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- 1 

## Chapter 7 Impacts of the Proposed Rule on Emissions and Air

Quality

This chapter presents the overall emissions and air quality impacts of the proposed Tier 3 standards

. Section 7.1 describes national average impacts on criteria and toxic emissions resulting from the Tier 3 proposal. Section 7.2 describes the air quality effects of the proposed emission reductions. Because the air quality analysis requires emission inventories with greater geographical resolution than the national average inventories, the emissio n inventories described in the two sections were developed separately, as described in each portion of this chapter. Section 7.3 discusses the impact of the proposed program on greenhouse gas emissions.

7.1 Criteria and Toxic Pollutant Emission Impacts

7.1.1 Ov erview

This section presents the projected national emission impacts of the Tier 3 proposal on criteria and toxic air pollutants for selected calendar years, and the methodology used to estimate these reductions. The proposed fuel and vehicle standards w ill directly reduce emissions of nitrogen oxides ( NO X )

, including nitrogen dioxide (NO 2 ) , volatile organic compounds ( VOC)

, carbon monoxide (CO), particulate matter (PM 2.5 ) , air toxics, and sulfur dioxide (SO 2 ). The implementation of lower sulfur gasoline will reduce criteria and air toxic emissions from the existing gasolin e fleet, and cause some reductions in SO 2 from the nonroad gasoline sector. The largest reductions come immediately following the implementation of the fuel standard, as a significant share of overall emissions are produced by Tier 2 and older vehicles. To reflect these early reductions, we are presenting emission reductions in calendar year 2017, near the beginning of the proposed fuel program.

The proposed vehicle standards will incu r reductions as the cleaner cars and trucks begin
to enter the fleet in model year 2017 . The magnitude of reduction will grow as the contribution of these vehicles to fleet emissions becomes more prominent

– to reflect this, we are also
pr esenting emissio n reductions through calendar year 2030, when 2017 and later model year cars and trucks comprise 80 percent of the light

- duty fleet, and travel 90 percent of vehicle miles travelled (VMT) . 2030 is a standard out

- year for evaluation ; it is used for air qual ity modeling in this proposal as well as recent EPA rules . H owever, the full impact of the vehicle program would be realized after 2030

. For this reason, we are also presenting emissions reductions in calendar year 2050, when the fleet will have fully tu rned over to the proposed vehicle standards.

Emission impacts presented in this section are estimated on an annual basis, for all 50 U.S. states plus the District of Columbia, Puerto Rico and the U.S. Virgin Islands. The reductions from onroad sources w ere estimated using an updated version of EPA’s MOVES model, as described in detail in Section 7.1.3 ; and the NONROAD model for offroad sources. Reductions were estimated compared to a reference case that assumed continuation of t he Tier 2 vehicle program indefinitely and an average gasoline sulfur level of 30 ppm (10 ppm in California).*** E.O. 12866 Review

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- 2 The emission inventory methodology applied to generate these national estimates does differ from the methodology used to generate the finely res olved emission inventories needed for the air quality modeling

, leading to some differences in absolute estimates of tons reduced between the two analyses. These differences are discussed in Section 7

. 2.

7.1.2 Sc enarios Modeled

We analyzed emission impacts o f the Tier 3 vehicle emissions and fuel standards by comparing projected emissions for future years without the Tier 3 rule (reference scenario) to projected emissions for future years with the Tier 3 standards in place (control scenario). Table 7

- 1 below presents an overview of the reference and control scenarios for calendar years 2017 and 2030. Both scenarios reflect the renewable fuel volumes mandated in the Energy Policy Act of 2005 (EPAct) and Energy Independence and Security A ct of 2007 (EISA). We thus refer to this renewable fuel level as “post

- EPAct/EISA”. A key update in the analysis of renewable fuel volumes from that performed for the RFS2 final rule is the inclusion of gasoline containing 15 percent ethanol by volume ( E 15 ) in the fuel pool, per the approval in 2011 of a waiver which allows E15 to be used in model year 2001 and later cars and trucks. We estimated the market shares for E15 and gasoline containing 10 percent ethanol by volume ( E10 ) in the market in 2017

an d 2030 , as discussed in Section 7.1.3.2.1

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- 3 Table 7

- 1 Overview of Reference and Control Scenarios

Reference Scenario Control Scenario

2017 Renewable Fuels: RFS2 program a 21.6 B gallons renewable fuels (24 B e thanol

- equivalent gallons): 17.8 B gallons ethanol: E10 b

, E15 c

Fuel Sulfur Level: 30 ppm (10 ppm California)

Fleet: 100 percent Tier 2 and older vehicles Re n ewable Fuels: RFS2 program a 21.6 B gallons renewable fuels (24 B e thanol

- equivalent gallons): 17.8 B gallons ethanol: E10 b

, E15 c

Fuel Sulfur Level: 10 ppm

Fleet: d
93 percent Tier 2 and older vehicles 7 percent Tier 3 vehicles

2030 Renewable Fuels: RFS2 program a 30.5 B gallons renewable fuel s (36 B e thanol

- equivalent gallons): 22.2 B gallons ethanol: E15 c

Fuel Sulfur Level: 30 ppm (10 ppm California)

Fleet: 100 percent Tier 2 and older vehicles Renewable Fuels: RFS2 program a 30.5 B gallons renewable fuels (36 B e thanol

- equival ent gallons): 22.2 B gallons ethanol: E15 c

Fuel Sulfur Level: 10 ppm

Fleet: d
20 percent Tier 2 and older vehicles 80 percent Tier 3 vehicles

a RFS2 primary mid

- ethanol scenario , 7 5 FR 14670 (March 26, 2010)
b Gasoline containing 10 percent ethano l by volume

c Gasoline containing 15 percent ethanol by volume
d Fraction of the vehicle population
Our reference scenarios assumed an average fuel sulfur level of 30 ppm in accordance with the Tier 2 gasoline sulfur standards. Under the proposed Tier 3 program, federal gasoline would contain no more than 10 ppm sulfur on an annual avera ge basis by January 1, 2017 (Section V of the preamble), and we therefore assumed a nationwide fuel sulfur level of 10 ppm for both future year control cases. A more deta iled description of our fuel inputs and assumptions for this analysis can be found in Section 7.1.3.2

.
We assumed a continuation of the existing Tier 2 standards for model years 2017 and later in modeling emissions for our referenc e scenario. Our Tier 3 control scenario model ed the suite of exhaust and evaporative emission standards proposed for light

- duty vehicles (LDVs), light duty trucks (LDTs : 1

- 4), medium passenger vehicles (MDPVs) and large pick

- ups and vans (Class 2b and 3 t rucks) described in Section IV of the preamble, including:

 Fleet average Federal Test Procedure ( FTP ) NMOG+NO X standards of 30 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2025*** E.O. 12866 Review

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- 4  Fleet average Supplemental Federal Test Procedure ( SFTP ) NMOG+ NO X standards of 50 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2025

 Per

- vehicle FTP PM standard of 3 mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2022

 Per

- vehicle US06

- only PM standard of 10 mg/mi for LDVs through LDT2s and 25 mg/mi for LDT3s and LDT4s, phasing in for MYs 2017 to 2022

 New standards for Class 2b and 3 trucks phasing in by MY 2022 including NMOG+NO X declining fleet average, more stringent PM standards, and a regulatory useful life of 150,000 miles

 More stringe nt evaporative emission standards for diurnal plus hot soak emissions, a new canister bleed test and emissions standard, a new requirement to measure emissions using 9 RVP E15 certification test fuel, and

new requirements addressing evaporative leaks on in

- use vehicles

.

T he Tier 3 standards are expected to reduce onroad criteria and toxic emissions

, and to a much smaller extent, nonroad SO 2 emissions

, but will not affect upstream , refueling or portable fuel container criteria or toxic emissions. The method ology for estimating emission impacts and the results for onroad and nonroad emissions are described in Section 7.1.3 and Section 7.1.4, respectively.

Implementation of the proposed Tier 3 standards is aligned with the model year 2017

- 2025 Light

- Duty GHG standards 1 to achieve significant criteria pollutant and GHG emissions reductions while providing regulatory certainty and compliance efficiency to the auto and oil industries. The LD GHG standards were still in a preliminary state of development (pre

pr oposal) at the time we developed our assumptions for the Tier 3 emissions, air quality, and cost analyses presented here , so we were not able to reflect them in these analyses. However, our analyses for the final Tier 3 rule will include the final LD GHG standards in both the reference and control scenarios, and will thus account for their impacts on the future vehicle fleet and future fuel consumption. We do not anticipate that this change will substantially modify our results or conclusions for two majo r reasons. First, i mproved fuel efficiency does not have a direct impact on criteria pollutant emissions. Second, n on

- GHG emissions standards for light

duty vehicles are expressed as grams per mile driven; thus, non

- GHG emissions are a function of the em ission control systems rather than the amount of fuel burned. As a result, changes to vehicle efficiency as a result of the LD GHG standards will not affect the emissions benefits of the Tier 3 rule. The analysis described here accounts for the following national onroad rules:

 Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, Feb ruary 10, 2000)

 Heavy

- Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan uary 18, 2 001)*** E.O. 12866 Review

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- 5  Mobile Source Air Toxics Rule ( 72 FR 8428, February 26, 2007 )

 Regulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program (75 FR 14670, March 26, 2010)

 L ight

- D uty Vehicle G reenhouse G as Emission Standards and C orporate A verage F uel E conomy S tandards for 2012

- 2016 (75 FR 25324, May 7, 2010)

The analysis also accounts for many other national rules and standards

. In addition, the modeling accounts for state and local rules including local fuel standards, Inspection/Maintenance pr ograms, Stage II refueling controls, the National Low Emission Vehicle Program (NLEV), and the Ozone Transport Commission (OTC) LEV Program. See the Tier 3 emissions modeling TSD for more detail.

7.1.3 Onroad Emissions

7.1.3.1 Methodology Overview

EPA’s official model for use in estimating mobile source emissions is known as the Motor Vehicle Emission Simulator (MOVES), with the most recent version approved for use in State Implementation Plan (SIP) and transportation conformity analyses being MOVES2010 b

. 2 A version o f MOVES 2010a

, updated specifically for this analysis, was used to estimate emissions of criteria and air toxic emissions from on

- road gasoline and diesel vehicles for the entire U.S. for the reference and control scenarios described in Section 7.1.2 above , for calendar years 2017 through 2030, and 2050. A

The MOVES model updates affecting reference and control case emissions were extensive, and are documented in a separate memorandum to the Tier 3 docket. 3 Updates made to MOVE S 2010a for this analysis primarily incorporate d major new research in three areas. The first involves fuel effects on exhaust emissions from Tier

- 2 vehicles. The second involves improvements in estimation of evaporative emissions from all vehicles, includi ng Tier

- 2 vehicles. The third involves accounting for the effects of fuel sulfur level on exhaust emissions.

T he effects of changes in fuel properties on exhaust emissions of Tier 2 vehicles, which comprise the majority of the fleet by 2017 , were assessed through the results of the EPAct Phase

3 Program . Specific fuel properties addressed include ethanol level, aromatics, distillation properties, and volatility (Reid Vapor Pressure, or RVP)

. Methods used to account for the effects of these properties in inventory modeling are described in a separate memorandum to the docket. 4 Improvements in estimating and projecting evaporative emissions are described in this document (see 7.1.3.3.7 ). Finally, b ecause the updates to fuel sulf ur effects are critical for

A The MOVES updates are reflected in a version of the MOVES model code ( April 14, 2011 Version a ) and a concurrently updated version of the MOVES default database ( May 12, 2011 ) , available in t he Tier 3 docket As these updates are still draft, this code and/or database are not approved for official use in SIP and conformity analyses.*** E.O. 12866 Review

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- 6 estimating reductions from the Tier 3 program, they are also presented in detail in Section 7.1.3.4.

O ther than sulfur, t hese changes had more bearing on updating reference case emissions than on the projected reductions from the Tier 3 standards. One key exception to this , however, were updates made to PM 2.5 fuel effects t hat result in an increase in emissions with increases in aromatics and T90 (reduction in E300)

. As discussed in Section 7.1. 3.2.2 , this update had a n impac t on control case emissions, as changes in aromatics and E300 were projected by refinery modeling as sulfur levels were reduced from 30 ppm to 10 ppm. However, other recent studies suggest that the PM emission increases observed are likely linked to the i mpacts of heavier hydrocarbons, and in particular heavy aromatics. 5

, 6
, 7 More study is needed to understand these relationships, and whether the changes in refinery operation to make up for lost octane, should it occur, would be likely to impact heavy arom atics or not.

I n addition to fuel effects, w e also improved emission estimates in other areas. The sulfate, sulfur dioxide, organic carbon and elemental carbon emission rates for 2007

- and

- later heavy

- duty diesel vehicles were updated to include informat ion from a recent study that examined the composition of particulate emissions from advanced diesel engines. 8 HC, CO and NO X start and running emission rates for light heavy

- duty gasoline vehicles were updated to fix an error in these rates for 2007

- and

- la ter emissions, and we repaired errors in the MOVES2010a emission rates for NH 3 , NO and NO 2
. B
, 9

The MOVES version used for this analysis also includes an added capability to model many hazardous air pollutants. And other changes were made to the MOVES2010 a model to facilitate the large number of parallel runs that needed to be done to complete the Tier 3 air quality modeling inventories. These changes are also detailed in the docket memo addressing MOVES updates.

In addition to the model updates needed to incorporate new research

, a set of custom inputs were developed to allow MOVES to model the reference and control scenarios

. Some of
t hese inputs were required to reflect regional variations in fuels for both the reference and control scenarios, as d is cussed in detail in Section 7

. 1.3.2

. Other inputs were required to model the vehicle program for exhaust and evaporative emissions , discussed in Section 7

. 1.3.3.

The national emission inventories presented in this section were developed with a simpler

a nd quicker method than we used for air quality modeling, because the abbreviated approach enabled analysis for calendar years in addition to 2017 and 2030 , and makes the analysis easier for stakeholders and other comment e rs to replicate. National emission inventories were developed using the pre

- aggregation feature of MOVES

. For criteria pollutants, the default pre

aggregation level of ‘hour’ was selected, which condenses the county

- level temperatures into a

B The changes to the NO and NO 2 rates did not impact the total NO X emissions, but facilitated the output of se parate results for nitric oxide and nitrogen dioxide.*** E.O. 12866 Review

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- 7 single national average temperature by hour of the day

. C While the model and many of the inputs are identical for the emission inventory modeling performed for the air quality analysis, th e pre

- aggregation approach is coarser than the approach described in Section 7.2 used to develop the gridded/hourl y emission inventories needed for air quality modeling

. In addition to this difference in temperature resolution (national average vs. gridded hourly temperature), the national emission inventory analysis also used information contained in the MOVES2010a default database for vehicle miles travelled, fleet age distributions, activity inputs (e.g., speeds), temperatures, emission standards, and inspection/maintenance programs. As discussed in Section 7.2, this contrasts with the air quality modeling invento ry methodology, which used data supplied by state and regional modelers for many of these inputs, employed hourly meteorological data, and account s for Section 177 states adoption of California LEV standards in the modeling baseline.

To assure that adeq uate temperature resolution was incorporated into the national emission inventory processes, MOVES was run separately for January and July, and annual emissions were extrapolated (for all pollutants except PM) by scaling up each month by a factor of 5.88. For PM, to offset the disproportionate effect of the colder temperature January results, a scaling factor of 7.5 was applied to July and 4.3 to January; these factors were determined based on analysis of annual PM emissions during modeling for the RFS2 ru le. 10 The updated MOVES version, and all inputs and outputs that produced the results presented in Section 7.1.5 of this Chapter are contained in the Tier 3 rulemaking docket.

7.1.3.2 Fuel Inputs

Estimating national emission inventor ies required translation of the reference and control fuel scenarios presented in Table 7

- 1 into a discrete set of fuels (defined by RVP, sulfur content, ethanol level, aromatics content , olefin content , T50 and T90)

, and the market share of these fuels

, b y month and county. These data were converted into “fuel supply” database tables used by MOVES to estimate emission inventories. Even for the national emission inventories calculated at a pre

- aggregated level, these county

- level fuel supply tables are re tained to develop composite emissions that reflect the market share of the entire set of fuels that define the U.S. fuel pool. The crux of estimating emission impacts for the Tier 3 fuel program was the develop ment of fuel supply database tables that refl ect ed the difference between the reference and control scenarios , discussed in the following sections.

7.1.3.2.1 Reference Scenario

The reference scenario was developed to reflect updated assumptions regarding the ethanol blends used to meet the EISA

- mandated vo lumes. For simplicity, we assumed the same biofuel volumes analyzed in the RFS2 final rule, referred to in that rule as the primary “mid

ethanol” case. 11 The RFS2 analysis also considered a “low

- ethanol” and a “high

- ethanol”

C For air toxics, pre

- aggregation level of ‘month’ was used instead to improve the model run time. Monthly pre

aggregation averages the temperatures of all selected days and hours into a national aver age hourly temperature for the month.*** E.O. 12866 Review

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- 8 scenario; the difference being an increase in cellulosic ethanol and a decrease in cellulosic diesel going from the low

- ethanol to the high

- ethanol scenario. We believe that the RFS2 renewable fuel volumes still bracket the realm of realistic potential fuel scenarios assuming the EISA mandate is met. However, given the practical limitations of conducting air quality modeling for all three scenarios, we focused our Tier 3 modeling on the primary mid

- ethanol case.

We relied on biofuel volumes taken directly from the RFS2 mid

- ethanol ca se, which made projections for every year between 2010 and 2022. We assumed that renewable fuel production/consumption would remain constant after EISA reaches full implementation in 2022. Accordingly, for 2030, we relied on 2022 biofuel volumes from the RFS2 primary mid

- ethanol

case.

In the RFS2 final rule, we assumed that all ethanol would be consumed as either E10 or E85. Since then , EPA issued a waiver permitting 15 volume percent ethanol blends (E15) to be used in 2001 and newer model year light

- du ty vehicles. D While E15 has only limited commercial availab ility cur r ently

, EPA believe s it may compete favorably with E85 in the marketplace and could become a major gasoline blend in the future to meet the mandated RFS2 volumes

. Accordingly, the refere nce scenario assumes an increasing utilization of E15 over time as infrastructure ramps up and owners become more aware of their vehicle’s ethanol usage potential.

To estimate future E15 fuel consumption, we started by estimating the fraction of vehicl es capable of legally refueling on E15 (or higher ethanol blends) in the future. To do this, we relied on many vehicle assumptions made under the RFS2 final rule. E For flexible fuel vehicles (FFVs), we started with EPA certification data and assumed that the “Detroit 3” (GM, Ford and Chrysler) would follow through with their vol untary commitment to produce 50 percent FFVs by 2012

. F We also assumed that the Detroit 3 would contin ue to comprise approximately 45 percent of total light

- duty vehicle sales. In addition, we relied on total light

duty vehicle sales projections from EIA’s Annual Energy Outlook (AEO). Factoring in EPA’s projected vehicle scrappage rates, vehicle miles traveled (VMT) by model year (MY) , and fue l economy assumptions based on EPA’s 2012

- 2016 Light

- Duty Greenhouse Gas Rule

, G we estimate that by 2017, 13.6 percent of gasoline demand would come from FFVs, 68.7 percent would come from 2001 and later model year non

- FFVs, and 17.7 percent would come from legacy vehicles, nonroad, motorcyc les and other small engines not approved for E15. By 2030, the contributions would be 22.0, 66.8, and 11.2 percent , respectively. A graph of the projected gasoline fuel fractions over time is provided in Figure 7

- 1 Figure 7

- 1

.

D On Nov. 4 2010, EPA issued a partial waiver for MY2007 and newer light

- duty vehicles (75 FR 68094). On January 26, 2011, EPA extended the waiver to MY 2001

- 2006 light

- duty vehicles (76 FR 4662).
E Refer to Sectio n 1.7 of the RFS2 RIA for more on our FFV/vehicle assumptions.
F We assumed that non

- domestic automakers would produce around 2 percent FFVs in current and future years.
G 75 FR 25324 (May 7, 2010). Formatted: Font: Not Bold*** E.O. 12866 Review

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- 9 Figure 7

- 1 Gasoline Fuel Fraction by Vehicle Type

The next step was to estimate how often E15

- capable vehicles would fill up on E15. E15 utilization is a function of how acc essible E15 is to consumers (i.e., number of stations offering it) and how often E15

- capable vehicles choose to fill up on it (based on retail pricing and other factors). We assumed that E15 utilization for 2001 and later model year vehicles w ould ramp up to 50 percent by 2017, increasing to almost 100 percent utilization by 2030. We assumed that new nonroad equipment (and other small engines not covered by the waiver) would be designed for and/or certified on E15 in the future. As such, we assumed that E15 utilization in nonroad equipment w ould ramp up from zero percent in 2017 to almost 100 percent by 2030. This resulted in a very small amount of E85 needing to be used in 2017 and 2030 to reach the RFS2 primary mid

- ethanol volumes. FFV owners were assu med to utilize E15 at the same rate as 2001 and later model year conventional vehicles when not refueling on E85. When not refueling on E15 (or E85), all E15

- capable vehicles were assumed to refuel on E10. As a simplifying assumption, we assumed E0 usage was negligible. Figure 7

- 2 Figure 7

- 2 shows the E15 and E85 utilization assumptions and Figure 7

- 3 shows the r esulting fuel volumes by year . Total fuel volumes were computed based on motor gasoline ener gy projections provided in the Annual Energy Outlook ( AEO ) 2011 early release. Formatted: Font: Not Bold*** E.O. 12866 Review

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- 10 Figure 7

- 2 Post

- EPAct/EISA Fuel Utilization Assumptions/Results*** E.O. 12866 Review

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- 11 Figure 7

- 3 Assumed Post

- EPAct/EISA Gasoline Fuel Volumes by Calendar Year

As shown above, our post

- EPAct/EISA ethanol blend assumptions (i.e., ramp up of E15) are capable of meeting the RFS2 primary mid

- ethanol scenario with little to no E8 5 needed

– especially in our 2017 and 2030 modeling years. Therefore, for emissions and air quality modeling

, we made the simplifying assumption that E85 use would be negligible

. While E85 used in FFVs could be part of the future biofuel picture, we beli eve that given EPA’s waiver decision and the ability for E15 to compete in today’s gasoline marketplace, E15 is a viable ethanol blend for meeting the RFS2 volumes

. These projected fuel scenarios do not reflect the impact of the LD GHG standard s , as the rule was still in a preliminary state at the time we needed to finalize our assumptions for these Tier 3 analyses. T he LD GHG standards w ill reduce gasoline demand in 2017 and beyond which has two impacts on our analysis. First, the reduced fuel consumpt ion associated with the LD GHG standards will likely result in somewhat lower costs for sulfur control, because less gasoline will need to be desulfurized. Second, i f ethanol volumes are constant but gasoline demand is reduced due to the LD GHG standards, perhaps as much as five percent of the fuel pool c ould b e E85 or other higher

- level ethanol blends by 2030. However, we do not expect this to significantly impact criteria pollutant reference or control case emissions because recent data

on NMOG and NO X emissions do not show significant differences when run on E85

, 12

, 13 and the*** E.O. 12866 Review

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- 12 percentage of the fleet impacted is small. There could be a small impact on sulfur related costs and emission reductions due to an effect of E85 on overall fuel pool sulfur concentr ations

, depending on what could be assumed for future E85 (or other higher level ethanol blend) sulfur levels

. An increase in E85 volumes could also lead to changes in toxic emissions, with increases in acetaldehyde emissions from ethanol combustion, but decreases in most other toxics due to dilution. However, EPA air quality modeling suggests that changes in ambient levels of acetaldehyde are likely to be much smaller than changes in direct emissions. 14

, 15 This is because emissions of acetaldehyde precurs ors, particularly alkenes, are lower in E85 emissions. For the Tier 3 final rule's analysis, we will be including the impact of the LD GHG standards, and we will revisit our assumed future fuel scenarios accordingly, including higher

- level ethanol blends as appropriate

. For this analysis we assumed that E15 would first appear in significant volumes in reformulated gasoline ( RFG ) areas

. All RFG is subject to a de facto 7

- psi RVP standard. And since neither E10 nor E15 can take advantage of the 1

- psi waive r in RFG areas, the two fuels are essentially the same with respect to vapor pressur e limits

. In conventional gasoline (CG) areas, E15 is held to a 9

- psi standard whereas E10 is allowed to be 10

- psi in

- use (through the use of a 1

- psi waiver). As a result , i n order for E15 to enter into CG areas, refiners would need to remove butanes/pentanes to make an 8

- psi blendstock to account for the 1

- psi increase associated with ethanol blending. Conversely, refiners could keep their CG blendstocks around 9

- psi to blend up 10

- psi E10 which is eligible for the 1

- psi waiver and thus is considered 9

- psi for compliance
purposes

. Due to the additional actions needed to market E15 during the summer in CG areas, it makes sense that more E15 would be used in RFG areas, at least in the near term. Accordingly, we assumed that in 2017, 75 percent of RFG would be E15 and the balance of E15 would be used in CG (resulting in 25 percent E15 utilization and 75 percent E10 utilization in those areas). By 2030, since we modeled a m uch larger volume of ethanol, E 15 was assumed to be used virtually everywhere.

Converting these assumptions into post

- EPAct/EISA fuel properties on a county

- by

county basis required an assessment o f fuel properties prior to the EPAct/EISA requirements for 2017 and 2030 , termed “pre

- EPAct/EISA”. We developed these starting with 2005 fuel volumes as the baseline for our analysis of renewable fuel impacts because these volumes were in use immediately before EPAct was enacted, creati ng volume requirements for 2006, and because they prov ide consistency with the base case used for air quality modeling described in Section 7.2. In 2005, gasoline contained over 4 billion gallons of ethanol

. In 2005, gasoline also contained approximately 2 billion gallons of MTBE

.
For translation into the fuel supply inputs needed by MOVES, the MOVES2010a default fuel supply for 2005 (based on the National Emission Inventory 16 ) was updated to serve as a better basis for the other modeling cases. These updates are detailed below.

 In 2005, California fuel was required to have an ethanol level of 5.7 percent for all fuel. The MOVES2010a default fuel supply listed California counties with a 57 percent market share of E10 and a 43 percent market share of E0 . The fuel supplies for these c ounties

were refined to indicate E5.7 at 100 percent market share. Also, Al a meda C ounty , California

, had anomalous fuel property data. The fuel properties for this county were replaced with an average of the fuel properties of the neighboring counties

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- 13  Cou nties in the southeast Michigan fuel program area erroneously had been assigned higher sulfur and benzene levels than the rest of the state of Michigan. Sulfur and benzene for these counties were corrected to an average of all other Michigan counties. Some Michigan counties also had very low levels of MTBE (<0.3 percent ), despite an MTBE ban in Michigan in 2005. These fuels have been corrected to a 0 percent MTBE conventional fuel to properly reflect the absence of MTBE in Michigan.

 Many counties in the 200 5 default fuel supply had fuel RVP levels that were drastically inconsistent with the mandated level for RVP in that county for a given summer month. Summer RVP levels for all counties were corrected to their mandated level (including a 0.25psi compliance margin) based on knowledge of fuel programs in place for 2005. Winter RVP levels remain unchanged from the 2005 default fuel supply.

 The corrections above were applied to the months of January (winter fuel properties) and July (summer fuel properties) for the 2005 base case. The fuel properties for these two months were then duplicated to other months to complete the 2005 fuel supply for all months. The corrected January fuel supply was duplicated to February, March, April, October, November and December; t he corrected July fuel supply was duplicated to May, June, August and September. Although this duplication eliminates fuel propert y changes during the shoulder seasons in April and September, these intermediate month fuel
proper ti es were used only when mod el ing refueling for the air quality inventories ; we expect that this simplification will not significantly impact the modeling run results. Diesel fuel properties were not adjusted in the 2005 fuel supply.

The 2017 pre

- EPACT/EISA base was developed using t he corrected 2005 fuel supply discussed above as a foundation. From this fuel supply, benzene and sulfur levels for all counties were further corrected to properly reflect the introduction of the Control of Hazardous Air Pollutants from Mobile Sources (MSA T2) (2007) rule and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (1999). Benzene corrections were made by PADD following results from the MSAT2 analysis of downstream benzene levels. No other fuel property changes w ere found to change significantly with a change in benzene levels. Benzene levels by PADD follow in Table 7

- 2 below:

Table 7

- 2

- MSAT2 Downstream Fuel Benzene Levels

PADD CG RFG 1 0.6 1 0.54 2 0.63 0.60 3 0.63 0.54 4 0.86 N/A 5 0.65 0.61 CA N/A 0.62

Sulfur corrections were made to all counties based on the default sulfur level found in the 2005 fuel supply. Counties with a sulfur level higher than 30 ppm were reduced to 30 ppm to reflect the gasoline sulfur standards of the Tier 2 rule ( counties subjected to lower fuel sulfur*** E.O. 12866 Review

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- 14 standards, such as in California, were not changed ) . Refinery modeling showed that there is a significant effect on aromatics level when sulfur is reduced. Co rrections to the aromatics level based on refinery modeling for counties with reduced sulfur level were made as follows:

1) A “ high sulfur” aromatics level was determine d using the following equation:

Equation 7

- 1 Aromatics Level from Initial Sulfur Concentration
2) A “low sulfur” aromatics level was determined using the same equation, substituting 30ppm for the initial sulfur level of the county

3) An aromatics delta was calculated by subtracting the “low sulfur” aromatics level from the “high sulfur” aromatics level

4) This aromatics delta was applied as a correction for sulfur reduction to the original aromatics level for the county as appearing in the 2005 fuel supply

Diesel fuel sulfur levels were also adjusted to 30ppm to reflect low sulfur diesel levels. There were no other changes from the 2005 base case to the 2017 base case. The fuel properties for the 2030 base case were identical to the 2017 base case for every county; the only modification made to create the 2030 pre

- EPAct/EISA base case is a change in the year of the fuel supply.

The post

- EPAct/EISA case s used as a reference for Tier 3 were developed using the pre

EPAct/EISA cases for 2017 and 2030 as a foundation, and were then created by inser ting the ethanol market share assumptions for each county discussed at the beginning of this section. Fuel properties for all counties were then adjusted based on relationships found in refinery model ing for this additional ethanol . The process used for a djusting ethanol market shares and fuel properties for every county is described below:

1) Counties with multiple fuels were aggregated to one set of temporary fuel properties using the market shares of these fuels. Counties with only one fuel remain uncha nged in this step.

2) Two new fuels, a 10 percent ethanol blend and a 15 percent ethanol blend , were created from the temporary fuel properties by raising the ethanol level from the temporary aggregate to the appropriate new ethanol level.

3) For each new fuel, o ther fuel properties were adjusted based on refinery modeling to reflect changes due to increased ethanol use. These fuel property changes (on a percent ethanol basis) are shown in Table 7

- 3 below:*** E.O. 12866 Review

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- 15 Table 7

- 3

- Reference Case Fuel Property Changes

Summer Winter Fuel Property a E10 E15 E10 E15 E200 0.54834 0.76481 0.73693 1.04067 E300 0.13946 0.09549 0.00634 0.39788 Aromatics

- 0.40887

- 0.46684

- 0.50555

- 0.49956 Olefins

- 0.137 88

- 0.15915

- 0.25198

- 0.15296
Note:

a T hese fuel p ropert y changes are listed as a per

- ethanol percent change.
4) Market shares for the E10 and E15 fuels were adjusted to reflect RFS2 level ethanol

use. As explained above, in RFG counties, E10 was a dded to the fuel supply at a 25 percent market share, E15 was added to the fuel supply at a 75 percent market share. For counties not using RFG, E10 was a dded to the fuel supply at a 75 percent mark et share, and E15 was added at a 25 percent market share. In 20 30 , the E10 market share is set at 1 percent and E15 market share set at 99 percent , regardless of fuel program.

5) RVP levels for the new E10 and E15 fuels were corrected to reflect E15 fuel not receiving a 1psi waiver for maximum RVP level. As discussed for the base case fuel supplies in the previous section, RVP levels were adjusted to within a 0.25 psi compliance margin depending on county fuel programs.

The result of this effort was two alternate fuel supply databases tables for use in MOVES, reflecti ng the reference case fuels in 2017 and 2030

– these tables were used for the development of national emissions inventory as well as air quality modeling. New fuel formulations were also required for MOVES, and were created for each of the new E10 and E15 fuels created for the 2017 and 2030 reference case.

7.1.3.2.2 Control Scenario

The Tier 3 control fuel scenarios for the years 2017 and 2030 used the fuel supplies constructed for the 2017 and 2030 reference cases described in the previous section as a foundation

. To develop the control scenario fuel supplies, w e modified the reference fuel supplies to reflect the sulfur program proposed in the Tier 3 control case by reducing sulfur from 30 ppm to 10 ppm for all gasoline

. H Associated fuel properties determined by refinery modeling were also adjusted to reflect the implications of sulfur reductions on other fuel properties , such as an increase in aromatics and decrease in olefins and distillation properties , as shown in Table 7

- 4

H The processing of control case sulfur levels nationwid e introduced an error in California counties. Control case fuels were set to 10ppm in all California counties, whereas the reference case sulfur levels ranged from 8

- 19ppm. This led to small changes in emissions due to fuel changes between reference and c ontrol, whereas in reality we expect no change in California fuel. The error had a negligible effect on national emission totals, though the 2017 air quality modeling results captured regional California impacts associated with the error that we judged no t valid. While this error also affected 2030 air quality changes related to the Tier 3 rule , the impact was far outweighed by emission reductions from the vehicle program projected for this year. Our analysis therefore includes the 2030 air quality impact s in California.*** E.O. 12866 Review

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- 16 below. Th ese changes were made to every county with fuel exceeding a sulfur level of 1 0ppm. No changes were made to the diesel fuel supply for the control scenario

.

