### Au gust 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 Standard s for Hazardous Air Pollutants and New Sou rce Performance Standards (NSPS) for

the Portland Cement Manufacturing Industry Final Report Au gust 2010 U.S. Environmental Protection Agency Office of Air Quality Planning and Standards (OAQPS) Air Benefit and Cost Group

(MD - C439 - 02) Research Triang le 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 Types of Portland Cement ................................ ................................ .................. 2 - 3 2.1.3 Production Costs ................................ ................................ ................................ . 2 - 3 2.2 The Demand Side ................................ ................................ ................................ ............. 2 - 8 2.3 Industry Organization ................................ ................................ ................................ .... 2 - 10 2.3.1 Market Structure ................................ ................................ ............................... 2 - 10 2.3.2 Manufacturing Plants ................................ ................................ ........................ 2 - 11 2.3.3 Firm Characteristics ................................ ................................ .......................... 2 - 16 2.4 Markets ................................ ................................ ................................ .......................... 2 - 20 2.4.1 Market Volumes ................................ ................................ ............................... 2 - 21 2.4.2 Market Prices ................................ ................................ ................................ .... 2 - 22 2.4.3 Future Projections ................................ ................................ ............................. 2 - 22 3 Economic Impact Anal ys is ................................ ................................ ........................... 3 - 1 3.1 Regulatory Program Costs ................................ ................................ ............................... 3 - 2 3.2 Partial - Equilibrium Analysis ................................ ................................ ........................... 3 - 5 3.2.1 Regional Structure and Baseline Data ................................ ................................ 3 - 6 3.2.2 Near - Term

Cement Plant Production Decisions ................................ ................. 3 - 6 3.2.3 Economic Impact Model Results ................................ ................................ ........ 3 - 9 3.3 Other Economic Analyses: Direct Compliance Cost Methods ................................ ...... 3 - 18 3.4 Social Cost Estimates ................................ ................................ ................................ ..... 3 - 19 3.5 Energy Impacts ................................ ................................ ................................ .............. 3 - 21iv 3.6 Assessment ................................ ................................ ................................ ..................... 3 - 23 4 Small Business Impact Analy sis ................................ ................................ ................... 4 - 1 4.1 Id entify 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 B enefits 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 Benefits Analysis Approach ................................ ................................ ............... 6 - 3 6.2. 3 Health Impact Analysis (HIA) ................................ ................................ ............ 6 - 4 6.2.4 Estimating PM 2.5 -related premature mortality ................................ .................... 6 - 8 6.2.5 Economic va luation of health impacts ................................ .............................. 6 - 11 6.3 Health Benefits Results ................................ ................................ ................................ .. 6 - 13 6.4 Energy Disbenefits ................................ ................................ ................................ ......... 6 - 20 6.4.1 PM 2.5 Disb enefits ................................ ................................ .............................. 6 - 25 6.4.2 Social Co st of Carbon and Greenhouse Gas Disbenefits ................................ .. 6 - 27 6.4.2 Total Disb enefits ................................ ................................ ............................... 6 - 27 6. 5 Unquantified or Nonmonetized Benefits ................................ ................................ ....... 6 - 20 6. 5 .1 Other 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 Carlo Analysis ................................ ................................ ....................... 6 - 41 6. 6 .2 Alternate Concentration - Response Func ti ons for PM Mortality ...................... 6 - 42 6. 6 .3 LML Assessment ................................ ................................ .............................. 6 - 42 6. 6 .4 Qualitative Assessment of Uncertainty and Other Analysis Limitations .......... 6 - 43 6. 7 Comparison of Benefits and Cos ts ................................ ................................ ................. 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 - 1vi LIST OF FIGURES Number Page 2 - 1. Simplified Flow Sheet of Clinker and Cement Manufacture ................................ ........ 2 - 2 2 - 2. Labor Costs per Metric Ton of Cement ($2005) ................................ .......................... 2 - 7 2 - 3. Distribution of Energy Consumption ................................ ................................ ............ 2 - 8 2 - 4. End Uses of Cement: 1975 to 2003 ................................ ................................ .............. 2 - 9 2 - 5. Producer Price Indices for Competitive Building Materials: 2003 to 2008 ................ 2 - 10 2 - 6. Distribution of Cement Kilns in the United States ................................ ..................... 2 - 13 2 - 7. Historical U.S. Cement Price ................................ ................................ ...................... 2 - 23 2 - 8. Deviation from National Average Cement Price per Metric Ton b y Region: 2005 ................................ ................................ ................................ ............................. 2 - 24 3 - 1. Range of Per - Ton Total Annualized Compliance Costs (2005$) ................................ . 3 - 4 5 - 1. Map of the Ph otochemical 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 inp uts and outputs for the BenMAP model ................................ .......................... 6 - 6 6 - 4. Breakdown of Monetized PM 2.5 Health Benefits using Mortality Function from Pope et al. (2002) ................................ ................................ ................................ ........ 6 - 16 6 - 5. Total Monetized PM 2.5 Benefits for the Final C ement 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 o f Total PM - related Mortalities Avoided by Baseline Air Quality

L evel for Final Portland Cement NESHAP and NSPS ........................... 6 - 20 6 - 8. Estimated County

L evel Carcinogenic Risk from HAP exposure from outdoor sources (NATA, 2002) ................................ ................................ ................................ 6 - 28 6 - 9. Estimated County

L evel 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 Portl and Cement NESHAP in 2013 (millions of 2005$) ................................ .... 1 - 4 2 - 1. Portland Cement Shipped from Plants in the United States to Domestic Customers, by

Ty pe ................................ ................................ ................................ ...... 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 Ty

pe: 2000 to 2005 (employee hours per metric ton) ................................ ............................. 2 - 6 2 - 5. Energy

Consumption by

Ty pe 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 Ty

pe ............................. 2 - 14 2 - 8. Clinker Capacity , Production, and Capacity Utilization in the United States: 2000 to 2005 ................................ ................................ ................................ ............... 2 - 15 2 - 9. Capacity

Utilization Rates by State: 2005 ................................ ................................ .. 2 - 17 2 - 10. Cement Manufacturing Employ ment (NAI CS 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 Re gional Market Impacts: 2005 ................................ ............................. 3 - 12 3 - 8. Distribution of Industry Impacts: 2005 ................................ ................................ ....... 3 - 13 3 - 9. Cement Plants with Significant Utilization Changes: 2005 ................................ ........ 3 - 14 3 - 10. Job L osses/ Gains Associated with the Final Rule ................................ ...................... 3 - 16 3 - 11. Distribution of Social Costs ($10 6 ): 2005 ................................ ................................ ... 3 - 20 3 - 12. U.S. Cement Sector Energy Consumption (Trillion BTUs): 2013 ............................. 3 - 23viii

4 - 1. Small Entity

Anal ys is ................................ ................................ ................................ ... 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 Benef its and Emission Reductions for Final Cement NESHAP and NSPS in 2013 (2005$) ................................ ................................ ......... 6 - 17 6 - 5 . Summary

of Monetized PM 2.5 Energy Disb enefits 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 Cos ts, and Net Benefits for the final Portland Cement NESHAP in 2013 (millions of 2005$) ................................ ............ 6 - 461 - 1 SECTION 1 INTRODUCTION The U.S. Environmental Protection Agency (EPA) is finalizing amendments to the National Emission Standards for Hazardou s 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 hydro carbons (THC), and particulate matter (PM) from kilns located at a major or an area sources, and hydrochloric acid (HCl) from kilns and located at major sources. EPA is also adopting separate standards for these pollutants that apply during startup, shutdo wn, and operating modes. Finally , EPA is adopting performance specifications for use of Hg continuous emission monitors (CEMS) and updating recordkeeping and testing requirements. The final amendments to the NSPS add or revise, as applicable, emission limi ts 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 requirem ents for affected sources. As part of the regulatory

process, EPA is required to develop a regulatory impact analy sis (RIA). The RIA includes an economic impact anal ys is (EIA) and a small entity impacts analy sis and documents the RIA methods and results. 1.1 Executive Summary The key results of the RIA are as follows:

 Options Analyzed: EPA’s anal ys is focuses on the results of the final NESHAP and NSPS. W e also present additional information on different combinations of the regulatory

programs to help stakehold ers better unders tand 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 C ost Analysis: EPA estimates that total annualized costs with the final NESHAP and NSPS will be $ 466 million (2005$) .  Market Analysis: The partial - equilibrium economic model suggests the average national price for Portland cement could be 5 % higher with the NESHAP, or $ 4 . 5 0 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 o perating profits f all by $2 41 million ; EPA also identified 10 domestic plants with negative operating profits and significant utilization changes that could temporarily idle until market demand conditions improve. The plants have unit compliance costs close to $ 8 per ton of clinker capacity and $ 116 million total change in operating profits . Since these plants account for approximately 8 % of domestic capacity , a decision to permanentl y shut down these plants would reduce domestic supply and could lead to additional projected market price increases and reductions in pollution control costs .  Employment Changes : EPA uses two methods for estimating emplo ym ent impacts. A simplistic, limited assessment narrowl y focused on output changes in the Portland cement industry

indicates that the final rule’s gross impact on employ ment is 1,500 job losses. However, this approach inherentl y overstates job losses, as it is based on the assumption that employ ment is proportional to output, and because it ignores offsetting general equilibrium and other effects as discussed in detail in Chapter 3. A more sophisticated anal yt ical approach that includes other types of employment effects estimates changes in net employ ment 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 cap acity utilization. The social cost estimates are significantl y higher than the engineering anal ys is estimates , which estimated annualized costs of $ 466 million. This is a direct consequence of EPA’s assumptions about existing market structure discussed ex tensively

in previous cement industry rulemakings and S ection 2, Appendix A , and Appendix B of this RIA. Under baseline conditions without regulation, the existing domestic cement plants are assumed to choose a production level that is less than the level produced under perfect competition. As a result, a pre existing market distortion exists in the markets covered b y 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 between price and marginal cost in these markets and contributes to additional social costs.

 Energy Impacts: EPA concludes that the rule when implemented will not have a sign ificant 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 ye ar and the natural gas use to be 1.0 million MMB TU per ye ar 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 ACIs ys tems, 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. E PA performed a screening anal ys is for impacts on the 4 small entities by comparing compliance costs to average company revenues. EPA’s anal ys is found that the ratio of compliance cost to company revenue falls below 1% for two of the four small entities ( includes a Tribal government) . Two small entities would have an annualized cost of between 1% and 3% of sales. No small business es would have an annualized cost greater than 3% of sales.

 Benefits Analysis: Inthe year of full implementation (2013), EPA estimates that the total monetized benefits of the final N ESHAP and NSPS are $ 7. 4 billion to $ 1 8 billion and $ 6. 7 billion to $ 1 6 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 m onetized in this anal ys is, including reducing 4,4 00 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 ye ar. In addition, ecos ys tem benefits and visibility benefits have not been monetized in this analy sis. These estimates include the energy disbenefits associated with increased electricity usage b y the control devices .  Net Benefits: In the ye ar of full implementation (2013), EPA estimates the net benefits of the final NESHAP and NSPS are $ 6. 5 billion to $ 1 7 billion and $ 5.8 billion to $ 16 billion, at 3% and 7% discount rates , respectivel y. All estimates are in 2005 dollars for the ye ar 2013. 1.2 Organization of this Report The remainder of this report supports and details the methodology and the results of the EIA:

 Section 2 presents a profile of the affected industry.

 Section 3 describes the economic impact anal ys is and energy impacts.

 Section 4 describes the small business impact analy sis.

 Section 5 presents the air quality modeling of emission reductions.

 Section 6 presents the benefits anal ys is.

 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 . Summa ry 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, 4 00 to $1 8 ,000 $6, 7 00 to $ 1 6 ,000 Total Social Costs c $926 to $950 $926 to $950 Net Benefits $6, 5 00 to $1 7 ,000 $ 5,8 00 to $1 6 ,000 Nonmo netized Benefits d 4,400 tons of NO x (includes energy disbenefits) 5,200 tons of organic HAPs 5,900 to ns 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 b $510 to $1,300 $460 to $1,100 Total Social Costs c $ 72 $ 72 Net Benefits $4 4 0 to $1, 2 00 $ 390 to $1, 0 00 Nonmo netized 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% D iscount Rate Total Monetized Benefits b $7, 4 00 to $1 8 ,000 $6,7 00 to $ 1 6 ,000 Total Social Costs c $904 to $930 $904 to $930 Net Benefits $6, 5 00 to $1 7 ,000 $ 5,800 to $16,000 Nonmo netized Benefits d 5 ,200 tons of organic HAPs 5,900 tons of HCl 16, 4 00 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 Mon etized Benefits b $7, 4 00 to $1 8 ,000 $6,7 00 to $ 1 6 ,000 Total Social Costs c $955 to $979 $955 to $979 Net Benefits $6, 5 00 to $1 7 ,000 $ 5, 7 00 to $ 1 5 ,000 Nonmo netized 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 Health effects from HAPs, NO 2, and SO 2 exposure Ecosystem effects Visibility impairment a All estimates are for the implementation year (2013) and are rounded to two significant figures. b The total monetized benefits reflect the human health benefits associated with reducing exposure to 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 chemical compositi on, are equally potent in causing premature mortality because there is no clear scientific evidence that would support the development of differential effects estimates by particl e type. The total monetized benefits include the energy disbenefits. c The me thodology used to estimate social costs for 1 year in the multimarket model using surplus changes results in the same social costs for both discount rates. d Due to data, methodology, and resource limitations, we were unable to monetize the benefits associ ated with these categories of benefits.2 - 1 SECTION 2 INDUSTRY PROFILE Hydraulic cement (primarily Portland cement) is a key component of an important construction material: concrete. Concrete is used in a wide variety of applications (e.g., residential and commercial buildings, public works projects), and cement demand is influenced by national and regional trends in these sectors. Recent data for 2007 show that the U.S. cement industry produced over 9 0 million metric tons of Portland cement ( Department of Interior [D OI ], U.S. G eological S urvey

[USGS] , 2008 b ). The value of total U.S. sales, including imported cement, was about $11.8 billion, with an average value of approximately $100 per metric ton. The vast majority

of cem ent 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, 2008 b ). 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 t echnical and economic aspects of the industry

that must be addressed in the economic impact anal ys is. Section 2.1 provides an overview of the production processes and costs data. Section 2.2 discusses the uses, consumers, and substitutes for cement. Sectio n 2.3 summarizes the organization of the Portland cement industry . The industry

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

outlook. 2.1 The Supply Side 2.1.1 Production Process As shown in Figure 2 - 1, the manufacturing pro cess 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 resulti ng clinker to a fine powder and mixing with gy psum 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

dif ferences between the wet and dry processes have led to a substantial decline in clinker capacity provided b y 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 Pa dovani, 2002 ). Data also show that the number of wet process plants fell from 32 in 2000 to 23 in 2005 (DOI, USGS, 2007 ).2 - 3

2.1.2 Types of Portland Cement Portland cement manufacturers produce a variety of ty pes 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 typ es that include air - entraining properties (IA, IIA, IIIA) (PCA, 2008a). We describe these below. Ty pes I and IA: These types are the usual product used in general concrete construction, most commonly

known as gray

cement because of its color. Ty pes 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. Ty pe III and IIIA: These ty

pes are made from raw materials with a lime - to - silic a ratio higher than that of Ty

pe I cement and are ground finer than Ty pe I cements. They

contain a higher proportion of tricalcium silicate than regular Portland cements. Ty pe IV: This ty

pe contains a lower percentage of tricalcium silicate and tricalcium aluminate than Ty

pe I, thus lowering the heat evolution. Consequently , the percentage of tetracalcium aluminoferrite is increased. Ty pe IV cements are produced to attain a low heat of hydration. Ty pe V: This ty

pe resists sulfates better than the other four ty pes. As shown in Table 2 - 1, the vast majority of Portland cement shipments 1 in 2005 were Ty pes I and II grey cement. However, Type V (sulfate - resisting) is a growing market ( DOI , USGS, 200 7 a); since 2000, Ty pe V cement has incre ased 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 f uel. U.S. Census data for the cement industry (North American Industry Classification Sy stem [ 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 to tal value of shipments was $10.6 billion, and the industry spent approximately

$1.7 billion on materi als, parts, and packaging, or 16 % of the value of shipments. Total compensation for all employees (includes pay roll and fringe benefits)

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

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

Customers, by 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 (T ype 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 10 0%

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 T ypes I and II but may include T ypes III thro ugh 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. 200 7 a . 2005 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Table 15. U.S. Department of the Interior, U.S. Geological Survey. 2002 . 2001 Minerals Yearbook, Cement . Washington, DC: U.S. Department of the Interior. Table 15. amounted to $1. 4 billion (13 %). 1 Fuels and electricity expenditures were approximately $1.7 billion (16 %). 2.1.3.1 Raw Material Costs According to the USGS, approximately 159.7 million tons of raw materials were required to produce approximately 95.5 million tons of cement in 2005 or 1.67 tons of raw materials per ton of cement. Table 2 - 2 summarizes the amount of raw material inputs used per t on 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 essentiall y constant during this 6 - ye ar period.

