portland cement increases by 5%, or $4.50 per metric ton, while 

overall U.S. cement consumption falls by approximately 5%. Domestic production falls by 11%, 

or 10 million tons per year. Cement imports increase in response to higher domestic cement 

prices; imports increase by 10%, or 3 million metric tons. 

Table 3-5. National-Level Market Impacts: 2005 

Changes from Baseline 

 Baseline Absolute Percent 

Market Price ($/metric ton) $83.70 $4.50 5.4% 

Market Output (million metric tons) 126 −6 −4.8% 

Domestic production 93 −10 −10.8% 

Imports 33 3 10.0% 

As shown in Table 3-6, price increases are the highest in regions with high compliance 

costs per metric ton. For example, the Cincinnati market price increase ($10 per metric ton) also 

includes kilns with higher average compliance costs and a kiln with the highest per-unit 

 

5

 This ultimately influences the partial-equilibrium model’s estimates of the social cost of the regulatory program 

since bigger existing market distortions tend to widen the gap between price and marginal cost in these markets 

and lead to higher deadweight loss estimates than under the case of perfectly competitive markets. The Office of 

Management and Budget (OMB) explicitly mentions the need to consider market power–related welfare costs in 

evaluating regulations under Executive Order 12866 (EPA, 1999a). 
3-10 

Table 3-6. Regional Compliance Costs and Market Price Changes ($/metric ton of 

cement): 2005 

Incremental Compliance Costs 

($/metric ton of estimated 

cement production) Baseline 

Price

Market Price Change 

Market Mean Minimum Maximum Absolute Percent 

Atlanta $3.60 $1.10 $5.90 $81.30 $2.80 3.4% 

Baltimore/Philadelphia $6.20 $1.20 $10.00 $81.70 $6.10 7.5% 

Birmingham $3.60 $1.10 $4.80 $82.60 $3.80 4.6% 

Chicago $6.80 $0.90 $10.10 $66.90 $4.80 7.2% 

Cincinnati $8.10 $4.00 $14.10 $84.20 $10.40 12.4% 

Dallas $5.60 $3.50 $8.50 $75.10 $4.90 6.5% 

Denver $3.00 $1.00 $8.10 $88.70 $6.30 7.1% 

Detroit $6.50 $4.00 $10.30 $92.70 $4.20 4.5% 

Florida $3.40 $1.20 $5.50 $90.70 $3.50 3.9% 

Kansas City $8.60 $3.80 $13.80 $86.10 $8.20 9.5% 

Los Angeles $6.00 $3.20 $13.10 $78.20 $4.30 5.5% 

Minneapolis $6.30 $4.50 $8.80 $92.20 $8.50 9.2% 

New York/Boston $2.50 $1.00 $4.50 $89.00 $1.80 2.0% 

Phoenix $1.90 $1.00 $6.00 $83.10 $4.20 5.1% 

Pittsburgh $7.60 $6.90 $8.00 $88.00 $4.60 5.2% 

St. Louis $4.80 $3.80 $5.60 $84.10 $4.50 5.4% 

Salt Lake City $5.90 $1.60 $9.90 $91.40 $10.40 11.4%

San Antonio $4.00 $0.80 $7.70 $82.30 $3.30 4.0% 

San Francisco $3.10 $1.00 $5.00 $96.90 $3.30 3.4% 

Seattle $1.20 $1.00 $1.40 $88.00 $0.70 0.8% 

Grand Total $5.20 $0.80 $14.10 $83.90 $4.50 5.4% 

compliance costs ($14 per metric ton).6 It is important to note that EPA uses a time horizon 

where transportation costs between regions are high enough that interregional trade is unlikely to 

occur, at least in the short run. The regional differences in unit compliance costs and the 

 

6

 The per-unit compliance costs were calculated by dividing the total annualized cost per kiln by the kiln’s estimated 

cement production within the economic impact model. 
3-11 

significant simulated changes in relative regional prices suggest domestic cement plants may be 

more likely to consider short-run shipments of cement between regional markets. Choices would 

depend on the additional benefits of selling cement to these markets and the costs of transporting 

the cement outside the regional market. Although EPA has not quantified this effect, additional 

flexibility would tend to temper prices increases in some of these markets. 

Imports also tend to limit price increases in certain regions. This tends to reinforce U.S. 

production declines because cement plants have more difficulty passing on compliance costs in 

the form of higher prices when compared with similar plants operating in regions without import 

competition. Because imports are only modeled for markets with imports in the baseline without 

regulation, Table 3-7 separates the results into markets with and without imports as well as 

providing the results for all markets. As shown in Table 3-7, median price increases in regions 

with imports are lower than the median price increases in regions without import competition. In 

some regions with imports, the reductions in U.S. production are significant. As shown in Table 

3-7, the maximum simulated U.S. regional production change is 23%. To the extent there are any 

unobserved constraints on import supply that are not captured in the import supply elasticity 

parameter, price and U.S. production adjustments for regional markets with imports would tend 

to become more similar to regional markets without imports. 

3.2.3.2 Industry-Level Results 

As shown in Table 3-8, compliance costs vary by cement plant, and this variation 

suggests some plants will be more adversely affected than others. To assess these differences, 

EPA collected industry operating profit data and identified plants with operating profit increases 

and losses. Absent plant-specific data, EPA assumed each plant’s baseline profits were consistent 

with the median operating profit margin reported by the PCA (2008c, Table 44). In 2005, this 

value was $18 per metric ton, or 16%. Using this assumption, total operating profits for 59 plants 

(58%) decrease by $387 million with regulation. These plants tend to have higher per ton 

compliance costs. The remaining plants’ compliance burden is offset by higher regional cement 

prices, and total plant operating profits increase by $147 million. These 44 plants have lower unit 

compliance costs compared with their competitors. 
3-12 

Table 3-7. Summary of Regional Market Impacts 

 Regional Markets 

With Imports Without Imports All Markets 

Change in Market Price 

Absolute ($/metric ton) 

Mean $4.70 $6.40 $4.50 

Median $4.20 $5.40 $4.40 

Minimum $0.70 $4.20 $0.70 

Maximum $10.40 $10.40 $10.40 

Percentage of baseline price 

Mean 5.5% 7.5% 5.4% 

Median 4.9% 6.2% 5.3% 

Minimum 0.8% 5.0% 0.8% 

Maximum 11.4% 12.4% 12.4% 

Change in Domestic Production 

Absolute (thousand metric tons) 

Mean −559 −271 −501 

Median −421 −247 −372 

Minimum −74 −189 −74 

Maximum −1,539 −403 −1,539 

Percentage of baseline production 

Mean −11.8% −6.6% −10.8% 

Median −11.6% −5.5% −10.4% 

Minimum −6.8% −4.4% −4.4% 

Maximum −22.8% −10.9% −22.8% 

Within the group of plants with operating losses, EPA identified 10 domestic plants with 

negative operating profits and significant utilization changes that could temporarily idle until 

market demand conditions improve (see Table 3-9). The plants have unit compliance costs close 

to $8 per ton; they account for approximately 8% of domestic capacity. These plants are modeled 

as continuing to operate despite low capacity utilization and short run negative profits. The 

model results for them are included in the summary results for Tables 3-5, 3-6, 3-7, and 3-8 but 

are also reported separately in Table 3-9. 
3-13 

If the plant owners did decide to permanently shut down these plants, the reduction in 

domestic supply would lead to additional projected market price increases. This would lead to an 

increased production at other plants, a possible increase in imports (depending if the plant that 

chooses to close is in a market where imports are anticipated) and a decrease in control cost. 

This scenario cannot be easily modeled. In an effort to bound this effect, the price increase 

needed to reduce national consumption by the amount of production that would be lost if the ten 

plants dropped from 55.5% capacity utilization to 0.0% capacity utilization was estimated using 

the demand elasticity of 0.88. This ignores changes in other plants is response to an increased 

potential market share and increases in imports. Both of these would tend diminish the price 

increase. The predicted price change was multiplied by the change in production associated with 

the ten plants dropping capacity utilization to zero and multiplied by one half to estimate the 

change in surplus associated with the price and quantity change. This gave a result of a $10 

million increase in social cost. This number was then reduced by the avoided pollution control 

cost of $34 million at the ten plants. This resulted in a net reduction of $24 million in social cost 

due to the adjustment. Because of the method of estimating this adjustment it cannot be 

distributed between producer and consumer surplus. An estimate of the social cost is provided 

with and without this adjustment. 

Table 3-8. Distribution of Industry 2005 

Changes in Total Operating Profit: 

Plants with Loss Plants with Gain All Plants 

Number 58 44 102

Cement Capacity (million metric tons) 

Total 55,202 38,145 93,346

Average per plant 952 867 915

Compliance Costs 

Total (thousand) $308,740 $68,806 $377,546

Average ($/metric cement) $5.59 $1.80 $4.04

Capacity Utilization (percent) 

Baseline 100.3% 98.7% 99.6%

With regulation 81.0% 100.3% 88.9%

Change in total operating profits (million) −$387 $147 −$241
3-14 

Table 3-9. Cement Plants with Significant Utilization Changes 2005 

 Total 

Number 10 

Cement Capacity (thousand metric tons) 

Total 7,815 

Average per plant 782 

Compliance Costs 

Total (thousand) $62,222 

Average ($/metric ton) $7.96 

Capacity Utilization (%) 

Baseline 99.0% 

With regulation 55.5% 

Change in Operating Profit (million) −$116 

3.2.3.3 Job Effects 

Precise job effect estimates cannot be estimated with certainty. Ideally, whenever a 

regulatory change results in a reallocation of labor or other factors of production in an economy, 

a general equilibrium approach should be applied to estimate the attendant economic impacts. 

Unfortunately, time and resource constraints prevented the creation of a model with the spatial 

and sectoral resolution necessary to analyze the final rule. However, Morgenstern et al (2002) 

provides a theoretical framework which allows us to approximate some of the relevant general 

equilibrium effects by identifying three economic mechanisms by which pollution abatement 

activities can indirectly influence: 

 higher production costs raise market prices, higher prices reduce consumption, and 

employment within an industry falls (“demand effect”); 

 pollution abatement activities require additional labor services to produce the same 

level of output (“cost effect”); and 

 post-regulation production technologies may be more or less labor intensive (i.e., 

more/less labor is required per dollar of output) (“factor-shift effect”). 

Several empirical studies, including Morgenstern et al. (2002), suggest the net 

employment decline is zero or economically small (e.g., Cole and Elliot, 2007; Berman and Bui, 

2001). However, others show the question has not been resolved in the literature (Henderson, 

1996; Greenstone, 2002). Morgenstern et al. use a 6-year panel (U.S. Census data for plant-level 
3-15 

prices, inputs [(including labor], outputs, and environmental expenditures) to econometrically 

estimate the production technologies and industry-level demand elasticities. Their identification 

strategy leverages repeat plant-level observations over time and uses plant-level and year fixed 

effects (e.g., dummy variables for plant and years). After estimating their model, Morgenstern 

show and compute the change in employment associated with an additional $1 million ($1987) in 

environmental spending. Their estimates cover four manufacturing industries (pulp and paper, 

plastics, petroleum, and steel) and Morgenstern et al. present results separately for the cost, 

factor shift, and demand effects, as well as the net effect. They also estimate and report an 

industry-wide average parameter that combines the four industry-wide estimates and weight 

them by each industry’s share of environmental expenditures. 

Historically, EPA has most often estimated employment changes associated with plant 

closures due to environmental regulation or changes in output for the regulated industry (EPA, 

1999a; EPA, 2000). This partial equilibrium approach focuses only on the “demand” portion of 

the projected change in employment and neglects other employment changes. EPA provides this 

estimate because it employs the most detailed modeling for the industry being regulated even if it 

does not capture all types of employment impacts. In addition to the employment effects 

identified by Morgenstern et al., we also expect that the substitutes for cement (e.g., asphalt) 

would expand production as consumers shift away from cement to other products. This would 

also lead to increased employment in those industries. Focusing only on the “demand effect”, it 

can be seen that the estimate from the historical approach is within the range presented by the 

Morgenstern “demand effect” portion. This strengthens our comfort in the reasonableness of 

both estimates. In April of this year, EPA started including an estimate based on the 

Morgenstern approach because it is thought to be a broader measure of the employment impacts 

of this type of environmental regulation. Thus, this analysis goes beyond what EPA has 

typically done because the parameters estimated in the Morgenstern paper were used to estimate 

all three effects (“demand,” “cost,” and “factor shift”). This transfer of results from the 

Morgenstern study is uncertain but avoids ignoring the “cost effect” and the “factor-shift effect.” 

 Using the historical approach, we calculated “demand effect” employment changes 

by assuming that the number of jobs declines proportionally with the economic 

model’s simulated output changes. As shown in Table 3-10, using this limited 

approach, the employment falls by an 1,500 jobs, or approximately −10%.7

 By 

comparison, using the Morgenstern approach, we estimate that the net employment 

effects could range between 600 job losses to 1,300 job gains. 

 

7

 To place this reduction in context, it is a similar to the decline experienced during the latest economic downturn; 

approximately 2,000 jobs (see Appendix A, Table A-3). 
3-16 

EPA has solely used this historical estimate in the past as a measure of the projected 

employment change associated with a regulation. However there are a number of serious 

shortcomings with this approach. First, and foremost, the historical approach only looks at the 

employment effects on the regulated industry from reduced output. Second, to arrive at that 

estimate, EPA needed to string together a number of strong assumptions. The employment 

impacts are independent of the performance of the overall economy. This rule takes effect in 

three years. If the economy is strong, the demand for cement strong, it is unlikely that any 

contraction in the industry will take place, even with the regulation. Second, we assume that all 

plants have the same limited ability to pass on the higher costs. In reality, plants should be 

modeled as oligopolists for each of their regional markets. Finally, EPA assumed that 

employment is directly proportional to output. This is unlikely, and biases the results towards 

higher employment losses. The Morgenstern methodology is a more complete consideration of 

probable impacts of a regulation on the economy. 

Table 3-10. Job Losses/Gains Associated with the Final Rule 

Method 1,000 Jobs 

Partial equilibrium model −1.5 

(demand effect only) 

Literature-based estimate (net effect [A + B + C below]) 0.3 

(−0.6 to +1.3) 

A. Literature-based estimate: Demand effect −0.8 

(−1.7 to +0.1) 

B. Literature-based estimate: Cost effect 0.5 

(+0.2 to +0.9) 

C. Literature-based estimate: Factor shift effect 0.6 

(+0 to +1.2) 

We calculated a similar “demand effect” estimate that used the Morgenstern paper. EPA 

selected this paper because the parameter estimates (expressed in jobs per million [$1987] of 

environmental compliance expenditures) provide a transparent and tractable way to transfer 

estimates for an employment effects analysis. Similar estimates were not available from other 

studies. To do this, we multiplied the point estimate for the total demand effect (−3.56 jobs per 

million [$1987] of environmental compliance expenditure) by the total environmental 

compliance expenditures used in the partial equilibrium model. For example, the jobs effect 
3-17 

estimate for is estimated to be 807 jobs (−3.56 × $378 million × 0.6).8

 Demand effect results are 

provided in Table 3-10. It is not appropriate to substitute the data from that approach in to the 

Morgenstern due to the incompatibilities of the underlying data. Since the result from the 

historical approach is within the confidence bounds for the Morgenstern results for the “demand 

effect”, we are comfortable that the more general Morgenstern result is a good representation of 

the change in employment. 

We also present the results of using the Morgenstern paper to estimate employment 

“cost” and “factor-shift” effects. Although using the Morgenstern parameters to estimate these 

“cost” and “factor-shift” employment changes is uncertain, it is helpful to compare the potential 

job gains from these effects to the job losses associated with the “demand” effect. Table 3-10 

shows that using the “cost” and “factor shift” employment effects may offset employment loss 

estimates using either “demand” effect employment losses. The 95% confidence intervals are 

shown for all of the estimates based on the Morgenstern parameters. As shown, at the 95% 

confidence level, we cannot be certain if net employment changes are positive or negative. 