Table 7

- 4 Tier 3 Control Case Sulfur Fuel Property Changes

Fuel Property Summer Winter E200 (%)

- 0.78

- 1.27 E300 (%)

- 0.75

- 0.68 Aromatics (%) 0.63 0.48 Olefins (%)

- 0.82

- 1.12

The result of this effort was two additional alternate fuel supply databases tables for use in MOVES, reflecting the control case fuels in 2017 and 2030 ; these tables were used for the development of national emissions inventory as well as air quality modeling. For the national emission inventories the 2017 fuel supply was applied to 2018 through 2021 as well, to approximate th e fuel supply prior to full RFS2 implementation; the 2030 fuel supplies were applied to 2022 through 2029, reflecting full RFS2 implementation. New fuel formulations were also required for MOVES, and were created for the low sulfur versions of the E10 and E15 fuels created for the 2017 and 2030 reference case.

Upon further analysis, we believe that the increase in aromatics and reduction in E300 shown in Table 7

- 4 is unlikely. The process of hydrotreating in the fluid catalytic cracking ( FCC ) of gasoline to reduce sulfur tends to saturate olefins formed by the FCC unit and which are present in FCC gasoline, thus reducing its octane. Because refiners have historically been pressed to make as much octane as possible to supply market needs, various technolo gies and catalyst formulations have been developed for hydrotreating FCC gasoline to avoid and/or minimize this loss of octane. Nevertheless, our analysis estimated that desulfurizing gasoline to an average of 10 ppm would result in about a half number lo ss in the octane of FCC gasoline, and we also conservatively assumed that this octane loss would need to be compensated for with other changes to gasoline, such as increased reformate (an aromatic rich stream), isomerate and alkylate. These assumptions a re conservative and not consistent with more recent trends. For example, there is less demand for octane due to the dramatic rise in the use of ethanol (which has very high octane), and the declining demand for gasoline. It is not clear that the loss in octane resulting from gasoline desulfurization would need to be compensated for. In fact, there is considerable “give away” of octane in the gasoline pool today as ethanol is still often splash blended on top of finished gasoline instead of blending it wi th a sub

- octane gasoline blendstock. Nevertheless, for our Tier 3 analysis, we conservatively assumed that the full half number loss in the octane of FCC gasoline would have to be compensated for by the refinery and not made up by blending in more ethanol . In particular , in running the refinery model to make up for the lost octane, we conservatively constrained the refinery model such that the only options for additional octane were changes within the refinery. Additional ethanol was not a modeled option , despite the fact that ethanol will be providing additional octane to the gasoline pool as ethanol use continues to rise through 2017 and beyond.

Furthermore, the increase in aromatics and decrease in E300 may simply be a function of additional assumption s in the refinery modeling. We did not model any revamps in FCC feed hydrotreaters. For some refineries, revamping FCC pretreaters is expected to realize the*** E.O. 12866 Review

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- 17 decrease in FCC gasoline sulfur levels without any decrease in FCC gasoline octane levels. Furt hermore, when refiners are faced with tighter sulfur standards, one strategy for compliance would be to undercut the heaviest portion of the FCC naphtha, which is also the portion highest in sulfur, into either jet fuel or diesel fuel. By doing so, refine rs would not only reduce the hydrotreating severity of their FCC post t reaters (reducing olefin saturation and some of the octane loss), but they would be increasing E300 (lightening up the gasoline pool). One check of our refinery modeling output, which s hows increasing aromatics and decreasing E300, would be to compare the output of our refinery modeling with that of other studies. Our refinery modeling estimated a 0.48 volume percent increase in the aromatic content and 0.68 percent reduction in the E30 0 of gasoline in the winter, and a 0.63 volume percent increase in the aromatic content and 0.75 percent reduction in the E300 of gasoline in the summer. Recent modeling performed by Mathpro for the International Council on Clean Transportation (ICCT) sho wed a 0.1 volume percent increase in aromatic content and a 0.2 volume percent decrease in E300 in the summer, and a 0.3 volume percent decrease in aromatics and 0.8 percent volume percent increase in E300 in the winter. 17 Overall, the Mathpro refinery mode ling showed an annual average decrease in aromatics and increase in E300. Recent modeling by Baker & O’Brien for API (Sensitivity Case 3) showed about half the increase in aromatics that our analysis showed, however the API study also included the octane impacts of lower RVP, so we could not determine whether modeling by API would show an increase in aromatics or not if it solely modeled sulfur control

. 18 API’s addendum to its original study which added an additional control case that solely modeled a 10
pp m gasoline sulfur standard (no change in RVP)

, showed a 0.2 volume percent decrease in aromatics during the summertime (no wintertime fuel quality data was presented, nor was any distillation data presented either summer or winter). Furthermore, after we completed this analysis for the NPRM, we discovered that the LP refinery cost model that we licensed to use required some improvements to correctly characteriz e the qualities of the light and heavy naphtha streams from the reformer to improve its estimatio n of E300 and aromatics content. Thus, unlike our modeling results shown in Table 7

- 4 , which show a meaningful impact on aromatics and E300, we believe, consistent with the Mathpro and Baker and O’Brien refinery modeling studies, that there will be little to no change

. Note that these improvements are not expected to have any impact on the cost estimates made by the refinery model.

The air quality analysis included the changes to aromatics and E300 shown in Table 7

- 4. However, because of the concerns ab ove, we did not reflect these changes in the national emission inventories presented in Section 7.1.5.

7.1.3.3 Vehicle Program Inputs

Modeling t he controls introduced by the Tier 3 vehicle program required the development of an other set of alternate MOVES database tables to reflect each aspect of the proposed Tier 3
program. Th ese database tables included :

 G aseous exhaust emissions rates (HC/CO/N O X ) for light duty cars, trucks, and light

- heavy trucks (gas and diesel) to reflect the proposed Tier 3 FTP and US06
st andards and their phase

- in

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- 18  E lemental carbon (EC) and organic carbon (OC) exhaust emission rates for light duty cars, trucks, and light

- heavy trucks (gas and diesel) to reflect the proposed Tier 3 FTP and US06 PM standards and phase

- in

.

 E vaporative HC pe rmeation emission rates to reflect the proposed diurnal test standard , certification fuel, and phase

- in.

 Leak prev a lence rates for tank vapor venting and liquid leaks to reflect proposed requirements for evaporative leak detection

. The development of th ese alternative emission rates is discussed below by pollutant, fuel and vehicle regulatory class

.

7.1.3.3.1 Gasoline LD HC/CO/NO X Exhaust

Gaseous emission rates in MOVES are contained in a database table (EmissionRateByAge) that expresses emission rates as mass per time, distinguished by emission process (start and running), fuel type (gas and diesel), vehicle regulatory class (LDV, LDT, Light HD, etc.), model year, age, and operating mode (power/speed for running, vehicle soak time for start). Developing these rates on Tier 3 vehicles required accounting for expected changes in each of these dimensions.

The development of Tier 3 emission rates follow ed the same procedures used to develop National LEV (NLEV, covering model years 2001

- 2003) and Tier 2 rates (cov ering model years 2004 and later) in the default MOVES database, as described in the documentation for development of light

- duty exhaust emission rates for MOVES201 0 ( known as the “MOVES Light

- Duty r eport” )

. 19 However, specific modifications were made to r epresent the introduction

o f Tier 3 standards, summarized below

. Where no modifications to methods were made, we will refer the reader to the appropriate section of the MOVES2010 report. In particular, see Section 1.3.4.

MOVES emission rates are estimat ed by standard level , model year , age , and vehicle regulatory class. There are separate rates for areas with Inspection/Maintenance programs (I/M) and those without. Developing the rates involves six steps, listed below.

1. Project a verage Federal Test Procedure ( FTP ) results by standard level and vehicle regulatory class. As in the development of the default MOVES2010 database outlined in the MOVES Light Duty Report , we made use of data measured on the FTP cycle in the course of EPA’s In

- use Verificati on Program (IUVP) to project emissions under the proposed standards

. For Tier 3, we developed estimates of FTP results for Tier 3 vehicles based on IUVP data from Tier 2 Bin 2 and 3 vehicles , including composite results, “cold

- start” emissions” (Bag1 minu s Bag3) and “hot

- running” emissions (Bag 2 FTP and US06).

2. Develop phase

- in assumptions for model years (MY) 2017

– 2031, by standard leve l, vehicle class and model year, including phase

- in assumptions repres enting the introduction of Tier 3 standards.*** E.O. 12866 Review

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- 19 3. Merge FTP results and Phase

- in assumptions . For running emissions, calculate weighted ratios of emissions in each model year relative to those for cars (LDV) in MY 2000, which represent Tier 1 LDV (as discussed in the MOVES Light Duty report, default MOVES rates were projected forward based on model year 2000 data from state I/M data, in conjunction with IUVP data for later model years).

4. Estimate Emissions by Operating Mode

. C alculate emissions by operating mode in each model year by multiplyin g the MY2000 emission rates by the weighted ratio for each model
year. We assume that the emissions control at high power (outside ranges of speed and acceleration covered by Bag 2 of the FTP) is not as effective as at lower power (within the range of spe ed and acceleration covered by Bag 2).

5. Apply Deterioration to estimate emissions for three additional age g roups (4

- 5, 6

- 7 and 8

- 9). We assume that Tier 3 vehicles will deteriorate similarly to other vehicles, wh en viewed in logarithmic terms , but w e modified deterioration to represent a useful life of 150,000 miles, as opposed to a useful li fe of 120K mi les , assumed for Tier 2 and NLEV vehicles

. This is the outcome of applying l n

- linear deterioration to the rates developed in steps 1

- 4. For the

rema ining three groups (10

- 14, 15

- 19 and 20+) , emissions are assumed to stabilize as described in the MOVES2010 report.

6. Es timate non

- I/M reference rates

. The rates in steps 1

- 6 represent rates under a reference inspection/maintenance ( I/M ) program. Corres ponding non

- I/M rates are calculated by applying the ratios applied to the Tier 1 and pre

- Tier 1 rates.
Each of these six steps is described in more detail below. Addition information is available in a separate memo available in th e docket. 20

7.1.3.3.1.1 Averag e FTP R esults (Step 1) (Standard)
Our projected emissions for Tier 3 vehicles are driven by the proposed NMOG+NO X standard, set at 30 mg/mi. However, because MOVES projects NO X and THC emissions separately, we apportioned the aggregate standard into NMOG and NO X components, which we will refer to as the “effective standards” for each pollutant. For purposes of apportionment, we assumed that NMOG control would pose a greater technical challenge than NO X control. Accordingly, we assumed “effective standards” for N MOG and NO X would be 20 mg/mi and 10 mg/mi, respectively. To implement this assumption, we further assumed that for N O X , vehicles would be effectively brought into Bin 2, and that for NMOG, vehicles would be brought to a level between Bin 2 and Bin 3, but closer to Bin 2.

In addition, MOVES models start and running processes separately. It is therefore necessary to translate the composite standard into start and running components. One component represents a “cold start” on the F TP cycle, represented as “ Bag1 minus Bag3” emissions. A second component represents “hot

- running” emissions, represented by the hot

running phase of the FTP (Bag 2). A third component represents emissions on the US06 cycle, representing emissions at high speed and power.*** E.O. 12866 Review

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- 20 Estimated FTP and US06 emissions levels for hydrocarbons ( NMOG and NMHC) are shown in T able 7

- 5 , for several Tier 2 Bins and for Tier 3. Values for all standards except Tier 3 are identical to those used to develop rates in the default da tabase. The values for Tier 3 are calculated as a weighted average of those for Bins 2 and 3, using Equation 7

- 2 Equation 7

- 2

.

Equation 7

- 2

T able 7

- 5 Hydrocarbons (HC): Useful

- Life FTP Standards and Associated Cold

- Start and Hot

- Running Results on the FTP and US06 Cycles. Values for the FTP and US06 represent NMOG and NMHC, respectively.

Bin Useful

- life Standard (mg/mi) FTP Composite a

(mg/mi) FTP Cold Start a

(mg) FTP hot Running a

(Bag 2) (mg/mi) US06 b

(mg/mi)

8 125 41.3 591 3.56 35.8 5 90 35.5 534 2.63 35.8 4 70 24.8 383 2. 28 35.8 3 55 21.5 329 1.74 35.8 2 10 5.6 87 0.42 2.6 Tier 3 c 20 9.2 142 0.7 10.0

a Value s represent “non

- methane organic gases” (NMOG).
b Values represent “non

- methane hydrocarbons” (NMHC).

c Values for Tier 3 calculated using Equation 7

- 2 Equation 7

- 2

. Under a general assumption that CO standards are not forcing, but that CO emi ssions tend to track NMOG emissions, corresponding values for CO were calculated in the same manner, and are presented in Table 7

- 6

.
Table 7

- 6 CO: Useful

- Life FTP Standards and Associ ated Cold

- Start and Hot

- Running Results on the FTP and US06 Cycles.

Bin Useful

- life Standard (mg/mi) FTP Composite

(mg/mi) Cold Start

(mg) FTP hot Running

(Bag 2) (mg/mi) US06

(mg/mi)

8 4,200 861 6,680 451 2,895 5 4,200 606 5,510 238 2,895 4 4,200 5 37 5,500 201 2,895 3 2,100 463 3,470 119 2,895 2 2,100 235 1,620 70 948 Tier 3 a 2,100 286 2,040 81 1,390

a Values for Tier 3 calculated using Equation 7

- 2 Equation 7

- 2

. Corresponding results for NO X are presented in Table 7

- 7 . In contrast to HC and CO, the values for Tier 2 Bin 2 were adopted for Tier 3, as the FTP composite of 5.5 mg/mi suggest s that Bin 2 vehicles can meet the “effective standard” of 10 mg/mi with a reasonable compliance margin.*** E.O. 12866 Review

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- 21 Table 7

- 7 N O X : Useful

- Life FTP Standards and Associated Cold

- Start and Hot

- Running Results on the FTP and US06 Cycles.

Bin Useful

- life Standard (mg/mi) FTP Composite

(mg/mi) Cold Start

(mg) FTP hot Running

(Bag 2) (mg/mi) US0 6

(mg/mi)

8 200 64.2 418 35.1 61.3 5 70 21.2 165 8.2 45.9 4 40 8.7 90 4.7 30.6 3 30 5.7 71 3.8 30.6 2 20 5.5 67 0.4 18.4 Tier 3 10 5.5 67 0.4 18.4

7.1.3.3.1.2 Develop Phase

- In Assumptions (Step 2)
We designed phase

- in assumptions so as to project compli ance with the Tier 3 fleet average NMOG+NO X requirements . The requirements are illustrated in Figure 7

- 4

. The phase

in begins in model year 2017 and ends in model year 2025

. Note the sharp drop in emissions at the outset of the Tier 3 phase

- in, also that the truck standards (LDT2,3,4) are slightly higher than the lighter vehicles ’ (LDV

- T1). After 2017, the reduction in the fleet average is linear . T he fleet averages for cars and trucks no longer differ at the completion of the phase

- in.

Figure 7

- 4 NMOG+NO X FTP Fleet Average Requirements during Phase

- In of the Tier 3

Exhaust Emissions Standards for Light

- Duty Vehicles.

7.1.3.3.1.3 Merge Cycle Results and Phase

- In Assumptions (Step 3)
The go al of this step is to calculate weighted averages of the FTP (cold

- start and hot

running) results for all standards in each model year, with the emissions results weighted by applicable phase

- in fractions. We do this step for each vehicle class separately, then weight the four truck classes together using a set of fractions also derived from the weighted sales estimates.*** E.O. 12866 Review

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- 22 Start and running emissions in each model year are simply calculated as weighted averages of the emissions estimates and the phase

- in fr actions. The resulting weighted start estimates are used directly to represent cold

- start emissions for young vehicles in each model year (ages 0

- 3). For running emissions, however, the averages are not used directly; rather, each is expressed as a ratio to the corresponding Tier 1 value.

7.1.3.3.1.4 Estimat e Emissions by Operating Mode (Step 4)
To project emissions for the 2016

- and

- later vehicles , we divided the operating modes for running exhaust into two groups. These groups represent the ranges of speed and power covered by the hot

- running phase (Bag 2) of the FTP standards (< ~20 kW/Mg), and the ranges covered by the SFTP standards (primarily the US06 cycle). For convenience, we refer to these two regions as “the hot

- running FTP region” and “US06 region,” respecti vely (See Figure 7

- 5 ). Data measured on the SC03 cycle did not play a role in emission rate development.

To estimate emissions by operating mode, the approach was to multiply the emission rates for MY 2000, representing Tier 1, b y a specific ratio for each model year from 2016 to 2025, to represent emissions for that model year.

To estimate rates for the US06 modes, we followed a procedure similar to that for the “FTP” modes, but using the “US06” columns in T able 7

- 5 through Table 7

- 7 . For HC and CO, we used Equation 7

- 2 Equation 7

- 2 , as before. For NO X , we applied the Bin

- 2 values. Figure 7

- 6 and Figure 7

- 7 sho w application of the ratios to the FTP and US06 operating modes in model years 2010, 2017, and 2025, representing fully phased

- in Tier 2 standards, an interim year during the Tier 3 phase

– in, and the fully phased

- in Tier 3 standards, respectively. Figure 7

- 3 displays the information on linear scale, highlighting the differences at the higher operating modes, while Figure 7

- 7 shows the same information on a logarithmic scale, illustrating the patterns for the lower operating modes.*** E.O. 12866 Review

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- 23 Figure 7

- 5 Operating modes for running Exhaust Emissions, divided broadly into “hot

running FTP” and “US06” regions.*** E.O. 12866 Review

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- 24 Figure 7

- 6 Projected Emission Rate s for Cars in Operating modes 21

- 30, vs. VSP, in ageGroup 0

- 3 years, for three model years, for (a) CO, (b) THC and (c) NO X (LINEAR SCALE).*** E.O. 12866 Review

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- 25 Figure 7

- 7 Projected Emission Rates for Cars in Operating modes 2 1

- 30, vs. VSP, in ageGroup 0

- 3 years, for Four Model Years, for (a) CO, (b) THC and (c) NO X (LOGARITHMIC SCALE).*** E.O. 12866 Review

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- 26 7.1.3.3.1.5 Apply Deterioration (Step 5)

Based on our extensive emissions analysis during MOVES2010 development , we assume that deterioration for differen t technologies is best represented by a multiplicative model, in which different technologies, represented by successive model

- year groups, show similar deterioration in relative terms but markedly different deterioration in absolute terms. We implemented this approach by translating emissions for the 0

- 3 age g roup, as calculated above, into natural logarithms and applying uniform logarithmic age trends to all model

- year groups. We derived logarithmic deterioration slopes for Tier 1 vehicles (MY 1996

- 98) and applied them
to Tier 2 vehicles. In this process we applied the same logarithmic slope to each operating mode, which is an extension of the multiplicative deterioration assumption. For 2017 and later model
year vehicles, the deterioration assumptions were modified to represent the extension of the full useful life (FUL), which is increased from 120,000 mi to 150,000 mi. However, we did not extrapolate the deterioration trend beyond the 8

- 9 year age g roup, as we know that emissions tend to stabilize bey ond this age, while the ln

- linear emissions model would project an increasingly steep and unrealistic exponential emissions trend. For the 10

- 14, 15

- 19 and 20+ age
g roups, the “stabilization of emissions with age” was estimated as for MOVES2010 ( MOVES Lig ht Duty report, section 1.3.3.7).

7.1.3.3.1.6 Estimate N

on

- I/M References (Step 6)
Completion of the preceding steps provided a set of rates representing I/M reference rates for MY 2016

- 2025. As a final step, we estimated non

- I/M reference rates by applying the same ratios used in MOVES2010 (section 1.3.3. 6 ).

7.1.3.3.1.7 Start Emissions

The values for “Cold S tart” shown in Tables 8

- 4 through 6 a bove were used to represent cold

- start emissions for the various standard levels. These are designated as opModeID=108 in the emissionR ateByAge table; emission rates for starts following shorter soak periods were developed by applying standard soak curves (found in the MOVES Light Duty report) to the updated cold start rates . Deterioration was applied to start emissions, using the same a pproach as used for developing MOVES2010 base rates discussed in the MOVES Light Duty report. Start deterioration is expressed relative to deterioration for running emissions.

7.1.3.3.1.8 Final Estimates of Composite FTP and US06
In producing emission inventory estim ates, MOVES combines emission rates with activity patterns derived from surveys of in

- use vehicles. These emissions do not necessarily correlate directly with the test procedures used for compliance; for example, in

- use activity shows that more miles are driven per start event than assumed on the FTP. Likewise, the US06 cycle is focused on compliance, and represents a relatively small portion of in

- use driving. However, to give a relative sense of the changes projected by the proposed Tier 3 standards,

e missions can be constructed for FTP composite and US06 from MOVES emission rates for the Tier 2 (labeled as model year 2010) and Tier 3 (labeled as model year 2025) cases. These are shown in Figures 7

- 5 through 7

- 8 below . Note that the Tier 3 rates shown below are for the MOVES base fuel of 30 ppm. In modeling the control scenarios on 10 ppm, these emission rates

were further lowered by the sulfur reductions outlined in Section 7.1.3.4.1.*** E.O. 12866 Review

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- 27 Figure 7

- 8 F TP C omposite NO X emissions for reference (2010) and Tier 3 (2025) constructed from MOVES rates

Figure 7

- 6 FTP Composite THC emissions constructed from MOVES rates*** E.O. 12866 Review

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- 28 Figure 7

- 7 US06 NO X emissions constructed from MOVES rates

Figure 7

- 8 US06 THC emissi ons constructed from MOVES rates*** E.O. 12866 Review

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- 29 7.1.3.3.2 Diesel LD HC/CO/NO X Exhaust

Emission rates representing light

- duty diesel vehicles under Tier 3 standards were calculated identically to those representing gasoline vehicles, with the exception that the “effective standard s” were set differently. Again, diesel vehicles are projected to meet the same NMOG+NO X standard as gasoline vehicles (30 mg/mi). However, for diesel vehicles, we assumed that light

- duty vehicles would meet Bin

- 2 standards following completion of the p has e in. Accordingly, the “effective standards” for NMOG and NO X were set at 10 and 20 mg/mi, respectively. As mentioned, all remaining steps were conducted as described in 7

. 1.3.3.1 above. As a result of the different effective standards, however, the ratio s and other numeric results specific to diesel vehicles vary slightly from their counterparts for gasoline vehicles.

7.1.3.3.3 Gasoline MD HC/CO/N O X The proposed Tier 3 program will affect not just light duty vehicles (below 8,500 pounds GVW), but also chassis certi fied vehicles between 8,500 and 14,000 pounds. These vehicles are referred to here as medium duty vehicles, but are also commonly known as C lass 2b and 3 heavy trucks. In MOVES, these vehicles are designated regulatory class 41. Regulatory class 41 also captures certain other vehicles, namely engine certified trucks and medium duty passenger vehicles, which are not regulated underneath the proposed medium duty standards. As described in the reference case medium duty updates to MOVES, 21 we assumed that d uring this timeframe engine certified vehicles and medium

- duty passenger vehicles (MDPV) are five percent and fifteen percent of the regulatory class respectively

.
Table 7

- 8 Population Percentage Category P ercent of Reg Class 41 MDPV 15% Class 2B 60% Class 3 20% Engine Certified 5%

The C lass 2 b and C lass 3 vehicle program was modeled to begin in model year 2017 and fully phase in during the 2022 model year ( Figure 7

- 9 ). This y ields an aggregate standard of 0.178 g/mile NMOG+N O X for C lass 2 b vehicles and 0.247 gram/mile for C lass 3 vehicles in 2022.*** E.O. 12866 Review

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- 30 Figure 7

- 9 Class 2B and 3 Standard Phase

- in

We combined the proposed Tier 3 pha se

- in for C lass 2 b and 3 vehicles with the existing emission standards MDPV and e ngine certified vehicles that comprise MOVES regulatory class 41. For this analysis, we assumed that MDPVs met the Tier 3 SULEV 30 standard, and that engine certified vehicle s would perform at 1.2x their standard on the chassis FTP. 22 Calculated using Table 7

- 8 , the weighted average of the C lass 2 b

, C lass 3, MDPV, and engine standards is 0.181. I To account for the real world performance of these vehicles, we related this aver age back to Tier 2 light duty vehicles, for which we have a significant amount of data from EPA’s in

- use verification program. Using the same tool that was used for developing the Tier 2 and Tier 3 light duty emission rates, we modeled MOVES Regulatory Cla ss 41 in 2022 as 90 percent Bin 5 and 10 percent Bin 8 vehicles. J For the phase

- in years of 2017

- 2021, we calculated new MOVES rates as a weighted average of the Tier 3 rates and the existing MOVES rates for regulatory class 41 such that the appropriate w eighted composite was calculated each year Table 7

- 9

.

I Due to a calculation error, an NMOG+ NO X standard of 0.177 was used for Regulatory Class 41. As a result, a slightly different phase

- in of the NMOG+ NO X and CO rates was used in the proposal modeling. This error overstates the NMO G+ NO X reductions for this vehicle class by approximately 2.5% in 2022.
J By basing the data on light duty vehicles, it is possible that we are misstating the emission profile of these larger vehicles, but as emissions decrease, it is also possible that the emission profile for the larger vehicles will more closely resemble that of light duty vehicles.*** E.O. 12866 Review

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- 31 Table 7

- 9 Phase

- in of MD Tier 3 Rates

Model Year Tier 3 Rate MOVE S DB Rates Composite Standard (g/mile) 2017 34% 66% 0.33 2 018 49% 51% 0.29 2019 62% 38% 0.26 2020 75% 25% 0.24 2021 87% 13% 0.21 2022 100% 0% 0.18

The CO standards for MD vehicles are less stringent than those for Tier 2 Bin 5 and Bin 8 vehicles. For Bin 5 and Bin 8 vehicles, the CO standard is 4.2 g/mile. For engine certified vehicles, the standard is approximately 17.3 grams per mile (14.4 grams per bhp multiplied by 1.2), 23 and for the Tier 3 MD vehicles, the standard ranges from 4.2 to 7.3 g/mile. Using the same weighted averages as before, we calculat ed an aggregate CO standard of 4.4 grams/mile, which is 5.5 percent higher than the Tier 2 Bin 5/8 standards. To compensate for the lower CO emissions in the Tier 2 vehicles that were used to develop the Tier 3 MD emission rates, we multiplied the running CO rates by 1.1 and the start CO rates by 1.05.

Like in the light duty vehicles, deterioration modeled to represent a 150,000 mile useful life. The same methodology was used for light duty and medium duty vehicles.

7.1.3.3.4 Diesel MD HC/CO/N O X

For medium duty d iesel vehicles, the emission rates currently in MOVES are significantly below the proposed Tier 3 HC and CO standards. When MOVES is used to generate a simulated FTP estimate for NMHC , the model calculates a rate of approximately 0.05 grams per mile, whil e the simulated FTP estimate for CO is less than 1 gram/mile. Consequently, we assumed no HC and CO emission benefits from Tier 3 s tandards on MD d iesel vehicles.

By contrast, we estimate that the Tier 3 NO X standar d will produce a reduction in d iesel C la ss 2 b and Class 3 NO X emissions. Because data on current NO X emissions are limited, as there is little in

- use data on MY 2010 and 2011 vehicles which use selective catalytic reduction as a NO X control strategy , we used a proportional approach to estimate the Tier 3 effect, reducing NO X in proportion to the change in the emission standard. Because emission standards tend to impact start and running emissions differently, we applied a greater portion of the reduction to running emissions and a smaller reduc tion to start emissions. These reductions were phased

- in over the same schedule as for gasoline vehicles, as detailed in Table 7

- 10 . Also, to account for the change in “useful life”, we duplicated the Tier 3 age 0

- 3 NO X rates to the 4

- 5
year age

- group.*** E.O. 12866 Review

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- 32 Table 7

- 10 Phase

- in of MD Diesel Tier 3 NO X Rates

Model Year Tier 3 Phase In Reduction in NO X Running Emission Rate Reduction in NO X

Start Emission Rate

2017 20% 12% 5% 2018 38% 23% 9 % 2019 54% 33% 12% 2020 69% 42% 16% 2021 85% 52% 19% 2022 100% 61% 23%

7.1.3.3.5 Gasoline PM 2.5

Tier 3 will reduce direct particulate matter (PM 2.5 ) emissions from light

- duty vehicles through a full

- useful life (FUL) PM standard on the FTP of 3 mg/mi. Addition ally, the Tier 3 standards will include more stringent SFTP PM standards for light

- duty vehicles, with a 10 mg/mi standard on the US06 for light

- duty passenger vehicles, and 20 mg/mi on the US06 for light

- duty trucks. These standards are targeting several processes that contribute to particulate matter in light

- duty gasoline vehicles: cold starts, high

- power operation, and deterioration of engine and emission control technology over the life of the vehicle.

To achieve the FUL PM standards without sacrifici ng fuel economy, it is anticipated that manufacturers may try a variety of strategies including reduc ing lubrication oil consumption over the life of the vehicle. For our analysis, we projected that r eductions in lube oil consumption would reduce the organ ic carbon (OC) fraction of PM by 30 percent for both cold start and running emissions. T hese reductions are based on an analysis of the Kansas City Light

- duty Vehicle Emissions Study, which collected PM emissions from a randomly recruited sample of vehicle s in the Kansas City area in 2005. Using chemical tracers found in both the lube oil and the particulate samples, the lube oil contribution to PM was estimated to be 25 percent of the PM emissions, primarily from the OC fraction of PM. The 25 percent reduc tion is estimated by weighting the sample to represent the distribution of vehicle ages and vehicle types in the Kansas City Metropolitan Area in 2004.

From this analysis, a 30 percent OC reduction is projected in Tier 3 gasoline vehicles due to decrease d lube oil consumption, which represents approximately a 25 percent PM reduction for gasoline vehicles during cold start and running emission processes. The modeling assumptions and overview of the Kansas City analysis are located in the Supporting Technic al Document: Estimated Reductions in Particulate Matter Emissions from Light

- duty and Medium

duty Gasoline Vehicles through Implementation of Tier 3 Regulations. 24

For the modeling runs described here, the OC reductions are anticipated to be phased

- in

over a four year period, with the full

- implementation occurring in 2020. The OC reductions are 7.5 (2017), 15 (2018), 22.5 (2019), and 30 percent (2020). The percent reductions are applied to*** E.O. 12866 Review

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- 33 cold

- start and running emission processes, to passenger car and truck sources, and to PM emissions as the vehicles age.

7.1.3.3.6 Diesel PM

The Tier 3 controls were modeled as having no impact on light duty diesel PM emissions.

7.1.3.3.7 Gasoline Evaporative Emissions

The proposed Tier 3 evaporative program , requiring lower emissions on the diurnal test procedure on E15 certification fuel and strengthened in

- use detection of vapor leaks

, is projected to cause a significant reduction in evaporative hydrocarbon emissions. For this analysis, tighter diurnal standards in conjunction with E15 cer tification fuel were attributed to reductions in evaporative permeation emissions, since the current certification standards are aimed at not allowing any vented vapor emissions during the test. The new requirements for in

- use leak detection w ere modeled as resulting in a reduced prevalence (frequency rate) of fuel system vapor and liquid leak s

. K

7.1.3.3.7.1 P ermeation Improvements
Permeation emissions include fuel vapors that escape from a vehicle through micro pores in the various fuel system components and material s. Tier 3 will reduce the allowable emissions from this process. Light duty vehicles will see a reduction from 0.50 g/test to 0 .300 g/test, approximately a 40 percent reduction.

The Tier 2 permeation rate in MOVES is 0.0102 g/hour on E0 fuel. Analysis of the impact of ethanol on permeation emissions conducted as part of the RFS2 final rule, and included in MOVES2010, suggests that t he use of E15 as Tier 3 certification test fuel will effectively double permeation emissions over the test procedure . Therefo re, the combination of lowering the vehicle standard and certifying on a fuel with higher propensity to permeate must be accounted for in Tier 3 permeation rates.

The Tier 3 rate is developed by estimating permeation emissions over one day of diurnal acti vity (65F

- 105F) on an e thanol

- containing fuel using various reductions in the base rate. The total permeation emissions fo r the day should equal about 75 percent of the sta ndard (~0.225g) as the other 25 percent of the standard can be attributed to the Hot Soak portion of the certification test. The result is a Tier 3 permeation rate of 0. 0026g/hour (a 75 percent reduction from the Tier 2 rate)

.

K One of the updates to MOVES for this analysis was to enable direct input of the leak prevalence rates.*** E.O. 12866 Review

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- 34 Table 7

- 11 Tier 3 Permeation Rates

Model Year Tier 3 Phase

in Per meation

(g/hr) Tier 2 0% 0.0102 2016_2017 40% 0.0072 2018_2019 60% 0.0056 2020_2021 80% 0.0041 2022 100% 0.0026

7.1.3.3.7.2 Reduce d frequency of vapor leaks
EPA, in conjunction with the state of Colorado and the Coordinating Research Council

, undertook multiple research program s to help quantify the prevalence of evaporative system leaks in the real world, and the emissions they cause. 25

, 26 The proposed evaporative leak provisions grew from this work, and inform ed the emission inventory contribution of evaporative leaks, and the level of reductions possible from a n in

- use program focused on reducing the incidence of these leaks

. To establish the reference case, the frequency of evaporative system leaks were estimated from the prevalence of high evaporative emissio n vehicles in the Colorado field study

. Because the Colorado study was not able to distinguish leaks from other high evaporative emission sources in the broader population, we analyzed data from CRC’s E

- 77 program and found that insufficient canister purg e is also contributing to high evaporative emissions, as this causes the canisters to become oversaturated, venting vapor into the atmosphere.