1 Wages paid to production workers were $0.8 billion (8% of the value of shipments) at an average hourly rate of $27.2 - 5

Table 2 - 2. Raw Material Input Ratios for the U.S. Cement Industry: 2000 to 2005 2000 2001 2002 2003 2004 2005 Raw material input (10 3 me tric 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 b y state. In 2005, the prices of raw materials were highest in Hawaii where they

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

sold for an average of $3.89 per metric ton. 2.1.3.2 Labor Costs In 2005, the Portland Cement Association (PCA) reported labor productivity measures (in terms of metric tons of cement per emplo ye e 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 ty picall y 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 b y 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 ce ment production. Weights for labor are 85% clinker and 15% finished cement production. Weights for energy are 92% clinker and 8% finished cement production (PCA, 2005 ). 2 The labor costs reported in Figure 2 - 3 were calculated by first multiplying the number of employee hours per metric ton of cement reported in Table 2 - 4 by the average hourly earnings of production workers for each year (BLS, 2007a and 2007b ). Next, these cost estimates were adjusted for inflation and exp ressed 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 ($/m etric ton) a State(s) Price of Raw Materials ($/m etric 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. 2007 b. 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 0.45 0 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 Resear ch 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 Pl ants We t Pro ce ss 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). 2007 a . “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 Dat a Tables.” Available at <http://www.bls.gov/cpi/>. As obtained on March 14, 2008. 2.1.3.3 Energy Costs Figure 2 - 3 provides a detailed breakdown of U.S. energy consumption in 2005. As this figure shows, the vast majority of energy

in U.S. cement plants is derived f rom 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 o f cement) (see Table 2 - 5). As these data show, wet process plants are typicall y 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 slightl y during this period .2 - 8 Co al & C ok

e 75. 4% Pe tr ol eum Pr odu cts 0. 8% Ele ctricity 11. 0% Nat ural G as 3. 6% Was te 9. 2% Figure 2 - 3. Distribution of Energy Consumption Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005 . Skokie, IL: PCA’s Economic Research Department. Table 2 - 5. Energy Consumption by Type of U.S. Cement Plant (million BTU per metric

ton) Year 2000 2001 2002 2003 2004 2005 All plants 4.982 4.93 4.858 4.762 4.755 4.699 Wet process 6.25 6.442 6.676 6.647 6.807 6.387 Dry process 4.673 4.655 4.498 4.433 4 .407 4.433 Source: Portland Cement Association. December 2005 . U.S. and Canadian Labor-Energy Input Survey 2005 . Skokie, IL: PCA’s Economic Research Department. 2.2 The Demand Side The demand for Portland ce ment 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 con struction in 1998 (USI TC, 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 s ys tem. 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 earl y 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

Read y - mi x co ncre te Co nc re te pr oduc ts Co nt ra ct or s Bui ld in g mater ial de al er s Oil wel l, m in in g, and was te stabi li zati on Go ve rn ment and mi sc el la neous Mas onr y ce ment Figure 2 - 4. End Uses of Cement: 1975 to 2003 Source: Kelly, T. and G. Matos. 2007 a . “Historical Statistics for Mineral and Material Commodities in the United States: Cement End Use Statistics. ” U.S. Geological Survey Data Series 140, Version 1.2. Available at http://minerals.usgs.gov/ds/2005/140/ . 74% of cement sales in 2005, followed by concrete product manufacturers (14%), contractors (6%) , and other (6%) ( Kelly and Matos, 2007a). Cement competes with other construction materials such as steel, asphalt, and lumber. Lumber is the primary

substitute in the residential construction market, while steel is the prim ary substitute in commercial applications. Asphalt is a key substitute in transportation projects such as road and parking lot surfacing. However, concrete has advantages over these substitutes because it tends to be available locally and has lower long - term maintenance costs (Van Oss and Padovani, 2002 ). The PCA regularl y reports price trends for these competing building materials (PCA, 2008b ). As shown in Figure 2 - 5, steel and asphalt have risen sharp ly relative to cement since 2003 while lumber has declined.2 - 10

Figure 2 - 5. Producer Price Indices for Competitive Building Materials: 2003 to 2008 Sourc e: Portland Cement Association. 2008b . “Market Research: Producer Price Indices

—Com petitive Building Materials.” Available at <http://www.cement.org/market/> . 2.3 Industry Organization 2.3.1 Market Structure A review and description of market characteristics (i.e., degree of concentration, entry barriers, and product differentiation) can enhance our understanding of how U.S. cement markets operate. These characteristics provide indicators of a firm’s ability to influence market prices b y varying the quantity

of cement it sells. For example, in markets with large numbers of sellers and identical products, firms are unlikel y to be able to influence market prices via their productio n decisions (i.e., they

are “price takers”). However, in markets with few firms, significant barriers to entry (e.g., licenses, legal restrictions, or high fixed costs), or products that are similar but can be differentiated, the firm may have some degree of market power (i.e., set or significantl y influence market prices). Cement sales are often concentrated locall y among a small number of firms for two reasons: high transportation costs and production economies of scale. 1 Transportation costs significantl y 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] 3273 1) 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 typicall y pointed to Census of Transportation data showing over 80% of cement shipments were made within a 200 - mile radius (Jans and Rosenbaum, 1997 ) 1 and reported evidence of high transportation costs per dollar of product value from case studies (Ryan, 2006 ). The cement industry is also very

capital intensive and entry requires substantial investments. In additional, large plants are typically more economical because they

can produce cement at lower unit costs; this reduces entry incentives for small sized 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 uni form quality, and recent industry reviews (USITC, 2006 ) suggest that there is little or no brand loyalty that allows firms to differentiate their products. 2.3.2 Manufacturing Plants During 2005, 107 cement manufacturing plants with 186 cement kilns were operating in the United States. This section describes the location, age, production capacity, and employment of these manufacturing facilities. Section 2.3.2 concludes with a discussion of future trends. Section 2.3.3 provides a detailed discussion of the characteristics of the firms owning these facilities. 2.3.2.1 Location Table 2 - 6 summarizes the geographic location of cement kilns in the United States and clinker capacity. The top five states in order of clinker capacity are California, Texas, Pe nnsylvania, 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 I sland, Minnesota, North Carolina, and Vermont) and the District of Columbia had no clinker - producing facilities in 200 5.

1 A recent USITC study of California cement markets found more than 75% of gray Portland cement shipments in the state were shipped to cu stomers 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 me tric 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 26 0 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

Ta ble 2 - 6. Number of Kilns and Clinker Capacity by State: 2005 (continued) No. Kilns Clinker Capacity (10 3 me tric tons per year) PA 21 6,414 RI 0 SC 6 3,480 SD 3 851 TN 2 1,438 TX 22 11,688 UT 2 1,514 VA 1 1,120 VT 0 WA 2 1,100 WI 0 WV 3 708 WY 2 597 Total 186 93,785 Source: Portland Cement Association (PCA). 2004 . U.S. and Canadian Portland Cement Industry: Plant Information Summary. Skokie, IL: PCA’s Economic Research Department.

Figure 2 - 6. Distribution of Cement Kilns in the United States Source: Portland Cement Association (PCA). December 2004 . U.S. and Canadian Portland Cement Industry: Plant Information Summary . Skokie, IL: Portland Cement Association Economic Research Department. 2.3.2.2 Age In 2005, 72% (134) of all kilns in the United States used the dry manufacturing process, and it accounted for 83% (78 million metric tons) of national clinker capacity . The growing prevalence of the dry

process among cement manufacturers is part of a long - term trend. As the data in Table 2 - 7 indicate, no new wet clinker capacity has been added within the past 30 years.2 - 14

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

No. Kil ns Clinker Capacity (10 3 me tric 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 me tric 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. Skoki e, 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 b y approximately 14% (Table 2 - 8). Because capacity tends to grow more rapidly

than production, total capacity utilization decreased sl ightly 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) 89,264 100,360 101,0 00 102,000 105,000 104,000 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 (USG S, 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. How ever,2 - 16

throughout these movements in clinker capacity and production, capacity utilization tended to remain between 80% and 85%. Capacity utilization often varies by geographic region as a result of fluctuations in regional construction activity . For exampl e, 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 utilizatio n rates by

state in 2005. 2.3.2.4 Employment Each ye ar, the Annual Survey of Manufactures (ASM) collects employ ment, pay roll, sales, and other data for all manufacturing establishments. Table 2 - 10 summarizes the employ ment data collected by

the ASM for the cement manufacturing industry (NAICS 327310) from 2000 to 2005. As these data indicate, total employ ment fell approximately

2% over this 6 - ye ar period, from approximately 17,000 employees in 2000 to 16,900 in 2005. 2.3.2.5 Trends As previously

discussed, clinker capacity has been increasing at a slower pace since 2001. However, according to the PCA, the cement industry has announced that it will increase clinker capacity b y 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 suppl y could increase b y as much as 2 million additional tons by

2012. Increases in EPA production variances could also add another 1.1 million metric tons of domestic supply (PCA, 2007 ) . 2.3.3 Firm Characteristics EPA has reviewed industry information and publicly available sales and employ ment databases to identify

the chain of ownership by accounting for subsidiaries, divisions, and joint ventures to appropriatel y group companies b y size. Ta ble 2 - 11 provides sales and emplo ym ent 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, Ne vada, 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, Nev ada, 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. 2007 b . 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/2003pu bs/m01as - 1.pdf>. As obtained on March 14, 2008. 2.3.3.1 Distribution of Small and Large Companies Firms are grouped into small and large categories using Small Business Administration (SBA) general size standard definitions for NAICS codes. These size standards ar e presented either b y number of employees or b y annual receipt levels, depending on the NAICS code. The manufacture of Portland cement is covered b y 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 emplo yees 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 t otal 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 Am erica, Inc.; Buzzi Unicem USA, Inc.; Heidelberg Cement 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 cap acity , and the top 10 accounted for 83% of total capacity . Small companies accounted for less than 5% of clinker capacity .

1 In cases where no employment data were available, we used information from previous EPA analyses to determine firm size.2 - 19

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

Ultim ate Parent Nam e Annual

Sales ($10 6 ) Employ me nt Type Sm all

Business Plant s Kilns Clinker Capacity (10 3 me tric 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 Com pany $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)

Ultim ate Parent Nam e Annual

Sales ($10 6 ) Employ me nt Type Sm all

Business Plants Kilns Clinker Capacity (10 3 me tric tons per year) Capacity

Share Monarch Cement Com pany $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 Com pany, 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 Com panies $29 350 Private Yes 1 2 286 0.3%

a Enterprise is owned by Salt River Pima - Maricopa Indian Community. b EPA estimate. Sources: Dun & Bradstreet, Inc. 2007 . D&B million dollar directory. Bethlehem, PA. LexisNexis. LexisNexis Academic [electronic resource]. Dayton, OH : Lexi s Nexis . 2.3.3.3 Company Revenue and Ownership Type Cement manufacturing is a capital - intensive industry . The vast majority of stakeholders are large global companies with sales exceeding $1 billion. In 2005, ultimate parent company sales ranged from $30 million to $22.3 billion (Table 2 - 11), with average (median) sales of $4,565 ($1,589) million. Small companies accounted for 0.3% share b y s ales. Ultimate parent companies were e ither privately or publicl y owned or jointly operated by

several companies. A majority of the companies (52%) were publicl y owned. Private companies had a slightl y smaller share (41%), and onl y two (or 7%) were joint ventures. 2.4 Markets Portland cement is produced and consumed domestically as well as traded internationally . The United States meets a substantial fraction of its cement needs through imports; in contrast, it exports only

a small fraction of domestically produced cement to other countries. We pro vide 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 onl y for hydraulic cement, which includes Portland and masonry cement. 2.4.1 Market Vol

umes 2.4.1.1 Domestic Production In 2007, the domestic shipments of Portland cement were 90.6 million metric tons, reflecting an 8.5% increase from 2000 and, more recentl y, a 3% decrease from 2006 (see Table 2 - 12). Year - end stocks remained relativel y level durin g 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 steadil y since 2000, reaching 128 million tons in 2006. However, affected b y decline s 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. 2008 b . 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 . Washi ngton, DC: U.S. Department of the Interior. Available at <http://minerals.er.usgs.gov/minerals/pubs/ commodity/cement/>. 2.4.1.2 International Trade Cement imports are a significant share of domestic consumption (approximately 20%); they also grew by

30% from 2000 to 2006 (see Table 2 - 13). Major importing countries in 2007 included Canada (18% of total imports in 2006), China (16%), and Thailand (11%) (DOI , USGS, 2008 b ). In 2007, the falling value of the dollar and construction activity dec lines 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 Im ports (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. 2008 b . 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 relativel y constant at about 800,000 tons and ty

pically did not exceed 1% of production. However, the level of U.S. exports has increased during the l ast 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, 2008 b ). 2.4.2 Market Prices Correcting for the effects of inflation, we find that the real price of cement per metric ton ( 2005 dollars) has typically ranged between $75 and $95 since 1990 (see Figure 2 - 7). However, data for the last 2 years suggest the average price of cement is at its highest level in over 2 decades (approximately

$100). Because of transportation constraints , there are regional differences in the price of cement across states. For example, remote locations such as Alaska and Hawaii had the highest deviation from the national average ($48 in 2005) (see Figure 2 - 8). In the contiguous states, prices in Arizona, New Mexico, and California were higher than the national averages, while prices in Texas, Indiana, and South Carolina were among the lowest. 2.4.3 Future Projections Although estimates of future cement demand are not publicly available, the Energy In formation 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 Cu rren t U.S . Do llars Co ns ta nt 2005 U.S . Dollar s Figure 2 - 7. Historical U.S. Cement Price Sources: 1990

–2003: Kelly, T. and G. Matos. 2007 b . “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. 2008 b . 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 So uth Car

olina Indian a Texas, southe rn Alabama Marylan d Arkansas and Ok lah oma

Texas, nor

the rn Kan sas Ge or gi a, Virg in ia, West V irg inia Kent ucky, Mississippi, Tennessee Misso ur i Pen nsylvania, east ern Color ado an d Wyo mi ng Or egon and Wash ingto n

Illinois Maine and New Yo rk Pen ns ylvania, we stern

Ohio Fl orida Iowa, Neb raska, So uth Dakota Michigan and Wisco nsin Idah o, M onta na , Nevada, Ut ah

Calif orni a, southe rn Califo rn ia, nor

the rn Ariz onaa nd New M exico Alaska and Hawaii

Dollar Differ ence Fr om National Averag e F. O. B. Cemen t Price ($90 pe r me tric ton): 2005 Figure 2 - 8. Devi ation from National Average Cement Price per Metric Ton by Region:

2005 Source: U.S. Department of the Interior, U.S. Geological Survey. 200 7 a . 2005 Minerals Yearbook, Cement . Washingto n, 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 ANAL YSIS EPA prepares an EIA to provide decision makers with a measure of the social costs of using resources t o 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 anal ys is that estimates market changes (e.g., price and consumption) and economic welfare changes (e.g., changes in consumer and producer surplus). The Office of Air Quality Planning and Standards ( OAQPS ) has adopted the standard industry- level anal ys is described in the Office’s resource manual (EPA, 1999a). This approach is consistent with previous EPA anal ys es of the Port land cement industry (EPA, 1998 ; EPA, 1999b , and 2009 a) 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 analy sis is the intermediate run where producers have some constraints on their flexibility

to adjust factors of production. This time horizon allows us to capture important transitory impacts of the program on existing producers. Key measures in this analy sis 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 employ ment),

 facility- level effe cts (plant utilization changes), and

 social costs (changes in producer and consumer surplus). Absent forecasts and the uncertainties of future economic baselines, the partial equilibrium market anal ysis can only cover a subset of plants presumed to be operating in conditions similar to 2005. Thus, this analy sis does not reflect changes in the state of the US econom y which may occur by the anal ys is year of 2013 which could significantly influence the quantity of cement needed. As shown in the following sections, the market anal ys is covers $37 8 million of the total $4 66 million in regulatory program costs, or 8 1 %; simulated post policy outcomes described throughout Section 3.2 should be interpreted in light of this modeling choice. EPA analyzed the remaining $8 8 million in NESHAP and NSPS regulatory program costs “outside” of the partial equilibrium market analy ses 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 NESHAP from the Portland cement manufacturing ind ustry and (NSPS for Portland cement pl ants. The final amen dments 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 app ly 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 limit s 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. Although EPA’s anal ys is focuses on the final NESHAP and NSPS engineering cost estimates , EPA also presents additional information on different combinations of the regulatory

programs . This information help s stakeholders better und erstand the size and scope of the each. These include

 f inal NSPS only ,  f inal NESHAP only , and  alternative: m ore stringent NSPS and f inal NESHAP. F or the ye ar 2013, EPA’s engineering cost anal ys is 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 ov er the expected life of the control equipment which is 20 years for all devices except RTOs which are 15 ye ars. The majority of the costs ($4 55 million, or 98 %, are associated with the final NESHAP. The remaining costs ($11 million) are associated with th e 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 combinations of the regulatory programs . InTable 3 - 2, we report state - level summary statistics for total annualized compliance costs per metric ton of clinker cap acity 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 Co mpliance Costs Description Final NSPS Only More Stringent NSPS Only Total: $ 40 a $5 6 a Final NESHAP Only Partial Equilibrium Analysis (1 36 Kilns) $37 8 NSPS kilns (7 kilns) $2 9 Other kilns (13 kilns) $4 8 Total: $4 55 Final NESHAP and NSPS Final NESHA P and More Stringent NSPS 1 36 Kilns $37 8 $37 8 20 Kilns $8 8 $ 104 Total: $4 66 $4 82 a The f inal NSPS only also includes the $2 9 million in NESHAP costs for 7 kilns. The 7 kilns will also incur an additional $11 in compliance costs to meet the f inal NSPS l imits for SO 2 and NO x . Alternatively, the 7 kilns wo uld 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. 00Number of Kilns

To ta l Annualize d Compliance Costs ($/metric ton of clinke r cap acit y), 2005$ Final NE SHAP only (136 Kilns included in E conom ic Impac t Mode l) Final NE SHAP only (20 Kilns notincluded in Econom ic Impac t Mode l) Final NE SHAP only (7 N SPS Kilns notincluded in E conom ic Impac t Mode l) Final NE SHAP and Select ed NS PS (7 N SPS Kilns not included in Economic I mp ac t Mode l) Final NE SHAP and Stringe nt NS PS (7 N SPS Kilns notinc luded in Ec onomic I mp ac t Mode l) Figure 3 - 1. Range of Per - Ton Total Annu alized Compliance Costs (2005$)3 - 5

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

ST Data

Average ($/ ton of clinker capacity) Minim um($/ 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 Analys is The partial - equilibrium analy sis develops a cement market model that simulates how stakeholders (consumers and firms) might respond to the additional regulatory program costs. In this section, we provide an overview of the economic model used during proposal (EPA, 2009 ) . Appendix A provides additional details about economic model updates made since proposal , model equations , and parameters.3 - 6

3.2.1 Regional Structure and Baseline Data Cement sales are often concentrated locall y among a small number of firms for two reasons: high transportation costs and production economies of scale. 1 Transportation costs significantl y 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 ty picall y 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 cem ent 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 stat e 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 Appendi x A (see Table 3 - 4 ). We obtained estimates of Portland cement imports from the USGS and mapped them to each market based on the port of entry . 3.2.2 Near

- Term Cement Plant Production Decisions A cement company acts in the best interest of its shareholders and maximizes profits. When deciding whether to make another ton of cement, the company considers the production effect on profits b y comparing the current market price of cement and the marginal production cost; if price is above marginal production cost, produ cing 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 nation al 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 ($/metric tons): 2005 Market Price ($/m etric ton) Atlanta $81 Baltimore/Phil adelphia $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 widel y accepted for modeling many industries regardless of the model time horizon (EPA, 2000 ), the cement industry has two characteristics that influenc ed 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. Timel y market entry is also constrained b y the high capital costs that involve purchases and c onstruction of large rotary kilns that are not readil y 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 , an d 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 the y can produce cement at lower unit costs; this reduces entry incentives for small er capacity cement plants .3 - 8

