76%
Louisiana
                                                                        118,230
                                                                        139,204
                                                                        -20,973
                                                                           -18%
Maine
                                                                          2,355
                                                                          2,355
                                                                              0
                                                                             0%
Maryland
                                                                         42,926
                                                                         30,368
                                                                         12,558
                                                                            29%
Massachusetts
                                                                         13,364
                                                                         13,363
                                                                              1
                                                                             0%
Michigan
                                                                        269,434
                                                                        162,632
                                                                        106,802
                                                                            40%
Minnesota
                                                                         70,937
                                                                         49,622
                                                                         21,315
                                                                            30%
Mississippi
                                                                         30,972
                                                                         32,109
                                                                         -1,137
                                                                            -4%
Missouri
                                                                        390,287
                                                                        186,899
                                                                        203,388
                                                                            52%
Montana
                                                                         15,447
                                                                         22,826
                                                                         -7,379
                                                                           -48%
Nebraska
                                                                         73,073
                                                                         71,339
                                                                          1,734
                                                                             2%
Nevada
                                                                         14,416
                                                                         14,416
                                                                              0
                                                                             0%
New Hampshire
                                                                          6,453
                                                                          6,742
                                                                           -289
                                                                            -4%
New Jersey
                                                                         38,856
                                                                          6,243
                                                                         32,614
                                                                            84%
New Mexico
                                                                         11,857
                                                                         13,926
                                                                         -2,068
                                                                           -17%
New York
                                                                         42,887
                                                                         15,160
                                                                         27,727
                                                                            65%
North Carolina
                                                                        126,048
                                                                         69,377
                                                                         56,670
                                                                            45%
North Dakota
                                                                        103,633
                                                                        103,624
                                                                              9
                                                                             0%
Ohio
                                                                        851,199
                                                                        178,975
                                                                        672,224
                                                                            79%
Oklahoma
                                                                        137,981
                                                                        138,072
                                                                            -91
                                                                             0%
Oregon
                                                                         11,336
                                                                         11,336
                                                                              0
                                                                             0%
Pennsylvania
                                                                        509,649
                                                                        125,545
                                                                        384,105
                                                                            75%
Rhode Island
                                                                              0
                                                                              0
                                                                              0
                                                                             -
South Carolina
                                                                        213,281
                                                                        100,788
                                                                        112,494
                                                                            53%
South Dakota
                                                                         29,711
                                                                         29,711
                                                                              0
                                                                             0%
Tennessee
                                                                        284,468
                                                                         64,721
                                                                        219,747
                                                                            77%
Texas
                                                                        453,332
                                                                        266,648
                                                                        186,685
                                                                            41%
Tribal
                                                                              0
                                                                              0
                                                                              0
                                                                             -
Utah
                                                                         33,498
                                                                         33,968
                                                                           -469
                                                                            -1%
Vermont
                                                                            263
                                                                            263
                                                                              0
                                                                             0%
Virginia
                                                                         77,256
                                                                         51,144
                                                                         26,112
                                                                            34%
Washington
                                                                          3,430
                                                                          3,430
                                                                              0
                                                                             0%
West Virginia
                                                                        498,507
                                                                         84,344
                                                                        414,163
                                                                            83%
Wisconsin
                                                                        130,538
                                                                         50,137
                                                                         80,401
                                                                            62%
Wyoming
                                                                         51,817
                                                                         58,530
                                                                         -6,714
                                                                           -13%
Total
                                                                      7,159,569
                                                                      3,356,577
                                                                      3,802,991
                                                                            53%

Table 3-12.	State Specific Changes in Annual EGU NOX for the Lower 48 States
State
                           2014 Base Case NOX (tons)
                         2014 Policy Case  NOX (tons)
                           EGU NOX reduction (tons)
                             EGU NOX reduction (%)
Alabama
                                                                         76,012
                                                                         69,192
                                                                          6,820
                                                                             9%
Arizona
                                                                         35,616
                                                                         35,613
                                                                              4
                                                                             0%
Arkansas
                                                                         36,347
                                                                         37,640
                                                                         -1,293
                                                                            -4%
California
                                                                         26,874
                                                                         26,776
                                                                             97
                                                                             0%
Colorado
                                                                         49,381
                                                                         49,331
                                                                             50
                                                                             0%
Connecticut
                                                                          2,854
                                                                          2,860
                                                                             -6
                                                                             0%
Delaware
                                                                          1,701
                                                                          1,717
                                                                            -15
                                                                            -1%
District of Columbia
                                                                              0
                                                                              0
                                                                              0
                                                                              -
Florida
                                                                        100,581
                                                                         78,508
                                                                         22,073
                                                                            22%
Georgia
                                                                         49,411
                                                                         41,484
                                                                          7,927
                                                                            16%
Idaho
                                                                            608
                                                                            608
                                                                              0
                                                                             0%
Illinois
                                                                         55,269
                                                                         49,162
                                                                          6,107
                                                                            11%
Indiana
                                                                        117,832
                                                                        110,740
                                                                          7,092
                                                                             6%
Iowa
                                                                         48,400
                                                                         42,231
                                                                          6,169
                                                                            13%
Kansas
                                                                         32,637
                                                                         24,328
                                                                          8,308
                                                                            25%
Kentucky
                                                                         83,544
                                                                         76,088
                                                                          7,456
                                                                             9%
Louisiana
                                                                         31,573
                                                                         31,582
                                                                             -9
                                                                             0%
Maine
                                                                          5,402
                                                                          5,402
                                                                              0
                                                                             0%
Maryland
                                                                         17,566
                                                                         17,190
                                                                            375
                                                                             2%
Massachusetts
                                                                          6,992
                                                                          7,033
                                                                            -41
                                                                            -1%
Michigan
                                                                         67,705
                                                                         60,907
                                                                          6,798
                                                                            10%
Minnesota
                                                                         41,474
                                                                         34,429
                                                                          7,045
                                                                            17%
Mississippi
                                                                         26,294
                                                                         26,080
                                                                            214
                                                                             1%
Missouri
                                                                         57,318
                                                                         52,103
                                                                          5,216
                                                                             9%
Montana
                                                                         19,399
                                                                         19,303
                                                                             96
                                                                             0%
Nebraska
                                                                         45,047
                                                                         28,211
                                                                         16,836
                                                                            37%
Nevada
                                                                         14,074
                                                                         14,050
                                                                             24
                                                                             0%
New Hampshire
                                                                          5,126
                                                                          4,971
                                                                            156
                                                                             3%
New Jersey
                                                                          8,006
                                                                          7,720
                                                                            286
                                                                             4%
New Mexico
                                                                         64,745
                                                                         64,833
                                                                            -88
                                                                             0%
New York
                                                                         21,689
                                                                         20,528
                                                                          1,160
                                                                             5%
North Carolina
                                                                         49,322
                                                                         45,008
                                                                          4,314
                                                                             9%
North Dakota
                                                                         53,265
                                                                         53,267
                                                                             -2
                                                                             0%
Ohio
                                                                        104,149
                                                                         89,753
                                                                         14,396
                                                                            14%
Oklahoma
                                                                         66,966
                                                                         44,143
                                                                         22,823
                                                                            34%
Oregon
                                                                          9,584
                                                                          9,632
                                                                            -48
                                                                             0%
Pennsylvania
                                                                        134,092
                                                                        118,981
                                                                         15,110
                                                                            11%
Rhode Island
                                                                            442
                                                                            442
                                                                              0
                                                                             0%
South Carolina
                                                                         39,018
                                                                         36,747
                                                                          2,271
                                                                             6%
South Dakota
                                                                         14,270
                                                                         14,273
                                                                             -3
                                                                             0%
Tennessee
                                                                         29,276
                                                                         20,512
                                                                          8,764
                                                                            30%
Texas
                                                                        142,087
                                                                        137,964
                                                                          4,123
                                                                             3%
Tribal
                                                                             11
                                                                             11
                                                                              0
                                                                             0%
Utah
                                                                         67,434
                                                                         67,434
                                                                              0
                                                                             0%
Vermont
                                                                            455
                                                                            455
                                                                              0
                                                                             0%
Virginia
                                                                         40,469
                                                                         39,734
                                                                            735
                                                                             2%
Washington
                                                                         13,322
                                                                         13,322
                                                                              0
                                                                             0%
West Virginia
                                                                         64,824
                                                                         53,975
                                                                         10,849
                                                                            17%
Wisconsin
                                                                         40,750
                                                                         33,537
                                                                          7,212
                                                                            18%
Wyoming
                                                                         70,207
                                                                         70,778
                                                                           -571
                                                                            -1%
Total
                                                                      2,089,422
                                                                      1,890,590
                                                                        198,832
                                                                            10%

CHAPTER 4
		      AIR QUALITY MODELING AND IMPACTS

4.1  Air Quality Impacts
	This section summarizes the methods for and results of estimating air quality for the 2014 base case and control scenario for the purposes of the benefit analysis.  EPA has focused on the health, welfare, and ecological effects that have been linked to air quality changes.  These air quality changes include the following:
      1.	Ambient fine particulate matter (PM2.5) and ground-level ozone (O3) - as estimated using a national-scale applications of the Comprehensive Air Quality Model with Extensions (CAMx; Environ, 2010);
      2.	Visibility degradation (i.e., regional haze), as developed using empirical estimates of light extinction coefficients and efficiencies in combination with CAMx modeled reductions in pollutant concentrations.
	The air quality estimates in this section are based on the emission changes summarized in the preceding section.  These air quality results are in turn associated with human populations and ecosystems to estimate changes in health and welfare effects.  In Section 4.1.1, we describe the air quality modeling platform and in Section 4.2, we cover the impacts on PM2.5 and ozone. Lastly, in Section 4.3, we discuss the estimation of visibility degradation.

