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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 emission 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 Overview 

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 will directly reduce emissions of 

nitrogen oxides (NOX), including nitrogen dioxide (NO2), volatile organic compounds (VOC), 

carbon monoxide (CO), particulate matter (PM2.5), air toxics, and sulfur dioxide (SO2). The 

implementation of lower sulfur gasoline will reduce criteria and air toxic emissions from the 

existing gasoline fleet, and cause some reductions in SO2 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 incur 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 

presenting emission 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 quality modeling in 

this proposal as well as recent EPA rules. However, 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 turned 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 were 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 the Tier 2 

vehicle program indefinitely and an average gasoline sulfur level of 30 ppm (10 ppm in 

California). 
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The emission inventory methodology applied to generate these national estimates does 

differ from the methodology used to generate the finely resolved 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 Scenarios Modeled 

We analyzed emission impacts of 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 Act 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 (E15) 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 

and 2030, as discussed in Section 7.1.3.2.1. 
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Table 7-1 Overview of Reference and Control Scenarios

Reference Scenario Control Scenario

2017

Renewable Fuels: RFS2 programa

 21.6 B gallons renewable fuels 

 (24 B ethanol-equivalent gallons):

17.8 B gallons ethanol: E10 b, E15c

Fuel Sulfur Level: 

 30 ppm (10 ppm California)

Fleet:

 100 percent Tier 2 and older vehicles

Renewable Fuels: RFS2 programa

 21.6 B gallons renewable fuels 

 (24 B ethanol-equivalent gallons):

17.8 B gallons ethanol: E10 b, E15c

Fuel Sulfur Level: 

 10 ppm

Fleet:d

 93 percent Tier 2 and older vehicles

 7 percent Tier 3 vehicles 

2030

Renewable Fuels: RFS2 programa

 30.5 B gallons renewable fuels

 (36 B ethanol-equivalent gallons):

22.2 B gallons ethanol: E15c

Fuel Sulfur Level: 

 30 ppm (10 ppm California)

Fleet:

 100 percent Tier 2 and older vehicles

Renewable Fuels: RFS2 programa

 30.5 B gallons renewable fuels

 (36 B ethanol-equivalent gallons):

22.2 B gallons ethanol: E15c

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, 75 FR 14670 (March 26, 2010)

b Gasoline containing 10 percent ethanol 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 average 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 detailed 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 reference scenario. Our Tier 3 control scenario modeled 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 trucks) described in Section IV of the preamble, including: 

 Fleet average Federal Test Procedure (FTP) NMOG+NOX standards of 30 

mg/mi for LDVs, LDTs and MDPVs, phasing in from MYs 2017 to 2025
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 Fleet average Supplemental Federal Test Procedure (SFTP) NMOG+NOX

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+NOX declining fleet average, more stringent PM standards, and a 

regulatory useful life of 150,000 miles

 More stringent 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.

The Tier 3 standards are expected to reduce onroad criteria and toxic emissions, and to a 

much smaller extent, nonroad SO2 emissions, but will not affect upstream, refueling or portable 

fuel container criteria or toxic emissions. The methodology 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 standards1to 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 (preproposal) 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 major reasons. First, improved fuel efficiency does not have a 

direct impact on criteria pollutant emissions. Second, non-GHG emissions standards for lightduty vehicles are expressed as grams per mile driven; thus, non-GHG emissions are a function of 

the emission 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, February 10, 2000)

 Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur 

Control Requirements (66 FR 5002, January 18, 2001)
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 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) 

 Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average 

Fuel Economy Standards 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 programs, 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 MOVES2010b.

2

 

A version of MOVES2010a, 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 MOVES2010a for this analysis primarily incorporated 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, including Tier-2 

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

The 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 Phase3 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.4Improvements in estimating and projecting evaporative emissions are described in this 

document (see 7.1.3.3.7). Finally, because the updates to fuel sulfur 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 the 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. 
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estimating reductions from the Tier 3 program, they are also presented in detail in Section 

7.1.3.4. 

Other than sulfur, these 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 PM2.5 fuel effects that 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 an

impact 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 impacts 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 aromatics or not. 

In addition to fuel effects, we 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 information from a recent study that 

examined the composition of particulate emissions from advanced diesel engines.8 HC, CO and 

NOX start and running emission rates for light heavy-duty gasoline vehicles were updated to fix 

an error in these rates for 2007-and-later emissions, and we repaired errors in the MOVES2010a 

emission rates for NH3, NO and NO2.

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 MOVES2010a 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 

these inputs were required to reflect regional variations in fuels for both the reference and control 

scenarios, as discussed 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

and 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 commenters to replicate. National emission inventories were 

developed using the pre-aggregation feature of MOVES. For criteria pollutants, the default preaggregation level of ‘hour’ was selected, which condenses the county-level temperatures into a 

 

B The changes to the NO and NO2 rates did not impact the total NOX emissions, but facilitated the output of separate 

results for nitric oxide and nitrogen dioxide. 
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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, 

the pre-aggregation approach is coarser than the approach described in Section 7.2 used to 

develop the gridded/hourly 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 inventory methodology, which used data 

supplied by state and regional modelers for many of these inputs, employed hourly 

meteorological data, and accounts for Section 177 states adoption of California LEV standards in 

the modeling baseline. 

To assure that adequate 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 rule.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 inventories 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, by 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 retained 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 

development of fuel supply database tables that reflected 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 volumes. For simplicity, we assumed the same 

biofuel volumes analyzed in the RFS2 final rule, referred to in that rule as the primary “midethanol” 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 preaggregation averages the temperatures of all selected days and hours into a national average hourly temperature for 

the month. 
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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 case, 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-duty vehicles.D While E15 has only limited 

commercial availability currently, EPA believes 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 reference 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 

vehicles 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.EFor 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 voluntary commitment to produce 50 

percent FFVs by 2012.

F We also assumed that the Detroit 3 would continue to comprise 

approximately 45 percent of total light-duty vehicle sales. In addition, we relied on total lightduty 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 fuel 

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, motorcycles 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 7-1Figure 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 Section 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).

Formatte
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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 accessible 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 would 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 would 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 assumed 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 7-2Figure 7-2 shows the E15 and E85 

utilization assumptions and Figure 7-3shows the resulting fuel volumes by year. Total fuel 

volumes were computed based on motor gasoline energy projections provided in the Annual 

Energy Outlook (AEO) 2011 early release.

Formatte
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Figure 7-2 Post-EPAct/EISA Fuel Utilization Assumptions/Results
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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 E85 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 believe 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 standards, as the 

rule was still in a preliminary state at the time we needed to finalize our assumptions for these 

Tier 3 analyses. The LD GHG standards will reduce gasoline demand in 2017 and beyond which 

has two impacts on our analysis. First, the reduced fuel consumption 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, if 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 could be 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 NOX emissions do not show significant differences when run on E85,

12,13 and the 
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7-12

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 concentrations, 

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 precursors, 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 waiver in RFG areas, the two fuels are 

essentially the same with respect to vapor pressure 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, in 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 much larger volume of ethanol, E15 was assumed to be used

virtually everywhere.

Converting these assumptions into post-EPAct/EISA fuel properties on a county-bycounty basis required an assessment of 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, creating volume requirements for 2006, and 

because they provide 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 Inventory16) 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 counties 

were refined to indicate E5.7 at 100 percent market share. Also, Alameda County, 

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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 Counties 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 2005 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; the corrected July fuel supply was duplicated to May, 

June, August and September. Although this duplication eliminates fuel property changes 

during the shoulder seasons in April and September, these intermediate month fuel 

properties were used only when modeling 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 the 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 (MSAT2) (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 were 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 0.61 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 – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-14

standards, such as in California, were not changed). Refinery modeling showed that there is a 

significant effect on aromatics level when sulfur is reduced. Corrections 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 determined 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 cases used as a reference for Tier 3 were developed using the preEPAct/EISA cases for 2017 and 2030 as a foundation, and were then created by inserting 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 

modeling for this additional ethanol. The process used for adjusting 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 unchanged 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, other 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:
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Table 7-3- Reference Case Fuel Property Changes

Summer Winter

Fuel Propertya 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 -0.13788 -0.15915 -0.25198 -0.15296

Note:

a These fuel property 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 added 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 added to the fuel supply at a 75 percent market share,

and E15 was added at a 25 percent market share. In 2030, 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, 

reflecting 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, we 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 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 between reference and control, 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 not 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 impacts in California. 
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below. These changes were made to every county with fuel exceeding a sulfur level of 10ppm. 

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 the 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 technologies 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 loss 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 are 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 with 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 assumptions 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 
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decrease in FCC gasoline sulfur levels without any decrease in FCC gasoline octane levels. 

Furthermore, 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, refiners would not only reduce the 

hydrotreating severity of their FCC posttreaters (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 shows 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 E300 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) showed 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 modeling 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 

ppm 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 characterize the qualities of the light and heavy 

naphtha streams from the reformer to improve its estimation 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 above, 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 the controls introduced by the Tier 3 vehicle program required the development 

of another set of alternate MOVES database tables to reflect each aspect of the proposed Tier 3 

program. These database tables included:

 Gaseous exhaust emissions rates (HC/CO/NOX) for light duty cars, trucks, and 

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

standards and their phase-in.
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 Elemental 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.

 Evaporative HC permeation emission rates to reflect the proposed diurnal test 

standard, certification fuel, and phase-in.

 Leak prevalence rates for tank vapor venting and liquid leaks to reflect proposed 

requirements for evaporative leak detection. 

The development of these alternative emission rates is discussed below by pollutant, fuel 

and vehicle regulatory class. 

7.1.3.3.1 Gasoline LD HC/CO/NOX 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 followed the same procedures used to develop 

National LEV (NLEV, covering model years 2001-2003) and Tier 2 rates (covering model years 

2004 and later) in the default MOVES database, as described in the documentation for 

development of light-duty exhaust emission rates for MOVES2010 (known as the “MOVES 

Light-Duty report”).

19

 However, specific modifications were made to represent the introduction 

of 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 estimated 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 average 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 Verification 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 minus 

Bag3) and “hot-running” emissions (Bag 2 FTP and US06).

2. Develop phase-in assumptions for model years (MY) 2017 – 2031, by standard level, 

vehicle class and model year, including phase-in assumptions representing the introduction of 

Tier 3 standards. 
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7-19

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 MY2000, 

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. Calculate emissions by operating mode in 

each model year by multiplying 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 speed and acceleration covered by Bag 2). 