Table 3 - 4 . Portland Cement Markets (10 6 metric tons): 2005

M arket 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 near term/transitory

stakeholder outcomes , EPA continued to use the economic impact model designed for previous analy ses (EPA, 1998, 1999b , 2009 ). The model consider s 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 cem ent 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 producin g 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, comp anies 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 c ement markets prior to regulation). 5 The size of the existing distortion depends on the seller’s market share and how responsive cement consumers are to changes in the cement price. Economic theory suggests the market distortion will ty

pically be higher th e smaller the number of sellers and when the quantity demanded is less sensitive to price (i.e., the demand elasticity is inelastic) (see Appendix A). 3.2.3 Economic Impact Model Results 3.2.3.1 Market-Level Results Market - level impacts include the regional price and qu antity 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 . 5 0 per metric ton, while overall U.S. cement consumption fall s by approximately

5 % . D omestic 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 fromBaseline Baseline Absolute Percent Market Price ($/metric ton) $83.70 $4. 5 0 5. 4 % Market Output ( million metric tons) 126

− 6

− 4. 8 % Domestic production 93

− 10

− 10.8 % Im ports 33 3 10.0 % As shown in Table 3 - 6 , price increases are the highest in regions with high compliance cost s per metric ton. For example, the Cincinnati market price increase ($ 10 per metric ton) also inclu des kilns with higher average compliance costs and a kiln with the highest per- unit

5 This ultimately influences the partial - equilibrium model’s estimates of the social cost of the regulatory program since bigger existing market distortions tend to widen the gap between price and marginal cost in these markets and lead to higher deadweight loss estimates than under the case of perfectly competitive marke ts. The Office of Management and Budget (OMB) explicitly ment ions 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

($/m etric ton of estim ated cem ent production) B aseline Price Market Price Change Market Me an Minim um 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% Pittsburg h $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 Itis important to note that EPA uses a time horizon where transportation costs between regions are high enough that interregional trade is unlikel y to occur, at least in the short run. The regional differences in unit compliance costs and the 6 The per - unit compliance costs were calculated by dividing the total annualized cost per kiln by the kiln’s estimated cement production within the economic impact model.3 - 11

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

to consider short - run shipments of cement between regional markets. Choices would depend on the additional benefits of selling cement to these markets and the costs of transporting the cement outside the regional market. Although EPA has not quantified this effect, additional flexibility would t end to temper prices increases in some of these markets. Imports also tend to limit p rice increases in certain region s. This tends to reinforce U.S. production declines because cement plants have more difficulty passing on compliance costs in the form of h igher prices when compared with similar plants operating in regions without import competition. Because imports are onl y modeled for markets with imports in the baseline without regulation, Table 3 - 7 separates the results into markets with and without impo rts as well as providing the results for all markets. As shown in Table 3 - 7, median price increases in regions with imports are lower than the median price increases in regions without import competition. In some regions with imports, the reductions in U.S . production are significant. As shown in Table 3 - 7, the maximum simulated U.S. regional production change is 23 % . To the extent there are any unobserved constraints on import supply that are not captured in the import supply elasticity parameter, price an d U.S. production adjustments for regional markets with imports would tend to become more similar to regional markets without imports. 3.2.3.2 Industry-Level Results As shown in Table 3 - 8, compliance costs vary by cement plant, and this variation suggests some plants will be more adversel y affected than others. To assess these differences, EPA collected industry

operating profit data and identified plants with operating profit increases and losses. Absent plant - specific data, EPA assumed each plant’s baseline profi ts 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 (5 8 %) decrease b y $38 7 million with regulation. These plants tend to have higher per ton compliance costs. The remaining plants’ compliance burden is offset b y higher regional cement prices, and total plant operating profits increase by $14 7 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 metri c tons) Mean

− 559

− 271

− 501 Median

− 421

− 247

− 372 Minimum

− 74

− 189

− 74 Maximum

− 1,539

− 403

− 1,539 Percentage of baseline production Mean

− 11.8%

− 6.6%

− 10. 8 % Median

− 11.6%

− 5.5%

− 10.4% Minimum

− 6.8%

− 4.4%

− 4.4% Maximum

− 22.8%

− 10.9%

− 22.8% Within the group of plants with operating losses, EPA identified 1 0 domestic plants with negative operating profits and significant utilization changes that could temporarily idle until m arket 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 th e summary results for Tables 3 - 5, 3 - 6, 3 - 7, and 3 - 8 but are also reported separately in Table 3 - 9.3 - 13

If the plant owners did decide to permanentl y shut down these plants, the reduction in domestic supply

would lead to additional projected market price increa ses. This would lead to an increased production at other plants, a possible increase in imports (depending if the plant that chooses to close is in a market where imports are anticipated) and a decrease in control cost. This scenario cannot be easily mode led. In an effort to bound this effect, the price increase needed to reduce national consumption by the amount of production that would be lost if the ten plants dropped from 55.5% capacity utilization to 0 .0% capacity utilization was estimated using the demand elasticity of 0.88. This ignores changes in other plants is response to an increased potential market share and increases in imports. Both of these would tend diminish the price increase. The predicted price change was multiplied by the change in production associated with the ten plants dropping capacity utilization to zero and multiplied by one half to estimate the change in surplus associated with the price and quantity change. This gave a result of a $10 million increase in social cost. This number was then reduced b y the avoided pollution control cost of $34 million at the ten plants. This resulted in a net reduction of $24 million in social cost due to the adjustment. Because of the method of estimating this adjustment it cannot be distributed between producer and consumer surplus. An estimate of the social cost is provided with and without this adjustment. Table 3 - 8. Distribution of Industry 2005 Changes in Total Operating Profit: Plants with Loss Plants with Gain All Plants Number 58 44 102 Cement Capacity (million metric tons) Total 55,202 38,145 93,346 Average per plant 952 867 915 Com pliance Costs Total (thousand) $308,740 $68,806 $377,546 Average ($/metric cement) $5.59 $1.80 $4.04 Capacity Utilization (percent) B aseline 100.3% 98.7% 99.6% With regulation 81.0% 100.3% 88.9% Change in total operating profits (million)

− $387 $147

− $2413 - 14

Table 3 - 9. Cement Plants with Significant Utilization Changes 2005 Total Number 1 0 Cement Capacity ( thousand metric tons) T otal 7,815 Average per plant 782 Com pliance Costs Total (thousand) $ 62,222 Average ($/metric ton) $7. 96 Capacity Utilization (%) Baseline 9 9.0 % With regulation 55.5 % Change in Operating Profit ( million) −$1 16 3.2.3.3 Job Effects Precise job effect estimates cannot be estimated with certainty . I deall y, whenever a regulatory

change results in a reallocation of labor or other factors of production in an econom y, a general equilibrium approach should be applied to estimate the attendant economic impacts. Unfortunatel y, time and resource constraints prevented the creation of a model with the spatial and sectoral resolution necessary to anal yz e the final rule. However, Morgenstern et al (2002) provides a theoretical framework which allow s us to approximate some of the relevant general equilibrium effects b y identifying three economic mechanisms by which pollution abatement activities can indirectl y i nfluence :

 higher production costs raise market prices, higher prices reduce consumption, a nd employ ment within an industry

falls (“demand effect”);

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

 post - regulation production technologies may be more or less labor intensive (i.e., more/less labor is required per dollar of output) (“factor- shift effect”). Several empirical studies, including Morgenstern et al. (2002 ), suggest the net employ ment decline is zero or economically small (e.g., Cole an d Elliot, 2007 ; Berman and Bui, 2001 ). However, others show the question has not been resolved in the literature (Henderson, 1996 ; Greenstone, 2002 ). Morgen stern et al. use a 6 - ye ar panel (U.S. Census data for plant - level3 - 15

prices, inputs [(including labor], outputs, and environmental expenditures) to econometricall y estimate the production technologies and industry - level demand elasticities. Their identificati on strategy leverages repeat plant - level observations over time and uses plant - level and ye ar fixed effects (e.g., dumm y variables for plant and ye ars ). After estimating their model, Morgenstern show and compute the change in emplo ym ent associated with an additional $1 million ($1987) in environmental spending. Their estimates cover four manufacturing industries (pulp and paper, plastics, petroleum, and steel) and Morgenstern et al. present results separately for the cost, factor shift, and demand effects, as well as the net effect. They also estimate and report an industry- wide average parameter that combines the four industry- wide estimates and weight them by each industry ’s share of environmental expenditures. Historically , EPA has most often estimated employ ment 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 onl y on the “demand” portion of the projected change in emplo ym ent and neglects other employ ment changes. EPA provides this estimate because it employ s the most detailed modeling for the industry being regulated even if it does not capture all ty

pes of employ

ment impacts. In addition to the employ ment 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 employ ment in those industries. Focusing onl y on the “demand effect”, it can be seen that the estimate from the historical approach is within the range presented by the Morgenstern “demand effect” portion. This strengthens our comfort in the reasonableness of both estimates. In April of this year, EPA started including an estimate based on the Morgenstern approach because it is thought to be a broader measure of the employ ment impacts of this ty pe of environmental regulation. Thus, this analy sis goes beyond what EPA has ty picall y done because the param eters estimated in the Morgenstern paper were used to estimate all three effects (“demand,” “cost,” and “factor shift”). This transfer of results from the Morgenstern study is uncertain but avoids ignoring the “cost effect” and the “factor- shift effect.”

 U sing the historical approach, w e calculated “demand effect” employ ment 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 employ

me nt falls by

an 1 , 500 jobs, or approximately

− 10% . 7 By comparison, using the Morgenstern approach, we estimate that the net employ ment effects could range between 600 job losses to 1,300 job gains.

7 To place this reduction in context, it is a similar to the decline experienced during the latest economic downturn; approximatel y 2,000 jobs (see Appendix A, Table A - 3).3 - 16

EPA has solely

used this historical estimate in the past as a measure of the projected employ ment ch ange associated with a regulation. However there are a number of serious shortcomings with this approach. First, and foremost, the historical approach onl y looks at the employ ment 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 employ ment impacts are independent of the performance of the overall economy. This rule takes effect in three ye ars . If the economy is strong, the demand for cement stro ng, it is unlikely that any contraction in the industry will take place, even with the regulation. Second, we assume that all plants have the same limited ability to pass on the higher costs. In reality , plants should be modeled as oligopolists for each of their regional markets. Finall y, EPA assumed that employ ment is directly

proportional to output. This is unlikely , and biases the results towards higher employ

ment losses. The Morgenstern methodology is a more complete consideration of probable impac ts of a regulation on the econom y. Table 3 - 10. Job Losses/Gains Associated with the Final Rule Me thod 1,000 Jobs Partial equilibriumm odel − 1.5 (dem and effect only) Literature - based estimate (net effect [A + B + C below]) 0.3 ( −0.6 to +1.3) A. Liter ature - 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 anal ys is. Similar estimates were not availa ble 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) b y the total environmental compliance expenditures used in the partial equilibrium mod el. For example, the jobs effect3 - 17

estimate for is estimated to be 80 7 jobs ( −3.56 × $37 8 million × 0.6). 8 Demand effect results are provided in Table 3 - 10. It is not appropriate to substitute the data from that approach in to the Morgenstern due to the inc ompatibilities 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 employ

ment. We also present the results of using the Morgenstern paper to estimate employ ment “cost” and “factor - shift” effects. Although using the Morgenstern parameters to estimate these “cost” and “factor - shift” employ ment changes is un certain, 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” employ ment effects may

offset employ

ment loss estimates using eith er “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 employ ment changes are positive or negative. Altho ugh the Morgenstern paper provides additional information about the potential job effects of environmental protection programs, there are several qualifications EPA considered as part of the anal ys is. First, EPA has used the weighted average parameter esti mates for a narrow set of manufacturing industries (pulp and paper, plastics, petroleum, and steel). Absent other data and estimates, this approach seems reasonable and the estimates come from a respected peerreviewed source. However, EPA acknowledges the final rule covers an industry not considered in the original empirical study . By transferring the estimates to the cement sector, we make the assumption that estimates are similar in size. In addition, EPA assumes also that Morgenstern et al.’s estimates derived from the 1979

– 1991 are still applicable for policy taking place in 2013, almost 20 years later. Second, the economic impact model only considers near - term employ ment effects in the cement industry where production technologies are fixed. As a result, the economic impact model places more emphasis on the short - term “demand effect,” whereas the Morgenstern paper emphasizes other important long - term responses. For example, positive job gains associated with “factor shift effects” are more plausible when production choices become more flexible over time and industries can substitute labor for other production inputs. Third, the Morgenstern paper estimates rel y on sector demand elasticities that are different (typically bigger) from the demand elasticity parameter used in the cement model. As a result, the demand effects are not directl y comparable with the demand effects estimated b y the cement model.

8 Since Morgenstern’s analysis reports environmental expenditures in 1987 dollars, we make an inflation adjustment to the engineering cost analysis using the consumer price index (195.3/113.6) = 0.6)3 - 18

Fourth, Morgenstern identifies the industry average as economically and statistically insignificant effec t (i.e., the point estimates are small, measured imprecisely , and not distinguishable from zero). EPA acknowledges this fact and has reported the 95% confidence intervals in Table 3 - 10. Fifth, Morgenstern’s methodology assumes large plants bear most of the regulatory

costs. By transferring the estimates, EPA assumes a similar distribution of regulatory costs by plant size and that the regulatory burden does not disproportionately fall on smaller plants. 3.3 Other Economic Analyses: Direct Compliance Cost Method s In addition to the market - level partial equilibrium anal ys is, EPA developed a separate economic analy sis for the remaining 20 kilns that EPA anticipates will be affected by the final rule . These costs ($8 8 million, or 1 9 % ) were not included in the economic impact model anal ys is 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 ap proximately $ 9 per metric ton of cement production . Using reported 2005 data from the USGS on the average mill net value of whi te cement ($176 per metric ton), this cost represents 5 % of the product value. EPA also conducted sales tests for 18 other kilns that were not included in the partial equilibrium analy sis. T he 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 the se 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 ty pically 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, 200 8c ). 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 lead3 - 19

companies to consider delaying rates of construction of new kilns until market conditions change (e.g., increases in demand that lead to rising cement prices) to cover additional control costs. 3.4 Social Cost Estimates For the kilns modeled in our partial equilibrium model, the market adjus tments in price and quantity

were used to estimate the changes in aggregate economic welfare using applied welfare economics principles (see Appendix C ) . Higher cement prices and reduced consumption lead to consumer welfare losses ($ 540 million). Domestic producers (in aggregate) experience a net loss of $ 239 million. As noted in the previous section, individual domestic producers may gain or lose depending on the change in compliance costs versus the change in the regional market prices. The total domesti c surplus loss (consumer and producers) totals $ 7 92 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 oligopo ly market response. Thus the social cost estimate for the 20 kilns not in the partial equilibrium model is $158 million. Because of the approximation used, we cannot estimate how the $158 million is distributed between consumers and producers.3 - 20

Table 3 - 1 1. 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 $5 51 $5 51 Partial - equilibrium model (baseline year 2005) Change in domestic producer surplus $2 41 $2 41 Partial - equilibrium model (baseline year 2005) 20 Kilns $ 158 $ 1 87 Direct Compliance Method (scaled by 1.8 for oligopoly) Change in domestic surplus $ 950 $ 979 Com bined methods Adjustment if ten low utilization facilities idle or close - $ 24 - $24

Total: $ 926 - $950 $ 955 - $979 Change with and without adjustment Final NSPS Only More Stringent NSPS Only Total: $ 72 $ 101 Direct compliance cost method (scaled by 1.8 for oligopoly) Final NESHAP Only Change in consumer surplus $5 51 Partial - equilibrium model (baseline year 2005) Change in domestic producer surplus $2 41 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 Com bined methods Adjustment if ten low utilization facilities idle or close $ - 24 Total: $ 904 - $930 Change with and without adjustment The estimated social cost of the final rule is $ 880 million. This estimate includes the results for existing kilns included in the partial - equilibrium analy sis ($ 792 million) , the final3 - 21

NESHAP direct compliance costs 20 kilns not included in the economic impact model, ($ 7 7 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 anal ys is estimate of annualized costs totaling $ 4 66 mill ion. This is a direct consequence of EP A’s assumptions about existing market structure discussed extensively in previous cement industry rulemakings and in Section 2 and Appendix B of this RIA. Under baseline conditions without regulation, the existing dom estic cement plants are assumed to choose a production level that is less than the level produced under perfect competition. As a result, a preexisting market distortion exists in the cement markets covered by the final rule (i.e., the observed baseline market price is higher than the [unobserved] market price that a model of perfect competition would predict). The imposition of additional regulatory costs tends to widen the gap between price and marginal cost in these markets and contributes to additional social costs . The above social costs for 2013 include annualized capital costs over the expected lifetime of the equipment and an opportunity cost of capital (7%) discount rate . To facilitate comparisons of benefits and costs when estimates vary of time ac ross multiple years, EPA typicall y estimate s 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 si nce the cost and benefit anal ys e s onl y produce estimates for a single ye ar (OAQPS, 1999a) . 3.5 Energy Impacts Executive Order 13211 (66 FR 28355, May 22, 2001) provides that agencies will prepare and submit to the Administrator of the Office of Information and Regulatory Affairs, OMB, a Statement of Energy

Effects for certain actions identified as “significant energy actions.” Section 4(b) of Executive Order 13211 defines “significant energy actions” as any action by an agency (normally published in the Federal Register) that promulgates or is expected to lead to the promulgation of a rule or regulation, including notices of inquiry , advance notices of final rulemaking, and notices of final rulemaking: (1) (i) that is a significant regulatory action under Executive Order 1 2866 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 a ction. This rule is not a significant energy action as designated by

the Administrator of the Office of Information and Regulatory Affairs because it is not likely to have a significant adverse impact on the supply , distribution, or use of energy . EPA has prepared an analy sis of energy impacts that explains this conclusion below.3 - 22

To enhance understanding regarding the regulation’s influence on energy consumption, EPA examined publicly

available data describing the cement sector’s energy consumption. The AEO 20 10 (DOE, 20 10 ) provides energy consumption data. As shown in Table 3 - 1 2 , this industry accounts for approximately 0. 4 % of the U.S. total energy consumption. As a result, any3 - 23