Because the Colorado data suggested high evaporative emissions on newer vehicles as well as old, we assumed tha t the leak prevalence rates from the Colorado study include d a degree of emissions from insufficient purge. Because insufficient purge would not be a function of age,

we assume most ( all but 1 percent ) of the Colorado prevalence rates at age 0

- 3 are due to insufficient purge

, whic h is assumed constant with age. The increase in high evaporative emissions incidence with age was then attributed to an increasing prevalence of leaks

. T his is illustrated in the chart to the left in Figure 7

- 10

. To model the control scenario, because the proposed leak requirement would only address leaks and not insufficient purge, only the leak portion of high evaporative emissions were reduced. Colorado data on a subset of vehicles that were selected for m ore detailed observation and testing suggested that 70 percent of the evaporative leaks detected were due to durability of the evaporative and/or fuel system

– e.g. problems like corroded fuel lines, filler neck, cracked hoses etc. that could be addressed with improved durability (the other 30 percent were due to issues beyond the manufacturers control, such as improper maintenance or missing gas caps). We estimated that the leak program would lead to a 70 percent reduction in the occurrence of vapor leaks

. This is reflected in the chart on the right in Figure 7

- 10 ; note that the emissions attributed to insufficient purge were not changed from the reference case.*** E.O. 12866 Review

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- 35 Figure 7

- 10 Vapor Leak Occurrence A ssumed for Reference (Left) and Tier 3 (Right)

7.1.3.3.7.1 Reduced frequency of liquid leaks
Similar to vapor leaks, we expect a reduction in the occurrence of liquid leaks due to improved system design and integrity. We believe that liquid leaks occu rring in advanced evaporative systems will be primarily caused by tampering and mal

- maintenance. Therefore we have reduced the frequency rate for leaks for vehicles less than 15 years of age, and expect vehicles older than 15 to have the same rate of leak occurrence as current technologies.

Table 7

- 12 Reductions of Liquid Leaks in Tier 3
Age Operating Hot Soak Cold Soak 0

- 9 45% 45% 45% 10

- 14 30% 30% 30% 15

- 19 0% 0% 0% 20+ 0% 0% 0%

7.1.3.4 Updates to MOVE S Sulfur Effects

In order to evaluate the emission impacts of the proposed sulfur standards, the version of MOVES used for this analysis made significant updates to the effect of fuel sulfur levels below 30ppm on exhaust emissions. In MOVES2010a these effects were based on extrapolation of data on sulfur levels above 30 ppm. 27 The updates made for this analysis were based on significant new data generated from EPA research conducted in 2010

- 11, summarized below. A draft report on this research is available in the docket. 28

7.1.3.4.1 EPA Sulfur Research Program

Fuel sulfur content has long been understood to affect the performance of emission aftertreatment catalysts in light duty vehicles, where the sulfur and/or its oxides adsorb to the active precious metal sites, reducing the catalyst’s efficiency in destroying harmful pollutants. This can severely impair the effectiveness of the catalyst to convert the products of combustion, leading to increases in these emissions relative to a “clean” catalyst. The quantity of sulfur
p resent on the catalyst at any given time is a function of its temperature and the fuel sulfur level, with elevated catalyst temperatures and lower fuel sulfur concentration both reducing sulfur*** E.O. 12866 Review

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7

- 36 loading. Numerous studies have shown the direct impact of fue l sulfur levels above 30 ppm on emissions; these formed the basis of the Tier 2 rulemaking, which considered the impact of sulfur in terms of immediate impact, and irreversible impact due to permanent catalyst damage. 29

With the advent of the Tier 2 sulfur standards, new research has focused on the emission reduction potential of lowering sulfur levels below 30 ppm, particularly on Tier 2 technology vehicles, under the hypothesis that increased reliance on the catalytic convertor would result in a higher se nsitivity to sulfur accumulation. A study conducted by EPA and the auto industry on nine Tier 2 vehicles in support of the Mobile Source Air Toxics (MSAT) rule, found significant reductions in NO X , CO and t otal HC when the vehicles were tested on low sulf ur fuel, relative to 32 ppm fuel. 30 In particular, the study found a nearly 50 percent increase in NO X when sulfur was increased from 6 ppm to 32 ppm. Another recent study by Umicore showed reductions of 41 percent for NO X and 17 percent for HC on a PZEV operating on fuel with 33 ppm and 3 ppm sulfur

. 31 Both of these studies conducted testing on high and low sulfur after running the test vehicles through test cycles meant to clean the catalyst from the effects of prior sulfur exposure.

Both of these stud ies showed the emission reduction potential of lower sulfur fuel on Tier 2 and later technology vehicles over the FTP cycle. However, assessing the potential for reduction on the in

- use fleet requires understanding how sulfur exposure over time impacts

em issions, and the state of sulfur loading for the typical vehicle in field. In response, EPA conducted a new study to assess the emission reductions expected from the in

- use Tier 2 fleet with a reduction in fuel sulfur level from current levels. It was de signed to take into consideration what was known from prior studies on sulfur build

- up in catalysts over time and the effect of periodic regeneration events that may result from higher speed and load operation over the course of day

- to

- day driving

.
The s tudy sample described in this analysis consisted of 81 cars and light trucks recruited from owners in southeast Michigan, covering model years 2007

- 9 with approximately 20,000

- 40,000 odometer miles. The makes and models targeted for recruitment were chose n to be representative of high sales vehicles covering a range of types and sizes. Test fuels were two

non

- ethanol gasolines with properties typical of certification fuel, one at a sulfur level of 5 ppm and the other at 28 ppm. A nominal concentration of approximately 25 ppm was targeted for the high level to be representative of retail fuel available to the public in the vehicle recruiting area. All emissions data was collected using the FTP cycle at a nominal temperature of 75°F.

Using the 28 ppm test fuel, emissions data were collected from vehicles in their as

received state, and then following a high

- speed/load “clean

- out” procedure consisting of two back

- to

- back US06 cycles intended to reduce sulfur loading in the catalyst. A statistical analysis

o f this data showed highly significant reductions in several pollutants including NO X and hydrocarbons ( Table 7

- 11 ), suggesting that reversible sulfur loading exists in the in

- use fleet and has a measurable effect on aftertreatment performance; for example, Bag 2 NO X emissions dropped 32 percent between the pre

- and post

- cleanout tests on 28 ppm fuel.*** E.O. 12866 Review

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- 37 Table 7

- 11 Average Clean

- out Effect on In

- use Emissions using 28 ppm Test F uel

NO x (p

- value) THC

(p

- value) CO

(p

- value) NMHC

(p

- value) CH 4

(p

- value) PM

(p

- va lue) Bag 1

– – 4.7% (0.0737) – – 15.4% (< 0.0001) Bag 2 31.9%

(0.0009) 16.5%

(0.0024) – 17.8% (0.0181) 15.3%

(0.0015) –
Bag 3 38.3%

(<0.0001) 21.4%

(<0.0001) 19.5%

(0.0011) 27.8%

(<0.0001) 12.0%

(<0.0001) 24.5%

(<0.0001) FTP Composite 11.4%

(<0.0001) 4 .1%

(0.0187) 7.6%

(0.0008) 3.0%

(0.0751) 6.9%

(0.0003) 13.7%

(<0.0001) Bag 1

– Bag 3

– – 4.2% (0.0714) – – –

Where no reduction estimate is provided , t he clean

- out effect is not significant at α = 0.10.
To assess the impact of lower sulfur fuel on in

- use emissions, a representative subset of vehicles was kept to conduct testing on 28 ppm and 5 ppm fuel with accumulated mileage. A first step in this portion of the study was to assess differences in the effectiveness of the clean

out procedure when done us ing different fuel sulfur levels . Table 7

- 12 presents a comparison of emissions immediately following (<50 miles) the clean

- out procedures at the low vs. high sulfur level. These results show significant emission reductions for the 5 ppm fuel relative to the 28 ppm fuel immediately after this clean

- out; for example, Bag 2 NO X emissions were 47 percent lower on the 5 ppm fuel vs. the 28 ppm fuel. This indicates that the catalyst is not fully desulfurized, even after a clean out procedure, as long as there is sulfur in the fuel.

Table 7

- 12 R eduction in emissions from 28 ppm to 5 ppm immediately following clean

- out

NO x (p

- value) THC

(p

- value) CO

(p

- value) NMHC

(p

- value) CH 4

(p

- value) PM ‡

Bag 1 5.9%

(0.0896) 5.4%

(0.0118) 7.3%

(0.0023) 4.6%

(0.0465) 11.1%

(<0.0001) –
Bag 2 47.3%

(0.0010) 40.2%

(<0.0001) – ‡ 34.4% (0.0041) 53.6%

(<0.0001) –
Bag 3 51.2%

(<0.0001) 35.0%

(<0.0001) 10.1%

(0.0988) 45.0%

(<0.0001) 25.4%

(<0.0001) –

FTP Composite 17.7%

(0.0001) 11.2%

(<0.0001) 8.3%

(0.0003) 8.8%

(0.0003) 21.4%

(<0.0001) –
Bag 1

– Bag 3

– ‡

– ‡ 5.8% (0.0412) – ‡

– ‡

–

‡ Sulfur level not significant at α = 0.10.

To assess the overall in

- use reduction between high and low sulfur fuel, a mixed model analysis of all data as a function of fuel sulfur level and miles driven after cleanout was performed. This analysis found highly significant reductions for several pollutants, as shown in Table 7

- 13 ; reductions for Bag 2 NO X were particularly high, estimated at 59 percent between 28ppm and 5ppm overall For some pollutants, such as Bag 2 NO X , the model fitting did not find*** E.O. 12866 Review

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- 38 a signific ant miles

- by

- sulfur interaction, suggesting the relative differences were not dependent on miles driven after clean

- out. Other results, such as Bag 1 hydrocarbons, did show a significant miles

- by

- sulfur interaction. In this case, determination of a sulfu r level effect for the in

- use fleet required estimation of a miles

- equivalent level of sulfur loading, which can be gleaned from the cleanout results obtained from the baseline testing on the vehicles as

- received.

Table 7

- 13 : In

- use emission reductions from 28 to 5 ppm sulfur

NO X (p

value) THC

(p

- value) CO

(p

value) NMHC

(p

- value) CH 4

(p

- value) NO x +NMO

G (p

- value) PM

‡

Bag 1 10.7%

(0.0033) 8.5% †

(0.0382) 7.5% †

(0.0552) 7.5%

(< 0.0001) 13.9% †

(< 0.0001) N/A

– Bag 2 59.2%

(< 0.0001) 48.8%

(< 0.0001) – ‡ 44.8% † (0.0260) 49.9%

(< 0.0001) N/A

– Bag 3 62.1%

(< 0.0001) 40.2%

(< 0.0001) 20.1%

(< 0.0001) 49.9%

(< 0.0001) 29.2%

(< 0.0001) N/A

–

FTP Composite 23.0% †

(0.0180) 13.0% †

(0.0027) 11.9% †

(0.0378) 10.6% †

(0. 0032) 25.8% †

(< 0.0001) 17.3%

(0.0140) –
Bag 1

– Bag 3 – ‡ 5.2% (0.0063) 4.3%

(0.0689) 5.1%

(0.0107) 4.6%

(0.0514) N/A

–

† Model with significant sulfur and mileage interaction term. ‡ Sulfur level not significant at α = 0.10. For THC B ag 1 and CH 4 B ag 1, because the effect of clean

- out was not statistically significant, the reduction estimates are based on the estimates of least squares means.

Major findings from this study include:

 Reversible sulfur loading is occu rring in the in

- use fleet of Tier 2 vehicles and has a measureable effect on emissions of NO X , hydrocarbons, and other pollutants of interest

.

 The effectiveness of high speed/load procedures in restoring catalyst efficiency is limited when operating on hig her sulfur fuel

.

 Reducing fuel sulfur levels from 28 to 5 ppm is expected to achieve significant reductions in emissions of NO X , hydrocarbons, and other pollutants of interest in the in

- use fleet

. The overall reductions found in this study are in agreement with other low sulfur studies conducted on Tier 2 vehicles. The magnitude of NO X and HC reductions found in this study when switching from 28 ppm to 5 ppm fuel are consistent with those found in MSAT and Umicore studies mentioned above.

7.1.3.4.2 Implementation in MOVES

The results shown in Table 7

- 13 were applied in MOVES for model year 2004 and later gasoline vehicles (the nominal start of the Tier 2 phase

- in) to estimate sulfur effects below 30
ppm. The sulfur fuel effect applies multiplicatively to other g asoline fuel effect s in MOVES, and applies only for sulfur levels below 30ppm. For sulfur levels above 30 ppm, and for all pre

-*** E.O. 12866 Review

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- 39 2004 model year vehicles, the sulfur effect originally used in MOVES remains in place. Equation 7

- 3 sho ws the generic form of the new sulfur correction; Table 7

- 14 shows the specific values for the sulfur co efficients by pollutant , process, and vehicle type.

Equation 7

- 3 Low Sulfur Effect

Table 7

- 14 Low Sulfur Coefficients by Vehicle Type, Process and Pollutant

Veh icle Type THC CO N O X PM

Starts Running Starts Running Starts Running Starts Running Motorcycle 0 0 0 0 0 0 0 0 Passenger Car, Passenger Truck & Light Commercial Truck 0.002237 0.020336 0.001866 0 0 0.024459 0 0

All other Vehicle Types 0 0.015488 0 0.009436 0 0.027266 0 0

These eq u ations were then used to fill in the database table that houses fuel effect

e quations in the MOVES database (“ GeneralFuelRatioExpression ”) . This table allows the MOVES model to compute fuel effects based on the fuel properties of any fuel contained in the fuel supply and fuel formulation database table s.

7.1.4 Nonroad Emissions

The no nroad sector includes a wide

- range of mobile emission sources ranging from locomotives and construction equipment to hand

- held lawn tools. In the nonroad sector, the only emissions that are directly affected by the proposed Tier 3 regulation are the emissi ons from gasoline

- powered equipment such as lawn

- mowers, recreational boats and all

- terrain vehicles. Their SO 2 emissions are reduced with the proposed decrease in gasoline sulfur levels. As with onroad, reference and control case emissions were generate d using the fuel supply inputs reflecting the post

- EISA/EP A ct renewable fuel volumes.

Gasoline and land

- based diesel nonroad emissions were estimated using EPA’s NONROAD 2008 model, as run by the EPA’s consolidated modeling system known as the National Mo bile Inventory Model (NMIM). 32 The fuels in the NMIM database, NCD2010201Tier 3

, were developed from the reference and control fuels used for onroad vehicles, as described in Section 7.1.3 . In 2005, onroad and nonroad gasoline formulations are assumed to b e identical. In 2017, E10 and E15 ethanol blends are available in every county, but nonroad equipment is assumed to use E10 only. In 2030, we assume almost all nonroad equipment can use either E10 or E15, so onroad and nonroad gasoline formulations are aga in assumed to be identical. For all years, the reference case included the higher sulfur reference gasoline and the control case met the proposed sulfur limits.*** E.O. 12866 Review

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- 40 Since aircraft, locomotive and commercial marine emission sources do not burn gasoline, thei r emission factors are unaffected by the changes in gasoline fuels that were developed for this rule . Hence, their emissions are the same for both the reference and control cases . The emissions from these sources used for this rule are the same as they w ere for the Heavy

- Duty Greenhouse Gas Rule (2011) 33 . The procedures for calculating emissions from locomotives and C1/C2 commercial marine were developed for the Locomotive Marine Rule (2008) and are detailed in the RIA for that rule. 34 The procedures used for calculating C3 commercial marine emissions are those developed in the recent C3 Rule (2010). 35

7.1.5 Criteria and Toxic Emission Impact Results

The proposed Tier 3 rule will reduce NO X (including NO 2 ), VOC, CO, and SO 2 from all gasoline

- powered on road vehi cles immediately upon implement ation of lower sulfur fuel, and will further reduce these emissions as well as PM 2.5 from cars, light trucks and light heavy

- duty trucks (gas and diesel) as tighter emission standards from these vehicles phase in. There also will be reductions in SO 2 emissions from the nonroad gasoline fleet as a result of sulfur standards. The reductions are summarized in this section for each pollutant.

NO X reductions are shown in Table 7

- 15 for Calendar Years 2017 through 2030, and 2050 when the fleet will have turned over completed to Tier 3 standards. W e project significant reductions immediately upon implementation of the program, growing to a nearly 30 percent reduction in onroad emissions by 2030, and nearly 4 0 percent reduction in onroad emissions with full fleet turnover by 2050.

Table 7

- 15 Tier 3 NO X Reductions by Calendar Year ( A nnual U.S. Short T ons)

Year Onroad mobile reference Onroad

mobile with control R e duction Percent

reduction in onroad 2017 3,452,314 3,167,934 284,381 8.2% 2018 3,179,942 2,890,474 289,469 9.1% 2019 2,942,238 2,646,150 296,088 10.1% 2020 2,732,380 2,429,517 302,863 11.1% 2021 2,557,573 2,241,209 316,364 12.4% 2022 2,427,305 2,091 ,971 335,335 13.8% 2023 2,304,619 1,950,687 353,932 15.4% 2024 2,196,463 1,823,335 373,128 17.0% 2025 2,115,326 1,717,083 398,244 18.8% 2026 2,040,468 1,617,809 422,659 20.7% 2027 1,989,071 1,540,270 448,801 22.6% 2028 1,942,891 1,469,181 473,710 24. 4% 2029 1,913,717 1,413,482 500,234 26.1% 2030 1,890,403 1,365,613 524,790 27.8% 2050 2,410,373 1,507,204 903,170 37.5%*** E.O. 12866 Review

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- 41 Table 7

- 16 shows the reduction in NO X emissions, in annual short tons, projected in calendar years 2017 and 20 30. The reductions are split into those attributable to the introduction of low sulfur fuel in the pre

- Tier 3 fleet (defined for this analysis as model years prior to 2017) ; and reductions attributable to vehicle standards enabled by low sulfur fuel (mode l year 2017 and later ) . As shown, in 2017 over 90 percent of the program reductions are coming from lower sulfur gasoline on the fleet already on the road . By 2030, nearly 90 percent of the reduction is coming from 2017 and later model year vehicles, wit h remaining reduction coming from lower sulfur fuel on pre

- Tier 3 vehicles

. L W e project that about one tenth of the reductions from the Tier 3 fleet in 2030 are attributable to the proposed Tier 3 heavy

- duty emission standards.

Table 7

- 16 Projected NO X Reductions from Tier 3 Program (Annual U.S. Short Tons)

2017 2030 Total reduction 28 4 , 381 524,790 Reduction from pre

- Tier 3 fleet due to sulfur standard 26 4

, 653 66,286

Reduction from Tier 3 fleet due to vehi cle and sulfur standards 19,7 28 458,504

VOC reductions are shown in Table 7

- 17 for Calendar Years 2017 through 2030, and 2050 when the fleet will have turned over completed to Tier 3 standards. We project reductions of over 40,000 tons (3 percent of the onroad fleet emissions) immediately upon implementation of the program, growing to a 23 percent reduction in onroad emissions by 2030, and 36 percent reduction in onroad emissions with full fleet turnover by 2050.

L This is an approximate breakdown, as there w ill be some NO X emission reduction from heavy

- duty gasoline vehicles greater than 14,000 pounds beyond the 2017 model year that are counted in the “Tier 3 fleet” here*** E.O. 12866 Review

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7

- 42 Table 7

- 17 Tier 3 VOC Reductions by Calendar Year (Annual U.S. Short Tons)

CY Onroad mobile reference Onroad

mobile with control Reduction Percent reduction in onroad 2017 1,614,384 1,569,603 44,782 2.8% 2018 1,506,001 1,456, 342 49,659 3.3% 2019 1,411,787 1,356,874 54,913 3.9% 2020 1,333,867 1,272,497 61,370 4.6% 2021 1,266,250 1,195,112 71,138 5.6% 2022 1,211,710 1,128,740 82,970 6.8% 2023 1,163,986 1,067,208 96,778 8.3% 2024 1,123,476 1,012,446 111,029 9.9% 2025 1,084 ,012 955,680 128,332 11.8% 2026 1,045,808 899,298 146,509 14.0% 2027 1,025,657 858,927 166,730 16.3% 2028 1,004,922 818,008 186,913 18.6% 2029 988,529 781,665 206,863 20.9% 2030 977,067 751,040 226,028 23.1% 2050 1,151,908 735,548 416,361 36.1%

Table 7

- 18 shows the VOC reductions in 2017 and 2030 split into those attributable to the pre

- Tier 3 fleet, and the Tier 3 fleet. The Tier 3 fleet reductions are further subdivided into the contribution of the proposed exhaust and evapor ative standards. In 2017 over 80 percent of the program reductions are coming from lower sulfur gasoline on the fleet already on the road. By 2030, over 90 percent of the reduction is coming from 2017 and later model year vehicles, with remaining reducti on coming from lower sulfur fuel on pre

- Tier 3 vehicles. The evaporative standards account for about one quarter of the Tier 3 fleet reductions in 2030.

Table 7

- 18 Projected VOC Reductions from Tier 3 Progra m (Annual U.S. Short Tons)

2017 2030 Total reduction 44, 782 226,028 Reduction from pre

- Tier 3 fleet due to sulfur standard 3 9,561 13,739

Reduction from Tier 3 fleet due to vehicle and sulfur standards 5,222 212,289

Exhaust 41,433 168,264 Evaporative 3,349 57,76 4

CO reductions are shown in Table 7

- 19 for Calendar Years 2017 through 2030, and 2050 when the fleet will have turned over completed to Tier 3 standards. We project significant reductions immediately upon implementatio n of the program, growing to a 30 percent reduction in onroad emissions by 2030, and 46 percent reduction in onroad emissions with full fleet turnover by 2050.*** E.O. 12866 Review

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- 43 Table 7

- 19 Tier 3 CO Reductions by Calendar Yea r (Annual U.S. Short Tons)

CY Onroad

mobile reference Onroad

mobile with control Reduction Percent reduction in onroad 2017 20,915,593 20,168,910 746,683 3.6% 2018 20,297,089 19,454,190 842,899 4.2% 2019 19,782,476 18,830,656 951,820 4.8% 2020 19,374, 712 18,295,010 1,079,702 5.6% 2021 19,122,796 17,747,325 1,375,471 7.2% 2022 18,830,157 17,119,383 1,710,775 9.1% 2023 18,736,417 16,610,543 2,125,874 11.3% 2024 18,647,649 16,095,479 2,552,170 13.7% 2025 18,647,177 15,584,593 3,062,585 16.4% 2026 18 ,621,982 15,054,696 3,567,286 19.2% 2027 18,699,029 14,558,766 4,140,263 22.1% 2028 18,745,355 14,047,003 4,698,352 25.1% 2029 18,850,303 13,594,884 5,255,420 27.9% 2030 18,951,626 13,186,263 5,765,362 30.4% 2050 24,839,365 13,339,791 11,499,574 46.3%

Table 7

- 20 shows the reductions for CO, broken down by pre

- and post

- Tier 3 in the manner described for NO X and VOC above. T he immediate reductions in the onroad fleet from sulfur control comprise about 80 percent of total reductio ns in 2017. By 2030, the proposed vehicle standard, enabled by low sulfur fuel, are accounting for 98 percent of program reductions. Of the Tier 3 vehicle standard reductions in 2030, we estimate that about 5 percent are contributed by the proposed heavy

- duty tailpipe standards.

Table 7

- 20 CO Reductions from Tier 3 Program (Annual U.S. Short Tons)

2017 2030 Total reduction 746,683 5,765,362 Reduction from pre

- Tier 3 fleet due to sulfur standard 608,502 139 ,074

Reduction from Tier 3 fleet due to vehicle and sulfur standards 138,181 5, 626

, 288

Direct PM 2.5 impacts are shown in Table 7

- 21 for calendar years 2017 through 2030, and 2050 when the fleet will have turned over completed to Tie r 3 standards. For direct PM, the impact shown is solely from the proposed tailpipe standards. Thus, u nlike other pollutants, reductions do not become significant until the fleet has turned over to cleaner vehicles. By 2030, we project a reduction of ab out 7,500 tons annually, which represents approximately 10 percent of the onroad direct PM 2.5 inventory. However, since the PM standards are mainly focused on improving engine durability through the end of a vehicle’s useful life, the relative reduction i n onroad emissions is projected to grow to 1 7 percent with full fleet turnover in 2050.*** E.O. 12866 Review

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- 44 As discuss ed in Section 7.2.1.1, the control scenario emissions inventory prepared for air quality modeling included the impact of an increase in aromatics as sulfur is reduced from 30 ppm to 10 ppm fuel . While this assumption had a minor effect on control case emissions for several pollutants, the effect was mo re visible for direct PM 2.5 emission s , as it result ed in a projected increase in emissions (roughly 700 ton s nationwide) in 2017

. As discussed in Section 7.1.3.2.2, t his emissions increase results from a series of conservative assumptions and uncertainties related to fuel parameters in 2017, and is not expected to occur in reality. As a result, we have not include d the PM emissions increase from sulfur reduction in the emission inventory impacts shown in Table 7

- 21.

Table 7

- 21 Tier 3 PM 2.5 Reductions by Calendar Year (Annual U.S. Short Tons)

CY Onroad mobile

r eference Onroad mobile

with control Reduction Percent reduction in onroad 2017 115,098 114,977 121 0.1% 2018 107,295 106,932 362 0.3% 2019 100,885 100,162 724 0.7 % 2020 95,192 93,992 1,200 1.3 % 2021 90,480 88,767 1,713 1. 9 % 2022 85,144 82,943 2,201 2.6 % 2023 81,859 79,081 2,778 3.4 % 2024 78,955 75,561 3,393 4.3 % 2025 76,935 72,890 4,045 5.3% 2026 75,204 70,502 4,702 6.3% 2027 73,880 68,496 5,384 7.3% 2028 72,504 66,431 6, 073 8.4% 2029 71,990 65,210 6, 780 9.4% 2030 71,554 64,096 7,458 10.4%

2050 92,895 77, 279 15,616 16.8 %

Emissions of air toxics also would be reduced by the proposed sulfur, exhaust and evaporative standards. Air toxics are generally a subset of compounds making up VOC, so the reduction trends tend to track the VOC re ductions presented above . Table 7

- 22 presents reductions for certain gaseous air toxics and polycyclic aromatic hydrocarbons (PAHs) M

, reflecting reductions of a few percent in 2017, and 20 to 40 percent of onroad emissions, depending on the individual pol lutant, in 2030.

M PAHs represents the sum of the following 15 PAH compounds: acenaphthene, acenaphthalene , anthracene, benz(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benze(g,h,i)perylene, benzo(k)fluoranthene, chrysene, dibenzo(a,h)anthracene, fluoranthene, fluorine, indeno(1,2,3,cd)pyrene, phenanthrene, and pyrene. These PAHs are included in EPA’s national emissions inventory (NEI).*** E.O. 12866 Review

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- 45 Table 7

- 22 Reductions for Certain Individual Compounds (Annual U.S. Short Tons)

2017 Reduction Percent r eduction

in onroad 2030 Reduction Percent reduction in onroad Acetaldehyde 7 62 3% 4,414 26% Formaldehyde 727 3% 2,707 12% Acrolein 23 1% 184 15% 1,3

- Butadiene 322 5% 1,087 37% Benzene 1,625 4% 8,581 3 6 % Naphthalene 9 6 2% 420 17% Ethanol 2, 684 2% 27,8 21 24% 2,2,4

- Trimethylpentane 840 2% 5

, 616 18%

Ethyl Benzene 724 3% 3

, 204 22% Hexane 857 3% 3

, 525 30% Propionaldehyde 29 2% 102 20% Styrene 98 4% 755 38 % Toluene 3

, 504 3% 16, 965 22% Xylene 2

, 856 3% 14, 238 22% PAHs 7 1% 67 17%

The totals shown in Table 7

- 23 represent the sum of all toxic species in Table 7

- 22

. As sh own, in 2030 the overall on

- road inventory of total toxics would be reduced by over 20 percent, with nearly one third of the vehicle program reductions coming from the proposed evaporative standards.

Table 7

- 23 Reductions in Total Mobile Source Air Toxics (Annual U.S. Short Tons)

2017 2030 Total reduction 15,156 89,685 Reduction from pre

- Tier 3 fleet due to sulfur standard 13,184 5,022

Reduction from Tier 3 fleet due to vehicle and sulfur standards 1,972 84,663

Exhaust 13,748 64,144 Evaporative 1,408 25,541

SO 2 emissions from mobile sources are a direct function of sulfur in the fuel, and reducing sulfur in gasoline would result in immediate reductions in SO 2 from the on and off

- road fleet. The reduct ions, shown in Table 7

- 24

, represent a roughly 50 percent reduction in onroad SO 2 emissions. The breakdown of the relative contribution of on

- road vehicles and off

- road equipment is shown ; the contribution of off

- road sources is a fu nction of off

- road gasoline consumption account ing for approximately 5 percent of overall gasoline use. 36*** E.O. 12866 Review

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- 46 Table 7

- 24 Projected SO 2 Reductions from Tier 3 Program (Annual U.S. Short Tons)

2017 2030 Total reduc tion 16, 261 17,267 Reduction from onroad vehicles due to sulfur standard 15,494 16,370

Reduction from off

- road equipment due to sulfur standard 767 897

Percent reduction in onroad SO 2 emissions 51% 51%

7.2 Criteria and Toxic Pollutant Air Quality Impacts

7.2.1 Emission Inventories for Air Quality Modeling

As noted in Section 7.1, emission inventories for a ir quality modeling were required for the entire U.S. by 12 km grid cell and hour of the day for each day of the year, requiring a methodology with much grea ter detail than the national emission inventories presented above. While most of the modeling tools and inputs used for estimating national emission inventories

were also used in developing inputs for air quality modeling, the application of these tools ( particularly MOVES) to produce the gridded / hourly emissions was quite different, and in essence a separate analysis. As explained in Section 7.2.1.1 , the different analyses generated different onroad inventory totals, but th e relative reduction from reference to control scenarios

was consistent. The summary of the methodology for each sector is contained in the following sections; for brevity, details of the process for developing air

- quality ready emission inventories

are a vailable in a separate technical support document. 37

7.2.1.1 Onroad Emissions

For the onroad vehicle emissions inputs to our air quality modeling, we used an emission inventory approach that provided more temporal and geographical resolution than the approach used for the national inventories described above. While modeling at this level is time

consuming and resource intensive, t his detail is needed when generating inputs to air quality models because it allows us much more precision in accounting for local ambie nt temperatures and local fuel properties in our air quality modeling. For this purpose, we used county

- specific inputs and tools that integrated the MOVES model of onroad emissions with the Sparse Matrix Operator Kernel Emissions tool (SMOKE) emission in ventory model to take advantage of the gridded hourly temperature information used in air quality modeling.

In particular, we used an automated process to run MOVES to produ ce emission factors by temperature and speed for the fleet mix, fuels, and I/M pro gram for more than 100 “representing counties” , to which every other county could be mapped

. The emission factors then were multiplied by activity at the grid

- cell

- hour level to produce gridded hourly emissions for the entire continental U.S . These emiss ions were input into the Community Multiscale Air Quality Modeling System ( CMAQ )

. We summarize this approach in the sections below.*** E.O. 12866 Review

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- 47 We used the same approach to model both the reference and the control cases, except, for the Tier 3 control case , we used the Tier 3 emission rates and fuels developed for the national inventories and described in Section 7.1.3

. Because of differences in methodology, particularly the treatment of vehicle speed distributions and th e non

- linear temperature effects in MOVES, the more detailed approach used for the air quality inventory produced different emission estimates than those described in the national inventory section above. The two sets of results are compared in Table 7

- 25 below.

