Fuel Effects on Exhaust Emissions
from Onroad Vehicles in MOVES3
gPk United States
Environmental Protection
^1 Agency
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Fuel Effects on Exhaust Emissions
from Onroad Vehicles in MOVES3
This technical report does not necessarily represent final EPA decisions
or positions. It is intended to present technical analysis of issues using
data that are currently available. The purpose in the release of such
reports is to facilitate the exchange of technical information and to
inform the public of technical developments.
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
&EPA
United States
Environmental Protection
Agency
EPA-420-R-20-016
November 2020
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Contents
1 Introduction 4
2 "Base" and "Target" Gasolines 6
2.1 Base Gasoline 7
2.2 Target Gasolines 7
2.2.1 Relevant Database Tables 8
3 Fuel Sulfur Effects 9
3.1 Introduction 9
3.2 The MOBILE6 Sulfur Model (M6Sulf) 10
3.2.1 Data Used in Developing the M6Sulf Model 10
3.2.2 Analysis of Short-Term Sulfur Effects 12
3.2.3 Analysis of Long-Term Sulfur Effects 19
3.2.4 Application in MOVES 22
3.3 Tier 2 Low Sulfur Model (T2LowSulf) 29
3.3.1 B ackground 29
3.3.2 Data Used in Developing the T2LowSulf Model 29
3.3.3 Data Analysis and Results 32
3.3.4 Application in MOVES 48
3.4 Results: Sulfur Effects in MOVES3 50
4 Use of the Complex Model (for CO Emissions) 53
4.1 Overview of the Complex Model 53
4.2 Application of the Complex Model 56
5 Use of the EPA Predictive Model (HC and NOx Emissions) 58
5.1 Data Used in Developing the EPA Predictive Model 58
5.2 Analytic Approaches 58
5.2.1 Standardization of Fuel Properties 59
5.3 Application in MOVES 62
6 Gasoline Fuel Effects for Vehicles certified to Tier 2 Standards (EPAct Models: HC, CO,
NOx, PM) 63
6.1 Introduction: the EPAct Project 63
6.2 Analysis and Model Fitting 68
6.2.1 Standardizing Fuel Properties 68
6.2.2 Fitting Procedures 70
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6.3 Scope and Implementation 76
6.4 De-standardization of Model Coefficients 77
6.4.1 De-standardizing Linear Terms 77
6.4.2 De-standardizing 2nd-order Terms 78
6.5 Fuel Effect Adjustments 81
6.6 The Database Table "GeneralFuelRatioExpression" 83
6.6.1 Examples 83
7 High-Level Ethanol Blends (E85) 86
7.1 Introduction 86
7.2 Data Analysis and Results 87
7.3 Application in MOVES 93
7.3.1 Example MOVES Results 96
8 Biodiesel Blends 99
8.1 Pre-2007 Diesel Engines 99
8.2 2007 and later Diesel Engines 99
8.3 Modeling Biodiesel 99
9 Sulfate and Sulfur Dioxide Emissions 100
9.1 Introduction 100
9.2 Sulfate Calculator Summary 101
9.3 Gasoline Vehicles 104
9.3.1 Pre-2004 Light-duty Gasoline Vehicles 104
9.3.2 2004 and later Light-duty Gasoline Vehicles 105
9.3.3 High Ethanol Blend (E85) Gasoline Vehicles 106
9.3.4 Motorcycles Heavy-duty Gasoline Vehicles 106
9.4 Diesel Vehicles 106
9.4.1 Pre-2007 Diesel Vehicles 106
9.4.2 2007 and Later Technology Diesel Vehicles 107
9.5 Compressed Natural Gas 107
9.6 Example Comparisons 108
9.7 Sulfur Dioxide Emissions Calculator 110
9.8 Summary 112
Appendix A Derivation of the Sulfate Equation and Parameters 114
Appendix B Estimation of Weight % Oxygenates for the Complex and Predictive Models 129
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Appendix C High Ethanol (E85) Fuel Adjustments and HC species VOC and NMOG 130
10 References 133
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1 Introduction
The MOVES model estimates emissions inventories for different vehicle types operating on
several fuels. Fuels in the model include gasoline, diesel, compressed natural gas (CNG),
liquified petroleum gas (LPG), "Ethanol (E-85)" and "electricity." The "Ethanol" category
includes blends of ethanol and gasoline in which the ethanol fraction exceeds 70 vol.%. This
document discusses adjustments or other calculations designed to account for the effects of
changes in fuel properties on exhaust emissions of THC, CO, NOx and PM. Similar calculations
applied to emissions of air toxics and evaporative emissions are discussed in separate reports.1'2
Clearly, fully electrified vehicles do not emit exhaust pollutants, and will not be further discussed
in this report.a Note that MOVES3 estimates emissions using LPG only for the NONROAD
component of the model.b
The different fuels are handled with widely varying levels of detail and sophistication, depending
on factors such as the prevalence of use and availability of data. Given its historic and current
importance in the market and in inventory modeling, the treatment for gasoline is the most
extensive and detailed. MOVES estimates "gasoline" emissions for gasoline blends with ethanol
up to 15 vol.%. The treatment for ethanol (E-85), diesel and CNG is much simpler.
Estimation of emissions from gasoline plays a very important role in MOVES. Gasoline plays a
substantial role in transportation, both in terms of the numbers of vehicles on U.S. roadways, and
in terms of volumes consumed. Gasoline is also important in terms of historic and current
policies and control measures, which often incorporate features involving control of fuel
properties or content. Policies and programs that MOVES incorporates include reformulated
gasoline (RFG), local fuel requirements, i.e., the so-called "boutique" gasolines, oxygenate-
blending requirements, and sulfur-control requirements. Control of gasoline vapor pressure is
also important, particularly for evaporative emissions, but is not discussed in this report, which is
concerned with exhaust emissions. Estimation of evaporative hydrocarbons is discussed in a
separate report.2
Ethanol mandates are reflected in the model, including the renewable fuels standards (RFS1 and
RFS260). The MOVES fuel supply currently reflects the fact that most gasolines in the U.S.
contain approximately 10 vol.% ethanol. In addition, MOVES includes the capability to model
the fuel effects of gasolines containing up to 15 vol.% ethanol, i.e., "El 5" fuels; however, these
fuels are intended for specific modeling scenarios and are not included in the default fuel supply.