Although the Morgenstern paper provides additional information about the potential job 

effects of environmental protection programs, there are several qualifications EPA considered as 

part of the analysis. First, EPA has used the weighted average parameter estimates for a narrow 

set of manufacturing industries (pulp and paper, plastics, petroleum, and steel). Absent other data 

and estimates, this approach seems reasonable and the estimates come from a respected peerreviewed source. However, EPA acknowledges the final rule covers an industry not considered 

in the original empirical study. By transferring the estimates to the cement sector, we make the 

assumption that estimates are similar in size. In addition, EPA assumes also that Morgenstern et 

al.’s estimates derived from the 1979–1991 are still applicable for policy taking place in 2013, 

almost 20 years later. Second, the economic impact model only considers near-term employment 

effects in the cement industry where production technologies are fixed. As a result, the economic 

impact model places more emphasis on the short-term “demand effect,” whereas the 

Morgenstern paper emphasizes other important long-term responses. For example, positive job 

gains associated with “factor shift effects” are more plausible when production choices become 

more flexible over time and industries can substitute labor for other production inputs. Third, the 

Morgenstern paper estimates rely on sector demand elasticities that are different (typically 

bigger) from the demand elasticity parameter used in the cement model. As a result, the demand 

effects are not directly comparable with the demand effects estimated by the cement model. 

 

8

 Since Morgenstern’s analysis reports environmental expenditures in 1987 dollars, we make an inflation adjustment 

to the engineering cost analysis using the consumer price index (195.3/113.6) = 0.6) 
3-18 

Fourth, Morgenstern identifies the industry average as economically and statistically 

insignificant effect (i.e., the point estimates are small, measured imprecisely, and not 

distinguishable from zero). EPA acknowledges this fact and has reported the 95% confidence 

intervals in Table 3-10. Fifth, Morgenstern’s methodology assumes large plants bear most of the 

regulatory costs. By transferring the estimates, EPA assumes a similar distribution of regulatory 

costs by plant size and that the regulatory burden does not disproportionately fall on smaller 

plants. 

3.3 Other Economic Analyses: Direct Compliance Cost Methods 

In addition to the market-level partial equilibrium analysis, EPA developed a separate 

economic analysis for the remaining 20 kilns that EPA anticipates will be affected by the final 

rule. These costs ($88 million, or 19%) were not included in the economic impact model analysis 

because of uncertainties and difficulties with developing an appropriate set of baseline cement 

market conditions for future years. 

The total annualized costs for two white cement kilns are $2 million, or approximately $9 

per metric ton of cement production. Using reported 2005 data from the USGS on the average 

mill net value of white cement ($176 per metric ton), this cost represents 5% of the product 

value. 

EPA also conducted sales tests for 18 other kilns that were not included in the partial 

equilibrium analysis. The total annualized NESHAP cost for these 18 kilns is approximately $75 

million. The median cost per ton is approximately $3.80 and ranges from $1.90 to $4.40 per ton 

of cement production. In addition, 7 of these 18 kilns would face an additional control cost above 

the NESHAP (approximately $1 dollar per metric ton) to meet the NSPS limits for SO2 and NOx. 

The USGS reports that the real price of cement per metric ton (2005 dollars) has typically 

ranged between $75 and $100 since 1990. A sales test using these price data shows cost-to-sales 

ratios (CSRs) could range between 2% and 6% 

Sales Test Ratio = Control Costs ($/ton)/F.O.B Cement Prices ($/ton). 

From 2000 to 2006, the PCA reports that the average operating profit rates for the 

industry ranged from 17 to 21% (PCA, 2008c). If these profit data are representative of operating 

profit rates for new kilns, kilns could potentially significantly reduce their operating profit rates. 

As a result, companies may have the incentive to look for less expensive alternatives to meet the 

emission standards. If these alternatives are limited or not cost effective, the final rule may lead 
3-19 

companies to consider delaying rates of construction of new kilns until market conditions change 

(e.g., increases in demand that lead to rising cement prices) to cover additional control costs. 

3.4 Social Cost Estimates 

For the kilns modeled in our partial equilibrium model, the market adjustments in price 

and quantity were used to estimate the changes in aggregate economic welfare using applied 

welfare economics principles (see Appendix C). Higher cement prices and reduced consumption 

lead to consumer welfare losses ($540 million). Domestic producers (in aggregate) experience a 

net loss of $239 million. As noted in the previous section, individual domestic producers may 

gain or lose depending on the change in compliance costs versus the change in the regional 

market prices. The total domestic surplus loss (consumer and producers) totals $792 million. 

 For the kilns not modeled in our partial equilibrium model, the $88 million in 

engineering costs were multiplied by 1.8 to approximate the likely additional social cost 

associated with oligopoly market response. Thus the social cost estimate for the 20 kilns not in 

the partial equilibrium model is $158 million. Because of the approximation used, we cannot 

estimate how the $158 million is distributed between consumers and producers. 
3-20 

Table 3-11. Distribution of Social Costs ($106

): 2005 

Social Cost Estimates 

Description 

Final NESHAP and 

NSPS 

Final NESHAP and 

More Stringent NSPS EIA Social Cost Method 

Change in consumer surplus $551 $551 Partial-equilibrium model 

(baseline year 2005) 

Change in domestic 

producer surplus 

$241 $241 Partial-equilibrium model 

(baseline year 2005) 

 

20 Kilns $158 $187 Direct Compliance Method 

(scaled by 1.8 for oligopoly) 

Change in domestic surplus $950 $979 Combined methods 

Adjustment if ten low 

utilization facilities idle 

or close 

-$24 -$24 

 

Total: $926-$950 $955-$979 Change with and without 

adjustment 

Final NSPS Only 

More Stringent NSPS 

Only 

Total: $72 $101 Direct compliance cost 

method (scaled by 1.8 for 

oligopoly)

 Final NESHAP Only 

Change in consumer surplus $551 Partial-equilibrium model 

(baseline year 2005) 

Change in domestic 

producer surplus 

$241 Partial-equilibrium model 

(baseline year 2005) 

 

NSPS kilns (7 kilns) $52 Direct compliance cost method 

(scaled by 1.8 for oligopoly)

Other kilns (13 kilns) $86 Direct compliance cost method 

(scaled by 1.8 for oligopoly)

Change in domestic surplus $930 Combined methods 

Adjustment if ten low 

utilization facilities idle 

or close 

$-24 

Total: $904-$930 Change with and without 

adjustment 

The estimated social cost of the final rule is $880 million. This estimate includes the 

results for existing kilns included in the partial-equilibrium analysis ($792 million),the final 
3-21 

NESHAP direct compliance costs 20 kilns not included in the economic impact model, ($77 

million), and the additional NSPS direct compliance cost for 7 kilns coming on line in the future 

($11 million). The social estimates are significantly higher than the engineering analysis estimate 

of annualized costs totaling $466 million. This is a direct consequence of EPA’s assumptions 

about existing market structure discussed extensively in previous cement industry rulemakings 

and in Section 2 and Appendix B of this RIA. Under baseline conditions without regulation, the 

existing domestic cement plants are assumed to choose a production level that is less than the 

level produced under perfect competition. As a result, a preexisting market distortion exists in 

the cement markets covered by the final rule (i.e., the observed baseline market price is higher 

than the [unobserved] market price that a model of perfect competition would predict). The 

imposition of additional regulatory costs tends to widen the gap between price and marginal cost 

in these markets and contributes to additional social costs. The above social costs for 2013 

include annualized capital costs over the expected lifetime of the equipment and an opportunity 

cost of capital (7%) discount rate. To facilitate comparisons of benefits and costs when estimates 

vary of time across multiple years, EPA typically estimates a “consumption equivalent” present 

value measure of costs. This could be computed using a consumption rate of interest of 3% and 

7%. However, this calculation was not necessary since the cost and benefit analyses only 

produce estimates for a single year (OAQPS, 1999a). 

3.5 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, OMB, 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 rule or regulation, including notices of inquiry, advance notices of final 

rulemaking, and notices of final 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 below. 
3-22 

To enhance understanding regarding the regulation’s influence on energy consumption, 

EPA examined publicly available data describing the cement sector’s energy consumption. The 

AEO 2010 (DOE, 2010) provides energy consumption data. As shown in Table 3-12, this 

industry accounts for approximately 0.4% of the U.S. total energy consumption. As a result, any 
3-23 

Table 3-12. U.S. Cement Sector Energy Consumption (Trillion BTUs)a

: 2013 

 Quantity Share of Total Energy Use 

Residual fuel oil 0.9 0.00% 

Distillate fuel oil 10.8 0.00% 

Petroleum coke 47.3 0.10% 

Other petroleumb

 30.2 0.00% 

Petroleum subtotal 89.2 0.10% 

Natural gas 19.8 0.00% 

Steam coal 206.6 0.20% 

Metallurgical coal 6.8 0.00% 

Coal subtotal 213.4 0.20% 

Purchased electricity 38.9 0.00% 

Total 399.44 0.40% 

Delivered Energy Use 72,407 72.20% 

Total Energy Use 100,592 100.00% 

a

 Fuel consumption includes consumption for combined heat and power. 

b

 Includes petroleum coke, lubricants, and miscellaneous petroleum products. 

Source: U.S. Department of Energy, Energy Information Administration. 2010. Supplemental Tables to the Annual 

Energy Outlook 2010. Table 10 and Table 39. Available at 

<http://www.eia.doe.gov/oiaf/aeo/supplement/supref.html>. 

energy consumption changes attributable to the regulatory program should not significantly 

influence the supply, distribution, or use of energy. EPA has also estimated the amount of 

additional electricity consumption associated with add-on controls. The analysis shows the 

additional national electrical demand to be 780 million kWh per year and the natural gas use to 

be 1.2 million MMBTU per year for existing kilns. For new kilns, assuming that of the 16 new 

kilns to start up by 2013, all 16 will add alkaline scrubbers and ACI systems, the electrical 

demand is estimated to be 199 million kWh per year. This is less than 0.1% of AEO 2010

forecasts of total electricity and natural gas use. 

3.6 Assessment 

Although the economic analyses presented in this section cannot provide precise 

estimates of the final NESHAP’s and NSPS’s economic impacts, the evidence presented in this 

section suggests that the economic impacts may be significant across several dimensions (price, 

consumption, production, and international trade). There are several broad issues we emphasize 

as stakeholders review the analysis. First, OAQPS’s partial equilibrium analysis of NESHAPs 

has traditionally been designed to assess small (marginal) changes in industry conditions. The 
3-24 

overall engineering cost analysis estimates are significant relative to the size of the U.S. cement 

market; EPA acknowledges that use of demand and import supply elasticities can be tenuous in 

these cases because the exact functional relationships (demand and supply) are less certain when 

simulated outcomes move further away from the observed pre-policy equilibrium. Second, the 

partial equilibrium assumes that transportation costs between regions are high enough that 

interregional trade is unlikely to occur, at least in the short run. Allowing interregional trade 

would expand the cement market definitions and increase the number of producers in each 

market. As discussed above, as the number of producers in a market increases, the production 

decision becomes more consistent with decisions made in pure competition; the additional 

trading opportunities may tend to moderate the relative price changes simulated within the 

model. Third, as discussed earlier in this section, the choice of market structure increases the 

agency’s social cost estimate; it is almost 2 times higher than a model that assumes perfect 

competition. Therefore, the analysis may overstate the social costs of the rule. EPA continues to 

believe the market structure is reasonable and provides an upper-bound social cost estimate for 

the following reasons: (1) high transportation costs and other production economics tend to limit 

the number of sellers (particularly over a short time horizon), so each seller has a substantial 

regional market share; (2) timely market entry is also constrained by the high capital costs that 

involve purchases and construction of large rotary kilns that are not readily movable or 

transferable to other uses9

; (3) cement producers offer very similar or identical products; and 

(4) the Office of Management and Budget (OMB) explicitly mentions the need to consider 

market power–related welfare costs in evaluating regulations under Executive Order 12866. 

 

9

 In addition, large plants are typically more economical because they can produce cement at lower unit costs; this 

reduces entry incentives for smaller capacity cement plants. 
4-1 

SECTION 4

SMALL BUSINESS IMPACT ANALYSIS 

The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a 

regulatory flexibility analysis of any rule subject to notice and comment rulemaking 

requirements under the Administrative Procedure Act or any other statute unless the agency 

certifies that the rule will not have a significant economic impact on a substantial number of 

small entities (SISNOSE). The first step in this assessment was to determine whether the rule 

will have SISNOSE. To make this determination, EPA used a screening and market analysis to 

indicate whether EPA can certify the rule as not having a SISNOSE. The elements of this 

analysis included 

 identifying affected small entities, 

 selecting and describing the measures and economic impact thresholds used in the 

analysis, and 

 completing the assessment and determining the SISNOSE certification category. 

4.1 Identify Affected Small Entities 

For the purposes of assessing the impacts of the final rule on small entities, small entity is 

defined as (1) a small business as defined by the Small Business Administration’s regulations at 

13 CFR 121.201; according to these size standards, ultimate parent companies owning Portland 

cement manufacturing plants are categorized as small if the total number of employees at the 

firm is fewer than 750 (see Table 4-1 for list); (2) a small governmental jurisdiction that is a 

government of a city, county, town, school district, or special district with a population of less 

than 50,000; and (3) a small organization that is any not-for-profit enterprise that is 

independently owned and operated and is not dominant in its field. As reported in Section 2, 

EPA has identified four small entities (see Table 4-1). One of the four entities is owned by a 

small Tribal government (Salt River Pima-Maricopa Indian Community). The remaining three 

entities are small businesses. 

4.2 Sales and Revenue Test Screening Analysis 

In the next step of the analysis, EPA assessed how the regulatory program may influence 

the profitability of ultimate parent companies by comparing pollution control costs to total sales 

(i.e., a “sales” test). To do this, we divided an ultimate parent company’s total annualized 

compliance costs by its reported revenue: 
4-2 

Table 4-1. Small Entity Analysis 

Owner 

Entity 

Type 

Annual 

Sales ($106) Employees Plants Kilns 

Clinker 

Capacity (103

metric tons 

per year) 

Cost-toSales 

Ratio 

Salt River 

Materials Groupa

Tribal 

government 

$184b

 NA 1 1 1,477 0.7% 

Monarch Cement 

Company 

Business $154 600 1 2 787 3.0% 

Continental 

Cement 

Company, LLC 

Business $93c

 <750 1 1 1,164 0.0% 

Snyder Associate 

Companies 

Business $29 350 1 2 286 2.0% 

a

 Enterprise is owned by Salt River Pima-Maricopa Indian Community. b

 EPA estimate. Estimate uses revenue data for four of the six enterprises owned by Salt River Pima-Maricopa 

Indian Community. 

c

 EPA estimate. Estimate uses cement production levels and average market prices. 

j

n

i

TR

TACC

CSR

∑

= (4.1) 

where 

CSR = cost-to-sales ratio, 

TACC = total annualized compliance costs, 

i = index of the number of affected plants owned by company j, 

n = number of affected plants, and 

TRj = total sales from all operations of ultimate parent company j or annual 

government revenue. 

The results of the screening analysis, presented in Table 4-1, show that no small 

businesses have a CSR greater than 3%. Two small business have an estimated CSR between 1 

and 3%. 
4-3 

4.3 Additional Market Analysis 

In additional to the screening analysis, EPA also examined small entity effects after 

accounting for market adjustments. Under this assumption, the entities recover some of the 

regulatory program costs as the market price adjusts in response to higher cement production 

costs. Even after accounting for these adjustments, small entity operating profits fall by less than 

1 million. 

4.4 Assessment 

After considering the economic impact of this final rule on small entities, EPA has 

determined it will not have a significant economic impact on the four small entities. No small 

companies have cost-to-sales ratios greater than 3% and only 4 of the over 40 cement companies 

are small entities. 
5-1 

SECTION 5

AIR QUALITY MODELING OF EMISSION REDUCTIONS 

5.1 Synopsis 

This section describes the air quality modeling performed by EPA in support of the 

Portland cement NESHAP and NSPS. A national scale air quality modeling analysis was 

performed to estimate the impact of the sector emissions changes on future years: annual and 24-

hour PM2.5 concentrations, total Hg deposition, as well as visibility impairment. Air quality 

benefits are estimated with the Comprehensive Air Quality Model with Extensions (CAMx) 

model. CAMx simulates the numerous physical and chemical processes involved in the 

formation, transport, and destruction of ozone, PM, and air toxics. In addition to the CAMx 

model, the modeling platform includes the emissions, meteorology, and initial and boundary 

condition data which are inputs to this model. 

Emissions and air quality modeling decisions are made early in the analytical process. 

For this reason, it is important to note that the inventories used in the air quality modeling and 

the benefits modeling are slightly different than the final adjusted cement kiln sector inventories 

presented in the RIA. However, the air quality inventories and the final rule inventories are 

generally consistent, so the air quality modeling adequately reflects the effects of the rule. 

The 2005-based CAMx modeling platform was used as the basis for the air quality 

modeling for this final rule. This platform represents a structured system of connected modelingrelated tools and data that provide a consistent and transparent basis for assessing the air quality 

response to projected changes in emissions. The base year of data used to construct this platform 

includes emissions and meteorology for 2005. The platform is intended to support a variety of 

regulatory and research model applications and analyses. This modeling platform and analysis is 

described fully below. Additional details about the modeling system are available in a separate 

technical support document: Air Quality Modeling Technical Support Document: National 

Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing 

Industry (U.S. EPA, 2010c). 

