MEMORANDUM

SUBJECT: Updates to MOVES for Tier 3 FRM Analyses

FROM: David Choi Assessment and Standards Division Office of Transportation and Air Quality

TO: Docket Number EPA-HQ-OAR-2011-0135 DATE: February 27, 2014

The U.S. Environmental Protection Agency (E PA) is finalizing a co mprehensive program designed to reduce air pollution from light-duty and medium-dut y vehicles. This program, known as “Tier 3”, will establish more stringent vehicle emissions standards and reduce the sulfur content of gasoline, considering the vehicle and its fuel as an integrated system. In the course of the Regulatory Impact Analysis, we estim ated the impact of the Tier 3 vehicle and fuel standards on nationwide highway vehicle emissions of criteria po llutants, criteria pollutant precursors and selected air toxics for selected calendar years. We made these estimates using an updated version of the EPA’s Motor Ve hicle Emission Simulator (MOVES2010b).

The attached document details how MOVES defa ult code and database were updated for the Tier 3 FRM air quality a nd inventory analyses.

# Updates to MOVES for the Tier 3 FRM Analysis

February, 2014

Air Quality & Modeling Center

Assessment & Standards Division Office of Transportation & Air Quality

U.S. EPA2 TABLE OF CONTENTS

I. Introduction .................................................................................................................. .... 4

II. Fuel Effect Updates .......................................................................................................... 5

A. Fuel Sulfur Impacts ................................................................................................... 5

B. Gasoline Non-sulfur Fuel Effects (Model Year 2001 and later) ............................... 9

1. Scope and Implementation in MOVES Code ......................................................... 10

2. Standardizing Fuel Properties ................................................................................. 12

3. Fuel Effect Adjustments ......................................................................................... 14

4. Fuel Effects for Selected Air Toxics ....................................................................... 16
a) Constant Fractions for Running Emissions ..................................................... 21
b) Post-Model Adjustments ................................................................................. 22

C. Modeling Vehicle Emissions with E85 Fuel .......................................................... 23

1. Exhaust Emissions .................................................................................................. 23

2. Evaporative Emissions ............................................................................................ 28

3. AVFT and FuelUsageFraction for E85 ................................................................... 29

III. Evaporative Emission Updates ................................................................................... 29

A. Changes to Vapor Leak Frequencies ...................................................................... 30

B. Changes to Hot Soak Calculations .......................................................................... 30

C. Changes to Vehicle Trip Patterns ........................................................................... 31

D. Changes to Temperature and Fuel Effects For Running Loss ................................ 31

E. Changes to Rates for Inspection and Maintenance ..................................................... 31

IV. Incorporation of New Rules ....................................................................................... 31

A. Changes to account for the 2017 LDGHG Rule ..................................................... 32

B. Changes to account for the HDGHG Rule .............................................................. 33

C. Changes to account for Californi a’s LEV III and 177 States ................................. 37

V. Other Changes ............................................................................................................ 38

A. Changes to Future Year VMT and Vehicle Populations ........................................ 38

B. I/M Model Year Covera ge in 2018 and 2030 ......................................................... 38

C. Virginia Early Low Emission Vehicle Program ..................................................... 393 D. Application of LEV III Fuel Adjustments to VA, NH, and DC (in the Reference Case)....... ...................................................................................................................... .......... 39

E. Effect of Certification Fuels on Base Emission Rates ................................................ 39

References .................................................................................................................... .......... 414 

## I. Introduction

The U.S. Environmental Protection Agency (E PA) is finalizing a co mprehensive program designed to reduce air pollution from light-duty and medium-dut y vehicles. This program, known as “Tier 3”, will establish more stringent vehicle emissions standards and reduce the sulfur content of gasoline, considering the vehicle and its fuel as an integrated system. In the Tier 3 Regulatory Impact Analysis, we estimate d the impact of the Tier 3 vehicle and fuel standards on U.S. highway vehicl e emissions of criteria pollutant s, criteria pollutant precursors and selected air toxics for a ra nge of calendar years. We made these estimates using an updated version of the EPA’s Motor Vehicle Emi ssion Simulator (MOVES2010b). This document describes how the MOVES code and default databa se were updated for the Tier 3 FRM analysis and explains the difference between the models used for the FRM air quality analysis and the inventory analysis.

MOVES2010b is EPA’s official model for estima ting emissions from cars and trucks. At the time of its release, it incor porated EPA's most up-to-date information on gasoline vehicle emissions. Exhaust emission rates for hydrocar bons (HC), carbon monoxide (CO) and oxides of nitrogen (NOx) were developed based on an an alysis of state inspection/maintenance and roadside remote sensing data from thousands of vehicles. 1 Emissions of particulate matter were based on EPA's groundbreaking Kansas City gasoline PM Study. 2,3 Evaporative emission rates were updated based on extensive evaporative testing conducted by EPA and the Coordinating Research Council (CRC). 4

However, since the release of MOVES2010b, we have continued to collect and analyze emissions data and improve the science of inventory modeling. In particular, EPA has completed two major studies of fuel effects on emissions from Tier 2 light-duty gasoline vehicles, and an important collection of studies on evaporative emissions. Because fuels and evaporative emissions are important elements of the Tier 3 rule, it was very important to include this data in our analysis. For the analyses supporting the Tier 3 Notice of Proposed Rulemaking (NPRM), we first updated MOVES i nputs to include the draft results from these fuel-effect and evaporative emissions studies. We also made additional improvements, such as updating calculation of the air toxics and hydrocarbon speciation, adding th e capabilities to estimate additional air toxics, and updating heavy-duty emi ssion rates for diesel and gasoline vehicles. The updates to MOVES supporting the NPRM an alysis are described in a separate memorandum. 5 The MOVES version developed to s upport the NPRM analysis is called “MOVEST3NPRM”
Since the NPRM analysis was completed, ou r understanding of vehicle emissions has continued to improve and we have incorporated this new information into our modeling. We have finalized the collection and analysis of the fuel-effects data initially included in MOVEST3NPRM, and have collected and analy zed additional data on evaporative emissions. We have also incorporated changes that refl ect recent EPA rules on light-duty and heavy-duty greenhouse gas emissions, and we have updated MOVE S to enable estimation of emissions from vehicles running on fuels contai ning 70% or more ethanol by volume, i.e., “E85” fuel. The MOVES version developed to support the FRM air quality and national inventory analyses is5 called “MOVEST3FRM”. Compared to M OVEST3NPRM, the emission changes in MOVEST3FRM are generally small (i.e. the models are quite similar from the perspective of quantifying Tier 3 reductions). a The main exception is evapor ative hydrocarbon emissions via the vapor-venting process. As explained below, new information on vapor leak frequencies and on high hot-soak emissions from vehicles with vapor leaks led to substantial increases in emissions we estimate from these processes. However, since the updates incorporating the new data apply to both reference and control cases, th e percent reductions from Tier 3 standards are expected to be minimally affected.

The sections below describe the new data and analyses in detail and explain how they were incorporated into the versions of the MOVES database and code used to support the air quality and the subsequent inventory analyses for the final rule. Because the precise modeling methodology used in generating the inventories fo r the air quality modeling is time consuming and resource intensive, it had to begin months before the national inventory modeling and used a slightly older version of the MOVES model (MOVESGHGSource20121002f code and MOVESDB20121002k_truncatedGFRE database). b The national inventory analysis was done using a version of MOVES with additional updates and improvements (MOVESGHGSource20121002f code and MOVESDB201 21002l_truncatedGFREIM database). Collectively, this memo refers to these versions of the software and database as MOVEST3FRM. The MOVES code and databases, as well as th e county databases and scenario-specific userinput databases, are in the docket for the final rule.
Both MOVEST3NPRM, and MOVEST3FRM were developed with the sole purpose of supporting the Tier 3 rulemaking. The models are not a substitute for MOVES2010b nor the upcoming MOVES2014, and thus they se rve no other official role.



## II. Fuel Effect Updates

A. Fuel Sulfur Impacts
EPA recently completed a large and important study investigating the effect of gasoline sulfur levels below 30 ppm on the emissions of Tier 2 cars and tr ucks. It was designed to take into consideration what was known from prior studi es on sulfur build-up in catalysts over time

a Note, this memo describes only changes to the MOVES code and default database. Between the NPRM and FRM analysis, there were also changes to MOVES inputs and post-processing. These revisions are described in the Chapter 7 of RIA for the Tier 3 final rule.

b The default database MOVESDB20121002i was used for the 2007 air quality baseline run. The default database movesdb20121002k_truncatedGFRE was used for the 2018 and 2030 reference and control air quality analyses. The two databases are identical except for two corrections, a fix to evaporative running loss rates for E85 vehicles and a change in the number of vehicles in th e “sampleVehicleDay” table. These revisions are described in detail in Section II.C and Section III.C of this memo.6 and the effect of periodic regeneration events that may result from higher speed and load operation over the course of da y-to-day driving. The study samp le included in the analysis consisted of 93 cars and light tr ucks 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 repr esentative of high sales vehicles covering a range of types and sizes. Test fuels were two non- ethanol gasolines with properties typical of certification test fuel, one at a su lfur level of 5 ppm and the other at 28 ppm. All emissions data was collected using the FTP cycle at a nominal temperature of 75 °F.

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 su lfur level and miles driven after cleanout was performed. The study found substantial reduc tions in hydrocarbon, CO and NOx emissions attributable to reduced sulfur le vels. For all pollutants, the model fitting did not find a significant miles-by-sulfur interaction, suggesting the rela tive differences were not dependent on miles driven after clean-out. This study has undergone extensive statis tical analysis, followed by an independent peer review covering the design, analysis methods, a nd results. The peer review was conducted according to guidelines describe d in EPA’s Peer Review Handbook, and did not produce any significant adverse findings. We th en used the results to quantify the emission reductions for gasoline cars and trucks unde r the lower sulfur st andards in Tier 3.
At the time the new standards were proposed, our analysis of the sulfur study was still in a draft form and the NPRM modeling was based on analysis of the 81 pass enger cars and trucks that had been tested up to that time. Since th e NPRM, twelve additional Tier 2 vehicles were tested and included in the statistical analysis described in the docketed final report, examining the effect of sulfur on emi ssions from Tier 2 vehicles. 6 The analysis based on the complete set of 93 Tier 2 vehicles is reflected in the results presented in this memo and the emissions modeling for FRM. Table 1 summarizes the final average reductions measured in the in-use sulfur test
program.