Table 7

- 25 Comparison of Calendar Year 2030 Onroad Emission National Inve nt ories and

Inventories Used for Air Quality Modeling [U.S. Short tons]

Pollutant Reference Control

National Inventory Air Q uality

Inventory Difference

AQ vs. NI National

Inventory Air Quality

Inventory Difference

AQ vs. NI NO X 1,890,403 1,846,571

- 2% 1,365,613 1,371,925 0% VOC 977,067 911,513

- 7 % 751,040 699,592

- 7 % CO 18,951,626 17,021,674

- 10% 13,186,263 11,984,061

- 9% P M 2

. 5 71,554 88,516 24% 64,917 83,842 29% Benzene 23,654 22,221

- 6% 15,073 14,352

- 5% Ethanol 107,912 116,762 8% 80,091 89,574 12% Acrolein 1,223 863

- 29% 1,039 699

- 33% 1

, 3

- Butadiene 2,915 2,932 1% 1,828 1,955 7% Formaldehyde 21,967 14,810

- 33% 19,260 12,270

- 36% Acetaldehyde 16,757 13,926

- 17% 12,343 9,946

- 19% SO 2 31,983 30,526

- 5% 15,613 15,068

- 3%

The differences between the n ational i nventories and air quality inventories r eflect the

non

- linear response to the more detailed handling of temperat ure and other local variables such as speed in the air quality inventory ; this is pronounced in pollutants with strong temperature sensitivities in MOVES, such as PM 2.5 , where the finer temperature resolution in the air quality approach produced significan tly higher emissions than the aggregate national inventory approach

.
Because the reference and control case emissions rates were the same for the national inventory and air quality inventory runs, the percent reductions due to the propos ed Tier 3 rule

ar e very similar, as shown in Table 7

- 26

. The exception is PM 2.5 , where the air quality inventory shows a slight increase in emissions in 2017. As discussed in Section 7.1. 3.2.2

, this increases resulted from a series of conservative assumptions and uncerta inties related to fuel parameters in 2017 which we d o n o t expect to occur in reality

.*** E.O. 12866 Review

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- 48 Table 7

- 26 Comparison of Emission Reductions from Reference to Control Case in

“National” and “Air Quality” Onroad Inven tories

2017 2030

Pollutant National

Inventory Reduction Air Quality

Inventory Reduction National Inventory

Reduction Air Quality

Inventory Reduction NO X - 8%

- 7%

- 28%

- 26% VOC

- 3%

- 3%

- 25%

- 23% CO

- 4%

- 3%

- 30%

- 30% PM 2

. 5 - 0.1% 1%

- 10 %

- 5% Benzene

- 4%

- 4%

- 36%

- 35% Ethanol

- 3%

- 2%

- 26%

- 23% Acrolein

- 1%

- 1%

- 15%

- 19% 1

, 3

- Butadiene

- 5%

- 5%

- 37%

- 33% Formaldehyde

- 2%

- 3%

- 12%

- 17% Acetaldehyde

- 3%

- 3%

- 26%

- 29% SO 2 - 5 1 %

- 50%

- 51%

- 51%

The following sections summarize the analysis done to generate the air quality inventories.

7.2.1.1.1 Representing Counties

Air quality modeling requires emission inventories for nearly all of the more than 3,000 counties in the United States. Although EPA compiles county

- specific databases for all counties in the nation, actu al county

- specific data is rare. Instead, much of our “county” data is based on
state

- wide estimates or national defaults. For this proposal, r ather than explicitly model every county in the nation, we have done detailed modeling for some counties and le ss detailed estimates for the other counties.

In this approach, we group counties that have similar properties that would result in similar emission rates. W e explicitly model only one county in the group (the "representing" county) to determine emissio n rates. These rates are then used in combination with county specific activity and meteorology data

, to generate inventories for all of the counties in the group. This approach dramatically reduces the number of modeling runs required to generate invent ories and still takes into accoun t differences between counties.

The grouping of counties was based on several characteristics as summarized in Table 7

- 27 b elow.*** E.O. 12866 Review

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- 49 Table 7

- 27 Characteristics for Representing Cou nty Groupings County Grouping Characteristic Description PADD Petroleum Administration for Defense Districts (PADDs). PADD 1 is divided into three sub

- PADD groupings and each sub

- group is treated as a separate PADD (1a, 1b and 1c). Each state belongs to a PADD and all counties in any state are within the same PADD. Fuel Parameters Average gasoline fuel properties for January and July 2005, including RVP, sulfur level, ethanol fraction and percent benzene Emission Standards Some states have adopted Cali fornia highway vehicle emission standards or plan to adopt them. Since implementation of the standards varies, each state with California standards is treated separately. Inspection/Maintenance Programs Counties were grouped within a state according to wh ether or not they had an I/M program. All I/M programs within
a state were considered as a single program, even though each county may be administered separately and have a different program design.

Altitude Counties were categorized as high or low alti tude based on the criteria set forth by EPA certification procedures (4,000 feet above sea level).

T he result is a set of 106 county groups with similar fuel, emission standards, altitude and I/M program s. For each group, the county with the highest VMT was chosen as the representing county. Of these, only 10 3 were needed to model the 48 states included in the air quality analysis inventory.

For each county group, SMOKE

- MOVES generated a set of rates that varied by vehicle type, speed and temperature, t hus we did not need to consider the fleet mix, speed or temperature range in our grouping characteristics. This greatly increases the number of counties that can be in each grouping, and reduces the number of MOVES runs required.

More detail on the proc ess for selecting representative counties and a list of all of the 3,322 counties in the nation and the c ounties selected to represent is provided in the emission inventory technical support document. 38

7.2.1.1.2 SMOKE

- MOVES

The official EPA highway vehicle emissions model (MOVES) was updated as described in Section 7.1.3 for national emission inventory development, but i n order to take advantage of the gridded hourly temperature information used in air quality modeling, MOVES and SMOKE ha ve been integrated into an inventory generation system called SMOKE

- MOVES. 39 MOVES can be run in “inventory mode” to calculate the mass of pollutant emissions, as was done for the national inventories, or in “emission rate” mode, in which it calculates emi ssions in grams per*** E.O. 12866 Review

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- 50 mile (for running emissions) or grams per vehicle (for start and evaporative emissions) . For our air qu ality runs, we used the rates approach. This creates a set of “lookup tables” with emission rates by temperatu re, speed, pollutant, and vehicle class (Source Classification Code (SCC)). SMOKE then transform s these rates into emission inventories for the air quality modeling by multiplying these emission factors by activity specific to each grid cell hour, 40

The SMOKE

- MOVES process ge nerates MOVES run specification files to produce the emission rate lookup tables (in MOVES there are three per run to cover all emission processes: Rate Per Distance, Rate per Vehicle, and Rate per Profile) covering the range of temperatures needed, across each combination of fuel and I/M program in the nation. For a given scenario, this resulted in over 16,000 run specification files. A series of post

- processing scripts were developed to take the raw MOVES emission rate table results and translate it int o the emission rates tables needed by SMOKE to produce mass emissions by 12 km grid and hour of the day, for an entire year. Note, a n update to these post

- processing scripts was made between the reference case and control case runs , which inadvertently in troduced a small inconsistency in emission rates between reference and control for about one

- third of the counties

. Specifically, there were 1

, 218 counties, out of 3

, 109 total counties, which were impacted. T he result was that for some counties, control case emission rates were a few percent higher than they should have been; fixing this error w ould increase the magnitude of reduction in the air quality analysis. 41

For expediency, MOVES lookup tables were generated for July and January to get the full ra nge of temperatures needed for an entire year’s worth of meteorology data. Thi s efficiency step introduces uncertainty because it does not account for fuel “shoulder” seasons in the fall and spring , where the actual fuel pool is a blend of winter and summ er fuel. This is mainly an issue for fuel RVP, which is not changing between the reference and control scenarios.

7.2.1.1.1 Inputs to MOVES

The county

- level fuel

- property inputs for the air quality runs were the same as for the national inventories described in S ection 7.1.3 . However, for the air quality runs , we were able to use grid

- level tempera tures. We also needed county

- specific information on vehicle populations, age distributions

, and inspection

- maintenance programs for each of the representing counties. The source data for each of these inputs is described below.

7.2.1.1.1.1 Temperature and Humidity
Ambient temperature can have a large impact on emissions. C old temperatures are associated with high start emissions for many pollutants. High temperatures are associated with greater running emissions due to the higher engine load of air conditioning. High temperatures also are associated with higher evaporative emissions. And, of course, the interaction between emissions and ambient tem peratures is an important consideration in air quality modeling. Thus accurately accounting for ambient temperatures was important for our air quality modeling work.

The gridded meteorological input data for the entire year of 2005 were derived from simu lations of the Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model. 42 This model, commonly referred to as MM5, is a limited

- area, nonhydrostatic, terrain

- following system that solves for the full set of physical and*** E.O. 12866 Review

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- 51 the rmodynamic equations which govern atmospheric motions. A description of how this tool was used to determine temperatures is found in the documentation for the recent Heavy

- Duty Greenhouse Gas rule. 43

SMOKE

- MOVES uses the MM5 temperatures and the county gro ups described in Section 7.2.1.1.1 to generate a list of all the possible temperatures and temperature profiles that are needed in the lookup tables. (Temperature profiles are vectors of 24 temperatures that describe how tempe ratures change over a day. They are needed to estimate vapor venting emissions.) To do this, SMOKE

- MOVES determine s the minimum and maximum temperatures in the county group for January and for July , and the minimum and maximum temperatures for each hour o f the day. It then generates a list of all possible temperatures between these limits, using a five degree Fahrenheit interval. The model also uses these temper atures (using a 10 degree interval) to develops a collection of possible temperature profile s.

SMOKE

- MOVES then runs MOVES for each of the listed temperatures and temperature profiles, generating emission rate look

- up tables that cover the desired temperature ranges. Finally, the original grid cell temperatures are used to find the appropriate emi ssion rate for each cell. The 2005 temperatures were used for all scenarios.

The treatment of humidity is simpler. SMOKE

- MOVES calculates an average day

- time (6am to 6pm) relative humidity for the county group for July and for January. The appropriate (J uly or January) humidity is used for all runs of the county group.

7.2.1.1.1.2 Vehicle Population Inputs
Vehicle population data is a required input for MOVES when modeling on a county basis. Using the technical guidan ce provided to states by EPA, a contractor genera ted appropriate estimates for vehicle populations for use in the MOVES databases using the county specific VMT and national average ratios of vehicle populations versus vehicle VMT from the MOVES application. This method is described in Section 3.3 of the document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory Preparation in State Implementation Plans and Transportation Conformity" (EPA

- 420

- B

- 10

- 023, April 2010), which is available on the EPA web site at: http://www.epa. gov/otaq/models /moves/index.htm

7.2.1.1.1.3 Other Local Inputs

In addition to temperature, vehicle population and fuels, we also needed inputs such as age distribution and Inspection Maintenance program descriptions for each of the representing counties. These inputs are required for the model to run at the county level and provided an opportunity to assure that the model was properly accounting for the most recent available local data. These county inputs were derived from the inputs used for the National Emissions Inventory (NEI ). This inventory covers the 50 United States (U.S.), Washington DC, Puerto Rico and U.S. Virgin Islands. The NEI was created by the U.S. Environmental Protection Agency's (EPA's) Emission Inventory Group (EIG) in Research Triangle Park, North Carolina, i n cooperation with the Office of Transportation and Air Quality in Ann Arbor, Michigan. The inputs for the NEI are stored in the National Mobile Inventory Model (NMIM) county database*** E.O. 12866 Review

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- 52 (NCD). Details of how the NCD was developed are documented for the NEI. 44 These inputs were then converted to a format consistent with MOVES

.

7.2.1.1.2 Parallel Processing on the “ Cloud ”

Providing the level of detail desired for the air quality modeling required an enormous amount of data

. Even with the “representing county” approach , we ran MOVES over 83, 000 times to support Tier3 rule making. Processing just one of the Tier 3 scenarios required 16,604 runs, which if run serially would take over 200 days. Early on, we recognized that this would be infeasible, even with the fastest computers available to us. Therefore, we developed a Linux

based environment at Amazon Web Services that enable d us to process the Tier 3 b ase case in less than 48 hours. We split the 16,604 runs into 206 batches (103 representative counties, for January and July). We then r an 206 Linux processor instances in parallel, with each instance processing 80

- 120 individual MOVES runs.

7.2.1.1.3 VMT, Population, and Speed

In addition to the lookup tables, SMOKE requires county VMT, population, and average speed by road type to calculate the necessary emissions for air quality modeling.

VMT by county and Source Classification Code (SCC) was developed using MOVES2010a and the National County Database. MOVES2010a has the EPA’s most recent projections of VMT growth at th e national level, based on estimates from the Annual Energy Outlook 45

. The National County Database (NCD20101201) 46 has our most recent estimates of 2005 VMT and our best estimates of allocation of VMT from national to the county level. Accordingly, for th e 2005 base year, our estimates of VMT by county and SCC were taken directly from the NCD. For the 2017 and 2030 inventories, we ran MOVES2010a with default inputs to generate total national VMT by SCC. But, because MOVES uses a static (1999) default all ocation of VMT to county, we did not use MOVES for these allocations. Instead, the 2017 county VMT was created by interpolating between the NCD VMT values for 2015 and those for 2020 and computing the NCD fraction for each county, then multiplying these f ractions by the MOVES VMT. The 2030 county allocation was computed similarly, using the NCD VMT for 2030. The VMT was also adjusted to account for increased onroad transportation of ethanol fuels and the resulting increase in travel by large tanker truck s. For both the reference and control scenarios, impacts of this activity on emissions from tank trucks (Class 8) are accounted by adjusting VMT used in SMOKE

- MOVES. 47 The VMT adjustments were derived from the Oak Ridge National Laboratory analysis of eth anol transport, scaled to account for the ethanol volumes.

Vehicle populations by county and SCC were developed similarly to the VMT , using MOVES to generate national totals for each year and using the NCD to allocate to county. However, the NCD does not include population estimates, so we used MOVES to generate the 2005 national population and we assumed that, for each calendar year (2005, 2017 and 2030) and for each SCC, the allocation of national vehicle population to county was proportional to the al location of VMT (summed across roadtypes).*** E.O. 12866 Review

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- 53 The average speeds provided to SMOKE for each county were derived from the default national average speed distributions found in the default MOVES 2010a database AvgSpeedDistribution table. These average speeds are the average speeds developed for the previous EPA highway vehicle emission factor model, MOBILE6. 48 In MOVES, there is a distribution of average speeds for each hour of the day for each road type. The average speeds in these distributions were used to calculate an overall average speed for each hour of the day. These hourly average speeds were weighted together using the default national average hourly vehicle miles traveled (VMT) distribution found in the MOVES default database HourlyVMTFraction tabl e, to calculate an average speed for each road type. This average speed by road type was provided to SMOKE for each county.

7.2.1.2 Nonroad Emissions

The “primary” nonroad emissions used in air quality modeling are identical to those used for national inventories as presented in Section 7.1.4 above. The NMIM model was run to generate county

- month inventories by SCC, which were processed to gridded

- hourly emissions by SMOKE. For more details on SMOKE processing of nonroad emissions, see t he emissions modeling technical support document

. 49

Table 7

- 28 Comparison of Calendar Year 2030 Nonroad Emission National Inve nt ories

and Inventories Used for Air Quality Modeling [U.S. Short tons]

Pollutant Re ference Control

National Inventory Air Quality

Inventory Difference National Inventory Air Quality

Inventory Difference N O X 765,026 765,026 0.0% 765,026 765,026 0.0% VOC 1,209,534 1,209,534 0.0% 1,209,452 1,209,452 0.0% CO 12,921,772 12,921,772 0.0% 1 2,921,772 12,921,772 0.0% PM 2

. 5 68,308 68,308 0.0% 68,308 68,308 0.0% Benzene 23,246 23,390 0.6% 23,245 23,389 0.6% Acrolein 571 570

- 0.2% 571 570

- 0.2% 1

, 3

- Butadiene 1,772 1,774 0.1% 1,772 1,774 0.1% Formaldehyde 13,580 13,522

- 0.4% 13,580 13,522

- 0. 4% Acetaldehyde 7,899 7,888

- 0.1% 7,899 7,888

- 0.1% SO 2 3,154 3,154 0.0% 2,257 2,257 0.0%

7.2.1.3 Refueling

7.2.1.3.1 Methodology

This section describes how the emission inventories for refueling from on

- road vehicles in calendar years 2017 and 2030 for Tier 3 reference and control cases were generated for air quality modeling. The refueling inventory includes emissions from spillage loss and displacement vapor loss. The displacement emissions vary from scenario to scenario depending on the RVP of the modeled fuels. Fo r this analysis, the refueling emissions were estimated using the revised version of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county*** E.O. 12866 Review

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- 54 level for all twelve months. We used the same fuel supply inputs as we used for the onroad inventories described in Section 7.1.3.2

.
As for onroad emissions, described above , we used a “representing county” approach to reduce MOVES runtime. Additional information on the use of representing counties to model refueling emissions is available in a technical support document. 50

7.2.1.3.2 Emission Inventory Results

The annual refueling emission inventories for air quality modeling for the lower 48 states are shown in Table 7

- 29

, along with the percent changes between the two scenarios. The Tier 3 proposal to eliminate the RVP waiver for E10 fuels reduces refueling emissions in 2017, but has no impact in 2030, where we model E15 as the only gasoline available for onroad vehicles in both the reference and control cases.

Table 7

- 29 Refueling Emissions for Tier 3 reference and control (U.S. Short Tons)

2017 2030

R eference C ontrol Percent R eduction R eference C ontrol Percent R eduction VOC 75,397 74,235

- 1.5% 56,405 56,402 0.0 % Benzene 158 160 1.6% 90 90 0.0% Ethanol 8,582 8,426

- 1.8% 7,143 7,142 0.0%

7.2.1.4 Portable Fuel Container and Upstream Emissions

While the Tier 3 proposed rule has no impact on portable fuel container (PFC) or upstream emissions associated with fuel producti on and transport/distribution , a significant number of modifications were made to the 2005v.4.2 platform inventory to account for impacts of renewable fuel requirements under EISA in the reference case air quality inventory . These modifications are descri bed in detail in a memorandum to the docket

. 51 Modifications to point and nonpoint inventories include adjustments to agricultural emissions, increases in emissions associated with production of corn ethanol, cellulosic ethanol, cellulosic diesel, and biod iesel, decreases in petroleum refinery emissions to account for gasoline displacement, and increased vapor loss emissions from transport of ethanol and g asoline/ethanol fuel blends. Modifications to mobile source inventories include increases in combustio n emissions from water, rail and truck transport of biofuels. PFC emissions were adjusted to account for impacts of RVP changes associated with use of gasoline/ethanol blends.

7.2.1.5 Hydrocarbon Speciation Profiles and SMOKE

We used the Communit y Multi

- scale Air Quality (CMAQ) model, described in detail in the following section, to conduct air quality modeling for this analysis. T he SMOKE tool is used
to process emission inventories for air quality modeling. N Specifically, SMOKE converts our air

N For more information, please see the website for SMOKE: http://www.smoke

- model.org/index.cfm

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- 55 quality emissio ns inventories into CMAQ

- ready inputs by transforming the emission inventories based on the temporal allocation, chemical spec iation, and spatial allocation requirements of CMAQ. In processing our Tier 3 emissions inventories for CMAQ, SMOKE uses hydrocarb on speciation profiles to break total hydrocarbons down into i ndividual constituent compounds and create the needed chemical speciation inputs required for CMAQ. Given the complexity of the atmospheric chemistry, the hydrocarbon speciation can have an imp ortant influence on the air quality modeling results. We recently created a number of new hydrocarbon speciation profiles for vehicle exhaust and evaporative emissions and headspace vapor. These profiles were created using data from the EPAct test program

, 52 CRC’s E

- 77 series of evaporative emissions programs described in Section 7.2 above, 53

, 54 and r ecent measurements of speciated gasoline headspace vapors collected by EPA’s Office of Research and Development (ORD)

. 55 Mobile source h ydrocarbon speciation pro files used in this analysis are listed in a memo to the docket. 56

7.2.2 Air Quality Modeling Methodology

Air quality models use mathematical and numerical techniques to simulate the physical and chemical processes that affect air pollutants as they disperse and react in the atmosphere. Based on inputs of meteorological data and source information, these models are designed to characterize primary pollutants that are emitted directly into the atmosphere and secondary pollutants that are formed as a result of comp lex chemical reactions within the atmosphere. Photochemical air quality models have become widely recognized and routinely utilized tools for regulatory analysis by assessing the effectiveness of control strategies. These models are applied at multiple s patial scales

- local, regional, national, and global. This section provides detailed information on the photochemical model used for our air quality analysis (the Community Multi

scale Air Quality (CMAQ) model), atmospheric reactions and the role of chem ical mechanisms in modeling, and model uncertainties and limitations. Further discussion of the modeling methodology is included in the Air Quality Modeling Technical Support Document (AQM TSD) found in the docket for this rule . Results of the air qualit y modeling are presented in Section 7.2.4

.
7.2.2.1 Modeling Methodology

A national

- scale air quality modeling analysis was performed to estimate future year 8

- hour ozone concentrations, annual PM 2.5 concentrations, 24

- hour PM 2.5 concen trations, annual NO 2 concentrations, air toxics concentrations, visibility levels and nitrogen and sulfur deposition levels for 2017 and 2030 . The 200 5

- based CMAQ modeling platform was used as the basis for the air quality modeling for this proposed rule. This platform represents a structured system of connected modeling

- related tools and data that provide a consistent and transparent basis for assessing the air quality response to projected changes in emissions. The base year of data used
to construct t his platform includes emissions and meteorology for 200 5 . The platform was developed by the U.S. EPA’s Office of Air Quality Planning and Standards in collaboration with the Office of Research and Development and is intended to support a variety of regula tory and research model applications and analyses.

The CMAQ modeling system is a non

- proprietary , publicly available, peer

- reviewed,
state

- of

- the

- science, three

- dimensional, grid

- based Eulerian air quality model designed to estimate the formation and fate of oxidant precursors, primary and secondary PM concentrations

,*** E.O. 12866 Review

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- 56 acid deposition, and air toxics, over regional and urban spatial scales for given input sets of meteorological conditions and emissions. 57

, 58

, 59 The CMAQ model version 4. 7 was most recently peer

- reviewed in February of 200 9 for the U.S. EPA. O The CMAQ model is a well

known and well

- respected tool and has been used in numerous national and international applications. 60

, 61

, 62 T his 200 5 multi

- pollutant modeling platform used the most recent CMAQ code available at the time of air quality modeling ( CMAQ version 4. 7.1 P ) with a minor internal change made by the U.S. EPA CMAQ model developers intended to speed model runtimes when only a small subset of toxics species are of interest.

CMAQ includes many s cience modules that simulate the emission, production, decay, deposition and transport of organic and inorganic gas

- phase and particle

- phase pollutants in the atmosphere. We used CMAQ v4.7.1 which reflect s updates to version 4.7 to improve the underlying science. These include aqueous chemistry mass conservation improvements, improved vertical convective mixing and lowered CB05 mechanism unit yields for acrolein from 1,3

- butadiene tracer reactions which were updated to be consistent with laboratory

measur ements. Section 7.2.3 of this draft RIA discusses the chemical mechanism and SOA formation.

7.2.2.2 Model Domain and Configuration

The CMAQ modeling domain encompasses all of the lower 48 States and portions of Canada and Mexico. The mo deling domain is made up of a large continental U.S. 36 kilometer ( km ) grid and two 12 km grids (an Eastern U.S. and a Western U.S. domain), as shown in Figure 7

- 11

. The modeling domain contains 14 vertical layers with the top of the modeling domain at about 16,200 meters, or 100 millibars (mb) of atmospheric pressure

.

O Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009). Report on the Peer Review of the Atmo spheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC.

, http://www.epa.gov/amad/peer/2009_AMAD_PeerReviewReport.pdf

. P CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and Analysis System (CMAS) as well as previous peer

- review reports at: http://www.cmascenter.org.*** E.O. 12866 Review

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- 57 Figure 7

- 11 Map of the CMAQ Modeling Domain

7.2.2.3 Model Inputs

The key inputs to the CMAQ model include emissions from anthr opogenic and biogenic sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological input files were derived from simulation s of the Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model 63 for t he entire year of 2 00 5 over model domains that are slightly larger than those shown in Figure 7

- 11 . This model, commonly referred to as MM5, is a limited

- area, nonhydrostatic, terrain

- following system that solves for the full set of physical and thermodynamic equations which govern atmospheric motions. 64 The meteorology for the national 36 km grid and the two 12 km grid s were developed by EPA and are described in more detail within the AQM TSD. The meteorological outputs from MM5 wer e processed to create model

- ready inputs for CMAQ using the Meteorology

- Chemistry Interf ace Processor (MCIP) version 3.4

. Outputs include : horizontal wind components (i.e., speed and direction), temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. 65

The lateral boundary and initial species concentrations are provided by a three

dimensional global atmospheric chemistry model, the GEOS

- CHEM model. 66 The global GEOS

- CHEM model simulates atmospheric chemi cal and physical processes driven by assimilated meteorological observations from the NASA’s Goddard Earth Observing System (GEO S). This model was run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitude

longitude) and 20 vertical layers. T he predictions were used to provide one

- way dynamic boundary conditions at three

- hour intervals and an initial concentration field for the 36 km CMAQ simulations. The future base conditions from the 36 km coarse grid modeling were used as the initial/boun dary state for all subsequent 12 km finer grid modeling.*** E.O. 12866 Review

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- 58 The emissions inputs used for the 200 5 base year and each of the future year base cases and control scenarios analyzed for this rule are summarized in Section 7.2.1 of th is draft RIA.

7.2.2.4 CMAQ Evaluation

A n operational model performance evaluation for ozone, PM 2.5 and its related speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.), nitrate and sulfate deposition, and specific air toxics (forma ldehyde, acetaldehyde, benzene, 1,3

- butadiene, and acrolein) was conducted using 2005 state/local monitoring data in order to estimate the ability of the CMAQ modeling system to replicate base year concentrations. Model performance statistics were calcula ted for observed/predicted pairs of daily/monthly/seasonal/annual concentrations. Statistics were generated for the following geographic groupings: domain wide, Eastern vs. Western (divided along the 100th meridian), and each Regional Planning Organizatio n (RPO) region. Q The “acceptability” of model performance was judged by comparing our results to those found in recent regional PM 2.5 model applications for other, non

- EPA studies. R Overall, the performance for the 2005 modeling platform is within the ra nge or close to that of these other applications. The performance of the CMAQ modeling was evaluated over a 2005 base case

. The model was able to reproduce historical concentrations of ozone and PM 2.5 over land with low bias and error results. Model pre dictions of annual formaldehyde, acetaldehyde and benzene showed relatively small bias and error results when compared to observations. The model yielded larger bias and error results for 1,3 butadiene and acrolein based on limited monitoring sites. A mo re detailed summary of the 2005 CMAQ model performance evaluation is available within the AQM TSD found in the docket of this rule.

7.2.2.5 Model Simulation Scenarios

As part of our analysis for this rulemaking, the CMAQ modeling system was used to calculate 8

- hou r ozone concentrations, daily and annual PM 2.5 concentrations, annual NO 2 concentrations, annual and seasonal (summer and winter) air toxics concentrations, visibility levels and annual nitrogen and sulfur deposition total levels for each of the following emissions scenarios:

- 2005 base year

- 20 17 Tier 3 reference case

- 20 17 Tier 3 control case

- 20 30 Tier 3 reference case

Q Regional Planning Organization regions include: Mid

- Atlantic/Northeast Visibility Union (MANE

- VU), Midwest Regional Planning Organization

– Lake Michigan Air D irectors Consortium (MWRPO

- LADCO), Visibility Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air Partnership (CENRAP), and Western Regional Air Partnership (WRAP).
R These other modeling studies represent a wide range of modeling analyses which cover various models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules.*** E.O. 12866 Review

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- 59 - 20 30 Tier 3 control case

T he emission inventories used in the air quality and benefits modeling are different from the proposed rule inventories due to the considerable length of time required to conduct the modeling. As noted above, emission inventories for air quality modeling were required for the entire U.S. by 12 km grid cell and hour of the day for each day of the year, req uiring a methodology of much greater detail than the national emission inventories presented in Section 7.1. While most of the modeling tools and inputs used for estimating national emission inventories were also used in developing inputs for air quality modeling as well, the application of these tools (particularly MOVES) to produce the gridded / hourly emissions was quite different, and in essence a separate analysis. As explained in Section 7.2.1.1 , the different analyses g enerated different onroad inventory totals, but the reduction from reference to control scenarios was consistent. The emission inventories used for air quality modeling are discussed in Section 7.2.1 of this draft RIA. The em issions modeling TSD , found in the docket for this rule (EPA

- HQ

- OAR

- 20 11

- 0135 ), contains a detailed discussion of the emissions inputs used in our air quality modeling.

We use the predictions from the model in a relative sense by combining the 2005 base

year predictions with predictions from each future

- year scenario and applying these modeled ratios to ambient air quality observations to estimate 8

- hour ozone concentrations

, daily and annual PM 2.5 concentrations, annual NO 2 concentrations and visibility impairment for each of the 2017 and 2030 scenarios. The ambient air quality observations are average conditions, on a site

by

- site basis, for a period centered around the model base year (i.e., 2003

- 2007 ).

The projected daily and annual PM 2.5 design val ues were calculated using the Speciated Modeled Attainment Test (SMAT) approach. The SMAT uses a Federal Reference Method (FRM) mass construction methodology that results in reduced nitrates (relative to the amount measured by routine speciation networks) , higher mass associated with sulfates (reflecting water included in FRM measurements), and a measure of organic carbonaceous mass that is derived from the difference between measured PM 2.5 and its non

- carbon components. This characterization of PM 2.5 mas s also reflects crustal material and other minor constituents. The resulting characterization provides a complete mass balance. It does not have any unknown mass that is sometimes presented as the difference between measured PM 2.5 mass and the characteri zed chemical components derived from routine speciation measurements. However, the assumption that all mass difference is organic carbon has not been validated in many areas of the U.S. The SMAT methodology uses the following PM 2.5 species components: su lfates, nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a fixed value of 0.5 μg/m 3 ). More complete details of the SMAT procedures can be found in the report "Procedures for Estimating Future PM 2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)". 67 For this latest analysis, several datasets and techniques were updated. These changes are fully described within the technical support document for the Final Tran sport Rule AQM TSD. 68 The projected 8

- hour ozone design values were calculated using the approach identified in EPA's guidance on air quality modeling attainment demonstrations. 69

Additionally, we conducted an analys i s to compare the absolute and percent differences between the future year reference and control cases for annual and seasonal ethanol,*** E.O. 12866 Review

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- 60 formaldehyde, acetaldehyde, benzene, 1,3

- butadiene, and acrolein, as well as annual nitrate and sulfate deposition. These data were not compared in a relative sense due to the limited observational data available.

7.2.3 Chemical Mechanisms in Modeling

This rule presents inventories for NO X , VOC, CO, PM 2.5 , SO 2 , NH 3 , and seven air toxics: benzene, 1,3

- butadiene, formaldehyde, acetaldehyde, ethanol, naphthalene and acrolein. T he air toxics are explicit model species in the CMAQv4.7 model with carbon bond 5 (CB05) mechanisms. 70 Emissions of all the pollutants included in the rule inventories, except ethanol, were generated using the Motor Vehicle Emissions Simulator (MOVES) VOC emissions and toxic

- to

- VOC ratios calculated using EPAct data. 71 Ethanol emissions for air quality modeling were based on speciation of VOC using different ethanol profiles (E0, E10 and E85) ( see Section 7.2.1.5 for mor e information ) . In addition to direct emissions, photochemical processes mechanisms are responsible for formation of some of these compounds in the atmosphere from precursor emissions. For some pollutants such as PM, formaldehyde, and acetaldehyde, many photochemical processes are involved. CMAQ therefore also requires inventories for a large number of other air toxics and precursor pollutants. Methods used to develop the air quality inventories can be found in Section 7.2.1

. In the CB05 mechanism, the chemistry of thousands of different VOCs in the atmosphere are represented by a much smaller number of model species which characterize the general behavior of a subset of chemical bond types; this condensation is necessary to al low the use of complex photochemistry in a fully 3

- D air quality model. 72

Complete combustion of ethanol in fuel produces carbon dioxide (CO 2 ) and water (H 2 O). Incomplete combustion results in the production of other air pollutants, such as acetaldehyde and other aldehydes, and the release of unburned ethanol. Ethanol is also present in evaporative emissions. In the atmosphere, ethanol from unburned fuel and evaporative emissions can undergo photodegradation to form aldehydes (acetaldehyde and formaldehyde ) and peroxyacetyl nitrate (PAN), and also plays a role in ground

- level ozone formation. Mechanisms for these reactions are included in CMAQ. Additionally, alkenes and other hydrocarbons are considered because any increase in acetyl peroxy radicals due t o ethanol increases might be counterbalanced by a decrease in radicals resulting from decreases in other hydrocarbons.

CMAQ includes 63 inorganic reactions to account for the cycling of all relevant oxidized nitrogen species and cycling of radicals, includ ing the termination of NO 2 and formation of nitric acid (HNO 3 ) without PAN formation. S

NO 2 + ∙OH + M → HNO 3 + M k = 1.19 x 10 - 11 cm 3 molecule - 1 s - 1 73

The CB05 mechanism also includes more than 90 organic reactions that include alternate pathways for the formation of acetyl peroxy radical, such as by reaction of ethene and other

S All rate coefficients are listed at 298 K and, if applicable, 1 bar of air.*** E.O. 12866 Review

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- 61 alkenes, alkane s, and aromatics . Alternate reactions of acetyl peroxy radical, such as oxidation of NO to form NO 2 , which again leads to ozone formation, are also included.

Atmospheric reactions and chemical mechanisms involving several key formation pathways are discus sed in more detail in the following sections.