4.1.1	Air Quality Modeling Platform
      We use the emissions inputs summarized above with national scale and regional scale application of the CAMx modeling system to estimate PM2.5 and ozone air quality in the contiguous U.S.  CAMx is a three-dimensional grid-based Eulerian photochemical model designed to estimate PM2.5 and ozone concentrations over annual time periods.  Consideration of the different processes that affect primary (directly emitted) and secondary (formed by atmospheric processes) PM2.5 in different locations is fundamental to understanding and assessing the effects of pollution control measures that affect PM2.5 and ozone concentrations at the surface.  Because it accounts for spatial and temporal variations as well as differences in the reactivity of emissions, CAMx is useful for evaluating the impacts of the rule on PM2.5 and ozone concentrations. Version 5.3 of CAMx was employed for this Transport Rule modeling, as described in the Air Quality Modeling Technical Support Document (EPA, 2011).
      For this analysis we used CAMx to simulate air quality for every hour of every day of the year.  These model applications required a variety of input files that contain information pertaining to the modeling domain and simulation period.  In addition to the CAMx model, our modeling system includes (1) emissions for a 2005 base year and emissions for the 2014 base case and control scenario, (2) meteorology for the year 2005, and (3) estimates of intercontinental transport (i.e., boundary concentrations) from a global photochemical model.  Using these data, CAMx generates hourly predictions of ozone and PM2.5 component species concentrations.  As discussed in the Air Quality Modeling TSD, we use the relative predictions from the model by combining the 2005 base-year and each future-year scenario with speciated ambient air quality observations to determine the expected change in 2014 concentrations due to the rule.  After completing this process, we then calculated annual mean PM2.5 and seasonal mean ozone air quality metrics as inputs to the health and welfare C-R functions of the benefits analysis.
<IP_ADDRESS>	Simulation Periods
		For use in this benefits analysis, the simulation period modeled by CAMx included separate full-year application for each of the three emissions scenarios (i.e., 2005 base year and the 2014 base case and 2014 control scenario).
<IP_ADDRESS>	Air Quality Modeling Domain
      Although air quality estimate are provided for the entire U.S., the focus of our analysis is on the Eastern U.S. since this is the geographic area of importance for this rule.  The areas modeled (i.e., modeling domains) are segmented into rectangular blocks referred to as grid cells.  The model actually predicts pollutant concentrations for each of these grid cells.  Our modeling for the East (referred to as the Eastern regional scale domain) was performed at a horizontal resolution of 12 x 12 km.  Modeling for the remainder of the U.S. (referred to as the national scale domain) was performed at a resolution of 36 x 36 km.  The national and regional scale modeling domains contain 14 vertical layers with the top of the modeling domain at about 16,200 meters, or approximately 100 mb.  The Eastern domain is nested within the National domain, as shown in Figure 4-1.

     Figure 4-1.  National and Eastern U.S. air quality modeling domains.
Nationwide 36 km domain
Eastern 12 km domain

<IP_ADDRESS>	Air Quality Model Inputs
		CAMx requires a variety of input files that contain information pertaining to the modeling domain and simulation period.  These include gridded, hourly emissions estimates and meteorological data, and initial and boundary conditions.  Separate emissions inventories were prepared for the 2005 base year and each future-year scenario.  All other inputs were specified for the 2005 base year model application and remained unchanged for each future-year modeling scenario.
      CAMx requires detailed emissions inventories containing temporally allocated emissions for each grid-cell in the modeling domain for each species being simulated.  The previously described annual emission inventories were preprocessed into model-ready inputs through the SMOKE emissions preprocessing system.  Meteorological inputs reflecting 2005 conditions across the contiguous U.S. were derived from Version 5 of the Mesoscale Model (MM5).  These inputs included horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer.  Details of the annual 2005 MM5 modeling are provided in the Air Quality Modeling TSD.

      The lateral boundary and initial species concentrations are provided by a three-dimensional global atmospheric chemistry and transport model (GEOS-CHEM).  The lateral boundary species concentrations varied with height and time (every 3 hours).  Terrain elevations and land use information were obtained from the U.S. Geological Survey database at 10 km resolution and aggregated to the roughly 36 km horizontal resolution used for this CAMx application.  The development of model inputs is discussed in greater detail in the Air Quality Modeling TSD, which is available in the docket for this rule.

4.2 Results for PM2.5 and Ozone
4.2.1	Converting CAMx PM2.5 Outputs to Benefits Inputs
	CAMx generates predictions of hourly PM2.5 species concentrations for every grid.  The species include a primary fraction and several secondary particles (e.g., sulfates, nitrates, and organics).  PM2.5 is calculated as the sum of the primary and the secondary formed particles.  Future-year estimates of PM2.5 were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM2.5 species concentrations.  Gridded fields of species concentrations were created by interpolating ambient data from the PM2.5 speciation network and IMPROVE data.  The ambient data were interpolated to the 36 km and 12 km grid resolutions.
	The procedures for determining the RRFs are similar to those in EPA guidance for modeling the PM2.5 standard (EPA, 2007).  This guidance recommends that model predictions be used in a relative sense to estimate changes expected to occur in each PM2.5 species.  The procedure for calculating future year PM2.5 values is called the Modeled Attainment Test Software (MATS).  EPA used this procedure to estimate the ambient impacts of the Transport Rule emissions controls.  For the purposes of projecting future PM2.5 concentrations for input to the benefits calculations, we applied the MATS procedure using the base year 2005 modeling results and each of the results from each of the 2014 base case and 2014 control scenario.   In our application of MATS for PM2.5 we used temporally scaled speciated PM2.5 monitoring data from 2005 as the set of base-year measured concentrations.  Temporal scaling is based on the ratios of model-predicted future case PM2.5 species concentrations to the corresponding model-predicted 2005 concentrations. Output files from this process include both quarterly and annual mean PM2.5 mass concentrations which are then manipulated within SAS to produce a BenMAP input file containing 364 daily values (created by replicating the quarterly mean values for each day of the appropriate season).
	The MATS procedures documented in the Air Quality Modeling TSD are applicable for projecting future nonattainment and maintenance sites and downwind receptor areas for the transport analysis.  Those procedures are similar as those performed for the PM benefits analysis in Chapter 5 with the following exceptions:
1) The benefits analysis uses interpolated PM2.5 data that cover all of the grid cells in the modeling domain, whereas the nonattainment analysis is performed at each ambient monitoring site using measured PM2.5 data (only the species data are interpolated).
2) The benefits analysis is anchored by the interpolated PM2.5 data from the single year of 2005, whereas the nonattainment analysis uses design values from three, 3-year periods (i.e., 2003-2005, 2004-2006, and 2005-2007) at individual monitoring sites.
4.2.2 PM2.5 Air Quality Results
      Table 4-1 summaries the projected ambient PM2.5 concentrations for the 2014 base case and 2014 impacts associated with rule.  This table includes the annual mean concentrations averaged across all model grid cells in the East along with the average change between the 2014 base and control concentrations.  We also provide the population-weighted average that better reflects the baseline levels and predicted changes for more populated areas of the East.  This measure, therefore better reflects the potential benefits of these predicted changes through exposure changes to the affected populations.  As shown, the average annual mean concentrations of PM2.5 across populated areas of the East declines by roughly 9 percent (or 0.6 ug/m[3]) in 2014.  The population-weighted average mean concentration declined by 9 percent (or 0.87 ug/m[3]) in 2014. This indicates the rule generates greater absolute air quality improvements in more populated, urban areas.

Table 4-1.  Summary of Base Case PM2.5 Air Quality and Changes Due to the Transport Rule.

                                     2014

                                   Statistic

                                   Base Case

                                 Control Case

                               Percent Change[a]

PM2.5  (ug/m[3])

Minimum Annual Mean
                                     1.84
                                     1.84
                                      0%

Maximum Annual Mean
                                      19
                                      19
                                      0%

Average Annual Mean
                                      7.3
                                      6.7
                                      9%

Pop-Weighted Average Annual Mean [b]
                                     9.95
                                     9.08
                                      9%

[a] The percent change is defined as the control case value minus the base case value multiplied by 100. A negative value denotes an increase in PM2.5 concentration.

[b] Calculated by summing the product of the projected CAMx grid-cell population and the estimated concentration, for that grid-cell and then dividing by the total population.

	Table 4-2 provides information on the populations in 2014 that will experience improved PM air quality.  Significant populations that live in areas with meaningful reductions in annual mean PM2.5 concentrations resulting from the rule.  As shown, in 2014, about 12percent of the U.S. population located in the modeling domain are predicted to experience reductions of greater than 1.75 ug/m[3].  Furthermore, nearly 30 percent of this population will benefit from reductions in annual mean PM2.5 concentrations of greater than 1.25 ug/m[3] and 68 percent will live in areas with reductions of greater than 0.5ug/m[3].

Table 4-2.  Distribution of PM2.5 Air Quality Improvements Over Population in 2014 Due to the Transport Rule for the Eastern U.S.

Change in Annual Mean PM2.5 Concentrations (ug/m[3])

                              2014 Population[b]

                               Number (millions)
                                  Percent (%)
-0.25 > Δ PM2.5 Conc < 0
                                   6,594,134
                                      2%
0 = Δ PM2.5 Conc
                                  34,315,378
                                      11%
0 > Δ PM2.5 Conc  0.25
                                  36,670,522
                                      11%
0.25 > Δ PM2.5 Conc   0.5
                                  27,463,668
                                      9%
0.5 > Δ PM2.5 Conc   0.75
                                  43,099,722
                                      13%
0.75 > Δ PM2.5 Conc   1.0
                                  53,403,287
                                      17%
1.0 > Δ PM2.5 Conc   1.25
                                  28,161,111
                                      9%
1.25 > Δ PM2.5 Conc   1.5
                                  30,602,368
                                      10%
1.5 > Δ PM2.5 Conc   1.75
                                  21,163,369
                                      7%
Δ PM2.5 Conc > 1.75
                                  39,290,133
                                      12%
[a] The change is defined as the control case value minus the base case value.
[b] Population counts and percentages are for the fraction of the national population located in the eastern 37 state modeling domain (as shown in Figure 4-1) considered in modeling health benefits for the rule.

