### From: Tom Walton/RTP/USEPA/US To: "Higgins, Cortney" <Cortney_Higgins@omb.eop.gov>, "Higgins, Cortney" <Cortney_Higgins@omb.eop.gov> | Cc: Keith Barnett/RTP/USEPA/US@EPA, Charlotte Bertrand/DC/USEPA/US@EPA, Ron Evans/RTP/USEPA/US@EPA, Amy Lamson/RTP/USEPA/US@EPA | Date: 08/06/2010 05:14 PM Subject: Portland Cement RIA with attachments

Cortney,

Attached are our agreed upon changes in both a redline and final version.

Thank you.

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_trackchanges.doc)(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 Plann ing 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 Plann ing and Standards (OAQPS) Air Benefit and Cost Group (MD-C439-02) Research Triangle Park, NC 27711iii 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 Ty pes of Portland Cement .................................................................................. 2-3 2.1.3 Production Co sts ................................................................................................. 2-3

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

2. 3 Industry Organization .................................................................................................... 2-10 2.3.1 Mark et Structure ............................................................................................... 2-10 2.3.2 Manuf acturing Plants ........................................................................................ 2-11 2.3.3 Firm Characteristics .......................................................................................... 2-16

2. 4 Markets .......................................................................................................................

... 2-20 2.4.1 Mark et Volumes ............................................................................................... 2-21 2.4.2 Mark et Prices .................................................................................................... 2-22 2.4.3 Future Pr ojections ............................................................................................. 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 R egional Structure and Baseline Data ................................................................ 3-6 3.2.2 N ear-Term Cement Plant Production Decisions ................................................. 3-6 3.2.3 Econ omic Impact Model Results ........................................................................ 3-9

3. 3 Other Economic Analyses: Direct Compliance Cost Methods ...................................... 3-19

3. 4 Social Cost Estimates ..................................................................................................... 3-20

3. 5 Energy Impacts .............................................................................................................. 3-22iv 3.6 Assessment..................................................................................................................... 3-24

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 PM 2.5 Human Health Benefits .................................................................. 6-2 6.2.1 Methodology Improvements since Proposal ....................................................... 6-2 6.2.2 B enefits Analysis Approach ............................................................................... 6-3 6.2.3 H ealth Impact Analysis (HIA) ............................................................................ 6-4 6.2.4 Es timating PM 2.5 -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 PM 2.5 Disbenefits .............................................................................................. 6-25 6.4.2 Soc ial Cost of Carbon and Greenhouse Gas Disbenefits .................................. 6-27 6.4.2 Total D isbenefits ............................................................................................... 6-27

6. 5 Unquantified or Nonmonetized Benefits ....................................................................... 6-20 6.5.1 Oth er SO 2 and PM Benefits .............................................................................. 6-25v 6.5.2 HAP Benefits .................................................................................................... 6-27

6. 6 Limitations and Uncertainties ........................................................................................ 6-41 6.6.1 Monte Carl o Analysis ....................................................................................... 6-41 6.6.2 A lternate Concentration-Response Functions for PM Mortality ...................... 6-42 6.6.3 LML A ssessment .............................................................................................. 6-42 6.6.4 Q ualitative Assessment of Uncertainty and Other Analysis Limitations ......... 6-43

6. 7 Comparison of Benefits and Costs ................................................................................. 6-44

7 References ....................................................................................................................

. 7-1

Ap pendixes
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 PM 2.5 -related Mortality ...................................... D-1vi LIST OF FIGURES

Number Page 2-1. Simplified Flow Sheet of Clinke r 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 Competitiv e Building Materials: 2003 to 2008................ 2-10 2-6. Distribution of Cement Kilns in the United States ..................................................... 2-13 2-7. Histo rical 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. B reakdown 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-re lated 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 ..........................................................................................................................

... 6-48vii 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, Producti on, 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 (10 6 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 ($/metric tons): 2005 ............................................ 3-7 3-4. Portland Cement Markets (10 6 metric tons): 2005 ....................................................... 3-8 3-5. National-Level Market Impacts: 2005 .......................................................................... 3-9 3-6. Regional Compliance Costs and Market Price Changes ($/metric ton of cement): 2005.............................................................................................................. 3-10 3-7. Summary of Regional Market Impacts: 2005 ............................................................. 3-12 3-8. Distribution of Industry Imp acts: 2005 ....................................................................... 3-13 3-9. Cement Plants with Significant Utilization Changes: 2005 ........................................ 3-15 3-10. Job Losses/Gains Associated with the Final Rule ...................................................... 3-17 3-11. Distribution of Social Costs ($10 6 ): 2005 ................................................................... 3-21 3-12. U.S. Cement Sector Energy Consumption (Trillion BTUs): 2013 ............................. 3-24viii 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$) .............................................................................. 6-14 6-3. Summary of Reductions in Health Incidences and Monetized Benefits from PM 2.5 Benefits for the Final Cement NESHAP and NSPS in 2013 (95 th percentile confidence interval) .................................................................................... 6-15 6-4. Comparison of Monetized Benefits and Emission Reductions for Final Cement NESHAP and NSPS in 2013 (2005$) ......................................................................... 6-17 6-5. Summary of Monetized PM 2.5 Energy Disbenefits for the Final Portland Cement NSPS and NESHAP in 2013 (2005$) ......................................................................... 6-17 6-6. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2013 ........................ 6-17 6-7. Monetized Disbenefits of CO 2 Emission Increases in 2013 ....................................... 6-17 6-8. Summary of the Monetized Benefits, Social Costs, and Net Benefits for the final Portland Cement NESHAP in 2013 (millions of 2005$) ............................................ 6-461-1 SECTION 1 INTRODUCTION

Th e 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 par ticulate 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 upda ting recordkeeping and testing requirements. The final amendments to the NSPS add or revise, as applicable, emission limits for particulate matter (PM), opacity, nitrogen oxides (NO x ), and sulfur dioxide (SO 2 ) for facilities that commence construction, modification, or reconstruction after June 16, 2008. The final rule also includes additional testing and monitoring requir ements 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
Th e 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 annua lized costs with the final NESHAP and NSPS will be $466 million (2005$).

 Market Analysis: The partial-equilibrium econom ic 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 tons1-2 per year. Because of higher domestic prices, imports rise by 10%, or 3 million metric tons per year.

 Industry Analysis: Net industry operating profits fa ll by $241 million; EPA also identified 10 domestic plants with ne gative 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 operati ng profits. Since these plants account for approximately 8% of domestic capacity, a decision to permanently shut down these plants would reduce domestic supply and coul d 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 focu sed 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 inherent ly 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 effect s 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 (54 55.5.8 percent) capacity utilization. The social co st estimates are significantly higher than the engineering analysis estimates, which estimated annualized costs of $466 million. This is a direct conseque nce 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 leve l produced under perfect competition. As a result, a preexisting market distortion exists in the markets covere d by the final rule (i.e., the observed baseline market price is higher than the [unobserved] market price that a model of perfect competition woul d predict). The imposition of additional regulatory costs tends to widen the gap be tween price and marginal cost in these markets and contributes to additional social costs.

 Energy Impacts: EPA concludes that the rule wh en 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 governmen t). 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 NE SHAP 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 PM 2.5 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 NO x , 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 disbenef its 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 approximately $6.5 billion to $17 billion and $5.8 billion to $ 16 15 billion, at 3% and 7% discount rates, respectively. All estimates are in 2005 dollars for the year 2013.

1.2 Organization of this Report
Th e 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 Benefits b $7,400 to $18,000 $6,700 to $16,000

Total Social Costs c $926 to $950 $926 to $950 Net Benefits $ 6, 5 00 450

– 6,474 6,500 to $17, 000 050

- 17,074 000 $5, 8 00 750

- 5,774 800 to $1 6 ,000 $15,050

- 15,074 000

Nonmonetized Benefits d 4,400 tons of NO x (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, NO 2 , and SO 2 exposure Ecosystem effects Visibility impairment Final NSPS onl y 3% Discount Rate 7% Discount Rate Total Monetized Benefits b $510 to $1,300 $460 to $1,100

Total Social Costs c $72 $72
Net Benefits $440 to $1,200 $390 to $1,000

Nonmonetized Benefits d 6,600 tons of NO x 520 tons of HCl Health effects from HAPs, NO 2 , and SO 2 exposure Ecosystem effects Visibility impairment Final NESHAP only 3% Discount Rate 7% Discount Rate Total Monetized Benefits b $7,400 to $18,000 $6,700 to $16,000

Total Social Costs c $904 to $930 $904 to $930
Net Benefits $6,500 to $17,000 $5,800 to $16,000

Nonmonetized Benefits d 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, SO 2 exposure Ecosystem effects Visibility impairment Alternative: More Stringent NSPS and Final NESHAP 3% Discount Rate 7% Discount Rate Total Monetized Benefits b $7,400 to $18,000 $6,700 to $16,000

Total Social Costs c $955 to $979 $955 to $979
Net Benefits $6,500 to $17,000 $5,700 to $15,000

Nonmonetized Benefits d 7,800 tons of NO x (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Formatted Table

Formatted Table

Formatted Table

Formatted Table

Formatted Table1-5 Health effects from HAPs, NO 2 , and SO 2 exposure Ecosystem effects Visibility impairment

a All estimates are for the implementation year (2 013) and are rounded to two significant figures. b The total monetized benefits reflec t the human health benefits associated with reducing exposure to PM 2.5 through reductions of

directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 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 ch emical composition, are equally pot ent in causing premature mortal ity 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 soc ial

costs for both discount rates. Range represents the estimated difference in su rplus if ten facilities with low estimated post regulation capacity utilization choose to idle or close rather than operate at a low (54 5.8 5 percent) capacity utilization. d Due to data, methodology, and resource limitations, we were unab le to monetize the benefits associated with these categories of benefits.2-1 SECTION 2 INDUSTRY PROFILE

H ydraulic 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 work s 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 va lue 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 summari zes 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
Por tland 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 tri calcium silicate than regular Portland cements.
Type IV: This type contains a lower percen tage 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 shipments 1 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. Cens us 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 ship ments 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 compensatio n for all employees (includes payroll and fringe benefits)

1 USGS notes these shipment data include ceme nt 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 Type a, 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% White d 894 1% 1,190 1% Blended 1,296 1% 3,160 3% Expansive and regulated fast setting 60 0% 6 0% Other e 1,786 2% 1,997 2%

Total f 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
Accord ing 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 shipme nts) 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 (10 3 metric tons) 144,949 147,300 153,100 150,500 158,200 159,700 Cement production (10 3 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

I n 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. Weight s for labor are 85% clinker and 15% finished cement production. Weights for energy are 92% clinker an d 8% finished cement production (PCA, 2005). 2 The labor costs reported in Figure 2-3 were calculate d by first multiplying the num ber of employee hours per metric ton of cement reported in Table 2-4 by the av erage 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 Statis tics (BLS). 2008. “Consumer Price Index All Items – U.S. City Average Data: Customizab le Data Tables.” Available at <http://www.bls.gov/cpi/>. As obtained on March 14, 2008.

2.1.3.3 Energy Costs Fig ure 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
Th e 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 for2-9 0 20,000,000 40,000,000 60,000,000 80,000,000 100,000,000 120,000,000

1975 1980 1985 1990 1995 2000Metric 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 Statis tics 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 mate rials such as steel, asphalt, and lumber. Lumber is the primary substitute in the residential construction ma rket, 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 ha s 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 rev iew 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 de gree 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 additi onal, large plants are typically more economicalbecause 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, a nd 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
Duri ng 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
Tab le 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 mo re 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 (10 3 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 (10 3 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

I n 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 (10 3 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 (10 3 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 (10 3 metric tons) 89,264 100,360 101,000 102, 000 105,000 104,000

Production (10 3 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. Av ailable 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 geogra phic 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 prev iously discussed, clinker capacity ha s been increasing at a slower pace since 2001. However, according to the PCA, the cemen t 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 h as 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 Manufacture s. 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 Manufacture s. 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
Firm s are grouped into small and large categories using Small Business Administration (SBA) general size standard definitions for NA ICS 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 employe es 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 us ed 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 ($10 6 ) Employment Type Small Business Plants Kilns Clinker Capacity (10 3 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 $150 b <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 ($10 6 ) Employment Type Small Business Plants Kilns Clinker Capacity (10 3 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 $50 b <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 Ri ver 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
Cem ent manufacturing is a capital-intensive i ndustry. 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

Por tland 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 statistics2-21 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

I n 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 ( 10 6 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. Av ailable at <http://minerals.er.usgs.gov/minerals/pubs/ commodity/cement/>.
2.4.1.2 International Trade
Cem ent 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 (10 6 metric tons) 0.7 0.7 0.9 0.8 0.7 0.8 1.5 1.9 Imports (10 6 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
Corr ecting 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
Altho ugh 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 $110F.O.B. Cement Price ($/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 p repares 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 St andards (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 intermedia te run where producers have some constraints on their flexibility to adjust factor s 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 offuture 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 do es 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 the3-2 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 NES HAP 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 particulat e matter (PM), opacity, nitrogen oxides (NO x ), and sulfur dioxide (SO 2 ) for facilities that commence constructi on, 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 he lps 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 at a discount rate of 7% 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 ($11 million) are associated with the final NSPS limits for SO 2 and NO x . Figure 3-1 illustrates the distribution of annualized compliance costs per metric ton of clinker capacity by different combinati ons of the regulatory programs. In Table 3-2, we report state-level summary statistics for total annualized compliance costs per metric ton of clinker capacity for the final NESHAP and NSPS to highlight any regional differences in control costs.3-3 Table 3-1. Summary of Direct Total Annualized Compliance Costs (million, 2005$)

Total Annualized Compliance Costs Description Final NSPS Only More Stringent NSPS Only
Total: $40 a $56 a 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 SO 2 and NO x . Alternatively, the 7 kilns would also incur an additional $27 in compliance costs to meet the stringent NSPS limits for SO 2 and NO x .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 >$5.00NumberofKilns

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

T he 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
Cem ent 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 cem ent 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 prod uce 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 sm aller capacity cement plants.3-8 Table 3-4. Portland Cement Markets (10 6 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, 199 9b, 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 depend s 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 sm aller 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

M arket-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 approxi mately 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 co st 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 n eed to consider market power–related welfare costs in evaluating regulations under Ex ecutive 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 hi gh 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 th e 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 market s 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 si milar plants operating in regions without import competition. Because imports are only modeled fo r 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 in creases 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 adjustment s for regional markets with imports would tend to become more similar to re gional 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 iden tified 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%

EPA notes that since conducting this anal ysis, one high mercury-emitting plant has invested in control technology estimated to reduce emissions by approximately 85 percent. The current analysis does not include these actual costs in the baseline but rather estimates aggregate compliance costs based on the averaging methodologi es applied to all other modeled plants. In addition, because this investment occurred after the analysis was conducted, the baseline benefits likewise do not include the approximately 85% emissions reduction. Finally, EPA did not estimate the change in social costs that would o ccur if the 2 high mercury-emitting plants were to shut down, because the Agency believes these pl ants will ultimately be able to meet the emissions limit by applying multiple mercury controls, which were accounted for in the cost3-13 analysis. EPA also acknowledges that if these 2 hi gh mercury-emitting plants ultimately are able to meet the emissions limit, they will not likely be able to do so by the required compliance date.
Within the group of plants with operating lo sses, EPA identified 10 domestic plants with negative operating profits and si gnificant 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 u tilization 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.
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 in crease 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 zer o 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 wa s then reduced by the avoided pollution control cost of $34 million at the ten plants

. because if the plants were to idle or shut down, they would not incur compliance costs. This resulted in a net reduction of $24 million in social cost due to the ad justment. when these firms idle or shut down as compared to the modeled scenario, where firms continue to operate a low capacity but incur compliance costs. Because of the method of estimating this adjustment it cannot be distributed between producer and consumer surplus. An estimate of the social cost is provid ed 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)3-14 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

− $2413-15 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

Pr ecise job effect estimates cannot be estimated with certainty and the economic literature does not give clear evidence on the effect of regulation on job effects

. 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

econo mic 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 th eoretical 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 emplo yment within an industry falls (“demand effect”);

 pollution abatement activities require add itional 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.,

m ore/less labor is required per dollar of output) (“factor

- shift effect”).
Several empirical studies , , including Morgenstern et al. (2002 ) including Morgenstern et al., , suggest the net employment decline is zero or economically small (e.g., Cole and Elliot, Formatted: body text,bt,body tx,indent,flush, No bullets or numbering3-16 2007; Berman and Bui, 2001). However, others show the question has not been resolved in the literature job effects are not trivial (Henderson, 1996; Greenstone, 2002).
Morgenstern et al. use a 6

- year panel (U.S. Census data for plant

- level prices, inputs [(including labor], outputs, and environmental expenditures) to econometrically estimate the
pro duction 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 m odel, 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 sha re of environmental expenditures.
Historically, EPA has most often estimated employme nt 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 indus tries. Focusing only on the “demand effect”, it can be seen that the estimate from the historical approach is within th e range presented by the Morgenstern “demand effect” portion. This strengthens our comfort in the reasonableness of both estimates. In April of this year, EP A 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. T hus, this analysis goes beyond what EPA has typically done , and uses Morgenstern et al. (2002) to provide the basis for the estimates. because the parameters estimated in the Morgenstern et al. (2002) model three economic mechanisms by which pollution abatement ac tivities can indirectly influence jobs:

 higher production costs raise market prices, higher prices reduce consumption, and employment within an industry falls (“demand effect”);

 pollution abatement activities require additio nal labor services to produce the same level of output (“cost effect”); and3-17  postregulation production technologies may be more or less labor intensive (i.e., more/less labor is required per dollar of output) (“factor-shift effect”).
paper were used to estimate all three effe cts (“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

. ” In examinining job effects. EPA selected this paper because the parameter estimates provide a transparent and tractable way to transfer estimates for an employment effects analysis. Simialr estimates were not available from other studies.

 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.
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 fore most, 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 w ith 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 Asso ciated 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)

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). Formatted: Indent: First line: 0 pt3-18 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 complian ce expenditure) by the total environmental compliance expenditures used in the partial equilibriu m model. For example, the jobs effect estimate for is estimated to be 807 jobs (

− 3.56 × $378 million × 0.6). 8 The timeframe for EPA’s regulatory analysis focuses on a single year effect, by contrast the Morganstern analysis used annualized inputs, and translates to annualized impacts. Demand effect results are provided in Table 3-10. It is not appropriate to substitute th e 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” em ployment 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 ne t 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

8 Since Morgenstern’s analysis reports environmental expend itures in 1987 dollars, we make an inflation adjustment to the engineering cost analysis using the consumer price index (195.3/113.6) = 0.6)3-19 part of the analysis. First, EPA has used the weighted average parameter estimates for a narrow set of manufacturing industries (pulp and paper, pl astics, petroleum, and steel). Absent other dataand 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 st ill 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” ar e 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 dema nd 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. 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 simi lar 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

I n 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.3-20 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 SO 2 and NO x . 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 companies to consider delaying rates of construction of new kilns until market conditions change (e.g., increases in demand that lead to rising ce ment 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 prev ious 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 distribu ted between consumers and producers.3-21 Table 3-11. Distribution of Social Costs ($10 6 ): 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 (net negative because shut down facilities will not incur compliance costs). -$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 consum er 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 adjustment3-22 The estimated social cost of the final rule is $ 880 926-950 million. This estimate includes the results for existing kilns included in the part ial-equilibrium analysis ($792 million),the final 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 significantl y 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 extens ively in previous cemen t 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 resu lt, 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 regulator y 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

Ex ecutive 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 id entified 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 adverse3-23 impact on the supply, distribution, or use of en ergy. EPA has prepared an analysis of energy impacts that explains this conclusion below.
To enhance understanding regarding the regulation’s influence on energy consumption, EPA examined publicly available data describi ng 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, any3-24 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 petroleum b 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 alka line 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
Altho ugh 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 smal l (marginal) changes in industry conditions. The3-25 overall engineering cost analysis estimates are si gnificant 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 th e observed pre-policy eq uilibrium. Second, the partial equilibrium assumes that transportati on costs between regions are high enough that interregional trade is unlikely to occur, at leas t 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 decisi ons 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 mode l 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 uses 9 ; (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 sm aller capacity cement plants.4-1 SECTION 4 SMALL BUSINESS IMPACT ANALYSIS

T he Regulatory Flexibility Act (RFA) genera lly 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 determ ine 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 determin ing the SISNOSE certification category.

4.1 Identify Affected Small Entities

For the purposes of assessing the impacts of th e final rule on small entities, small entity is defined as (1) a small business as defined by th e 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

I n 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 ($10 6 ) Employees Plants Kilns Clinker Capacity (10 3 metric tons per year) Cost-toSales Ratio
Salt River Materials Group a Tribal government $184 b NA 1 1 1,477 0.7%

Monarch Cement Company Business $154 600 1 2 787 3.0%

Continental Cement Company, LLC Business $93 c <750 1 1 1,164 0.0%

Snyder Associate Companies Business $29 350 1 2 286 2.0%

a Enterprise is owned by Salt Ri ver 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

TR j = 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 sm all 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 si gnificant 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 em issions changes on future years: annual and 24- hour PM 2.5 concentrations, total Hg deposition, as well as visibility impairment. Air quality benefits are estimated with the Comprehensiv e 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 repres ents 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 an alyses. 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 sec ondary 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 conde nsable organic gases between gas and particle phase (Strader et al., 1999), Regional Acid Depo sition 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 PM 2.5 , 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 (a pproximately 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 determin ing 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 platfo rm. 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 emis sions 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 20135-3 reference case. The 2002 v3.1 platform 2020 projec tion factors were the starting point for most of the 2013 SMOKE-based projections.

Figure 5-1. Map of the Photochemical Modeling Domain a

a The black outer box denotes the 36 km national modeling do main; 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 Do mains 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 es timates with the exception that facilities that closed between 2005 and 2010 were removed from the 2013 inventory. The length of time5-4required 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 importan t 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 PM 2.5 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 PrimaryPM 2.5 16,758 15,403
PM 2.5 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 SO X and 86% reductions in primarily emitted PM 2.5 for the NSPS and NESHAP, more stringent NSPS and NESHAP, and NESHAP only

• 6% reductions in SO X and 5% reductions in primarily emitted PM 2.5 for NSPS only
As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM 2.5 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 PM 2.5 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 do ne 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 consider ed 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 PM 2.5 , total Hg deposition levels, and visibility impairment. In this section, we presen t 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/m 2 . A reduction of 26,047 μg/m 2 is estimated for the western United States. The reductions to total annual Hg deposition estimated by the photochemi cal model show that the reductions tend to be greatest nearest the sources.
This section summarizes the results of our modeling of annual average PM 2.5 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/m 3 in annual PM 2.5 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/m 3 . An annual PM 2.5 design value is the concentration that determines whether a monitoring site meets the annual NAAQS for PM 2.5 . The full details involved in calculating an annual PM 2.5 design value are given in Appendix N of 40 CFR part 50.5-6 Projected air quality benefits are estimated usi ng procedures outlined by EPA modeling guidance (U.S. EPA, 2007).

This section summarizes the results of our modeling of 24-hour average PM 2.5 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/m 3 in 24-hour average PM 2.5 design values at most monitor locations in the United States is possible given an area’s proximi ty to controlled sources and the amount of reduced sulfur dioxide emissions. The median reduction over all monitor locations is 0.1 μg/m 3

. A 24-hour PM 2.5 design value is the concentration that determines whether a monitoring site meets the 24-hour NAAQS for PM 2.5 . The full details involved in calculating a 24-hour PM 2.5 design value are given in Appendix N of 40 CF R 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 ru le 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 us ed in the valuation of visibility. The deciview metric provides a scale for perceived visual ch anges 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 ar e indicative of worse visibility. Thus, an improvement in visibility is a decrease in d eciview 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 repres ent 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 th e 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 PM 2.5 reductions are the consequence of the technologies installed to meet these multiple limits. These estimates include th e number of cases of avoided morbidity and premature mortality among populations exposed to PM 2.5 , 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 $17 bi llion in the implementation year. All estimates are in 2005$. These estimates include the energy di sbenefits 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, revi sed 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 NO x , 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 from 2 epidemiology functions and 12 expert functions 3% DR 7% DR

Figure 6-1. Total Monetized PM 2.5 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-respon se 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, NO 2 , and SO 2 , ecosystem effects, and visibility effects.

6.2 Calculation of PM 2.5 Human Health Benefits In addition to pollutants we cannot monetize, this rulemaking would reduce emissions of PM 2.5 and SO 2 . Because SO 2 is also a precursor to PM 2.5, reducing and SO 2 emissions would also reduce PM 2.5 formation, human exposure, and the incidence of PM 2.5 -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 PM 2.5 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 na tional 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 func tion. Consistent with recent scientific advice, we are replacing the previous threshold sensitivity analysis with a new LML assessment to highlight the uncertainty associ ated 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, a nd 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 mercurymethylation, 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 benef its 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-overlapping6-4 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, includi ng 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 PM 2.5 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 re sulting 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>.6-5The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMxgenerated projections of PM 2.5 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 y 0 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; Δ x 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 out puts for the BenMAP model.