Table 1 Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur on In-Use Tier 2 Vehicles

Test Phase NO X (p-value) THC (p-value) CO (p-value) NMHC (p-value) CH 4 (p-value) NO x +NMOG (p-value) PM a
Bag 1 7.1% (0.0216) 9.2% (0.0002) 6.7% (0.0131) 8.1% (0.0017) 16.6% (< 0.0001) N/A –
Bag 2 51.9% (< 0.0001) 43.3% (< 0.0001) – a 42.7% (0.0003) 51.8% (< 0.0001) N/A –
Bag 3 47.8% (< 0.0001) 40.2% (< 0.0001) 15.9% (0.0003) 54.7% (< 0.0001) 29.2% (< 0.0001) N/A –

FTP Composite 14.1% (0.0008) 15.3% (< 0.0001) 9.5% (< 0.0001) 12.4% (< 0.0001) 29.3% (< 0.0001) 14.4% (< 0.0001) –
Bag 1 – Bag 3

– a 5.9% (0.0074) – a – b

– b N/A –

a The differences in sensitivity to sulfur not significant at α = 0.10.
b Inconclusive because the mixed model did not converge.7 The results shown in Table 1 were incorporated into MOVES and were applied to model year 2001-and-later gasoline vehicles to estimate the sulf ur effects when modeling fuel sulfur levels at or below 30 ppm. In applying the results from the study of Tier 2 vehicles to model year as early as 2001, it was assumed that NLEV vehicles are mo re similar to upcoming Tier 2 vehicles than the Tier 1 vehicles.

The new sulfur model is applied multiplicativ ely in conjunction with other gasoline fuel effects in MOVES, and applies onl y for sulfur levels equal to and below 30 ppm. For sulfur levels above 30 ppm, and for all pre-2001 model year vehicles, we did not attempt to update the fuel sulfur algorithm in MOVES2010b because we were not aware of any additional data that provided information on fuel sulfur belo w 30 ppm for the legacy vehicles.

Equation 1 shows the generic form of the new sulfur model. β s represents the sulfur coefficient. χ s represents the actual in-use sulfur levels in the region being modeled. 

#    
S S 

### 0

. 1 adjustment sulfur x S A base

S 

###     Equation 1

The sulfur base ( S base ) in the sulfur model varies as a function of the scenario being modeled and the model year group (see Table 2). For most states, the sulfur base is 30 ppm except for 2017 and later model years for the control scenario. For California, the sulfur base is 10 ppm for all model years and scenarios. For th e Section 177 states that have adopted the LEV III program c , the sulfur base of 30 ppm and 10 ppm is used for the model years 2001 to 2016 and model years 2017 and la ter, respectively.

The results of the final versi on of the sulfur analysis were applied in MOVEST3FRM for model year 2001-and-later gasoline vehicles by standardizing the emissions reductions estimated from the in-use sulfur study (Table 1) to a base sulfur level of 30 ppm. The sulfur coefficient simply represents the slope of the interpolated line between 28 and 5 ppm fuel sulfur levels on emissions. The linearity of sulfur impacts on emissions is supported by past studies with multiple fuel sulfur levels all of which compare gasoline with differing sulfur levels that are below 100 ppm (e.g., CRC E-60 and 2001 AAM/AIAM programs as well as comments submitted to this rulemaking by MECA cited within Section IV.A.6 of the preamble). The emission reductions from FTP bag 2 and FTP ba g1-bag3 were used to calculate the sulfur coefficients for running exhaust and start exhaust, respectively. The percent reduction estimates from all Tier 2-certified vehicles from the in-use sulfur test program (Table 1) were used to develop the sulfur coefficients for passenger cars , passenger trucks, and ligh t commercial trucks. For heavier gasoline vehicles, a separate mixed model analyses were performed on Tier 2 Bin 8 vehicles from the in-use sulfur test program (Dodge Caravan and Ford F150), and the resulting estimates of percent reduction (T able 3) were used to develop the coefficients for heavy-duty gasoline vehicles, assuming that the catalyst respon se of heavier gasoline trucks to fuel sulfur

c These states include Connecticut, Delaware, Maryland, Maine, Massachusetts, New Jersey, New York, Oregon, Pennsylvania, Rhode Island, Washington, and Vermont.8 level is closer to Tier 2 Bin 8 ve hicles than others. Due to lack of data, we assumed no impact of sulfur on emissions for 2001- and-later motorcycles.
Table 4 shows the specific values of the su lfur coefficients used in MOVES by pollutant, process, and vehicle type.

Table 2 Base Sulfur Levels in the Low Sulfur Model

Region Reference Control MYG 2001-2016 MYG 2017+ MYG 2001-2016 MYG 2017+ Most States 30 ppm 30 ppm 30 ppm 10 ppm California 10 ppm 10 ppm 10 ppm 10 ppm Section-177 States 30 ppm 10 ppm 30 ppm 10 ppm

Table 3 Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur on In-Use Tier 2 Bin 8 Vehicles

Test Phase NO X (p-value) THC (p-value) CO (p-value) Bag 2 66.3% (0.0751) 36.8% (< 0.0001) 22.1% (0.0061) Bag 1 – Bag 3 – a – a – a

a The differences in sensitivity to sulfur not significant at α = 0.10.

Table 4 Tier 2 Low Sulfur Coefficients by Vehicle Type, Process and Pollutant

Vehicle Type THC CO NO X PM Starts Running Starts Running Starts Running Starts Running Motorcycle 0 0 0 0 0 0 0 0 Passenger Car, Passenger Truck & Light Commercial Truck 0.00257 0.01813 0 0 0 0.02158 0 0

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

Equation 1 has been applied using the coefficien ts in Table 4 to populat e the database table that stores fuel-effect equations in the MOVES database (“Gene ralFuelRatioExpression”). This table allows the MOVES model to compute fuel effects based on the properties of any fuel contained in the “FuelSupply” and “F uelFormulation” database tables.
Lastly, to estimate the impacts of the updates ma de to the sulfur model in MOVEST3FRM, MOVES2010b and MOVEST3FRM were run for a single county at 30 ppm and 10 ppm fuel sulfur levels in cale ndar years 2018 and 2030, using MOVES defa ult databases (Table 5). Total Hydrocarbon (THC) showed similar magnitude of impact from reduced sulfur between MOVES2010b and MOVEST3FRM in both calendar years. MOVEST3FRM was less sensitive to the changes in sulfur levels for carbon mono xide (CO), but showed more sensitivity to the9 changes in sulfur levels for oxides of nitrogen (NO x ). These results are consistent with the expected impacts from the updates made to the sulfur model in MOVEST3FRM.

Table 5 Percent Reduction in Emissions from 30 ppm to 10 ppm Sulfur Estimated in MOVES

CY2018 CY2030 MOVES2010b MOVEST3FRM MOVES2010b MOVEST3FRM THC 6.7% 6.4% 9.5% 11.5%

CO 8.8% 0.8% 10.8% 0.6% NO x 9.5% 19.4% 7.4% 26.2%



### B. Gasoline Non

­ sulfur Fuel Effects (Model Year 2001 and later)
An important function of mobile sour ce air pollution inventory models, including MOBILE6 and MOVES, is to account for the e ffects of different fuel properties on exhaust emissions. For this purpose, MOBILE6 relied on existing fuel effect models, known as the “EPA Predictive Model” and the “Complex Model”. These models were developed using data collected on 1990s-technology vehicles meeting Tier 0 emission standards, levels an order of magnitude higher than those for currently manufactured vehicles compliant with Federal Tier-2 or equivalent LEV-II standards. While these mode ls are still in use in MOVES to estimate fuel effects for vehicles manufactured prior to m odel year 2001, for example, equations from the Predictive Model are used to calc ulate fuel effects for total hydr ocarbons and oxides of nitrogen, and equations from the Complex Model are used to estimate fuel effects for toxic compounds, including carbon monoxide and air toxics , such as benzene and the aldehydes 7 --their applicability to vehicles employing more recen t engine and emission control technologies has been questioned. Since the initia tion of the MOVES project, it ha s become clear that an updated fuel-effects model representing Ti er-2 certified vehicles would be needed. In addition, Congress included provisions for the development of such a model in the 2005 Energy Policy Act (EPAct).
To meet this goal, EPA entered a partners hip with the Department of Energy (DOE) and the Coordinating Research Council (CRC) to unde rtake the largest fuel s research program conducted since the Auto/Oil prog ram in the early 1990’s, aimed specifically at understanding the effects of fuel property changes on exha ust emissions on recently manufactured Tier 2 vehicles. The resulting res earch program was dubbed the “EPAct/V2/E-89” program (or “EPAct” for short), with the th ree components of the label deno ting the designation given to the study by the EPA, DOE and CRC, respectively.
The program was conducted in three phases. Phases 1 and 2 were pilot efforts involving measurements on 19 light-duty car s and trucks on three fuels, at two temperatures. These preliminary efforts laid the groundwork for design of a full-scale research program, designated as Phase 3.
Initiated in March 2009, the Phase 3 program involved measurement of exhaust emissions from fifteen high-sales-volume Tier-2 certified vehicles (model year 2008), using twenty-seven test fuels spanning wide ranges of five fuel prop erties (ethanol, aromatics, vapor pressure, and two distillation parameters: T50 and T90). The prope rties of the test fuels were not assigned to10 represent in-use fuels, but rather to allow deve lopment of statistical models that would enable estimation of relative differences in emissions acro ss the ranges of fuel properties expected in the U.S. fuel pool. A fuel matrix was designed for Ph ase 3 to span the ranges of the five selected properties seen in commercially available fuels.
An initial sample of 19 test vehicles was c hosen with the intent ofrepresenting the latesttechnology light-duty vehicles on the market at the time the program was launched (model year 2008). The 19 vehicles represented a high fraction of vehicle sales at the time. The sample was to conform on average to Tier-2 Bin-5 exhaust levels and employ a variety of emission control technologies, to be achieved by including a rang e of vehicle sizes and manufacturers. Due to budget constraints, the sample was reduced from 19 to 15 vehicles for the Phase-3 program.
The LA92 test cycle was used with emissi ons measured over thre e phases analogous to those in the Federal Test Proce dure (FTP), at an ambient temperature of 75°F. In MOVES, the EPAct results are applied at temperatures higher and lower than this le vel, under an assumption that effects for fuels and temperature are independent and multiplicative.
Emissions measured include carbon dioxide (CO 2 ), carbon monoxide (CO), total hydrocarbons (THC), methane (CH 4 ), oxides of nitrogen (NO x ), and particulate matter (PM 2.5 ). In addition, hydrocarbons were spec iated for subsets of vehicles and fuels, allowing calculation of derived parameters such as non-meth ane organic gases (NMOG) and non-methane hydrocarbons (NMHC). Speciation also allowed independent analyses of selected toxics including acetaldehyde, formaldehyde, acr olein, benzene and 1,3-butadiene.
Phase 3 data collection was completed in J une 2010. Dataset construction and analysis was conducted between January 2010 and N ovember 2012. This process involved ongoing collaboration among EPA staff, DOE staff and c ontractors, and CRC repr esentatives. Following the completion of data collecti on, construction of the dataset i nvolved intensive evaluation and quality assurance. The analysis involved seve ral iterations between analysis and additional physical and chemical review of the data. Success ive rounds of statistical modeling were applied to the data, to achieve severa l goals, including identificati on of potential candidate models, identification and review of outlying observations, identification and review of subsets of data from influential vehicles, and identification of models including subsets of terms that best explain the results obtained.
The EPAct exhaust research program and an alysis are extensivel y documented in the “EPAct Test Program Report” 8 and “EPAct Analysis Report.” 9 This section describes how the statistical models developed during the EPAct study are appl ied in the MOVES model for purposes of the Tier 3 FRM.



### 1. Scope and Implementation in MOVES Code
Within MOVES, the steps described in this document are applied within the scope listed below.