5. Apply Deterioration to estimate emissions for three additional age groups (4-5, 6-7 

and 8-9). We assume that Tier 3 vehicles will deteriorate similarly to other vehicles, when 

viewed in logarithmic terms, but we modified deterioration to represent a useful life of 150,000 

miles, as opposed to a useful life of 120K miles, assumed for Tier 2 and NLEV vehicles. This is 

the outcome of applying ln-linear deterioration to the rates developed in steps 1-4. For the 

remaining three groups (10-14, 15-19 and 20+), emissions are assumed to stabilize as described 

in the MOVES2010 report.

6. Estimate non-I/M reference rates. The rates in steps 1-6 represent rates under a 

reference inspection/maintenance (I/M) program. Corresponding 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 the docket.20

7.1.3.3.1.1 Average FTP Results (Step 1) (Standard)

Our projected emissions for Tier 3 vehicles are driven by the proposed NMOG+NOX

standard, set at 30 mg/mi. However, because MOVES projects NOX and THC emissions 

separately, we apportioned the aggregate standard into NMOG and NOX 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 NOX control. 

Accordingly, we assumed “effective standards” for NMOG and NOX would be 20 mg/mi and 10 

mg/mi, respectively. To implement this assumption, we further assumed that for NOX, 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 FTP cycle, represented as “Bag1 minus Bag3” 

emissions. A second component represents “hot-running” emissions, represented by the hotrunning phase of the FTP (Bag 2). A third component represents emissions on the US06 cycle, 

representing emissions at high speed and power.
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Estimated FTP and US06 emissions levels for hydrocarbons (NMOG and NMHC) are 

shown in Table 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 database. The values for Tier 3 are 

calculated as a weighted average of those for Bins 2 and 3, using Equation 7-2Equation 7-2.

T3  0.775B20.225B3

Equation 7-2

Table 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 Compositea

(mg/mi)

FTP Cold Starta

(mg)

FTP hot Runninga

(Bag 2) 

(mg/mi)

US06b

(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 3c20 9.2 142 0.7 10.0

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

b Values represent “non-methane hydrocarbons” (NMHC).

c Values for Tier 3 calculated using Equation 7-2Equation 7-2.

Under a general assumption that CO standards are not forcing, but that CO emissions 

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 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)

US06 

(mg/mi)

8 4,200 861 6,680 451 2,895

5 4,200 606 5,510 238 2,895

4 4,200 537 5,500 201 2,895

3 2,100 463 3,470 119 2,895

2 2,100 235 1,620 70 948

Tier 3a2,100 286 2,040 81 1,390

a Values for Tier 3 calculated using Equation 7-2Equation 7-2.

Corresponding results for NOX 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 suggests that 

Bin 2 vehicles can meet the “effective standard” of 10 mg/mi with a reasonable compliance 

margin.
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Table 7-7 NOX: 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)

US06 

(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 compliance with the Tier 3 fleet 

average NMOG+NOX requirements. The requirements are illustrated in Figure 7-4. The phasein 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. The fleet 

averages for cars and trucks no longer differ at the completion of the phase-in. 

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

201520162017201820192020202120222023202420252026

FTP Composite Standard (g/mi)

Model Year

LDV/LDT1

LDT234

Figure 7-4 NMOG+NOX 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 goal of this step is to calculate weighted averages of the FTP (cold-start and hotrunning) 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. 
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Start and running emissions in each model year are simply calculated as weighted 

averages of the emissions estimates and the phase-in fractions. 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 Estimate 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,” respectively (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, by 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 Table 7-5 through Table 7-7. For HC and CO, 

we used Equation 7-2Equation 7-2, as before. For NOX, we applied the Bin-2 values. Figure 7-6

and Figure 7-7 show 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.

 
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Figure 7-5 Operating modes for running Exhaust Emissions, divided broadly into “hotrunning FTP” and “US06” regions.
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0

100

200

300

400

500

600

700

800

900

1,000

-5 0 5 10 15 20 25 30 35 40

Emission Rate (g/hr)

Vehicle Specific Power (kW/Mg)

2010

2017

2025

0

1

2

3

4

5

6

7

-5 0 5 10 15 20 25 30 35 40

Emission Rate (g/hr)

Vehicle Specific Power (kW/Mg)

2010

2017

2025

0

2

4

6

8

10

12

14

16

18

-5 0 5 10 15 20 25 30 35 40

Emission Rate (g/hr)

Vehicle Specific Power (kW/Mg)

2010

2017

2025

(b) THC

(c) NOx

(a) CO

Figure 7-6 Projected Emission Rates 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) NOX (LINEAR 

SCALE).
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opMode

21

22

23

24

25

27

28

29

30

0.01

0.10

1.00

10.00

100.00

-10 0 10 20 30 40

Emission Rate (g/hr)

Vehicle Specific Power (kW/Mg)

2000

2010

2017

2025

0.001

0.010

0.100

1.000

10.000

100.000

1,000.000

-5 0 5 10 15 20 25 30 35 40

Emission Rate (g/hr)

Vehicle Specific Power (kW/Mg)

2000

2010

2017

2025

(c) NOx

1

10

100

1,000

10,000

-10 0 10 20 30 40

Emission Rate (g/hr)

Vehicle Specific Power (kW/Mg)

2000

2010

2017

2025

(a) CO

(b) THC

Figure 7-7 Projected Emission Rates for Cars in Operating modes 21-30, vs. VSP, in 

ageGroup 0-3 years, for Four Model Years, for (a) CO, (b) THC and (c) NOX

(LOGARITHMIC SCALE).
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7.1.3.3.1.5 Apply Deterioration (Step 5)

Based on our extensive emissions analysis during MOVES2010 development, we assume 

that deterioration for different 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 group, 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 group, as we know that emissions 

tend to stabilize beyond 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 

groups, the “stabilization of emissions with age” was estimated as for MOVES2010 (MOVES 

Light Duty report, section 1.3.3.7).

7.1.3.3.1.6 Estimate Non-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 Start” shown in Tables 8- 4 through 6 above were used to represent 

cold-start emissions for the various standard levels. These are designated as opModeID=108 in 

the emissionRateByAge 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 approach 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 estimates, 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, 

emissions 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. 
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Figure 7-8 FTP Composite NOX emissions for reference (2010) and Tier 3 (2025) 

constructed from MOVES rates

Figure 7-6 FTP Composite THC emissions constructed from MOVES rates
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Figure 7-7 US06 NOX emissions constructed from MOVES rates

Figure 7-8 US06 THC emissions constructed from MOVES rates
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7.1.3.3.2 Diesel LD HC/CO/NOX 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 standards” were set differently. Again, diesel vehicles are projected to meet the same 

NMOG+NOX 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 phase 

in. Accordingly, the “effective standards” for NMOG and NOX 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 ratios 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/NOX

The proposed Tier 3 program will affect not just light duty vehicles (below 8,500 pounds 

GVW), but also chassis certified vehicles between 8,500 and 14,000 pounds. These vehicles are 

referred to here as medium duty vehicles, but are also commonly known as Class 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 during 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 Percent of Reg Class 41

MDPV 15%

Class 2B 60%

Class 3 20%

Engine Certified 5%

The Class 2b and Class 3 vehicle program was modeled to begin in model year 2017 and 

fully phase in during the 2022 model year (Figure 7-9). This yields an aggregate standard of 

0.178 g/mile NMOG+NOX for Class 2b vehicles and 0.247 gram/mile for Class 3 vehicles in 

2022. 
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Figure 7-9 Class 2B and 3 Standard Phase-in

We combined the proposed Tier 3 phase-in for Class 2b and 3 vehicles with the existing 

emission standards MDPV and engine 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 vehicles would perform at 1.2x their standard on the chassis FTP.22 Calculated 

using Table 7-8, the weighted average of the Class 2b, Class 3, MDPV, and engine standards is 

0.181.I To account for the real world performance of these vehicles, we related this average 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 Class 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 weighted composite was calculated each year Table 

7-9. 

 

I Due to a calculation error, an NMOG+ NOX standard of 0.177 was used for Regulatory Class 41. As a result, a 

slightly different phase-in of the NMOG+NOX and CO rates was used in the proposal modeling. This error 

overstates the NMOG+ NOX 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. 
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Table 7-9 Phase-in of MD Tier 3 Rates

Model Year Tier 3 Rate MOVES DB Rates Composite Standard 

(g/mile)

2017 34% 66% 0.33

2018 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 calculated 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/NOX

For medium duty diesel 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, while the simulated FTP estimate for CO is less than 1 gram/mile. 

Consequently, we assumed no HC and CO emission benefits from Tier 3 standards on MD diesel 

vehicles.

By contrast, we estimate that the Tier 3 NOX standard will produce a reduction in diesel 

Class 2b and Class 3 NOX emissions. Because data on current NOX emissions are limited, as 

there is little in-use data on MY 2010 and 2011 vehicles which use selective catalytic reduction 

as a NOX control strategy, we used a proportional approach to estimate the Tier 3 effect, 

reducing NOX 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 reduction 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 NOX rates to the 4-5 

year age-group.
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Table 7-10 Phase-in of MD Diesel Tier 3 NOX Rates

Model Year Tier 3 Phase In Reduction in NOX

Running Emission 

Rate

Reduction in NOX

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 (PM2.5) emissions from light-duty vehicles 

through a full-useful life (FUL) PM standard on the FTP of 3 mg/mi. Additionally, 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 sacrificing fuel economy, it is anticipated that 

manufacturers may try a variety of strategies including reducing lubrication oil consumption over 

the life of the vehicle. For our analysis, we projected that reductions in lube oil consumption 

would reduce the organic carbon (OC) fraction of PM by 30 percent for both cold start and 

running emissions. These 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 

vehicles 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 reduction 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 decreased 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 Technical 

Document: Estimated Reductions in Particulate Matter Emissions from Light-duty and Mediumduty 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 
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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 certification 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 were modeled as resulting in a reduced prevalence (frequency rate) of fuel system 

vapor and liquid leaks.

