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 (EPA) is finalizing a comprehensive program 

designed to reduce air pollution from light-duty and medium-duty 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 estimated the impact of the Tier 3 vehicle and fuel 

standards on nationwide highway vehicle emissions of criteria pollutants, criteria pollutant 

precursors and selected air toxics for selected calendar years. We made these estimates using an 

updated version of the EPA’s Motor Vehicle Emission Simulator (MOVES2010b). 

The attached document details how MOVES default code and database were updated for the 

Tier 3 FRM air quality and 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. EPA 

 
2 

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 California’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 Coverage in 2018 and 2030 ......................................................... 38

C. Virginia Early Low Emission Vehicle Program ..................................................... 39
3 

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 .............................................................................................................................. 41

 
4 

I. Introduction

The U.S. Environmental Protection Agency (EPA) is finalizing a comprehensive program 

designed to reduce air pollution from light-duty and medium-duty 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 estimated the impact of the Tier 3 vehicle and fuel 

standards on U.S. highway vehicle emissions of criteria pollutants, criteria pollutant precursors 

and selected air toxics for a range of calendar years. We made these estimates using an updated 

version of the EPA’s Motor Vehicle Emission Simulator (MOVES2010b). This document 

describes how the MOVES code and default database 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 estimating emissions from cars and trucks. At the 

time of its release, it incorporated EPA's most up-to-date information on gasoline vehicle 

emissions. Exhaust emission rates for hydrocarbons (HC), carbon monoxide (CO) and oxides of 

nitrogen (NOx) were developed based on an analysis 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 inputs 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 the capabilities to estimate 

additional air toxics, and updating heavy-duty emission rates for diesel and gasoline vehicles. 

The updates to MOVES supporting the NPRM analysis are described in a separate 

memorandum.5

 The MOVES version developed to support the NPRM analysis is called 

“MOVEST3NPRM” 

Since the NPRM analysis was completed, our 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 analyzed additional data on evaporative emissions. 

We have also incorporated changes that reflect recent EPA rules on light-duty and heavy-duty 

greenhouse gas emissions, and we have updated MOVES to enable estimation of emissions from 

vehicles running on fuels containing 70% or more ethanol by volume, i.e., “E85” fuel. The 

MOVES version developed to support the FRM air quality and national inventory analyses is 
5 

called “MOVEST3FRM”. Compared to MOVEST3NPRM, 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 evaporative 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, the 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 for 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 MOVESDB20121002l_truncatedGFREIM database). 

Collectively, this memo refers to these versions of the software and database as MOVEST3FRM. 

The MOVES code and databases, as well as the 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 serve 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 trucks. It was designed to take 

into consideration what was known from prior studies 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 the “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 day-to-day driving. The study sample included in the analysis 

consisted of 93 cars and light trucks recruited from owners in southeast Michigan, covering 

model years 2007-9 with approximately 20,000-40,000 odometer miles. The makes and models 

targeted for recruitment were chosen to be representative of high sales vehicles covering a range 

of types and sizes. Test fuels were two non-ethanol gasolines with properties typical of 

certification test fuel, one at a sulfur 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 sulfur level and miles driven after cleanout was 

performed. The study found substantial reductions in hydrocarbon, CO and NOx emissions 

attributable to reduced sulfur levels. For all pollutants, the model fitting did not find a significant 

miles-by-sulfur interaction, suggesting the relative differences were not dependent on miles 

driven after clean-out. This study has undergone extensive statistical analysis, followed by an 

independent peer review covering the design, analysis methods, and results. The peer review 

was conducted according to guidelines described in EPA’s Peer Review Handbook, and did not 

produce any significant adverse findings. We then used the results to quantify the emission 

reductions for gasoline cars and trucks under the lower sulfur standards 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 passenger cars and trucks 

that had been tested up to that time. Since the 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 emissions 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 NOX 

(p-value) 

THC 

(p-value) 

CO 

(p-value) 

NMHC 

(p-value) 

CH4 

(p-value) 

NOx+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 sulfur 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 more similar to upcoming Tier 2 vehicles than 

the Tier 1 vehicles. 

