### From: Ed Nam/AA/USEPA/US

To: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> Cc: Lucie Audette/AA/USEPA/US@EPA, Robin Moran/AA/USEPA /US@EPA, William Charmley/AA/USEPA/US@EPA,

Kathryn Sargeant/AA/USEPA/US@EPA Date: 07/20/2012 11:02 PM Subject: Re: Executive Summary



## Chandana.

Attached is our Executive summary. WE will attach to our Preamble for final.

-Ed

Executive Summary 2017-2025 CAFE & GHG to OMB 7-20- 2012b.docx

From: Robin Moran/AA/USEPA/US To: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop .gov> Cc: Ed Nam/AA/USEPA/US@EPA, William Charmley/AA/USEPA/U S@EPA, james.tamm@dot.gov,

rebecca.yoon@dot.gov Date: 07/26/2012 04:51 PM Subject: EPA Placeholders on Air Quality Modeling and Cost S ensitivities



## Chandana,

In our 7/18 package submittal, we had a couple plac eholders for our analysis of air quality modeling i mpacts, the health impacts analysis, and the cost sensitivities analys is. The following files contain our EPA RIA and Pr eamble Section III write-ups for these issues.

Please let me know if you have any questions.

Robin

Robin Moran Senior Policy Advisor Assessment and Standards Division U.S. EPA, Office of Transportation and Air Quality 2000 Traverwood Dr. Ann Arbor, MI 48105 (734) 214-4781 (734) 214-4821 (fax)

RIA 3.11_Cost Sensitivities.docx

Preamble III.G and H_ AQ Modeling and Health Impact s.docx

RIA 6.2.2 and 6.3_AQ Impacts and Health Benefits.do cx

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 1 of 7 Executive Summary

I. 

## Purpose of the Regulatory Action

a. The Need for the Action and How the Action addre sses the Need



# NHTSA, on behalf of the Department of Transportatio n, and EPA are issuing final rules to further reduce greenhouse gas emissions and impr ove fuel economy for light-duty vehicles for model years 2017and beyond. On May 21, 2010, Presi dent Obama issued a Presidential Memorandum requesting that EPA and NHTSA develop th rough notice and comment rulemaking a coordinated National Program to improv e fuel economy and reduce greenhouse gas emissions of light-duty vehicles for model years 20 17-2025, building on the success of the first phase of the National Program for these vehicles fo r model years 2012-2016. These final rules are consistent with the President’s request and res pond to the country’s critical need to address global climate change and to reduce oil consumption

. These standards apply to passenger cars, light-duty trucks, and medium-duty passenger vehicles (i.e. sport utility vehicles, cross-over u tility vehicles, and light trucks), and represent t he continuation of a harmonized and consistent Nationa l Program for these vehicles. Under the National Program automobile manufacturers will be a ble to continue building a single light-duty national fleet that satisfies all requirements unde r both programs.

The National Program is estimated to save approxima tely 4 billion barrels of oil and to reduce GHG emissions by the equivalent of approxima tely 2 billion metric tons over the lifetimes of those light duty vehicles produced in MYs 2017-2025. The agencies project that fuel savings will far outweigh higher vehicle costs, and that the net benefits to society of the MYs 2017-2025 National Program will be in the range of $326 billion to $451 billion (7 and 3 percent discount rates, respectively) over the lifetimes of those light duty vehicles sold in MYs 2017- 2025.

The National Program is projected to provide signif icant savings for consumers due to reduced fuel use. Although the agencies estimate t hat technologies used to meet the standards will add, on average, about $1,800 to the cost of a new light duty vehicle in MY 2025, consumers who drive their MY 2025 vehicle for its e ntire lifetime will save, on average, $5,500 to $7,200 (7 and 3 percent discount rates, respecti vely) in fuel, for a net lifetime savings of $3,200 to $4,800. This estimate assumes gasoline p rices of $3.87 per gallon in 2025 with small increases most years throughout the vehicle’s lifet ime.

b. Legal Authority
EPA and NHTSA are finalizing separate sets of stand ards for passenger cars and for light trucks, under their respective statutory authority. EPA is setting national CO 2 

# emissions standards for passenger cars and light-trucks under section 202 (a) of the Clean Air Act (CAA)

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 2 of 7 ((42 USC section 7521 (a)), and under its authorit y to measure passenger car and passenger car fleet fuel economy pursuant to the Energy Policy an d Conservation Act (EPCA) 49 USC section 32904 (c). NHTSA is setting national corporate a verage fuel economy (CAFE) standards under the Energy Policy and Conservation Act (EPCA), as a mended by the Energy Independence and Security Act (EISA) of 2007 (49 U.S.C. 32902).

Section 202 (a) of the Clean Air Act requires EPA t o establish standards for emissions of pollutants from new motor vehicles which emissions cause or contribute to air pollution which may reasonably be anticipated to endanger public he alth or welfare. See Coalition for Responsible Regulation v. EPA, No. 09-1322 (D.C. Cir. June 26, 2012) slip op. p. 41 (“’[i]f EPA makes a finding of endangerment, the Clean Air Act requires the [a]gency to regulate emissions of the deleterious pollutant from new motor vehicle s. ‘...Given the non-discretionary duty in Section 202 (a)(1) and the limited flexibility avai lable under Section 202 (a)(2), which this court has held relates only to the motor-vehicle industry ,...EPA had no statutory basis on which it could ‘ground [any] reasons for further inaction” ( quoting State of Massachusetts v. EPA , 549 U.S. 497, 533, 535 (2007). In establishing such st andards, EPA must consider issues of technical feasibility, cost, and available lead time. Standa rds under section 202 (a) thus take effect only “after providing such period as the Administrator f inds necessary to permit the development and application of the requisite technology, giving app ropriate consideration to the cost of compliance within such period” (CAA section 202 (a )(2) (42 USC section 7512 (a)(2)).

EPCA, as amended by EISA, contains a number of prov isions regarding how NHTSA must set CAFE standards. EPCA requires that NHTSA establish separate passenger car and light truck standards (49 U.S.C. 32902(b)(1)) at “the max imum feasible average fuel economy level that it decides the manufacturers can achieve in th at model year (49 U.S.C. 32902(a)),” based on the agency’s consideration of four statutory factor s: technological feasibility, economic practicability, the effect of other standards of th e Government on fuel economy, and the need of the nation to conserve energy (49 U.S.C. 32902(f)). EPCA does not define these terms or specify what weight to give each concern in balanci ng them; thus, NHTSA defines them and determines the appropriate weighting that leads to the maximum feasible standards given the circumstances in each CAFE standard rulemaking. F or MYs 2011–2020, EPCA further requires that separate standards for passenger cars and for light trucks be set at levels high enough to ensure that the CAFE of the industry-wide combined fleet of new passenger cars and light trucks reaches at least 35 mpg not later than MY 2020 (49 U.S.C. 32902(b)(2)(A))]. For model years after 2020, standards need simply be set at the max imum feasible level [CITE].

Section I.D of the preamble contains a detailed dis cussion of both agencies’ statutory authority.

II. Summary of the Major Provisions of the Final Ru le

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 3 of 7 NHTSA and EPA are finalizing rules for light-duty v ehicles that the agencies believe represent the appropriate levels of fuel economy an d GHG emissions standards for model years 2017 and beyond pursuant to their respective statut ory authorities.

Standards

EPA is establishing standards that are projected to require, on an average industry fleet wide basis, 163 grams/mile of carbon dioxide (CO 2 

# ) in model year 2025, which is equivalent to 54.5 mpg if this level were achieved solely through improvements in fuel efficiency. 1 

# Consistent with its statutory authority, NHTSA has developed two phases of passenger car an d light truck standards in this rulemaking action. T he first phase, from MYs 2017-2021, includes final standards that are projected to require, on a n average industry fleet wide basis, a range from 40.3 – 41.0 mpg in MY 2021. The second phase of th e CAFE program, from MYs 2022-2025, includes standards that are not final, due to the s tatutory requirement that NHTSA set average fuel economy standards not more than 5 model years at a time. Rather, those standards are augural, meaning that they represent NHTSA’s curren t best estimate, based on the information available to the agency today, of what levels of st ringency might be maximum feasible in those model years. NHTSA projects that those standards c ould require, on an average industry fleet wide basis, a range from 48.7 – 49.7 mpg in model y ear 2025.

Both the CO 2 

# and CAFE standards are footprint-based, as are the standards currently in effect for these vehicles through model year 2016. The standards will become more stringent on average in each model year from 2017 through 2025. Generally, the larger the vehicle footprint, the less numerically stringent the corresponding ve hicle CO 2 

# emissions and MPG targets. As a result of the footprint-based standards, the burden of compliance is distributed across all vehicle footprints and across all manufacturers. Manufactur ers are not compelled to build vehicles of any particular size or type (nor do the rules create an incentive to do so), and each manufacturer will have its own fleet-wide standard that reflects the light duty vehicles it chooses to produce.

Mid-Term Evaluation

The agencies will conduct a comprehensive mid-term evaluation and agency decisionmaking process for the MYs 2022-2025 standards as d escribed in the proposal. The mid-term evaluation reflects the rules’ long time frame and, for NHTSA, the agency’s statutory obligation to conduct a de novo rulemaking in order to establish final standards f or MYs 2022-2025. In order to align the agencies’ proceedings for MYs 20 22-2025 and to maintain a joint national program, EPA and NHTSA will finalize their actions related to MYs 2022-2025 standards

1 

## Real-world CO 2 

## is typically 25 percent higher and real-world fuel economy is typically 20 percent lower than the CO 2 

## and CAFE compliance values discussed here. 163g/m i would be equivalent to 54.5 mpg, if the entire fl eet were to meet this CO 2 

## level through tailpipe CO 2 

## and fuel economy improvements. The agencies expect , however, that a portion of these improvements will be made through improvements in air conditioning leakage and throug h use of alternative refrigerants, which would not contribut e to fuel economy.

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 4 of 7 concurrently. If the EPA determination is that sta ndards may change, the agencies will issue a joint NPRM and joint Final Rules. NHTSA and EPA fu lly expect to conduct this mid-term evaluation in coordination with the California Air Resources Board, given our interest in maintaining a National Program to address GHG emiss ions and fuel economy. Further discussion of the mid-term evaluation is found in S ections III.B.3 and IV.A.3.b.

Compliance Flexibilities

As proposed, the agencies are finalizing several pr ovisions which provide compliance flexibility to manufacturers to meet the standards without compromising the program’s overall environmental and energy security objectives. Furth er discussion of compliance flexibilities is in Sections I. B.4, II.F, III.B, III.C, IV.I.

Credit Averaging, Banking and Trading

The agencies are continuing to allow manufacturers to generate credits for overcompliance with the CO 2 

# and CAFE standards. 2 

# A manufacturer will generate credits if its car and/or truck fleet achieves a fleet average CO 2 

# /CAFE level better than its car and/or truck standards. Conversely, a manufacturer will incur a debit/shortfall if its fleet average CO 2 

# /CAFE level does not meet the standard when all credits a re taken into account. As in the prior CAFE and GHG programs, a manufacturer whose fleet genera tes credits in a given model year would have several options for using those credits, inclu ding credit carry-back, credit carry-forward, credit transfers, and credit trading.

Air Conditioning Improvement Credits

As proposed, EPA is establishing that the maximum t otal A/C credits available for cars will be 18.8 grams/mile CO 2 

# -equivalent and 24.4 grams/mile for trucks CO 2 

# -equivalent. 3 

# The approaches used to calculate these credits for dire ct and indirect A/C improvement (i.e., improvements to A/C leakage (including substitution of low GHG refrigerant) and A/C efficiency) are generally consistent with those of the MYs 2012-2016 program, although there are several revisions. Most notably, a new test fo r A/C efficiency, optional under the GHG program starting in MY 2014, will be used exclusive ly in MY 2017 and beyond. Under its EPCA authority, EPA proposed and is finalizing prov isions to allow manufacturers to generate fuel consumption improvement values for purposes of CAFE compliance based on these same improvements in air conditioner efficiency.

Off-Cycle Credits

2 

## This credit flexibility is required by EPCA/EISA, see 49 U.S.C. 32903, and is well within EPA’s discreti on under section 202 (a) of the CAA.
3 

## This is further broken down by 5.0 and 7.2 g/mi re spectively for car and truck A/C efficiency credits , and 13.8 and 17.2 g/mi respectively for car and truck alternativ e refrigerant credits.

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 5 of 7 EPA proposed and is finalizing provisions allowing manufacturers to continue to generate and use off-cycle credits to demonstrate c ompliance with the GHG standards. These credits are for measureable GHG emissions and fuel economy improvements attributable to use of technologies whose benefits are not measured by the two-cycle test mandated by EPCA. Under its EPCA authority, EPA proposed and is final izing provisions to allow manufacturers to generate fuel consumption improvement values for pu rposes of CAFE compliance based on the use of off-cycle technologies.

Incentives for Electric Vehicles, Plug-in Hybrid El ectric Vehicles, Fuel Cell Vehicles and compressed Natural Gas Vehicles

In order to provide temporary regulatory incentives to promote the penetration of certain “game changing” advanced vehicle technologies into the light duty vehicle fleet, EPA is finalizing, as proposed, an incentive multiplier fo r CO 2 

# emissions compliance purposes for all electric vehicles (EVs), plug-in hybrid electric ve hicles (PHEVs), and fuel cell vehicles (FCVs) sold in MYs 2017 through 2021. The incentives are expected to promote increased application of these advanced technologies in the program’s ear ly model years, which could achieve economies of scale that will support the wider appl ication of these technologies to help achieve the more stringent standards in MYs 2022-2025. In addition, in response to public comments persuasively explaining how infrastructure for comp ressed natural gas (CNG) vehicles could serve as a bridge to use of advanced technologies s uch as hydrogen fuel cells, EPA is finalizing an incentive multiplier for CNG vehicles sold in MY s 2017 through 2021.

NHTSA currently interprets EPCA and EISA as preclud ing it from offering incentives for the alternative fuel operation of EVs, PHEVs, FCVs, and NGVs, except as specified by statute, and thus did not propose and is not including incen tive multipliers comparable to the EPA incentive multipliers described above.

Incentives for Use of Advanced Technologies includi ng Hybridization for full-Size Pickup Trucks

The agencies recognize that the standards presented in this final rule for MYs 2017-2025 will be challenging for large vehicles, including f ull-size pickup trucks. To help address this challenge, the program will, as proposed, contain i ncentives for the use of hybrid electric and other advanced technologies in full-size pickup tru cks.

III. Costs and Benefits of National Program



# It is important to note that NHTSA’s CAFE standards and EPA’s GHG standards will both be in effect, and both will lead to increases in average fuel economy and reductions in GHGs. The two agencies’ standards together compris e the National Program, and the following discussions of the respective costs and benefits of NHTSA’s CAFE standards and EPA’s GHG

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 6 of 7 standards does not change the fact that both the CA FE and GHG standards, jointly, are the source of the benefits and costs of the National Pr ogram

The costs and benefits projected by NHTSA to result from the CAFE standards are presented first, followed by those projected by EPA to result from the GHG emissions standards. For several reasons, the estimates for costs and be nefits presented by NHTSA and EPA for their respective rules, while consistent, are not directl y comparable, and thus should not be expected to be identical. See Section I.C of the preamble f or further details and discussion.

NHTSA has analyzed in detail the projected costs an d benefits for the 2017-2025 CAFE standards for light-duty vehicles. NHTSA estimates that the required fuel economy increases would lead to fuel savings totaling a range from 18 0 billion to 184 billion gallons throughout the lives of light duty vehicles sold in MYs 2017-2025. At a 3 percent discount rate, the present value of the economic benefits resulting from those fuel savings is between $513 billion and $525 billion; at a 7 percent private discount rate, the present value of the economic benefits resulting from those fuel savings is between $400 b illion and $409 billion. The agency further
estimates that these new CAFE standards will lead t o corresponding reductions in CO 2 

# emissions totaling 1.9 billion metric tons during the lives o f light duty vehicles sold in MYs 2017-2025. The present value of the economic benefits from avo iding those emissions is approximately $53 billion, based on a global social cost of carbon va lue of about $26 per metric ton (in 2017, and growing thereafter).

NHTSA’s Estimated MYs 2017-2025 Costs, Benefits, an d Net Benefits (Billions of 2010 dollars)) under the CAFE Standards 4



# 3% discount rate 7% discount rate

Lifetime present value Annualized

value Lifetime

present value Annualized

value

Costs 2008

2010 (150) – (143) (6.4) –

(6.2) (148) –

(140) (10.7) –

(10.4)

Benefits 2008

2010 657 – 641 26.3 –

25.6 525 –

512 36.7 –

35.8

Net Benefits 2008

2010 507 – 498 19.9 –

19.4 377 –

372 26.0 –

25.4

EPA has analyzed in detail the projected costs and benefits of the 2017-2025 GHG standards for light-duty vehicles. The Table below shows EPA’s estimated lifetime discounted cost, fuel savings, and benefits for all such vehic les projected to be sold in model years 2017-

4 

## “Estimated Required” analysis that does not accoun ting for the compliance flexibilities and advanced technologies that NHTSA is prohibited from consider ing when determining the maximum feasible level of new CAFE standards, since manufacturers’ decisions to u se those flexibilities and technologies are volunta ry.

# *** E.O. 12866 Review – Draft – Do Not Cite, Quote, or Release During Review ***

Page 7 of 7 2025. The benefits include impacts such as climate -related economic benefits from reducing emissions of CO 2 

# (but not other GHGs), reductions in energy securit y externalities caused by U.S. petroleum consumption and imports, the value o f certain particulate matter-related health benefits (including premature mortality), the value of additional driving attributed to the VMT rebound effect, the value of reduced refueling time needed to fill up a more fuel efficient vehicle. The analysis also includes estimates of economic im pacts stemming from additional vehicle use, such as the economic damages caused by accidents, c ongestion and noise (from increased VMT rebound driving).

EPA’s Estimated 2017-2025 Model Year Lifetime Disco unted Costs, Benefits, and Net Benefits assuming the 3% discount rate SCC Value 5 

# (Billions of 2010 dollars)

Lifetime Present Value d 

# – 3% Discount Rate Program Costs -$150 Fuel Savings $475
Benefits $126
Net Benefits d 

# $451

Annualized Value f



# – 3% Discount Rate Annualized costs -$6.49 Annualized fuel savings $20.5 Annualized benefits $5.46 Net benefits $19.5

Lifetime Present Value d 

# - 7% Discount Rate Program Costs -$144 Fuel Savings $364
Benefits $106
Net Benefits e



# $326
Annualized Value f



# – 7% Discount Rate Annualized costs -$10.8 Annualized fuel savings $27.3 Annualized benefits $7.96 Net benefits $24.4

5 

## Further notes and details concerning these SCC Val ue are found in Section I.C.2. Table I-17.

### 2017 Regulatory Impact Analysis

1 Regulatory Impacts Analysis, Chapter 3.11

3.11 Cost Sensitivities

3.11.1 Overview

We have conducted several sensitivity analyses on a variety of input parameters. We have run the OMEGA model to generate 2025MY results for each of these sensitivities. We have looked at different levels of mass reduction c osts, battery pack costs, indirect cost multipliers, and learning rates. These sensitiviti es are summarized in

Table 3.11-1, followed by a discussion of the methods, with the results in Table 3.11-10 . Additional sensitivities with regard to benefits ar e shown in RIA Chapter 4.

Table 3.11-1 Summary of Cost Sensitivities
Sensitivity parameter Low side sensitivity High sid e sensitivity Mass reduction direct manufacturing costs 40% lower 40% higher

Battery pack direct manufacturing
costs 10% lower for P2 HEVs 20% lower for PHEV/EV 10% higher for P2 HEVs 20% higher for PHEV/EV Indirect cost multipliers Low side of 95% confidenc e interval of modified Delphi survey results High side of 95% confidence interval of modified Delphi survey results Learning rates a P-value of 30% on steep portion of the curve; cost reductions of 4%/3%/2% per year for each 5 year increment on the flat portion of the learning curve P-value of 10% on steep portion of the curve; cost reductions of 2%/1%/0% per year for each 5 year increment on the flat portion of the learning curve

a Higher learning rates results in lower costs, henc e the low side sensitivity uses the higher learning rates while the high side sensitivity uses the lower lear ning rates.



### 3.11.2 Mass Sensitivity

For the mass reduction cost sensitivity, we adjuste d the mass reduction DMC cost equation by +/-40%. That cost equation is shown in Table 3.11-2 along with the cost equation used for each side of the mass reduction cost sensi tivity.

Table 3.11-2 Mass Reduction Cost Sensitivities
Sensitivity parameter Mass reduction DMC equation used Low side DMC=$2.60x, where x=% mass reduction Primary case DMC=$4.33x, where x=% mass reduction High side DMC=$6.06x, where x=% mass reduction



### As mass reduction is a relatively cost effective technology, even with higher costs, OMEGA still choose s a relatively similar , but somewhat diminished degree of mass reduction. By contrast, even with lower costs, mas s reduction is still limited by the constraints given by the safety analysis. . These impacts would be greater on manufacturers that use more mass reduction technology, and less o n those that use less. Formatted: Indent: First line: 44 px, Space Before: 16 px, After: 16 px, Don't hyphenate Deleted: For the analyses presented in and

Formatted: Space Before: 16 px, After: 16 px, Don't hyphenate Deleted: summarized

Deleted: ¶

Deleted:

Deleted: ¶

Deleted: We did not re-rank OMEGA packages for the mass reduction cost sensitivities but rather us ed the same input files used for our primary analysis with new mass costs. This should have no impact on the results other than making them conservative since re-ranking packages would serve to move the modeling to more cost effective technologies, thus reducing the cost impact of this sensitivity. Beca use
Deleted: would

Deleted: given the stringency of the MY 2025 standards

Deleted: would

Deleted: still

Deleted: be

Deleted: fatality

Deleted: analysis

Deleted: As a result, the mass reduction sensitivity does not have any impact on the relative ranking of packages or the engine and hybridization technologies that would be added in response to our standards.¶ The high mass cost inputs increased the average compliance costs of the reference case by $XX andthe control case by $XX, for a net average pervehicle cost increase of $102 in MY 2025. The low mass cost inputs decreased the average compliance costs of the reference case by $xx and the control case by $xx for a net average cost decrease of $xx

### 2017 Regulatory Impact Analysis

2 3.11.3 Battery Sensitivity

For the battery pack cost sensitivities, we decreas ed/increased the battery pack DMCs by the amounts shown in Table 3.11-3. As presented in Chapter 3 of the joint TSD, we have developed linear regressions for our battery pack c osts. These linear regressions provide battery pack DCM as a function of net weight reduct ion of the vehicle. Table 3.11-3 and Table 3.11-5 show the linear regressions used for our low side and high side sensitivity analyses, respectively, while Table 3.11-4 presents the linear regressions used for our primary analysis (as presented in Chapter 3 of the joint T SD).

Table 3.11-3 Linear Regressions of Battery Pack Dir ect Manufacturing Costs vs Net

Weight Reduction used for Low Side Sensitivity (201 0$)

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

Small car -$163x+$653 -$688x+$2,026 -$1,214x+$2,917 -$1,488x+$4,105 -$1,734x+$4,892 -$1,636x+$6,464 Standard

car -$216x+$721 -$1,235x+$2,370 -$1,756x+$3,511 -$2,203x+$4,818 -$2,367x+$5,645 -$2,042x+$7,803
Large car -$332x+$843 -$1,505x+$2,987 -$3,760x+$4,808 -$3,485x+$6,180 -$3,718x+$6,904 -$2,272x+$8,896 Small MPV

- $202x+$701

- $858x+$2,268

- $1,565x+$3,397

- $1,649x+$4,797

- $2,119x+$5,834

- $15x+$8,087 Large MPV -$272x+$788 Truck -$330x+$909 Notes: “x” in the equations represents the net weight redu ction as a percentage, so a subcompact P2 HEV batte ry pack with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (- $163)x(15%)+$653=$629. The agencies did not regress PHEV or EV costs for t he large MPV and truck vehicle classes since we do not believe these vehicle classes would use the technol ogies. 

### Table 3.11-4 Linear Regressions of Battery Pack Di rect Manufacturing Costs vs Net

Weight Reduction used for the Primary Analysis (201 0$)

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

Small car -$181x+$726 -$861x+$2,533 -$1,517x+$3,646 -$1,859x+$5,131 -$2,168x+$6,115 -$2,045x+$8,080 Standard

car -$240x+$801 -$1,543x+$2,962 -$2,195x+$4,389 -$2,754x+$6,023 -$2,958x+$7,056 -$2,552x+$9,753 Large car -$369x+$937 -$1,881x+$3,734 -$4,700x+$6,010 -$4,356x+$7,725 -$4,647x+$8,630 -$2,840x+$11,120 Small MPV -$224x+$779 -$1,073x+$2,835 -$1,957x+$4,247 -$2,061x+$5,997 -$2,649x+$7,293 -$19x+$10,109 Large MPV -$303x+$876 Truck -$367x+$1,010 Notes: “x” in the equations represents the net weight redu ction as a percentage, so a subcompact P2 HEV batte ry pack with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (- $181)x(15%)+$726=$699. The agencies did not regress PHEV or EV costs for t he large MPV and truck vehicle classes since we do not believe these vehicle classes would use the technol ogies.



### Table 3.11-5 Linear Regressions of Battery Pack Di rect Manufacturing Costs vs Net

Weight Reduction used for the High Side Sensitivity (2010$)

Vehicle Class P2 HEV PHEV20 PHEV40 EV75 EV100 EV150

Small car -$200x+$798 -$1,033x+$3,039 -$1,821x+$4,376 -$2,231x+$6,157 -$2,601x+$7,338 -$2,455x+$9,696 Standard

car -$264x+$881 -$1,852x+$3,555 -$2,633x+$5,266 -$3,305x+$7,227 -$3,550x+$8,467 -$3,063x+$11,704

### 2017 Regulatory Impact Analysis

3 Large car -$406x+$1,031 -$2,257x+$4,480 -$5,639x+$7,212 -$5,227x+$9,269 -$5,577x+$10,357 -$3,407x+$13,344 Small MPV -$247x+$857 -$1,287x+$3,402 -$2,348x+$5,096 -$2,473x+$7,196 -$3,179x+$8,752 -$23x+$12,131 Large MPV -$333x+$963 Truck -$404x+$1,111 Notes: “x” in the equations represents the net weight redu ction as a percentage, so a subcompact P2 HEV batte ry pack with a 20% applied weight reduction and, therefore, a 15% net weight reduction would cost (- $200)x(15%)+$798=$768. The agencies did not regress PHEV or EV costs for t he large MPV and truck vehicle classes since we do not believe these vehicle classes would use the technol ogies.



### In the high case, the penetration of EVs decreased slightly, and MHEVs declined slightly as companies and MY 2025 TDS 24, start stop and HEV penetration s increased slightly . In the low cost case, the MY 2025 penetration of EVs increased

, while the HEV penetration decreased . In general, these shifts were slight, as this ru lemaking doesn’t rely heavily on strong hybrids, EVs, or other battery t echnology vehicles. In general, changing the battery costs shifted the choice between HEVs a nd EVs. As both EVs and HEVs are less cost effective (in this set of inputs) than convent ional technologies, the penetrations of nonbattery dependent technologies was generally little changed.

3.11.4 ICM Sensitivity

For the ICM sensitivity, we looked at the 95% confi dence intervals of the survey responses gathered as part of the modified Delphi p rocess used to generate our low, medium and high2 complexity ICMs. We discuss this modifie d Delphi process in Chapter 3 of the joint TSD and provide details in a memorandum to th e docket (EPA-HQ-OAR-2010-0799). A 

### In that memorandum, the survey responses from each respondent are presented for each element of the ICM along with average responses, st andard deviations and other statistical measures. Using these, we calculate the ICM elemen ts at the low side of the 95% confidence interval and at the high side. Table 3.11-6 and Ta ble 3.11-8 show the ICMs used for the low side and high side sensitivity analyses, respective ly, while Table 3.11-7 shows the ICMs used for our primary analysis. For the High1 ICM, since it was generated using a consensus approach rather than blind surveys, we have scaled the ICM elements using the same ratios as resulted from the 95% confidence intervals for the High2 ICM.

Table 3.11-6 ICMs used for the Low Side Sensitivit y Near term Long term Summed Complexity Warranty Non-warranty Warranty Non-warra nty Near term Long term Low 0.004 0.113 0.001 0.090 1.118 1.091 Medium 0.037 0.225 0.025 0.148 1.262 1.174 High1 0.043 0.361 0.027 0.217 1.404 1.243 High2 0.048 0.479 0.041 0.272 1.528 1.313



### Table 3.11-7 ICMs used for the Primary Analysis
Near term Long term Summed

A “Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies,” Helfand, G., and Sherwood, T., Memorandum dated Aug ust 2009. Deleted: For the battery pack sensitivities, unlike the mass reduction sensitivities, we did re-rank OMEGA packages. However, we started with the master-sets of packages as described in Chapter 1 o f this RIA for both the 2016MY and 2025MY and did not start with preliminary-sets of packages and conduct a full package building/ranking process. Using the master-sets of packages, we inserted new battery pack costs, re-ranked the master-sets of packages to get the proper ordering of packages for each sensitivity case, then ran OMEGA. As noted above, this should have no impact on the results other than making them conservative since starting with a preliminary-set of packages and re-ranking would serve to move the modeling to more cost effective technologies, thus redu cing the cost impact. The high battery cost inputs increased the average compliance costs of the reference case by $X and th e control case by $XX, for a net cost increase of $XX

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### 2017 Regulatory Impact Analysis

4 Complexity Warranty Non-warranty Warranty Non-warra nty Near term Long term Low 0.012 0.230 0.005 0.187 1.242 1.193 Medium 0.045 0.343 0.031 0.259 1.387 1.290 High1 0.065 0.499 0.032 0.314 1.564 1.345 High2 0.074 0.696 0.049 0.448 1.770 1.497



### Table 3.11-8 ICMs used for the High Side Sensitiv ity
Near term Long term Summed Complexity Warranty Non-warranty Warranty Non-warra nty Near term Long term Low 0.019 0.347 0.010 0.284 1.366 1.294 Medium 0.052 0.461 0.037 0.369 1.513 1.406 High1 0.087 0.637 0.037 0.411 1.723 1.447 High2 0.099 0.914 0.057 0.623 2.012 1.680



### 3.11.5 Learning Rate Sensitivity

For the learning rate sensitivity, we increased the learning effects for the low side case and decreased the learning effects for the high sid e case. This sounds counterintuitive, but we have done this because the increased learning rates result in lower technology costs so, therefore, are more appropriate for the low side se nsitivity. The reverse is true when decreasing the learning rates. For our primary ana lysis, as described in Chapter 3 of the joint TSD, we have used a 20% p-value for technologies on the steep portion of the learning curve and then have used learning rates of 3% per year fo r five years, 2% per year for 5 years, then 1% per year for 5 years for technologies on the fla t portion of the learning curve. Table 3.11-9 shows how we have adjusted these learning ra tes for both the low and high side sensitivities.

Table 3.11-9 Learning Rates used for our Learning Rate Sensitivity

Sensitivity Steep learning rate Flat learning rate Low side 30% 4%, 3%, 2% Primary case 20% 3%, 2%, 1% High side 10% 2%, 1%, 0%



### 3.11.6 Summary of Sensitivity Impacts

The average per-vehicle impacts of the sensitivity runs are shown in Table 3.11-10. Note that the majority of these impacts are less th an $ 150 relative to the primary analysis costs. The ICM impacts are larger. For those sensitivities that change technology co sts, generally, an increase in the cost of a single tech nology will provide a smaller incremental change in total cost than a equivalent decrease in cost of a single technology. This is due to the TARF function in the model which attempts to mi nimize incremental cost. By contrast, learning and ICM changes, because they affect every technology, tend to produce more symmetrical increases and decreases.

Table 3.11-10 Summary of Per-vehicle Cost Impacts of Sensitivity Analyses in MY 2025 relative to Primary Analysis (2010$) Deleted: Like done for the battery pack sensitivities, we re-ranked OMEGA packages using the master-sets of packages as described in Chapter 1 of this RIA for the 2016MY and 2025MY. We did not start with preliminary-sets of packages and conduct a full package building/ranking process. Using the master-sets of packages, we inserted new costs calculated using the low/high IMCs, re-ranked the master-sets of packages to get the proper ordering of packages for each sensitivity case, the n ran OMEGA. As noted above, this should have no impact on the results other than making them conservative since starting with a preliminary-set of packages and re-ranking would serve to move the modeling to more cost effective technologies, thus reducing the cost impact. ¶ The high ICM inputs increased the average compliance costs of the reference case by $XX andthe control case by $XX, for a net average pervehicle cost increase of $XX9 in MY 2025. The low ICM inputs decreased the average compliance costs of the reference case by $XX5 and the control case by $XX for a net average cost decrease of $XX. ¶

Deleted: For the learning sensitivity, we re-ranked OMEGA packages using the master-sets of packages as described in Chapter 1 of this RIA for the 2016MY and 2025MY. We did not start with preliminary-sets of packages and conduct a full package building/ranking process. Using the master

sets of packages, we inserted new costs calculated using the low/high learning rates, re-ranked the master-sets of packages to get the proper ordering of packages for each sensitivity case, then ran OMEGA. As noted above, this should have no impact on the results other than making them conservative since starting with a preliminary-set of packages and re-ranking would serve to move the modeling to more cost effective technologies, thus reducing the cost impact. ¶ The high learning inputs increased the average compliance costs of the reference case by $48 andthe control case by $XX, for a net average pervehicle cost increase of $XX in MY 2025. The low ICM inputs decreased the average compliance costs of the reference case by $XX and the control case b y $XX for a net average cost decrease of $XX. ¶

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### 2017 Regulatory Impact Analysis

5 Sensitivity Title Reference Case Change Control Case

Change Impact

Primary Case



### -- -- -- Mass Cost

High 

### $21 $65 $44
Mass Cost Low 

### -$24 -$87 -$63 Battery Cost High 

### $2 $72 $69 Battery Cost Low 

### -$2 -$74 -$72 ICM High 

### $110 $316 $206
ICM Low 

### -$114 -$317 -$203 Learning Rate High 

### $48 $177 $129 Learning Rate Low 

### -$44 -$159 -$115

3.11.7 NAS report

As in the proposal, w e note that EPA has decided not to base a sensitivi ty case on the 2010 National Academy of Science Report “Assessment of Technologies for Improving LightDuty Vehicle Fuel Economy, Assessment for Fuel Econ omy Technologies for Light-Duty Vehicles” (The National Academies Press, June 2010).