5.2 Photochemical Model Background 

CAMx version 5.10 is a freely available computer model that simulates the formation and 

fate of photochemical oxidants, primary and secondary PM concentrations, and air toxics, over 

regional and urban spatial scales for given input sets of meteorological conditions and emissions. 

CAMx includes numerous science modules that simulate the emission, production, decay, 
5-2 

deposition and transport of organic and inorganic gas-phase and particle-phase pollutants in the 

atmosphere (Nobel, McDonald-Buller et al., 2001; Baker and Scheff, 2007; Russell, 2008). 

CAMx is applied with ISORROPIA inorganic chemistry (Nenes et al., 1999), a 

semivolatile equilibrium scheme to partition condensable organic gases between gas and particle 

phase (Strader et al., 1999), Regional Acid Deposition Model (RADM) aqueous phase chemistry 

(Chang et al., 1987), and Carbon Bond 05 (CB05) gas-phase chemistry module (Gery et al., 

1989; ENVIRON, 2008). All modeling domains were modeled for the entire year of 2005. Data 

from the entire year were used when looking at the estimation of PM2.5, total Hg deposition, and 

visibility impacts from the regulation. 

5.3 Model Domain and Grid Resolution 

The modeling analyses were performed for a domain covering the continental United 

States, as shown in Figure 5-1. This domain has a parent horizontal grid of 36 km with two finerscale 12 km grids over portions of the eastern and western United States. The model extends 

vertically from the surface to 100 millibars (approximately 15 km) using a sigma-pressure 

coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken 

from a global model and did not change over the simulations. In turn, the 36 km grid was only 

used to establish the incoming air quality concentrations along the boundaries of the 12 km grids. 

Only the finer grid data were used in determining the impacts of the emission standard program 

changes. Table 5-1 provides some basic geographic information regarding the photochemical 

model domains. 

5.4 Emissions Input Data 

The emissions data used in the base year and future reference and future emissions 

adjustment case are based on the 2005 v4 platform. The emissions cases use some different 

emissions data than the official v4 platform to use data intended only for the rule development 

and not for general use. Unlike the 2005 v4 platform, the configuration for this modeling 

application included some additional HAPs and a cement kiln sector emissions inventory more 

consistent with the engineering analysis of potential control options. 

The 2013 reference case is intended to represent the emissions associated with growth 

and controls in that year. The U.S. EGU point source emissions estimates for the future year 

reference and control case are based on an Integrated Planning Model (IPM) run for criteria 

pollutants, HCl, and Hg in 2013 (although HCl was not modeled). Both control and growth 

factors were applied to a subset of the 2005 non-EGU point and nonpoint to create the 2013 
5-3 

reference case. The 2002 v3.1 platform 2020 projection factors were the starting point for most 

of the 2013 SMOKE-based projections. 

Figure 5-1. Map of the Photochemical Modeling Domaina

a The black outer box denotes the 36 km national modeling domain; the red inner box is the 12 km western U.S. 

grid; and the blue inner box is the 12 km eastern U.S. grid. 

Table 5-1. Geographic Elements of Domains Used in Photochemical Modeling 

Photochemical Modeling Configuration 

National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level 

The 2013 reference scenario for the cement kiln sector assumed no growth or control for 

the industry from the 2005 sector emissions estimates with the exception that facilities that 

closed between 2005 and 2010 were removed from the 2013 inventory. The length of time 
5-4 

required to conduct emissions and photochemical modeling precludes using the final facilityspecific emissions estimates based on controls implemented for this rule. A 2013 “control” or 

emissions adjustment case was developed by removing all Portland cement sector emissions 

from the 2013 baseline inventory. This “zero-out” of the sector creates a policy space where 

potential controls would be maximized at all locations. Since this is unrealistic, the air quality 

estimates from the 2013 “zero-out” or “control” case are adjusted to reflect nationwide estimates 

of control percentages by pollutant. It is important to note that the scenario without cement kilns 

includes the zeroing-out of emissions from hazardous waste kilns. Out of 181 kilns nationwide, 

there are 14 hazardous waste kilns, which represent 10 to 20% of total kiln emissions. This leads 

to a slight overestimate of the reduction in PM2.5 levels and mercury deposition. 

Table 5-2. Cement Kiln Emissions in 2005 Base and Estimated Future Year (2013) in 

tons per year 

Specie 2005 2013 

Nitrogen Oxides 216,525 199,391 

Volatile Organic Compounds 8,817 8,419 

Sulfur Dioxide 158,560 149,013 

PrimaryPM2.5 16,758 15,403 

PM2.5 Mercury 0.8 0.7 

Reactive Gas Phase Mercury 6.2 6.0 

Elemental Mercury 3.8 3.6 

 The air quality estimates associated with 2013 zero-out of the cement kiln sector are 

adjusted nationally to reflect various options. 

• A 90% reduction in mercury emissions for the NSPS and NESHAP , more stringent 

NSPS and NESHAP, and NESHAP only 

• 82% reductions in SOX and 86% reductions in primarily emitted PM2.5 for the NSPS and 

NESHAP, more stringent NSPS and NESHAP, and NESHAP only 

• 6% reductions in SOX and 5% reductions in primarily emitted PM2.5 for NSPS only 

As part of the analysis for this rulemaking, the modeling system was used to calculate 

daily and annual PM2.5 concentrations, annual total Hg deposition levels, and visibility 

impairment. Model predictions are used in a relative sense to estimate scenario-specific future-
5-5 

year design values of PM2.5 and ozone. Specifically, we compare a 2013 reference scenario, a 

scenario without the cement kiln controls, to a 2013 control scenario that includes the 

adjustments to the cement kiln sector. This is done by calculating the simulated air quality ratios 

between any particular future year simulation and the 2005 base. These predicted ratios are then 

applied to ambient base year design values. The design value projection methodology used here 

followed EPA guidance for such analyses (U.S. EPA, 2007). Additionally, the raw model outputs 

are also used in a relative sense as inputs to the health and welfare impact functions of the 

benefits analysis. Only model predictions for Hg deposition were analyzed using absolute model 

changes, although these parameters also considered percentage changes between the control case 

and two future baselines. 

5.5 Model Results: Air Quality Impacts 

As described above, we performed a series of air quality modeling simulations for the 

continental United States to assess the impacts of emissions adjustments to the Portland cement 

kiln sector. We looked at impacts on future ambient PM2.5, total Hg deposition levels, and 

visibility impairment. In this section, we present information on current and projected levels of 

pollution for 2013. 

This section summarizes the results of our modeling of differences in total Hg deposition 

impacts in the future based on changes to the cement kiln emissions. Specifically, we compare a 

2013 reference scenario to a 2013 emissions change scenario (approximating a nationwide 90% 

reduction to mercury emissions). Model results for the eastern and central United States indicate 

that total Hg deposition (wet and dry forms) would be reduced by a total of 63,518 μg/m2

. A 

reduction of 26,047 μg/m2

 is estimated for the western United States. The reductions to total 

annual Hg deposition estimated by the photochemical model show that the reductions tend to be 

greatest nearest the sources. 

This section summarizes the results of our modeling of annual average PM2.5 air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2013 reference scenario to a 2013 control scenario. The modeling assessment indicates that a 

decrease up to 0.3 μg/m3

 in annual PM2.5 design values is possible given an area’s proximity to 

controlled sources and the amount of reduced sulfur dioxide emissions. The median reduction 

over all monitor locations is 0.09 μg/m3

. An annual PM2.5 design value is the concentration that 

determines whether a monitoring site meets the annual NAAQS for PM2.5. The full details 

involved in calculating an annual PM2.5 design value are given in Appendix N of 40 CFR part 50. 
5-6 

Projected air quality benefits are estimated using procedures outlined by EPA modeling guidance 

(U.S. EPA, 2007). 

This section summarizes the results of our modeling of 24-hour average PM2.5 air quality 

impacts in the future due to reductions in emissions from this sector. Specifically, we compare a 

2013 reference scenario to a 2013 control scenario. The modeling assessment indicates that a 

decrease up to 0.5 μg/m3

 in 24-hour average PM2.5 design values at most monitor locations in the 

United States is possible given an area’s proximity to controlled sources and the amount of 

reduced sulfur dioxide emissions. The median reduction over all monitor locations is 0.1 μg/m3

. 

A 24-hour PM2.5 design value is the concentration that determines whether a monitoring site 

meets the 24-hour NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5

design value are given in Appendix N of 40 CFR part 50. Projected air quality benefits are 

estimated using procedures outlined by EPA modeling guidance (U.S. EPA, 2007). 

Air quality modeling conducted for this final rule was used to project visibility conditions 

in 138 mandatory Class I federal areas across the United States in 2013 (U.S. EPA, 2007). The 

level of visibility impairment in an area is based on the light-extinction coefficient and a unitless 

visibility index, called a “deciview,” that is used in the valuation of visibility. The deciview 

metric provides a scale for perceived visual changes over the entire range of conditions, from 

clear to hazy. Under many scenic conditions, the average person can generally perceive a change 

of one deciview. Higher deciview values are indicative of worse visibility. Thus, an 

improvement in visibility is a decrease in deciview value. The modeling assessment indicates 

that a decrease up to 0.31 deciviews in annual 20% worst visibility days is possible given an 

area’s proximity to controlled sources and the amount of reduced sulfur dioxide emissions. 

Median reductions are 0.01 deciviews to the 20% worst days and 20% best days over all monitor 

locations. 

5.6 Limitations (Uncertainties) Associated with the Air Quality Modeling 

Any deficiencies with the emissions or meteorological inputs may lead to control scenario 

estimates that may not fully characterize the source contribution mix at a receptor location. This 

application used a complete year of meteorology to capture the variety of meteorological 

formation regimes conducive to eleveted pollution. However, it is possible that the meteorology 

used for these model applications may not represent all elevated pollution formation regimes at 

every individual receptor location in the continental United States. 
6-1 

SECTION 6

BENEFITS OF EMISSIONS REDUCTIONS 

6.1 Synopsis 

In this section, we provide an estimate of the monetized benefits associated with reducing 

exposure to particulate matter (PM) for the final Portland Cement NESHAP and NSPS. The PM 

reductions are the result of emission limits on PM as well as emission limits on other pollutants, 

including hazardous air pollutants (HAPs) for the NESHAP and criteria pollutants for the NSPS. 

The total PM2.5 reductions are the consequence of the technologies installed to meet these 

multiple limits. These estimates include the number of cases of avoided morbidity and 

premature mortality among populations exposed to PM2.5, as well as the monetized value of 

those avoided cases. Using a 3% discount rate, we estimate the total monetized benefits of the 

final Cement NESHAP and NSPS to be $7.4 billion to $18 billion in the implementation year 

(2013). Using a 7% discount rate, we estimate the total monetized benefits of the final Cement 

NESHAP and NSPS to be $6.7 billion to $17 billion in the implementation year. All estimates 

are in 2005$. These estimates include the energy disbenefits associated with increased electricity 

usage by the control devices. 

These monetized estimates reflect EPA’s most current interpretation of the scientific 

literature and several methodology updates introduced in the proposal analysis. In addition, 

these estimates incorporate an array of improvements since the proposal, including cement 

sector-specific air quality modeling data, revised value-of-a-statistical-life (VSL), lowest 

measure level (LML) assessment, qualitative benefits for ecosystems and HAPs, and mercury 

deposition maps. Higher or lower estimates of benefits are possible using other assumptions; 

examples of this are provided in Figure 6-1. 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 4,400 tons of NOx, 5,800 tons of HCl, 5,200 tons of organic HAPs, and 

16,400 pounds of mercury each year. 
6-2 

$0

$5,000

$10,000

$15,000

$20,000

$25,000

Pope et al.

Laden et al.

Millions(2005$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

3% DR

7% DR

Figure 6-1. Total Monetized PM2.5 Benefits for the Final Cement NESHAP and NSPS in 

2013 a

a This graph shows the estimated 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. These estimates do 

not include benefits from reducing HAP emissions, but they do include the energy disbenefits. Due to data, 

methodology, and resource limitations, we were unable to monetize the benefits associated with several categories 

of benefits, including exposure to HAPs, NO2, and SO2, ecosystem effects, and visibility effects. 

6.2 Calculation of PM2.5 Human Health Benefits 

In addition to pollutants we cannot monetize, this rulemaking would reduce emissions of 

PM2.5 and SO2. Because SO2 is also a precursor to PM2.5, reducing and SO2 emissions would also 

reduce PM2.5 formation, human exposure, and the incidence of PM2.5-related health effects. The 

PM reductions are the result of emission limits on PM as well as emission limits on other 

pollutants, including hazardous air pollutants for the NESHAP and criteria pollutants for the 

NSPS. The total PM2.5 reductions are the consequence of the technologies installed to meet these 

multiple limits. 

6.2.1 Methodology Improvements since Proposal 

This benefits analysis incorporates an array of policy and technical improvements since 

the proposal RIA in 2009 (U.S. EPA, 2009a), including: 
6-3 

1. Cement sector-specific air quality modeling data. The benefits estimates for this final 

analysis are based on air quality data modeled by CAMx that reflect the emissions from 

the cement sector and the reductions anticipated as a result of this rule. This data provides 

a superior representation of the geographic distribution of the emission reductions and 

resulting ambient concentrations than the national average benefit-per-ton estimates used 

in the proposal. For more information regarding the modeling inputs and assumptions, 

please see Section 5 of this RIA. 

2. Use of a revised Value of Statistical Life (VSL). The Agency continues to update its 

guidance on valuing mortality risk reductions and until a final report is available, EPA 

now uses a single, peer-reviewed mean VSL estimate of $6.3 million (2000$). We 

discuss this issue in more detail in Section 6.2.5. 

3. Lowest Measured Level (LML) assessment. Consistent with the rationale outlined in the 

proposal RIA, EPA now estimates PM-related mortality without assuming an arbitrary 

threshold in the concentration-response function. Consistent with recent scientific 

advice, we are replacing the previous threshold sensitivity analysis with a new LML 

assessment to highlight the uncertainty associated with benefits estimated at low air 

quality levels. We discuss this issue in more detail in Section 6.2.4 and provide the 

results of this LML assessment in Section 6.3. 

4. Qualitative benefits for ecosystems and HAPs. Data, resource, and methodological 

limitations prevented EPA from quantifying or monetizing the benefits from several 

important benefit categories, including benefits from reducing toxic air pollutant 

emissions, ecosystem effects, and visibility impairment. Instead, we provide a qualitative 

description of the benefits anticipated as a result of the emission reductions from this 

rule. These unquantified benefits are described in Section 6.5. 

5. Mercury deposition. The air quality modeling data provide an estimate of the reduction in 

mercury deposition associated with the mercury emission reductions anticipated as a 

result of this rule. We provide maps of the reduced mercury deposition in Section 

6.3.2.1. Due to time and resource limitations, we were unable to model mercury 

methylation, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits 

from reducing mercury emissions. 

6.2.2 Benefits Analysis Approach 

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality. This approach estimates changes in individual health and 

welfare endpoints and assigns values to those changes assuming independence of the individual 

values. Total benefits are calculated simply as the sum of the values for all non-overlapping 
6-4 

health and welfare endpoints. The “damage-function” approach is the standard method for 

assessing costs and benefits of environmental quality programs and has been used in several 

recent published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et al., 2009). 

To assess economic value in a damage-function framework, the changes in environmental 

quality must be translated into effects on people or on the things that people value. For changes 

in PM, a health impact analysis (HIA) must first be conducted to convert air quality changes into 

effects that can be assigned dollar values. For this RIA, the health impacts analysis is limited to 

those health effects that are directly linked to ambient levels of air pollution and specifically to 

those linked to PM. We also provide qualitative discussions of the impact of changes in other 

environmental and ecological effects, including the benefits associated with decreasing 

deposition of sulfur to terrestrial and aquatic ecosystems, but we are unable to place an economic 

value on these changes due to time and resource limitations. 

We note at the outset that EPA rarely has the time or resources to perform extensive new 

research to measure directly either the health outcomes or their values for regulatory analyses. 

Thus, similar to Kunzli et al. (2001) and other recent health impact analyses, our estimates are 

based on the best available methods of benefits transfer. Benefits transfer is the science and art 

of adapting primary research from similar contexts to obtain the most accurate measure of 

benefits for the environmental quality change under analysis. Adjustments are made for the level 

of environmental quality change, the socio-demographic and economic characteristics of the 

affected population, and other factors to improve the accuracy and robustness of benefits 

estimates. 