Also, although it was used in some historical gasoline blends, MOVES does not model emissions
from gasoline with methyl-tertiary-butyl-ether (MTBE), but instead substitutes equivalent
ethanol blending to account for oxygenate requirements in years where MTBE was present. The
construction and composition of the default fuel supply is described in greater detail in a separate
report.3
Sulfur requirements incorporated in the gasoline supply include the Tier 2 and Tier 3 emissions
standards, which imposed reductions in the sulfur content of gasoline. Under the Tier 2 program,
a MOVES does not model upstream or lifecycle emissions.
b It is visible in the fuelType table and in the GUI due to sharing of tables between the onroad and NONROAD
components of the model.
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maximum and average sulfur levels were reduced from 300 to 80 and 120 to 30 ppm from 2004
and 2006, respectively.4 Under the Tier 3 program, further reductions to an average gasoline
sulfur level of 10 ppm were achieved starting in 2017.5
For gasoline fuels, the model applies "adjustments" to account for changes in selected fuel
properties in the geographic area(s) and time periods covered in MOVES runs. The properties
considered to be relevant include fuel-content parameters, as well as bulk properties. Fuel-
content variables include levels of oxygenate, ethanol, olefins, aromatics and sulfur. Bulk
properties include vapor pressure, distillation properties, expressed as temperatures (T50, T90) or
as volumes evaporated at specific distillation temperatures (E200, E300).
The basis for calculating adjustments is the differences between "base" emissions, assumed to
reflect the properties of a specific reference fuel (the typical fuel in-use during base rate emission
collection), and "target" emissions, intended to reflect the set of "target" fuels in the areas and
periods covered in a MOVES run. This "base" reference fuel has been updated in MOVES3 to
reflect more recent exhaust emission testing and analysis conducted since the release of the
previous model version. The concept and updated definitions of the base gasoline properties are
discussed below in Section 2.
During a run, MOVES combines emission rates and vehicle activity, e.g., vehicle-miles traveled,
to generate the "base" emissions estimate, prior to applying adjustments for other factors, such as
humidity, temperature and fuel properties. With respect to fuel properties, the "base estimate" is
assumed to reflect the properties of an associated "base" gasoline. To indicate this aspect of
model design, the emission rates stored in tables such as emissionRate or emissionRateByAge are
designated as "mean base rates."
Adjustments for sulfur are calculated separately and applied independently of those for other
properties. For pre-2001 model year gasoline vehicles, the sulfur adjustments are calculated
using an approach adapted from the MOBILE6 model, here designated as the "M6Sulf' model.
The adaptation of this model for use in MOVES, incorporating "short-term" and "long-term"
sulfur effects, is described Section 3.2. For 2001 and later model year gasoline vehicles,
including those certified to Tier 2 standards, we have applied recent research to develop simple
fractional adjustments for vehicles operating on gasolines with sulfur content less than 30 ppm.
The model, designated as the "T2LowSulf' model, is described in Section 3.3.
For other non-sulfur properties, approaches to calculating adjustments also differ for different
subsets of vehicles.
For all gasoline vehicles manufactured prior to MY 2001, we apply the "Complex Model" to
calculate adjustments for CO and the "EPA Predictive Model" to calculate adjustments for THC
and NOx. The Complex and Predictive Models, described in Chapters 4 and 5, account for the
effects of selected fuel properties, including oxygenates, aromatics, olefins, vapor pressure and
distillation parameters. While broadly similar in their overall approaches, the data and analysis
methods used in developing these models differ in important respects. The underlying datasets
were composed of cycle aggregate emissions results, and thus, we calculate and apply
adjustments that are applied to both start and running exhaust emissions.
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For all gasoline vehicles manufactured after 2001, we apply a set of statistical models developed
from the results of the "EPAct Phase-3 Project," a large-scale controlled experiment conducted
under a congressional mandate in the Energy Policy Act of 2005 (EPAct). Based on the results of
this project, we apply adjustments for THC, CO, NOx and PM2.5, although in this case, distinct
adjustments are applied to start and running emissions. The design and analysis of these data
incorporated advances in methods developed since development of the Complex and Predictive
models. The development and application of these adjustments are described in Chapter 6.
In MOVES, fuel sulfur plays yet another role in estimating emissions of sulfate (SO4) as a
component of the non-elemental-carbon component of PM2.5. The model also accounts for the
contribution of lubricating oil to sulfate emissions.
The estimation of sulfate components is performed by the "sulfate calculator." The calculator is
designed to estimate sulfate emissions for user-specified fuels during model runs, by relating
them to a set of "reference sulfate fractions" associated with "reference fuel sulfur levels." The
sulfate contribution from lubricating oil is assumed to be independent of the fuel sulfur level.
The specific assumptions applied to gasoline fuels are described in Section 9.3. In addition,
MOVES estimates emissions of sulfur dioxide (SO2) as a function of gasoline consumption and
sulfur level. Unlike the sulfate calculation, the SO2 calculation assumes that all emissions are
contributed by the fuel. As with the sulfate calculation, the SO2 calculation uses the same
structure for all fuels. Assumptions specific to gasoline are shown in Table 9-3.
Lastly, fuels containing 70 to 85 vol.% ethanol (E85) have been available for many years and
their use as transportation fuels has been growing. Vehicles designed to run on either gasoline or
"high-level" ethanol blends are designated as flexible-fuel or "flex-fuel" vehicles (FFVs).
MOVES estimates emissions from FFVs running on fuels containing 70 to 85 vol.% ethanol.
The algorithm for estimating the effects of E85 on emissions is described in Section 7.
Some sections of a draft version of this document underwent external peer review. The draft
reports, peer reviewer comments, the Agency's responses, and related peer-review for the
updates made to MOVES20146 and MOVES37 are provided on EPA's Science Inventory
webpage.
2 "Base" and "Target" Gasolines
As previously described, the concept of "base" and "target" fuels is applied to gasoline fuels in
the calculation of fuel adjustments using the Complex Model, EPA Predictive Model and the
EPAct models (excluding the Tier 2 Sulfur Model, see Section 3.3.4). The research and analysis
underlying these adjustments are described in Chapters 4, 5, and 6, respectively.