The new sulfur model is applied multiplicatively in conjunction with other gasoline fuel 

effects in MOVES, and applies only 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 below 30 ppm for the legacy vehicles. 

Equation 1 shows the generic form of the new sulfur model. βs represents the sulfur 

coefficient. χs represents the actual in-use sulfur levels in the region being modeled. 

    S S sulfur adjustment A 1.0 S x S    base  Equation 1

The sulfur base (Sbase) 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 the Section 177 states that have adopted the LEV 

III programc, the sulfur base of 30 ppm and 10 ppm is used for the model years 2001 to 2016 and 

model years 2017 and later, respectively. 

The results of the final version 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 bag1-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 light 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 (Table 3) were used to develop the coefficients for heavy-duty 

gasoline vehicles, assuming that the catalyst response 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 vehicles 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 sulfur 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 NOX 

(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 NOX PM

Starts Running Starts Running Starts Running Starts Running 

Motorcycle 0 0 0 0 0 0 0 0 

Passenger Car, 

Passenger Truck & 

Light Commercial Truck 

0.00257 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 coefficients in Table 4 to populate the database table 

that stores fuel-effect equations in the MOVES database (“GeneralFuelRatioExpression”). This 

table allows the MOVES model to compute fuel effects based on the properties of any fuel 

contained in the “FuelSupply” and “FuelFormulation” database tables. 

Lastly, to estimate the impacts of the updates made 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 calendar years 2018 and 2030, using MOVES default 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 monoxide (CO), but showed more sensitivity to the 
9 

changes in sulfur levels for oxides of nitrogen (NOx). 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% 

NOx 9.5% 19.4% 7.4% 26.2% 

B. Gasoline Non­sulfur Fuel Effects (Model Year 2001 and later)   

 An important function of mobile source air pollution inventory models, including 

MOBILE6 and MOVES, is to account for the effects 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 models are still in use in MOVES to estimate fuel 

effects for vehicles manufactured prior to model year 2001, for example, equations from the 

Predictive Model are used to calculate fuel effects for total hydrocarbons 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 aldehydes7

--their 

applicability to vehicles employing more recent engine and emission control technologies has 

been questioned. Since the initiation of the MOVES project, it has become clear that an updated 

fuel-effects model representing Tier-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 partnership with the Department of Energy (DOE) and 

the Coordinating Research Council (CRC) to undertake the largest fuels research program 

conducted since the Auto/Oil program in the early 1990’s, aimed specifically at understanding 

the effects of fuel property changes on exhaust emissions on recently manufactured Tier 2 

vehicles. The resulting research program was dubbed the “EPAct/V2/E-89” program (or 

“EPAct” for short), with the three components of the label denoting 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 cars 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 properties (ethanol, aromatics, vapor pressure, and 

two distillation parameters: T50 and T90). The properties of the test fuels were not assigned to 
10 

represent in-use fuels, but rather to allow development of statistical models that would enable 

estimation of relative differences in emissions across the ranges of fuel properties expected in the 

U.S. fuel pool. A fuel matrix was designed for Phase 3 to span the ranges of the five selected 

properties seen in commercially available fuels. 

 An initial sample of 19 test vehicles was chosen with the intent of representing 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 range 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 emissions measured over three phases analogous to 

those in the Federal Test Procedure (FTP), at an ambient temperature of 75°F. In MOVES, the 

EPAct results are applied at temperatures higher and lower than this level, under an assumption 

that effects for fuels and temperature are independent and multiplicative. 

 Emissions measured include carbon dioxide (CO2), carbon monoxide (CO), total 

hydrocarbons (THC), methane (CH4), oxides of nitrogen (NOx), and particulate matter (PM2.5). 

In addition, hydrocarbons were speciated for subsets of vehicles and fuels, allowing calculation 

of derived parameters such as non-methane organic gases (NMOG) and non-methane 

hydrocarbons (NMHC). Speciation also allowed independent analyses of selected toxics 

including acetaldehyde, formaldehyde, acrolein, benzene and 1,3-butadiene. 