7.2.3.1 Acetaldehyde

Acetaldehyde is the main photodegradation product of ethanol, as well as other precursor hydrocarbons. Acetaldehyde is also a product of fuel combustion. In the atmosphere, acetaldehyde can rea ct with the OH radical and O 2 to form the acetyl peroxy radical [CH 3 C(O)OO∙]. T When NO X is present in the atmosphere t his radical species can then further react with nitric oxide (NO), to produce formaldehyde (HCHO), or with nitrogen dioxide (NO 2 ), to pro duce PAN [CH 3 C(O)OONO 2 ]. An overview of these reactions and the corresponding reaction rates are provided below. U

CH 3 CHO + ∙OH → CH 3 C∙O + H 2 O k = 1.5 x 10 - 11 cm 3 molecule - 1 s - 1 74

CH 3 C∙O + O 2 + M → CH 3 C(O)OO∙ + M

CH 3 C(O)OO∙ + NO → CH 3 C(O)O∙ + NO 2 k = 2.0 x 10 - 11 cm 3 molecule - 1 s - 1 75

CH 3 C(O)O∙ → ∙CH 3 + CO 2

∙CH 3 + O 2 + M → CH 3 OO∙ + M

CH 3 OO∙ + NO → CH 3 O∙ + NO 2

CH 3 O∙ + O 2 → HCHO + HO 2

CH 3 C(O)OO∙ + NO 2 + M → CH 3 C(O)OONO 2 + M k = 1.0 x 10 - 11 cm 3 molecule - 1 s - 1 76

Acetaldehyde can react with the NO 3 radical, gro und state oxygen atom (O 3 P) and chlorine, although these reactions are much slower. Acetaldehyde can also photolyze (hν), which predominantly produces ∙CH 3 (which reac ts as shown above to form CH 3 OO

∙ ) and HCO ( which rapidly forms HO 2 and CO ) :

CH 3 CHO + hν +2 O 2 → CH 3 OO∙ + HO 2 + CO  = 240

- 380 nm 77

As mentioned above, CH 3 OO∙ can react in the atmosphere to produce formaldehyde (HCHO). Formaldehyde is also a product of hydrocarbon combustion. In the atmosphere, the most important reactions of formaldehyde ar e photolysis and reaction with the OH , with

T A cetaldehyde is not the only source of acetyl peroxy radicals in the atmosphere. For example, dicarbonyl compounds (methylglyoxal, biacetyl, and others) also form acetyl radicals , which can further react to form peroxyacetyl nitrate (PAN)

. U All rate coefficients are listed at 29 8 K and, if applicable, 1 bar of air.*** E.O. 12866 Review

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- 62 atmospheric lifetimes of approximately 3 hours and 13 hours, respectively. 78 Formaldehyde can also react with NO 3 radical, ground state oxygen atom (O 3 P) and chlorine, although these reactions are much slower. F ormaldehyde is removed mainly by photolysis whereas the higher aldehydes, those with two or more carbons such as acetaldehyde, react predominantly with OH radicals. The photolysis of formaldehyde is a n important source of new hydroperoxy radicals (HO 2 )

, w hich can lead to ozone formation and regenerate OH radicals

.
HC H O + hν + 2 O 2 → 2 H O 2 + CO  = 240

- 360 nm 79

HO 2 + NO → NO 2 + OH

Photolysis of HCHO can also proceed by a competing pathway which makes only stable products : H 2 and CO.

CB05 mechanisms for a cetaldehyde formation warrant a detailed discussion given the increase in vehicle and engine exhaust emissions for this pollutant and ethanol, which can form acetaldehyde in the air. Acetaldehyde is represented explicitly in the CB05 chemical mechanism 80

, 81 by the ALD2 model species, which can be both formed from other VOCs and can decay via reactions with oxidants and radicals. The reaction rates for acetaldehyde, as well as for the inorganic reactions that produce and cycle radicals, and the representativ e reactions of other VOCs have all been updated to be consistent with recommendations in the literature. 82

The decay reactions of acetaldehyde are fewer in number and can be characterized well because they are explicit representations. In CB05, a cetaldehyd e can photolyze in the presence of sunlight or react with molecular oxygen (O 3 (P)), hydroxyl radical (OH), or nitrate radicals. The reaction rates are based on expert recommendations, 83 and the photolysis rate is from IUPAC recommendations.

In CMAQ v4.7, the acetaldehyde that is formed from photochemical reactions is tracked separately from that which is due to direct emission and transport of direct emissions. In CB05, there are 25 different reactions that form acetaldehyde in molar yields ranging from 0 .02 (ozone reacting with lumped products from isoprene oxidation) to 2.0 (cross reaction of acylperoxy radicals, CXO 3 ). The specific parent VOCs that contribute the most to acetaldehyde concentrations vary spatially and temporally depending on characteris tics of the ambient air, but alkenes in particular are found to play a large role. The IOLE model species, which represents internal carbon

- carbon double bonds, has high emissions and relatively high yields of acetaldehyde. The OLE model species, represe nting terminal carbon double bonds, also plays a role because it has high emissions although lower acetaldehyde yields. Production from peroxyproprional nitrate and other peroxyacylnitrates (PANX) and aldehydes with 3 or more carbon atoms can in some inst ances increase acetaldehyde but because they also are a sink of radicals, their effect is smaller. Thus, the amount of acetaldehyde (and formaldehyde as well) formed in the ambient air as well as emitted in the exhaust (the latter being accounted for in

e mission inventories) is affected by changes in these precursor compounds due to the addition of ethanol to fuels (e.g., decreases in alkenes would cause some decrease of acetaldehyde, and to a larger extent, formaldehyde).*** E.O. 12866 Review

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- 63 The reaction of ethanol (CH 3 CH 2 OH) with OH is slower than some other important reactions but can be an important source of acetaldehyde if the emissions are large. Based on kinetic data for molecular reactions, the only important chemical loss process for ethanol (and other alcohols) i s reaction with the hydroxyl radical (∙OH). 84 This reaction produces acetaldehyde (CH 3 CHO) with a 90 percent yield. 85 The lifetime of ethanol in the atmosphere can be calculated from the rate coefficient, k, and due to reaction with the OH radical, occurs o n the order of a day in polluted urban areas or several days in unpolluted areas. V

In CB05, reaction of one molecule of ethanol yields 0.90 molecules of acetaldehyde. It assumes the majority of the reaction occurs through H

- atom abstraction of the more weakly

bonded methylene group, which reacts with oxygen to form acetaldehyde and hydroperoxy radical (HO 2 ), and the remainder of the reaction occurs at the

– CH3 and

– OH groups, creating formaldehyde (HCHO), oxidizing NO to NO 2 (represented by model specie s XO 2 ) and creating glycoaldehyde, which is represented as ALDX:

CH 3 CHOH + OH → HO 2 + 0.90 CH 3 CHO + 0.05 ALDX + 0.10 HCHO + 0.10 XO 2

7.2.3.2 Secondary Organic Aerosols (SOA)

Secondary organic aerosol (SOA) chemistry research described below has led to implementati on of new pathways for secondary organic aerosol (SOA) in CMAQ 4.7, based on recommendations of Edney et al. and the recent work of Carlton et al. 86

, 87 In previous versions of CMAQ, all SOA was semivolatile and resulted from the oxidation of compounds emit ted entirely in the gas

- phase. In CMAQ v4.7, parameters in existing pathways were revised and new formation mechanisms were added. Some of the new pathways, such as low

- NO X oxidation of aromatics and particle

- phase oligomerization, result in nonvolatile SOA.

Organic aerosol can be classified as either primary or secondary depending on whether it is emitted into the atmosphere as a particle (primary organic aerosol, POA) or formed in the atmosphere (SOA). SOA precursors include volatile organic compounds (VOCs) as well as low

volatility compounds that can react to form even lower volatility compounds. Current research suggests SOA contributes significantly to ambient organic aerosol (OA) concentrations, and in Southeast and Midwest States may make up more than 50 percent (although the contribution varies from area to area) of the organic fraction of PM 2.5 during the summer (but less in the winter). 88

, 89 A wide range of laboratory studies conducted over the past twenty years show that anthropogenic aromatic h ydrocarbons and long

- chained alkanes, along with biogenic isoprene, monoterpenes, and sesquiterpenes, contribute to SOA formation. 90

, 91

, 92

, 93

, 94 Modeling studies, as well as carbon isotope measurements

, indicate that a significant fraction of SOA results from the oxidation of biogenic hydrocarbons

. 95

, 96 Based on parameters derived from laboratory chamber experiments , SOA chemical mechanisms have been developed and integrated into air quality models such as the CMAQ model and have been used to predict OA concentr ations. 97

Over the past 10 years, ambient OA concentrations have been routinely measured in the U.S. and some of these data have been used to determine, by employing source/receptor

V All rate coefficients are listed at 298 K and, if applicable, 1 bar of air.*** E.O. 12866 Review

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- 64 methods, the contributions of the major OA sources, including biomass bur ning and vehicular gasoline and diesel exhaust. Since mobile sources are a significant source of VOC emissions, currently accounting for a lmost 4 0 percent of anthropogenic VOC, 98 mobile sources are also an important source of SOA , particularly in populated areas

. Toluene is an important contributor to anthropogenic SOA. Mobile sources are the most significant contributor to ambient toluene concentrations as shown by analyses done for the 200 5 National Air Toxics Assessment (NATA) 99 and the Mobile Source Air Toxics (MSAT) Rule. 100
The 2005 NATA indicate s that onroad and nonroad mobile sources accounted for almost 60 percent ( 1

. 46 μg/m 3 ) of the total average nationwide ambient concentration of toluene ( 2.48 μg/m 3 ) , when the contribution of the estimated “backg round” is apportioned among source
sectors

. The amount of toluene in gasoline influences the amount of toluene emitted in vehicle exhaust and evaporative emissions, although, like benzene, some toluene is formed in the combustion process. In turn, levels of toluene and other aromatics in gasoline are potentially influenced by the amount of ethanol blended into the fuel. Due to the high octane quality of ethanol, it greatly reduces the need for and levels of other high

- octane components such as aromatics i ncluding toluene (which is the major aromatic compound in gasoline). Since toluene contributes to SOA and the toluene level of gasoline is decreasing, it is important to assess the effect of these reductions on ambient PM.

In addition to toluene, other mo bile

- source hydrocarbons such a s benzene, xylene, and alkanes form SOA. Similar to toluene, the SOA produced by benzene and xylene from low

- NO X pathways is expected to be less volatile and be produced in higher yields than SOA from high

NO X cond itions

. 101 Alkanes form SOA with higher yields resulting from the oxidation of longer chain as well as cyclic alkanes

. 102

It is unlikely that ethanol would form directly fr o m SOA or affect SOA formation indirectly through changes in the radical populations from incre asing ethanol exhaust. Nevertheless, scientists at the U.S. EPA’s Office of Research and Development recently directed experiments to investigate ethanol’s SOA forming potential. 103 The experiments were conducted under conditions where peroxy radical react ions would dominate over reaction with NO ( i.e., irradiations performed in the absence of NO X and OH produced from the photolysis of hydrogen peroxide). This was the most likely scenario under which SOA formation could occur, since a highly oxygenated C4 o rganic would be potentially made. As expected, no SOA was produced. From these experiments, the upper limit for the aerosol yield would have been less than 0.01 percent based on s canning mobility particle sizer (SMPS) data. Given the expected negative

re sult based on these initial smog chamber experiments, these data were not published.

In general, measurements of organic aerosol represent the sum of POA and SOA and the fraction of aerosol that is secondary in nature can only be estimated. One of the most widely applied method of estimating total ambient SOA concentrations is the EC tracer method using ambient data which estimates the OC/EC ratio in primary source emissions. 104

, 105 SOA concentrations have also been estimated using OM (organic mass) to OC (org anic carbon) ratios, which can indicate that SOA formation has occurred, or by subtracting the source/receptor

- based total primary organic aerosol (POA) from the measured OC concentration. 106 Aerosol mass*** E.O. 12866 Review

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- 65 spectrometer (AMS) measurements along with positive matrix factorization (PMF) can also be used to identify surrogates for POA and SOA in ambient as well as chamber experiments. Such methods, however, may not be quantitatively accurate and provide no information on the contribution of individual biogenic an d anthropogenic SOA sources, which is critical information needed to assess the impact of specific sources and the associated health risk. These methods assume that OM containing additional mass from oxidation of OC comes about largely (or solely) from SO A formation. In particular, the contributions of anthropogenic SOA sources, including those of aromatic precursors, are required to determine exposures and risks associated with replacing fossil fuels with biofuels.

Upon release into the atmosphere, numer ous VOC compounds can react with free radicals in the atmosphere to form SOA. While this has been investigated in the laboratory, there is relatively little information available on the specific chemical composition of SOA compounds themselves from specif

ic VOC precursors. This absence of compositional data from the precursors has largely prevented the identification of aromatically

- derived SOA in ambient samples which, in turn, has prevented observation

- based measurements of the aromatic and other SOA co ntributions to ambient PM levels.

As a first step in determining the ambient SOA concentrations, EPA has developed a
tracer

- based method to estimate such concentrations. 107

, 108 The method is based on using mass fractions of SOA tracer compounds, measured in s mog chamber

- generated SOA samples, to convert ambient concentrations of SOA tracer compounds to ambient SOA concentrations. This method consists of irradiating the SOA precursor of interest in a smog chamber in the presence of NO X , collecting the SOA prod uced on filters, and then analyzing the samples for highly polar compounds using advanced analytical chemistry methods. Employing this method, candidate tracers have been identified for several VOC compounds which are emitted in significant quantities and known to produce SOA in the atmosphere. Some of these SOA

- forming compounds include toluene, a variety of monoterpenes, isoprene, and β

- caryophyllene

, the latter three of which are emitted by vegetation and are more significant sources of SOA than toluene. Smog chamber work can also be used to investigate SOA chemical formation mechanisms. 109

, 110

, 111

, 112

Although these concentrations are only e stimates, due to the assumption that the mass fractions of the smog chamber SOA samples using these tracers are equal to those in the ambient atmosphere, there are presently no other means available for estimating the SOA concentrations originating from in dividual SOA precursors. Among the tracer compounds observed in ambient PM 2.5 samples are two tracer compounds that have been identified in smog chamber aromatic SOA samples. 113 To date, these aromatic tracer compounds have been identified, in the laborato ry, for toluene and m

- xylene SOA. Additional work is underway by the EPA to determine whether these tracers are also formed by benzene and other alkylbenzenes (including

o

- xylene, p

- xylene, 1,2,4

- trimethylbenzene, and ethylbenzene).

One caveat regarding t his work is that a large number of VOCs emitted into the atmosphere, which have the potential to form SOA, have not yet been studied in this way. It is possible that these unstudied compounds produce SOA species which are being used as tracers for other V OCs. This means that the present work could overestimate the amount of SOA formed in the atmosphere by the VOCs studied to date. This approach may also estimate entire*** E.O. 12866 Review

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- 66 hydrocarbon classes (e.g., all methylsubstituted

- monoaromatics or all monoterpenes) an d not individual precursor hydrocarbons. Thus the tracers could be broadly representative and not indicative of individual precursors. This is still unknown. Also, anthropogenic precursors play a role in formation of atmospheric radicals and aerosol aci dity, and these factors influence SOA formation from biogenic hydrocarbons. This anthropogenic and biogenic interaction, important to EPA and others, needs further study. The issue of SOA formation from aromatic precursors is an important one to which EP A and others are paying significant attention.

The aromatic tracer compounds and their mass fractions have also been used to estimate monthly ambient aromatic SOA concentrations from March 2004 to February 2005 in five U.S. Midwestern cities. 114 The annua l tracer

- based SOA concentration estimates were 0.15, 0.18, 0.13, 0.15, and 0.19 μg carbon/m 3 for Bondville, IL, East St. Louis, IL, Northbrook, IL, Cincinnati, OH and Detroit, MI, respectively, with the highest concentrations occurring in the summer. On average, the aromatic SOA concentrations made up 17 percent of the total SOA concentration. Thus, this work suggests that we are finding ambient PM levels on an annual basis of about 0.15 μ g/m 3 associated with present toluene levels in the ambient air in these Midwest cities. Based on preliminary analysis of recent laboratory experiments, it appears the toluene tracer could also be formed during photooxidation of some of the xylenes. 115

Over the past decade a variety of modeling studies have been conducted to predict ambient SOA levels

. While early studies focus ed on the contribution of biogenic monoterpenes

, additional precursors, such as sesquiterpenes, isoprene, benzene, toluene, and xylene, have been implemented in atmospheric models such as GEOS

- Chem, PMCAMx, and CMAQ. 116

, 117

, 118

, 119

, 120

, 121

, 122 Studies have indicated that ambient OC levels may be underestimated by current model parameterizations. 123 While the treatment of new precursors has likely reduced the model/measurement bias, underestimates can persist. 124 I n general, modeling studies focus on comparing the sum of the POA and SOA concentrations with ambient OC or estimated OA concentrations. Without a method to attribute measured OC to different sources or precursors, identifying causes of the underestimates in modeled OC via model/measurement comparisons can be challenging. Oxidation of low

- volatility organic compounds as well as particle

- phase reactions resulting from acidity have been explored as potential missing sources of OC in models. 125

, 126

7.2.3.3 Ozone

As mentio ned above, the addition of ethanol to fuels has been shown to contribute to PAN formation and this is one way for it to contribute therefore to ground

- level ozone formation downwind of NO X sources . PAN is a reservoir and carrier of NO X and is the product of acetyl radicals reacting with NO 2 in the atmosphere. One source of PAN is the photooxidation o f acetaldehyd e (Section 7.2.3.1 ), but many VOCs have the potential for forming acetyl radicals and therefore PAN or a PAN

- type compo und

. W PAN can undergo thermal decomposition with a lifetime of approximately 1 hour at 298K or 148 days at 250K. X

W M any aromatic hydrocarbons

, particularly those present in high percentages in gasoline (toluene, m

- , o

- , p

- xylene, and 1,3,5

- , 1,2,4

- trime thylbenzene), form methylglyoxal and biacetyl, which are also strong generators of acetyl*** E.O. 12866 Review

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- 67 CH 3 C(O)OONO 2 + M → CH 3 C(O)OO∙ + NO 2 + M k = 3.3 x 10 - 4 s - 1 127

The reaction above shows how NO 2 is released in the thermal decomposition of PAN

, along with a peroxy radical which can oxidize NO to NO 2 as previously shown in Section 7.2.3.1

. NO 2 can also be formed in ph otodegradation reactions where NO is converted to NO 2 (see OH radical reaction of acetaldehyde in Section 7.2.3.1 ). In both cases, NO 2 further photolyzes to produce ozone (O 3 ).

NO 2 + h ν → NO + O( 3 P)  = 300

- 800 nm 128

O( 3 P) + O 2 + M → O 3 + M

The temperature sensitivity of PAN allows it to be stable enough at low temperatures to be transported long distances before decomposing to release NO 2 . NO 2 can then participate in ozone formation in regions remote from the original NO X source

. 129 A discussion of CB05 mechanisms for ozone formation can be found in Yarwood et al. (2005). 130

Another important way that ethanol fuels contribute to ozone formation is by increasing the formation of new radicals through increases in formaldehyde and acet aldehyde. As shown in Section 7.2.3.1 , the photolysis of both aldehydes results in two molecules of either hydroperoxy radical or methylperoxy radical, both of which oxidize NO to NO 2 leading to ozone formation.

7.2.3.4 Uncertainties As sociated with Chemical Mechanisms

A key source of uncertainty with respect to the air quality modeling results is the photochemical mechanisms in CMAQ 4.7 .1

. Pollutants such as ozone, PM, acetaldehyde, formaldehyde, acrolein, and 1,3

- butadiene can be form ed 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 handl e reactions of thousands of chemicals in the atmosphere. Mechanisms for formation of ozone, PM, acetaldehyde and peroxyacetyl nitrate (PAN) are discussed in Section 7.2.3

. For PM, there are a number of uncertainties associated w ith SOA formation that should be addressed explicitly. As mentioned in Section 7.2.3

, a large number of VOCs emitted into the atmosphere, which have the potential to form SOA, have not yet been studied in detail. In addition, the amount of ambient SOA that comes from 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 Communit y Modeling and Analysis System (CMAS) website

. 131

radicals ( Smith, D.F., T.E. Kleindienst, C.D. McIver ( 1999 ) Primary product distribution from the reaction of OH with m

- , p

- xylene and 1,2,4

- and 1,3,5

- Trimethylbenze ne. J. Atmos. Chem., 34: 339

- 364 .).
X All rate coefficients are listed at 298 K and, if applicable, 1 bar of air.*** E.O. 12866 Review

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- 68 7.2.4 Impacts of the Proposed Rule on Air Quality

Air quality m odeling performed for this proposed rule estimates the changes in ambient concentrations of PM 2.5 , ozone and NO 2 , as well as changes in ambient conce ntrations of ethanol and the following air toxics: acetaldehyde, acrolein, benzene, 1,3

- butadiene, and formaldehyde

. The air quality modeling results also include changes in deposition of nitrogen and sulfur and changes in visibility levels due to this pr oposed rule

.
This section describes current ambient levels of the modeled pollutants an d presents the projected future ambient levels resulting from the proposed rule

.

7.2.4.1 Ozone

As described in Section 6.2.1 of this draft RIA , ozone causes adverse health e ffects, and the EPA has set national ambient air quality standards (NAAQS) to protect against those health effects. In this section, we present information on current and model

- projected future ozone levels.

7.2.4.1.1 Current Levels of Ozone

Figure 7

- 12 shows a snapshot of measured ozone concentrations in 20 10 . The highest ozone concentrations were located in California.*** E.O. 12866 Review

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- 69 Figure 7

- 12 Ozone Concentrations (fourth highest daily maximum 8

- hour concentration) in ppm for 20 10 Y
The primary and secondary NAAQS for ozone are 8

- hour 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, 2004). Z As of December 14 , 2012, there

were 41 8

- hour ozone nonattainment areas for the 1997 ozone NAAQS

, composed of 2 21 full or partial counties

, w ith a total population of over 1 29 million . Nonattainment areas for the 1997 8

- hour ozone NAAQS are pictured in Figure 7

- 13

. Nonattainment designations for the 2008 ozone standard s we re finalized on April 30, 2012 and May 31, 20 12

. 132 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 pictured in Figure 7

- 14

. As of December 14, 2012, over 138 million people are liv ing in ozone nonattainment areas. AA

Y From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA

- 454/R

- 12

- 001. February 2012. Available at: http://www.epa.gov/ airtrends/2011/.
Z A nonattainment area is defined in the Clean Air Act (CAA) as an area that is violating an ambient standard or is contributing to a nearby area that is violating the standard.
AA The 138 million total is calculated by summing, without d ouble counting, the 1997 and 2008 ozone nonattainment populations contained in the Summary Nonattainment Area Population Exposure report*** E.O. 12866 Review

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- 70 Figure 7

- 13 1997 8

- hour Ozone Nonattainment Areas

(http://www.epa.gov/oar/oaqps/greenbk/popexp.html). If there is a population associated with both the 1997 and 2008 no nattainment areas, and they are not the same, then the larger of the two populations is included in the sum.*** E.O. 12866 Review

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- 71 Figure 7

- 14 2008 8

- hour Ozone Nonattainment Areas

States with ozone no nattainment areas are required to take action to bring those areas into attainment in the future. The attainment date assigned to an ozone 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 then to maintain it thereafter. BB T he attainment dates for areas designated nonattainment for the 2008 8

- hour ozone NAAQS are in the 2015 to 203 2 timeframe, depending on the severity of the proble m in each area. In addition, EPA is working to complete the current review of the ozone NAAQS by mid

- 2014. If EPA revises the ozone standards in 2014 pursuant to that review, t he attainment dates associated with areas designated nonattainment for that NA AQS would likely be in the 2019 to 2036 timeframe, depending on the severity of the problem in each area.

7.2.4.1.2 Projected Levels of Ozone W ithout this Proposed Rule

EPA has already adopted many mobile source emission control programs that are expected to reduce ambient ozone levels. These control programs include the New Marine

BB The Los Angeles South Coast Air Basin 8

- hour ozone nonattainment area and t he San Joaquin Valley Air Basin 8

- hour ozone nonattainment area are de signated as extreme and will have to attain before June 15, 2024

. The Sacramento, Coachella Valley, Western Mojave and Houston 8

- hour ozone nonattainment area s are designated as severe and will have to attain by June 15, 2019.*** E.O. 12866 Review

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- 72 Compression

- Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Marine Spark

- Ignition a nd Small Spark

- Ignition Engine R ule (73 FR 59034, October 8, 2008), the Locomotive and Marine Rule (73 FR 25098, May 6, 2008), the Clean Air Nonroad Diesel R ule (69 FR 38957, June 29, 2004), the Heavy

- Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan uary 18, 2001 ) and the Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, Feb ruary 10, 2000) . As a result of these and other federal, state and local programs, 8

- hour ozone levels are expected to improve in the future. Howe ver, even with the implementation of all current state and federal regulations, there are projected to be counties violating the ozone NAAQS well into the future. Thus additional federal control programs, such as Tier 3, can assist areas with attainment d ates in 2017 and beyond in attaining the NAAQS as expeditiously as practicable and may relieve areas with already stringent local regulations from some of the burden associated with adopting additional local controls.

The a ir quality modeling projects that in 2017 , with all current controls in effect but excluding the emissions changes expected to occur as a result of this proposed action or any other additional controls , at least 40 counties, with a projected population of almost 50 million people, would h ave projected design values above the level of the 2008 8

- hour ozone standard of 75 ppb

. Even in 2030 the modeling projects there will be 12 counties with a population of almost 32 million people that would have projected design values above the level of the 2008 8

- hour ozone standard of 75 ppb without additional controls

. Since the emission changes from this proposal go into effect during the period when some areas are still working to attain the ozone NAAQS, the projected emission changes will help stat e and local agencies in their effort to attain and maintain the ozone standard. In the following section we discuss the projected ozone reductions associated with the proposed standards.

7.2.4.1.1 Projected Levels of Ozone W ith this Proposed Rule

This section sum marizes the results of our modeling of ozone air quality impacts in the future with the proposed standards . Specifically, for the years 2017 and 2030 we compare a reference scenario (a scenario without the proposed standards) to a control scenario that in cludes the proposed standards. Our modeling indicates that ozone design value concentrations will decrease dramatically in many areas of the country as a result of this proposal and in some places those decreases will be enough to move the projected desig n values from being above the NAAQS to being below the NAAQS

. Additional information on the emissions reductions that are projected with this final action is available in Section 7.2.1 of this draft RIA

. Figure 7

- 15 and Figure 7

- 16 present the changes in 8

- hour ozone design value concentration s in 2017 and 20 30 respectively

. Note that the projected results for 2017 do not include California , while the projected results for 2030 do. CC T his issue does no t have a significant impact on the AQ modeling results for the rest of the country

.

CC The processing of contro l case sulfur levels nationwide introduced an error in California counties. Control case fuels were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8

- 19 ppm. This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we*** E.O. 12866 Review

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- 73 Figure 7

- 15 Projected Change in 2017 8

- hour Ozone Design Values Between the Reference Case and Control Case

expect no change in California fu el. The error had a negligible effect on national emission totals, thoug h the 2017 air quality modeling results captured regional California impacts associated with the error that we judged not valid.*** E.O. 12866 Review

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- 74 Figure 7

- 16 Projected Change in 2030 8

- hour Ozone Design Values Between the Reference Case and Control Case

As can be seen in Figure 7

- 15 , the majority of the design value de creases in 2017 are between 0.5 and 1.0 ppb

. The projected population

- weighted average design value concentration without the proposed rule is 71.3 ppb in 2017. T here are also 7 counties with projected 8

- hour ozone design value de creases of more than 1 ppb; these counties are in Arizona, Texas and Tennessee

. The maximum projected de crease in an 8

- hour ozone design value in 2017 is 1.09 ppb in Tarrant County, Texas near Dallas, which is projected to be above the ozone standard. Figure 7

- 16 present s the ozone design value changes for 2030. The projected population

weighted average design value concentration without the proposed rule is 66.7 ppb in 2030. In 2030 the ozone design value decreases are larger than in 2017; most decreases are projected to be between 1.0 and 1.5 ppb, and over 200 counties have design values with projected decreases greater than 1.5 ppb. The maximum projected de crease in an 8

- hour ozone design value in 2030 is 3.2 ppb in Maricopa County, Arizona, where Phoenix is located.

Table 7

- 30 and Table 7

- 31 s how the average change

, due to this proposed rule, in 2017 and 2030 8

- hour ozone design values for: (1) all counties with 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 percent of it, (4) counties with 2017/ 20 30 design values that exceeded the 2008 ozone stan dard, and (5) counties with 2017/ 20 30 design values that did not exceed the stand ard, but were within 10 percent of it. Counties within 10 percent of the standard are intended to reflect counties that although not violating the standards, will also be imp acted by changes in ozone as they work to*** E.O. 12866 Review

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- 75 ensure long

- term maintenance of the ozone NAAQS. All of these metrics show a decrease in 2017 and 2030, indicating in five different ways the overall improvement in air quality.

On a population

- weighted basis, t he average modeled future

- year 8

- hour ozone design values are projected to de crease by 0. 47 ppb in 2017 and 1.55 ppb in 20 30. On a population

weighted basis

, design values in those counties that are projected to be above the 2008 ozone standard in 2017 an d 2030 are projected to decrease by 0.30 and 1.62 ppb respectively due to the proposed standards.

Table 7

- 30 Average Change in Projected 8

- hour Ozone Design Value in 2017
Average a Number of U.S. Counties 20 2 0

Population Change in

20 17 design value (ppb)
All 676 238,026,106

- 0.50 All, population

- weighted

- 0.47 Counties whose 2005 base year is violating the 2008 8

- hour ozone standard 393 176,910,535

- 0.56

Counties whose 2005 base year is violating th e 2008 8

- hour ozone standard, population

- weighted - 0.51

Counties whose 2005 base year is within 10 percent of the 2008 8

- hour ozone standard 201 40,516,171

- 0.47

Counties whose 2005 base year is within 10 percent of the 2008 8

- hour ozone standard, po pulation

weighted - 0.42

Counties whose 2017 control case is violating the 2008 8

- hour ozone standard 37 47,659,433

- 0.35

Counties whose 2017 control case is violating the 2008 8

- hour ozone standard, population

- weighted - 0.30

Counties whose 2017 co ntrol case is within 10 percent of the 2008 8

- hour ozone standard 124 68,625,934

- 0.51

Counties whose 2017 control case is within 10 percent of the 2008 8

- hour ozone standard, population

- weighted - 0.49

Note s :

a Averages are over counties with 2005 mo deled design values

. b Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by Single Year of Age CD.*** E.O. 12866 Review

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- 76 Table 7

- 31 Average Change in Projected 8

- hour Ozone Design Va lue in 2030
Average a Number of U.S. Counties 2030

Population Change in

2030 design value (ppb)
All 676 261,497,900

- 1.35 All, population

- weighted

- 1.55 Counties whose 2005 base year is violating the 2008 8

- hour ozone standard 393 194,118,748

- 1.5 4

Counties whose 2005 base year is violating the 2008 8

- hour ozone standard, population

- weighted - 1.69

Counties whose 2005 base year is within 10 percent of the 2008 8

- hour ozone standard 201 44,436,103

- 1.18

Counties whose 2005 base year is within 10 percent of the 2008 8

- hour ozone standard, population

weighted - 1.25

Counties whose 2030 control case is violating the 2008 8

- hour ozone standard 10 30,619,714

- 1.49

Counties whose 2030 control case is violating the 2008 8

- hour ozone standard, pop ulation

- weighted - 1.62

Counties whose 2030 control case is within 10 percent of the 2008 8

- hour ozone standard 40 21,541,863

- 1.37

Counties whose 2030 control case is within 10 percent of the 2008 8

- hour ozone standard, population

- weighted - 1.50

N ote s :

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

There are still 12 counties, most of them in California, that a re projected to have 8

- hour ozone design values above the 2008 NAAQS in 20 30 with out the proposed standards or any other additional controls in place

. T able 7

- 32 below presents the changes in design values for these counties.*** E.O. 12866 Review

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- 77 T able 7

- 32 Change in Ozone Design Values (ppb) for Counties Projected to be Above the 2008 Ozone NAAQS in 2030 County Name Change in 8

- hour Ozone Design Value (ppb ) Population in 2030 a San Bernardino, Californ ia

- 1.91 2,784,490 Riverside, California

- 1.68 2,614,198 Los Angeles, California

- 1.68 10,742,722 Kern, California

- 1.00 981,806 Orange, California

- 1.13 4,431,071 Harris, Texas

- 2.05 5,268,889 Tulare, California

- 1.00 528,663 Suffolk, New York

- 1.3 3 1,705,822 Fresno, California

- 1.15 1,196,950 Harford, Maryland

- 1.91 365,103 Brazoria, Texas

- 1.96 364

, 257 Hudson, New Jersey

- 0.88 747

, 407
Note:

a Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. Population by S ingle Year of Age CD.
The proposed rule would also reduce ozone design values in some counties from above the level of the standard to below it

. In 2017

, ozone design values in three counties ( Bucks County in Pennsylvania, Arlington County in Virginia and St Louis County in Missouri ) move from being above the standard to below. The projected population in these three counties in 2017 is almost 2 million people. There are two more counties whose design values would be reduced from above the standard to bel ow by the proposed rule in 2030. These counties are Hudson County in New Jersey and Brazoria County in Texas. The projected population in these two counties in 2030 is over 1 million people.

In terms of modeling accuracy, the count of modeled nonatt ainment counties is much less certain than the average changes in air quality. For example, actions by states to meet their SIP obligations would not be expected to significantly change the overall concentration changes induced by this final rule, but they could substantially change the number of counties in or out of attainment. If state actions resulted in an increase in the number of areas that are very close to, but still above, the NAAQS, then this rule might bring many of those counties down sufficien tly to change their attainment status. On the other hand, if future state actions brought several counties we project to be very close to the standard into attainment status, then the air quality improvements from this rule might change the actual attainme nt status of very few counties. Bearing this limitation in mind, our modeling indicates that the emission reductions from this proposed rule will decrease the number of ozone nonattainment counties by 3 in 2017 and by 2 in 2030, without consideration of ne w state or local programs.