As discussed in detail in Chapter 3 of the Joint T echnical Support Document for this final rule, EPA and NHTSA have utilized the best av ailable information in order to estimate the cost and effectiveness for a large number of te chnologies which can be used to reduce GHG emissions and improve fuel efficiency.

In 2007, NHTSA commissioned the National Academy of Science to perform an assessment of, among other things, the cost and eff ectiveness of technologies for improving the fuel economy of light-duty vehicles. The 2010 NAS Committee published their results of their assessment in June of 2010. EPA has reviewed this report in detail and for the reasons discussed below, we have not relied upon this repor t as a primary assessment for our cost and effectiveness estimates for this final rule, and we have also not used the report to perform a sensitivity assessment based on the 2010 NAS report for the same reasons.

Our principal reasons are twofold. First, the 2010 NAS Committee focused their report on the near-term, specifically the 2010-2015 time frame, and not on the time frame of this final rule, which is 2017 to 2025. Second, on a range of topics EPA and NHTSA have relied upon newer information for cost and effectiv eness estimates.

With respect to the time frame of interest, in the Summary of the NAS 2010 report (pages S-1 and S-2), the NAS Committee discusses th at their costs estimates are for the 2010- 2015 time frame. The 2010 NAS Report also discusse s that there are longer-term technologies which are in the 5 to 15 year time hor izon which are not the focus of the NAS 2010 report. There are a number of specific exampl es where this difference in time frame is relevant to any potential comparison between the 20 10 NAS report and the EPA and NHTSA assessment for this final rule. For example, there are a number of technologies that EPA and NHTSA discuss in Chapter 3 of the Joint TSD which a re not a single, discrete piece of hardware, but rather a continuum of improvements wh ere the level of improvement can change given the potential time horizon. The 2010 NAS Committee considered at least six of Formatted Table Deleted: Low ($)

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### 2017 Regulatory Impact Analysis

6 these technologies: low friction lubricants, engine friction reduction, improved accessories, lower rolling resistance tires, aerodynamic drag im provement, and improved internals for automatic transmissions. The 2010 NAS report provi des cost and effectiveness estimates for one increment of improvement for each of these tech nologies applicable to the 2010-2015 time frame. This is similar to the approach utiliz ed by NHTSA and EPA for the MYs 2012- 2016 rulemaking. However, for the MYs 2017-2025 final rules, where EPA and NHTSA are using a model year baseline set of vehicles, the ag encies estimate that for each these technologies two increments of improvement can be i mplemented across the fleet between between promulgation of the final rule and MY 2025. Using the NAS Report estimates for these technologies thus, without basis, would not c onsider the further projected incremental improvements in these technologies.

A second example of the importance of the time fram e is evaluation of the effectiveness of gasoline direct injection with tur bocharging and downsizing. The 2010 NAS Committee considered one level of downsizing in the 2010-2015 time frame, and EPA and NHTSA took a similar approach for the MYs 2012-2016 rule. But, for the MYs 2017-2025 final rule, based on data in the literature, our di scussions with the auto companies and automotive suppliers, and a 2011 Ricardo study comm issioned by EPA, in the longer term additional levels of downsizing are achievable, inc luding in some cases with the use of cooled exhaust gas recirculation, that provide additional CO 2 

### /fuel consumption reductions. Those additional levels of downsizing were not considered by the 2010 NAS Committee in their assessment of near-term costs and effectiveness.

In addition to the difference in time frames being considered by the 2010 NAS report and this final rule, a second significant differenc e between the two assessments were the additional studies and information available to EPA and NHTSA which were not reviewed by 2010 NAS Committee. In many cases this was due to the additional two years EPA and NHTSA had available (while the NAS Committee’s repo rt was published in 2010, the bulk of their assessments occurred between 2007 and 2009), and in other areas this new information was the result of the many confidential meetings EP A and NHTSA had with auto companies and auto suppliers over the past two years.

The additional publically available studies which E PA and NHTSA utilized included new studies on the costs for mass reduction, lithi um-ion battery packs, 8 speed automatic transmissions, 8 speed dual-clutch transmissions, h ybrid electric vehicle, plug-in hybrid electric vehicle, and all electric vehicles. EPA a nd NHTSA also utilized new reports dealing with the use of indirect cost multipliers for estim ating indirect manufacturing costs. EPA and NHTSA also are using a number of new studies which were not available to the 2010 NAS Committee for the estimation of the effectiveness o f a large number of the 2017-2025 technologies; these include peer reviewed papers in the literature as well as the 2011 Ricardo study (discussed in detail in Chapter 3 of the Join t TSD). A partial list of the studies and data sources regarding technology feasibility, costs, l ead time, and effectiveness considered by EPA which were not reviewed by the 2010 NAS Committ ee or were published after they completed their work, or was obtained confidentiall y from automotive suppliers includes:

- 2011 Ricardo Report “Computer Simulation of Light -duty Vehicle Technologies for Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe” 1 

### , this report has Formatted: Subscript

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### 2017 Regulatory Impact Analysis

7 been peer reviewed and the peer review report and t he response to peer review comments are available in the EPA docket EPA-HQ-OAR -2010-0799.

- Argonne National Laboratories 2011 Report “Modeli ng the Performance and Cost of Lithium-Ion Batteries for Electric-Drive Vehicles” 2 

### and the accompanying Battery Performance and Cost Model, which estimates lithium -ion battery pack cost for the 2020 time frame. This report was peer reviewed and revised in 2011, and the model, report, and peer review report are available in the EPA docket EPA-HQ-OAR-2010- 0799.

- 2011 FEV Report “Light-Duty Technology Cost Analy sis Power-split and P2 HEV Case Studies.” 3 

### This report was peer reviewed, and a copy of the report, the peer review report, and the response to peer review comm ents report are available in the EPA docket EPA-HQ-OAR-2010-0799.

- 2011 FEV Report “Light-Duty Technology Cost Analy sis: Advanced 8-speed Transmissions” 4 

### . A copy of this report is available in the EPA do cket EPA-HQ-OAR2010-0799.

- 2010 Lotus Engineer Study “An Assessment of Mass Reduction Opportunities for a 2017 – 2020 Model Year Vehicle Program” 5 

### , this report has been peer reviewed, and a copy of the report and the peer review report are a vailable in the EPA docket EPAHQ-OAR-2010-0799.

- EPA vehicle fuel economy certification data from MY2011 and MY2012 vehicles, including for example the MY2011 Ford F-150 with th e 3.5L Ecoboost engine, MY2011 Sonata Hybrid, MY2012 Infiniti M35h hybrid, and several other advanced technology production vehicles.

- “EBDI - Application of a Fully Flexible High BME P Downsized Spark Ignited Engine.” Society of Autmotive Engineers (SAE) Techn ical Paper No. 2010-01-0587, Cruff, L., Kaiser, M., Krause, S., Harris, R., Krue ger, U., Williams, M., 2010. 6



### - “Water Cooled Exhaust Manifold and Full Load EGR Technology Applied to a Downsized Direct Injection Spark Ignition Engine.” SAE Technical Paper Series No. 2010-01-0356. Taylor, J., Fraser, N., Wieske, P., 2010. 7



### - “Requirements of External EGR Systems for Dual C am Phaser Turbo GDI Engines.” SAE Technical Paper Series No. 2010-01-0588. Roth, D.B., Keller, P, Becker, M., 2010. 8



### - “Doing More with Less - The Fuel Economy Benefit s of Cooled EGR on a Direct Injected Spark Ignited Boosted Engine,” SAE Technic al Paper Series, No. 2010-01- 0589. Kaiser, M., Krueger, U., Harris, R., Cruff, L., 2010. 9 

### EPA-HQ-OAR-2010- 0799

### 2017 Regulatory Impact Analysis

8 - “Using indirect cost multipliers to estimate the total cost of adding new technology in the automobile industry,” International Journal of Production Economics Rogozhin, A.,et al., 2009. 10



### - “Documentation of the Development of Indirect Cost Multipliers for Three Automotive Technologies,” EPA Technical Memorandum, Helfand, G., Sherwood, T., August 2009. 11



### - Confidential business information regarding the dev elopment status, effectiveness and costs for a large number of technologies obtained b y EPA in meetings during 2010 and 2011 with more than a dozen worldwide automotiv e suppliers involved in the development and production of a wide range of techn ologies, including but not limited to fuel injection systems, transmissions, turbochar gers, lower mass automotive components, tires, and automotive lithium-ion batte ries.

With the exception of the confidential business inf ormation and copyrighted information, copies of the reports and studies list ed above are available in the EPA docket for this final rule, EPA-HQ-OAR-2010-0799. Information on how to obtain copies of the SAE papers is also available in the EPA docket, or they can be order from SAE on-line at http://papers.sae.org/.

For the reasons described above, EPA has elected no t to perform a sensitivity assessment based on the 2010 NAS Report, nor have w e used the 2010 NAS Report as our primary basis for assessing the costs and effective ness of technologies for the proposal or this final rule.

EPA requested comment on our overall approach for b asing our assessment on technology feasibility, lead time, costs and effect iveness on the full range of information described in the Joint Technical Support Document (which includes consideration of the 2010 NAS Study), as opposed to an alternative appro ach in which EPA would base our technology feasibility, lead time, costs and effect iveness primarily on the 2010 NAS Study and place lower weighting or no weighting on the ad ditional information which has become available since the 2010 NAS Study (including those data sources, studies and reports listed above). EPA received public comment from the Delph i Corportation recommending that “the National Research Council technology cost estimates and implementation cadence data be included in the agencies' analyses and be considere d a primary source of information.” This comment is discussed in the Response to Comment doc ument, section 12.3.

EPA also requested comment specifically on EPA’s us e of the 2011 Ricardo study (listed above), and any ways to improve our estimat es of technology effectiveness, including the use of full vehicle simulation modeling as was used in the 2011 Ricardo study or alternative approaches. We also requested comment on the 2011 Ricardo Study and the Ricardo response to comments report with respect to the peer review conducted on the Ricardo report. We received comments from the ICCT, and several o ther organizations, these comments are discussed in section 12 of the Respons e to Comments document. These documents are all available in the EPA docket for t his rulemaking (EPA-HQ-OAR-2010-

### 2017 Regulatory Impact Analysis

9 0799). Significant additional detail regarding the 2011 Ricardo study and how it was used to inform EPA’s estimates of technology effectiveness is contained in Chapter 3 of the Joint Technical Support Document.

### 2017 Regulatory Impact Analysis

10 1 

### 2011 Ricardo Report “Computer Simulation of Light- duty Vehicle Technologies for Greenhouse Gas Emission Reductions in the 2020-2025 Timeframe”

2 

### Argonne National Laboratories 2011 Report “Modelin g the Performance and Cost of Lithium-Ion Batteries for Electric-Drive Vehicles”

3 

### 2011 FEV Report “Light-Duty Technology Cost Analys is Power-split and P2 HEV Case Studies”

4 

### 2011 FEV Report “Light-Duty Technology Cost Analys is: Advanced 8-speed Transmissions”

5 

### 2010 Lotus Engineer Study “An Assessment of Mass R eduction Opportunities for a 2017 – 2020 Model Year Vehicle Program”

6 

### “EBDI - Application of a Fully Flexible High BMEP Downsized Spark Ignited Engine.” Society of Autmotive Engineers (SAE) Technical Pape r No. 2010-01-0587, Cruff, L., Kaiser, M., Krause, S., Harris, R., Krueger, U., Williams, M., 2010

7 

### “Water Cooled Exhaust Manifold and Full Load EGR T echnology Applied to a Downsized Direct Injection Spark Ignition Engine.” SAE Techni cal Paper Series No. 2010-01-0356. Taylor, J., Fraser, N., Wieske, P., 2010

8 

### “Requirements of External EGR Systems for Dual Ca m Phaser Turbo GDI Engines.” SAE Technical Paper Series No. 2010-01-0588. Roth, D.B ., Keller, P, Becker, M., 2010

9 

### “Doing More with Less - The Fuel Economy Benefits of Cooled EGR on a Direct Injected Spark Ignited Boosted Engine,” SAE Technical Paper Series, No. 2010-01-0589. Kaiser, M., Krueger, U., Harris, R., Cruff, L., 2010

10 

### “Using indirect cost multipliers to estimate the t otal cost of adding new technology in the automobile industry,” International Journal of Prod uction Economics Rogozhin, A.,et al., 2009

11 

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# Preamble III.G.4 (Air Quality Impacts of Non-GHG Pollutants); III.H.7.a (Quantified and Monetized No n-GHG Health Impacts); III.10 (Summary of Costs and Benefits)

Preamble Section III.G.4

4. Air Quality Impacts of Non-GHG Pollutants

Air quality modeling was performed to assess the im pact of the vehicle standards on criteria and air toxic pollutants. In this section , we present information on current levels of pollution as well as projections for 2030, with res pect to ambient PM 2.5 

### , ozone, selected air toxics, visibility levels and nitrogen and sulfur d eposition. The results are discussed in more detail in Section 6.2.2 of the RIA.

a. Ozone

i. Current Levels

Concentrations that exceed the level of the ozone N AAQS occur in many parts of the country. The primary and secondary NAAQS for ozone are 8-hou r standards with a level of 0.075 ppm. The most recent revision to the ozone standards was in 2008; the previous 8-hour ozone standards, set in 1997, had a level of 0.08 ppm. I n 2004, the U.S. EPA designated nonattainment areas for the 1997 8-hour ozone NAAQS. 1,2 

### As of July 20 , 2012, there were 43 ozone nonattainment areas for the 1997 ozone NAAQS compos ed of 237 full or partial counties, with a total population of over 129 million. Nonattainmen t designations for the 2008 ozone standard s were finalized on April 30, 2012 and May 31, 2012

. 3 

### These designations include 46 areas, composed of 227 full or partial counties, with a population of over 123 million. As of July 20, 2012, 140 million people are living in ozone nonatt ainment areas.

.

ii. Projected Levels without the Vehicle Standards

States with ozone nonattainment areas are required to take action to bring those areas into attainment. The attainment date assigned to an ozo ne nonattainment area is based on the area’s classification. Most ozone nonattainment areas are required to attain the 1997 8-hour ozone NAAQS in the 2007 to 2013 time frame and attainment dates for the 2008 8-hour ozone NAAQS

1 69 FR 23858 (April 30, 2004).
2 A nonattainment area is defined in the Clean Air A ct (CAA) as an area that is violating an ambient st andard or is contributing to a nearby area that is violating the standard.
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### Page 2 of 27

are in the 2015 to 2032 timeframe. 4 

### Once an ozone nonattainment area has attained the NAAQS they are then required to maintain it thereafter.

EPA has already adopted many emission control progr ams that are expected to reduce ambient ozone levels. As a result of these and oth er federal, state and local programs, 8-hour ozone levels are expected to improve in the future. Even so, our air quality modeling projects that in 2030, with all current controls but excludi ng the impacts of the vehicle standards, up to 10 counties with a population of over 30 million would have projected design values above the level of the 2008 ozone standard of 0.075 ppm (75 ppb). These numbers do not account for those areas that are close to (e.g., within 10 percent of ) the 2008 ozone standard. These areas, although not above the standards, will also be impacted by c hanges in ozone concentrations as they work to ensure long-term maintenance of the ozone NAAQS.

iii. Projected Levels with the Vehicle Standards

Our modeling indicates that there will be very smal l changes in ambient ozone concentrations across most of the country. However , there will be small decreases in ozone design value concentrations in some areas of the co untry and small increases in ozone design value concentrations in other areas. 5 

### The increases in ozone design values are likely d ue mainly to the VMT rebound effect in some places and in oth er places are likely due mainly to increased electricity generation. The ozone decreases are li kely due mainly to changes in the location of EGUs, or power plants, in some places and in other places are likely due mainly to reduced fuel production. The average modeled 8-hour ozone desig n values are projected to increase by 0.01 ppb in 2030 and the design values for those countie s that are projected to be above the 2008 ozone standard in 2030 will decrease by 0.14 ppb du e to the vehicle standards.

b. Particulate Matter

i. Current Levels

There are many areas of the country that are curren tly in nonattainment for the PM 2.5 

### NAAQS. There are two NAAQS for PM 2.5 

### : an annual standard (15 micrograms per cubic meter ( μ g/m 3 

### ) and a 24-hour standard (35 μ g/m 3). 

### The most recent revisions to these standards were in 1997 and 2006. In June 2012, EPA proposed to revis e the PM 2.5 

### NAAQS and is scheduled to

4 The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area and the San Joaquin Valley Air Basin 8- hour ozone nonattainment area are designated as ext reme and will have to attain before June 15, 2024. The Sacramento, Coachella Valley, Western Mojave and Ho uston 8-hour ozone nonattainment areas are designat ed as severe and will have to attain by June 15, 2019.
5 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour ozone NAAQS. The full details involved in calculat ing an 8-hour ozone design value are given in appen dix I of 40 CFR part 50. Deleted: [placeholder]

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### Page 3 of 27

issue final revisions in December 2012 under a cour t-ordered schedule. The proposed changes include revising the annual PM 2.5 

### standard to a level between 12 and 13 μg/m 3 

### , and establishing a distinct secondary PM 2.5 

### standard for the protection of visibility, particu larly in urban areas.

In 2005 EPA designated nonattainment areas for the 1997 PM 2.5 

### NAAQS. 6 

### As of July 20 , 2012, over 91 million people lived in the 35 area s that are designated as nonattainment for the 1997 PM 2.5 

### NAAQS. These PM 2.5 

### nonattainment areas are comprised of 191 full or p artial counties. On October 8, 2009, the EPA issued final nonattainment area designations for the 2006 24-hour PM 2.5 

### NAAQS. 7 

### These designations include 32 areas, composed of 121 full or partial counties, with a population of over 70 million. In total, there are 50 PM 2.5 

### nonattainment areas with a population of over 105 million people. 8



### ii. Projected Levels without the Vehicle Standards

States with PM 2.5 

### nonattainment areas will be required to take actio n to bring those areas into attainment in the future. The 1997 PM 2.5 

### nonattainment areas are required to attain the 199 7
PM 2.5 

### NAAQS in the 2010 to 2015 time frame and then main tain it thereafter. 9 

### The 2006 24- hour PM 2.5 

### nonattainment areas are required to attain the 200 6 24-hour PM 2.5 

### NAAQS in the 2014 to 2019 time frame and then maintain it therea fter. 10



### EPA has already adopted many mobile source emission control programs that are expected to reduce ambient PM levels. As a result of these and other federal, state and local programs, the number of areas that fail to meet the PM 2.5 

### NAAQS in the future is expected to decrease. Even so, our air quality modeling projec ts that in 2030, with all current controls but excluding the impacts of the vehicle standards adop ted here, at least 4 counties with a population of almost 7 million would have projected design val ues above the level of the 1997 annual PM 2.5 

### standard of 15 μg/m 3 

### and 21 counties with a population of over 31 milli on would have projected design values above the level of the 2006 24-hour P M 2.5 

### standard of 35 μg/m 3 

### . These numbers do not account for those areas that are close to (e .g., within 10 percent of) the PM 2.5 

### standards. These areas, although not above the standards, will also be impacted by any changes in PM 2.5 

### concentrations as they work to ensure long-term mai ntenance of the PM 2.5 

### NAAQS.

6 70 FR 19844 (April 14, 2005).
7 74 FR 58688 (November 13, 2009).
8 Data come from Summary Nonattainment Area Populati on Exposure Report, current as of July 20 , 2012 at: http://www.epa.gov/oar/oaqps/greenbk/popexp.html and contained in Docket EPA-HQ-OAR-2010-0799.
10 U.S. EPA. (2011). PM Standards Revision – 2006: Ti meline. Available at http://www.epa.gov/PM/naaqsrev2006.html#timeline. A ccessed December 31, 2011.
10 U.S. EPA. (2011). PM Standards Revision – 2006: Ti meline. Available at http://www.epa.gov/PM/naaqsrev2006.html#timeline. A ccessed December 31, 2011. Deleted: 2.5

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### Page 4 of 27 iii. Projected Levels with the Vehicle Standards

Our modeling indicates that there will be very sm all changes in ambient PM 2.5 

### concentrations across most of the country. However , there will be small decreases in PM 2.5 

### design value concentrations in some areas of the co untry and small increases in PM 2.5 

### design value concentrations in other areas. 11 

### The decreases in PM 2.5 

### design values for some counties are likely due to emission reductions related to lo wer fuel production and the increases are likely due to increased emissions from the VMT rebound eff ect or increased electricity generation.

c. Air Toxics

i. Current Levels

The majority of Americans continue to be exposed to ambient concentrations of air toxics at levels which have the potential to cause adverse health effects. 12 

### The levels of air toxics to which people are exposed vary depending on where pe ople live and work and the kinds of activities in which they engage, as discussed in de tail in U.S. EPA’s 2007 Mobile Source Air Toxics Rule. 13 

### According to the National Air Toxic Assessment (N ATA) for 2005, 14 

### mobile sources were responsible for 43 percent of outdoor toxic emissions and over 50 percent of the cancer risk and noncancer hazard associated with pr imary emissions. Mobile sources are also large contributors to precursor emissions which rea ct to form secondary concentrations of air toxics. Formaldehyde is the largest contributor to cancer risk of all 80 pollutants quantitatively assessed in the 2005 NATA. Mobile sources were re sponsible for over 40 percent of primary emissions of this pollutant in 2005, and are major contributors to formaldehyde precursor emissions. Benzene is also a large contributor to c ancer risk, and mobile sources account for over 70 percent of ambient exposure. Over the years, E PA has implemented a number of mobile source and fuel controls which have resulted in VOC reductions, which also reduced formaldehyde, benzene and other air toxic emissions

.

11 An annual PM 2.5 design value is the concentration that determines whether a monitoring site meets the annual NAAQS for PM 2.5 . The full details involved in calculating an annu al PM 2.5 design value are given in appendix N of 40 CFR part 50. A 24-hour PM 2.5 design value is the concentration that determines whether a monitoring site meets the 24-hour NAAQS for PM 2.5 . The full details involved in calculating a 24-ho ur PM 2.5 design value are given in appendix N of 40 CFR part 50.
12 U..S. Environmental Protection Agency (2007). Cont rol of Hazardous Air Pollutants from Mobile Sources ; Final Rule. 72 FR 8434, February 26, 2007.
13 U.S. Environmental Protection Agency (2007). Contr ol of Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007.
14 U.S. EPA. (2011). 2005 National-Scale Air Toxics A ssessment. http://www.epa.gov/ttn/atw/nata2005/. Formatted: Font: Not Bold, Not Superscript/ Subscript

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### Page 5 of 27 ii. Projected Levels

Our modeling indicates that national average ambien t concentrations of the modeled air toxics change less than 1 percent across most of the coun try due to the final standards

. Additional detail on the air toxics results can be found in Section 6.2.2.3 of the RIA.

d. Nitrogen and Sulfur Deposition

i. Current Nitrogen and Sulfur Deposition Levels

Over the past two decades, the EPA has undertaken n umerous efforts to reduce nitrogen and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show that deposition of both nitrogen and sulfur compoun ds has decreased over the last 17 years. The data show that reductions were more substantial for sulfur compounds than for nitrogen compounds. In the eastern U.S., where data are mos t abundant, total sulfur deposition decreased by about 44 percent between 1990 and 2007, while to tal nitrogen deposition decreased by 25 percent over the same time frame. 15 

### These numbers are generated by the U.S. national monitoring network and they likely underestimate ni trogen deposition because neither ammonia nor organic nitrogen is measured. Although total n itrogen and sulfur deposition has decreased over time, many areas continue to be negatively imp acted by deposition. Deposition of inorganic nitrogen and sulfur species routinely measured in t he U.S. between 2005 and 2007 were as high as 9.6 kilograms of nitrogen per hectare (kg N/ha) averaged over three years and 20.8 kilograms of sulfur per hectare (kg S/ha) averaged over three years. 16



### ii. Projected Nitrogen and Sulfur Deposition Levels

Our air quality modeling projects increases in nitr ogen deposition in some localized areas across the U.S. along with a few areas of decreases in nitrogen deposition as a result of the GHG standards. The increases in nitrogen deposition ar e likely due to projected upstream emissions increases in NO X 

### from increased electricity generation and increase d driving due to the VMT rebound effect. The decreases in nitrogen depositi on are likely due to projected upstream emissions decreases in NO X 

### from changes in the location of electricity genera tion. The remainder of the country will experience only minim al changes in nitrogen deposition, ranging from decreases of less than 0.5% to increases of le ss than 0.5%.

Our air quality modeling also projects both increas es and decreases in sulfur deposition as a result of the GHG standards. The decreases in sulfur deposition are likely due to projected

15 U.S. EPA. (2012). U.S. EPA’s Report on the Environm ent. Data accessed online February 15, 2012 at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=deta il.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.list By Chapter&r=216610 and contained in Docket EPA-HQ-OAR -2010-0799.
16 U.S. EPA. (2012). U.S. EPA's Report on the Environ ment. Data accessed online February 15, 2012 at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=deta il.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.list By Chapter&r=216610 and contained in Docket EPA-HQ-OAR -2010-0799. Deleted: the 2017-2025 LD GHG standards have relatively little impact on

### Page 6 of 27

upstream emissions decreases from changes in the lo cation of electricity generation and from reduced gasoline production. The increases in sulf ur deposition are likely due to projected upstream emissions increases from increased electri city generation. The remainder of the country will experience only minimal changes in sul fur deposition, ranging from decreases of less than 0.5% to increases of less than 0.5%.

For maps of 2030 deposition impacts and additional information on these impacts see Section 6.2.2.4 of the RIA.

e. Visibility

i. Current Visibility Levels

As mentioned in Section III.G.4.i, millions of peop le live in nonattainment areas for the
PM 2.5 

### NAAQS. These populations, as well as large number s of individuals who travel to these areas, are likely to experience visibility impairme nt. In addition, while visibility trends have improved in mandatory class I federal areas, the mo st recent data show that these areas continue to suffer from visibility impairment. In summary, visibility impairment is experienced throughout the U.S., in multi-state regions, urban areas, and remote mandatory class I federal

areas.

ii. Projected Visibility Levels

Air quality modeling was used to project visibility conditions in 139 mandatory class I federal areas across the U.S. The results show tha t in 2030 all the modeled areas would continue to have annual average deciview levels above backgr ound. 17 

### Overall the vehicle standards will have a very small impact on visibility. The averag e visibility at all modeled mandatory class I federal areas on the 20 percent worst days is proje cted to improve by 0.003 deciviews, or 0.03 percent, in 2030. Section 6.2.2.5 of the RIA conta ins more detail on the visibility portion of the air quality modeling.

5. Other Unquantified Health and Environmental Effects

In the NPRM, EPA sought comment on whether there ar e any other health and environmental impacts associated with advancements in vehicle GHG reduction technologies that the agency should consider. In particular, EP A requested information on studies or research

17 The level of visibility impairment in an area is b ased on the light-extinction coefficient and a unit less visibility index, called a “deciview”, which is used in the va luation of visibility. The deciview metric provide s a scale for perceived visual changes over the entire range of c onditions, from clear to hazy. Under many scenic c onditions, the average person can generally perceive a change of o ne deciview. The higher the deciview value, the wo rse the visibility. Thus, an improvement in visibility is a decrease in deciview value. Deleted: [placeholder]¶

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### Page 7 of 27

underway on vehicle’s life-cycle impacts (e.g., mat erials usage, manufacturing, end of life disposal) beyond issues regarding fuel production a nd distribution (upstream) discussed in Section III.C. 18



### EPA received a mix of comments on this topic, many highlighting recent or upcoming studies including new research from the University of California, Davis and the University of Michigan. Some commenters argued that EPA should b ase future GHG standards on lifecycle emissions in order to avoid favoring technologies t hat have lower emissions during operation or the “use phase,” but higher total greenhouse gas em issions when production and other stages of a vehicle’s life are considered. For example, severa l organizations from the steel industry recommended that EPA and NHTSA consider incorporati ng life-cycle assessment into vehicle regulations as part of the 2018 mid-term evaluation and outlined one potential framework for establishing such life-cycle based standards.

Other commenters agreed with the agencies’ proposal not to consider life-cycle impacts as part of the standards, arguing that life-cycle a nalysis (LCA) is beyond the intended scope of the rulemaking and that regulating emissions from v ehicle operation addresses the majority of GHG emissions. The American Chemistry Council also noted, “Further, this type of rulemaking is not an appropriate place to apply LCA because of the lack of consensus regarding how to calculate inputs and outputs in an LCA evaluation a t this time.”

EPA is glad to see the advances in research on this important topic and plans to monitor new work in this area. However, the agency continue s to believe that, as of the time of this rulemaking, there is too much uncertainty about the life-cycle impacts of future advanced technologies to conduct the type of detailed, vehic le-specific assessments that would be needed in a regulatory context. See the EPA Response to C omments document for a more detailed discussion on this topic and a fuller summary of co mments received. 19

18 See 76 FR at 75112.
19 Also, see Ch. 6 of NHTSA’s Environmental Impact S tatement for this rulemaking, “ Literature Synthesi s of Lifecycle Environmental Impacts of Certain Vehicle Mate rials and Technologies,” Docket No. NHTSA-2011-0056

. The range of different models and approaches utiliz ed in the surveyed LCA studies, and the sensitivity of the results to study assumptions, demonstrate the challenge of developing a fair and robust method to evaluate lif e-cycle impacts across a range of different vehicle technol ogies at this time.

### Page 8 of 27



# Preamble III.H.7.a – Quantified and Monetized Non-G HG Health Impacts

7. Non-Greenhouse Gas Health and Environmental Impacts

This section presents EPA’s analysis of the criteri a pollutant-related health and environmental impacts that will occur as a result o f the final standards. Light-duty vehicles and fuels are significant sources of mobile source air pollution such as direct PM, NO X 

### , SO X 

### , VOCs and air toxics. The impact that improved fuel econ omy will have on rebound driving will affect exhaust and evaporative emissions of these pollutan ts from vehicles. In addition, increased fuel savings associated with improved fuel economy achie ved under the standards will affect emissions from upstream sources (see Section III.G for a complete description of emission impacts associated with the final standards). Emis sions of NO X 

### (a precursor to ozone formation and secondarily-formed PM 2.5 

### ), SO X 

### (a precursor to secondarily-formed PM 2.5 

### ), VOCs (a precursor to ozone formation and, to a lesser degre e, secondarily-formed PM 2.5 

### ) and directlyemitted PM 2.5 

### contribute to ambient concentrations of PM 2.5 

### and ozone. Exposure to ozone and
PM 2.5 

### is linked to adverse human health impacts such as premature deaths as well as other important public health and environmental effects.

As many commenters noted, it is important to quanti fy the health and environmental impacts associated with the final rule because it a llows us to more accurately assess the net costs and benefits of the standards. Moreover, co-pollut ant impacts tend to accrue in the near term, while any effects from reduced climate change mostl y accrue over a time frame of several decades or longer.

This section is split into two sub-sections: the fi rst presents the PM- and ozone-related health and environmental impacts associated with th e final rule in calendar year (CY) 2030; the second presents the PM-related dollar-per-ton value s used to monetize the PM-related cobenefits associated with the model year (MY) analys is (i.e., over the lifetimes of the MY 2017- 2025 vehicles) of the final rule. 20



### EPA did receive adverse comments regarding the omis sion of some non-GHG impacts in the proposal. In that analysis, we used “dollar-pe r-ton” estimates to monetize the health-related

20 EPA typically analyzes rule impacts (emissions, ai r quality, costs and benefits) in the year in which they occur; for this analysis, we selected 2030 as a representative future year. We refer to this analysis as the “Ca lendar Year” (CY) analysis. EPA also conducted a separate analy sis of the impacts over the model year lifetimes of the 2017 through 2025 model year vehicles. We refer to this analysis as the “Model Year” (MY) analysis. In co ntrast to the CY analysis, the MY lifetime analysis shows the lif etime impacts of the program on each MY fleet over the course of its lifetime.

### Page 9 of 27

impacts of reduced exposure to PM 2.5 

### . We continue to apply these values in the MY anal ysis for the final rule. No “dollar-per-ton” method exists for ozone or toxic air pollutants due to complexity associated with atmospheric chemistry (f or ozone and toxics) and a lack of economic valuation data/methods (for air toxics). However, we have conducted full-scale photochemical air quality modeling to estimate the change in ambi ent concentrations of ozone, PM 2.5 

### and air toxics for the CY analysis in 2030 and used these m odeling results as the basis for estimating the human health impacts and their economic value of th e rule in 2030. EPA had neither the time nor resources to conduct such modeling for the Mode l Year analysis.

a. Quantified and Monetized Non-GHG Human Health Benef its of the 2030 Calendar Year (CY) Analysis

This analysis reflects the impact of the final ligh t-duty GHG rule in 2030 compared to a future-year reference scenario without the rule in place.

We estimate that the final rule will lead to a smal l net reduction in PM 2.5 

### -related health impacts - the reduction in population-weighted national average PM 2.5 

### exposure results in a small

net reduction in adverse PM-related human health impacts (the reduction in national populationweighted annual average PM 2.5 

### is 0.0065 μ g/m3).

The air quality modeling also projects a very small increase in ozone concentrations in

many areas (population-weighted maximum 8-hour average ozone increases by 0.0009 ppb)

. While the ozone-related impacts are very small, the increase in population-weighted national average ozone exposure results in a very small increase in ozone-related health impacts.

We base our analysis of the final rule’s impact on human health in 2030 on peerreviewed studies of air quality and human health ef fects. 21,22 

### These methods are described in more detail in the RIA that accompanies this action . Our benefits methods are also consistent with recent rulemaking analyses such as the propose d Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA

, 23 

### the final NO 2 

### NAAQS, 24 

### and the

21 U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for the Prop osed National Ambient Air Quality Standards for Particul ate Matter . Prepared by: Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn /ecas/ria.html
22 U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis . Prepared by: Office of Air and Radiation, Office of Air Quality Planning and Standards. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html
23 U.S. Environmental Protection Agency (U.S. EPA). 2009a. Regulatory Impact Analysis: National Emissi on Standards for Hazardous Air Pollutants from the Por tland Cement Manufacturing Industry. Office of Air Quality Planning and Standards, Research Triangle Park, NC. April. Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandce mentria_4-20-09.pdf. Accessed March 15, 2010.
24 U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory Impact Analysis (RIA). Office of Air Quality Planning and Standard s, Research Triangle Park, NC. April. Available o n the Internet
at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RI Afulldocument.pdf . Accessed March 15, 2010. Deleted: very
Deleted: XX

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Deleted: very
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Deleted: (population-weighted maximum 8-hour
average ozone XX by XX ppb)

### Page 10 of 27

final Category 3 Marine Engine rule, 25 

### and the final Cross State Air Pollution Rule. 26 

### To model the ozone and PM air quality impacts of the final r ule, we used the Community Multiscale Air Quality (CMAQ) model (see Section III.G.4). The mo deled ambient air quality data serves as an input to the Environmental Benefits Mapping and Ana lysis Program (BenMAP). 27 

### BenMAP is a computer program developed by the U.S. EPA that int egrates a number of the modeling elements used in previous analyses (e.g., interpolation func tions, population projections, health impact functions, valuation functions, analysis and poolin g methods) to translate modeled air concentration estimates into health effects inciden ce estimates and monetized benefits estimates.