Fuels : The adjustments apply to gasoline (fuelTypeID = 1) a nd high-level ethanol fuels (E85, fuelTypeID = 5). Note that the emission ra tes stored in “emissionRateByAge” for E85 are11 identical to those for gasoline. Note also that the handling of the adjust ments differs for the two fuel types. For lower level blends (vol. % <=20, fuelTypeid = 1), the adjustments are applied to the “in-use” fuels specified for the model runs . For high-level blends, however (vol. % > 20, fuelTypeid = 5), the adjustments are applied as though the fuel had a 10% ethanol level, and additional adjustments are applied to estimate emi ssions from the high-level blend (relative to an E10 fuel). Development and application of the supplementary E85 adjustments is described in Section C below.

Model Years : Adjustments apply to m odel year 2001 and later.

SourceType : The adjustments apply to all sourceT ypes (running on gasoline blends).

Emission Processes : Adjustments are developed and app lied separately to running exhaust (processID = 1) and start exhaus t emissions (processID = 2).

Pollutants : The pollutants covered incl ude those listed in Table 6.

Table 6 Pollutants Modified by Fuel Adjustments
pollutantID pollutantName Acronym
1 Total Gaseous Hydrocarbons THC

2 Carbon Monoxide CO 3 Oxides of Nitrogen (NO x ) NO x 111 Primary PM 2.5 – Organic Carbon PM(OC) 2
112 Primary PM 2.5 – Elemental Carbon PM(EC) 2

20 Benzene

21 Ethanol
24 1,3-Butadiene 25 Formaldehyde 26 Acetaldehyde

27 Acrolein

2 As the same adjustments are applied to OC and EC, they will be referred to more generically as “PM” in this document.

Database Table : MOVES allows a very flexible input data format for incorporating and applying coefficients within a wi de variety of mathematical fo rms. These “fuel-effect ratio expressions” can include up to 32,000 characters and are stored in a database table dedicated to this purpose (“GeneralFuelRatioEx pression”). A description of th is table is shown in Table 7.12 Table 7 Description of the Databa se Table “GeneralFuelRatioExpression”
Field Description Values fuelTypeID Identifies fuel types as broa d classes, i.e., “gasoline,” “diesel,” etc. 1 = gasoline 2 = diesel, Etc. polProcessID Identifies combinations of pollutant and process. e.g., 301 = hotrunning NOx, etc.

minModelYearID The earliest model year to which a specific value of fuelEffectRatioExpression is applied. e.g., 2001

maxModelYearID The latest model year to which a specific value of fuelEffectRatioExpression is applied. e.g., 2050

minAgeID The minimum vehicle age at which the value of fuelEffectRatioExpression is applied. e.g., 0 years

maxAgeID The maximum vehicle age at which the value of fuelEffectRatioExpression is applied. e.g., 30 years

sourceTypeID Identifies vehicles by functional type. See table “sourceUseType.” 11= motorcycle 21= passenger car 31=passenger truck 32=light commercial truck, etc.

fuelEffectRatioExpression A mathematical expression containing up to 32,000 characters.



### 2. Standardizing Fuel Properties
In applying the EPAct models to estimate emissi ons effects for a given fuel, it is necessary to first “center” and “scale” the properties for th e fuel, also known as “standardization.” This process simply involves first “centering” the m easured fuel properties by subtracting the given value from the sample mean, and then “scaling” by then dividing the centered values by their respective standard deviations (with the sample stat istics calculated from the fuel set used in the program), as shown in Equation 2. The result is a “ Z score,” representing a “standard normal distribution” with a mean of 0.0 and a standard deviation of 1.0.

s x x Z i

i 

 Equation 2

For the linear effects in the model, standard ization is performed usi ng the values of each fuel property, each in their respective scales (vol. %, psi, °F.). Using aromatics as an example, the standardization of the linear term is shown in Equation 3.13 arom arom arom

arom 

### s x x Z 

 Equation 3

For second-order terms, however, the process is not performed on the values of the fuel properties themselves. Rather, quad ratic and interaction terms are constructed from the Z scores for the linear terms, and the process is repeate d. Using the quadratic term for ethanol as an example (etOH×etOH), the standardized value, denoted by ZZ etOH×etOH , is calculated as shown in Equation 4, where 

### m Z etOH Z etOH and 

### s Z etOH Z etOH are the mean and standard devi ation of the quadratic term constructed from the Z sc ore for the linear effect.

etOH etOH etOH etOH etOH etOH etOH
Z Z Z Z

s m Z Z ZZ 

  Equation 4

Standardized terms for interaction effects are constructed similarly. For example, Equation 5 shows the standardization of an interaction term between ethanol and aromatics.

Arom etOH A etOH Arom etOH eArom etOH
Z Z Z Z

s m Z Z ZZ 

  Equation 5

Means and standard deviations for relevant model terms are shown in Table 8.14 Table 8 Means and Standard deviations for Fuel Properties, based on Fuel Matrix in the EPAct Phase-3 Project

Model Term Statistics for Full Fuel Matrix 1
Mean Standard deviation Ethanol (%) 10.3137 7.87956 Aromatics (%) 25.6296 10.0154 RVP (psi) 8.5178 1.61137 T50 (°F) 190.611 28.5791 T90 (°F) 320.533 19.4801

etOH × etOH 0.962963 0.802769
T50 × T50 0.962963 0.739766

etOH × Arom -0.03674 0.978461
etOH × RVP -0.0992352 0.999615
etOH × T50 -0.541342 0.769153
etOH × T90 0.0163277 0.972825

1 Fifteen vehicles measured on 27 fuels.



### 3. Fuel Effect Adjustments
In MOVES, emissions of the pollutants THC, CO, NO x and PM are calculated starting with “base emission rates” (meanBaseRate, meanBase RateIM) stored in the database table, “emissionRateByAge”. 10 The base rates are assumed to represent emissions on a “base fuel” which are multiplied by an adjustment to represent emissions on an appropriate in-use fuel. Different fuel adjustments have been develope d to represent selected pollutants and emission processes. Adjustments also vary depending on vehicle type and model year. A general discussion of the development and application of fuel effects used in MOVES2010 is provided in a separate technical report. 11 This document describes the appl ication of the EPAct study results to derive fuel adjustments for the subsets of vehicles and model years described above.
The models generated using EPAct results allo w estimation of emissions effects related to five fuel properties: ethanol c ontent (%), aromatics content (%), RVP (psi), T50 (°F) and T90 (°F), as well as selected interaction terms among these five parameters. The statistical models generated from the EPAct exhaust data follow the general structure shown in Equation 6 below. In this example equation, which por trays the model for cold-start NOx, β denotes a model coefficient, Z etOH denotes a “standardized” fuel term for this property, and Z etOH×Arom denotes a “standardized” etOH×Arom interaction term. d Finally, the term s ε 2 represents the residual error

d Note that these coefficients apply to fuel properties that have been “standardiz ed,” i.e., centered on their means and scaled by their standard deviations, based on the fuel-property matrix used to develop the models. To properly apply15 or “mean square error” for the model. Note that the subsets of the potential terms vary by emission and process, depending on the results of the statistical model fitting.

Two sets of exhaust fuel effect coeffici ents were employed for each pollutant; one set representing start emissions and a second set re presenting hot-running emissions. In some cases fuel effects estimated for these two processes differed substantially, as the effects of fuel properties on start emissions are dominated by changes in combustion and catalyst warm-up, while the impact of running emissions is dictated by catalyst efficiency when fully operational.
Relative fuel effects are calculated by applying the models to specific “in-use” fuels and predefined “base fuels” and taking th e ratio of the emissions on the in -use fuel to those on the base fuel, as shown in Equation 7.

      

 

    

 

 
2 Ar o etOH Arom etOH T50 T50 Arom Arom etOH etOH 0

5

. 0 exp e (g/mi) Emissions

     

s Z Z

Z Z X β

Equation 6



##       base use in base

use in 

### exp exp exp Effect Fuel X β X

β X

β X

β

  

  Equation 7

Table 9 shows the coefficients used in E quation 6 and Equation 7 for four individual pollutants, including total hydrocarbons (THC) and the criteri a pollutants CO, NO x , and PM. The application of these models has been integrated into MOVES for the FRM analyses. For implementation in MOVES, Equation 7 is input di rectly into the “Gener alFuelRatioExpression” table. Note that the expressions in the table also incorporate adju stments for sulfur effects, as described in Section II.A above.

the models, this process must be applied for the specific fuels under consideration. For more information on this technique, please see the EPAct analysis report.16 The table presents two sets of coefficients for each pollutant, representing the effects of the fuel properties on start and runni ng exhaust emissions, respectively. e The coefficients can be understood as the change in the na tural logarithm of emissions (e.g., Δ lnCO) associated with a change in the fuel property of 1.0 standard de viations. Because these coefficients apply to “standardized” fuel properties, as mentioned ab ove, the magnitude and signs of the terms are comparable, giving a sense of the influence of each term in the estimation of that pollutant relative to the others. For exam ple, for PM start emissions, ch anges in aromatics and T90 are very influential, and are positivel y related with PM emissions (e.g., when aromatics increase, PM increases). For THC start emissions, on the ot her hand, the negative sign of the coefficient indicates that the relationship is inverse, i.e., in creases in RVP are associated with decreases in THC.

Table 9 Standardized Coefficients for Models representing Regulated Gaseous Emissions and Particulates, from the EPAct Program

Model Term THC CO NO x PM Start Running Start Running Start Running Start Running
Intercept -0.8664 -4.6533 1.3466 -1.389 3 -2.8594 -4.5692 0.6559 -1.3107
etOH 0.0548 0.0327 -0.1049 0.06750 0.06299 0.1582 0.1126
aromatics 0.0676 -0.0195 -0.01242 0.0913 0.1339 0.04407 0.3833 0.1662

RVP -0.0445 -0.0355 -0.00762 0.0299
T50 0.1288 0.0501 -0.03273 0.0261 0.04783 0.0550
T90 0.0183 0.0514 -0.1571 0.0440 0.2923 0.1072
etOH×etOH 0.0436 0.07304
T50×T50 0.0736 0.0337 0.05358 0.0935
etOH×Arom 0.0179 0.02086 -0.02369
etOH×RVP 0.01596
etOH×T50 0.0445 0.1064
etOH×T90 0.0214
Variance ( s 2

ε ) 0.2012 0.9057 0.4641 2.0443 0.7383 0.6556 1.4610 1.9164



### 4. Fuel Effects for Selected Air Toxics
Aside from the pollutants represented in Section II.B.3 above, the remaining compounds addressed in this memorandum, i.e., the aldehyde s, acrolein, benzene, ethanol and 1,3-butadiene, are not calculated from emission ra tes stored in the database, but rather as fractions of total

e For all models, “start” and “running” emissions are represented by results measured on Bags 1 and 2 of the LA92 cycle, respectively.17 volatile organic compounds (VOC). During a run, MOVES estimates VOC emissions from THC emissions.
Thus, for use in MOVES, models developed fr om EPAct results were applied in a different way than described for the fuel-effects adjustme nts. Rather than estimating the change in emissions for one compound on two different fuel s, the approach for toxics estimates the emissions of the toxic as a fraction of emissi ons for VOC, on the same fuel. So, to model the behavior of the fraction with respect to changes in fuel properties, it was necessary to develop models for NMOG and Ethane, as well as the toxics, because MOVES estimates VOC as NMOG minus Ethane.
Thus, if we let X β toxic , X α NMOG and X θ ethane represent models for a selected toxic compound, NMOG and ethane, respectively, calcul ated by applying Equation 6 to each compound for a specified fuel, the toxic emissions as a fraction of VOC emissions is given by

e NMOG toxic



### e e e Fraction Toxic toxic X θ X α X β



###    f Equation 8

For these compounds, the calculation show n in Equation 8 is applied in the “GeneralFuelRatioExpression” table. Table 11 a nd Table 12 show coefficients for statistical models for ethanol and the five toxics representing “cold st art” and “hot-running” emissions, respectively. These models were developed usi ng techniques similar to the models shown in Table 9, using speciated results obtaine d from the EPAct Phase-3 program.