Fuel adjustments are designed to represent differences between "base" and "target" emissions.
"Base" emissions are emissions assumed to reflect a default set of conditions, including
temperature, humidity and fuel properties. A "base gasoline" is defined as a set of selected
gasoline properties assumed to be associated with, and implicit in, estimates of "base" emissions.
A "base" emissions estimate is the result of a calculation in which base emission rates, i.e., from
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the emissionRateByAge table, are combined with appropriate measures of activity, i.e., vehicle
miles traveled or numbers of vehicle starts, prior to the application of adjustments for
temperature, fuel properties or other factors.
MOVES3 uses a single base fuel for adjustments made based on the Complex, Predictive, and
EPAct models. This base fuel was updated in the MOVES3 model to better represent the fuels
seen in-use as part of the study deriving base emission rates.8 The properties of this fuel are
defined in the database table BaseFuel, and are further described in sub-section 2.1.
2.1 Base Gasoline
For gasoline, MOVES3 uses a single base fuel for the calculation of fuel adjustments for non-
sulfur properties. This fuel is assumed to represent the "typical" in-use gasoline seen in the
Denver metropolitan area between calendar years 2009 and 2017. The emission rates for
gaseous emissions from light-duty vehicles are based on random evaluation samples from the
Denver Inspection and Maintenance (I/M) Program during this time period and from Denver
remote sensing data (RSD) in a similar time period. The development of these "I/M reference
rates" (meanBaseRatelM) is described in detail in a separate report.8 Because fuel properties for
individual vehicles in the I/M lanes and RSD testing are unknown, we assume that the
"averaged" fuel properties, based on refinery batch data in the same area during the same time
period, are representative and can be associated with the average emission rates. The properties
of this fuel are shown in Table 2-1 below and are represented by fuelFormulationID 99 in the
MOVES3 fuelFormulation database table.0
2.2 Target Gasolines
The "target" gasoline is the gasoline which is to be evaluated for its effect on emissions, i.e., the
fuel(s) assigned to the areas and periods covered in specific MOVES runs. The properties of
target gasolines vary by county, year, and month. The MOVES database contains a set of fuel
formulations and associated fuel market-share fractions for each county in the United States, for
each month and for calendar years 1990 and 1999 through 2060. In addition to the default fuel
formulations, the user may generate custom fuels through the "Fuel Wizard" feature. The
development of the fuel supply tables and the "fuel wizard" is described in a separate document.3
0 Although the MOVES3 baseFuel database table also contains base fuels with fuelFormulationlDs 96 and 98 for
model years 2001-2050, this year range has been entirely superseded by the fuel adjustment models contained in the
generalFuelRatioExpression database table and thus the properties of these two fuels do not have any effect on
model results (see Section 6.6).
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Table 2-1. Properties of the MOVES3 Base Gasoline.
Fuel Property Name
Fuel Property Value
Fuel Sub-Type
E-10 (fuelSubtypelD 12)
fuelFormulationID
99
RVP (psi)
8.8
Sulfur Level (ppm)
30.0
Ethanol Volume (%)
10.0
Aromatic Content (%)
25.77
Olefin Content (%)
8.44
Benzene Content (%)
0.65
E200(%)
47.61
E300 (%)
84.89
T50 (°F)
212.3
T90 (°F)
321.7
Volume to percent Oxygen (%)
0.3653
2.2.1 Relevant Database Tables
The database tables listed below are relevant to the calculation of the fuel adjustments described
in this report:
BaseFuel: this table contains properties for the base fuel used by MOVES3 in calculation of fuel
adjustments, as shown in Table 2-1 above.
FuelEngTechAssoc: This table stores associations of fuel type and engine technology that apply
to each sourceType.
FuelModelName: This table identifies the individual statistical models used in applications of the
Complex and EPA Predictive Models for CO and air toxics. The applications of these models in
estimation of air toxic emissions are discussed in a separate report.1
fuelModelWtFactor \ Contains sets of factors used to weight the results of the various individual
equations used in the application of the Complex Model. See Chapter 4.
FuelParameterName: This table defines the various fuel parameters included in MOVES
calculations.
GeneralFuelRatio: This table is empty by design; it is populated during a model run.
GeneralFuelRatioExpressiorr. this table contains mathematical expressions that calculate some
of the fuel adjustments described in this chapter. It is described in greater detail in 6.6.
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The additional tables listed below are described in the Fuel Supply report:3
FuelFormulation,
FuelSupply,
RegionCounty,
Fuel UsageFraction,
FuelWizardFactors,
ElOFuelProperties.
3 Fuel Sulfur Effects
3.1 Introduction
Fuel sulfur content has long been understood to affect the performance of emission after-
treatment catalysts in light-duty vehicles, where the sulfur and its oxides occupy active precious-
metal sites and oxygen storage materials, reducing the catalyst's efficiency in removing
pollutants. For light-duty vehicles, "three-way," or "oxidation-reduction" catalysts play a major
role in reducing pollutant concentrations in exhaust streams. Catalysts contain precious metals
and metal oxides to selectively oxidize hydrocarbons and carbon monoxide and reduce nitrogen
oxides in the exhaust gases. Sulfur oxides from fuel combustion preferentially bind to active
sites in the catalyst, inhibiting their ability to participate in the intended conversion reactions (a
phenomenon often referred to as "sulfur poisoning"). The amount of sulfur retained by the
catalyst is a function of the type and arrangement of active materials and coatings within the
catalyst, its operating temperature, as well as the air-to-fuel ratio and concentration of sulfur in
the exhaust gas.9'10
Modern engines operate with rapid rich-lean oscillations that maintain the proper oxidation-
reduction condition of the catalyst. Under typical driving conditions, however, a non-zero
equilibrium level of sulfur is retained, which can accumulate over time. Regular operation at
high temperatures under net reducing conditions can release much of the retained sulfur oxides
from the catalyst and can mitigate the effects of accumulated sulfur on catalyst efficiency.