6.2.3 Health Impact Analysis (HIA) 

The HIA quantifies the changes in the incidence of adverse health impacts resulting from 

changes in human exposure to PM2.5 air quality. HIAs are a well-established approach for 

estimating the retrospective or prospective change in adverse health impacts resulting from 

population-level changes in exposure to pollutants (Levy et al. 2009). Analysts have applied the 

HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant 

levels (Hubbell et al. 2005; Davidson et al. 2007, Tagaris et al. 2009). For this analysis, we used 

the environmental Benefits Mapping and Analysis Program (BenMAP), which is a PC-based tool 

that can systematize health impact analyses by applying a database of key input parameters, 

including health impact functions and population projections.1

 

1

 For this analysis, we used BenMAP version 3.0 (Abt Associates, 2008). This model is available for free download 

on the Internet at <http://www.epa.gov/air/benmap>.
6-5 

The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMxgenerated projections of PM2.5 air quality and estimating the change in the spatial distribution of 

the ambient air quality; (2) determining the subsequent change in population-level exposure; (3) 

calculating health impacts by applying concentration-response relationships drawn from the 

epidemiological literature (Hubbell et al. 2009) to this change in population exposure. 

A typical health impact function might look as follows: 

where y0 is the baseline incidence rate for the health endpoint being quantified (for 

example, a health impact function quantifying changes in mortality would use the baseline, or 

background, mortality rate for the given population of interest); Pop is the population affected by 

the change in air quality; Δx is the change in air quality; and β is the effect coefficient drawn 

from the epidemiological study. For this analysis, we systematize the HIA calculation process 

using BenMAP’s library of existing air quality monitoring data, population data and health 

impact functions. Figure 6-2 provides a simplified overview of this approach, and Figure 6-3 

identifies the data inputs and outputs for the BenMAP model. 

Figure 6-2. Illustration of BenMAP Approach 
6-6 

Figure 6-3. Data inputs and outputs for the BenMAP model 

The benefits estimates in this analysis were derived using modified versions of the health 

impact functions used in the PM NAAQS Regulatory Impact Analysis (RIA) (U.S. EPA, 2006). 

While many of the functions are identical to those used in the PM NAAQS RIA, we have 

updated a few of the underlying assumptions over the last few years. For a detailed description 

of the underlying functions, studies, baseline incidence rates, and population data used in this 

analysis, please refer to Chapter 5 of the recently proposed Transport Rule (U.S. EPA, 2010a). 

Table 6-1 identifies which human health and welfare endpoints are included in the monetized 

benefits and which endpoints are unquantified. In summary, the monetized PM benefits include 

premature mortality and 11 morbidity endpoints. 

Census 

Population 

Baseline and 

Post-Control 

PM2.5

Concentrations 

PM2.5 Health 

Functions 

Economic 

Valuation 

Functions 

Population 

Projections 

PM2.5 Incremental 

Air Quality 

Change 

PM2.5 Related 

Health Impacts

Woods & 

Poole 

Population 

Projections 

Background 

Incidence and 

Prevalence Rates 

Monetized PM2.5-

related Benefits 

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP model
6-7 

Table 6-1. Human Health and Welfare Effects of Pollutants Affected 

Pollutant/ 

Effect 

Quantified and monetized in primary 

estimate Unquantified 

PM: 

healtha

Premature mortality based on cohort 

study estimatesb Low birth weight 

Premature mortality based on expert 

elicitation estimates Pulmonary function 

Hospital admissions: respiratory and 

cardiovascular Chronic respiratory diseases other than chronic bronchitis 

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

Nonfatal heart attacks (myocardial 

infarctions) UVb exposure (+/-)c

Lower and upper respiratory illness 

Minor restricted activity days 

Work loss days 

Asthma exacerbations (among 

asthmatic populations 

Respiratory symptoms (among 

asthmatic populations)

Infant mortality 

PM: 

welfare 

Visibility in Class I areas in SE, SW, and CA regions

Visibility in residential areas

Visibility in non-class I areas and class 1 areas in NW, NE, 

and Central regions

UVb exposure (+/-)c

Global climate impactsc

SO2: 

health 

 Respiratory hospital admissions

Asthma emergency room visits

 Asthma exacerbation

 Acute respiratory symptoms

 Premature mortality

 Pulmonary function

SOX: 

welfare 

Commercial fishing and forestry from acidic deposition effects 

Recreation in terrestrial and aquatic ecosystems from acid 

deposition effects 

 Increased mercury methylation

Mercury: 

health 

 Incidence of neurological disorders

Incidence of learning disabilities

Incidences in developmental delays

Potential cardiovascular effects including:

--Altered blood pressure regulation 

--Increased heart rate variability 

--Incidences of Myocardial infarction 

Potential reproductive effects

Mercury: 

welfare 

Impact on birds and mammals (e.g. reproductive effects)

Impacts to commercial, subsistence and recreational fishing a

 In addition to primary economic endpoints, there are a number of biological responses that have been associated with 

PM health effects including morphological changes and altered host defense mechanisms. The public health impact of 

these biological responses may be partly represented by our quantified endpoints. b

 Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk 

estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of 

this issue). While some of the effects of short term exposure are likely to be captured by the cohort estimates, there 

may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in 

the primary analysis. c May result in benefits or disbenefits.
6-8 

6.2.4 Estimating PM2.5-related premature mortality 

Consistent with the proposal RIA for this rule (U.S. EPA, 2009a), the PM2.5 benefits 

estimates utilize the concentration-response functions as reported in the epidemiology literature, 

as well as the 12 functions obtained in EPA’s expert elicitation study as a characterization of 

uncertainty. 

 One estimate is based on the concentration-response (C-R) function developed from 

the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope 

et al. (2002), a study that EPA has previously used to generate its primary benefits 

estimate. When calculating the estimate, EPA applied the effect coefficient as 

reported in the study without an adjustment for assumed concentration threshold of 10 

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 

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).2

 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 benefits 

estimates. Previously, EPA had calculated benefits based on these two empirical studies, but 

derived the range of 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., 

 

2

 These two studies specify multi-pollutant models that control for SO2, among other pollutants. 
6-9 

2006).3

 Within this assessment, we include the 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 concentrationresponse functions, benefits estimates using these functions generally fall between results using 

these epidemiology studies (see Figure 6-1). In general, the expert elicitation results support the 

conclusion that the benefits of PM2.5 control are very likely to be substantial. 

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. This analysis continues to use the updated assumptions first applied in the proposal 

RIA for this rule (U.S. EPA, 2009a), including the updated population dataset in BenMAP 3.0 

and the functions directly from the epidemiology studies without an adjustment for an assumed 

threshold.4

 Removing the threshold assumption is a key difference between the method used in 

this analysis of PM benefits and the methods used in RIAs prior to the proposal RIA for this rule, 

and we now calculate incremental benefits down to the lowest modeled PM2.5 air quality levels. 5

Prior to the proposal RIA for this rule, EPA presented the results using an assumed threshold at 

10 μg/m3

 in the PM-mortality health impact function as the primary PM-related benefits results. 

Using a threshold of 10 μg/m3

 was an arbitrary choice, and we could have assumed thresholds at 

other points in the lower end of the observed range the analysis. Since the proposal RIA for this 

rule, EPA included a sensitivity analysis with an assumed threshold at 10 μg/m3 to illustrate that 

the fraction of benefits that occur at lower air pollution concentration levels are inherently more 

uncertain. 

In the proposal RIA for this rule, EPA solicited comment on the use of the no-threshold 

model for benefits analysis within the preamble.6

 Based on our review of the public comments as 

well as 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 

 

3

 Please see the Section 5.2 of the proposal RIA for this rule for more information regarding the change in the 

presentation of benefits estimates. 4

The benefits methodology has also been updated since the proposal RIA to incorporate a revised VSL, as discussed 

in the next section. 5

It 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. 6

 The comment period for the proposed rule closed on September 4, 2009 (Docket ID No. EPA–HQ–OAR–2002–

0051 available at http://www.regulations.gov). All public comments received as well as the responses to those 

comments are available in this docket. 
6-10 

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 please consult the Technical Support 

Document (TSD) entitled Summary of Expert Opinions on the Existence of a Threshold in the 

Concentration-Response Function for PM-related Mortality (U.S. EPA, 2010b), which is 

provided in Appendix D of this RIA. 

Consistent with recent scientific advice, we are replacing the previous threshold 

sensitivity analysis with a new “Lowest Measured Level” (LML) assessment. This approach 

summarizes the distribution of avoided PM mortality impacts according to the baseline PM2.5

levels experienced by the population receiving the PM2.5 mortality benefit. In the results section, 

we identify on the figures the lowest air quality levels measured in each of the primary cohort 

studies that estimate PM-related mortality. This information allows readers to determine the 

portion of PM-related mortality benefits occurring above or below the LML of each study; in 

general, our confidence in the estimated PM mortality decreases as we consider air quality levels 

further below the LML in the two epidemiological studies. 

While an LML assessment provides some insight into the level of uncertainty in the 

estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to 

quantify PM-related mortality impacts using a full range of modeled air quality concentrations. 

Unlike an assumed threshold, which is a modeling assumption that reduces the magnitude of the 

estimated health impacts, the LML is a characterization of the fraction of benefits that are more 

uncertain. It is important to emphasize that just because we have greater confidence in the 

benefits above the LML, this does not mean that we have no confidence that benefits occur 

below the LML. 
6-11 

Analyses of these cohorts using data from more recent years, during which time PM 

concentrations have fallen, continue to report strong associations with mortality. As we model 

mortality impacts among populations exposed to levels of PM2.5 that are successively lower than 

the LML of each study, our confidence in the results diminishes. As air pollution emissions 

continue to decrease over time, there will be more people in areas where we do not have 

published epidemiology studies. However, each successive cohort study has shown evidence of 

effects at successively lower levels of PM2.5. As more 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 recent studies indicate that 

confidence in PM2.5-related mortality effects down to at least 7.5 μg/m3 is high. 

For these rules the SO2 reductions represent a large fraction of the total benefits from 

reducing PM2.5 , but it is not possible to isolate the portion if the total benefits attributable to the 

emission reductions of SO2 resulting from the application of HCl controls. The benefits 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.

6.2.5 Economic valuation of health impacts 

After quantifying the change in adverse health impacts, the final step is to estimate the 

economic value of these avoided impacts. Please refer to Table 5-11 in the recently proposed 

Transport Rule (U.S. EPA, 2010a) for a detailed description of the underlying valuation 

functions and the monetized unit values for each endpoint incorporated into this analysis.7

 The 

monetized mortality benefits dominate the total benefits estimates. 

 

7

 To comply with Circular A-4, EPA provides monetized benefits using discount rates of 3% and 7% (OMB, 2003). 

These benefits are estimated for a specific analysis year (i.e., 2013), and most of the PM 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 benefits 

using a 7% discount rate are only approximately 10% less than the monetized benefits using a 3% discount rate. 
6-12 

As is the nature of RIAs, the assumptions and methods used to estimate air quality 

benefits evolve over time to reflect the Agency’s most current interpretation of the scientific and 

economic literature. For a period of time (2004–2006), the Office of Air and Radiation (OAR) 

valued mortality risk reductions using a value-of-a-statistical-life (VSL) estimate derived from a 

limited analysis of some of the available studies. OAR arrived at a VSL using a range of $1 

million to $10 million (2000$) consistent with two meta-analyses of the wage-risk literature. The 

$1 million value represented the lower end of the interquartile range from the Mrozek and Taylor 

(2002) meta-analysis of 33 studies. The $10 million value represented the upper end of the 

interquartile range from the Viscusi and Aldy (2003) meta-analysis of 43 studies. The mean 

estimate of $5.5 million (2000$)8

 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)9

 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 

 

8

After adjusting the VSL to account for a different currency year (2005$) and to account for income growth to 2015, 

the $5.5 million VSL is $7.2 million. 9

In the (draft) update of the Economic Guidelines (U.S. EPA, 2006), 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. 
6-13 

1991. The mean VSL across these studies is $6.3 million (2000$).10 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. 

6.3 Health Benefits Results 

Table 6-2 provides a summary of the monetized PM2.5 benefits for the final Portland 

Cement NESHAP and NSPS using the anchor points of Pope et al. and Laden et al. as well as the 

results from the expert elicitation on PM mortality at discount rates of 3% and 7%. Table 6-3 

provides a summary of the reductions in health incidences as a result of the pollution reductions 

for the final Portland Cement NESHAP and NSPS. Table 6-4 compares the monetized PM2.5 

benefits attributable to the final NSPS only, the final NESHAP only, and the more stringent 

NSPS and final NESHAP. Figure 6-4 illustrates the relative breakdown of the monetized PM2.5 

health benefits. Figure 6-5 provides a graphical representation of all 14 of the PM2.5 benefits, at 

both a 3 percent and 7% discount rate. 

The very large proportion of the avoided PM-related impacts we estimate in this analysis 

occur among populations exposed at or above the lowest LML of the cohort studies (Figures 6-6 

and 6-7), increasing our confidence in the PM mortality analysis. Figure 6-6 shows a bar chart of 

the percentage of the estimated mortalities at each PM2.5 level. Figure 6-7 shows a cumulative 

distribution function of the same data. Both figures identify the LML for each of the major 

cohort studies. 

Using the Pope et al. (2002) study, approximately 94% of the mortality impacts occur 

among populations with baseline exposure 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 mortality impacts occur at or 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. 

 

10In this analysis, we adjust the VSL to account for a different currency year (2005$) and to account for income 

growth to 2015. After applying these adjustments to the $6.3 million value, the VSL is $8.3 million. 
6-14 

Table 6-2. Summary of Monetized Benefits Estimates for Final Cement NESHAP and 

NSPS in 2013 (millions of 2005$)a

 3% 7% 

Based on Epidemiology Literature 

Pope et al. $7,600 $6,900 

($620--$23,000) ($560--$21,000) 

Laden et al. $19,000 $17,000 

($1,600--$55,000) ($1,500--$49,000) 

Based on Expert Elicitation 

Expert A $20,000 $18,000 

($1,100--$65,000) ($1,000--$59,000) 

Expert B $15,000 $13,000 

($550--$61,000) ($500--$55,000) 

Expert C $15,000 $14,000 

($870--$57,000) ($790--$52,000) 

Expert D $11,000 $9,700 

($690--$34,000) ($620--$31,000) 

Expert E $24,000 $22,000 

($2,100--$73,000) ($1,900--$66,000) 

Expert F $14,000 $12,000 

($1,300--$41,000) ($1,200--$37,000) 

Expert G $9,000 $8,200 

($56--$33,000) ($53--$30,000) 

Expert H $11,000 $10,000 

($75--$44,000) ($71--$40,000) 

Expert I $15,000 $13,000 

($820--$50,000) ($740--$45,000) 

Expert J $12,000 $11,000 

($900--$47,000) ($810--$42,000) 

Expert K $2,900 $2,700 

($56--$19,000) ($53--$17,000) 

Expert L $10,000 $9,100 

($370--$39,000) ($330--$35,000) 

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. These estimates do not 

include benefits from reducing HAP emissions, and they do not include the energy disbenefits described in the 

next section. 
6-15 

Table 6-3. Summary of Reductions in Health Incidences and Monetized Benefits from 

PM2.5 Benefits for the Final Cement NESHAP and NSPS in 2013 (95th

percentile confidence interval)a

Health Endpoint Incidence 3% Discount 

(millions of 2005$) 

7% Discount 

(millions of 2005$)

Avoided Premature Mortality 

Pope et al. (ACS cohort) 960 $7,000 $6,300 

(320--1,600) ($0,560--$21,000) ($0,500--$19,000) 

Laden et al. (H6C cohort) 2,500 $18,000 $16,000 

(1,200--3,700) ($1,600--$53,000) ($1,400--$47,000) 

Woodruff et al. (Infant Mortality) 

4 $35 $35 

(-4--13) (-$38--$160) (-$38--$160) 

Avoided Morbidity 

Chronic Bronchitis 

650 $19 $19 

(70--1,200) ($1.1--$90) ($1.10--$90.00) 

Acute Myocardial Infarction 1,500 $11 $11 

(470--2,600) ($2.0--$27) ($1.8--$26) 

Hospital Admissions, Respiratory 

240 $0.21 $0.21 

(100--360) ($0.10--$0.31) ($0.10--$0.31) 

Hospital Admissions, Cardiovascular 

500 $0.90 $0.90 

(360--590) ($0.47--$1.20) ($0.47--$1.2) 