The range of total monetized ozone- and PM-related health impacts is presented in Table III-1. We present total benefits (the sum of morbidity-related benefits and mortality-related benefits) based on the PM- and ozone-related premature mortal ity function used. The benefits ranges therefore reflect the addition of each estim ate of ozone-related premature mortality ( across six selected studies, each with its own row in Table III-1) to each estimate of PM-related premature mortality (based on either Pope et al., 2002 or Laden et al. , 2006), along with all morbidity-related benefits . These estimates represent EPA’s preferred appro ach to characterizing a best estimate of monetized impacts . As is the nature of Regulatory Impact Analyses (RIAs), the assumptions and methods used t o estimate air quality impacts evolve to reflect the Agency’s most current interpretation of the scientific and economic literature.

Table III-1: Estimated 2030 Monetized PM-and Ozone- Related Health Impacts a
2030 Total Ozone and PM Benefits – PM Mortality Der ived from American Cancer Society Analysis and Six-Cities Analysis a
Premature Ozone Mortality Function Reference Total Benefits ( B illions, 2010$, 3% Discount Rate) b,c,d Total Benefits ( B illions, 2010$, 7% Discount Rate) b,c,d Multi-city analyses Bell et al., 2004 Total: $1.0 – $2.6 PM: $1.1 - $2.6 Ozone: -$0.006 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.006 Huang et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.006 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.006 Schwartz, 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.009 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.009

25 U.S. Environmental Protection Agency. 2009. Regu latory Impact Analysis: Control of Emissions of Air Pollution from Category 3 Marine Diesel Engines. E PA-420-R-09-019, December 2009. Prepared by Office of Air and Radiation. http://www.epa.gov/otaq/regs/nonroa d/marine/ci/420r09019.pdf. Accessed February 9, 20 10.
26 U.S. Environmental Protection Agency. 2011. Regu latory Impact Analysis for the Federal Implementati on Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 States; Correction of SIP Ap provals for 22 States. EPA-HQ-OAR-2009-0491, June 2011. Prepa red by Office of Air and Radiation. http://www.epa.gov/airtransport/pdfs/FinalRIA.pdf . Accessed May 16, 2012.
27 Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ec as/ benmodels.html. Deleted: impacts

Deleted: monetized

Deleted: s

Deleted: M

Deleted: M

### Page 11 of 27

Meta-analyses Bell et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.019 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.019 Ito et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.026 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.026 Levy et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.027 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.027

Notes:

a Total includes premature mortality-related and morb idity-related ozone and PM 2.5 benefits. Range was developed by adding the estimate from the ozone pre mature mortality function to the estimate of PM 2.5 -related premature mortality derived from either the ACS stu dy (Pope et al., 2002) or the Six-Cities study (Lad en et al., 2006).
b Note that totals presented here do not include a n umber of unquantified health impact categories. A detailed listing of unquantified health and welfare effects is provided in Table III-2.

c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines f or Preparing Economic Analyses and OMB Circular A-4. Results are rounded to two significant digits for e ase of presentation and computation.
d Negatives indicate a disbenefit, or an increase in health effect incidence. Monetized impacts are ro unded to two significant digits. Totals may not sum due to rounding.



### The monetized impacts in Table III-1 include all of the human health impacts we are able to quantify and monetize at this time. However, th e full complement of human health and welfare effects associated with PM, ozone and other criteria pollutants remain unquantified because of current limitations in methods or availa ble data. We have not quantified a number of known or suspected health effects linked with ozone , PM and other criteria pollutants for which appropriate health impact functions are not availab le or which do not provide easily interpretable outcomes (e.g., changes in heart rate variability). Additionally, we are unable to quantify a number of known welfare effects, including reduced acid and particulate deposition damage to cultural monuments and other materials, and environ mental benefits due to reductions of impacts of eutrophication in coastal areas. These are list ed in Table III-2. As a result, the health benefit s quantified in this section do not reflect the full range of possible impacts attributable to the final rule.

### Page 12 of 27 Table III-2: Unquantified and Non-Monetized Potenti al Effects
Pollutant/Effects Effects Not Included in Analysis - Changes in:
Ozone Health a Chronic respiratory damage b

Premature aging of the lungs b Non-asthma respiratory emergency room visits Exposure to UVb (+/-) e
Ozone Welfare Yields for

-commercial forests -some fruits and vegetables -non-commercial crops Damage to urban ornamental plants Impacts on recreational demand from damaged forest aesthetics Ecosystem functions Exposure to UVb (+/-) e
PM Health c Premature mortality - short term exposures d

Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bro nchitis Non-asthma respiratory emergency room visits Exposure to UVb (+/-) e PM Welfare Residential and recreational visibility in non-Class I areas

Soiling and materials damage Damage to ecosystem functions Exposure to UVb (+/-) e Nitrogen and Sulfate Deposition Welfare Commercial forests due to acidic sulfate and nitrat e deposition

Commercial freshwater fishing due to acidic deposit ion Recreation in terrestrial ecosystems due to acidic deposition Existence values for currently healthy ecosystems Commercial fishing, agriculture, and forests due to nitrogen deposition Recreation in estuarine ecosystems due to nitrogen deposition Ecosystem functions Passive fertilization CO Health Behavioral effects
HC/Toxics Health f Cancer (benzene, 1,3-butadiene, formaldehyde, aceta ldehyde)

Anemia (benzene) Disruption of production of blood components (benze ne) Reduction in the number of blood platelets (benzene ) Excessive bone marrow formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmental effects (1,3-butadie ne) Irritation of eyes and mucus membranes (formaldehyd e) Respiratory irritation (formaldehyde) Asthma attacks in asthmatics (formaldehyde) Asthma-like symptoms in non-asthmatics (formaldehyd e) Irritation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion ( acrolein) HC/Toxics Welfare Direct toxic effects to animals

Bioaccumulation in the food chain Damage to ecosystem function Odor

Notes:

a The public health impact of biological responses s uch as increased airway responsiveness to stimuli, inflammation

### Page 13 of 27

in the lung, acute inflammation and respiratory cel l damage, and increased susceptibility to respirato ry infection are likely partially represented by our quantified endp oints.
b The public health impact of effects such as chroni c respiratory damage and premature aging of the lun gs may be partially represented by quantified endpoints such as hospital admissions or premature mortality, but a number of other related health impacts, such as doctor visits and decreased athletic performance, remain unquant ified.

c In addition to primary economic endpoints, there a re a number of biological responses that have been associated with PM health effects including morphological chan ges and altered host defense mechanisms. The publi c health impact of these biological responses may be partly represented by our quantified endpoints.
d While some of the effects of short-term exposures are likely to be captured in the estimates, there m ay be premature mortality due to short-term exposure to P M not captured in the cohort studies used in this a nalysis. However, the PM mortality results derived from the expert elicitation do take into account premature m ortality effects of short term exposures.

e May result in benefits or disbenefits.
f Many of the key hydrocarbons related to this rule are also hazardous air pollutants listed in the CAA

.



### While there will be impacts associated with air tox ic pollutant emission changes that result from the final rule, we do not attempt to mo netize those impacts. This is primarily because currently available tools and methods to assess air toxics risk from mobile sources at the national scale are not adequate for extrapolation to inciden ce estimations or benefits assessment. The best suite of tools and methods currently available for assessment at the national scale are those used in the National-Scale Air Toxics Assessment (N ATA). The EPA Science Advisory Board specifically commented in their review of the 1996 NATA that these tools were not yet ready for use in a national-scale benefits analysis, because they did not consider the full distribution of exposure and risk, or address sub-chronic health ef fects. 28 

### While EPA has since improved the tools, there remain critical limitations for estima ting incidence and assessing benefits of reducing mobile source air toxics. EPA continues to work to address these limitations; however, we did not have the methods and tools available for nation al-scale application in time for the analysis of the final rule. 29



### EPA is also unaware of specific information identif ying any effects on listed endangered species from the small fluctuations in pollutant co ncentrations associated with this rule (see Section III.G.4). Furthermore, our current modelin g tools are not designed to trace fluctuations in ambient concentration levels to potential impact s on particular endangered species.

28 Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an SAB Advisory. http://www.epa.gov/ttn/atw/sab/sabre v.html.
29 In April, 2009, EPA hosted a workshop on estimatin g the benefits or reducing hazardous air pollutants . This workshop built upon the work accomplished in the Ju ne 2000 Science Advisory Board/EPA Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on approaches to estimating human health benefits from reductions in air toxics exposure, but no consensu s was reached on methods that could be implemented in the near term for a broad selection of air toxics. Pl ease visit http://epa.gov/air/toxicair/2009workshop.html for m ore information about the workshop and its associat ed materials.

### Page 14 of 27 i. Quantified Human Health Impacts

Table III-3 and Table III-4 present the annual PM 2.5 

### and ozone health impacts in the 48 contiguous U.S. states associated with the final ru le for 2030. For each endpoint presented in Table III-3 and Table III-4, we provide both the me an estimate and the 90% confidence interval.

Using EPA’s preferred estimates, based on the Ameri can Cancer Society (ACS) and SixCities studies and no threshold assumption in the m odel of mortality, we estimate that the final rule will reduce between 110 and 280 cases of PM 2.5 

### -related premature mortality annually in 2030. For ozone-related premature mortality in 203 0, we estimate a range of between 1 to 3 cases of additional premature mortality.

Table III-3: Estimated PM 2.5 

### -Related Health Impacts a

Health Effect 2030 Annual Reduction in Incidence (5 th % - 95 th %ile) Premature Mortality – Derived from epidemiology lit erature b Adult, age 30+, ACS Cohort Study (Pope et al., 20 02)

Adult, age 25+, Six-Cities Study (Laden et al., 2 006)

Infant, age <1 year (Woodruff et al., 1997) 110 (30 – 190) 280 (130 – 440) 0 (0 – 1) Chronic bronchitis (adult, age 26 and over) 76 (1 – 150) Non-fatal myocardial infarction (adult, age 18 and over) 130 (32 – 230) Hospital admissions - respiratory (all ages) c 20 (8 – 32) Hospital admissions - cardiovascular (adults, age > 18) d 50 (33 – 60) Emergency room visits for asthma (age 18 years and younger) 72 (34 – 110) Acute bronchitis, (children, age 8-12) 160 (-42 – 370) Lower respiratory symptoms (children, age 7-14) 2,100 (770 – 3,400) Upper respiratory symptoms (asthmatic children, age 9-18) 1,600

(260 – 2,900) Asthma exacerbation (asthmatic children, age 6-18) 3,500 (-120 – 9,700) Work loss days 14,000 (12,000 – 16,000) Minor restricted activity days (adults age 18-65) 81,000 (65,000 – 96,000)
Notes:

a Incidence is rounded to two significant digits. Es timates represent incidence within the 48 contiguou s United States.
b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 2002) and the Six-Cities Study (Laden et al., 2006) . Note that these are two alternative estimates of adult Deleted: result in

Deleted: XX

Deleted: XX

Deleted: XX

Deleted: XX

### Page 15 of 27 mortality and should not be summed. PM-related inf ant mortality based upon a study by Woodruff, Grillo, and Schoendorf, (1997).30

c Respiratory hospital admissions for PM include adm issions for chronic obstructive pulmonary disease (COPD), pneumonia and asthma.
d Cardiovascular hospital admissions for PM include total cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.



### Table III-4: Estimated Ozone-Related Health Impacts a

Health Effect 2030 Annual Reduction in Incidence (5th% - 95th%ile) Premature Mortality, All ages b Multi-City Analyses Bell et al. (2004) – Non-accidental

Huang et al. (2005) – Cardiopulmonary

Schwartz (2005) – Non-accidental

Meta-analyses: Bell et al. (2005) – All cause

Ito et al. (2005) – Non-accidental

Levy et al. (2005) – All cause -1 (-4 – 3) -1 (-5 – 4) -1 (-6 – 4)

-2 (-10 – 6) -3 (-11 – 6) -3 (-10 – 4) Hospital admissions- respiratory causes (adult, 65 and older)c -6 (-30 – 15) Hospital admissions -respiratory causes (children, under 2) -3 (-12 – 6) Emergency room visit for asthma (all ages) -1 (-18 – 15) Minor restricted activity days (adults, age 18-65) -930 (-18,000 – 16,000) School absence days -850 (-6,700 – 5,100)
Notes:

a Negatives indicate a disbenefit, or an increase in health effect incidence. Incidence is rounded to two significant digits. Estimates represent incidence w ithin the 48 contiguous U.S.

b Estimates of ozone-related premature mortality are based upon incidence estimates derived from severa l alternative studies: Bell et al. (2004); Huang et a l. (2005); Schwartz (2005) ; Bell et al. (2005); It o et al. (2005); Levy et al. (2005). The estimates of ozone -related premature mortality should therefore not b e summed.

c Respiratory hospital admissions for ozone include a dmissions for all respiratory causes and subcategories for COPD and pneumonia.

30 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1 997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air P ollution in the United States.” Environmental Health Perspectives 105(6):608-612.

### Page 16 of 27 ii. Monetized Impacts

Table III-5 presents the estimated monetary value o f changes in the incidence of ozone and PM 2.5 

### -related health effects. All monetized estimates a re stated in 2010$. These estimates account for growth in real gross domestic product ( GDP) per capita between the present and 2030. Our estimate of total monetized impacts in 2 030 for the final rule, using the ACS and SixCities PM mortality studies and the range of ozone mortality assumptions, is between $1.0 and $2.6 b illion, assuming a 3 percent discount rate , and between $0.92 and $2.3 billion, assuming a 7 percent discount rate. As the results below indicate, monetized impacts are driven primarily by the change in premature fatalities in 2030

. Deleted: XX

Deleted: XX

Deleted: m
Deleted: both

Deleted: and 7

Deleted: (totals are the same due to a rounding convention of two significant digits).

Deleted: ozone-related

Deleted: each year

### Page 17 of 27

Table III-5: Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects (in millions of 2010$) a,b

2030

PM 2.5 -Related Health Effect (5 th and 95 th %ile) Premature Mortality – Derived from Epidemiology Studies c,d, Adult, age 30+ - ACS study (Pope et al., 2002) 3% discount rate

7% discount rate $980 ($110 - $2,600) $880 ($97 - $2,400) Adult, age 25+ - Six-Cities study (Laden et al., 2006) 3% discount rate

7% discount rate $2,500 ($340 - $6,300) $2,300 ($310 - $5,700) Infant Mortality, <1 year – (Woodruff et al. 1997) $3.8 (-$3.9 - $15) Chronic bronchitis (adults, 26 and over) $42 ($0.4 - $140) Non-fatal acute myocardial infarctions 3% discount rate

7% discount rate $14 ($2.3 - $36) $12 ($1.8 - $30) Hospital admissions for respiratory causes $0.32 ($0.13 - $0.51) Hospital admissions for cardiovascular causes $0.73 ($0.07 - $1.4) Emergency room visits for asthma $0.03 ($0.01 - $0.05) Acute bronchitis (children, age 8–12) $0.08 (-$0.02 - $0.21) Lower respiratory symptoms (children, 7–14) $0.04 ($0.01 - $0.09) Upper respiratory symptoms (asthma, 9–11) $0.05 ($0.009 - $0.12)
Asthma exacerbations $0.20 (-$0.007 - $0.58) Work loss days $2.2 ($1.9 - $2.6) Minor restricted-activity days (MRADs) $5.6 ($3.2 - $8.1)
Ozone-related Health Effect Premature Mortality, All ages – Derived from Multi-city analyses Bell et al., 2004 -$5.8 (-$45 - $27) Huang et al., 2005 -$6.2 (-$60 - $41)

### Page 18 of 27 Schwartz, 2005 -$8.7 (-$71 - $44) Premature Mortality, All ages – Derived from Meta-analyses Bell et al., 2005 -$19 (-$120 - $38) Ito et al., 2005 -$26 (-$140 - $58) Levy et al., 2005 -$27 (-$120 - $38) Hospital admissions- respiratory causes (adult, 65 and older) -$0.16 (-$0.77 - $0.39) Hospital admissions- respiratory causes (children, under 2) -$0.03 (-$130 - $0.07) Emergency room visit for asthma (all ages) -$0.0003 (-$0.007 - $0.006) Minor restricted activity days (adults, age 18-65) -$0.06 (-$1.3 - $1.1) School absence days -$0.08 (-$0.65 - $0.49)

Notes:

a Negatives indicate a disbenefit, or an increase in health effect incidence. Monetized impacts are ro unded to two significant digits for ease of presentation and computation. PM and ozone benefits are nationw ide.
b Monetary benefits adjusted to account for growth i n real GDP per capita between 1990 and the analysis year (2030).

c Valuation assumes discounting over the SAB recomme nded 20 year segmented lag structure. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses.



### iii. What Are the Limitations of the Benefits Analysis?

Every benefit-cost analysis examining the potential effects of a change in environmental protection requirements is limited to some extent b y data gaps, limitations in model capabilities (such as geographic coverage), and uncertainties in the underlying scientific and economic studies used to configure the benefit and cost mode ls. Limitations of the scientific literature often result in the inability to estimate quantitat ive changes in health and environmental effects, such as potential increases in premature mortality associated with increased exposure to carbon monoxide. Deficiencies in the economics literature often result in the inability to assign economic values even to those health and environmen tal outcomes which can be quantified. These general uncertainties in the underlying scien tific and economics literature, which can lead to valuations that are higher or lower, are discuss ed in detail in the RIA and its supporting references. Key uncertainties that have a bearing on the results of the benefit-cost analysis of the final rule include the following:

• The exclusion of potentially significant and unquan tified benefit categories (such as health, odor, and ecological benefits of reduction in air toxics, ozone, and PM);

### Page 19 of 27

• Errors in measurement and projection for variables such as population growth;

• Uncertainties in the estimation of future year emis sions inventories and air quality;

• Uncertainty in the estimated relationships of healt h and welfare effects to changes in pollutant concentrations including the shape of the concentration-response function, the size of the effect estimates, and the relative toxi city of the many components of the PM mixture;

• Uncertainties in exposure estimation; and

• Uncertainties associated with the effect of potenti al future actions to limit emissions.

As Table III-5 indicates, total benefits are driven primarily by the reduction in premature mortalities each year. Some key assumptions underl ying the premature mortality estimates include the following, which may also contribute to uncertainty:

• Inhalation of fine particles is causally associated with premature death at concentrations near those experienced by most Americans on a daily basis. Although biological mechanisms for this effect have not yet been comple tely established, the weight of the available epidemiological, toxicological, and exper imental evidence supports an assumption of causality. The impacts of including a probabilistic representation of causality were explored in the expert elicitation-b ased results of the 2006 PM NAAQS RIA.

• All fine particles, regardless of their chemical co mposition, are equally potent in causing premature mortality. This is an important assumpti on, because PM produced via transported precursors emitted from stationary sour ces may differ significantly from PM precursors released from mobile sources and other i ndustrial sources. However, no clear scientific grounds exist for supporting differentia l effects estimates by particle type.

• The C-R function for fine particles is approximatel y linear within the range of ambient concentrations under consideration. Thus, the esti mates include health benefits from reducing fine particles in areas with varied concen trations of PM, including both regions that may be in attainment with PM 2.5 

### standards and those that are at risk of not meeting the standards.

• There is uncertainty in the magnitude of the associ ation between ozone and premature mortality. The range of ozone impacts associated with the final standards is estimated based on the risk of several sources of ozone-relat ed mortality effect estimates. In a 2008 report on the estimation of ozone-related premature mortality published by the National Research Council, a panel of experts and reviewers concluded that short-term exposure to ambient ozone is likely to contribute to premature deaths and that ozone-related mortality should be included in estimates of the health benef its of reducing ozone exposure. 31 

### EPA has requested advice from the National Academy of S ciences on how best to quantify uncertainty in the relationship between ozone expos ure and premature mortality in the context of quantifying benefits.

Acknowledging the data limitations and uncertaintie s, we present a best estimate of the total monetized health impacts based on our interpr etation of the best available scientific

31 National Research Council (NRC), 2008. Estimating Mortality Risk Reduction and Economic Benefits fro m Controlling Ozone Air Pollution. The National Acad emies Press: Washington, D.C. Deleted: benefits associated with the coordinated strategy

### Page 20 of 27

literature and methods supported by EPA’s technical peer review panel, the Science Advisory Board’s Health Effects Subcommittee (SAB-HES). The National Academies of Science (NRC, 2002) has also reviewed EPA’s methodology for analy zing the health benefits of measures taken to reduce air pollution. EPA addressed many of the se comments in the analysis of the final PM NAAQS. 32,33 

### The analysis in this final rule incorporates this most recent work to the extent possible.

32 National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollutio n Regulations. The National Academies Press: Washingt on, D.C.
33 U.S. Environmental Protection Agency. October 200 6. Final Regulatory Impact Analysis (RIA) for the Prop osed National Ambient Air Quality Standards for Particul ate Matter . Prepared by: Office of Air and Radiation. Available at http://www.epa.gov/ttn/ecas/ria.html.

### Page 21 of 27



# Preamble III.H.10 Summary of Costs and Benefits

10. Summary of Costs and Benefits

In this section, the agencies present a summary of costs, benefits, and net benefits of the proposed program. Table Error ! No text of specified style in document. -6 shows the estimated annual monetized costs of the proposed program for the indicated calendar years. The table also shows the net present values of those costs for the calendar years 2012-2050 using both 3 percent and 7 percent discount rates. 34 

### Table Error ! No text of specified style in document. -7 shows the undiscounted annual monetized fuel savings of the p roposed program. The table also shows the net present values of those fuel savings for the sa me calendar years using both 3 percent and 7 percent discount rates. In this table, the aggrega te value of fuel savings is calculated using pretax fuel prices since savings in fuel taxes do not represent a reduction in the value of economic resources utilized in producing and consuming fuel. Note that the fuel savings shown here result from reductions in fleet-wide fuel use. Thus, fuel savings grow over time as an increasing fraction of the fleet meets the proposed standards.

Table Error! No text of specified style in document. -6 Undiscounted Annual Costs & Costs of the Proposed Program Discounted Back to 2012 at 3% and 7% Discount Rates (Millions, 2010$) a
2017 2020 2030 2040 2050 NPV, Years 2012-2050, 3% Discount Rate NPV, Years

2012-2050, 7% Discount Rate Technology Costs $2,440 $8,860 $33,700 $37,400 $42,000 $521,000 $231 ,000

Maintenance Costs $37 $330 $2,260 $3,630 $4,540 $39,500 $15,600

Vehicle Program Costs $2,470 $9,190 $35,900 $41,000 $46,500 $561,000 $247 ,000

Note:

a Technology costs for separate light-duty vehicle s egments can be found in Section III.H.2. Annual co sts shown are undiscounted values.



### Table Error! No text of specified style in document. -7 Undiscounted Annual Fuel Savings & Proposed Program Fuel Savings Discounted Back to 20 12 at 3% and 7% Discount Rates (Millions, 2010$) a
2017 2020 2030 2040 2050 NPV, Years 2012-2050, 3% NPV, Years

2012-2050, 7%

34 For the estimation of the stream of costs and bene fits, we assume that after implementation of the pr oposed MY 2017-2025 standards, the 2025 standards apply to ea ch year thereafter.

### Page 22 of 27 Discount Rate Discount Rate

Fuel Savings (pre-tax) $651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $6 07,000

Note:

a Fuel savings for separate light-duty vehicle segme nts can be found in Section III.H.3. Annual costs s hown are undiscounted values.



### Table Error ! No text of specified style in document. -8 presents estimated annual monetized benefits for the indicated calendar years . The table also shows the net present values of those benefits for the calendar years 2012-2050 using both 3 percent and 7 percent discount rates. The table shows the benefits of reduced CO 2 

### emissions—and consequently the annual quantified benefits ( i.e. , total benefits)—for each of the four social cost of carbon (SCC) values estimated by the interagency working group. As dis cussed in the RIA Chapter 7.2, there are some limitations to the SCC analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non-cata strophic impacts, their incomplete treatment of adaptation and technological change, uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding risk aversi on.

In addition, these monetized GHG benefits exclude t he value of net reductions in nonCO 2 

### GHG emissions (CH 4 

### , N 2 

### O, HFC) expected under this action. Although EPA h as not monetized the benefits of reductions in non-CO 2 

### GHGs, the value of these reductions should not be interpreted as zero. Rather, the net reductions in non-CO 2 

### GHGs will contribute to this program’s climate benefits, as explained in Section III.H.5.

Table Error! No text of specified style in document. -8 Monetized Undiscounted Annual Benefits & Benefits of the Proposed Program Discoun ted Back to 2012 at 3% and 7% Discount Rates (Millions, 2010$)
2017 2020 2030 2040 2050 NPV, Years 2012-2050, 3% Discount Rate a NPV, Years

2012-2050, 7% Discount Rate a Reduced CO 2 Emissions at each assumed SCC value b 5% (avg SCC) $14 $164 $2,500 $5,510 $8,540 $32,400 $32,400 3% (avg SCC) $55 $633 $8,410 $17,000 $24,400 $170,0 00 $170,000 2.5% (avg SCC) $87 $1,000 $12,900 $25,400 $35,400 $ 290,000 $290,000 3% (95th %ile) $167 $1,940 $25,700 $51,800 $74,100 $519,000 $519,000 Energy SecurityBenefits (macrodisruption costs) $33 $371 $4,560 $8,320 $10,400 $84,500 $32,200

Accidents, Congestion, Noise Costs g -$54 -$564 -$5,710 -$9,650 -$12,100 -$101,000 -$39, 200

Increased Travel Benefits $79 $865 $9,560 $17,000 $14,500 $167,000 $64,800

Refueling Time Savings $25 $282 $3,360 $6,350 $8,870 $64,900 $24,500

Non-GHG Related Health Impacts c,d,e B B $920 - $1,000 $920 -

$1,000 $920 -

$1,000 $9,190 $3,050

### Page 23 of 27

Non-CO 2 GHG Impacts f n/a n/a n/a n/a n/a n/a n/a

Total Annual Benefits at each assumed SCC value b 5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000 3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000 2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000 3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000 Notes:

a Net present value of reduced CO 2 emissions is calculated differently than other ben efits. The same discount rate used to discount the value of damages from future e missions (SCC at 5, 3, 2.5 percent) is used to calc ulate net present value of SCC for internal consistency. Ref er to the SCC TSD for more detail. Annual costs sho wn are undiscounted values.
b Section III.H.5 notes that SCC increases over time . For the years 2012-2050, the SCC estimates range as follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; an d for 95 th percentile SCC at 3%: $70-$140.

c Note that “B” indicates unquantified criteria poll utant benefits in years prior to 2030 (2017-2029). For the final rule, EPA only conducted full-scale photochemical a ir quality modeling to estimate the rule’s PM 2.5 - and ozonerelated impacts in the calendar year 2030. For the purposes of estimating a stream of future-year cri teria pollutant benefits associated with the final standards, we as sume that the annual benefits out to 2050 are equal to, and no less than, those modeled in 2030 as reflected by the str eam of estimated future emission reductions. The N PV of criteria pollutant-related benefits should therefore be cons idered a conservative estimate of the potential ben efits associated with the final rule.
d The PM 2.5 -related portion of the health benefits presented i n this table are based on an estimate of premature mortality derived from the ACS study (Pope et al., 2002). However, EPA’s primary method of characteri zing PMrelated premature mortality is to use both the ACS and the Six Cities study (Laden et al., 2006) to ge nerate a coequal range of benefits estimates. The decision to present only the ACS-based estimate in this table does not convey any preference for one study over the other. We no te that this is also the more conservative of the t wo estimates - PM-related benefits would be approximately 245 perc ent (or nearly two-and-a-half times) larger had we used the per-ton benefit values based on the Six Cities stud y instead. Refer to Section III.H.7 to see the ful l range of nonGHG related health benefits in Calendar Year 2030.

e The range of calendar year non-GHG benefits presen ted in this table assume either a 3% discount rate in the valuation of PM-related premature mortality ($1,000 million) or a 7% discount rate ($920 million) to a ccount for a twenty-year segmented cessation lag. Note that the benefits estimated using a 3% discount rate were u sed to calculate the NPV using a 3% discount rate and the benefits estimated using a 7% discount rate were us ed to calculate the NPV using a 7% discount rate. f The monetized GHG benefits presented in this analy sis exclude the value of changes in non-CO 2 GHG emissions expected under this program ( See RIA Chapter 7.1). Although EPA has not monetized changes in non-CO 2 GHGs, the value of any increases or reductions should not be interpreted as zero. We seek comment on a meth od of quantifying non-CO 2 GHG benefits in Section III.H.5.

g Negative values for Accidents, Congestion, and Nois e costs represent disbenefits.



### Table Error ! No text of specified style in document. -9 presents estimated annual net benefits for the indicated calendar years. The tab le also shows the net present values of those net benefits for the calendar years 2012-2050 using bot h 3 percent and 7 percent discount rates. The table includes the benefits of reduced CO 2 

### emissions (and consequently the annual net benefit s) for each of the four SCC values considered by EPA.

Table Error! No text of specified style in document. -9 Undiscounted Annual Monetized Net Benefits & Net Benefits of the Proposed Program Dis counted Back to 2012 at 3% and 7% Discount Rates (Millions, 2009$)
2017 2020 2030 2040 2050 NPV, 3% a NPV, 7% a Vehicle Program $2,470 $9,190 $35,900 $41,000 $46,5 00 $561,000 $247,000

### Page 24 of 27

Costs Fuel Savings $651 $7,430 $86,400 $155,000 $212,000 $1,600,000 $607,000 Total Annual Benefits at each assumed SCC value b 5% (avg SCC) $97 $1,120 $15,300 $28,500 $31,300 $257,000 $118,000 3% (avg SCC) $138 $1,590 $21,200 $40,000 $47,200 $395,000 $256,000 2.5% (avg SCC) $171 $1,960 $25,600 $48,400 $58,100 $515,000 $376,000 3% (95th %ile) $250 $2,890 $38,500 $74,800 $96,900 $743,000 $604,000 Monetized Net Benefits at each assumed SCC value c 5% (avg SCC) -$1,690 -$316 $68,000 $146,000 $201,000 $1,290,000 $478,000 3% (avg SCC) -$1,650 $153 $73,900 $158,000 $217,000 $1,430,000 $616,000 2.5% (avg SCC) -$1,610 $524 $78,300 $166,000 $228,000 $1,550,000 $736,000 3% (95th %ile) -$1,530 $1,460 $91,200 $192,000 $267,000 $1,780,000 $964,000 Notes:

a Net present value of reduced CO 2 emissions is calculated differently than other ben efits. The same discount rate used to discount the value of damages from future e missions (SCC at 5, 3, 2.5 percent) is used to calc ulate net present value of SCC for internal consistency. Ref er to the SCC TSD for more detail. Annual costs sho wn are undiscounted values.
b Section VIII.H.5 notes that SCC increases over tim e. For the years 2012-2050, the SCC estimates rang e as follows: for Average SCC at 5%: $5-$16; for Avera ge SCC at 3%: $23-$46; for Average SCC at 2.5%: $ 38-$67; and for 95 th percentile SCC at 3%: $70-$140 Section VIII.H.5 a lso presents these SCC estimates.

c Net Benefits equal Fuel Savings minus Technology C osts plus Benefits.


### EPA also conducted a separate analysis of the total benefits over the model year lifetimes of the 2017 through 2025 model year vehicles. In c ontrast to the calendar year analysis presented above in Table Error ! No text of specified style in document. -6 through Table Error ! No text of specified style in document. -9, the model year lifetime analysis below shows th e impacts of the proposed program on vehicles produce d during each of the model years 2017 through 2025 over the course of their expected life times. The net societal benefits over the full lifetimes of vehicles produced during each of the n ine model years from 2017 through 2025 are shown in Table Error ! No text of specified style in document. -10 and Table Error ! No text of specified style in document. -11 at both 3 percent and 7 percent discount rates, respectively.