However, producing these conditions at sustained and/or regular intervals may accelerate thermal
degradation of the catalyst and may also raise other challenges for emission control and fuel
economy. Additionally, failures to maintain high catalyst temperatures (e.g., due to cold
weather, extended idle or rich operation), can severely impair the effectiveness of the catalyst in
converting the products of combustion, leading to increases in emissions relative to "clean"
catalysts.
This chapter describes how MOVES adjusts exhaust emissions of hydrocarbons (HC), carbon
monoxide (CO), and nitrogen oxides (NOx) in response to varying levels of fuel sulfur in
gasoline. Because the quantity of sulfur present on the catalyst at any given time is primarily a
function of operating temperature and the fuel sulfur level, the effects of gasoline sulfur content
are modeled as though they are independent of the effects of other fuel properties.
Note that MOVES assumes that there is no direct impact of fuel sulfur on criteria emissions from
diesel vehicles. Note also that emissions of sulfate (SO4) and sulfur dioxide (SO2) are discussed
in Chapter 9.
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MOVES includes two separate sulfur effects models. The two models are the "MOBILE6 Fuel
Sulfur Model" (M6Sulf) and "Tier 2 Low Sulfur Model" (T2LowSulf). The M6Sulf model
applies to (1) all model years for sulfur levels above 30 ppm, and (2) pre-2001 model years for
sulfur level equal to and below 30 ppm. Section 3.2 details the M6Sulf model algorithm, as well
as the underlying data and analyses, and discusses the minor changes and assumptions applied to
adapt the M6Sulf model into the MOVES framework.
The T2LowSulf model applies only to 2001-and-later model year vehicles operating on sulfur
levels equal to or below 30 ppm. Section 3.3 describes how the results of a study specifically
designed to measure sulfur effects on Tier 2 gasoline vehicles were applied in MOVES.
3.2 The MOBILE6 Sulfur Model (M6Sulf)
The M6Sulf model was developed through the analysis of several studies examining the effect of
sulfur on exhaust emissions, described below. Vehicle technologies included in the analysis
were Tier 0, Tier 1, Low-Emitting Vehicles (LEV), and Ultra Low-Emitting Vehicles (ULEV).
For additional details, see "Fuel Sulfur Effects on Exhaust Emissions for MOBILE6."11
3.2.1 Data Used in Developing the M6Sulf Model
In developing the M6Sulf model, we relied on the following data sources:
Auto/Oil Phase I Sulfur Study—As a part of the extensive testing program, ten 1989 model year
light-duty gasoline vehicles (representing a subset of the fleet tested in all the other Auto/Oil
studies) were tested using two fuels with sulfur levels of 466 and 49 ppm (other fuel parameters
were held constant). The results indicated that overall HC, CO, and NOx emissions were
reduced by approximately 16%, 13% and 9%, respectively, when fuel sulfur content was reduced
from the higher to the lower level.
Auto/Oil Phase II Sulfur Study—-This study expanded on the Phase I study by testing the same
vehicle fleet over a wider range of sulfur levels with more intermediate points. This additional
work was performed to identify non-linear trends of emissions in relation to sulfur content. Two
fuel sets were used. The first, termed "Part I", was a five-fuel set ranging from a nominal sulfur
level of 450 ppm down to 50 ppm in increments of 100 ppm. The second, termed "Part II", was
a three-fuel set having sulfur levels of 50 ppm to 10 ppm in increments of 20 ppm. This study
confirmed the results of the Phase I study and further found that reducing fuel sulfur from 50
ppm to 10 ppm resulted in a reduction in HC of 6% and CO of 10%; there was no statistically
significant effect on NOx emissions in this range.
T.WT9o/Sulfur Study—-The study was designed to investigate possible non-linear impacts of the
fuel distillation parameter T9o, interactive impacts of two fuel distillation parameters (T5o and T9o)
and sulfur on emissions from light-duty vehicles. Three vehicle fleets were tested: Tier 0
vehicles assessed in the Phase I and Phase II Studies above (consisting of ten vehicles), a Federal
Tier 1 fleet (consisting of six vehicles), and an "Advanced Technology" fleet (six production
type LEV and ULEV vehicles). Only the Tier 0 and Tier 1 fleets were tested for their responses
to changes in sulfur levels. Two fuel sets tested in this program were used to investigate the
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impact of fuel sulfur on exhaust emissions: a low T9o set and a high T9o set with approximate
sulfur levels of 33 and 317 ppm.
API Extension Fuel Set—-In this program, the Tier 0 vehicle fleet (consisting of ten vehicles)
from the Auto/Oil program was tested at sulfur levels of 450 and 900 ppm to investigate the
impact of the higher levels of fuel sulfur observed in U.S. gasoline. The results from this
program showed emission reductions of 5%, 2%, and 3% for HC, CO, and NOx respectively, as
a result of reducing sulfur from 900 to 450 ppm.
EPA RFG Phase I Study—-Phase I was an initial investigation of the impacts of oxygenates,
volatility, distillation properties, and sulfur on emissions. The vehicles included in this program
represented 1990 model-year or equivalent technology (Tier 0 vehicles). Two fuels examined in
this program had differing sulfur levels (112 ppm and 371 ppm) with the other fuel parameters at
approximately constant levels. The results indicated that decreasing sulfur from 371 ppm to 112
ppm caused a 5% reduction in HC emissions, a 7% reduction in NOx emissions, and a 9%
reduction in CO emissions in the tested fleet.
EPA RFG Phase II Study—-Phase II was a continuation of Phase I, investigating further the
effects of oxygen content, oxygenate type, volatility, sulfur, olefins, and distillation parameters.
Relevant testing included fuels with sulfur levels of 59 and 327 ppm. Again, vehicles with 1990
model-year or equivalent technology were tested. For the fleet tested, the results indicated that a
reduction in sulfur from 327 to 59 ppm caused a 7% reduction in HC, a 5% reduction in NOx
emissions, and an 8% reduction in CO emissions.