As descri bed in Section 6.1.2.1 of this draft RIA t he science of ozone formation, transport, and accumulation is complex. The air quality modeling projects ozone decreases as a result of emissions changes from the proposed fuel and vehicle standards

. This change in ozone*** E.O. 12866 Review

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- 78 results from interactions between photochemistry

, background concentrations of VOC and NO X , local emissions and meteorolog y.
There is one county in 2017 that is projected to have an increase in modeled ozone d esign value concentration (Multnomah County, Oregon, where Portland is located). When NO X levels are relatively high and VOC levels relatively low, NO X forms inorganic nitrates ( i.e. , particles) but relatively little ozone. Such conditions are called “ N O X - saturat ed

. ” Under these conditions, VOC reductions are effective in reducing ozone, but NO X reductions can actually increase local ozone under certain circumstances. We believe that this is the case in Multnomah County, Oregon in 2017. In 2030, when t he fleet would be composed of vehicles meeting the new standards and the NO X and VOC emissions reductions are larger, this ozone disbenefit is eliminated, and the design values for all the modeled counties are decreasing

.

7.2.4.2 Particulate Matter

As described in Section 6.2.2 of this draft RIA , PM causes adverse health effects, and the EPA has set national ambient air quality standards (NAAQS) to protect against those health effects. In this section we present information on current and mo del

- projected future PM levels.

7.2.4.2.1 Current Levels of PM

Figure 7

- 17 and Figure 7

- 18 Figure 7

- 18 respectively show a snapshot of annual and 24

- hour PM 2.5 concentrations in 20 10

. In 2010 , t he highest annual 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

.*** E.O. 12866 Review

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- 79 Figure 7

- 17 A nnual Average PM 2.5 Concentrations in μg/m 3 for 20 10 DD

DD From U.S. EPA, 2011. Our Nation’s Air: Status and Trends through 2010. EPA

- 454/R

- 12

- 001. February 2012. Available at: http://www.epa.gov/airtrends/2011/.*** E.O. 12866 Review

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- 80 Figure 7

- 18 24

- hour (98 th percentile 24

- hour concentrations) PM 2.5 Concentrations in μg/m 3 for 20 10 EE

There are two NAAQS for PM 2.5 : an annual standard ( 1 2 .0 μg/m 3 ) and a 24

- hour standard (35 μg/m 3 ). The most recent revisions to these standards were in 1997, 2006 and in December 2012. The December 2012 rule revised the level of the annual PM 2.5 standard from 15 μg/m 3 to 12 μg/m 3

. 133

In 2005 the U.S. EPA de signated nonattainment areas for the 1997 PM 2.5 NAAQS (70 FR 19844, April 14, 2005)

. As of December 14 , 2012, over 91 million people lived in the 35 areas 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. Nonattainment areas for the 1997 PM 2.5 NAAQS are pictured in Figure 7 19. EPA anticipates making attainment/nonattainment designations for the 2012 PM 2.5 NAAQS by December 2014, with those designations likely be coming effective in early 2015. 134 On October 8, 2009, the EPA issued final nonattainment area designations for the 2006 24

- hour PM 2.5 NAAQS (74 FR 58688, November 13, 2009). These designations include 32 areas composed of 121 full or partial counties, wi th a population of over 70 million. Nonattainment areas for the 2006 PM 2.5 NAAQS are pictured in Figure 7 20. In total, there are 50 PM 2.5 nonattainment areas with a population of over 105 million people. FF

EE From U.S. EPA, 2011. Our Nation’s Air: Status and Tr ends through 2010. EPA

- 454/R

- 12

- 001. February 2012. Available at: http://www.epa.gov/airtrends/2011/.
FF Data come from Summary Nonattainment Area Population Exposure Report, current as of December 14, 2012 at: http://www.epa.gov/oar/oaqps/greenbk/popexp.html and contained in Docket EPA

- HQ

- OAR

- 20 11

- 0 135.*** E.O. 12866 Review

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- 81 States with PM 2.5 nonattainment areas will be required to take action to bring those areas into attainment in the future. Most 1997 PM 2.5 nonattainment areas are required to attain the 1997 PM 2.5 NAAQS in the 20 09 to 201 4 time frame and then required to maintain the 1997 PM 2.5 NAAQS thereafter

. 135 Th e 2006 24

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

- hour PM 2.5 NAAQS in the 201 5 to 2019 time frame and then be required to maintain the 2006 24

- hour PM 2.5 NAAQS thereafter. 136 2012 PM 2.5 nonattainment areas will likely be requir ed to attain the 2012 PM 2.5 NAAQS in the 20 2 1 to 20 25 time frame

, depending on the severity of an area’s fine particle pollution problems and the availability of pollution controls

.

Figure 7

- 19 1997 PM 2.5 N onattainment Areas*** E.O. 12866 Review

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- 82 Figure 7

- 20 2006 PM 2.5 Nonattainment Areas

As of December 14 , 20 1 2 , over 2 9 million people live in the 4 6 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 PM 10 NAAQS are pictured in Figure 7

- 21

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- 83 Figure 7

- 21 PM 10 Nonattainment Areas

7.2.4.2.2 Projected Levels of PM 2.5 W ithout this Proposed Rule

EPA has already adopted many mobile source emission control programs that are expected to reduce ambient PM levels. These control programs include the Heavy

- Duty Greenhouse Gas Rule (76 FR 57106, September 15, 20 11), the New Marine Compression

Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Marine Spark

- Ignition a nd Small Spark

- Ignition Engine R ule (73 FR 59034, October 8, 2008), the Locomotive and Mari ne Compression

- Ign ition Engine R ule (73 FR 25098, May 6, 2008), the Clean Air Nonroad Diesel (69 FR 38957, June 29, 2004), the Heavy

- Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, Jan uary 18, 2001) and the Tier 2 Motor Veh icle Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, Feb ruary 10, 2000). 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 decreas e. However, even with the implementation of all current state and federal regulations, there are projected to be counties violating the PM 2.5 NAAQS well into the future. Thus additional federal control programs, such as Tier 3, can assist areas with atta inment dates in 2017 and beyond in attaining the NAAQS as expeditiously as practicable and may relieve areas with already stringent local regulations from some of the burden associated with adopting additional local controls.

The air quality modeling con ducted for this proposal projects that in 20 30 , with all current controls in effect but excluding the emissions changes expected to occur as a result of this proposal or any other additional controls , at least 14 counties, with a projected population of

ov er 28 million people, would have projected design values above the level of the annual*** E.O. 12866 Review

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- 84 standard of 12 .0 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 proposed action would go into effect during the period when some areas are still working to attain the PM 2.5 NAAQS, the projected emission changes will help state and local agencies in their effort to attain and maintain the PM 2.5 standard. In the following section we discuss projected PM 2.5 reductions from the se proposed standards.

7.2.4.2.3 Projected Annual Average Levels of PM 2.5 With this Proposed Rule

This section summarizes the results of our modeling of annual average PM 2.5 air quality impacts in the future due to the standards proposed in this action

. Specifically, for the years 2017 and 2030 we compare a reference scenario (a scenario without the proposed standards) to a control scenario th at includes the proposed standards. Our modeling indicates that by 2030 annual PM 2.5 design values in the majority of the modeled counties would decrease due to the proposed standards. The decreases in annual PM 2.5 design values are likely due to the pro jected reductions in primary PM 2.5 , NO X , SO X and VOC emissions. Additional information on the emissions reductions that are projected with this proposed action is available in Section 7.2.1 of this draft RIA

. It is important t o note that, as discussed in Section 7.1.5 and 7.2.1 .1

, the control scenario emissions inventory prepared for air quality modeling included direct PM 2.5 vehicle emissions increase s that we do not expect to occur in reality. These increases resulted from a series of conservative assumptions and uncertainties related to fuel parameters in 2017, and also an emissions processing issue which erroneously increased direct PM emissions in about one third of modeled counties (see Section 7.2.1.1.2 for more detail). Because our air quality modeling assumes this increase, our air quality results overestimate ambient PM and underestimate the reductions that would result from the proposed Tier 3 standards.

Figure 7

- 22 and Figure 7

- 23 p resent the changes in annual PM 2.5 design values in 2017 and 20 30 respectively

. GG Note that the projected results for 2017 do not include California, while the projected results f or 2030 do

. HH This issue do es not have a significant impact on the AQ modeling results for the rest of the country.

GG 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 de tails involved in calculating an annual PM 2.5 design value are given in appendix N of 40 CFR part 50.
HH The processing of control case sulfur levels nationwide introduced an error in California counties. Control case fuels were set to 10 ppm in all Califo rnia counties, whereas the reference case sulfur levels ranged from 8

- 19 ppm. This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we expect no change in California fu el. The error had a negligible e ffect on national emission totals, though the 2017 air quality modeling results captured regional California impacts associated with the error that we judged not valid.*** E.O. 12866 Review

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- 85 Figure 7

- 22 Projected Change in 20 17 Annual PM 2.5 Design Values Between the Reference Case and Control Case*** E.O. 12866 Review

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- 86 Figure 7

- 23 Projected Change in 2030 Annual PM 2.5 Design Values Between the Reference Case and Control Case

The projected population

- weighted average design value concentration without the proposed rule is 9.3 μg/m 3 in 2017. As shown in Figure 7

- 22 , we project that in 2017 seven counties will have design value decreases of between 0.01 μg/m 3 and 0.05 μg/m 3 . These counties are in Utah, Pennsylvania and Wisconsin. The maxim um projected decrease in a 2017 annual PM 2.5 design value is 0.03 μg/m 3 in Weber County, Utah. As mentioned above the decreases in ambient annual PM 2.5 concentrations are due to reductions in NO X , SO X and VOCs and the subsequent reductions in secondarily formed PM due to this proposed rule in 2017, which offset the small increases in direct PM emissions that were modeled but we do not expect
to occur (see Section 7.1.5 and Section 7.2.1.1.2 of this draft RIA for more detail )

. As a result, the projected decreases in design values are underestimates of the actual effects of the proposed rule. There are a few counties with projected small increases in annual PM 2.5 in 2017 , but as explained, we do not expect that the se localized small increases will actually happen.

The projected population

- weighted average design value concentration without the proposed rule is 9.5 μg/m 3 in 2030. Figure 7

- 23 presents the annual PM 2.5 design value changes in 2030. In 2030 all the modeled counties have decreases in annual PM 2.5 design value s

. T he annual PM 2.5 design value decreases in 2030 are larger than the decreases in 2017; most design values are projected to decrease between 0.01 and 0.0 5 μg/m 3 and over 100 additional counties have projected design value decreases greater than 0.05 μg/m 3 . The maximum projected decrease in an annual PM 2.5 design value in 2030 is 0.20 μg/ m 3 in Tulare County, California.*** E.O. 12866 Review

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- 87 Table 7

- 33 and Table 7

- 34 s how the average change in 2017 and 2030 annual PM 2.5 design values for: (1) all counties with 2005 baseline design values, (2) counties with 2005 baseline design values that exceeded the 20 12 annual PM 2.5 standard, (3) counties with 2005 baseline design values that did not exceed the 20 12 standard, but were within 10 percent of it, (4) counties with 2017/ 20 30 design values that exceeded the 20 12 annual PM 2.5 standard, and (5) counties with 2017/ 20 30 design values th at did not exceed the stand ard, but were within 10 percent of it. Counties within 10 percent of the standard are intended to reflect counties that although not violating the standards, will also be impacted by changes in PM 2.5 as they work to ensure long

- term maintenance of the annual PM 2.5 NAAQS. All of these metrics show either no change or a small increase or decrease in 2017 and show a decrease in 2030 ; these results are underestimates for the reasons explained above . The row s in Table 7

- 33 that compare the 2017 control case to the standard do not include California a s we were not able to model design value changes in CA due to an inventory error. II On a population

- weighted basis, there is no change ( 0. 00 μg/m 3 ) in the average mo deled future

- year annual PM 2.5 design values in 2017 and a 0. 06 μg/m 3 decrease in 20 30. On a population

- weighted basis design values in those counties that are projected to be above the annual PM 2.5 standard in 2030 would decrease by 0. 1 1 μg/m 3 due to the proposed standards.

II The processing of control case sulfur levels nationwide introduced an error in Califo rnia counties. Control case fuels were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8

- 19 ppm. This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we expect no change in California fu el. The error had a negligible effect on national emission totals, though the 2017 air quality modeling results captured regional California impacts associated with the error that we judged not valid.*** E.O. 12866 Review

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- 88 Table 7

- 33 Average Change in 2017 Annual PM 2.5 Design Value as a Result of the Proposed Rule

AVERAGE Number of U.S. Counties 2020

Population Change in 2017

design value ( μ g/m 3 ) All 576 226,563,544 0.00 All, population

- weighted 0.00 Counties whose 2005 base year is violating the annual PM 2.5 standard

314 140,706,274 0.00

Counties whose 2005 base year is violating the annual PM 2.5 standard, population

- weighted 0.00

Counties whose 2005 base year is within 10 percent of the annual PM 2.5 standard 8 3 29

, 253

, 128

- 0.01

Counties whose 2005 base year is within 10 percent of the annual PM 2.5 standard, population

- weighted 0.00

Counties whose 2017 control case is violating the annual PM 2.5 standard 4 1

, 992

, 506 0.01

Counties whose 2017 control case is violating the annual PM 2.5 standard, population

- weighted 0.01

Counties whose 2017 control case is within 10 percent of the annual PM 2.5 standard 13 1 2

, 158

, 988

- 0.01

Counties whose 2 017 control case is within 10 percent of the annual PM 2.5 standard, population

- weighted - 0.01

Note s :

a Averages are over counties with 2005 modeled design values
b Population numbers based on Woods & Poole data. Woods & Poole Economics, Inc. 2001. P opulation by Single Year of Age CD.*** E.O. 12866 Review

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- 89 Table 7

- 34 Average Change in 2030 Annual PM 2.5 Design Value as a Result of the Proposed Rule

AVERAGE Number of U.S. Counties 20 3 0

Population Change in 20 30

design value ( μ g /m 3 ) All 576 247,415,381 - 0.05 All, population

- weighted

- 0.06 Counties whose 2005 base year is violating the annual PM 2.5 standard

314 1 5 2 ,10 9

, 56 9 - 0.0 5 Counties whose 2005 base year is violating the annual PM 2.5 standard, population

- we ighted

- 0.0 7 Counties whose 2005 base year is within 10 percent of the annual PM 2.5 standard

8 3 3 1 ,8 63

, 37 6 - 0.05 Counties whose 2005 base year is within 10 percent of the annual PM 2.5 standard, population

- weighted

- 0.0 5 Counties whose 2030 control case is violating the annual PM 2.5 standard

14 28

, 624 ,7 58 - 0.1 1 Counties whose 2030 control case is violating the annual PM 2.5 standard, population

- weighted

- 0.1 0 Counties whose 2030 control case is within 10 percent of the annual PM 2 .5 standard

28 2 3

, 84 0, 2 7 2 - 0. 07 Counties whose 2030 control case is within 10 percent of the annual PM 2.5 standard, population

- weighted

- 0. 09

Note s :

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

There are fourteen counties in California that are projected to have annual PM 2.5 design values above the NAAQS in 20 30 with out the proposed standards or any other additional standards in place

. Table 7

- 35 below presents the changes in design values for these counties.*** E.O. 12866 Review

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- 90 Table 7

- 35 Change in Annual PM 2.5 Design Values (μg/m 3 ) for Counties Proj ected 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

- 0.14 2,614,198 San Bernardino County, California

- 0.11 2,784,489 Kern County, California

- 0.19 9 81,806 Tulare County, California

- 0.2 528,662 Fresno County, California

- 0.16 1,196,949 Kings County, California

- 0.17 195,067 Los Angeles County, California

- 0.09 10,742,722 Allegheny County, Pennsylvania

- 0.05 1,234,930 Lincoln County, Montan a

- 0.02 20,454 Jefferson County, Alabama

- 0.05 697,239 Wayne County, Michigan

- 0.09 1,838,269 Santa Cruz County, Arizona

- 0.03 55,393 Orange County, California

- 0.06 4,431,070
Note:

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

7.2.4.2.4 Projected 24

- hour Average Levels of PM2 .5 With this Proposed Rule

This section summarizes the results of our modeling of 24

- hour PM 2.5 air quality impacts in the future due to the proposed rule

. Specifically, for the years 2017 and 2030 we compare a reference scenario (a scenario without the proposed standards) to a control scenario that includes the proposed standards

. Our modeling indicates that by 2030 24

- hour PM 2.5 design values in the majo rity of the modeled counties would decrease due to the proposed standards. The decreases in 24

- hour PM 2.5 design values are likely due to the projected reductions in primary PM 2.5 , NO X , SO X and VOCs. Additional information on the emissions reductions tha t are projected with this proposed action is available in Section 7.2.1 of this draft RIA

. It is important to note that, as discussed in Section 7.1.5 and 7.2.1.1, the control scenario emissions inventory prepa red for air quality modeling included direct PM 2.5 vehicle emissions increase s that we do not expect to occur in reality. These increases resulted from a series of conservative assumptions and uncertainties related to fuel parameters in 2017, and also an emissions processing issue which erroneously increased direct PM emissions in about one third of modeled counties (see Section 7.2.1.1.2 for more detail). Because our air quality modeling assumes this increase, our air quality results overestimate ambient PM and underestimate the reductions that would result from the proposed Tier 3 standards.*** E.O. 12866 Review

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- 91 Figure 7

- 24 and Figure 7

- 25 p resent the changes in 24

- hour PM 2.5 design values in 2 017 and 20 30 respectively

. JJ Note that the projected results for 2017 do not include California, while the projected results for 2030 do. KK This issue does not have a significant impact on the AQ modeling results for the rest of the country.

Figure 7

- 24 Projected Change in 20 17 24

- hour PM 2.5 Design Values Between the Reference Case and the Control Case

JJ A 24

- hour PM 2.5 de sign value is the concentration that determines whether a monitoring site meets the 24

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

- hour PM 2.5 design value are given in appendix N of 40 CFR part 50.
KK The processing of control case sulfur levels nationwide introduced an error in California counties. Control case fuels were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8

- 19 ppm. This led to small changes in emissions due to fuel change s between reference and control, whereas in reality we expect no change in California fu el. The error had a negligible effect on national emission totals, though the 2017 air quality modeling results captured regional California impacts associated with th e error that we judged not valid.*** E.O. 12866 Review

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- 92 Figure 7

- 25 Projected Change in 2030 24

- hour PM 2.5 Desig n Values Between the Reference Case and the Control Case

The projected population

- weighted average design value concentration without the proposed rule is 23.4 μg/m 3 in 2017. A s shown in Figure 7

- 24 , in 2017 there are 72 counties with projected 24

- hour PM 2.5 design value decreases greater than 0.05 μg/m 3 . These counties are in Utah, Pennsylvania and scattered throughout the Midwest . The maximum projected de crease in a 2017 24

- hour PM 2.5 design value is 0.20 μg/m 3 in Tooele Count y, Utah

. As mentioned above

, the decreases in ambient annual PM 2.5 concentrations are due to reductions in NO X , SO X and VOCs and the subsequent reductions in secondarily formed PM due to this proposed rule in 2017, which offset the small increases in dire ct PM emissions that were modeled but we do not expect to occur (see Section 7.1.5 and Section 7.2.1.1.2 of this draft RIA for more detail). As a result, the projected decreases in design values ar e underestimates of the actual effects of the proposed rule. T here are some counties with projected small increases in 24

- hour PM 2.5 in 2017

, but as explained, we do not expect that the se localized small increases will actually happen.

The projected popu lation

- weighted average design value concentration without the proposed rule is 24.3 μg/m 3 in 20 30

. Figure 7

- 25 presents the 24

- hour PM 2.5 design value changes in 2030. In 2030 the 24

- hour PM 2.5 design value decreases are larger ; most design values are projected to decrease between 0.05 and 0.15 μg/m 3 and almost 200 counties have projected design value decreases greater than 0.15 μg/m 3 . The maximum projected decrease in a 24

- hour PM 2.5 design value in 2030 is 0.81 μg/ m 3 in Cache County, Utah. As shown in Figure 7

- 25

, design values in 68 counties would decrease by more than 0.25 μg/m 3

. These counties are in Idaho, Nevada, Montana, Louisiana, northern Utah, and the upper Midwest. The dec reases in*** E.O. 12866 Review

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- 93 24

- hour PM 2.5 design values that are projected in some counties are likely due to emission reductions related to reductions in PM 2.5 precursor emissions (NO X
, SO X , and VOCs )

. There is one county, Richmond County, Georgia, with a projected 24

- hou r PM 2.5 design value increase of less than 0.15 μg/m 3

. Additional information on the emissions reductions that are projected with this proposed action is available in Section 7.2.1

.
Table 7

- 36 show s the average change in 20 30 24

- hour PM 2.5 design values for: (1) all counties with 2005 baseline design values, (2) counties with 2005 baseline design values that exceeded the 24

- hour PM 2.5 standard, (3) counties with 2005 baseline design values that did not exceed the standard, but were within 10 percent of it, (4) counties with 20 30 design values that exceeded the 24

- hour PM 2.5 standard, and (5) counties with 20 30 design values that did not exceed the standa rd, but were within 10 percent of it. Counties within 10 percent of the standard are intended to reflect counties that although not violating the standards, will also be impacted by changes in PM 2.5 as they work to ensure long

- term maintenance of the 24

- hour PM 2.5 NAAQS. On a population

- weighted basi s, the average modeled future

- year 24

- hour PM 2.5 design valu es are projected to decrease by 0.14 μg/m 3 due to the proposed standards . On a population

weighted basis

, 24

- hour PM 2.5 design values in those counties that are projected to be above the 24

- hour PM 2.5 standard in 20 30 would decrease by 0. 13 μg/m 3

.
Table 7

- 36 Average Change in Projected 24

- hour PM 2.5 Design Value s in 2030
Average a Number of U.S. Counties 2030

Population b Change in

2030 design value (μg /m 3 )
All
569 245,111,480 - 0.13 All, population

- weighted

- 0.14 Counties whose 2005 base year is violating the 2006 24

- hour PM 2.5 standard

108 91,474,036 - 0.19 Counties whose 2005 base year is violating the 2006 24

- hour PM2 .5 standard, population

- weigh ted

- 0.15 Counties whose 2005 base year is within 10 percent of the 2006 24

- hour PM 2.5 standard

140 53,990,060 - 0.16 Counties whose 2005 base year is within 10 percent of the 2006 24

- hour PM 2.5 standard, population

weighted

- 0.17

Counties whose 2030 control case is violating the 2006 24

- hour PM 2.5 standard

21 31,002,272 - 0.13 Counties whose 2030 control case is violating the 2006 24

- hour PM 2.5 standard, population

- weighted

- 0.05

Counties whose 2030 control case is within 10 percent of the 2006 2 4

- hour PM 2.5 standard

7 4,212,913 - 0.12

Counties whose 2030 control case is within 10 percent of the 2006 24

- hour PM 2.5 standard, population

- weighted

- 0.10*** E.O. 12866 Review

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- 94 Note:

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

There are 21 counties t hat are projected to have 24

- hour PM 2.5 design values above the NAAQS in 20 30 with out the proposed standard s or any other additional co ntrols in place

. Table 7

- 37 Table 7

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

Table 7

- 37 Change in 24

- hour PM 2.5 Design Values (μg/m 3 ) for Counties Projected to be

Ab ove 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

Riverside County, California 0.00 2,614,198 Kern County, California 0.00 981,806 San Bernardino County, California 0.00 2,784,490 Fresno Co unty, California 0.00 1,196,950 Sacramento County, California 0.00 1,856,971 Cache County, Utah

- 0.81 141,446 Kings County, California 0.00 195,067 Lane County, Oregon

- 0.12 460,993 Los Angeles County, California 0.00 10,742,722 Allegheny County, Pen nsylvania

- 0.11 1,234,931 Tulare County, California 0.00 528,663 Lake County, Montana

- 0.07 40,126 Stanislaus County, California 0.00 688,246 Ravalli County, Montana

- 0.19 63,914 Missoula County, Montana

- 0.27 141,264 Klamath County, Oregon

- 0.13 77, 200 Salt Lake County, Utah

- 0.59 1,431,946 Lincoln County, Montana

- 0.12 20,454 Butte County, California 0.00 287,236 Pierce County, Washington

- 0.24 1,082,579 Orange County, California 0.00 4,431,071 Note:

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

7.2.4.3 Nitrogen Dioxide

As described in Section 6.2.3 of this draft RIA

, NO 2 causes adverse health effects, and the EPA has set national ambient air quality standards (NAAQS) to pr otect against those health effects. In this section we present information on current and model

- projected future NO 2 levels.*** E.O. 12866 Review

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- 95 7.2.4.3.1 Current Levels of NO 2
Between 2003 and 2005, national mean concentrations of NO 2 were about 15 ppb for averaging periods ranging f rom a day to a year. LL There are two NAAQS for NO 2 : an annual standard ( 53 ppb ) and a 1

- hour standard (100 ppb). The primary NAAQS for NO 2 was revised in January 2010

. EPA completed area designations in January 2012 and there are currently no nonattainmen t areas . The designations were based on the existing community

- wide monitoring network. Once the expanded network of NO 2 monitors is fully deployed and three years of air quality data have been collected, EPA intends to redesignate areas in 2016 or 2017

, as appropriate, based on the air quality data from the new monitoring network. 137

, 138

7.2.4.3.2 Projected Levels of NO 2 without this Proposed Rule

EPA has already adopted many mobile source emission control programs that are expected to reduce ambient NO 2 levels. Thes e control programs include the Heavy

- Duty Greenhouse Gas Rule (76 FR 57106, September 15, 2011), New Marine Compression

- Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the Locomotive and Marine Compression

- Ignition E ngine Rule (73 FR 25098, May 6, 2008), the Clean Air Nonroad Diesel (69 FR 38957, June 29, 2004), the Heavy

- Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements (66 FR 5002, January 18, 2001) and the Tier 2 Motor Vehicle Em issions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, Feb ruary 10, 2000). As a result of these and other federal, state and local programs, ambient concentrations of NO 2 in the future are expected to decrease. However, even with the imp lementation of all current state and federal regulations, there are projected to be parts of the country with elevated NO 2 concentrations in the future

.
As mentioned in Section 7.2.4.3.1 , nonattainment designations for the NO 2 NAAQS will potentially be o ccurring in 2016 or 2017. T he emission changes from this proposal would go into effect during the period when any NO 2 nonattainment areas would be working to at tain the NO 2 NAAQS

.

7.2.4.3.3 Projected Levels of NO 2 with this Proposed Rule

This section summarizes t he results of our modeling of annual average NO 2 air quality impacts in the future due to the proposed standards. Specifically, for the years 2017 and 2030
we compare a reference scenario (a scenario without the proposed standards) to a control scenario t hat includes the proposed standards. Figure 7

- 26 and Figure 7

- 27 p resent the changes in annual NO 2 concentration s in 2017 and 20 30 respectively

.

LL EPA, 2010. Final Regulatory Impact Analysis (RIA) for the NO2 National Ambient Air Quality Standards (NAAQS)

. http://www.epa.gov/ttn/ecas/regdata/RIAs/FinalNO2RIAfulldocument.pdf*** E.O. 12866 Review

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- 96 Figure 7

- 26 Projected Change in 20 17 Annual NO 2 Concentrations Between the Reference Case and Control Case

Figure 7

- 27 Projected Change in 2030 Annual NO 2 Concentrations Between the Reference Case and Control Cas e*** E.O. 12866 Review

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- 97 As shown in Figure 7

- 27 , our modeling indicates that by 2030 annual NO 2 concentrations in the majority of the country would decrease less than 0.1 ppb due to this proposal. However decreases in annual NO 2 concen trations are greater than 0.3 ppb in most urban areas. These emissions reductions would also likely decrease 1

- hour NO 2 concentrations and help any potential nonattainment areas to attain and maintain the standard. Note that the projected results for 201 7 do not include California, while the projected results for 2030 do. MM This issue does not have a significant impact on the AQ modeling results for the rest of the country.

7.2.4.4 Air Toxics

As described in Section 6.2.5 of this draft RIA

, air toxics cause ad verse health effects . In this section we present information on current and model

- projected future levels of air toxics.

7.2.4.4.1 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. 139 The levels of air toxics to which people are exposed vary depending on where people live and work and the kinds of activities in which they engage, as discussed in detail in U.S. EPA’s most recent Mobil e Source Air Toxics (MSAT) Rule. 140 In order to identify and prioritize air toxics, emission source types and locations which are of greatest potential concern, U. S. EPA conducts the National

- Scale Air Toxics Assessment (NATA). The most recent NATA was co nducted for calendar year 200 5 , and was released in March 2011

. 141 NATA for 200 5 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 U nited States

3) Estimating population exposures across the United States

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

Figure 7

- 28 and Figure 7

- 29 Figure 7

- 29 depict estimated tract

- level carcinogenic risk and

noncancer respiratory hazard from the assessment. The respiratory hazard is dominated by a single pollutant, acrolein.

According to the NATA for 200 5 , mobile sourc es were responsible for 43 percent of outdoor toxic emissions and over 50 percent of the cancer risk and noncancer hazard attributable

MM The processing of control case sulfu r levels nationwide introduced an error in California counties. Control case fuels were set to 10ppm in all California counties, whereas the reference case sulfur levels ranged from 8

- 19ppm. This led to small changes in emissions due to fuel changes betwe en reference and control, whereas in reality we expect no change in California fu el. The error had a negligible effect on national emission totals, though the 2017 air quality modeling results captured regional California impacts associated with the erro r that we judged not valid. Formatted: Font: Not Bold*** E.O. 12866 Review

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- 98 to direct emissions from mobile and stationary sources

. NN
, OO

, 142 Mobile sources are also large contributors to precursor emi ssions which react to form secondary concentrations of air toxics

. Formaldehyde is the largest contributor to cancer risk of all 80 pollutants quantitatively assessed in the 200 5 NATA, and mobile sources were responsible for over 40 percent of primary emis sions of this pollutant in 200 5

, 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 years, 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.

Figure 7

- 28 Tract Level Average Carcinogenic Risk , 200 5 NATA

NN NATA also includes estimates of risk attributable to background concentrations, which includes contributions from long

- range transport, persistent air toxics, and natural sources; as well as secondary concentrations, where
tox ics are formed via secondary formation. Mobile sources substantially contribute to long

- range transport and secondarily formed air toxics.
OO NATA relies on a Guassian plume model, Assessment System for Population Exposure Nationwide (ASPEN), to estimate t oxic air pollutant concentrations. Projected air toxics concentrations presented in this final action were modeled with CMAQ 4.7.1.*** E.O. 12866 Review

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- 99 Figure 7

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

7.2.4.4.2 Projected Levels of Air Toxics

In the following sections, we describe results of our modeling of air toxic s levels in the fut ure with the proposed standards

. Although there are a large number of compounds which are considered air toxics, we focused on those which were identified as national and regional

- scale cancer and noncancer risk drivers in past NATA assessments and were a lso likely to be significantly impacted by the standards. These compounds include benzene, 1,3

- butadiene, formaldehyde, acetaldehyde, and acrolein. I mpacts on ethanol concentrations were also included in our analyses. Information on the air quality mode ling methodology is contained in Section 7.2.2

. Additional detail can be found in the air quality modeling technical support document (AQM TSD ) in the docket for this rule

. Additional maps, including the seasonal concentration maps for 2030 and 2017

, are included in Appendix 7. A

. It should be noted that EPA has adopted many mobile source emission control programs that are expected to reduce ambient air toxics levels. These control programs include the Heavy

duty Onboard Diagnostic Rule (74 FR 8310, Febru ary 24, 2009), Small SI and Marine SI Engine Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098, May 6, 2008), Mobile Source Air Toxics Rule ( 72 FR 8428, February 26, 2007 ), Clean Air Nonroad Diesel Rule (69 FR 38957, J une 29, 2004), Heavy

- 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 Sulfur Control Requirements (65 FR 6698, Feb. 10, 2000). A s a result of these programs, the ambient concentration of air toxics in the*** E.O. 12866 Review

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- 100 future is expected to decrease. The reference case and control case scenarios include these controls.

Our modeling indicates that the impacts of the proposed standards include generally small decreases in ambie nt concentrations of air toxics, with the greatest reductions in urban

areas. Air toxics pollutants dominated by primary emissions (or a decay product of a directly emitted pollutant)

, such as benzene and 1,3

- butadiene

, h ave the largest impacts. A ir toxics that primarily result from photochemical transformation

, such as formaldehyde and acetaldehyde , are not impacted as much as those dominated by direct emissions

. Our modeling shows decreases in ambient air toxics concen trations for both 2017 and 2030. Reductions are greater in 2030 , when Tier 3 cars and trucks would contribute nearly 90 percent of fleet

- wide vehicle miles travelled

, than in 2017, which is the first year of the proposed program. However, our modeling pr ojects there would be small immediate reductions in ambient concentrations of air toxics due to the proposed sulfur controls in 2017. Furthermore, t he full reduction of the vehicle program would be realized after 2030, when the fleet has fully turned over to Tier 3 vehicles. B ecause overall impacts are relatively small in both future years , we concluded that assessing exposure to ambient concentrations and conducting a quantitative risk assessment of air toxic impacts was not warranted. However, we did d evelop population metrics, including the population living in areas with increases or decreases in concentrations of various magnitudes.