 Phase 3 data collection was completed in June 2010. Dataset construction and analysis 

was conducted between January 2010 and November 2012. This process involved ongoing 

collaboration among EPA staff, DOE staff and contractors, and CRC representatives. Following 

the completion of data collection, construction of the dataset involved intensive evaluation and 

quality assurance. The analysis involved several iterations between analysis and additional 

physical and chemical review of the data. Successive rounds of statistical modeling were applied 

to the data, to achieve several goals, including identification 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 analysis are extensively 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 applied 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) and high-level ethanol fuels (E85, 

fuelTypeID = 5). Note that the emission rates stored in “emissionRateByAge” for E85 are 
11 

identical to those for gasoline. Note also that the handling of the adjustments 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 emissions 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 model year 2001 and later. 

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

Emission Processes: Adjustments are developed and applied separately to running exhaust 

(processID = 1) and start exhaust emissions (processID = 2). 

Pollutants: The pollutants covered include 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 (NOx) NOx

111 Primary PM2.5 – Organic Carbon PM(OC)2

112 Primary PM2.5 – 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 wide variety of mathematical forms. These “fuel-effect ratio 

expressions” can include up to 32,000 characters and are stored in a database table dedicated to 

this purpose (“GeneralFuelRatioExpression”). A description of this table is shown in Table 7. 
12 

Table 7 Description of the Database Table “GeneralFuelRatioExpression” 

Field Description Values 

fuelTypeID Identifies fuel types as broad 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 emissions effects for a given fuel, it is necessary 

to first “center” and “scale” the properties for the fuel, also known as “standardization.” This 

process simply involves first “centering” the measured 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 statistics 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, standardization is performed using 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, quadratic and interaction terms are constructed from the Z scores 

for the linear terms, and the process is repeated. Using the quadratic term for ethanol as an 

example (etOH×etOH), the standardized value, denoted by ZZetOH×etOH, is calculated as shown in 

Equation 4, where mZetOHZetOH and sZetOHZetOH are the mean and standard deviation of the quadratic term 

constructed from the Z score for the linear effect. 

 

etOH etOH

etOH etOH etOH

etOH etOH

Z Z

Z Z

s

Z Z m

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. 

etOH Arom

etOH Arom etOH A

etOH eArom

Z Z

Z Z

s

Z Z m

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 

Matrix1

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, NOx and PM are calculated starting with 

“base emission rates” (meanBaseRate, meanBaseRateIM) 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 developed 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 application 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 allow estimation of emissions effects related to 

five fuel properties: ethanol content (%), 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 portrays the model for cold-start NOx, β denotes a model 

coefficient, ZetOH denotes a “standardized” fuel term for this property, and ZetOH×Arom denotes a 

“standardized” etOH×Arom interaction term.d Finally, the term sε

2 represents the residual error 

 

d

 Note that these coefficients apply to fuel properties that have been “standardized,” 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 apply 
15 

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 coefficients were employed for each pollutant; one set 

representing start emissions and a second set representing 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 the ratio of the emissions on the in-use fuel to those on the base 

fuel, as shown in Equation 7. 

































 

2

etOH Arom etOH Aro

T50 T50

Arom Arom

0 etOH etOH

0.5

exp

Emissions (g/mi) e









 

s

Z

Z

Z

Z

Xβ

Equation 6

 

    in use base

base

in use exp

exp

exp FuelEffect Xβ Xβ XβXβ      Equation 7

Table 9 shows the coefficients used in Equation 6 and Equation 7 for four individual 

pollutants, including total hydrocarbons (THC) and the criteria pollutants CO, NOx, and PM. 