API "Reversibility" Study—- American Petroleum Institute (API) tested a series of vehicles in
response to the issue of sulfur reversibility in LEV and "advanced technology" vehicles. Sulfur
"reversibility" refers to the ability of a vehicle to return to low emissions on low sulfur fuel after
temporary use of high sulfur fuel. Only one of the vehicles was used in this analysis as part of
the LEV emissions data set (all of which had approximately 100K mileage). The other vehicles
from this test program were not included in the analysis either because: 1) they did not meet the
criteria of mileage accumulation of 100K (see discussion below on why only the vehicles with
the mileage accumulation of 100K was considered to be appropriate) or, 2) the testing was not
completed at the time of the analysis.
CRC Sulfur/LEV Study— This study involved six light-duty vehicles certified for sale in
California as LEVs in 1997. Two fuel sets were investigated under this program: one fuel set
was a California RFG with two sulfur levels (nominally 40 ppm and 150 ppm); the other set of
five fuels had five different sulfur levels (nominally 40, 100, 150, 330, and 600 ppm). The
vehicles were first tested in an "as-received" condition (average vehicle mileage of 10,000 miles)
and with the catalysts bench-aged to simulate 100,000 miles of operation (although the oxygen
sensors were original, low mileage sensors). The 10,000 mile emissions data will hereafter be
referred to as the "10K data" and the 100,000 mile data will be referred to as the "100K data."
The conclusions from this study included:
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• For the 10,000-mile catalysts, reducing sulfur from 600 to 40 ppm resulted in emission
reductions of 46%, 63%, and 57% forNMHC, NOx, and CO, respectively, over the FTP
composite.
• For the aged 100,000-mile catalysts, reducing sulfur from 600 to 40 ppm resulted in
emission reductions of 32%, 61%, and 46% forNMHC, NOx, and CO, respectively, over
the FTP composite.
• The fleet response to the changes in fuel sulfur level was found to be linear for the 10,000-
mile catalysts and non-linear for the 100,000-mile catalysts. The effect of sulfur change
was more pronounced at lower sulfur levels for the aged catalysts.
In the current analysis, only the 100K data was used since the other major LEV/ULEV testing
program only tested vehicles with aged components to simulate 100,000 miles of driving. The
emissions data from both fuel sets (conventional and RFG gasoline) were used in this analysis.
AAMA/AIAM Sulfur/LEV Studv^-This study tested 21 vehicles - 9 LEV LDVs, 1 LEV LDT1,
7 LEV LDT2s, and 4 ULEV LDVs. The vehicles were equipped with emission control
components that were aged to mimic 100,000 miles of on-road driving. The base fuel used in
the program was a California RFG with a nominal sulfur level of 40 ppm. The base fuel was
then doped with sulfur compounds to obtain nominal sulfur levels of 100, 150, 330, and 600
ppm. Based on the 21 vehicle fleet, AAMA/AIAM reached the following conclusions:
• The emissions benefits of the technologies in low-emission vehicles are diminished as
fuel sulfur level is increased above 40 ppm.
• The LEVs and ULEVs tested in this program showed a larger detrimental effect from fuel
sulfur increases than the Tier 0 or Tier 1 vehicles tested in the Auto/Oil program.
• The emissions response of LEVs and ULEVs to fuel sulfur is non-linear for all pollutants
and is more pronounced at lower sulfur levels.
3.2.2 Analysis of Short-Term Sulfur Effects
Unless otherwise specified, all data sets were analyzed using the following regression
methodology. Individual fuel/vehicle data points were analyzed using a regression procedure in
the SAS statistical software package "ABSORB". The dummy variables were used to "absorb"
the vehicles' effect on emissions, thereby allowing the fuel sulfur effect to be isolated and better
approximated. This approach is similar to that used in the development of the reformulated
gasoline Complex model in which a "dummy" variable was created for each vehicle in the data
set. Repeat tests on vehicles (and for the same vehicle(s) used in different programs) at a given
sulfur level were averaged to represent one data point. Emissions were regressed against the raw
("as-reported") sulfur concentrations (ppm). In all cases, two different mathematical fits were
considered in modeling the relationship between emissions and fuel sulfur level - log-log and
log-linear. The selections were made based on the accuracy of the fit.
The original M6Sulf algorithm in MOBILE6 was based on the analyses that distinguished the
vehicles into two emitter categories, "Normal" and "High", based on the definition in Table 3-1
below.
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Table 3-1. Definitions of "Normal" and "High" Emitter in the M6Sulf Model.
Emitter Category
Definition
Normal
Less than or equal to two times the emission
standard for NOx, or HC, or less than or equal to
three times the emissions standard for CO
High
Greater than two times the emission standard for
either NOx, or HC, or greater than three times the
emission standard for CO
The algorithm produced separate sulfur corrections for "Normal" and "High" emitters. Because
MOVES does not attempt to distinguish "normal" and "high" emitter classes and because the
weights applied to effects for both classes were frequently about equal, the sets of model
coefficients for "normal" and "high" emitters were regarded as independent models and assigned
equal weights for consistency with the MOBILE6 model. For the purpose of describing the
analyses that formed the basis of the M6Sulf model, the analyses of "Normal" and "High"
emitters are presented separately in Section 3.2.2.1 and Section 3.2.2.2, respectively. Table 3-2
shows the numbers of vehicles in each emitter category for the studies included in developing the
M6Sulf model.
Table 3-2. Number of Vehicles in Each of the Emitter Categories.
Study
Normal Emitters
High Emitters
All Auto/Oil (all Tier 0 Vehicles)
10
0
EPA RFG Phase I (all Tier 0 Vehicles)
20
19
EPA RFG Phase II (all Tier 0 Vehicles)
24
15
Tier 1 T50/T90 Study (all Tier 1 vehicles)
6
0
CRC Sulfur/LEV Study (LEV and ULEV Vehicles)
12
0
AAMA/AIAM Sulfur/LEV Study (LEV and ULEV
Vehicles and Trucks)
21
0
TOTALS:
93
34
3.2.2.1 Normal Emitters
3.2.2.1.1 Tier 0 Vehicles
The sulfur impacts for normal-emitting Tier 0 vehicles are based on combined analysis of the
following studies: Auto/Oil data, the API extension fuel data, and the EPA RFG Phase I and
Phase II data. Using the SAS "ABSORB" procedure described earlier, it was found that the log-
log fit was consistently better than the log-linear fit. The resulting correlations are shown below
in Table 3-3.