Table 3 - 1 2 . 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% Metallur gical 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 pe troleum coke, lubricants, and miscellaneous petroleum products. Source: U.S. Department of Energy, Energy Information Administration. 2010. Supplemental Tables to the Annual Energy Outlook 2010. Ta ble 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 significantl y i nfluence the suppl y, distribution, or use of energy. EPA has also estimated the amount of additional electricity consumption associated with add - on controls. The analy sis shows the additional national electrical demand to be 780 million kWh per year and th e 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 s ys tems, the electrical demand is estimated to be 199 million kWh per ye ar. This is less than 0.1% of AEO 2010 forecasts of total electricity and natural gas use. 3.6 Assessment Although the economic analy ses presented in this section cannot provide precise estimates of the final NESHAP ’s and NSPS’s economic impacts, the evidence present ed 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 anal ys is . First, OAQ PS’s partial equilibrium analy sis of NESHAPs has traditionally

been designed to assess small (marginal ) changes in industry conditions. The3 - 24

overall engineering cost anal ys is estimates are significant relative to the size of the U.S. cement market; EPA ackn owledges that use of demand and import supply elasticities can be tenuous in th ese case s because the exact functional relationships (demand and suppl y) are less certain when simulated outcomes move further away from the observed pre - policy equilibrium. Sec ond, the partial equilibrium assumes that transportation costs between regions are high enough that interregional trade is unlikely to occur, at least in the short run. Allowing interregional trade would expand the cement market definitions and increase th e 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 dec isions 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. Theref

ore, the anal ys is 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 economi cs tend to limit the number of sellers (particularl y 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 readil y 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 additio n, large plants are typically more economical because they can produce cement at lower unit costs; this reduces entry incentives for small er capacity cement plants .4 - 1 SECTION 4 S MALL B USINESS I MPACT ANALYSIS The Regulatory

Flexibility

Act (RFA) generall y requires an agency to prepare a regulatory

flexibility

analy sis 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 (SI SNOSE). The first step in this assessment was to determine whether the rule will have SI SNOSE. To make this determination, EPA used a screening and market anal ys is to indicate whether EPA can certify the rule as not having a SI SNOSE. The elements of this anal ys is included

 identifying affected small enti ties,

 selecting and describing the measures and economic impact thresholds used in the anal ys is, and

 completing the assessment and determining the SISNOSE certification category . 4.1 Identify Affected Small Entities For the purposes of assessing the impacts of the final rule on small entities, small entity is defined as (1) a small business as defined b y the Small Business Administration’s regulations at 13 CFR 121.201; according to these size standards, ultimate parent companies owning Portland cement manufacturing plants are categorized as small if the total number of employees at the firm is fewer than 750 (see Table 4 - 1 for list); (2) a small governmental jurisdiction that is a government of a city , county , town, school district, or special district with a p opulation of less than 50,000; and (3) a small organization that is any not - for - profit enterprise that is independentl y 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). O ne of the four entities is owned by a small Tribal government (Salt River Pima - Maricopa Indian Community). The remaining three entities are small businesses. 4.2 Sales and Revenue Test Screening Analysis In the next step of the analy sis, EPA assessed how the r egulatory 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 b y 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 - to Sales Ratio Salt River Materials Group a Tribal

government $184 b NA 1 1 1,477 0. 7 % Monarch Cement Com pany Business $154 600 1 2 787 3.0 % Continental Cement Com pany, LLC Business $93 c <750 1 1 1,164 0.0 % Snyder Associate Com panies Business $29 350 1 2 286 2.0 %

a Enterprise is owned by Salt River Pima - Maricopa Indian Community. b EPA estimate. Esti mate 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, TA CC = total annualized compliance costs, i = index of the number of affected plants owned b y 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 sc reening anal ys is, presented in Table 4 - 1 , show that no small business es have a CSR greater than 3%. Two small business have an estimated CSR between 1 and 3%.4 - 3

4.3 Additional Market Analysis In additional to the screening analy sis, EPA also examined small enti ty 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 As sess ment 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 companie s have cost - to - sales ratios greater than 3 % and o nl y 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 b y EPA in support of the Portland c ement NESHAP and NSPS . A national scale air quality modeling anal ys is was performed to estimate the impact of the sector emissions changes on future year s : annual and 24 - hour PM 2.5 concentrations, total Hg deposition, as well as visibility impairment. Air quality benefits are estimated with the Comprehensive Air Quality Model with Extensions (CAMx) model. CAMx simulates the numerous phy sical and chemical processes involved in the formation, transport, and destruction of ozone, PM, and air toxics. In additio n 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 anal ytical 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 r ule inventories are generall y consistent, so the air quality modeling adequatel y reflects the effects of the rule. The 2005 - based CAMx modeling platform was used as the basis for the air quality modeling for this final rule. This platform represents a structured s ys tem of connected modeling related 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 an d meteorology for 2005. The platform is intended to support a variety of regulatory

and research model applications and analy ses. This modeling platform and analy sis is described full y below. Additional details about the modeling s ys tem 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 freel y available computer model that simulates the formation and fate of photochemical oxidants, primary and secondary

PM concentrations, and air toxics, over regional and urban spatial scales for given input sets of meteorological conditions and emissions. CAMx includes numerous science modules that simulate the emission, production, decay ,5 - 2

deposition and transport of organic and inorganic gas - phase and particle - phase pollutants in the atmosphere (Nobel, McDonald - Buller et al. , 2001 ; Baker and Scheff , 2007 ; Russell , 2008 ). CAMx is applied with I SORROPIA inorganic chemistry (Nenes et al. , 1999 ), a semivolatile equilibrium schem e to partition condensable organic gases between gas and particle phase (Strader et al., 1999 ), Regional Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al. , 1987 ), and Carbon Bond 05 ( CB05) gas - phase chemistry module (Gery

et al. , 1989 ; ENVIRON , 2008 ). All modeling domains were modeled for the entire year of 2005. Data from the entire ye ar were u s ed 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 anal ys es 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 finer scale 12 km grids over portions of the eastern and western United States . The model extends verticall y from the surface to 100 millibars (approximately 15 km) using a sigma - pressure coordinate s ys tem. Air quality conditions at the out er boundary of the 36 km domain were taken from a global model and did not change over the simulations. In turn, the 36 km grid was only used to establish the incoming air quality concentrations along the boundaries of the 12 km grids. Only the finer grid data were used in determining the impacts of the emission standard program changes. Table 5 - 1 provides some basic geographic information regarding the photochemical model domains. 5.4 Emissions Input Data The emissions data used in the base year and future ref erence and future emissions adjustment case are based on the 2005 v4 platform. The emissions cases use some different emissions data than the official v4 platform to use data intended onl y for the rule development and not for general use. Unlike the 2005 v4 platform, the configuration for this modeling application included some additional HAPs and a cement kiln sector emissions inventory more consistent with the engineering analy sis of potential control options. The 2013 reference case is intended to repre sent 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 20 13 (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 projection factors were the starting point for most of the 201 3 SMOKE - based projections.

Figure 5 - 1. Map of the Photochemical Modeling Do main 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 Domains Used in Photochemical Modeling Photochemical Modeling Configuration National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid Map Projection Lambert Conformal Projection Grid Resolution 36 km 12 km 12 km Co ordinate Center 97 deg W, 40 deg N True Latitudes 33 deg N and 45 deg N Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 Vertical extent 14 Layers: Surface to 100 millibar level The 2013 reference scenario for the cement kiln sector assumed no growth or control for the industry

from the 2005 sector emissions estimates with the exception that facilities that closed between 2005 and 2010 were removed from the 2013 inventory . The length of time5 - 4

required to conduct emissions and photochemical modeling preclude s using the final facilityspecific emissions estimates based on controls implemented for this rule. A 2013 “control” or emissions adjustment case was developed b y removing all Portland c ement 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 o f control percentages b y pollutant. Itis important to note that the scenario without cement kilns includes the zeroing - out of emissions from hazardous waste kilns. Out of 181 kilns nationwide, there are 14 hazardous waste kilns, which represent 10 to 20% of total kiln emissions. This leads to a slight overestimate of the reduction in 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 V olatile 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 anal ys is for this rulemaking, the modeling s ys tem 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

ye ar 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 done by calculating the simulated air quality ratios between any particular future year simulation and the 2005 base. These predicted ratios are then applied to ambient base year design values. The design value projection methodology used here followed EPA guidance for such anal ys es (U.S. EPA, 2007). Additionally , the raw model outputs are also used in a relative se nse as inputs to the health and welfare impact functions of the benefits anal ys is. Onl y m odel predictions for Hg deposition were anal yz ed using absolute model changes, although these parameters also considered percent age changes between the control case an d 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 present information on current and projected levels of pollution for 2013. This section summarizes the results of our modeling of differe nces 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 b y 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 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 inv olved 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 using procedures outlined by EPA modeling guidance (U.S. EPA, 2007 ). This section summarizes th e results of our modeling of 24 - h our 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 de crease up to 0.5 μg/m 3 in 24 - h our average PM 2.5 design values at most monitor locations in the United States is possible given an area’s proximity to controlled sources and the amount of reduced sulfur dioxide emissions. The median reduction over all monit or 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 rule was used to project visibility conditions in 138 mandato ry

Class I federal areas across the United States in 2013 (U.S. EPA, 2007 ). The level of visibility

impairment in an area is based on the light - extinction coefficient and a unitless visibility index, called a “deciview , ” that is used in the valuation of visibility . The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to haz y. Under many scenic conditions, the average person can generally perceive a change of one deciview. Hi gher deciview values are indicative of worse visibility . Thus, an improvement in visibility is a decrease in deciview value. The modeling assessment indicates that a decrease up to 0.31 deciviews in annual 20% worst visibility day s is possible given an are a’s proximity

to controlled sources and the amount of reduced sulfur dioxide emissions. Median reductions are 0.01 deciviews to the 20% worst day s and 20% best day s over all monitor locations. 5.6 Limitations (Uncertainties) Associated with the Air Quality Mo deling Any deficiencies with the emissions or meteorological inputs may lead to control scenario estimates that may

not fully characterize the source contribution mix at a receptor location. This application used a complete year of meteorology to capture the variety of meteorological formation regimes conducive to eleveted pollution. However, it is possible that the meteorology used for these model applications may not represent all elevated pollution formation regimes at every individual receptor location in the continental United States.6 - 1 SECTION 6 BENEFITS OF EMISSION S REDUCTIONS 6.1 Synopsis In this section, we provide an estimate of the monetized benefits associated with reducing exposure to particulate matter (PM) for the final Portland Cement NESHAP and NSPS. Th e 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 te chnologies 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 monetized benefits of the final Cement NESHAP and NSPS to be $7. 4 billion to $1 8 billion in the implementation year (2013). Using a 7% discount rate, we estimate the total monetized benefits of the final Cement NESHAP and NSPS to be $6. 7 billion to $17 billion in the implementation year. All estimates are in 2005$. These estimates include the energy disbenefits associated with increased electricity usage b y the control devices . These monetized estimates reflect EPA’s most current interpretation of the scientific literature and several methodology updates introduced in the proposal anal ysis. Inaddition, these estimates incorporate an array of improvements since the proposal, including cement sector - specific air quality modeling data, revised value - of - a - statistical - life (VSL), lowest measure level (LML) assessment, qualitative benefits for ecos ys tems 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, ecosy stem effects, and visibility impairment. The benefits from reducing other air pollutants have not been monetized in this analy sis, including reducing 4,4 00 tons of NO x , 5,800 tons of HCl, 5,200 tons of organic HAPs, and 16, 4 00 pounds of mercury each ye ar.6 - 2 $0 $5,000 $10,000 $15,000 $20,000 $25,000

Pope eta l. Laden et al.Millions (2005$)

Benef its estimates der iv ed from 2 epidemiology f unctions and 12 exper t f unctions 

### 2013* 3% D R 7% D R

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 - response function provided in those studies. These estimates do not include benef

its 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 P M 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 t o meet these multiple limits. 6.2.1 Methodology Improvements since Proposal This benefits anal ys is incorporates an array of policy and technical improvements since the proposal RIA in 2009 (U.S. EPA, 2009 a), including:6 - 3

1. Cement sector-specific air quality modeling data . The benefits estimates for this final anal ys is are based on air quality data modeled by

CAMx th at reflect the emissions from the cement sector and the reductions anticipated as a result of this rule. This data provides a superior representation of the geographic distribution of the emission reductions and resulting ambient concentrations than the national average benefit - per - ton estimates used in the proposal. For more information regarding the modeling inputs and assumptions, please see Section 5 of this RIA. 2. Use of a revised Value of Statistical Life (VSL) . The Agency continues to update its guid ance on valuing mortality risk reductions and until a final report is available, EPA now uses a single, peer - reviewed mean VSL estimate of $6.3 million (2000$). We discuss this issue in more detail in Section 6.2.5. 3. Lowest Measured Level (LML) assessment . Consistent with the rationale outlined in the proposal RIA , EPA now estimates PM - related mortality without assuming an arbitrary threshold in the concentration - response function . Consistent with recent scientific advice , we are replacing the previous threshold sensitivity analy sis 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 LM L assessment in Sectio n 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, ecosy stem effects, and visibility impairment. Instead, we provide a qualitative description of the benefits anticipated as a result of the emission reductions from this rule. These unquantified benefits are described in Section 6. 5 . 5. Mercury d

eposition . The air quality modeling data provide an estimate of the reduction in mercury deposition associated with the mercury emission reductions anticipated as a result of this rule. We provide maps of the reduced mercury deposition in Section 6.3.2.1 . Due to time and resource limitations, we were unable to model mercury methylation, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits from reducing mercury emissions. 6.2.2 Benefits Analysis Approach We follow a “damage - function” approach in calculating total benefits of the modeled changes in environmental quality . This approach estimates changes in individual health and welfare endpoints and assigns values to those changes assuming independence of the individual values. Total benefits are calculated simply as the sum of the values for all non - 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 anal ys es (Lev y 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 analy sis (HIA) must first be conducted to convert air quality changes into effects that can be assigned dollar values. For this RIA, the he alth impacts anal ys is 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 ecolog ical effects, including the benefits associated with decreasing deposition of sulfur to terrestrial and aquatic ecos ystems, 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 rarel y has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory anal ys es. Thus, similar to Kunzli et al. (200 1 ) and other recent health impact anal ys es, our estimates are base d 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 anal ys is. Adjustm ents 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 resultin g from population - level changes in exposure to pollutants (Lev y 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, Tagari s et al. 2009). For this analy sis, we used the environmental Benefits Mapping and Anal ys is Program (BenMAP) , which is a PC - based tool that can sy stematize health impact anal ys es b y 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 avail able for free dow nload on the Internet at <http://www.epa.gov/air/benmap>.6 - 5

The HIA approach used in this analy sis involves three basic steps: (1) utilizing CAMx generated projections of PM 2.5 air quality and estimating the change in the spatial distribution of the ambient air quality; (2) determi ning the subsequent change in population - level exposure; (3) calculating health impacts by appl yi ng concentration - response relationships drawn from the epidemiological literature (Hubbell et al. 2009) to this change in population exposure. A ty pical 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 th e given population of interest); Pop is the population affected by the change in air quality ;  x is the change in air quality ; and β is the effect coefficient drawn from the epidemiological study . For this anal ys is, we s ys tematize the HIA calculation proce ss using BenMAP’s library

of existing air quality

monitoring data, population data and health impact functions. Figure 6 - 2 provides a simplified overview of this approach, and Figure 6 - 3 identifies the data inputs and outputs for the BenMAP model.

Fig ure 6 - 2. Illustration of BenMAP Approach6 - 6

Figure 6 - 3. Data inputs and outputs for the BenMAP model The benefits estimates in this analy sis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analy sis (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 underl yi ng fun ctions , studies, baseline incidence rates, and population data used in this anal ys is, 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 mo netized benefits and which endpoints are unquantified . In summary , the monetized PM benefits include premature mortality

and 11 morbidity

endpoints. Census Population Data 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 Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 - related Benefits Blue identifies a user - selected input within the BenMAP model Green identifies a data input generated outside of the BenMAP model6 - 7

Table 6 - 1 . Human Health and Welfare Effects of Pollutants Affected Pollutant/ Effect Quantified and mo netized in primary

estimate Unquantified

PM: health a Premature mortality based on cohort

study estimates b Lo w birth weight Premature mortality based on expert elicitation estimates Pulmonary function Hospital admissions: respiratory and cardiovascular Chronic respiratory diseases other than chronic bronchitis Emergency room visits for asthma Non - asthma respiratory emergency room visits Nonfatal heart attacks (myocardial infarctions) UVb exposure (+/ - ) c Lo wer 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 Ac ute 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 methylati on 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

-- Inc idences of Myocardial infarction Potential reproductive effects Mercury: welfare Im pact on birds and mammals (e.g. reproductive effects) Im pacts to commercial, subsistence and recreational fishing a In addition to primary economic endpoints, there ar e a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms. The public health impact of these biological responses may be partly represented by our quantified endpoints. b Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discus sion 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 an alysis. 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 function s as reported in the epidemiology literature, as well as the 12 functions obtained in EPA’s expert elicitation study as a characterization of uncertainty.