The coefficients for the toxics models are interpreted and applied in the same way. However, two differences must be noted. As re sources available for the EPAct project limited the number of tests for which hydrocarbons could be speciated, some models were developed using smaller subsets of data representing fewe r vehicles and fuels and are thus simpler in structure, including only the four linear terms ethanol, aromatics, T50 and T90, and no secondorder terms. For simplicity, the smaller set of ve hicles and fuels is termed the “reduced” fuel matrix, in contrast to the “full” fuel matrix as previously desc ribed (see Table 8). In addition, it is necessary to note that the fuel terms for mode ls fit to the “reduced” design are standardized differently than those fit under the “full” desi gn. The fuel property statistics for the reduced design are presented below in Table 10.

Coefficients for start emissi ons are presented in Table 11, as start emissions for four compounds were measured on the “Full Fuel Matrix” (see Table 8), with two compounds measured on the “reduced” fuel matrix. However, as all models representing running emissions were developed using the reduced design, repres enting 5 vehicles measured on 11 fuels, these models are presented separately in Table 12.
In addition, because VOC are calculated as non-methane organic gases (NMOG) minus ethane (H 3 CCH 3 ), it was necessary to develop analogous models for both NMOG and ethane. Coefficients for these two models are shown in Table 13 and Table 14 for start and running18 emissions, respectively. Models for these two com pounds were calculated in the same manner as those shown in Table 9. Note that models fo r both start and running ethane were developed using the reduced design, as with some of the toxics models.
In calculating toxic fractions, we elected to use models for NMOG and ethane fit using study designs and datasets similar to those for th e toxic compounds. That is to say, if the toxic model was fit with the reduced design, we combined it with the NMOG and ethane models also fit with the reduced design. We followed this co urse to prevent the calculation and propagation of artifacts in the estimated fractions resul ting from differing levels of information and complexity in the numerator and denominator in Equation 8. In this context we considered it important to apply “information parity” to th e toxic model in the numerator and the NMOG model in the denominator, as the vast major ity of VOC mass is represented by NMOG, with ethane comprising only a small fraction.

Table 10 Means and Standard deviations for Fuel Properties, based on Reduced Fuel Matrix in the EPAct Phase-3 Project

Model Term Reduced Fuel Matrix 1
Mean Standard deviation Ethanol (%) 11.0182 8.05925 Aromatics (%) 24.3909 9.92426 T50 (°F) 197.000 23.4536 T90 (°F) 323.527 19.6015

1 Applies to models fit with data from 5 vehicles measured on 11 fuels.19 Table 11 Models representing “Cold-start” Emissions for Selected Air Toxics
Model term Compound

Acetaldehyde 1 Formaldehyde 1 Acrolein 1 Ethanol 1 Benzene 2 1,3- Butadiene 2 Intercept -5.2323 -5.9771 -7.9338 -4.9080 -4.1029 -5.8371

etOH 0.81449 0.2299 0.2476 1.4627 -0.00468 -0.01729

Arom 0.03483 0.02822 0.1122 0.4056 0.02673

RVP -0.04170 -0.04718 -0.06450 -0.06054

T50 0.08670 0.1672 0.1880 0.07029 0.04242 0.1247

T90 0.03801 0.1302 0.2489 -0.09923 0.01133 0.1004

etOH × etOH -0.1669 -0.08310 -0.4970

T50 × T50 0.06665 0.05262 0.1108

etOH × Arom 0.01840 0.01651

etOH × RVP 0.02194

etOH × T50 -0.01627 -0.1186

etOH × T90 0.02004 0.04617

T50 × T90 0.03959 0.03489 0.05986

Variance ( s 2

ε ) 0.2034 0.4765 0.4661 0.7013 0.4614 0.3281
1 Models fit under the Full fuel matrix, including 15 vehicles measured on 27 fuels. See Table 8. 2 Models fit under the Reduced fuel matrix, including 15 vehicles measured on 11 fuels. Note that these models do not include a linear term for RVP, and do not include any 2 nd –order terms. See Table 10

.

Table 12 Models representing “Hot-Running” Emissions for Selected Air Toxics 1

Model term Acetaldehyde Formaldehyde Acrolein Ethanol Benzene 1,3- Butadiene Intercept -9.4189 -8.6574 -9.3072

etOH 0.1520 0.08456 NO 0.9233 NO NO

Arom 0.07991 0.01575 MODEL -0.3772 MODEL MODEL

T50 -0.02997 0.01863 -0.01910

T90 -0.07836 -0.08138 -0.3017

Variance ( s 2

ε ) 0.4379 0.4583 1.4596

1 Models fit using data from from the Reduced fuel matrix, i.e., 5 vehicles measured on 11 fuels. See Table 10

.20 Table 13 NMOG and Ethane (Bag 1): Models representing “Cold-start” Emissi ons under Full and Reduced Fuel Matrices

Model term Full Fuel Matrix 1 Reduced Fuel Matrix 2
NMOG NMOG 3 Ethane 4
Intercept -0.95209 -0.8943 -4.3079

etOH 0.08019 0.1040 0.1204

Arom 0.08782 0.09435 -0.1728

RVP -0.04224

T50 0.1345 0.1527 0.2169

T90 0.02127 0.09531

etOH × etOH 0.04432

T50 × T50 0.07579

etOH × Arom 0.01693

etOH × RVP

etOH × T50 0.04653

etOH × T90 -0.95209

Variance ( s 2

ε ) 0.2012 0.1982 0.6377
1 Represents 15 vehicles measured on 27 fuels; this model is used with the “full-fuel-matri x” models for Acetaldehyde, Formaldehyde, Acrolein and Ethanol, shown in Table 11 above.
2 Represents 15 vehicles measured on 11 fuels.
3 This model used with models for Benzene and 1,3-butadiene shown in Table 11 above.
4 This model used with all m odels shown in Table 11; note that ethane was measured only under the reduced fuel matrix.

Table 14 NMOG and Ethane (Bag 2): Models representing “Hot-Running” Emis sions under the Reduced Fuel Matrix

Model Term Model 1

NMOG Ethane Intercept -4.7775 -7.7241
etOH 0.01778 0.07345
Arom 0.03320 -0.1260
T50 0.04258 0.1815
T90 0.09051 0.1322

Variance ( s 2

ε ) 1.2431 2.8243

1 These models used with all models listed in Table 12

.21 

### a) Constant Fractions for Running Emissions
In Table 12 above, note that models represen ting running emissions are not available for three compounds: acrolein, ben zene and 1,3-butadiene. For thes e compounds, the relevant subsets of data were inadequate to allow m odel fitting. Therefore, for these compounds, running emissions were represented as cons tant fractions of VOC, with va lues derived from the available data.
Thus, for acrolein, benzene and 1,3-butadiene , the values of the fractions were 0.00077, 0.047 and 0.0, respectively. These values were deri ved as “ratios of means” (ROM), in which the toxic and VOC values were averaged first by vehicle and then across vehicles, as described below. The ROM approach is generally preferred as it provides an unbiased estimator of the true fraction as the sample size increases. 12

For benzene, results were available for four vehicles, differing widely in their benzene and VOC levels, and also in numb ers of available measurements, as shown in Table 15. The averaging was performed in two steps so that the vehicle(s) with th e greatest numbers of measurements would not dominate the overall mea n. In the first step, the benzene and VOC values were averaged for each vehicle. In the second step, the four vehicle means were averaged to give an overall mean. Finally, the overall m ean for benzene was divided by that for VOC to give a simple ratio estimator for benzene as a fraction of VOC.

Table 15 Benzene (Bag 2): Derivation of a Ratio-of-Means Estimator for Benzene as a Fraction of VOC

Vehicle n Benzene (mg) VOC (mg) Ratio of means (ROM)
Corolla 2 0.053752 2.2694
F150 10 2.2241 28.427 Impala 3 0.10825 10.670 Silverado 4 0.29381 16.216

All vehicles 4 0.66997 1 14.396 0.0465

1 This value is a simple average of the m eans for all four vehi cles, as listed above

.

The VOC fraction for acrolein wa s derived similarly (Table 16). For this compound results were available for five vehicles. Values for acrol ein are considerably lower than for benzene, so results are expressed in μ g, rather than mg. The resulting fr action is two orders of magnitude lower than that for benzene.22 Table 16 Acrolein (Bag 2): Derivation of a Ratio-of-Means Estimator for Acrolein as a Fraction of VOC

Vehicle n Acrolein ( μ g) VOC ( μ g) Ratio of means (ROM) Civic 3 5.4190 3,038.9 Corolla 5 2.8934 2,929.6 F150 5 8.3558 24,321 Impala 6 8.0180 10,408 Silverado 10 19.662 17,192

All vehicles 5 8.8696 1 11,578 0.0007661

1 This value is a simple average of the m eans for all five vehicles, as listed above

.

For 1,3-butadiene in Bag 2, measurements were extremely low; in fact, we considered the dataset so heavily affected by “left-censoring” th at we did not consider it adequate for either model fitting or development of ratio estimators. Accordingly, for modeling purposes, we have adopted an assumption that this compound is not emitted during hot-running operation, i.e., the ROM estimator is 0.0.



### b) Post

‐ Model Adjustments
For two compounds, benzene and 1,3-butadiene, additional refinements were applied to supplement the study design of the EPAct fuel set. These adjustments are applied to both start and running emissions, as appropriate.
For benzene, the issue is that the fuel matrix included aromatics generally, but not benzene specifically. As we considered it inadequate to model benzene in exhaust without explicitly accounting for benzene levels in fuel, we developed a “ post-model” refinement using data external to the EPAct program. In this case the source was a program conducted in support of the 2007 MSAT2 rule. This program performed meas urements on nine Tier-2 certified vehicles on fuels with benzene levels rangin g from 0.6 to 1.1 percent by weight. 13,14 With benzene represented as a fraction of VOC (as in Equation 8) denoted as f benzene , a value modified to account for benzene levels in different fuels ( f

* benzene ) is calculated as shown in Equation 9, where x benzene is the benzene level for the fuel modeled (weight percent), A is the mean benzene level in the EPAct exhaust program fu el set (0.66 weight percent), and B is a coefficient, taking a value of 0.24.



