$2.48
$7.76
$0.99
$18.72
$40.53
$42.35
                                     92811
National Security
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
                                      NA
Notes: . National Security is included in this table but does not have size standards.
[a]	NAICS codes 221111, 221112, 221113, 221119, 221121, 221122: A firm in these industries is defined as small by SBA if, including its affiliates, it is primarily engaged in the generation, transmission, and/or distribution of electric energy for sale and its total electric output for the preceding fiscal year did not exceed 4 million megawatt hours.
NA = Not available. SUSB did not report this data for disclosure or other reasons.
Source: U.S. Census Bureau. 2009a. "Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail Employment Sizes: 2009." <http://www.census.gov/csd/susb/

Annual entity compliance costs vary depending on the size of the diesel engines used at the affected establishment. Absent facility-specific information, we computed per-entity compliance costs based for three different cases based on representative establishments -- Cases 1, 2, and 3 (see Table 6-4). Each representative establishment differs based on the size and number of diesel engines being used. Compliance costs are calculated by summing the total annualized compliance costs for the relevant engine categories, dividing the sum by the total existing population of those engines, and multiplying the average engine cost by the number of engines assumed to be at the establishment. Since NAICS 2211 and 622110 are fundamentally different than other industries considered in this analysis, we used different assumptions about what constitutes the representative establishment and report these assumptions separately.
Case 1: The representative establishment for all industries uses three 750+ hp engines with an average compliance cost of $1,878 per engine, resulting in a total annualized compliance cost of approximately $5,634 for this representative establishment.
Case 2: The representative establishment in NACIS 2211 and 622110 uses two 50 to 750+ hp engines with an average compliance cost of $419 per engine, resulting in a total annualized compliance cost of $838 for this representative establishment. For all other industries, the representative establishment uses two 50 to 300 hp engines with an average compliance cost of $146 per engine, resulting in a total compliance cost of $292 for this representative establishment.
Case 3: The representative establishment for all industries uses two 50 to 100 hp engines with an average compliance cost of $68 per engine, resulting in a total compliance cost of $137 for this representative establishment.
EPA believes that small entities are most likely to face costs similar to Case 2 (columns shaded in gray in Table 6-4) because most of the engines to be affected by this proposal in NAICS 335312, 333992, 211111, and 211112 are under 300 hp capacity, and most small entities in these industries will own engines of this size or smaller. This is corroborated by Figure 6-1 and 6-2 which shows the distribution of engine population and compliance costs by engine size for all industries. However, it is difficult to make a similar claim for NAICS 2211 and 622110 based on the existing distribution of engines in these industries.6  As noted earlier in the RIA, only 20 percent of the existing distribution of engines is expected to be classified as non-emergency.
For the sales test, we divided the representative establishment compliance costs reported in Table 6-4 by the representative receipts reported in Tables 6-2 and 6-3. This is known as the cost-to-receipt (i.e., sales) ratio, or the "sales test." The "sales test" is the impact
Table 6-4.	Representative Establishment Costs Used for Small Entity Analysis ($2008)

                                    Case 1
                                    Case 2
                                    Case 3

                       NAICS 2211, 622110
(+750 hp only)
                        All Other NAICS
(+750 hp only)
                       NAICS 2211, 622110
(50 - 750+ hp)
                         All Other NAICS
(50 - 300 hp)
                     NAICS 2211, 622110
(50 - 100 hp only)
                      All Other NAICS
(50 - 100 hp only)
Total Annualized Costs ($)
                                                                    $62,068,406
                                                                     $7,875,707
                                                                   $304,817,985
                                                                    $40,407,406
                                                                     $9,093,118
                                                                     $6,745,516
Engine Population
                                                                         33,052
                                                                          4,194
                                                                        727,090
                                                                        276,374
                                                                        133,099
                                                                         98,736
Average Engine Cost ($/engine)
                                                                         $1,878
                                                                         $1,878
                                                                           $419
                                                                           $146
                                                                            $68
                                                                            $68
Assumed Engines Per Establishment
                                                                              3
                                                                              3
                                                                              2
                                                                              2
                                                                              2
                                                                              2
Total Annualized Costs per Establishment
                                                                         $5,634
                                                                         $5,634
                                                                           $838
                                                                           $292
                                                                           $137
                                                                           $137
* Engine population estimates taken from  "Impacts Associated with NESHAP for Existing Stationary CI RICE," prepared by Bradley Nelson, EC/R, Inc. for Melanie King, U.S. EPA, Office of Air Quality Planning and Standards, February 19, 2010.

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

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

methodology EPA employs in analyzing small entity impacts as opposed to a "profits test," in which annualized compliance costs are calculated as a share of profits.
This is because revenues or sales data are commonly available data for entities normally impacted by EPA regulations and profits data normally made available are often not the true profit earned by firms because of accounting and tax considerations. Revenues as typically published are usually correct figures and are more reliably reported when compared to profit data. The use of a "sales test" for estimating small business impacts for a rulemaking such as this one is consistent with guidance offered by EPA on compliance with SBREFA7 and is consistent with guidance published by the U.S. SBA's Office of Advocacy that suggests that cost as a percentage of total revenues is a metric for evaluating cost increases on small entities in relation to increases on large entities.8
If the cost-to-receipt ratio is less than 1%, then we consider the final rule to not have a significant impact on the establishment company in question. We summarize the industries with cost-to-receipt ratios exceeding 1% below:
Primary Analysis:
Case 2: NAICS 2211
Case 3: No industries
Sensitivity Analysis (unlikely):
Case 1: NAICS 211111, 211112, with less than 20 employees, NAICS 2211 with receipts less than $500,000 per year, NAICS 234 with receipts less than $500,000 per year, and irrigated farms with receipts of $500,000 or less per year
In the Case 2 primary analysis, only establishments in NAICS 2211 with receipts less than $100,000 per year have cost-to-receipt ratios above 1%. These establishments represent less than 5 percent of affected small establishments.  However, establishments earning this level of receipts are likely to be using smaller engines than those assumed in Case 2, such as 50 to 100 hp engines. The results of our Case 3 analysis demonstrate that these establishments are not significantly impacted when taking this engine size into account.
      After considering the economic impacts of this proposed rule on small entities, we certify that this action will not have a significant economic impact on a substantial number of small entities.  This certification is based on the economic impact of this proposal action to all affected small entities across all industries affected.  The percentage of small entities impacted by this proposal having annualized costs greater than 1 percent of sales is less than 2 percent according to this analysis. We thus conclude that there is no significant economic impact on a substantial number of small entities (SISNOSE) for this proposed rule.
      Although the proposed rule would not have a significant economic impact on a substantial number of small entities, EPA nonetheless tried to reduce the impact of the rule on small entities.  When developing the revised standards, EPA took special steps to ensure that the burdens imposed on small entities were minimal.  EPA conducted several meetings with industry trade associations to discuss regulatory options and the corresponding burden on industry, such as recordkeeping and reporting. In addition, as mentioned earlier in this preamble, EPA proposes to reduce regulatory requirements for a variety of area sources affected under this RICE rule with amendments to the final RICE rules promulgated in 2010. We continue to be interested in the potential impacts of this proposed rule on small entities and welcome comments on issues related to such impacts.