 One estimate is based on the concentration - response (C - R) function developed from the extended anal y sis 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 analy sis of the Harvard Six Cities cohort, as reported b y 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 sinc e 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 anal ys is 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 practice has been to develop independent estimates of PM 2.5 - mortality estimates corresponding to the concentration - response function provided b y each of the twelve exp erts, 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 inh erent in each, which we believe argues for using both studies to generate benefits estimates. Previously , EPA had calculated benefits based on these two empirical studies, but derived the range of benefits, including the minimum and maximum results, from a n 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 provide d by each of the twelve experts to better characterize the uncertainty in the concentration - response function for mortality and the degree of variability in the expert responses. Because the experts used these cohort studies to inform their concentration response functions, benefits estimates using these functions generall y 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 like ly to be substantial. EPA strives to use the best available science to support our benefits anal ys es, and we recognize that interpretation of the science regarding air pollution and health is dy namic and evolving. This analy sis continues to use the updated assumptions first applied in the proposal RIA for this rule (U.S. EPA, 2009a), including the updated population dataset in BenMAP 3.0 and the functions directly from the epidemiology studies without an adjustment for an assumed threshold. 4 Removing the threshold assumption is a key difference between the method used in this analy sis 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 model ed PM 2.5 air quality levels. 5 Prior to the proposal RIA for this rule, EPA presented 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 wa s an arbitrary choice, and we could have assumed thresholds at other points in the lower end of the observed range the anal ys is. Since the proposal RIA for this rule, EPA included a sensitivity analy sis with an assumed threshold at 10 μg/m 3 to illustrat e 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 anal ys is 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 recentl y reviewed b y 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 s hape 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 p roposed 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 d ocket.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 adequatel y portray s 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 HE S full y 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. Analy ses 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 (TS D) 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 replacin g the previous threshold sensitivity

anal ys is with a new “Lowest Measured L evel” (LML) assessment . This approach summarizes the distribution of avoided PM mortality impacts according to the baseline PM 2.5 levels experienced b y 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 - rel ated mortality benefits occurring above or below the LML of each study ; in general, our confidence in the estimated PM mortality decreases as we consider air quality levels further below the LML in the two epidemiological studies. While an LMLassessment provides some insight into the level of uncertainty in the estimated PM mortality

benefits, EPA does not view the LMLas 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 LMLis a characterization of the fraction of benefits that are more uncertain. It is important to emphasize that just because we have greater confidence in the benefits above the LML, this does not mean that we have no confidence that benefits occur below the LML.6 - 11

Analy ses of these cohorts using data from more recent years, during which time PM concentrations have fallen, continue to repor t strong associations with mortality . As we model mortality impacts among populations exposed to levels of PM 2.5 that are successivel y lower than the LMLof each study , our confidence in the results diminishes. As air pollution emissions continue to decre ase 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 successivel y lower levels of PM 2.5 . As more large cohort studies follow populati ons 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 LMLfrom recent studies indicate that confidence in PM 2.5 - related mortal ity 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 red

uctions 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 th at would support the development of differential effects estimates b y 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. Ple ase refer to Table 5 - 11 in the recentl y 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 analy sis. 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 y ear with two exceptions: acute myocardial infarctions (AMIs) and premature mortality. For AMIs, we assume 5 years of follow - up medical costs and lost wages. For premature mortality, we assume that there is a “cessation” lag between PM exposures and the tot al 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, an d 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 than the monetized benefits using a 3% dis count 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 - s tatistical - life (VSL) estimate derived from a limited analy sis 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 - anal ys es of the wage - risk literature. The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta - anal ys is of 3 3 studies. The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta - anal ys is of 43 studies. The mean estimate of $5.5 million (2000$) 8 was also consistent with the mean VSLof $5.4 million estimated in the Kochi et al. (2006) meta - anal ys is. However, the Agency neither changed its official guidance on the use of VSL in rule - makings nor subjected the interim estimate to a scientific peer - review process through the Science Advisory Board (SAB) or other peer - review group. During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a report from meta - anal ytic experts to evaluate m ethodological questions raised b y 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 - anal ytic experts, the SAB - EEAC advised the Agency to update its guidance using specific, appropriate meta - anal ytic techniques to combine estimates from unique data sources and different studies, including those using different methodol ogies (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 r eceived. Therefore, the Agency has decided to apply

the VSL that was vetted and endorsed b y the SAB in the Guidelines for Preparing Economic Analy ses (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 VSLestimates 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 grow th 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 usi ng scientifically

sound, appropriatel y 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 red

uctions 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 analy sis occur among populations exposed at or above the lowest LMLof the cohort studies (Figures 6 - 6 and 6 - 7), increasing our confidence in the PM mortality analy sis. Figure 6 - 6 shows a bar chart of the p erc entage 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 LMLof 10 μg/m 3

. As we model mortal ity 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 analy sis 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 LMLof the major cohort studies.

10 In this analysis, we ad

just the VSL to account for a different currency year (2005$) and to account for income grow th 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

NS PS in 2013 ( millions of 2005$) a 3% 7% Based on Epide miology 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 $18,00 0 ($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 describ ed 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 o f 2005$) 7% Discount

(millions of 2005$) Avoided Prem ature 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) Ac ute 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) Ac ute 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) Lo wer Respiratory Symptoms 18,000 $0.02 $0.02 (7,800 -- 28,000) ($0.01 -- $0.05) ($0.01 -- $0.05) Upper Respiratory Sym

ptoms 14,000 $0.03 $0.03

(3,400 -- 24,000) ($0.01 -- $0.07) ($0.01 -- $0.07) a All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures. All fine particles are assumed to have equivalent health effects. T hese estimates do not include benefits from reducing HAP emissions , and they do not include the energy disbenefits described in the next section .6 - 16 Adul t Mortal ity 93% Chronic Br onchit is 3. 8% AMI 2. 1% Minor R es tr ic te d Activity Days 0.6 %

Infant Mor tality 0. 4%

Work Los s Days 0. 2% HA, Cardio 0. 2% Ha, Re sp 0.04% As thma Exac erba tion 0. 01% Uppe r Re sp Symp 0. 01% ER Visits 0. 005% Low er Re sp Sym p 0.004% Acut e Br onchit is 0 .0 01% Ot he r 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 breakdow n is illustrative, using the results based on Pope et al. (2002) as an example. Using the Laden et al. (2006) function for premature mortality, the percentage of total monetize d benefits due to adult mo rtality wo uld 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 , an d 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 F inal 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 mo netized 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$)

Ben ef its estimates der iv ed from 2 epidemiology f unctions and 12 e xper t f unctions 

### 2013* 3% D R 7% D R

F igure 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 - response function provided i n those studies. These estimates do not include benef

its 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 20Percentage of Avoided PM2.5Mortalities

Bas eline Annual Mea n PM 2.5 Le ve l (μ g/ m 3 ) LM L of Krewski et al. ( 2009) stu dy LM L of Laden et al. ( 2006) st udy LM L of Pope et al. ( 2002) st udy

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. ( 200 2 ).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 20Cumulative percentage of avoided PM Mortalities

Bas eline Annual Mea n PM 2. 5 Le ve l (μ g/m 3 ) LM L of Pope et al. (2002) st udy LM L of L ade n et al. (2 006) st udy

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 9 4 % 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. ( 200 2 ). 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 suppl y electricity to the Portland cement facilities. We estimate increased energy demand associated with the installation of scrubbers, ACI s ys tems, and RTO. The increases fo r 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 p umps 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 P M 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 dis benefits using air quality modeling . Instead, we used the “benefit - per - ton” approach to estimate these dis benefits 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 anal ys is, 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 analy sis because we assume that all of the increased PM to ns 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 Disb enefits for the Final Portland

Cement NSPS and NESHAP i n 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%) Dire ct 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 t o $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 estimat es vary because each ton of precursor reduced has a different propensity to become PM 2.5 . The mo netized disbenefits incorporate the conversion from precursor emissions to ambient fine particles. Confidence intervals are unavailable for this analysis becaus e of the benefit - per - ton methodology. T he 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.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. Itis 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 ecos ys tem services due to climate change. The SCC estimates used in this analy sis 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 establi sh L ight - Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Econom y 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 anal yses, which we have applied in this anal ysis: $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. L ow probability , high impact events are incorporated into all of the SCC values through explicit consideration of their effects in two of the three models as well as the use of a probability density function for equilibrium climate sensitivity . Treating climate sensitivity probabilistically

results in more high temperature outcomes, which in turn lead to higher projections of damage s.

11 Docket ID EPA - HQ - OAR - 2009 - 0472 - 114577, Technical Support Documen t: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, Interagency Working Group on Social Cost of Carbon, with participation by Council of Economic Advisers, Council on Environmental Quality, Department of Agriculture, Departme nt 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 im pacts 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, wo uld not result in accurate estimates 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 phy sical 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 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 anal ys is ye ars of 201 3 , in 200 5 dollars are provided in Table 6 - 6. When attempting to assess the incremental economic impacts of carbon dioxide emissions, the analy st faces a number of serious challenges. A recent report from the National Academies of Science (NRC , 200 8 ) points out that any assessment will suffer from uncertainty, speculation, and lack of information about (1) future emissions of greenhouse gases, (2) the effects of past and future emissions on the climate sy stem, (3) the impact of changes in clim ate on the phy sical and biological environment, and (4) the translation of these environmental impacts into economic damages. As a result, any effort to quantify and monetize the harms associated with climate change will raise serious questions of science , economics, and ethics and should be viewed as provisional. The interagency

group noted a number of limitations to the SCC analysis, including the incomplete way

in which the integrated assessment models capture catastrophic and non catastrophic 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 make s 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

anal ys is by

the Federal government will continue to ev olve 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 clim ate change and its impacts on society improves over time. Specificall y, 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 dis benefits captured by the models for each analy sis year. Fo r internal consistency , the annual dis benefits 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 - ye ar and emissions - year specific. SCC value s represent only a partial accounting of climate impacts. Table 6 - 7. Monetized Disb enefits 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% Averag e $36 3% 95%ile $67

a The SCC values are dollar - ye ar 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 de vices would increase emissions of several pollutants . Inthis anal ys is, 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 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 analy sis onl y 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 anal ys is, 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 anal ys is estimates that domestic production would decrease by 10 million tons, but imports would increase b y only 3 million tons . The net effect on gl obal pollutants like CO 2 and mercury is difficult to determine because it depends on many factors, and quantifying the benefits associated with either omission is beyond the scope of this analy sis. 6.5 Unquantified or Nonmonetized Benefits The monetized benef

its estimated in this RIA onl y 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 f rom several important benefit categories, including benefits from reducing toxic emissions, ecosy stem effects, and visibility impairment. The health benefits from reducing hazardous air pollutants (HAPs) have not been monetized in this analy sis. In additio n to being a PM 2.5 precursor, SO 2 emissions also contribute to adverse effects from acidic deposition in aquatic and terrestrial ecosy stems, increase mercury methylation, as well as visibility impairment. 6.5.1 Other SO2 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. Unfortunatel y, we were unable to estimate the health benefits associated with reduced SO 2 exposure in this analysis because we do not have air quality modelin g 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 nearb y populations. Therefore, this analy sis onl y quantifies and monetizes the PM 2.5 bene fits 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 b etween respiratory health effects and short - term exposure to SO 2 (U.S. EPA, 200 8b ). 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 s ys tem in humans is6 - 26

bronchocons triction ” (U.S. EPA, 200 9c ) . In addition, the REA summarized from the ISA that “ a sthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. ” A clear concentration - response relationship ha s 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 adversel y affected (U.S. EPA, 200 9c ) . Based on our review of this in formation, we identified four short term morbidity

endpoints that the SO 2 IS A identified as a “causal relationship”: asthma exacerbation, respiratory - related emergency department visits, and respiratory- related hospitalizations. The differing evidence and associated strength of the evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 IS A also concluded that the relationship between short - term SO 2 exposure and premature mortality was “suggestive of a causal relationship” becaus e it is difficult to attribute the mortality risk effects to SO 2 alone. Although the SO 2 IS A 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 welfare effects from acidic deposition, visibility impairment, and enhanced mercury methylation. Deposition of sulfur causes acidification, which can cause a loss of biodiversity of fishes, zooplankton, and macro invertebrates in aquatic ecos ys tems, as well as a decline in sensitive tree species, such as red spruce ( Picea rubens ) and sugar maple (

Acer saccharum ) in terrestrial ecos ystems . Inthe northeastern United States, the surf ace 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. B iological effec ts of acidification in terrestrial ecos ys tems are generall y 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 sensitivi ty of these plants to stresses, such as droughts, cold temperatures, insect pests, and disease leading to increased mortality of canopy

trees. Terrestrial acidification affects several important ecological services, including declines in habitat for threat ened and endangered species (cultural), declines in forest aesthetics (cultural), declines in forest productivity (provisioning), and increases in forest soil erosion and reductions in water retention (cultural and regulating). (U.S. EPA, 2008c ) Reducing S O 2 and PM emissions would improve the level of visibility throughout the United States. Fine particles with significant light - extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler, 1996). These suspended par ticles 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 levels. In fact , particulate sulfate is the largest contributor to regional haze in the eastern U.S. (i.e., 40% or more annuall y and 75% during summer). In the western U.S., particulate sulfate contributes to 20 - 50% o f regional haze (U.S. EPA, 2009b ). Visibility has dire ct significance to people’s enjoyment of daily activities and their overall sense of wellbeing. Good visibility increases the quality of life where individuals live and work, and where they

engage in recreational activities. Due to time and resource limit ations, we were unable to estimate the monetized benefits associated with visibility improvements. Previous anal ys es (U.S. EPA, 2006; U.S. EPA, 2010c) show that visibility benefits are a significant welfare benefit category . 6.5.2 HAP Benefits Americans are exp osed 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 en gage. In order to identify

and prioritize air toxics, emission source types and locations which are of greatest potential concern, U.S. EPA conducts the National - Scale Air Toxics Assessment (NATA). The most recent NATA was conducted for calendar ye ar 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 populatio n 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 t o air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive sy stems. According to the 2002

14 U.S. EPA. (2009) 2002 National - Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 15 U.S. EPA. (2009) 2002 National - Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 16 Chronic exposure is defined in the glossary of the Integrated Risk Information (IRIS) database (http://www.epa.gov/iris) as repeated exposure by the oral, dermal, or inhalation route for more than approximately 10% of the life span in humans (more than approximately 90 days to 2 years in typically used laboratory animal species). 17 Defined in the IRIS database as exposure to a substance spanning approximately 10% of the lifetime of an organism. 18 Defined in the IRIS database as exposure by the oral, dermal, or inhalation route for 24 hours or less.6 - 28

NATA, nearl y 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 sing le pollutant, acrolein. This rule is anticipated to reduce 16,400 pounds of mercury , 5,800 tons of HCl, and 5,200 tons of organic HAPs each year . Due to data, resource, and methodology limitations, we were unable to estimate the benefits associated with the thousands tons of hazardous air pollutants that would be reduced as a result of this rule. Available emissions data show that several different HAPs are emitted from Portland cement manufacturing plants, either released from kilns sy stems, raw material dryers, clinker coolers, raw mills, finish mills, storage bins, conveying sy stem transfer points, bagging s ys tems, or bulk loading and unloading sy stems.

Figure 6 - 8. Estimated County Level Carcinogenic Risk from HAP exposure from outdoor sources (NATA, 2 002)

19 The NATA mode ling frame work 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 mo deling 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 highl y 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 b y 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

likel y results in methylmercury accumulation in fish (Drevnick et al., 2007; Munthe et al, 2007). The SO 2 IS A concluded that evidence is sufficient to infer a casual relationship between sulfur deposition and increased mercury methylation in wetlands and aquatic environments (U.S. EPA, 2008c ). In addition to the role of sulfate deposition on methylation, these rules would also reduce mercury emissions. Mercury

is em itted to the air from various man - made and natural sources. These emissions transport through the atmosphere and eventuall y deposit to land or water bodies. This deposition can occur locally , regionall y, or globally, depending on the form of mercury emitte d 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 di valent mercury (reactive gas phase mercury) (58%) , and particulate bound mercury (7%) (U.S. EPA, 2010c) . Gaseous elemental mercury

can be transported very

long distances, even globally , to regions far from the emissions source (becoming part of the global “pool”) before deposition occurs. Inorganic divalent and particulate bound mercury have a shorter atmospheric lifetime and can deposit to land or water bodies closer to the emissions source. Furthermore, elemental mercury in the atmosphere can undergo tran sformation 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 methyl mercury . The primary route of human exposure to mercury emissions from industrial sources is generally indirectl y through the consumption of fish containing methyl mercury . As described above, mercury that has been emitted to the air eventuall y settles into water bodies or onto land where it can either move directl y or be leached into waterbodies. Once deposited, certain microorganisms can change it into methylmercury , a highl y toxic form that builds up in fish, shellfish and animals that eat fish. Consumption of fish and shellfish are the main sources of methylmercury exposure to humans. Methylmercury builds up more in some ty

pes of fish and shellfish than in others. The levels of methylmercury in fish and shellfish vary

widel y depending on what they eat, how long they live, and how high they are in the food chain. Most fish, including ocean species and local freshwater fish, contain some methylmercury . For example, in recent studies by EPA and the U.S. Geological Survey (USGS) of fish tissues, every

fish sampled contained some methyl mercury (Scudder, 2009) . The majority

of fish consumed in the U.S. are ocean species. The methylmercury concentrations in ocean fish species are primaril y influenced b y the global mercury pool. However, the meth yl mercury found in local fish can be due, at least partl y, 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 rel y heavil y on fish for a substantial part of their diet). It has been demonstrated that high levels of meth yl mercury

in the bloodstream of unborn babies and young children may harm the devel oping nervous s ys tem, making the child less able to think and learn. Moreover, mercury exposure at high levels can harm the brain, heart, kidney s, lungs, and immune sy stem of people of all ages.6 - 31

Several studies suggest that the methylmercury content of fis h may

reduce these cardio protective effects of fish consumption. Some of these studies also suggest that methylmercury may cause adverse effects to the cardiovascular s ystem. 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 methyl mercury exposure via fish consumption and acute myocardial infarction (AMIor heart attacks), coronary heart disease, cardiovascular disease, and all - cause mortality . 21 The NRC also noted a study of 917 seven year old chil dren 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 s ys tem appears to be a target for methylmercury toxicity .” 23 Since publication of the NRC report there have been some 30 published papers presenting the findings of studies that have examined the possible cardiovascular effects of methylmercury

exposure. These studies include epidemiological, toxicolo gical, and toxicokinetic investigations. Over a dozen review papers have also been published. If there is a causal relationship between methylmercury exposure and adverse cardiovascular effects, then reducing exposure to methylmercury would result in public health benefits from reduced cardiovascular effects. In earl y 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 an d to advise EPA on methodologies for estimating population

20 National Research Council (N RC). 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. 1 995. “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. Weihe, and Grandje an, 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 Methyl mercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. p. 229.6 - 32

level cardiovascular health impacts of reduced methylmercury exposure. The report from that workshop is in preparation. Portland cement manufacturing plants emitted about 16 tons of mercury in the air in 2006 in the U.S. Based on the EPA’s National Emission Inventory , and about 103 tons of mercury were emitted from all anthropogenic sources in the U.S. in 2005. Moreover, the United Nations has estimated that about 2,100 tons of mercury were emitted worldwide by anthropogenic sources in 2005. We believe that total mercury emissions in the U.S. and globally in 2006 were about the same magnitude in 2005. Therefore, we estimate that in 2006, these sources emitted about 16% of the total anthropogenic mercury emissions in the U.S. and about 0.8% of the global emissions in 2005. Using 2008 inventory estimates, the mercury emissions from Portland cement kilns only were approximately

9.1 tons. Overall, the NESHAP and NSPS would reduce mercury emissions by ab out 8.2 tons (90%) per year from current levels, and therefore, contribute to reductions in mercury exposures and health effects. Due to time and resource limitations, we were unable to model mercury

methylation, bioaccumulation in fish tissue, and human consumption of mercurycontaminated fish that would be needed in order to estimate the human health benefits from reducing mercury

emissions. However, we were able to model the change in mercury deposition using CAMx for the final Portland Cement NESHAP and NSPS. 24 These modeling results indicate significantly

reduced total mercury

deposition (wet and dry

forms) , including reducing deposition by

up to 30% in the West and up to 17% in the East in 2013 . This modeling indicates that mercury

deposition reduct ions tend to be greatest nearest the sources. Figure 6 - 10 shows the change in mercury

deposition as a result of the final Portland Cement NESHAP and NSPS in the Eastern U.S., and Figure 6.11 shows the change in mercury deposition in the Western U.S.