K

7.1.3.3.7.1 Permeation Improvements

Permeation emissions include fuel vapors that escape from a vehicle through micro pores 

in the various fuel system components and materials. 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 the use of E15 as Tier 3 certification test fuel will 

effectively double permeation emissions over the test procedure. Therefore, 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 

activity (65F-105F) on an ethanol-containing fuel using various reductions in the base rate. The 

total permeation emissions for the day should equal about 75 percent of the standard (~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.
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Table 7-11 Tier 3 Permeation Rates 

Model Year

Tier 3 PhaseinPermeation 

(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 Reduced frequency of vapor leaks

EPA, in conjunction with the state of Colorado and the Coordinating Research Council, 

undertook multiple research programs 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 informed the emission inventory contribution of evaporative

leaks, and the level of reductions possible from an 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 emission 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 purge 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 that the leak prevalence rates from the Colorado study included 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, which is assumed constant with age. The increase in high evaporative 

emissions incidence with age was then attributed to an increasing prevalence of leaks. This 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 more 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. 
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Figure 7-10 Vapor Leak Occurrence Assumed 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 occurring 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 MOVES 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 

present 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 
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loading. Numerous studies have shown the direct impact of fuel 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 sensitivity 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 NOX, CO and total HC when the vehicles were tested on low sulfur fuel, relative to 

32 ppm fuel.30 In particular, the study found a nearly 50 percent increase in NOX when sulfur 

was increased from 6 ppm to 32 ppm. Another recent study by Umicore showed reductions of 

41 percent for NOX 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 studies 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 

emissions, 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 designed 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 study 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 chosen 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 asreceived 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 

of this data showed highly significant reductions in several pollutants including NOX 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 NOX emissions 

dropped 32 percent between the pre- and post-cleanout tests on 28 ppm fuel.
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Table 7-11 Average Clean-out Effect on In-use Emissions using 28 ppm Test Fuel

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(p-value)

PM

(p-value)

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, the 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 cleanout procedure when done using 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 NOX 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 Reduction in emissions from 28 ppm to 5 ppm immediately following clean-out

NOx

(p-value)

THC

(p-value)

CO

(p-value)

NMHC

(p-value)

CH4

(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 NOX were particularly high, estimated at 59 percent between 

28ppm and 5ppm overall For some pollutants, such as Bag 2 NOX, the model fitting did not find 
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a significant 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 sulfur 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

NOX (pvalue)

THC

(p-value)

CO

(pvalue)

NMHC

(p-value)

CH4

(p-value)

NOx+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 Bag 

1 and CH4 Bag 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 occurring in the in-use fleet of Tier 2 vehicles and has 

a measureable effect on emissions of NOX, hydrocarbons, and other pollutants of 

interest.

 The effectiveness of high speed/load procedures in restoring catalyst efficiency is 

limited when operating on higher sulfur fuel.

 Reducing fuel sulfur levels from 28 to 5 ppm is expected to achieve significant 

reductions in emissions of NOX, 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 NOX 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 gasoline fuel effects in MOVES,

and applies only for sulfur levels below 30ppm. For sulfur levels above 30 ppm, and for all pre-
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2004 model year vehicles, the sulfur effect originally used in MOVES remains in place. Equation 

7-3 shows the generic form of the new sulfur correction; Table 7-14 shows the specific values for 

the sulfur coefficients by pollutant, process, and vehicle type.

Equation 7-3 Low Sulfur Effect

Table 7-14 Low Sulfur Coefficients by Vehicle Type, Process and Pollutant

Vehicle Type THC CO NOX 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 equations were then used to fill in the database table that houses fuel effect 

equations 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 tables. 

7.1.4 Nonroad Emissions 

The nonroad 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 emissions from 

gasoline-powered equipment such as lawn-mowers, recreational boats and all-terrain vehicles. 

Their SO2 emissions are reduced with the proposed decrease in gasoline sulfur levels. As with 

onroad, reference and control case emissions were generated using the fuel supply inputs 

reflecting the post-EISA/EPAct renewable fuel volumes. 

Gasoline and land-based diesel nonroad emissions were estimated using EPA’s 

NONROAD2008 model, as run by the EPA’s consolidated modeling system known as the 

National Mobile 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 be 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 again 

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. 
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Since aircraft, locomotive and commercial marine emission sources do not burn gasoline, 

their 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 were 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 NOX (including NO2), VOC, CO, and SO2 from all 

gasoline-powered on road vehicles immediately upon implementation of lower sulfur fuel, and 

will further reduce these emissions as well as PM2.5 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 SO2 emissions from the nonroad gasoline fleet as a result of sulfur 

standards. The reductions are summarized in this section for each pollutant. 

NOX 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. We project significant 

reductions immediately upon implementation of the program, growing to a nearly 30 percent 

reduction in onroad emissions by 2030, and nearly 40 percent reduction in onroad emissions with 

full fleet turnover by 2050. 

Table 7-15 Tier 3 NOX Reductions by Calendar Year (Annual U.S. Short Tons)

Year

Onroad 

mobile 

reference

Onroad 

mobile 

with 

control Reduction

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%
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Table 7-16 shows the reduction in NOX emissions, in annual short tons, projected in 

calendar years 2017 and 2030. 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 (model 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, with remaining reduction coming from lower 

sulfur fuel on pre-Tier 3 vehicles.

L

 We 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 NOX Reductions from Tier 3 Program (Annual U.S. Short Tons)

2017 2030

Total reduction 284,381 524,790

Reduction from pre-Tier 3 

fleet due to sulfur standard

264,653 66,286

Reduction from Tier 3 fleet

due to vehicle and sulfur 

standards

19,728 458,504

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 will be some NOX 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
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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 evaporative 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 reduction 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 Program (Annual U.S. Short Tons)

2017 2030

Total reduction 44,782 226,028

Reduction from pre-Tier 3 

fleet due to sulfur standard

39,561 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 57,764

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 implementation 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. 
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Table 7-19 Tier 3 CO Reductions by Calendar Year (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 NOX and VOC above. The immediate reductions in the onroad fleet from 

sulfur control comprise about 80 percent of total reductions 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 PM2.5 impacts are shown in Table 7-21 for calendar years 2017 through 2030, and 

2050 when the fleet will have turned over completed to Tier 3 standards. For direct PM, the 

impact shown is solely from the proposed tailpipe standards. Thus, unlike other pollutants, 

reductions do not become significant until the fleet has turned over to cleaner vehicles. By 2030, 

we project a reduction of about 7,500 tons annually, which represents approximately 10 percent 

of the onroad direct PM2.5 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 in 

onroad emissions is projected to grow to 17 percent with full fleet turnover in 2050. 
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As discussed 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 more visible for direct PM2.5 emissions, as it resulted in a 

projected increase in emissions (roughly 700 tons nationwide) in 2017. As discussed in Section 

7.1.3.2.2, this 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 included the PM emissions increase from sulfur reduction in the emission 

inventory impacts shown in Table 7-21. 

Table 7-21 Tier 3 PM2.5 Reductions by Calendar Year (Annual U.S. Short Tons)

CY

Onroad mobile 

reference

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 reductions 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 pollutant, 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).
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Table 7-22 Reductions for Certain Individual Compounds (Annual U.S. Short Tons)

2017 

Reduction 

Percent reduction 

in onroad

2030 Reduction Percent reduction 

in onroad

Acetaldehyde 762 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 36%

Naphthalene 96 2% 420 17%

Ethanol 2,684 2% 27,821 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 

shown, 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

SO2 emissions from mobile sources are a direct function of sulfur in the fuel, and 

reducing sulfur in gasoline would result in immediate reductions in SO2 from the on and off-road 

fleet. The reductions, shown in Table 7-24, represent a roughly 50 percent reduction in onroad 

SO2 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 function of off-road gasoline 

consumption accounting for approximately 5 percent of overall gasoline use.36
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Table 7-24 Projected SO2 Reductions from Tier 3 Program (Annual U.S. Short Tons)

2017 2030

Total reduction 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 SO2 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 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, requiring a 

methodology with much greater 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 the 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 available 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 timeconsuming and resource intensive, this detail is needed when generating inputs to air quality 

models because it allows us much more precision in accounting for local ambient 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 inventory 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 produce emission factors 

by temperature and speed for the fleet mix, fuels, and I/M program 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 emissions were input into the Community Multiscale Air 

Quality Modeling System (CMAQ). We summarize this approach in the sections below. 
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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 the 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 Inventories and 

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

Pollutant

Reference Control

National 

Inventory

Air Quality 

Inventory

Difference

AQ vs. NI

National 

Inventory

Air Quality 

Inventory

Difference

AQ vs. NI

NOX 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%

PM2.5 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%

SO2 31,983 30,526 -5% 15,613 15,068 -3%

The differences between the national inventories and air quality inventories reflect the 

non-linear response to the more detailed handling of temperature 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 PM2.5, where the finer temperature resolution in the air quality 

approach produced significantly 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 proposed Tier 3 rule 

are very similar, as shown in Table 7-26. The exception is PM2.5, 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 uncertainties related to fuel parameters in 

2017 which we do not expect to occur in reality. 
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Table 7-26 Comparison of Emission Reductions from Reference to Control Case in 

“National” and “Air Quality” Onroad Inventories

2017 2030

Pollutant

National 

Inventory 

Reduction

Air Quality 

Inventory 

Reduction

National Inventory 

Reduction

Air Quality 

Inventory 

Reduction

NOX -8% -7% -28% -26%

VOC -3% -3% -25% -23%

CO -4% -3% -30% -30%

PM2.5 -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%

SO2 -51% -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, actual county-specific data is rare. Instead, much of our “county” data is based on 

state-wide estimates or national defaults. For this proposal, rather than explicitly model every 

county in the nation, we have done detailed modeling for some counties and less detailed 

estimates for the other counties. 

In this approach, we group counties that have similar properties that would result in 

similar emission rates. We explicitly model only one county in the group (the "representing" 

county) to determine emission 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 

inventories and still takes into account differences between counties.

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

27 below.
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Table 7-27 Characteristics for Representing County 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 California 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 whether 

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 altitude based 

on the criteria set forth by EPA certification procedures 

(4,000 feet above sea level).

The result is a set of 106 county groups with similar fuel, emission standards, altitude and 

I/M programs. For each group, the county with the highest VMT was chosen as the representing 

county. Of these, only 103 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, thus 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 process for selecting representative counties and a list of all of the 

3,322 counties in the nation and the counties 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 in order to take advantage of 

the gridded hourly temperature information used in air quality modeling, MOVES and SMOKE 

have 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 emissions in grams per 
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mile (for running emissions) or grams per vehicle (for start and evaporative emissions). For our 

air quality runs, we used the rates approach. This creates a set of “lookup tables” with emission 

rates by temperature, speed, pollutant, and vehicle class (Source Classification Code (SCC)). 

SMOKE then transforms 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 generates 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 into 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, an update to these post-processing scripts was made between the

reference case and control case runs, which inadvertently introduced 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. The result was that 

for some counties, control case emission rates were a few percent higher than they should have 

been; fixing this error would 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 range of temperatures needed for an entire year’s worth of meteorology data. This 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 summer 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 Section 7.1.3. However, for the air quality runs, we were able 

to use grid-level temperatures. 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. Cold 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 temperatures 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 

simulations 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 
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thermodynamic 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 groups 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 temperatures change over a day. They are needed to estimate vapor venting 

emissions.) To do this, SMOKE-MOVES determines the minimum and maximum temperatures 

in the county group for January and for July, and the minimum and maximum temperatures for 

each hour of 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 temperatures (using a 10 

degree interval) to develops a collection of possible temperature profiles.