Emergency Room Visits, Respiratory 1,000 $0.03 $0.03 

(550--1,500) ($0.01--$0.04) ($0.01--$0.04) 

Acute Bronchitis 1,500 $0.01 $0.01 

(-200--3,200) ($0.00--$0.02) ($0.00--$0.02) 

Work Loss Days 130,000 $1.2 $1.2 

(110,000--140,000) ($1.1--$1.4) ($1.1--$1.4) 

Asthma Exacerbation 17,000 $0.06 $0.06 

(1,200--52,000) ($0.00--$0.21) ($0.00--$0.21) 

Minor Restricted Activity Days 750,000 $3.0 $3.0 

(620,000--880,000) ($1.6--$4.6) ($1.6--$4.6) 

Lower Respiratory Symptoms 18,000 $0.02 $0.02 

(7,800--28,000) ($0.01--$0.05) ($0.01--$0.05) 

Upper Respiratory Symptoms 14,000 $0.03 $0.03 

(3,400--24,000) ($0.01--$0.07) ($0.01--$0.07) 

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. These estimates do not include benefits from reducing 

HAP emissions, and they do not include the energy disbenefits described in the next section. 
6-16 

AdultMortality

93%

Chronic Bronchitis

3.8%

AMI 2.1%

Minor Restricted Activity

Days 0.6%

Infant Mortality 0.4%

Work Loss Days 0.2%

HA,Cardio 0.2%

Ha, Resp 0.04%

Asthma Exacerbation 0.01%

Upper Resp Symp 0.01%

ER Visits 0.005%

Lower Resp Symp 0.004%

Acute Bronchitis 0.001%

Other 1.5%

Figure 6-4. Breakdown of Monetized PM2.5 Health 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 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. The monetized estimates do not include benefits from reducing HAP 

emissions or NOx, and they do not include the energy disbenefits described in the next section. 
6-17 

Table 6-4. Comparison of Monetized Benefits and Emission Reductions for Final Cement 

NESHAP and NSPS in 2013 (2005$)a

 

Final NESHAP and 

NSPS 

Final NSPS 

only 

Final NESHAP 

only 

Final NESHAP and 

Stringent NSPS 

3% Pope $7,600 $510 $7,600 $7,600 

Laden $19,000 $1,300 $19,000 $19,000 

7% Pope $6,900 $460 $6,900 $6,900 

Laden $17,000 $1,100 $17,000 $17,000 

Emission 

Reductions 

PM (tpy) 11,000 590 11,000 11,000 

SO2 (tpy) 124,000 9,000 124,000 124,000 

NOx (tpy) 6,600 6,600 0 11,000 

HCl (tpy) 5,900 520 5,900 5,900 

Organic HAPs (tpy) 5,200 0 5,200 5,200 

Hg (pounds) 16,400 0 16,400 16,400 

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. The monetized estimates do not include benefits from 

reducing HAP emissions or NOx, and they do not include the energy disbenefits described in the next section. 
6-18 

$0

$5,000

$10,000

$15,000

$20,000

$25,000

Pope et al.

Laden et al.

Millions(2005$)

Benefits estimates derived from2 epidemiology functions and 12 expert functions

3% DR

7% DR

Figure 6-5. Total Monetized PM2.5 Benefits for the Final Cement NESHAP and NSPS in 

2013a

a This graph shows the estimated 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. These estimates do 

not include benefits from reducing HAP emissions, and they do not include the energy disbenefits described in the 

next section. 
6-19 

0%

5%

10%

15%

20%

25%

1 2 3 4 5 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Percentage of AvoidedPM2.5Mortalities

Baseline Annual MeanPM2.5 Level (μg/m3)

LML of Krewski et al. (2009)study LML of Laden et

al. (2006) study

LML of Pope et

al. (2002) study

Figure 6-6. Percentage of Total PM-Related Mortalities Avoided by Baseline Air Quality 

Level for Final Portland Cement NESHAP and NSPS a

a Approximately 94% of the mortality impacts occur among populations with baseline exposure to annual mean 

PM2.5 levels at or above 7.5 μg/m3

, which is the lowest air quality level considered in the ACS cohort study by 

Pope et al. (2002).
6-20 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 7.5 8 9 10 11 12 13 14 15 16 17 18 19 20

Cumulative percentage of avoidedPMMortalities

Baseline Annual MeanPM2.5 Level(μg/m3)

LML of Pope et al.

(2002)study

LML of Laden et al.

(2006)study

Figure 6-7. Cumulative Percentage of Total PM-related Mortalities Avoided by Baseline 

Air Quality Level for Final Portland Cement NESHAP and NSPS a 

a Approximately 94% of the mortality impacts occur among populations with baseline exposure to annual mean 

PM2.5 levels at or above 7.5 μg/m3

, which is the lowest air quality level considered in the ACS cohort study by 

Pope et al. (2002).

6.4 Energy Disbenefits 

Electricity usage associated with the operation of control devices is anticipated to 

increase emissions of criteria pollutants from utility boilers that supply electricity to the Portland 

cement facilities. We estimate increased energy demand associated with the installation of 

scrubbers, ACI systems, and RTO. The increases for kilns subject to existing source standards 

are estimated to be 2,000 tpy of NOx, 1,000 tpy of CO, 3,500 tpy of SO2 and about 100 tpy of 

PM. For kilns subject to new source standards increases in secondary air pollutants are estimated 

to be 200 tpy of NOX, 100 tpy of CO, 400 tpy of SO2 and 10 tpy of PM. We also estimated 

increases of CO2 to be 1.1 million tpy for kilns subject to existing source standards and 4,000 tpy 

for kilns subject to new source standards. The increase in electricity usage for the pumps used in 

the SNCR system to deliver reagent to the kiln is negligible. 
6-21 

6.4.1 PM2.5 Disbenefits 

The additional energy usage required for the emission control devices would increase 

emissions of PM, NOx, SO2. Because NOx and SO2 are also precursors to PM2.5, increasing 

these emissions would also increase PM2.5 formation, human exposure, and the incidence of 

PM2.5-related health effects. Due to time and resource limitations, it was not possible to provide 

a comprehensive estimate of the PM2.5-related disbenefits using air quality modeling. Instead, 

we used the “benefit-per-ton” approach to estimate these disbenefits based on the methodology 

described in Fann, Fulcher, and Hubbell (2009). These PM2.5 benefit-per-ton estimates provide 

the total monetized human health 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 perton technique in several previous RIAs, including the proposal for this rule (U.S. EPA, 2009a). 

For this analysis, we use the benefit-per-ton estimates associated with the EGU sector. It is 

important to note that the disbenefits associated with directly emitted PM are overestimated in 

this analysis because we assume that all of the increased PM tons are in the PM2.5 fraction. 

Table 6-5 summarizes the benefit-per-ton estimates and the monetized PM2.5 disbenefits at 

discount rates of 3% and 7%. 

Table 6-5. Summary of Monetized PM2.5 Energy Disbenefits for the Final Portland 

Cement NSPS and NESHAP in 2013 (2005$) 

Pollutant 

Emissions 

Reductions 

(tons) 

Benefit 

per ton 

(Pope, 

3%) 

Benefit 

per ton 

(Laden, 

3%) 

Benefit 

per ton 

(Pope, 

7%) 

Benefit 

 per ton 

(Laden, 

7%) 

Monetized PM2.5 

Disbenefits 

(millions, 3%) 

Monetized PM2.5 

Disbenefits 

(millions, 7%) 

Direct PM2.5 110 $210,000 $510,000 $190,000 $460,000 $23 to $56 $21 to $50 

PM2.5 Precursors 

SO2 3,900 $37,000 $91,000 $34,000 $82,000 $150 to $360 $130 to $320 

NOX 2,200 $6,800 $17,000 $6,100 $15,000 $15 to $36 $13 to $33 

 Total $180 to $450 $170 to $400 

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 disbenefits incorporate the conversion from precursor emissions to ambient fine particles. 

Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology. The 

disbenefits associated with directly emitted PM are overestimated in this analysis because we assume that all of 

the increased PM tons are in the PM2.5 fraction. 
6-22 

6.4.2 Social Cost of Carbon and Greenhouse Gas Disbenefits 

EPA has assigned a dollar value to reductions in carbon dioxide (CO2) emissions using 

recent estimates of the “social cost of carbon” (SCC). The SCC is an estimate of the monetized 

damages associated with an incremental increase in carbon emissions in a given year. It is 

intended to include (but is not limited to) changes in net agricultural productivity, human health, 

property damages from increased flood risk, and the value of ecosystem services due to climate 

change. The SCC estimates used in this analysis were developed through an interagency process 

that included EPA and other executive branch entities, and concluded in February 2010. EPA 

first used these SCC estimates in the benefits analysis for the final joint EPA/DOT Rulemaking 

to establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average 

Fuel Economy Standards; see the rule’s preamble for discussion about application of SCC (75 

FR 25324; 5/7/10). The SCC Technical Support Document (SCC TSD) provides a complete 

discussion of the methods used to develop these SCC estimates.11 

The interagency group selected four SCC values for use in regulatory analyses, which we 

have applied in this analysis: $5, $21, $35, and $65 per metric ton of CO2 emissions12 in 2010, in 

2007 dollars. The first three values are based on the average SCC from three integrated 

assessment models, at discount rates of 2.5, 3, and 5 percent, respectively. SCCs at several 

discount rates are included because the literature shows that the SCC is quite sensitive to 

assumptions about the discount rate, and because no consensus exists on the appropriate rate to 

use in an intergenerational context. The fourth value is the 95th percentile of the SCC from all 

three models at a 3 percent discount rate. It is included to represent higher-than-expected 

impacts from temperature change further out in the tails of the SCC distribution. Low 

probability, high impact events are incorporated into all of the SCC values through explicit 

consideration of their effects in two of the three models as well as the use of a probability density 

function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results 

in more high temperature outcomes, which in turn lead to higher projections of damages. 

 

11 Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document: Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of 

Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of 

Agriculture, Department of Commerce, Department of Energy, Department of Transportation, Environmental 

Protection Agency, National Economic Council, Office of Energy and Climate Change, Office of Management 

and Budget, Office of Science and Technology Policy, and Department of Treasury (February 2010). Also 

available at http://www.epa.gov/otaq/climate/regulations.htm 12 The interagency group decided that these estimates apply only to CO2 emissions. Given that warming profiles and 

impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded 

“transforming gases into CO2-equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, 

would not result in accurate estimates of the social costs of non-CO2 gases” (SCC TSD, pg 13). 
6-23 

The SCC increases over time because future emissions are expected to produce larger 

incremental damages as physical and economic systems become more stressed in response to 

greater climatic change. Note that the interagency group estimated the growth rate of the SCC 

directly using the three integrated assessment models rather than assuming a constant annual 

growth rate. This helps to ensure that the estimates are internally consistent with other modeling 

assumptions. The SCC estimates for the analysis years of 2013, in 2005 dollars are provided in 

Table 6-6. 

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A recent report from the National 

Academies of Science (NRC, 2008) points out that any assessment will suffer from uncertainty, 

speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the 

effects of past and future emissions on the climate system, (3) the impact of changes in climate 

on the physical and biological environment, and (4) the translation of these environmental 

impacts into economic damages. As a result, any effort to quantify and monetize the harms 

associated with climate change will raise serious questions of science, economics, and ethics and 

should be viewed as provisional. 

The interagency group noted a number of limitations to the SCC analysis, including the 

incomplete way in which the integrated assessment models capture catastrophic and noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk 

aversion. The limited amount of research linking climate impacts to economic damages makes 

the interagency modeling exercise even more difficult. The interagency group hopes that over 

time researchers and modelers will work to fill these gaps and that the SCC estimates used for 

regulatory analysis by the Federal government will continue to evolve with improvements in 

modeling. Additional details on these limitations are discussed in the SCC TSD. 

In light of these limitations, the interagency group has committed to updating the current 

estimates as the science and economic understanding of climate change and its impacts on 

society improves over time. Specifically, the interagency group has set a preliminary goal of 

revisiting the SCC values within two years or at such time as substantially updated models 

become available, and to continue to support research in this area. 

Applying the global SCC estimates to the estimated increases in CO2 emissions for the 

range of policy scenarios, we estimate the dollar value of the climate-related disbenefits captured 

by the models for each analysis year. For internal consistency, the annual disbenefits are 
6-24 

discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. 5%, 3%, 

and 2.5%) rather than 3% and 7%.13 These estimates are provided in Table 6-7. 

Table 6-6. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2013 a

Discount Rate and Statistic SCC estimate (2005$) 

5% Average $5.0 

3% Average $21.5 

2.5% Average $34.9 

3% 95%ile $65.6 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

Table 6-7. Monetized Disbenefits of CO2 Emission Increases in 2013 a

Discount Rate and Statistic 

SCC-derived disbenefits 

(millions of 2005$) 

5% Average $5.1 

3% Average $22 

2.5% Average $36 

3% 95%ile $67 

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts. 

6.4.3 Total Monetized Disbenefits 

The additional energy usage required for the emission control devices would increase 

emissions of several pollutants. In this analysis, we were able to monetize the disbenefits 

associated with the increased emissions of PM, NOX, SO2, and CO2, but we were unable to 

monetize the disbenefits associated with the increased emissions of CO. We estimate that the 

total monetized disbenefits at a 3% discount rate are $210 to $470 million. Therefore, these 

disbenefits reduce the total monetized benefits to $7.4 billion to $18 billion and $6.7 billion to 

$17 billion, at discount rates of 3% and 7% respectively. 

In addition, we were unable to quantify the emission increases or monetize the disbenefits 

associated with “leakage” of emissions to other counties. This benefits analysis only 

incorporates the domestic emission changes, but this regulation could lead to increased imports 

 

13 It is possible that other benefits or costs of proposed regulations unrelated to CO2 emissions will be discounted at 

rates that differ from those used to develop the SCC estimates. 
6-25 

and production in other countries. For this analysis, because we do not have sufficient 

information on origin of these imports, the specific location of the additional emissions, or the 

level of control on those facilities, we are unable to estimate the potential disbenefits associated 

with increased emissions in other countries that might occur as a result of this regulation. 

However, the monetized benefits estimates do not account for the decrease in domestic emissions 

associated with the decrease in domestic production and transportation. The economic analysis 

estimates that domestic production would decrease by 10 million tons, but imports would 

increase by only 3 million tons. The net effect on global pollutants like CO2 and mercury is 

difficult to determine because it depends on many factors, and quantifying the benefits associated 

with either omission is beyond the scope of this analysis. 

6.5 Unquantified or Nonmonetized Benefits 

The monetized benefits estimated in this RIA only reflect the portion of benefits 

attributable to the health impacts associated with exposure to 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 benefits from reducing 

hazardous air pollutants (HAPs) have not been monetized in this analysis. In addition to being a 

PM2.5 precursor, SO2 emissions also contribute to adverse effects from acidic deposition in 

aquatic and terrestrial ecosystems, increase mercury methylation, as well as visibility 

impairment. 

6.5.1 Other SO2 and PM Benefits 

In addition to being a precursor to PM2.5, SO2 emissions are also associated with a variety 

of respiratory health effects. Unfortunately, we were unable to estimate the health benefits 

associated with reduced SO2 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 SO2 for nearby 

populations. Therefore, this analysis only quantifies and monetizes the PM2.5 benefits associated 

with the reductions in SO2 emissions. 

Following an extensive evaluation of health evidence from epidemiologic and laboratory 

studies, the Integrated Science Assessment (ISA) for Sulfur Dioxide concluded that there is a 

causal relationship between respiratory health effects and short-term exposure to SO2 (U.S. EPA, 

2008b). According to summary of the ISA in EPA’s risk and exposure assessment (REA) for the 

SO2 NAAQS“the immediate effect of SO2 on the respiratory system in humans is 
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bronchoconstriction” (U.S. EPA, 2009c). In addition, the REA summarized from the ISA that 

“asthmatics are more sensitive to the effects of SO2 likely resulting from preexisting 

inflammation associated with this disease.” A clear concentration-response relationship has been 

demonstrated in laboratory studies following exposures to SO2 at concentrations between 20 and 

100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversely 

affected (U.S. EPA, 2009c). Based on our review of this information, we identified four shortterm morbidity endpoints that the SO2 ISA identified as a “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 SO2 ISA. The SO2 ISA also concluded that the 

relationship between short-term SO2 exposure and premature mortality was “suggestive of a 

causal relationship” because it is difficult to attribute the mortality risk effects to SO2 alone. 

Although the SO2 ISA stated that studies are generally consistent in reporting a relationship 

between SO2 exposure and mortality, there was a lack of robustness of the observed associations 

to adjustment for pollutants. 