Figure 6-2. Illustration of BenMAP Approach6-6 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 incide nce 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 PM 2.5 Concentrations

PM 2.5 Health

Functions

Economic Valuation Functions Population Projections

PM 2.5 Incremental Air Quality

Change

PM 2.5 Related Health Impacts W oods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 - related Benefits

Blue identifies a user-selected input w ithin the BenMAP model Green identifies a data in p ut g enerated outside of the BenMAP model6-7 Table 6-1. Human Health and Welfare Effects of Pollutants Affected
Pollutant/ Effect Quantified and monetized in primary estimate Unquantified

PM: health a Premature mortality based on cohort study estimates b Low birth weight Premature mortality based on expert elicitation estimates Pulmonary function Hospital admissions: respiratory and cardiovascula r 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 impacts c

SO 2 : health Respiratory hospital admissions Asthma emergency room visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function

SO X : 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 shorte r term exposures (see Kunzli et al., 2001 for a discussion of this issue). While some of the effects of short term expo sure are likely to be captured by the cohort estimates, there may be additional premature mortality from short term PM ex posure not captured in the cohort estimates included in the primary analysis.

c May result in benefits or disbenefits.6-8 6.2.4 Estimating PM 2.5 -related premature mortality Consistent with the proposal RIA for this rule (U.S. EPA, 2009a), the PM 2.5 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 concentratio n-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/m 3 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 PM 2.5 NAAQS, has been used as an alternative estimate in the PM 2.5 NAAQS RIA and PM 2.5 benefits estimates in RIAs completed since the PM 2.5 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/m 3 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 PM 2.5 -mortality relationship and interpreted for benefits analysis in EPA’s final RIA for the PM 2.5 NAAQS. For that study, twelve experts (labeled A through L) provided independent estimates of the PM 2.5 -mortality concentration-response function. EPA practice has been to develop independent estimates of PM 2.5 -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 argue s for using both studies to generate benefits estimates. Previously, EPA had calculated benef its 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 PM 2.5 and premature mortality (Roman et al.,

2 These two studies specify multi-pollutant models that control for SO 2 , 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 th ese 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 PM 2.5 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 up dated 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 PM 2.5 air quality levels. 5 Prior to the proposal RIA for this rule, EPA pres ented the results using an assumed threshold at 10 μg/m 3 in the PM-mortality health impact function as the primary PM-related benefits results. Using a threshold of 10 μg/m 3 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/m 3 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 shap e 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 PM 2.5 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 PM 2.5 levels experienced by the population receiving the PM 2.5 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 morta lity decreases as we consider air quality levels further below the LML in the two epidemiological studies.
While an LML assessment provides some insi ght 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 characteri zation 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 stro ng associations with mortality. As we model mortality impacts among populations exposed to levels of PM 2.5 that are successively lower than the LML of each study, our confidence in the re sults 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 PM 2.5 . 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 PM 2.5 -related mortality effects down to at least 7.5 μg/m 3 is high.

For these rules the SO 2 reductions represent a large fract ion of the total benefits from reducing PM 2.5 , but it is not possible to isolate the portion if the total benefits attributable to the emission reductions of SO 2 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 effect s 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 be nefits 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 premat ure mortality, we assume that there is a “cessation” lag between PM exposures and the total realization of cha nges 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 PM 2.5 (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 th an 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 re port 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 th e 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 PM 2.5 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 PM 2.5 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 PM 2.5 health benefits. Figure 6-5 provides a graphical representation of all 14 of the PM 2.5 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 PM 2.5 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 PM 2.5 levels at or above the LML of 7.5 μg/m 3 . Using the Laden et al. (2006) study, 40% of the mortality impacts occur at or above the LML of 10 μg/m 3

. As we model mortality impacts among populations exposed to levels of PM 2.5 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 PM 2.5 -related effects down to the lowest LML of the major cohort studies.

10 In this analysis, we adjust the VS L to account for a different currency y ear (2005$) and to account for income

growth to 2015. After applying th ese 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 PM 2.5 Benefits for the Final Cement NESHAP and NSPS in 2013 (95 th 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 ef fects. 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 Adult Mortality 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 PM 2.5 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, th e percentage of total monetized benefits due to adult mortality would be 97%. This chart shows the breakdown using a 3% discou nt 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 NO x , 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 $17,000EmissionReductions PM (tpy) 11,000 590 11,000 11,000 SO 2 (tpy) 124,000 9,000 124,000 124,000 NO x (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 eff ects. The monetized estimates do not include benefits from reducing HAP emissions or NO x , 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 from 2 epidemiology functions and 12 expert functions 3% DR 7% DR

Figure 6-5. Total Monetized PM 2.5 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-respon se 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 20PercentageofAvoidedPM2.5Mortalities

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) 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 am ong populations with baseline exposure to annual mean

PM 2.5 levels at or above 7.5 μg/m 3 , 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 20CumulativepercentageofavoidedPMMortalities

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) 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 am ong populations with baseline exposure to annual mean

PM 2.5 levels at or above 7.5 μg/m 3 , 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 NO x , 1,000 tpy of CO, 3,500 tpy of SO 2 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 NO X , 100 tpy of CO, 400 tpy of SO 2 and 10 tpy of PM. We also estimated increases of CO 2 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 reag ent to the kiln is negligible.6-21 6.4.1 PM 2.5 Disbenefits
The additional energy usage required for the emission control devices would increase emissions of PM, NOx, SO 2 . Because NOx and SO 2 are also precursors to PM 2.5, increasing these emissions would also increase PM 2.5 formation, human exposure, and the incidence of PM 2.5 -related health effects. Due to time and resource limitations, it was not possible to provide a comprehensive estimate of the PM 2.5 -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 PM 2.5 benefit-per-ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of PM 2.5 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 PM 2.5 fraction. Table 6-5 summarizes the benefit-per-ton estimates and the monetized PM 2.5 disbenefits at discount rates of 3% and 7%.
Table 6-5. Summary of Monetized PM 2.5 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 PM 2.5 Disbenefits (millions, 3%) Monetized PM 2.5 Disbenefits (millions, 7%)

Direct PM 2.5 110 $210,000 $510,000 $190,000 $460,000 $23 to $56 $21 to $50
PM 2.5 Precursors

SO 2 3,900 $37,000 $91,000 $34,000 $82,000 $150 to $360 $130 to $320
NO X 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 PM 2.5 . 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 overes timated in this analysis because we assume that all of the increased PM tons are in the PM 2.5 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 (CO 2 ) 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 CO 2 emissions 12 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. Trea ting climate sensitivity probabilistically results in more high temperature outcomes, which in tu rn 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 Advi sers, 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 CO 2 emissions. Given that warming profiles and

impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded “transforming gases into CO 2 -equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, would not result in accurate estimat es of the social costs of non-CO 2 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 th at 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 climat e system, (3) the impact of changes in climate on the physical and biological environment, a nd (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, eco nomics, and ethics and should be viewed as provisional.
The interagency group noted a number of limitations to the SCC analysis, including theincomplete 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 CO 2 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 are6-24 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 CO 2 ) 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 specifi c. SCC values represent only a partial accounting of climate impacts.
Table 6-7. Monetized Disbenefits of CO 2 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 specifi c. 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, NO X , SO 2 , and CO 2 , but we were unable to monetize the disbenefits associated with the incr eased 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 CO 2 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 CO 2 and mercury is difficult to determine because it depends on many f actors, 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 PM 2.5 precursor, SO 2 emissions also contribute to adve rse effects from acidic deposition in aquatic and terrestrial ecosystems, increase me rcury methylation, as well as visibility impairment.

6.5.1 Other SO 2 and PM Benefits In addition to being a precursor to PM 2.5 , SO 2 emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits associated with reduced SO 2 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 SO 2 for nearby populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 benefits associated with the reductions in SO 2 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 SO 2 (U.S. EPA, 2008b). According to summary of the ISA in EPA’s risk and exposure assessment (REA) for the SO 2 NAAQS“the immediate effect of SO 2 on the respiratory system in humans is6-26 bronchoconstriction” (U.S. EPA, 2009c). In addition, the REA summarized from the ISA that “asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease.” A cl ear concentration-response relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adverselyaffected (U.S. EPA, 2009c). Based on our review of this information, we identified four shortterm morbidity endpoints that the SO 2 ISA identified as a “causal relationship”: asthma exacerbation, respiratory-related emergency department visits, and respiratory-related hospitalizations. The differing evidence and a ssociated strength of the evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short-term SO 2 exposure and premature mortality was “suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness of the observed associations to adjustment for pollutants.

SO 2 emissions also contribute to adverse welf are 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 th e 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 pl ants 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 regu lating). (U.S. EPA, 2008c)
Reducing SO 2 and PM emissions would improve th e level of visibility throughout the United States. Fine particles with significant light-extinction efficien cies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended particles and6-27 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 le vels. 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 qua lity 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) sh ow 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, em ission 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 wa s 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 acute 18 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 th e 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 subs tance 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 th e 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 Ca rcinogenic Risk from HAP exposure from outdoor sources (NATA, 2002)

19 The NATA modeling framework has a number of limitati ons 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 SO 2 ISA concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and in creased 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 on6-30 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 di stances, 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, shellfis h 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 sp ecies 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, hear t, kidneys, lungs, and immune system of people of all ages.6-31Several 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), cor onary 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 in clude epidemiological, toxicological, and toxicokinetic investigations. Over a dozen review papers have also been published. If there is a causal relationship between methylmercury exposu re 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. 21 Salonen, 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. 22 Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. We ihe, and Grandjean, P., 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375. 23 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the

Toxicological Effects of Methylmercury, Board on Envi ronmental 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 a bout 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 tomodel 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 cha nge in mercury deposition using CAMx for the final Portla nd 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 th e 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/m 2 ) in the Eastern U.S.6-34 Figure 6-11. Reductions in Total Mercury Deposition (μg/m 2 ) 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 concentrati ons can rapidly lead to swelling and spasm of the throat and suffocation. Most seriously exposed persons ha ve immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the br onchioles. 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 fr om: 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 Toluene 26

Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessme nt, there is inadequate information to assess the carcinogenic potential of toluene because studi es 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; symp toms 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 irritati on 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 fr om: U.S. EPA. 2005. “Full IRIS Summary for Toluene

(CASRN 108-88-3)” Environmental Protection Agency, Inte grated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Crit eria 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 to6-36 recently published epidemiological data. For instance, research conducted by the National Cancer Institute (NCI) found an increa sed 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. J ournal of the National Cancer Institute 95: 1615-1623. 29 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mort ality 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 garmen t workers exposed to formaldehyde: an update. Occup.

Environ. Med. 61: 193-200. 32 Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Exte nded 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 car cinogenicity in the F344 rat. Tox Sci 75: 432-447. 34 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Ka lisak, 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 rode nt and human dataset. Tox Sci 82: 279-296. 35 Chemical Industry Institute of Toxicology (CIIT).199 9. Formaldehyde: Hazard charact erization 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 Sensitiv ity 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 e xperimental 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 irri tation, chronic bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia. Anim al studies suggest that formaldehyde may also cause airway inflammation – including eosinophil in filtration 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 limite d 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 limite d 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 the6-38 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 leve ls. 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 defo rmities, 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 fr amework of the Inter-Organization Programme for the Sound Management of Chemicals. Geneva. 45 All health effects language for this section came fr om: 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 (CDF s). Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on th e Internet at <http://www.at sdr.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 chemi cals and dyestuffs, Internati onal 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 granul ocyte/macrophage colony-stimulating factor in vitro, Proc. Natl. Acad. Sci. 89:3691-3695.6-40lymphocytic 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 h ealth 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 observe d 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 Institu te (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 Na tional 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. Occupation al 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.ep a.gov/iris/sub st/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 & Evaluatio n 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. (200 4). 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 (I RIS) database is available at: www.epa.gov/iris6-41 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 appropriat e 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 thos e 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 th e 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 ar e 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 Mo nte 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 f unctions for PM mortality

PM 2.5 mortality benefits are the largest benefit category that we monetized in this analysis. To better understand the concentration-response relationship between PM 2.5 exposure and premature mortality, EPA conducted an expert elicitation in 2006 (Roman et al., 2008; IEc, 2006). In general, the results of the expert elicit ation support the conclusion that the benefits of PM 2.5 control are very likely to be substantial. In previous RIAs, EPA presented benefits estimates using concentration response functions derived from the PM 2.5 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-base d 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 PM 2.5 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 derive d 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

PM 2.5 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 PM 2.5 , we included the LML assessment. We have more confidence in the mortality impacts among populations exposed to levels of PM 2.5 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 that6-43 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, rega rdless of their chemical composition, are equally potent in causing premature mortality. This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 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 functio n 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 PM 2.5, 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 PM 2.5 and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on re sults of the expert elicitation study in addition to our core estimates. Even thes e 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 PM 2.5 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 PM 2.5 benefits, please consult the PM 2.5 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 re gulatory scenario. By assuming that each kiln6-44 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 decr ease in domestic cement production and transportation, we are underestimating the air quality impacts and mone tized 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 mone tize as many of the benefits anticipated from this rule as possible, but the mo netized 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 limita tions include limited staff and extramural funding in conjunction with a h eavy regulatory workload. Methodological limitations include an absence of concentration-response functions for ma ny 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 deta iled uncertainty assessment found in the PM NAAQS RIA. However, the results of the Mont e 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 $926 to $950 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 mi nus annualized costs) utilizing the 14 different PM 2.5 mortality functions at discount rates of 3% and 7%. Data, resource, and methodological limitations prevented EPA from monetizing th e benefits from several important benefit categories, including benefits from reducing hazardous air pollutants, ecosystem effects, and visibility impairment. EPA believes that the bene fits 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 cost s, 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 Benefits 2 $7,400 to $18,000 $6,700 to $16,000 Total Social Costs 3 $926 to $950 $926 to $950 Net Benefits $6,500 to $17,000 $5,800 to $16,000

Non-monetized Benefits d 4,400 tons of NO x (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, NO 2 , and SO 2 exposure Ecosystem effects Visibility impairment Final NSPS onl y 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 $510 to $1,300 $460 to $1,100 Total Social Costs 3 $72 $40 Net Benefits $470 to $1,300 $420 to $1,100

Non-monetized Benefits d 6,600 tons of NO x 520 tons of HCl Health effects from HAPs, NO 2 , and SO 2 exposure Ecosystem effects Visibility impairment Final NESHAP only 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 $7,400 to $18,000 $6,700 to $16,000 Total Social Costs 3 $904 to $930 $904 to $930 Net Benefits $6,500 to $17,000 $5,800 to $16,000

Non-monetized Benefits d 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, SO 2 exposure Ecosystem effects Visibility impairment Alternative: More Stringent NSPS and Final NESHAP 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 $7,400 to $18,000 $6,700 to $16,000 Total Social Costs 3 $955 to $979 $955 to $979 Net Benefits $6,500 to $17,000 $5,700 to $15,000

Non-monetized Benefits 4 7,800 tons of NO x (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, NO 2 , and SO 2 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 PM 2.5 through reductions of directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine parti cles, regardless of their chemical composition, are equally potent in causing premature mortality because there is no clear scientific evidence that woul d 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 from 2 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 PM 2.5 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 PM 2.5 . 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 m onetize the benefits associated with several categories of benefits, including exposure to HAPs, NO 2 , and SO 2 , 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 from 2 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 PM 2.5 benefits for implementation year (2013). This graph shows 14 benefits estimates combined with the cost estimate. All combinati ons are treated as independent and equally probable. All fine particles are assumed to have equivalent health eff ects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM 2.5 . 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 una ble to monetize the benefits associated with several categories of benefits, including exposure to HAPs, NO 2 , and SO 2 , ecosystem effects, and visibility effects.7-1 SECTION 7 REFERENCES Abt Associates, Inc. 2008. Environmental Benefits and Mapping Program (Version 3.0). Bethesda, MD. Prepared for U.S. Environmental Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. Available on the Internet at <http://www.epa.gov/air/benmap>. Baker, K. and P. Scheff. 2007. “Photochemical model performance for PM 2.5 , sulfate, nitrate, ammonium, and precursor species SO 2 , HNO3, and NH 3 at background monitor locations in the central and eastern United States.” Atmospheric Environment 41: 6185-6195. Berman, E. and L. T. M. Bui. 2001. Environmental regulation and labor demand: Evidence from the south coast air basin. Journal of Public Economics 79(2):265-295.
Broda, C., N. Limao, and D.E. Weinstein. 2 008b. “Optimal Tariffs and Market Power: The Evidence.” American Economic Review 98(5):2032-2065. Chang, J. S., R. A. Brost, Et Al. 1987. “A 3-Dimensional Eulerian Acid Deposition Model – Physical Concepts And Formulation.” Journal Of Geophysical Research-Atmospheres 92(D12): 14681-14700.
Cole, M. A. and R. J. Elliott. 2007. Do environm ental regulations cost jobs? An industry-level analysis of the UK. B E Journal of Economic Analysis & Policy 7(1) (Topics), Article 28. Davidson K, Hallberg A, McCubbin D, Hubbell BJ. 2007. “Analysis of PM 2.5 Using the Environmental Benefits Mapping a nd Analysis Program (BenMAP)”. J Toxicol Environ Health 70: 332—346. Drevnick, P.E., D.E. Canfield, P.R. Gorski, A.L.C. Shinneman, D.R. Engstrom, D.C.G. Muir, G.R. Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and J.T. Oris. 2007. Deposition and cycling of sulfur controls mercury accumulation in Isle Royale fish. Environmental Science and Technology 41(21):7266-7272.
Dun & Bradstreet, Inc. 2007. D&B Million Dollar Directory . Bethlehem, PA: Dun & Bradstreet, Inc.

ENVIRON. 2008. User's Guide Comprehensive Air Quality Model With Extensions . Novato, ENVIRON International Corporation. Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution. Air Qual Atmos Health 2:169-176. Gery, M. W., G. Z. Whitten, Et Al. 1989. “A Photochemical Kinetics Mechanism For Urban And Regional Scale Computer Modeling.” Journal Of Geophysical Research-Atmospheres 94(D10): 12925-12956.7-2 Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy 110(6):1175-1219. Henderson, J. V. 1996. “Effects of Air Quality Regulation.” American Economic Review 86(4):789-813. Hubbell BJ, Fann N, Levy JI. 2009. “Methodological Considerations in Developing Local-Scale Health Impact Assessments: Balancing National, Regional and Local Data.” Air Qual Atmos Health 2:99–110.
Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessment of the ConcentrationResponse Relationship Between PM 2.5 Exposure and Mortality . Prepared for the U.S. EPA, Office of Air Quality Planning and Standa rds, September. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/Uncertainty/pm_ee_report.pdf>.
Industrial Economics, Inc. 2008. Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study – Health Benefits of Benzene Reductions in Houston, 1990-2020. Prepared for the U.S. EPA, Office of Policy Analysis and Review, March. Available on the Internet at <http://www.epa.gov/oar/sect812/mar08/ 812CAA_Benzene_Houston_Draft_Report_Mar ch2008.pdf>. Jans, I. and D. I. Rosenbaum. 1997. “Multimarket Contact and Pricing: Evidence from the U.S. Cement Industry.” International Journal of Industrial Organization 15:391-412. Kelly, T. and G. Matos. 2007a. “Historical Sta tistics 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://m inerals.usgs.gov/ds/2005/140/>. 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

m odified April 11, 2006. Kochi, I., B. Hubbell, and R. Kramer. 2006. “An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Li fe for Environmental Policy Analysis.” Environmental and Resource Economics 34:385-406.

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr., and M. Studnicka. 2001. “Assessment of Deaths Attributable to Air Pollution: S hould We Use Risk Estimates Based on Time Series or on Cohort Studies?” American Journal of Epidemiology 153(11):1050-55. Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine 173:667-672.7-3 Levy JI, Baxter LK, Schwartz J. 2009. “Uncertainty and variability in health-related damages from coal-fired power plants in the United States.” Risk Anal. 29 (7): 1000-1013. LexisNexis. LexisNexis Academic [electronic resource]. Dayton, OH: LexisNexis. Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. “Jobs versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and Management 43(3):412-436. Mrozek, J.R., and L.O. Taylor. 2002. “What Determines the Value of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270. Munthe, J., R.A. Bodaly, B.A. Branfireun, C.T. Driscoll, C.C. Gilmour, R. Harris, M. Horvat, M. Lucotte, and O. Malm. 2007. “Recovery of Mercury-Contaminated Fisheries.” AMBIO: A Journal of the Human Environment 36:33-44. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The National Academies Press. National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC.
Nenes, A., S. N. Pandis, et al. 1999. “C ontinued development and testing of a new thermodynamic aerosol module for urban and regional air quality models.” Atmospheric Environment 33(10): 1553-1560. Nobel, C. E., E. C. McDonald-Buller, et al. 2001. “Accounting for spatial variation of ozone productivity in NO x emission trading.” Environmental Science & Technology 35(22): 4397-4407. Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis

. Washington, DC. Available on the internet at http://www.whitehouse.gov/omb/circulars/a004/a-4.html. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. Portland Cement Association (PCA). 2008a. “Cement and Concrete Basics: History & Manufacture of Portland Cement.” Available at <http://www.cement.org/basics/ concretebasics_history.asp>. Portland Cement Association (PCA). 2008b. “Market Research: Producer Price Indices— Competitive Building Materials.” Availa ble at <http://www.cement.org/market/>.7-4 Portland Cement Association (PCA). 2008c. “20 07 North American Cement Industry Annual Yearbook.” Available at <http://testinter.cement.org/bookstore/prof ile.asp?printpage=true&store=&id=15743>. Portland Cement Association (PCA). December 2004. U.S. and Canadian Portland Cement Industry: Plant Information Summary . Skokie, IL: Portland Cement Association Economic Research Department. Portland Cement Association (PCA). December 2005. U.S. and Canadian Labor-Energy Input Survey 2005 . Skokie, IL: Portland Cement Association Economic Research Department. Portland Cement Association (PCA). September 28, 2007. “Flash Report: Capacity Expansion Update.” Available at < http://www.cement.org/econ/pdf/NONMEMBERCapacity Ex pansion.pdf>. As obtained on March 31, 2008. 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. Rossi, G., M.A. Vigotti, A. Zanobetti, F. Repetto , V. Gianelle, and J. Schwartz. 1999. “Air Pollution and Cause-Specific Mortality in Milan, Italy, 1980–1989.” Arch Environ Health 54(3):158-164. Russell, A. G. (2008). “EPA Supersites Program-related emissions-based particulate matter modeling: Initial applications and advances.” Journal of the Air & Waste Management Association 58(2): 289-302. Ryan, S. 2006. “The Cost of Environmental Regulation in a Concentrated Industry.” Available at
< http://econ-www.mit.edu/files/1166>.

Scudder , B.C., Chasar, L.C., Wentz, D.A., Bauch, N.J., Brigham, M.E., Moran, P.W., and Krabbenhoft, D.P. 2009. Mercury in fish, bed sediment, and water from streams across the United States, 1998–2005: U.S. Geological Su rvey Scientific Investigations Report 2009–5109, 74 p. Sisler, J.F. 1996. Spatial and seasonal patterns and long-term variability of the composition of the haze in the United States: an analysis of data from the IMPROVE network. CIRA Report, ISSN 0737-5352-32, Colorado State University. Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “Pot ential impact of climate change on air-pollution related human health effects.” Environ. Sci. Technol . 43: 4979—4988. U.S. Department of Commerce, Bureau of the Census. 2003. 2001 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March 14, 2008.7-5 U.S. Department of Commerce, Bureau of the Census. 2006. 2005 Annual Survey of Manufacture s. 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. 2010. Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007 . Available at < http://factfinder. census.gov/servlet/IBQTable?_bm=y&- ds_name=EC0731I1&-ib_type=NAICS2007&-NAICS2007=327310>. U.S. Department of Energy, Energy Information Administration. 2007. Annual Energy Outlook 2007 . Supplemental Washington, DC: U.S. Energy Information Administration. U.S. Department of Energy, Energy Information Administration. 2010. Annual Energy Outlook 2010 . Washington, DC: U.S. Energy Information Administration. U.S. Department of Energy, Energy Information Administration. 2010. Supplemental Tables to the Annual Energy Outlook 2010. Available at <://www.eia.doe.gov/oiaf/aeo /supplement/supref.html > 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). 2007b. “State and Area Employment, Hours and Earnings 2005.” Washington, DC: U.S. Department of Labor. U.S. Department of Labor, Bureau of Labor Statistics (BLS). 2008. “Consumer Price Index All Items – U.S. City Average Data: Custom izable Data Tables.” Available at <http://www.bls.gov/cpi/>. 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: Custom izable Data Tables.” Available at <http://www.bls.gov/cpi/>. As obtained on March 14, 2008. U.S. Department of the Interior, U.S. Geological Survey. 2000. Minerals Yearbook, Cement.

. Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/pubs/comm odity/cement/>. As obtained on March 14, 2008. U.S. Department of the Interior, U.S. Geological Survey. 2001. 2000 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at ,<http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interior, U.S. Geological Survey. 2002. 2001 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>.7-6 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/>. U.S. Department of the Interior, U.S. Geological Survey. 2004. 2003 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interior, U.S. Geological Survey. 2005. 2004 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interior, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Tables 11 and 15. http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/. U.S. Department of the Interior, U.S. Geological Survey. 2007b. 2005 Minerals Yearbook, Crushed Stone . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interior, U.S. Geological Survey. 2008a. 2006 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. 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/c ommodity/cement/mcs-2008-cemen.pdf>. U.S. Department of the Interior, U.S. Geological Survey. 2010. Minerals Commodity Summaries, Cement 2010 . Washington, DC: U.S. Department of the Interior. Available at < http://minerals.usgs.gov/minerals/pubs/commodity/cement/mcs-2010-cemen.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking . Appendix C, p. 12. June Washington, DC: U.S. Environmental Protection Agency. Available at
< http://www.epa.gov/epaoswer/other/ckd/ckd/ckdriafn.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 1999. Office of Air Quality Planning and Standards. OAQPS Economic Analysis Resource Document. Available at <http://www.epa.gov/ttn/ecas/analguid.html>. U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses . EPA 240-R-00-003. September. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC: EPA. Available at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html>.7-7 U.S. Environmental Protection Agency (U.S. EPA). 2004. Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines. EPA420-R-04-007. Prepared by Office of Air and Radiation. Available at <http://www.e pa.gov/nonroad-diesel/2004fr/420r04007.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Available at <http://www.epa.gov/ttn/ecas/regdata/R IAs/Chapter%205--Benefits.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5

, and Regional Haze . April. Office of Air Quality Planning and Standards RTP, NC. Available on the Internet at <http://www.epa.gov/ ttn/scram/guidance/guide/final-03-pm-rhguidance.pdf> U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Available at <http://www.epa.gov/ttn/ecas/regdata /RIAs/6-ozoneriachapter6.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Guidelines for Preparing Economic Analyses: External Review Draft. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. Available at <http://yosemite.epa.gov/ee/epa/eermfile.n sf/vwAN/EE-0516-01.pdf/$File/EE-0516- 01.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. September. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. December. Available at <http://cfpub.epa.g ov/ncea/cfm/recordisplay.cfm?deid=201485>. U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry . Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available at
< http://www.epa.gov/ttn/ecas /regdata/RI As/portlandcementria_4-20-09.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment—RTP Divi sion. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U. S. Environmental Protection Agency (U.S. EPA). 2009c. Risk and Exposure Assessment to Support the Review of the SO 2 Primary National Ambient Air Quality Standards: Final Report. Office of Air Quality Planning and Standards, Research Triangle Park, NC. August.7-8 Available on the Internet at <http://www.epa.gov/ttn/naaqs/standards/s o2/data/Risk%20and%20Exposure%20Assessmen t%20to%20Support%20the%20Review%20of%20the%20SO2%20Primary%20National%20 Ambient%20Air%20Quality%20Standards-%20Final%20Report.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010a. Regulatory Impact Analysis for the Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http://w ww.epa.gov/ttn/ecas/ria.html>. U.S. Environmental Protection Agency (U.S. EPA). 2010b. Summary of Expert Opinions on the Existence of a Threshold in the C oncentration-Response Function for PM 2.5 -related Mortality: Technical Support Document. Comp iled by Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/ecas/benefits.html>. U.S. Environmental Protection Agency (U.S. EPA). 2010c. Air Quality Modeling Technical Support Document: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Comp iled by Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/scram/re ports/EPA-454_R-10-004.pdf>. 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%5CSABPR ODUCT.NSF/08E1155AD 24F871C85256E540 0433D5D/$File/council_adv_04002.pdf>. U.S. Environmental Protection Agency – Science Advisory Board (U.S. EPA-SAB). 2005. EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter (Second Draft PM Staff Paper, January 2005) . EPA-SAB-CASAC-05-007. June. U.S. Environmental Protection Agency – Science Advisory Board (U.S. EPA-SAB). 2007. SAB Advisory on EPA’s Issues in Va luing Mortality Risk Reductio n. EPA-SAB-08-001. October. Available at
< http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F i le/sab-08-001.pdf >. U.S. Environmental Protection Agency – Science Advisory Board (U.S. EPA-SAB). 2009a. Review of EPA’s Integrated Scien ce Assessment for Particulate Matter (First External Review Draft, December 2008). EPA-COUNCIL-09-008. May. Available at
< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/ 73ACCA834AB44A10852575BD0064346B/$File/EPA-CASAC-09-008- unsigned.pdf>.7-9 U.S. Environmental Protection Agency – Science Advisory Board (U.S. EPA-SAB). 2009b. Consultation on EPA’s Particulate Matter National Ambient Air Quality Standards: Scope and Methods Plan for Health Risk and Exposure Assessment . EPA-COUNCIL-09- 009. May. Available on the Internet at
< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/ 723FE644C5D758DF852575BD00763A32/$File/EPA-CASAC-09-009-unsigned.pdf>. 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. Av ailable on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB2852577450073877 6/$File/EPA-COUNCIL-10-001-unsigned.pdf>. U.S. International Trade Commission (USITC). 2006. Gray Portland Cement and Cement Clinker from Japan Investigation No. 731-TA-461 (Second Review). Publication 3856. Available at < http://hotdocs.usitc.gov/docs/pubs/701_731/pub3856.pdf>.

Van Oss, H.G., and A.C. Padovani. 2002. “Cement Manufacture and the Environment Part I: Chemistry and Technology.” Journal of Industrial Ecology 6(1):89-105.
Varian, H. 1992. Microeconomic Analysis. 3 rd Ed. New York: W.W. Norton & Company. Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.APPENDIX A SHORT-RUN REGIONAL PORTLAND CEMENT ECONOMIC MODELA-1 The Office of Air Quality Planning and St andards (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. Th is 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 0A-3 Table A-2. Revised Portland Cement Markets (10 6 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 2009 a 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.pdfA-4 A.2 Partial Equilibrium Model The partial equilibrium analysis performed fo r 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 structure 1 . 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 influen ce 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 Share i /Demand Elasticity) = Marginal Cost i . This equation shows the relationship between th e oligopoly model and perfect competition. The market distortion will typically be higher when market share i 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 (dq i ),

 change in imports (dq imports ),

 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 (c i ) is applied to each kiln, and the difference in the highest cost kiln with-regula tion and the highest cost kiln in the baseline approximates the plant’s change in the marginal cost of production (dMC i ). 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 ow n production de cision. 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 Revenue i = dmarginal Cost i

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 × (dpr ice/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, 200 0). In previous analyses, EPA econometrically

1 To highlight and make transparent the assumptions regardi ng seller behavior, this equa tion 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 REPRESENTATIONB-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

q i Domestic plant i’s output
FC i Plant fixed costs
VC i Plant variable costs

Step 1: First, we assume the plant’s goal is to maximize profits: 

### ( ) ( ) iiii i FCqVCqQPQ

− − = )( max

i q 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 d ecisions influence the market price:

0 q P

i ≠ ∂ ∂

 Plant (i) output decisions do not affect thos e 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 ii i

i = ∂

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ∂

∂ +

.

Step 6: Use and apply the following definitions:

elasticity demand inverse 1 q Q P P(Q)

i == ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ∂

∂
η

share market splant' Qq Q q 1 i i = ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= −

.

We derive the following expression:


## ( )

i ii i

q qVC Q 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 1 Q q
1 2 i

= ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

+ Q q P dQ Q P dq dP i i η

η η .APPENDIX C SOCIAL COST METHODOLOGYC-1 The Office of Air Quality Planning and St andards (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. Th is 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, P 0 , and the quantity, Q 0 . Without the regulation, the total benefits of consuming cement are given by the area under the demand curve up to the market output, Q 0 . This equals the area filled by the letters ABCDEFGHIJ. The total variable cost to society of producing Q 0 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 Q 0 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 th e suppliers of cement and reflects the value they receive in the market for producing Q 0 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., P 0 . Consumer surplus is thereby given by the area ABC.C-2 Q 1 P 1

D

$

Output P 0

P 1 c

P 0 c

Q 0 C B A

D E

F

J I 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 occu rs at a higher market price for cement, P 1 , and a lower level of output, Q 1 . In this scenario, the total benefits of consumption are equal to areaABDFI and the total variable costs of production are equal to area FI. This yields a withregulation social surplus equal to area ABD w ith area BD representing the new producer surplus and area A being the new consumer surplus. The so cial 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

Δ PS = 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

Δ CS = – (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 th e 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 = Δ PS + Δ CS = – (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 competi tive structure of the markets for cement. A comparison with the outcome un der 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, P 0 C , is now equal to MC and Q 0 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 P 1 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 engineeringC-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 Q 1 , 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.,

SC imp – SC perf = − [(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 PM 2.5 -RELATED MORTALITYD-1 

## Summary of Expert Opinions on th e Existence of a Threshold in the Concentration-Respon se Function for PM 2.5 

## -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 estima tion, 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, wh ich 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, contin ue 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 Environmen tal 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, CAD-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, DCD-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 PM 10 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 PM 2.5 and mortality risk observed in cohort studies that extends below present-day regulations of 15 μg/m 3 for mean annual levels, without a discernable “safe” threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008)
Pg 2364: “The PM 2.5 concentration– cardiovascular risk relationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m 3 (the 2006 annual NAAQS level) without a discernable “safe” threshold.” Pg 2365: “This updated review by the AHA wr iting 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 PM 2.5 concentrations even below present-day annual (15 μg/m 3 ) and 24-hour (35 μg/m 3 ) 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, MPHD-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, MPHD-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 concentratio n-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 i ndividual 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, O 3 , lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely mechanisms include nonlinear processes for so me 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 ad mission and mortality studies that examined the PM 10 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 th e 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 identi fied that attempt to characterize the shape of the concentration-response curve along with possi ble 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 ho spital admissions and ED visits and mortality associated with short-term exposure to PM 10 and mortality associated with long-term exposure to PM 2.5 . “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 theseD-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, lo g-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-thres hold, log-linear model; however, one study did observe heterogeneity in the shape of the con centration-response curv e 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 infl uence 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 ev idence was observed to suggest that a threshold exists in the association between long-term exposure to PM 2.5 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 rela tionship 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 areD-8 several challenges in determining and interpre ting 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) investigat e 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 PM 10 data in 20 cities from the 1987-1994 NMMAPS data. They reported that the spline model, combined across the cities, showed a linear relation withoutindicating a threshold for the relative risks of death for all-causes and for cardiovascularrespiratory causes in relation to PM 10 , but “the other cause” deaths (i.e., all cause minus cardiovascular-respiratory) showed an apparent threshold at around 50 μ g/m 3 PM 10 , as shown in Figure 6-35. For all-cause and cardio-respirator y 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 PM 10 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 μ g/m 3 , between 25 and 34 μ g/m 3 , between 35 and 44 μ g/m 3 , and 45 μ g/m 3 and above. In the model, days with concentrations below 15 μ g/m 3 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-ci ty variation in the concentration-response relationship in this study.

“PM 10 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 μ g/m 3 ) 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 lo g-linear model. However, in an ancillary analysis of the concentration-response curves for the largest citi es 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 supportD-9 no-threshold relationships between PM 10 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 rela tionship 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 Protec tion 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, NCD-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, MAD-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 Re search, 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, NCD-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, NCD-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 O ccupational Health, Emory University,

Atlanta, GA Dr. Frances Silverman , Gage Occupational and Environmenta l 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, MAD-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 sp ecific cut-point, and not simply try different cutpoints to see what effect this has on the analys is. 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/sa b/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 Environmenta l Physiology, Department of Environmental

Health, Harvard School of Public Heal th, Harvard University, Boston, MA Dr. Ellis B. Cowling , University Distinguished Professor At-Large Emeritus, Colleges of Natural

Resources and Agriculture and Life Sciences, No rth Carolina State University, Raleigh, NCD-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 Caroli na State University, Raleigh, NC Dr. Armistead (Ted) Russell , Professor, Department of Civil a nd 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 Schoo l 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 Enviro nmental Medicine, New York University

School of Medicine, Tuxedo, NY
Dr. Helen Suh MacIntosh , Associate Professor, Environmenta l 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 Polluti on 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, ILD-16 Dr. Kathy Weathers , Senior Scientist, Cary Institute of Ecosystem Studies, Millbrook, NYD-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 Repor t, 140, Health Effects Institute, Boston, MA.
Pg 119: [About Pope et al. (2002)] “Each 10- μ g/m 3 increase in long-term average ambient PM 2.5 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 PM 2.5 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 forwardedthe 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 PM 2.5 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 th e exposure-response function within the range of observed PM 2.5 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, NYD-18 Dr. Stephanie London, MD, PhD , Senior Investigator, Epidemio logy 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 Pulm onary 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 WashingtonD-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 μ g/m 3 .”
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 PM 2.5 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/eca s/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 eff ect estimate distributions above and below some cut-off concentration. The adjustments these experts made to median estimates and/or uncertainty at lower PM 2.5 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 μ g/m 3 , and a 20% chance that it would fall between 5 and 10 μ g/m 3 .”
Pg ix: “Compared to the pilot study, experts in this study were in general more confident in a causal relationship, less likely to incorporate th resholds, and reported higher mortality effect estimates. The differences in results compared wi th 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 PM 2.5 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 PM 2.5 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 theo retical 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 give n 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 c ohort 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 studi es 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 th at 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 exposu re and a long follow-up period. Furthermore, two experts (B and I) cited studies documentin g 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 toxicol ogical 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 th at 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 μ g/m 3 , and a 20% chance that it would fall between 5 and 10 μ g/m 3 .”
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 PM 2.5 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 μ g/m 3 ) = 0.8 and P(5 μ g/m 3 < T ≤ 10 μ g/m 3 ) = 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 Enviro nmental 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 DentistryD-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 pur pose of estimating public health impacts, the Panel favored the primary use of an assumed threshold of 10 μ g/m 3 . The original approach of using background or LML, as well as the other po stulated 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 theexistence 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 Re search 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, En vironmental Health , School of Public

Health, Harvard University (HSPH), Boston, MAD-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 Polluti on 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, NVD-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 PM 2.5 and both allcause and cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. Graphical analyses of th ese 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, CAD-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, MDD-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 func tions may influence the overall estimate of benefits. The shape is particularly important fo r 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 concen trations 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 PM 10 and PM 2.5 , 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 PM 10 in 88 cities indicates that the concentration-response functions are not due to th e high concentrations and that the slopes of these functions do not appear to increase at hi gher 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 polluti on concentrations have similar effects per unit change in concentration as those studies conducte d 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 (D ockery 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 pollutio n regulations affect even larger groups of people (millions). It is reasonabl e to assume that among such large groups susceptibility to air pollution healthD-28 effects varies considerably across individuals and de pends 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 th e 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 ch anges 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/m 3 ) mean threshold for mortality associated with long-term exposure to PM 2.5 . The low-end benefits estimate assumed a 15- μg/m 3 threshold for all PM-related health effects. The st udies, however, included concentrations as low as 7.5 μg/m 3 . 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 c oncentration-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 concen tration-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 por tion 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 shou ld 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 relativ e 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 woul d have resulted in only a slight widening of the probability distribution for avoided mort ality 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 judgme nts 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 epidemiologi cal studies assume that these functions are linearD-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, epidem iological 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 conc entration-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 co rrelations between model components that are structurally dependent upon each other, or th e 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 inco rporating 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 fo r 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, BerkeleyD-30 Dr. PETER A. VALBERG , Gradient Corporation, Cambridge, Massachusetts
Dr. SCOTT L. ZEGER , Johns Hopkins University, Baltimore, Maryland

## 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 Port land Cement .................................................................................. 2-3 2.1.3 Production Costs

................................................................................................. 2-3

2.2 The Demand Side

............................................................................................................. 2-8

2.3 Industr y 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 Econom ic Impact Model Results ........................................................................ 3-9

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

3.4 Social Cost Estim ates ..................................................................................................... 3-19

3.5 Energy Impacts .............................................................................................................. 3-22

## iv 3.6 Assessment..................................................................................................................... 3-24



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

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

4.2 Sales and Revenue Test Screening Analy sis.................................................................... 4-1

4.3 Additional M arket Analysis ............................................................................................. 4-3

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



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

5.1 Synopsis ......................................................................................................................

..... 5-1

5.2 Photochem ical Mode l Background .................................................................................. 5-1

5.3 Model Dom ain and Grid Resolution ................................................................................ 5-2

5.4 Em issions Input Data ....................................................................................................... 5-2

5.5 Model Results: Air Quality Im pacts ................................................................................ 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 PM 2.5 

### Human Health Benefits .................................................................. 6-2 6.2.1 Methodology Im provements since Proposal ....................................................... 6-2 6.2.2 Benefits Analy sis Approach ............................................................................... 6-3 6.2.3 Health Im pact Analysis (HIA) ............................................................................ 6-4 6.2.4 Estimating PM 2.5 

### -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 Disb enefits ......................................................................................................... 6-20 6.4.1 PM 2.5 

### 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 Nonm onetized Benefits ....................................................................... 6-20 6.5.1 Other SO 2 

### and PM Benefits .............................................................................. 6-25

## v 6.5.2 HAP Benefits .................................................................................................... 6-27

6.6 Lim itations and Uncertainties ........................................................................................ 6-41 6.6.1 Monte Carlo Analy sis ....................................................................................... 6-41 6.6.2 Alternate Co ncentration-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 Co mparison 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 PM 2.5 

### -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 Compe titive Building Materials: 2003 to 2008................ 2-10 2-6. Distribution of Cement K ilns in the United States ..................................................... 2-13 2-7. Historical U.S. Cement Price ...................................................................................... 2-23 2-8. Deviation from National Average Ceme nt Price per Metric Ton by Region: 2005...........................................................................................................................

.. 2-24

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

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

6-1. Total Monetized Benefits for the Fi nal 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 th e Final Cement NESHAP and NSPS in 2013...........................................................................................................................

.. 6-18 6-6. Percentage of Total PM-Related Mo rtalities Avoided by Baseline Air Quality Level for Final Portland Ce ment NESHAP and NSPS ............................................... 6-19 6-7. Cumulative Percentage of Total PM -related Mortalities Avoided by Baseline Air Quality Level for Final Port land 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 (R espiratory) Risk from HAP exposure from outdoor sources (NATA, 2002) .......................................................................... 6-29 6-10. Reductions in Total Mercury Deposi tion (μg/m2) in the Eastern U.S. ...................... 6-33 6-11. Reductions in Total Mercury Deposi tion (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, Soci al 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 Ma rket 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 Clinke r Capacity by State: 2005 .............................................. 2-12 2-7. Number of Kilns and Clinker Capa city by Age and Process Type ............................. 2-14 2-8. Clinker Capacity, Production, and Capaci ty Utilization in the United States: 2000 to 2005 ............................................................................................................... 2-15 2-9. Capacity Utilization Rates by State: 2005 .................................................................. 2-17 2-10. Cement Manufacturing Employme nt (NAICS 327310): 2000 to 2005 ...................... 2-18 2-11. Ultimate Parent Compan y Summary Data: 2005 ........................................................ 2-19 2-12. Historical U.S. Cement Statistics (10 6 

## metric tons) .................................................... 2-21 2-13. U.S. Cement Trade Data: 2000 to 2007 ...................................................................... 2-22

3-1. Summary of Direct Total Annuali zed 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 Mark et ($/metric tons): 2005 ............................................ 3-7 3-4. Portland Cement Markets (10 6 

## 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 Ma rket Impacts: 2005 ............................................................. 3-12 3-8. Distribution of Indus try Impacts: 2005 ....................................................................... 3-13 3-9. Cement Plants with Significan t Utilization Changes: 2005 ........................................ 3-15 3-10. Job Losses/Gains Associated with the Final Rule ...................................................... 3-17 3-11. Distribution of Social Costs ($10 6 

## ): 2005 ................................................................... 3-21 3-12. U.S. Cement Sector Energy Consumption (Trillion BTUs): 2013 ............................. 3-24

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

5-1. Geographic Elements of Domains Us ed in Photochemical Modeling .......................... 5-3 5-2. Cement Kiln Emissions in 2005 Base a nd Estimated Future Year (2013) in tons per year .......................................................................................................................... 5-3

6-1. Human Health and Welfare E ffects of Pollutants Affected .......................................... 6-7 6-2. Summary of Monetized Benefits Esti mates for Final Cement NESHAP and NSPS in 2013 (millions of 2005$) .............................................................................. 6-14 6-3. Summary of Reductions in Health Incidences and M onetized Benefits from PM 2.5 

## Benefits for the Final Cement NESHAP and NSPS in 2013 (95 th 

## 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 PM 2.5 

## 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 CO 2 

## ) for 2013 ........................ 6-17 6-7. Monetized Disbenefits of CO 2 

## 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. Environm ental 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 Perfor mance 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 part iculate matter (PM) from kilns located at a major or an area sources, and hydrochloric acid (H Cl) from kilns and located at major sources. EPA is also adopting separate standards for th ese pollutants that appl y during startup, shutdown, and operating modes. Finally, EPA is adopting performance specifications for use of Hg continuous emission monitors (CEMS) and updati ng recordkeeping and testing requirements. The final amendments to the NSPS add or revise, as applicable, emission limits for particulate matter (PM), opacity, nitrogen oxides (NO x 

## ), and sulfur dioxide (SO 2 

## ) 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 requ ired to develop a regulatory imp act analysis (RIA). The RIA includes an economic impact anal ysis (EIA) and a small entity impacts analysis and documents the RIA methods and results.

1.1 Executive Summary The key results of the R IA are as follows:

 Options Analyzed: EPA’s analysis focuses on the results of the final NESHAP and NSPS. We also present additional inform ation on different combinations of the regulatory programs to help stakeholders be tter understand the si ze 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 resu lts of analyzing the final NESHAP and NSPS.

 Engineering Cost Analysis: EPA estimates that total annua lized costs with the final NESHAP and NSPS will be $466 million (2005$).

 Market Analysis: The partial-equilibrium economi c 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 pr oduction 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 ne gative operating prof its and significant utilization changes that could temporarily idle until market demand conditions improve. The plants have unit compliance cost s close to $8 per ton of clinker capacity and $116 million total change in operating pr ofits. Since these plants account for approximately 8% of domestic capacity, a decision to permanently shut down these plants would reduce domestic supply and c ould lead to additiona l projected market price increases and re ductions in polluti on control costs.

 Employment Changes : EPA uses two methods for estimating employment impacts. A simplistic, limited assessment narrowly fo cused on output changes in the Portland cement industry indicates that the final ru le’s gross impact on employment is 1,500 job losses. However, this approach inherent ly overstates job losses, as it is based on the assumption that employment is propor tional to output, and because it ignores offsetting general equilibrium and other effect s as discussed in detail in Chapter 3. A more sophisticated analytical approach th at includes other ty pes 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 u tilization choose to idle or close rather th an operate at a low (55.5. percent) capacity utiliza tion. The social cost estima tes 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 previ ous cement industry rulemakings and Section 2, Appendix A, and Appendix B of this RIA. Under baseline conditions without regulation, the existing domestic cement pl ants 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 ma rkets covered by the final rule (i.e., the observed baseline market price is higher th an the [unobserved] market price that a model of perfect competition would predic t). The imposition of additional regulatory costs tends to widen the gap between price and marginal cost in these markets and contributes to additi onal 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 th e 16 new kilns to start up by 2013 all 16will add alkaline scrubbers and ACI systems, th e 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 comp any 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 governm ent). 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 implementa tion (2013), EPA estimates that the total monetized benefits of the fina l 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 do llars for the year 2013. Using alternate relationships between PM 2.5 

## and premature mortality suppl ied 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 NO x 

## , 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 disbenefit s associated with increased electricity usage by the control devices.

 Net Benefits: In the year of full implementa tion (2013), EPA estimates the net benefits of the final NESHAP and NSPS ar e approximately $6.5 billion to $17 billion and $5.8 billion to $15 billion, at 3% and 7% discount rates, respectively. All estimates are in 2005 dolla rs for the year 2013.

1.2 Organization of this Report
The rem ainder of this report supports and details the methodology a nd the results of the EIA:

 Section 2 presents a profile of the affected industry.

 Section 3 describes the economic imp act 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 th e 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 Benefits b 

### $7,400 to $18,000 $6,700 to $16,000 Total Social Costs c 

### $926 to $950 $926 to $950 Net Benefits $6,500 to $17,000 $5,800 to $15,000

Nonmonetized Benefits d 

### 4,400 tons of NO x 

### (includes ener gy disbenefits) 5,200 t ons of or g anic HAPs 5,900 tons of HCl 16,400 pounds of mercur y Health effects from HAPs, NO 2 , 

### and SO 2 

### exposure Ecos y stem effects Visibilit y impairment Final NSPS only 3% Discount Rate 7% Discount Rate Total Monetized Benefits b 

### $510 to $1,300 $460 to $1,100 Total Social Costs c 

### $72 $72 Net Benefits $440 to $1,200 $390 to $1,000

Nonmonetized Benefits d 

### 6,600 tons of NO x 

### 520 tons of HCl Health effects from HAPs, NO 2 , 

### and SO 2 

### exposure Ecos y stem effects Visibilit y impairment Final NESHAP only 3% Discount Rate 7% Discount Rate Total Monetized Benefits b 

### $7,400 to $18,000 $6,700 to $16,000 Total Social Costs c 

### $904 to $930 $904 to $930 Net Benefits $6,500 to $17,000 $5,800 to $16,000

Nonmonetized Benefits d 

### 5,200 tons of or g anic HAPs 5,900 tons of HCl 16,400 pounds of mercur y Health effects from HAPs, SO 2 

### exposure Ecosystem effects Visibilit y impairment Alternative: More Stringen t NSPS and Final NESHAP 3% Discount Rate 7% Discount Rate Total Monetized Benefits b 

### $7,400 to $18,000 $6,700 to $16,000 Total Social Costs c 

### $955 to $979 $955 to $979 Net Benefits $6,500 to $17,000 $5,700 to $15,000

Nonmonetized Benefits d 

### 7,800 tons of NO x 

### (includes ener gy disbenefits) 5,200 tons of or g anic HAPs 5,900 tons of HCl 16,400 pounds of mercur y Health effects from HAPs, NO 2 , 

### and SO 2 

### exposure Ecos y stem effects Visibilit y impairment

a All estimates are for the implementation year (20 13) and are rounded to two significant figures. b The total monetized benefits reflect the human health benefits a ssociated with reducing exposure to PM 2.5 through reductions of

directly emitted PM 2.5 and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits include many but not all health effects associated with PM 2.5 exposure. Benefits are s hown as a range from Pope et al. (2 002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortal ity 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 th e multimarket model using surplus changes results in the same soc ial

costs for both discount rates. 