Small Government Entities
The rule also covers sectors that include entities owned by small and large governments. However, given the uncertainty and data limitations associated with identifying and appropriately classifying these entities, we computed a "revenue" test for a model small government, where the annualized compliance cost is a percentage of annual government revenues (U.S. Census, 2005a,b). The use of a "revenue test" for estimating impacts to small governments for a rulemaking such as this one is consistent with guidance offered by EPA on compliance with SBREFA,9 and is consistent with guidance published by the US SBA's Office of Advocacy.10 For example, from the 2002 Census (in 2008 dollars), the average revenue for small governments (counties and municipalities) with populations fewer than 10,000 are $3 million per entity, and the average revenue for local governments with populations fewer than 50,000 is $8 million per entity. For the smallest group of local governments (<10,000 people), the cost-to-revenue ratio would be 0.2% or less under each case. For the larger group of governments (<50,000 people), the cost-to-revenue ratio is 0.1% or less under all cases.

Human Health Benefits of Emissions Reductions
Synopsis

      Implementation of emissions controls required by the proposed CI RICE NESHAP reconsideration is expected to reduce emissions of hazardous air pollutants (HAP) and have ancillary co-benefits that would lower ambient concentrations of PM2.5 and ozone. In this section, we quantify the monetized co-benefits for this rule associated with reducing exposure to ambient fine particulate matter (PM2.5) by reducing emissions of precursors. We estimate the total monetized co-benefits to be $770 million to $1.9 billion at a 3% discount rate and $690 million to $1.7 billion at a 7% discount rate in 2013. All estimates are in 2010$.  These estimates reflect the monetized human health benefits of reducing cases of morbidity and premature mortality among populations exposed to PM2.5 reduced by this rule.  These estimates reflect EPA's most current interpretation of the scientific literature. Higher or lower estimates of benefits are possible using other assumptions; examples of this are provided in Figure 7-2.  Data, resource, and methodological limitations prevented EPA from monetizing the benefits from several important benefit categories, including benefits from reducing exposure to HAP, carbon monoxide, and ozone, as well as visibility impairment. In addition to reducing emissions of PM precursors, this rule would reduce 1,000 tons of HAP and 14,000 tons of carbon monoxide each year.
Calculation of PM2.5-Related Human Health Co-Benefits

      Assuming that the baseline for this RIA does not include implementation of the 2010 final CI RICE rule, as we state earlier in this document, this proposed reconsideration would reduce emissions of directly emitted particles and VOCs.  Because these emissions are precursors to PM2.5, reducing these emissions would also reduce PM2.5 formation, human exposure and the incidence of PM2.5-related health effects.  Due to analytical limitations, it was not possible to provide a comprehensive estimate of PM2.5-related benefits or provide estimates of the health benefits associated with exposure to HAP, CO, or ozone. Instead, we used the "benefit-per-ton" approach to estimate these benefits. The methodology employed in this analysis is similar to the work described in Fann, Fulcher, and Hubbell (2009), but represents an improvement that EPA believes would provide more reliable estimates of PM2.5-related health benefits for emissions reductions in specific sectors. The key assumptions are described in detail below. These PM2.5 benefit-per-ton estimates provide the total monetized human health benefits (the sum of premature mortality and premature morbidity) of reducing one ton of PM2.5 from a specified source. EPA has used the benefit per-ton technique in several previous RIAs, including the recent SO2 NAAQS RIA (U.S. EPA, 2010).

      The Integrated Science Assessment (ISA) for Particulate Matter (U.S. EPA, 2009b) identified the human health effects associated with ambient PM2.5, which include premature morality and a variety of morbidity effects associated with acute and chronic exposures. Table 7-1 shows the quantified and unquantified benefits captured in those benefit-per-ton estimates, but this table does not include entries for the unquantified health effects associated with exposure to HAP, CO, or ozone nor welfare effects such visibility impairment that are described in section 7.2.  It is important to emphasize that the list of unquantified benefit categories is not exhaustive, nor is quantification of each effect complete.

Table 7-1:	Human Health Effects of PM2.5
Category
                                Specific Effect
                          Effect Has Been Quantified
                           Effect Has Been Monetized
                    More Information (refers to CSAPR RIA)
Improved Human Health
Reduced incidence of premature mortality from exposure to PM2.5
Adult premature mortality based on cohort study estimates and expert elicitation estimates (age >25 or age >30)
                                      
                                      
Section 5.4

Infant mortality (age <1)
                                      
                                      
Section 5.4
Reduced incidence of morbidity from exposure to PM2.5
Non-fatal heart attacks (age > 18)
                                      
                                      
Section 5.4

Hospital admissions -- respiratory  (all ages)
                                      
                                      
Section 5.4

Hospital admissions -- cardiovascular (age >20)
                                      
                                      
Section 5.4

Emergency room visits for asthma (all ages)
                                      
                                      
Section 5.4

Acute bronchitis (age 8-12)
                                      
                                      
Section 5.4

Lower respiratory symptoms (age 7-14)
                                      
                                      
Section 5.4

Upper respiratory symptoms (asthmatics age 9-11)
                                      
                                      
Section 5.4

Asthma exacerbation (asthmatics age 6-18)
                                      
                                      
Section 5.4

Lost work days  (age 18-65)
                                      
                                      
Section 5.4

Minor restricted-activity days (age 18-65)
                                      
                                      
Section 5.4

Chronic Bronchitis (age >26)
                                      
                                      
Section 5.4

Emergency room visits for cardiovascular effects (all ages)
                                      --
                                      --
Section 5.4

Strokes and cerebrovascular disease (age 50-79)
                                      --
                                      --
Section 5.4

Other cardiovascular effects (e.g., other ages)
                                      --
                                      --
PM ISA[2]

Other respiratory effects (e.g., pulmonary function, non-asthma ER visits, non-bronchitis chronic diseases, other ages and populations)
                                      --
                                      --
PM ISA[2]

Reproductive and developmental effects (e.g., low birth weight, pre-term births, etc)
                                      --
                                      --
PM ISA[2,3]

Cancer, mutagenicity, and genotoxicity effects
                                      --
                                      --
PM ISA[2,3]
1 We assess these benefits qualitatively due to time and resource limitations for this analysis.
[2] We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.
[3] We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other significant concerns over the strength of the association.

      Consistent with the Portland Cement NESHAP (U.S. EPA, 2009a), the benefits estimates utilize the concentration-response functions as reported in the epidemiology literature, as well as the 12 functions obtained in EPA's expert elicitation study as a sensitivity analysis.

One estimate is based on the concentration-response (C-R) function developed from the extended analysis of American Cancer Society (ACS) cohort, as reported in Pope et al. (2002), a study that EPA has previously used to generate its primary benefits estimate.  When calculating the estimate, EPA applied the effect coefficient as reported in the study without an adjustment for assumed concentration threshold of 10 ug/m[3] as was done in recent (2006-2009) Office of Air and Radiation RIAs.
One estimate is based on the C-R function developed from the extended analysis of the Harvard Six Cities cohort, as reported by Laden et al (2006).  This study, published after the completion of the Staff Paper for the 2006 PM2.5 NAAQS, has been used as an alternative estimate in the PM2.5 NAAQS RIA and PM2.5 benefits estimates in RIAs completed since the PM2.5 NAAQS.  When calculating the estimate, EPA applied the effect coefficient as reported in the study without an adjustment for assumed concentration threshold of 10 ug/m[3] as was done in recent (2006-2009) RIAs.
Twelve estimates are based on the C-R functions from EPA's expert elicitation study (Roman et al., 2008) on the PM2.5 -mortality relationship and interpreted for benefits analysis in EPA's final RIA for the PM2.5 NAAQS.  For that study, twelve experts (labeled A through L) provided independent estimates of the PM2.5 -mortality concentration-response function.  EPA practice has been to develop independent estimates of PM2.5 -mortality estimates corresponding to the concentration-response function provided by each of the twelve experts, to better characterize the degree of variability in the expert responses.