Benzene

Our modeling projects that the proposed standards w ould have a notable impact on ambient benzene concentratio ns. In 2017, the first year the proposed Tier 3 standards take effect, ambient benzene reductions are generally between 0.001 and 0.01 μg/m3 , or between 1 and 2.5 percent in some areas ( Figure 7

- 30 Figure 7

- 30 ). In 2030, our modeling projects t hat the proposal would decrease ambient benzene concentrations across much of the country on the order of 1 to 5 percent , with reductions ranging from 10 to 25 percent in some urban areas ( Figure 7

- 31 Figure 7

- 31 ). A bsolute decreas es in ambi ent concentrations of benzen e are generally between 0.001 and 0. 0 1 μg/m3 in rural areas and as much as 0.1 μg/m3 in urban areas ( Figure 7

- 31 Figure 7

- 31 )

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- 101 Figure 7

- 30 Changes in Benzene Am bient Concentrations Between the Reference Case and the Control Case in 2017 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

Figure 7

- 31 Changes in Benzene Ambient Concentrations Between th e Reference Case and the Control Case in 2030 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

1,3

- Butadiene

Our modeling also shows reductions of ambient 1,3

- butadiene concentrations in 2017 and 2030. Figure 7

- 32 Figure 7

- 32 show s that in 2017, ambient concentrations of 1,3

- butdiene
gen erally decrease between 1 and 5 percent across the country, corresponding to small decreases*** E.O. 12866 Review

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- 102 in absolute concentrations (less than 0.001 μg/m3). In 2030, r eductions of 1,3

- butadiene concentrations range between 1 and 25 percent , with decreases of at least 0.005 μg/m3 in urban

areas ( Figure 7

- 33 Figure 7

- 33 ).

Figure 7

- 32 Cha nges in 1,3

- Butadiene Ambient Concentrations Between the Reference Case and the Control Case in 2017 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

Figure 7

- 33 Changes in 1,3

- Butadiene Amb ient Concentrations Between the Reference Case and the Control Case in 2030 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )*** E.O. 12866 Review

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- 103 Acrolein

Our modeling indicates the proposed standards would reduc e ambient concentrations of acrolein in 2017 and 2030. Figure 7

- 34 Figure 7

- 34 shows decreases in ambient concentrations of acrolein generally between 1 and 2.5 percent across the parts of the country in 2017, corresponding to small decreases in absolute concentrations (less than 0.001 μg/m3). Reductions of acrolein concentrations in 2030 range between 1 and 25 percent , with decreases as high as 0.003 μg/m3 in a few urban areas

.

Figure 7

- 34 Changes in Acrolein Ambient Concentrations Between th e Reference Case and the Control Case in 2017 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

Figure 7

- 35 Changes in Acrolein Ambient Concentrations Between the Reference Case and the Contro l Case in 2030 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )*** E.O. 12866 Review

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- 104 Ethanol

Our modeling projects that the proposed standards would slightly decrease ambient ethanol concentrations in 2030, with negligible impact in 2017 . As shown in Figure 7

- 36 Figure 7

- 36

, in 2017, a nnual percent change s in ambient concentrations of ethanol are less than 1 percent across the country , with absolute concentrations of ± 0.01 ppb

. I n 2030, some parts of the country , especially urban areas , are projected to have reductions in ethanol concentrations on the order of 1 to 5 percent as a result of the proposal ( Figure 7

- 37 Figure 7

- 37 )

. Figure 7

- 37 Figure 7

- 37 also shows that absolute decreas es in ambient concentrations of ethanol are generally between 0.001 and 0.1 ppb in 2030 with decreases in a few urban areas as high as 0.2 ppb

.

Figure 7

- 36 Changes in Ethanol Ambient Concentrations Between the Reference C ase and the Control Ca se in 2017 : Percent Changes (left) and Absolute Changes in μg/m3 (right)*** E.O. 12866 Review

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- 105 Figure 7

- 37 Changes in Ethanol Ambient Concentrations Between the Reference Case and the Control Case in 2030: Percent Changes (lef t) and Absolute Changes in μg/m3 (right)

Formaldehyde

Our modeling projects that formaldehyde concentrations would slightly decrease in parts of the country (mainly urban areas) as a result of the proposal. As shown in Figure 7

- 38 Figure 7

- 38 and Figure 7

- 39 Figure 7

- 39

, a nnual percent change s in ambient concentrations of form aldehyde are less than 1 percent across much of the country for 2017 but are on the order of 1 to 5 percent in 2030 in some urban areas as a result of the pr oposal. Figure 7

- 38 Figure 7

- 38 and Figure 7

- 39 Figure 7

- 39 also show that absolute changes in ambient concentrations of formaldehyde are generally between 0.001 and 0. 0 1 μg/m3 in both years, with some areas as high as 0.1 μg/m3 in 2030

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- 106 Figure 7

- 38 Changes in Form aldehyde Ambient Concentrations Between the Reference Case and the Control Case in 2017 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

Figure 7

- 39 Changes in Form aldehyde Ambient Concentrations Between the Reference Case and the Control Case in 2030 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

Acetaldehyde

Our air quality modeling shows a nnual percent changes in ambient concentrations of acetaldehyde of generally less than 1 percent across the U.S ., although the proposal may decrease acetaldehyde concentrations in some urban areas by 1 to 2.5 percent in 2030 ( Figure 7

- 40 Figure 7

- 40 ). Changes in ambient concentrations of acetaldehyde are generally in the range*** E.O. 12866 Review

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- 107 of 0. 0 1 μg/m3 to

- 0. 0 1 μg/m3 with decreases happening in the more populated areas and increases happening in more rural areas ( Figure 7

- 40 Figure 7

- 40 ).

The complex photochemistry associated with NO X emissions and acetaldehyde formation appears to be the explanation for the split between increased rural concentrations and decreased urban concentrations. In the atmosphere, acetaldehyde pr ecursors react with NO X to form peroxyacylnitrate (PAN). Reducing NO X allows acetaldehyde precursors to be available to form acetaldehyde instead

. This phenomenon is more prevalent in rural areas where NO X is low. The chemistry involved is further descr ibed by a recent study done by EPA’s Office of Research and Development and Region 3 evaluating the complex effects of reducing multiple emissions on reactive air toxics and criteria pollutants. 143

Figure 7

- 40 Changes in Acetaldehyde Ambient Concentrations Between the Reference Case and the Control Case in 2017 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )*** E.O. 12866 Review

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- 108 Figure 7

- 41 Changes in Acetaldehyde Ambient Concentrations Between the Reference Case and the Control Case in 2030 : Percent Changes ( left) and Absolute Changes in μg/m3 (right )

Population Metrics

Although the reductions in ambient air t oxics concentrations expected from the proposed Tier 3 standards are generally small, they are projected to benefit the majority of the U.S. population. As shown in Table 7

- 38 , over 80 percent of the total U.S. population is projecte d to experience a decrease in ambient benzene and acrolein con centrations of at least 2.5 percent

. M ore than 85 percent of the population is projected to experience decrease in 1,3

- butadiene concentrations of at least 5 percent

. Table 7

- 38 also shows that o ver 80 percent of the U.S population is projected to experience at least a 1 percent decrease in ambient ethanol concentrations, and over a 60 percent would experience a similar decrease in ambient formaldehyde concentrations with the proposed standards.

Table 7

- 38 Percent of Total Population Experiencing Changes in Annual Ambient Concentrations of Toxic Pollutants in 2030 as a Result of the Proposed Standards
Percent Change Benzene A crolein 1,3

- Butadiene Formaldehyde Ethanol Acetaldehyde
≤

- 50

>

- 50 to ≤

- 25 0.1 %

>

- 25 to ≤

- 10 2.8 % 0.7 % 56.8 %

>

- 10 to ≤

- 5 23.7 % 36.8 % 30.8 %

>

- 5 to ≤

- 2.5 54.5 % 43.7 % 7.1 % 1.2 % 33.0 % 0.3 %

>

- 2.5 to ≤

- 1 17.7 % 15.3 % 3.4 % 63.2 % 55 .3 % 25.1 %

>

- 1 to < 1 1.4 % 3.5 % 1.7 % 35.6 % 11.6 % 74.6 %*** E.O. 12866 Review

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- 109 ≥ 1 to < 2.5 0.0 %

≥ 2.5 to < 5
≥ 5 to < 10
≥ 10 to < 25
≥ 25 to < 50
≥ 50

Of note, the proposed rule is expected to decrease population exposure to acrole in which is currently a national risk driver for noncancer respiratory healt h effects as described in S ection 7.2.5.4.1. Our modeling projects that acrolein concentrations would decrease to levels below the inhalation reference concentration for acrolein ( 0.02 μg/m3 ) for over 5 million people in 2030

, meaning that as a result of the proposed Tier 3 standards, 5 million fewer Americans will be exposed to ambient levels of acr olein high enough to present a potent ial for adverse health effects. The inhalatio n reference concentration for acrolein and othe r risk drivers is described in S ection 6.1.5.6. In addition, the decrease in population exposure to the toxic compounds in Table 7

- 36 will decrease canc er risks that are described in S ection 6.1.5.

7.2.4.5 Visibilit y

As described in Section 6.3.1 of this draft RIA , PM also causes adverse visibility effects, and the EPA has set national ambient air quality standards (NAAQS) and regional haze rules to protect against visibility impairment . In this section we present i nformation on current and

mo del

- projected future visibility levels at Mandatory Class I Federal Areas.

7.2.4.5.1 Current Visibility Levels

D esignated PM 2.5 nonattainment areas indicate that, as of July 20

, 20 1 2

, over 105 million people live in nonattainment areas fo r the PM 2.5 NAAQS. Thus, at least these populations would likely be experiencing visibility impairment, as well as many thousands of individuals who travel to these areas. In addition, while visibility trends have improved in Mandatory Class I Federal

ar eas , these areas continue to suffer from visibility impairment. 144 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 km ± 35 km (i.e., 120 to 165 miles) in the West

. 145

, 146

, 147 I n summary, visibility impairment is experienced throughout the U.S., in multi

- state regions, urban areas, and remote Mandatory Class I Federal areas

.

7.2.4.5.2 Projected Visibility Levels

Air quality modeling conducted for the final action was used to project visibility conditions in 139 Mandatory Class I Federal areas across the U.S. The results show that in 2030 all the modeled areas would continue to have annual average deciview levels above background*** E.O. 12866 Review

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- 110 and the proposed rule would improve visibility in all these areas

. PP The average visibility on the 20 percent worst days at all modeled Mandatory Class I Federal areas is projected to improve by 0. 04 deciviews, or 0. 28 percent , in 2030. The greatest improvement in visibilities will be seen in Joshua Tree National Monument, where visibility is projected to improve by 0. 99 percent (0. 16 DV) in 2030 due to the proposed standards

. Table 7

- 39 contains the full vis ibi lity results from 2030 for the 139 analyzed areas.

Table 7

- 39 Visibility Levels ( in Deciviews ) for Mandatory Class I Federal Areas on the 20

Percent Worst Days with and without this Proposed Rule

Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Sipsey Wilderness AL 29.03 21.67 21.80 21.84 21.76 11.39

Caney Creek Wilderness AR 26.36 21.00 21.03 21.10 21 .02 11.33

Upper Buffalo Wilderness AR 26.27 21.24 21.30 21.35 21.28 11.28

Chiricahua NM AZ 12.89 12.29 12.28 12.23 12.21 6.92

Chiricahua Wilderness AZ 12.89 12.27 12.27 12.22 12.20 6.91

Galiuro Wilderness AZ 12.89 12.37 12.35 12.21 12.15 6.88

Grand Ca nyon NP AZ 11.86 11.03 11.02 10.89 10.86 6.95

Mazatzal Wilderness AZ 13.95 12.87 12.84 12.61 12.55 6.91

Mount Baldy Wilderness AZ 11.32 10.91 10.91 10.85 10.84 6.95

Petrified Forest NP AZ 13.56 12.90 12.89 12.75 12.72 6.97

Pine Mountain Wilderness AZ 1 3.95 12.81 12.78 12.54 12.48 6.92

PP The level of visibility impairment in an area is based on the light

- extinction coefficient and a unitless visibility index , called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the average person can generally perceive a change of one deciview. The higher the deciview value, the worse the visibility. Thus, an improvement in visibility is a decrease in deciview value.*** E.O. 12866 Review

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- 111 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Saguaro NM AZ 14.39 13.72 13.71 13.57 13.55 6.84 Sierra Ancha Wilderness AZ 14.45 13.55 13.53 13.33 13.28 6.92

Superstition Wilderness AZ 14.15 13.15 13.13 12.99 12.93 6.88

Sycamore Canyon Wilderness AZ 15.45 14.83 14. 81 14.70 14.67 6.96

Agua Tibia Wilderness CA 22.36 18.87 18.87 18.19 18.09 7.17

Ansel Adams Wilderness (Minarets) CA 15.24 14.48 14.48 14.29 14.25 7.12

Caribou Wilderness CA 13.65 12.75 12.75 12.61 12.57 7.29

Cucamonga Wilderness CA 18.44 15.83 15.83 1 5.42 15.32 7.17

Desolation Wilderness CA 12.87 11.89 11.88 11.76 11.73 7.13

Emigrant Wilderness CA 16.87 15.86 15.85 15.69 15.65 7.14

Hoover Wilderness CA 11.61 11.05 11.05 10.95 10.94 7.12

John Muir Wilderness CA 15.24 14.41 14.42 14.25 14.22 7.14

Jo shua Tree NM CA 18.90 16.74 16.72 16.14 15.98 7.08

Kaiser Wilderness CA 15.24 14.21 14.21 13.99 13.95 7.13

Kings Canyon NP CA 23.73 22.29 22.29 21.99 21.91 7.13

Lassen Volcanic NP CA 13.65 12.78 12.77 12.61 12.56 7.31

Lava Beds NM CA 14.13 13.14 13.13 13.20 13.17 7.49

Mokelumne Wilderness CA 12.87 12.04 12.03 11.91 11.88 7.14

Pinnacles NM CA 17.90 15.64 15.61 15.43 15.31 7.34*** E.O. 12866 Review

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- 112 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Point Reyes NS CA 22.40 20.89 20.87 21.08 20.99 7.39

Redwood NP CA 18.55 17.99 17.97 17.77 17.73 7.81

San Gabriel Wilderness CA 18.44 15.86 15.86 15.37 15.26 7.17

San Gorgonio Wilderness CA 21.43 19.50 19.50 19.01 18.89 7.10

San Jacinto Wilderness CA 21.43 18.70 18.71 17.67 17.52 7.12

San Rafael Wilderness CA 19.43 17.63 17.61 17.30 17.21 7.28

Sequoia NP CA 23.73 21.90 21.8 8 21.52 21.42 7.13 South Warner Wilderness CA 14.13 13.34 13.33 13.30 13.27 7.32

Thousand Lakes Wilderness CA 13.65 12.76 12.75 12.60 12.55 7.32

Ventana Wilderness CA 17.90 16.48 16.45 16.21 16.07 7.32

Yosemite NP CA 16.87 15.87 15.86 15.71 15.67 7.14

Black Canyon of the Gunnison NM CO 10.00 9.32 9.32 9.29 9.28 7.06

Eagles Nest Wilderness CO 8.82 8.27 8.26 8.22 8.20 7.08

Flat Tops Wilderness CO 8.82 8.43 8.43 8.39 8.38 7.07

Great Sand Dunes NM CO 11.82 11.34 11.34 11.31 11.30 7.10

La Garita Wildern ess CO 10.00 9.59 9.59 9.54 9.54 7.06

Maroon Bells

Snowmass Wilderness CO 8.82 8.38 8.38 8.36 8.35 7.07*** E.O. 12866 Review

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- 113 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Mesa Verde NP CO 12.14 11.46 11.45 11.48 11.46 7.09

Mount Zirkel Wilderness CO 9.72 9.29 9.29 9.28 9.27 7.08

Rawah Wilderness CO 9.72 9.29 9.29 9.26 9.25 7.08

Rocky Mountain NP CO 12.85 12.37 12.36 12.34 12.32 7.05

Weminuche Wilderness CO 10.00 9.58 9.58 9.51 9.51 7.06

West Elk Wilderness CO 8.82 8.35 8.35 8.33 8.32 7.07

Everglades NP FL 22.31 19.30 19.06 19.10 19.04 11.15

Okefenokee GA 27.13 21. 29 21.44 21.47 21.40 11.45 Wolf Island GA 27.13 21.10 21.12 21.18 21.12 11.42 Craters of the Moon NM ID 14.06 13.30 13.30 13.10 13.05 7.13

Sawtooth Wilderness ID 14.97 14.75 14.74 14.75 14.75 7.15

Selway

Bitterroot Wilderness ID 17.11 16.83 16.83 16.86 16.85 7.32

Mammoth Cave NP KY 31.37 22.87 23.09 23.14 23.07 11.53

Acadia NP ME 22.89 18.51 18.80 18.83 18.80 11.45 Moosehorn ME 21.72 17.81 18.01 18.03 18.01 11.36 Roosevelt Campobello International Park ME 21.72 17.68 17.95 17.96 17.94 11.36

Isle Ro yale NP MI 20.74 18.69 18.64 18.74 18.68 11.22

Seney MI 24.16 21.32 21.33 21.44 21.35 11.37 Boundary Waters Canoe Area MN 20.20 17.13 17.05 17.16 17.10 11.21*** E.O. 12866 Review

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- 114 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Voyageurs NP MN 19.27 17.03 16.95 17.05 16.99 11.09

Hercules

Glades Wilderness MO 26.75 21.92 21.99 22.04 21.97 11.27

Anaconda

Pintler Wilderness MT 17.11 16.73 16.72 16.77 16.77 7.28

Bob Marshall Wilderness MT 16.13 15.71 15.71 15.74 15.73 7.36

Cabinet Mountains Wilderness MT 14.31 13.74 13.74 13.79 13.78 7.43

Gates of the Mountains Wilderness MT 11.94 11.56 11.56 11.57 11.57 7.22

Glacier NP MT 19.62 18.81 18.82 18.81 18.81 7.56 Medicine Lake MT 18.21 17.65 17.65 17.58 17.57 7.30

Mission Mountains Wilderness MT 16.13 15.57 15.57 15.62 15.61 7.39

Red Rock Lakes MT 11.19 10.78 10.78 10.74 10. 72 7.14

Scapegoat Wilderness MT 16.13 15.68 15.68 15.70 15.70 7.29

UL Bend MT 15.49 15.17 15.17 15.13 15.12 7.18 Linville Gorge Wilderness NC 28.77 20.85 21.23 21.24 21.19 11.43

Shining Rock Wilderness NC 28.54 20.47 20.78 20.81 20.73 11.45

Lostwood N D 19.57 18.48 18.28 18.39 18.36 7.33 Theodore Roosevelt NP ND 17.74 16.71 16.51 16.61 16.58 7.31

Great Gulf Wilderness NH 22.82 16.73 17.01 17.05 17.01 11.31*** E.O. 12866 Review

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- 115 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Presidential Range

- Dry River Wilderness NH 22.82 16.68 16.97 17.00 16.96 11.33

Brigantine NJ 2 9.01 21.56 21.88 21.93 21.84 11.28 Bandelier NM NM 11.97 10.89 10.88 10.77 10.75 7.02

Bosque del Apache NM 13.81 12.78 12.76 12.63 12.61 6.97

Carlsbad Caverns NP NM 17.19 14.93 15.00 15.03 15.00 7.02

Gila Wilderness NM 13.12 12.57 12.56 12.53 12.52 6.9 5

Pecos Wilderness NM 9.60 9.08 9.07 9.01 8.99 7.04

Salt Creek NM 18.27 16.70 16.70 16.67 16.64 6.99 San Pedro Parks Wilderness NM 10.42 9.87 9.87 9.78 9.77 7.03

Wheeler Peak Wilderness NM 9.60 8.92 8.92 8.82 8.80 7.07

White Mountain Wilderness NM 13. 01 12.16 12.16 12.20 12.19 6.98

Jarbidge Wilderness NV 12.26 11.98 11.98 11.98 11.97 7.10

Wichita Mountains OK 23.81 19.38 19.18 19.29 19.18 11.07

Crater Lake NP OR 13.21 12.52 12.51 12.62 12.60 7.71

Diamond Peak Wilderness OR 13.21 12.45 12.44 12.56 1 2.54 7.77

Eagle Cap Wilderness OR 17.34 16.36 16.36 16.51 16.48 7.34

Gearhart Mountain Wilderness OR 13.21 12.69 12.68 12.68 12.66 7.46*** E.O. 12866 Review

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- 116 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Hells Canyon Wilderness OR 19.00 18.00 18.00 17.82 17.76 7.32

Kalmiopsis Wilderness OR 16.38 15.48 15.46 15.60 15.56 7.71

Mount Hood Wilderness OR 14.68 13.33 13.30 13.53 13.46 7.77

Mount Jefferson Wilderness OR 15.80 14.96 14.95 15.12 15.09 7.81

Mount Washington Wilderness OR 15.80 14.95 14.93 15.12 15.09 7.89

Mountain Lakes Wilderness OR 13.21 12.44 12.43 12.57 12 .55 7.57

Strawberry Mountain Wilderness OR 17.34 16.66 16.65 16.55 16.51 7.49

Three Sisters Wilderness OR 15.80 15.02 15.01 15.18 15.15 7.87

Cape Romain SC 26.48 20.61 20.72 20.76 20.69 11.36

Badlands NP SD 17.14 15.56 15.50 15.56 15.54 7.30

Wind Cave NP SD 15.84 14.81 14.72 14.77 14.75 7.24

Great Smoky Mountains NP TN 30.28 22.32 22.57 22.62 22.52 11.44

Joyce

Kilmer

Slickrock Wilderness TN 30.28 22.03 22.29 22.34 22.25 11.45

Big Bend NP TX 17.30 15.76 15.71 15.75 15.72 6.93

Guadalupe Mountains NP TX 17.19 14.95 15.03 15.06 15.03 7.03*** E.O. 12866 Review

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- 117 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

Arches NP UT 10.77 10.13 10.13 10.20 10.18 6.99 Bryce Canyon NP UT 11.62 10.95 10.95 10.93 10.93 6.99

Canyonlands NP UT 10.77 10.15 10.13 10.24 10.21 7.01

Capitol Reef NP UT 10.86 10.46 10.46 10.53 10.52 7.03

Jame s River Face Wilderness VA 29.12 20.45 20.61 20.65 20.56 11.24

Shenandoah NP VA 29.31 20.24 20.67 20.69 20.61 11.25

Lye Brook Wilderness VT 24.45 17.72 17.75 17.80 17.67 11.25

Alpine Lake Wilderness WA 16.99 15.59 15.55 15.35 15.22 7.86

Glacier Peak Wi lderness WA 13.29 12.26 12.25 12.26 12.24 7.80

Goat Rocks Wilderness WA 12.67 11.54 11.52 11.60 11.56 7.82

Mount Adams Wilderness WA 12.67 11.57 11.56 11.65 11.61 7.78

Mount Rainier NP WA 17.07 15.77 15.75 15.80 15.75 7.90

North Cascades NP WA 13.29 12 .24 12.23 12.18 12.17 7.78

Olympic NP WA 15.83 14.63 14.61 14.71 14.65 7.88 Pasayten Wilderness WA 15.35 14.34 14.32 14.48 14.46 7.77

Dolly Sods Wilderness WV 29.05 20.23 20.82 20.84 20.79 11.32

Otter Creek Wilderness WV 29.05 20.34 20.90 20.93 20.87 1 1.33

Bridger Wilderness WY 10.73 10.38 10.38 10.39 10.39 7.08

Fitzpatrick Wilderness WY 10.73 10.38 10.38 10.38 10.38 7.09

Grand Teton NP WY 11.19 10.73 10.72 10.68 10.66 7.09*** E.O. 12866 Review

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- 118 Class 1 Area (20% worst days) State 2005

Baseline Visibility (dv) a 2017 Reference 2017

Tier 3 Control 2030

Reference 2030

Tier 3 Control Natural

Background

North Absaroka Wilderness WY 11.30 10.99 10.99 10.97 10.97 7.09

Teton Wilde rness WY 11.19 10.81 10.80 10.77 10.75 7.09

Washakie Wilderness WY 11.30 10.99 10.99 10.97 10.96 7.09

Yellowstone NP WY 11.19 10.76 10.76 10.70 10.69 7.12

a The level of visibility impairment in an area is based on the light

- extinction coefficient and a unitless visibility index, called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a scale for perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the

av erage person can generally perceive a change of one deciview. The higher the deciview value, the worse the visibility. Thus, an improvement in visibility is a decrease in deciview value.

7.2.4.6 Deposition of Nitrogen and Sulfur

As described in Section 6.3.2.1 o f this draft RIA

, deposition of nitrogen and sulfur can

cause adverse environmental effects

. In this section we present information on current and

mo del

- projected future nitrogen and sulfur deposition levels.

7.2.4.6.1 Current Levels of Nitrogen and Sulfur Depositi on
Over the past two decades, the EPA has undertaken numerous 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 compounds 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 most abundant, total sulfur deposition decreased by about 44 percent between 1990 and 2007, whil e total nitrogen deposition decreased by 25 percent over the same time frame. QQ These numbers are generated by the U.S. national monitoring network and they likely underestimate nitrogen deposition because neither ammonia nor organic nitrogen is measured. A lthough total nitrogen and sulfur deposition has decreased over time, many areas continue to be negatively impacted by deposition. Deposition of inorganic nitrogen and sulfur species routinely measured in the U.S. between 2005 and 2007 were as high

QQ U.S. EPA. (2012). U.S. EPA’s Report on the Environment. Data accessed online February 15, 2 012 at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.listBy Chapter&r=216610 and contained in Docket EPA

- HQ

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- 0135.*** E.O. 12866 Review

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- 119 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. RR

RR U.S. EPA. (2012). U.S. EPA's Report on the Environment. Data accessed online February 15, 2012 at: http://cfpub.epa.gov/eroe/index.cfm?fuseaction=detail.viewPDF&ch=46&lShowInd=0&subtop=341&lv=list.listBy Chapter&r=216610 and contained in Docket EPA

- HQ

- OAR

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- 0135.*** E.O. 12866 Review

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- 120 Figure 7

- 42 Total Sulfur Deposition in the Contiguo us U.S., 1989

- 1991 and 200 5

- 200 7*** E.O. 12866 Review

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- 121 Figure 7

- 43 Total Nitrogen Deposition in the Contiguous U.S., 1989

- 1991 and 2005

- 2007*** E.O. 12866 Review

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- 122 7.2.4.6.2 Projected Levels of Nitrogen and Sulfur Deposition

Our air quality modeling projects d ecreases in both nitrogen and sulfur deposition due to this proposed rule

. Figure 7

- 44 shows that for nitrogen deposition by 2030 the proposed standards would result in annual percent decreases of more than 5 percent in most urba n areas with decreases of more than 7 percent in urban areas in Nevada, Arizona and Florida. In addition, smaller decreases, in the 1 to 1.5 percent range, would occur over most of the rest of the country.

Figure 7

- 44 Percent Change in Annual Total Nitrogen over the U.S. Modeling Domain as a Result of the Proposed Standards

Figure 7

- 45 shows that for sulfur deposition the proposed standards will result in annual percent decreases of more than 2 percent in some areas in 2030 . The decreases in sulfur deposition are likely due to projected reductions in the sulfur level in fuel

. Minimal changes in sulfur deposition, ranging from decreases of less than 0.5 percent to no change, are projected for
t he rest of the country.*** E.O. 12866 Review

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- 123 Figure 7

- 45 Percent Change in Annual Total Sulfur over the U.S. Modeling Domain as a Result of the Proposed Standards

7.3 Greenhouse Gas Emission Impacts

Reductions in nitrous oxide (N 2 O) emissions and methane (CH 4 ) emissions , both potent greenhouse gas emissions (wi th global warming potentials 298 and 25 times greater than CO 2 , respectively) , are projected for gasoline cars and trucks due to the proposed sulfur and tail pipe standards. These projections are based on studies that provid e a basis for reductions in N 2 O and CH 4 emissions due to the Tier 3 sulfur and vehicle standard s

. With respect to sulfur, a study published in 2004 by the University of California at River side found a 29 percent reduction in N 2 O emissions over the FTP and a 50 percent reduction over the US06 when sulfur was reduced from 30 to 5 ppm

. 148 EPA’s sulfur study, detailed in Section 7.2, found a 25 percent reduction in CH 4 emissions when sulf ur was reduced from 28 to 5 ppm (the EPA study did not measure N 2 O emissions)

.
Several s tudies have also established that reductions in tailpipe standards for NO X and HC results in reductions in and N 2 O and CH 4 , respectively. N 2 O is unique in that it is not fo rmed primarily during combustion, but in the catalyst during catalyst warm

- up, before the catalyst reaches the temperatures r equired for full effectiveness ( known as “light

- off” ) . Improvements to catalyst technology required to meet lower emission standar ds reduce the time required for the catalyst to reach light

- off, which reduces the window of N 2 O formation. Studies conducted by EPA and Environment Canada found that N 2 O emission are lower on vehicles certified to more stringent NO X emission standards. 149

, 150 A study by Meffert, et al. established a strong correlation between improvements in NO X catalytic conversion efficiency and reductions in N 2 O*** E.O. 12866 Review

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- 124 emissions. 151 A study published by Behrentz, et al. in 2004 examined the relationship between N 2 O and NO X from data collected by the California Air Resources Board on cars and light trucks ranging from the mid

- 1980s through early 2000’s Low Emission Vehicle (LEV) technology

. 152
T he study reported an N 2 O: NO X ratio of 0.095± 0.035 ( with the lower end of the range
comp rised of older oxidation catalyst technologies, and the higher end of the range comprised of modern three

- way catalyst technologies ) , and supported the application of N 2 O: NO X ratios to NO X emissions as a reasonable method for estimating N 2 O emission inven tories. Subsequent analysis of this dataset by Meszler also f ound that for vehicles equipped with more modern controls, N 2 O emissions increased with vehicle mileage, suggesting deterioration in N 2 O emissions as vehicles age

. 153

The Meszler and Environm ent Canada studies cited above also established that vehicles certified to more stringent HC standards emit less CH 4

, even though HC standards from Tier 1 and later do not include methane in the regulated standards

. This trend is also reflected in the MO VES model, based on analysis of correlation between CH 4 and HC emissions. MOVES esti mates methane as a function of total HC emissions, so the CH 4 emission inventories account for effects such as deterioration, temperature, aggressive driving, and reductio n s in tailpipe emission standards. Because of this, CH 4 reductions from the Tier 3 program can be estimated directly by MOVES, as a function of reductions in HC from the sulfur and vehicle standards ( although this will provide a conservative estimate of reductions, as the percent reduction in CH 4 from using low sulfur fuel is about double that for total HC (Table 7

- 13) )

. The MOVES results

are shown in Table 7

- 40.

Table 7

- 40: Estimated Reduction in CH 4 from Tier 3 Program (MMTCO 2 eq)

2017 2030 Reference case onroad mobile emissions 2.1 2.5

Control case onroad mobile emissions 2.0 2.0

Reduction 0.1 0.5

MOVES N 2 O emission s are based directly on a limited sample of N 2 O emission data, rather than link ing N 2 O emissions to NO X emissions as suggested by Behr entz; as a result

, the model does not estimate potential N 2 O reduction concurrent with the Tier 3 program. Because of this, t he MOVES

- based inventories are significantly lower than inventories that take into account the N 2 O:NO X link, because they do not account for factors affecting light

- duty NO X emissions, like deterioration or aggressive driving, and lower NO X standards. To estimate N 2 O reductions , we have calculated N 2 O reductions due to vehicle standards on the Tier 3 fleet (2017 and later model ye ar vehicles) , using the 0.095 N 2 O:NO X factor from Behrentz. For the pre

- Tier 3 fleet, we then bounded the N 2 O reductions due to sulfur control using two methods: 1) applying the 0.095 N 2 O: NO X ratio directly to the sulfur

- related NO X reductions reported in Section 7.2; and 2 ) applying the percent reduction in N 2 O from the UC Riverside study to current MOVES inventory estimates for onroad gasoline vehicles. These two methods are outlined in Table 7

- 41, along with the range of reductions that result.*** E.O. 12866 Review

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- 125 Tabl e 7

- 41: Estimated Reduction in N 2 O from the Tier 3 Program

2017 2030 Reduction from Tier 3 fleet NO X r eduction from Tier 3 fleet due to vehicle and sulfur standards ( U.S. Short Tons ) 19,728 458,504

N 2 O reduction based on N 2 O:NO X ratio of

0.095 ( MMT CO 2 eq a ) 0.5 11.8

Reduction from pre

- Tier 3 fleet Method 1 Reference case onroad g asoline NO X emissions (U.S. Short Tons) 1,844,772 980,679

Reference case onroad g asoline N 2 O emissions based on N 2 O:NO X ratio of 0.095

( MMTCO 2 eq a ) 47.4 25.2

Reduction from pre

- Tier 3 fleet due to sulfur standard ( U.S. Short Tons ) 264,653 66,286

N 2 O reduction based on N 2 O:NO X ratio of

0.095 ( MMTCO 2 eq a ) 6.8 1.7

Method 2 U.S. o nroad gasoline N 2 O e missions from
pre

- Tier 3 fleet using MOVES (MMTCO 2 e) 10.3 2.0

Percen t r eduction in N 2 O going from 30 to 10

ppm 23% b 23%

N 2 O reduction (MMTCO 2 eq) 2.4 0.5 Total Range of N 2 O Reduction (MMTCO 2 eq) 2.9

– 7.3 12.3

- 13.5

a Using GWP of 298 SS

b 29 percent from 25ppm sulfur reduction in UC Riverside study scaled to 20ppm reductio n
Summing the results from Tables 7

- 40 and 7

- 41, the range of reductions calculated for CH 4 and N 2 O is from 3.0 to 7.4 million metric tons of carbon dioxide equivalent (MMTCO 2 e q ) in 2017, growing to 12.8 to 1 4 .0 MMTCO 2 e q in 2030. These reductions would be offset to some degree by CO 2 emissions associated with h igher energy use required in the process of removing sulfur. To assess refinery permitting implications, EPA has conducted an analysis of these refinery impacts, and estimates an increase of up to 4 .6 MMTCO 2 e q million metric tons of carbon dioxide equivalent (MMTCO 2 e) in 2017 with the implementation of the lower sulfur standards. 154 The actual increase will be lower, since refineries will not be operating at their permit capacity. The actual increase will also be a function of several factors, including

SS The global warming potentials (GWP) used in this rule are consi stent with the 100

- year time frame values in the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). At this time, the 1996 IPCC Second Assessment Report (SAR) 100

- year GWP values are used in the official U.S. greenhouse gas inventory submission to the United Nations Framework Convention on Climate Change (per the reporting requirements under that international convention, which were last updated in 2006) . N 2 O has a 100

- year GWP of 298 and CH 4 has a 100

- year GWP of 25 ac cording to the 2007 IPCC AR4

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- 126 technology options selected by the refineries and the projected use of averaging, banking and trading in avoiding the need for investments at some refineries. As a result, 4.6 MMTCO 2 e represents an upp er

- bound estimate of the possible increase in refinery CO 2 emissions due to the need for additional process heat and hydrogen production to enable additional hydrotreating.