4.2.3	Converting CAMx Outputs to Full-Season Profiles for Benefits Analysis
		This study extracted hourly, surface-layer ozone concentrations for each grid-cell from the standard CAMx output file containing hourly average ozone values.  These model predictions are used in conjunction with the observed concentrations obtained from the Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the entire ozone season.[,]  The predicted changes in ozone concentrations from the future-year base case to future-year control scenario serve as inputs to the health and welfare C-R functions of the benefits analysis (i.e., BenMAP).
      To estimate ozone-related health and welfare effects, full-season ozone data are required for every grid cell.  Given available ozone monitoring data, we generated full-season ozone profiles for each location in the contiguous 48 States in two steps:  (1) we combine monitored observations and modeled ozone predictions to interpolate hourly ozone concentrations to a grid of 8 km by 8 km population grid-cells, and (2) we converted these full-season hourly ozone profiles to an ozone measure of interest, such as the daily average.[,]

4.2.4	Ozone Air Quality Results
	This section provides a summary of the predicted ambient ozone concentrations from the CAMx model for the 2014 base case and changes associated with the rule.  Table 4-3 provides those ozone metrics for grid cells in the Eastern U.S. that enter the C-R functions for health benefits endpoints.  The population-weighted average reflects the baseline levels and predicted changes for more populated areas of the nation.  This measure, therefore, will better reflect the potential benefits of these predicted changes through exposure changes to these populations.
Table 4-3.   Summary of CAMx Derived Population-Weighted Ozone Season Air Quality Metrics for Health Benefits Endpoints Due to the Transport Rule for the Eastern U.S.

                                     2014

                                 Statistic[a]

                                   Base Case

                                  Change [b]

                               Percent Change b
Population-Weighted Average (ppb) [c]

Daily 8-Hour Average Concentration
                                     44.85
                                     0.09
                                     0.2%

[a] This ozone metric is calculated at the CAMx grid-cell level for use in health effects estimates based on the results of spatial and temporal Voronoi Neighbor Averaging.

[b] The change is defined as the control case value minus the base case value.  The percent change is the "Change" divided by the "Base Case," and then multiplied by 100 to convert the value to a percentage.

[c] Calculated by summing the product of the projected CAMx grid-cell population and the estimated CAMx grid-cell seasonal ozone concentration, and then dividing by the total population.

4.3	Visibility Degradation Estimates
			Visibility degradation is often directly proportional to decreases in light transmittal in the atmosphere.  Scattering and absorption by both gases and particles decrease light transmittance.  To quantify changes in visibility, our analysis computes a light-extinction coefficient, based on the work of Sisler (1996), which shows the total fraction of light that is decreased per unit distance.  This coefficient accounts for the scattering and absorption of light by both particles and gases, and accounts for the higher extinction efficiency of fine particles compared to coarse particles.  Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon, elemental carbon (soot), and soil (Sisler, 1996).

      Based upon the light-extinction coefficient, we also calculated a unitless visibility index, called a "deciview," which is used in the valuation of visibility.  The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy.  Under many scenic conditions, the average person can generally perceive a change of one deciview.  The higher the deciview value, the worse the visibility.  Thus, an improvement in visibility is a decrease in deciview value.
	Table 4-4 provides the visibility improvements, measured in annual average deciviews, expected to occur in the Eastern and Western U.S. As shown, Class I visibility regions in the Eastern U.S., including such regions as the Great Smoky Mountains and Shenandoah, are expected to see significant improvements in visibility.  By 2014, such regions in the Eastern U.S. are expected to see improvements of over 1.5 deciview (11 percent), and such regions in the Western U.S. are expected to see improvements of over 0.02 deciviews (or less than 1 percent).

Table 4-4.  Summary of Basecase Recreational Visibility and Changes by Region: (annual average deciviews)

                                     2014

                           Class I Visibility Regions

                             Base Case (Deciviews)

                     Change in Annual Average (Deciviews)

                     Percent Change in Annual Average (%)
Eastern US
                                     14.84

                                     1.57
                                     11.0

Western US
                                     9.09

                                     0.02
                                     <1

4.4   References
Environ, 2010. Comprehensive Air Quality Model with Extensions Version 5.3 User's Guide. Environ International Corporation. Novato, CA. December 2010.

Sisler, J.F.  July 1996.  Spatial and Seasonal Patterns and Long Term Variability of the Composition of the Haze in the United States: An Analysis of Data from the IMPROVE Network.  Fort Collins, CO:  Cooperative Institute for Research in the Atmosphere, Colorado State University.
U.S. Environmental Protection Agency (EPA). 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 2011. Air Quality Modeling Technical Support Document for the Transport Rule.  Office of Air Quality Planning and Standards, Research Triangle Park, NC.

--------------------------------------------------------------------------------
					   Chapter 5
--------------------------------------------------------------------------------
 				Benefits Analysis and Results

Synopsis

      This chapter contains a subset of the estimated health and welfare benefits of the Transport Rule remedy in 2014. This rule is expected to yield significant reductions in SO2 and NOx from EGUs, which in turn would lower overall ambient levels of PM2.5 and ozone across much of the eastern U.S. In this chapter we quantify the health and welfare benefits resulting from these air quality improvements.
      We estimate the monetized benefits of the selected remedy to be $120 billion to $280 billion at a 3% discount rate and $110 billion to $250 billion at a 7% discount rate in 2014.  The benefits of the more and less stringent alternatives may be found in the benefit-cost comparison chapter. All estimates are in 2007$. We estimate the benefits of the selected remedy using modeled changes in ambient pollution concentrations while the benefits of the more and less stringent remedies are based on a benefit per ton approach described below. This benefits analysis accounts for both decreases and increases in emissions across the country. These estimates omit the benefits from several important categories, including ecosystem benefits, mercury benefits, and the direct health benefits from reducing exposure to NO2 and SO2 due to time constraints.
      The estimated benefits of this rule are substantial, particularly when viewed within the context of the total public health burden of PM2.5 and ozone air pollution. A recent EPA analysis estimated that 2005 levels of PM2.5 and ozone were responsible for between 130,000 and 320,000 PM2.5-related and 4,700 ozone-related premature deaths, or about 6.1% of total deaths from all causes in the continental U.S. (Fann et al. 2011). This same analysis attributed almost 200,000 non-fatal heart attacks, 90,000 hospital admissions due to respiratory or cardiovascular illness and 2.5 million cases of aggravated asthma among children--among many other impacts. We estimate the Transport Rule to reduce the number of PM2.5-related premature deaths in 2014 by between 13,000 and 34,000, 15,000 non-fatal heart attacks, 8,700 fewer hospital admissions and 400,000 fewer cases of aggravated asthma. By 2014, in combination with other federal and state air quality actions, the Transport Rule will address a substantial fraction of the total public health burden of PM2.5 and ozone air pollution.
      EPA expects greater emission reductions due to this rule in 2012 than in 2014, due to substantial emission reductions expected to occur in the baseline (i.e., unrelated to the Transport Rule) between those years.  As a result, we anticipate that the avoided health impacts and monetized benefits would also be greater in 2012, though we have not calculated these estimates for this analysis.

       Appendix A to this RIA contains an assessment of the distribution of health benefits among different populations. In this analysis, we considered the level of PM2.5 mortality risk according to the race, income and educational attainment of the population before and after the implementation of the Transport Rule. We found those populations whose PM2.5 mortality risk was before the implementation of the rule received the greatest risk reduction from the Transport Rule -- irrespective of the race of the population. We also found that populations with lower levels of educational attainment, an attribute that may be associated with increased vulnerability to PM2.5 mortality risk, also received a significant reduction in risk.
      	Finally, Appendix E provides an  alternate presentation of the benefits as an attempt to incorporate the recommendations from EPA's recently published Guidelines for Preparing Economic Analyses (U.S. EPA, 2010).

5.1 Overview

   This chapter contains a subset of the estimated health and welfare benefits of the selected and alternate rule remedies for the Transport Rule in 2014. The Transport Rule is expected to yield significant aggregate reductions in SO2 and NOx from EGUs, which in turn would lower overall ambient levels of PM2.5 and ozone across much of the eastern U.S. To perform this analysis, EPA followed an approach that is generally consistent with the proposal Transport Rule analysis, with the exception of the baseline incidence rates that are an input into the health impact calculation for PM2.5 and ozone health outcomes. These updated rates are both more current and provide better spatial resolution in many areas of the U.S. As we describe in section 5.4 below, these updated data are likely to yield a better overall estimate of PM and ozone-related health impacts.
   The analysis in this chapter aims to characterize the benefits of the selected remedy by answering two key questions:
   1. What are the health and welfare effects of changes in ambient particulate matter (PM2.5) and ozone air quality resulting from reductions in precursors including NOx and SO2?
   2. What is the economic value of these effects?
      In this analysis we consider an array of health and welfare impacts attributable to changes in PM2.5 and ozone air quality. The 2009 PM2.5 Integrated Science Assessment (U.S. EPA, 2009d) and the 2006 ozone criteria document (U.S. EPA, 2006a) identify the human health effects associated with these ambient pollutants, which include premature mortality and a variety of morbidity effects associated with acute and chronic exposures. PM welfare effects include visibility impairment and materials damage. Ozone welfare effects include damages to agricultural and forestry sectors. NOx welfare effects include aquatic and terrestrial acidification and nutrient enrichment (U.S. EPA, 2008f).  SO2 welfare effects include aquatic and terrestrial acidification and increased mercury methylation (U.S. EPA, 2008f).  Though models exist for quantifying these ecosystem impacts, time and resource constraints precluded us from quantifying most of those effects in this analysis.

      Table 5-1 summarizes the total monetized benefits of the final Transport Rule remedy in 2014.  This table reflects the economic value of the change in PM2.5 and ozone-related human health impacts occurring as a result of the Transport Rule.
      Table 5-2 summarizes the human health and welfare benefits categories contained within the primary benefits estimate, those categories that were unquantified due to limited data or time.