      The effect coefficients are drawn from epidemiology studies examining two large population cohorts: the American Cancer Society cohort (Pope et al., 2002) and the Harvard Six Cities cohort (Laden et al., 2006).  These are logical choices for anchor points in our presentation because, while both studies are well designed and peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for using both studies to generate  benefits estimates.  Previously, EPA had calculated benefits based on these two empirical studies, but derived the range of benefits, including the minimum and maximum results, from an expert elicitation of the relationship between exposure to PM2.5 and premature mortality (Roman et al., 2008).  Within this assessment, we include the benefits estimates derived from the concentration-response function provided by each of the twelve experts to better characterize the uncertainty in the concentration-response function for mortality and the degree of variability in the expert responses.  Because the experts used these cohort studies to inform their concentration-response functions, benefits estimates using these functions generally fall between results using these epidemiology studies (see Figure 7-2).  In general, the expert elicitation results support the conclusion that the benefits of PM2.5 control are very likely to be substantial.

      Readers interested in reviewing the general methodology for creating the benefit-per-ton estimates used in this analysis should consult the draft Technical Support Document (TSD) on
estimating the benefits per ton of reducing PM2.5 and its precursors in the "Other Non-EGU Point" category (U.S. EPA, 2012). The primary difference between the estimates used in this analysis and the estimates reported in Fann, Fulcher, and Hubbell (2009) is the air quality modeling data utilized. The air quality modeling data used in this analysis use more narrow sectors. In addition, the updated air quality modeling data reflects more recent emissions data (2005 rather than 2001) and has a higher spatial resolution (12km rather than 36 km grid cells). The benefits methodology, such as health endpoints assessed, risk estimates applied, and valuation techniques applied did not change. As noted below in the characterization of uncertainty, these updated estimates still have similar limitations as all national-average benefit-per-ton estimates in that they reflect the geographic distribution of the modeled emissions, which may not exactly match the emission reductions in this rulemaking, and they may not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors for any specific location.  In this analysis, we apply these national benefit-per-ton estimates calculated for this category for directly emitted particles and multiply them by the corresponding emission reductions.

      These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence is not yet sufficient to allow differentiation of effects estimates by particle type. Directly emitted particles are the primary PM2.5 precursors affected by this rule. Even though we assume that all fine particles have equivalent health effects, the benefit-per-ton estimates vary between precursors depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The sector-specific modeling does not provide estimates of the PM2.5-related benefits associated with reducing VOC emissions, but these unquantified benefits are generally small compared to other PM2.5 precursors (U.S. EPA, 2012).
      The benefit-per-ton coefficients in this analysis were derived using modified versions of the health impact functions used in the PM NAAQS Regulatory Impact Analysis (U.S. EPA, 2006).  Specifically, this analysis uses the method first applied in the Portland Cement NESHAP RIA (U.S. EPA, 2009a), which applied the functions directly from the epidemiology studies without an adjustment for an assumed threshold.  Removing the threshold assumption is a key difference between the method used in this analysis of PM benefits and the methods used in RIAs prior to Portland Cement proposal, and we now calculate incremental benefits down to the lowest modeled PM2.5 air quality levels.

      Based on our review of the current body of scientific literature, EPA now estimates PM-related mortality without applying an assumed concentration threshold.  EPA's Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was reviewed by EPA's Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 2009b), concluded that the scientific literature consistently finds that a no-threshold log-linear model most adequately portrays the PM-mortality concentration-response relationship while recognizing potential uncertainty about the exact shape of the concentration-response function.
Consistent with this finding, we have conformed the previous threshold sensitivity analysis to the current state of the PM science by incorporating a "Lowest Measured Level" (LML) assessment, which is a method EPA has employed in several recent RIAs including the Cross-State Air Pollution Rule (U.S. EPA, 2011b).  This information allows readers to determine the portion of population exposed to annual mean PM2.5 levels at or above the LML of each study; in general, our confidence in the estimated PM mortality decreases as we consider air quality levels further below the LML in major cohort studies that estimate PM-related mortality. While an LML assessment provides some insight into the level of uncertainty in the estimated PM mortality benefits, EPA does not view the LML as a threshold and continues to quantify PM-related mortality impacts using a full range of modeled air quality concentrations.  It is important to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of the major cohort studies, which is 5.8 ug/m[3].  Just because we have greater confidence in the benefits above the LML, this does not mean that we have no confidence that benefits occur below the LML.    For a summary of the scientific review statements regarding the lack of a threshold in the PM2.5-mortality relationship, see the Technical Support Document (TSD) entitled Summary of Expert Opinions on the Existence of a Threshold in the Concentration-Response Function for PM2.5-related Mortality (U.S. EPA, 2010b).
      For this analysis, policy-specific air quality data is not available due to time or resource limitations.  For these rules, we are unable to estimate the percentage of premature mortality associated with this specific rule's emission reductions at each PM2.5 level.  However, we believe that it is still important to characterize the distribution of exposure to baseline air quality levels.  As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM2.5 level using the source apportionment modeling used to calculate the benefit-per-ton estimates for this sector.  It is important to note that baseline exposure is only one parameter in the health impact function, along with baseline incidence rates population, and change in air quality.  In other words, the percentage of the population exposed to air pollution below the LML is not the same as the percentage of the population experiencing health impacts as a result of a specific emission reduction policy.  The most important aspect, which we are unable to quantify for rules without rule-specific air quality modeling, is the shift in exposure associated with this specific rule.  Therefore, caution is warranted when interpreting the LML assessment for this rule.  The results of this analysis are provided in Section 7.3.
      As is the nature of RIAs, the assumptions and methods used to estimate air quality benefits evolve over time to reflect the Agency's most current interpretation of the scientific and economic literature.  For a period of time (2004-2008), the Office of Air and Radiation (OAR) valued mortality risk reductions using a value of statistical life (VSL) estimate derived from a limited analysis of some of the available studies.  OAR arrived at a VSL using a range of $1 million to $10 million (2000$) consistent with two meta-analyses of the wage-risk literature.  The $1 million value represented the lower end of the interquartile range from the Mrozek and Taylor (2002) meta-analysis of 33 studies.  The $10 million value represented the upper end of the interquartile range from the Viscusi and Aldy (2003) meta-analysis of 43 studies.  The mean estimate of $5.5 million (2000$) was also consistent with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) meta-analysis.  However, the Agency neither changed its official guidance on the use of VSL in rulemakings nor subjected the interim estimate to a scientific peer-review process through the Science Advisory Board (SAB) or other peer-review group.

      During this time, the Agency continued work to update its guidance on valuing mortality risk reductions, including commissioning a report from meta-analytic experts to evaluate methodological questions raised by EPA and the SAB on combining estimates from the various data sources.  In addition, the Agency consulted several times with the Science Advisory Board Environmental Economics Advisory Committee (SAB-EEAC) on the issue.  With input from the meta-analytic experts, the SAB-EEAC advised the Agency to update its guidance using specific, appropriate meta-analytic techniques to combine estimates from unique data sources and different studies, including those using different methodologies (i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007).

      Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied consistently best reflects the SAB-EEAC advice it has received.  Therefore, the Agency has decided to apply the VSL that was vetted and endorsed by the SAB in the Guidelines for Preparing Economic Analyses (U.S. EPA, 2000) while the Agency continues its efforts to update its guidance on this issue.  This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published between 1974 and 1991.  The mean VSL across these studies is $6.3 million (2000$).  The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to the SAB-EEAC's specific recommendations.

In implementing these rules, emission controls may lead to reductions in ambient PM2.5 below the National Ambient Air Quality Standards (NAAQS) for PM in some areas and assist other areas with attaining the PM NAAQS. Because the PM NAAQS RIAs also calculate PM benefits, there are important differences worth noting in the design and analytical objectives of each RIA. The NAAQS RIAs illustrate the potential costs and benefits of attaining a new air quality standard nationwide based on an array of emission control strategies for different sources. In short, NAAQS RIAs hypothesize, but do not predict, the control strategies that States may choose to enact when implementing a NAAQS. The setting of a NAAQS does not directly result in costs or benefits, and as such, the NAAQS RIAs are merely illustrative and are not intended to be added to the costs and benefits of other regulations that result in specific costs of control and emission reductions. However, some costs and benefits estimated in this RIA account for the same air quality improvements as estimated in the illustrative PM2.5 NAAQS RIA.
By contrast, the emission reductions for implementation rules are from a specific class of well-characterized sources. In general, EPA is more confident in the magnitude and location of the emission reductions for implementation rules rather than illustrative NAAQS analyses. Emission reductions achieved under these and other promulgated rules will ultimately be reflected in the baseline of future NAAQS analyses, which would reduce the incremental costs and benefits associated with attaining the NAAQS. EPA remains forward looking towards the next iteration of the 5-year review cycle for the NAAQS, and as a result does not issue updated RIAs for existing NAAQS that retroactively update the baseline for NAAQS implementation. For more information on the relationship between the NAAQS and rules such as analyzed here, please see Section 1.2.4 of the SO2 NAAQS RIA (U.S. EPA, 2010a).

      Figure 7.1 illustrates the relative breakdown of the monetized PM2.5 health benefits.

Figure 7-1:	Breakdown of Monetized PM2.5 Health Benefits using Mortality Function from Pope et al. (2002)*
*This pie chart breakdown is illustrative, using the results based on Pope et al. (2002) as an example.  Using the Laden et al. (2006) function for premature mortality, the percentage of total monetized benefits due to adult mortality would be 97%.  This chart shows the breakdown using a 3% discount rate, and the results would be similar if a 7% discount rate was used.

	Table 7-2 provides a general summary of the monetized PM-related health benefits by precursor, including the emission reductions and benefit-per-ton estimates at discount rates of 3% and 7%.  Table 7-3 provides a summary of the reductions in health incidences as a result of the pollution reductions.  In Table 7-4, we provide the benefits using our anchor points of Pope et al. and Laden et al. as well as the results from the expert elicitation on PM mortality.  Figure 7-2 provides a visual representation of the range of PM2.5-related benefits estimates using concentration-response functions supplied by experts.  Figure 7-3 shows a breakdown of monetized benefits by engine size.

      The benefit-per-ton estimates shown in this RIA are different than the benefit-per-ton estimates cited in the 2010 final SI RICE RIA for two reasons.  First, these estimates are based on updated air quality modeling, which results in slightly higher benefits per ton for other non-EGU point sources than the previous modeling.  Second, these estimates have been inflated to 2010$. Third, the new air quality modeling did not provide estimates associated with reducing VOCs.

Table 7-2:	General Summary of Monetized PM2.5-Related Health Co-Benefits Estimates for the Proposed CI RICE NESHAP Reconsideration (millions of 2010$)[*]
                                   Pollutant
                          Emissions Reductions (tons)
                               Benefit per ton
                                  (Pope, 3%)
                               Benefit per ton
                                  (Laden, 3%)
                               Benefit per ton
                                  (Pope, 7%)
                               Benefit per ton
                                  (Laden, 7%)
              Total Monetized Benefits (millions of 2010$ at 3%)
              Total Monetized Benefits (millions of 2010$ at 7%)
PM2.5 Precursors

Direct PM2.5
                                     2,818
                                   $270,000
                                   $670,000
                                   $240,000
                                   $610,000
                                                                           $770
                                      to
$1,900
                                                                           $690
                                      to
$1,700

                                     Total
                                                                           $770
                                      to
$1,900
                                                                           $690
                                      to
$1,700
[*] All estimates are for the analysis year (2013), and are rounded to two significant figures so numbers may not sum across columns.  It is important to note that the monetized benefits do not include reduced health effects from direct exposure to NO2, ozone exposure, ecosystem effects, or visibility impairment. All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to form PM2.5.  The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.  Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology.

Table 7-3:	Summary of Reductions in Health Incidences from PM2.5-Related Co-Benefits for Proposed CI RICE NESHAP Reconsideration*
Avoided Premature Mortality

Pope et al.
                                      85
Laden et al.
                                      220
Avoided Morbidity

Chronic Bronchitis
                                      59
Emergency Department Visits, Respiratory
                                      66
Hospital Admissions, Respiratory
                                      16
Hospital Admissions, Cardiovascular
                                      35
Acute Bronchitis
                                      130
Lower Respiratory
                                     1,700
Upper Respiratory
                                     1,300
Minor Restricted Activity Days
                                    68,000
Work Loss Days
                                    12,000
Asthma Exacerbation
                                     2,800
Acute Myocardial Infarction
                                      94
* All estimates are for the analysis year (2013) and are rounded to whole numbers with two significant figures.  All fine particles are assumed to have equivalent health effects because the scientific evidence is not yet sufficient to allow differentiation of effects estimates by particle type. Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology.

Table 7-4:	All PM2.5 Co-Benefits Estimates for the Proposed CI RICE NESHAP Reconsideration at discount rates of 3% and 7% in 2013 (in millions of 2010$)*

                                      3%
                                      7%
Benefit-per-ton Coefficients Derived from Epidemiology Literature
Pope et al.
                                     $770
                                     $690
Laden et al.
                                    $1,900
                                    $1,700
Benefit-per-ton Coefficients Derived from Expert Elicitation
Expert A
                                    $2,000
                                    $1,800
Expert B
                                    $1,500
                                    $1,400
Expert C
                                    $1,500
                                    $1,400
Expert D
                                    $1,100
                                     $970
Expert E
                                    $2,500
                                    $2,200
Expert F
                                    $1,400
                                    $1,200
Expert G
                                     $920
                                     $820
Expert H
                                    $1,200
                                    $1,000
Expert I
                                    $1,500
                                    $1,400
Expert J
                                    $1,200
                                    $1,100
Expert K
                                     $290
                                     $260
Expert L
                                    $1,000
                                     $910
[*]All estimates are rounded to two significant figures. The benefits estimates from the Expert Elicitation are provided as a reasonable characterization of the uncertainty in the mortality estimates associated with the concentration-response function.  Confidence intervals are unavailable for this analysis because of the benefit-per-ton methodology

Figure 7-2:	Total Monetized PM2.5 Co-Benefits of Proposed CI RICE NESHAP Reconsideration in 2013
*This graph shows the estimated benefits at discount rates of 3% and 7% using effect coefficients derived from the Pope et al. study and the Laden et al study, as well as 12 effect coefficients derived from EPA's expert elicitation on PM mortality.  The results shown are not the direct results from the studies or expert elicitation; rather, the estimates are based in part on the concentration-response function provided in those studies.