The application of these models has been integrated into MOVES for the FRM analyses. For 

implementation in MOVES, Equation 7 is input directly into the “GeneralFuelRatioExpression” 

table. Note that the expressions in the table also incorporate adjustments 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 running exhaust emissions, respectively.e The coefficients can be 

understood as the change in the natural logarithm of emissions (e.g., ΔlnCO) associated with a 

change in the fuel property of 1.0 standard deviations. Because these coefficients apply to 

“standardized” fuel properties, as mentioned above, 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 example, for PM start emissions, changes in aromatics and T90 are 

very influential, and are positively related with PM emissions (e.g., when aromatics increase, PM 

increases). For THC start emissions, on the other hand, the negative sign of the coefficient 

indicates that the relationship is inverse, i.e., increases 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 NOx PM 

Start Running Start Running Start Running Start Running 

Intercept -0.8664 -4.6533 1.3466 -1.3893 -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 aldehydes, acrolein, benzene, ethanol and 1,3-butadiene, 

are not calculated from emission rates 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 from EPAct results were applied in a different 

way than described for the fuel-effects adjustments. Rather than estimating the change in 

emissions for one compound on two different fuels, the approach for toxics estimates the 

emissions of the toxic as a fraction of emissions 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 Xβtoxic, XαNMOG and Xθethane represent models for a selected toxic 

compound, NMOG and ethane, respectively, calculated by applying Equation 6 to each 

compound for a specified fuel, the toxic emissions as a fraction of VOC emissions is given by 

NMOG e

toxic

e e

e Toxic Fraction toxic Xα Xθ

Xβ

  f  Equation 8

For these compounds, the calculation shown in Equation 8 is applied in the 

“GeneralFuelRatioExpression” table. Table 11 and Table 12 show coefficients for statistical 

models for ethanol and the five toxics representing “cold start” and “hot-running” emissions, 

respectively. These models were developed using techniques similar to the models shown in 

Table 9, using speciated results obtained 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 resources 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 fewer 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 vehicles and fuels is termed the “reduced” fuel 

matrix, in contrast to the “full” fuel matrix as previously described (see Table 8). In addition, it 

is necessary to note that the fuel terms for models fit to the “reduced” design are standardized 

differently than those fit under the “full” design. The fuel property statistics for the reduced 

design are presented below in Table 10. 

 Coefficients for start emissions 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, representing 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 (H3CCH3), 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 running 
18 

emissions, respectively. Models for these two compounds were calculated in the same manner as 

those shown in Table 9. Note that models for 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 the 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 course to prevent the calculation and propagation 

of artifacts in the estimated fractions resulting 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 the toxic model in the numerator and the NMOG 

model in the denominator, as the vast majority 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 Matrix1

 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 

Acetaldehyde1 Formaldehyde1 Acrolein1 Ethanol1 Benzene2 1,3-

Butadiene2

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 2nd –order terms. SeeTable 10.

Table 12 Models representing “Hot-Running” Emissions for Selected Air Toxics1

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” Emissions under Full and Reduced Fuel Matrices 

Model term Full Fuel 

Matrix1

 Reduced Fuel 

Matrix2

NMOG NMOG3 Ethane4

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-matrix” 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 models 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” Emissions under the Reduced Fuel Matrix 

Model 

Term Model1

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 representing running emissions are not available for 

three compounds: acrolein, benzene and 1,3-butadiene. For these compounds, the relevant 

subsets of data were inadequate to allow model fitting. Therefore, for these compounds, running 

emissions were represented as constant fractions of VOC, with values 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 derived 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 numbers of available measurements, as shown in Table 15. The 

averaging was performed in two steps so that the vehicle(s) with the greatest numbers of 

measurements would not dominate the overall mean. 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 mean 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 0.669971 14.396 0.0465 

1 This value is a simple average of the means for all four vehicles, as listed above. 

The VOC fraction for acrolein was derived similarly (Table 16). For this compound results 

were available for five vehicles. Values for acrolein are considerably lower than for benzene, so 

results are expressed in μg, rather than mg. The resulting fraction 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 (μg) VOC (μg) 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 8.86961 11,578 0.0007661 

1 This value is a simple average of the means 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” that 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 measurements on nine Tier-2 certified vehicles 

on fuels with benzene levels ranging 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 fbenzene, a value modified to 

account for benzene levels in different fuels (f*benzene) is calculated as shown in Equation 9, 

where xbenzene is the benzene level for the fuel modeled (weight percent), A is the mean benzene 

level in the EPAct exhaust program fuel set (0.66 weight percent), and B is a coefficient, taking a 

value of 0.24. 