13
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Table 3-3. Results of Regression Analysis for Tier 0 Normal-Emitting Vehicles.
Pollutant
Emissions Process
Type of Regression Fit
Regression Coefficient
R2
HC
Running
Ln-Ln
0.15262
0.947
CO
Running
Ln-Ln
0.19086
0.886
NOx
Running
Ln-Ln
0.02083
0.944
HC
Start
Ln-Ln
0.0027436
0.959
CO
Start
Ln-Ln
-0.01792
0.860
NOx
Start
Ln-Ln
0.04772
0.862
The estimated effects of the fuel sulfur level on emissions based on model predictions are shown
in Table 3-4.
Table 3-4. Modeled Effects of Fuel Sulfur Level on Emissions for Tier 0 Normal-Emitting Vehicles.
Pollutant
Emissions
Process
% Increase in Emissions when Sulfur is Increased from 30 ppm to:
75 ppm
150 ppm
330 ppm
600 ppm
HC
Running
15.0
27.8
44.2
58.0
CO
Running
19.1
36.0
58.0
77.1
NOx
Running
1.93
3.41
5.12
6.44
HC
Start
0.25
0.44
0.66
0.83
CO
Start
-1.63
-2.84
-4.21
-5.23
NOx
Start
4.47
7.98
12.1
15.4
The Tier 0 analysis summarized in Table 3-3 and Table 3-4 is applied to all normal emitters of
Tier 0 and earlier vehicles (all vehicles equipped with a catalyst) since very little data is available
to support an evaluation of the effect of sulfur on pre-Tier 0 vehicles. For vehicles not equipped
with catalysts, sulfur is assumed to have no direct effect on exhaust emissions from those
vehicles.
14
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For comparison, Table 3-5 shows the estimated effects of reducing sulfur from 450 ppm to 50
ppm on emissions using the regressions listed in Table 3-3 for Tier 0 normal emitters and the
effects computed from the Complex Model for normal emitters. The results are similar for CO,
but the effects of sulfur on HC and NOx estimated from M6Sulf model are smaller compared to
the effects predicted by the Complex Model. This difference is probably due to the inclusion of
the T50/T90 sulfur data set in the current analysis. Inspection of the T50/T90 sulfur data shows
somewhat muted HC effects and much lower NOx effects for sulfur variations. The T50/T90
sulfur data was not available at the time the Complex Model was constructed.
Table 3-5. Comparison of the Effects of Sulfur on Composite Emissions from M6Sulf Model and Complex
Model when Sulfur is Reduced from 450 to 50 ppm.
Model
HC (% Reduction)
NOx (% Reduction)
CO (% Reduction)*
M6Sulf
13.0
6.6
15.4
Complex Model*
19.0
13.6
18.5
* CO emissions were not included in the original RFG Complex Model. The CO model estimates are based on the CO
model developed separately (using the same statistical techniques used to construct the RFG Complex Model) from the
RFG rulemaking and discussed in SAE paper 961214.21
3.2.2.1.2 Tier 1 Vehicles
For the analysis of Tier 1 vehicles, only T50/T90 Sulfur Study, tested at the fuel sulfur levels of
330 ppm and 30 ppm, was available. Because only two sulfur levels were available, the log-
linear fit was chosen to represent the data. The regression coefficients and the estimated effects
on emissions based on model predictions are shown in Table 3-6 and Table 3-7, respectively. It
is interesting to note that the emission reductions from lower fuel sulfur are generally greater for
Tier 1 vehicles than Tier 0 vehicles.
15
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Table 3-6. Results of Regression Analysis for Tier 1 Normal-Emitting Vehicles.
Pollutant
Emissions
Process
Type of Regression Fit
Regression
Coefficient
R2
HC
Running
Ln-Linear
0.002457
0.818
CO
Running
Ln-Linear
0.001746
0.911
NOx
Running
Ln-Linear
0.0006337
0.853
HC
Start
Ln-Linear
0.00009516
0.941
CO
Start
Ln-Linear
-0.0002338
0.820
NOx
Start
Ln-Linear
0.0008023
0.692
Table 3-7. Modeled Effects of Fuel Sulfur Level on Emissions for Tier 1 Normal-Emitting Vehicles.
Pollutant
Emissions
Process
% Increase in Emissions when Sulfur is Increased from 30
ppm to:
75 ppm
150 ppm
330 ppm
600 ppm1
HC
Running
11.7
34.3
109.0
143.0
CO
Running
8.17
23.3
68.8
91.4
NOx
Running
2.90
7.90
20.9
26.3
HC
Start
0.43
1.15
2.90
3.65
CO
Start
-1.05
-2.77
-6.77
-8.41
NOx
Start
3.68
10.1
27.2
34.6
Please see the explanation below about how the effects at 600 ppm were estimated.
Since the underlying data for Tier 1 vehicles included the sulfur level of only up to 330 ppm, it
would be inappropriate to extrapolate using the log-linear regression beyond 330 ppm.
Therefore, for any sulfur level between 330 ppm and 600 ppm (the high end of the sulfur range
in MOVES), the following equations were used to estimate the effect of fuel sulfur on Tier 1
16
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vehicles. The fractional effect for Tier-1 vehicles at any sulfur level X> 330 ppm (fn..v) is given
by Equation 3-1,
fn,x ~ /n,330
where:
r f ^
JTQ,X
\ fro,330 J
Equation 3-1
/n,330 = the fractional change in emissions for Tier 1 vehicles at 330 ppm relative
to a 30-ppm baseline (available in Table 3-7),
frojc = the fractional change in emissions for Tier 0 vehicles at level Xrelative to
a 30-ppm baseline (can be estimated from Table 3-4),
fn,330 = the fractional change in emissions for Tier 0 vehicles at 330 ppm relative
to a 30-ppm baseline (available in Table 3-4).
For example, using the equation above, the effect of increasing sulfur to 600 ppm from 30 ppm
on running HC emissions for Tier 1 vehicles would be: 1.09 (0.58/0.442) = 1.43 (i.e., 143%).
The values 58.0% and 44.2% were obtained from Table 3-4 and 109.0% was obtained from
Table 3-7.