Table 5-1: Estimated monetized benefits of the final Transport Rule remedy (billions of 2007$)[A]
Benefits Estimate
                          Within Transport Region[B]
                           Outside Transport Region
                                     Total
Pope et al. (2002) PM2.5 mortality and Bell et al. (2004) ozone mortality estimates
  Using a 3% discount rate
                                    $110 +B
                                 ($8.8 -- $340)
                                   $0.28 +B
                                ($0.01 -- $0.9)
                                    $120 +B
                                 ($14 -- $350)
  Using a 7% discount rate
                                    $100 +B
                                  ($8 -- $310)
                                   $0.25 +B
                                ($0.01 -- $0.85)
                                    $110 +B
                                 ($13 -- $320)
Laden et al. (2006) PM2.5 mortality and Levy et al. (2005) ozone mortality estimates
  Using a 3% discount rate
                                    $270 +B
                                 ($24 -- $800)
                                    $0.7 +B
                                ($0.05 -- $0.21)
                                    $280 +B
                                 ($29 -- $810)
  Using a 7% discount rate
                                    $250 +B
                                 ($22 -- $720)
                                    $0.6 +B
                                ($0.04 -- $1.9)
                                    $250 +B
                                 ($26 -- $730)
A For notational purposes, unquantified benefits are indicated with a "B" to represent the sum of additional monetary benefits and disbenefits. Data limitations prevented us from quantifying these endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to quantify. A detailed listing of unquantified health and welfare effects is provided in Table 5-2. Estimates here are subject to uncertainties discussed further in the body of the document.  Estimates include the value of CO2-related benefits and the monetized benefits of visibility improvements in Class I areas.

B Rounded to two significant figures.

The benefits analysis in this chapter relies on an array of data inputs -- including air quality modeling, health impact functions and valuation estimates among others -- which are themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in this analysis. As a means of characterizing this uncertainty we employ two primary techniques. First, we use Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and economic valuation functions. Second, because this characterization of random statistical error may omit important sources of uncertainty we also employ the results of an expert elicitation on the relationship between premature mortality and ambient PM2.5 concentration (Roman et al., 2008); this provides additional insight into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. Both approaches have different strengths and weaknesses, which are fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006).
Given that reductions in premature mortality dominate the size of the overall monetized benefits, more focus on uncertainty in mortality-related benefits gives us greater confidence in our uncertainty characterization surrounding total benefits. Certain EPA RIA's including the 2008 Ozone NAAQS RIA (U.S. EPA, 2008a) contained a suite of sensitivity analyses, only some of which we include here due in part to time constraints. In particular, these analyses characterized the sensitivity of the monetized benefits to the specification of alternate cessation lags and income growth adjustment factors. The estimated benefits increased or decreased in proportion to the specification of alternate income growth adjustments and cessation lags, making it possible for readers to infer the sensitivity of the results in this RIA to these parameters by referring to the PM NAAQS RIA (2006d) and Ozone NAAQS RIA (2008a).
For example, the use of an alternate lag structure would change the PM2.5-related mortality benefits discounted at 3% discounted by between 10.4% and  - 27%; when discounted at 7%, these benefits change by between 31% and -49%. When applying higher and lower income growth adjustments, the monetary value of PM2.5 and ozone-related premature changes between 30% and -10%; the value of chronic endpoints change between 5% and -2% and the value of acute endpoints change between 6% and -7%.
Below we include a new analysis (Figures 5-19 and 5-20) in which we bin the estimated number of avoided PM2.5-related premature mortalities resulting from the implementation of the Transport Rule according to the projected 2014 baseline PM2.5 air quality levels. This presentation is consistent with our approach to applying PM2.5 mortality risk coefficients that have not been adjusted to incorporate an assumed threshold.  The very large proportion of the avoided PM-related impacts we estimate in this analysis occur among populations exposed at or above the LML of each study, increasing our confidence in the PM mortality analysis. Approximately 69% of the avoided impacts occur at or above an annual mean PM2.5 level of 10 ug/m[3] (the LML of the Laden et al. 2006 study); about 96% occur at or above an annual mean PM2.5 level of 7.5 ug/m3 (the LML of the Pope et al. 2002 study). As we model mortality impacts among populations exposed to levels of PM2.5 that are successively lower than the LML of each study our confidence in the results diminishes. However, the analysis below confirms that the great majority of the impacts occur at or above each study's LML.

Table 5-2: Human Health and Welfare Effects of Pollutants Affected by the Transport Rule
Pollutant/ Effect
Quantified and monetized in base estimate
Unquantified
PM: health[a]
Premature mortality based on cohort study estimatesb  and expert elicitation estimates
Low birth weight, pre-term birth and other reproductive outcomes

Hospital admissions: respiratory and cardiovascular
Pulmonary function

Emergency room visits for asthma
Chronic respiratory diseases other than chronic bronchitis

Nonfatal heart attacks (myocardial infarctions)
Non-asthma respiratory emergency room visits

Lower and upper respiratory illness
UVb exposure (+/-)[c]

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
Household soiling

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]
Ozone: health
Premature mortality based on short-term study estimates
Chronic respiratory damage

Hospital admissions: respiratory
Premature aging of the lungs

Emergency room visits for asthma
Non-asthma respiratory emergency room visits

Minor restricted activity days
UVb exposure (+/-)[c]

School loss days

Ozone: welfare

Decreased outdoor worker productivity
Yields for:
--Commercial forests
--Fruits and vegetables, and
--Other commercial and noncommercial crops

Damage to urban ornamental plants

Recreational demand from damaged forest aesthetics

Ecosystem functions

UVb exposure (+/-)[c]
Climate impacts
NO2: health

Respiratory hospital admissions

Respiratory emergency department visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function
NOX: welfare

Commercial fishing and forestry from acidic deposition effects

Commercial fishing, agriculture and forestry from nutrient deposition effects

Recreation in terrestrial and estuarine ecosystems from nutrient deposition effects

Other ecosystem services and existence values for currently healthy ecosystems
Coastal eutrophication from nitrogen deposition effects
SO2: health

Respiratory hospital admissions

Asthma emergency room visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function
SOX: welfare

Commercial fishing and forestry from acidic deposition effects

Recreation in terrestrial and aquatic ecosystems from acid deposition effects

Increased mercury methylation
Mercury: health

Incidence of neurological disorders

Incidence of learning disabilities

Incidences in developmental delays

Mercury: welfare

Impact on birds and mammals (e.g. reproductive effects)

Impacts to commercial, subsistence and recreational fishing
[A] In addition to primary economic endpoints, there are a number of biological responses that have been associated with PM health effects including morphological changes and altered host defense mechanisms.  The public health impact of these biological responses may be partly represented by our quantified endpoints.
[B] Cohort estimates are designed to examine the effects of long term exposures to ambient pollution, but relative risk estimates may also incorporate some effects due to shorter term exposures (see Kunzli et al., 2001 for a discussion of this issue). While some of the effects of short term exposure are likely to be captured by the cohort estimates, there may be additional premature mortality from short term PM exposure not captured in the cohort estimates included in the primary analysis.
[C] May result in benefits or disbenefits.

5.2 Benefits Analysis Methods
      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 (specific effects that can be associated with changes in air quality) and assigns values to those changes assuming independence of the individual values.  Total benefits are calculated simply as the sum of the values for all non-overlapping health and welfare endpoints. The "damage-function" approach is the standard method for assessing costs and benefits of environmental quality programs and has been used in several recent published analyses (Levy et al., 2009; Hubbell et al., 2009; Tagaris et al., 2009).
      To assess economic value in a damage-function framework, the changes in environmental quality must be translated into effects on people or on the things that people value.  In some cases, the changes in environmental quality can be directly valued, as is the case for changes in visibility.  In other cases, such as for changes in ozone and PM, a health and welfare impact analysis must first be conducted to convert air quality changes into effects that can be assigned dollar values.
      For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health effects that are directly linked to ambient levels of air pollution and specifically to those linked to ozone and PM.  There may be other, indirect health impacts associated with implementing emissions controls, such as occupational health impacts for coal miners.
      The welfare impacts analysis is limited to changes in the environment that have a direct impact on human welfare.  For this analysis, we are limited by the available data to examine impacts of changes in visibility in Class 1 areas.  We also provide qualitative discussions of the impact of changes in other environmental and ecological effects, for example, changes in deposition of nitrogen and sulfur to terrestrial and aquatic ecosystems, but we are unable to place an economic value on these changes due to time and resource limitations.
      We note at the outset that EPA rarely has the time or resources to perform extensive new research to measure directly either the health outcomes or their values for regulatory analyses.  Thus, similar to Kunzli et al. (2000) and other recent health impact analyses, our estimates are based on the best available methods of benefits transfer.  Benefits transfer is the science and art of adapting primary research from similar contexts to obtain the most accurate measure of benefits for the environmental quality change under analysis.  Adjustments are made for the level of environmental quality change, the socio-demographic and economic characteristics of the affected population, and other factors to improve the accuracy and robustness of benefits estimates.

5.2.1 Health Impact Assessment

      The Health Impact Assessment (HIA) quantifies the changes in the incidence of adverse health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs are a well-established approach for estimating the retrospective or prospective change in adverse health impacts expected to result from population-level changes in exposure to pollutants (Levy et al. 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program (BenMAP) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and population projections. Analysts have applied the HIA approach to estimate human health impacts resulting from hypothetical changes in pollutant levels (Hubbell et al. 2005; Davidson et al. 2007, Tagaris et al. 2009). EPA and others have relied upon this method to predict future changes in health impacts expected to result from the implementation of regulations affecting air quality (U.S. EPA, 2008a).
      The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMx-generated projections of PM2.5 and ozone air quality and estimating the change in the spatial distribution of the ambient air quality; (2) determining the subsequent change in population-level exposure; (3) calculating health impacts by applying concentration-response relationships drawn from the epidemiological literature (Hubbell et al. 2009) to this change in population exposure.

A typical health impact function might look as follows:

                        ∆y=yo ∙eβ∙∆x- 1∙Pop

      where y0 is the baseline incidence rate for the health endpoint being quantified (for example, a health impact function quantifying changes in mortality would use the baseline, or background, mortality rate for the given population of interest); Pop is the population affected by the change in air quality; x is the change in air quality; and β is the effect coefficient drawn from the epidemiological study. Tools such as BenMAP can systematize the HIA calculation process, allowing users to draw upon a library of existing air quality monitoring data, population data and health impact functions.
      Figure 5-1 provides a simplified overview of this approach.