SO2 emissions also contribute to adverse welfare effects from acidic deposition, visibility 

impairment, and enhanced mercury methylation. Deposition of sulfur 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, 2008c) 

Reducing SO2 and PM emissions 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 
6-27 

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. In fact, particulate sulfate is the largest contributor to 

regional haze in the eastern U.S. (i.e., 40% or more annually and 75% during summer). In the 

western U.S., particulate sulfate contributes to 20-50% of regional haze (U.S. EPA, 2009b). 

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. Due to time and resource limitations, we were 

unable to estimate the monetized benefits associated with visibility improvements. Previous 

analyses (U.S. EPA, 2006; U.S. EPA, 2010c) show that visibility benefits are a significant 

welfare benefit category. 

6.5.2 HAP Benefits 

Americans are exposed to ambient concentrations of air toxics at levels which have the 

potential to cause adverse health effects.14 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 2009.15 NATA for 2002 includes four steps: 

1) Compiling a national emissions inventory of air toxics emissions from outdoor sources 

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,16 subchronic,17 or acute18 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 

 

14 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 15 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 16 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). 17 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an 

organism. 18 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less. 
6-28 

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. 19 Figures 6-8 and 6-9 

depict estimated county-level carcinogenic risk and noncancer respiratory hazard from the 

assessment. The respiratory hazard is dominated by a single pollutant, acrolein. 

This rule is anticipated to reduce 16,400 pounds of mercury, 5,800 tons of HCl, and 5,200 

tons of organic HAPs each year. 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 Portland cement manufacturing plants, either released from kilns 

systems, raw material dryers, clinker coolers, raw mills, finish mills, storage bins, conveying 

system transfer points, bagging systems, or bulk loading and unloading systems. 

Figure 6-8. Estimated County Level Carcinogenic Risk from HAP exposure from 

outdoor sources (NATA, 2002) 

 

19 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/ 
6-29 

Figure 6-9. Estimated County Level Noncancer (Respiratory) Risk from HAP exposure 

from outdoor sources (NATA, 2002) 

6.5.2.1 Mercury 

Mercury is a highly neurotoxic contaminant that enters the food web as a methylated 

compound, methylmercury (U.S. EPA, 2008c). The contaminant is concentrated in higher 

trophic levels, including fish eaten by humans. Experimental evidence has established that only 

inconsequential amounts of methylmercury can be produced in the absence of sulfate (U.S. EPA, 

2008c). Current evidence indicates that in watersheds where mercury is present, increased sulfate 

deposition very likely results in methylmercury accumulation in fish (Drevnick et al., 2007; 

Munthe et al, 2007). The SO2 ISA concluded that evidence is sufficient to infer a casual 

relationship between sulfur deposition and increased mercury methylation in wetlands and 

aquatic environments (U.S. EPA, 2008c). 

In addition to the role of sulfate deposition on methylation, these rules would also reduce 

mercury emissions. Mercury is emitted to the air from various man-made and natural sources. 

These emissions transport through the atmosphere and eventually deposit to land or water bodies. 

This deposition can occur locally, regionally, or globally, depending on the form of mercury 

emitted and other factors such as the weather. The form of mercury emitted varies depending on 
6-30 

the source type and other factors. Available data indicate that the mercury emissions from these 

sources are a mixture of gaseous elemental mercury (35%), inorganic divalent mercury (reactive 

gas phase mercury) (58%), and particulate bound mercury (7%) (U.S. EPA, 2010c). Gaseous 

elemental mercury can be transported very long distances, even globally, to regions far from the 

emissions source (becoming part of the global “pool”) before deposition occurs. Inorganic 

divalent and particulate bound mercury have a shorter atmospheric lifetime and can deposit to 

land or water bodies closer to the emissions source. Furthermore, elemental mercury in the 

atmosphere can undergo transformation into divalent mercury, providing a significant pathway 

for deposition of emitted elemental mercury. 

Potential exposure routes to mercury emissions include both direct inhalation and 

consumption of fish containing methylmercury. The primary route of human exposure to 

mercury emissions from industrial sources is generally indirectly through the consumption of 

fish containing methylmercury. As described above, mercury that has been emitted to the air 

eventually settles into water bodies or onto land where it can either move directly or be leached 

into waterbodies. Once deposited, certain microorganisms can change it into methylmercury, a 

highly toxic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish 

and shellfish are the main sources of methylmercury exposure to humans. Methylmercury builds 

up more in some types of fish and shellfish than in others. The levels of methylmercury in fish 

and shellfish vary widely depending on what they eat, how long they live, and how high they are 

in the food chain. Most fish, including ocean species and local freshwater fish, contain some 

methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) 

of fish tissues, every fish sampled contained some methylmercury (Scudder, 2009). 

The majority of fish consumed in the U.S. are ocean species. The methylmercury 

concentrations in ocean fish species are primarily influenced by the global mercury pool. 

However, the methylmercury found in local fish can be due, at least partly, to mercury emissions 

from local sources. Research shows that most people’s fish consumption does not cause a 

mercury-related health concern. However, certain people may be at higher risk because of their 

routinely high consumption of fish (e.g., tribal and other subsistence fishers and their families 

who rely heavily on fish for a substantial part of their diet). It has been demonstrated that high 

levels of methylmercury in the bloodstream of unborn babies and young children may harm the 

developing nervous system, making the child less able to think and learn. Moreover, mercury 

exposure at high levels can harm the brain, heart, kidneys, lungs, and immune system of people 

of all ages. 
6-31 

Several studies suggest that the methylmercury content of fish may reduce these cardioprotective effects of fish consumption. Some of these studies also suggest that methylmercury 

may cause adverse effects to the cardiovascular system. For example, the NRC (2000) review of 

the literature concerning methylmercury health effects took note of two epidemiological studies 

that found an association between dietary exposure to methylmercury and adverse cardiovascular 

effects.20 Moreover, in a study of 1,833 males in Finland aged 42 to 60 years, Solonen et al. 

(1995) observed a relationship between methylmercury exposure via fish consumption and acute 

myocardial infarction (AMI or heart attacks), coronary heart disease, cardiovascular disease, and 

all-cause mortality.21 The NRC also noted a study of 917 seven year old children in the Faroe 

Islands, whose initial exposure to methylmercury was in utero although post natal exposures may 

have occurred as well. At seven years of age, these children exhibited an increase in blood 

pressure and a decrease in heart rate variability.22 Based on these and other studies, NRC 

concluded in 2000 that, while “the data base is not as extensive for cardiovascular effects as it is 

for other end points (i.e. neurologic effects) the cardiovascular system appears to be a target for 

methylmercury toxicity.”23 

Since publication of the NRC report there have been some 30 published papers 

presenting the findings of studies that have examined the possible cardiovascular effects of 

methylmercury exposure. These studies include epidemiological, toxicological, and 

toxicokinetic investigations. Over a dozen review papers have also been published. If there is a 

causal relationship between methylmercury exposure and adverse cardiovascular effects, then 

reducing exposure to methylmercury would result in public health benefits from reduced 

cardiovascular effects. 

In early 2010, EPA sponsored a workshop in which a group of experts were asked to 

assess the plausibility of a causal relationship between methylmercury exposure and 

cardiovascular health effects and to advise EPA on methodologies for estimating population 

 

20 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies 

Press. Washington, DC. pp.168-173. 21Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995. “Intake of mercury from fish lipid peroxidation, and the risk of 

myocardial infarction and coronary, cardiovascular and any death in Eastern Finnish men.” Circulation, 91 

(3):645-655. 22Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P., 1999. “Prenatal Methylmercury 

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375. 23National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the 

Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies 

Press. Washington, DC. p. 229. 
6-32 

level cardiovascular health impacts of reduced methylmercury exposure. The report from that 

workshop is in preparation. 

Portland cement manufacturing plants emitted about 16 tons of mercury in the air in 2006 

in the U.S. Based on the EPA’s National Emission Inventory, and about 103 tons of mercury 

were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations 

has estimated that about 2,100 tons of mercury were emitted worldwide by anthropogenic 

sources in 2005. We believe that total mercury emissions in the U.S. and globally in 2006 were 

about the same magnitude in 2005. Therefore, we estimate that in 2006, these sources emitted 

about 16% of the total anthropogenic mercury emissions in the U.S. and about 0.8% of the global 

emissions in 2005. 

Using 2008 inventory estimates, the mercury emissions from Portland cement kilns only 

were approximately 9.1 tons. Overall, the NESHAP and NSPS would reduce mercury emissions 

by about 8.2 tons (90%) per year from current levels, and therefore, contribute to reductions in 

mercury exposures and health effects. Due to time and resource limitations, we were unable to 

model mercury methylation, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits from 

reducing mercury emissions. However, we were able to model the change in mercury deposition 

using CAMx for the final Portland Cement NESHAP and NSPS.24 These modeling results 

indicate significantly reduced total mercury deposition (wet and dry forms), including reducing 

deposition by up to 30% in the West and up to 17% in the East in 2013. This modeling indicates 

that mercury deposition reductions tend to be greatest nearest the sources. Figure 6-10 shows the 

change in mercury deposition as a result of the final Portland Cement NESHAP and NSPS in the 

Eastern U.S., and Figure 6.11 shows the change in mercury deposition in the Western U.S. 

 

24 See Section 5 of this RIA for more information on the air quality modeling. 
6-33 

Figure 6-10. Reductions in Total Mercury Deposition (μg/m2

) in the Eastern U.S. 
6-34 

Figure 6-11. Reductions in Total Mercury Deposition (μg/m2) in the Western U.S. 

6.5.2.2 Hydrogen Chloride (HCl) 25

Hydrogen chloride gas is intensely irritating to the mucous membranes of the nose, 

throat, and respiratory tract. Brief exposure to 35 ppm causes throat irritation, and levels of 50 to 

100 ppm are barely tolerable for 1 hour. The greatest impact is on the upper respiratory tract; 

exposure to high concentrations can rapidly lead to swelling and spasm of the throat and 

suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue 

coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures 

may develop an accumulation of fluid in the lungs. Exposure to hydrogen chloride can lead to 

Reactive Airway Dysfunction Syndrome (RADS), a chemically- or irritant-induced type of 

asthma. Children may be more vulnerable to corrosive agents than adults because of the 

relatively smaller diameter of their airways. Children may also be more vulnerable to gas 

 

25 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). Medical Management Guidelines for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S. 

Department of Health and Human Services, Public Health Service. Available on the Internet at 

<http://www.atsdr.cdc.gov/Mhmi/mmg173.html>. 
6-35 

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly 

when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 

6.5.2.3 Toluene26

Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under 

the 2005 Guidelines for Carcinogen Risk Assessment, there is inadequate information to assess 

the carcinogenic potential of toluene because studies of humans chronically exposed to toluene 

are inconclusive, toluene was not carcinogenic in adequate inhalation cancer bioassays of rats 

and mice exposed for life, and increased incidences of mammary cancer and leukemia were 

reported in a lifetime rat oral bioassay. 

The central nervous system (CNS) is the primary target for toluene toxicity in both 

humans and animals for acute and chronic exposures. CNS dysfunction (which is often 

reversible) and narcosis have been frequently observed in humans acutely exposed to low or 

moderate levels of toluene by inhalation; symptoms include fatigue, sleepiness, headaches, and 

nausea. Central nervous system depression has been reported to occur in chronic abusers exposed 

to high levels of toluene. Symptoms include ataxia, tremors, cerebral atrophy, nystagmus 

(involuntary eye movements), and impaired speech, hearing, and vision. Chronic inhalation 

exposure of humans to toluene also causes irritation of the upper respiratory tract, eye irritation, 

dizziness, headaches, and difficulty with sleep. 

Human studies have also reported developmental effects, such as CNS dysfunction, 

attention deficits, and minor craniofacial and limb anomalies, in the children of women who 

abused toluene during pregnancy. A substantial database examining the effects of toluene in 

subchronic and chronic occupationally exposed humans exists. The weight of evidence from 

these studies indicates neurological effects (i.e., impaired color vision, impaired hearing, 

decreased performance in neurobehavioral analysis, changes in motor and sensory nerve 

conduction velocity, headache, dizziness) as the most sensitive endpoint. 

6.5.2.4 Formaldehyde 

Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on 

evidence in humans and in rats, mice, hamsters, and monkeys.27 EPA is currently reviewing 

 

26 All health effects language for this section came from: U.S. EPA. 2005. “Full IRIS Summary for Toluene 

(CASRN 108-88-3)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Office of 

Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH. Available 

on the Internet at <http://www.epa.gov/iris/subst/0118.htm>. 27 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to 
6-36 

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.28,29 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.30 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.31 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.32

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.33,34,35 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.36,37,38,39 These findings are not supportive of interpreting the CIIT model results as 

 Formaldehyde, Office of Pesticides and Toxic Substances, April 1987. 28 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. 29 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. 30 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. 31 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. 

Environ. Med. 61: 193-200. 32 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. 33 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. 34 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. 35 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. 36 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, 2006 37 Subramaniam, R; Chen, C; Crump, K; .et .al. (2006) Uncertainties in biologically-based modeling of 

formaldehyde-induced cancer risk: identification of key issues. Risk Anal 28(4):907-923. 
6-37 

providing a conservative (health protective) estimate of human risk.40 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.41

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.”42 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.43,44

 38 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 39 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 40 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of biologically motivated model for formaldehydeinduced respiratory cancer in humans. Ann Occup Hyg 52:481-495. 41 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 42 International Agency for Research on Cancer (2006) Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxypropan-2-

ol. Monographs Volume 88. World Health Organization, Lyon, France. 43 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 44 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 
6-38 

6.5.2.5 Dioxins (Chlorinated dibenzodioxins (CDDs) 45

A number of effects have been observed in people exposed to 2,3,7,8-TCDD levels that 

are at least 10 times higher than background levels. The most obvious health effect in people 

exposure to relatively large amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin 

disease with acne-like lesions that occur mainly on the face and upper body. Other skin effects 

noted in people exposed to high doses of 2,3,7,8-TCDD include skin rashes, discoloration, and 

excessive body hair. Changes in blood and urine that may indicate liver damage also are seen in 

people. Alterations in the ability of the liver to metabolize (or breakdown) hemoglobin, lipids, 

sugar, and protein have been reported in people exposed to relatively high concentrations of 

2,3,7,8-TCDD. Most of the effects are considered mild and were reversible. However, in some 

people these effects may last for many years. Slight increases in the risk of diabetes and 

abnormal glucose tolerance have been observed in some studies of people exposed to 2,3,7,8-

TCDD. We do not have enough information to know if exposure to 2,3,7,8-TCDD would result 

in reproductive or developmental effects in people, but animal studies suggest that this is a 

potential health concern. 

In certain animal species, 2,3,7,8-TCDD is especially harmful and can cause death after a 

single exposure. Exposure to lower levels can cause a variety of effects in animals, such as 

weight loss, liver damage, and disruption of the endocrine system. In many species of animals, 

2,3,7,8-TCDD weakens the immune system and causes a decrease in the system's ability to fight 

bacteria and viruses at relatively low levels (approximately 10 times higher than human 

background body burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused 

reproductive damage and birth defects. Some animal species exposed to CDDs during pregnancy 

had miscarriages and the offspring of animals exposed to 2,3,7,8-TCDD during pregnancy often 

had severe birth defects including skeletal deformities, kidney defects, and weakened immune 

responses. In some studies, effects were observed at body burdens 10 times higher than human 

background levels. 

 World Health Organization, and produced within the framework of the Inter-Organization Programme for the 

Sound Management of Chemicals. Geneva. 45 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6). 

Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at 

http://www.atsdr.cdc.gov/tfacts104.html. 
6-39 

6.5.2.6 Furans (Chlorinated dibenzofurans (CDFs)) 46

Most of the information on the adverse health effects comes from studies in people who 

were accidentally exposed to food contaminated with CDFs. The amounts that these people were 

exposed to were much higher than are likely from environmental exposures or from a normal 

diet. Skin and eye irritations, especially severe acne, darkened skin color, and swollen eyelids 

with discharge, were the most obvious health effects of the CDF poisoning. CDF poisoning also 

caused vomiting and diarrhea, anemia, more frequent lung infections, numbness, effects on the 

nervous system, and mild changes in the liver. Children born to exposed mothers had skin 

irritation and more difficulty learning, but it is unknown if this effect was permanent or caused 

by CDFs alone or CDFs and polychlorinated biphenyls in combination. 