### 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 (55.5 percent) capacity utilization.

## 1-5 d Due to data, methodology, and resource limitati ons, we were unable to monetize the be nefits associated with these categories of

benefits.

## 2-1 SECTION 2 INDUSTRY PROFILE
Hydraulic c ement (primarily Portland cemen t) 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 work s 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 va lue of total U.S. sales, including imported cement, was about $11.8 billion, with an averag e value of approximately $100 per metric ton. The vast majority of cement sales went to re ady-mixed concrete producers and concrete product manufacturers (88%). Since 2003, the United Stat es has relied on cement imports to meet approximately 20% to 23% of its consumpti on needs. However, this share dropped to approximately 17% in 2007 as overall constr uction demand for cement fell (DOI, USGS, 2008b).
The remainder of this section provides an introduction to the Po rtland cement industry. The purpose is to give the reader a general und erstanding of the techni cal 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 da ta. Section 2.2 discusses the uses, consumers, and substitutes for cement. Section 2.3 summari zes the organization of the Portland cement industry. The industry profile concludes with a di scussion 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 m anufacturing proce ss of an integrated cement plant includes

 quarrying and crushing the raw materials,

 grinding the carefully proportioned materi als 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 pr ocess and the dry process. In the wet process, water is added to the raw materials during the ble nding process and before feeding the mixture into the rota ry kiln. In contrast, the dry pr ocess feeds the blended materials directly into the rotary kiln in a dry state. Newer dry process plants also use preheater and precalciner technologies that part ially heat and calcine the blended raw materials before they enter the rotary kiln. These tec hnologies 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 falli ng 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 cem ent manufacturers produce a variet y of types of cement in the United States designed to meet different requirements. The Am erican Society for Testing Materials (ASTM) specification C-150 provides for eight types of Portla nd cement: five standard types (I, II, III, IV, V) and three additional types th at include air-entraining properties (IA, IIA, IIIA) (PCA, 2008a). We describe these below.
Types I and IA: These types are the usual pr oduct used in general concrete construction, most commonly known as gray cement because of its color.
Types II and IIA: These types ar e intended for use when mode rate heat of hydration is required or for general concrete construc tion 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 sili cate than regular Portland cements.
Type IV: This type contains a lower percen tage of tricalcium silicate and tricalcium aluminate than Type I, thus lowering the heat evolution. Consequentl y, 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 ma jority of Portland cement shipments 1 

## in 2005 were Types I and II grey cement. However, Type V (s ulfate-resisting) is a growing market (DOI, USGS, 2007a); since 2000, Type V cement has incr eased its share of sh ipments from 4% to 15%. Shipment shares for other types of ceme nt remained constant during this period.

2.1.3 Production Costs Portland cement is produced using a combin ation 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 employ ees (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 Plan ts in the United States to Domestic Customers, by Type a, 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% White d 

### 894 1% 1,190 1% Blended 1,296 1% 3,160 3% Expansive and regulated fast setting 60 0% 6 0% Other e 

### 1,786 2% 1,997 2%

Total f 

### 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, approxim ately 159.7 milli on tons of raw materials were required to produce approximately 95.5 million tons of cem ent in 2005 or 1.67 tons of raw materials per ton of cement. Table 2-2 summarizes the amount of raw material inputs us ed 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 e ssentially 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 fo r the U.S. Cement Industry: 2000 to 2005

2000 2001 2002 2003 2004 2005 Raw material input (10 3 

### metric tons) 144,949 147,300 153,100 150,500 158,200 159,700 Cement production (10 3 

### 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 Cem ent 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% mo re 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 ma nufacturing 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 pl ants labor costs have risen in recent y ears (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 aver age hourly earnings of produc tion 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 th e 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 Associatio n. December 2005. U.S. and Canadian Labor-Energy Inpu t 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 As sociation. 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 consum ption 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 electricit y, 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) (s ee Table 2-5). As these data show, wet process plants are typically more energy intensive, c onsuming 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 ener gy 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 consumpti on 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 As sociation. 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 As sociation. December 2005. U.S. and Canadian Labor- Energy Input Survey 2005

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



## 2.2 The Demand Side
The dem and for Portland cement is consider ed a “derived” demand because it depends on the construction demands for its end product—conc rete. A recent study by the U.S. International Trade Commission suggests that 0.192 metric tons of grey Portla nd 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 represente d only a small share of the total value of construction expenditure s (less than 2%).
Concrete is used in a wide variety of cons truction applications, in cluding residential and commercial buildings, and public works projects su ch as the national highway system. As shown in Figure 2-4, ready-mixed concre te producers have historically accounted for over half of the Portland cement consumpti on. Although government and mis cellaneous expenditures saw substantial increases in the ear ly 1990s, their consumption shar e 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 2000Metric 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 Sta tistics for Mineral and Material Commodities in the United States: Cement End Use Statistics.” U.S. Geological Su rvey Data Series 140, Ve rsion 1.2. Available at http://minerals.usgs.gov/ds/2005/140/

. 

## 74% of cement sales in 2005, followed by concre te product manufacturers (14%), contractors (6%), and other (6%) (Kelly and Matos, 2007a).
Cement competes with other construction mate rials such as steel, asphalt, and lumber. Lumber is the primary substitute in the residentia l 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, conc rete 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 pr ice 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 Co mpetitive Building Materials: 2003 to 2008
Source: Portland Cement Association. 2008b. “Marke t 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 descriptio n of m arket characteristics (i.e ., degree of con centration, entry barriers, and product differentiat ion) 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 se lls. For example, in markets w ith large numbers of sellers and identical products, firms are unlikely to be able to influence market pric es via their production decisions (i.e., they are “price take rs”). However, in markets with few firms, significant barriers to entry (e.g., licenses, legal rest rictions, or high fixed costs), or products that are similar but can be differentiated, the firm may have some degr ee of market power (i.e., set or significantly influence market prices).

Cement sales are often concen trated locally among a sma ll 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 empiri cal literature has typi cally pointed to Cens us of Transportation data showing over 80% of cement shipments we re made within a 200-mile radius (Jans and Rosenbaum, 1997) 1 

## and reported evidence of high tran sportation costs pe r 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 co sts; this reduces entry incentives for smallsized cement plants. Using recent data for pl anned 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, consum ers are likely to view cement produced by different firms as very good substitutes. Ameri can Society for Testing and Materials (ASTM) specifications tend to ensure uniform quality, a nd recent industry reviews (USITC, 2006) suggest that there is little or no bra nd loyalty that allows firms to differentiate their products.

2.3.2 Manufacturing Plants During 2005, 107 cem ent manufacturing plants w ith 186 cement kilns were operating in the United States. This section describes the lo cation, age, production ca pacity, and employment of these manufacturing facilities. Section 2.3.2 co ncludes 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 cem ent kilns in the United States and clinker capacity. The top five states in order of clinker capacity ar e California, Texas, Pennsylvania, Florida, and Alabam a. 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 ha d 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 (10 3 

### 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 (10 3 

### 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 Asso ciation (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 St ates used the dry m a nufacturing process, and it accounted for 83% (78 million metric tons) of national clinker capacity. The growing prevalence of the dry process am ong cement manufacturers is part of a long-term trend. As the data in Table 2-7 indicate, no new wet clinker cap acity 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 (10 3 

### 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 Capaci ty by Age and Process Type (continued)

No. Kilns Clinker Capacity (10 3 

### 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 y ears use the dry manufacturing process. These new kilns account for 64% (60 milli on metric tons) of national clinker capacity.

2.3.2.3 Production Capacity and Utilization Between 2000 and 2005, apparent annual cli nker capacity grew approxim ately 17%, while clinker production grew by approximately 14% (Table 2-8). Because capacity tends to grow more rapidly than production, total capacity utiliz ation decreased sligh tly 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 (10 3 

### metric tons) 89,264 100,360 101,000 102,000 105,000 104,000

Production (10 3 

### 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% eac h year, while production grew an average of 2%. As a result, capacity utilization has risen sl owly since 2001. However,

## 2-16 throughout these movements in clinker capacity a nd production, capacity u tilization tended to remain between 80% and 85%.
Capacity utilization often varies by geogra phic region as a result of fluctuations in regional construction activity. Fo r example, 2005 data show that Idaho, Montana, and Nevada shared a capacity utiliz ation rate of 95.5%—well above the na tional average. In contrast, South Carolina used only 64.5% of its clinker capacity . Table 2-9 provides a complete listing of capacity utilization ra tes by state in 2005.

2.3.2.4 Employment Each year, the Annual Survey of Manufactur es (ASM) collects em ployment, payroll, sales, and other data for all manufacturi ng establishments. Table 2-10 summarizes the employment data collected by the ASM for th e cement manufacturi ng 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 ha s been increasing at a s lower 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 amount s to a $5.9 billion investment (PCA, 2007).
In addition to these expected capacity expans ions, likely changes in U.S. specifications allowing the use of limestone in Portland cem ent could also increas e production capacity. According to the PCA, domestic cement supply could increase by as much as 2 million additional tons by 2012. Increase s 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 inform ation and publicly available sa les and employment databases to identify the chain of ownership by accounting for subsidiari es, divisions, and joint ventures to appropriately gr oup 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 Commer ce, Bureau of the Census. 2006. 2005 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.p df>. As obtained on March 14, 2008. U.S. Department of Commerce, Bureau of the Census. 2003. 2001 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Government Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.p df>. As obtained on March 14, 2008.



## 2.3.3.1 Distribution of Small and Large Companies
Firm s 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 ow ning 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 com panie s 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 Ce ment Co.); Ash Grove Cement Co.; Texas Industries, Inc.; Italcementi S.p.A.; Taiheiyo Cement Corporatio n; Titan Cement; and VICAT. The top 5 had about 57% of total U.S. clinker capacity, and the top 10 acc ounted 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 us ed 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 ($10 6 

### ) Employment Type Small Business Plants Kilns Clinker Capacity (10 3 

### 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 

### $150 b 

### <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 Compan y Summary Data: 2005 (continued)

Ultimate Parent Name Annual Sales ($10 6 

### ) Employment Type Small Business Plants Kilns Clinker Capacity (10 3 

### 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 $50 b 

### <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 [elect ronic resource]. Dayton, OH: LexisNexis.



## 2.3.3.3 Company Revenue and Ownership Type
Ce ment manufacturing is a capit al-intensive industry. The vast majority of stakeholders are large global companies with sales exceedi ng $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 owne d 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 (o r 7%) were joint ventures.

2.4 Markets
Portland cem ent is produced and consumed dome stically 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 pr oduced cement to other countries. We provide value, quantity, and price trends over the past de cade for Portland cement when detailed statistics

## 2-21 are available. In the case of international trade, we can repo rt 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 dom estic shipments of Portla nd 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 a nd equaled 8.9 million tons in 2007. As Table 2-12 shows, shipments to customers increased stead ily since 2000, reaching 128 million tons in 2006. However, affected by declines in the housi ng market, the shipments fell by 9% in 2007.
Table 2-12. Historical U.S. Cement Statistics ( 10 6 

## 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://m inerals.er.usgs.gov/minerals/pubs/ commodity/cement/>.



## 2.4.1.2 International Trade
Cem ent imports are a significant share of domestic consumption (approximately 20%); they also grew by 30% from 2000 to 2006 (see Ta ble 2-13). Major impor ting 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 an d construction activity d eclines 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 (10 6 

### metric tons) 0.7 0.7 0.9 0.8 0.7 0.8 1.5 1.9 Imports (10 6 

### 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 pr oduction. However, the level of U.S. exports has increased during the last 2 years. In 2007, U.S. exports tota led 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 Ba hamas, 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 cem ent 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 pric e of cement is at its highest level in over 2 decades (approximately $100). Because of tran sportation 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 Ca lifornia were higher than the national averages, while prices in Texas, Indi ana, and South Carolina were among the lowest.

2.4.3 Future Projections Although estim ates of future cement dema nd are not publicly available, the Energy Information Administration provide s projections for the real valu e 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 $110F.O.B. Cement Price ($/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. “Histo rical 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. 20 04–2007: 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 Yor k 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 Interi or, 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 m ake rs 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 presente d 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 cons umer and producer surplus).
The Office of Air Quality Planning and Sta ndards (OAQPS) has adopted the standard industry-level analysis describe d 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-peri od static partial-equilibrium model to compare pre-policy cement market baselines with expected post-po licy outcomes in these markets. The benchmark time horizon for the analysis is the intermedia te run where producers have some constraints on their flexibility to adjust fact ors of production. This time horizon a llows us to capture important transitory impacts of the program on existing produ cers. 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 produ cer and consumer surplus).

Absent forecasts and the uncertainties offuture economic baselines, the partialequilibrium market analysis can only cover a su bset 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 foll owing sections, the market analysis covers $378 million of the total $466 million in regulatory pr ogram 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 milli on 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 co mplete 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 am endments to the NESHAP from the Portland cement manufacturing industry and (NSPS for Portland ce ment plants. The final amendments to the NESHAP add or revise, as applicable, emission lim its 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 pollutant s 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 fina l amendments to the NSPS add or revise, as applicable, emission limits for particulate matter (PM), opacity, nitrogen oxides (NO x 

## ), and sulfur dioxide (SO 2 

## ) for facilities that commence constr uction, 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 he lps stakeholders better understand the size and scope of the each. These include

 final NSPS only,

 final NESHAP only, and

 alternative: more stringe nt NSPS and final NESHAP.
For the year 2013, EPA’s engineerin g cost analysis estimates the total annualized costs of the final NESHAP and NSPS are $466 million (i n 2005 dollars) (see Table 3-1). These costs include a variety of pollution control expendi tures: equipment installation, operating and maintenance, recordkeeping, and performance-testi ng activities. Cap ital costs are a nnualized at a discount rate of 7% 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 remaini ng costs ($11 million) are associated with the final NSPS limits for SO 2 

## and NO x 

## . Figure 3-1 illustrates the distribution of annualized compliance costs per metric ton of clinker capaci ty by different combinations of the regulatory programs. In Table 3-2, we report state-level su mmary statistics for total annualized compliance costs per metric ton of clinker capacity fo r 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: $40 a 

### $56 a


### 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 SO 2 

### and NO x . 

### Alternatively, the 7 kilns would also incur an additional $27 in compliance costs to meet the stringent NSPS limits for SO 2 

### and NO x 

### .

## 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 >$5.00NumberofKilns

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 Annuali zed 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 ec onomic 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 dur ing 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
Ce ment sales are often concen trated locally among a sma ll 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 empiri cal literature has typi cally pointed to Cens us of Transportation data showing over 80% of cement shipments we re made within a 200-mile radius (Jans and Rosenbaum, 1997) 2 

## and reported evidence of high tran sportation costs pe r 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 numbe r of independent region al markets with each having a single market-clearing price.
The freight-on-board (f.o.b.) price of Portland cement for each regiona l market is derived as the production weighted average of the stat e level f.o.b. prices repo rted 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 adju stments 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 ce ment company acts in the best interest of its shareholders and maximizes profits. When deciding whether to make another t on 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 co st, 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 co st) 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 ceme nt industry has two characteristics that influenced EPA’s modeling choice relating to mark et structure. First, high transportation costs and other production economics tend to limit the num ber of sellers (particularly over a short time horizon), so each seller has a subs tantial regional market share. Timely market entry is also constrained by the high capital cost s that involve purchases and cons truction of larg e 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 industr y 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 cemen t at lower unit costs; this reduces entry incentives for sm aller capacity cement plants.

## 3-8 Table 3-4. Portland Cement Markets (10 6 

## 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 c ontinued 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 wh en 1) the number of companies is limited and 2) the companies sell similar or identical products. 4 

## Under these circumst ances, the short-run production decision rule that a cement compa ny 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 produ cing 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 ot her 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 ma rket prices tend to be higher than marginal production costs (i.e., there may be a preexisti ng market distortion within cement markets prior to regulation). 5 

## The size of the existing distortion depe nds 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 de mand elasticity is inelastic) (see Appendix A).

3.2.3 Economic Impact Model Results

3.2.3.1 Market-Level Results
Market-level im pacts 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 in creases by 5%, or $4.50 per metric ton, while overall U.S. cement consumption falls by appr oximately 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 th e highest in regions with high compliance costs per metric ton. For example, the Cincinnati market price increa se ($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 th e 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 cost s between regions are high enough that interregional trade is unlikely to occur, at least in the short run. The regiona l 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 ce rtain regions. This tends to reinforce U.S. production declines because cement plants have mo re 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 region s 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 producti on 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 adjustment s 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, com pliance costs va ry 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 iden tified plants with operating profit increases and losses. Absent plant-specific data, EPA assume d each plant’s baseline profits were consistent with the median operating profit margin repo rted 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’ complia nce burden is offset by higher regional cement prices, and total plant operating profits increas e by $147 million. These 44 plants have lower unit compliance costs compared with their competitors.

## 3-12 Table 3-7. Summary of Regi onal 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%



## EPA notes that since conducting this analysis, one high mercury-emitting plant has invested in control technology estimated to re duce emissions by approximately 85 percent. The current analysis does not include these actual cost s in the baseline but rather estimates aggregate compliance costs based on the averaging methodologies applied to all other modeled plants. In addition, because this investment occurred after the analysis was conducted, the baseline benefits likewise do not include the approximately 85 % emissions reduction. Finally, EPA did not estimate the change in social costs that would oc cur if the 2 high mercury-emitting plants were to shut down, because the Agency believes these pl ants will ultimately be able to meet the emissions limit by applying multiple mercury cont rols, which were accounted for in the cost

## 3-13 analysis. EPA also acknowledges that if these 2 high mercury-emitting plants ultimately are able to meet the emissions limit, they will not likely be able to do so by the required compliance date.
Within the group of plants with operating lo sses, EPA identified 10 domestic plants with negative operating profits and signi ficant utilization changes that could temporar ily 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 summ ary results for Tables 3-5, 3-6, 3-7, and 3-8 but are also reported separately in Table 3-9.
If the plant owners did deci de to permanently shut down these plants, the reduction in domestic supply would lead to add itional projected market price incr eases. This would lead to an increased production at other plan ts, a possible increase in imports (depending if the plant that chooses to close is in a market where imports ar e 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 woul d be lost if the ten plants dropped from 55.5% capac ity 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 multiplie d 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 w ith 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 because if the plants were to idle or shut down, they would not incur compliance costs. This resulted in a net reduction of $24 million in social cost when these firms idle or shut down as compared to the modeled scenario, where firms continue to operate a low capacity but incur compliance costs. 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)

## 3-14 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

## 3-15 Table 3-9. Cement Plants with Si gnificant 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 estim ates cannot be es timated with certainty and the economic literature does not give clear evidence on the effect of regulation on job effects. Several empirical studies, including Morgenstern et al.,su ggest the net employment decline is zero or economically small (e.g., Cole and Elliot, 2007; Be rman and Bui, 2001). However, others show the job effects are not trivia l (Henderson, 1996; Greenstone, 2002).
EPA has most often estimated employment cha nges associated with plant closures due to environmental regulation or changes in output for the regulated i ndustry (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. Focu sing only on the “demand effect”, it can be seen that the estimate from the historical approach is within the range pres ented by the Morgenstern “demand effect” portion. This strengthens our co mfort 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 analys is goes beyond what EPA has typically done, and

## 3-16 uses Morgenstern et al. (2002) to provide the basis for the estimat es. Morgenstern et al. (2002) model three economic mechanisms by which pol lution abatement activities can indirectly influence jobs:

 higher production costs raise market prices , higher prices reduce consumption, and employment within an indus try falls (“demand effect”);

 pollution abatement activities require additi onal labor services to produce the same level of output (“cost effect”); and

 postregulation production technologies may be more or less labor intensive (i.e., more/less labor is required per dollar of output) (“factor -shift effect”).
This transfer of results from the Morgenstern st udy is uncertain but av oids ignoring the “cost effect” and the “factor-shift effect” In examinining job effects. EPA selected this paper because the parameter estimates provide a transparent an d tractable way to tran sfer estimates for an employment effects analysis. Simialr estimate s were not available from other studies.

 Using the historical approach, we calcula ted “demand effect” employment changes by assuming that the number of jobs d eclines 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 approac h, we estimate that the net employment effects could range between 600 job losses to 1,300 job gains.
EPA has solely used this historical estimate in the past as a measure of the projected employment change associated with a regula tion. However there are a number of serious shortcomings with this approach. First, and fo remost, 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 stri ng together a number of strong assumptions. The employment impacts are independent of the pe rformance of the overa ll 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 th e higher costs. In real ity, plants should be modeled as oligopolists for each of their re gional markets. Finally, EPA assumed that employment is directly proportiona l to output. This is unlikely, and biases the results towards higher employment losses. The Morgenstern me thodology is a more comple te consideration of probable impacts of a regulation on the economy.

7 

### To place this reduction in context, it is a similar to th e decline experienced during the latest economic downturn; approximately 2,000 jobs (see Appendix A, Table A-3).

## 3-17 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” estim ate that used the Morgenstern paper. EPA selected this paper because the parameter esti mates (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 equi librium model. For example, the jobs effect estimate is estimated to be 807 jobs (

− 3.56 × $378 million × 0.6). 8 

## The timeframe for EPA’s regulatory analysis focuses on a single year eff ect, by contrast the Morganstern analysis used annualized inputs, and translates to annualized impacts. Demand e ffect 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 incompatibiliti es of the underlying data. Since the re sult from the historical approach is within the confidence bounds for the Morgenst ern results for the “demand effect”, we are comfortable that the more general Morgenstern re sult is a good representa tion of the change in employment.
We also present the results of using th e Morgenstern paper to estimate employment “cost” and “factor-shift” effects. Although using the Morgenstern pa rameters to estimate these “cost” and “factor-shift” employment changes is uncertain, it is he lpful to compare the potential job gains from these effects to the job losses a ssociated with the “demand” effect. Table 3-10 shows that using the “cost” and “factor shift” employment effects may offset employment loss

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 estimates using either “demand” effect employ ment 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 add itional information about the potential job effects of environmental protection programs, ther e are several qualificatio ns EPA considered as part of the analysis. First, EPA has used the weighted average parameter estimates for a narrow set of manufacturing industries (p ulp and paper, plastics, petroleum, and steel). Absent other dataand estimates, this approach seems reasonable and the estimates come from a respected peerreviewed source. However, EPA acknowledges the fi nal 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 st ill applicable for policy taking place in 2013, almost 20 years later. Second, the economic imp act model only considers near-term employment effects in the cement industry where production t echnologies 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 sh ift effects” are more plausible when production choices become more flexible over time and industries can substi tute labor for other prod uction inputs. Third, the Morgenstern paper estimates rely on sector de mand 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 compar able with the demand effects estimated by the cement model. Fourth, Morgenstern identifies the industry average as economically and statistically insignificant effect (i.e., the point estimate s are small, measured imprecisely, and not distinguishable from zero). EPA acknowledges th is fact and has reported the 95% confidence intervals in Table 3-10. Fifth, Mo rgenstern’s methodology assumes larg e 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 bur den does not disproportionately fall on smaller plants.

3.3 Other Economic Analyses: Dire ct Compliance Cost Meth ods In addition to the market-level partial equi librium 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.

## 3-19 The total annualized costs for two white ceme nt kilns are $2 million, or approximately $9 per metric ton of cement production. Using re ported 2005 data from the USGS on the average mill net value of white cement ($176 per metric ton), this cost repres ents 5% of the product value.

EPA also conducted sales tests for 18 other k ilns that were not in cluded in the partial equilibrium analysis. The total annualized NESHAP cost for these 18 kilns is approximately $75 million. The median cost per ton is approxi mately $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 metr ic ton) to meet the NSPS limits for SO 2 

## and NO x 

## . The USGS reports that the real price of cement per metric ton (2005 dollars) has typically ranged between $75 and $100 since 1990. A sales test using these price data shows cost-to-sales ratios (CSRs) could rang e between 2% and 6%
Sales Test Ratio = Control Costs ($ /ton)/F.O.B Cement Prices ($/ton).
From 2000 to 2006, the PCA reports that th e average operating profit rates for the industry ranged from 17 to 21% (PCA, 2008c). If th ese profit data are representative of operating profit rates for new kilns, kilns could potentially significantly reduce thei r operating profit rates. As a result, companies may have the incentive to look for less expensive alte rnatives to meet the emission standards. If these alternatives are limited or not cost effective, the final rule may lead companies to consider delaying rates of construc tion of new kilns until market conditions change (e.g., increases in demand that lead to rising cemen t prices) to cover addi tional control costs.