Figure 7-3:	Breakdown of Total Monetized PM2.5 Co-Benefits of Proposed CI RICE NESHAP Reconsideration by Engine Size
Unquantified Benefits
The monetized benefits estimated in this RIA only reflect a subset of benefits attributable to the health effect reductions associated with ambient fine particles.  Data, time, and resource limitations prevented EPA from quantifying the impacts to, or monetizing the benefits from several important benefit categories, including benefits from reducing exposure to HAP, CO, and ozone exposure, as well as ecosystem effects, and visibility impairment. This does not imply that there are no benefits associated with these emission reductions.  These benefits are described qualitatively in this section.
7.2.1 HAP Benefits
     Even though emissions of air toxics from all sources in the U.S. declined by approximately 42% since 1990, the 2005 National-Scale Air Toxics Assessment (NATA) predicts that most Americans are exposed to ambient concentrations of air toxics at levels that have the potential to cause adverse health effects (U.S. EPA, 2011c).  The levels of air toxics to which people are exposed vary depending on where people live and work and the kinds of activities in which they engage.  In order to identify and prioritize air toxics, emission source types and locations that are of greatest potential concern, U.S. EPA conducts the NATA.  The most recent NATA was conducted for calendar year 2005 and was released in March 2011.  NATA includes four steps:
      1) Compiling a national emissions inventory of air toxics emissions from outdoor sources
      2) Estimating ambient and exposure concentrations of air toxics across the United States
      3) Estimating population exposures across the United States
      4) Characterizing potential public health risk due to inhalation of air toxics including both cancer and noncancer effects
      Based on the 2005 NATA, EPA estimates that about 5% of census tracts nationwide have increased cancer risks greater than 100 in a million.  The average national cancer risk is about 50 in a million.  Nationwide, the key pollutants that contribute most to the overall cancer risks are formaldehyde and benzene.  Secondary formation (e.g., formaldehyde forming from other emitted pollutants) was the largest contributor to cancer risks, while stationary, mobile and background sources contribute almost equal portions of the remaining cancer risk.
      Noncancer health effects can result from chronic, subchronic, or acute inhalation exposures to air toxics, and include neurological, cardiovascular, liver, kidney, and respiratory effects as well as effects on the immune and reproductive systems.  According to the 2005 NATA, about three-fourths of the U.S. population was exposed to an average chronic concentration of air toxics that has the potential for adverse noncancer respiratory health effects. Results from the 2005 NATA indicate that acrolein is the primary driver for noncancer respiratory risk.
      Figure 7-4 and Figure 7-5 depict the estimated census tract-level carcinogenic risk and noncancer respiratory hazard from the assessment.  It is important to note that large reductions in HAP emissions may not necessarily translate into significant reductions in health risk because toxicity varies by pollutant, and exposures may or may not exceed levels of concern.  For example, acetaldehyde mass emissions are more than double acrolein emissions on a national basis, according to EPA's 2005 National Emissions Inventory (NEI).  However, the Integrated Risk Information System (IRIS) reference concentration (RfC) for acrolein is considerably lower than that for acetaldehyde, suggesting that acrolein could be potentially more toxic than acetaldehyde.  Thus, it is important to account for the toxicity and exposure, as well as the mass of the targeted emissions.

Figure 7-4	Estimated Chronic Census Tract Carcinogenic Risk from HAP exposure from outdoor sources (2005 NATA)

Figure 7-5	Estimated Chronic Census Tract Noncancer (Respiratory) Risk from HAP exposure from outdoor sources (2005 NATA)
Due to methodology and time limitations under the court-ordered schedule, we were unable to estimate the benefits associated with the hazardous air pollutants that would be reduced as a result of these rules. In a few previous analyses of the benefits of reductions in HAP, EPA has quantified the benefits of potential reductions in the incidences of cancer and non-cancer risk (e.g., U.S. EPA, 1995). In those analyses, EPA relied on unit risk factors (URF) developed through risk assessment procedures. These URFs are designed to be conservative, and as such, are more likely to represent the high end of the distribution of risk rather than a best or most likely estimate of risk. As the purpose of a benefit analysis is to describe the benefits most likely to occur from a reduction in pollution, use of high-end, conservative risk estimates would overestimate the benefits of the regulation. While we used high-end risk estimates in past analyses, advice from the EPA's Science Advisory Board (SAB) recommended that we avoid using high-end estimates in benefit analyses (U.S. EPA-SAB, 2002). Since this time, EPA has continued to develop better methods for analyzing the benefits of reductions in HAP.
As part of the second prospective analysis of the benefits and costs of the Clean Air Act (U.S. EPA, 2011a), EPA conducted a case study analysis of the health effects associated with reducing exposure to benzene in Houston from implementation of the Clean Air Act (IEc, 2009). While reviewing the draft report, EPA's Advisory Council on Clean Air Compliance Analysis concluded that "the challenges for assessing progress in health improvement as a result of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response functions, uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk estimates to low doses and the challenges of tracking health progress for diseases, such as cancer, that have long latency periods" (U.S. EPA-SAB, 2008).
In 2009, EPA convened a workshop to address the inherent complexities, limitations, and uncertainties in current methods to quantify the benefits of reducing HAP. Recommendations from this workshop included identifying research priorities, focusing on susceptible and vulnerable populations, and improving dose-response relationships (Gwinn et al., 2011).
In summary, monetization of the benefits of reductions in cancer incidences requires several important inputs, including central estimates of cancer risks, estimates of exposure to carcinogenic HAP, and estimates of the value of an avoided case of cancer (fatal and non-fatal). Due to methodology and time limitations under the court-ordered schedule, we did not attempt to monetize the health benefits of reductions in HAP in this analysis. Instead, we provide a qualitative analysis of the health effects associated with the HAP anticipated to be reduced by these rules.EPA remains committed to improving methods for estimating HAP benefits by continuing to explore additional concepts of benefits, including changes in the distribution of risk.
Although numerous HAP may be emitted from CI RICE, a few HAP account for over 90% of the total mass of HAP emissions emitted. These HAP are formaldehyde (72%), acetaldehyde (8%), acrolein (7%), methanol (3%), and benzene (3%). Although we do not have estimates of emission reductions for each HAP, this rule for existing CI engines is anticipated to reduce 1,000 tons of HAP each year. Below we describe the health effects associated with the top 5 HAP by mass emitted from CI RICE.
Formaldehyde
      Since 1987, EPA has classified formaldehyde as a probable human carcinogen based on evidence in humans and in rats, mice, hamsters, and monkeys.  Substantial additional research since that time informs current scientific understanding of the health effects associated with exposure to formaldehyde. These include recently published research conducted by the National Cancer Institute (NCI) which found an increased risk of nasopharyngeal cancer and lymphohematopoietic malignancies such as leukemia among workers exposed to formaldehyde.,  In an analysis of the lymphohematopoietic cancer mortality from an extended follow-up of these workers, NCI confirmed an association between lymphohematopoietic cancer risk and peak formaldehyde exposures. A recent NIOSH study of garment workers also found increased risk of death due to leukemia among workers exposed to formaldehyde.  Extended follow-up of a cohort of British chemical workers did not find evidence of an increase in nasopharyngeal or lymphohematopoietic cancers, but a continuing statistically significant excess in lung cancers was reported.
      In the past 15 years there has been substantial research on the inhalation dosimetry for formaldehyde in rodents and primates by the Chemical Industry Institute of Toxicology (CIIT, now renamed the Hamner Institutes for Health Sciences), with a focus on use of rodent data for refinement of the quantitative cancer dose-response assessment.[,][,]  CIIT's risk assessment of formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. These data were modeled using a biologically-motivated two-stage clonal growth model for cancer and also a point of departure based on a Benchmark Dose approach.  However, it should be noted that recent research published by EPA indicates that when two-stage modeling assumptions are varied, resulting dose-response estimates can vary by several orders of magnitude.[,][,][,]  These findings are not supportive of interpreting the CIIT model results as providing a conservative (health protective) estimate of human risk.  EPA research also examined the contribution of the two-stage modeling for formaldehyde towards characterizing the relative weights of key events in the mode-of-action of a carcinogen.  For example, the model-based inference in the published CIIT study that formaldehyde's direct mutagenic action is not relevant to the compound's tumorigenicity was found not to hold under variations of modeling assumptions.
      Based on the developments of the last decade, in 2004, the working group of the IARC concluded that formaldehyde is carcinogenic to humans (Group 1), on the basis of sufficient evidence in humans and sufficient evidence in experimental animals - a higher classification than previous IARC evaluations.  After reviewing the currently available epidemiological evidence, the IARC (2006) characterized the human evidence for formaldehyde carcinogenicity as "sufficient," based upon the data on nasopharyngeal cancers; the epidemiologic evidence on leukemia was characterized as "strong."
      Formaldehyde exposure also causes a range of noncancer health effects, including irritation of the eyes (burning and watering of the eyes), nose and throat.  Effects from repeated exposure in humans include respiratory tract irritation, chronic bronchitis and nasal epithelial lesions such as metaplasia and loss of cilia.  Animal studies suggest that formaldehyde may also cause airway inflammation  -  including eosinophil infiltration into the airways. There are several studies that suggest that formaldehyde may increase the risk of asthma  -  particularly in the young.[,]
	The above-mentioned rodent and human studies, as well as mechanistic information and their analyses, were evaluated in EPA's recent Draft Toxicological Review of Formaldehyde  -  Inhalation Assessment through the Integrated Risk Information System (IRIS) program.  This draft IRIS assessment was released in June 2010 for public review and comment and external peer review by the National Research Council (NRC).  The NRC released their review report in April 2011 (http://www.nap.edu/catalog.php?record_id=13142).  The EPA is currently revising the draft assessment in response to this review.
Acetaldehyde
Acetaldehyde is classified in EPA's IRIS database as a probable human carcinogen, based on nasal tumors in rats, and is considered toxic by the inhalation, oral, and intravenous routes.  Acetaldehyde is reasonably anticipated to be a human carcinogen by the U.S. Department of Health and Human Services (DHHS) in the 11[th] Report on Carcinogens and is classified as possibly carcinogenic to humans (Group 2B) by the IARC.[,]  The primary noncancer effects of exposure to acetaldehyde vapors include irritation of the eyes, skin, and respiratory tract.