Many of the same effects that occurred in people accidentally exposed also occurred in 

laboratory animals that ate CDFs. Animals also had severe weight loss, and their stomachs, 

livers, kidneys, and immune systems were seriously injured. Some animals had birth defects and 

testicular damage, and in severe cases, some animals died. These effects in animals were seen 

when they were fed large amounts of CDFs over a short time, or small amounts over several 

weeks or months. Nothing is known about the possible health effects in animals from eating 

CDFs over a lifetime. 

6.5.2.7 Benzene 

According to NATA for 2002, benzene is the largest contributor to cancer risk of all 124 

pollutants quantitatively assessed in the 2002 NATA.47 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.48 EPA states in its IRIS 

database that data indicate a causal relationship between benzene exposure and acute 

 

46 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry 

(ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and 

Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/tfacts32.html>. 47 U.S. EPA. (2009) 2002 National-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 48 U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is available electronically at: 

http://www.epa.gov/iris/subst/0276.htm. 

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. 

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. 
6-40 

lymphocytic leukemia and suggest a relationship between benzene exposure and chronic nonlymphocytic 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.49 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.50 The most sensitive noncancer effect observed in humans, based on current data, is the 

depression of the absolute lymphocyte count in blood.51 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.52 EPA’s 

IRIS program has not yet evaluated these new data. 

6.5.2.8 Other Air Toxics 

In addition to the compounds described above, other compounds in gaseous hydrocarbon 

and PM emissions would be affected by this rule, including metal and organic HAPs. 

Information regarding the health effects of these compounds can be found in EPA’s IRIS 

database.53

 

49 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. 

U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens 

available at: http://ntp.niehs.nih.gov/go/16183. 50 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Environ. Health Perspect. 82: 193-197. 

Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Reviews. 3: 541-554. 51 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. 

U.S. EPA 2002 Toxicological Review of Benzene (Noncancer Effects). Environmental Protection Agency, 

Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental 

Assessment, Washington DC. This material is available electronically at http://www.epa.gov/iris/subst/0276.htm. 52 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. 

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. 

Lan, Qing, Zhang, L., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers Exposed to Low Levels of 

Benzene. Science 306: 1774-1776. 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. 53 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris 
6-41 

6.6 Limitations and Uncertainties

The National Research Council (NRC) (2002) concluded that EPA’s general 

methodology for calculating the benefits of reducing air pollution is reasonable and informative 

in spite of inherent uncertainties. To address these inherent uncertainties, NRC highlighted the 

need to conduct rigorous quantitative analysis of uncertainty and to present benefits estimates to 

decisionmakers in ways that foster an appropriate appreciation of their inherent uncertainty. In 

response to these comments, EPA’s Office of Air and Radiation (OAR) is developing a 

comprehensive strategy for characterizing the aggregate impact of uncertainty in key modeling 

elements on both health incidence and benefits estimates. Components of that strategy include 

emissions modeling, air quality modeling, health effects incidence estimation, and valuation. 

In this analysis, we use three methods to assess uncertainty quantitatively: Monte Carlo 

analysis, alternate concentration-response functions for PM mortality, and LML assessment. We 

also provide a qualitative assessment for those aspects that we are unable to address 

quantitatively in this analysis. Each of these analyses is described in detail in the following 

sections. 

This analysis includes many data sources as inputs, including emission inventories, air 

quality data from models (with their associated parameters and inputs), population data, health 

effect estimates from epidemiology studies, and economic data for monetizing benefits. Each of 

these inputs may be uncertain and would affect the benefits estimate. When the uncertainties 

from each stage of the analysis are compounded, small uncertainties can have large effects on the 

total quantified benefits. In this analysis, we are unable to quantify the cumulative effect of all 

of these uncertainties, but we provide the following analyses to characterize many of the largest 

sources of uncertainty. 

6.6.1 Monte Carlo analysis 

Similar to other recent RIAs, we used Monte Carlo methods for characterizing random 

sampling error associated with the concentration response functions and economic valuation 

functions. Monte Carlo simulation uses random sampling from distributions of parameters to 

characterize the effects of uncertainty on output variables, such as incidence of morbidity. 

Specifically, we used Monte Carlo methods to generate confidence intervals around the 

estimated health impact and dollar benefits. The reported standard errors in the epidemiological 

studies determined the distributions for individual effect estimates, as shown in Tables 6-2 and 

6-3. 
6-42 

6.6.2 Alternate concentration-response functions for PM mortality 

PM2.5 mortality benefits are the largest benefit category that we monetized in this 

analysis. To better understand the concentration-response relationship between PM2.5 exposure 

and premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 

2006). In general, the results of the expert elicitation support the conclusion that the benefits of 

PM2.5 control are very likely to be substantial. In previous RIAs, EPA presented benefits 

estimates using concentration response functions derived from the PM2.5 Expert Elicitation as a 

range from the lowest expert value (Expert K) to the highest expert value (Expert E). However, 

this approach did not indicate the agency’s judgment on what the best estimate of PM benefits 

may be, and EPA’s Science Advisory Board described this presentation as misleading. 

Therefore, we began to present the cohort-based studies (Pope et al, 2002; and Laden et al., 

2006) as our core estimates in the proposal RIA for this rule (U.S. EPA, 2009a). 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 the two 

epidemiology-based estimates (Roman et al., 2008). 

In this analysis, we present the results derived from the expert elicitation as indicative of 

the uncertainty associated with a major component of the health impact functions, and we 

provide the independent estimates derived from each of the twelve experts to better characterize 

the degree of variability in the expert responses. In this section, we provide the results using the 

concentration-response functions derived from the expert elicitation in both tabular (Tables 6-2 

and 6-3) and graphical form (Figure 6-5). Please note that these results are not the direct results 

from the studies or expert elicitation; rather, the estimates are based in part on the concentrationresponse function provided in those studies. 

6.6.3 LML assessment 

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. 

We have more confidence in the mortality impacts among populations exposed to levels of PM2.5

above the lowest LML of the large cohort studies, and our confidence in the results diminish as 

we model that are lower than the LML. 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 just because we have greater 

confidence in the benefits above the LML, this does not mean that we have no confidence that 
6-43 

benefits occur below the LML. In section 6.3, we provide the results of the LML assessment in 

Figures 6-6 and 6-7. 

6.6.4 Qualitative assessment of uncertainty and other analysis limitations 

Although we strive to incorporate as many quantitative assessments of uncertainty, there 

are several aspects for which we are only able to address qualitatively. These aspects are 

important factors to consider when evaluating the relative benefits of the attainment strategies for 

each of the alternative standards: 

Above we present the estimates of the total monetized benefits, based on our 

interpretation of the best available scientific literature and methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estimates are subject to a number of assumptions 

and uncertainties. For example, the key assumptions underlying the estimates for premature 

mortality, which typically account for at least 90% of the total monetized benefits, include the 

following: 

1. 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. 

2. 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 

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. 

3. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality (which typically accounts for 85% to 95% of total monetized 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 benefits, please consult the 

PM2.5 NAAQS RIA (Table 5.5). 

In addition, there is some uncertainty associated with the specificity of the air quality 

inputs to benefits model for this particular regulatory scenario. By assuming that each kiln 
6-44 

proportionately reduces their emissions by the same percentage as the national percentage 

reduction, we may be slightly under or overestimating the air quality impacts at specific 

locations and the associated monetized benefits. By including the hazardous waste kilns in the 

emissions inventory, we may be slightly overestimating the air quality impacts and monetized 

benefits. By omitting the decrease in domestic cement production and transportation, we are 

underestimating the air quality impacts and monetized benefits. By omitting the increase in 

cement imports, we may be overestimating the monetized benefits by not accounting for 

additional global pollutants. By using national average benefit-per-ton estimates to calculate the 

energy disbenefits, we may be under or overestimating these monetized disbenefits. Despite our 

inability to fully characterize and quantify these relatively small effects, we believe that, on net, 

the air quality impacts and associated monetized benefits are representative of the magnitude of 

benefits anticipated from this regulation. 

As previously described, we strive to monetize as many of the benefits anticipated from 

this rule as possible, but the monetized benefits estimated in this RIA inevitably only reflect the 

portion of benefits. Specifically, only the benefits attributable to the health impacts associated 

with exposure to ambient fine particles have been monetized in this analysis. 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. Data limitations include limited monitoring for 

HAPs, incomplete emissions inventories for HAPs, and limited photochemical air quality 

modeling for non-mercury HAPs. Resource limitations include limited staff and extramural 

funding in conjunction with a heavy regulatory workload. Methodological limitations include an 

absence of concentration-response functions for many HAP health effects, with issues such as 

exposure misclassification, small number of cases, confounding, and extrapolation of 

toxicological effects down to ambient levels (IEc, 2008). Despite our inability to monetize all of 

the benefit categories, the monetized benefits still exceed the costs by a substantial margin. 

This RIA does not include the type of detailed uncertainty assessment found in the PM 

NAAQS RIA. However, the results of the Monte Carlo analyses of the health and welfare 

benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty 

surrounding the benefits results presented in this analysis. 

6.7 Comparison of Benefits and Costs 

Using a 3% discount rate, we estimate the total monetized benefits of the final Portland 

Cement NESHAP and NSPS to be $7.4 billion to $18 billion in the implementation year (2013). 
6-45 

Using a 7% discount rate, we estimate the total monetized benefits of the final Portland Cement 

NESHAP and NSPS to be $6.7 billion to $16 billion. These estimates include the energy 

disbenefits associated with increased electricity usage by the control devices. The annualized 

social costs of the final NESHAP and NSPS are $880 million.54 Thus, net benefits are $6.5 

billion to $17 billion at a 3% discount rate for the benefits and $5.8 billion to $16 billion at a 7% 

discount rate. In addition, the benefits from reducing 16,400 pounds of mercury, 4,400 tons of 

NOx, 5,800 tons of HCl, and 5,200 tons of organic HAPs each year have not been included in 

these estimates. All estimates are in 2005$. 

Table 6-5 shows a summary of the monetized benefits, social costs, and net benefits for 

the final Portland Cement NESHAP and NSPS, the final NSPS only, the final NESHAP only, 

and the more stringent NSPS and final NESHAP. Figures 6-12 and 6-13 show the full range of 

net benefits estimates (i.e., annual benefits minus annualized costs) utilizing the 14 different 

PM2.5 mortality functions at discount rates of 3% and 7%. 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. EPA believes that the benefits are likely to exceed the costs under this 

rulemaking even when taking into account uncertainties in the cost and benefit estimates. 

 

54 For more information on the annualized costs, please refer to Section 4 of this RIA. 
6-46 

Table 6-8. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the 

final Portland Cement NESHAP in 2013 (millions of 2005$)1

Final NESHAP and NSPS 

3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $7,400 to $18,000 $6,700 to $16,000 

Total Social Costs3 $926 to $950 $926 to $950 

Net Benefits $6,500 to $17,000 $5,800 to $16,000 

Non-monetized Benefitsd

4,400 tons of NOx (includes energy disbenefits) 

5,200 tons of organic HAPs 

5,900 tons of HCl 

16,400 pounds of mercury 

Health effects from HAPs, NO2, and SO2 exposure 

Ecosystem effects 

Visibility impairment 

Final NSPS only

 3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $510 to $1,300 $460 to $1,100 

Total Social Costs3 $72 $40 

Net Benefits $470 to $1,300 $420 to $1,100 

Non-monetized Benefitsd

6,600 tons of NOx

520 tons of HCl 

Health effects from HAPs, NO2, and SO2 exposure 

Ecosystem effects 

Visibility impairment 

Final NESHAP only 

3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $7,400 to $18,000 $6,700 to $16,000 

Total Social Costs3 $904 to $930 $904 to $930 

Net Benefits $6,500 to $17,000 $5,800 to $16,000

Non-monetized Benefitsd

5,200 tons of organic HAPs 

5,900 tons of HCl 

16,400 pounds of mercury 

Health effects from HAPs, SO2 exposure 

Ecosystem effects 

Visibility impairment 

Alternative: More Stringent NSPS and Final NESHAP 

3% Discount Rate 7% Discount Rate 

Total Monetized Benefits2 $7,400 to $18,000 $6,700 to $16,000 

Total Social Costs3 $955 to $979 $955 to $979 

Net Benefits $6,500 to $17,000 $5,700 to $15,000 

Non-monetized Benefits4

7,800 tons of NOx (includes energy disbenefits) 

5,200 tons of organic HAPs 

5,900 tons of HCl 

16,400 pounds of mercury 

Health effects from HAPs, NO2, and SO2 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 benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of directly emitted PM2.5 

and PM2.5 precursors such as SO2. 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. The total monetized benefits include the energy disbenefits. 

3

 The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both 

discount rates. 

4 Due to data, methodology, and resource limitations, we were unable to monetize the benefits associated with these categories of benefits. 
6-47 

$0

$5,000

$10,000

$15,000

$20,000

$25,000

Pope et al.

Laden et al.

Millions(2005$)

Cost estimate combined with total monetized benefits estimates derived from2

epidemiology functions and 12 expert functions

Figure 6-12. Net Benefits for the Final Portland Cement NESHAP and NSPS at 3% 

Discount Rate a

a

 Net Benefits are quantified in terms of PM2.5 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 benefits incorporate the conversion from 

precursor emissions to ambient fine particles. These net benefits include the energy disbenefits. Due to data, 

methodology, and resource limitations, we were unable to monetize the benefits associated with several categories of 

benefits, including exposure to HAPs, NO2, and SO2, ecosystem effects, and visibility effects. 
6-48 

$0

$5,000

$10,000

$15,000

$20,000

$25,000

Pope et al.

Laden et al.

Millions(2005$)

Cost estimate combined with total monetized benefits estimates derived from2

epidemiology functions and 12 expert functions

Figure 6-13. Net Benefits for the Final Portland Cement NESHAP and NSPS at 7% 

Discount Rate a

a

 Net Benefits are quantified in terms of PM2.5 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 benefits incorporate the 

conversion from precursor emissions to ambient fine particles. These net benefits include the energy disbenefits. Due 

to data, methodology, and resource limitations, we were unable to monetize the benefits associated with several 

categories of benefits, including exposure to HAPs, NO2, and SO2, ecosystem effects, and visibility effects.
7-1 

SECTION 7

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7-8 

Available on the Internet at 

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Existence of a Threshold in the Concentration-Response Function for PM2.5-related 

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the Health Effects Subcommittee of the Advisory Council on Clean Air Compliance 

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0433D5D/$File/council_adv_04002.pdf>. 

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October. Available at 

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unsigned.pdf>. 
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Scope and Methods Plan for Health Risk and Exposure Assessment. EPA-COUNCIL-09-

009. May. Available on the Internet at 

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6/$File/EPA-COUNCIL-10-001-unsigned.pdf>. 

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Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76. 
APPENDIX A 

SHORT-RUN REGIONAL PORTLAND CEMENT ECONOMIC MODEL 
A-1 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standardindustry level analysis described in the Office’s resource manual (EPA, 1999a). This approach is 

consistent with previous EPA analyses of the Portland cement industry (EPA, 1998; EPA, 

1999b) and uses a single-period static partial-equilibrium model to compare prepolicy cement 

market baselines with expected postpolicy outcomes in these markets. The benchmark time 

horizon for the analysis is the intermediate run where producers have some constraints on their 

flexibility to adjust factors of production. This time horizon allows us to capture important 

transitory impacts of the program on existing producers. Key measures in this analysis include 

 market-level effects (market prices, changes in domestic production and 

consumption, and international trade), 

 industry-level effects (changes in operating profits and employment), 

 facility-level effects (plant utilization changes), and 

 social costs (changes in producer and consumer surplus). 

In this appendix, we provide additional details about economic model updates, model 

equations and parameters. 

A.1 Economic Impact Model Updates Since Proposal 

The need for a complete set of statistics makes the use of a 2005 baseline the best choice 

for a typical year. At the time of proposal model development, it was the latest year for which the 

PCA had published their plant information summary and complete statistics for updating variable 

cost functions were available. Details of model development are provided in EPA (2009), 

Appendix A. Since proposal, EPA identified several plants where operations had changed (see 

Table A-1). As a result, EPA modified the baseline U.S. production quantities to approximate 

these changes and maintain consistency with 2005 market conditions (Table A-2). 