3.2.2.1.3 LEVs and ULEVs
As discussed in Section 3.2.1 above, AAMA/AIAM and CRC Sulfur programs were used to
estimate the effect of fuel sulfur on LEVs and ULEVs. While the analyses for Tier 0 and Tier 1
vehicles were based only on light-duty vehicles, the data for LEVs and ULEVs also included
light-duty trucks. Separate analyses were conducted for light-duty vehicles (passenger cars and
light trucks) and for light-duty trucks (LDT2, LDT3, and LDT4). These data were analyzed in
the same manner as described above using the SAS "ABSORB" procedure.
Because we were unable to get the bag data from the testing programs to determine the start and
running coefficients separately, the regression was run on the composite and the resulting
coefficients were applied to both running and start emissions. Consistent with the findings from
the AAMA/AIAM and CRC reports, log-log regression model was found to be a better fit for the
data.
The regression coefficients for estimating the effects of fuel sulfur on emissions from LEV (and
cleaner technology) are summarized in Table 3-8. Compared to Tier 0 and Tier 1 vehicles,
ULEV and LEV vehicles were more sensitive to the changes in fuel sulfur levels.
17
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Table 3-8. Results of Regression Analysis for normal-emitting LEVs and ULEVs.
Pollutant
Passenger cars (LDV)
Light Trucks (LDT2,3,4)
Composite
Emissions
Running
Emissions
Start
Emissions
Composite
Emissions
Running
Emissions
Start
Emissions
HC
0.168
0.168
0.168
0.125
0.125
0.125
CO
0.236
0.236
0.236
0.151
0.151
0.151
NOx
0.351
0.351
0.351
0.146
0.146
0.146
3.2.2.2 High Emitters
The vehicles meeting the emissions criteria for high emitters (Table 3-1) were available only in
the EPA RFG Phase 1 and 2 datasets (Table 3-2). These data were used to estimate regression
coefficients for high-emitting Tier 0 vehicles, which were, however, also applied for LEV and
Tier 2 vehicles A log-linear fit was used since the volume of high-emitter data available was
small and only two sulfur levels were tested in the EPA RFG programs. The regression
coefficients for high emitters are shown in Table 3-9. and the corresponding emission effects are
shown in Table 3-10.
Table 3-9. Results of Regression Analysis for Tier 0 High-Emitting Vehicles (Also applied to LEV and Tier 2
"High-Emitting" Vehicles).
Pollutant
Emissions
Process
rype of Regression Fit
Regression
Coefficient
R2
HC
Running
Ln-Linear
1.138E-4
0.996
CO
Running
Ln-Linear
1.111E-4
0.993
NOx
Running
Ln-Linear
2.848E-4
0.998
HC
Start
Ln-Linear
-2.227E-4
0.985
CO
Start
Ln-Linear
-5.336E-4
0.962
NOx
Start
Ln-Linear
2.519E-4
0.889
18
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Table 3-10. Effects of Fuel Sulfur Level on Emissions for Tier 0 High-Emitting Vehicles.
Pollutant
Emissions
Process
% Increase in Emissions when Sulfur is Increased from 30 ppm to:
75 ppm
150 ppm
330 ppm
600 ppm
HC
Running
0.51
1.37
3.47
6.70
CO
Running
0.50
1.34
3.39
6.54
NOx
Running
1.29
3.48
8.92
17.6
HC
Start
-1.00
-2.64
-6.46
-11.9
CO
Start
-2.37
-6.20
-14.8
-26.2
NOx
Start
1.14
3.07
7.85
15.4
Table 3-11 compares the estimated effects of reducing sulfur from 450 ppm to 50 ppm on
emissions using the regression coefficients listed in Table 3-9 for Tier 0 high emitters and the
effects computed from the Complex Model for high emitters.
Table 3-11. Comparison of the Effects of Sulfur on Composite Emissions from Tier 0 High Emitters using
M6Sulf Model and Complex Model when Sulfur is Reduced from 450 to 50 ppm.
Model
HC (% Reduction)
CO (% Reduction)*
NOx (% Reduction)
M6Sulf
1.5
0.3
11.2
Complex Model
-5.0
1.4
10.0
CO emissions were not in the original RFG Complex Model. The CO model was developed separately (using the
same statistical techniques used to construct the RFG Complex Model) and is discussed in SAE paper 96121413.
3.2.3 Analysis of Long-Term Sulfur Effects
In addition to adsorbing onto the surface of the catalyst and acting as a "poison," sulfur can also
penetrate the precious metal layer, especially into palladium (the metal of choice for LEV
catalysts), and into the oxygen storage material and further damage the catalyst. Full penetration
may not have occurred during the very few miles of operation prior to short-term emission
testing on high sulfur fuel. The short-term exposure in the test programs (evaluated previously
in Section 3.2.2) typically consisted only of running several emission tests (FTP or LA4). Since
19
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each FTP is approximately 18 miles in length, the short-term exposure usually amounted to just
under 100 miles of operation, all of which was in a controlled laboratory environment.
To address this concern, API and EPA conducted test programs on a total of six light-duty
vehicles for sulfur sensitivity after both short-term and long-term exposures to sulfur.18 The
long-term exposure consisted of between 1,500 and 4,000 miles of in-use operation over urban,
rural, and highway roads. Two of the vehicles were 1999 models, while the other four were all
1998 models. All six were either LEV or ULEV vehicles. Three of the vehicles were equipped
with catalyst systems aged to either 50,000 or 100,000 miles. The other three vehicles had low
mileage catalyst systems aged to only about 4,000 miles.
All of the vehicles were tested for short-term exposure prior to the long-term testing. Each
vehicle was tested using a FTP baseline tested on low sulfur fuel (30 or 40 ppm). The number of
tests used to establish the baseline varied from two to four. The vehicles were then tested with
the high sulfur fuel (EPA at 350 ppm, API at 540 ppm). Sulfur sensitivity was determined by
calculating the percent increase in average emissions with the high sulfur fuel compared to the
average emissions with the low sulfur fuel. Table 3-12 lists both the short-term and the long-term
sulfur sensitivity data for all six vehicles.