24 See Section 5 of this RIA for more information on the air quality modeling.6 - 33

Figu re 6 - 1 0 . 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 barel y tolerable for 1 hour. The greatest impact is on the upper respiratory tract; exposure to high concentrations can rapidly lea d to swelling and spasm of the throat and suffocation. Most seriously exposed persons have immediate onset of rapid breathing, blue coloring of the skin, and narrowing of the bronchioles. Patients who have massive exposures may develop an accumulation of f luid in the lungs. Exposure to hydrogen chloride can lead to Reactive Airway

Dy sfunction Sy

ndrome (RADS), a chemically- or irritant - induced ty pe of asthma. Children may

be more vulnerable to corrosive agents than adults because of the relativel y smaller di ameter of their airway s. Children may also be more vulnerable to gas

25 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). Medical Management Guidelines for Hydrogen Chloride (HCl) . CAS# : 7647 - 01 - 0 . Atlanta , GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc.gov/Mhmi/mmg173.html >.6 - 35

exposure because of increased minute ventilation per kg and failure to evacuate an area promptly when exposed. Hydrogen chloride has not been classified for carcinogenic effects. 6.5.2.3 Toluene 26 Toluene is found in evaporative as well as exhaust emissions from motor vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment, there is inadequate information to assess the carcinogenic potential of toluene because studies of humans chronically exposed to toluene are inconclusive, toluene was not carcinogenic in adequate inhalation cancer bioassay s 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 s ys tem (CNS) is the primary target for toluene toxicity in both humans and animals for acute and chronic exposures. CNS dy sfunction (which is often reversible) and narcosis have been frequentl y observed in humans acutel y exposed to low or moderate levels of toluene by inhalation; sy

mptoms include fatigue, sleepiness, headaches, and nausea. Central nervous system depression has been reported to occur in chronic abusers exposed to high levels of toluene. Sy mptoms include ataxia, tremors, cerebral atrophy , ny stagmus (involuntary

eye movements), and impaired speech, hearing, and vision. Chronic inhalation exposure of humans to toluene also causes irritation of the upper respiratory tract, eye irritation, dizziness, headaches, and difficulty with sleep. Human studies have also reported developmental effects, such as CNS dy sfunction, 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 occupationall y exposed humans exists. The weight of evidence from these studies indicates neurological effects (i.e., impaired color vision, impaired hearing, decreased performance in neurobehavioral analy sis, 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 formaldeh yde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkey s. 27 EPA is currently reviewing 26 All health effects language for this section came from: U.S. EPA. 2005. “Full IRIS Summary for Toluene (CASRN 108 - 88 - 3)” Environmental Protection Agency, Integrated Risk Information System (IRIS), Office of Health and Environmental Assessment, Environmental Criteria and Assessment Office, Cincinnati, OH. Available on the Internet at <http://www.epa.gov/iris/subst/0118.htm>. 27 U.S. EPA. 1987. Assessment of Health Risks to Garment Workers and Certain Home Residents from Exposure to6 - 36

recentl y published epidemiological data. For instance, research conducted by the National Cancer Institute (NCI) found an increased risk of nasophary ngeal cancer and ly mphohematopoietic malignancies such as leukemia among worker s exposed to formaldeh yde. 28 , 29 Inan analy sis of the l ymphohematopoietic cancer mortality from an extended follow - up of these workers, NCIconfirmed an association between l ymphohematopoietic cancer risk and peak exposures. 30 A recent National Institute of O ccupational Safety and Health (NIOSH) study of garment workers also found increased risk of death due to leukemia among workers exposed to formaldeh yde. 31 Extended follow - up of a cohort of British chemical workers did not find evidence of an increase in nas opharyngeal or l ymphohematopoietic 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 formaldeh yde in rodents and primates b y th e CIIT Centers for Health Research (formerl y 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 formaldeh yd e incorporated mec hanistic 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. Mo rtality from lymphohematopoetic malignancies among workers in formaldehyde industries. Journal of the National Cancer Institute 95: 1615 - 1623. 29 Hauptmann, M..; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A. 2004. Mortality from solid cancers among workers in formaldehyde industries. American Journal of Epidemiology 159: 1117 - 1130. 30 Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Hoover, R. N.; Hauptmann, M. 2009. Mortality from lymphohematopoietic malignancies among workers in formaldehyde industries: The National Cancer Institute cohort. J. National Cancer Inst. 101: 751 - 761. 31 Pinkerton, L. E. 2004. Mortality among a cohort of garment workers exposed to formaldehyde: an update. Occup. Environ. Med. 61: 193 - 200. 32 Coggo n, D, EC Harris, J Poole, KT Palmer. 2003. Extended follow - up of a cohort of British chemical workers exposed to formaldehyde. J National Cancer Inst. 95:1608 - 1615. 33 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 20 03. Biologically mo tivated computational modeling of formaldehyde carcinogenicity in the F344 rat. Tox Sci 75: 432 - 447. 34 Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston, and FJ Miller. 2004. Human respiratory tract cancer risks of i nhaled formaldehyde: Dose - response predictions derived from biologically - mo tivated computational modeling of a combined rodent and human dataset. Tox Sci 82: 279 - 296. 35 Chemical Industry Institute of Toxicology (CIIT).1999. Formaldehyde: Hazard characteriz ation 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 - induc ed 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 charact erizing 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 formaldeh yde’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, i n 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 suffi cient evidence in experimental animals - a higher classification than previous IARC evaluations. After reviewing the currentl y available epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyd e carcinogenicity as “suffici ent,” based upon the data on nasophary ngeal cancers; the epidemiologic evidence on leukemia was characterized as “strong.” 42 EPA is reviewing the recent work cited above from the NCIand NIOSH, as well as the anal ys is by the CIIT Centers for Health Research and other studies, as part of a reassessment of the human hazard and dose - response associated with formaldeh yde. Formaldeh yd e exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of the eyes), no se and throat. Effects from repeated exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia. Animal studies suggest that formaldehyde may also cause airway

inflammation

– including eosinophil infiltration into the airways. There are several studies that suggest that formaldehyde may increase the risk of asthma

– particularl y in the yo ung. 43 , 44

38 Subramaniam, R; Chen, C; Crump, K; .et .al. (2007). Uncertainties in the CIIT 2 - stage model for formaldehyde induced nasal cancer in the F344 rat: a limited sensitivity analysis - I. Ri sk Anal 27:1237 39 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity analysis of biologically motivated model for formaldehyde induced respiratory cancer in humans. Ann Occup Hyg 52:481 - 495. 40 Crump, K; Chen, C; Fox, J; .et .al. (2006) Sensitivity anal ysis of biologically motivated model for formaldehyde induced 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 formaldehyde induced 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 Toxi c 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 Ch emical 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 observ ed in people exposed to 2,3,7,8 - TCDD levels that are at least 10 times higher than background levels. The most obvious health effect in people exposure to relatively

large amounts of 2,3,7,8 - TCDD is chloracne. Chloracne is a severe skin disease with acne - l ike lesions that occur mainl y 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 relativel y 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 informati on 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 a fter 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 s ys tem. In many species of animals, 2,3,7,8 - TCDD weakens the immune sy stem and causes a decrease in the s ystem'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 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 including skeletal deformities, kidney defects, and weakened immune responses. In some studies , effects were observed at body burdens 10 times higher than human background levels.

World Health Organization, and produced within the framework of the Inter - Organiz ation Programme for the Sound Management of Chemicals. Geneva. 45 All health effects language for this section came from: Agency for Toxic Substances and Disease Registry (ATSDR). 1999. ToxFAQs for Chlorinated Dibenzo - p - dioxins (CDDs) (CAS# : 2,3,7,8 - TCDD 17 46 - 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 cont aminated with CDFs. The amounts that these people were exposed to were much higher than are likel y from environmental exposures or from a normal diet. Skin and eye irritations, especiall y 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 s ys tem, and mild changes in the liver. Children born to exposed mothers ha d 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 accidentall y exposed also occurred in laboratory

animals that ate CDFs. Animals also had severe weight loss, and their stomachs, livers, kidney s, and immune sy stems were seriously injured. Some animals had birth defects and testicular damage, and in severe cases, some animals died. These e ffects 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 IRI S database that data indicate a causal relationship between benzen e exposure and acute

46 All health effects language for this s ection came from: Agency for Toxic Substances and Disease Registry (ATSDR). 1995. ToxFAQsTM for Chlorodibenzofurans (CDFs) . Atlanta, GA: U.S. Department of Health and Human Services, Public Health Service. Available on the Internet at <http://www.atsdr.cdc. gov/tfacts32.html>. 47 U.S. EPA. (2009) 2002 National - Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/ 48 U.S. EPA. 2000. Integrated Risk Information System File for Benzene. This material is available electronically at: http://www.epa.gov/iris/subst/0276.htm. International Agency for Research on Cancer, IARC monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Some industrial chemicals and dyestuffs, International Agency for Research on Cancer, World Health Organization, Lyon, France, p. 345 - 389, 1982. Irons, R.D.; Stillman, W.S.; Colagiovanni, D.B.; Henry, V.A. (1992) Synergistic action of the benzene metabolite hydroquinone on myelopoietic stimulating activity of granulocyte/macrophage colony - stimulating fact or in vitro, Proc. Natl. Acad. Sci. 89:3691 - 3695.6 - 40

ly mphocy tic leukemia and suggest a relationship between benzene exposure and chronic non ly mphocy tic leukemia and chronic l ymphocy tic leukemia. The International Agency for Research on Carcinogens (IARC) has determined that benzene is a human carcinogen and the U.S. Department of Health and Human Services (DHHS) has characterized benzene as a known human carcinogen. 49 A number of adverse noncancer health effects including blood disorders, such as preleukemia and aplastic anemia, have also been associated with long - term exposure to benzene. 50 The most sensitive noncancer effect observed in humans, based on current data, is the depression of the absolute ly mphocy te count in blood. 51 Inaddition, recent work, including studies sponsored by

the Health Effects Institute (HEI), provides evidence that biochemical responses are occurring at lower levels of benzene exposure than previously known. 52 EPA’s IRIS program has not yetevaluated 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 b y this rule, including metal and organic HAPs. In formation regarding the health effects of these compounds can be found in EPA’s IRI S database. 53

49 International Agency for Research on Cancer (IARC). 1987. Monographs on the evaluation of carcinogenic risk of chemicals to humans, Volume 29, Supplement 7, Some industrial chemicals and dyestuffs, World Health Organization, Lyon, France. U.S. Department of Health and Human Services National Toxicology Program 11th Report on Carcinogens available at: http://ntp.niehs.nih.gov/go/16183. 50 Aksoy, M. (1989). Hematotoxicity and carcinogenicity of benzene. Envir on. Health Perspect. 82: 193 - 197. Goldstein, B.D. (1988). Benzene toxicity. Occupational medicine. State of the Art Revie ws. 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. Tit enko - 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 Revie w of Benzene (Noncancer Effects). Environmental Protection Ag ency, Integrated Risk Information System (IRIS), Research and Development, National Center for Environmental Assessment, Washington DC. This material is available electronically at http://www.epa.gov/iris/subst/0276.htm. 52 Qu, O.; Shore, R.; Li, G.; Jin, X .; Chen, C.L.; Cohen, B.; Melikian, A.; Eastmond, D.; Rappaport, S.; Li, H.; Rupa, D.; Suramaya, R.; Songnian, W.; Huifant, Y.; Meng, M.; Winnik, M.; Kwok, E.; Li, Y.; Mu, R.; Xu, B.; Zhang, X.; Li, K. (2003). HEI Report 115, Validation & Evaluation of Bio markers in Workers Exposed to Benzene in China. Qu, Q., R. Shore, G. Li, X. Jin, L.C. Chen, B. Cohen, et al. (2002). Hematological changes among Chinese workers with a broad range of benzene exposures. Am. J. Industr. Med. 42: 275 - 285. Lan, Qing, Zhang, L ., Li, G., Vermeulen, R., et al. (2004). Hematotoxically in Workers Exposed to Low Levels of Benzene. Science 306: 1774 - 1776. Turtletaub, K.W. and Mani, C. (2003). Benzene metabolism in rodents at doses relevant to human exposure from Urban Air. Research Reports Health Effect Inst. Report No.113. 53 U.S. EPA Integrated Risk Information System (IRIS) database is available at: www.epa.gov/iris6 - 41

6.6 Limitations and Un certainties 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 uncertaintie s , NRC highlighted the need to conduct rigorous quantitative anal ys is of uncertainty and to present benefits estimates to decisionmakers in way s that foster an appropriate appreciation of their inherent uncertainty . In response to these comments, EPA’s Of fice 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 analy sis, we use three methods to assess uncertainty quantitativel y: Monte Carlo anal ys is , alternate concentration - response functions for PM mortality , and LML assessment . We also provide a qualitative assessment for those aspects that we are unable to address quantitatively

in this analy sis. Each of these anal yses is described in detail in the following sections. This analy sis includes many data sources as inputs, inclu ding emission inventories, air quality data from models (with their associated parameters and inputs), population data, health effect estimates from epidemiology studies, and economic data for monetizing benefits. Each of these inputs may

be uncertain and would affect the benefits estimate. When the uncertainties from each stage of the analy sis are compounded, small uncertainties can have large effects on the total quantified benefits. In this analy sis, we are unable to quantify the cumulative effect of all of these uncertainties, but we provide the following anal ys es 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 associat ed 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 . Sp ecificall y, 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 s hown in Tables 6 - 2 and 6 - 3.6 - 42

6.6.2 Alternate concentration -response functions for PM mortality PM 2.5 mortality

benefits are the largest benefit category that we monetized in this anal ys is. 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 elicitation 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 - based studies (Pope et al, 2002; and L aden 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 plausib le, but most of the expert - based estimates fall between the two epidemiology- based estimates (Roman et al., 2008 ). In this analy sis, we present the results derived from the expert elicitation as indicative of the uncertainty asso ciated 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 result s 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 concentration response 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 anal ys is. To better characterize the uncertainty assoc iated with mortality impacts that are estimated to occur in areas with low baseline levels of PM 2.5 , we included the LMLassessment. 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 hav e 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 onl y able to address qualitativel y. 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 SAB HES and the NAS (NRC, 2002 ). The benefits estimates are subject to a number of assumptions and uncertainties. For example, the key assumptions underlying the estimates for premature mortality , which typically account for at least 90% of the total monetized benefits, include the following: 1. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality . This is an important assumption, because PM 2.5 produced via transported precursors emitted from EGUs may differ significantl y from direct PM 2.5 re leased from diesel engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by

particle type. 2. We assume that the health impact function for fine particles is linear down to the lowest air qu ality

levels modeled in this analy sis. 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 me et 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 estimate s 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 estim ate 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 anal ys is. 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 regulatory scenario. By

assuming that each kiln6 - 44

proportionatel y reduces their emissions by the same percentage as the national percentage reduction, we may

be slightl y 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 slightl y overestimating the air quality impacts and monetized benefits . By

omitting the decrease in domestic cement pro duction and transportation , we are underestimating the air quality impacts and monetized benefits . By omitting the increase in cement imports, we may

be overestimating the monetized benefits by not accounting for additional global pollutants. By using national average benefit - per - ton estimates to calculate the energy disbenefits, we may

be under or overestimating these monetized disbenefits. Despite our inability to full y characterize and quantify these relatively

small effects, we believe that, on ne t, the air quality

impacts and associated monetized benefits are representative of the magnitude of benefits anticipated from this regulation. As previously

described, we strive to monetize as many of the benefits anticipated from this rule as possible, but the monetized benefits estimated in this RIA inevitably onl y reflect the portion of benefits . Specifically , onl y the benefits attributable to the health impacts associated with exposure to ambient fine particles have been monetized in this analysis . D ata, resource, and methodological limitations prevented EPA from quantifying or monetizing the benefits from several important benefit categories, including benefits from reducing toxic emissions, ecos ys tem effects, and visibility impairment. Data limitati ons include limited monitoring for HAPs, incomplete emissions inventories for HAPs, and limited photochemical air quality modeling for non - mercury HAPs . Resource limitations include limited staff and extramural funding in conjunction with a heav y regulato ry workload. Methodological limitations include an absence of concentration - response functions for many HAP health effects, with issues such as exposure misclassification, small number of cases, confounding, and extrapolation of toxicological effects down to ambient levels (IEc, 2008). Despite our inability to monetize all of the benefit categories, the monetized benefits still exceed the costs by a substantial margin. This RIA does not include the ty pe of detailed uncertainty assessment found in the PM NAAQS RIA. However, the results of the Monte Carlo anal ys es 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 analy sis. 6.7 Comparison of Be nefits 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 $1 8 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 $ 1 6 billion. These estimates include the energy disbenefits associated with increased electricity usage by the control devices . The annualized social costs of the final NESHAP and NSPS are $ 880 million . 54 Thus, net benefits are $6. 5 billion to $17 billion at a 3% discount rate for the benefits and $ 5.8 billion to $16 billion at a 7% discount rate. In addition, the benefits from reducing 16,400 pounds of mercury , 4,400 tons of NO x, 5,800 tons of HCl, and 5,200 tons of organic HAPs each ye ar have not been included in these estimates. All estimates are in 2005$. Table 6 - 5 shows a summary of the monetized benefits, social costs, and net benefits for the final Portland Cement NESHAP and NSPS , the final NSPS only , the final NESHAP only , and the more stringent NSPS and final NESHAP . Figures 6 - 12 and 6 - 13 show the full range of net benefits estimates (i.e., annual benefits minus annualized costs) utilizing the 14 different PM 2.5 mortal ity

functions at discount rates of 3% and 7%. Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several important benefit categories, including benefits from reducing hazardous air pollutants, ecos ystem effects , and visibility impairment. EPA believes that the benefits are likel y to exceed the costs under this rulemaking even when taking into account uncertainties in the cost and benefit estimates.