Table Error! No text of specified style in document. -10 Monetized Technology Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2017-2025 Model Year

Light-Duty Vehicles (Millions, 2009$; 3% Discount R ate) h

2017 MY 2018

MY 2019

MY 2020

MY 2021

MY 2022

MY 2023

MY 2024

MY 2025

MY Sum

Vehicle Program Costs $2,770 $5,460 $7,720 $10,100 $14,000 $19,900 $25,40 0 $30,900 $33,600 $150,000

Fuel Savings (pre-tax) $7,040 $15,500 $24,300 $34,100 $50,400 $64,900 $78, 500 $92,800 $107,000 $475,000

Energy Security Benefits(macrodisruption costs) $365 $807 $1,260 $1,780 $2,650 $3,430 $4,170 $4,950 $5,750 $25,200

Accidents, Congestion, Noise Costs f -$548 -$1,150 -$1,770 -$2,440 -$3,480 -$4,420 -$5,2 70 -$6,160 -$7,040 -$32,300

### Page 25 of 27

Increased Travel Benefits $1,000 $2,180 $3,390 $4,700 $6,840 $8,650 $10,200 $ 11,900 $13,600 $62,500

Refueling Time Savings $273 $604 $945 $1,330 $1,970 $2,550 $3,100 $3,680 $ 4,280 $18,700

PM 2.5 Related Health Impacts c,d,e $74 $171 $271 $385 $606 $776 $928 $1,090 $1,250 $5, 540

Non-CO 2 GHG Impacts g n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

Reduced CO 2 Emissions at each assumed SCC value a, b 5% (avg SCC) $152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2, 820 $11,800

3% (avg SCC) $642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,2 60 $10,800 $46,600

2.5% (avg SCC) $1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100

3% (95th %ile) $1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000

Monetized Net Benefits at each assumed SCC value a, b 5% (avg SCC) $5,590 $13,000 $21,200 $30,500 $46,200 $57,500 $68, 100 $79,700 $94,400 $416,000

3% (avg SCC) $6,080 $14,100 $22,900 $33,000 $49,900 $62,200 $73, 900 $86,600 $102,000 $451,000

2.5% (avg SCC) $6,480 $15,000 $24,300 $34,900 $52,800 $66,000 $78, 500 $92,000 $109,000 $479,000

3% (95th %ile) $7,400 $17,100 $27,600 $39,600 $59,800 $75,200 $89, 800 $106,000 $125,000 $547,000

Notes:

a Net present value of reduced CO 2 emissions is calculated differently than other ben efits. The same discount rate used to discount the value of damages from future e missions (SCC at 5, 3, 2.5 percent) is used to calc ulate net present value of SCC for internal consistency. Ref er to the SCC TSD for more detail.
b Section III.H.5 notes that SCC increases over time . For the years 2012-2050, the SCC estimates range as follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; an d for 95 th percentile SCC at 3%: $70-$140. Section III.H.5 also presents these SCC estimates.

c Note that the non-GHG impacts associated with the standards presented here do not include the full co mplement of endpoints that, if quantified and monetized, would change the total monetized estimate of rule-related impacts. Instead, the non-GHG benefits are based on benefit- per-ton values that reflect only human health impac ts associated with reductions in PM 2.5 exposure. Ideally, human health and environmental benefits would be based on changes in ambient PM 2.5 and ozone as determined by full-scale air quality modeling. However, EPA was unable to conduct a full-scale air quality modeling for the Model Year analysis. Full scale air quality modeling was cond ucted for the Calendar Year analysis. See Section III.G and III. H.7 for a discussion of that analysis.
d The PM 2.5 -related health benefits (derived from benefit-per- ton values) presented in this table are based on an estimate of premature mortality derived from the AC S study (Pope et al., 2002). However, EPA’s primar y method of characterizing PM-related premature mortality is to use both the ACS and the Six Cities study (Lade n et al., 2006) to generate a co-equal range of benefits estimates. The decision to present only the ACS-based estima te in this table does not convey any preference for one study over t he other. We note that this is also the more conse rvative of the two estimates - PM-related benefits would be appro ximately 245 percent (or nearly two-and-a-half time s) larger had we used the per-ton benefit values based on the Six Cities study instead. See Joint TSD 4 for a detai led description of the dollar-per-ton values used in this analysis.

e The PM 2.5 -related health benefits (derived from benefit-per- ton values) presented in this table assume a 3% discount rate in the valuation of premature mortali ty to account for a twenty-year segmented cessation lag. If a 7% discount rate had been used, the values would be ap proximately 9% lower. f Negative values for Accidents, Congestion, and Noi se costs represent disbenefits.

### Page 26 of 27 g The monetized GHG benefits presented in this analy sis exclude the value of changes in non-CO 2 GHG emissions expected under this action ( See RIA Chapter 7.1). Although EPA has not monetized changes in non-CO 2 GHGs, the value of any increases or reductions should not be interpreted as zero. We seek comment on a method of quantifying non-CO 2 GHG benefits in Section III.H.5.
h Model year values are discounted to the first year of each model year; the “Sum” represents those dis counted values summed across model years.



### Table Error! No text of specified style in document. -11 Monetized Technology Costs, Fuel Savings, Benefits, and Net Benefits Associated with the Lifetimes of 2017-2025 Model Year

Light-Duty Vehicles (Millions, 2009$; 7% Discount R ate) h

2017 MY 2018

MY 2019

MY 2020

MY 2021

MY 2022

MY 2023

MY 2024

MY 2025

MY Sum

Vehicle Program Costs $2,650 $5,220 $7,370 $9,610 $13,300 $19,200 $24,600 $29,900 $32,500 $144,000

Fuel Savings (pre-tax) $5,410 $11,900 $18,600 $26,100 $38,600 $49,700 $60, 100 $71,100 $82,300 $364,000

Energy Security Benefits(macrodisruption costs) $279 $615 $964 $1,360 $2,020 $2,620 $3,180 $3,780 $4,400 $19,200

Accidents, Congestion, Noise Costs f -$425 -$893 -$1,370 -$1,890 -$2,690 -$3,410 -$4,070 -$4,760 -$5,440 -$24,900

Increased Travel Benefits $761 $1,650 $2,550 $3,530 $5,120 $6,470 $7,640 $8,8 70 $10,100 $46,700

Refueling Time Savings $209 $461 $721 $1,020 $1,500 $1,940 $2,360 $2,800 $ 3,260 $14,300

PM 2.5 Related Health Impacts c,d,e $59 $136 $215 $305 $478 $607 $721 $840 $959 $4,320

Non-CO 2 GHG Impacts g n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a

Reduced CO 2 Emissions at each assumed SCC value a, b 5% (avg SCC) $152 $344 $551 $794 $1,210 $1,590 $1,970 $2,380 $2, 820 $11,800

3% (avg SCC) $642 $1,440 $2,270 $3,230 $4,850 $6,330 $7,740 $9,2 60 $10,800 $46,600

2.5% (avg SCC) $1,040 $2,320 $3,660 $5,190 $7,760 $10,100 $12,300 $14,700 $17,100 $74,100

3% (95th %ile) $1,970 $4,390 $6,950 $9,880 $14,800 $19,300 $23,600 $28,300 $33,000 $142,000

Monetized Net Benefits at each assumed SCC value a, b 5% (avg SCC) $3,800 $9,010 $14,900 $21,600 $32,900 $40,300 $47,3 00 $55,100 $65,800 $291,000

3% (avg SCC) $4,290 $10,100 $16,600 $24,100 $36,500 $45,000 $53, 100 $62,000 $73,800 $326,000

2.5% (avg SCC) $4,690 $11,000 $18,000 $26,000 $39,400 $48,800 $57, 600 $67,400 $80,100 $353,000

### Page 27 of 27

3% (95th %ile) $5,610 $13,100 $21,300 $30,700 $46,500 $58,000 $69, 000 $81,000 $96,100 $421,000

Notes:

a Net present value of reduced CO 2 emissions is calculated differently than other ben efits. The same discount rate used to discount the value of damages from future e missions (SCC at 5, 3, 2.5 percent) is used to calc ulate net present value of SCC for internal consistency. Ref er to the SCC TSD for more detail.
b Section III.H.5 notes that SCC increases over time . For the years 2012-2050, the SCC estimates range as follows: for Average SCC at 5%: $5-$16; for Average SCC at 3%: $23-$46; for Average SCC at 2.5%: $38-$67; an d for 95 th percentile SCC at 3%: $70-$140. Section III.H.5 also presents these SCC estimates.

c Note that the non-GHG impacts associated with the standards presented here do not include the full co mplement of endpoints that, if quantified and monetized, would change the total monetized estimate of rule-related impacts. Instead, the non-GHG benefits are based on benefit- per-ton values that reflect only human health impac ts associated with reductions in PM 2.5 exposure. Ideally, human health and environmental benefits would be based on changes in ambient PM 2.5 and ozone as determined by full-scale air quality modeling. However, EPA was unable to conduct a full-scale air quality modeling for the Model Year analysis. Full scale air quality modeling was cond ucted for the Calendar Year analysis. See Section III.G and III. H.7 for a discussion of that analysis.
d The PM 2.5 -related health benefits (derived from benefit-per- ton values) presented in this table are based on an estimate of premature mortality derived from the AC S study (Pope et al., 2002). However, EPA’s primar y method of characterizing PM-related premature mortality is to use both the ACS and the Six Cities study (Lade n et al., 2006) to generate a co-equal range of benefits estimates. The decision to present only the ACS-based estima te in this table does not convey any preference for one study over t he other. We note that this is also the more conse rvative of the two estimates - PM-related benefits would be appro ximately 245 percent (or nearly two-and-a-half time s) larger had we used the per-ton benefit values based on the Six Cities study instead. See Joint TSD 4 for a detai led description of the dollar-per-ton values used in this analysis.

e The PM 2.5 -related health benefits (derived from benefit-per- ton values) presented in this table assume a 3% discount rate in the valuation of premature mortali ty to account for a twenty-year segmented cessation lag. If a 7% discount rate had been used, the values would be ap proximately 9% lower

. f Negative values for Accidents, Congestion, and Noi se costs represent disbenefits.

g The monetized GHG benefits presented in this analy sis exclude the value of changes in non-CO 2 GHG emissions expected under this action ( See RIA Chapter 7.1). Although EPA has not monetized changes in non-CO 2 GHGs, the value of any increases or reductions should not be interpreted as zero. We seek comment on a method of quantifying non-CO 2 GHG benefits in Section III.H.5.
h Model year values are discounted to the first year of each model year; the “Sum” represents those dis counted values summed across model years.

### 2017 Regulatory Impact Analysis

1 

# Regulatory Impacts Analysis, Chapter 6.2.2 and 6.3 – Air Quality Modeling Results; Quantified and Monetized Non-GHG Health Impacts

6.2.1.3 Modeling Uncertainties and Limitations

All the results presented below must be interpreted with the understanding that there are uncertainties in inventories, atmospheric processes in CMAQ, and other aspects of the modeling process. While it is beyond the scope of this RIA to include a comprehensive discussion of all limitations and uncertainties associated with air q uality modeling, several sources of uncertainty that impact analyses for this rule are discussed.

A source of uncertainty is the photochemical mechan isms in CMAQ 4.7.1. Pollutants such as ozone, PM, acetaldehyde, formaldehyde, acro lein, and 1,3-butadiene can be formed secondarily through atmospheric chemical processes. Since secondarily formed pollutants can result from many different reaction pathways, there are uncertainties associated with each pathway. Simplifications of chemistry must be made in order to handle reactions of thousands of chemicals in the atmosphere. Mechanisms for format ion of ozone, PM, acetaldehyde and peroxyacetyl nitrate (PAN) are discussed in Section Error! Reference source not found.

. For PM, there are a number of uncertainties associa ted with SOA formation. As mentioned in Section Error! Reference source not found. , a large number of VOCs emitted into the atmosphere, which have the potential to fo rm SOA, have not yet been studied in detail. In addition, the amount of ambient SOA that comes f rom benzene is uncertain. Simplifications to the SOA treatment in CMAQ have also been made in order to preserve computational efficiency. These simplifications are described in release notes for CMAQ 4.7 on the Community Modeling and Analysis System (CMAS) websi te. 1



## 6.2.2 Air Quality Modeling Results

6.1.1 Air Quality Modeling Results

6.1.1.1 Ozone

As described in Section Error! Reference source not found. , exposure to ozone causes adverse health effects, and the EPA has set nationa l standards to provide requisite protection against those health effects. In this section, we present information on current and modelprojected future ozone levels.

6.1.1.1.1 Current Levels of Ozone

Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 1 shows a snapshot of measured ozone concentrations in 2010. The highest ozone concentrations were located in California. Formatted: Right: 0.88"

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### Chapter 6

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- 1 Ozone Concentrations (average of annual fourth hi ghest daily maximum 8- hour concentration) in ppm for 2010 2


### The primary and secondary NAAQS for ozone are 8-hou r standards set at 0.075 ppm. The most recent revision to the ozone standards was in 2008; the previous 8-hour ozone standards, set in 1997, had been set at 0.08 ppm. In 2004, the U.S. EPA designated nonattainment areas for the 1997 8-hour ozone NAAQS (69 FR 23858, April 30, 200 4). A 

### As of July 20 , 2012, there were 43 8-hour ozone nonattainment areas for the 1997 ozone NAAQS, composed of 237 full or partial counties, with a total population of over 129 milli on. Nonattainment areas for the 1997 8-hour ozone NAAQS are pictured in Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 2. Nonattainment designations for the 2008 ozone standard s were finalized on April 30, 2012 and May 31, 2012

. 3 

### These designations include 46 areas, composed of 227 full or partial counties, with a population of over 123 million. Nonattainment areas for the 2008 ozone NAAQS are pi ctured in Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 8 . As of July 20, 2012, 140 million people are living in ozone no nattainment areas.

A A nonattainment area is defined in the Clean Air A ct (CAA) as an area that is violating an ambient st andard or is contributing to a nearby area that is violating the standard. Deleted: 4

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Deleted: XX

Deleted: XX

Formatted: Font: (Default) Times New Roman, 12 pt, Font color: Auto

### 2017 Regulatory Impact Analysis

3 .

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- 2 1997 8-hour Ozone Nonattainment Areas Deleted: In total, there are XX ozone nonattainment areas, composed of XX counties, with a population of XX million people

Deleted: 5

### Chapter 6

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Figure Error! No text of specified style in document.

- 3 2008 8-hour Ozone Nonattainment Areas

States with ozone nonattainment areas are required to take action to bring those areas into attainment in the future. The attainment date assi gned to an ozone nonattainment area is based on the area’s classification. Most ozone nonattain ment areas are required to attain the 1997 8- hour ozone NAAQS in the 2007 to 2013 time frame. B 

### Once an ozone nonattainment area has attained the NAAQS they are then required to mainta in it thereafter. The attainment dates for areas designated nonattainment for the 2008 8-hour ozone NAAQS are in the 2015 to 2032 timeframe, depending on the severity of the problem in each area.

6.1.1.1.2 Projected Levels of Ozone

In the following sections, we describe projected oz one levels in the future with and without the vehicle standards. Our modeling indica tes that there will be very small changes in ozone across most of the country. In addition, ozo ne concentrations in some areas will decrease and ozone concentrations in some other areas will i ncrease. The impacts of the standards on

B The Los Angeles South Coast Air Basin 8-hour ozone nonattainment area and the San Joaquin Valley Air Basin 8- hour ozone nonattainment area are designated as ext reme and will have to attain before June 15, 2024. The Sacramento, Coachella Valley, Western Mojave, and H ouston 8-hour ozone nonattainment areas are designa ted as severe and will have to attain by June 15, 2019. Formatted: Keep with next

Formatted: Heading 4,Tech Assessment Heading 4,TSD Heading 4,RIA: Heading 4,Preamble Heading 4,ECA Heading 4,Heading 4 - Preamble,Preamble Heading 41,RIA: Heading 41,Preamble Heading 42,RIA: Heading 42,Preamble Heading 43,RIA: Heading 43,Preamble Heading 44, Indent: Hanging: 0.2", Outline numbered + Level: 5 + Numbering Style: 1, 2, 3, ... + Start at: 1 + Alignment: Left + Aligned at: 0 px + Tab after: 68 px + Indent at: 68 px

Deleted: ¶

### 2017 Regulatory Impact Analysis

5 ozone are a function of VMT increases from rebound, upstream reductions in petroleum consumption from crude oil production and transport , and gasoline production, distribution and transport, and changes in location and amount of el ectricity generation. Information on the air quality modeling methodology is contained in Sectio n Error! Reference source not found. and additional detail can be found in the air quality m odeling technical support document (AQM TSD).

Projected Levels of Ozone without this Final Actio n


### EPA has already adopted many emission control prog rams that are expected to reduce ambient ozone levels. These control programs inclu de the New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Marine Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October 8, 2008), the Locomotive and Marine Rule (73 FR 25098, May 6, 200 8), the Clean Air Interstate Rule (70 FR 25162, May 12, 2005), the Clean Air Nonroad Diesel Rule (69 FR 38957, June 29, 2004), and the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, January 18, 2001). As a result of these and other federal, state and local programs, 8-hour ozone levels are expected to improve in the future. However, even with the implementation of all current state and federal regulations, there are projected to be counties that would have projected design values above the l evel of the ozone NAAQS well into the future.

The air quality modeling projects that in 2030, wit h all current controls in effect but excluding the emissions changes expected to occur a s a result of this final action, at least 10 counties, with a projected population of over 30 mi llion people, would have projected design values above the level of the 2008 8-hour ozone sta ndard of 75 ppb. Since the emission changes from this final action go into effect during the pe riod when some areas are still working to attain the ozone NAAQS, the projected emission changes wil l impact state and local agencies in their effort to attain and maintain the ozone standard. In the following section we discuss the projected ozone impacts associated with the vehicle standards.

Projected Levels of Ozone with this Final Action



### This section summarizes the results of our modelin g of ozone air quality impacts in the future with the vehicle standards. Specifically, w e compare a 2030 reference scenario, a scenario without the vehicle standards, to a 2030 control sc enario which includes the vehicle standards. Our modeling indicates that there will be very smal l changes in ambient ozone concentrations across most of the country. However, there will be small decreases in ozone design value concentrations in some areas of the country and sma ll increases in ozone design value concentrations in other areas. C

C An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour ozone NAAQS. The full details involved in calculat ing an 8-hour ozone design value are given in appen dix I of 40 CFR part 50.

### Chapter 6

6

Figure Error ! No text of specified style in document.

- 4 presents the changes in 8-hour ozone design value concentrations in 2030 between t he reference case and the control case. The ozone impacts are related to downstream emissions c hanges from VMT rebound and upstream emissions changes in electrical power generation an d fuel production. In some areas the ozone impact is a result of a combination of the various emissions changes but in other areas the impact is likely mainly the result of one of the types of emissions changes. Some of the ozone increases and decreases are related mainly to upstream emissi ons changes in electricity generation. For example, the projected increases in Las Vegas, Dayt on, and Little Rock are due mainly to increased demand for electricity from electric vehi cles and the projected decrease in ozone in northeast West Virginia is due mainly to reductions in power plant emissions. D 

### Some of the ozone decreases are mainly related to upstream emis sions reductions from reduced refinery demand as fuel production decreases (e.g. the Gulf Coast) and some of the ozone increases are mainly related to increased emissions of NO X 

### from the VMT rebound effect (e.g., Knoxville and Atlanta).

Figure Error! No text of specified style in document.

- 4 Projected Change in 2030 8-hour Ozone Design Values Due to the Final Standards

D Section 4.7.3.1 has more information on the Integr ated Planning Model (IPM) analysis which was done t o project future electricity demand and plant locations.

### 2017 Regulatory Impact Analysis

7 As can be seen in Figure Error ! No text of specified style in document.

- 4 , the majority of the ozone design value impacts are between + 0.3 ppb and -0.3 ppb. However, there are two counties that will experience 8-hour ozone design v alue decreases of more than 0.3 ppb: Garrett County, Maryland, and Harris County, Texas. The ma ximum projected decrease in an 8-hour ozone design value is 0.47 ppb in Garrett County, M aryland and is likely related to the projected reductions in power plant emissions in northeast WV . There is also one county, Pulaski County in Arkansas, with a projected design value increase greater than 0.3 ppb. The projected increase in Pulaski County is 0.37 ppb. There are 10 counties, most of them in California, that are projected to have 8-hour ozone design values above the 2008 NAAQS in 2030 with the vehicle standards in place. Table Error ! No text of specified style in document.

- 1 below presents the changes in design values for these counties.

Table Error! No text of specified style in document.

- 1 Change in Ozone Design Values (ppb)

for Counties Projected to be Above the 2008 Ozone N AAQS in 2030

County Name Change in 8-hour Ozone Design Value (ppb ) 

### Population in

2030 a

San Bernardino Co, California -0.20 2,784,490 Riverside Co, California -0.23 2,614,198 Los Angeles Co, California -0.13 10,742,722 Kern Co, California 0.05 981,806 Harris Co, Texas -0.31 5,268,889 Tulare Co, California -0.02 528,663 Orange Co, California -0.12 4,431,071 Fresno Co, California -0.04 1,196,950 Suffolk Co, New York -0.06 1,705,822 Brazoria Co, Texas -0.30 364,257

Note:

a Population numbers based on Woods & Poole data. W oods & Poole Economics, Inc. 2001. Population by Single Year of Age CD.



### Table Error ! No text of specified style in document.

- 2 shows the average change in 2030 8-hour ozone design values for: (1) all counties wi th 2005 baseline design values, (2) counties with 2005 baseline design values that exceeded the 2008 ozone standard, (3) counties with 2005 baseline design values that did not exceed the 2008 standard, but were within 10% of it, (4) counties with 2030 design values that exceeded the 2008 ozone standard, and (5) counties with 2030 design values that did not exceed the standard , but were within 10% of it. Counties within 10% of the standard are intended to reflect countie s that although not violating the standards, will also be impacted by changes in ozone as they work t o ensure long-term maintenance of the ozone NAAQS. The average modeled future-year 8-hour ozon e design values are projected to increase by 0.01 ppb in 2030. Average design values in thos e counties that are projected to be above the 2008 ozone standard in 2030 will decrease by 0.14 p pb due to the vehicle standards. Formatted: Space Before: 16 px, After: 16 px, Don't hyphenate

Formatted: Indent: First line: 48 px, Space Before: 16 px, After: 16 px, Don't hyphenate

### Chapter 6

8

Table Error! No text of specified style in document.

- 2 Average Change in Projected 8-hour Ozone Design Value

Average a Number of US Counties 2030

Population b Change in

2030 design value (ppb) All 675 261,439,344 0.01 All, population-weighted 0.00 Counties whose 2005 base year is above the 2008 8-hour ozone standard

393 194,118,748 0.02 Counties whose 2005 base year is above the 2008 8-hour ozone standard, population-weighted 0.00 Counties whose 2005 base year is within 10 percent of the 2008 8-hour ozone standard

201 44,436,103 0.02 Counties whose 2005 base year is within 10 percentof the 2008 8-hour ozone standard, populationweighted 0.01 Counties whose 2030 control case is above the 2008 8-hour ozone standard

10 30,618,868 -0.14 Counties whose 2030 control case is above the 2008 8-hour ozone standard, population-weighted -0.16 Counties whose 2030 control case is within 10% of the 2008 8-hour ozone standard

40 29,661,201 -0.02 Counties whose 2030 control case is within 10% ofthe 2008 8-hour ozone standard, populationweighted 0.00
Notes:

a Averages are over counties with 2005 modeled desig n values
b Population numbers based on Woods & Poole data. W oods & Poole Economics, Inc. 2001. Population by Single Year of Age CD.


### Ground-level ozone pollution is formed by the react ion of VOCs and NO X 

### in the atmosphere in the presence of heat and sunlight. T he science of ozone formation, transport, and accumulation is complex. 4 

### The projected ozone impacts which are seen in the air quality modeling for this final action are a result of the emissions changes due to the vehicle standards combined with the photochemistry involved, the diff erent background concentrations of VOCs and NO X 

### in different areas of the country, and the differe nt meteorological conditions in different areas of the country.

When VOC levels are relatively high, relatively sma ll amounts of NO X 

### enable ozone to form rapidly. Under these conditions, VOC reductio ns have little effect on ozone and while NO X 

### reductions are highly effective in reducing ozone, conversely NO X 

### increases lead to increases in ozone. Such conditions are called “NO X 

### -limited.” Because the contribution of VOC emissi ons from biogenic (natural) sources to local ambient oz one concentrations can be significant, even some areas where man-made VOC emissions are relativ ely low can be NO X 

### -limited. Rural areas are usually NO X 

### -limited, due to the relatively large amounts of b iogenic VOC emissions in such

areas.

When NO X 

### levels are relatively high and VOC levels relative ly low, NO X 

### forms inorganic nitrates ( i.e. , particles) but relatively little ozone. Such con ditions are called “NOx-saturated.”

### 2017 Regulatory Impact Analysis

9 Under these conditions, VOC reductions are effectiv e in reducing ozone, but NO X 

### reductions can actually increase local ozone under certain circums tances.

6.1.1.2 Particulate Matter

As described in Section Error! Reference source not found. , exposure to PM 2.5 

### causes adverse health effects, and the EPA has set nationa l standards to provide requisite protection against those health effects. In this section, we present information on current and modelprojected future PM 2.5 

### levels.

6.1.1.2.1 Current Levels of Particulate Matter

Figure Error ! No text of specified style in document. .2

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- 5 and Figure Error ! No text of specified style in document. .2

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- 6 respectively show a snapshot of annual and 24-hou r PM 2.5 

### concentrations in 2010. In 2010, the highest annua l average PM 2.5 

### concentrations were in California, Indiana, Pennsylvania, and Hawaii and t he highest 24-hour PM 2.5 

### concentrations were in California and Alaska.

Figure Error! No text of specified style in document. .2

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- 5 Annual Average PM 2.5 

### Concentrations in μg/m 3 

### for 2010 5 Deleted: [placeholder]

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### Chapter 6

10

Figure Error! No text of specified style in document. .2

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- 6 24-hour (98 th 

### percentile 24- hour concentrations) PM 2.5 

### Concentrations in μg/m 3 

### for 2010 6



### There are two NAAQS for PM 2.5 

### : an annual standard (15.0 μ g/m 3 

### ) and a 24-hour standard (35 μ g/m 3 

### ). The most recent revisions to these standards we re in 1997 and 2006. In June 2012, EPA proposed to revise the PM 2.5 

### NAAQS and is scheduled to issue final revisions in December 2012 under a court-ordered schedule. The proposed changes include revising the annual PM 2.5 

### standard to a level between 12 and 13 μg/m 3 

### , and establishing a distinct secondary PM 2.5 

### standard for the protection of visibility, particularly in u rban areas.

In 2005 the U.S. EPA designated nonattainment areas for the 1997 PM 2.5 

### NAAQS (70 FR 19844, April 14, 2005). As of July 20 , 2012, over 91 million people lived in the 35 area s that are designated as nonattainment for the 1997 PM 2.5 

### NAAQS. These PM 2.5 

### nonattainment areas are comprised of 191 full or partial counties. Nonatta inment areas for the 1997 PM 2.5 

### NAAQS are pictured in Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 7. On October 8, 2009, the EPA issued final nonatt ainment area designations for the 2006 24-hour PM 2.5 

### NAAQS (74 FR 58688, November 13, 2009). These des ignations include 32 areas, composed of 121 full or partial c ounties, with a population of over 74 million. Nonattainment areas for the 2006 PM 2.5 

### NAAQS are pictured in Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 8. In total, there are 50 PM 2.5 

### nonattainment areas with a population of over 105 million people.

States with PM 2.5 

### nonattainment areas will be required to take actio n to bring those areas into attainment in the future. The 1997 PM 2.5 

### nonattainment areas are required to attain the 199 7 PM 2.5 

### NAAQS in the 2009 to 2015 time frame and then main tain the 1997 PM 2.5 

### NAAQS Deleted: 78

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### 2017 Regulatory Impact Analysis

11 thereafter. 7 

### The 2006 24-hour PM 2.5 

### nonattainment areas will be required to attain the 2006 24- hour PM 2.5 

### NAAQS in the 2014 to 2019 time frame and then main tain the 2006 24-hour PM 2.5 

### NAAQS thereafter. 8



### Figure Error! No text of specified style in document. .2

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document.

- 7 1997 PM 2.5 

### Nonattainment Areas Deleted: 89

### Chapter 6

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- 8 2006 PM 2.5 

### Nonattainment Areas

As of July 20 , 2012, over 29 million people live in the 46 areas that are designated as nonattainment for the PM 10 

### NAAQS. There are 39 full or partial counties that make up the PM 10 

### nonattainment areas. Nonattainment areas for the P M 10 

### NAAQS are pictured in Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 9. Deleted: 910

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- 9 PM 10 

### Nonattainment Areas

6.1.1.2.2 Projected Levels of PM 2.5


### In the following sections we describe projected PM 2.5 

### levels in the future, with and without the standards. Our modeling indicates that there will be very small changes in PM 2.5 

### across most of the country. The impacts of the sta ndards on PM 2.5 

### are a function of VMT increases from rebound, upstream reductions in petr oleum consumption from crude oil production and transport, and gasoline production, distribution and transport, and changes in location and amount of electricity generation. Inf ormation on the air quality modeling methodology is contained in Section Error! Reference source not found. . Additional detail can be found in the air quality modeling technical supp ort document (AQM TSD).

Projected Levels of PM2.5 without this Final Actio n


### EPA has already adopted many mobile source emission control programs that are expected to reduce ambient PM levels. These contro l programs include the New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Marine Spark-Ignition and Small Spark-Ig nition Engine Rule (73 FR 59034, October Deleted: 10 11

Formatted: Indent: Hanging: 0.2", Outline numbered + Level: 5 + Numbering Style: 1, 2, 3, ... + Start at: 1 + Alignment: Left + Aligned at: 0 px + Tab after: 68 px + Indent at: 68 px
Deleted: ¶

### Chapter 6

14

8, 2008), the Locomotive and Marine Compression-Ign ition Engine Rule (73 FR 25098, May 6, 2008), the Clean Air Nonroad Diesel (69 FR 38957, J une 29, 2004), the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Co ntrol Requirements (66 FR 5002, January 18, 2001) and the Tier 2 Motor Vehicle Emissions St andards and Gasoline Sulfur Control Requirements (65 FR 6698, February 10, 2000). As a result of these and other federal, state and local programs, the number of areas that fail to me et the PM 2.5 

### NAAQS in the future is expected to decrease. However, even with the implementation of all current state and federal regulations, there are projected to be counties that would have projected design values above the level of the PM 2.5 

### NAAQS well into the future.

The air quality modeling conducted projects that in 2030, with all current controls in effect but excluding the emissions changes expected to occur as a result of this final action, at least 4 counties, with a projected population of ne arly 7 million people, would have projected design values above the level of the annual standar d of 15 μg/m 3 

### and at least 21 counties, with a projected population of over 31 million people, would have projected design values above the level of the 2006 24-hour standard of 35 μg/m 3 

### . Since the emission changes from this final action go into effect during the period when some a reas are still working to attain the PM 2.5 

### NAAQS, the projected emission changes will impact s tate and local agencies in their effort to attain and maintain the PM 2.5 

### standard. In the following section we discuss the PM 2.5 

### impacts associated with the vehicle standards.

Projected Annual Average PM 2.5 

### Design Values with this Final Action



### This section summarizes the results of our modeling of annual average PM 2.5 

### air quality impacts in the future due to the vehicle standards finalized in this action. Specifically, we compare a 2030 reference scenario (a scenario witho ut the vehicle standards) to a 2030 control scenario which includes the vehicle standards. Our modeling indicates that the majority of the modeled counties will experience small changes of b etween 0.05 μg/m 3 

### and -0.05 μg/m 3 

### in their annual PM 2.5 

### design values due to the vehicle standards.

Figure Error ! No text of specified style in document.

- 10 presents the changes in annual PM 2.5 

### design values in 2030. E

E An annual PM 2.5 design value is the concentration that determines whether a monitoring site meets the annual NAAQS for PM 2.5 . The full details involved in calculating an annu al PM 2.5 design value are given in appendix N of 40 CFR part 50.

### 2017 Regulatory Impact Analysis

15 Figure Error! No text of specified style in document.

- 10 Projected Change in 2030 Annual

PM 2.5 

### Design Values Due to the Final Standards

Figure Error! No text of specified style in document.

- 10 , eight counties will experience decreases larger than 0.05 μg/m 3 

### . These counties are in the Gulf Coast and in Miss ouri. The maximum projected decrease in an annual PM 2.5 

### design value is 0.16 μg/m 3 

### in West Baton Rouge County, Louisiana. The decreases in annual PM 2.5 

### design values in the Gulf Coast are likely due to emission reductions related to lower fuel produc tion. Additional information on the emissions reductions that are projected with this final actio n is available in Section 4.7.

There are 4 counties, all in California, that are p rojected to have annual PM 2.5 

### design values above the NAAQS in 2030 with the vehicle sta ndards in place. Table Error ! No text of specified style in document.

- 3 below presents the changes in design values for th ese counties. Formatted: Indent: First line: 48 px

### Chapter 6

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Table Error! No text of specified style in document.

- 3 Change in Annual PM 2.5 

### Design Values (μg/m 3 

### ) for Counties Projected to be Above the Annual PM 2.5 

### NAAQS in 2030

County Name Change in Annual PM 2.5 

### Design Value (μg/m 3) 

### Population

in 2030 a

Riverside County, California -0.01 2,614,198 San Bernardino County, California 0 2,784,489 Kern County, California -0.02 981,806 Tulare County, California -0.01 528,662

a Population numbers based on Woods & Poole data. W oods & Poole Economics, Inc. 2001. Population by S ingle Year of Age CD.



### Average changes in 2030 annual PM 2.5 

### design values for a variety of metrics are all between 0.00 and -0.03 μg/m 3 

### illustrating the small decrease in annual PM 2.5 

### design values in 2030. These metrics include: (1) all counties with 2005 baseline design values, (2) counties with 2005 baseline design values that exceeded the annua l PM 2.5 

### standard, (3) counties with 2005 baseline design values that did not exceed the stan dard, but were within 10% of it, (4) counties with 2030 design values that exceeded the annual PM 2.5 

### standard, and (5) counties with 2030 design values that did not exceed the standard, but were within 10% of it.

Projected 24-hour Average PM 2.5 

### Design Values with this Final Action



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

### air quality impacts in the future due to the vehicle standards. Specif ically, we compare a 2030 reference scenario (a scenario without the vehicle standards) to a 2030 c ontrol scenario which includes the vehicle standards. Our modeling indicates that the majorit y of the modeled counties will experience changes of between -0.05 μg/m 3 

### and 0.05 μg/m 3 

### in their 24-hour PM 2.5 

### design values. Figure Error! No text of specified style in document.

- 11 presents the changes in 24-hour PM 2.5 

### design values in 2030. F

F 

### A 24-hour PM 2.5 

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

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

### design value are given in appendix N of 40 CFR part 50. Formatted Table

Formatted: Subscript

Formatted: Subscript

Formatted: Subscript

### 2017 Regulatory Impact Analysis

17 Figure Error! No text of specified style in document.

- 11 Projected Change in 2030 24-hour PM 2.5 

### Design Values Due to the Final Standards

As shown in Figure Error! No text of specified style in document.

- 11 , design value concentrations will increase more than 0.05 μg/m 3 

### in six counties and design value concentrations will decrease more than 0.05 μg/m 3 

### in 23 counties. The decreases in 24-hour PM 2.5 

### design values in some counties are likely due to e mission reductions related to lower fuel production. The maximum projected decrease in a 24 -hour PM 2.5 

### design value is 0.76 μg/m 3 

### in East Baton Rouge County, Louisiana. The increases in 24-hour PM 2.5 

### design values in some counties are likely due to increased emissions from the VMT rebound effect or increased electricity generation. The maximum projected incr ease in a 24-hour PM 2.5 

### design value is 0.14 μg/m 3 

### in El Paso County, Colorado. Additional informati on on the emissions changes that are projected with this final action is available in Se ction 4.7.

There are 21 counties, mainly in California, that a re projected to have 24-hour PM 2.5 

### design values above the NAAQS in 2030 with the vehi cle standards in place. Table Error ! No text of specified style in document.

- 4 

### below presents the changes in design values for th ese counties.

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18

Table Error! No text of specified style in document.