Acrolein
       EPA determined in 2003 that the human carcinogenic potential of acrolein could not be determined because the available data were inadequate.  No information was available on the carcinogenic effects of acrolein in humans and the animal data provided inadequate evidence of carcinogenicity.  The IARC determined in 1995 that acrolein was not classifiable as to its carcinogenicity in humans.
Acrolein is extremely acrid and irritating to humans when inhaled, with acute exposure resulting in upper respiratory tract irritation, mucus hypersecretion and congestion.  The intense irritancy of this carbonyl has been demonstrated during controlled tests in human subjects, who suffer intolerable eye and nasal mucosal sensory reactions within minutes of exposure.  These data and additional studies regarding acute effects of human exposure to acrolein are summarized in EPA's 2003 IRIS Human Health Assessment for acrolein.  Evidence available from studies in humans indicate that levels as low as 0.09 ppm (0.21 mg/m[3]) for five minutes may elicit subjective complaints of eye irritation with increasing concentrations leading to more extensive eye, nose and respiratory symptoms.  Lesions to the lungs and upper respiratory tract of rats, rabbits, and hamsters have been observed after subchronic exposure to acrolein.  Acute exposure effects in animal studies report bronchial hyper-responsiveness.  In a recent study, the acute respiratory irritant effects of exposure to 1.1 ppm acrolein were more pronounced in mice with allergic airway disease by comparison to non-diseased mice which also showed decreases in respiratory rate.  Based on these animal data and demonstration of similar effects in humans (i.e., reduction in respiratory rate), individuals with compromised respiratory function (e.g., emphysema, asthma) are expected to be at increased risk of developing adverse responses to strong respiratory irritants such as acrolein.
Benzene
      The EPA's IRIS database lists benzene as a known human carcinogen (causing leukemia) by all routes of exposure, and concludes that exposure is associated with additional health effects, including genetic changes in both humans and animals and increased proliferation of bone marrow cells in mice.[,][,]  EPA states in its IRIS database that data indicate a causal relationship between benzene exposure and acute lymphocytic leukemia and suggest a relationship between benzene exposure and chronic non-lymphocytic leukemia and chronic lymphocytic leukemia.  The IARC has determined that benzene is a human carcinogen and the DHHS has characterized benzene as a known human carcinogen.[,] A number of adverse noncancer health effects including blood disorders, such as preleukemia and aplastic anemia, have also been associated with long-term exposure to benzene.[,]