3.4 Social Cost Estimates
For the kilns modeled in our partial equilibri um model, the market adjustments in price and quantity were used to estimate the change s in aggregate economic welfare using applied welfare economics principles (s ee Appendix C). Higher cement pr ices 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 prev ious section, individual domestic producers may gain or lose depending on the change in comp liance costs versus the change in the regional market prices. The total domestic surplus lo ss (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 a pproximate the likely additional social cost associated with oligopoly market re sponse. Thus the social cost estimate for the 20 kilns not in

## 3-20 the partial equilibrium model is $158 million. Because of th e approximation used, we cannot estimate how the $158 million is distributed between consumers and producers.

## 3-21 Table 3-11. Distribution of Social Costs ($10 6 

## ): 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 (net negative because shut down facilities will not incur compliance costs). -$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

## 3-22 The estimated social cost of the final rule is $926-950 million. This estimate includes the results for existing kilns included in the partia l-equilibrium analysis ($792 million),the final 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 m illion. This is a direct cons equence of EPA’s assumptions about existing market structure discussed extens ively 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 resu lt, 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 be tween 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 singl e year (OAQPS, 1999a).

3.5 Energy Impacts
Executive O rder 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 id entified as “significant energy actions.” Section 4(b) of Executive Order 13211 defines “significa nt energy actions” as an y 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, includi ng notices of inquiry, advance notices of final rulemaking, and notices of final rulemaking: (1) (i) that is a significan t regulatory action under Executive Order 12866 or any successor order, and (i i) 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 Regulator y Affairs as a signif icant energy action.
This rule is not a signifi cant 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

## 3-23 impact on the supply, distribution, or use of ener gy. EPA has prepared an analysis of energy impacts that explains this conclusion below.
To enhance understanding regarding the regu lation’s influence on energy consumption, EPA examined publicly available data describi ng 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

## 3-24 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 petroleum b 

### 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 consum ption 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 attr ibutable to the regulatory pr ogram should not significantly influence the supply, distributi on, 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 m illion 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 electric ity and natural gas use.

3.6 Assessment Although the econom ic analyses presented in this section cannotprovide precise estimates of the final NESHAP’s and NSPS’s econo mic impacts, the evidence presented in this section suggests that the economic impacts may be significant acro ss 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 (m arginal) changes in i ndustry conditions. The

## 3-25 overall engineering cost analysis estimates are significant relative to the size of the U.S. cement market; EPA acknowledges that use of demand a nd import supply elasticities can be tenuous in these cases because the exact f unctional relationships (demand a nd supply) are less certain when simulated outcomes move further away from the observed pre-policy equilibrium. Second, the partial equilibrium assumes that transportati on costs between regions are high enough that interregional trade is unlikely to occur, at leas t in the short run. Allowing interregional trade would expand the cement market definitions an d 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 a nd 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 shor t 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 constructi on of large rotary kilns that are not readily movable or transferable to other uses 9 

## ; (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 eval uating regulations under Executive Order 12866.

9 

### In addition, large plants are typically more economical because they can produce cemen t at lower unit costs; this reduces entry incentives for sm aller capacity cement plants.

## 4-1 SECTION 4 SMALL BUSINESS IMPACT ANALYSIS
The Regulatory Flexibility Act (RFA) gene rally requires an agency to prepare a regula tory 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 purp oses of assessing the impacts of the final rule on small entities, small entity is defined as (1) a small business as defined by th e 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 domi nant 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-Maric opa 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 asse ssed how the regulatory program m ay influence the profitability of ultimate parent companies by comparing pollution control costs to total sales (i.e., a “sales” test). To do th is, we divided an ultimate pa rent company’s total annualized compliance costs by its reported revenue:

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

Owner Entity Type Annual Sales ($10 6 

### ) Employees Plants Kilns Clinker Capacity (10 3



### metric tons

per year) Cost-toSales Ratio
Salt River Materials Group a 

### Tribal government $184 b 

### NA 1 1 1,477 0.7%

Monarch Cement Company Business $154 600 1 2 787 3.0%

Continental Cement Company, LLC Business $93 c 

### <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 pro duction 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

TR j 

## = total sales from all operations of ultimate parent company j or annual government revenue.
The results of the screening analysis, pres ented in Table 4-1, show that no small businesses have a CSR greater than 3%. Two sm all 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 ad justs 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 REDUCTI ONS

5.1 Synopsis


## This section describes the air quality modeling performe d by EPA in support of the Portland cement NESHAP and NSPS. A nationa l scale air quality modeling analysis was performed to estimate the impact of the sector emissions changes on future years: annual and 24- hour PM 2.5 

## concentrations, total Hg deposition, as we ll as visibility impairment. Air quality benefits are estimated with the Comprehensiv e 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 em issions, meteorology, and initial and boundary condition data which are inputs to this model.
Emissions and air quality modeling decisions ar e made early in the analytical process. For this reason, it is important to note that the inventories used in the ai r 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 ai r quality modeling adequately re flects 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 repres ents 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 em issions. The base year of data us ed to construct this platform includes emissions and meteorology for 2005. The pl atform is intended to support a variety of regulatory and research model appl ications and analyses. This mode ling platform and analysis is described fully below. Additional details about th e modeling system are available in a separate technical support document: Air Quality Mode ling Technical Support Document: National Emission Standards for Hazardous Air Pollutant s from the Portland Cement Manufacturing Industry (U.S. EPA, 2010c).

5.2 Photochemical Model Background 

## CAMx version 5.10 is a freely available comput er model that simulates the formation and fate of photochemical oxidants, primary and seco ndary 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 organi c and inorganic gas-phase and pa rticle-phase pollutants in the atmosphere (Nobel, McDonald-Buller et al., 200 1; Baker and Scheff, 2007; Russell, 2008).
CAMx is applied with ISORROPIA inorga nic 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 (R ADM) 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 PM 2.5 

## , 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. Th is 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 (a pproximately 15 km) using a sigma-pressure coordinate system. Air quality conditions at th e 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 ai r quality concentrations along th e boundaries of the 12 km grids. Only the finer grid data were used in determin ing 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 platfo rm. 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 pl atform, the configuration for this modeling application included some additional HAPs and a cement kiln sector emissions inventory more consistent with the engineering anal ysis of potential control options.
The 2013 reference case is intended to represen t the emissions associ ated 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 Integr ated Planning Model (IPM) run for criteria pollutants, HCl, and Hg in 2013 (although HCl wa s not modeled). Both control and growth factors were applied to a s ubset of the 2005 non-EGU point and nonpoint to create the 2013

## 5-3 reference case. The 2002 v3.1 platform 2020 projecti on factors were the starting point for most of the 2013 SMOKE-based projections.

Figure 5-1. Map of the Photochemical Modeling Domain a

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 Doma ins 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 assume d no growth or control for the industry from the 2005 sector emissions estimates with the ex ception that facilities that closed between 2005 and 2010 were removed fr om the 2013 inventory. The length of time

## 5-4 required to conduct emissions and photochemical modeling precludes usin g 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 create s a policy space where potential controls would be maximized at all loca tions. 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 importa nt to note that the scen ario without cement kilns includes the zeroing-out of emissions from hazar dous 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 PM 2.5 

## levels and mercury deposition.

Table 5-2. Cement Kiln Emissions in 2005 Ba se 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 PrimaryPM 2.5 

### 16,758 15,403
PM 2.5 

### 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 fo r the NSPS and NESHAP , more stringent NSPS and NESHAP, and NESHAP only

• 

## 82% reductions in SO X 

## and 86% reductions in primarily emitted PM 2.5 

## for the NSPS and NESHAP, more stringent NSPS a nd NESHAP, and NESHAP only

• 

## 6% reductions in SO X 

## and 5% reductions in primarily emitted PM 2.5 

## for NSPS only
As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM 2.5 

## concentrations, annual total Hg de position levels, and visibility impairment. Model predictions are used in a rela tive sense to estimate scenario-specific future-

## 5-5 year design values of PM 2.5 

## and ozone. Specifically, we compare a 2013 reference scenario, a scenario without the cement kiln controls, to a 2013 control scenar io 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 an d the 2005 base. These predicted ratios are then applied to ambient base year design values. The design value projecti on 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 consider ed 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 PM 2.5 

## , total Hg deposition levels, and visibility impairment. In this section, we pres ent information on current and projected levels of pollution for 2013.

This section summarizes the results of our m odeling of differences in total Hg deposition impacts in the future based on changes to the ce ment kiln emissions. Specifically, we compare a 2013 reference scenario to a 2013 emissions chan ge 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 form s) would be reduced by a total of 63,518 μg/m 2 

## . A reduction of 26,047 μg/m 2 

## 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 PM 2.5 

## air quality impacts in the future due to reductions in emissi ons from this sector. Specifically, we compare a 2013 reference scenario to a 2013 control scenar io. The modeling assessment indicates that a decrease up to 0.3 μg/m 3 

## in annual PM 2.5 

## design values is possible gi ven an area’s proximity to controlled sources and the amount of reduced su lfur dioxide emissions. The median reduction over all monitor locations is 0.09 μg/m 3 

## . An annual PM 2.5 

## design value is the concentration that determines whether a monitoring site meets the annual NAAQS for PM 2.5 

## . The full details involved in calculating an annual PM 2.5 

## design value are given in Appendix N of 40 CFR part 50.

## 5-6 Projected air quality benefits are estimated us ing procedures outlined by EPA modeling guidance (U.S. EPA, 2007).

This section summarizes the results of our modeling of 24-hour average PM 2.5 

## air quality impacts in the future due to reductions in emissi ons from this sector. Specifically, we compare a 2013 reference scenario to a 2013 control scenar io. The modeling assessment indicates that a decrease up to 0.5 μg/m 3 

## in 24-hour average PM 2.5 

## design values at most monitor locations in the United States is possible given an area’s proximity to controll ed sources and the amount of reduced sulfur dioxide emissions. The median reduction over all monitor locations is 0.1 μg/m 3



## . A 24-hour PM 2.5 

## design value is the concentration that determines whether a monitoring site meets the 24-hour NAAQS for PM 2.5 

## . The full details involved in calculating a 24-hour PM 2.5 

## 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 th e United States in 2013 (U.S. EPA, 2007). The level of visibility impairment in an area is ba sed on the light-extinction coefficient and a unitless visibility index, called a “deciview ,” that is used in the valuat ion of visibility. The deciview metric provides a scale for perceived visual ch anges 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 ar e 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 th e amount of reduced sulfur dioxide emissions. Median reductions are 0.01 decivi ews to the 20% worst days and 20% best days over all monitor locations.

5.6 Limitations (Uncertainties) Associat ed w ith the Air Quality Modeling


## Any deficiencies with the emissions or meteor ological 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 y ear of meteorology to capture th e variety of meteorological formation regimes conducive to eleveted pollution. However, it is possible that the meteorology used for these model applications may not repres ent all elevated polluti on 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 fina l 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 crit eria pollutants for the NSPS. The total PM 2.5 

## reductions are the conseque nce 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 PM 2.5 

## , as well as the monetized value of those avoided cases. Using a 3% discount rate, we estimate the total mone tized benefits of the final Cement NESHAP and NSPS to be $7.4 bill ion 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 $17 billi on in the implementatio n year. All estimates are in 2005$. These estimates include the energy di sbenefits 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 introdu ced in the proposal analysis. In addition, these estimates incorporate an array of improvements since the proposal, including cement sector-specific air qual ity modeling data, revised value-of-a-statistical-life (VSL), lowest measure level (LML) assessment, qualitative be nefits 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, eco system effects, and visibility impairment. The benefits from reducing other air pollutants have not been monetized in this analysis, including reducing 4,400 tons of NO x 

## , 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 from 2 epidemiology functions and 12 expert functions 3% DR 7% DR



## Figure 6-1. Total Monetized PM 2.5 

## 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-respon se 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 associat ed with several categories of benefits, including exposure to HAPs, NO 2 

### , and SO 2 

### , ecosystem effects, and visibility effects.



## 6.2 Calculation of PM 2.5 

## Human Health Benefits 

## In addition to pollutants we cannot monetize, this rulemaking would reduce emissions of PM 2.5 

## and SO 2 

## . Because SO 2 

## is also a precursor to PM 2.5, 

## reducing and SO 2 

## emissions would also reduce PM 2.5 

## formation, human exposure, and the incidence of PM 2.5 

## -related health effects. The PM reductions are the result of emission limits on PM as well as emission limits on other pollutants, including hazardous ai r pollutants for the NESHAP a nd criteria pollutants for the NSPS. The total PM 2.5 

## 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 a rray 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 quali ty data modeled by CAMx that reflect the emissions from the cement sector and the reducti ons anticipated as a result of this rule. This data provides a superior representation of the geographic distribution of the em ission reductions and resulting ambient concentrations than the na tional average benefit-per-ton estimates used in the proposal. For more information re garding 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 func tion. Consistent with recent scientific advice, we are replacing the previous thre shold 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 fo r ecosystems and HAPs. Data, resource, and methodological limitations prevented EPA from quantifying or monetizing the benefits from several important benefit categories, including be nefits from reducing toxic air pollutant emissions, ecosystem effects, and visibility im pairment. Instead, we provide a qualitative description of the benefits an ticipated 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 modeli ng data provide an estimate of the reduction in mercury deposition associated with the merc ury 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 ti ssue, 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 bene fits of the modeled changes in environmental quality. This approa ch estimates changes in individual health and welfare endpoints and assigns va lues 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-functi on 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 firs t be conducted to convert air quality changes into effects that can be assigned dollar values. For th is RIA, the health impacts analysis is limited to those health effects that are dir ectly linked to ambient levels of air pollution and specifically to those linked to PM. We also pr ovide qualitative discus sions of the impact of changes in other environmental and ecological effects, includi ng the benefits associated with decreasing deposition of sulfur to terrestri al 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 th e time or resources to perform extensive new research to measure directly eith er the health outcomes or their values for regulatory analyses. Thus, similar to Kunzli et al. (2001) and other re cent health impact analyses, our estimates are based on the best available methods of benefits tran sfer. 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-de mographic and economic characteristics of the affected population, and other factors to impr ove the accuracy and robustness of benefits estimates.

6.2.3 Health Impact Analysis (HIA) 

## The HIA quantifies the changes in the inciden ce of adverse health impacts resulting from changes in human exposure to PM 2.5 

## air quality. HIAs are a well-established approach for estimating the retrospective or prospective chan ge 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, Ta garis et al. 2009). For th is 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 func tions 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 invo lves three basic steps: (1) utilizing CAMxgenerated projections of PM 2.5 

## air quality and estimating the cha nge in the spatial distribution of the ambient air quality; (2) determining the subs equent change in population-level exposure; (3) calculating health impacts by applying concentr ation-response relationships drawn from the epidemiological literature (Hubbell et al. 2009 ) to this change in population exposure.
A typical health impact f unction might look as follows:

where y 0 

## is the baseline incidence rate for th e health endpoint being quantified (for example, a health impact function quantifying ch anges in mortality would use the baseline, or background, mortality rate for th e given population of interest); Pop is the population affected by the change in air quality; Δ 

## x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. For this analys is, we systematize the HIA calculation process using BenMAP’s library of ex isting air quality monitoring da ta, population data and health impact functions. Figure 6-2 provides a simplifie d overview of this approach, and Figure 6-3 identifies the data inputs and out puts 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 Regulat ory 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 inci dence rates, and population data used in this analysis, please refer to Chapte r 5 of the recently proposed Tran sport Rule (U.S. EPA, 2010a). Table 6-1 identifies which human health and we lfare 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 PM 2.5 

## Concentrations

PM 2.5 

## Health

Functions

Economic Valuation Functions Population Projections

PM 2.5 

## Incremental Air Quality

Change

PM 2.5 

## Related Health Impacts W oods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 

## - related Benefits

Blue identifies a use r -selected input w ithin the BenMAP model Green identifies a data in p ut g enerated 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: health a 

### Premature mortalit y based on cohort study estimates b 

### Low birth weight Premature mortality based on expert elicitation estimates Pulmonary function Hospital admissions: respiratory and cardiovascula r Chronic respiratory diseases other than chronic bronchitis Emergency room visits for asthma N on-asthma respiratory emergency room visits N onfatal 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 impacts c



### SO 2 

### : health Respiratory hospital admissions Asthma emergency room visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function

SO X 

### : 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 Mercur y : welfare Impact on bir d s 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 re sponses that have been associated with

PM health effects including mo rphological changes and altered host defense mechanisms. Th e public health impact of these biological responses may be partly represented by our quantified endpoints. b 

### Cohort estimates are designed to exam ine 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 PM 2.5 

## -related premature mortality 

## Consistent with the proposal RIA for this rule (U.S. EPA, 2009a), the PM 2.5 

## benefits estimates utilize the concentratio n-response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s e xpert elicitation study as a characterization of uncertainty.

 

## One estimate is based on the concentrati on-response (C-R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), a study th at 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/m 3 

## 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 PM 2.5 

## NAAQS, has been used as an alternative estimate in the PM 2.5 

## NAAQS RIA and PM 2.5 

## benefits estimates in RIAs completed since the PM 2.5 

## NAAQS. When calculating the estimate, EPA applied the effect coeffici ent as reported in the study without an adjustment for assumed concentration threshold of 10 μg/m 3 

## as was done in recent (2006-2009) RIAs.

 

## Twelve estimates are based on the C-R func tions from EPA’s e xpert elicitation study (IEc, 2006; Roman et al., 2008) on the PM 2.5 

## -mortality relationship and interpreted for benefits analysis in EPA’s final RIA for the PM 2.5 

## NAAQS. For that study, twelve experts (labeled A through L) provi ded independent estimates of the PM 2.5 

## -mortality concentration-response function. EPA pr actice has been to develop independent estimates of PM 2.5 

## -mortality estimates corresponding to the concentration-response function provided by each of the twelve expe rts, 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, whic h we believe argues for using both studies to generate benefits estimates. Previously, EPA had calculated benefi ts based on these two empirical studies, but derived the range of benefits, including the mi nimum and maximum results, from an expert elicitation of the relationsh ip between exposure to PM 2.5 

## and premature mortality (Roman et al.,

2 

### These two studies specify multi-pollutant models that control for SO 2 

### , 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 functio n for mortality and the degree of variability in the expert responses. Because the experts used these c ohort studies to inform their concentrationresponse functions, benefits estimates using thes e functions generally fall between results using these epidemiology studies (see Figu re 6-1). In general, the expe rt elicitation results support the conclusion that the benefits of PM 2.5 

## control are very likely to be substantial.

EPA strives to use the best available science to support our benefits analyses, and we recognize that interpreta tion of the science rega rding air pollution and h ealth 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), includi ng the updated population dataset in BenMAP 3.0 and the functions directly from the epidemiology studies without an adju stment for an assumed threshold. 4 

## Removing the threshold assumption is a key difference between the method used in this analysis of PM benefits a nd the methods used in RIAs prior to the proposal RIA for this rule, and we now calculate incremental bene fits down to the lowest modeled PM 2.5 

## air quality levels. 5 

## Prior to the proposal RIA for this rule, EPA pres ented the results using an assumed threshold at 10 μg/m 3 

## in the PM-mortality health impact function as the primary PM-related benefits results. Using a threshold of 10 μg/m 3 

## 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/m 3 

## to illustrate that the fraction of benefits that occu r at lower air pollution concentra tion 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 scien tific 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 concen tration-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 us ing 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 pr ovided a basis for reconsidering the application of thresholds in PM 2.5 

## concentration-response functions used in EPA’s RIAs. For a summary of these scientific review statements and the pa nel members please consu lt 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 th e previous threshold sensitivity analysis with a new “Lowest Measur ed Level” (LML) assessment. This approach summarizes the distribution of avoided PM mortality impacts according to the baseline PM 2.5 

## levels experienced by the population receiving the PM 2.5 

## mortality benefit. In the results section, we identify on the figures the lowe st 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 occurr ing above or below the LML of each study; in general, our confidence in the estimated PM mortal ity 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 modeli ng 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 th at 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 st rong associations with mortality. As we model mortality impacts among populations exposed to levels of PM 2.5 

## 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 PM 2.5 

## . 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 thre shold, we have more confidence in the benefits estimates above the LML of the large cohort studie s. 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 PM 2.5 

## -related mortality effects down to at least 7.5 μg/m 3 

## is high.

For these rules the SO 2 

## reductions represent a large fraction of the total benefits from reducing PM 2.5 

## , but it is not possible to isolate the portion if the total benefits attributable to the emission reductions of SO 2 

## resulting from the application of HC l 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 cl ear scientific evidence that would support the development of differential eff ects 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 detail ed description of th e 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 lo st 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 PM 2.5 

### (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 aff ects the AMI costs after the analysis year and the valuation of premature mort alities 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-lif e (VSL) estimate derived from a limited analysis of some of the available studi es. 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 in terquartile range from the Mrozek and Taylor (2002) meta-analysis of 33 studies. The $10 mil lion 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-analy sis. However, the Agency neither changed its official guidance on the use of VSL in rule-mak ings nor subjected the interim estimate to a scientific peer-review process through the Scie nce 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 repo rt 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 i ssue. With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic techniques to comb ine estimates from unique data sources and different studies, including those using differe nt 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 re ceived. 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 st udies 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 review ed evidence in valuing mortality risk reductions and has made significant progr ess in responding to the SAB-EEA C’s specific recommendations.

6.3 Health Benefits Results 

## Table 6-2 provides a summary of the monetized PM 2.5 

## 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 elicitat ion on PM mortality at discount rates of 3% and 7%. Table 6-3 provides a summary of the reductions in health in cidences as a result of the pollution reductions for the final Portland Cement NESHAP and NSPS. Table 6-4 compares the monetized PM 2.5 

## 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 PM 2.5 

## health benefits. Figure 6-5 provides a gra phical representation of all 14 of the PM 2.5 

## benefits, at both a 3 percent and 7% discount rate.
The very large proportion of the avoided PM-re lated impacts we estimate in this analysis occur among populations exposed at or above the lo west LML of the cohort studies (Figures 6-6 and 6-7), increasing our confidence in the PM mort ality analysis. Figure 6-6 shows a bar chart of the percentage of the estima ted mortalities at each PM 2.5 

## 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, approxima tely 94% of the mortality impacts occur among populations with baseline exposure to annual mean PM 2.5 

## levels at or above the LML of 7.5 μg/m 3 

## . Using the Laden et al. (2006) study, 40% of the mortality impacts occur at or above the LML of 10 μg/m 3



## . As we model mortality impacts among populations exposed to levels of PM 2.5 

## that are successively lower than the LML of the lowest cohort study, our confidence in the results diminishes. However, the analysis above c onfirms that the great majority of the impacts occur at or above the lowest c ohort study’s LML. It is important to emphasize that we have high confidence in PM 2.5 

## -related effects down to the lowest LML of the major cohort studies.

10 

### In 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 implementati on 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 PM 2.5 

## Benefits for the Final Cement NESHAP and NSPS in 2013 (95 th 

## 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 eff ects. These estimates do not in clude benefits from reducing HAP emissions, and they do not include the energy disbenefits described in the next section.

## 6-16 Adult Mortality 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 PM 2.5 

## 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 premat ure 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 NO x 

### , 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 $17,000EmissionReductions 

### PM (tpy) 11,000 590 11,000 11,000 SO 2 

### (tpy) 124,000 9,000 124,000 124,000 NO x 

### (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 NO x 

### , 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 from 2 epidemiology functions and 12 expert functions 3% DR 7% DR



## Figure 6-5. Total Monetized PM 2.5 

## 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-respon se 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 20PercentageofAvoidedPM2.5Mortalities

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) 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

PM 2.5 

### levels at or above 7.5 μg/m 3 

### , 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 20CumulativepercentageofavoidedPMMortalities

Baseline Annual Mean PM 2.5 Level (μg/m 3 ) 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 Po rtland Cement NESHAP and NSPS a

a 

### Approximately 94% of the mortality impacts occur among populations with baseline exposure to annual mean

PM 2.5 

### levels at or above 7.5 μg/m 3 

### , 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 utilit y 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 NO x 

## , 1,000 tpy of CO, 3,500 tpy of SO 2 

## and about 100 tpy of PM. For kilns subject to new sour ce standards increases in secondary air pollutants are estimated to be 200 tpy of NO X 

## , 100 tpy of CO, 400 tpy of SO 2 

## and 10 tpy of PM. We also estimated increases of CO 2 

## 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 in crease in electricity usage for the pumps used in the SNCR system to deliver reagent to the kiln is negligible.