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 emission 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 

(July 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 guidance provided to states by EPA, a contractor generated 

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, 

in 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 
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(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 Linuxbased environment at Amazon Web Services that enabled us to process the Tier 3 base case in 

less than 48 hours. We split the 16,604 runs into 206 batches (103 representative counties, for 

January and July). We then ran 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 the national level, based on estimates from the Annual Energy 

Outlook45. 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 the 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 allocation 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 fractions 

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 trucks. 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 ethanol 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 

allocation of VMT (summed across roadtypes). 
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The average speeds provided to SMOKE for each county were derived from the default 

national average speed distributions found in the default MOVES2010a 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 table, 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 the emissions 

modeling technical support document.

49

Table 7-28 Comparison of Calendar Year 2030 Nonroad Emission National Inventories 

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

Pollutant

Reference Control

National 

Inventory

Air Quality 

Inventory Difference

National 

Inventory

Air Quality 

Inventory Difference

NOX 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% 12,921,772 12,921,772 0.0%

PM2.5 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%

SO2 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. For this analysis, the refueling emissions were estimated using 

the revised version of EPA’s Motor Vehicle Emissions Simulator (MOVES2010a) at the county 
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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

Reference Control Percent

Reduction Reference Control Percent Reduction

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 production 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 described 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 biodiesel, 

decreases in petroleum refinery emissions to account for gasoline displacement, and increased 

vapor loss emissions from transport of ethanol and gasoline/ethanol fuel blends. Modifications 

to mobile source inventories include increases in combustion 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 Community Multi-scale Air Quality (CMAQ) model, described in detail in 

the following section, to conduct air quality modeling for this analysis. The 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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quality emissions inventories into CMAQ-ready inputs by transforming the emission inventories

based on the temporal allocation, chemical speciation, and spatial allocation requirements of 

CMAQ. In processing our Tier 3 emissions inventories for CMAQ, SMOKE uses hydrocarbon 

speciation profiles to break total hydrocarbons down into individual 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 important 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 recent measurements of speciated gasoline headspace 

vapors collected by EPA’s Office of Research and Development (ORD).

55 Mobile source 

hydrocarbon speciation profiles 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 complex 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 spatial scales - local, regional, national, and global. This section provides detailed 

information on the photochemical model used for our air quality analysis (the Community Multiscale Air Quality (CMAQ) model), atmospheric reactions and the role of chemical 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 quality 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 PM2.5 concentrations, 24-hour PM2.5 concentrations, annual 

NO2 concentrations, air toxics concentrations, visibility levels and nitrogen and sulfur deposition

levels for 2017 and 2030. The 2005-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 this platform includes emissions and meteorology for 2005. 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 regulatory 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,
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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 2009 for the U.S. EPA.O The CMAQ model is a wellknown and well-respected tool and has been used in numerous national and international 

applications.60,61,62 This 2005 multi-pollutant modeling platform used the most recent CMAQ 

code available at the time of air quality modeling (CMAQ version 4.7.1P) 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 science 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 reflects 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 

measurements. 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 modeling 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 Atmospheric 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.
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36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary

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 anthropogenic and biogenic 

sources, meteorological data, and initial and boundary conditions. The CMAQ meteorological 

input files were derived from simulations of the Pennsylvania State University/National Center 

for Atmospheric Research Mesoscale Model63 for the entire year of 2005 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 grids were developed by EPA and are described in 

more detail within the AQM TSD. The meteorological outputs from MM5 were processed to 

create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface 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 threedimensional global atmospheric chemistry model, the GEOS-CHEM model.66

 The global 

GEOS-CHEM model simulates atmospheric chemical and physical processes driven by 

assimilated meteorological observations from the NASA’s Goddard Earth Observing System 

(GEOS). This model was run for 2005 with a grid resolution of 2 degree x 2.5 degree (latitudelongitude) and 20 vertical layers. The 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/boundary state for all subsequent 12 km finer grid modeling.
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7-58

The emissions inputs used for the 2005 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 this draft RIA.

7.2.2.4 CMAQ Evaluation

An operational model performance evaluation for ozone, PM2.5 and its related speciated 

components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.), nitrate and sulfate 

deposition, and specific air toxics (formaldehyde, 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 calculated 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 Organization (RPO) 

region.Q The “acceptability” of model performance was judged by comparing our results to 

those found in recent regional PM2.5 model applications for other, non-EPA studies.R Overall, 

the performance for the 2005 modeling platform is within the range 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 PM2.5 over land with 

low bias and error results. Model predictions 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 more 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-hour ozone concentrations, daily and annual PM2.5 concentrations, annual NO2

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

- 2017 Tier 3 reference case 

- 2017 Tier 3 control case 

- 2030 Tier 3 reference case 

 

Q Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANE-VU), Midwest 

Regional Planning Organization – Lake Michigan Air Directors 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.
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- 2030 Tier 3 control case 

The 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, requiring 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 generated 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 emissions modeling TSD, found in the docket for this rule 

(EPA-HQ-OAR-2011-0135), 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 baseyear 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 PM2.5 concentrations, annual NO2 concentrations and visibility impairment for each of the 

2017 and 2030 scenarios. The ambient air quality observations are average conditions, on a siteby-site basis, for a period centered around the model base year (i.e., 2003-2007). 

The projected daily and annual PM2.5 design values 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 PM2.5 and its non-carbon components. This 

characterization of PM2.5 mass 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 PM2.5 mass and the 

characterized 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 PM2.5 species components: sulfates, 

nitrates, ammonium, organic carbon mass, elemental carbon, crustal, water, and blank mass (a 

fixed value of 0.5 μg/m3). More complete details of the SMAT procedures can be found in the 

report "Procedures for Estimating Future PM2.5 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 Transport 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 analysis to compare the absolute and percent differences 

between the future year reference and control cases for annual and seasonal ethanol, 
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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 NOX, VOC, CO, PM2.5, SO2, NH3, and seven air 

toxics: benzene, 1,3-butadiene, formaldehyde, acetaldehyde, ethanol, naphthalene and acrolein. 

The 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 more 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 allow the 

use of complex photochemistry in a fully 3-D air quality model.72

Complete combustion of ethanol in fuel produces carbon dioxide (CO2) and water (H2O). 

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 to 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, including the termination of NO2 and formation of nitric 

acid (HNO3) without PAN formation.S

NO2 + ∙OH + M → HNO3 + M k = 1.19 x 10-11 cm3molecule-1s

-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.
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alkenes, alkanes, and aromatics. Alternate reactions of acetyl peroxy radical, such as oxidation 

of NO to form NO2, which again leads to ozone formation, are also included.

Atmospheric reactions and chemical mechanisms involving several key formation 

pathways are discussed 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 react with the OH radical and O2 to form the acetyl peroxy radical 

[CH3C(O)OO∙].T When NOX is present in the atmosphere this radical species can then further 

react with nitric oxide (NO), to produce formaldehyde (HCHO), or with nitrogen dioxide (NO2), 

to produce PAN [CH3C(O)OONO2]. An overview of these reactions and the corresponding 

reaction rates are provided below. U

CH3CHO + ∙OH → CH3C∙O + H2O k = 1.5 x 10-11cm

3molecule-1

s

-1 74

CH3C∙O + O2 + M → CH3C(O)OO∙ + M

CH3C(O)OO∙ + NO → CH3C(O)O∙ + NO2 k = 2.0 x 10-11cm3molecule-1s

-1

 

75

CH3C(O)O∙ → ∙CH3 + CO2

∙CH3 + O2 + M → CH3OO∙ + M 

CH3OO∙ + NO → CH3O∙ + NO2

CH3O∙ + O2 → HCHO + HO2

CH3C(O)OO∙ + NO2 + M → CH3C(O)OONO2 + M k = 1.0 x 10-11 cm3molecule-1s

-1

 

76

Acetaldehyde can react with the NO3 radical, ground state oxygen atom (O3P) and 

chlorine, although these reactions are much slower. Acetaldehyde can also photolyze (hν), 

which predominantly produces ∙CH3 (which reacts as shown above to form CH3OO∙) and HCO

(which rapidly forms HO2 and CO):

CH3CHO + hν +2 O2 → CH3OO∙ +HO2 + CO  = 240-380 nm 77

As mentioned above, CH3OO∙ 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 are photolysis and reaction with the OH, with 

 

T Acetaldehyde 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 298 K and, if applicable, 1 bar of air.
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atmospheric lifetimes of approximately 3 hours and 13 hours, respectively.78 Formaldehyde can 

also react with NO3 radical, ground state oxygen atom (O3P) and chlorine, although these 

reactions are much slower. Formaldehyde 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 an important source of new hydroperoxy radicals

(HO2), which can lead to ozone formation and regenerate OH radicals. 

HCHO + hν + 2 O2 → 2 HO2 + CO  = 240-360 nm 79

HO2 + NO → NO2+ OH

Photolysis of HCHO can also proceed by a competing pathway which makes only stable 

products: H2 and CO. 

CB05 mechanisms for acetaldehyde 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 

mechanism80,81 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 representative 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, acetaldehyde can photolyze in the presence 

of sunlight or react with molecular oxygen (O3(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, CXO3). The specific parent VOCs that contribute the most to acetaldehyde 

concentrations vary spatially and temporally depending on characteristics 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, representing 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 instances 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 

emission 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). 
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The reaction of ethanol (CH3CH2OH) 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) is reaction with the hydroxyl radical (∙OH).84 This reaction produces 

acetaldehyde (CH3CHO) 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 on 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 weaklybonded methylene group, which reacts with oxygen to form acetaldehyde and hydroperoxy 

radical (HO2), and the remainder of the reaction occurs at the –CH3 and –OH groups, creating 

formaldehyde (HCHO), oxidizing NO to NO2 (represented by model species XO2) and creating 

glycoaldehyde, which is represented as ALDX:

CH3CHOH + OH → HO2 + 0.90 CH3CHO + 0.05 ALDX + 0.10 HCHO + 0.10 XO2

7.2.3.2 Secondary Organic Aerosols (SOA)

Secondary organic aerosol (SOA) chemistry research described below has led to 

implementation 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 emitted 

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-NOX 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 lowvolatility 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 PM2.5 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 hydrocarbons 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 concentrations.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.
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methods, the contributions of the major OA sources, including biomass burning and vehicular 

gasoline and diesel exhaust. Since mobile sources are a significant source of VOC emissions, 

currently accounting for almost 40 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 2005

National Air Toxics Assessment (NATA)99 and the Mobile Source Air Toxics (MSAT) Rule.100 

The 2005 NATA indicates that onroad and nonroad mobile sources accounted for almost 60

percent (1.46 μg/m3) of the total average nationwide ambient concentration of toluene (2.48

μg/m3), when the contribution of the estimated “background” 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 including 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 mobile-source hydrocarbons such as benzene, xylene, and 

alkanes form SOA. Similar to toluene, the SOA produced by benzene and xylene from low-NOX

pathways is expected to be less volatile and be produced in higher yields than SOA from highNOX conditions.