- 4 Change in 24-hour PM 2.5 Design Values (μg/m 3 ) for

Counties Projected to be Above the 24-hour PM 2.5 NAAQS in 2030



### County Name Change in 24- hour PM 2.5 

### Design Value (μg/m 3) 

### Population in 2030 a

Kern Co, California 0.02 981,806 Riverside Co, California -0.01 2,614,198 Fresno Co, California -0.02 1,196,950 San Bernardino Co, California -0.27 2,784,490 Sacramento Co, California 0 1,856,971 Kings Co, California -0.03 195,067 Los Angeles Co, California 0.03 10,742,722 Tulare Co, California -0.03 528,663 Lane Co, Oregon 0 460,993 Cache Co, Utah 0.04 141,446 Allegheny Co, Pennsylvania -0.01 1,234,931 Stanislaus Co, California -0.02 688,246 Lake Co, Montana 0 40,126 Orange Co, California 0.05 4,431,071 Klamath Co, Oregon 0 77,200 Salt Lake Co, Utah 0.02 1,431,946 Ravalli Co, Montana 0 63,914 Butte Co, California 0 287,236 Missoula Co, Montana 0.01 141,264 Pierce Co, Washington 0.03 1,082,579 Lincoln Co, Montana 0 20,454
Note:

a Population numbers based on Woods & Poole data. W oods & Poole Economics, Inc. 2001. Population by S ingle Year of Age CD.



### Average changes in 2030 24-hour PM 2.5 

### design values for a variety of metrics are all between 0.00 and -0.03 μg/m 3 

### illustrating the small decrease in 24-hour PM 2.5 

### design values in 2030. These metrics include: (1) all counties with 2005 baseline design values, (2) counties with 2005 baseline design values that exceeded the 24-ho ur PM 2.5 

### standard, (3) counties with 2005 baseline design values that did not exceed the stan dard, but were within 10% of it, (4) counties with 2030 design values that exceeded the 24-hour P M 2.5 

### standard, and (5) counties with 2030 design values that did not exceed the standard, but were within 10% of it. Formatted Table

### 2017 Regulatory Impact Analysis

19 6.1.1.3 Air Toxics

According to the National Air Toxics Assessment (NA TA) for 2005, mobile sources were responsible for 43 percent of outdoor toxic emissio ns and over 50 percent of the cancer risk and noncancer hazard attributable to direct emissions f rom mobile and stationary sources. G,H,9 

### According to the 2005 NATA, about three-fourths of the U.S. population was exposed to an average chronic concentration of air toxics that ha s the potential for adverse noncancer respiratory health effects. In 2007 EPA finalized vehicle and fuel controls to reduce mobile source air toxics. 10 

### In addition, over the years, EPA has implemented a number of mobile source and fuel controls resulting in VOC reductions, whic h also reduce air toxic emissions. Modeling from the Mobile Source Air Toxics (MSAT) rule sugge sts that the mobile source contribution to ambient benzene concentrations is projected to decr ease over 40% by 2015, with a decrease in ambient benzene concentration from all sources of a bout 25%. Although benzene is used as an example, the downward trend is projected for other air toxics as well. See the RIA for the final MSAT rule for more information on ambient air toxic s projections. 11



### 6.1.1.3.1 Current Levels of Air Toxics

The majority of Americans continue to be exposed to ambient concentrations of air toxics at levels which have the potential to cause adverse health effects. 12 

### The levels of air toxics to which people are exposed vary depending on where pe ople live and work and the kinds of activities in which they engage, as discussed in de tail in U.S. EPA’s 2007 Mobile Source Air Toxics (MSAT) Rule. 13 

### In order to identify and prioritize air toxics, e mission source types and locations which are of greatest potential concern, U. S. EPA conducts the National-Scale Air Toxics Assessment (NATA). The most recent NATA was conducted for calendar year 2005, and was released in March 2011. 14 

### NATA for 2005 includes four steps:

1) Compiling a national emissions inventory of air toxics emissions from outdoor sources

2) Estimating ambient concentrations of air toxics across the United States

3) Estimating population exposures across the Unit ed States

4) Characterizing potential public health risk due to inhalation of air toxics including both cancer and noncancer effects

G NATA also includes estimates of risk attributable to background concentrations, which includes contri butions from long-range transport, persistent air toxics, and na tural sources; as well as secondary concentrations, where toxics are formed via secondary formation. Mobile sources sub stantially contribute to long-range transport and s econdarily formed air toxics.
H NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to estimate toxic air pollutant concentrations. Projec ted air toxics concentrations presented in this fin al action were modeled with CMAQ 4.7.1. Formatted: Normal

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### Chapter 6

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Figure Error ! No text of specified style in document. .2

- Error! No text of specified style in document.

- 12 and Figure Error ! No text of specified style in document. .2

- Error! No text of specified style in document.

- 13 depict estimated tract-level carcinogenic risk a nd noncancer respiratory hazard from the assessment. The respiratory hazard is dominated by a single pollutant, acrolein.

According to the NATA for 2005, mobile sources were responsible for 43 percent of outdoor toxic emissions and over 50 percent of the cancer risk and noncancer hazard attributable to direct emissions from mobile and stationary sour ces. I,J,15 

### Mobile sources are also large contributors to precursor emissions which react to form secondary concentrations of air toxics. Formaldehyde is the largest contributor to cancer r isk of all 80 pollutants quantitatively assessed in the 2005 NATA, and mobile sources were responsib le for over 40 percent of primary emissions of this pollutant in 2005, and are major contributors to formaldehyde precursor emissions. Benzene is also a large contributor to cancer risk, and mobile sources account for over 70 percent of ambient exposure. Over the year s, EPA has implemented a number of mobile source and fuel controls which have resulted in VOC reductions, which also reduced formaldehyde, benzene and other air toxic emissions

.

I NATA also includes estimates of risk attributable to background concentrations, which includes contri butions from long-range transport, persistent air toxics, and na tural sources; as well as secondary concentrations, where toxics are formed via secondary formation. Mobile sources sub stantially contribute to long-range transport and s econdarily formed air toxics
J NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to estimate toxic air pollutant concentrations. Projec ted air toxics concentrations presented in this fin al action were modeled with CMAQ 4.7.1.

### 2017 Regulatory Impact Analysis

21 Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 12 Tract Level Average Carcinogenic Risk, 2005 NATA

Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 13 County Level Average Noncancer Hazard Index, 200 5 NATA

6.1.1.3.2 Projected Levels of Air Toxics

In the following sections, we describe results of o ur modeling of air toxics levels in the future with the finalized standards. Although ther e are a large number of compounds which are considered air toxics, we focused on those which we re identified as national and regional-scale cancer and noncancer risk drivers in past NATA asse ssments and were also likely to be significantly impacted by the standards. These com pounds include benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Informat ion on the air quality modeling methodology is contained in Section Error! Reference source not found. . Additional detail, including seasonal concentration maps, can be found in the ai r quality modeling technical support document (AQM TSD) in the docket for this rule.

It should be noted that EPA has adopted many mobile source emission control programs that are expected to reduce ambient air toxics leve ls. These control programs include the Heavyduty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI and Marine SI Engine Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098, Deleted: 11 14

Deleted: 1215

### Chapter 6

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May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007), Clean Air Nonroad Diesel Rule (69 FR 38957, June 29, 2004), H eavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan. 18, 2001) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Su lfur Control Requirements (65 FR 6698, Feb. 10, 2000). As a result of these programs, the ambient concentration of air toxics in the future is expected to decrease. The reference case and control case scenarios include these controls.

Our modeling indicates that national average ambien t concentrations of the modeled air toxics change less than 1 percent across most of the coun try due to the final standards . Because overall impacts are relatively small in future year s, we concluded that assessing exposure to ambient concentrations and conducting a quantitativ e risk assessment of air toxic impacts was not warranted. However, we did develop population metr ics, including the population living in areas with changes in concentrations of various magnitude s.
Acetaldehyde

Our air quality modeling results show that this rul e does not have substantial impacts on ambient concentrations of acetaldehyde. Figure Error ! No text of specified style in document.

- 14 shows nationwide changes in ambient acetaldehyde i n 2030 are between ± 1 percent, with decreases up to 10 percent in a few urban areas. Reductions in ambient acetaldehyde in 2030 range between 0.001 and 0.01 μg/m3 across much of the country with decreases as high as 0.1 μg/m3 in urban areas; these changes are mainly associated with reductions from upstream sources including fuel pro duction, refining, storage and transport (Figure Error ! No text of specified style in document.

- 14 ).

Figure Error! No text of specified style in document.

- 14 Changes in Acetaldehyde Ambient Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute Changes in μg/m3 (right) Deleted: the 2017-2025 LD GHG standards have relatively little impact on

Deleted: Percent Changes

Deleted: Between the Reference Case and the Control Case

### 2017 Regulatory Impact Analysis

23 Formaldehyde

Our air quality modeling results do not show substa ntial impacts on ambient concentrations of formaldehyde as a result of the final standards . In 2030, annual percent changes in ambient concentrations of formaldehyde a re less than 1 percent across much of the country, with a decrease ranging from 2.5 to 10 per cent in Oklahoma (Figure Error ! No text of specified style in document.

- 15 ). Ambient formaldehyde reductions in 2030 general ly range from 0.001 to 0.1 μg/m3 and are associated with ups tream reductions in fuel production, refining, storage and transport (Figure Error ! No text of specified style in document.

- 15 ). Decreases in Oklahoma are over 0.3 μg/m3 and due to reductions i n emissions from refineries in that area. Increases in ambient formaldehyde concentrations ra nge between 0.001 to 0.1 μg/m3 in areas associated with increased emissions from power plants

.

Figure Error! No text of specified style in document.

- 15 Changes in Formaldehyde Ambient Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute Changes in μg/m3 (right)

Benzene

Our air quality modeling results do not show substa ntial impacts on ambient concentrations of benzene as a result of this rule. In 2030, percent changes in ambient concentrations of benzene are ± 1 percent nationwid e (Figure Error! No text of specified style in document.

- 16 ); a few areas, mainly in the Gulf Coast region, ar e projected to have benzene reductions from 1 to 10 percent , likely due to decreases in refinery emissions. Ab solute changes in ambient benzene in 2030 are generally ± 0.001 μg/m3 in the western half of the U.S. with decreases up to 0.01 μg/m3 across the eastern half of the U.S due to upstream reductions in fuel Deleted: the 2017-2025 LD GHG standards

Deleted: EGUs

Deleted: Percent

Deleted: Between the Reference Case and the Control Case

Deleted: %

### Chapter 6

24

production, refining, storage and transport (Figure Error! No text of specified style in document.

- 16 ).

Figure Error! No text of specified style in document.

- 16 Changes in Benzene Ambient Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute Changes in μg/m3 (right)

1,3-Butadiene

Our modeling also shows that this rule do es not have a significant impact on ambient 1,3- butadiene concentrations in 2030. Figure Error ! No text of specified style in document.

- 17 shows that ambient concentrations of 1,3-but a diene generally range between ± 1 percent across the country in 2030. Some areas have 1,3-butadiene increases on the order of 1 to 2.5 percent; however, as shown in the map on the right, all chan ges in absolute concentrations are between ± 0.001 μ g/m3 nationwide (Figure Error ! No text of specified style in document.

- 17 ). Deleted: Percent

Deleted: Between the Reference Case and the Control Case

Deleted: ¶

Deleted: the 2017-2025 LD GHG standards

Deleted: are

### 2017 Regulatory Impact Analysis

25 Figure Error! No text of specified style in document.

- 17 Changes in 1,3-Butadiene Ambient Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute Changes in μg/m3 (right)

Acrolein

Our air quality modeling results do not show substa ntial impacts on ambient concentrations of acrolein as a result of this rule . In 2030, percent changes in ambient acrolein concentrations are generally ± 1 percent nationwide (Figure Error ! No text of specified style in document.

- 18 ). Parts of the Midwest and Texas have decreases i n ambient acrolein concentrations generally between 1 and 10 percent a nd increases of similar magnitude in a few urban areas; however, all absolute changes in ambie nt acrolein concentrations are between ± 0.001 μ g/m3 in 2030 (Figure Error ! No text of specified style in document.

- 18 ). Deleted: Percent

Deleted: Between the Reference Case and the Control Case

### Chapter 6

26

Figure Error! No text of specified style in document.

- 18 Changes in Acrolein Ambient Concentrations in 2030 due to the Final Standards: Percent Changes (left) and Absolute Changes in μg/m3 (right)

Population Metrics

To assess the impact of this rule’s projected changes in air quality, we developed population metrics that show the population experiencing changes in annual ambient concentrations across the modeled air toxics. As s hown in Table Error ! No text of specified style in document.

- 5 , over 98 percent of the U.S. population is project ed to experience a less than one percent change in formaldehyde and 1,3-but adiene. Over 83 percent of the U.S. population is projected to experience a less than o ne percent change in acetaldehyde, benzene and acrolein, and over 12 percent are projected to expe rience a 1 to 5 percent decrease in these pollutants. Deleted: Percent

Deleted: Between the Reference Case and the Control Case

Deleted: with the 2017

- 2025 LD GHG standards

### 2017 Regulatory Impact Analysis

27 Table Error! No text of specified style in document.

- 5 Percent of Total Population Experiencing Changes in Annual Ambient Concentratio ns of Toxic Pollutants in 2030 as a Result of the Final Standards
Percent Change Acetaldehyde Formaldehyde Benzene 1,3-Butadiene Acrolein
≤ -100 > -100 to ≤ -50 > -50 to ≤ -10 > -10 to ≤ -5 0.0% 0.0% 0.8% 0.2% > -5 to ≤ -2.5 1.5% 0.1% 1.8% 0.0% 2.0% > -2.5 to ≤ -1 15.3% 1.2% 13.0% 0.2% 10.3% > -1 to < 1 83.1% 98.7% 84.4% 99.2% 86.1%

≥ 1 to < 2.5 0.0% 0.6% 0.9%
≥ 2.5 to < 5 0.0% 0.0%
≥ 5 to < 10 0.0%
≥ 10 to < 50

≥ 50 to < 100

≥ 100



### 6.1.1.4 Deposition of Nitrogen and Sulfur

6.1.1.4.1 Current Levels of Nitrogen and Sulfur Deposition

Over the past two decades, the EPA has undertaken n umerous efforts to reduce nitrogen and sulfur deposition across the U.S. Analyses of long-term monitoring data for the U.S. show that deposition of both nitrogen and sulfur compoun ds has decreased over the last 17 years. The data show that reductions were more substantial for sulfur compounds than for nitrogen compounds. In the eastern U.S., where data are mos t abundant, total sulfur deposition decreased by about 44 percent between 1990 and 2007, while to tal nitrogen deposition decreased by 25 percent over the same time frame. 16 

### These numbers are generated by the U.S. national monitoring network and they likely underestimate ni trogen deposition because neither ammonia nor organic nitrogen is measured. Although total n itrogen and sulfur deposition has decreased over time, many areas continue to be negatively imp acted by deposition. Deposition of inorganic nitrogen and sulfur species routinely measured in t he U.S. between 2005 and 2007 were as high as 9.6 kilograms of nitrogen per hectare (kg N/ha) averaged over three years and 20.8 kilograms of sulfur per hectare (kg S/ha) averaged over three years. 17 Deleted: LD GHG Rule

Deleted: [placeholder]

### Chapter 6

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Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 19 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2005 -2007 Deleted: 13 28

### 2017 Regulatory Impact Analysis

29

### Chapter 6

30

Figure Error! No text of specified style in document. .2

- Error! No text of specified style in document.

- 20 Total Nitrogen Deposition in the Contiguous U.S. , 1989-1991 and 2005- 2007

6.1.1.4.2 Projected Levels of Nitrogen and Sulfur Deposition

Our air quality modeling projects increases in nitr ogen deposition in some localized areas across the U.S. along with a few areas of decreases in nitrogen deposition. Figure Error! No text of specified style in document.

- 21 shows that for nitrogen deposition the vehicle sta ndards will result in annual percent increases of more tha n 2 percent in some areas. The increases in nitrogen deposition are likely due to projected ups tream emissions increases in NO X 

### from increased electricity generation and increased driv ing due to the rebound effect. Figure Error! No text of specified style in document.

- 21 also shows that for nitrogen deposition the vehicl e standards will result in annual percent decreases o f more than 2 percent in a few areas in West Virginia and New Mexico. The decreases in nitrogen deposition are likely due to projected upstream emissions decreases in NO X 

### from changes in the location of electricity genera tion. The remainder of the country will experience only minim al changes in nitrogen deposition, ranging from decreases of less than 0.5 percent to increase s of less than 0.5 percent.

Figure Error! No text of specified style in document.

- 21 Percent Change in Annual Total Nitrogen Deposition as a Result of the Final Standa rds Deleted: 1429

### 2017 Regulatory Impact Analysis

31 Our air quality modeling projects both increases an d decreases in sulfur deposition in localized areas across the U.S. Figure Error! No text of specified style in document.

- 22 shows that for sulfur deposition the vehicle standards wi ll result in annual percent decreases of more than 2% in some areas. The decreases in sulfur dep osition are likely due to projected upstream emissions decreases from changes in the location of electricity generation and from reduced gasoline production. Figure Error! No text of specified style in document.

- 22 also shows that for sulfur deposition the vehicle standards will re sult in annual percent increases of more than 2% in some areas. The increases in sulfur deposition are likely due to projected upstream emissions increases from increased electricity generation. T he remainder of the country will experience only minimal changes in sulfur deposition, ranging from decreases of less than 0.5% to increases of less than 0.5%.

Figure Error! No text of specified style in document.

- 22 Percent Change in Annual Total Sulfur Deposition as a Result of the Final Standard s

6.1.1.5 Visibility Degradation

6.1.1.5.1 Current Visibility Levels

A s of August 30, 2011, approximately 101 million peo ple live in nonattainment areas for the PM 2.5 

### NAAQS. Thus, at least these populations would lik ely be experiencing visibility impairment, as well as many thousands of individual s who travel to these areas. While visibility trends have improved in most Class I areas, the rec ent data show that these areas continue to Deleted: [placeholder]¶

Deleted: Designated PM 2.5 nonattainment areas indicate that, a

### Chapter 6

32

suffer from visibility impairment. 18 

### Calculated from light extinction efficiencies from Trijonis et al. (1987, 1988), annual average visual range under natural conditions in the East is estimated to be 150 km ± 45 km (i.e., 65 to 120 miles) and 230 k m ± 35 km (i.e., 120 to 165 miles) in the West. 19,20,21 

### In summary, visibility impairment is experienced th roughout the U.S., in multi-state regions, urban areas, and remote Mandatory Class I Federal areas.

6.1.1.5.2 Projected Visibility Levels

Air quality modeling conducted for the final action was used to project visibility conditions in 139 mandatory class I federal areas a cross the U.S. in 2030. The results show that all the modeled areas will continue to have annual average deciview levels above background in 2030. K 

### The results also indicate that the majority of th e modeled mandatory class I federal areas will see very little change in their visibility. S ome mandatory class I federal areas will see improvements in visibility due to the vehicle stand ards and a few mandatory class I federal areas will see visibility decreases. The average visibil ity at all modeled mandatory class I federal areas on the 20% worst days is projected to improve by 0. 003 deciviews, or 0.03%, in 2030. The greatest projected improvement in visibility, 0.1% improvement (0.02 DV) in 2030 due to the vehicle standards, occurs in Sipsey Wilderness in A L, Agua Tibia Wilderness in CA, and Alpine Lake Wilderness in WA. The following seven areas w ill see small degradations in visibility in 2030 as a result of the heavy-duty standards: Wolf Island GA, 0.03 deciview degradation; Joshua Tree National Monument CA, 0.02 deciview degradatio n, San Gorgonio Wilderness CA, 0.01 deciview degradation; Upper Buffalo Wilderness AR, 0.01 deciview degradation; San Jacinto Wilderness CA, 0.01 deciview degradation; Okefenoke e GA, 0.01 deciview degradation; and Hells Canyon Wilderness OR, 0.01 deciview degradati on. Error! Reference source not found. contains the full visibility results from 2030 for the 138 analyzed areas.

Table Error! No text of specified style in document.

- 6 Visibility Levels (in Deciviews) for Class I Areas on the 20% Worst Days

Class 1 Area (20% worst days) State 2005 Base 2030

LDGHG Reference 2030

LDGHG Control Natural

Background

Sipsey Wilderness AL 29.88 20.54 20.52 11.39 Caney Creek Wilderness AR 26.69 19.84 19.84 11.33 Upper Buffalo Wilderness AR 26.97 20.17 20.18 11.28 Chiricahua NM AZ 12.89 12.08 12.07 6.92 Chiricahua Wilderness AZ 12.89 12.08 12.07 6.91 Galiuro Wilderness AZ 12.89 12.09 12.09 6.88 Grand Canyon NP AZ 11.86 10.92 10.91 6.95 Mazatzal Wilderness AZ 13.95 12.46 12.45 6.91

K The level of visibility impairment in an area is b ased on the light-extinction coefficient and a unit less visibility index, called a “deciview”, which is used in the va luation of visibility. The deciview metric provide s a scale for perceived visual changes over the entire range of c onditions, from clear to hazy. Under many scenic c onditions, the average person can generally perceive a change of o ne deciview. The higher the deciview value, the wo rse the visibility. Thus, an improvement in visibility is a decrease in deciview value. Formatted: Heading 4,Tech Assessment Heading 4,TSD Heading 4,RIA: Heading 4,Preamble Heading 4,ECA Heading 4,Heading 4 - Preamble,Preamble Heading 41,RIA: Heading 41,Preamble Heading 42,RIA: Heading 42,Preamble Heading 43,RIA: Heading 43,Preamble Heading 44, Indent: Hanging: 0.2", Outline numbered + Level: 5 + Numbering Style: 1, 2, 3, ... + Start at: 1 + Alignment: Left + Aligned at: 0 px + Tab after: 68 px + Indent at: 68 px Deleted: ¶

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### 2017 Regulatory Impact Analysis

33 Mount Baldy Wilderness AZ 11.32 10.74 10.74 6.95 Petrified Forest NP AZ 13.56 12.65 12.65 6.97 Pine Mountain Wilderness AZ 13.95 12.42 12.41 6.92 Saguaro NM AZ 14.39 13.43 13.43 6.84 Sierra Ancha Wilderness AZ 14.45 13.28 13.28 6.92 Superstition Wilderness AZ 14.15 12.85 12.85 6.88 Sycamore Canyon Wilderness AZ 15.45 14.67 14.67 6.96 Agua Tibia Wilderness CA 22.36 18.41 18.39 7.17 Ansel Adams Wilderness (Minarets) CA 15.24 14.39 14.39 7.12 Caribou Wilderness CA 13.65 12.68 12.67 7.29 Cucamonga Wilderness CA 18.44 15.64 15.64 7.17 Desolation Wilderness CA 12.87 12.10 12.09 7.13 Emigrant Wilderness CA 16.87 15.94 15.94 7.14 Hoover Wilderness CA 11.61 11.07 11.06 7.12 John Muir Wilderness CA 15.24 14.34 14.34 7.14 Joshua Tree NM CA 18.90 16.39 16.41 7.08 Kaiser Wilderness CA 15.24 14.11 14.10 7.13 Kings Canyon NP CA 23.73 22.19 22.19 7.13 Lassen Volcanic NP CA 13.65 12.66 12.66 7.31 Lava Beds NM CA 14.13 13.19 13.19 7.49 Mokelumne Wilderness CA 12.87 12.08 12.07 7.14 Pinnacles NM CA 17.90 15.42 15.42 7.34 Point Reyes NS CA 22.40 21.00 21.00 7.39 Redwood NP CA 18.55 17.66 17.66 7.81 San Gabriel Wilderness CA 18.44 15.54 15.53 7.17 San Gorgonio Wilderness CA 21.43 19.27 19.28 7.10 San Jacinto Wilderness CA 21.43 18.10 18.11 7.12 San Rafael Wilderness CA 19.43 17.40 17.39 7.28 Sequoia NP CA 23.73 21.68 21.67 7.13 South Warner Wilderness CA 14.13 13.31 13.31 7.32 Thousand Lakes Wilderness CA 13.65 12.65 12.64 7.32 Ventana Wilderness CA 17.90 16.37 16.37 7.32
Yosemite NP CA 16.87 15.95 15.95 7.14 Black Canyon of the Gunnison NM CO 10.00 9.21 9.21 7.06 Eagles Nest Wilderness CO 8.82 8.05 8.05 7.08 Flat Tops Wilderness CO 8.82 8.32 8.31 7.07 Great Sand Dunes NM CO 11.82 11.20 11.20 7.10 La Garita Wilderness CO 10.00 9.49 9.49 7.06 Maroon Bells-Snowmass Wilderness CO 8.82 8.27 8.26 7.07 Mesa Verde NP CO 12.14 11.31 11.31 7.09 Mount Zirkel Wilderness CO 9.72 9.20 9.19 7.08 Rawah Wilderness CO 9.72 9.15 9.14 7.08 Rocky Mountain NP CO 12.85 12.15 12.15 7.05 Weminuche Wilderness CO 10.00 9.46 9.46 7.06 West Elk Wilderness CO 8.82 8.21 8.21 7.07 Everglades NP FL 22.48 18.43 18.43 11.15 Okefenokee GA 27.21 20.28 20.29 11.45 Wolf Island GA 27.21 20.12 20.15 11.42

### Chapter 6

34

Craters of the Moon NM ID 14.06 12.94 12.94 7.13 Sawtooth Wilderness ID 14.97 14.70 14.70 7.15 Mammoth Cave NP KY 32.00 22.29 22.29 11.53 Acadia NP ME 22.75 18.34 18.33 11.45 Moosehorn ME 21.19 17.58 17.58 11.36 Roosevelt Campobello International Park ME 21.19 17.57 17.56 11.36 Isle Royale NP MI 21.31 18.19 18.19 11.22 Seney MI 25.05 20.80 20.80 11.37 Boundary Waters Canoe Area MN 20.20 16.56 16.56 11.21 Voyageurs NP MN 19.62 16.61 16.61 11.09 Hercules

- Glades Wilderness MO 26.95 21.00 21.00 11.27 Anaconda-Pintler Wilderness MT 17.11 16.69 16.68 7.28 Bob Marshall Wilderness MT 16.13 15.63 15.63 7.36 Cabinet Mountains Wilderness MT 14.31 13.65 13.65 7.43 Gates of the Mountains Wilderness MT 11.94 11.48 11.47 7.22 Glacier NP MT 19.62 18.73 18.73 7.56 Medicine Lake MT 18.21 17.17 17.17 7.30 Mission Mountains Wilderness MT 16.13 15.50 15.49 7.39 Red Rock Lakes MT 11.19 10.62 10.62 7.14 Scapegoat Wilderness MT 16.13 15.59 15.59 7.29 Selway-Bitterroot Wilderness MT 17.11 16.74 16.74 7.32 UL Bend MT 15.49 15.00 15.00 7.18 Linville Gorge Wilderness NC 29.66 20.08 20.07 11.43 Shining Rock Wilderness NC 28.54 19.49 19.48 11.45 Lostwood ND 19.61 17.64 17.64 7.33 Theodore Roosevelt NP ND 17.88 16.02 16.02 7.31 Great Gulf Wilderness NH 21.43 16.46 16.46 11.31 Presidential Range-Dry River Wilderness NH 21.43 16.39 16.39 11.33 Brigantine NJ 28.68 20.96 20.95 11.28 Bandelier NM NM 11.97 10.51 10.51 7.02 Bosque del Apache NM 13.81 12.40 12.40 6.97 Carlsbad Caverns NP NM 16.51 14.48 14.47 7.02 Gila Wilderness NM 13.12 12.41 12.40 6.95 Pecos Wilderness NM 9.60 8.85 8.85 7.04 Salt Creek NM 18.27 16.19 16.18 6.99 San Pedro Parks Wilderness NM 10.42 9.63 9.62 7.03 Wheeler Peak Wilderness NM 9.60 8.66 8.65 7.07 White Mountain Wilderness NM 13.01 12.05 12.05 6.98 Jarbidge Wilderness NV 12.26 11.92 11.92 7.10 Wichita Mountains OK 23.63 18.27 18.26 11.07 Crater Lake NP OR 13.21 12.49 12.49 7.71 Diamond Peak Wilderness OR 13.21 12.39 12.39 7.77 Eagle Cap Wilderness OR 17.34 16.31 16.31 7.34 Gearhart Mountain Wilderness OR 13.21 12.61 12.61 7.46 Hells Canyon Wilderness OR 19.00 17.57 17.58 7.32 Kalmiopsis Wilderness OR 16.38 15.36 15.36 7.71 Mount Hood Wilderness OR 14.68 13.03 13.03 7.77 Mount Jefferson Wilderness OR 15.80 14.78 14.78 7.81

### 2017 Regulatory Impact Analysis

35 Mount Washington Wilderness OR 15.80 14.78 14.77 7.89 Mountain Lakes Wilderness OR 13.21 12.42 12.42 7.57 Strawberry Mountain Wilderness OR 17.34 16.37 16.37 7.49 Three Sisters Wilderness OR 15.80 14.87 14.87 7.87 Cape Romain SC 27.43 19.70 19.70 11.36 Badlands NP SD 16.82 14.91 14.90 7.30 Wind Cave NP SD 15.95 14.21 14.21 7.24 Great Smoky Mountains NP TN 30.56 21.28 21.27 11.44 Joyce-Kilmer-Slickrock Wilderness TN 30.56 20.97 20.97 11.45 Big Bend NP TX 17.21 15.35 15.34 6.93 Guadalupe Mountains NP TX 16.51 14.47 14.46 7.03 Arches NP UT 10.77 9.98 9.97 6.99 Bryce Canyon NP UT 11.62 10.95 10.95 6.99 Canyonlands NP UT 10.77 10.12 10.11 7.01 Capitol Reef NP UT 10.86 10.39 10.39 7.03 James River Face Wilderness VA 28.93 19.62 19.62 11.24 Shenandoah NP VA 29.42 19.58 19.58 11.25 Lye Brook Wilderness VT 24.11 16.87 16.86 11.25 Alpine Lake Wilderness WA 16.99 15.06 15.04 7.86 Glacier Peak Wilderness WA 13.29 12.18 12.17 7.80 Goat Rocks Wilderness WA 12.67 11.35 11.34 7.82 Mount Adams Wilderness WA 12.67 11.39 11.39 7.78
Mount Rainier NP WA 17.07 15.36 15.35 7.90 North Cascades NP WA 13.29 12.15 12.14 7.78 Olympic NP WA 15.83 14.31 14.31 7.88 Pasayten Wilderness WA 15.35 14.36 14.36 7.77 Dolly Sods Wilderness WV 29.94 19.65 19.64 11.32 Otter Creek Wilderness WV 29.94 19.73 19.72 11.33 Bridger Wilderness WY 10.73 10.29 10.29 7.08 Fitzpatrick Wilderness WY 10.73 10.29 10.28 7.09 Grand Teton NP WY 11.19 10.57 10.56 7.09 North Absaroka Wilderness WY 11.30 10.90 10.90 7.09 Teton Wilderness WY 11.19 10.68 10.68 7.09 Washakie Wilderness WY 11.30 10.90 10.90 7.09
Yellowstone NP WY 11.19 10.61 10.61 7.12

### Chapter 6

36

6.3 

## Quantified and Monetized Non-GHG Health and Environ mental Impacts

This section presents EPA’s analysis of the non-GHG , or co-pollutant, health and environmental impacts that can be expected to occur as a result of the final light-duty vehicle GHG rule. GHG emissions are predominantly the bypr oduct of fossil fuel combustion processes that also produce criteria and hazardous air pollut ants. The vehicles that are subject to the final standards are also significant sources of mobile so urce air pollution such as direct PM, NOx, VOCs and air toxics. The standards will affect exh aust emissions of these pollutants from vehicles. They will also affect emissions from ups tream sources related to changes in fuel consumption and electricity generation. Changes in ambient ozone, PM 2.5 

### , and air toxics that will result from the standards are expected to affect hu man health in the form of premature deaths and other serious human health effects, as well as othe r important public health and welfare effects.

It is important to quantify the health and environm ental impacts associated with the final standards because it allows us to more accurately a ssess the net costs and benefits of the standards. Moreover, co-pollutant impacts tend to accrue in the near term, while effects from reduced climate change mostly accrue over a time fr ame of several decades or longer.

This section is split into two sub-sections: the fi rst presents the PM- and ozone-related health and environmental impacts associated with fi nal rule in calendar year (CY) 2030; the second presents the PM-related benefits-per-ton val ues used to monetize the PM-related cobenefits associated with the model year (MY) analys is (i.e., over the lifetimes of the MY 2017- 2025 vehicles) of the final rule. L



### Though EPA is characterizing the changes in emissio ns associated with toxic pollutants, we were not able to quantify or monetize the human health effects associated with air toxic pollutants for this final rule analysis due to data and methodological limitations. Please refer to Chapter 4 of this RIA for more information about th e air toxics emissions impacts associated with the final standards.

6.3.1 Quantified and Monetized Non-GHG Human Health Benef its of the 2030 Calendar Year (CY) Analysis

This section presents EPA’s analysis of the criteri a pollutant-related health and environmental impacts that will occur as a result o f the final standards. Light-duty vehicles and fuels are significant sources of mobile source air pollution such as direct PM, NO X 

### , SO X 

### , VOCs and air toxics. The impact that improved fuel econ omy will have on rebound driving will affect

L EPA typically analyzes rule impacts (emissions, ai r quality, costs and benefits) in the year in which they occur; for this analysis, we selected 2030 as a representative future year. We refer to this analysis as the “Ca lendar Year” (CY) analysis. EPA also conducted a separate analysis o f the impacts over the model year lifetimes of the 2017 through 2025 model year vehicles. We refer to this analysi s as the “Model Year” (MY) analysis. In contrast t o the CY analysis, the MY lifetime analysis shows the lifeti me impacts of the program on each MY fleet over the course of its lifetime. Deleted: [placeholder]

### 2017 Regulatory Impact Analysis

37 exhaust and evaporative emissions of these pollutan ts from vehicles. In addition, increased fuel savings associated with improved fuel economy achie ved under the standards will affect emissions from upstream sources (see Chapter 4 for a complete description of emission impacts associated with the final standards). Emissions of NO X 

### (a precursor to ozone formation and secondarily-formed PM 2.5 

### ), SO X 

### (a precursor to secondarily-formed PM 2.5 

### ), VOCs (a precursor to ozone formation and, to a lesser degree, secondaril y-formed PM 2.5 

### ) and directly-emitted PM 2.5 

### contribute to ambient concentrations of PM 2.5 

### and ozone. Exposure to ozone and PM 2.5 

### is linked to adverse human health impacts such as premature d eaths as well as other important public health and environmental effects.