## 6-21 6.4.1 PM 2.5 

## Disbenefits


## The additional energy usage required for the emission control devices would increase emissions of PM, NOx, SO 2 

## . Because NOx and SO 2 

## are also precursors to PM 2.5, 

## increasing these emissions would also increase PM 2.5 

## formation, human exposure, and the incidence of PM 2.5 

## -related health effects. Due to time and resource limitations , it was not possible to provide a comprehensive estimate of the PM 2.5 

## -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 PM 2.5 

## benefit-per-ton estimates provide the total monetized human health benefits (t he sum of premature mortality and premature morbidity) of reducing one ton of PM 2.5 

## 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 esti mates 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 a ll of the increased PM tons are in the PM 2.5 

## fraction. Table 6-5 summarizes the benefit-per- ton estimates and the monetized PM 2.5 

## disbenefits at discount rates of 3% and 7%.
Table 6-5. Summary of Monetized PM 2.5 

## 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 PM 2.5 

### Disbenefits (millions, 3%) Monetized PM 2.5 

### Disbenefits (millions, 7%)

Direct PM 2.5 

### 110 $210,000 $510,000 $190,000 $460,000 $23 to $56 $21 to $50
PM 2.5 

### Precursors
SO 2 

### 3,900 $37,000 $91,000 $34,000 $82,000 $150 to $360 $130 to $320
NO X 

### 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 PM 2.5 

### . 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 em itted PM are overestimated in this analysis because we assume that all of the increased PM tons are in the PM 2.5 

### fraction.

## 6-22 6.4.2 Social Cost of Carbon an d Greenhouse Gas Disbenefits


## EPA has assigned a dollar value to reductions in carbon dioxide (CO 2 

## ) emissions using recent estimates of the “social cost of carbon” (S CC). The SCC is an estimate of the monetized damages associated with an incr emental increase in carbon emissi ons in a given year. It is intended to include (but is not limited to) change s in net agricultural productivity, human health, property damages from increased flood risk, and the valu e of ecosystem services due to climate change. The SCC estimates used in this analysis we re developed through an interagency process that included EPA and other executive branch entit ies, 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 Vehi cle Greenhouse Gas Emission Sta ndards and Corporate Average Fuel Economy Standards; see th e rule’s preamble for discussi on 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 CO 2 

## emissions 12 

## in 2010, in 2007 dollars. The first three values are based on the average SCC fr om three integrated assessment models, at discount rates of 2.5, 3, and 5 percent, respectivel y. SCCs at several discount rates are included because the literatu re 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 disc ount 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 tw o of the three models as well as the use of a pr obability density function for equilibrium climate sensitivity. Treating climate sensitivity probabilistically results in more high temperature outcomes, which in tu rn 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 Advi sers, 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 CO 2 

### emissions. Given that warming profiles and

impacts other than temperature cha nge (e.g. ocean acidifica tion) vary across GHGs, the group concluded “transforming gases into CO 2 

### -equivalents using GWP, and then multiplying the carbon-equivalents by the SCC, would not result in accurate estimate s of the social costs of non-CO 2 

### 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 sy stems 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 integr ated assessment models rather than assuming a constant annual growth rate. This helps to ensure that the estimat es 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 incremen tal 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 climat e 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 limita tions to the SCC analysis, including the incomplete way in which the integrated a ssessment models capture catastrophic and noncatastrophic impacts, their in complete treatment of adapta tion and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversion. The limited amount of research linki ng climate impacts to economic damages makes the interagency modeling exercise even more di fficult. The interagency group hopes that over time researchers and modelers will work to fill th ese 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 interagenc y group has committed to updating the current estimates as the science and economic understa nding of climate change and its impacts on society improves over time. Specifically, the in teragency 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 CO 2 

## emissions for the range of policy scenarios, we estimate the dollar value of the climate-rela ted disbenefits captured by the models for each analysis year. For inte rnal consistency, the annual disbenefits are

## 6-24 discounted back to NPV terms using the same di scount 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 CO 2 

## ) 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 CO 2 

## 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, NO X 

## , SO 2 

## , and CO 2 

## , 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 bil lion 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 ot her counties. This be nefits analysis only incorporates the domestic emission changes, but th is regulation could lead to increased imports

13 

### It is possible that other benefits or costs of proposed regulations unrelated to CO 2 

### 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 specif ic 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 produc tion and transportation. The economic analysis estimates that domestic production would decr ease by 10 million tons, but imports would increase by only 3 million tons. The net effect on global pollutants like CO 2 

## and mercury is difficult to determine because it depends on many f actors, 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 imp acts associated with exposure to ambient fine particles. Data, resource, and methodological limitations preven ted EPA from quantifyi ng or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecosystem effects, and visibility im pairment. The health benefits from reducing hazardous air pollutants (HAPs) have not been mone tized in this analysis. In addition to being a PM 2.5 

## precursor, SO 2 

## emissions also contribute to adve rse effects from acidic deposition in aquatic and terrestrial ecosystems, increase me rcury methylation, as well as visibility impairment.

6.5.1 Other SO 2 

## and PM Benefits 

## In addition to being a precursor to PM 2.5 

## , SO 2 

## emissions are also associated with a variety of respiratory health effects. Unfortunately, we were unable to estimate the health benefits associated with reduced SO 2 

## 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 concentra tions, we were unable to es timate the exposure to SO 2 

## for nearby populations. Therefore, this analysis only quantifies and monetizes the PM 2.5 

## benefits associated with the reductions in SO 2 

## 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 heal th effects and short-term exposure to SO 2 

## (U.S. EPA, 2008b). According to summary of the ISA in EPA’ s risk and exposure assessment (REA) for the SO 2 

## NAAQS“the immediate effect of SO 2 

## on the respiratory system in humans is

## 6-26 bronchoconstriction” (U.S. EPA, 2009c). In addi tion, the REA summarized from the ISA that “asthmatics are more sensitive to the effects of SO 2 

## likely resulting from preexisting inflammation associated with this disease.” A clear concentrati on-response relationship has been demonstrated in laboratory studies following exposures to SO 2 

## 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 SO 2 

## ISA identified as a “causal relationship”: asthma exacerbation, respiratory-related emergency de partment visits, and respiratory-related hospitalizations. The differing evidence and asso ciated strength of the evidence for these different effects is described in detail in the SO 2 

## ISA. The SO 2 

## ISA also concluded that the relationship between short-term SO 2 

## exposure and premature mort ality was “suggestive of a causal relationship” because it is difficult to attribute the mortality risk effects to SO 2 

## alone. Although the SO 2 

## ISA stated that studies are generally consistent in repo rting a relationship between SO 2 

## exposure and mortality, ther e was a lack of robustness of the observed associations to adjustment for pollutants.

SO 2 

## emissions also contribute to adverse welf are effects from acidic deposition, visibility impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, z ooplankton, 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. Biologi cal effects of acidification in terrestrial ecosystems are genera lly linked to aluminum toxicity , which can cause reduced root growth, which restricts the ability of the plant to take up water a nd nutrients. These direct effects can, in turn, increase the sensit ivity 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 ecologica l services, including declines in habitat for threatened and endangered species (cultural), declin es in forest aesthetics (cultural), declines in forest productivity (provi sioning), and increases in forest so il erosion and reductions in water retention (cultural and regul ating). (U.S. EPA, 2008c)
Reducing SO 2 

## and PM emissions would improve th e level of visibility throughout the United States. Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and so il (Sisler, 1996). These suspended particles and

## 6-27 gases degrade visibility by scattering and absorb ing 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, pa rticulate 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 enjo yment of daily activities and their overall sense of wellbeing. Good visibility in creases the quality of life wher e 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 associ ated with visibility improvements. Previous analyses (U.S. EPA, 2006; U.S. EPA, 2010c) sh ow 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 prioriti ze air toxics, emission source t ypes 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 fo r 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 acute 18 

## inhalation exposures to air toxics, and incl ude neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune a nd reproductive systems. According to the 2002

14 

### U.S. EPA. (2009) 2002 National-Scale Air Toxi cs Assessment. http://www.epa.gov/ttn/atw/nata2002/ 15 

### U.S. EPA. (2009) 2002 National-Scale Air Toxi cs 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 expose d to an average concen tration 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 noncance r 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 a ssociated 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 manufact uring 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 maki ng 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 th at enters the food web as a methylated compound, methylmercury (U.S. EPA, 2008c). Th e contaminant is concentrated in higher trophic levels, including fish eat en by humans. Experimental ev idence 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 methylmerc ury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007). The SO 2 

## ISA concluded that evidence is sufficient to infer a casual relationship between sulfur de position and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008c).

In addition to the role of su lfate 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 a nd 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 merc ury (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 di stances, even globally, to regions far from the emissions source (becoming part of the global “pool”) before deposit ion occurs. Inorganic divalent and particulate bound me rcury have a shorter atmosphe ric lifetime and can deposit to land or water bodies closer to the emissions so urce. Furthermore, elemental mercury in the atmosphere can undergo transformation into diva lent mercury, providi ng a significant pathway for deposition of emitted elemental mercury.
Potential exposure routes to mercury emi ssions include both di rect inhalation and consumption of fish containing methylmercur y. The primary route of human exposure to mercury emissions from industrial sources is ge nerally 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 wh ere it can either move directly or be leached into waterbodies. Once deposited, certain microor ganisms 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 methylme rcury 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 sp ecies and local freshwat er fish, contain some methylmercury. For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every fish sampled cont ained some methylmercury (Scudder, 2009).
The majority of fish consumed in the U. S. are ocean species. The methylmercury concentrations in ocean fish species are pr imarily 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 subs tantial 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, hear t, kidneys, lungs, and immune system of people of all ages.

## 6-31 Several studies suggest that the methylmerc ury content of fish may reduce these cardioprotective effects of fish consum ption. Some of these studies also suggest that methylmercury may cause adverse effects to the cardiovascular sy stem. For example, the NRC (2000) review of the literature concerning methylme rcury health effects took note of two epidemiological studies that found an association between dietary exposure to methylmerc ury and adverse cardiovascular effects. 20 

## Moreover, in a study of 1,833 males in Finl and aged 42 to 60 years, Solonen et al. (1995) observed a relationship between methylmerc ury exposure via fish consumption and acute myocardial infarction (AMI or he art attacks), coronary heart dis ease, cardiovascular disease, and all-cause mortality. 21 

## The NRC also noted a study of 917 se ven 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 da ta base is not as extensive fo r cardiovascular effects as it is for other end points (i.e. neurologic effects) the cardiovascular syst em 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 in clude epidemiological, toxicological, and toxicokinetic investigations. Over a dozen review papers have also been published. If there is a causal relationship between methylmercury exposur e and adverse cardiovascular effects, then reducing exposure to methylmercury would resu lt 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 methodol ogies 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. 21 

### Salonen, 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. 22 

### Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weih e, and Grandjean, P., 1999. “Prenatal Methylmercury

Exposure As A Cardiovascular Risk Factor At Seven Years of Age”, Epidemiology, pp370-375. 23 

### 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. p. 229.

## 6-32 level cardiovascular health imp acts 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 Emissi on 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 merc ury 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 emissi ons in the U.S. and about 0.8% of the global emissions in 2005.
Using 2008 inventory estimates, the mercury em issions from Portland cement kilns only were approximately 9.1 tons. Overall, the NE SHAP and NSPS would reduce mercury emissions by about 8.2 tons (90%) per year from current levels, and therefor e, contribute to reductions in mercury exposures and health effects. Due to time and resource limitations, we were unable tomodel 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 ab le 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 de position (wet and dry fo rms), 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 grea test 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 Tota l Mercury Deposition (μg/m 2 

## ) in the Eastern U.S.

## 6-34 Figure 6-11. Reductions in Tota l Mercury Deposition (μg/m 2 

## ) in the Western U.S.



## 6.5.2.2 Hydrogen Chloride (HCl) 25


## Hydrogen chloride gas is intensely irritati ng 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 grea test impact is on the upper respir atory tract; exposure to high concentrations can rapidly lead to swelling and spasm of th e throat and suffocation. Most seriously expos ed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronc hioles. Patients who have massive exposures may develop an accumulation of fluid in the lung s. Exposure to hydrogen chloride can lead to Reactive Airway Dysfunction S yndrome (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 al so be more vulnerable to gas

25 

### All health effects language for this section came fro m: 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. Availabl e on the Internet at <http://www.atsdr.cdc.gov/Mhmi/mmg173.html>.

## 6-35 exposure because of increased minute ventilation pe r kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not b een classified for car cinogenic effects.

6.5.2.3 Toluene 26



## Toluene is found in evaporative as well as ex haust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment , there is inadequate information to assess the carcinogenic potential of toluene because st udies of humans chronically exposed to toluene are inconclusive, toluene was not carcinogenic in adequa te inhalation cancer bioassays of rats and mice exposed for life, and increased inci dences of mammary cancer and leukemia were reported in a lifetime rat oral bioassay.
The central nervous system (CNS) is the pr imary 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 ob served in humans acutely exposed to low or moderate levels of toluene by inhalation; sympto ms 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 spe ech, hearing, and vision. Chronic inhalation exposure of humans to toluene also causes irritati on of the upper respirator y tract, eye irritation, dizziness, headaches, and difficulty with sleep.

Human studies have also re ported 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 occupati onally exposed humans exists. The weight of evidence from these studies indicates neurologi cal effects (i.e., impaired colo r vision, impaired hearing, decreased performance in neurobehavioral an alysis, changes in motor and sensory nerve conduction velocity, headache, dizzine ss) as the most sensitive endpoint.

6.5.2.4 Formaldehyde
Since 1987, EPA has classified formaldehyde as a probable huma n 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, In tegrated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Crit eria 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 increas ed risk of nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to formaldehyde. 28,29 

## In an analysis of the lymphohematopoi etic 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 incr eased 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 nasopha ryngeal or lymphohematopoietic cancers, but a continuing statistically significant excess in lung cancers was reported. 32


## In the past 15 years there has been substa ntial 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, result ing dose-response estimates can vary by several orders of magnitude. 36 , 37 , 38 , 39 

## These findings are not supportive of in terpreting 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. Mort ality from lymphohematopoetic

malignancies among workers in formaldehyde industries. Jo urnal of the National Cancer Institute 95: 1615-1623. 29 

### Hauptmann, M..; Lubin, J. H.; Stewar t, 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 garmen t workers exposed to formaldehyde: an update. Occup.

Environ. Med. 61: 193-200. 32 

### Coggon, D, EC Harris, J Poole, KT Palmer. 2003. Exte nded 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, an d 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 can cer risk: identification of key issues. Risk Anal 28(4):907-923.

## 6-37 providing a conservative (health prot ective) 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 m ode-of-action of a carcinogen. For example, the model-based inference in the publ ished CIIT study that formalde hyde’s direct mutagenic action is not relevant to the compound’s tumorigenic ity was found not to hold under variations of modeling assumptions. 41


## Based on the developments of the last decad e, 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 expe rimental animals - a higher classification than previous IARC evaluations. After reviewing the currently available epidemiological evidence, the IARC (2006) characterized the human evid ence for formaldehyde carcinogenicity as “sufficient,” based upon the data on nasophar yngeal cancers; the epid emiologic 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 hum an 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 th e eyes), nose and throat. Effects from repeated exposure in humans include resp iratory tract irritation, chroni c bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia. Animal studies su ggest that formaldehyde may also cause airway inflammation – including eosinophil infi ltration into the airways. There are several studies that suggest that formal dehyde 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 (A TSDR). 1999. Toxicological profile for Formaldehyde.

Atlanta, GA: U.S. Department of Health an d Human Services, Pub lic Health Service. http://www.atsdr.cdc.gov/to xprofiles/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 leve ls. The most obvious health effect in people exposure to relatively large amounts of 2,3,7,8-TC DD is chloracne. Chloracne is a severe skin disease with acne-like lesions th at 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 th at 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 re ported in people exposed to rela tively 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. Sl ight 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 peopl e, but animal studies suggest that this is a potential health concern.
In certain animal species, 2,3,7,8-TCDD is es pecially 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 cause s a decrease in the syst em's ability to fight bacteria and viruses at rela tively low levels (approximately 10 times higher than human background body burdens). In other animal st udies, exposure to 2,3,7,8-TCDD has caused reproductive damage and birth defects. Some anim al 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 includi ng skeletal deformities, kidney defects, and weakened immune responses. In some studies, effects were obser ved 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 fro m: 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, es pecially 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 fre quent lung infections, numbness, effects on the nervous system, and mild changes in the live r. Children born to exposed mothers had skin irritation and more difficulty lear ning, but it is unknown if this effect was permanent or caused by CDFs alone or CDFs and polychlor inated 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 an imals died. These effects in animals were seen when they were fed large amounts of CDFs ove r a short time, or small amounts over several weeks or months. Nothing is known about the pos sible health effects in animals from eating CDFs over a lifetime.

6.5.2.7 Benzene
According to NATA for 2002, benzene is the la rgest 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 al l routes of exposure, and concludes that exposure is associated with additional health eff ects, 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 indicat e a causal relationship between benzene exposure and acute

46 

### All health effects language for this section came fro m: 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 Toxi cs Assessment. http://www.epa.gov/ttn/atw/nata2002/ 48 

### U.S. EPA. 2000. Integrated Risk Inform ation System File for Benzene. This ma terial is available electronically at:

http://www.epa.gov/iris/subst/0276.htm. International Agency for Re search on Cancer, IARC mon ographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs, In ternational 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 actio n 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 relations hip 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 Servic es (DHHS) has characteri zed benzene as a known human carcinogen. 49 

## A number of adverse noncancer hea lth effects including blood disorders, such as preleukemia and aplastic anemia, have al so 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 Institut e (HEI), provides eviden ce 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, So me industrial chemicals an d 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. Occupationa l 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 availabl e 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 (IR IS) 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 bene fits 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 quantitativ e analysis of uncertainty and to present benefits estimates to decisionmakers in ways that foster an appropriate apprec iation 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 aggr egate impact of uncertainty in key modeling elements on both health incidence and benefits es timates. Components of that strategy include emissions modeling, air quality mo deling, health effects inciden ce 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 t hose 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 emi ssion inventories, air quality data from models (with their associated parameters and inputs), population data, health effect estimates from epidemiology studies, and ec onomic 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 ha ve 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 followi ng 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 concentra tion response functions and economic valuation functions. Monte Carlo simulati on 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 individua l effect estimates, as shown in Tables 6-2 and 6-3.

## 6-42 6.6.2 Alternate concentration-response fu nctions for PM mortality



## PM 2.5 

## mortality benefits are the largest benef it category that we monetized in this analysis. To better unders tand the concentration-res ponse relationship between PM 2.5 

## exposure and premature mortality, EPA conducted an expe rt elicitation in 2006 (Rom an et al., 2008; IEc, 2006). In general, the results of the expert elicit ation support the conclusion that the benefits of PM 2.5 

## control are very likely to be substantial. In previous RIAs, EP A presented benefits estimates using concentration respon se functions derived from the PM 2.5 

## 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 de scribed this presentation as misleading. Therefore, we began to presen t the cohort-based studies (Pope et al, 2002; and Laden et al., 2006) as our core estimates in the proposal RIA fo r this rule (U.S. EPA, 2009a). Using alternate relationships between PM 2.5 

## and premature mortality supplie d 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 derive d from the expert elicitation as indicative of the uncertainty associated with a major compone nt of the health imp act 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 (Figur e 6-5). Please note that these re sults are not the direct results from the studies or expert elicitation; rather, th e estimates are based in part on the concentrationresponse function provided in those studies.

6.6.3 LML assessment



## PM 2.5 

## mortality benefits are the largest benef it category that we monetized in this analysis. To better characterize the uncertainty associated with mort ality impacts that are estimated to occur in areas with low baseline levels of PM 2.5 

## , we included the LML assessment. We have more confidence in the mortality im pacts among populations exposed to levels of PM 2.5 

## above the lowest LML of the larg e 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 mort ality 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 qua ntitative 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 avai lable scientific literature a nd methods and supported by the SABHES and the NAS (NRC, 2002). The benefits estima tes are subject to a number of assumptions and uncertainties. For example, the key assu mptions 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, rega rdless of their chemical composition, are equally potent in causing pr emature mortality. This is an important assumption, because PM 2.5 

## produced via transported precursors emitted from EGUs may differ significantly from direct PM 2.5 

## released from diesel engi nes and other industrial sources, but no clear scien tific grounds exist for s upporting 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 PM 2.5, 

## including both regions that are in attainment with fine particle st andard and those that do not meet the standard down to th e lowest modeled concentrations.

3. To characterize the uncertainty in the relationship between PM 2.5 

## 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 PM 2.5 

## 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 PM 2.5 

## benefits, please consult the PM 2.5 

## NAAQS RIA (Table 5.5).
In addition, there is some uncertainty associ ated with the specificity of the air quality inputs to benefits model for this particular re gulatory 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 overes timating 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 av erage benefit-per-ton estimates to calculate the energy disbenefits, we may be under or overestima ting 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 be nefits 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, includ ing benefits from reducing toxic emissions, ecosystem effects, and visibility impairment . Data limitations include limited monitoring for HAPs, incomplete emissions inventories fo r HAPs, and limited photochemical air quality modeling for non-mercury HAPs. Resource limita tions include limited staff and extramural funding in conjunction with a heav y regulatory workload. Methodol ogical 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 am bient levels (IEc, 2008). Despite our inability to monetize all of the benefit categories, the moneti zed 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 Mont e Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RI A 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 be nefits of the final Portland Cement NESHAP and NSPS to be $7.4 billion to $18 billion in the implem entation year (2013).

## 6-45 Using a 7% discount rate, we estim ate 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 in creased electricity usage by the control devices. The annualized social costs of the final NESH AP and NSPS are $926 to $950 million. 54 

## Thus, net benefits are $6.5 billion to $17 billion at a 3% discount rate for the benefits a nd $5.8 billion to $16 billion at a 7% discount rate. In add ition, the benefits from reduc ing 16,400 pounds of mercury, 4,400 tons of NOx, 5,800 tons of HCl, and 5,200 tons of organic HAPs each year have not been included in these estimates. A ll estimates are in 2005$.
Table 6-5 shows a summary of the monetized be nefits, 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 PM 2.5 

## mortality functions at discount rates of 3% and 7%. Data, resour ce, and methodological limitations prevented EPA from monetizing the benefits from several important benefit categories, including benefits fr om reducing hazardous air pollutants, ecosystem effects, and visibility impairment. EPA believes that the be nefits are likely to exceed the costs under this rulemaking even when taking into account uncerta inties in the cost a nd benefit estimates.

54 

### For more information on the annualized cost s, 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 Benefits 2 

### $7,400 to $18,000 $6,700 to $16,000 Total Social Costs 3 

### $926 to $950 $926 to $950 Net Benefits $6,500 to $17,000 $5,800 to $16,000

Non-monetized Benefits d 

### 4,400 tons of NO x 

### (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, NO 2 , 

### and SO 2 

### exposure Ecosystem effects Visibility impairment Final NSPS only 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 

### $510 to $1,300 $460 to $1,100 Total Social Costs 3 

### $72 $40 Net Benefits $470 to $1,300 $420 to $1,100

Non-monetized Benefits d 

### 6,600 tons of NO x 

### 520 tons of HCl Health effects from HAPs, NO 2 , 

### and SO 2 

### exposure Ecosystem effects Visibility impairment Final NESHAP only 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 

### $7,400 to $18,000 $6,700 to $16,000 Total Social Costs 3 

### $904 to $930 $904 to $930 Net Benefits $6,500 to $17,000 $5,800 to $16,000

Non-monetized Benefits d 

### 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, SO 2 

### exposure Ecosystem effects Visibility impairment Alternative: More Stringen t NSPS and Final NESHAP 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 

### $7,400 to $18,000 $6,700 to $16,000 Total Social Costs 3 

### $955 to $979 $955 to $979 Net Benefits $6,500 to $17,000 $5,700 to $15,000

Non-monetized Benefits 4 

### 7,800 tons of NO x 

### (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 tons of HCl 16,400 pounds of mercury Health effects from HAPs, NO 2 , 

### and SO 2 

### 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 hea lth benefits associated with reducing exposure to PM 2.5 through reductions of directly emitted PM 2.5

and PM 2.5 precursors such as SO 2 . It is important to note that the monetized benefits in clude many but not all health effects associated with PM 2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine parti cles, regardless of their chemical composition, are equally potent in causing premature mort ality because there is no clear scientific evidence that woul d support the development of differential effects estimate s by particle type. The total monetized be nefits 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 from 2 epidemiology functions and 12 expert functions


## Figure 6-12. Net Benefits for the Final Po rtland Cement NESHAP and NSPS at 3% Discount Rate a

a 

### Net Benefits are quantified in terms of PM 2.5 

### 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 PM 2.5 

### . The monetized benefits inco rporate 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 m onetize the benefits associated with several categories of benefits, including exposure to HAPs, NO 2 

### , and SO 2 

### , 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 from 2 epidemiology functions and 12 expert functions


## Figure 6-13. Net Benefits for the Final Po rtland Cement NESHAP and NSPS at 7% Discount Rate a

a 

### Net Benefits are quantified in terms of PM 2.5 

### benefits for implementation year (2013). This graph shows 14 benefits

estimates combined with the cost estimate. All combinatio ns 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 PM 2.5 

### . 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 unab le to monetize the benefits associated with several categories of benefits, including exposure to HAPs, NO 2 

### , and SO 2 

### , ecosystem effects, and visibility effects.

## 7-1 SECTION 7 REFERENCES


## Abt Associates, Inc. 2008. Environmental Be nefits and Mapping Program (Version 3.0). Bethesda, MD. Prepared for U.S. Environmen tal Protection Agency Office of Air Quality Planning and Standards. Research Triangle Park, NC. Available on the Internet at <http://www.epa.gov/air/benmap>.
Baker, K. and P. Scheff. 2007. “Phot ochemical model performance for PM 2.5 

## , sulfate, nitrate, ammonium, and precu rsor species SO 2 

## , HNO3, and NH 3 

## at background monitor locations in the central and eastern United States.” Atmospheric Environment 41: 6185-6195.
Berman, E. and L. T. M. Bui. 2001. Environmen tal regulation and labor demand: Evidence from the south coast air basin. Journal of Public Economics 79(2):265-295.
Broda, C., N. Limao, and D.E. Weinstein. 2008b. “Optimal Tariffs and Market Power: The Evidence.” American Economic Review 98(5):2032-2065. Chang, J. S., R. A. Brost, Et Al. 1987. “A 3-Dimensional Eulerian Acid Deposition Model – Physical Concepts And Formulation.” Journal Of Geophysical Research-Atmospheres 92(D12): 14681-14700.
Cole, M. A. and R. J. Elliott. 2007. Do environm ental regulations cost jo bs? An industry-level analysis of the UK. B E Journal of Economic Analysis & Policy 7(1) (Topics), Article 28. Davidson K, Hallberg A, McCubbin D, Hubbell BJ. 2007. “Analysis of PM 2.5 

## Using the Environmental Benefits Mapping an d Analysis Program (BenMAP)”. J Toxicol Environ Health 70: 332—346.
Drevnick, P.E., D.E. Canfield, P.R. Gorski, A. L.C. Shinneman, D.R. Engstrom, D.C.G. Muir, G.R. Smith, P.J. Garrison, L.B. Cleckner, J.P. Hurley, R.B. Noble, R.R. Otter, and J.T. Oris. 2007. Deposition and cycling of sulfur controls mercury accumulation in Isle Royale fish. Environmental Scie nce and Technology 41(21):7266-7272.
Dun & Bradstreet, Inc. 2007. D&B Million Dollar Directory . Bethlehem, PA: Dun & Bradstreet, Inc.