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 from SOA or affect SOA formation 

indirectly through changes in the radical populations from increasing 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 reactions would dominate over reaction with NO (i.e., 

irradiations performed in the absence of NOX 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 organic 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 scanning mobility particle sizer (SMPS) data. Given the expected negative 

result 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 (organic 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 
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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 and 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 SOA 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, numerous 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 specific 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 contributions 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 smog 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 NOX, collecting the SOA produced 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 estimates, 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 individual SOA precursors. Among the tracer compounds observed in ambient 

PM2.5 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 

laboratory, 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 this 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 VOCs. 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 
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hydrocarbon classes (e.g., all methylsubstituted-monoaromatics or all monoterpenes) and 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 acidity, 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 EPA 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 annual tracer-based SOA concentration estimates were 0.15, 0.18, 

0.13, 0.15, and 0.19 μg carbon/m3for 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 μg/m3associated 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 focused 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 In 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 mentioned 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 NOX sources. PAN is a reservoir and carrier of NOX and is the product of acetyl 

radicals reacting with NO2 in the atmosphere. One source of PAN is the photooxidation of 

acetaldehyde (Section 7.2.3.1), but many VOCs have the potential for forming acetyl radicals 

and therefore PAN or a PAN-type compound.

W PAN can undergo thermal decomposition with a 

lifetime of approximately 1 hour at 298K or 148 days at 250K. X

 

W Many aromatic hydrocarbons, particularly those present in high percentages in gasoline (toluene, m-, o-, p-xylene, 

and 1,3,5-, 1,2,4-trimethylbenzene), form methylglyoxal and biacetyl, which are also strong generators of acetyl 
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CH3C(O)OONO2 + M → CH3C(O)OO∙ + NO2 + M k = 3.3 x 10-4 s

-1 127

The reaction above shows how NO2 is released in the thermal decomposition of PAN,

along with a peroxy radical which can oxidize NO to NO2 as previously shown in Section 

7.2.3.1. NO2 can also be formed in photodegradation reactions where NO is converted to NO2

(see OH radical reaction of acetaldehyde in Section 7.2.3.1). In both cases, NO2 further 

photolyzes to produce ozone (O3).

NO2 + hν → NO + O(3P)  = 300-800 nm 128

O(3P) + O2 + M → O3 + M

The temperature sensitivity of PAN allows it to be stable enough at low temperatures to 

be transported long distances before decomposing to release NO2. NO2 can then participate in 

ozone formation in regions remote from the original NOX 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 acetaldehyde. 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 NO2 leading to ozone formation. 

7.2.3.4 Uncertainties Associated 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 formed secondarily through atmospheric 

chemical processes. Since secondarily formed pollutants can result from many different reaction 

pathways, there are uncertainties associated with each pathway. Simplifications of chemistry 

must be made in order to handle reactions of thousands of chemicals in the atmosphere. 

Mechanisms for 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 with 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 Community 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-Trimethylbenzene. J. Atmos. Chem., 34: 339- 364.).

X All rate coefficients are listed at 298 K and, if applicable, 1 bar of air.
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7.2.4 Impacts of the Proposed Rule on Air Quality

Air quality modeling performed for this proposed rule estimates the changes in ambient 

concentrations of PM2.5, ozone and NO2, as well as changes in ambient concentrations 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 proposed rule. 

This section describes current ambient levels of the modeled pollutants and 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 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 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 2010. The highest 

ozone concentrations were located in California. 
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Figure 7-12 Ozone Concentrations (fourth highest daily maximum 8-hour concentration) in 

ppm for 2010Y

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 221 full or 

partial counties, with a total population of over 129 million. Nonattainment areas for the 1997 8-

hour ozone NAAQS are pictured in Figure 7-13. Nonattainment designations for the 2008 ozone 

standards were finalized on April 30, 2012 and May 31, 2012.

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 living 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 double counting, the 1997 and 2008 ozone 

nonattainment populations contained in the Summary Nonattainment Area Population Exposure report 
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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 nonattainment areas, and they are not the same, then the larger of the two populations is included in the sum.
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Figure 7-14 2008 8-hour Ozone Nonattainment Areas

States with ozone nonattainment areas are required to take action to bring those areas into 

attainment in the future. The attainment date 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 The 

attainment dates for areas designated nonattainment for the 2008 8-hour ozone NAAQS are in 

the 2015 to 2032 timeframe, depending on the severity of the problem in each area. 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, the attainment dates associated with 

areas designated nonattainment for that NAAQS 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 Without 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 the San Joaquin Valley Air Basin 

8-hour ozone nonattainment area are designated as extreme and will have to attain before June 15, 2024. The 

Sacramento, Coachella Valley, Western Mojave and Houston 8-hour ozone nonattainment areas are designated as 

severe and will have to attain by June 15, 2019. 
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Compression-Ignition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 

2010), the Marine Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October 

8, 2008), the Locomotive and Marine Rule (73 FR 25098, May 6, 2008), the Clean Air Nonroad 

Diesel Rule (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 Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 6698, 

February 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. However, 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 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 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 have 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 state 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 With this Proposed Rule

This section summarizes 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 includes 

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 design 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 

concentrations in 2017 and 2030 respectively. Note that the projected results for 2017 do not 

include California, while the projected results for 2030 do.CC This issue does not have a 

significant impact on the AQ modeling results for the rest of the country.

 

CC 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 changes between reference and control, whereas in reality we 
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Figure 7-15 Projected Change in 2017 8-hour Ozone Design Values Between the Reference 

Case and Control Case

 

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 not valid.
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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 decreases 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. There are also 7 counties with projected 8-hour 

ozone design value decreases of more than 1 ppb; these counties are in Arizona, Texas and 

Tennessee. The maximum projected decrease 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 presents the ozone design value changes for 2030. The projected populationweighted 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 decrease 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 show 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 2017/2030 design values that exceeded the 2008 ozone standard, 

and (5) counties with 2017/2030 design values that did not exceed the standard, 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 ozone as they work to 
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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, the average modeled future-year 8-hour ozone design 

values are projected to decrease by 0.47 ppb in 2017 and 1.55 ppb in 2030. On a populationweighted basis, design values in those counties that are projected to be above the 2008 ozone 

standard in 2017 and 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

Averagea Number 

of U.S.

Counties

2020 

Population

Change in 

2017 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 the 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, populationweighted

-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 control 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

Notes:

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.
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Table 7-31 Average Change in Projected 8-hour Ozone Design Value in 2030

Averagea 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 -1.54

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, populationweighted

-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, population-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

Notes:

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 are projected to have 8-hour 

ozone design values above the 2008 NAAQS in 2030 without the proposed standards or any 

other additional controls in place. Table 7-32 below presents the changes in design values for 

these counties. 
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Table 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 

2030a

San Bernardino, California -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 -1.33 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 Single 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 below 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 nonattainment 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 sufficiently 

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 attainment 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 new state or local programs.

As described in Section 6.1.2.1 of this draft RIA the 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 
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results from interactions between photochemistry, background concentrations of VOC and NOX,

local emissions and meteorology. 

There is one county in 2017 that is projected to have an increase in modeled ozone design 

value concentration (Multnomah County, Oregon, where Portland is located). When NOX levels 

are relatively high and VOC levels relatively low, NOX forms inorganic nitrates (i.e., particles) 

but relatively little ozone. Such conditions are called “NOX-saturated.” Under these conditions, 

VOC reductions are effective in reducing ozone, but NOX 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 the fleet would be composed of vehicles meeting the new 

standards and the NOX 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 model-projected future PM levels.

7.2.4.2.1 Current Levels of PM

Figure 7-17 and Figure 7-18Figure 7-18 respectively show a snapshot of annual and 24-

hour PM2.5 concentrations in 2010. In 2010, the highest annual average PM2.5 concentrations 

were in California, Indiana, Pennsylvania and Hawaii and the highest 24-hour PM2.5

concentrations were in California and Alaska.
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Figure 7-17 Annual Average PM2.5 Concentrations in μg/m3for 2010DD

 

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/. 
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Figure 7-18 24-hour (98th percentile 24- hour concentrations) PM2.5 Concentrations in 

μg/m3for 2010EE

There are two NAAQS for PM2.5: an annual standard (12.0 μg/m3) and a 24-hour standard 

(35 μg/m3). 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 PM2.5 standard from 15 μg/m3to 

12 μg/m3.

133

 

In 2005 the U.S. EPA designated nonattainment areas for the 1997 PM2.5 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 PM2.5 NAAQS. These PM2.5 nonattainment 

areas are comprised of 191 full or partial counties. Nonattainment areas for the 1997 PM2.5

NAAQS are pictured in Figure 7 19. EPA anticipates making attainment/nonattainment 

designations for the 2012 PM2.5 NAAQS by December 2014, with those designations likely 

becoming effective in early 2015.134 On October 8, 2009, the EPA issued final nonattainment 

area designations for the 2006 24-hour PM2.5 NAAQS (74 FR 58688, November 13, 2009). 

These designations include 32 areas composed of 121 full or partial counties, with a population 

of over 70 million. Nonattainment areas for the 2006 PM2.5 NAAQS are pictured in Figure 7 20. 

In total, there are 50 PM2.5 nonattainment areas with a population of over 105 million people.FF 

 

EE 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/. 

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-2011-0135.
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States with PM2.5 nonattainment areas will be required to take action to bring those areas 

into attainment in the future. Most 1997 PM2.5 nonattainment areas are required to attain the 

1997 PM2.5 NAAQS in the 2009 to 2014 time frame and then required to maintain the 1997 

PM2.5 NAAQS thereafter.

135

 The 2006 24-hour PM2.5 nonattainment areas will be required to 

attain the 2006 24-hour PM2.5 NAAQS in the 2015 to 2019 time frame and then be required to 

maintain the 2006 24-hour PM2.5 NAAQS thereafter.136 2012 PM2.5 nonattainment areas will 

likely be required to attain the 2012 PM2.5 NAAQS in the 2021 to 2025 time frame, depending 

on the severity of an area’s fine particle pollution problems and the availability of pollution 

controls.