#   

#  

b e n benzene benzene *
benzene 

## f f B A x f      Equation 923 Similarly, given the importance of olefins to estimation of emissions for 1,3-butadiene, and that the EPAct exhaust program study design di d not incorporate olef ins as a factor, we considered it appropriate to develop a post-mode l adjustment explicitly accounting for olefin level. This adjustment was derived by va rying olefin levels in the Complex Model 15 and fitting a polynomial trend to the results. 16 Starting with an unadjusted toxic fraction for 1,3-butadiene ( f

buta ), the modified fraction f * buta is calculated using Equation 10, in which x olefin is the olefin level, and A
, B
, C and D are coefficients, taking values of 0.000008, 0.0002, 0.0069 and 0.008823, respectively.


###        

 D C Bx Ax f f olefin 2
olefin buta *
buta

Equation 10 1,3-Butadiene Correction
To estimate emissions for other gaseous air toxics, additional updates were incorporated into MOVES. Additional compounds includ e 2,2,4-trimethylpentane, ethylbenzene, n -hexane, methyl-tert-butyl ether (MTBE), propionaldehyde, styrene, toluen e, and xylenes. For these pollutants, toxic-to-VOC ratios were derived fr om speciation profiles and applied to VOC as estimated by MOVES. These ratios varied between pre-2004 and 2004-and-later vehicles, and also among E0, E10 and E15 blends. 17



### C. Modeling Vehicle Emissions with E85 Fuel

1. Exhaust Emissions

The version of MOVES used for the Tier 3 NPRM analysis was not capable of modeling emissions from vehicles using fuels with 70% or more ethanol by volume, i.e., “E85” fuel. Because the fuel supplies assumed in the NPRM analysis for future years included only a negligible fraction of E85, no E85 was modeled. However, the FRM analysis assumes higher usage of E85 fuel based on the fuel volumes projected by AEO2013 f , and thus, it was necessary to update the MOVES model to add this capability. g
For model year 2000 and earlier gasoline light-dut y vehicles, the toxic fractions for vehicles running on fuels containing 70-100% ethanol were de rived from measurements on four flexiblefuel vehicles conducted during the phase 3 of the EPAct program. The vehicles were tested on a single E85 blend. Since no test was acquired on an E70 blend (mor e typically used in winter)

f See Chapter 7.1.3.2 of the Regulatory Impact Analysis for the final rule

g The default database MOVESDB20121002i used for the 2007 baseline air quality inventory had zero values for the E85 evaporative running loss rates. This was fixed by replicating the running loss rates for gasoline in MOVESDB20121002k (the version used for the 2018 and 2030 reference and control air quality inventories). This error has no impact in the 2007 run since no E85 fuel usage was modeled in that year.24 and on blends with ethanol cont ent above 85%, the same toxic fr actions are used for blends containing 70-100% ethanol.
Of the fractions shown in Table 17, those with pollutantID from 40- 46 are stored in the database table “minorHAPRatio.” Fractions fo r compounds with polluta ntID from 20-27 are stored in the database table “ATRatioNonGas.”

Table 17 Toxic Fractions for model year 2000 and earlier light-duty vehicles running on E70/E85.

Pollutant (pollutantID) Toxic Fraction

2,2,4-Trimethylpentane (40) 0.0078 Ethyl Benzene (41) 0.0055 Hexane (42) 0.0045 Propionaldehyde (43) 0.0025 Styrene (44) 0.0003 Toluene (45) 0.0177 Xylene(s) (46) 0.0185 1,3-Butadiene (24) 0.0011 Acetaldehyde (26) 0.1644 Acrolein (27) 0.0010 Benzene (20) 0.0170 Ethanol (21) 0.3724 Formaldehyde (25) 0.029125 Table 18 Description of the Database Table “ATRatio NonGas,” as Applied to Light-Duty Vehicles

Field Description RelevantValues

polProcessID Identifies the pollutant (1 st two digits and Emissions Process (last two digits). Pollutants are identified in the table above; Relevant processes include: “Running Exhaust” (processID = 1) “Start Exhaust” (processID = 2) “Extended Idle Exhaust” (processID = 90) “Auxiliary Power Exhaust” (processID = 91)

sourceTypeID Identifies types of vehicles, classified by function Passenger Car (21) Passenger Truck (31) Light Commercial Truck (32)

fuelSubTypeID Identifies specific fuel classes within the fuelTypeID 51 = “Ethanol (E85)” 52 = “Ethanol (E70)”

modelYearGroupID Identifies a set of model years covered by a specific value of atRatio. atRatio Fraction, or “ratio” of the toxic relative to total VOC.

atRatioCV “Coefficient of Variation of the Mean” or “relative standard error” of the atRatio.

dataSourceID Indicates source data and methods used to estimate ATRatio.

For model year 2001 and later gasoline light-dut y vehicles, the impact of E85 on emissions for criteria pollutants (THC, CO, NO x ), particulate matter (PM), and a set of compounds designated as “major hazardous air pollutants” ( HAPs) were examined using the results from four test programs, namely the phase 3 of the EPAct program, National Renewable Energy Laboratory (NREL) E40, 18 Coordinating Resear ch Council (CRC) E-80, 19 and the “PM Speciation” Program. 20 All programs measured emissions on the LA92 cycle on both E10 and E85 blends, except CRC E-80, which measured E6 a nd E85 blends. Only the vehicles tested on both E10 (E6) and E85 were included in the anal ysis. Numbers of vehicles in each program are summarized in Table 19.
The preliminary analyses showed consistent emission trends across da tasets and thus, all available datasets were pooled to examine the effect of E85 on emissions , relative to E10. The tests of significance for differences between E10 and E85 were performed using Student’s paired
t-tests. 2126 Table 19 Numbers of vehicles included in the anal ysis of major HAPs from high-level ethanol blends

Test Program Number of Vehicles EPAct (phase 3) 3 NREL E40 9 CRC E-80 7 PM Speciation 2

For THC, CO, NOx, and PM, no statistica lly significant differences in emissions were observed between E10 and E85 ( α =0.05) and thus, the base emi ssion rates, IM factor, and IM coverage are assumed to be the same as gasolin e and the same fuel effects as E10 are applied. For most major HAPs, statistically significant differences in emissions between E10 and E85 were observed and the adjustment factors represen ting the ratios of emissions on an E85 blend to those on an E10 blend were derive d using Equation 11, instead of de riving toxic fractions. The adjustment factor was set to zero when the di fferences in emissions were not statistically significant (i.e., acrolein).

Equation 11

The resulting adjustment fact ors are shown in Table 20, and are stored in the database table, “GeneralFuelRatioExpression”, for fuel TypeID = 5. Note that in the GFRE, the adjustments shown in Table 20 ar e applied in conjunction with the complex fuel adjustments for non-sulfur effects, as described in Section B above.

Table 20 E70/E85 adjustment factors for major HAPs for model year 2001 and later vehicles

Pollutant (pollutantID) Adjustment Factor for E70/E85 Benzene (20) 0.6672 Ethanol (21) 7.587 1,3-butadiene (24) 0.2167 Formaldehyde (25) 1.572 Acetaldehyde (26) 7.126 Acrolein (27) 027 The toxic fractions for the additional “minor HAPs” (pollutantID from 40-46) for model year 2001 and later are identical to the fractions used for model year 2000 and earlier.
Limited emissions data exist for polyaro matic hydrocarbons (PAH) emissions from vehicles running on E85. Thus, the toxic fract ions for PAH were estimated by multiplying the fractions for E0 fuels by the fraction of gasolin e in E85 fuels. We assumed that the annual average ethanol content of E85 is 74%. Thus , E0 fractions were multiplied by 0.26. This approach assumes that no PAH emissions re sult from ethanol combustion. The resulting fractions are presented in Table 21. Note that as PAH are emitted in both particulate as well as gaseous phases, fractions are presented for both phases, with the fracti on for the gaseous phase relative to VOC, and that for the particulate phase relative to organi c carbon less than 2.5 microns (OC 2.5 ).

Table 21 Toxic Fractions for PAH Species for Vehicles running on E85 blends, by Phase and Process

Species pollutantID CAS Number Toxic fraction Gaseous

Phase Particulate Phase Start Running Naphthalene 23 91203 4.60E-02 4.37E-03 3.35E-03 Acenaphthylene 42 208968 4.03E-03 1.30E-03 9.97E-04

Acenaphthene 41 83329 8.87E-04 0.00E+00 0.00E+00
Fluorene 54 86737 1.80E-03 0.00E+00 0.00E+00
Anthracene 44 120127 7.44E-04 1.35E-03 1.03E-03
Phenanthrene 57 85018 4.77E-03 4.71E-03 3.61E-03
Fluoranthene 53 206440 1.24E-03 4.77E-03 3.65E-03 Pyrene 59 129000 1.42E-03 5.16E-03 3.95E-03 Benz(a)anthracene 45 56553 1.20E-04 1.24E-02 9.48E-03 Chrysene 50 218019 1.34E-04 1.05E-02 8.01E-03 Benzo(a)pyrene 46 50328 6.54E-06 3.10E-02 2.37E-02 Benzo(b)fluoranthene 47 2059 92 8.91E-05 1.51E-02 1.16E-02 Benzo(k)fluoranthene 49 2070 89 8.91E-05 1.51E-02 1.16E-02 Benzo(g,h,i)perylene 48 191242 0.00E+00 8.39E-02 6.42E-02 Indeno(1,2,3,c,d)pyrene 56 1933 95 0.00E+00 3.15E-02 2.41E-02 Dibenzo(a,h)anthracene 51 53703 0.00E+00 7.24E-04 5.55E-04

Since metal emissions can result from trace leve l contamination of fuel and engine oil, as well as attrition from exhaust emission compone nts, there is no way to estimate the metal emissions for vehicles running on E85 or E70 fuel in the absen ce of data. Thus, the metal28 emission rates were assumed to remain unchanged from those applied to vehicles with earlier fuel delivery and emissi ons control technologies.

No emissions data exist for dioxin and furan em issions from vehicles running on E85 or E70. Thus, the dioxin emission factors for E85 and E70 were estimated by multiplying fractions for vehicles running on E0 fuels by the fraction of gasoline in the fuel, assuming no emission of dioxins or furans resulting from the combustion of ethanol. Resulting rati os are given in Table 22.