                  Figure 5-1: Illustration of BenMAP Approach

Baseline Air QualityPost-Policy Scenario  Air QualityIncremental Air QualityImprovementPM2.5 ReductionPopulationAges 18-65BackgroundIncidenceRateEffectEstimateMortality Reduction

5.2.2 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. The appropriate economic value for a change in a health effect depends on whether the health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air pollution generally lower the risk of future adverse health effects by a small amount for a large population. The appropriate economic measure is therefore ex ante Willingness to Pay (WTP) for changes in risk. However, epidemiological studies generally provide estimates of the relative risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to use this data in a consistent framework is to convert probabilities to units of avoided statistical incidences. This measure is calculated by dividing individual WTP for a risk reduction by the related observed change in risk. For example, suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected population is automatically taken into account by the number of incidences predicted by epidemiological studies applied to the relevant population. The same type of calculation can produce values for statistical incidences of other health endpoints.

      For some health effects, such as hospital admissions, WTP estimates are generally not available. In these cases, we use the cost of treating or mitigating the effect as a primary estimate. For example, for the valuation of hospital admissions we use the avoided medical costs as an estimate of the value of avoiding the health effects causing the admission. These cost of illness (COI) estimates generally (although not in every case) understate the true value of reductions in risk of a health effect. They tend to reflect the direct expenditures related to treatment but not the value of avoided pain and suffering from the health effect.
      We use the BenMAP model (Abt Associates, 2008) to estimate the health impacts and monetized health benefits for the preferred remedy.  Figure 5-2 below shows the data inputs and outputs for the BenMAP model.

           Figure 5-2: Data inputs and outputs for the BenMAP model
                            Census Population Data
Modeled Baseline and Post-Control 2014 Ambient PM2.5 and O3 Concentrations
                        PM2.5 & O3 Health Functions
                         Economic Valuation Functions
                          2014 Population Projections
                 PM2.5 & O3 Incremental Air Quality Change
                     PM2.5 & O3-Related Health mpacts
Woods & Poole Population Projections
                   Background Incidence and Prevalence Rates
                    Monetized PM2.5 and O3-related Benefits
Blue identifies a user-selected input within the BenMAP model
Green identifies a data input generated outside of the BenMAP model

5.2.3 Benefit Per-Ton Estimates
   Benefit per-ton (BPT) estimates quantify the health impacts and monetized human health benefits of an incremental change in air pollution precursor emissions. In situations when we are unable to perform air quality modeling because of resource or time constraints, this approach can provide a reliable estimate of the benefits of emission reduction scenarios. EPA has used the benefit per-ton technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 2008) and NO2 NAAQS RIA (U.S. EPA, 2010b).  Time constraints prevented the Agency from modeling the air quality changes resulting from the more and less stringent SO2 caps and so we estimate a subset of these health benefits using PM2.5 benefit per-ton estimates. The assessment of the alternate scenarios omits ozone-related benefits for two reasons. First, the overall level of ozone-related benefits in the modeled case is relatively small compared to those associated with PM2.5 reductions (see table 5-17 below), due in part to the fairly modest summer time NOx emission reductions under this scenario. The level of summertime NOx emission reductions of the alternate scenarios are very similar to the modeled scenario, suggesting that the omission of ozone-related impacts would not greatly influence the overall level of benefits. Second, the complex non-linear chemistry of ozone formation introduces uncertainty to the development and application of a benefit per ton estimate. Taken together, these factors argued against developing an ozone benefit per ton estimate for this RIA.
   For this analysis, EPA applies PM2.5 BPT estimates that are methodologically consistent with those reported in Fann et al. (2009), but have been adjusted for this analysis to better match the spatial distribution of air quality changes projected for the Transport Rule. To derive the BPT estimates for this analysis, we:
   1. Quantified the PM2.5 -related human and monetized health benefits of the SO2 emission reductions of the proposed remedy. We first quantified the health impacts and monetized benefits of total PM2.5 mass formed from the SO2 reductions of the proposed remedy, allowing us to isolate the PM air quality impacts from SO2 reductions alone. This procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-related benefits of incremental changes in SO2 emissions. Because reductions in NOx emissions are relatively small in each scenario, and previous EPA modeling indicates that PM2.5 formation is less sensitive to NOx emission reductions on a per-ug/m[3] basis (Fann et al, 2009), we did not quantify the NOx-related PM2.5 changes.

   2. Divided the health impacts and monetized benefits by the emission reduction. This calculation yields BPT estimates for PM-related SO2.

   The resulting BPT estimates were then multiplied by the projected SO2 emission reductions for the more and less stringent scenarios to produce an estimate of the PM- and ozone-related health impacts and monetized benefits. There is no analogous approach for estimating a BPT for visibility, and so the benefits of the alternative remedies omit this important monetized benefit.

5.3 Uncertainty Characterization

      In any complex analysis using estimated parameters and inputs from numerous models, there are likely to be many sources of uncertainty and this analysis is no exception.  As outlined both in this and preceding chapters, many inputs were used to derive the estimate of benefits for the proposed remedy, including emission inventories, air quality models (with their associated parameters and inputs), epidemiological health effect estimates, estimates of values (both from WTP and COI studies), population estimates, income estimates, and estimates of the future state of the world (i.e., regulations, technology, and human behavior).  Each of these inputs may be uncertain and, depending on its role in the benefits analysis, may have a disproportionately large impact on estimates of total benefits.  For example, emissions estimates are used in the first stage of the analysis.  As such, any uncertainty in emissions estimates will be propagated through the entire analysis.  When compounded with uncertainty in later stages, small uncertainties in emission levels can lead to large impacts on total benefits.
      The National Research Council (NRC) (2002, 2008) highlighted the need for EPA to conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these estimates to decision makers in ways that foster an appropriate appreciation of their inherent uncertainty. In general, the NRC concluded that EPA's general methodology for calculating the benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties.  Since the publication of these reports, EPA's Office of Air and Radiation (OAR) continues to make progress toward the goal of characterizing the aggregate impact of uncertainty in key modeling elements on both health incidence and benefits estimates in two key ways: Monte Carlo analysis and expert-derived concentration-response functions.  In this analysis, we use both of these two methods to assess uncertainty quantitatively, as well as provide a qualitative assessment for those aspects that we are unable to address quantitatively.

      First, we used Monte Carlo methods for characterizing random sampling error associated with the concentration response functions from epidemiological studies and random effects modeling to characterize both sampling error and variability across the 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 premature mortality. Specifically, we used Monte Carlo methods to generate confidence intervals around the estimated health impact and dollar benefits. The reported standard errors in the epidemiological studies determined the distributions for individual effect estimates.
      Second, because characterization of random statistical error omits important sources of uncertainty (e.g., in the functional form of the model -- e.g., whether or not a threshold may exist), we also incorporate the results of an expert elicitation on the relationship between premature mortality and ambient PM2.5 concentration (Roman et al., 2008).  Use of the expert elicitation and incorporation of the standard errors approaches provide insights into the likelihood of different outcomes and about the state of knowledge regarding the benefits estimates. However, there are significant unquantified uncertainties present in upstream inputs including emission and air quality. Both approaches have different strengths and weaknesses, which are fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006).
      In benefit analyses of air pollution regulations conducted to date, the estimated impact of reductions in premature mortality has accounted for 85% to 95% of total monetized benefits. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. The health impact functions used to estimate avoided premature deaths associated with reductions in ozone have associated standard errors that represent the statistical errors around the effect estimates in the underlying epidemiological studies. In our results, we report credible intervals based on these standard errors, reflecting the uncertainty in the estimated change in incidence of avoided premature deaths. We also provide multiple estimates, to reflect model uncertainty between alternative study designs.
      For premature mortality associated with exposure to PM, we follow the same approach used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting two empirical estimates of premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation study. Even these multiple characterizations, including confidence intervals, omit the contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations. Furthermore, the approach presented here does not yet include methods for addressing correlation between input parameters and the identification of reasonable upper and lower bounds for input distributions characterizing uncertainty in additional model elements. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the estimates. This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis.
   In 2006 the EPA requested an NAS study to evaluate the extent to which the epidemiological literature to that point improved the understanding of ozone-related mortality. The NAS found that short-term ozone exposure was likely to contribute to ozone-related mortality (NRC, 2008) and issued a series of recommendations to EPA, including that the Agency should:
   1. Present multiple short-term ozone mortality estimates, including those based on multi-city analyses such as the National Morbidity, Mortality and Air Pollution Study (NMMAPS) as well as meta-analytic studies.
   2. Report additional risk metrics, including the percentage of baseline mortality attributable to short-term exposure.
   3. Remove reference to a no-causal relationship between ozone exposure and premature mortality.
	The quantification and presentation of ozone-related premature mortality in this chapter is responsive to these NRC recommendations and generally consistent with EPA's recent ozone reconsideration analysis (U.S. EPA, 2010a).
	Some key sources of uncertainty in each stage of both the PM and ozone health impact assessment are the following:
   * gaps in scientific data and inquiry;
   * variability in estimated relationships, such as epidemiological effect estimates, introduced through differences in study design and statistical modeling;
   * errors in measurement and projection for variables such as population growth rates;
   * errors due to misspecification of model structures, including the use of surrogate variables, such as using PM10 when PM2.5 is not available, excluded variables, and simplification of complex functions; and
   * biases due to omissions or other research limitations.

In Table 5-3 we summarize some of the key uncertainties in the benefits analysis.

      Table 5-3.  Primary Sources of Uncertainty in the Benefits Analysis
1.  Uncertainties Associated with Impact Functions
      The value of the ozone or PM effect estimate in each impact function.
      Application of a single impact function to pollutant changes and populations in all locations.
      Similarity of future-year impact functions to current impact functions.
      Correct functional form of each impact function.
      Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the source epidemiological study.
      Application of impact functions only to those subpopulations matching the original study population.
2.  Uncertainties Associated with CAMx-Modeled Ozone and PM Concentrations
      Responsiveness of the models to changes in precursor emissions from the control policy.
      Projections of future levels of precursor emissions, especially ammonia and crustal materials.
      Lack of ozone and PM2.5 monitors in all rural areas requires extrapolation of observed ozone data from urban to rural areas.
3.  Uncertainties Associated with PM Mortality Risk
      Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.
      Direct causal agents within the complex mixture of PM have not been identified.
      The extent to which adverse health effects are associated with low-level exposures that occur many times in the year versus peak exposures.
      The extent to which effects reported in the long-term exposure studies are associated with historically higher levels of PM rather than the levels occurring during the period of study.
      Reliability of the PM2.5 monitoring data in reflecting actual PM2.5 exposures.
4.  Uncertainties Associated with Possible Lagged Effects
      The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM levels that would occur in a single year is uncertain as well as the portion that might occur in subsequent years.
5.  Uncertainties Associated with Baseline Incidence Rates
      Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore may not accurately represent the actual location-specific rates.
      Current baseline incidence rates may not approximate well baseline incidence rates in 2014.
      Projected population and demographics may not represent well future-year population and demographics.
6.  Uncertainties Associated with Economic Valuation
       Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and therefore have uncertainty surrounding them.
       Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates because of differences in income or other factors.
7.  Uncertainties Associated with Aggregation of Monetized Benefits
       Health and welfare benefits estimates are limited to the available impact functions.  Thus, unquantified or unmonetized benefits are not included.