   benzene benzene ben

*

benzene f  x  A B f  f Equation 9
23 

 Similarly, given the importance of olefins to estimation of emissions for 1,3-butadiene, and 

that the EPAct exhaust program study design did not incorporate olefins as a factor, we 

considered it appropriate to develop a post-model adjustment explicitly accounting for olefin 

level. This adjustment was derived by varying olefin levels in the Complex Model15 and fitting a 

polynomial trend to the results.16 Starting with an unadjusted toxic fraction for 1,3-butadiene 

(fbuta), the modified fraction f

*

buta is calculated using Equation 10, in which xolefin 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

Ax Bx C 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 include 2,2,4-trimethylpentane, ethylbenzene, n-hexane, 

methyl-tert-butyl ether (MTBE), propionaldehyde, styrene, toluene, and xylenes. For these 

pollutants, toxic-to-VOC ratios were derived from 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 AEO2013f

, and thus, it was necessary 

to update the MOVES model to add this capability.g

For model year 2000 and earlier gasoline light-duty vehicles, the toxic fractions for vehicles 

running on fuels containing 70-100% ethanol were derived 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 (more 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 content above 85%, the same toxic fractions 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 for compounds with pollutantID 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) 0.0291 
25 

Table 18 Description of the Database Table “ATRatioNonGas,” as Applied to Light-Duty Vehicles 

Field Description RelevantValues 

polProcessID Identifies the pollutant (1st 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-duty vehicles, the impact of E85 on emissions 

for criteria pollutants (THC, CO, NOx), 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 Research 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 and E85 blends. Only the vehicles tested on 

both E10 (E6) and E85 were included in the analysis. Numbers of vehicles in each program are 

summarized in Table 19. 

The preliminary analyses showed consistent emission trends across datasets 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.21
26 

Table 19 Numbers of vehicles included in the analysis 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 statistically significant differences in emissions 

were observed between E10 and E85 (α=0.05) and thus, the base emission rates, IM factor, and 

IM coverage are assumed to be the same as gasoline 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 representing the ratios of emissions on an E85 blend to 

those on an E10 blend were derived using Equation 11, instead of deriving toxic fractions. The 

adjustment factor was set to zero when the differences in emissions were not statistically 

significant (i.e., acrolein). 

Equation 11

The resulting adjustment factors are shown in Table 20, and are stored in the database 

table, “GeneralFuelRatioExpression”, for fuelTypeID = 5. Note that in the GFRE, the 

adjustments shown in Table 20 are 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) 0 
27 

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 polyaromatic hydrocarbons (PAH) emissions from 

vehicles running on E85. Thus, the toxic fractions for PAH were estimated by multiplying the 

fractions for E0 fuels by the fraction of gasoline 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 result 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 fraction for the gaseous phase 

relative to VOC, and that for the particulate phase relative to organic carbon less than 2.5 

microns (OC2.5). 

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 205992 8.91E-05 1.51E-02 1.16E-02 

Benzo(k)fluoranthene 49 207089 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 193395 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 level contamination of fuel and engine oil, as 

well as attrition from exhaust emission components, there is no way to estimate the metal 

emissions for vehicles running on E85 or E70 fuel in the absence of data. Thus, the metal 
28 

emission rates were assumed to remain unchanged from those applied to vehicles with earlier 

fuel delivery and emissions control technologies. 

No emissions data exist for dioxin and furan emissions 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 ratios are given in Table 

22. 

Table 22 Emission Factors for Dioxins and Furans, 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 ethanol, ranging from 70 to 100%, 

the toxic fractions were developed based on the results of two-day 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 test, 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, vapor-venting and refueling-spillage emissions 

processes. 
29 

Table 23 Toxic Fractions for Evaporative Emissions, 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 fraction 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 capable of fueling on E85. This results in a 

population fraction of 6.87% E85-capable vehicles. Next, the fraction of E85 in the total gasoline 

fuel pool must also be calculated. Motor gasoline and E85 volumes are reported in AEO2013 as 

energy and are converted to volumes for our use. 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 vehicles 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 fraction exists in the “fuelUsageFraction” table for calendar years 2010 

to 2030. 