Table 22 Emission Factors for Dioxins and Fur ans, for Vehicles Operating on E85 Blends
Congener Emission rate (mg/mile) 2,3,7,8-Tetrachlorodibenzo-p-dioxin 2.15E-10 1,2,3,7,8-Pentachlorodibenzo-p-Dioxin 9.61E-11 1,2,3,4,7,8-Hexachlorodibenzo-p-Dioxin 1.01E-11 1,2,3,6,7,8-Hexachlorodibenzo-p-Dioxin 2.06E-11 1,2,3,7,8,9-Hexachlorodibenzo-p-Dioxin 1.28E-11 1,2,3,4,6,7,8-Heptachlorodibenzo-p-Dioxin 1.55E-11 Octachlorodibenzo-p-dioxin 3.67E-12 2,3,7,8-Tetrachlorodibenzofuran 7.19E-11 1,2,3,7,8-Pentachlorodibenzofuran 1.03E-11 2,3,4,7,8-Pentachlorodibenzofuran 7.55E-11 1,2,3,4,7,8-Hexachlorodibenzofuran 2.84E-11 1,2,3,6,7,8-Hexachlorodibenzofuran 3.02E-11 1,2,3,7,8,9-Hexachlorodibenzofuran 8.24E-12 2,3,4,6,7,8-Hexachlorodibenzofuran 3.52E-11 1,2,3,4,6,7,8-Heptachlorodibenzofuran 3.16E-11 1,2,3,4,7,8,9-Heptachlorodibenzofuran 1.01E-12 Octachlorodibenzofuran 1.07E-12



### 2. Evaporative Emissions
For vehicles operating on fuels containing high levels of ethano l, ranging from 70 to 100%, the toxic fractions were develope d based on the results of two-da y diurnal evaporative emissions tests on four 2007 model year FFVs from CRC E-80 program. 22 Following typical speciation profile procedures, the fraction of each compound in a test was calculated by dividing its emission rate by the sum of all rates for that te st, and the fractions for a given compound were then averaged across all tests to form the composite profile. The resulting fractions presented in Table 23 were applied to the permeation, vapo r-venting and refueling-spillage emissions
processes.29 Table 23 Toxic Fractions for Evaporative Em issions, for Permeation, Vapor-venting and Refueling-spillage Processes

Pollutant Fraction
Ethanol 0.61042 Naphthalene 0 2,2,4-Trimethylpentane 0.00830 Ethyl Benzene 0.00124
N-Hexane 0.01276
Toluene 0.01608 Xylene a 0.00733
Benzene 0.00664



### 3. AVFT and FuelUsageFraction for E85
The Alternative Vehicle Fraction Table (AVFT) and “fuelUsageFraction” table are used in combination to provide the model with appropriate E85 fuel-volume usage. Both of these tables were filled using data derived from the Annual Energy Outlook 2013 final report (AEO2013). The AVFT specifies the fractions of vehicles for a given model year that are capable of refueling with alternative fuels. For example, in model year 2018, 16.3% of passenger and commercial trucks as well as 1.8% of passenger cars sold are capable of fueling with E85. The AVFT contains data for MY1998 through MY2030 for use in the Tier 3 modeling.
To derive the fuel usage fracti on of these vehicles, their prevalence in the overall fleet must be determined. For example, using AEO2013, we find a total of 212.8 million light-duty vehicles in the 2018 fleet; of which 15.70 million are capab le of fueling on E85. This results in a population fraction of 6.87% E85-capa ble vehicles. Next, the fraction of E85 in the total gasoline fuel pool must also be calculated. Motor gaso line and E85 volumes are reported in AEO2013 as energy and are converted to volumes for our us e. In 2018, the fuel pool contains 126.2 billion gallons of motor gasoline and 1.3 billion gallons of E85, resulting in 1.05% of the fuel pool as E85. Having both the population fraction of E85 vehi cles as well as the E85 fraction of the fuel pool, the fuel usage fraction can now be calculated. In this example, 1.05% of the fuel must be used by 6.87% of the vehicle population, resulting in these vehicles refueling on E85 15.27% of the time. This fuel usage fracti on exists in the “fuelUsageFrac tion” table for calendar years 2010 to 2030.



## III. Evaporative Emission Updates

Evaporative emissions account for a substa ntial portion of the gaseous hydrocarbon inventory. Over the last several years, EPA has invested in a ma jor research program to better understand “real-world” evaporative emissions from the current fleet of light duty vehicles. Much of this data was incorporated into th e modeling for the Tier 3 proposal, which is30 summarized in the MOVES docket memo for the NPRM. Specific revisions included changes to canister breakthrough emissions for vehicles re maining parked for multiple days, permeation emissions from vehicles using E15 fuels and vapor leaks.
Since the NPRM analysis, we have collected additional data and continued to improve our understanding of evaporative emissions. In partic ular, we have improved our estimates of the fractions of vehicles with vapor leaks in the fleet as a function of age and have updated our understanding of emissions in the hour after trip ends (hot soak emissions) from vehicles with vapor leaks. Together, these ch anges have had a substantial imp act on our estimates of vaporventing emissions. The new findings and analysis are detailed in two 23,24 technical reports. Details of the modeling are substantially differe nt from previous versions and are therefore documented in a separate memorandum to the dock et for evaporative emissions calculations for MOVEST3FRM. 25 This section of the docket memo su mmarizes the way the new information was incorporated in MOVEST3FRM.



### A. Changes to Vapor Leak Frequencies
Vapor leak frequencies in the Tier 3 NPRM anal ysis were based primarily on data from field research conducted at the Ken Caryl I/M st ation in Denver between 2008 and 2010. After the NPRM analysis was complete, we received peer review comments suggesting a different approach to our analysis of the data. In particular, the peer revi ewer questioned the use of remote sensing data in estimating leak rates and suggested that the leak rates be based only on direct measurements of hot-soak emissions. The updated an alysis addressing the pe er review comments led to significant reductions in our estimates of leak frequencies for the newest model year groups. We updated MOVEST3FRM to incorporate this change.
After the FRM inventory runs for air quality modeling were well underway, we did a final pre-publication review of the Ken Caryl report and compared it again to the original peer review comments. This involved performing an additional analysis of vehicle ag e and associated leak prevalence, which ultimately led to further reduci ng the rate at which leaks develop over the life of Tier 2 and later vehicles. Th e distribution of leak sizes shif ted towards smaller leaks causing a reduction in emissions from leaks. More detail s can be found on the modeling of leaks in the MOVES evaporative emissions docket memorandum referenced above. This change reduced EPA’s overall estimate of the average size of a vapor-venting leak. We incorporated these changes into a new version of the MOVES database (MOVESDB20121002l_truncatedGFREIM), and used this revised version for the inventory runs described in the RIA.
This final version of the Ken Caryl analysis di ffered substantially from the original, so for completeness, we conducted a second round of external peer review. The comments in the second review were minor and did not lead to additional model updates. 26,27



### B. Changes to Hot Soak Calculations
At the time of the Tier 3 NPRM analysis, we had not updated MOVES rates to include hotsoak evaporative emissions from vapor-leaking vehicles. Thus, the NPRM analysis did not sufficiently account for the impact of vapor leaks on hot soaks. For MOVEST3FRM, we31 updated the hot-soak base rates. The rates are no w calculated as a weighted average of leaking and non-leaking vehicles. The prope r weighting is performed using the leak frequencies for each specific model-year and age gr oup. Details are available in th e MOVES evaporative emissions docket memorandum referenced above.



### C. Changes to Vehicle Trip Patterns
The MOVESDB20121002k_truncatedGFRE databa se used for the 2018 and 2030, reference and control case, respectively, ha s the same trip pattern information as used in the NPRM air quality modeling database. This database accounts for “inactive” vehicles that park for long periods of time (more than a few days), as explained in the docket memo for the proposed rule. 28 However, the MOVESDB20121002i version used for the 2007 air quality base run inadvertently used a “sampleVehicleDay” table from M OVES2010b. This differen ce resulted in the 2007 baseline run having fewer light-duty multi-day park ing episodes and more vehicle starts than intended. Updating the table resulted in a minor increase in evaporative emissions.



### D. Changes to Temperature and Fuel Effects For Running Loss
The MOVES model had not incorpor ated fuel and temperature eff ects into the calculation of running-loss vapor emissions unt il it was developed and implemented in MOVEST3FRM. The coefficients used in the determination of this e ffect are the same as used in the MOBILE6 model. They apply to all gasoline source types and all model years, and remain constant below 40°F. The adjustment also remains constant at vapo r pressures of 7 psi a nd below as well as at pressures of 10 psi and above. The version of MOVES used to generate inventories for air quality modeling applied the multiplicative adjustment of 1 to 86°F. However, in the version of MOVES used for the national inventory, the adjust ment was revised to be applied at 95°F to accurately capture the effect of temperature on running loss. This resulted in lower running loss emissions for the national inventory compared to the inventories for the air quality. However, the overall evaporative emissions were minimally impacted.



### E. Changes to Rates for Inspection and Maintenance
Vapor-venting emissions from leaks can be detected by properly functioning on-board diagnostic (OBD) systems. However, OBD cannot prevent emissions caused by leaks, but rather alerts the driver by illuminating the Malfunction Indicator Light (MIL). MOVES estimates that a driver in an I/M enforcement area is more likely to repair their ve hicle in response to a MIL than a driver in a non-I/M (non-enforcing) area. MOVE S estimates that 90% of drivers in I/M areas will repair a leak compared to 10% in non-I/M areas.



## IV. Incorporation of New Rules

The version of MOVES used for the Tier 3 NPRM analysis did not incorporate emission changes expected due to EPA’ s recent final rule for reduci ng greenhouse gas (GHG) emissions from light-duty (LD) vehicles starting with m odel year (MY) 2017 (referred to here as the “2017 LD GHG” standards). 29 The MOVES version also did not include emission changes expected32 due to the Heavy-Duty Greenhouse Gas Rule (2011), 30 referred to here as “HDGHG”. MOVEST3FRM was updated to account for these rules. The revisions lead to changes in emissions in both the reference and control scenarios – most not ably, a reduction in NOx from heavy-duty diesel trucks.



### A. Changes to account for the 2017 LDGHG Rule
The 2017 LDGHG rule phased in new standards for passenger cars, light-duty trucks, and medium-duty passenger vehicles in MYs 2017 through 2025. After MY2025, the levels set by the standards are assumed to appl y indefinitely. The fina l standards are projec ted to result in an average industry fleet-wide level of 163 grams/mile of carbon dioxide (CO 2 ) in model year 2025, which is equivalent to 54.5 miles per gall on (MPG) if achieved exclusively through fuel economy improvements.
For MOVEST3FRM, we accounted for the 2017 L DGHG rule using techniques similar to those used in MOVES2010a to account for the 2012-2016 GHG rule. As described in the MOVES2010a report, 31 we adjusted the projected CO 2 g/mile rates from the rulemaking regulatory classes (car/truck) to the regulator y classes in MOVES (20/30), removed the air conditioning leakage improvements, and left the air conditioning efficiency improvements in the rates themselves. As in the MY 2012-2016 analysis, we also adjusted the emission rates for the on-road gap h of approximately 25% (multiplying the CO 2 rates by 1.25).
Additionally, we revisited th e MY 2008-MY2016 rates, and cha nged the ratio of reduction applied to the base rates be tween start and running. Many of the compliance technologies anticipated to be used in response to the light duty greenhouse gas regulation (transmission improvements, aerodynamic improvements, tires , etc) will not improve performance during vehicle start. Thus, the methodology used in MOVES2010a, which applied the same rate of reduction to both running and star ts, likely underestimates energy consumption from starts. For MOVEST3FRM, the reduction applied to starts was re vised to be half of that applied to running. i Because energy consumption at start is a small fracti on of the total, this increase has little impact on total CO 2 emissions; however to keep the composite emissions the same, we also decreased fuel consumption for running em issions by about one percent.
Similar to the MOVES2010a analysis, we cal culated ratios to produce the emission rates projected in the rulemaking, and then used these values to ap ply a ratio against the MY 2016 MOVES2010a emission rates.