5.4 Benefits Analysis Data Inputs

      In Figure 5-2, we summarized the key data inputs to the health impact and economic valuation estimate. Below we summarize the data sources for each of these inputs, including demographic projections, effect coefficients, incidence rates and economic valuation. We indicate where we have updated key data inputs since the 2005 CAIR benefits analysis.

5.4.1 Demographic Data

   Quantified and monetized human health impacts depend on the demographic characteristics of the population, including age, location, and income.  We use projections based on economic forecasting models developed by Woods and Poole, Inc (Woods and Poole, 2008).  The Woods and Poole (WP) database contains county-level projections of population by age, sex, and race out to 2030.  Projections in each county are determined simultaneously with every other county in the United States to take into account patterns of economic growth and migration.  The sum of growth in county-level populations is constrained to equal a previously determined national population growth, based on Bureau of Census estimates (Hollman et al., 2000).  According to WP, linking county-level growth projections together and constraining to a national-level total growth avoids potential errors introduced by forecasting each county independently.  County projections are developed in a four-stage process:

   1. First, national-level variables such as income, employment, and populations are forecasted.

   2. Second, employment projections are made for 172 economic areas defined by the Bureau of Economic Analysis, using an "export-base" approach, which relies on linking industrial-sector production of non-locally consumed production items, such as outputs from mining, agriculture, and manufacturing with the national economy.  The export-based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.

   3. Third, population is projected for each economic area based on net migration rates derived from employment opportunities and following a cohort-component method based on fertility and mortality in each area.

   4. Fourth, employment and population projections are repeated for counties, using the economic region totals as bounds.  The age, sex, and race distributions for each region or county are determined by aging the population by single year of age by sex and race for each year through 2014 based on historical rates of mortality, fertility, and migration.

5.4.2 Effect Coefficients

      The first step in selecting effect coefficients is to identify the health endpoints to be quantified. We base our selection of health endpoints on consistency with EPA's Integrated Science Assessments (which replace the Criteria Document), with input and advice from the EPA Science Advisory Board - Health Effects Subcommittee (SAB-HES), a scientific review panel specifically established to provide advice on the use of the scientific literature in developing benefits analyses for air pollution regulations (http://www.epa.gov/sab/). In general, we follow a weight of evidence approach, based on the biological plausibility of effects, availability of concentration-response functions from well conducted peer-reviewed epidemiological studies, cohesiveness of results across studies, and a focus on endpoints reflecting public health impacts (like hospital admissions) rather than physiological responses (such as changes in clinical measures like Forced Expiratory Volume (FEV1)).
      There are several types of data that can support the determination of types and magnitude of health effects associated with air pollution exposures. These sources of data include toxicological studies (including animal and cellular studies), human clinical trials, and observational epidemiology studies. All of these data sources provide important contributions to the weight of evidence surrounding a particular health impact. However, only epidemiology studies provide direct concentration-response relationships which can be used to evaluate population-level impacts of reductions in ambient pollution levels in a health impact assessment.
      For the data-derived estimates, we relied on the published scientific literature to ascertain the relationship between PM and adverse human health effects. We evaluated epidemiological studies using the selection criteria summarized in Table 5-4. These criteria include consideration of whether the study was peer-reviewed, the match between the pollutant studied and the pollutant of interest, the study design and location, and characteristics of the study population, among other considerations. The selection of C-R functions for the benefits analysis is guided by the goal of achieving a balance between comprehensiveness and scientific defensibility. In general, the use of results from more than a single study can provide a more robust estimate of the relationship between a pollutant and a given health effect. However, there are often differences between studies examining the same endpoint, making it difficult to pool the results in a consistent manner. For example, studies may examine different pollutants or different age groups. For this reason, we consider very carefully the set of studies available examining each endpoint and select a consistent subset that provides a good balance of population coverage and match with the pollutant of interest. In many cases, either because of a lack of multiple studies, consistency problems, or clear superiority in the quality or comprehensiveness of one study over others, a single published study is selected as the basis of the effect estimate.
            Table 5-4. Criteria Used when Selecting C-R functions
Consideration
Comments
Peer-Reviewed Research
Peer-reviewed research is preferred to research that has not undergone the peer-review process.
Study Type
Among studies that consider chronic exposure (e.g., over a year or longer), prospective cohort studies are preferred over ecological studies because they control for important individual-level confounding variables that cannot be controlled for in ecological studies.
Study Period
Studies examining a relatively longer period of time (and therefore having more data) are preferred, because they have greater statistical power to detect effects.  More recent studies are also preferred because of possible changes in pollution mixes, medical care, and lifestyle over time.  However, when there are only a few studies available, studies from all years will be included.
Population Attributes
The most technically appropriate measures of benefits would be based on impact functions that cover the entire sensitive population but allow for heterogeneity across age or other relevant demographic factors.  In the absence of effect estimates specific to age, sex, preexisting condition status, or other relevant factors, it may be appropriate to select effect estimates that cover the broadest population to match with the desired outcome of the analysis, which is total national-level health impacts.  When available, multi-city studies are preferred to single city studies because they provide a more generalizable representation of the C-R function.
Study Size
Studies examining a relatively large sample are preferred because they generally have more power to detect small magnitude effects.  A large sample can be obtained in several ways, either through a large population or through repeated observations on a smaller population (e.g., through a symptom diary recorded for a panel of asthmatic children).
Study Location
U.S. studies are more desirable than non-U.S. studies because of potential differences in pollution characteristics, exposure patterns, medical care system, population behavior, and lifestyle.
Pollutants Included in Model
When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is important to use properly specified impact functions that include both pollutants.  Using single-pollutant models in cases where both pollutants are expected to affect a health outcome can lead to double-counting when pollutants are correlated.
Measure of PM
For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the focus on reducing emissions of PM2.5 precursors, and because air quality modeling was conducted for this size fraction of PM.  Where PM2.5 functions are not available, PM10 functions are used as surrogates, recognizing that there will be potential downward (upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction.
Economically Valuable Health Effects
Some health effects, such as forced expiratory volume and other technical measurements of lung function, are difficult to value in monetary terms.  These health effects are not quantified in this analysis.
Non-overlapping Endpoints
Although the benefits associated with each individual health endpoint may be analyzed separately, care must be exercised in selecting health endpoints to include in the overall benefits analysis because of the possibility of double-counting of benefits.

      When several effect estimates for a pollutant and a given health endpoint have been selected, they are quantitatively combined or pooled to derive a more robust estimate of the relationship. The BenMAP Technical Appendices provides details of the procedures used to combine multiple impact functions (Abt Associates, 2008). In general, we used fixed or random effects models to pool estimates from different studies of the same endpoint. Fixed effects pooling simply weights each study's estimate by the inverse variance, giving more weight to studies with greater statistical power (lower variance). Random effects pooling accounts for both within-study variance and between-study variability, due, for example, to differences in population susceptibility. We used the fixed effects model as our null hypothesis and then determined whether the data suggest that we should reject this null hypothesis, in which case we would use the random effects model. Pooled impact functions are used to estimate hospital admissions and asthma exacerbations. For more details on methods used to pool incidence estimates, see the BenMAP Manual Appendices (Abt Associates, 2008), which are available with the BenMAP software at http://www.epa.gov/benmap.html.
      Effect estimates selected for a given health endpoint were applied consistently across all locations nationwide. This applies to both impact functions defined by a single effect estimate and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in fact, vary from one location to another (e.g., because of differences in population susceptibilities or differences in the composition of PM), location-specific effect estimates are generally not available.
      The specific studies from which effect estimates for the primary analysis are drawn are included in Table 5-5. We highlight in blue those studies that have been added since the 2005 CAIR benefits analysis and incorporated into the central benefits estimate. In all cases where effect estimates are drawn directly from epidemiological studies, standard errors are used as a partial representation of the uncertainty in the size of the effect estimate. Below we provide the basis for selecting these studies.

Table 5-5. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts
                                   Endpoint
                                   Pollutant
                                     Study
                               Study Population
Premature Mortality
 Premature mortality -- daily time series

O3
(8-hour max)

Bell et al.(2004) (NMMAPS study)
Huang et al. (2004) (multi-city)
Schwartz (2005) (multi-city)
Meta-analyses:
   Bell et al. (2005)
   Ito et al. (2005)
   Levy et al. (2005)
All ages
 Premature mortality -- cohort study, all-cause
PM2.5
(annual avg)
Pope et al. (2002)
Laden et al. (2006)
>29 years
>25 years
 Premature mortality, total exposures
PM2.5
(annual avg)
Expert Elicitation (Roman et al., 2008)
>24 years
 Premature mortality -- all-cause
PM2.5
(annual avg)
Woodruff et al. (2006)
Infant (<1 year)
Chronic Illness
 Chronic bronchitis
PM2.5
(annual avg)
Abbey et al. (1995)
>26 years
 Nonfatal heart attacks
PM2.5
(24-hour avg)
Peters et al. (2001)
Adults (>18 years)
Hospital Admissions
 Respiratory

O3
(8-hour max)
Pooled estimate:
Schwartz (1995) -- ICD 460 - 519 (all resp)
Schwartz (1994a; 1994b) -- ICD 480 - 486 (pneumonia)
Moolgavkar et al. (1997) -- ICD 480 - 487 (pneumonia)
Schwartz (1994b) -- ICD 491 - 492, 494 - 496 (COPD)
Moolgavkar et al. (1997) -- ICD 490 - 496 (COPD)
>64 years