In 2017, t he range of potential decrease in CH 4 and N 2 O emissions overlaps with the range of projected increase in CO 2 from refinery processes, suggesting that a net increase or decrease in GHG emissions cannot be quantified with certainty

. However, we estimate the
program w ould result in net GHG reductions as the program continues into the future, as shown by our 2030 estimates.*** E.O. 12866 Review

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- 127 References

1 2017 and Later Model Year Light

- Duty Vehicle Greenhous e Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule (77 FR 62623

– 63200 ), October 2012.

2 MOVES (Motor Vehicle Emission Simulator) website: http://www.epa.gov/otaq/models/moves/index.htm

3 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

4 U.S. EPA. 2012. Memorandum to Docket: “Development of fuel adjustments and toxic fractions for use in MOVES2010a using draft statistical models generated using results from the Phase

- 3 EPAct Project.”

5 Particle Emissions from a 2009 Gasoline Direct Injection Engine Using Different Commercially Available Fuels, SAE Paper 2010

- 01

- 2117, Imad A. Khalek and Thomas Bougher, October 25, 2010.

6 Development of a Predictive Model for Gasoline Vehicle Particulate Matter Emissiosn, SAE Paper 2010

- 01

- 2115, Koichiro Aikawa and Takayuki Sakurai, October 25, 2010.

7 Masashi Iisuka, Japan Petroleum Energy Center, “Effect of Fuel Properties on Emissions from Direct Injection Gasoline Vehicle,” 5 th Asian Petroleum Technology Symposium, Jaka rta, Indonesia, January 25, 2007.

8 Khalek, I., Bougher, T., and Merritt, P. M. 2009. Phase 1 of the Advanced Collaborative Emissions Study. Prepared by Southwest Research Institute for the Coordinating Research Council and the Health Effects Institute, June 2009. Available at www.crcao.org

.
9 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

10 U.S. EPA, 2008, Memorandum to Docket: Using MOVES to Generate Inventories for the RFS2 NPRM.

11 Re gulation of Fuels and Fuel Additives: Changes to Renewable Fuel Standard Program; Final Rule (75 FR 14670), March 2010.

12 RFS2 Final Rule Regulatory Impact Analysis, Chapter 3

13 Coordinating Research Council (2011), Exhaust and Evapor a tive Emissions Testi ng of Flexible Fuel Vehicles, CRC E

- 80

14 R. Cook, et al

. ( 2011 ) . Air quality impacts of increased use of ethanol under the United States’ Energy Independence and Security Act. Atmospheric Environment, 45: 7714

- 7724.

15 Luecken, D. J. et al. 2011. Regio nal sources of atmospheric formaldehyde and acetaldehyde, and implications for atmospheric modeling. Atmospheric Environment, in press

. doi:10.1016/j.atmosenv.2011.10.005

16 "DOCUMENTATION FOR THE 2005 MOBILE NATIONAL EMISSIONS INVENTORY, VERSION 2" ftp:// ftp.epa.gov/EmisInventory/2005_nei/mobile/2005_mobile_nei_version_2_report.pdf

17 Mathpro (October 2011 ). Refining Economics of a National Low Sulfur, Low RVP Gasoline Standard, Performed for The International Council for Clean Transportation, Available at : http://www.theicct.org/sites/default/files/publications/ICCT04_Tier3_Report_Final_v4_All.pdf. Accessed December 12 , 2011.

18 Baker and O’Brien ( July 2011 ) .Potential Supply and Cost Impacts of Lower Sulfur, Lower RVP Gasoline; prepared for T he American P etroleum Institute. Available at: http://www.api.org/Newsroom/upload/110715_LowerSulfur_LowerRVP_Final.pdf

. Accessed December 12 , 2011

19 U.S.. EPA.2011. Development of Emission Rates for Light

- Duty Vehicles in the Motor Vehicle Emissions Simulator (MOVES2 010): Final Report. (EPA

- 420

- R

- 11

- 011) http://www.epa.gov/otaq/models/moves/documents/420r11011.pdf

.
20 U.S. EPA. 2011. Memorandum to Docket: Tier 3 Emission Rates for Gasoline Light Duty HC/CO/NOx Exhaust

21 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

22 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”*** E.O. 12866 Review

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- 128 23 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

24 Sonntag, Darrell. 2013. Supporting Technical Document: Estima ted Reductions in Particulate Matter Emissions from Light

- duty and Medium

- duty Gasoline vehicles through Implementation of Tier 3 Regulations.

25 Defries, et. al. 2011. “ Estimates of the Fraction of the Fleet with High Evaporative Emissions based on the Ken Caryl Station “ (Denver, Colorado) Field Study , Eastern Research Group report to EPA 0218.04.004.001

26 Haskew, Harold, et al. 2011. “Vehicle Evaporative Emission Mechanisms: A Pilot Study,” available at http://crcao.org/reports/recentstudies2008/E

- 77PilotStudy/E

- 77PilotStudyFinalReport6.24.08.pdf

27 U.S. EPA. 2011. MOVES2010 Fuel Adjustment and Air Toxic Emission Calculation Algorithm

– Developmen t and Results ( EPA

- 420

- R

- 11

- 009 ) http://www.epa.gov/otaq/models/moves/documents/420r11009.pdf

28 U.S. EPA. 2013. "The Effects of Fuel Sulfur Level on Emissions from Tier 2 Vehicles In

- Use" (draft)

.
29 See Chapter III and Appendix B of the Regulatory Impact Analysis for the Tier2/Sulfur Final Rule EPA 420

- R

99

- 023.

30 See Chapter 6 of the Regulatory Impact Analysis for the Control of Hazardous Air Pollutants from Mobile Sources Final Rule, EPA 420

- R

- 07

- 002.

31 Ball D., Clark D., Moser D. 2011. “ Effects of Fuel Sulfur on FTP NOx Emissions from a PZEV 4 Cylinder Application ”

. SAE 2011 World Congress Paper 2011

- 01

- 0300. SAE International: Warrendale, PA.

32 U.S. EPA. 2005 EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling System for MOBILE6 and NONROAD ; EPA420

- R

- 05

- 024; Office of Transportation and Air Quality, Ann Arbor, MI. http://www.epa.gov/otaq/models/nmim/420r05024.pdf

33 Final Rulemaking: Greenhouse Gas Emissions Standar ds and Fuel Efficiency Standards for Medium

- and Heavy

Duty Engines and Vehicles (signed August 9, 2011). http://www.epa.gov/oms/climate/regulations.htm#1

- 2

34 Final Rule: Control of Emissions of Air Pollution from Locomotives and Marine Compression

- Igniti on Engines Less Than 30 Liters per Cylinder (published May 6, 2008 and republished June 30, 2008)

. http://www.epa.gov/oms/locomotives.htm#2008final

35 Final Rule: Control of Emissions from New Marine Compression

- Ignition Engines at or Above 30 Liters per C ylinder (published April 30, 2010)

. http://www.epa.gov/oms/oceanvessels.htm#car

- ems

.
36 U.S. Energy Information Administration (2011), Annual Energy Outlook 2011

, DOE/EIA

- 0383(2011)

37 U.S. EPA 2011, Tier 3 Emissions Inventory for Air Quality Modeling Tech nical Support Document

38 U.S. EPA 2011. Tier 3 Emissions Inventory for Air Quality Modeling Technical Support Document

39 User’s Guide for the SMOKE

– MOVES Integration Tool , July 14, 2010. Available at http://www.smoke

model.org/smoke_moves_tool/

.
40 For mo re information, please see the website for SMOKE: http://www.smoke

- model.org/index.cfm

.
41 Michaels, H. (2011, December). Documentation of Smoke

- MOVES Post

- Processing Script Inconsistency. Memorandum t o the docket.

42 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth

- Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN

- 398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.

43 "Air Quality Modeling Technical Supp ort Document: Heavy

- Duty Vehicle Greenhouse Gas Emission Standards Final Rule", (EPA

- 454

- R

- 11

- 004, August 2011) (http://www.epa.gov/otaq/climate/documents/454r11004.pdf)

44 "Documentation for the 2005 Mobile National Emissions Inventory, Version 2 ", (Decem ber 2008) ( ftp://ftp.epa.gov/EmisInventory/2005_nei/mobile/2005_mobile_nei_version_2_report.pdf )

45 U.S. EPA. 2010 MOVES2010 Highway Vehicle Population and Activity Data, EPA

- 420

- R

- 10

- 026

, http://www.epa.gov/otaq/models/moves/420r10026.pdf*** E.O. 12866 Review

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- 129 46 Documentation for the 2005 Mobile National Emissions Inventory, Version 2 ", (December 2008) ( ftp://ftp.epa.gov/EmisInventory/2005_nei/mobile/2005_mobile_nei_version_2_report.pdf )

47 U. S. EPA. 2011. Spreadsheet “Comparison_VMT_future_15jul2011.xlsx.” Docket EPA

- HQ

- OA R

- 2011

- 0135

48 "Development of Methodology for Estimating VMT Weighting by Facility Type", (EPA420

- R

- 01

- 009, M6.SPD.003, April 2001) . Prepared for EPA by Systems Applications International, Inc., ICF Consulting Group. (http://www.epa.gov/ otaq/models/mobile 6/r01009.pdf)

49 U.S. EPA 2011. Tier 3 Emissions Inventory for Air Quality Modeling Technical Support Document

.
50 U.S. EPA 2011. Tier 3 Emissions Inventory for Air Quality Modeling Technical Support Document

.
51 Cook, R. 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container Inventories for Tier 3 Proposal . Memorandum to Docket EPA

- HQ

- OAR

- 20 10

- 0 162.

52 U.S. EPA

. 2009. Exhaust Emission Profiles for EPA SPECIATE Database: Energy Policy Act (EPAct) Low

Level Ethanol Fuel Blends a nd Tier 2 Light

- Duty Vehicles. EPA

- 20

- R

- 09

- 002, June 2009.

53 U.S. EPA. 2010. Evaporative Emissions from In

- Use Vehicles: Test Fleet Expansion (CRC E

- 77

- 2b). EPA

- 420

- R

- 10

- 025. Available at: http://www.epa.gov/otaq/emission

- factors

- research/420r10025.pdf

. Accessed December 15, 2011.

54 Coordinating Research Council. 2010. Study To Determine Evaporative Emission Breakdown, Including Permeation Effects a nd Diurnal Emissions, Using E 20 Fuels o n Aging Enhanced Evaporative Emissions Certified Vehicles (CRC E

- 77

- 2c). Available at: http://www.crcao.com/reports/recentstudies2011/E

- 77

- 2c/E

- 77

- 2c%20Final%20Report%20for%20sure%201

- 28

- 11.pdf . Accessed December 15, 2011.

55 U.S. EPA. 2011. Hydrocarbon Composition of Gasoline Vapor Emissions from Enclosed Fuel Tanks. EP A

- 420

- R

- 11

- 018. Available in the docket to this rule.

56 U.S. EPA. 201 3

, January

. Mobile Source Hydrocarbon Speciation Profiles for the Tier 3 Rule NPRM and Anti

backsliding Study AQ Modeling. Memorandum to the docket.

57 Byun, D.W., Ching, J. K.S. 1999. Science algorithms of the EPA models

- 3 community multiscale air quality (CMAQ) modeling system . Washington, DC: U.S. Environmental Protection Agency, Office of Research and Development.

58 Byun, D.W., Schere, K.L. (2006). Review of the Governing Equations, Computational Algorithms, and Other Components of the Models

- 3 Community Multiscale Air Quality (CMAQ) Modeling System. Journal of Applied Mechanics Reviews, 59(2), 51

- 77.

59 Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M. A. (1996). The next generation of integrated air quality modeling: EPA’s Models

- 3, Atmospheric Environment, 30, 1925

- 1938.

60 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004). Simulating regional

- scale ozone climatolo gy over the eastern United States: model evaluation results. Atmospheric Environment, 38(17), 2627

- 2638.

61 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S. (2008). Long

- range transport of acidifying substances in East Asia

- Part I: M odel evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939

- 5955.

62 U.S. Environmental Protection Agency. (2008). Technical support document for the final locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N .C.: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Air Quality Assessment Division.

63 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth

- Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN

- 39 8+STR. , 138 pp, National Center for Atmospheric Research, Boulder CO.

64 Grell, G., Dudhia, J., Stauffer, D. (1994). A Description of the Fifth

- Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN

- 398+STR. , 138 pp, National Center for Atmospheric Rese arch, Boulder CO.*** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

7

- 130 65 Byu n, D.W., Ching, J. K.S. (1999). Science algorithms of EPA Models

- 3 Community Multiscale Air Quality (CMAQ) modeling system , EPA/600/R

- 99/030, Office of Research and Development). Please also see: http://www.cmascenter.org/

.
66 Le Sager, P. Yantosca, B., Carouge , C. (2008). GEOS

- CHEM v8

- 01

- 02 User’s Guide , Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, December 18, 2008

.

67 U.S. EPA, (2004), Procedures for Estimating Future PM 2.5 Values for the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test (SMAT)

- Updated 11/8/04.

68 U.S. EPA, (2011), Final Cross State Air Pollution Rule Air Quality Modeling TSD.

69 U.S. EPA (2007) Guidance on the Use of Models and Other Analyses For Demonstrating Attainment of Air Quality Goals for Ozone, PM 2.5 , and Regional Haze; EPA

- 454/B

- 07

- 002; Research Triangle Park, NC; April 2007.

70 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Che mical Mechanism: CB05. Final Report to the U.S. EPA, RT

- 0400675, December 8, 2005. http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.

71 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

72 Dodge, M.C., 2000. Chemical o xidant mechanisms for air quality modeling: critical review. Atmospheric Environment 34, 2103

- 2130.

73 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry

- IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac

- kinetic.ch.cam.ac.uk/index.html.

74 Atkinson, R., Baulch, D.L., Cox, R.A., Crowl ey, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry

- IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac

- kinetic.ch.cam.ac.uk/index.html.

75 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmosph eric Chemistry

- IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac

- kinetic.ch.cam.ac.uk/index.html.

76 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry

- IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac

- kinetic.ch.cam. ac.uk/index.html.

77 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson

- Pitts, B.J. (2003) Chemical Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html.

78 J.G. Calvert, A. Mellouki, J.J. Orlando, M.J. Pilling, and T.J. Wallington, 2011. The mechanisms of atmospheric oxidation of the oxygenates. Oxfo rd University Press, New York/Oxford.

79 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson

- Pitts, B.J. (2003) Chemical Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory http://jpldataeval.jpl.nasa.gov/index.html.

80 Yarwood, G., Rao, S., Yocke, M., Whitten , G.Z., 2005. Updates to the Carbon Bond Mechanism: CB05. Final Report to the U.S. EPA , RT

- 0400675. Yocke and Company, Novato, CA.*** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

7

- 131 81 Luecken, D.J., Phillips, S., Sarwar, G., Jang, C., 2008b. Effects of using the CB05 vs. SAPRC99 vs. CB4 chemical mechanism on model predictions: Ozone and gas

- phase photochemical precursor concentrations. Atm ospheric Environment 42, 5805

- 5820.

82 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson

- Pitts, B.J., 2003. Chemical Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory.

83 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson

- Pitts, B.J., 200 3. Chemical Kinetics and Photochemical Data for use in Atmospheric Studies, Evaluation Number 14. NASA Jet Propulsion Laboratory.

84 Atkinson R, Arey J (2003) Atmospheric Degradation of Volatile Organic Compounds. Chem Rev 103: 4605

- 4638.

85 Atkinson, R. , Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry

- IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmo spheric Chemistry. July 2005 web version. http://www.iupac

- kinetic.ch.cam.ac.uk/index.html.

86 Edney, E. O., T. E. Kleindienst, M. Lewandowski, and J. H. Offenberg, 2007. Updated SOA chemical mechanism for the Community Multi

- Scale Air Quality model, EPA 6 00/X

- 07/025, U.S. EPA, Research Triangle Park, NC.

87 Carlton, A.G., B. J. Turpin, K. Altieri, S. Seitzinger, R. Mathur, S. Roselle, R. J. Weber, (2008), CMAQ model performance enhanced when in

- cloud SOA is included: comparisons of OC predictions with mea surements, Environ. Sci. Technol. 42 (23), 8798

– 8802.

88 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern United States, Environ Sci Techn ol 42(9):3303

- 3309. http://pubs.acs.org/cgi

- bin/article.cgi/esthag/2008/42/i09/html/es0720412.html

.
89 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of biogenic and anthropogenic hydrocarbons to second ary organic aerosol at a southeastern U.S. location, Atmos Environ 41(37):8288

- 8300.

90 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007)

- - pinene,N O x /Air Mixture: Comparison of Results Using 14 C Content and SOA Organic Tracer Methods, Environ Sci Technol 41: 3972

- 3976.

91 Pandis, S.N., Harley, R.A., Cass, G.R., Seinfeld, J.H. (1992) Secondary organic aerosol formation and transport. Atmos Environ 26 , 2269

– 2282.

92 Takekawa, H. Minoura, H. Yamazaki , S. (2003) Temperature dependence of secondary organic aerosol formation by photo

- oxidation of hydrocarbons. Atmos Environ 37: 3413

– 3424.

93 Kleeman, M.J., Ying, Q., Lu, J., Mysliwiec, M.J., Griffin, R.J., Chen, J., Clegg , S. (2007) Source apportionment of secondary organic aerosol during a severe photochemical smog episode. Atmos Environ 41: 576

– 591.

94 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T . E.; Pierce, J. R.; Pandis, S. N. (2007) Rethinking organic aerosol: Semivolatile emissions and photochemical aging. Science 315: 1259

- 1262.

95 Griffin, R. J.; Cocker, D. R.; Seinfeld, J. H.; Dabdub, D. (1999) Estimate of global atmospheric organic aeroso l from oxidation of biogenic hydrocarbons. Geophys Res Lett 26 ( 17) 2721

– 2724

96 Lewis, C. W.; Klouda, G. A.; Ellenson, W. D. (2004) Radiocarbon measurement of the biogenic contribution to summertime PM

- 2.5 ambient aerosol in Nashville, TN. Atmos Environ 38 ( 35) 6053

– 6061.

97 Byun DW, Schere, KL (2006) Review of the Governing Equations, Computational Algorithms, and Other Components of the Models

- 3 Community Multiscale Air Quality (CMAQ) Modeling System, J Applied Mechanics*** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

7

- 132 Reviews 59: 51

- 76.

98 U. S. EPA (20 10 ) Our Nations Air, Status and Trends through 2008 . EPA 454/ R

- 09

- 002

, February 2010. http://www.epa.gov/airtrends/2010.

99 U. S. EPA. (20 11 ) 200 5 National

- Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata200 5 /risksum.html

.
100 U. S. EPA ( 2007) Regulatory Impact Analysis for the Control of Hazardous Air Pollutants from Mobile Sources Rule, Chapter 3, Air Quality and Resulting Health and Welfare Effects of Air Pollution from Mobile Sources. 72 FR 8428, February 26, 2007. http://www.epa.gov/o taq/regs/toxics/420r07002.pdf

.
102 Lim, Y.B., Ziemann, P.J. (2009) Effects of Molecular Structure on Aerosol Yields from OH Radical

- Initiated Reactions of Linear, Branched, and Cyclic Alkanes in the Presence of NO X . Environ Sci Technol 43 (7): 2328

- 2334.

103 Kleindienst , T.E. (2008) Hypothetical SOA Production from Ethanol Photooxidation. Memo to the Docket EPA

HQ

- OAR

- 2005

- 0161.

104 Turpin, B.J., Huntzicker, J.J., Larson, S.M., Cass , G.R. (1991) Los Angeles Summer Midday Particulate Carbon: Primary and Secon dary Aerosol. Environ Sci Technol 25: 1788

- 1793.

105 Turpin, B.J., Huntzicker, J.J. (1995) Identification of Secondary Organic Aerosol Episodes and Quantitation of Primary and Secondary Organic Aerosol Concentrations During SCAQS. Atmos Environ 29(23): 3527

- 3544.

106 Bae M

- S, Schauer JJ, Turner JR (2006) Estimation of the Monthly Average Ratios of Organic Mass to Organic Carbon for Fine Particulate Matter at an Urban Site, Aerosol Sci Technol 40(12): 1123

- 1139. http://dx.doi.org/10.1080/02786820601004085.

107 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern U.S. location. Atmos Environ 41(37):8288

- 8300.

108 Offenberg JH, CW Lewis, M Lewandowski, M Jaoui, TE Kleindienst, EO Edney (2007) Contributions of

- - pinene,NO x /Air Mixture: Comparison of Results Using 14 C Content and SOA Organic Tracer Methods, Environ Sci Te chnol 41: 3972

- 3976.

109 Claeys M, R Szmigielski, I Kourtchev, P Van der Veken, R Vermeylen, W Maenhaut, M Jaoui, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2007) Hydroxydicarboxylic acids: Markers for secondary organic aerosol from the photoox

- pinene. Environ Sci Technol 41(5): 1628

- 1634.

110 Edney EO, TE Kleindienst, M Jaoui, M Lewandowski, JH Offenberg, W Wang, M Claeys (2005) Formation of 2

- methyl tetrols and 2

- methylglyceric acid in secondary organic aerosol from laboratory irrad iated isoprene/NO X /SO 2 /air mixtures and their detection in ambient PM 2.5 samples collected in the Eastern United States. Atmos Environ 39: 5281

- 5289.

111 Jaoui M, TE Kleindienst, M Lewandowski, JH Offenberg, EO Edney (2005) Identification and quantificatio n of aerosol polar oxygenated compounds bearing carboxylic or hydroxyl groups. 2. Organic tracer compounds from monoterpenes. Environ Sci Technol 39: 5661

- 5673.

112 Kleindienst TE, TS Conver, CD McIver, EO Edney (2004) Determination of secondary organic aer osol products from the photooxidation of toluene and their implications in ambient PM 2.5 . J Atmos Chem 47: 70

- 100.

113 Kleindienst TE, TS Conver, CD McIver, EO Edney (2004) Determination of secondary organic aerosol products from the photooxidation of tolu ene and their implication in ambient PM 2.5 , J Atmos Chem 47: 70

- 100.

114 Lewandowski M, M Jaoui , JH Offenberg , TE Kleindienst, EO Edney, RJ Sheesley, JJ Schauer (2008) Primary and secondary contributions to ambient PM in the midwestern United States, E nviron Sci Technol 42(9):3303

- 3309. http://pubs.acs.org/cgi

- bin/article.cgi/esthag/2008/42/i09/html/es0720412.html.

115 Kleindienst TE, M Jaoui, M Lewandowski, JH Offenberg, EO Edney (2007) Estimates of the contributions of biogenic and anthropogenic hydro carbons to secondary organic aerosol at a southeastern U.S. location. Atmos Environ 41(37):8288

- 8300.*** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

7

- 133 116 Henze DK, JH Seinfeld (2006) Global secondary organic aerosol from isoprene oxidation. Geophys Res Lett 33: L09812. doi:10.1029/2006GL025976.

117 Hild ebrandt, L., Donahue1, N. M, Pandis1, S. N. (2009) High formation of secondary organic aerosol from the photo

- oxidation of toluene. Atmos Chem Phys 9: 2973

- 2986. Docket EPA

- HQ

- OAR

- 2011

- 0135 9.

118 Ng, N. L., Kroll, J. H., Chan, A. W. H., Chabra, P. S., Fla gan, R. C., Seinfield, J. H., Secondary organic aerosol formation from m

- xylene, toluene, and benzene, Atmospheric Chemistry and Physics Di scussion, 7, 3909

- 3922, 2007. Docket EPA

- HQ

- OAR

- 2011

- 0135

119 Henze, D. K., Seinfeld, J. H., Ng, N. L., Kroll, J. H., Fu, T.

- M., Jacob, D. J., and Heald, C. L. (2008) Global modeling of secondary organic aerosol formation from aromatic hydrocarbons: high

- vs. low

- yield pathways, Atmos. Chem. Phys., 8, 2405

- 2420, doi:10.5194/acp

- 8

- 2405

- 2008

120 Lane, T. E., Donahue, N.M. an d Pandis, S.N. (2008) Simulating secondary organic aerosol formation using the volatility basis

- set approach in a chemical transport model, Atmos. Environ., 42, 7439

- 7451, doi: 10.1016/j.atmosenv.2008.06.026

121 Carlton, A.G., Bhave, P.V., Napelenok, S.L., E dney, E.O., Sarwar, g., Pinder, R.W., Pouliot, G.A., Houyoux, M., (2010). Model Representation of Secondary Organic Aerosol in CMAQv4.7. Environ Sci Technol 44(22), 8553

- 8560.

122 Parikh, H.M., Carlton, A.G., Vizuete, W., and Kamen, R.M. (2011) Modeling seco ndary organic aerosol using a dynamic partitioning approach incorporating particle aqueous

- phase chemistry, Atmospheric Environment, 45, 1126

- 1137.

123 Volkamer, R., J.L. Jimenez, F. SanMartini,K.Dzepina,Q. Zhang,D. Salcedo,L. T. Molina, D. R.Worsnop, and M. J. Molina (2006), Secondary organic aerosol formation from anthropogenic air pollution: Rapid and higher than expected, Geophys. Res. Lett., 33, L17811, doi:10.1029/2006GL026899.

124 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.O., Sarwar, g., Pind er, R.W., Pouliot, G.A., Houyoux, M., (2010). Model Representation of Secondary Organic Aerosol in CMAQv4.7

. Environ Sci Technol 44(22), 8553

- 8560.

125 Robinson, A. L.; Donahue, N. M.; Shrivastava, M.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.; Pierce, J. R.; Pandis, S. N. (2007) Rethinking organic aerosol: Semivolatile emissions and photochemical aging. Science 315: 1259

- 1262. Docket EPA

- HQ

- OAR

- 2011

- 0135

126 Carlton, A.G., Bhave, P.V., Napelenok, S.L., Edney, E.O., Sarwar, g., Pinder, R.W., Po uliot, G.A., Houyoux, M., (2010). Model Representation of Secondary Organic Aerosol in CMAQv4.7

. Environ Sci Technol 44(22), 8553

- 8560.

127 Atkinson, R., Baulch, D.L., Cox, R.A., Crowley, J.N., Hampson, R.F. Jr., Hynes, R.G., Jenkin, M.E., Kerr, J.A., Rossi, M.J., Troe, J. (2005) Evaluated Kinetic and Photochemical Data for Atmospheric Chemistry

- IUPAC Subcommittee on Gas Kinetic Data Evaluation for Atmospheric Chemistry. July 2005 web version. http://www.iupac

- kinetic.ch.cam.ac.uk/index.html. Docket EPA

- HQ

- OAR

- 2011

- 0135

128 Sander, S.P., Friedl, R.R., Golden, D.M., Kurylo, M.J., Huie, R.E., Orkin, V.L., Moortgat, G.K., Ravishankara, A.R., Kolb, C.E., Molina, M.J., Finlayson

- Pitts, B.J. (2003) Chemical Kinetics and Photochemical Data for use in Atmospheric Stud ies, Evaluation Number 14. NASA Jet Propulsion Laboratory. http://jpldataeval.jpl.nasa.gov/index.html. Docket EPA

- HQ

- OAR

- 2011

- 0135

129 Finlayson

- Pitts BJ, Pitts JN Jr. (1986) Atmospheric Chemistry: Fundamentals and Experimental Techniques, Wiley, New York.

130 Yarwood G, Rao S, Yocke M, Whitten GZ (2005) Updates to the Carbon Bond Chemical Mechanism: CB05. Final Report to the U.S. EPA, RT

- 0400675, December 8, 2005. http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf. Docket EPA

- HQ

- OAR

- 2011

- 0135

131 http:/ /www.cmascenter.org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt

132 77FR 30088 (May 21, 2012)*** E.O. 12866 Review

– Revised Version

– Do Not Cite, Quote, or Release During Review ***

7

- 134 133 US EPA (2012). National Ambient Air Quality Standards for Particulate Matter. http://www.epa.gov/PM/2012/finalrule.pdf

134 US EPA (2012). Fact Sheet: Implementing the Standards. http://www.epa.gov/airquality/particlepollution/2012/decfsimp.pdf

135 U.S. EPA. (2007). PM 2.5 National Ambient Air Quality Standard Implementation Rule (Final). Washington, DC: U.S. EPA. 72 FR 20586, April 25, 2007.

136 PM Standards Revision

– 2006: Timeline. http://www.epa.gov/PM/naaqsrev2006.html#timeline

137 U.S. EPA. (201 2 ). Fact Sheet

– Air Quality Designations for the 2010 Primary Nitrogen Dioxide (NO 2 ) National Ambient A ir Quality Standards. http://www.epa.gov/airquality/nitrogenoxides /designations/pdfs/20120120FS.pdf

138 U.S. Environmental Protection Agency (2012). Revision to Ambient Nitrogen Dioxide Monitoring Requirements. 77 FR 64244, October 19, 2012.

139 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollut ants from Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007.

140 U. S. Environmental Protection Agency (2007). Control of Hazardous Air Pollutants from Mobile Sources; Final Rule. 72 FR 8434, February 26, 2007.

141 U.S. EPA. (2011) 2005 National

- Sc ale Air Toxics Assessment. http://www.epa.gov/ttn/atw/n ata2005/. Docket EPA

- HQ

- OAR

- 2011

- 0135

142 U.S. EPA. (2011) Summary of Results for the 2005 National

- Scale Assessment. http://www.epa.gov/ttn/atw/nata2005/05p df/sum_results.pdf.

143 Luecken, D,J, Clmorel, A.J. 2008. Codependencies of Reactive Air Toxic and Criteria Pollutants on Emission Reductions. J. Air & Waste Manage. Assoc. 58:693

– 701. DOI:10.3155/1047

- 3289.58.5.693

144 U.S. Environmental Protection Agency (E PA). 2009. Integrated Science Assessment for Particulate Matter. U.S. Environmental Protection Agency. Research Triangle Park. EPA/600/R

- 08/139F

145 Trijonis, J.C. et al. 1987. Preliminary extinction budget results from the RESOLVE program, pp. 872

- 883. In: P.J. Bhardwaja, et. al. Visibility Protection Research and Policy Aspects. Air Pollution Control Assoc., Pittsburgh, PA.

146 Trijonis, J.C. et al. 1988. RESOLVE Project Final Report: Visibility conditions and Causes of Visibility Degradation in the Mojave D esert of California. NWC TP #6869. Naval Weapons Center, China Lake, CA.

147 Irving, Patricia M., e.d., 1991. Acid Deposition: State of Science and Technology, Volume III, Terrestrial, Materials, Health, and Visibility Effects, The U.S. National Acid Precipi tation Assessment Program, Chapter 24, page 24

– 76.

148 Huai, et al. (2004) Estimates of the emission rates of nitrous oxide from light

- duty vehicles using different chassis dynamometer test cycles Atmospheric Environment 6621

- 6629

149 Michaels, H. (1998) Emi ssions of Nitrous Oxide from Highway Mobile Sources , U.S. EPA EPA420

- R

- 98

- 009

150 Graham, L. Greenhouse Gas Emissions from 1997

- 2005 Model Year Light Duty Vehicles Environment Canada ERMD Report #04

- 44

151 Meffert, et. al (2000) Analysis of Nitrous Oxide Emis sions from Light Duty Passenger Cars , SAE 2000

- 01

- 1952

152 Behrentz, et al. (2004), Measurements of nitrous oxide emissions from light

- duty motor vehicles: a pilot study Atmospheric Environment 4291

- 4303

153 Meszler, D. (2004), Light Duty Vehicle Methane and Nitrous Oxide Emissions: Greenhouse Gas Impacts Study for Northeast States Center for a Clean Air Future