h Final Rulemaking: Light-Duty Vehicle Greenhouse Gas Emissions Standards and Corporate Average Fuel Economy Standards (published May 7, 2010). In miles per gallon, there is approximately a 25% gap between laboratory measured and real world achieved fuel economies.

i This ratio is consistent with the ratio typically seen in criteria pollutant reduction; see Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator (MOVES2010) (PDF) (198 pp, 5.2MB, EPA-420-R-11-011, August 2011) Some technologies will affect start fuel co nsumption during start, such as engine downsizing. Given the minimal contribution of starts to total energy consumption, and that the national average emission rates are equivalent, a 2:1 ratio of reductions is a reasonable balance of concerns with little impact on the emission inventory.33 Table 24 Overall Reductions applied to MY 2016 CO 2 Emission rates (g/mi)

MY shortModYrGroupID Cars (regClassID=20) Trucks (regClassID=30)

Emission Rate Ratio Emission Rate Ratio

2017 37 269 93% 348 97%
2018 38 258 89% 340 95%
2019 39 247 86% 332 93%
2020 40 236 82% 324 90%
2021 41 226 79% 304 85%
2022 42 217 75% 290 81%
2023 43 207 72% 276 77%
2024 44 198 69% 262 73%
2025 45 190 66% 250 70%
2026 46 190 66% 250 70%
2027 47 190 66% 250 70%
2028 48 190 66% 250 70%
2029 49 190 66% 250 70%
2030 50 190 66% 250 70% 2031- 2050 9 190 66% 250 70%

We made a number of additional minor updates to database tables. These revisions included updating the “PollutantProcessModelYear” table to include references to new MY groups for MY 2020 and later. This table now includes MYGs 2020-2030 and a new final MY group of MY 2031-2050. We also updated the emission rates for al ternative fuels where we assumed that the fuel consumption rate for each alternative fuel was equivalent to gasoline energy consumption multiplied by the CO 2 density adjustment contained in MOVES. In the absence of data, we assumed that these fuels all had to meet the same CO 2 standard. We also a dded start rates that had been unintentionally dropped out of MOVES2010a. Because starts are a small part of energy consumption, this change produced less than a 0.1% increase in total energy for the country.
These changes to light-duty en ergy consumption rates reduce CO 2 , SO 2 , and SO 4 emissions and evaporative emissions from refueling. The changes were made for both the reference and control case, so they have little impact on the redu ctions predicted as a resu lt of the Tier 3 rule.



### B. Changes to account for the HDGHG Rule
The changes to account for the HDGHG rule were more involved than those for the LDGHG rule. The medium and heavy duty greenhouse gas program set in place by the rule begins with 2014 model year and increases in stringency th rough 2018. The levels set by the standards are34 assumed to apply indefinitely after 2018 unle ss changed by a future rulemaking. The program breaks the diverse truck sector in to three distinct categories:

 Line-haul tractors (largest hea vy-duty tractors used to pull trailers, i.e., semi trucks)

 Heavy-duty pickups and vans (3/4 and 1 ton trucks and vans)

 Vocational trucks (everything else: buses, re fuse trucks, concrete mixers, ambulances...)
The program set separate standards for engine s and vehicles and ensures improvements in both. It also sets separate st andards for fuel consumption, CO 2 , N 2 O, CH 4 and hydrofluorocarbons (HFCs). j

In MOVES, the HD GHG Rule is implemented through three key elements. These are: (a) revised running emission rates for total energy, (b) new aerodynamic coef ficients and vehicle masses for use in MOVES operating mode genera tion, (c) auxiliary power units (APUs) that largely replace extended idle in long haul trucks and are added to MOVES as a new process for combination long-haul trucks. These changes prim arily affect long haul diesel but other heavyduty source types are also affected slightly. There is a substantial impact on VOC, NOx, CO, PM, energy and toxic emissions from the intr oduction of APUs and the other changes.
The revised running emission rates for total energy are drawn from the modeling for the HDGHG rulemaking. k Expected improvements in engine technologies and performance were quantified as new running, start and extended id le rates for heavy dut y gasoline and diesel vehicles for the 2014-and-later model years. Table 25 describes the improvements expected from the HD engine technologies. The reductions from the baseline were applied to the appropriate source bins in the MOVES “emissionRate” table.

Table 25 Estimated Reductions in Engine CO 2 Emission Rates from this Program

GVWR Class Fuel Model Years CO 2 Reduction From Baseline

HHD (8a-8b) Diesel 2014-2016 3%
2017+ 6% MHD (6-7) and LHD 4-5 Diesel 2014-2016 5%

2017+ 9%

Gasoline 2016+ 5%

Table 26 contains the combination long-haul tr actor and vocational vehicle tire rolling resistance, coefficient of dra g, and weight reductions expected from the technologies which could be used to meet the standards. The va lue in the table reflects a 400-pound mass reduction.

j HFCs are not modeled in MOVES, and the N2O and CH4 standards are not considered forcing on emissions.

k Final Regulatory Impact Analysis, Chapter 5 (RIA) (PDF) (553 pp, EPA-420-R-11-901, August 2011)35 As discussed in the HDGHG rulemaking, EPA used a sales mix of 10 percent Class 7 low-roof, 10 percent Class 7 high-roof, 45 percent Class 8 low-roof, and 35 percent Class 8 high-roof based on feedback from the manufacturers. The s hort-haul values in the table reflect a modeling assumption that eight percent of all tractors would be considered vocational tractors and therefore will only be required to meet the vo cational vehicle standa rds and not show any aerodynamic or weight improvement. The vocat ional tractor population (assumed to be 9,773 vehicles in 2014 based on confidential sales information shared by a truck manufacturer) was applied entirely to short-haul tractor population which represen ts 19.7 percent of short-haul tractor population. The weight re duction applied to short-haul tractors is 321 pounds which is calculated from a 400 pound weight reduction reduced by 19.7 percent. The tire rolling resistance reduction is assumed to be five per cent based on the data deri ved in the tire testing program conducted by EPA and described in the rulemaking. The reductions from the baseline summarized in Table 26 were applied to the corres ponding source types in the MOVES “sourceUseType” table. For de tails on these assumptions, pl ease see the HDGHG rulemaking documents. 32

Table 26 Estimated Reductions in Rolling Resist ance and Aerodynamic Drag Coefficients from Reference Case for Alternative 3 (Model Years 2014 and Later) Truck Type Reduction In Tire Rolling Resistance Coefficient From Baseline Reduction In Aerodynamic Drag Coefficient From Baseline Weight Reduction (Lb)

Combination long-haul 9.6% 12.1% 400 Combination short-haul 7.0% 5.9% 321 Vocational vehicles (Single-unit trucks, refuse trucks, motor homes, buses, and light commercial trucks) 5.0% 0% 0

These changes are represented in MOVEST3F RM through new aerodynamic coefficients and weights. In MOVES2010b, the h eavy-duty sourcetype coefficien ts were based on a set of published estimates. l However, for MOVEST3FRM, we revi sed the rolling, rotating, drag and inertia terms (i.e., A
, B and C ) in the scaled-tractive-power (STP) calculation. These changes primarily affect combination short and long-haul source types which are 10 percent lower, and begin with model year 2014. Becau se the heavy duty emission standards are per unit of work, and these changes affect the amount of work that is done, the cha nges to the coefficients affect all pollutants. The largest impact is on total energy emissions, and is on the order of a few percent.

l Petrushov, V., "Coast Down Method in Time-Distance Variables," SAE Technical Paper 970408, 1997, doi:10.4271/970408.36 Table 27 – Current and Revised Road-Load Coefficients Category Rolling term Drag term Mass (mg)

current revised current revised current revised Intercity Bus 1.295 1.230 0.0037 same 19.5937 same Transit Bus 1.094 1.040 0.0036 same 16.5560 same School Bus 0.747 0.709 0.0022 same 9.0699 same Refuse Truck 1.417 1.346 0.0036 same 20.6845 same Single Unit Short Haul 0.562 0.534 0.0016 same 7.6416 same Combination Short Haul 1.964 1.826 0.0040 0.0038 29.3275 29.1819 Combination Long Haul 2.081 1.881 0.0042 0.0037 31.4038 31.2224

In MOVEST3FRM, we added a new emissions pr ocess, “Auxiliary Power Unit” (APU). This process is calculated only for the heavy-duty lo ng-haul combination truck source type. The APU emission rates are consistent with HDGH G Rulemaking, and drawn from the NONROAD model. The APU is modeled as a nonroad Tier 4, less-than-8 kW, compression-ignition engine. The rulemaking assumed a 30 percent penetrat ion of APUs in 2010-2014 model years and 100 percent penetration in 20 14-and-later model years. m Thus, by CY 2030 APU emissions largely replace extended idle emissions.

Table 28 APU Emission Rates
Pollutant Rates Units CO 36 g/hr NOx 26.88 g/hr THC 6.72 g/hr NMHC 6.72 g/hr

VOC 6.72 g/hr CH 4 0.3 % of THC PM2.5* 1.8 g/hr OC 1.35 g/hr EC 0.45 g/hr SO 2 0.0188 g/hr
CO 2 10.18 kg /gal Fuel consumption 0.2 gallons/hr Total Energy 27,430 kJ/hr *PM 2.5 is split into EC and OC using the same split as the regular extended idle.

m

This assumes that 100% of extended idling is replaced in vehi cles with an APU.37 Unlike the HHD standards, the HD pickup truck sta ndards are evaluated in terms of grams of CO 2 per mile or gallons of fuel per 100 miles. Si nce nearly all HD pickup trucks and vans will be certified on a chassis dynamometer, the CO 2 reductions for these vehicles are not represented as engine and road load reduction co mponents, but as total vehicle CO 2 reductions. Table 29 describes the estimated expected change s in engine and vehicle technologies.

Table 29 Estimated Total Vehicle CO 2 Reductions for HD Pickup Trucks and Vans

GVWR CLASS FUEL MODEL YEARS CO 2 REDUCTION FROM BASELINE
LHD 2b-3 Gasoline 2014 1.5%

2015 2%
2016 4%
2017 6%
2018+ 10%
Diesel 2014 2.3%
2015 3%
2016 6%
2017 9%
2018+ 15%

Engine CO 2 reductions and HD pickup/van total vehicle CO 2 reductions were applied to the energy consumption rates in the “emissionRate” table in MOVES by applying the percentage reductions to the default energy rates. The impr ovements in tire rolling resistance and drag coefficient were applied in the “sourceUseType” table; the percentage reductio ns were applied to the road load coefficients. It was assumed that 100 percent of Class 7/ 8 combination long-haul tractors model year 2014 and late r use APUs during extended idling.
These changes to heavy-duty energy consumption rates reduce CO 2 emissions and also reduced evaporative (spillage) em issions from refueling, and SO 2 and SO 4 emissions. In addition, the changes to the road load coeffi cients have an impact on the operating mode distribution used to calculate all emissions. This causes an em ission reduction, most notably for NOx emissions. There is also an additional impact on VOC, NOx, CO, PM, energy and toxic emissions from the introduction of APUs. The changes were made for both the reference and control case, so they have little impact on the redu ctions predicted as a resu lt of the Tier 3 rule.