ENVIRON. 2008. User's Guide Comprehensive Air Quality Model With Extensions . Novato, ENVIRON International Corporation.
Fann, N., C.M. Fulcher, B.J. Hubbell. 2009. The in fluence of location, source, and emission type in estimates of the human health benef its of reducing a ton of air pollution. Air Qual Atmos Health 2:169-176. Gery, M. W., G. Z. Whitten, Et Al. 1989. “A Photochemical Kinetics Mechanism For Urban And Regional Scale Computer Modeling.” Journal Of Geophysical Research-Atmospheres 94(D10): 12925-12956.

## 7-2 Greenstone, M. 2002. “The Impacts of Environm ental Regulations on Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” Journal of Political Economy 110(6):1175-1219. Henderson, J. V. 1996. “Effects of Air Quality Regulation.” American Economic Review 86(4):789-813. Hubbell BJ, Fann N, Levy JI. 2009. “Methodological Considerations in Developing Local-Scale Health Impact Assessments: Balancing National, Regional and Local Data.” Air Qual Atmos Health 2:99–110.
Industrial Economics, Inc. 2006. Expanded Expert Judgment Assessm ent of the ConcentrationResponse Relationship Between PM 2.5 

## Exposure and Mortality . Prepared for the U.S. EPA, Office of Air Quality Planning and Sta ndards, September. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata /Uncertainty/pm_ee_report.pdf>.
Industrial Economics, Inc. 2008. Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act: Air Toxics Case Study – He alth Benefits of Benzene Reductions in Houston, 1990-2020. Prepared for the U.S. EPA, Offi ce of Policy Analysis and Review, March. Available on the Internet at <http://www.epa.gov/oar/sect812/mar08/812C AA_Benzene_Houston_Draft_Report_Mar ch2008.pdf>.
Jans, I. and D. I. Rosenbaum. 1997. “Multimarket Contact and Pricing: Evidence from the U.S. Cement Industry.” International Journal of Industrial Organization 15:391-412. Kelly, T. and G. Matos. 2007a. “H istorical Statistics for Minera l and Material Commodities in the United States: Cement End Use Statistics.” U.S. Geological Surv ey Data Series 140, Version 1.2. Available at <http://minerals.usgs.gov/ds/2005/140/>. Kelly, T. and G. Matos. 2007b. “H istorical Statistics for Minera l and Material Commodities in the United States: Cement Supply and Demand St atistics.” U.S. Geological Survey Data Series 140, Version 1.2. Available at < http://minerals.usgs.gov/ds/2005/140/>. Last modified April 11, 2006.
Kochi, I., B. Hubbell, and R. Kra mer. 2006. “An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analysis.” Environmental and Resource Economics 34:385-406.

Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Horak Jr., and M. Studnicka. 2001. “Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies?” American Journal of Epidemiology 153(11):1050-55. Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respir atory and Critical Care Medicine 173:667-672.

## 7-3 Levy JI, Baxter LK, Schwartz J. 2009. “Uncerta inty and variability in health-related damages from coal-fired power plan ts in the United States.” Risk Anal. 29 (7): 1000-1013. LexisNexis. LexisNexis Academic [elect ronic resource]. Dayton, OH: LexisNexis. Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. “Jobs versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and Management 43(3):412-436. Mrozek, J.R., and L.O. Taylor. 2002. “What Dete rmines the Value of Life? A Meta-Analysis.” Journal of Policy Analysis and Management 21(2):253-270. Munthe, J., R.A. Bodaly, B.A. Br anfireun, C.T. Driscoll, C.C. Gi lmour, R. Harris, M. Horvat, M. Lucotte, and O. Malm. 2007. “Recovery of Mercury-Contaminated Fisheries.” AMBIO: A Journal of the Human Environment 36:33-44. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The Na tional Academies Press. National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations . Washington, DC: The Na tional Academies Press. National Research Council (NRC). 2008. Estimating Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. National Academies Press. Washington, DC.
Nenes, A., S. N. Pandis, et al. 1999. “C ontinued development and testing of a new thermodynamic aerosol module for urban and regional air quality models.” Atmospheric Environment 33(10): 1553-1560.
Nobel, C. E., E. C. McDonald-Buller, et al . 2001. “Accounting for spa tial variation of ozone productivity in NO x 

## emission trading.” Environmental Science & Technology 35(22): 4397-4407. Office of Management and Budget (OMB). 2003. Circular A-4: Regulatory Analysis

. Washington, DC. Available on the internet at http://www.whitehouse.gov/omb/circulars/a004/a-4.html. Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortal ity, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 287:1132-1141. Portland Cement Association (PCA). 2008a. “Cement and Concrete Basics: History & Manufacture of Portland Cement.” Availa ble at <http://www.cement.org/basics/ concretebasics_history.asp>. Portland Cement Association (PCA). 2008b. “Market Research: Producer Price Indices— Competitive Building Materials.” Availabl e at <http://www.cement.org/market/>.

## 7-4 Portland Cement Association (PCA). 2008c. “ 2007 North American Cement Industry Annual Yearbook.” Available at <http://testinter.cement.org/bookstore/profi le.asp?printpage=true&store=&id=15743>. Portland Cement Association (PCA). December 2004. U.S. and Canadian Portland Cement Industry: Plant Information Summary . Skokie, IL: Portland Cement Association Economic Research Department. Portland Cement Association (PCA). December 2005. U.S. and Canadian Labor-Energy Input Survey 2005 . Skokie, IL: Portland Cement Associa tion Economic Research Department. Portland Cement Association (PCA). Septembe r 28, 2007. “Flash Report: Capacity Expansion Update.” Available at < http://www.cement.org/econ/pdf/NONMEMBERCapacity Expansion.pdf>. As obtained on March 31, 2008.

Rom an, 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 Ambien t Fine Particulate Matter in the U.S. Environ. Sci. Technol ., 42(7):2268-2274. Rossi, G., M.A. Vigotti, A. Zanobetti, F. Repetto, V. Gianelle, and J. Schwartz. 1999. “Air Pollution and Cause-Specific Mort ality in Milan, Italy, 1980–1989.” Arch Environ Health 54(3):158-164. Russell, A. G. (2008). “EPA Supersites Progra m-related emissions-based particulate matter modeling: Initial applic ations and advances.” Journal of the Air & Waste Management Association 58(2): 289-302. Ryan, S. 2006. “The Cost of Environmental Regula tion in a Concentrated Industry.” Available at
< http://econ-www.mit.edu/files/1166>.
Scudder, B.C., Chasar, L.C., W entz, D.A., Bauc h, N.J., Brigham, M.E., Moran, P.W., and Krabbenhoft, D.P. 2009. Mercury in fish, be d sediment, and water from streams across the United States, 1998–2005: U.S. Geological Su rvey Scientific Investigations Report 2009–5109, 74 p. Sisler, J.F. 1996. Spatial and s easonal patterns and long-term vari ability of the composition of the haze in the United States: an analysis of data from the IMPROVE network. CIRA Report, ISSN 0737-5352-32, Colorado State University. Tagaris E, Liao KJ, Delucia AJ, et al. 2009. “Pot ential impact of climate change on air-pollution related human health effects.” Environ. Sci. Technol . 43: 4979—4988. U.S. Department of Commerce, Bureau of the Census. 2003. 2001 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Govern ment Printing Office. Available at <http://www.census.gov/prod/2003pubs/m01as-1.pdf>. As obtained on March 14, 2008.

## 7-5 U.S. Department of Commerce, Bureau of the Census. 2006. 2005 Annual Survey of Manufacture s. M05(AS)-1. Washington, DC: Govern ment 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. 2010. Sector 31: EC0731I1: Manufacturing: Industry Series: Detailed Statistics by Industry for the United States: 2007 . Available at < http://factfinder.census.gov/servlet/IBQTable?_bm=y&- ds_name=EC0731I1&-ib_type=NAICS2007&-NAICS2007=327310>. U.S. Department of Energy, Ener gy Information Administration. 2007. Annual Energy Outlook 2007 . Supplemental Washington, DC: U.S. Energy Information Administration. U.S. Department of Energy, Ener gy Information Administration. 2010. Annual Energy Outlook 2010 . Washington, DC: U.S. Energy Information Administration. U.S. Department of Energy, Energy Informati on Administration. 2010. Supplemental Tables to the Annual Energy Outlook 2010. Available at <://www.eia.doe.gov/oiaf/aeo/supplement/supref.html > 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 La bor Statistics (BLS). 2007b. “State and Area Employment, Hours and Earnings 2005.” Wash ington, DC: U.S. Department of Labor. U.S. Department of Labor, Bur eau of Labor Statistics (BLS). 2008. “Consumer Price Index All Items – U.S. City Average Data: Custom izable Data Tables.” Available at <http://www.bls.gov/cpi/>. As obtained on March 14, 2008. U.S. Department of Labor, Bur eau of Labor Statistics (BLS). 2008. “Consumer Price Index All Items – U.S. City Average Data: Custom izable Data Tables.” Available at <http://www.bls.gov/cpi/>. As obtained on March 14, 2008. U.S. Department of the Interi or, U.S. Geological Survey. 2000. Minerals Yearbook, Cement.

. Washington, DC: U.S. Department of the Interior. Available at <http://minerals.usgs.gov/minerals/pubs/commodity/cement/>. As obtained on March 14, 2008. U.S. Department of the Interi or, U.S. Geological Survey. 2001. 2000 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at ,<http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interi or, U.S. Geological Survey. 2002. 2001 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>.

## 7-6 U.S. Department of the Interi or, 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/>. U.S. Department of the Interi or, U.S. Geological Survey. 2004. 2003 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interi or, U.S. Geological Survey. 2005. 2004 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interi or, U.S. Geological Survey. 2007a. 2005 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Tables 11 and 15. http://minerals.er.usgs.gov/mi nerals/pubs/commodity/cement/. U.S. Department of the Interi or, U.S. Geological Survey. 2007b. 2005 Minerals Yearbook, Crushed Stone . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interi or, U.S. Geological Survey. 2008a. 2006 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/>. U.S. Department of the Interi or, 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 Interi or, U.S. Geological Survey. 2010. Minerals Commodity Summaries, Cement 2010 . Washington, DC: U.S. Department of the Interior. Available at < http://minerals.usgs.gov/m inerals/pubs/commodity/cement/mcs-2010-cemen.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 1998. Regulatory Impact Anal ysis of Cement Kiln Dust Rulemaking . Appendix C, p. 12. June Wash ington, DC: U.S. Environmental Protection Agency. Available at
< http://www.epa.gov/epaoswer/other/ckd/ckd/ckdriafn.pdf>.

U.S. Environm ental Protection Agency (U.S. EPA). 1999. Office of Air Quality Planning and Standards. OAQPS Economic Analysis Resource Document. Available at <http://www.epa.gov/ttn/ecas/analguid.html>. U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses . EPA 240-R-00-003. September. Natio nal Center for Environmental Economics, Office of Policy Economics a nd Innovation. Washington, DC: EPA. Available at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html>.

## 7-7 U.S. Environmental Protection Agency (U.S. EPA). 2004. Final Regulatory Analysis: Control of Emissions from Nonroad Di esel Engines. EPA420-R-04-007. Prepared by Office of Air and Radiation. Available at <http://w ww.epa.gov/nonroad-diesel/2004fr/420r04007.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particulate Matter, Chapter 5. Available at <http://www.epa.gov/ttn/ecas/regdata/R IAs/Chapter%205--Benefits.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attain ment of Air Quality Goals for Ozone, PM 2.5 

## , and Regional Haze . April. Office of Air Quality Planning and Standards RTP, NC. Available on the Internet at <http://www.epa.gov /ttn/scram/guidance/guide/final-03-pm-rhguidance.pdf> U.S. Environmental Protection Agency (U.S. EPA). 2008a. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-level Ozone, Chapter 6. Available at <http://www.epa.gov/ttn/ecas/regdata /RIAs/6-ozoneriachapter6.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Guidelines for Preparing Economic Analyses: External Review Draft. National Center for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC. Available at <http://yosemite.epa.gov/ee/epa/eermfile .nsf/vwAN/EE-0516-01.pdf/$File/EE-0516- 01.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2008b. Integrated Science Assessment for Sulfur Oxides—Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park , NC. September. Available at <http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=198843>. U.S. Environmental Protection Agency (U.S. EPA). 2008c. Integrated Science Assessment for Oxides of Nitrogen and Sulfur–Ecological Criteria National (Final Report). National Center for Environmental Assessment, Res earch Triangle Park, NC. EPA/600/R-08/139. December. Available at <http://cfpub.epa. gov/ncea/cfm/recordisplay.cfm?deid=201485>. U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry . Office of Air Quality Planni ng and Standards, Research Triangle Park, NC. April. Available at
< http://www.epa.gov/ttn/ecas/regdata/RIA s/po rtlandcementria_4-20-09.pdf >. U.S. Environmental Protection Agency (U.S. EPA). 2009b. Integrated Science Assessment for Particulate Matter (Final Report). EPA-600-R- 08-139F. National Center for Environmental Assessment—RTP Di vision. December. Available at <http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=216546>.

U. 

## S. Environmental Protection Agency (U.S. EP A). 2009c. Risk and Exposure Assessment to Support the Review of the SO 2 

## Primary National Ambient Ai r Quality Standards: Final Report. Office of Air Quality Planning and Stan dards, Research Triangle Park, NC. August.

## 7-8 Available on the Internet at <http://www.epa.gov/ttn/naaqs/standards/ so2/data/Risk%20and%20Exposure%20Assessmen t%20to%20Support%20the%20Review%20of%20t he%20SO2%20Primary%20National%20 Ambient%20Air%20Quality%20Sta ndards-%20Final%20Report.pdf>. U.S. Environmental Protection Agency (U.S. EPA). 2010a. Regulatory Impact Analysis for the Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http: //www.epa.gov/ttn/ecas/ria.html>. U.S. Environmental Protection Agency (U.S. EP A). 2010b. Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM 2.5 

## -related Mortality: Technical Support Document. Compiled by Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/ecas/benefits.html>. U.S. Environmental Protection Agency (U.S. EPA). 2010c. Air Quality Modeling Technical Support Document: National Emission Standards for Hazardous Air Pollutants from the Portland Cement Manufacturing Industry. Compiled by Office of Air Quality Planning and Standards, Research Triangle Park, NC. July. Available at <http://www.epa.gov/ttn/scram/reports/EPA-454_R-10-004.pdf>. U.S. Environmental Protection Agency – Sc ience Advisory Board (U.S. EPA-SAB). 2004. Advisory on Plans for Health Effects Analysis in the Anal ytical 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-COUNCI L-ADV-04-002. March. Availa ble on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E540 0433D5D/$File/council_adv_04002.pdf>. U.S. Environmental Protection Agency – Sc ience Advisory Board (U.S. EPA-SAB). 2005. EPA’s Review of the National Ambient Air Quality Standards for Particulate Matter (Second Draft PM Staff Paper, January 2005) . EPA-SAB-CASAC-05-007. June. U.S. Environmental Protection Agency – Sc ience Advisory Board (U.S. EPA-SAB). 2007. SAB Advisory on EPA’s Issues in Va luing Mortality Risk Reductio n. EPA-SAB-08-001. October. Available at
< http://yosemite.epa.gov/sab/sabproduct.nsf/4128007E7876B8F0852573760058A978/$F ile/sab-08-001.pdf >.

U.S. Environm ental Protection Agency – Sc ience Advisory Board (U.S. EPA-SAB). 2009a. Review of EPA’s Integrated Scienc e Assessment for Particulate Matter (First External Review Draft, December 2008). EPA-CO UNCIL-09-008. May. Available at
< http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256ead006be86 e/73ACCA834AB44A10852575BD0064346B /$File/EPA-CASAC-09-008- unsigned.pdf>.

## 7-9 U.S. Environmental Protection Agency – Sc ience Advisory Board (U.S. EPA-SAB). 2009b. 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/81e39f4c09954fcb85256ead006be86 e/723FE644C5D758DF852575BD00763A32/$File/E PA-CASAC-09-009-unsigned.pdf>.

U.S. Environm ental Protection Agency – Sc ience 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. Av ailable on the Internet at <http://yosemite.epa.gov/sab/sabproduct.nsf/0/72D4EFA39E48CDB2852577450073877 6/$File/EPA-COUNCIL-10-001-unsigned.pdf>. U.S. International Trade Commission (USITC ). 2006. Gray Portland Cement and Cement Clinker from Japan Investigation No. 731- TA-461 (Second Review). Publication 3856. Available at < http://hotdocs.usitc.gov/docs/pubs/701_731/pub3856.pdf>.
Van Oss, H. G., and A.C. Padovani. 2002. “Cem ent Manufacture and the Environment Part I: Chemistry and Technology.” Journal of Industrial Ecology 6(1):89-105.
Varian, H. 1992. Microeconomic Analysis. 3 rd 

## Ed. New York: W.W. Norton & Company. Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal of Risk and Uncertainty 27(1):5-76.

## APPENDIX A SHORT-RUN REGIONAL PORTLAND CEMENT ECONOMIC MODEL

## A-1 The Office of Air Quality Planning and Sta ndards (OAQPS) has adopted the standardindustry level analysis describe d 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 st atic 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. Th is time horizon allows us to capture important transitory impacts of the program on existing producers. Key measur es in this analysis include

 

## market-level effects (market prices, changes in domestic production and consumption, and international trade),

 

## industry-level effects (changes in op erating profits and employment),

 

## facility-level effects (plant utilization changes), and

 

## social costs (changes in produ cer and consumer surplus).
In this appendix, we provide additional de tails about economic model updates, model equations and parameters. A.1 Economic Impact Model Updates Since Proposal 

## The need for a complete set of statistics ma kes the use of a 2005 baseline the best choice for a typical year. At the time of proposal model development, it wa s the latest year for which the PCA had published their plant in formation summary and complete statistics for upda ting variable cost functions were available. Details of model development are provided in EPA (2009), Appendix A. Since proposal, EPA identified seve ral plants where operations had changed (see Table A-1). As a result, EPA m odified the baseline U.S. production quantities to approximate these changes and maintain consistency w ith 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 constr uction. As a result, business cycles also significantly influence the cement industry (see Tabl e A-3). If 2013 is more or less favorable for the cement industry than 2005, then impacts w ould 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 (10 6 

## 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 2009 a 

### 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

## A-4 A.2 Partial Equilibrium Model 

## The partial equilibrium analysis performed fo r 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 ad ditional regulatory program costs. In the near term, the regional cement markets are assumed to have few sellers th at offer similar/identical products. As a result, EPA used an oligopoly market structure 1 

## . 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 factor s 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 Share i 

## /Demand Elasticity) = Marginal Cost i 

## . This equation shows the relationship between the oligopoly model and perfect competition. The market distortion will typically be higher when market share i 

## is high (there are few sellers) and in markets where the quantity demanded is less se nsitive to price (i.e., the demand elasticity is inelastic). A.2.1 Model Equations To estimate the economic impacts of the regu lation, EPA used four linear equations to calculate the following unknown variables:

 

## change in domestic plant production (dq i 

## ),

 

## change in imports (dq imports 

## ),

 

## 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 (c i 

## ) 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 (dMC i 

## ). 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 comp anies will react to its ow n production de cision. EPA selected the Cournot model where th e company assumes competing companies’ output is fixed in its own production decision.

## A-5 equilibrium, the change in margin al revenue (dMRi) must equal the change in the marginal cost (dMCi) for each plant. 1


## dmarginal Revenue i 

## = dmarginal Cost i



## 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 × (dpr ice/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 pri ce 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 indi vidual 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/base line price) × baseline consumption.
The use of published estimates from previous rule makings 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 regard ing 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% in crease in the cement pr ice 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 additi onal 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 cemen t plant’s profit function. Table B-1. Variable Descriptions



## P Market price Q= 

# ∑ i 

### q 

## Market output

q i 

## Domestic plant i’s output
FC i 

## Plant fixed costs
VC i 

## Plant variable costs

Step 1: First, we assume the plant’s goal is to maximize profits: 

## ( ) ( ) iiii i 

### FCqVCqQPQ

− − = 

### )( max

i q 

### π


## .
Step 2: The first-order conditi ons 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 ow n production decisions influe nce the market price:

0 q P

i 

### ≠ ∂ ∂



##  

## Plant (i) output decisions do not affect thos e 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 ii i

i 

### = ∂

− ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ∂

∂ +



## .

Step 6: Use and apply the following definitions:

elasticity demand inverse 1 q Q P P(Q)

i 

### == ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ∂

∂
η



### share market splant' Qq Q q 1 i i 

### = ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

= −



## .

We derive the following expression:


# ( )

i ii i



## q qVC Q q
1 ∂

= ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

+ η P

.

Step 7: The total differential of this equa tion is determined and gives us the optimal decision rule for the plant:

dMC 1 Q q
1 2 i



## = ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + ⎥ ⎥ ⎥ ⎥

⎦ ⎤

⎢ ⎢ ⎢ ⎢

⎣ ⎡

⎟ ⎟ ⎟ ⎟

⎠ ⎞

⎜ ⎜ ⎜ ⎜

⎝ ⎛

+ Q q P dQ Q P dq dP 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 describe d 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 st atic partial-equilibrium model to compare prepolicy cement market baselines with expected postpolicy outcomes in thes e 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 pr oducers. The model provides an estimate of the social costs (changes in producer and consumer su rplus) associated with controls applying to existing kilns (see Section 4). Sin ce the social cost methodology is id entical to the approach used in previous cement analysis (EPA, 1998, Appendi x C), we have included elements of the previous report’s Appendix C in this RIA.
Figure C-1 illustrates the conceptual fram ework for evaluating the social cost and distributive impacts under the imperfectly compe titive structure of U.S. cement markets. The baseline equilibrium is given by the price, P 0 

## , and the quantity, Q 0 

## . Without the regulation, the total benefits of consuming cement are give n by the area under the demand curve up to the market output, Q 0 

## . This equals the area filled by the le tters ABCDEFGHIJ. The total variable cost to society of producing Q 0 

## 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 Q 0 

## equals the total benefits minus the total cost s, 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 surplu s accrues to the suppliers of cem ent and reflects the value they receive in the market for producing Q 0 

## 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) le ss what they must pay on the market, i.e., P 0 

## . Consumer surplus is thereby given by the area ABC.

## C-2 Q 1 

### P 1



### D

$

Output P 0



### P 1 c



### P 0 c



### Q 0 

### C B A

D E

F

J I H G MC + Control Costs

MR MC



## Figure C-1. Social Cost of Regula tion 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 co mpliance costs. As shown in Figure C-1, this results in a new market equilibrium that occurs at a higher market price for cement, P 1 

## , and a lower level of output, Q 1 

## . In this scenario, the total benefits of consumption are equal to area ABDFI and the total vari able 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 so cial cost of the regul ation equals the total change in social surplus caused by the regulation. Therefore, the so cial cost of the regulation is represented by the area FGHEC in Figure C-1.
The distributive effects are estimated by separa ting the social cost into producer surplus and consumer surplus losses. First, the change in producer surplus is given by

Δ PS = B – F – (G+H+E) (C.1)
Producers gain B from the increase in price (a tr ansfer from consumers to producers), but lose F from the increase in production co sts due to the incremental compliance costs. Furthermore, the reduction of cement production leads to fo regone baseline profits of G+H+E.

## C-3 The change in consumer surplus is given by

Δ CS = – (B + C) (C.2)
This change results from the reduction in cons umer 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-regulati on 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 su rplus can then be derived simply by adding the changes in producer and consumer surplus, i.e.,

Social Cost = Δ PS + Δ CS = – (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 competi tion will assist in i llustrating this point.
Suppose that the MR curve in Figure C-1 was the demand function for a competitive market, rather than the marginal revenue func tion for an imperfectly competitive producer. Similarly, let the MC function be the aggregate supply function fo r 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 compe titive demand function, the baseline (competitive) market price, P 0 C 

## , is now equal to MC and Q 0 

## 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 cove rs their costs of production.
In the with-regulation perfec tly competitive equilibrium, the market price would rise by the per unit control cost amount to P 1 c 

## . The social cost of the regul ation 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 regu lation under perfect competition. The overstatement results from the fact that the incremental comp liance costs are estimated based on the baseline market level of cement output. With regula tion, output is projected to decline to Q 1 

## , so that the actual incremental compliance costs incurred by the industry are given by area F. Area G represents the foregone value of cement consump tion to consumers, also referred to as the deadweight loss (analogous to area C unde r 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.,

SC imp 

## – SC perf 

## = − [(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 cu rve (D) and the marginal revenue curve (MR) that exist under imperfectly competitive market structure. This area doe s 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 cem ent. The pre-existing social inefficiency of imperfect competition is exacerbated as the regul ation moves society further away from the socially optimal level of cement production, whic h 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 PM 2.5 

## -RELATED MORTALITY

## D-1 

# Summary of Expert Opinions on th e Existence of a Threshold in the Concentration-Response Function for PM 2.5 

# -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 Associati on 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 - Sc ience 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/sabpro duct.nsf/0/72D4EFA39E48CDB28525774500738776/$ File/EPA-COUNCIL-10-001-unsigned.pdf>.