III. Evaporative Emission Updates

Evaporative emissions account for a substantial portion of the gaseous hydrocarbon 

inventory. Over the last several years, EPA has invested in a major research program to better 

understand “real-world” evaporative emissions from the current fleet of light duty vehicles. 

Much of this data was incorporated into the modeling for the Tier 3 proposal, which is 
30 

summarized in the MOVES docket memo for the NPRM. Specific revisions included changes to 

canister breakthrough emissions for vehicles remaining 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 particular, 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 changes have had a substantial impact on our estimates of vaporventing emissions. The new findings and analysis are detailed in two23,24 technical reports. 

Details of the modeling are substantially different from previous versions and are therefore 

documented in a separate memorandum to the docket for evaporative emissions calculations for 

MOVEST3FRM.25 This section of the docket memo summarizes the way the new information 

was incorporated in MOVEST3FRM. 

A. Changes to Vapor Leak Frequencies

Vapor leak frequencies in the Tier 3 NPRM analysis were based primarily on data from field 

research conducted at the Ken Caryl I/M station 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 reviewer 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 analysis addressing the peer 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 age and associated leak 

prevalence, which ultimately led to further reducing the rate at which leaks develop over the life 

of Tier 2 and later vehicles. The distribution of leak sizes shifted towards smaller leaks causing a 

reduction in emissions from leaks. More details 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 differed 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, we 
31 

updated the hot-soak base rates. The rates are now calculated as a weighted average of leaking 

and non-leaking vehicles. The proper weighting is performed using the leak frequencies for each 

specific model-year and age group. Details are available in the MOVES evaporative emissions 

docket memorandum referenced above. 

C. Changes to Vehicle Trip Patterns

The MOVESDB20121002k_truncatedGFRE database used for the 2018 and 2030, reference 

and control case, respectively, has 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 MOVES2010b. This difference resulted in the 2007 

baseline run having fewer light-duty multi-day parking 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 incorporated fuel and temperature effects into the calculation of 

running-loss vapor emissions until it was developed and implemented in MOVEST3FRM. The 

coefficients used in the determination of this effect 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 vapor pressures of 7 psi and 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 adjustment 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 vehicle in response to a MIL than 

a driver in a non-I/M (non-enforcing) area. MOVES 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 reducing greenhouse gas (GHG) emissions 

from light-duty (LD) vehicles starting with model year (MY) 2017 (referred to here as the “2017 

LD GHG” standards).29 The MOVES version also did not include emission changes expected 
32 

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 notably, 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 apply indefinitely. The final standards are projected to result in an 

average industry fleet-wide level of 163 grams/mile of carbon dioxide (CO2) in model year 2025, 

which is equivalent to 54.5 miles per gallon (MPG) if achieved exclusively through fuel 

economy improvements. 

For MOVEST3FRM, we accounted for the 2017 LDGHG 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 CO2 g/mile rates from the rulemaking 

regulatory classes (car/truck) to the regulatory 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 gaph of approximately 25% (multiplying the CO2 rates by 1.25). 

Additionally, we revisited the MY 2008-MY2016 rates, and changed the ratio of reduction 

applied to the base rates between 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 starts, likely underestimates energy consumption from starts. For 

MOVEST3FRM, the reduction applied to starts was revised to be half of that applied to running.i 

Because energy consumption at start is a small fraction of the total, this increase has little impact 

on total CO2 emissions; however to keep the composite emissions the same, we also decreased 

fuel consumption for running emissions by about one percent. 

Similar to the MOVES2010a analysis, we calculated ratios to produce the emission rates 

projected in the rulemaking, and then used these values to apply 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 consumption 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 CO2 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 alternative fuels where we assumed that the 

fuel consumption rate for each alternative fuel was equivalent to gasoline energy consumption 

multiplied by the CO2 density adjustment contained in MOVES. In the absence of data, we 

assumed that these fuels all had to meet the same CO2 standard. We also added 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 energy consumption rates reduce CO2, SO2, and SO4 emissions 

and evaporative emissions from refueling. The changes were made for both the reference and 

control case, so they have little impact on the reductions predicted as a result 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 through 2018. The levels set by the standards are 
34 

assumed to apply indefinitely after 2018 unless changed by a future rulemaking. The program 

breaks the diverse truck sector into three distinct categories: 

 Line-haul tractors (largest heavy-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, refuse trucks, concrete mixers, ambulances...) 