The analysis in this section aims to characterize t he benefits of the final standards by answering two key questions:

1. What are the health and welfare effects of chang es in ambient particulate matter (PM 2.5 

### ) and ozone air quality resulting from reductions i n precursors including NO X 

### and SO 2 

### ?

2. What is the economic value of these effects?

For the final rule, we have quantified and monetize d the health and environmental impacts in 2030, representing impacts associated wi th a year when the standards are fully implemented and reflects a limited degree of fleet turnover. Overall, we estimate that the final standards will lead to a net decrease in PM 2.5 

### -relatedhealth impacts in 2030. The decrease in population-weighted national average PM 2.5 

### exposure results in a net decrease in adverse PMrelated human health impacts (the decrease in natio nal population-weighted annual average PM 2.5 

### is 0.0065 μ 

### g/m 3 

### in 2030). M 

### We estimate that there is a very small increase in populationweighted national average ozone exposure , which results in a very small net in crease in ozonerelated health impacts (population-weighted maximum 8-hour average ozone in creases by 0.0009 ppb in 2030).

Using the most conservative premature mortality est imates (Pope et al., 2002 for PM 2.5 

### and Bell et al., 2004 for ozone), N,O 

### we estimate that by 2030, implementation of the final standards will reduce approximately 110 premature mortalities annually and yield approxima tely $0.95 billion in total annual benefits. The upper end o f the range of avoided premature mortality estimates associated with the standards (based on L aden et al., 2006 for PM 2.5 

### and Levy et al., 2005 for ozone) P,Q 

### results in approximately 280 premature mortalities avoided in 2030 and yields

M Note that the national, population-weighted PM 2.5 and ozone air quality metrics presented in this Ch apter represent an average for the entire, gridded U.S. CMAQ domain . These are different than the population-weighted PM 2.5 and ozone design value metrics presented in Chapter 7, which represent the average for areas with a curren t air quality monitor.

N Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Cal le, D. Krewski, K. Ito, and G.D. Thurston. (2002). Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure t o Fine Particulate Air Pollution. Journal of the American Medical Association, 287, 1132-1141.

O Bell, M.L., et al. (2004). Ozone and short-term m ortality in 95 US urban communities, 1987-2000. Journal of the American Medical Association

, 292(19) , 2372-2378.

P Laden, F., J. Schwartz, F.E. Speizer, and D.W. Doc kery. (2006). Reduction in Fine Particulate Air P ollution and Mortality. American Journal of Respiratory and Critical Care M edicine

. 173 , 667-672. Deleted: XX

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### Chapter 6

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approximately $2.6 billion in total benefits. Thus, even taking the most conservative premature mortality assumptions, the health impacts of the st andards presented in this rule are substantial.

6.3.1.1 Overview

This analysis reflects the impacts of the final MY 2017-2025 standards in 2030 compared to a future-year reference scenario without the sta ndards in place. Overall, we estimate that the final rule will lead to a net decrease in populatio n-weighted national average PM 2.5 

### exposure , which results in a net decrease in adverse PM-related hu man health and environmental impacts (the decrease in national population weighted annua l average PM 2.5 

### is 0.0065 μ 

### g/m3 in 2030).

The air quality modeling also projects a very small net increase in ozone concentrations ( population weighted maximum 8-hour average ozone in creases by 0.0009 ppb in 2030 ). The small increase in population-weighted national average ozone expos ure results in a very small increase in net ozone-related health and environmental impacts.

We base our analysis of the final rule’s impact on human health and the environment on peer-reviewed studies of air quality and human heal th effects. 22,23 

### Our benefits methods are also consistent with recent rulemaking analyses such as the final Transport Rule, 24 

### the final 2012- 2016 MY Light-Duty Vehicle Rule, 25 

### and the final Portland Cement National Emissions Standards for Hazardous Air Pollutants (NESHAP) RIA

. 26 

### To model the ozone and PM air quality impacts of the final standard, we used the Community Multiscale Air Quality (CMAQ) model (see Section 6.2). The modeled ambient air q uality data serves as an input to the Environmental Benefits Mapping and Analysis Program version 4.0 (BenMAP). R 

### BenMAP is a computer program developed by the U.S. EPA that int egrates a number of the modeling elements used in previous analyses ( e.g. 

### , interpolation functions, population projections, health impact functions, valuation functions, analysis and poolin g methods) to translate modeled air concentration estimates into health effects inciden ce estimates and monetized benefits estimates.

The range of total monetized ozone- and PM-related health impacts in 2030 is presented in Table 6 .3

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- 7. We present total benefits (the sum of morbidity-related benefits and mortality-rel ated benefits) based on the PM- and ozonerelated premature mortality function used. The ben efits ranges therefore reflect the addition of each estimate of ozone-related premature mortality ( across six selected studies, each with its own row) to each estimate of PM-related premature mortality (based on either Pope et al., 2002 or Laden et al., 2006), along with all morbidity-relat ed benefits . These estimates represent EPA’s preferred approach to characterizing a best estimat e of benefits. As is the nature of RIAs, the assumptions and methods used to estimate air qualit y benefits evolve to reflect the Agency’s most current interpretation of the scientific and e conomic literature.

Q Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (20 05). Ozone exposure and mortality: an empiric baye s metaregression analysis. Epidemiology. 16(4) , 458-68.

R Information on BenMAP, including downloads of the software, can be found at http://www.epa.gov/ttn/ec as/ benmodels.html. Deleted: XX

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### 2017 Regulatory Impact Analysis

39 Table 6 .3

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- 7: Estimated 2030 Monetized PMand Ozone-Related Health Benefits a

2030 Total Ozone and PM Benefits – PM Mortality Der ived from American Cancer Society Analysis and Six-Cities Analysis a Premature Ozone Mortality Function Reference Total Benefits (Billions, 2010$, 3% Discount Rate) b,c Total Benefits (Billions, 2010$, 7% Discount Rate) b,c Multi-city analyses Bell et al., 2004 Total: $1.0 – $2.6 PM: $1.1 - $2.6 Ozone: -$0.006 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.006 Huang et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.006 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.006 Schwartz, 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone:

- $0.009 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone:

- $0.009 Meta-analyses Bell et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.019 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.019 Ito et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.026 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.026 Levy et al., 2005 Total: $1.0 - $2.6 PM: $1.1 - $2.6 Ozone: -$0.027 Total: $0.92 - $2.3 PM: $0.95 - $2.3 Ozone: -$0.027 Notes:

a Total includes premature mortality-related and morb idity-related ozone and PM 2.5 benefits. Range was developed by adding the estimate from the ozone premature mor tality function to the estimate of PM 2.5 -related premature mortality derived from either the ACS study (Pope e t al., 2002) or the Six-Cities study (Laden et al., 2006).
b Note that total benefits presented here do not inc lude a number of unquantified benefits categories. A detailed listing of unquantified health and welfare effects is provided in Table 6 .3 -Error! No text of specified style in document.

- 8

. c Results reflect the use of both a 3 and 7 percent discount rate, as recommended by EPA’s Guidelines f or Preparing Economic Analyses and OMB Circular A-4. Results ar e rounded to two significant digits for ease of pre sentation and computation. Totals may not sum due to roundin g.



### The benefits in Table 6 .3

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- 7 include all of the human health impacts we are able to quantify an d monetize at this time. However, the full complement of human health and welfare effects asso ciated with PM

, ozone , and other criteria pollutants remain unquantified because of current limitations in methods or available data. We have not quantified a number of known or suspected health effects linked with ozone

, PM , and other criteria pollutants for which appropriate health impact functions are not available or which do not provide easily interpretable outcomes ( e.g. 

### , changes in heart rate variability). Additionally, we are unable to quantify a number of known welfare effects, including reduced acid and particulate deposition damage to cultural monuments and other materials, and environmental benefits due to reductions of impacts of eutrophication in coastal areas. These are listed in Table 6 .3 -Error! No text of specified style in document.

- 8 . As a result, the health benefits quantified in this section are likely unde restimates of the total benefits attributable to th e final standards. Deleted: 6

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### Chapter 6

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Table 6 .3

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- 8: Human Health and Welfare

Effects of Pollutants Affected by the Final Standar ds
Pollutant/ Effect Quantified and monetized in primary estimate Unquan tified

PM: health a Premature mortality based on cohort study

estimates b and expert elicitation estimates Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarctions) Lower and upper respiratory illness Minor restricted activity days Work loss days Asthma exacerbations (among asthmatic populations Respiratory symptoms (among asthmatic populations) Infant mortality Low birth weight, pre-term birth and other reproductive outcomes Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits UVb exposure (+/-) c

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 i n NW, NE, and Central regions UVb exposure (+/-) c Global climate impacts c Ozone: health Premature mortality based on short-term

study estimates Hospital admissions: respiratory Emergency room visits for asthma Minor restricted activity days School loss days Chronic respiratory damage Premature aging of the lungs Non-asthma respiratory emergency room visits UVb exposure (+/-) c

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 CO: health Behavioral effects Nitrate Deposition: 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 Deleted: 7

### 2017 Regulatory Impact Analysis

41 Pollutant/ Effect Quantified and monetized in primary estimate Unquan tified

Sulfate Deposition: welfare Commercial fishing and forestry from acidic deposition effects Recreation in terrestrial and aquatic ecosystems from acid deposition effects Increased mercury methylation HC/Toxics: health d Cancer (benzene, 1,3-butadiene, formaldehyde, acetaldehyde) Anemia (benzene) Disruption of production of blood components (benzene) Reduction in the number of blood platelets (benzene) Excessive bone marrow formation (benzene) Depression of lymphocyte counts (benzene) Reproductive and developmental effects (1,3- butadiene) Irritation of eyes and mucus membranes (formaldehyde) Respiratory irritation (formaldehyde) Asthma attacks in asthmatics (formaldehyde) Asthma-like symptoms in non-asthmatics (formaldehyde) Irritation of the eyes, skin, and respiratory tract (acetaldehyde) Upper respiratory tract irritation and congestion (acrolein) HC/Toxics: welfare Direct toxic effects to animals Bioaccumulation in the food chain Damage to ecosystem function Odor

Notes:

a In addition to primary economic endpoints, there a re a number of biological responses that have been associated with PM health effects including morphological chan ges and altered host defense mechanisms. The publi c health impact of these biological responses may be partly represented by our quantified endpoints.
b Cohort estimates are designed to examine the effec ts 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). 27 While some of the effects of short term exposure a re likely to be captured by the cohort estimates, t here may be additional premature mortality from short te rm PM exposure not captured in the cohort estimates included in the primary analysis.

c May result in benefits or disbenefits.
d Many of the key hydrocarbons related to this actio n are also hazardous air pollutants listed in the C AA.


### While there will be impacts associated with air tox ic pollutant emission changes that result from the final standards, we do not attempt to monetize those impacts. This is primarily because currently available tools and methods to as sess air toxics risk from mobile sources at the national scale are not adequate for extrapolation t o incidence estimations or benefits assessment. The best suite of tools and methods currently avail able for assessment at the national scale are those used in the National-Scale Air Toxics Assessm ent (NATA). The EPA Science Advisory Board specifically commented in their review of the 1996 NATA that these tools were not yet ready for use in a national-scale benefits analysis , because they did not consider the full distribution of exposure and risk, or address sub-c hronic health effects. 28 

### While EPA has since

### Chapter 6

42

improved these tools, there remain critical limitat ions for estimating incidence and assessing benefits of reducing mobile source air toxics.

As part of the second prospective analysis of the b enefits and costs of the Clean Air Act, 29 

### EPA conducted a case study analysis of the health e ffects associated with reducing exposure to benzene in Houston from implementation of the Clean Air Act. While reviewing the draft report, EPA’s Advisory Council on Clean Air Compliance Anal ysis concluded that “the challenges for assessing progress in health improvement as a resul t of reductions in emissions of hazardous air pollutants (HAPs) are daunting...due to a lack of e xposure-response functions, uncertainties in emissions inventories and background levels, the di fficulty of extrapolating risk estimates to low doses and the challenges of tracking health progres s for diseases, such as cancer, that have long latency periods.” 30 

### EPA continues to work to address these limitation s; however, we did not have the methods and tools available for national-s cale application in time for the analysis of the final standards. S



### Human Health Impacts



### Table 6 .3

- Error! No text of specified style in document.

- 9 and Table 6 .3 -Error ! No text of specified style in document.

- 10 present the annual PM 2.5 

### and ozone health impacts in the 48 contiguous U.S. states associated with the final st andards. For each endpoint presented in Table 6 .3

- Error! No text of specified style in document.

- 9 and Table 6 .3 -Error ! No text of specified style in document.

- 10, we provide both the point estimate and the 90 p ercent confidence interval.

Using EPA’s preferred estimates, based on the Ameri can Cancer Society (ACS) and SixCities studies and no threshold assumption in the m odel of mortality, we estimate that the final standards will result in between 110 and 280 cases of avoided PM 2.5 

### -related premature deaths annually in 2030. As a sensitivity analysis, when the range of expert opinion is used, we estimate between 36 and 370 fewer premature mortalities in 2030.

The range of ozone impacts is based on changes in r isk estimated using several sources of ozone-related mortality effect estimates. This ana lysis presents six alternative estimates for the association based upon different functions reported in the scientific literature, derived from both the National Morbidity, Mortality, and Air Pollutio n Study (NMMAPS) (Bell et al., 2004; Huang et al., 2005; Schwartz, 2005) and from a series of recent meta-analyses (Bell et al., 2005, Ito et al., 2005, and Levy et al., 2005). This approach i s not inconsistent with recommendations provided by the NRC in their recent report (NRC, 20 08) on the estimation of ozone-related mortality risk reductions, “The committee recommend s that the greatest emphasis be placed on estimates from new systematic multicity analyses th at use national databases of air pollution and

S In April, 2009, EPA hosted a workshop on estimatin g the benefits or reducing hazardous air pollutants . This workshop built upon the work accomplished in the Ju ne 2000 Science Advisory Board/EPA Workshop on the Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on approaches to estimating human health benefits from reductions in air toxics exposure, but no consensu s was reached on methods that could be implemented in the near te rm for a broad selection of air toxics. Please vis it http://epa.gov/air/toxicair/2009workshop.html for m ore information about the workshop and its associat ed materials. Deleted: XX

Deleted: XX

Deleted: XX

Deleted: XX

### 2017 Regulatory Impact Analysis

43 mortality, such as in the NMMAPS, without excluding consideration of meta-analyses of previously published studies.” 31 

### For ozone-related premature mortality in 2030, we estimate a range of between 1 to 3 additional premature mortalities.

Following these tables, we also provide a more comp rehensive presentation of the distributions of incidence generated using the avai lable information from empirical studies and expert elicitation.

Table 6 .3

- Error! No text of specified style in document.

- 11 presents the distributions of the reduction in PM 2.5 

### -related premature mortality based on the C-R distr ibutions provided by each expert, as well as that from the data-derived health impact functions, based on the statistical error associated with the ACS study (Pope et al., 2 002) and the Six-Cities study (Laden et al., 2006). The 90 percent confidence interval for each separate estimate of PM-related mortality is also provided.

In 2030, the effect estimates of nine of the twelve experts included in the elicitation panel fall within the empirically-derived range provided by the ACS and Six-Cities studies. Only one expert falls below this range, while two of the exp erts are above this range. Although the overall range across experts is summarized in these tables, the full uncertainty in the estimates is reflected by the results for the full set of 12 exp erts. The twelve experts’ judgments as to the likely mean effect estimate are not evenly distribu ted across the range illustrated by arraying the highest and lowest expert means.

Table 6 .3

- Error! No text of specified style in document.

- 9: Estimated PM 2.5 

### -Related Health Impacts a

Health Effect 2030 Annual Reduction in Incidence (5 th - 95 th percentile) Premature Mortality – Derived from epidemiology lit erature b Adult, age 30+, ACS Cohort Study (Pope et al., 20 02)

Adult, age 25+, Six-Cities Study (Laden et al., 2 006)

Infant, age <1 year (Woodruff et al., 1997) 110 (30 – 190) 280 (130 – 440) 0 (0 – 1) Chronic bronchitis (adult, age 26 and over) 76 (1 – 150) Non-fatal myocardial infarction (adult, age 18 and over) 130 (32 – 230) Hospital admissions - respiratory (all ages) c 20 (8 – 32) Hospital admissions - cardiovascular (adults, age > 18) d 50 (33 – 60) Emergency room visits for asthma (age 18 years and younger) 72 (34 – 110) Acute bronchitis, (children, age 8-12) 160 (-42 – 370) Lower respiratory symptoms (children, age 7-14) 2,100 (770 – 3,400) Upper respiratory symptoms (asthmatic children, age 9-18) 1,600 (260 – 2,900) Deleted: XX

Deleted: XX

Deleted: fewer

Deleted: 8

### Chapter 6

44 Asthma exacerbation (asthmatic children, age 6-18) 3,500 (-120 – 9,700) Work loss days 14,000 (12,000 – 16,000) Minor restricted activity days (adults age 18-65) 81,000 (65,000 – 96,000) Notes:

a Incidence is rounded to two significant digits. Est imates represent incidence within the 48 contiguous United States.
b PM-related adult mortality based upon the American Cancer Society (ACS) Cohort Study (Pope et al., 200 2) and the Six-Cities Study (Laden et al., 2006). Not e that these are two alternative estimates of adult mortality and should not be summed. PM-related infant mortality based upon a study by Woodruff, Grillo, and Schoend orf, (1997). T

c Respiratory hospital admissions for PM include admi ssions for chronic obstructive pulmonary disease (C OPD), pneumonia and asthma.
d Cardiovascular hospital admissions for PM include t otal cardiovascular and subcategories for ischemic heart disease, dysrhythmias, and heart failure.



### Table 6 .3

- Error! No text of specified style in document.

- 10: Estimated Ozone-Related Health Impacts a

Health Effect 2030 Annual Reduction in Incidence (5 th - 95 th percentile) Premature Mortality, All ages b Multi-City Analyses Bell et al. (2004) – Non-accidental

Huang et al. (2005) – Cardiopulmonary

Schwartz (2005) – Non-accidental

Meta-analyses: Bell et al. (2005) – All cause

Ito et al. (2005) – Non-accidental

Levy et al. (2005) – All cause -1 (-4 – 3) -1 (-5 – 4) -1 (-6 – 4)

-2 (-10 – 6) -3 (-11 – 6) -3 (-10 – 4) Hospital admissions- respiratory causes (adult, 65 and older)c -6 (-30 – 15) Hospital admissions -respiratory causes (children, under 2) -3 (-12 – 6) Emergency room visit for asthma (all ages) -1 (-18 – 15) Minor restricted activity days (adults, age 18-65) -930 (-18,000 – 16,000) School absence days -850 (-6,700 – 5,100)

T Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1 997. “The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air P ollution in the United States.” Environmental Health Perspectives 105(6):608-612. Deleted: 9

### 2017 Regulatory Impact Analysis

45 Notes:

a Negatives indicate a disbenefit, or an increase in health effect incidence. Incidence is rounded to two significant digits. Estimates represent incidence within the 48 contiguous U.S.
b Estimates of ozone-related premature mortality are based upon incidence estimates derived from severa l alternative studies: Bell et al. (2004); Huang et al. (2005); S chwartz (2005) ; Bell et al. (2005); Ito et al. (20 05); Levy et al. (2005). The estimates of ozone-related premature mortality should therefore not be summed.

c Respiratory hospital admissions for ozone include a dmissions for all respiratory causes and subcategor ies for COPD and pneumonia.



### Table 6 .3

- Error! No text of specified style in document.

- 11: Results of Application of Expert Elicitation: Annual Reductions in Premature Mortali ty in 2030 Associated with the Final Standards

Source of Mortality Estimate 2030 Incidence 5th Percentile Mean 95th Percentile Pope et al. (2002) 30 110 190 Laden et al. (2006) 130 280 440 Expert A 6 300 590 Expert B -23 220 530 Expert C -4 230 530 Expert D 23 160 280 Expert E 150 370 60 Expert F 120 200 290 Expert G 0 130 260 Expert H -38 170 430 Expert I 19 220 430 Expert J 19 180 440 Expert K 0 36 190 Expert L 8 140 330



### Monetized Estimates of Human Health and Environment al Impacts



### Table 6 .3

- Error! No text of specified style in document.

- 12 presents the estimated monetary value of changes in the incidence of ozone and PM 2.5 

### -related health and environmental effects. Total aggregate monetized benefits are pr esented in Table 6 .3

- Error! No text of specified style in document.

- 13. All monetized estimates are presented in 2010$ . Where appropriate, estimates account for growth in real g ross domestic product (GDP) per capita between 2000 and 2030. U 

### The monetized value of PM 2.5 

### -related mortality also accounts for a

U Our analysis accounts for expected growth in real income over time. Economic theory argues that WTP for most goods (such as environmental protection) will incre ase if real incomes increase. Benefits are therefo re adjusted by multiplying the unadjusted benefits by the appropri ate adjustment factor to account for income growth over time. For growth between 2000 and 2030, this factor is 1. 23 for long-term mortality, 1.27 for chronic health impacts, and 1.08 for minor health impacts. For a complete disc ussion of how these adjustment factors were derived , we refer the reader to the PM NAAQS regulatory impact analysis. 9 Note that similar adjustments do not exist for co st-of-illnessbased unit values. For these, we apply the same un it value regardless of the future year of analysis. Deleted: 10

### Chapter 6

46

twenty-year segmented cessation lag. V 

### To discount the value of premature mortality that occurs at different points in the future, we apply both a 3 and 7 percent discount rate. We also use both a 3 and 7 percent discount rate to value PM-related nonfatal heart attacks (myocardial infarctions). W



### In addition to omitted benefits categories such as air toxics and various welfare effects, not all known PM 2.5 

### - and ozone-related health and welfare effects coul d be quantified or monetized. The estimate of total monetized health benefits of the final standards is thus equal to the subset of monetized PM 2.5 

### - and ozone-related health impacts we are able to q uantify plus the sum of the nonmonetized health and welfare benefits . Our estimate of total monetized benefits in 2030 for the final standards, using the ACS and Six -Cities PM mortality studies and the range of ozone mortality assumptions, is between $ 1.0 and $ 2.6 billion, assuming a 3 percent discount rate, or between $ 0.92 and $ 2.3 billion, assuming a 7 percent discount rate. As t he results indicate, total benefits are driven primarily by th e reduction in PM 2.5 

### -related premature fatalities each year and represent the benefits of the final s tandards anticipated to occur annually when the program is fully implemented.

The next largest benefit is for reductions in chron ic illness (chronic bronchitis and nonfatal heart attacks), although this value is mor e than an order of magnitude lower than for premature mortality. Hospital admissions for respi ratory and cardiovascular causes, minor restricted activity days, and work loss days accoun t for the majority of the remaining benefits. The remaining categories each account for a small p ercentage of total benefit; however, they represent a large number of avoided incidences affe cting many individuals. A comparison of the incidence table to the monetary benefits table reve als that there is not always a close correspondence between the number of incidences avo ided for a given endpoint and the monetary value associated with that endpoint. For example, there are many more work loss days than PMrelated premature mortalities, yet work loss days a ccount for only a very small fraction of total monetized benefits. This reflects the fact that ma ny of the less severe health effects, while more common, are valued at a lower level than the more s evere health effects. Also, some effects, such as hospital admissions, are valued using a pro xy measure of willingness-to-pay ( e.g. 

### , costof-illness). As such, the true value of these effe cts may be higher than that reported here.

Table 6 .3

- Error! No text of specified style in document.

- 12: Estimated Monetary Value of Changes in Incidence of Health and Welfare Effects (millions of 2010$) a,b

2030 PM 2.5 -Related Health Effect (5 th and 95 th Percentile)

V Based in part on prior SAB advice, EPA has typical ly assumed that there is a time lag between changes in pollution exposures and the total realization of changes in h ealth effects. Within the context of benefits anal yses, this term is often referred to as “cessation lag”. The existenc e of such a lag is important for the valuation of p remature mortality incidence because economic theory suggests that ben efits occurring in the future should be discounted. In this analysis, we apply a twenty-year distributed lag to PM mortality reductions. This method is consisten t with the most recent recommendation by the EPA’s Science Advisory Board. Refer to: EPA – Science Advisory Board, 20 04. Advisory Council on Clean Air Compliance Analysis R esponse to Agency Request on Cessation Lag. Letter from the Health Effects Subcommittee to the U.S. Environ mental Protection Agency Administrator, December.

W Nonfatal myocardial infarctions (MI) are valued us ing age-specific cost-of-illness values that reflec t lost earnings and direct medical costs over a 5-year period follo wing a nonfatal MI. Deleted: XX

Deleted: XX

Deleted: XX

Deleted: XX

Deleted: and ozoneDeleted: 11

### 2017 Regulatory Impact Analysis

47 Premature Mortality – Derived from Epidemiology Studies c,d Adult, age 30+ - ACS study (Pope et al., 2002) 3% discount rate

7% discount rate $980 ($110 - $2,600) $880 ($97 - $2,400) Adult, age 25+ - Six-Cities study (Laden et al., 2006) 3% discount rate

7% discount rate $2,500 ($340 - $6,300) $2,300 ($310 - $5,700) Infant Mortality, <1 year – (Woodruff et al. 1997) $3.8 (-$3.9 - $15) Chronic bronchitis (adults, 26 and over) $42 ($0.4 - $140) Non-fatal acute myocardial infarctions 3% discount rate

7% discount rate $14 ($2.3 - $36) $12 ($1.8 - $30) Hospital admissions for respiratory causes $0.32 ($0.13 - $0.51) Hospital admissions for cardiovascular causes $0.73 ($0.07 - $1.4) Emergency room visits for asthma $0.03 ($0.01 - $0.05) Acute bronchitis (children, age 8–12) $0.08 (-$0.02 - $0.21) Lower respiratory symptoms (children, 7–14) $0.04 ($0.01 - $0.09) Upper respiratory symptoms (asthma, 9–11) $0.05 ($0.009 - $0.12) Asthma exacerbations $0.20 (-$0.007 - $0.58) Work loss days $2.2 ($1.9 - $2.6) Minor restricted-activity days (MRADs) $5.6 ($3.2 - $8.1)

Premature Mortality, All ages – Derived from Multi-city analyses Bell et al., 2004 -$5.8 (-$45 - $27) Huang et al., 2005 -$6.2 (-$60 - $41) Schwartz, 2005 -$8.7 (-$71 - $44) Premature Mortality, All ages – Derived from Meta-analyses Bell et al., 2005 -$19 (-$120 - $38) Ito et al., 2005 -$26 (-$140 - $58) Levy et al., 2005 -$27 (-$120 - $38) Hospital admissions- respiratory causes (adult, 65 and older) -$0.16 (-$0.77 - $0.39) Hospital admissions- respiratory causes (children, under 2) -$0.03

### Chapter 6

48 (-$130 - $0.07) Emergency room visit for asthma (all ages) -$0.0003 (-$0.007 - $0.006) Minor restricted activity days (adults, age 18-65) -$0.06 (-$1.3 - $1.1) School absence days -$0.08 (-$0.65 - $0.49) Notes:

a Negatives indicate a disbenefit, or an increase in health effect incidence. Monetary benefits are rounded to two significant digits for ease of presentation and computation. PM and ozone benefits are nationwide

. b Monetary benefits adjusted to account for growth i n real GDP per capita between 1990 and the analysis year (2030).

c Valuation assumes discounting over the SAB recomme nded 20 year segmented lag structure. Results reflect the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses.



### Table 6 .3

- Error! No text of specified style in document.

- 13: Total Monetized Ozone and PMrelated Benefits Associated with the Final Standard s in 2030

Total Ozone and PM Benefits (billions, 2010$) – PM Mortality Derived from the ACS and Six-Cities St udies 3% Discount Rate 7% Discount Rate Ozone Mortality Function Reference Mean Total Benefits Ozone

Mortality Function Reference Mean Total Benefits

Multi-city Bell et al.,

2004 $1.0 - $2.6 Multi-city Bell et al., 2004 $0.95 - $2.3

Huang et al., 2005 $1.0 - $2.6 Huang et al., 2005 $0.94 - $2.3

Schwartz, 2005 $1.0 - $2.6 Schwartz, 2005 $0.94 - $2.3

Meta-analysis Bell et al.,

2005 $1.0 - $2.6 Meta-analysis Bell et al., 2005 $0.93 - $2.3

Ito et al., 2005 $1.0 - $2.6 Ito et al., 2005 $0.92 - $2.3

Levy et al., 2005 $1.0 - $2.6 Levy et al., 2005 $0.92 - $2.3

Total Ozone and PM Benefits (billions, 2010$) – PM Mortality Derived from Expert Elicitation (Lowes t and Highest Estimate) 3% Discount Rate 7% Discount Rate Ozone Mortality Function Reference Mean Total Benefits Ozone

Mortality Function Reference Mean Total Benefits

Multi-city Bell et al.,

2004 $0.39 - $3.4 Multi-city Bell et al., 2004 $0.35 - $3.1

Huang et al., 2005 $0.39 - $3.4 Huang et al., 2005 $0.35 - $3.1

Schwartz, 2005 $0.39 - $3.4 Schwartz, 2005 $0.35 - $3.1

Meta-analysis Bell et al.,

2005 $0.38 - $3.4 Meta-analysis Bell et al., 2005 $0.34 - $3.1

Ito et al., 2005 $0.37 - $3.4 Ito et al., 2005 $0.33 - $3.0

Levy et al., $0.37 - $3.4 Levy et al., $0.33 - $3.0 Deleted: 12

### 2017 Regulatory Impact Analysis

49 2005 2005



### Methodology

Human Health Impact Functions



### Health impact functions measure the change in a hea lth endpoint of interest, such as hospital admissions, for a given change in ambient ozone or PM concentration. Health impact functions are derived from primary epidemiology stu dies, meta-analyses of multiple epidemiology studies, or expert elicitations. A st andard health impact function has four components: (1) an effect estimate from a particula r study; (2) a baseline incidence rate for the health effect (obtained from either the epidemiolog y study or a source of public health statistics such as the Centers for Disease Control); (3) the s ize of the potentially affected population; and (4) the estimated change in the relevant ozone or P M summary measures.

A typical health impact function might look like: 

# ( ) 1 0 

### − ⋅ = ∆ ∆⋅

x 

### e y y β



### ,
where y 0 

### is the baseline incidence (the product of the base line incidence rate times the potentially affected population), β 

### is the effect estimate, and ∆ 

### x is the estimated change in the summary pollutant measure. There are other functio nal forms, but the basic elements remain the same. The following subsections describe the sourc es for each of the first three elements: size of the potentially affected populations; PM 2.5 

### and ozone effect estimates; and baseline incidence rates. We also describe the treatment of potential thresholds in PM-related health impact functions. Section 8.2 describes the ozone and PM a ir quality inputs to the health impact functions.

Potentially Affected Populations



### The starting point for estimating the size of poten tially affected populations is the 2000 U.S. Census block level dataset. 32 

### Benefits Modeling and Analysis Program (BenMAP) incorporates 250 age/gender/race categories to matc h specific populations potentially affected by ozone and other air pollutants. The software const ructs specific populations matching the populations in each epidemiological study by access ing the appropriate age-specific populations from the overall population database. BenMAP proje cts populations to 2030 using growth factors based on economic projections. 33



### Effect Estimate Sources



### The most significant quantifiable benefits of reduc ing ambient concentrations of ozone and PM are attributable to reductions in human heal th risks. EPA’s Ozone and PM Criteria Documents 34,35 

### and the World Health Organization’s 2003 and 2004 36,37 

### reports outline numerous human health effects known or suspected to be linke d to exposure to ambient ozone and PM. EPA recently evaluated the ozone and PM literature for use in the benefits analysis for the final 2008 Ozone NAAQS and final 2006 PM NAAQS analyses. We use the same literature in this

### Chapter 6

50

analysis; for more information on the studies that underlie the health impacts quantified in this RIA, please refer to those documents.

It is important to note that we are unable to separ ately quantify all of the possible PM and ozone health effects that have been reported in the literature for three reasons: (1) the possibility of double counting (such as hospital admissions for specific respiratory diseases versus hospital admissions for all or a sub-set of respiratory dise ases); (2) uncertainties in applying effect relationships that are based on clinical studies to the potentially affected population; or (3) the lack of an established concentration-response (CR) relationship. Table 6 .3 -Error ! No text of specified style in document.

- 14 lists the health endpoints included in this anal ysis.

Table 6 .3

- Error! No text of specified style in document.

- 14: Health Impact Functions Used in

BenMAP to Estimate Impacts of PM 2.5 

### and Ozone Reductions

ENDPOINT POLLUTANT STUDY STUDY POPULATION Premature Mortality Premature mortality – daily time series O 3 Multi-city Bell et al (2004) (NMMAPS study) 38 – Nonaccidental Huang et al (2005) 39 - Cardiopulmonary Schwartz (2005) 40 – Non-accidental Meta-analyses: Bell et al (2005) 41 – All cause Ito et al (2005) 42 – Non-accidental Levy et al (2005) 43 – All cause All ages

Premature mortality—cohort study, allcause PM 2.5 Pope et al. (2002) 44 Laden et al. (2006) 45 >29 years >25 years

Premature mortality, total exposures PM 2.5 Expert Elicitation (IEc, 2006) 46 >24 years

Premature mortality — all-cause PM 2.5 Woodruff et al. (1997) 47 Infant (<1 year)

Chronic Illness
Chronic bronchitis PM 2.5 Abbey et al. (1995) 48 >26 years
Nonfatal heart attacks PM 2.5 Peters et al. (2001) 49 Adults (>18 years) Hospital Admissions Respiratory O 3 Pooled estimate: Schwartz (1995) - ICD 460-519 (all resp) 50 Schwartz (1994a; 1994b) - ICD 480-486 (pneumonia) 51

, 52 Moolgavkar et al. (1997) - ICD 480-487 (pneumonia) 53 Schwartz (1994b) - ICD 491-492, 494-496 (COPD) Moolgavkar et al. (1997) – ICD 490-496 (COPD) >64 years

Burnett et al. (2001) 54 <2 years Deleted: 13

### 2017 Regulatory Impact Analysis

51 ENDPOINT POLLUTANT STUDY STUDY POPULATION

PM 2.5 Pooled estimate:

Moolgavkar (2003)—ICD 490-496 (COPD) 55 Ito (2003)—ICD 490-496 (COPD) 56 >64 years

PM 2.5 Moolgavkar (2000)—ICD 490-496 (COPD) 57 20–64 years

PM 2.5 Ito (2003)—ICD 480-486 (pneumonia) >64 years

PM 2.5 Sheppard (2003)—ICD 493 (asthma) 58 <65 years
Cardiovascular PM 2.5 Pooled estimate: Moolgavkar (2003)—ICD 390-429 (all cardiovascular) Ito (2003)—ICD 410-414, 427-428 (ischemic heart disease, dysrhythmia, heart failure) >64 years

PM 2.5 Moolgavkar (2000)—ICD 390-429 (all

cardiovascular) 20–64 years

Asthma-related ER visits O 3 Pooled estimate: Peel et al (2005) 59 Wilson et al (2005) 60 All ages All ages
Asthma-related ER visits (cont’d) PM 2.5 Norris et al. (1999) 61 0–18 years

Other Health Endpoints
Acute bronchitis PM 2.5 Dockery et al. (1996) 62 8–12 years Upper respiratory symptoms PM 2.5 Pope et al. (1991) 63 Asthmatics, 9–11
years Lower respiratory symptoms PM 2.5 Schwartz and Neas (2000) 64 7–14 years

Asthma exacerbations PM 2.5 Pooled estimate: Ostro et al. (2001) 65 (cough, wheeze and shortness of breath) Vedal et al. (1998) 66 (cough) 6–18 years a

Work loss days PM 2.5 Ostro (1987) 67 18–65 years School absence days O 3 Pooled estimate: Gilliland et al. (2001) 68 Chen et al. (2000) 69 5–17 years b

Minor Restricted Activity Days (MRADs) O 3 Ostro and Rothschild (1989) 70 18–65 years

PM 2.5 Ostro and Rothschild (1989) 18–65 years

Notes:

a The original study populations were 8 to 13 for th e Ostro et al. (2001) study and 6 to 13 for the Ved al et al. (1998) study. Based on advice from the Science Advisory B oard Health Effects Subcommittee (SAB-HES), we exte nded the applied population to 6 to 18, reflecting the c ommon biological basis for the effect in children i n the broader age group. See: U.S. Science Advisory Board. 2004. Adv isory Plans for Health Effects Analysis in the Anal ytical Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Coun cil (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 an d 10. Chen et al. (2000) studied children 6 to 11. Based on recent advice from the National Research Council and the E PA 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

. 