Figure 7-19 1997 PM2.5 Nonattainment Areas
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Figure 7-20 2006 PM2.5 Nonattainment Areas

As of December 14, 2012, over 29 million people live in the 46 areas that are designated 

as nonattainment for the PM10 NAAQS. There are 39 full or partial counties that make up the 

PM10 nonattainment areas. Nonattainment areas for the PM10 NAAQS are pictured in Figure 

7-21.
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Figure 7-21 PM10 Nonattainment Areas

7.2.4.2.2 Projected Levels of PM2.5 Without 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, 2011), the New Marine CompressionIgnition Engines at or Above 30 Liters per Cylinder Rule (75 FR 22895, April 30, 2010), the 

Marine Spark-Ignition and Small Spark-Ignition Engine Rule (73 FR 59034, October 8, 2008), 

the Locomotive and Marine Compression-Ignition Engine 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 Emissions Standards and Gasoline Sulfur Control 

Requirements (65 FR 6698, February 10, 2000). As a result of these and other federal, state and 

local programs, the number of areas that fail to meet the PM2.5 NAAQS in the future is expected 

to decrease. However, even with the implementation of all current state and federal regulations, 

there are projected to be counties violating the PM2.5 NAAQS well into the future. Thus 

additional federal control programs, such as Tier 3, can assist areas with attainment 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 conducted for this proposal projects that in 2030, 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 

over 28 million people, would have projected design values above the level of the annual 
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standard of 12.0 μg/m3and 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/m3. Since the emission changes from this proposed action would go into effect during the 

period when some areas are still working to attain the PM2.5 NAAQS, the projected emission 

changes will help state and local agencies in their effort to attain and maintain the PM2.5

standard. In the following section we discuss projected PM2.5 reductions from these proposed

standards. 

7.2.4.2.3 Projected Annual Average Levels of PM2.5 With this Proposed Rule

This section summarizes the results of our modeling of annual average PM2.5 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 that includes the proposed standards. Our modeling indicates that by 2030 

annual PM2.5 design values in the majority of the modeled counties would decrease due to the 

proposed standards. The decreases in annual PM2.5 design values are likely due to the projected 

reductions in primary PM2.5, NOX, SOX 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 to 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 PM2.5 vehicle emissions 

increases 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 present the changes in annual PM2.5 design values in 2017 

and 2030 respectively.

GG Note that the projected results for 2017 do not include California, 

while the projected results for 2030 do.

HH

 This issue does not have a significant impact on the 

AQ modeling results for the rest of the country. 

 

GG An annual PM2.5 design value is the concentration that determines whether a monitoring site meets the annual 

NAAQS for PM2.5. The full details involved in calculating an annual PM2.5 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 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 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 not valid.
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Figure 7-22 Projected Change in 2017 Annual PM2.5 Design Values Between the Reference 

Case and Control Case
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Figure 7-23 Projected Change in 2030 Annual PM2.5 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/m3in 2017. As shown in Figure 7-22, we project that in 2017 seven 

counties will have design value decreases of between 0.01 μg/m3 and 0.05 μg/m3. These 

counties are in Utah, Pennsylvania and Wisconsin. The maximum projected decrease in a 2017 

annual PM2.5 design value is 0.03 μg/m3in Weber County, Utah. As mentioned above the 

decreases in ambient annual PM2.5 concentrations are due to reductions in NOX, SOX 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 PM2.5 in 2017, but as 

explained, we do not expect that these localized small increases will actually happen.

The projected population-weighted average design value concentration without the 

proposed rule is 9.5 μg/m3in 2030. Figure 7-23 presents the annual PM2.5 design value changes 

in 2030. In 2030 all the modeled counties have decreases in annual PM2.5 design values. The 

annual PM2.5 design value decreases in 2030 are larger than the decreases in 2017; most design 

values are projected to decrease between 0.01 and 0.05 μg/m3and over 100 additional counties 

have projected design value decreases greater than 0.05 μg/m3. The maximum projected 

decrease in an annual PM2.5 design value in 2030 is 0.20 μg/m

3

in Tulare County, California. 
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Table 7-33 and Table 7-34 show the average change in 2017 and 2030 annual PM2.5

design values for: (1) all counties with 2005 baseline design values, (2) counties with 2005 

baseline design values that exceeded the 2012 annual PM2.5 standard, (3) counties with 2005 

baseline design values that did not exceed the 2012 standard, but were within 10 percent of it, (4) 

counties with 2017/2030 design values that exceeded the 2012 annual PM2.5 standard, and (5) 

counties with 2017/2030 design values that did not exceed the standard, 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 PM2.5 as they work to 

ensure long-term maintenance of the annual PM2.5 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 rows in Table 7-33 that compare the 2017 

control case to the standard do not include California as 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/m3) in the average modeled future-year annual PM2.5 design values in 2017 and a 0.06 

μg/m3decrease in 2030. On a population-weighted basis design values in those counties that are 

projected to be above the annual PM2.5 standard in 2030 would decrease by 0.11 μg/m3due to 

the proposed standards.

 

II 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 changes between reference and control, 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 not valid.
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Table 7-33 Average Change in 2017 Annual PM2.5 Design Value as a Result of the Proposed 

Rule

AVERAGE Number of U.S.

Counties

2020 

Population

Change in 2017 

design value 

(μg/m3) 

All 576 226,563,544 0.00

All, population-weighted 0.00

Counties whose 2005 base year is 

violating the annual PM2.5 standard

314 140,706,274

0.00

Counties whose 2005 base year is 

violating the annual PM2.5 standard, 

population-weighted

0.00

Counties whose 2005 base year is 

within 10 percent of the annual PM2.5 

standard

83 29,253,128 -0.01

Counties whose 2005 base year is 

within 10 percent of the annual PM2.5 

standard, population-weighted

0.00

Counties whose 2017 control case is 

violating the annual PM2.5 standard

4 1,992,506 0.01

Counties whose 2017 control case is 

violating the annual PM2.5 standard, 

population-weighted

0.01

Counties whose 2017 control case is 

within 10 percent of the annual PM2.5 

standard

13 12,158,988 -0.01

Counties whose 2017 control case is 

within 10 percent of the annual PM2.5 

standard, population-weighted

-0.01

Notes:

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.
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Table 7-34 Average Change in 2030 Annual PM2.5 Design Value as a Result of the Proposed 

Rule

AVERAGE Number of U.S.

Counties

2030 

Population

Change in 2030

design value 

(μg/m3) 

All

576 247,415,381 

-0.05

All, population-weighted -0.06

Counties whose 2005 base year is 

violating the annual PM2.5 standard

314

 

152,109,569 

-0.05

Counties whose 2005 base year is 

violating the annual PM2.5 standard, 

population-weighted -0.07

Counties whose 2005 base year is 

within 10 percent of the annual PM2.5 

standard

83 31,863,376 

-0.05

Counties whose 2005 base year is 

within 10 percent of the annual PM2.5 

standard, population-weighted -0.05

Counties whose 2030 control case is 

violating the annual PM2.5 standard

14 28,624,758

-0.11

Counties whose 2030 control case is 

violating the annual PM2.5 standard, 

population-weighted -0.10

Counties whose 2030 control case is 

within 10 percent of the annual PM2.5 

standard

28 23,840,272

-0.07

Counties whose 2030 control case is 

within 10 percent of the annual PM2.5 

standard, population-weighted -0.09

Notes:

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 fourteen counties in California that are projected to have annual PM2.5 design 

values above the NAAQS in 2030 without the proposed standards or any other additional 

standards in place. Table 7-35 below presents the changes in design values for these counties. 
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Table 7-35 Change in Annual PM2.5 Design Values (μg/m3) for Counties Projected to be 

Above the Annual PM2.5 NAAQS in 2030

County Name Change in 

Annual 

PM2.5

Design 

Value 

(μg/m3)

Population 

in 2030a

Riverside County, California -0.14 2,614,198 

San Bernardino County, California -0.11 2,784,489 

Kern County, California -0.19 981,806 

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, Montana -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 & Poole 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 PM2.5 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 PM2.5 design values in the 

majority of the modeled counties would decrease due to the proposed standards. The decreases 

in 24-hour PM2.5 design values are likely due to the projected reductions in primary PM2.5, NOX, 

SOX and VOCs. 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 to 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 PM2.5 vehicle emissions 

increases 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.
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Figure 7-24 and Figure 7-25 present the changes in 24-hour PM2.5 design values in 2017 

and 2030 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 2017 24-hour PM2.5 Design Values Between the Reference 

Case and the Control Case

 

JJ A 24-hour PM2.5 design value is the concentration that determines whether a monitoring site meets the 24-hour 

NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5 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 changes between reference and control, 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 not valid.
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Figure 7-25 Projected Change in 2030 24-hour PM2.5 Design 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/m3in 2017. As shown in Figure 7-24, in 2017 there are 72 counties

with projected 24-hour PM2.5 design value decreases greater than 0.05 μg/m3. These counties are 

in Utah, Pennsylvania and scattered throughout the Midwest. The maximum projected decrease 

in a 2017 24-hour PM2.5 design value is 0.20 μg/m3in Tooele County, Utah. As mentioned 

above, the decreases in ambient annual PM2.5 concentrations are due to reductions in NOX, SOX

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 some counties with projected small increases in 24-hour PM2.5 in 2017, 

but as explained, we do not expect that these localized small increases will actually happen.

The projected population-weighted average design value concentration without the 

proposed rule is 24.3 μg/m3in 2030. Figure 7-25 presents the 24-hour PM2.5 design value

changes in 2030. In 2030 the 24-hour PM2.5 design value decreases are larger; most design 

values are projected to decrease between 0.05 and 0.15 μg/m3and almost 200 counties have 

projected design value decreases greater than 0.15 μg/m3. The maximum projected decrease in a 

24-hour PM2.5 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/m3. These counties are 

in Idaho, Nevada, Montana, Louisiana, northern Utah, and the upper Midwest. The decreases in 
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24-hour PM2.5 design values that are projected in some counties are likely due to emission 

reductions related to reductions in PM2.5 precursor emissions (NOX, SOX, and VOCs). There is 

one county, Richmond County, Georgia, with a projected 24-hour PM2.5 design value increase of 

less than 0.15 μg/m3. Additional information on the emissions reductions that are projected with 

this proposed action is available in Section 7.2.1. 

Table 7-36 shows the average change in 2030 24-hour PM2.5 design values for: (1) all 

counties with 2005 baseline design values, (2) counties with 2005 baseline design values that 

exceeded the 24-hour PM2.5 standard, (3) counties with 2005 baseline design values that did not 

exceed the standard, but were within 10 percent of it, (4) counties with 2030 design values that 

exceeded the 24-hour PM2.5 standard, and (5) counties with 2030 design values that did not 

exceed the standard, 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 PM2.5 as they work to ensure long-term maintenance of the 24-hour PM2.5 NAAQS. 