Methanol
      Exposure of humans to methanol by inhalation or ingestion may result in central nervous system depression and degenerative changes in the brain and visual systems.  After inhaled or ingested, methanol is converted to formate, a highly toxic metabolite that within the course of a few hours can cause narcosis, metabolic acidosis, headaches, severe abdominal and leg pain and visual degeneration that can lead to blindness.
      Methanol has been demonstrated to cause developmental toxicity in rats and mice, and reproductive and developmental toxicity in monkeys.  A number of studies have reported adverse effects in the offspring of rats and mice exposed to methanol by inhalation including reduced weight of brain pituitary gland, thymus, thyroid, reduced overall fetal body weight and increased incidence of extra ribs and cleft palate.,,  Methanol inhalation studies using rhesus monkeys have reported a decrease in the length of pregnancy, and limited evidence of impaired learning ability in offspring.,,,  EPA has not classified methanol with respect to its carcinogenicity.
Other Air Toxics
In addition to the compounds described above, other toxic compounds might be affected by these rules.  Information regarding the health effects of those compounds can be found in EPA's IRIS database.
7.2.2	Ozone Co-Benefits
      In the presence of sunlight, NOx and VOCs can undergo a chemical reaction in the atmosphere to form ozone.  Reducing ambient ozone concentrations is associated with significant human health benefits, including mortality and respiratory morbidity (U.S. EPA, 2008a).  Epidemiological researchers have associated ozone exposure with adverse health effects in numerous toxicological, clinical and epidemiological studies (U.S. EPA, 2006c).  These health effects include respiratory morbidity such as fewer asthma attacks, hospital and ER visits, school loss days, as well as premature mortality.
7.2.3	Carbon Monoxide Co-Benefits
      Carbon monoxide in ambient air is formed primarily by the incomplete combustion of carbon-containing fuels and photochemical reactions in the atmosphere. The amount of CO emitted from these reactions, relative to carbon dioxide (CO2), is sensitive to conditions in the combustion zone, such as fuel oxygen content, burn temperature, or mixing time. Upon inhalation, CO diffuses through the respiratory system to the blood, which can cause hypoxia (reduced oxygen availability). Carbon monoxide can elicit a broad range of effects in multiple tissues and organ systems that are dependent upon concentration and duration of exposure. The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010a) concluded that short-term exposure to CO is "likely to have a causal relationship" with cardiovascular morbidity, particularly in individuals with coronary heart disease. Epidemiologic studies associate short-term CO exposure with increased risk of emergency department visits and hospital admissions. Coronary heart disease includes those who have angina pectoris (cardiac chest pain), as well as those who have experienced a heart attack. Other subpopulations potentially at risk include individuals with diseases such as chronic obstructive pulmonary disease (COPD), anemia, or diabetes, and individuals in very early or late life stages, such as older adults or the developing young. The evidence is suggestive of a causal relationship between short-term exposure to CO and respiratory morbidity and mortality. The evidence is also suggestive of a causal relationship for birth outcomes and developmental effects following long-term exposure to CO, and for central nervous system effects linked to short- and long-term exposure to CO.
7.2.4	Visibility Impairment Co-Benefits
      Reducing secondary formation of PM2.5 would improve visibility throughout the U.S.
Fine particles with significant light-extinction efficiencies include sulfates, nitrates, organic
carbon, elemental carbon, and soil (Sisler, 1996). Suspended particles and gases degrade
visibility by scattering and absorbing light. Higher visibility impairment levels in the East are
due to generally higher concentrations of fine particles, particularly sulfates, and higher average
relative humidity levels. Visibility has direct significance to people's enjoyment of daily
activities and their overall sense of wellbeing. Good visibility increases the quality of life where
individuals live and work, and where they engage in recreational activities. Previous analyses
(U.S. EPA, 2006; U.S. EPA, 2011a; U.S. EPA, 2011b) show that visibility benefits are a significant welfare benefit category. Without air quality modeling, we are unable to estimate
visibility related benefits, nor are we able to determine whether VOC emission reductions would be likely to have a significant impact on visibility in urban areas or Class I areas.
       7.3	Characterization of Uncertainty in the Monetized Co-Benefits
In any complex analysis, there are likely to be many sources of uncertainty.  Many inputs are used to derive the final estimate of economic benefits, including emission inventories, air quality models (with their associated parameters and inputs), epidemiological estimates of concentration-response (C-R) functions, estimates of values, population estimates, income estimates, and estimates of the future state of the world (i.e., regulations, technology, and human behavior).  For some parameters or inputs it may be possible to provide a statistical representation of the underlying uncertainty distribution.  For other parameters or inputs, the necessary information is not available. As discussed in the PM2.5 NAAQS RIA (Table 5.5) (U.S. EPA, 2006), there are a variety of uncertainties associated with these PM benefits.  Therefore, the estimates of annual benefits should be viewed as representative of the magnitude of benefits expected, rather than the actual benefits that would occur every year.
It is important to note that the monetized benefit-per-ton estimates used here reflect specific geographic patterns of emissions reductions and specific air quality and benefits modeling assumptions. For example, these estimates do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors. Use of these $/ton values to estimate benefits may lead to higher or lower benefit estimates than if benefits were calculated based on direct air quality modeling. Great care should be taken in applying these estimates to emission reductions occurring in any specific location, as these are all based on national or broad regional emission reduction programs and therefore represent average benefits-per-ton over the entire United States. The benefits-per-ton for emission reductions in specific locations may be very different than the estimates presented here. For more information, see the TSD describing the calculation of the new benefit-per-ton estimates (U.S. EPA, 2012).

PM2.5 mortality benefits are the largest benefit category that we monetized in this analysis.  To better characterize the uncertainty associated with mortality impacts that are estimated to occur in areas with low baseline levels of PM2.5, we included the LML assessment.  For this analysis, policy-specific air quality data is not available due to time or resource limitations, thus we are unable to estimate the percentage of premature mortality associated with this specific rule's emission reductions at each PM2.5 level. As a surrogate measure of mortality impacts, we provide the percentage of the population exposed at each PM2.5 level using the source apportionment modeling used to calculate the benefit-per-ton estimates for this sector.   A very large proportion of the population is exposed at or above the lowest LML of the cohort studies (Figures 7-6 and 7-7), increasing our confidence in the PM mortality analysis. Figure 7-6 shows a bar chart of the percentage of the population exposed to various air quality levels in the pre- and post-policy policy.  Figure 7-7 shows a cumulative distribution function of the same data.  Both figures identify the LML for each of the major cohort studies.  As the policy shifts the distribution of air quality levels, fewer people are exposed to PM2.5 levels at or above the LML.  Using the Pope et al. (2002) study, the 77% of the population is exposed to annual mean PM2.5 levels at or above the LML of 7.5 ug/m[3].  Using the Laden et al. (2006) study, 25% of the population is exposed above the LML of 10 ug/m[3].  As we model avoided premature deaths among populations exposed to levels of PM2.5, we have lower confidence in levels below the LML for each study.   It is important to emphasize that we have high confidence in PM2.5-related effects down to the lowest LML of the major cohort studies.  Just because we have greater confidence in the benefits above the LML, this does not mean that we have no confidence that benefits occur below the LML.
      A large fraction of the baseline exposure occurs below the level of the National Ambient Air Quality Standard (NAAQS) for annual PM2.5 at 15 ug/m[3], which was set in 2006. It is important to emphasize that NAAQS are not set at a level of zero risk.  Instead, the NAAQS reflect the level determined by the Administrator to be protective of public health within an adequate margin of safety, taking into consideration effects on susceptible populations. While benefits occurring below the standard may be less certain than those occurring above the standard, EPA considers them to be legitimate components of the total benefits estimate.

Figure 7-6.	Percentage of Adult Population by Annual Mean PM2.5 Exposure in the Baseline

Figure 7-7.	Cumulative Distribution of Adult Population by Annual Mean PM2.5 Exposure in the Baseline

Above we present the estimates of the total benefits, based on our interpretation of the best available scientific literature and methods and supported by the SAB-HES and the NAS (NRC, 2002).  The benefits estimates are subject to a number of assumptions and uncertainties.  For example, for key assumptions underlying the estimates for premature mortality, which typically account for more than 90% of the total benefits, we were able to quantify include the following:

      1. PM2.5 benefits were derived through benefit per-ton estimates, which do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local factors that might lead to an over-estimate or under-estimate of the actual benefits of controlling directly emitted fine particulates.  We do not have data on the specific location of the air quality changes associated with this rulemaking; as such, it is not feasible to estimate the proportion of benefits occurring in different locations, such as designated nonattainment areas.  In addition, the benefit-per-ton estimates are based on emissions from existing sources.  To the extent that the geographic distribution of the emissions reductions for this rule are different than the modeled emissions, the benefits may be underestimated or overestimated. In general, there is inherently more uncertainty for new sources, which may not be included in the emissions inventory, than existing sources.
      2. We assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality.  This is an important assumption, because PM2.5 produced via transported precursors emitted from EGUs may differ significantly from direct PM2.5 released from diesel engines and other industrial sources, but no clear scientific grounds exist for supporting differential effects estimates by particle type.
      3. We assume that the health impact function for fine particles is linear down to the lowest air quality levels modeled in this analysis.  Thus, the estimates include health benefits from reducing fine particles in areas with varied concentrations of PM2.5, including both regions that are in attainment with fine particle standard and those that do not meet the standard down to the lowest modeled concentrations.
      4. To characterize the uncertainty in the relationship between PM2.5 and premature mortality (which typically accounts for 85% to 95% of total monetized benefits), we include a set of twelve estimates based on results of the expert elicitation study in addition to our core estimates.  Even these multiple characterizations omit the uncertainty in air quality estimates, baseline incidence rates, populations exposed and transferability of the effect estimate to diverse locations.  As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM2.5 estimates.  This information should be interpreted within the context of the larger uncertainty surrounding the entire analysis.  For more information on the uncertainties associated with PM2.5 benefits, please consult the PM2.5 NAAQS RIA (Table 5.5).