### In selecting epidemiological studies as sources of effect estimates, we applied several criteria to develop a set of studies that is likely to provide the best estimates of impacts in the

### Chapter 6

52

U.S. To account for the potential impacts of diffe rent health care systems or underlying health status of populations, we give preference to U.S. s tudies over non-U.S. studies. In addition, due to the potential for confounding by co-pollutants, we give preference to effect estimates from models including both ozone and PM over effect esti mates from single-pollutant models. 71,72



### Baseline Incidence Rates



### Epidemiological studies of the association between pollution levels and adverse health effects generally provide a direct estimate of the relationship of air quality changes to the relative risk 

### of a health effect, rather than estimating the abso lute number of avoided cases. For example, a typical result might be that a 100 ppb decrease i n daily ozone levels might, in turn, decrease hospital admissions by 3 percent. The baseline inc idence of the health effect is necessary to convert this relative change into a number of cases . A baseline incidence rate is the estimate of the number of cases of the health effect per year i n the assessment location, as it corresponds to baseline pollutant levels in that location. To der ive the total baseline incidence per year, this rat e must be multiplied by the corresponding population number. For example, if the baseline incidence rate is the number of cases per year per 100,000 people, that number must be multiplied by the number of 100,000s in the populat ion.

Table 6 .3 -Error ! No text of specified style in document.

- 15 summarizes the sources of baseline incidence rates and provides average incid ence rates for the endpoints included in the analysis. Table 6 .3

- Error! No text of specified style in document.

- 16 presents the asthma prevalence rates used in this analysis. For both b aseline incidence and prevalence data, we used age-specific rates where available. We applied con centration-response functions to individual age groups and then summed over the relevant age ra nge to provide an estimate of total population benefits. In most cases, we used a sing le national incidence rate, due to a lack of more spatially disaggregated data. Whenever possib le, the national rates used are national averages, because these data are most applicable to a national assessment of benefits. For some studies, however, the only available incidence info rmation comes from the studies themselves; in these cases, incidence in the study population is a ssumed to represent typical incidence at the national level. Regional incidence rates are avail able for hospital admissions, and county-level data are available for premature mortality. We hav e projected mortality rates such that future mortality rates are consistent with our projections of population growth. 73



### Table 6 .3

- Error! No text of specified style in document.

- 15: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact Funct ions, General Population
Endpoint Parameter Rates Value Source Mortality Daily or annual mortality rate projected to 2020 Age-, cause-, and county-specific rate CDC Wonder ( 2006–2008 ) 74
U.S. Census bureau Hospitalizations Daily hospitalization rate Age-, r egion-, state-,county- and causespecific rate 2007 HCUP data files a, 75

Asthma ER Visits Daily asthma ER visit rate Age-, r egion-, state-,county- and causespecific rate 2007 HCUP data files a

Chronic Bronchitis Annual prevalence rate per
person 0.0367 1999 NHIS (American Lung Association, 2002, Table 4) 76 Deleted: 14

### 2017 Regulatory Impact Analysis

53 Endpoint Parameter Rates Value Source Aged 18–44 Aged 45–64 Aged 65 and older 0.0505 0.0587

Annual incidence rate per
person 0.00378 Abbey et al. (1993, Table 3)

Nonfatal Myocardial Infarction (heart attacks) Daily nonfatal myocardial infarction incidence rate per person, 18+ Age-, region-, state-, and county- specific
rate 2007 HCUP data files a ; adjusted by

0.93 for probability of surviving after 28 days (Rosamond et al., 1999) Asthma Exacerbations Incidence among asthmatic African-American children daily wheeze daily cough daily dyspnea 0.076 0.067 0.037 Ostro et al. (2001)

Acute Bronchitis Annual bronchitis incidence rate, children 0.043 American Lung Association (2002, Table 11) 77 Lower Respiratory Symptoms Daily lower respiratory symptom incidence among children b 0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory Symptoms Daily upper respiratory symptom incidence among asthmatic children 0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate per person (18–65) Aged 18–24 Aged 25–44 Aged 45–64 0.00540 0.00678 0.00492 1996 HIS (Adams, Hendershot, and

Marano, 1999, Table 41); 78 U.S. Bureau of the Census (2000) 79

School Loss Days Rate per person per year, assuming 180 school days
per year 9.9 National Center for Education Statistics (1996) 80 and 1996 HIS (Adams et al., 1999, Table 47);Minor RestrictedActivity Days Daily MRAD incidence rate per person 0.02137 Ostro and Rothschild (1989, p. 243)

Notes:

a Healthcare Cost and Utilization Program (HCUP) dat abase contains individual level, state and regional -level hospital and emergency department discharges for a variety of ICD codes.
b Lower respiratory symptoms are defined as two or m ore of the following: cough, chest pain, phlegm, a nd wheeze.



### Table 6 .3

- Error! No text of specified style in document.

- 16: Asthma Prevalence Rates Used for this Analysis

Population Group Asthma Prevalence Rates Value Source All Ages 0.0780

American Lung Association (2010, Table 7) < 18 0.0941 5–17 0.1070 18–44 0.0719 45–64 0.0745 65+ 0.0716 African American, 5 to 17 0.1776 American Lung Asso ciation (2010, Table 9) African American, <18 0.1553 American Lung Associat ion b
Notes:

a See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/D atasets/NHIS/2000/.
b Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008). 81 Deleted: 15

### Chapter 6

54 Economic Values for Health Outcomes



### Reductions in ambient concentrations of air polluti on generally lower the risk of future adverse health effects for a large population. The refore, the appropriate economic measure is willingness-to-pay (WTP) for changes in risk of a h ealth effect rather than WTP for a health effect that would occur with certainty (Freeman, 19 93). 82 

### Epidemiological studies generally provide estimates of the relative risks of a partic ular health effect that is avoided because of a reduction in air pollution. We converted those to u nits of avoided statistical incidence for ease of presentation. We calculated the value of avoided st atistical incidences by dividing individual WTP for a risk reduction by the related observed ch ange in risk. For example, suppose a pollution-reduction regulation is able to reduce th e risk of premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If ind ividual WTP for this risk reduction is $100, then the WTP for an avoided statistical premature death is $1 million ($100/0.0001 change in risk).

WTP estimates generally are not available for some health effects, such as hospital admissions. In these cases, we used the cost of tr eating or mitigating the effect as a primary estimate. These cost-of-illness (COI) estimates ge nerally understate the true value of reducing the risk of a health effect, because they reflect t he direct expenditures related to treatment, but no t the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). 83,84 

### We provide unit values for health endpoints (along wit h information on the distribution of the unit value) in Table 6 .3 -Error ! No text of specified style in document.

- 17. All values are in constant year 2010 dollars, adjusted for growth in real inco me out to 2030 using projections provided by Standard and Poor’s. Economic theory argues that W TP for most goods (such as environmental protection) will increase if real income increases. Many of the valuation studies used in this analysis were conducted in the late 1980s and early 1990s. Because real income has grown since the studies were conducted, people’s willingness to pay for reductions in the risk of premature death and disease likely has grown as well. We did not adjust cost of illness-based values because they are based on current costs. Similarly , we did not adjust the value of school absences, because that value is based on current wa ge rates. For details on valuation estimates for PM-related endpoints, see the 2006 PM NAAQS RIA

. 85 

### For details on valuation estimates for ozone-related endpoints, see the 2008 Ozone NAA QS RIA. 86



### Table 6 .3

- Error! No text of specified style in document.

- 17: Unit Values for Economic

Valuation of Health Endpoints (2010$)

Health Endpoint Central Estimate of Value Per Statistical Incidence 2000 Income Level 2030

Income Level Derivation of Distributions of Estimates

Premature Mortality (Value of a Statistical Life) $8,000,000 $9,900,000 EPA currently recommends a central VSL o f $6.3m (2000$) based on a Weibull distribution fitted to 26 published VSL est imates (5 contingent valuation and 21 labor market studies). The underl ying studies, the distribution parameters, and other useful informati on are available in Appendix B of EPA's current Guidelines for Preparin g Economic Analyses (U.S. EPA, 2000). Deleted: 16

### 2017 Regulatory Impact Analysis

55 Chronic Bronchitis (CB) $450,000 $550,000 The WTP to avoid a case of pollution-relat ed CB is calculated as where x is the severity of an average CB case, WTP 13 is the WTP for a severe case of CB, and $ is the parameter relating WTP to severity, based on the regression results reported in Krupnick and Cro pper (1992). The distribution of WTP for an average severity-level c ase of CB was generated by Monte Carlo methods, drawing from each of three distributions: (1) WTP to avoid a severe case of CB is assigned a 1/9 probability of being each of the first nine deciles of the distribution of WTP responses in Viscusi et al. (1991); (2) the sev erity of a pollutionrelated case of CB (relative to the case described in the Viscusi study) is assumed to have a triangular distribution, with the most likely value at severity level 6.5 and endpoints at 1.0 and 12.0 ; and (3) the constant in the elasticity of WTP with respect to severity i s normally distributed with mean = 0.18 and standard deviation = 0.0669 (f rom Krupnick and Cropper [1992]). This process and the rationale for choosing it is described in detail in the Costs and Benefits of th e Clean Air Act, 1990 to 2010 (U.S. EPA, 1999). Nonfatal Myocardial Infarction (heart attack) 3% discount rate Age 0–24 Age 25–44

Age 45–54 Age 55–65 Age 66 and over

7% discount rate Age 0–24 Age 25–44 Age 45–54 Age 55–65 Age 66 and over $89,373

$100,690$1 06,053 $185,785 $89,373

$88,547 $98,680 $103,481 $174,866 $88,548 $89,373

$100,690 $106,053 $185,785 $89,373

$88,547 $98,680 $103,481 $174,866 $88,548 No distributional information available. Age-specif ic cost-of-illness values reflect lost earnings and direct medical cos ts over a 5-year period following a nonfatal MI. Lost earnings estim ates are based on Cropper and Krupnick (1990). Direct medical costs a re based on simple average of estimates from Russell et al. (19 98) and Wittels et al. (1990). Lost earnings: Cropper and Krupnick (1990). Present discounted val ue of 5 years of lost earnings: age of onset: at 3% at 7% 25–44 $8,774 $7,855 45–54 $12,932 $11,578 55–65 $74,746 $66,920 Direct medical expenses: An average of: 1. Wittels et al. (1990) ($102,658—no discounting) 2. Russell et al. (1998), 5-year period ($22,331 a t 3% discount rate; $21,113 at 7% discount rate) Hospital Admissions Chronic Obstructive Pulmonary Disease (COPD) $17,996 $17,996 No distributional information available. The COI es timates (lost earnings plus direct medical costs) are based on IC D-9 code-level information ( e.g. , average hospital care costs, average length of hospital stay, and weighted share of total COPD cat egory illnesses) reported in Agency for Healthcare Research and Qual ity (2000) (www.ahrq.gov). Asthma Admissions $11,957 $11,957 No distributional information available. The COI es timates (lost earnings plus direct medical costs) are based on IC D-9 code-level information ( e.g. , average hospital care costs, average length of ho spital stay, and weighted share of total asthma category i llnesses) reported in Agency for Healthcare Research and Quality (2000) ( www.ahrq.gov). All Cardiovascular $30,256 $30,256 No distributional information available. The COI es timates (lost earnings plus direct medical costs) are based on IC D-9 code-level information ( e.g. , average hospital care costs, average length of ho spital stay, and weighted share of total cardiovascular ca tegory illnesses) reported in Agency for Healthcare Research and Qual ity (2000) (www.ahrq.gov). All respiratory (ages 65+) $25,413 $25,413 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD-9 code level information ( e.g. , average hospital care costs, average length of hosp ital stay, and

### Chapter 6

56 weighted share of total COPD category illnesses) re ported in Agency for Healthcare Research and Quality, 2000 (www.ahrq .gov). All respiratory (ages 0–2) $10,943 $10,943 No distributions available. The COI point estimates (lost earnings plus direct medical costs) are based on ICD-9 code level information ( e.g. , average hospital care costs, average length of hosp ital stay, and weighted share of total COPD category illnesses) re ported in Agency for Healthcare Research and Quality, 2000 (www.ahrq .gov). Emergency Room Visits for Asthma $405 $405 No distributional information available. Simple average of two unit COI values: (1) $311.55, from Smith et al. (1997) and (2) $260.67, from Stanford et al. (1999). Respiratory Ailments Not Requiring Hospitalization Upper Respiratory Symptoms (URS) $32 $34 Combinations of the three symptoms for whic h WTP estimates are available that closely match those listed by Pope e t al. result in seven different “symptom clusters,” each describing a “ty pe” of URS. A dollar value was derived for each type of URS, usin g mid-range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. In the absence of info rmation surrounding the frequency with which each of the se ven types of URS occurs within the URS symptom complex, we assumed a uniform distribution between $9.2 and $43.1. Lower Respiratory Symptoms (LRS) $20 $21 Combinations of the four symptoms for which WTP estimates are available that closely match those listed by Schwar tz et al. result in 11 different “symptom clusters,” each describing a “ty pe” of LRS. A dollar value was derived for each type of LRS, usin g mid-range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. The dollar value for L RS is the average of the dollar values for the 11 different types of LRS. In the absence of information surrounding the frequency with which ea ch of the 11 types of LRS occurs within the LRS symptom complex, we as sumed a uniform distribution between $6.9 and $24.46. Asthma Exacerbations $55 $57 Asthma exacerbations are valued at $45 per incidence, based on the mean of average WTP estimates for the four severity definitions of a “bad asthma day,” described in Rowe and Chestnut (1 986). This study surveyed asthmatics to estimate WTP for avoidance o f a “bad asthma day,” as defined by the subjects. For purposes of v aluation, an asthma exacerbation is assumed to be equivalent to a day i n which asthma is moderate or worse as reported in the Rowe and Chest nut (1986) study. The value is assumed have a uniform distribution be tween $15.6 and $70.8. Acute Bronchitis $452 $494 Assumes a 6-day episode, with the distribution of the daily value specified as uniform with the low and high values b ased on those recommended for related respiratory symptoms in Neu mann et al. (1994). The low daily estimate of $10 is the sum of the mid-range values recommended by IEc 1994 for two symptoms bel ieved to be associated with acute bronchitis: coughing and ches t tightness. The high daily estimate was taken to be twice the value of a minor respiratory restricted-activity day, or $110. Work Loss Days (WLDs) Variable (U.S. median = $137) Variable

(U.S. median = $137) No distribution available. Point estimate is based on county-specific

median annual wages divided by 50 (assuming 2 weeks of vacation) and then by 5—to get median daily wage. U.S. Year 2 000 Census, compiled by Geolytics, Inc.

### 2017 Regulatory Impact Analysis

57 Minor Restricted Activity Days (MRADs) $64 $69 Median WTP estimate to avoid one MRAD from Tolley et al. (1986). Distribution is assumed to be triangular with a min imum of $22 and a maximum of $83, with a most likely value of $52. Ra nge is based on assumption that value should exceed WTP for a singl e mild symptom (the highest estimate for a single symptom—for eye irritation—is $16.00) and be less than that for a WLD. The triang ular distribution acknowledges that the actual value is likely to be closer to the point estimate than either extreme.

School Absence Days $95 $95 No distribution available



### Processing Air Quality Modeling Data for Health Impacts Analys is



### In Section 6.2, we summarized the methods for and r esults of estimating air quality for the standards. These air quality results are in tu rn associated with human populations to estimate changes in health effects. For the purposes of thi s analysis, we focus on the health effects that have been linked to ambient changes in ozone and PM 2.5 

### related to emission reductions estimated to occur due to the implementation of the standards . We estimate ambient PM 2.5 

### and ozone concentrations using the Community Multiscale Air Q uality model (CMAQ). This section describes how we converted the CMAQ modeling output into full-season profiles suitable for the health impacts analysis.

General Methodology

First, we extracted hourly, surface-layer PM and oz one concentrations for each grid cell from the standard CMAQ output files. For ozone, th ese model predictions are used in conjunction with the observed concentrations obtain ed from the Aerometric Information Retrieval System (AIRS) to generate ozone concentra tions for the entire ozone season. X,Y 

### 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 impact functions of the benefits analysis ( i.e.



### , BenMAP).

To estimate ozone-related health effects for the co ntiguous United States, full-season ozone data are required for every BenMAP grid-cell. Given available ozone monitoring data, we generated full-season ozone profiles for each locat ion in two steps: (1) we combined monitored observations and modeled ozone predictions to inter polate hourly ozone concentrations to a grid of 12-km by 12-km population grid cells for the con tiguous 48 states, and (2) we converted these full-season hourly ozone profiles to an ozone measu re of interest, such as the daily 8-hour maximum. Z,AA

X The ozone season for this analysis is defined as t he 5-month period from May to September.

Y Based on AIRS, there were 961 ozone monitors with sufficient data ( i.e. , 50 percent or more days reporting at least nine hourly observations per day [8 am to 8 pm] dur ing the ozone season).

Z The 12-km grid squares contain the population data used in the health benefits analysis model, BenMAP

. AA This approach is a generalization of planar interp olation that is technically referred to as enhanced Voronoi Neighbor Averaging (EVNA) spatial interpolation. S ee the BenMAP manual for technical details, availab le for download at http://www.epa.gov/air/benmap. Deleted: Manipulating

### Chapter 6

58

For PM 2.5 

### , we also use the model predictions in conjunction with observed monitor data. CMAQ generates predictions of hourly PM species con centrations for every grid. The species include a primary coarse fraction (corresponding to PM in the 2.5 to 10 micron size range), a primary fine fraction (corresponding to PM less tha n 2.5 microns in diameter), and several secondary particles ( e.g. 

### , sulfates, nitrates, and organics). PM 2.5 

### is calculated as the sum of the primary fine fraction and all of the secondarily fo rmed particles. Future-year estimates of PM 2.5 

### were calculated using relative reduction factors (R RFs) applied to 2005 ambient PM 2.5 

### and PM 2.5 

### species concentrations. A gridded field of PM 2.5 

### concentrations was created by interpolating Federal Reference Monitor ambient data and IMPROVE ambient data. Gridded fields of PM 2.5 

### species concentrations were created by interpolatin g EPA speciation network (ESPN) ambient data and IMPROVE data. The ambient data were inter polated to the CMAQ 12 km grid.

The procedures for determining the RRFs are similar to those in EPA’s draft guidance for modeling the PM 2.5 

### standard (EPA, 2001). 87 

### The guidance recommends that model predictions be used in a relative sense to estimate changes exp ected to occur in each major PM 2.5 

### species. The procedure for calculating future-year PM 2.5 

### design values is called the “Speciated Modeled Attainment Test (SMAT).” EPA used this procedure t o estimate the ambient impacts of the final standards.

Table 6 .3 -Error ! No text of specified style in document.

- 18 provides those ozone and PM 2.5 

### metrics for grid cells in the modeled domain that enter the health impact functions for health benefits endpoints. The population-weighted average reflects the baseline levels and predicted changes for more populated areas of the n ation. This measure better reflects the potential benefits through exposure changes to thes e populations.

Table 6 .3

- Error! No text of specified style in document.

- 18: Summary of CMAQ-Derived Population-Weighted Ozone and PM 2.5 

### Air Quality Metrics for Health Benefits Endpoints Associated with the Final Standards

2030 Statistic a 

### Baseline Change b 

### Ozone Metric: National Population-Weighted Average (ppb) c 

### Daily Maximum 8-Hour Average Concentration 42.3735 0.0009

PM 2.5 

### Metric: National Population-Weighted Average (μg/m 3 

### ) Annual Average Concentration 8.1135 -0.0065

Notes:

a Ozone and PM 2.5 metrics are calculated at the CMAQ grid-cell level for use in health effects estimates. Ozone metrics are calculated over relev ant time periods during the daylight hours of the “ozone season” ( i.e. , May through September). Note that the national, population-weighted PM 2.5 and ozone air quality metrics presented in this cha pter represent an average for the entire, gridded U.S. CMAQ domain. These are different than the pop ulation-weighted PM 2.5 and ozone design value metrics presented in Chapter 7, which represe nt the average for areas with a current air quality monitor.
b The change is defined as the control-case value minus the base-case value ; a negative value therefore indicates a reduction and a positive valu e an increase

. c Calculated by summing the product of the projected CMAQ grid-cell population and the estimated CMAQ grid cell seasonal ozone concentrati on and then dividing by the total population. Deleted: 17

Deleted: minus the control-case value

### 2017 Regulatory Impact Analysis

59 Emissions and air quality modeling decisions are ma de early in the analytical process. For this reason, the emission control scenarios use d in the air quality and benefits modeling are slightly different than the final emission inventor ies estimated for the final standards. Please refer to Section 6.2.1 for more information about t he inventories used in the air quality modeling that supports the health impacts analysis.

Methods for Describing Uncertainty



### 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 we re used to derive the estimate of benefits for the final standards, including emission inventories , air quality models (with their associated parameters and inputs), epidemiological health effe ct 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). Ea ch of these inputs may be uncertain and, depending on its role in the benefit s 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 emiss ions 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) 88,89 

### highlighted the need for EPA to conduct rigorous quantitative analysis of uncertain ty 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 an d informative in spite of inherent uncertainties. Since the publication of these reports, EPA’s Offic e 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 bene fits 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 quantitativ ely, as well as provide a qualitative assessment for those aspects that we are unable to address quantitatively.

First, we used Monte Carlo methods for characterizi ng random sampling error associated with the concentration response functions from epid emiological studies and random effects modeling to characterize both sampling error and va riability across the economic valuation functions. Monte Carlo simulation uses random sampl ing from distributions of parameters to characterize the effects of uncertainty on output v ariables, such as incidence of premature mortality. Specifically, we used Monte Carlo method s to generate confidence intervals around the estimated health impact and dollar benefits. Th e reported standard errors in the epidemiological studies determined the distribution s for individual effect estimates.

Second, because characterization of random statisti cal 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 exper t elicitation on the relationship between premature mortality and ambient PM 2.5 

### concentration (Roman et al., 2008). 90 

### Use of the expert elicitation and incorporation of the standard error s approaches provide insights into the likelihood

### Chapter 6

60

of different outcomes and about the state of knowle dge regarding the benefits estimates. However, there are significant unquantified uncerta inties present in upstream inputs including emission and air quality. Both approaches have diff erent 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 co nducted to date, the estimated impact of reductions in premature mortality has accounted for 85 to 95 percent of total monetized benefits. Therefore, it is particularly important to attempt to characterize the uncertainties associated with reductions in premature mortality. The health impac t functions used to estimate avoided premature deaths associated with reductions in ozon e have associated standard errors that represent the statistical errors around the effect estimates in the underlying epidemiological studies. In our results, we report credible interva ls 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 be tween 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 estim ates based on results of the expert elicitation study. Even these multiple characterizations, inclu ding confidence intervals, omit the contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, populations exposed and transferability of the effe ct estimate to diverse locations. Furthermore, the approach presented here does not yet include me thods for addressing correlation between input parameters and the identification of reasonab le upper and lower bounds for input distributions characterizing uncertainty in additio nal 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 shou ld 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 t he understanding of ozone-related mortality. The NAS found that short-term ozone exposure was li kely to contribute to ozone-related mortality (NRC, 2008) and issued a series of recomm endations to EPA, including that the Agency should:

1. Present multiple short-term ozone mortality estimat es, 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 perce ntage of baseline mortality attributable to short-term exposure.

3. Remove reference to a no-causal relationship betwee n ozone exposure and premature mortality.

The quantification and presentation of ozone-relate d premature mortality in this chapter is responsive to these NRC recommendations.

### 2017 Regulatory Impact Analysis

61 Some key sources of uncertainty in each stage of bo th the PM and ozone health impact assessment are the following:

• 

### gaps in scientific data and inquiry;

• 

### variability in estimated relationships, such as epi demiological 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 PM 10 

### when PM 2.5 

### is not available, excluded variables, and simplification of complex functions; and

• 

### biases due to omissions or other research limitatio ns.

In Table 6 .3

- Error! No text of specified style in document.

- 19 we summarize some of the key uncertainties in the benefits analysis.

Table 6 .3

- Error! No text of specified style in document.

- 19: Primary Sources of Uncertainty in the Benefits Analysis
1. Uncertainties Associated with Impact Functions

- The value of the ozone or PM effect estimate in eac h impact function.

- Application of a single impact function to pollutan t changes and populations in all locations.

- Similarity of future-year impact functions to curre nt impact functions.

- Correct functional form of each impact function.

- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the sourc e epidemiological study.

- Application of impact functions only to those subpo pulations matching the original study population. 2. Uncertainties Associated with CMAQ-Modeled Ozon e and PM Concentrations

- Responsiveness of the models to changes in precurso r emissions from the control policy.

- Projections of future levels of precursor emissions , especially ammonia and crustal materials.

- Lack of ozone and PM 2.5 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 b iological mechanism for observed epidemiological ev idence.

- Direct causal agents within the complex mixture of PM have not been identified.

- The extent to which adverse health effects are asso ciated with low-level exposures that occur many tim es in the year versus peak exposures.

- The extent to which effects reported in the long-te rm exposure studies are associated with historicall y higher levels of PM rather than the levels occurring durin g the period of study.

- Reliability of the PM 2.5 monitoring data in reflecting actual PM 2.5 exposures. 4. Uncertainties Associated with Possible Lagged E ffects

- The portion of the PM-related long-term exposure mo rtality effects associated with changes in annual P M levels that would occur in a single year is uncertain as w ell as the portion that might occur in subsequent y ears. 5. Uncertainties Associated with Baseline Incidenc e Rates

- Some baseline incidence rates are not location spec ific ( e.g. , those taken from studies) and therefore may not accurately represent the actual location-specific r ates.

- Current baseline incidence rates may not approximat e well baseline incidence rates in 2030.

- Projected population and demographics may not repre sent well future-year population and demographics. 6. Uncertainties Associated with Economic Valuatio n Deleted: 18

### Chapter 6

62

- Unit dollar values associated with health and welfa re endpoints are only estimates of mean WTP and the refore have uncertainty surrounding them.

- Mean WTP (in constant dollars) for each type of ris k reduction may differ from current estimates becau se of differences in income or other factors. 7. Uncertainties Associated with Aggregation of Mo netized Benefits

- Health and welfare benefits estimates are limited t o the available impact functions. Thus, unquantifi ed or unmonetized benefits are not included.



### 6.3.2 PM-related Monetized Benefits of the Model Year (MY ) Analysis

As described in Chapter 4, the final standards will reduce emissions of several criteria and toxic pollutants and precursors. In the MY analysi s, EPA estimates the economic value of the human health benefits associated with reducing PM 2.5 

### exposure. Due to analytical limitations, this analysis does not estimate benefits related to other criteria pollutants (such as ozone, NO 2 

### or SO 2 

### ) or toxics pollutants, nor does it monetize all of the potential health and welfare effects associated with PM 2.5


### . The MY analysis uses a “benefit-per-ton” method to estimate a selected suite of PM 2.5 

### - related health benefits described below. These PM 2.5 

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

### , or one ton of a pollutant that contributes to secondarily-formed PM 2.5 

### (such as NOx and SOx) from a specified source. Id eally, the human health benefits would be estimated based on changes in ambient PM 2.5 

### concentrations and population exposure, as determined by complete air quality and exposure modeling. However, conducting such detailed modeling for the model yea r analysis was not possible within the timeframe for the final rule. Note that EPA conduc ted full-scale photochemical air quality modeling for the calendar year analysis. Please re fer to Chapter 6.2 for a description of EPA’s air quality modeling results and to Chapter 6.3.1 f or a description of the quantified and monetized PM- and ozone-related health impacts of the FRM.

Due to analytical limitations, the estimated benefi t-per-ton values do not include comparable benefits related to reductions in other ambient concentrations of criteria pollutants (such as ozone, NO 2 

### or SO 2 

### ) or toxic air pollutants, nor do they monetize all of the potential health and welfare effects associated with PM 2.5 

### or the other criteria pollutants. As a result, monetizing PM-related health impacts alone underest imates the benefits associated with reductions of the suite of non-GHG pollutants that would be reduced by the final standards.

The dollar-per-ton estimates used to monetize reduc tions in emissions that contribute to ambient concentrations of PM 2.5 

### are provided in Table 6.3-Error ! No text of specified style in document.

- 20.

Table 6.3- Error! No text of specified style in document.

- 20 PM 2.5 -related Benefits-per-ton Values (2010$) a

Year All Sources d Upstream (Non-EGU) Sources d Mobile Sources

SO 2 NO X Direct PM 2.5 NO X Direct PM 2.5 Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated Using a 3 Percent Discount Rate c 2015 $30,000 $4,900 $230,000 $5,100 $280,000 Deleted: 19

### 2017 Regulatory Impact Analysis

63 2020 $33,000 $5,400 $250,000 $5,600 $310,000 2030 $38,000 $6,400 $290,000 $6,700 $370,000 2040 $45,000 $7,600 $340,000 $8,000 $440,000 Dollar-per-ton Derived from American Cancer Society Analysis (Pope et al., 2002) Estimated Using a 7 Percent Discount Rate c 2015 $27,000 $4,500 $210,000 $4,600 $250,000 2020 $30,000 $4,900 $230,000 $5,100 $280,000 2030 $35,000 $5,800 $270,000 $6,100 $330,000 2040 $41,000 $6,900 $310,000 $7,300 $400,000 Dollar-per-ton Derived from Six Cities Analysis (La den et al., 2006) Estimated Using a 3 Percent Discount Rate c 2015 $73,000 $12,000 $560,000 $12,000 $680,000 2020 $80,000 $13,000 $620,000 $14,000 $750,000 2030 $94,000 $16,000 $720,000 $16,000 $900,000 2040 $110,000 $19,000 $840,000 $20,000 $1,100,000 Dollar-per-ton Derived from Six Cities Analysis (La den et al., 2006) Estimated Using a 7 Percent Discount Rate c 2015 $66,000 $11,000 $510,000 $11,000 $620,000 2020 $72,000 $12,000 $560,000 $12,000 $680,000 2030 $84,000 $14,000 $650,000 $15,000 $810,000 2040 $99,000 $17,000 $760,000 $18,000 $960,000

a Total dollar-per-ton estimates include monetized PM 2.5 -related premature mortality and morbidity endpoint s. Range of estimates are a function of the estimate o f PM 2.5 -related premature mortality derived from either th e ACS study (Pope et al., 2002) or the Six-Cities study ( Laden et al., 2006).
b Dollar-per-ton values were estimated for the years 2015, 2020, and 2030. For 2040, EPA extrapolated exponentially based on the growth between 2020 and 2030.

c The dollar-per-ton estimates presented in this tab le assume either a 3 percent or 7 percent discount rate in the valuation of premature mortality to account for a t wenty-year segmented cessation lag.
d Note that the dollar-per-ton value for SO2 is base d on the value for Stationary (Non-EGU) sources; no SO2 value was estimated for mobile sources.



### As Table 6.3-Error ! No text of specified style in document.

- 20 indicates, EPA projects that the per-ton values for reducing emissions of c riteria pollutants from both vehicle use and stationary sources such as fuel refineries and stor age facilities will increase over time. BB 

### These projected increases reflect rising income levels, w hich are assumed to increase affected individuals’ willingness to pay for reduced exposur e to health threats from air pollution. They also reflect future population growth and increased life expectancy, which expands the size of the population exposed to air pollution in both urban a nd rural areas, especially in older age groups with the highest mortality risk. 91,CC



### For certain PM 2.5 

### -related pollutants (such as direct PM 2.5 

### and NOx), EPA estimates different per-ton values for reducing mobile sourc e emissions than for reductions in emissions of the same pollutant from stationary sources such as fuel refineries and storage facilities. These reflect differences in the typical geographic distr ibutions of emissions of each pollutant by

BB As we discuss in the emissions chapter of EPA’s DR IA (Chapter 4), the rule would yield emission reduc tions from upstream refining and fuel distribution due to decreased petroleum consumption.
CC For more information about EPA’s population projec tions, please refer to the following: http://www.epa.gov/air/benmap/models/BenMAPManualAp pendicesAugust2010.pdf (See Appendix K)

### Chapter 6

64

different sources, their contributions to ambient l evels of PM 2.5 

### , and resulting changes in population exposure. EPA applies these separate va lues to its estimates of changes in emissions from vehicle use and from fuel production and distr ibution to determine the net change in total economic damages from emissions of those pollutants

. The benefit per-ton technique has been used in prev ious analyses, including the 2012- 2016 Light-Duty Greenhouse Gas Rule, 92 

### the Ozone National Ambient Air Quality Standards (NAAQS) RIA, 93 

### the Portland Cement National Emissions Standards f or Hazardous Air Pollutants (NESHAP) RIA, 94 

### and the final NO 2 

### NAAQS. 95 

### Table 6 .3 -Error ! No text of specified style in document.

- 21 shows the quantified and monetized PM 2.5 

### -related co-benefits that are captured in these benefit-per-ton estimates, and al so lists other effects that remain un-quantified and are thus excluded from the estimates.