The program set separate standards for engines and vehicles and ensures improvements in 

both. It also sets separate standards for fuel consumption, CO2, N2O, CH4 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 coefficients and vehicle 

masses for use in MOVES operating mode generation, (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 primarily 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 introduction 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 idle rates for heavy duty 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 CO2 Emission Rates from this Program 

GVWR Class Fuel Model Years CO2 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 tractor and vocational vehicle tire rolling 

resistance, coefficient of drag, and weight reductions expected from the technologies which 

could be used to meet the standards. The value 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 short-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 vocational vehicle standards and not show any 

aerodynamic or weight improvement. The vocational 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 represents 19.7 percent of short-haul 

tractor population. The weight reduction 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 percent based on the data derived 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 corresponding source types in the MOVES 

“sourceUseType” table. For details on these assumptions, please see the HDGHG rulemaking 

documents.32

Table 26 Estimated Reductions in Rolling Resistance 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 MOVEST3FRM through new aerodynamic coefficients and 

weights. In MOVES2010b, the heavy-duty sourcetype coefficients were based on a set of 

published estimates.l However, for MOVEST3FRM, we revised 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. Because the heavy duty emission standards are per unit of work, 

and these changes affect the amount of work that is done, the changes 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 process, “Auxiliary Power Unit” (APU). This 

process is calculated only for the heavy-duty long-haul combination truck source type. The APU 

emission rates are consistent with HDGHG 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 penetration of APUs in 2010-2014 model years and 100 

percent penetration in 2014-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 

CH4 0.3 % of THC 

PM2.5* 1.8 g/hr 

OC 1.35 g/hr 

EC 0.45 g/hr 

SO2 0.0188 g/hr 

CO2 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 vehicles with an APU. 
37 

Unlike the HHD standards, the HD pickup truck standards are evaluated in terms of grams of 

CO2 per mile or gallons of fuel per 100 miles. Since nearly all HD pickup trucks and vans will be 

certified on a chassis dynamometer, the CO2 reductions for these vehicles are not represented as 

engine and road load reduction components, but as total vehicle CO2 reductions. Table 29

describes the estimated expected changes in engine and vehicle technologies. 

Table 29 Estimated Total Vehicle CO2 Reductions for HD Pickup Trucks and Vans 

GVWR 

CLASS 

FUEL MODEL 

YEARS 

CO2 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 CO2 reductions and HD pickup/van total vehicle CO2 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 improvements in tire rolling resistance and drag 

coefficient were applied in the “sourceUseType” table; the percentage reductions 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 later use APUs during extended idling. 

These changes to heavy-duty energy consumption rates reduce CO2 emissions and also 

reduced evaporative (spillage) emissions from refueling, and SO2 and SO4 emissions. In 

addition, the changes to the road load coefficients have an impact on the operating mode 

distribution used to calculate all emissions. This causes an emission 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 reductions predicted as a result of the Tier 3 rule. 

C. Changes to account for California’s LEV III and 177 States

California finalized LEV III requirements 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 states differs from Tier 3 as 
38 

the latter program has an additional Leak Standard with an In-Use Vehicle Program (IUVP) 

enforcement throughout the vehicle’s life. MOVEST3FRM assumes different rates of vapor leak 

prevented and rate of control from I/M and OBD programs for LEV III/177 States and for Tier 3. 

Additional modeling details can be found in the 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 Tier 3 FRM air quality runs, the updated 

future year VMT and vehicle population estimates were incorporated when the MOVES 

emission rates were applied using the SMOKE-MOVES 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 database, 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 “HPMSVTypeYear” and the SalesGrowthFactor in the 

“SourceTypeYear” tables in the MOVES database 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 (CDBs) 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 exempt the newest model years from the program. 