EPA also recognizes that the demand for cement is a derived demand because it depends 

on demand for sectors such as housing and construction. As a result, business cycles also 

significantly influence the cement industry (see Table A-3). If 2013 is more or less favorable for 

the cement industry than 2005, then impacts would be expected to change accordingly. 
A-2 

Table A-1. Economic Model Population Updates: 2005 

Market 

Approximate Clinker 

Capacity Removed 

(thousand metric tons) Description 

Approximate Clinker 

Capacity Added 

(thousand metric tons)

Net Change 

in Market 

Plant 

Population 

Atlanta 300 Closure 0 −1 

Baltimore/Philadelphia 500 Closure 400 −1 

Chicago 600 Replacement 1,100 0 

Dallas 800 Replacement 800 0 

Detroit 900 Closure 0 -1 

Kansas City 300 Closure 0 −1 

Los Angeles 1,100 Replacement 2,200 0 

Phoenix 600 Replacement 700 +1 

San Antonio 300 Closure 0 0 

St. Louis 500 Replacement 1,200 0 
A-3 

Table A-2. Revised Portland Cement Markets (106 metric tons): 2005 

Market 

U.S. Production 

Proposal 

U.S. Production 

Revised Difference 

Atlanta 6.1 5.8 −0.3 

Baltimore/Philadelphia 8.0 7.8 −0.2 

Birmingham 5.9 5.9 0.0 

Chicago 4.3 4.7 0.4 

Cincinnati 3.7 3.7 0.0 

Dallas 8.2 8.1 −0.1

Denver 3.4 3.4 0.0 

Detroit 4.8 3.8 −1.0 

Florida 5.6 5.5 −0.1 

Kansas City 5.3 5.0 −0.3 

Los Angeles 9.6 10.6 1.0 

Minneapolis 1.7 1.7 0.0 

New York/Boston 3.2 3.2 0.0 

Phoenix 4.1 4.3 0.2 

Pittsburgh 1.5 1.5 0.0 

St. Louis 5.4 6.0 0.6 

Salt Lake City 2.4 2.4 0.0 

San Antonio 5.7 5.5 −0.2 

San Francisco 3.4 3.4 0.0 

Seattle 1.1 1.1 0.0 

Total, Grey 93.6 93.6 0.0 

Source: EPA calculations. 

Table A-3. Recent Market Trends 

Economic Variable 2005 2006 2007 2008 2009a

Clinker production 

(million metric tons) 

87 89 86 78 58 

Price, average mill 

value ($/metric ton) 

$91 $102 $104 $103 $100 

Employment 

(thousand) 

16 16 16 15 14 

Share of 

consumption 

provided by imports 

(percent) 

25 27 19 11 8 

a

estimated. 

Source: USGS, 2010. Mineral Commodity Survey 2010. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2010-cemen.pdf 

Comment [A1]: Added number to table 

without track changes. 
A-4 

A.2 Partial Equilibrium Model 

The partial equilibrium analysis performed for this rule uses the cement market model 

developed during proposal (U.S. EPA, 2009). The model simulates how stakeholders (consumers 

and firms) may respond to the additional regulatory program costs. In the near term, the regional 

cement markets are assumed to have few sellers that offer similar/identical products. As a result, 

EPA used an oligopoly market structure1

. As described in Section 3, this market structure 

assumption suggests that the observed baseline market price will be higher than marginal 

production costs (i.e., there may be a preexisting market distortion prior to regulation). To 

provide some intuition about factors that influence the size of the existing distortion, we express 

a seller’s “best” supply decision as a function of the market price, the seller’s market share, the 

market demand elasticity, and the seller’s marginal costs (see Varian [1992], pp. 289–290): 

Price × (1 + Market Sharei/Demand Elasticity) = Marginal Costi. 

This equation shows the relationship between the oligopoly model and perfect competition. The 

market distortion will typically be higher when market sharei is high (there are few sellers) and 

in markets where the quantity demanded is less sensitive to price (i.e., the demand elasticity is 

inelastic). 

A.2.1 Model Equations 

To estimate the economic impacts of the regulation, EPA used four linear equations to 

calculate the following unknown variables: 

 change in domestic plant production (dqi), 

 change in imports (dqimports), 

 change in cement market quantity (dQ), and 

 change in cement price (dP). 

Equation 1: Domestic Supply. For each plant, we describe its response to the regulatory 

program as follows. The total compliance cost per ton (ci) is applied to each kiln, and the 

difference in the highest cost kiln with-regulation and the highest cost kiln in the baseline 

approximates the plant’s change in the marginal cost of production (dMCi). In with-regulation 

 

1

 There are different commonly used models of oligopoly in the economics literature. They differ with respect to the 

assumption about how a company believes competing companies will react to its own production decision. EPA 

selected the Cournot model where the company assumes competing companies’ output is fixed in its own 

production decision. 
A-5 

equilibrium, the change in marginal revenue (dMRi) must equal the change in the marginal cost 

(dMCi) for each plant.1

dmarginal Revenuei = dmarginal Costi

or 

( ) cost dmarginal elasticity dem

price qplant Qmarket 

Qdmarket 

elasticity dem

price

Qmarket 

qdplant 

elasticity dem

sharemkt 1dprice 2

i + × − ×× = ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ +×

Equation 2: Supply of Imports. If applicable to the market, an equation describing the supply 

of cement from other countries was included: 

dimports = import supply elasticity × (dprice/baseline price) × baseline imports. 

For import supply, EPA used the latest empirical work on how other countries who export (i.e., 

supply imports) to the United States respond to price changes. Broda et al. (2008) report that the 

export supply elasticity for commodities imported by the United States was approximately two. 

This implies that a 1% increase in prices results in a 2% increase in the volume of exports for a 

typical good. 

Equation 3: Market Supply. Market supply of Portland cement equals the change in domestic 

production and imports: 

dmarket Q = dtotal domestic production + dimports. 

This condition ensures that the market quantity is consistent with the individual supply decisions 

of domestic plants and imports in the new with-regulation equilibrium for each regional market. 

Equation 4: Market Demand. The demand for Portland cement is derived from the demand for 

concrete products, which, in turn, is derived largely from the demand for construction. Based on 

a linear demand equation, the market demand condition for Portland cement must hold based on 

the projected change in market price, that is, 

dMarketQ = demand elasticity (dprice/baseline price) × baseline consumption. 

The use of published estimates from previous rulemakings is appropriate in cases when the cost 

of preparing original estimates is high (EPA, 2000). In previous analyses, EPA econometrically 

 

1

 To highlight and make transparent the assumptions regarding seller behavior, this equation is formally derived in 

Appendix B. 
A-6 

estimated the demand elasticity for cement and reported a point estimate of −0.88 (EPA, 1998). 

This value suggests that a 1% increase in the cement price would lead to a 0.88% reduction in 

cement consumption. 
APPENDIX B 

THE CEMENT PLANT’S PRODUCTION DECISION: 

A MATHEMATICAL REPRESENTATION 
B-1 

This appendix provides additional detail about the cement ’s production decision used in 

the economic model (see Equation 1 in Section 3 of the RIA). Table B-1 identifies and describes 

the key variables of the cement plant’s profit function. 

Table B-1. Variable Descriptions 

P Market price 

Q=∑ i q Market output 

qi Domestic plant i’s output 

FCi Plant fixed costs 

VCi Plant variable costs 

Step 1: First, we assume the plant’s goal is to maximize profits: 

( ) ( ) i = − − FCqVCqQPQ iiii max )( qi

π . 

Step 2: The first-order conditions for a profit maximum are: 

( ) 0q

q

qVC

q

q

P(Q) P

q

π i

i

ii i

i i

i = ∂ − ∂

∂ += ∂ . 

Step 3: Apply two key assumptions in the Cournot price model: 

 Plant’s (i) recognizes its own production decisions influence the market price: 

0

q

P

i

≠ ∂

∂

 Plant (i) output decisions do not affect those of any other plant (j) (e.g., there is no 

strategic action among cement plants): 

0

q

q

i

j = ∂

∂

Step 4: Next, multiply second term by 

Q

P

P

Q 1 =
B-2 

( ) 0q

q

qVC

Q

P

P

Q q

q

P(Q) P i

i

ii i

i

= ∂ −⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂ + . 

Step 5: Rearranging terms: 

( ) 0q

q

qVC P

Q

q

q

Q

P

P(Q) P i

i

i ii

i

= ∂ − ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂ + . 

Step 6: Use and apply the following definitions: 

elasticity demand inverse 1

q

Q

P

P(Q)

i

==⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

∂

∂

η

Qq market splant' share

Q

q 1 i i =⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − . 

We derive the following expression: 

( )

i

ii

i

q

Q qVC

q

1 ∂ =

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

+

η

P . 

Step 7: The total differential of this equation is determined and gives us the optimal 

decision rule for the plant: 

dMC Q 1

q

1 2

i

=⎥

⎦

⎤ ⎢

⎣

⎡ −⎥

⎦

⎤ ⎢

⎣

⎡

+

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

+

Q

P q dQ QP dP dq i

i η η η . 
APPENDIX C 

SOCIAL COST METHODOLOGY 
C-1 

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standard 

industry-level analysis described in the Office’s resource manual (EPA, 1999a). This approach is 

consistent with previous EPA analyses of the Portland cement industry (EPA, 1998; EPA, 

1999b) and uses a single-period static partial-equilibrium model to compare prepolicy cement 

market baselines with expected postpolicy outcomes in these markets. The benchmark time 

horizon for the analysis is the intermediate run where producers have some constraints on their 

flexibility to adjust factors of production. This time horizon allows us to capture important 

transitory impacts of the program on existing producers. The model provides an estimate of the 

social costs (changes in producer and consumer surplus) associated with controls applying to 

existing kilns (see Section 4). Since the social cost methodology is identical to the approach used 

in previous cement analysis (EPA, 1998, Appendix C), we have included elements of the 

previous report’s Appendix C in this RIA. 

Figure C-1 illustrates the conceptual framework for evaluating the social cost and 

distributive impacts under the imperfectly competitive structure of U.S. cement markets. The 

baseline equilibrium is given by the price, P0, and the quantity, Q0. Without the regulation, the 

total benefits of consuming cement are given by the area under the demand curve up to the 

market output, Q0. This equals the area filled by the letters ABCDEFGHIJ. The total variable 

cost to society of producing Q0 equals the area under the MC function, given by the area IJ. 

Thus, the total surplus value to society from the production and consumption of output level Q0

equals the total benefits minus the total costs, or the area filled by the letters ABCDEFGH. 

This total surplus value to society can be further divided into producer surplus and 

consumer surplus. Producer surplus accrues to the suppliers of cement and reflects the value they 

receive in the market for producing Q0 units of cement less their costs of production, i.e., their 

profits. As shown in Figure C-1, producer surplus is given by the area DEFGH, which is the 

difference between cement revenues (i.e., area DEFGHIJ) and production costs (area IJ). 

Consumer surplus accrues to the consumers of cement and reflects the value they place on 

consumption (total benefits of consumption) less what they must pay on the market, i.e., P0. 

Consumer surplus is thereby given by the area ABC. 
C-2 

Q1

P1

D

$

Output

P0

P1

c

P0

c

Q0

C B

A

D E

F

I J

H

G

MC + Control Costs

MR

MC

Figure C-1. Social Cost of Regulation Under Imperfect Competition 

The final rule will increase the marginal cost of producing cement and thereby shift this 

curve upward by the amount of the incremental compliance costs. As shown in Figure C-1, this 

results in a new market equilibrium that occurs at a higher market price for cement, P1, and a 

lower level of output, Q1. In this scenario, the total benefits of consumption are equal to area 

ABDFI and the total variable costs of production are equal to area FI. This yields a withregulation social surplus equal to area ABD with area BD representing the new producer surplus 

and area A being the new consumer surplus. The social cost of the regulation equals the total 

change in social surplus caused by the regulation. Therefore, the social cost of the regulation is 

represented by the area FGHEC in Figure C-1. 

The distributive effects are estimated by separating the social cost into producer surplus 

and consumer surplus losses. First, the change in producer surplus is given by 

ΔPS = B – F – (G+H+E) (C.1) 

Producers gain B from the increase in price (a transfer from consumers to producers), but lose F 

from the increase in production costs due to the incremental compliance costs. Furthermore, the 

reduction of cement production leads to foregone baseline profits of G+H+E. 
C-3 

The change in consumer surplus is given by 

ΔCS = – (B + C) (C.2) 

This change results from the reduction in consumer surplus from the baseline value of ABC to 

the with-regulation value of A. In this case, consumers lose area B as a transfer to producers 

through the increase in the price they pay for the with-regulation level of cement consumption, 

while the reduction in cement consumption due to regulation leads to foregone baseline value of 

consumption equal to area C. 

The social cost or total change in social surplus can then be derived simply by adding the 

changes in producer and consumer surplus, i.e., 

 Social Cost = ΔPS + ΔCS = – (F + G + H + E + C) (C.3) 

This estimate can be compared to the engineering estimate of incremental compliance cost to 

demonstrate the difference between these two estimates of social cost. The incremental 

compliance cost estimate is given by the area FGH, which is simply the constant cost per unit 

times the baseline output level of cement. The social cost estimate from Equation (C.3) above, 

however, exceeds the engineering estimate by the area EC. In other words, the incremental 

compliance cost estimate understates the social costs of the regulation. The reason for this 

follows directly from the imperfectly competitive structure of the markets for cement. A 

comparison with the outcome under perfect competition will assist in illustrating this point. 

Suppose that the MR curve in Figure C-1 was the demand function for a competitive 

market, rather than the marginal revenue function for an imperfectly competitive producer. 

Similarly, let the MC function be the aggregate supply function for all producers in the market. 

The market equilibrium is still determined at the intersection of MC and MR, but given the 

revised interpretation of MR as the competitive demand function, the baseline (competitive) 

market price, P0

C

, is now equal to MC and Q0 is now interpreted as the competitive level of 

cement demand. In this case, all social surplus goes to the consumer. This is because producers 

receive a price that just covers their costs of production. 

In the with-regulation perfectly competitive equilibrium, the market price would rise by 

the per unit control cost amount to P1

c

. The social cost of the regulation is given entirely by the 

loss in consumer surplus as given by area FG. As shown in Figure C-1, this estimate of social 

cost is less than the incremental compliance cost estimate (i.e., area FGH) so that the engineering 
C-4 

estimate overstates the social cost of the regulation under perfect competition. The overstatement 

results from the fact that the incremental compliance costs are estimated based on the baseline 

market level of cement output. With regulation, output is projected to decline to Q1, so that the 

actual incremental compliance costs incurred by the industry are given by area F. Area G 

represents the foregone value of cement consumption to consumers, also referred to as the 

deadweight loss (analogous to area C under the imperfect competition scenario). 

In addition, the estimate of social cost under perfect competition is less than the estimate 

under imperfect competition by the area HEC, i.e., 

 SCimp – SCperf = −[(F+G+H+E+C) – (F+G)] = −(H + E + C) (C.4) 

The difference between these two measures results from the fact that the price paid by consumers 

(i.e., marginal value to society for cement) exceeds the cost of producing cement (i.e., the 

marginal cost to society of producing cement). As shown in Figure C-1, this difference in social 

cost is equal to the area between the demand curve (D) and the marginal revenue curve (MR) 

that exist under imperfectly competitive market structure. This area does not exist under perfect 

competition because the MR curve is interpreted as the demand curve so that the price paid by 

consumers equals the marginal cost of producing cement. The pre-existing social inefficiency of 

imperfect competition is exacerbated as the regulation moves society further away from the 

socially optimal level of cement production, which results in social costs greater than the 

incremental compliance cost imposed on the cement industry. 
APPENDIX D 

SUMMARY OF EXPERT OPINIONS ON THE EXISTENCE OF A THRESHOLD IN 

THE CONCENTRATION-RESPONSE FUNCTION FOR PM2.5-RELATED 

MORTALITY 
D-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) 

July 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) 
D-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 
D-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
D-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 (eg, linear or log-linear) concentration-response 

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 
D-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 
D-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 
D-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 
D-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 
D-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 
D-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 
D-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 
D-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 
D-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
D-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 
D-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 
D-16 

Dr. Kathy Weathers, Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NY
D-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 
D-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
D-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.” 
D-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% sure that a threshold existed. If there were 

a threshold, he thought that there was an 80% chance that it would be less than or 

equal to 5 μg/m3

, and a 20% 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. 
D-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% 

sure that a threshold existed. If there were a threshold, he thought that there was an 80% chance 

that it would be less than or equal to 5 μg/m3

, and a 20% 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.” 
D-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
D-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 

Dr. Petros Koutrakis, Professor of Environmental Science, Environmental Health , School of Public 

Health, Harvard University (HSPH), Boston, MA 
D-24 

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 
D-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 
D-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 
D-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 long-term 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, 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 
D-28 

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. 

7.1.1.1 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 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 concentration-response 

functions. Typically, EPA and authors of epidemiological studies assume that these functions are linear 
D-29 

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.” 

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 
D-30 

Dr. PETER A. VALBERG, Gradient Corporation, Cambridge, Massachusetts 

Dr. SCOTT L. ZEGER, Johns Hopkins University, Baltimore, Maryland