This RIA does not include the type of detailed uncertainty assessment found in the PM NAAQS RIA because we lack the necessary air quality input and monitoring data to run the benefits model.  In addition, we have not conducted any air quality modeling for this rule. Moreover, it was not possible to develop benefit-per-ton metrics and associated estimates of uncertainty using the benefits estimates from the PM RIA because of the significant differences between the sources affected in that rule and those regulated here.  However, the results of the Monte Carlo analyses of the health and welfare benefits presented in Chapter 5 of the PM RIA can provide some evidence of the uncertainty surrounding the benefits results presented in this analysis.

 Comparison of Co-Benefits and Costs
Using a 3% discount rate, we estimate the total combined monetized co-benefits of the reconsidered CI RICE NESHAP proposal to be $770 million to $1.9 billion in the implementation year (2013). Using a 7% discount rate, we estimate the total monetized co-benefits of the reconsidered CI RICE NESHAP proposal to be $690 million to $1.7 billion. The annualized social costs of the proposed NESHAP are $373 million (2010$) at a 7% interest rate. The annualized social costs of the proposed NESHAP are $372 million in 2008$.   As stated in Section 4 of this RIA,  the costs in 2008 dollars can be updated to 2010 dollars by applying the ratio of the 2010 Marshall & Swift (M&S) annual cost index and the 2008 M&S annual cost index, which is 1,457.4/1,449.3 = 1.01.  Thus, the net benefits are $400 million to $1.5 billion at a 3% discount rate and $320 million to $1.3 billion at a 7% discount rate. All of these estimates are in 2010$ for the year 2013.
Table 7-5 shows a summary of the monetized co-benefits, social costs, and net benefits for the CI RICE NESHAP, respectively. Figures 7-8 and 7-9 show the full range of net benefits estimates (i.e., annual co-benefits minus annualized costs) utilizing the 14 different PM2.5 mortality functions at discount rates of 3% and 7%. In addition, the benefits from reducing 14,000 tons of carbon monoxide and 1,000 tons of HAP each year from existing SI RICE have not been included in these estimates. EPA believes that the co-benefits are likely to exceed the costs under this rulemaking even when taking into account uncertainties in the cost and benefit estimates.

 Table 7-5.	Summary of the Monetized Benefits, Compliance Costs and Net benefits for the 2010 Rule with the Proposed Amendments to the Stationary CI Engine NESHAP in 2013 (millions of 2010 dollars)a

                               3% Discount Rate
                               7% Discount Rate
                                Proposed NESHAP
Total Monetized Benefits[2]
                                     $770
                                      to
                                    $1,900
                                     $690
                                      to
                                    $1,700
Total Compliance Costs[3]
                                     $373
                                     $373
Net Benefits
                                     $400
                                      to
                                    $1,500
                                     $320
                                      to
                                    $1,300
Non-monetized Benefits
Health effects from HAP exposure

Health effects from PM2.5 exposure from VOC emissions

Ecosystem effects

Visibility impairment
[1]All estimates are for the implementation year (2013), and are rounded to two significant figures. The annual ized compliance costs are $373 million in 2010$ as noted earlier in this RIA.  These costs, presented in 2008 dollars, can be updated to 2010 dollars by applying the ratio of the 2010 Marshall & Swift (M&S) annual cost index and the 2008 M&S annual cost index, which is 1,457.4/1,449.3 = 1.01.  Compliance costs are used as an approximation for social costs in this RIA.

[2] The total monetized benefits reflect the human health benefits associated with reducing exposure to PM2.5 through reductions of PM2.5 precursors such as directly emitted fine particles. Human health benefits are shown as a range from Pope et al. (2002) to Laden et al. (2006). These models assume that all fine particles, regardless of their chemical composition, are equally potent in causing premature mortality because the scientific evidence  is not yet sufficient to allow differentiation of effects estimates by particle type. Because these estimates were generated using benefit-per-ton estimates, we do not break down the total monetized benefits into specific components here.   See Figure 7-1 for an illustration of the breakdown, or the RIA for the final Cross-States Air Pollution Rule (EPA, 2011) for more information.

[3] The engineering compliance costs are annualized using a 7 percent discount rate.

Figure 7-8.	Net Benefits for Proposed CI RICE NESHAP Reconsideration at 3% discount rate*
*Net Benefits are quantified in terms of PM2.5 at a 3% discount rate for 2013 and are in 2010$.  This graph shows 14 benefits estimates combined with the cost estimate.  All fine particles are assumed to have equivalent health effects, but the benefit per ton estimates vary because each ton of precursor reduced has a different propensity to become PM2.5.  The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

Figure 7-9.	Net Benefits for Proposed CI RICE NESHAP Reconsideration at 7% discount rate*
*Net Benefits are quantified in terms of PM2.5 benefits at a 7% discount rate at a 7% discount rate for 2013 and are in 2010$.  This graph shows 14 benefits estimates combined with the cost estimate.  All fine particles are assumed to have equivalent health effects.  The monetized benefits incorporate the conversion from precursor emissions to ambient fine particles.

 7.5	References
Fann, N., C.M. Fulcher, B.J. Hubbell.  2009. "The influence of location, source, and emission type in estimates of the human health benefits of reducing a ton of air pollution." Air Qual Atmos Health 2:169 - 176.
Gwinn, M.R., J. Craig, D.A. Axelrad, R. Cook, C. Dockins, N. Fann, R. Fegley, D.E. Guinnup, G. Helfand, B. Hubbell, S.L. Mazur, T. Palma, R.L. Smith, J. Vandenberg, and B. Sonawane. 2011. "Meeting report: Estimating the benefits of reducing hazardous air pollutants -- summary of 2009 workshop and future considerations." Environ Health Perspect. Jan; 119(1): p. 125-30.
Kochi, I., B. Hubbell, and R. Kramer.  2006.  "An Empirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Environmental Policy Analysis."  Environmental and Resource Economics.  34: 385-406.
Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi, Y, et al. 2009. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.  HEI Research Report, 140, Health Effects Institute, Boston, MA.
Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006. Reduction in Fine Particulate Air Pollution and Mortality. American Journal of Respiratory and Critical Care Medicine. 173: 667-672.
Mrozek, J.R., and L.O. Taylor.  2002.  "What Determines the Value of Life?  A Meta-Analysis."  Journal of Policy Analysis and Management 21(2):253-270.
National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.
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United States
Environmental Protection
Agency
                 Office of Air Quality Planning and Standards
                   Health and Environmental Impacts Division
                          Research Triangle Park, NC