54 For more information on the annualized costs, please refer to Section 4 of this RIA.6 - 46

Table 6 - 8 . Summary of the Monetized Benefits, Social Costs, a nd Net Benefits for the

final Portland Cement NES HAP in 2013 (millions of 2005$) 1 Final NESHAP and NSPS 3% Discount Rate 7% Discount Rate Total Monetized Benefits 2 $7, 4 00 to $1 8 ,000 $6, 7 00 to $ 16 ,000 Total Social Costs 3 $926 to $950 $926 to $950 Net Benefits $6, 500 to $1 7 ,000 $ 5,8 00 to $16,000 Non - monetized Benefits d 4,4 00 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 $ 5,8 00 to $16,000 Non - monetized Benefits d 5,200 tons of organic HAPs 5,900 tons of HCl 16, 4 00 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, 7 00 to $ 15 ,000 Non - monetized Benefits 4 7 , 8 00 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 figure s. 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 . I t is important to note that the monetized benefits include many but not all health effects associated with PM2.5 exposure. Benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, re gardless of their chemical composition, are equally potent in causing p remature mortality because there is no clear scientific evidence that would support the development of differential effects estimates by particle type. The total monetized benefits include the energy disbenefits. 3 The methodology used to estimate social costs for one year in the multimarket model using surplus changes results in the same social costs for both discount rates. 4 Due to data, methodology, and resource limitations, we were unable to monetize the benefits associated with these categories of b enefits.6 - 47 $0 $5,000 $10,000 $15,000 $20,000 $25,000

Pope et al. Laden et al.Millions (2005$)

Cos t estimate combine d wi th total monetiz ed benefits estimates der iv ed f rom 2 ep idemiology functions and 12 exper t f unctions 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 cos t 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 monetize the benefits as sociated 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

a l. Laden et a l.Millions (2005$)

Cos t estimate combine d wi th total mone tiz ed benefits estimates der iv ed f rom 2 ep idemiology functions and 12 exper t f unctions Figure 6 - 13 . Net Benefits for the Final Portland Cement NESHAP and NSPS at 7%

Discount Rate a a Net Benefits are quantifi ed 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 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 Int ernet 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 Stat es. ” 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. 2008b. “O ptimal 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

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by the Health Effects Subcommittee of th e Advisory Council on Clean Air Compliance Analy sis. EPA - SAB - COUNCIL - ADV - 04 - 002. March. Available on the Internet at <http://yosemite.epa.gov/sab%5CSABPRODUCT.NSF/08E1155AD24F871C85256E540 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 (S econd 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 Valuing 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. Environmental Protection Agency

– Science Advisory Board (U.S. EPA - SAB). 2009a. Review of EPA’s Integrated Science Assessment for Particulate Matter (F irst 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

– Scien ce 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/0/72D4EFA39E4 8CDB2852577450073877 6/$File/EPA - COUNCIL - 10 - 001 - unsigned.pdf>. U.S. International Trade Commission (USI TC). 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 P ORTLAND C EMENT E CONOMIC M ODELA - 1

The Office of Air Quality Planning and Standards ( OAQPS ) has adopted the standard industry level anal ys is described in the Office’s resource manual (EPA, 1999a). This approach is consistent with previous EPA anal ys es of the Portland cement industry (EPA, 1998; EPA, 1999b ) and uses a single - period static partial - equilibrium model to compare prepolicy cement market baseline s with expected postpolicy outcomes in these markets. The benchmark time horizon for the anal ys is is the intermediate run where producers have some constraints on their flexibility to adjust factors of production. This time horizon allows us to capture imp ortant transitory impacts of the program on existing producers. Key measures in this analy sis include

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

 industry- level effects (changes in operatin g profits and employ ment),

 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 Ec onomic 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 ye ar . At the time of proposal model development, it was the latest year for which the PCA had published thei r 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 identifie d several plants where operations h ad changed ( s ee 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 significantl y influence the cement industry (see Table A - 3). If 2013 is more or less favorable for the cement industry

than 2005, then impacts wou ld be expected to change accordingl y.A - 2

Table A - 1. Economic Model Population Updates: 2005

Market Approxim ate Clinker

Capacity Rem oved (thousand metric tons) Description Approxim ate Clinker Capacity Added (thousand metric tons) Net Change in Market Plant Po pulation Atlanta 3 00 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. Tabl e 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 Em ployment (thousand) 16 16 16 15 14 Share of consumption p rovided 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 analy sis performed for this rule uses the cement market model developed during proposal (U.S. EPA, 2009 ). The model simulates how stakeholders (consumers and firms) may

respond to the additional regulatory program costs. In the near t erm, the regional cement markets are assumed to have few sellers that offer similar/identical products. As a result, EPA used an oligopol y market structure 1 . As described in S ection 3, this market structure assumption suggests that the observed baseline market price will be higher than marginal production costs (i.e., there may be a preexisting market distortion prior to regulation). To provide some intuition about factors that influence the size of the existing distortion, we express a seller’s “best” supp ly

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) = Mar ginal Costi . This equation shows the relationship between the oligopoly model and perfect competition. The market distortion will ty

pically be higher when market share i is high (there are few sellers) and in markets where the quantity demanded is less sens itive to price (i.e., the demand elasticity is inelastic). A. 2.1 Model Equations To estimate the economic impacts of the regulation, EPA used four linear equations to calculate the following unknown variables:

 change in domestic plant production (dqi ),

 c hange in imports (dqimports ),

 change in cement market quantity (dQ), and

 change in cement price (dP). Equation 1: Domestic Supply. For each plant, we describe its response to the regulatory program as follows. The total compliance cost per ton (c i ) is ap plied 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 companies will react to its own production decision. EPA selected the Cournot model where the company assumes competing com panies’ 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 q plant Q market Q dmarket elasticity demprice Q market q dplant elasticity dem share mkt 1 dprice 2 i                 Equation 2: Supply of Imports. If applicable to the market, an equation describing the supply of cement from other countries was included: dimports = import supply elasticity × (dprice/baseline price) × baseline imports. For import supply , EPA used the latest empirical work on how other countries who export (i.e., supply imports) to the United States respond to price changes. Broda et al. (2008 ) report that the export supply

elasticity for commodities imported by the United States was approximately two. This implies that a 1% increase in prices results in a 2% increase in the volume of expor ts for a ty pical 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) × base line consumption. The use of published estimates from previous rulemakings is appropriate in cases when the cost of preparing original estimates is high (EPA, 2000 ). In previous anal ys es, EPA econometrically

1 To highlight and make transparent the assumptions regarding seller behavior, this equation is formally derived in Appendix B.A - 6

estimated the demand elasticity for cement and reported a point estimate of −0.88 (EPA, 1998 ). This value suggests that a 1% increase in the cement price would lead to a 0.88% reduction in cement consumption.APPENDIX B T HE CEMENT PLANT’S PRODUCTION DECISION: A MATHEMATICAL REPRESENTATIONB - 1

This app endix 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 De scriptions 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: 

###     i i i i i FC

q VC q Q P Q



  ) ( max i q  . Step 2: The firs t - order conditions for a profit maximum are: 

###   0 q q q VC q q P(Q) P q π i i i i i i i i 



     

. Step 3: Apply

two key assumptions in the Cournot price model:

 Plant’s (i) recognizes its own production decisions influence the market price: 0 q P i   

 Plant (i) output d ecisions do not affect those of any other plant (j) (e.g., there is no strategic action among cement plants): 0 q q i j    Step 4: Next, multiply

second term by Q P P Q 1 B - 2 

###   0 q q q VC Q P P Q q q P(Q) P i i i i i i 



            . Step 5: Rearranging terms: 

###   0 q q q VC P Q q q Q P P(Q) P i i i i i i 



                    . Step 6: Use and appl y the following definitions: elasticity demand inverse 1 q Q P P(Q) i 

            share market s plant' Q q Q q 1 i i 

          . We derive the following expression: 

##   i i i i q q VC Q q 1 

                           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    .APPENDIX C SOCIAL COST METHODOL OGYC - 1

The Office of Air Quality Planning and Standards (OAQPS) has adopted the standard industry- level anal ys is described in the Office’s resource manual (EPA , 1999a). This approach is consistent with previous EPA anal ys es 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 anal ys is is the intermediate run where producers have some constraints on their flexibility to adjust factors of production. This time horizon allows us to capture important transitory impacts of the program on existing producers. The model provides an estimate of the social costs (changes in producer and consumer surplus) associated with controls applying to existing ki lns (see Section 4). Since the social cost methodology is identical to the approach used in previous cement anal ysis (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 b y the area under the demand curve up to the market output, Q 0 . This equals the area filled b y 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 b y the letters ABCDEFGH. This total surplus value to society can be further divided into producer surplus and consumer surplus. Producer surplus accrues to the suppliers of cement and reflects the value they receive in the market for producing Q 0 units of cement less their costs of production, i.e., their profits. As shown in Figure C - 1, producer surplus is g iven b y 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 b y the amount of the incremental compliance costs. As shown in Figure C - 1, this results in a new market equilibrium that occurs at a higher market price for cement, P 1 , and a lower level of output, Q 1 . In this scenario, the total benefit s of consumption are equal to area ABDFI and the total variable costs of production are equal to area FI. This yields a with regulation social surplus equal to area ABD with area BD representing the new producer surplus and area A being the new consumer surplus. The social cost of the regulation equals the total change in social surplus caused b y the regulation. Therefore, the social cost of the regulation is represented b y the area FGHEC in Figure C - 1. The distributive effects are estimated b y separating the social cost into producer surplus and consumer surplus losses. First, the change in producer surplus is given by ΔPS = B

– F

– (G+H+E) ( C .1) Producers gain B from the increase in price (a transfer from consumers to producers), but lose F from the increase in production costs due to the incremental compliance costs. Furthermore, the reduction of cement production leads to foregone baseline profits of G+H+E.C - 3

The change in consumer surplus is given b y ΔCS =

– (B + C) ( C .2) This change results from the reduction in consumer surplus from the baseline value of ABC to the with - regulation value of A. In this case, consumers lose area B as a transfer to producers through the increase in the price they pay for the with - regulation level of cement consumption, while the reduction in cement consumption due to regulation leads to foregone baseline value of consumption equal to are a C. The social cost or total change in social surplus can then be derived simply b y adding the changes in producer and consumer surplus, i.e.,

Soci al Cost = ΔPS + ΔCS =

– (F + G + H + E + C) ( C .3) This estimate can be compared to the engineering estimate of incremental compliance cost to demonstrate the difference between these two estimates of social cost. The incremental compliance cost estimate is given b y 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 b y the area EC. In other words, the incremental comp liance cost estimate understates the social costs of the regulation. The reason for this follows directly

from the imperfectl y competitive structure of the markets for cement. A comparison with the outcome under perfect competition will assist in illustrating this point. Suppose that the MR curve in Figure C - 1 was the demand function for a competitive market, rather than the marginal revenue function for an imperfectl y 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 perfectl y competitive equil ibrium, the market price would rise by the per unit control cost amount to P 1 c . The social cost of the regulation is given entirel y b y the loss in consumer surplus as given b y area FG. As shown in Figure C - 1, this estimate of social cost is less than the i ncremental 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 b y the industry are given b y area F. Area G represents the foregone value of cement consumption t o 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 socie ty

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 mov es 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 OP INIONS ON THE EXISTE NCE OF A THRESHOLD IN THE CONCENTRATION - RESPONSE FUNCTION FO R PM 2.5 - RELATED MORTALITYD - 1



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

## - related Mortality

Technical Support Document (TSD)

July 2010

C ompiled 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 Analy sis (2010) B. Ameri can Heart Association Scientific Statement (2010) C. Integrated Science Assessment for Particulate Matter (2009) D. CASAC comments on PM I SA 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 Analy sis (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 Ben efits 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: “ Th e HES generally agrees with other decisions made by the EPA project team with respect to PM, in particular, the PM mortality effect threshold model, the cessation lag model, the inclusion of infant mortality estimation, and differential toxicity of PM. ” P g 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: “T he HES fully supports EPA’s decision to use a no - threshold model to estimate mortality reductions. This decision is supported by the data, which are quite consistent in showing effects down to the lowest measured levels. Analyses of cohorts using data from more recent years, during which time PM concentrations have fallen, continue to report strong associations with mortality. Therefore, there is no evidence to support a truncation of the CRF.” HES Panel Members Dr. John Bailar , Chair of the Health Effects Subcommittee, Scholar in Residence, The National Academies, Washington, DC Dr. Michelle Bell , Associate Professor, School of Forestry and Environmental Studies, Yale University , New Haven, CT Dr. James K. Hammitt , Professor, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA Dr. Jonathan Levy , Associate Professor, Department of Environmental Health, Harvard School of Public Health, Boston, MA Dr. C. Arden Pope , III Professor, Department of Economics, Brigham Young Univ ersity , Provo, UT Mr. John Fintan Hurley , Research Director, Institute of Occupational Medicine (IOM), Edinburgh, United Kingdom, UK Dr. Patrick Kinney , Professor, Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University , New York, NY Dr. Michael T. Kleinman , Professor, Department of Medicine, Division of Occupational and Environmental Medicine, University of California, Irvine, Irvine, 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 Medic al 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 card iovascular 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 long term 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 b y the AHA writing group corroborates and strengthens the conclusions of the initial scientific statement. In this context, we agree with the concept and continue to support measures based on scientific evidence, such as the US EPA NAAQS, that seek to control PM levels to protect the public health. Because the evidence reviewed supports that there is no safe threshold, it appears that public health benefits would accrue from lowering 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 un derstanding of the PM cardiovascular 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 eliminate s 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 Bh atnagar , 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 (F i nal 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 pu blic health impacts associated with exposure to a pollutant is whether the concentration - response relationship is linear across the full concentration range encountered, or if nonlinear relationships exist along any part of this range. Of particular intere st 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 ty pe of health outcome, underlying biological mechanisms and dose. At the human popu lation level, however, various sources of variability and uncertainty tend to smooth and “linearize” the concentration - response function (such as the low data density in the lower concentration range, possible influence of measurement error, and individual differences in susceptibility to air pollution health effects). In addition, many chemicals and agents may act by perturbing naturall y 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 likel y mechanisms include nonlinear processes for some key events. These attributes of human population dose - response relationships have been extensively discussed in the b roader epidemiologic literature (Rothman and Greenland, 1998, 086599 ).” Pg 2 - 16: “In addition, cardiovascular hospital admission 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 the PM - morbidity and mortality

associatio n 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 I SA studies have been identi fied that attempt to characterize the shape of the concentration - response curve along with possible PM “thresholds” (i.e., levels which PM concentrations must exceed in order to elicit a health response). The epidemiologic studies evaluated that examined the shape of the concentration - response curve and the potential presence of a threshold have focused on cardiovascular hospital admissions and ED visits and mortality associated with short - term exposure to 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 anal ys is during model sel ection to determine if a linear curve most adequatel y represented the concentration - response relationship; whereas, only one study conducted an extensive anal ys is to examine the shape of the concentration - response curve at different concentrations (Section 6.2.10.10). Overall, the limited evidence from the studies evaluated supports the use of a no - threshold, log - linear model, which is consistent with the observations made in studies that examined the PM - mortality relationship. “Although multiple studies h ave previously examined the PM - mortality concentration - response relationship and whether a threshold exists, more complex statistical analy ses continue to be developed to analyze this association. Using a variety of methods and models, most of the studies evaluated support the use of a no - threshold, log - linear model; however, one study did observe heterogeneity

in the shape of the concentration - response curve across cities (Section 6.5). Overall, the studies evaluated further support the use of a no - thresho ld log - linear model, but additional issues such as the influence of heterogeneity in estimates between cities, and the effect of seasonal and regional differences in PM on the concentration - response relationship still require further investigation. “In ad dition to examining the concentration - response relationship between short - term exposure to PM and mortality, Schwartz et al. (2008, 156963) conducted an anal ys is of the shape of the concentration - response relationship associated with long - term exposure to PM. Using a variety of statistical methods, the concentration - response curve was found to be indistinguishable from linear, and, therefore, little evidence was observed to suggest that a threshold exists in the association between long - term exposure to 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 anal yz ed primarily

through studies that examined the relationship between PM and mortality . These studies, wh ich 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 anal yzed the PM concentration - response relationship, and contributed to the overall body o f evidence which suggests a log - linear, no - threshold PM concentration - response relationship. “The results from the three multicity studies discussed above support no - threshold log - linear models, but issues such as the possible influence of exposure error and heterogeneity of shapes across cities remain to be resolved. Also, given the pattern of seasonal and regional differences in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a concentration - response relationship estimated across cities and for all - ye ar 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 interpreting the shape of PM - mo rtality concentration response functions and the presence of a threshold, including: (1) limited range of available concentration levels (i.e., sparse data at the low and high end); (2) heterogeneity of susceptible populations; and (3) investigate the PM - m ortality 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 mo dels 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 without indicatin g a threshold for the relative risks of death for all - causes and for cardiovascular respiratory

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 μg/m 3 PM 10 , as shown in Figure 6 - 35. For all - cause and cardio - respiratory deaths, based on the Akaike’s Information Criterion (AIC), a log - linear model without threshold was preferred to the threshold model and to the spline model. “The HEI review committee commented that interpretation of these results required caution, because (1) the measurement error could obscure any threshold; (2) the city- specific concentration - response curves exhibited a variety of shapes; and (3) the use of AI C 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 etiologicall y credible reason suggested thus far to choose one model over others fo r 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 ) anal ys is 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 μg/m 3 , between 25 and 34 μg/m 3 , between 35 and 44 μg/m 3 , and 45 μg/m 3 and above. In the model, days with concentrations below 15 μg/m 3 served as the reference level. This model was fit using the single stage method, combining strata acros s all cities in the case - crossover design. Figure 6 - 36 shows the resulting relationship, which does not provide sufficient evidence to suggest that a threshold exists. The authors did not examine city- to - city variation in the concentration - response relatio nship 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 μg/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 adequatel y estimated using the log - linear model. However, in an ancillary anal ys is of the concentration - response curves for the largest cities in each of the three distinct geographic areas (western, southern, and eastern European cities): London, England; Athens, Greece; and Cracow, Poland, Samoli et al. (2005, 087436 ) observed a difference in the shape of the concentration response curve across cities. Thus, while the combined curves (Figure 6 - 37) appear to supportD - 9

no - threshold relationships between PM 10 and mortality, the he terogeneity of the shapes across cities makes it difficult to interpret the biological relevance of the shape of the combined curves. “The results from the three multicity studies discussed above support no - threshold log - linear models, but issues such as the possible influence of exposure error and heterogeneity of shapes across cities remain to be resolved. Also, given the pattern of seasonal and regional differences in PM risk estimates depicted in recent multicity study results (e.g., Peng et al., 2005, 087463 ), the very concept of a concentration - response relationship estimated across cities and for all - ye ar data may not be very

informative.” Authors of ISA Dr. Lindsay Wichers Stanek (PM Team Leader)

— National Center for Environmental Assessment (NCEA), U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, NC Dr. Jeffrey Arnold

—

NCEA, U.S. EPA, Research Triangle Park, NC (now at Institute for Water Resources, U.S. Arm y C orps 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., Co rvallis, 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 Univers ity , 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

—Lo velace 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 To ny 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 Sy stems, 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

—

Natio nal Oceanic and Atmospheric Administration, Las Vegas, NV Dr. Les Recio

—Genetic Toxicology Division, Integrated Laboratory Sy stems, Inc., Research Triangle Park, NC Dr. David Quincy Rich