### C. Changes to account for California’s LEV III and 177 States
California finalized LEV III requi rements in 2012, to initiate phase-in for model year 2018. After Tier 3 was proposed but not yet final, the Section 177 States adopted LEV III. The modeling of evaporative emissions for California and Section 177 st ates differs from Tier 3 as38 the latter program has an additional Leak Sta ndard with an In-Use Vehicle Program (IUVP) enforcement throughout the vehicle’s life. MOVEST 3FRM assumes different rates of vapor leak prevented and rate of control from I/M and OBD programs for LEV III/177 St ates and for Tier 3. Additional modeling details can be found in th e MOVES evaporative emissions calculations memorandum to the Tier 3 docket.



## V. Other Changes

A. Changes to Future Year VMT and Vehicle Populations
Since the NPRM modeling, the Department of Energy has released a new version of the Annual Energy Outlook (AEO), AEO2013. For the Ti er 3 FRM air quality runs, the updated future year VMT and vehicle population estim ates were incorporated when the MOVES emission rates were applied using the SMOKE-MOVE S model as described in Chapter 7 of the RIA. However for the inventory runs, these updated estimates of activity needed to be incorporated into the MOVES default da tabase, MOVESDB20121002l_truncatedGFREIM.

The same AEO2013 VMT estimates used to estimate the VMT for the SMOKE-MOVES runs for the air quality analysis were used to generate the VMT and population growth estimates for use in the VMTGrowthFactor in the “HPMSV TypeYear” and the SalesGrowthFactor in the “SourceTypeYear” tables in the MOVES da tabase used for the inventory runs.



### B. I/M Model Year Coverage in 2018 and 2030
The Inspection and Maintenance (I /M) program descriptions are stored in the “IMCoverage” table in the county databases (CDB s) provided as input to MOVES. The model year coverage in these “IMCoverage” tables are customized to each calendar year in order to account for the “grace” period in most I/M programs that exem pt the newest model years from the program. When the CDBs used for 2018 and 2030 were pr ojected using the 2007 CDB values, the model year coverages in the “IMCovera ge” tables were not updated. Th is inadvertent omission caused more model years to be exempted from the bene fits of the I/M program than intended by the program design. Since the exemption model y ears were based on the 2007 calendar year, the number of inappropriately exempted model years is greater in 2030 than in 2018. This resulted in increased base emission rates for both the refere nce and control cases used in the air quality analysis for those model year s inappropriately exempted in counties with I/M programs.
Out of the 3,109 counties included in the air qua lity analysis MOVES runs, 411 counties are affected by this omission. The 411 counties account for about 51% of the national vehicle miles traveled (activity) in the nation. Although the affected counties will see some increase in light duty gasoline emissions, since both the reference and control cases were a ffected by this issue, we expect minimal impact on the estimat ed reductions from the Tier 3 rule.

The tables were rectified for the national inventory runs and thus, the final inventory estimates (and the benefits calculations) in the final rule are not affected by this omission.39 

### C. Virginia Early Low Emission Vehicle Program
When MOVES is used to generate national inventories using the County Database, it is impractical to run every single county. The run times would be prohibitive. MOVES therefore reduces the number of counties by combining “like” counties into representative county groups and a single county from each group is run to gene rate emission rates used for all counties in the
group. The State of Virginia adopted early Califor nia Low Emission Vehicle standards, which require the use of an alternate emission rate tabl e in order to account for the differences between the emission performance of vehicle certified to the California standards a nd federal standards.
In the case of Virginia, the representing county chosen to represent selected counties in the state had adopted California Low Emission Vehicle (LEV) standards. As a result, the counties using the emission rates from the representing co unty did not use emission rates reflecting the early LEV standards when calculating the invent ories. These “early” LEV standards only affect the 1999 and 2000 model years. Virginia revert ed to using federal “National LEV” emission standards beginning with the 2001 model year.

The effect increased the overall emissions in the State of Virginia by about 5% in the air quality runs. The national invent ory analysis was not affected.



### D. Application of LEV III Fuel Adjustments to VA, NH, and DC (in the Reference Case)
The effects of fuel properties are applied to exha ust emission rates using a set of general fuel ratio expressions (GFRE) stored in the MOVES de fault database. These effects can differ for the different vehicle technologies. A special set of these GFRE were developed for vehicles certified using California standard s and were used in all states which had adopted the California standards in preference to federa l standards for highway vehicles . However, although the states of Virginia and New Hampshire and the District of Columbia had adopt ed the early California Low Emission Vehicle standards in the 1990’s, thes e locations had reverted to federal emission standards starting with the 2004 model year. As a result, the GFRE factors for California standards were inappropriately applied to the 20 04 and later model years in these locations for the reference case for the inventories for the air quality modeling. The same GFRE is applied to all states in the control case, so this problem does not affect the results in the control case.
The difference between the technologies used for federal standards for the 2004 and later model years and the California standards is small a nd the differences in the effect of changes in fuel properties is negligible. The nati onal inventory analysis was not affected.



### E. Effect of Certification Fuels on Base Emission Rates
An issue in the NPRM MOVES analysis was that it did not account for a change from certification fuels with no ethanol to the new fuel under Tier 3 st andards containing 10% ethanol. However, for the FRM analysis, we addresse d the question of changi ng certification fuels by incorporating fuel adjustments in the “GeneralFu elRatioExpression” calculations to account for the adoption of Tier 3 certific ation fuel starting in MY2017. On e such adjustment accounts for40 reduction in fuel sulfur to 10 ppm and is calcul ated by re-centering the preexisting sulfur model around a new base sulfur calculation at 10 ppm. A second adjustment accounts for the adoption of E10 certification fuel and is calculated by modifying the exis ting adjustments using the EPAct models and assigning the Tier 3 ce rtification fuel as the “base” fu el for MY2017 and later. With these adjustments in place, Tier 3 vehicles opera ting on in-use fuel with 10 ppm sulfur would show no sulfur effect, as the in -use and certification levels ma tch. Similarly, Tier 3 vehicles operating on an in-use fuel with the same non-su lfur properties as the Ti er 3 certification fuel would have no fuel adjustment for the non-sulfur fuel properties. Conversely, emissions for Tier 3 vehicles operating on fuels with properties diffe ring from the E10 certification fuel are adjusted appropriately relative to the certification fuel.41 

## References

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2 U.S. EPA. 2012. Development of Evaporative Emissions Calculations for the Motor Vehicle Emissions Simulator MOVES2010: Final Report. (EPA-420-R-12-027) http://www.epa.gov/otaq/models/moves/documents/420r12027.pdf

3 U.S. EPA. 2008. Analysis of Particulate Matter Emissions from Light-Duty Vehicles in Kansas City (EPA420-R08-010) http://www.epa.gov/otaq/emission-factors-research/420r08010.pdf

4 U.S. EPA, 2008. Modeling Evaporative Emissions in MOVES for the RFS2 NPRM. Memorandum from Prashanth Gururaja, U.S. EPA, November 2008.

5 U.S. EPA, 2013. Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM Analysis, EPA-HQ-OAR2011-0135.

6 The Effects of Ultra-Low Sulfur Gasoline on Emissions from Tier 2 Vehicles in the In-Use Fleet, EPA-420-R-14- 002.

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11 U.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

12 Zarnoch, S.J., and W.A. Bechtold. 2000. Estimating mapped-plot forest attributes with ratios of means. Canadian Journal of Forest Research . 30: 688-697.

13 U. S. EPA. 2007. Final Regulatory Impact Analysis : Control of Hazardous Air Po llutants from Mobile Sources; EPA420-R-07-002; U.S. Environmental Protection Agency ; Office of Transportation and Air Quality: Ann Arbor, MI. Chapter 6.11. http://www.epa.gov/otaq/toxics.htm.

14 U. S. EPA. 2011. Spreadsheet “Benz Buta Adju stments.xlsx.” EPA Docket EPA-HQ-OAR-2011-0135.

15 U. S. EPA. 1993. Final Regulatory Impact Analys is for Reformulated Gaso line. December 13, 1993. http://www.epa.gov/otaq/regs/fuels/rfg/42 16 U. S. EPA. 2011. Spreadsheets “Olefins versus Butadiene.xls” and Benz Buta Adjustments.xls.” EPA Docket EPA-HQ-OAR-2011-0135.

17 U.S. EPA. 2011 Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM.

18 Yanowitz, J., Knoll, K., Kemper, J., Luecke, J., and McCormick R. Impact of Adaptation on Flex-Fuel Vehicle Emissions When Fueled with E40. Environmental Science & Technology, 2013, 47(6), 2990-2997.

19 Haskew, H.M., and Liberty, T.F. Exhaust and Evaporative Emissions Testing of Flexible-Fuel Vehicles. Final Report. Coordinating Research Council. CRC Repo rt No. E-80, August, 2011. Available at www.crcao.org

.

20 Long, T., Herrington, J., Hays, M., Baldauf, R., and Snow, R. Air Toxic Emission from Passenger Cars Operating on Ethanol Blend Gasoline. 104 th Air and Waste Management Association Annual Conference and Exhibition, 2011, #118, 3053-3058.

21 Choi, D. Modeling of E8 5, April, 2013. http:// www.epa.gov/otaq/models/m oves/documents/faca-meetingapr2013/06-modeling-e85.pdf

22 Haskew, H.M., and Liberty, T.F. Exhaust and Evaporative Emissions Testing of Flexible-Fuel Vehicles. Final Report. Coordinating Research Council. CRC Repo rt No. E-80, August, 2011. Available at www.crcao.org

23 DeFries, T., Palacios, C., Weathe rby, M., Stanard, A., Kishan, S. (2013) Estimated Summer Hot-Soak Distributions for Denver’s Ken Caryl I/M Station Fleet.

24 Brown, J. (2011) The DELTA Model: Improved Evaporative Emissions Modeling for EPA MOVES

25 U.S. EPA 2014, Memorandum to Docket: Development of Evaporative Emissions Calculations for Tier 3 FRM, EPA-HQ-OAR-2011-0135

26 Browning, L. (2013) Peer Review of Draft Report “Estimated Summer Hot Soak Distributions for Denver’s Ken Caryl IM Station Fleet”.

27 U.S.EPA 2013, EPA Response to Comments on the 2 nd Peer Review of “Estimated Summer Hot-Soak Distributions for Denver’s Ken Caryl I/M St ation Fleet” Memorandum from Constance Hart.

28 U.S. EPA, 2013. Memorandum to Docket: Updates to MOVES for the Tier 3 NPRM Analysis, EPA-HQ-OAR2011-0135., Section III B 2.

29 77 FR 62623 (October 15, 2012).

30 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

31 U.S. EPA, 2012. Updates to the Greenhouse Gas and Energy Consumption Rates in MOVES2010a , EPA-420-R12-025, http://www.epa.gov/otaq/models/moves/documents/420r12025.pdf

32 Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles (76 FR 57106, September 15, 2011)