When the CDBs used for 2018 and 2030 were projected using the 2007 CDB values, the model 

year coverages in the “IMCoverage” tables were not updated. This inadvertent omission caused 

more model years to be exempted from the benefits of the I/M program than intended by the 

program design. Since the exemption model years 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 reference and control cases used in the air quality 

analysis for those model years inappropriately exempted in counties with I/M programs. 

Out of the 3,109 counties included in the air quality 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 affected by this issue, 

we expect minimal impact on the estimated 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 generate emission rates used for all counties in the 

group. 

The State of Virginia adopted early California Low Emission Vehicle standards, which 

require the use of an alternate emission rate table in order to account for the differences between 

the emission performance of vehicle certified to the California standards and 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 county did not use emission rates reflecting the 

early LEV standards when calculating the inventories. These “early” LEV standards only affect 

the 1999 and 2000 model years. Virginia reverted 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 inventory 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 exhaust emission rates using a set of general fuel 

ratio expressions (GFRE) stored in the MOVES default database. These effects can differ for the 

different vehicle technologies. A special set of these GFRE were developed for vehicles 

certified using California standards and were used in all states which had adopted the California 

standards in preference to federal standards for highway vehicles. However, although the states 

of Virginia and New Hampshire and the District of Columbia had adopted the early California 

Low Emission Vehicle standards in the 1990’s, these 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 2004 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 and the differences in the effect of changes in 

fuel properties is negligible. The national 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 standards containing 10% ethanol. 

 However, for the FRM analysis, we addressed the question of changing certification fuels by 

incorporating fuel adjustments in the “GeneralFuelRatioExpression” calculations to account for 

the adoption of Tier 3 certification fuel starting in MY2017. One such adjustment accounts for 
40 

reduction in fuel sulfur to 10 ppm and is calculated 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 existing adjustments using the EPAct 

models and assigning the Tier 3 certification fuel as the “base” fuel for MY2017 and later. With 

these adjustments in place, Tier 3 vehicles operating on in-use fuel with 10 ppm sulfur would 

show no sulfur effect, as the in-use and certification levels match. Similarly, Tier 3 vehicles 

operating on an in-use fuel with the same non-sulfur properties as the Tier 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 differing from the E10 certification fuel are adjusted 

appropriately relative to the certification fuel. 
41 

References

 

1

 U.S. EPA, 2011. Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions 

Simulator (MOVES2010): Final Report. (EPA-420-R-11-011) 

http://www.epa.gov/otaq/models/moves/documents/420r11011.pdf. 

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. 

7

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

Results. 

8

 U.S. EPA. EPAct/V2/E-89: Assessing the Effect of Five Gasoline Properties on Exhaust Emissions from LightDuty Vehicles certified to Tier-2 Standards: Final Report on Program Design and Data Collection. EPA-420-R-13-

004. Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI; National 

Renewable Energy Laboratory, Golden, CO; Coordinating Research Council, Alpharetta, GA. April, 2013. 

9

 U.S. EPA. Assessing the Effect of Five Gasoline Properties on Exhaust Emissions from Light-Duty Vehicles 

certified to Tier-2 Standards: Analysis of Data from EPAct Phase 3 (EPAct/V2/E-89). Final Report. EPA-420-R13-002. Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. April, 

2013. 

10 U.S. EPA. 2011. Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions 

Simulator (MOVES2010). EPA-420-R-11-011. Assessment and Standards Division, Office of Transportation and 

Air Quality, Ann Arbor, MI. August 2011. 

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 Pollutants 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 Adjustments.xlsx.” EPA Docket EPA-HQ-OAR-2011-0135. 

15 U. S. EPA. 1993. Final Regulatory Impact Analysis for Reformulated Gasoline. 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 Report 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. 104th Air and Waste Management Association Annual Conference and 

Exhibition, 2011, #118, 3053-3058. 

21 Choi, D. Modeling of E85, April, 2013. http://www.epa.gov/otaq/models/moves/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 Report No. E-80, August, 2011. Available at www.crcao.org

23 DeFries, T., Palacios, C., Weatherby, 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 2nd Peer Review of “Estimated Summer Hot-Soak 

Distributions for Denver’s Ken Caryl I/M Station 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)