On a population-weighted basis, the average modeled future-year 24-hour PM2.5 design values 

are projected to decrease by 0.14 μg/m3due to the proposed standards. On a populationweighted basis, 24-hour PM2.5 design values in those counties that are projected to be above the 

24-hour PM2.5 standard in 2030 would decrease by 0.13 μg/m3.

Table 7-36 Average Change in Projected 24-hour PM2.5 Design Values in 2030

Averagea Number 

of U.S.

Counties

2030 

Populationb

Change in 

2030 design 

value 

(μg/m3)

All

569 245,111,480

-0.13

All, population-weighted -0.14

Counties whose 2005 base year is violating the 2006 

24-hour PM2.5 standard

108 91,474,036

-0.19

Counties whose 2005 base year is violating the 2006 

24-hour PM2.5 standard, population-weighted -0.15

Counties whose 2005 base year is within 10 percent 

of the 2006 24-hour PM2.5 standard

140 53,990,060

-0.16

Counties whose 2005 base year is within 10 percent 

of the 2006 24-hour PM2.5 standard, populationweighted -0.17

Counties whose 2030 control case is violating the 

2006 24-hour PM2.5 standard

21 31,002,272

-0.13

Counties whose 2030 control case is violating the 

2006 24-hour PM2.5 standard, population-weighted -0.05

Counties whose 2030 control case is within 10

percent of the 2006 24-hour PM2.5 standard

7 4,212,913

-0.12

Counties whose 2030 control case is within 10

percent of the 2006 24-hour PM2.5 standard, 

population-weighted -0.10
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Note:

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 21 counties that are projected to have 24-hour PM2.5 design values above the 

NAAQS in 2030 without the proposed standards or any other additional controls in place. Table 

7-37Table 7-37 below presents the changes in design values for these counties. 

Table 7-37 Change in 24-hour PM2.5 Design Values (μg/m3) for Counties Projected to be 

Above the 24-hour PM2.5 NAAQS in 2030

County Name Change in 24-hour 

PM2.5 Design Value 

(μg/m3)

Population in 2030a

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 County, 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, Pennsylvania -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, NO2 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 model-projected future NO2 levels.
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7.2.4.3.1 Current Levels of NO2

Between 2003 and 2005, national mean concentrations of NO2 were about 15 ppb for 

averaging periods ranging from a day to a year.LL There are two NAAQS for NO2: an annual 

standard (53 ppb) and a 1-hour standard (100 ppb). The primary NAAQS for NO2 was revised in 

January 2010. EPA completed area designations in January 2012 and there are currently no 

nonattainment areas. The designations were based on the existing community-wide monitoring 

network. Once the expanded network of NO2 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 NO2 without this Proposed Rule

EPA has already adopted many mobile source emission control programs that are 

expected to reduce ambient NO2 levels. These 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 Engine 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 Emissions Standards and Gasoline Sulfur Control Requirements (65 FR 

6698, February 10, 2000). As a result of these and other federal, state and local programs, 

ambient concentrations of NO2 in the future are expected to decrease. However, even with the 

implementation of all current state and federal regulations, there are projected to be parts of the 

country with elevated NO2 concentrations in the future. 

As mentioned in Section 7.2.4.3.1, nonattainment designations for the NO2 NAAQS will 

potentially be occurring in 2016 or 2017. The emission changes from this proposal would go 

into effect during the period when any NO2 nonattainment areas would be working to attain the 

NO2 NAAQS. 

7.2.4.3.3 Projected Levels of NO2 with this Proposed Rule

This section summarizes the results of our modeling of annual average NO2 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 that includes the proposed standards. Figure 7-26 and Figure 7-27 present the changes 

in annual NO2 concentrations in 2017 and 2030 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
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Figure 7-26 Projected Change in 2017 Annual NO2 Concentrations Between the Reference 

Case and Control Case

Figure 7-27 Projected Change in 2030 Annual NO2 Concentrations Between the Reference 

Case and Control Case
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As shown in Figure 7-27, our modeling indicates that by 2030 annual NO2 concentrations 

in the majority of the country would decrease less than 0.1 ppb due to this proposal. However 

decreases in annual NO2 concentrations are greater than 0.3 ppb in most urban areas. These 

emissions reductions would also likely decrease 1-hour NO2 concentrations and help any 

potential nonattainment areas to attain and maintain the standard. Note that the projected results 

for 2017 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 adverse 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 Mobile 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 conducted for calendar year 2005, and 

was released in March 2011.

141

 NATA for 2005 includes four steps:

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

sources 

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

3) Estimating population exposures across the 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 7-29Figure 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 2005, mobile sources 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 sulfur 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 between reference and control, 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 not valid.

Formatte
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to direct emissions from mobile and stationary sources.

NN,OO,142

 Mobile sources are also large 

contributors to precursor emissions which react to form secondary concentrations of air toxics.

Formaldehyde is the largest contributor to cancer risk of all 80 pollutants quantitatively assessed 

in the 2005 NATA, and mobile sources were responsible for over 40 percent of primary 

emissions of this pollutant in 2005, and are major contributors to formaldehyde precursor 

emissions. Benzene is also a large contributor to cancer risk, and mobile sources account for 

over 70 percent of ambient exposure. Over the 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, 2005 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 

toxics 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 toxic air pollutant concentrations. Projected air toxics concentrations presented in this final action were 

modeled with CMAQ 4.7.1.
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Figure 7-29 County Level Average Noncancer Hazard Index, 2005 NATA

7.2.4.4.2 Projected Levels of Air Toxics

In the following sections, we describe results of our modeling of air toxics levels in the 

future 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 also likely to be 

significantly impacted by the standards. These compounds include benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde, and acrolein. Impacts on ethanol concentrations were also included 

in our analyses. Information on the air quality modeling 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 Heavyduty Onboard Diagnostic Rule (74 FR 8310, February 24, 2009), Small SI and Marine SI Engine 

Rule (73 FR 59034, October 8, 2008), Locomotive and Commercial Marine Rule (73 FR 25098, 

May 6, 2008), Mobile Source Air Toxics Rule (72 FR 8428, February 26, 2007), Clean Air 

Nonroad Diesel Rule (69 FR 38957, June 29, 2004), 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). As a result of these programs, the ambient concentration of air toxics in the 
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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 ambient 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, have the largest impacts. Air 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 concentrations 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 projects 

there would be small immediate reductions in ambient concentrations of air toxics due to the 

proposed sulfur controls in 2017. Furthermore, the full reduction of the vehicle program would 

be realized after 2030, when the fleet has fully turned over to Tier 3 vehicles. Because 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 develop 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 would have a notable impact on 

ambient benzene concentrations. 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 7-30Figure 7-30). In 2030, our modeling projects that 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 7-31Figure 

7-31). Absolute decreases in ambient concentrations of benzene are generally between 0.001 

and 0.01 μg/m3 in rural areas and as much as 0.1 μg/m3 in urban areas (Figure 7-31Figure 7-31). 
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Figure 7-30 Changes in Benzene Ambient 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 the 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 7-32Figure 7-32 shows that in 2017, ambient concentrations of 1,3-butdiene 

generally decrease between 1 and 5 percent across the country, corresponding to small decreases 
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in absolute concentrations (less than 0.001 μg/m3). In 2030, reductions of 1,3-butadiene 

concentrations range between 1 and 25 percent, with decreases of at least 0.005 μg/m3 in urban 

areas (Figure 7-33Figure 7-33). 

Figure 7-32 Changes 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 Ambient Concentrations Between the Reference Case 

and the Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 
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Acrolein 

Our modeling indicates the proposed standards would reduce ambient concentrations of 

acrolein in 2017 and 2030. Figure 7-34Figure 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 the 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 Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 
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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 7-36Figure 

7-36, in 2017, annual percent changes in ambient concentrations of ethanol are less than 1

percent across the country, with absolute concentrations of ± 0.01 ppb. In 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 7-37Figure 7-37). Figure 

7-37Figure 7-37 also shows that absolute decreases 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 Case and 

the Control Case in 2017: Percent Changes (left) and Absolute Changes in μg/m3 (right) 
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Figure 7-37 Changes in Ethanol Ambient Concentrations Between the Reference Case and the 

Control Case in 2030: Percent Changes (left) 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 7-38Figure 

7-38 and Figure 7-39Figure 7-39, annual percent changes in ambient concentrations of 

formaldehyde 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 proposal. Figure 7-38Figure 7-38

and Figure 7-39Figure 7-39 also show that absolute changes in ambient concentrations of 

formaldehyde are generally between 0.001 and 0.01 μg/m3 in both years, with some areas as high 

as 0.1 μg/m3 in 2030. 
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Figure 7-38 Changes in Formaldehyde 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 Formaldehyde 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 annual 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 

7-40Figure 7-40). Changes in ambient concentrations of acetaldehyde are generally in the range 
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of 0.01 μg/m3 to -0.01 μg/m3 with decreases happening in the more populated areas and increases 

happening in more rural areas (Figure 7-40Figure 7-40). 

The complex photochemistry associated with NOX emissions and acetaldehyde formation 

appears to be the explanation for the split between increased rural concentrations and decreased 

urban concentrations. In the atmosphere, acetaldehyde precursors react with NOX to form 

peroxyacylnitrate (PAN). Reducing NOX allows acetaldehyde precursors to be available to form 

acetaldehyde instead. This phenomenon is more prevalent in rural areas where NOX is low. The 

chemistry involved is further described 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) 
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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 toxics 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 projected to 

experience a decrease in ambient benzene and acrolein concentrations of at least 2.5 percent. 

More 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 over 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 Acrolein 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%
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≥ 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 acrolein which 

is currently a national risk driver for noncancer respiratory health effects as described in Section 

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 acrolein high enough to present a potential for adverse health 

effects. The inhalation reference concentration for acrolein and other risk drivers is described in 

Section 6.1.5.6. In addition, the decrease in population exposure to the toxic compounds in 

Table 7-36 will decrease cancer risks that are described in Section 6.1.5.

7.2.4.5 Visibility

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 information on current and 

model-projected future visibility levels at Mandatory Class I Federal Areas.

7.2.4.5.1 Current Visibility Levels

Designated PM2.5 nonattainment areas indicate that, as of July 20, 2012, over 105 million 

people live in nonattainment areas for the PM2.5 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 

areas, 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

 In 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 
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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 visibility 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 

Canyon 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 13.95 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.
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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 15.42 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

Joshua 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
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7-112

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 21.88 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 

Wilderness

CO 10.00 9.59 9.59 9.54 9.54 7.06

Maroon 

BellsSnowmass 

Wilderness

CO 8.82 8.38 8.38 8.36 8.35 7.07
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7-113

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

SelwayBitterroot 

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 Royale 

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
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7-114

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

HerculesGlades 

Wilderness

MO 26.75 21.92 21.99 22.04 21.97 11.27

AnacondaPintler 

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 ND 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
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7-115

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 29.01 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 6.95

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 12.54 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
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7-116

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

JoyceKilmerSlickrock 

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
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7-117

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

James 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 

Wilderness

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 11.33

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
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7-118

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 

Wilderness

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 

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.