—Department of Epidemiology , University of Medicine and Dentistry of N ew 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 Deacones s 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, M I 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. Rober t Devlin , National Health and Environmental Effects Research Laboratory , U.S. EPA, Research Triangle Park, NC Dr. David DeMarini , National Health and Environmental Effects Research L aboratory , U.S. EPA, Research Triangle Park, NC Dr. Neil Donahue , Departme nt 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 Gauderm an , Department of Environmental Medicine, Department of Preventive Medicine, University of Southern California, Los Angeles, CA Dr. Barbara Glenn , National Center for Environmental Research, U.S. EPA, Washington, D.C. Dr. Terry Gordon , School of Medicine, New York University, Tuxedo, NY Mr. Tim Hanley , Office of Air Quality Planning and Standards, U.S. EPA, Research Triangle Park, NC Dr. Jack Harkema , Department of Pathobiology and Diagnostic Investigation, Michigan State University , East Lansing, MI Ms. Be th 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 Aero nautical 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 Ric hm ond , 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 Occupational Health, Emory University , Atlanta, GA Dr. Frances Silverman , Gage Occupational and Environmental Health, University of Toronto, Toronto, ON Mr. Steven Silverman , Office of General Council, U.S. EPA, Washington, D.C. Dr. Barbara Turpin , Department of Environmental Sciences, Rutgers University , New Brunswick, NJ Dr. Robert Vanderpool, National Exposure Research Laboratory , U.S. EPA, Research Triangle Park, NC Dr. John Vandenberg (Director)— NCEA - RTP Division, U.S. EPA, Research Triangle Park, NC Dr. Ala n 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 Triang le 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 Wood all, 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 Bo ard (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/81e39f4c09954fcb8 5256ead006be86e/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 betwe en 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. E PA - 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.NS F/81e39f4c09954fcb85256ead006be86e/7 23FE644C5D758DF852575BD00763A32/$File/EPA - CASAC - 09 - 009 - unsigned.pdf>. Pg 6: “On the issue of cut - points raised on 3 - 18, the authors should be prepared to offer a scientifically

cogent reason for selection of a specific cut - point, and not simply try different cut points to see what effect this has on the anal ys is. The draft I SA was clear that there is little evidence for a population threshold in the C - R function.” U.S. Environmental Protection Agency - Science Advisory Board (U.S. EPA - SAB). 2009. Review of Integrated Science Assessment for Particulate Matter (Second External Review Draft, July 2009) . EPA - CASAC - 10 - 001. November. Available on the Internet at <http://yosemite.epa.gov/sab/SABPRODUCT.NSF/81e39f4c09954fcb85256 ead006be86e/151B1F8 3B023145585257678006836B9/$File/EPA- CASAC - 10 - 001 - unsigned.pdf>. Pg 2: “The paragraph on lines 22 - 30 of page 2 - 37 is not clearl y written. Twice in succession it states that the use of a no - threshold log - linear model is supported, but the n cites other studies that suggest otherwise. It would be good to revise this paragraph to more clearl y state

– well, I’m not sure what. Probabl y that more research is needed.” CASAC Panel Members Dr. Jonathan M. Samet, Professor and Chair, Department of Preventive Medicine, University of Southern California, Los Angeles, CA Dr. Joseph Brain , Philip Drinker Professor of Environmental Physiology, Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, MA Dr. Ellis B. Cowling , University Distinguished Professor At - Large Emeritus, Colleges of Natural Resources and Agriculture and Life Sciences, North Carolina State University, Raleigh, 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 Carolina State University, Raleigh, NC Dr. Armistead (Ted) Russell , Professor, Dep artment of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA Dr. Lowell Ashbaugh, Associate Research Ecologist, Crocker Nuclear Lab, University of California, Davis, Davis, CA Prof. Ed Avol, Professor, Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA Dr. Wayne Cascio, Professor, Medicine, Cardiology, Brody School of Medicine at East Carolina University, Greenville, NC Dr. David Grantz, Director, Botany and Plant Sciences and Air Pollution Research Center, Riverside Campus and Kearney Agricultural Center, University of California, Parlier, CA Dr. Joseph Helble, Dean and Professor, Thayer School of Engineering, Dartmouth College, Hanover, NH Dr. Rogene Henderson , Senior Scientist Emeritus, Lovelace Respiratory Research Institute, Albuquerque, NM Dr. Philip Hopke , Bayard D. Clarkson Distinguished Professor, Department of Chemical Engineering, Clarkson University, Potsdam, NY Dr. Morton Lippmann , Professor, Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo, NY Dr. Helen Suh MacIntosh , Associate Professor, Environmental Health, School of Public Health, Harvard University, Boston, MA Dr. William Malm , Research Physicist, National Park Se rvice 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 Res earch in the Atmosphere, Colorado State University, Fort Collins, CO Dr. Robert F. Phalen , Professor, Department of Community & Environmental Medicine; Director, Air Pollution Health Effects Laboratory; Professor of Occupational & Environmental Health, Center for Occupation & Environment Health, College of Medicine, University of California Irvine, Irvine, CA Dr. Kent Pinkerton , Professor, Regents of the University of California, Center for Health and the Environment, University of California, Davis, CA Mr. Richard L. Poirot , Environmental Analyst, Air Pollution Control Division, Department of Environmental Conservation, Vermont Agency of Natural Resources, Waterbury, VT Dr. Frank Speizer , Edward Kass Professor of Medicine, Channing Laboratory, Harvard M edical 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 Mich igan 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, Mich elle 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). Extende d follow - up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA. Pg 119: [About Pope et al. (2002)] “Each 10 - μg/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, respectivel y. There was no evidence of a threshold exposure level within the range o f 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 forwarded the letter on July 10, 2009 to EPA’s Beth Hassett - Sip ple. (letter placed in docket #EPA HQ - 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 ye ar 1998. The present report, which included follow - up through the ye ar 2000, also does not appear to demonstrate the existence of a threshold in the exposure - response function wi thin 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 Hygi ene 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 Lo ndon, MD, PhD , Senior Investigator, Epidemiology Branch, National Institute of Environmental Health Sciences Dr. William N. Rom, MD, MPH , Sol and Judith Bergstein Professor of Medicine and Environmental Medicine and Director of Pulmonary and Critical Care Medicine, New York University

Medical Center Dr. Armistead Russell , Georgia Power Distinguished Professor of Environmental Engineering, School of Civil and Environmental Engineering, Georgia Institute of Technology Dr. Lianne Sheppard, PhD , Professor, D epartment 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 μg/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 thi s 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/ecas/regdata/Uncertainty/pm_ee_report.pdf>. Pg v: “Each expert was given the option to integrate their judgments about the likelihood of a causal relationship and/or threshold in the C - R function into his distribution or to provide a distribution "conditional on" one or both of these factors.” Pg vii: “Only

one of 12 experts explicitly incorporated a threshold into his C - R function. 3 The rest believed there was a lack of empirical and/or theoretical support for a population threshold. However, three other experts gav e 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. I f 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 thresholds, and reported higher mortality ef fect estimates. The differences in results compared with the pilot appear to reflect the influence of new research on the interpretation of the key epidemiological studies that were the focus of both elicitation studies, more than the influence of changes to the structure of the protocol.” Pg 3 - 25: “3.1.8 THRESHOLDS The protocol asked experts for their judgments regarding whether a threshold exists in the 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 concentratio n - 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 generall y beli eved that individuals exhibit thresholds for PM - related mortality . However, 11 of them discounted the idea of a population threshold in the C - R function on a theoretical and/or empirical basis. Seven of these experts noted that theoretically one would be unlikely

to observe a population threshold due to the variation in susceptibility at any given time in the study

population resulting from combinations of genetic, environmental, and socioeconomic factors. 33 All 11 thought that there was insufficient empiri cal support for a population threshold in the C - R function. In addition, two experts (E and L) cited anal ys es of the ACS cohort data in Pope et al. (2002) and another (J) cited Krewski et al. (2000a & b) as supportive of a linear relationship in the study range.D - 21

“Seven of the experts favored epidemiological studies as ideall y 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 lev els. However, those who favored epidemiologic studies generall y acknowledged that definitive studies addressing thresholds would be difficult or impossible to conduct, because they would need to include a very large and diverse population with wide variati on 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 generall y thought that clinical and toxicological studies are best suited for researching mechanisms and for addressing thresholds in very narrowl y 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 biologicall y safe is fundamentally at odds with toxicological theory . He did not think that a population threshold was detectable in the currentl y available epidemiologic studies. He indicated that some of the cohort studies showed greater uncertainty in the shape of the C - R function at lower levels, which could be indicative of a threshold. “Expert K chose to incorporate a threshold into his C - R function. He indicated that he was 50 % sure that a threshold existed. If there were a threshold, he thought that there was an 80 % chance that it would be less than or equal to 5 μ g/m 3 , and a 20 % chance that it would fall between 5 and 10 μ g/m 3 .” Roman, H enry 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. Tech nol. , 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 app roach allowed them to express increased uncertainty in mortality

effects seen at lower concentrations in major epidemiological studies. Expert K thought the relationship would be log - linear above a threshold.” Pg 2271: “Expert K also applied a threshold, T, to his function, which he described probabilistically . He specified P(T > 0) = 0.5. Given T > 0, he indicated P(T ≤ 5 μg/m 3 ) = 0.8 and P(5 μg/m 3 < T ≤ 10 μg/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 analy sis.”D - 22

Experts: Dr. Doug W. Dockery , Harvard School of Public Health Dr. Kazuhiko Ito, Nelson Institute of Environmental Medicine, NYU School of Medicine, Tuxedo, NY Dr. Dan Krewski , University of Ottawa Dr. Nino Künzli , University of Southern California Keck School of Medicine Dr. Morton Lippmann , Professor, Nelson Institute of Environmental Medicine, New York University School of Medicine, Tuxedo, NY Dr. Joe Mauderly , Lovelace Respiratory Research Institute Dr. Bart Ostro , Chief, Air Pollution Epidemiology Unit, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency , Oakland, CA Dr. Arden Pope , Professor, Department o f 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 S chool 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 issue s. 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 anal ys is. The available epidemiological database on daily mortality and morbidity

does not establish either the presence or absence of threshold concentrati ons for adverse health effects. Thus, in order to avoid emphasizing an approach that assumes effects that extend to either predicted background concentrations or LML , and to standardize the approach across cities, for the purpose of estimating public health impacts, the Panel favored the primary use of an assumed threshold of 10 μg/m 3 . The original approach of using background or LML, as well as the other postulated thresholds, could still be used in a sensitivity

anal ys is of threshold assumptions. “The analy ses in this chapter highlight the impact of assumptions regarding thresholds, or lack of threshold, on the estimates of risk. The uncertainty associated with threshold or nonlinear models needs more thorough discussion. A major research need is for more work to determine the existence and level of any thresholds that may

exist or the shape of nonlinear concentration response curves at low levels of exposure that may exist, and to reduce uncertainty in estimated risks at the lowest PM concentrations.” CAS AC 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 D istinguished 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. Petro s Koutrakis , Professor of Environmental Science, Environmental 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 Ob erdorster , 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 Co nservation, 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, Seatt le, 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. Geo rge 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 Subcom mi 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%5CSABPRODUCT.NSF/08E1155AD24F871C85256E5400 433D5D/$File/council_adv_04002.pdf>. Pg 20: “The Subco mmittee 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 v iewed 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 al l cause and cardiopulmonary mortality were near linear within the relevant ranges, with no apparent threshold. Graphical anal ys es of these studies (Dockery et al., 1993, Figure 3 and Krewski et al., 2000, page 162) also suggest a continuum of effects down to lower levels. Therefore, it is reasonable for EPA to assume a no threshold model down to, at least, the low end of the concentrations reported in the studies.” HES Panel Members Dr. Bart Ostro , California Office of Environmental Health Hazard Assessment (OEHHA), Oakland, CA Mr. John Fintan Hurley , Institute of Occupational Medicine (IOM), Edinburgh, Scotland Dr. Patrick Kinney , Columbia University , New York, NY Dr. Michael Kleinman , University of California, Irvine, CA Dr. Nino Künzli , University of Southern California, L os 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 , Uni versity 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, Researc h 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 Stat e 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 Par k, 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: Th e National Academies Press. Pg 109: “ Linearity and Thresholds “The shape of the concentration - response functions may influence the overall estimate of benefits. The shape is particularly important for lower ambient air pollution concentrations to which a large portion of the population is exposed. For this reason, the impact of the existence of a threshold may

be considerable. “In epidemiological studies, air pollution concentrations are usuall y 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 indic ates that the concentration - response functions are not due to the high concentrations and that the slopes of these functions do not appear to increase at higher concentrations (Samet et al. 2000). Many other mortality

studies have examined the shape of the concentration - response function and indicated that a linear (nonthreshold) model fit the data well (Pope 2000). Furthermore, studies conducted in cities with very low ambient pollution concentrations have similar effects per unit change in concentration a s those studies conducted in cities with higher concentrations. Again, this finding suggests a fairly linear concentration - response function over the observed range of exposures. “Regarding the studies of long - term exposure, Krewski et al. (2000) found tha t the assumption of a linear concentration -response function for mortality outcomes was not unreasonable. However, the statistical power to assess the shape of these functions is weakest at the upper and lower end of the observed exposure ranges. Most of the studies examining the effects of long - term exposure on morbidity compare subjects living in a small number of communities (Dockery et al. 1996; Ackermmann - Liebrich 1997; Braun - Fahrländer et al. 1997). Because the number of long - term effects studies are few and the number of communities studied is relatively small (8 to 24), the ability to test formally the absence or existence of a no - effect threshold is not feasible. However, even if thresholds exist, they may not be at the same concentration for all he alth 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 i s 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 integrate s information from very large groups of people (thousands). Air pollution regulations affect even larger groups of people (millions). It is reasonable to assume that among such large groups susceptibility to air pollution healthD - 28

effects varies considerably across individuals and depends on a large set of underlying factors, including genetic makeup, age, exposure measurement error, preexisting disease, and simultaneous exposures from smoking and occupational hazards. This variation in individual susceptibil ities and the resulting distribution of individual thresholds underlies the concentration -response function observed in epidemiology. Thus, until biologically based models of the distribution of individual thresholds are developed, it may be productive to assume that the population concentration -response function is continuous and to focus on finding evidence of changes in its slope as one approaches lower concentrations. 7.1.1.1 EPA’s Use of Thresholds “In EPA’s benefits analyses, threshold issues were discussed and interpreted. For the PM and ozone National Ambient Air Quality Standards (NAAQS), EPA investigated the effects of a potential threshold or reference value below which health consequences were assumed to be zero (EPA 1997). Specifically, the high - end ben efits estimate assumed a 12 - microgram per cubic meter (μg/m3 ) 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/m3 . For the Tier 2 rule and the HD engine and diesel - fuel rule, no threshold was assumed (EPA 1999, 2000). EPA in these analyses acknowledged that there was no evidence for a threshold for PM. “Several points should be noted regarding the threshold assumptions. If a threshold is assumed where one was not apparent in the original study, then the data should be refit and a new curve generated with the assumption of a zero slope over a segment of the concentration -response function that was originally found to be positively sloped. The assumption of a zero slope over a portion of the curve will force the slope in the remaining segment of the positively sloped concentration -response function to be greater than was indicated i n the original study. A new concentration -response function was not generated for EPA’s benefits analysis for the PM and ozone NAAQS for which threshold assumptions were made. The generation of the steeper slope in the remaining portion of the concentratio n -response function may fully offset the effect of assuming a threshold. These aspects of assuming a threshold in a benefits analysis where one was not indicated in the original study should be conveyed to the reader. The committee notes that the treatment of thresholds should be evaluated in a consistent and transparent framework by using different explicit assumptions in the formal uncertainty analyses (see Chap ter 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 un certainty analysis.” Pg 136: “Two additional illustrative examples are thresholds for adverse effects and lag structures. 2 EPA considers implausible any thresho ld for mortality in the particulate matter (PM) exposure ranges under consideration (EPA 1999a, p. 3 - 8). Although the agency conducts sensitivity analyses incorporating thresholds, it provides no judgment as to their relative plausibility. In a probabilistic uncertainty analysis, EPA could assign appropriate weights to various threshold models. For PM -related mortality in the Tier 2 analysis, the committee expects that this approach would have resulted in only a slight widening of the probability distributi on for avoided mortality and a slight reduction in the mean of that distribution, thus reflecting EPA’s views about the implausibility of thresholds. The committee finds that such formal incorporation of EPA’s expert judgments about the plausibility of thresholds into its primary analysis would have been an improvement. “Uncertainty about thresholds is a special aspect of uncertainty about the shape of concentration -response functions. Typically, EPA and authors of epidemiological studies assume that these functions are 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, epidemiological investigators are employing analytic methods that permit the estimation of nonlinear shapes for concentration -response functions (Greenland et al. 1999). As a consequence, EPA will need to be prepared to incorporate nonlinear concentration -response functions from epidemiological studies into the agency’s health benefits analyses. Any source of error or bias that can distort an epidemiological association can also distort the shape of an estimated concentration -resp onse 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 comp onents), in addition to concentration -response thresholds and time lags between exposure and response. For example, additional features of the concentration -response function

— such as projection of the results from the study population to the target populations (which may have etiologically relevant characteristics outside the range seen in the study population) and the projection of baseline frequencies of morbidity and mortality into the future

— must be characterized probabilistically. Other uncertainties that might affect the probability distributions are the estimations of population exposure (or even concentration) from emissions, estimates of emissions themselves, and the relative toxicity of various classes of particles. Similarly, many aspects of the analysis of the impact of regulation on ambient concentrations and on population exposure involve considerable uncertainty and, therefore, may be beneficially modeled in this way. Depending on the analytic approach used, joint probability distributions will have to be specified to incorporate correlations between model components that are structurally dependent upon each other, or the analysis will have to be conducted in a sequential fashion that follows the model for the data - generating process. “EPA shoul d explore alternative options for incorporating expert judgment into its probabilistic uncertainty analyses. The agency possesses considerable internal expertise, which should be employed as fully as possible. Outside experts should also be consulted as ne eded, individually or in panels. In all cases, when expert judgment is used in the construction of a model component, the experts should be identified and the rationales and empirical bases for their judgments should be made available.” NRC members Dr. JOH N 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, Massachu setts 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 Bas el, 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 o f California, BerkeleyD - 30

Dr. PETER A. VALBERG , Gradient Corporation, Cambridge, Massachusetts Dr. SCOTT L. ZEGER , Johns Hopkins University, Baltimore, Maryland