Burnett et al. (2001)
<2 years

PM2.5
(24-hour avg)
Pooled estimate:
Moolgavkar (2003) -- ICD 490 - 496 (COPD)
Ito (2003) -- ICD 490 - 496 (COPD)
>64 years

PM2.5
(24-hour avg)
Moolgavkar (2000) -- ICD 490 - 496 (COPD)
20 - 64 years

PM2.5
(24-hour avg)
Ito (2003) -- ICD 480 - 486 (pneumonia)
>64 years

PM2.5
(24-hour avg)
Sheppard (2003) -- ICD 493 (asthma)
<65 years
 Cardiovascular
PM2.5
(24-hour avg)
Pooled estimate:
Moolgavkar (2003) -- ICD 390 - 429 (all cardiovascular)
Ito (2003) -- ICD 410 - 414, 427 - 428 (ischemic heart disease, dysrhythmia, heart failure)
>64 years

PM2.5
(24-hour avg)
Moolgavkar (2000) -- ICD 390 - 429 (all cardiovascular)
20 - 64 years
 Asthma-related ER visits
O3
(8-hour max)
Pooled estimate:
Peel et al. (2005)
Wilson et al.(2005)

All ages
All ages

PM2.5
(24-hour avg)
Norris et al. (1999)
0 - 18 years
Other Health Endpoints
 Acute bronchitis
PM2.5
(annual avg)
Dockery et al. (1996)
8 - 12 years
 Upper respiratory symptoms
PM10
(24-hour avg)
Pope et al. (1991)
Asthmatics, 9 - 11 years
 Lower respiratory symptoms
PM2.5
(24-hour avg)
Schwartz and Neas (2000)
7 - 14 years
 Asthma exacerbations
PM2.5
(24-hour avg)
Pooled estimate:
Ostro et al. (2001) (cough, wheeze and shortness of breath)
Vedal et al. (1998) (cough)
6 - 18 years[a]
 Work loss days
PM2.5
(24-hour avg)
Ostro (1987)
18 - 65 years
 School absence days
O3
(8-hour max)
Pooled estimate:
Gilliland et al. (2001)
Chen et al. (2000)

5 - 17 years[b]
 Minor Restricted Activity Days (MRADs)
O3
(8-hour max)
Ostro and Rothschild (1989)
18 - 65 years

PM2.5
(24-hour avg)
Ostro and Rothschild (1989)
18 - 65 years

[a] The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader age group. See: U.S. Science Advisory Board. 2004. Advisory 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. EPA-SAB-COUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.
[b] Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school absences for all school-aged children based on the biological similarity between children aged 5 to 17.

<IP_ADDRESS> PM2.5 Premature Mortality Effect Coefficients
      Both long- and short-term exposures to ambient levels of PM2.5 air pollution have been associated with increased risk of premature mortality. The size of the mortality risk estimates from epidemiological studies, the serious nature of the effect itself, and the high monetary value ascribed to prolonging life make mortality risk reduction the most significant health endpoint quantified in this analysis.

      Although a number of uncertainties remain to be addressed by continued research (NRC, 2002), a substantial body of published scientific literature documents the correlation between elevated PM2.5 concentrations and increased mortality rates (U.S. EPA, 2009d). Time-series methods have been used to relate short-term (often day-to-day) changes in PM2.5 concentrations and changes in daily mortality rates up to several days after a period of elevated PM2.5 concentrations. Cohort methods have been used to examine the potential relationship between community-level PM exposures over multiple years (i.e., long-term exposures) and community-level annual mortality rates. Researchers have found statistically significant associations between PM2.5 and premature mortality using both types of studies. In general, the risk estimates based on the cohort studies are larger than those derived from time-series studies. Cohort analyses are thought to better capture the full public health impact of exposure to air pollution over time, because they account for the effects of long-term exposures and possibly some component of short-term exposures (Kunzli et al., 2001; NRC, 2002). This section discusses some of the issues surrounding the estimation of PM2.5-related premature mortality. To demonstrate the sensitivity of the benefits estimates to the specific sources of information regarding the impact of PM2.5 exposures on the risk of premature death, we are providing estimates in our results tables based on studies derived from the epidemiological literature and from the EPA sponsored expert elicitation. The epidemiological studies from which these estimates are drawn are described below. The expert elicitation project and the derivation of effect estimates from the expert elicitation results are described in the 2006 PM2.5 NAAQS RIA and Roman et al. (2008). In the interest of brevity we do not repeat those details here. However, Figure 5-18 summarizes the estimated PM2.5-related premature mortalities avoided using risk estimates drawn from the expert elicitation.