## Pg 2: “The HES generally agrees with other decisions made by the EP A project team with respect to PM, in particular, th e PM mortality effect threshold model, the cessation lag model, the inclusion of infant mortality estima tion, 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, whic h are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts us ing data from more recent years, during which time PM concentrations have fallen, con tinue 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 Eff ects Subcommittee, Scholar in Residence, The National Academies, Washington, DC Dr. Michelle Bell 

## , Associate Professor, School of Forest ry and Environmental Studies, Yale University, New Haven, CT Dr. James K. Hammitt 

## , Professor, Department of Hea lth 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 Ec onomics, 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 Environm ental Health Sciences, Mailman School

of Public Health, Columbia University, New York, NY Dr. Michael T. Kleinman 

## , Professor, Department of Medi cine, 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 Environm ental Protection Agency, Oakland, CA Dr. Rebecca Parkin 

## , Professor and Associate Dean, Envi ronmental and Occupational Health,

School of Public Health and Health Services, The George Washington University Medical Center, Washington, DC

## D-4 B. Scientific Statement from Am erican Heart Association (2010) Brook RD, Rajagopalan S, Pope CA 3rd, Br ook 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 sc ientific statement fr om the American Heart Association.” Circulation . 121: 2331-2378.


## Pg 2338: “Finally, there appeared to be no lower-limit threshold below which PM 10 

## was not associated with excess mortality across all regions.”
Pg 2350: “There also appears to be a monotonic (eg, linear or log-linea r) concentration-response relationship between PM 2.5 

## and mortality risk observed in co hort studies that extends below present-day regulations of 15 μg/m 3 

## for mean annual levels, w ithout a discernable “safe” threshold.” (cites Pope 2004, Krewski 2009, and Schwartz 2008)
Pg 2364: “The PM 2.5 

## concentration– cardiovascular risk re lationships for both short- and longterm exposures appear to be monotonic, extending below 15 μg/m 3 

## (the 2006 annual NAAQS level) without a discer nable “safe” threshold.” Pg 2365: “This updated review by the AHA writi ng 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 scientif ic 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 bene fits would accrue from lowering PM 2.5 

## concentrations even below present-day annual (15 μg/m 3 

## ) and 24-hour (35 μg/m 3 

## ) NAAQS, if feasible, to optimally prot ect the most susceptible populations.” Pg 2366: “Although numerous insights have great ly enhanced our understanding of the PMcardiovascular relationship since the first AHA statement was published, the following list represents broad strategic avenues for future in vestigation: ... Determine whether any “safe” PM threshold concentration exists that eliminates both acute and chronic car diovascular effects in healthy and susceptible individua ls 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 Assessmen t 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 characteriz ing the public health im pacts associated with exposure to a pollutant is whether the concentrat ion-response relationship is linear across the full concentration range encountered, or if nonlinear re lationships exist along a ny part of this range. Of particular interest is the shape of the con centration-response curve at and below the level of the current standards. The shap e of the concentration-respons e curve varies, depending on the type of health outcome, underl ying 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 (s uch as the low data density in the lower concentration range, possible influence of measurement error, and in dividual differences in susceptibility to air pollution health effects). In addition, many chemi cals and agents may act by perturbing naturally occurring background processes that lead to disease, which also linearizes population concentration-response relations hips (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, O 3 

## , lead [Pb], ETS, radiation) do not exhibit evident thresholds for health effects, even though likely mechanisms include nonlinear processes for so me key events. These attributes of human population dose-response relationships have be en extensively discussed in the broader epidemiologic literature (Rothman and Greenland, 1998, 086599 ).”
Pg 2-16: “In addition, cardiovascular hospital ad mission and mortality studies that examined the PM 10 

## concentration-response relationship f ound evidence of a log-linear no-threshold relationship between PM expos ure and cardiovascular-relate d morbidity (Section 6.2) and mortality (Section 6.5).”
Pg 2-25: “2.4.3. PM Concentration-Response Relationship An important consideration in characterizing th e PM-morbidity and mortality association is whether the concentration-respons e relationship is linear across th e full concentration range that is encountered or if there are concentration ranges where there ar e departures from linearity (i.e., nonlinearity). In this ISA studies have been identified that atte mpt to characterize the shape of the concentration-response curve along with pos sible 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 concen tration-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 PM 10 

## and mortality associated with long-term exposure to PM 2.5 

## . “A limited number of studies have been identified that examined the shape of the PM cardiovascular hospital admission and ED visit concen tration-response rela tionship. Of these

## D-7 studies, some conducted an explor atory analysis during model sele ction to determine if a linear curve most adequately represented the concentr ation-response relationship; whereas, only one study conducted an extensive anal ysis to examine the shape of the concentr ation-response curve at different concentrations (Section 6.2.10.10). Ov erall, the limited eviden ce from the studies evaluated supports the us e 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 exam ined the PM-mortality concentration-response relationship and whether a threshol d exists, more complex statisti cal 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 con centration-response curve across cities (Section 6.5). Overall, the studies evaluate d further support the use of a no-th reshold log-linear model, but additional issues such as the infl uence of heterogeneity in estimate s 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 c oncentration-response relationshi p 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 wi th long-term exposure to PM. Using a variety of statistical methods, the concentration-respons e curve was found to be indistinguishable from linear, and, therefore, little evidence was observe d to suggest that a th reshold exists in the association between long-term exposure to PM 2.5 

## 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 cons istently 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 concentra tion-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 erro r and heterogeneity of shapes across cities remain to be resolved. Also, give n the pattern of seasonal and regional differences in PM risk estimates depicted in rece nt multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a co ncentration-response relationship estima ted across cities and for all-year data may not be very informative.”
Pg 6-197: “6.5.2.7. Investigation of C oncentration-Response Relationship The results from large multicity studies re viewed in the 2004 PM AQCD (U.S. EPA, 2004, 056905 ) suggested that strong evidence did not exis t 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 interpre ting the shape of PM-mortality concentrationresponse functions and the presence of a threshol d, including: (1) limited range of available concentration levels (i.e., sparse data at the low and high end); (2) hete rogeneity 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 PM 10 

## data in 20 cities from the 1987-1994 NMMAPS data. They reported that the spline model, combined acro ss the cities, showed a linear relation without indicating a threshold for the relative risks ofdeath for all-causes and for cardiovascularrespiratory causes in relation to PM 10 

## , but “the other cause” deaths (i.e., all cause minus cardiovascular-respiratory) showed an apparent threshold at around 50 μ g/m 3 

## PM 10 

## , as shown in Figure 6-35. For all-cause and cardio-respirator y deaths, based on the Akaike’s Information Criterion (AIC), a log-linear m odel without threshold was prefe rred to the threshold model and to the spline model.

“The HEI review committee commented that in terpretation of these results required caution, because (1) the measurement error could obscu re 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 appr opriate due to the fact it was not designed to assess scientific theories of etiology. Note, however, that ther e has been no etiologically credible reason suggested thus far to choose one model over ot hers 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 relati onship between PM10 and mortality.

“The Schwartz (2004, 078998 ) analysis of PM 10 

## and mortality in 14 U.S. cities, described in Section 6.5.2.1, also examined the shape of the c oncentration-response rela tionship by including indicator variables for days when c oncentrations were between 15 and 25 μ g/m 3 

## , between 25 and 34 μ g/m 3 

## , between 35 and 44 μ g/m 3 

## , and 45 μ g/m 3 

## and above. In the model, days with concentrations below 15 μ g/m 3 

## 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 eviden ce to suggest that a threshold exists. The authors did not examine city-to-c ity variation in the concentration-response relationship in this study.

“PM 10 

## 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 estim ated using a regression model relating co-located PM10 to BS or TSP. They used regression spline models with two knots (30 and 50 μ g/m 3 

## ) and then combined the individual city estimates of the splines across cities. The investigators concluded that the association between PM and mortal ity 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 citie s): 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 comb ined curves (Figure 6-37) appear to support

## D-9 no-threshold relationships between PM 10 

## and mortality, the heterogene ity 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 erro r and heterogeneity of shapes across cities remain to be resolved. Also, give n the pattern of seasonal and regional differences in PM risk estimates depicted in rece nt multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a co ncentration-response relationship estima ted 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 Protec tion 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, Res earch 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, Res earch Triangle Park, NC Dr. Tara Greaver 

## —NCEA, U.S. EPA, Resear ch Triangle Park, NC Dr. Erin Hines 

## —NCEA, U.S. EPA, Research Triangle Park, NC Dr. Douglas Johns 

## —NCEA, U.S. EPA, Resear ch 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, Resear ch Triangle Park, NC Dr. Qingyu Meng 

## —Oak Ridge Institute for Science a nd Education, Postdoctoral Research

Fellow to NCEA, U.S. EPA, Re search 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, Res earch 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, Resear ch Triangle Park, NC Dr. William Wilson 

## —NCEA, U.S. EPA, Resear ch Triangle Park, NC Dr. Lori White 

## — NCEA, U.S. EPA, Research Triangle Pa rk, 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, Pediatri c and Environmental Epidemiology,

Yale University, New Haven, CT Dr. Michelle Bell 

## —School of Forestry & E nvironmental 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 a nd 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 Environmenta l 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 Envi ronmental 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 Atmospheri c 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, Univer sity 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 Resear ch Unit, Beth Israel Deaconess

Medical Center, Boston, MA

## D-11 Dr. Eric Whitsel 

## —Departments of Epidemiology and Medi cine, University of North Carolina,

Chapel Hill, NC Peer Reviewers Dr. Sara Dubowsky Adar 

## , Department of Epidemiology, Univ ersity of Washington, Seattle,

WA Mr. Chad Bailey 

## , Office of Transportation a nd Air Quality, Ann Arbor, MI Mr. Richard Baldauf 

## , Office of Transportation a nd 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, Resear ch Triangle Park, NC Dr. Robert Devlin 

## , National Health and Environmental Eff ects 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 Eff ects 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 Sta ndards, U.S. EPA, Research Triangle

Park, NC Mr. Tyler Fox 

## , Office of Air Quality Planning and Sta ndards, U.S. EPA, Research Triangle

Park, NC Dr. Jim Gauderman 

## , Department of Environmental Me dicine, Department of Preventive

Medicine, University of Southern California, Los Angeles, CA Dr. Barbara Glenn 

## , National Center for Environmental Re search, U.S. EPA, Washington, D.C. Dr. Terry Gordon 

## , School of Medicine, New Yo rk 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 Dia gnostic 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-Riddl e Aeronautical Univ ersity, Prescott,

AZ Dr. Mike Kleeman 

## , Department of Civil and Environm ental 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, Univ ersity 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 Sta ndards, U.S. EPA, Research Triangle

Park, NC Dr. Karen Martin 

## , Office of Air Quality Planning and Stan dards, U.S. EPA, Research Triangle

Park, NC Ms. Connie Meacham 

## , NCEA, U.S. EPA, Res earch Triangle Park, NC
Mr. Tom Pace 

## , Office of Air Quality Planning and Sta ndards, U.S. EPA, Research Triangle

Park, NC Dr. Jennifer Peel 

## , Department of Environmental and Radi ological 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 St andards, U.S. EPA, Research Triangle

Park, NC Dr. Sanjay Rajagopalan 

## , Division of Cardiovascular Me dicine, 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 Sta ndards, 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 O ccupational Health, Emory University,

Atlanta, GA Dr. Frances Silverman 

## , Gage Occupational and Environmenta l 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. EP A, 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 Divisi on, 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, Un iversity of North

Carolina, Chapel Hill, NC Mr. Ronald Williams 

## , National Exposure Research Laborat ory, U.S. EPA, Research Triangle

Park, NC Dr. George Woodall, 

## NCEA, U.S. EPA, Resear ch Triangle Park, NC Dr. Antonella Zanobetti 

## , Department of Environmental He alth, Harvard School of Public

Health, Boston, MA

## D-14 D. CASAC comments on PM ISA and REA (2009)
U.S. Environmental Protection Agency - Sc ience 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/SABPRO DUCT.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 c onclusion should be followed by th e discussion of statistical methodology and the identif ication of any thres hold 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/SABPRO DUCT.NSF/81e39f4c09954fcb85256ead006be86e/7 23FE644C5D758DF852575BD00763A32/$File/E PA-CASAC-09-009-unsigned.pdf>.


## Pg 6: “On the issue of cut-points raised on 3- 18, the authors should be prepared to offer ascientifically 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 Ma tter (Second External Review Draft, July 2009)

. EPA-CASAC-10-001. November. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.N SF/81e39f4c09954fcb8525 6ead006be86e/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-thre shold log-linear model is supporte d, 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 Hea lth, 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, Cons truction and Environmental Engineering,

College of Engineering, North Carolin a 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 E ngineering, Dartmouth College, Hanover,

NH Dr. Rogene Henderson , Senior Scientist Emeritus, Lovel ace 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 Environm ental 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 Se rvice Air Resources Division, Cooperative

Institute for Research in the Atmosphere, Co lorado 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, Co llege 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 a nd 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, Pa t 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 sp atial analysis of the American Cancer Society study linking particulat e air pollution and mortality. HEI Research Repor t, 140, Health Effects Institute, Boston, MA.


## Pg 119: [About Pope et al. (2002)] “Each 10- μ g/m 3 

## increase in long-term average ambient PM 2.5 

## concentrations was associated with approximately a 4%, 6%, or 8% increase in risk of death from all causes, cardiopulmonary disease, a nd lung cancer, respectively. There was no evidence of a threshold exposure level w ithin the range of observed PM 2.5 

## concentrations. “
Krewski (2009). Letter from Dr. Daniel Krewsk i to HEI’s Dr. Kate Adams (dated July 7,

2009) regarding “EPA queries regarding HE I Report 140”. Dr. Adams then forwarded the letter on July 10, 2009 to EPA’s Beth Hasset t-Sipple. (letter plac ed in docket #EPAHQ-OAR-2007-0492).


## Pg 4: “6. The Health Review Committee comment ed that the Updated Analysis completed by Pope et al. 2002 reported “no evidence of a threshold exposure level within the range of observed PM 2.5 

## concentrations” (p. 119). In the Extende d Follow-Up study, did the analyses provide continued support for a no-threshold re sponse 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 functi on within the range of observed PM 2.5 

## concentrations.”

HEI Health Review Committee Members Dr. Homer A. Boushey, MD 

## , Chair, Professor of Medici ne, 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 Environmen tal 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 & Envir onmental Medicine, University of

Rochester Medical Center, Rochester, NY

## D-18 Dr. Stephanie London, MD, PhD 

## , Senior Investigator, Epidemio logy Branch, National Institute

of Environmental Health Sciences Dr. William N. Rom, MD, MPH 

## , Sol and Judith Bergstein Professor of Medicine and

Environmental Medicine and Dire ctor of Pulmonary and Criti cal Care Medicine, New York University Medical Center Dr. Armistead Russell 

## , Georgia Power Distinguished Profe ssor of Environmental Engineering,

School of Civil and Environmental Engin eering, Georgia Institute of Technology Dr. Lianne Sheppard, PhD 

## , Professor, Department of Biosta tistics, University of Washington

## D-19 F. Schwartz et al. (2008)
Schwartz J, Coull B, Laden F. (2008). The E ffect 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 ev idence for a threshold in the association between exposure to fine partic les and the risk of death on follow-up, which continues well below the U. S. EPA standard of 15 μ g/m 3 

## .”
Pg 68: “In conclusion, penalized spline smoothi ng 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 ha s no threshold down to close to background levels.”

## D-20 G. Expert Elicitation on PM-Mortality (2006, 2008)
Industrial Economics, Inc., 2006. Expanded Expert JudgmentAssessment of the

Concentration-Response Relationship Between PM 2.5 

## 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 inte grate their judgments a bout the likelihood of a causal relationship and/or threshold in the C-R function into his distri bution or to provide a distribution "conditiona l on" one or both of these factors.”
Pg vii: “Only one of 12 experts explicitly in corporated a threshold into his C-R function. 3 

## The rest believed there was a lack of empirical and/or theoreti cal support for a p opulation 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 PM 2.5 

## 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 μ g/m 3 

## , and a 20% chance that it would fall between 5 and 10 μ g/m 3 

## .”
Pg ix: “Compared to the pilot st udy, experts in this study were in general more confident in a causal relationship, less likely to incorporate th resholds, 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 epid emiological studies that were the focus of both elicitation studies, more than th e influence of changes to the structure of the protocol.”
Pg 3-25: “3.1.8 THRESHOLDS The protocol asked experts for their judgments regarding whethe r a threshold exists in the PM 2.5 

## mortality C-R function. The protocol focused on assessing expert judgmen ts regarding theory and evidential support for a popula tion threshold (i.e., the concen tration below which no member of the study population would experien ce an increased risk of death). 32 

## If an expert wished to incorporate a threshold in his characterization of the concentr ation-response relationship, the team then asked the expert to specify the threshold PM 2.5 

## concentration probabilistically, incorporating his uncertainty about the true threshold level.
“From a theoretical and conceptu al standpoint, all experts genera lly believed that individuals exhibit thresholds for PM-related mortality. Ho wever, 11 of them discounted the idea of a population threshold in the C-R function on a theore tical and/or empirical basis. Seven of these experts noted that theo retically 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, environm ental, and socioeconomic factors. 33 

## All 11 thought that there was insufficient empirical support for a populatio n threshold in the C-R function. In addition, two experts (E and L) cited analys es of the ACS cohort data in P ope et al. (2002) and another (J) cited Krewski et al. (2000a & b) as supportive of a linear rela tionship in the study range.

## D-21 “Seven of the experts favored epidemiological stud ies as ideally the best means of addressing the population threshold issue, because they are best able to evaluate the fu ll range of susceptible individuals at environmentall y relevant exposure levels. However, those who favored epidemiologic studies generally acknowledged th at 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 va riation 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 toxicologi cal studies are best su ited for researching mechanisms and for addressing thresholds in very narrowly defined groups. One expert, B, thought that a better understanding of the detailed biological mechan ism 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 demonstrat es 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 popu lation threshold was detect able in the currently available epidemiologic studies. He indicated th at some of the cohort studies showed greater uncertainty in the shape of the C-R function at lower levels, which c ould 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 th reshold, he thought that there was an 80% chance that it would be less than or equal to 5 μ g/m 3 

## , and a 20% chance that it would fall between 5 and 10 μ g/m 3 

## .”
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 Chan ges in Ambient Fine Particulate Matter in the U.S.” Environ. Sci. Technol. , 42(7):2268-2274.


## Pg 2271: “Eight experts thought th e true C-R function relating mo rtality to changes in annual average PM 2.5 

## was log-linear across the entire study range (ln(mortality) ) β × PM). Four experts (B, F, K, and L) specified a “piecewi se” log-linear functi on, 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 eff ects 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 threshol d, T, to his function, which he described probabilistically. He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μ g/m 3 

## ) = 0.8 and P(5 μ g/m 3 

## < T ≤ 10 μ g/m 3 

## ) = 0.2. Figure 3 does not include the impact of applying expert K’s threshold, as the size of the reduction in bene fits 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 E nvironmental Medicine, NYU School of Medicine,

Tuxedo, NY Dr. Dan Krewski 

## , University of Ottawa Dr. Nino Künzli 

## , University of Southern Califor nia Keck School of Medicine Dr. Morton Lippmann , Professor, Nelson Institute of Environm ental 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 Environm ental 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 - Sc ience Advisory Board (U.S. EPA-SAB). 2005. EPA’s Review of the National Ambient Ai r 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/sabproduc t.nsf/E523DD36175EB5AD8525701B007332AE/$Fil e/SAB-CASAC-05-007_unsigned.pdf>.



## Pg 6: “A second concern is with methodologi cal issues. The issue of the selection of concentration-response (C-R) rela tionships based on locally-deriv ed coefficients needs more discussion. The Panel did not agree with EPA st aff 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 anal ysis. The available epidemiological database on daily mortality and morbidity does not establis h 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 predic ted background concentrations or LML, and to standardize the approach across cities, for the pur pose of estimating public health impacts, the Panel favored the primary use of an assumed threshold of 10 μ g/m 3 

## . The original approach of using background or LML, as well as the other pos tulated thresholds, could still be used in a sensitivity analysis of threshold assumptions. “The analyses in this chapter hi ghlight 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 ma y exist or the shape ofnonlinear concentrationresponse curves at low levels of exposure that ma y 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 Environmenta l Health, School of Public Health and

Community Medicine, University of Washington, Seattle, WA
Dr. Petros Koutrakis , Professor of Environmental Science, Envi ronmental 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 W ood 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, Bl oomberg School of Public Health, Johns

Hopkins University, Baltimore, MD
Dr. Warren H. White , Visiting Professor, Crocker Nuclear Laborat ory, University of California -Davis,

Davis, CA Dr. George T. Wolff , Principal Scientist, General Motors Corporation, Detroit, MI 

## Dr. Barbara Zielinska 

## , Research Professor, Division of At mospheric Science, Desert Research

Institute, Reno, NV

## D-25 I. HES Comments on 812 Analysis (2004)
U.S. Environmental Protection Agency - Sc ience Advisory Board (U.S. EPA-SAB). 2004.

Advisory on Plans for Health Effects Analysis in the Analyt ical Plan for EPA’s Second Prospective Analysis – Benefits and Costs of the Clean Air Act, 1990-2020. Advisory by the Health Effects Subcommi ttee 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%5CSA BPRODUCT.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, biol ogic 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 PM 2.5 

## and both allcause and cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. Graphical an alyses of these studies (Doc kery 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 re ported 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 Medi cine (IOM), Edinburgh, Scotland Dr. Patrick Kinney 

## , Columbia University, New York, NY Dr. Michael Kleinman 

## , University of Calif ornia, 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 Washingt on 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 In c., 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, Portsm outh, NH Dr. Dale Hattis, Clark University, Worcester, MA Dr. D. Warner North, NorthWor ks Inc., Belmont, CA Dr. Thomas S. Wallsten, University of Maryland, College Park, MD

## D-27 J. NRC – Committee on Estimating the Health Ri sk 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 concentrati on-response functions may influe nce the overall estimate of benefits. The shape is particularly important fo r lower ambient air pollu tion concentrations to which a large portion of the population is exposed. Fo r this reason, the impact of the existence of a threshold may be considerable.
“In epidemiological studies, air pollution concen trations 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 st udies with the large number of repeated measurements, linearity and thresholds have been formally addressed with reasonable statistical power. For pollutants such as PM 10 

## and PM 2.5 

## , there is no evidence for any departure of linearity in the observed range of exposure, nor any indi cation of a threshold. For example, examination of the mortality effects of short-term exposure to PM 10 

## in 88 cities indicates that the concentration-response functions are not due to the high concentr ations and that the slopes of these functions do not appear to increase at hi gher concentrations (S amet et al. 2000). Many other mortality studies have examined the sh ape of the concentrati on-response function and indicated that a linear (nonthres hold) model fit the data well (Pope 2000). Furthermore, studies conducted in cities with very lo w ambient pollution concentrations have similar effects per unit change in concentration as thos e studies conducted in cities with higher concentrations. Again, this finding suggests a fairly lin ear concentration-response func tion 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 outcome s was not unreasonable. However, the statistical power to assess the shape of these functions is w eakest 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 epidemiol ogy, because epidemiology in tegrates 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 su ch large groups susceptibility to air pollution health

## D-28 effects varies considerably across individuals and de pends 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 concentrati on-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/m 3 

### ) mean threshold for mortality associated with long-term exposure to PM 2.5 

### . The low-end benefits estimate assumed a 15- μg/m 3 

### threshold for all PM-related health effects. The studies, however, included concentrations as low as 7.5 μg/m 3 

### . 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 thr eshold assumptions. If a threshold is assumed where one was not apparent in the original study, then the da ta should be refit and a ne w 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 zer o 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 concen tration-response function w as 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 aspec ts of assuming a threshold in a benefits analysis where one was not indicated in the original study sh ould be conveyed to th e reader. The committee notes that the treatment of thresholds should be evaluate d 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 ag ency conducts sensitivity analyses incorporating thresholds, it provides no judgment as to their relative pl ausibility. 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 pl ausibility 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 epidemiologi cal studies assume that these functions are linear

## D-29 on some scale. Often, the scale is a logarithmic transf ormation 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, epidemio logical 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 prepar ed 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 associati on 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 rea listic 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 stud y population to the target populations (which may have etiologically releva nt 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 emi ssions, estimates of emissions themselves, and the relative toxicity of various classes of particles. Simila rly, many aspects of the analysis of the impact of regulation on ambient concentrations and on populati on exposure involve considerable uncertainty and, therefore, may be beneficially modeled in this wa y. 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 cons ulted 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 fo r 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