7.2.4.6 Deposition of Nitrogen and Sulfur

As described in Section 6.3.2.1 of this draft RIA, deposition of nitrogen and sulfur can 

cause adverse environmental effects. In this section we present information on current and 

model-projected future nitrogen and sulfur deposition levels.

7.2.4.6.1 Current Levels of Nitrogen and Sulfur Deposition

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, while 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. Although 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, 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-2011-0135. 
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-119

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-2011-0135. 
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-120

Figure 7-42 Total Sulfur Deposition in the Contiguous U.S., 1989-1991 and 2005 -2007
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-121

Figure 7-43 Total Nitrogen Deposition in the Contiguous U.S., 1989-1991 and 2005-2007
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-122

7.2.4.6.2 Projected Levels of Nitrogen and Sulfur Deposition

Our air quality modeling projects decreases 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 urban 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 

the rest of the country. 
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7-123

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 (N2O) emissions and methane (CH4) emissions, both potent 

greenhouse gas emissions (with global warming potentials 298 and 25 times greater than CO2, 

respectively), are projected for gasoline cars and trucks due to the proposed sulfur and tailpipe 

standards. These projections are based on studies that provide a basis for reductions in N2O and 

CH4 emissions due to the Tier 3 sulfur and vehicle standards. With respect to sulfur, a study 

published in 2004 by the University of California at Riverside found a 29 percent reduction in 

N2O 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 

CH4 emissions when sulfur was reduced from 28 to 5 ppm (the EPA study did not measure N2O 

emissions). 

Several studies have also established that reductions in tailpipe standards for NOX and 

HC results in reductions in and N2O and CH4, respectively. N2O is unique in that it is not formed 

primarily during combustion, but in the catalyst during catalyst warm-up, before the catalyst 

reaches the temperatures required for full effectiveness (known as “light-off”). Improvements to 

catalyst technology required to meet lower emission standards reduce the time required for the 

catalyst to reach light-off, which reduces the window of N2O formation. Studies conducted by 

EPA and Environment Canada found that N2O emission are lower on vehicles certified to more 

stringent NOX emission standards.149,150 A study by Meffert, et al. established a strong 

correlation between improvements in NOX catalytic conversion efficiency and reductions in N2O 
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-124

emissions.151 A study published by Behrentz, et al. in 2004 examined the relationship between 

N2O and NOX 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

 

The study reported an N2O: NOX ratio of 0.095±0.035 (with the lower end of the range 

comprised of older oxidation catalyst technologies, and the higher end of the range comprised of 

modern three-way catalyst technologies), and supported the application of N2O: NOX ratios to 

NOX emissions as a reasonable method for estimating N2O emission inventories. Subsequent 

analysis of this dataset by Meszler also found that for vehicles equipped with more modern 

controls, N2O emissions increased with vehicle mileage, suggesting deterioration in N2O 

emissions as vehicles age.

153

 

The Meszler and Environment Canada studies cited above also established that vehicles 

certified to more stringent HC standards emit less CH4, even though HC standards from Tier 1 

and later do not include methane in the regulated standards. This trend is also reflected in the 

MOVES model, based on analysis of correlation between CH4 and HC emissions. MOVES

estimates methane as a function of total HC emissions, so the CH4 emission inventories account 

for effects such as deterioration, temperature, aggressive driving, and reductions in tailpipe 

emission standards. Because of this, CH4 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 CH4

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 CH4 from Tier 3 Program (MMTCO2eq)

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 N2O emissions are based directly on a limited sample of N2O emission data, 

rather than linking N2O emissions to NOX emissions as suggested by Behrentz; as a result, the 

model does not estimate potential N2O reduction concurrent with the Tier 3 program. Because 

of this, the MOVES-based inventories are significantly lower than inventories that take into 

account the N2O:NOX link, because they do not account for factors affecting light-duty NOX

emissions, like deterioration or aggressive driving, and lower NOX standards. To estimate N2O 

reductions, we have calculated N2O reductions due to vehicle standards on the Tier 3 fleet (2017 

and later model year vehicles), using the 0.095 N2O:NOX factor from Behrentz. For the pre-Tier 

3 fleet, we then bounded the N2O reductions due to sulfur control using two methods: 1) 

applying the 0.095 N2O: NOX ratio directly to the sulfur-related NOX reductions reported in 

Section 7.2; and 2) applying the percent reduction in N2O 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. 
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Table 7-41: Estimated Reduction in N2O from the Tier 3 Program

2017 2030

Reduction from Tier 3 fleet

NOX reduction from Tier 3 fleet due to 

vehicle and sulfur standards (U.S. Short Tons)

19,728 458,504

N2O reduction based on N2O:NOX ratio of 

0.095 (MMTCO2eqa)

0.5 11.8

Reduction from pre-Tier 3 fleet

Method 1

Reference case onroad gasoline NOX

emissions (U.S. Short Tons)

1,844,772 980,679

Reference case onroad gasoline N2O 

emissions based on N2O:NOX ratio of 0.095 

(MMTCO2eqa)

47.4 25.2

Reduction from pre-Tier 3 fleet due to sulfur 

standard (U.S. Short Tons)

264,653 66,286

N2O reduction based on N2O:NOX ratio of 

0.095 (MMTCO2eq a)

6.8 1.7

Method 2

U.S. onroad gasoline N2O emissions from 

pre-Tier 3 fleet using MOVES (MMTCO2e)

10.3 2.0

Percent reduction in N2O going from 30 to 10 

ppm

23% b23%

N2O reduction (MMTCO2eq) 2.4 0.5

Total Range of N2O Reduction 

(MMTCO2eq)

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 reduction

Summing the results from Tables 7-40 and 7-41, the range of reductions calculated for 

CH4 and N2O is from 3.0 to 7.4 million metric tons of carbon dioxide equivalent (MMTCO2eq) 

in 2017, growing to 12.8 to 14.0 MMTCO2eq in 2030. These reductions would be offset to some 

degree by CO2 emissions associated with higher 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 MMTCO2eq million metric tons of 

carbon dioxide equivalent (MMTCO2e) 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 consistent 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) . N2O has a 100-year GWP of 

298 and CH4 has a 100-year GWP of 25 according to the 2007 IPCC AR4.
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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 MMTCO2e 

represents an upper-bound estimate of the possible increase in refinery CO2 emissions due to the 

need for additional process heat and hydrogen production to enable additional hydrotreating. 

In 2017, the range of potential decrease in CH4 and N2O emissions overlaps with the 

range of projected increase in CO2 from refinery processes, suggesting that a net increase or 

decrease in GHG emissions cannot be quantified with certainty. However, we estimate the 

program would result in net GHG reductions as the program continues into the future, as shown 

by our 2030 estimates. 
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References

1

2017 and Later Model Year Light-Duty Vehicle Greenhouse 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,” 5th Asian Petroleum Technology Symposium, Jakarta, 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 Regulation 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 Evaporative Emissions Testing 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. Regional 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 The American Petroleum 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 (MOVES2010): 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 – Revised Version – Do Not Cite, Quote, or Release During Review ***

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23 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

24 Sonntag, Darrell. 2013. Supporting Technical Document: Estimated 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

27U.S. EPA. 2011. MOVES2010 Fuel Adjustment and Air Toxic Emission Calculation Algorithm –Development 

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-R99-023.

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 Standards and Fuel Efficiency Standards for Medium- and HeavyDuty 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-Ignition 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 

Cylinder (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 Technical 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.smokemodel.org/smoke_moves_tool/.

40 For more 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 to 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 Support 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", (December 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 – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-129

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-OAR-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/mobile6/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-2010-0162.

52U.S. EPA. 2009. Exhaust Emission Profiles for EPA SPECIATE Database: Energy Policy Act (EPAct) LowLevel Ethanol Fuel Blends and 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 and Diurnal Emissions, Using E20 Fuels on 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. EPA-420-

R-11-018. Available in the docket to this rule. 

56 U.S. EPA. 2013, January. Mobile Source Hydrocarbon Speciation Profiles for the Tier 3 Rule NPRM and Antibacksliding 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 climatology 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: Model 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-398+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 Research, Boulder CO.
*** E.O. 12866 Review – Revised Version – Do Not Cite, Quote, or Release During Review ***

7-130

65 Byun, 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 PM2.5 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, PM2.5, 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 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. 

71 U.S. EPA. 2013. “Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM”

72 Dodge, M.C., 2000. Chemical oxidant 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., 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.

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 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.

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. Oxford 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. 

Atmospheric 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., 2003. 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 Atmospheric 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 600/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 measurements, 

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 Technol 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 secondary 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,NOx/Air Mixture: Comparison of Results Using 14C 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 aerosol 

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 (2010) 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. (2011) 2005 National-Scale Air Toxics Assessment. 

http://www.epa.gov/ttn/atw/nata2005/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/otaq/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 NOX. Environ Sci Technol 43 (7): 2328-2334.

103 Kleindienst, T.E. (2008) Hypothetical SOA Production from Ethanol Photooxidation. Memo to the Docket EPAHQ-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 Secondary 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,NOx/Air Mixture: Comparison of Results 

Using 14C Content and SOA Organic Tracer Methods, Environ Sci Technol 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 irradiated 

isoprene/NOX/SO2/air mixtures and their detection in ambient PM2.5 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 quantification 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 aerosol products 

from the photooxidation of toluene and their implications in ambient PM2.5. 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 toluene and their implication in ambient PM2.5, 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, Environ 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 hydrocarbons to secondary organic aerosol at a southeastern U.S. location. Atmos 

Environ 41(37):8288-8300. 
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116 Henze DK, JH Seinfeld (2006) Global secondary organic aerosol from isoprene oxidation. Geophys Res Lett 33: 

L09812. doi:10.1029/2006GL025976. 

117 Hildebrandt, 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., Flagan, R. C., Seinfield, J. H., Secondary organic aerosol 

formation from m-xylene, toluene, and benzene, Atmospheric Chemistry and Physics Discussion, 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. and 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., Edney, 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 secondary 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., 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.

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., Pouliot, 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 Studies, 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). PM2.5 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. (2012). Fact Sheet – Air Quality Designations for the 2010 Primary Nitrogen Dioxide (NO2) National 

Ambient Air 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 Pollutants 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-Scale Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata2005/. 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/nata2005/05pdf/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 (EPA). 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 Desert 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 Precipitation 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) Emissions 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 Emissions 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