      Over a dozen epidemiological studies have found significant associations between various measures of long-term exposure to PM and elevated rates of annual mortality, beginning with Lave and Seskin (1977). Most of the published studies found positive (but not always statistically significant) associations with available PM indices such as total suspended particles (TSP). However, exploration of alternative model specifications sometimes raised questions about causal relationships (e.g., Lipfert et al., 1989). These early "ecological cross-sectional" studies (Lave and Seskin, 1977; Ozkaynak and Thurston, 1987) were criticized for a number of methodological limitations, particularly for inadequate control at the individual level for variables that are potentially important in causing mortality, such as wealth, smoking, and diet.
      Over the last 17 years, several studies using "prospective cohort" designs have been published that appear to be consistent with the earlier body of literature. These new "prospective cohort" studies reflect a significant improvement over the earlier work because they include individual level information with respect to health status and residence. The most extensive analyses have been based on data from two prospective cohort groups, often referred to as the Harvard "Six-Cities Study" (Dockery et al., 1993; Laden et al., 2006) and the "American Cancer Society or ACS study" (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004, Krewski et al. 2009); these studies have found consistent relationships between fine particle indicators and premature mortality across multiple locations in the United States. A third major data set comes from the California-based 7th Day Adventist Study (e.g., Abbey et al., 1999), which reported associations between long-term PM exposure and mortality in men. Results from this cohort, however, have been inconsistent, and the air quality results are not geographically representative of most of the United States, and the lifestyle of the population is not reflective of much of the U.S. population. Analysis is also available for a cohort of adult male veterans diagnosed with hypertension has been examined (Lipfert et al., 2000; Lipfert et al., 2003, 2006). The characteristics of this group differ from the cohorts in the Six-Cities, ACS, and 7th Day Adventist studies with respect to income, race, health status, and smoking status. Unlike previous long-term analyses, this study found some associations between mortality and ozone but found inconsistent results for PM indicators. Because of the selective nature of the population in the veteran's cohort, we have chosen not to include any effect estimates from the Lipfert et al. (2000) study in our benefits assessment.
      Given their consistent results and broad geographic coverage, and importance in informing the NAAQS development process, the Six-Cities and ACS data have been particularly important in benefits analyses. The credibility of these two studies is further enhanced by the fact that the initial published studies (Pope et al., 1995 and Dockery et al., 1993) were subject to extensive reexamination and reanalysis by an independent team of scientific experts commissioned by the Health Effect Institute (HEI) (Krewski et al., 2000). The final results of the reanalysis were then independently peer reviewed by a Special Panel of the HEI Health Review Committee. The results of these reanalyses confirmed and expanded the conclusions of the original investigators. While the HEI reexamination lends credibility to the original studies, it also highlights sensitivities concerning the relative impact of various pollutants, such as SO2, the potential role of education in mediating the association between pollution and mortality, and the influence of spatial correlation modeling. Further confirmation and extension of the findings of the 1993 Six City Study and the 1995 ACS study were recently completed using more recent air quality and a longer follow-up period for the ACS cohort was published over the past several years (Pope et al., 2002, 2004; Laden et al., 2006, Krewski et al. 2009). The follow up to the Harvard Six City Study both confirmed the effect size from the first analysis and provided additional confirmation that reductions in PM2.5 are likely to result in reductions in the risk of premature death. This additional evidence stems from the observed reductions in PM2.5 in each city during the extended follow-up period. Laden et al. (2006) found that mortality rates consistently went down at a rate proportionate to the observed reductions in PM2.5.
      A number of additional analyses have been conducted on the ACS cohort data (Jarrett et al., 2009; Pope et al., 2009). These studies have continued to find a strong significant relationship between PM2.5 and mortality outcomes and life expectancy. Specifically, much of the recent research has suggested a stronger relationship between cardiovascular mortality and lung cancer mortality with PM2.5, and a less significant relationship between respiratory-related mortality and PM2.5. The extended analyses of the ACS cohort data (Krewski et al. 2009) provides additional refinements to the analysis of PM-related mortality by (a) extend the follow-up period by 2 years to the year 2000, for a total of 18 years; (b) incorporate ecological., or neighborhood-level co-variates so as to better estimate personal exposure; (c) perform an extensive spatial analysis using land use regression modeling. These additional refinements may make this analysis well-suited for the assessment of PM-related mortality for EPA benefits analyses.
      In developing and improving the methods for estimating and valuing the potential reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel recommended using long-term prospective cohort studies in estimating mortality risk reduction (U.S. EPA-SAB, 1999b). This recommendation has been confirmed by a report from the National Research Council, which stated that "it is essential to use the cohort studies in benefits analysis to capture all important effects from air pollution exposure" (NRC, 2002, p. 108). More specifically, the SAB recommended emphasis on the ACS study because it includes a much larger sample size and longer exposure interval and covers more locations (e.g., 50 cities compared to the Six Cities Study) than other studies of its kind. Because of the refinements in the extended follow-up analysis, the SAB-HES recommended using the Pope et al. (2002) study as the basis for the primary mortality estimate for adults and suggests that alternate estimates of mortality generated using other cohort and time-series studies could be included as part of the sensitivity analysis (U.S. EPA-SAB, 2004b). The PM NAAQS Risk and Exposure Assessment (U.S EPA, 2010) utilized risk coefficients drawn from the Krewski et al. (2009) study. In a December of 2009 consultation with the SAB-HES, the Agency proposed utilizing the Krewski et al. (2009) extended analysis of the ACS cohort data. The panel was supportive of this approach.
      As noted above, since 2004 SAB review, an extended follow-up of the Harvard Six cities study has been published (Laden et al., 2006) and in recent RIAs (see for example the SO2 NAAQS, PM NAAQS, CAIR and Nonroad Diesel RIAs), we have included this estimate of mortality impacts based on application of the C-R function derived from this study. We use this specific estimate to represent the Six Cities study because it both reflects among the most up-to-date science and was cited by many of the experts in their elicitation responses. It is clear from the expert elicitation that the results published in Laden et al. (2006) are potentially influential, and in fact the expert elicitation results encompass within their range the estimates from both the Pope et al. (2002) and Laden et al. (2006) studies (see Figure 5-18 below). These are logical choices for anchor points in our presentation because, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for using both studies to generate  benefits estimates.
<IP_ADDRESS> Ozone Premature Mortality Effect Coefficients
While particulate matter is the criteria pollutant most clearly associated with premature mortality, recent research suggests that short-term repeated ozone exposure also likely contributes to premature death. The 2006 Ozone Criteria Document found that  "[c]onsistent with observed ozone-related increases in respiratory- and cardiovascular-related morbidity, several newer multi-city studies, single-city studies, and several meta-analyses of these studies have provided relatively strong epidemiologic evidence for associations between short-term ozone exposure and all-cause mortality, even after adjustment for the influence of season and PM" (U.S. EPA, 2006). The epidemiologic data are also supported by recent experimental data from both animal and human studies, which provide evidence suggestive of plausible pathways by which risk of respiratory or cardiovascular morbidity and mortality could be increased by ambient ozone. With respect to short-term exposure, the Ozone Criteria Document concluded, "This overall body of evidence is highly suggestive that ozone directly or indirectly contributes to non-accidental and cardiopulmonary-related mortality, but additional research is needed to more fully establish underlying mechanisms by which such effects occur" (U.S. EPA, 2006).
With respect to the time-series studies, the conclusion regarding the relationship between short-term ozone exposure and premature mortality is based, in part, upon recent city-specific time-series studies such as the Schwartz (2004) analysis in Houston and the Huang et al. (2004) analysis in Los Angeles. This conclusion is also based on recent meta-analyses by Bell et al. (2005), Ito et al. (2005), and Levy et al. (2005), and a new analysis of the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) data set by Bell et al. (2004), which specifically sought to disentangle the roles of ozone, PM, weather-related variables, and seasonality. The 2006 Criteria Document states that "the results from these meta-analyses, as well as several single- and multiple-city studies, indicate that co-pollutants generally do not appear to substantially confound the association between ozone and mortality" (p. 7-103). However, CASAC raised questions about the implications of these time-series results in a policy context. Specifically, CASAC emphasized that "...while the time-series study design is a powerful tool to detect very small effects that could not be detected using other designs, it is also a blunt tool" (U.S. EPA-SAB, 2006). They point to findings (e.g., Stieb et al., 2002, 2003) that indicated associations between premature mortality and all of the criteria pollutants, indicating that "findings of time-series studies do not seem to allow us to confidently attribute observed effects to individual pollutants" (id.). They note that "not only is the interpretation of these associations complicated by the fact that the day-to-day variation in concentrations of these pollutants is, to a varying degree, determined by meteorology, the pollutants are often part of a large and highly correlated mix of pollutants, only a very few of which are measured" (id.). Even with these uncertainties, the CASAC Ozone Panel, in its review of EPA's Staff Paper, found "...premature total non-accidental and cardiorespiratory mortality for inclusion in the quantitative risk assessment to be appropriate."
In 2006 the EPA requested an NAS study to answer four key questions regarding ozone mortality: (1) how did the epidemiological literature to that point improve our understanding of the size of the ozone mortality effect? (2) How best can EPA quantify the level of ozone mortality impacts from short-term exposure? (3) How might EPA estimate the change in life expectancy? (4) What methods should EPA use to estimate the monetary value of changes in ozone-related mortality risk and life expectancy?
In 2008 the NAS (NRC, 2008) issued a series of recommendations to the EPA regarding the quantification and valuation of ozone-related short-term mortality. Chief among these was that "...short-term exposure to ambient ozone is likely to contribute to premature deaths" and the committee recommended that "ozone-related mortality be included in future estimates of the health benefits of reducing ozone exposures..." The NAS also recommended that "...the greatest emphasis be placed on the multicity and NMMAPS studies without exclusion of the meta-analyses" (NRC, 2008).
Recent evidence also suggests a relationship between long-term exposure to ozone and premature respiratory mortality in the ACS cohort (Jerrett et al. 2009). Jerrett and colleagues find that long-term exposure to ozone is linked to respiratory premature mortality in a two-pollutant model that controls for PM2.5. This is the first long-term cohort study to have observed such a relationship.  In a December of 2009 consultation with the SAB-HES, the Agency proposed utilizing the Jerrett et al. (2009) analysis of the ACS cohort data. In its advisory, the panel recommended quantifying ozone-related mortality using risk coefficients drawn from the short-term time-series studies.
In view of the findings of the Criteria document and the NAS panel, we include used estimates of short-term ozone mortality from the Bell et al. (2004) NMMAPS analysis, the Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect estimates from the three meta-analyses (Bell et al. 2005, Levy et al. 2005 and Ito et al. 2005).
<IP_ADDRESS> Chronic Bronchitis
CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 months a year for several years in a row.  CB affects an estimated 5 percent of the U.S. population (American Lung Association, 1999).  A limited number of studies have estimated the impact of air pollution on new incidences of CB.  Schwartz (1993) and Abbey et al. (1995) provide evidence that long-term PM exposure gives rise to the development of CB in the United States.  Because the Transport Rule is expected to reduce primarily PM2.5, this analysis uses only the Abbey et al. (1995) study, because it is the only study focusing on the relationship between PM2.5 and new incidences of CB.

<IP_ADDRESS> Nonfatal Myocardial Infarctions (Heart Attacks)
Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United States (Peters et al., 2001) and other countries (Poloniecki et al., 1997).  We used a recent study by Peters et al. (2001) as the basis for the impact function estimating the relationship between PM2.5 and nonfatal heart attacks.  Peters et al. is the only available U.S. study to provide a specific estimate for heart attacks.  Other studies, such as Samet et al. (2000) and Moolgavkar (2000), show a consistent relationship between all cardiovascular hospital admissions, including those for nonfatal heart attacks, and PM.  Given the lasting impact of a heart attack on long-term health costs and earnings, we provide a separate estimate for nonfatal heart attacks.  The estimate used in the Transport Rule analysis is based on the single available U.S. effect estimate.  The finding of a specific impact on heart attacks is consistent with hospital admission and other studies showing relationships between fine particles and cardiovascular effects both within and outside the United States. Several epidemiologic studies (Liao et al., 1999; Gold et al., 2000; Magari et al., 2001) have shown that heart rate variability (an indicator of how much the heart is able to speed up or slow down in response to momentary stresses) is negatively related to PM levels.  Heart rate variability is a risk factor for heart attacks and other coronary heart diseases (Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997; Tsuji et al., 1996).  As such, significant impacts of PM on heart rate variability are consistent with an increased risk of heart attacks.
<IP_ADDRESS> Hospital and Emergency Room Admissions
Because of the availability of detailed hospital admission and discharge records, there is an extensive body of literature examining the relationship between hospital admissions and air pollution.  Because of this, many of the hospital admission endpoints use pooled impact functions based on the results of a number of studies.  In addition, some studies have examined the relationship between air pollution and emergency room visits.  Since most emergency room visits do not result in an admission to the hospital (the majority of people going to the emergency room are treated and return home), we treat hospital admissions and emergency room visits separately, taking account of the fraction of emergency room visits that are admitted to the hospital.
The two main groups of hospital admissions estimated in this analysis are respiratory admissions and cardiovascular admissions.  There is not much evidence linking ozone or PM with other types of hospital admissions.  The only type of emergency room visits that have been consistently linked to ozone and PM in the United States are asthma-related visits.
To estimate avoided incidences of cardiovascular hospital admissions associated with PM2.5, we used studies by Moolgavkar (2003) and Ito (2003).  Additional published studies show a statistically significant relationship between PM10 and cardiovascular hospital admissions.  However, given that the control options we are analyzing are expected to reduce primarily PM2.5, we focus on the two studies that examine PM2.5.  Both of these studies provide an effect estimate for populations over 65, allowing us to pool the impact functions for this age group.  Only Moolgavkar (2000) provided a separate effect estimate for populations 20 to 64.  Total cardiovascular hospital admissions are thus the sum of the pooled estimate for populations over 65 and the single study estimate for populations 20 to 64.  Cardiovascular hospital admissions include admissions for myocardial infarctions.  To avoid double-counting benefits from reductions in myocardial infarctions when applying the impact function for cardiovascular hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove admissions for myocardial infarctions.
To estimate total avoided incidences of respiratory hospital admissions, we used impact functions for several respiratory causes, including chronic obstructive pulmonary disease (COPD), pneumonia, and asthma.  As with cardiovascular admissions, additional published studies show a statistically significant relationship between PM10 and respiratory hospital admissions.  We used only those focusing on PM2.5.  Both Moolgavkar (2000) and Ito (2003) provide effect estimates for COPD in populations over 65, allowing us to pool the impact functions for this group.  Only Moolgavkar (2000) provides a separate effect estimate for populations 20 to 64.  Total COPD hospital admissions are thus the sum of the pooled estimate for populations over 65 and the single study estimate for populations 20 to 64.  Only Ito (2003) estimated pneumonia and only for the population 65 and older.  In addition, Sheppard (2003) provided an effect estimate for asthma hospital admissions for populations under age 65.  Total avoided incidences of PM-related respiratory-related hospital admissions are the sum of COPD, pneumonia, and asthma admissions.
To estimate the effects of PM air pollution reductions on asthma-related ER visits, we use the effect estimate from a study of children 18 and under by Norris et al. (1999).  As noted earlier, there is another study by Schwartz examining a broader age group (less than 65), but the Schwartz study focused on PM10 rather than PM2.5.  We selected the Norris et al. (1999) effect estimate because it better matched the pollutant of interest.  Because children tend to have higher rates of hospitalization for asthma relative to adults under 65, we will likely capture the majority of the impact of PM2.5 on asthma emergency room visits in populations under 65, although there may still be significant impacts in the adult population under 65.