Table 6 .3

- Error! No text of specified style in document.

- 21 Human Health and Welfare Effects of PM 2.5



### Quantified and Monetized in Primary Estimates Un-quantified Effects Changes in: Adult premature mortality Bronchitis: chronic and acute Hospital admissions: respiratory and cardiovascular Emergency room visits for asthma Nonfatal heart attacks (myocardial infarction) Lower and upper respiratory illness Minor restricted-activity days Work loss days Asthma exacerbations (asthmatic population) Infant mortality Subchronic bronchitis cases Low birth weight Pulmonary function Chronic respiratory diseases other than chronic bronchitis Non-asthma respiratory emergency room visits Visibility Household soiling



### Consistent with the NO 2 

### NAAQS, DD 

### the benefits estimates utilize concentration-respo nse functions as reported in the epidemiology literatur e. Readers interested in reviewing the complete methodology for creating the benefit-per-t on estimates used in this analysis can consult the Technical Support Document (TSD) 96 

### accompanying the final ozone NAAQS RIA. Readers can also refer to Fann et al. (2009) 97 

### for a detailed description of the benefit-per-ton methodology. EE

DD Although we summarize the main issues in this chap ter, we encourage interested readers to see benefit s chapter of the NO2 NAAQS for a more detailed description of re cent changes to the PM benefits presentation and pr eference for the no-threshold model.
EE The values included in this report are different f rom those presented in the article cited above. Be nefits methods change to reflect new information and evaluation of the science. Since publication of the June 2009 a rticle, EPA has made two significant changes to its benefits method s: (1) We no longer assume that a threshold exists in PM-related models of health impacts, which is consistent with the findings reported in published research; and (2 ) We have revised the Value of a Statistical Life to equal $6 .3 million (year 2000$), up from an estimate of $5. 5 million (year 2000$) used in the June 2009 report. Please refer to the following website for updates to the dollar- per-ton estimates: http://www.epa.gov/air/benmap/bpt.html Deleted: 20

Formatted Table

### 2017 Regulatory Impact Analysis

65 As described above, national per-ton estimates were developed for selected pollutant/source category combinations. The per-to n values calculated therefore apply only to tons reduced from those specific pollutant/source c ombinations ( e.g. 

### , NO 2 

### emitted from mobile sources; direct PM emitted from stationary sources) . Our estimate of total PM 2.5 

### benefits is therefore based on the total direct PM 2.5 

### and PM 2.5 

### –related precursor emissions (NOx, SOx, and VOCs) controlled from each source and multiplied by the respective per-ton values of reducing emissions from that source.

The benefit-per-ton coefficients in this analysis w ere derived using modified versions of the health impact functions used in the PM NAAQS Re gulatory Impact Analysis. Specifically, this analysis uses the benefit-per-ton estimates fi rst applied in the Portland Cement NESHAP RIA, which incorporated concentration-response func tions directly from the epidemiology studies, without any adjustment for an assumed thre shold. Removing the threshold assumption is a key difference between the method used in this an alysis to estimate PM co-benefits and the methods used in analyses prior to EPA’s Portland Ce ment NESHAP. FF 

### As a consequence, the benefit-per-ton estimates used in this analysis inc lude incremental benefits of reductions in PM 2.5 

### concentrations down to their lowest modeled levels. This approach is also consistent with EPA’s analysis of the 2012-2016 Light-Duty Vehicle Greenh ouse Gas rule.

Reductions in PM-related mortality provide the majo rity of the monetized value in each benefit-per-ton estimate. Typically, the premature mortality-related effect coefficients that underlie the benefits-per-ton estimates are drawn f rom epidemiology studies that examine two large population cohorts: the American Cancer Socie ty cohort (Pope et al., 2002) 98 

### and the Harvard Six Cities cohort (Laden et al., 2006). 99 

### The concentration-response (C-R) function developed from the extended analysis of American Ca ncer Society (ACS) cohort, as reported in Pope et al. (2002), has previously been used by EPA to generate its primary benefits estimate. The extended analysis of the Harvard Six Cities coh ort, as reported by Laden et al (2006), was published after the completion of the Staff Paper f or the 2006 PM 2.5 

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

### NAAQS RIA and PM 2.5 

### co-benefits estimates in analyses completed since the PM 2.5 

### NAAQS.

These studies provide logical choices for co-equal anchor points when presenting PMrelated benefits because, while both studies are we ll designed and peer-reviewed, there are strengths and weaknesses inherent in each. Althoug h EPA’s primary method of characterizing

FF Based on a review of the current body of scientifi c literature, EPA estimates PM-related mortality wi thout applying an assumed concentration threshold. EPA‘s Integrated Science Assessment for Particulate Matt er (U.S. Environmental Protection Agency. 2009. Integrated S cience Assessment for Particulate Matter (Final Rep ort). EPA600-R-08-139F. National Center for Environmental As sessment – RTP Division. December), which was revie wed by EPA‘s Clean Air Scientific Advisory Committee (U .S. Environmental Protection Agency - Science Advis ory Board. 2009. Review of EPA‘s Integrated Science Ass essment for Particulate Matter (First External Revi ew Draft, December 2008). EPA-COUNCIL-09-008. May.; U.S. Envi ronmental Protection Agency Science Advisory Board

. 2009. Consultation on EPA‘s Particulate Matter Nati onal Ambient Air Quality Standards: Scope and Metho ds Plan for Health Risk and Exposure Assessment. EPA-COUNCI L-09-009. May), concluded that the scientific liter ature consistently finds that a no-threshold log-linear m odel most adequately portrays the PM-mortality conc entrationresponse relationship while recognizing potential u ncertainty about the exact shape of the concentrati on-response function. This assumption is incorporated into the calculation of the PM-related benefits-per-ton val ues.

### Chapter 6

66

PM-related premature mortality is to use both studi es to generate a co-equal range of benefits estimates, EPA has chosen to present only the benef it-per-ton value derived from the ACS study in its summary tables of total Model Year costs and benefits (See RIA Chapter 7). This decision was made to provide the reader with summary tables that are easier to understand and interpret and does not convey any preference for one study ov er the other. We note that this is also the more conservative of the two estimates - PM-relate d benefits would be approximately 245 percent (or nearly two-and-a-half times) larger had we used the per-ton benefit values based on the Harvard Six Cities study instead. See RIA Chap ter 7.3 for the monetized PM-related health impacts of the Model Year analysis.

As is the nature of benefits analyses, assumptions and methods evolve over time to reflect the most current interpretation of the scientific a nd economic literature. For a period of time (2004-2008), EPA’s Office of Air and Radiation (OAR ) valued mortality risk reductions using a value of statistical life (VSL) estimate derived fr om 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 t he interquartile range from the Mrozek and Taylor (2002) 100 

### meta-analysis of 33 studies. The $10 million valu e represented the upper end of the interquartile range from the Viscusi and Aldy (2003) 101 

### 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) 102 

### 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 Sci ence Advisory Board (SAB) or other peerreview group.

Until updated guidance is available, EPA determined that a single, peer-reviewed estimate applied consistently best reflects the Science Advi sory Board Environmental Economics Advisory Committee (SAB-EEAC) advice it has receive d. Therefore, EPA 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) 103 

### while they continue efforts to update their guidan ce on this issue. GG 

### This approach calculates a mean value across VSL estimates derived from 26 labor market and contingent valuation studies published b etween 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). The dollar-per-ton estimates used in this analysis are based on this revised VSL. HH



### The benefit-per-ton estimates are subject to a numb er of assumptions and uncertainties.

GG In the update of the Economic Guidelines (U.S. EPA , 2011), EPA retained the VSL endorsed by the SAB w ith the understanding that further updates to the mortality risk valuation guidance would be forthcoming in th e near future. The update of the Economic Guidelines is available on the Internet at http://yosemite.epa.gov/ee/epa/eed.nsf/pages/Guidel ines.html/$file/Guidelines.pdf.

HH This value differs from the Department of Transpor tation’s most recent estimate of the value of preve nting transportation-related fatalities, which is $6.1 mi llion when expressed in today’s (2011) dollars.

### 2017 Regulatory Impact Analysis

67 • 

### They do not reflect local variability in population density, meteorology, exposure, baseline health incidence rates, or other local fac tors that might lead to an overestimate or underestimate of the actual benefits of controlling fine particulates in specific locations. Please refer to Chapter 6.3.1 for the description o f the agency’s quantification and monetization of PM- and ozone-related health impact s for the final standards.

• 

### This analysis assumes that all fine particles, rega rdless of their chemical composition, are equally potent in causing premature mortality. Thi s is an important assumption, because PM 2.5 

### produced via transported precursors emitted from st ationary sources may differ significantly from direct PM 2.5 

### released from engines and other industrial sources. At the present time, however, no clear scientific grounds exist for supporting differential effects estimates by particle type.

• 

### This analysis assumes that the health impact functi on for fine particles is linear within the range of ambient concentrations under consideration . Thus, the estimates include health benefits from reducing fine particles in areas with varied initial concentrations of PM 2.5, 

### including both regions that are in attainment with fine particle standard and those that do not meet the standard, down to the lowest modeled c oncentrations.

• 

### There are several health benefits categories that E PA was unable to quantify due to limitations associated with using benefits-per-ton estimates, several of which could be substantial. Because NO X 

### and VOC emissions are also precursors to ozone, cha nges in NO X 

### and VOC would also impact ozone formation and the h ealth effects associated with ozone exposure. Benefits-per-ton estimates for ozo ne do not exist due to issues associated with the complexity of the atmospheric a ir chemistry and nonlinearities associated with ozone formation. The PM-related be nefits-per-ton estimates also do not include any human welfare or ecological benefits. Please refer to Chapter 6.3.1 for a description of the unquantified co-pollutant benefi ts associated with this rulemaking.

As mentioned above, emissions changes and benefits- per-ton estimates alone are not a good indication of local or regional air quality an d health impacts, as the localized impacts associated with the rulemaking may vary significant ly. Additionally, the atmospheric chemistry related to ambient concentrations of PM 2.5 

### , ozone and air toxics is very complex. Full-scale photochemical modeling is therefore necessary to pr ovide the needed spatial and temporal detail to more completely and accurately estimate the chan ges in ambient levels of these pollutants and their associated health and welfare impacts. For this final rule, EPA conducted a national-scale air quality modeling analysis for 2030 to analyze t he impacts of the standards on PM 2.5 

### , ozone, and selected air toxics.

1 

### CMAS 2008. Release Notes for CMAQ v4.7. http://www.cmascenter.org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt Docket EPA-HQ-OAR2010-0162.

2 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 20 12. Available at: http://www.epa.gov/airtrends/2011/.
3 77FR 30088 (May 21, 2012)

4 

### U.S. EPA. Air Quality Criteria for Ozone and Relat ed Photochemical Oxidants (Final). U.S. EPA, Washington, DC, EPA/600/R-05/004aF-cF, 2006. Docke t EPA-HQ-OAR-2010-0162.

5 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 20 12. Available at: http://www.epa.gov/airtrends/2011/. Deleted:

.

### Chapter 6

68 6 From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA-454/R-12-001. February 20 12. Available at: http://www.epa.gov/airtrends/2011/.
7 U.S. EPA. (2007). PM 2.5 National Ambient Air Quality Standard Implementati on Rule (Final). Washington, DC: U.S. EPA. 72 FR 20586, April 25, 2007. Docket EPA-H Q-OAR-2011-0135. 8 PM Standards Revision – 2006: Timeline. Docket EPA- HQ-OAR-2011-0135. 

### http://www.epa.gov/PM/naaqsrev2006.html#timeline Docket EPA-HQ-OAR-2011-0135.
9 U.S. EPA. (2011) Summary of Results for the 2005 N ational-Scale Assessment. http://www.epa.gov/ttn/atw/nata2005/05pdf/sum_resul ts.pdf.
10 Control of Hazardous Air Pollutants From Mobile So urces (72 FR 8428; February 26, 2007)
11 US EPA (2007) Control of Hazardous Air Pollutants from Mobile Sources Regulatory Impact Analysis. EPA document number 420-R-07-002, February 2007.
12 U. S. Environmental Protection Agency (2007). Cont rol of Hazardous Air Pollutants from Mobile Sources ; Final Rule. 72 FR 8434, February 26, 2007. Docket EPA-HQ -OAR-2011-0135.
13 U. S. Environmental Protection Agency (2007). Cont rol of Hazardous Air Pollutants from Mobile Sources ; Final Rule. 72 FR 8434, February 26, 2007. Docket EPA-HQ -OAR-2011-0135.
14 U.S. EPA. (2011) 2005 National-Scale Air Toxics As sessment. http://www.epa.gov/ttn/atw/nata2005/. D ocket EPA-HQ-OAR-2011-0135
15 U.S. EPA. (2011) Summary of Results for the 2005 N ational-Scale Assessment. http://www.epa.gov/ttn/atw/nata2005/05pdf/sum_resul ts.pdf. Docket EPA-HQ-OAR-2011-0135.
16 U.S. EPA. (2012). U.S. EPA’s Report on the Environm ent. Data accessed online February 15, 2012 at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=deta il.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.list ByC hapter&r=216610 and contained in Docket EPA-HQ-OAR- 2011-0135.
17 U.S. EPA. (2012). U.S. EPA's Report on the Environ ment. Data accessed online February 15, 2012 at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=deta il.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.list ByC hapter&r=216610 and contained in Docket EPA-HQ-OAR- 2011-0135.

18 

### US EPA (2009)

19 

### Trijonis et al. 1987

20 

### Trijonis et al. 1988

21 

### Irving, 1991

22 

### U.S. Environmental Protection Agency. (2006). Final Regulatory Impact Analysis (RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter 

### . Prepared by: Office of Air and Radiation. Retrieved March, 26, 2009 at http://www.epa.gov/ttn/ecas/ria.html

. EPA-HQ-OAR-2009-0472-0240

23 

### U.S. Environmental Protection Agency. (2008). Final Ozone NAAQS Regulatory Impact Analysis 

### . Prepared by: Office of Air and Radiation, Office of Air Quality Planning and Standards. Retrieved March, 26, 2009 at http://www .epa.gov/ttn/ecas/ria.html . EPA-HQ-OAR2009-0472-0238

24 U.S. EPA, (2011), Final Transport Rule. Final Rule signed on July 6, 2011. Available online at: http://www.epa.gov/airtransport/

25 

### U.S. Environmental Protection Agency. (2010). Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corpo rate Average Fuel Economy Standards: Regulatory Impact Analysis 

### , Assessment and Standards Division, Office of Tran sportation and

### 2017 Regulatory Impact Analysis

69 Air Quality, EPA-420-R-10-009, April 2010. Availab le on the internet: http://www.epa.gov/otaq/climate/regulations/420r100 09.pdf

26 

### U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollu tants from the Portland Cement Manufacturing Industry. Office of Air Quality Plan ning and Standards, Research Triangle Park, NC. Augues. Available on the Internet at < http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandce mentfinalria.pdf >. EPA-HQ-OAR-2009- 0472-0241

27 

### Kunzli, N., S. Medina, R. Kaiser, P. Quenel, F. Ho rak Jr, and M. Studnicka. 2001. Assessment of Deaths Attributable to Air Pollution: Should We Use Risk Estimates Based on Time Series or on Cohort Studies? American Journal of Epidemiology 153(11):1050-55.

28 

### Science Advisory Board. 2001. NATA – Evaluating the National-Scale Air Toxics Assessment for 1996 – an SAB Advisory. http://www. epa.gov/ttn/atw/sab/sabrev.html.

29 

### U.S. Environmental Protection Agency (U.S. EPA). 2 011. The Benefits and Costs of the Clean Air Act from 1990 to 2020 

### . Office of Air and Radiation, Washington, DC. Mar ch. Available on the Internet at <http://www.epa.gov/air/sect812/feb 11/fullreport.pdf>.

30 

### U.S. Environmental Protection Agency—Science Advis ory Board (U.S. EPA-SAB). 2008. Benefits of Reducing Benzene Emissions in Houston, 1990–2020 

### . EPA-COUNCIL-08-001. July. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/D4D7EC9 DAEDA8A548525748600728A83/$File/ EPA-COUNCIL-08-001-unsigned.pdf>.

31 

### National Research Council (NRC). 2008. Estimatin g Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. Nat ional Academies Press. Washington, DC.

32 

### GeoLytics Inc. (2002). Geolytics CensusCD® 2000 Short Form Blocks. CD-ROM Release 1.0. GeoLytics, Inc. East Brunswick, NJ. Available : http://www.geolytics.com/ [accessed 29 September 2004]

33 

### Woods & Poole Economics Inc. 2008. Population by Single Year of Age CD. CD-ROM. Woods & Poole Economics, Inc. Washington, D.C. EPA- HQ-OAR-2009-0472-0011

34 

### U.S. Environmental Protection Agency. (2006). Air quality criteria for ozone and related photochemical oxidants (second external review draf t). 

### Research Triangle Park, NC: National Center for Environmental Assessment; report no. EPA /600R-05/004aB-cB, 3v. Available: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid =137307[March 2006] EPA-HQ-OAR2009-0472-0099, EPA-HQ-OAR-2009-0472-0100, EPA-HQ-O AR-2009-0472-0101

35 

### U.S. Environmental Protection Agency, 2004. Air Quality Criteria for Particulate Matter Volume II of II. 

### National Center for Environmental Assessment, Offi ce of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC EPA/600/P99/002bF. EPA-HQ-OAR-2009-0472-0097

### Chapter 6

70 36 

### World Health Organization (WHO). (2003). Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide: Report on a WHO Working Group. 

### World Health Organization. Bonn, Germany. EUR/03/5042688.

37 

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### Copenhagen, Denmark: World Health Organization.

38 

### Bell, M.L., et al. (2004). Ozone and short-term m ortality in 95 U.S. urban communities, 1987- 2000. JAMA 

### , 2004. 292(19): p. 2372-8. EPA-HQ-OAR-2009-0472-16 62

39 

### Huang, Y.; Dominici, F.; Bell, M. L. (2005) Bayesi an hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortal ity. Environmetrics 

### . 16: 547-562. EPAHQ-OAR-2009-0472-0233

40 

### Schwartz, J. (2005) How sensitive is the associati on between ozone and daily deaths to control for temperature? Am. J. Respir. Crit. Care Med. 

### 171: 627-631. EPA-HQ-OAR-2009-0472-1678

41 

### Bell, M.L., F. Dominici, and J.M. Samet. (2005). A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology 

### . 16(4): p. 436-45. EPA-HQ-OAR-2009-0472-0222

42 

### Ito, K., S.F. De Leon, and M. Lippmann (2005). Ass ociations between ozone and daily mortality: analysis and meta-analysis. Epidemiology 

### . 16(4): p. 446-57. EPA-HQ-OAR-2009- 0472-0231

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### Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. (20 05). Ozone exposure and mortality: an empiric bayes metaregression analysis. Epidemiology 

### . 16(4): p. 458-68. EPA-HQ-OAR-2009- 0472-0236

44 

### Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Cal le, D. Krewski, K. Ito, and G.D. Thurston. (2002). “Lung Cancer, Cardiopulmonary Mortality, a nd Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical Association 

### 287:1132-1141. EPA-HQ-OAR2009-0472-0263

45 Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dock ery. (2006). Reduction in Fine Particulate Air Po llution and Mortality. American Journal of Respiratory and Critical Care M edicine . 173: 667-672. EPA-HQ-OAR-2009-0472- 1661

46 

### Industrial Economics, Incorporated (IEc). (2006). Expanded Expert Judgment Assessment of the Concentration-Response Relationship Between PM2 .5 Exposure and Mortality. Peer Review Draft. 

### Prepared for: Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. Augu st. EPA-HQ-OAR-2009-0472-0242

47 Woodruff, T.J., J. Grillo, and K.C. Schoendorf. ( 1997). The Relationship Between Selected Causes of Postneonatal Infant Mortality and Particulate Air P ollution in the United States. Environmental Health Perspectives. 105(6):608-612. EPA-HQ-OAR-2009-0472-0382

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71 48 Abbey, D.E., B.L. Hwang, R.J. Burchette, T. Vancur en, and P.K. Mills. (1995). Estimated Long-Term A mbient Concentrations of PM(10) and Development of Respira tory Symptoms in a Nonsmoking Population. Archives of Environmental Health. 50(2): 139-152. EPA-HQ-OAR-2009-0472-0432

49 Peters, A., D.W. Dockery, J.E. Muller, and M.A. Mi ttleman. (2001). Increased Particulate Air Pollut ion and the Triggering of Myocardial Infarction. Circulation. 103:2810-2815. EPA-HQ-OAR-2009-0472-0239

50 

### Schwartz J. (1995). Short term fluctuations in a ir pollution and hospital admissions of the elderly for respiratory disease. Thorax. 

### 50(5):531-538.

51 

### Schwartz J. (1994a). PM(10) Ozone, and Hospital Admissions For the Elderly in Minneapolis St Paul, Minnesota. Arch Environ Health. 

### 49(5):366-374. EPA-HQ-OAR-2009-0472-1673

52 

### Schwartz J. (1994b). Air Pollution and Hospital Admissions For the Elderly in Detroit, Michigan. Am J Respir Crit Care Med. 

### 150(3):648-655. EPA-HQ-OAR-2009-0472-1674

53 

### Moolgavkar SH, Luebeck EG, Anderson EL. (1997). A ir pollution and hospital admissions for respiratory causes in Minneapolis St. Paul and Birm ingham. Epidemiology 

### . 8(4):364-370. EPAHQ-OAR-2009-0472-1673

54 

### Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. (2001). Association between ozone and hospitalization for a cute respiratory diseases in children less than 2 years of age. Am J Epidemiol 

### . 153(5):444-452. EPA-HQ-OAR-2009-0472-0223

55 Moolgavkar, S.H. (2003). “Air Pollution and Dail y Deaths and Hospital Admissions in Los Angeles and Cook Counties.” In Revised Analyses of Time-Series Studies of Air Poll ution and Health . Special Report. Boston, MA: Health Effects Institute.

56 Ito, K. (2003). “Associations of Particulate Mat ter Components with Daily Mortality and Morbidity i n Detroit, Michigan.” In Revised Analyses of Time-Series Studies of Air Poll ution and Health . Special Report. Health Effects Institute, Boston, MA. EPA-HQ-OAR-2009-0472-1674

57 

### Moolgavkar, S.H. (2000). Air Pollution and Hospi tal Admissions for Diseases of the Circulatory System in Three U.S. Metropolitan Areas

. Journal of the Air and Waste Management Association 

### 50:1199-1206. EPA-HQ-OAR-2009-0472-1664

58 

### Sheppard, L. (2003). Ambient Air Pollution and N onelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994. In Revised Analyses of Time-Series Studies of Air Poll ution and Health 

### . Special Report. Boston, MA: Health Effects Ins titute. EPA-HQ-OAR-2009-0472-0318

59 

### Peel, J. L., P. E. Tolbert, M. Klein, et al. (2005 ). Ambient air pollution and respiratory emergency department visits. Epidemiology 

### . Vol. 16 (2): 164-74. EPA-HQ-OAR-2009-0472- 1663

60 

### Wilson, A. M., C. P. Wake, T. Kelly, et al. (2005) . Air pollution, weather, and respiratory emergency room visits in two northern New England c ities: an ecological time-series study. Environ Res 

### . Vol. 97 (3): 312-21. EPA-HQ-OAR-2009-0472-0246

61 Norris, G., S.N. YoungPong, J.Q. Koenig, T.V. Lars on, L. Sheppard, and J.W. Stout. (1999). An Assoc iation between Fine Particles and Asthma Emergency Departm ent Visits for Children in Seattle. Environmental Health Perspectives 107(6):489-493. EPA-HQ-OAR-2009-0472-0318

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72 62 Dockery, D.W., J. Cunningham, A.I. Damokosh, L.M. Neas, J.D. Spengler, P. Koutrakis, J.H. Ware, M. Ra izenne, and F.E. Speizer. (1996). Health Effects of Acid Aerosols On North American Children-Respiratory Sym ptoms. Environmental Health Perspectives 104(5):500-505. EPA-HQ-OAR-2009-0472-0225

63 

### Pope, C.A., III, D.W. Dockery, J.D. Spengler, and M.E. Raizenne. (1991). Respiratory Health and PM 10 

### Pollution: A Daily Time Series Analysis. American Review of Respiratory Diseases 

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64 

### Schwartz, J., and L.M. Neas. (2000). Fine Partic les are More Strongly Associated than Coarse Particles with Acute Respiratory Health Effects in Schoolchildren. Epidemiology 

### 11:6-10.

65 Ostro, B., M. Lipsett, J. Mann, H. Braxton-Owens, and M. White. (2001). Air Pollution and Exacerbat ion of Asthma in African-American Children in Los Angeles. Epidemiology 12(2):200-208.

66 Vedal, S., J. Petkau, R. White, and J. Blair. (19 98). Acute Effects of Ambient Inhalable Particles in Asthmatic and Nonasthmatic Children. American Journal of Respiratory and Critical Care M edicine 157(4):1034-1043. EPAHQ-OAR-2009-0472-1671

67 Ostro, B.D. (1987). Air Pollution and Morbidity Revisited: A Specification Test. Journal of Environmental Economics Management 14:87-98. EPA-HQ-OAR-2009-0472-1670

68 

### Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. (2001). The effects of ambient air pollution on school abse nteeism due to respiratory illnesses. Epidemiology 

### 12(1):43-54. EPA-HQ-OAR-2009-0472-1675

69 

### Chen L, Jennison BL, Yang W, Omaye ST. (2000). E lementary school absenteeism and air pollution. Inhal Toxicol 

### 12(11):997-1016. EPA-HQ-OAR-2009-0472-0224

70 Ostro, B.D. and S. Rothschild. (1989). Air Pollu tion and Acute Respiratory Morbidity: An Observati onal Study of Multiple Pollutants. Environmental Research 50:238-247. EPA-HQ-OAR-2009-0472-0364

71 

### U.S. Science Advisory Board. (2004). Advisory Plans for Health Effects Analysis in the Analytical Plan for EPA’s Second Prospective Analys is –Benefits and Costs of the Clean Air Act, 1990—2020. 

### EPA-SAB-COUNCIL-ADV-04-004. EPA-HQ-OAR-2009-0472-4 664

72 

### National Research Council (NRC). (2002). Estimating the Public Health Benefits of Proposed Air Pollution Regulations. 

### Washington, DC: The National Academies Press.

73 Abt Associates, Inc. October 2005. Methodology for County-level Mortality Rate Project ions. Memorandum to Bryan Hubbell and Zachary Pekar, U.S. EPA.

74 

### Centers for Disease Control: Wide-ranging OnLine D ata for Epidemiologic Research (CDC Wonder) (data from years 1996-1998), Centers for Di sease Control and Prevention (CDC), U.S. Department of Health and Human Services, Available on the Internet at <http://wonder.cdc.gov>.

75 

### Agency for Healthcare Research and Quality (AHRQ). 2000. HCUPnet, Healthcare Cost and Utilization Project.

76 

### American Lung Association. 1999. Chronic Bronchiti s. Available on the Internet at <http://www.lungusa.org/diseases/lungchronic.html>.

### 2017 Regulatory Impact Analysis

73 77 

### American Lung Association. 2002. Trends in Asthma Morbidity and Mortality. American Lung Association, Best Practices and Program Servic es, Epidemiology and Statistics Unit. Available on the Internet at <http://www.lungusa.or g/data/asthma/ASTHMAdt.pdf>.

78 

### Adams PF, Hendershot GE, Marano MA. 1999. Current Estimates from the National Health Interview Survey, 1996. Vital Health Stat 10(200):1 -212.

79 

### U.S. Bureau of Census. 2000. Population Projection s of the United States by Age, Sex, Race, Hispanic Origin and Nativity: 1999 to 2100. Populat ion Projections Program, Population Division, U.S. Census Bureau, Washington, DC. Avail able on the Internet at <http://www.census.gov/population/projections/natio n/summary/np-t.txt>.

80 

### National Center for Education Statistics (NCHS). 1 996. The Condition of Education 1996, Indicator 42: Student Absenteeism and Tardiness. U. S. Department of Education. Washington, DC.

81 

### Centers for Disease Control: Wide-ranging OnLine D ata for Epidemiologic Research (CDC Wonder) (data from years 1996-1998), Centers for Di sease Control and Prevention (CDC), U.S. Department of Health and Human Services, Available on the Internet at <http://wonder.cdc.gov>.

82 

### Freeman(III), AM. 1993. The Measurement of Environ mental and Resource Values: Theory and Methods. Washington, DC: Resources for the Futu re.

83 

### Harrington, W., and P.R. Portney. 1987. Valuing th e Benefits of Health and Safety Regulation. Journal of Urban Economics 22:101-112.

84 

### Berger, M.C., G.C. Blomquist, D. Kenkel, and G.S. Tolley. 1987. Valuing Changes in Health Risks: A Comparison of Alternative Measures. The So uthern Economic Journal 53:977-984.

85 

### U.S. Environmental Protection Agency (U.S. EPA). 2006. Regulatory Impact Analysis, 2006 National Ambient Air Quality Standards for Particul ate Matter, Chapter 5. Office of Air Quality Planning and Standards, Research Triangle Park, NC. October. Available on the Internet at <http://www.epa.gov/ttn/ecas/regdata/RIAs/Chapter%2 05--Benefits.pdf>.

86 

### U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-l evel Ozone, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle P ark, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneri achapter6.pdf>.

87 

### U.S. EPA, (2001), “Guidance for Demonstrating Atta inment of Air Quality Goals for PM2.5 and Regional Haze”, http://www.epa.gov/ttn/scram/gu idance_sip.htm, Modeling Guidance, DRAFT-PM

88 

### National Research Council (NRC). 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The Nat ional Academies Press.

89 

### National Research Council (NRC). 2008. Estimatin g Mortality Risk Reduction and Economic Benefits from Controlling Ozone Air Pollution. Nat ional Academies Press. Washington, DC.

### Chapter 6

74 90 

### Roman, Henry A., Katherine D. Walker, Tyra L. Wals h, Lisa Conner, Harvey M. Richmond, Bryan J. Hubbell, and Patrick L. Kinney. 2008. Exp ert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matte r in the U.S. Environ. Sci. Technol., 42(7):2268-2274.

91 

### The issue is discussed in more detail in the PM NA AQS RIA from 2006. See U.S. Environmental Protection Agency. 2006. Final Regu latory Impact Analysis (RIA) for the Proposed National Ambient Air Quality Standards for Particulate Matter. Prepared by: Office of Air and Radiation. October 2006. Available at htt p://www.epa.gov/ttn/ecas/ria.html.

92 

### U.S. Environmental Protection Agency (U.S. EPA), 2 010. Regulatory Impact Analysis, Final Rulemaking to Establish Light-Duty Vehicle Greenhou se Gas Emission Standards and Corporate Average Fuel Economy Standards. Office of Transpor tation and Air Quality. April. Available at http://www.epa.gov/otaq/climate/regulations/420r 10009.pdf . EPA-420-R-10-009

93 

### U.S. Environmental Protection Agency (U.S. EPA). 2008. Regulatory Impact Analysis, 2008 National Ambient Air Quality Standards for Ground-l evel Ozone, Chapter 6. Office of Air Quality Planning and Standards, Research Triangle P ark, NC. March. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/6-ozoneri achapter6.pdf>. EPA-HQ-OAR-2009- 0472-0238

94 

### U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis: National Emission Standards for Hazardous Air Pollu tants from the Portland Cement Manufacturing Industry. Office of Air Quality Plan ning and Standards, Research Triangle Park, NC. Augues. Available on the Internet at < http://www.epa.gov/ttn/ecas/regdata/RIAs/portlandce mentfinalria.pdf >. EPA-HQ-OAR-2009- 0472-0241

95 

### U.S. Environmental Protection Agency (U.S. EPA). 2010. Final NO2 NAAQS Regulatory Impact Analysis (RIA). Office of Air Quality Plann ing and Standards, Research Triangle Park, NC. April. Available on the Internet at http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RI Afulldocument.pdf . Accessed March 15, 2010. EPA-HQ-OAR-2009-0472-0237

96 

### U.S. Environmental Protection Agency (U.S. EPA). 2008. Technical Support Document: Calculating Benefit Per-Ton estimates, Ozone NAAQS Docket #EPA-HQ-OAR-2007-0225- 0284. Office of Air Quality Planning and Standards , Research Triangle Park, NC. March. Available on the Internet at <http://www.regulation s.gov>. EPA-HQ-OAR-2009-0472-0228

97 

### Fann, N. et al. (2009). The influence of location , source, and emission type in estimates of the human health benefits of reducing a ton of air poll ution. Air Qual Atmos Health. Published online: 09 June, 2009. EPA-HQ-OAR-2009-0472-0229

98 

### Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Cal le, D. Krewski, K. Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution.” Journal of the American Medical As sociation 287:1132-1141. EPA-HQ-OAR2009-0472-0263

99 

### Laden, F., J. Schwartz, F.E. Speizer, and D.W. Doc kery. 2006. “Reduction in Fine Particulate Air Pollution and Mortality.” American Journal of Respiratory and Critical Care Medicine

### 2017 Regulatory Impact Analysis

75 173:667-672. Estimating the Public Health Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press. EPA-H Q-OAR-2009-0472-1661

100 

### Mrozek, J.R., and L.O. Taylor. 2002. “What Deter mines the Value of Life? A MetaAnalysis.” Journal of Policy Analysis and Manageme nt 21(2):253-270. EPA-HQ-OAR-2009- 0472-1677

101 

### Viscusi, V.K., and J.E. Aldy. 2003. “The Value of a Statistical Life: A Critical Review of Market Estimates throughout the World.” Journal ofRisk and Uncertainty 27(1):5-76. EPA-HQOAR-2009-0472-0245

102 

### Kochi, I., B. Hubbell, and R. Kramer. 2006. An E mpirical Bayes Approach to Combining Estimates of the Value of Statistical Life for Envi ronmental Policy Analysis. Environmental and Resource Economics. 34: 385-406. EPA-HQ-OAR-2009-0 472-0235

103 

### U.S. Environmental Protection Agency (U.S. EPA). 2000. Guidelines for Preparing Economic Analyses. EPA 240-R-00-003. National Cen ter for Environmental Economics, Office of Policy Economics and Innovation. Washington, DC . September. Available on the Internet at <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Gu idelines.html/$file/cover.pdf>. EPA-HQOAR-2009-0472-0226

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