In order to quantify the difference between short-term and long-term exposures, a fleet average
emission rate was determined for both low and high sulfur fuels for each pollutant, for both long-
term and short-term exposures. The percent change in emissions between low and high sulfur
fuels was calculated, and the ratio of long-term sensitivity to the short-term sensitivity was then
determined. As shown in Table 3-13, the percent increases from short-term to long-term were
quite large, especially for hydrocarbon emissions. Statistical tests performed to assess the
significance of the observed increases in sulfur sensitivity are discussed in Appendix B of the
Tier 2 Regulatory Impact Analysis.22
20
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Table 3-12. Vehicle-by-Vehicle Short-Term vs. Long-Term Sulfur Sensitivity.
Vehicle
Sulfur
Aging
Sulfur
Level
Exhaust Tailpipe Emissions (g/mi)
Sulfur Sensitivity (%)
HC
CO
NOx
HC
CO
NOx
Accord
Short
30
0.031
0.351
0.092
12.0
36.3
69.4
350
0.035
0.478
0.155
Long
30
0.033
0.330
0.09
21.7
121.1
158.5
350
0.040
0.731
0.234
Cavalier
Short
30
0.070
1.778
0.068
49.3
127.7
347.0
350
0.105
4.048
0.303
Long
30
0.070
1.778
0.068
216.0
306.4
411.8
350
0.223
7.224
0.324
Altima
Short
40
0.041
0.788
0.061
43.9
34.3
83.6
540
0.059
1.058
0.112
Long
40
0.041
0.788
0.061
39.0
25.3
116.4
540
0.057
0.987
0.132
Taurus
Short
40
0.033
0.522
0.075
54.5
59.4
34.7
540
0.051
0.832
0.101
Long
40
0.033
0.522
0.075
121.2
151.0
56.0
540
0.073
1.310
0.117
Accord
Short
40
0.029
0.285
0.100
10.3
4.9
92.0
540
0.032
0.299
0.192
Long
40
0.029
0.285
0.100
41.4
63.2
145.0
540
0.041
0.465
0.245
Avalon
Short
40
0.040
0.406
0.068
52.5
33.3
70.6
540
0.061
0.541
0.116
Long
40
0.040
0.406
0.068
50.0
80.8
108.8
540
0.060
0.734
0.142
21
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Table 3-13. Differences between Short-Term and Long-Term Sulfur Sensitivities.
Average
Sulfur Sensitivity (%)
Ratio of long-term to short-term sensitivity
HC
CO
NOx
HC
CO
NOx
Short-Term
40.2
75.7
111.3
2.50
2.36
1.47
Long-Term
100.3
178.7
163.4
3.2.4 Application in MO VES
In MOVES, the M6Sulf model is applied to (1) all model years for sulfur levels above 30 ppm,
and (2) pre-2001 model years for sulfur levels equal to and below 30 ppm. In addition, the
M6Sulf model is applied to all sourcetypes.
The M6Sulf model data, based on the analyses in Section 3.2.2, are stored in "sulfurmodelcoeff
table, described in Table 3-14.
Table 3-14. Description of the Database Table "sulfurmodelcoeff'
Field
Description
Values
processID
Identifies the emissions process.
1 = running exhaust
2 = start exhaust
pollutantID
Identifies the pollutant
1 = total hydrocarbons
(THC)
2 = carbon monoxide (CO)
3 = nitrogen oxides (NOx)
M6emitterID
Identifies the emitter classes. See "suljurmodelname" table
1 = normal emitter
2 = high emitter1
sourcetypelD
Identifies vehicles by functional type.
11= motorcycle
21= passenger car
3 l=passenger truck
32=light commercial truck,
etc.
fuelMY GroupID
The range of model year groups to which the sulfur
coefficients are applied
e.g., 1960-1974, 1997-2000,
etc.
sulfurFunctionID
Identifies the type of regression the coefficients are based
on. See "sulfurmodelname" table
1 = log-log
2 = log-linear
sulfurCoeff
The sulfur coefficients from the regression analyses
See Section 3.2.2
^OVES does not distinguish "high emitters" as such, but the calculator does apply both models and weights the results
equally.
22
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3.2.4.1 Short-Term Sulfur Effects
The Short-Term Sulfur Effect estimates the short-term effects on emissions due to adsorption of
sulfur onto the catalyst surface by calculating an adjustment to the base emissions as a function
of the sulfur content of the gasoline. The initial calculations use Equation 3-2 and Equation 3-3
in cases where the log-log relationship is required (sulfurFunctionID = 1), or Equation 3-4 and
Equation 3-5 when the log-linear relationship is required (sulfurFunctionID = 2).
In these equations, the coefficient (ft) represents the sulfurCoeff field in the sulfurModelCoeff
table, values of which are presented in 3.2.2 above. As shown in the tables, the sulfurCoeff
varies by pollutant, process and "emitter status."
The intermediate variable "sulfShortTarget" (Cshort,target) is the correction factor for the sulfur
level of the fuel being modeled, for which the sulfur content (xs) is expressed in ppm. The
parameter, Cshort,basis, is the correction factor for the base sulfur (sulfurBasis variable in the
SulfurBase table) level. The sulfur basis (xs,basis) is always set at 30 ppm.
Qhort,target = exP (P ^ *s ) Equation 3-2
Qhort,basis = exP (ftln *s,basis) Equation 3-3
Qhort,target = exP (ft xs) Equation 3-4
Qhort,basis — ®XP iP •"'S,basis ) Equation 3-5
The Short-term sulfur effect (SulfAdj, As,short) for all groups is computed using Equation 3-6.
C -C
j short, target short, basis
^s,short T, Equation 3-6
short,basis
In this application of Equation 3-6, the numerator is multiplied by 0.60 only for NOx to represent
high emitters, based on the analysis of the Complex Model which indicated that the NOx
sensitivity of high emitters is approximately 60 percent of the sensitivity for normal emitters.
3.2.4.2 Long-Term Sulfur Effects
As described in Section 3.2.3, the Long-Term Sulfur Effects are intended to account for
reversible effects of prolonged exposure to sulfur in the catalyst. The values used in MOVES
(Table 3-13) are stored in the sulfurLongCoeff variable (^s.iong) in "M6SulfurCoeff' table. The
values for sulfurLongCoeff are a function of pollutant. The long-term sulfur effects apply to
LEV and cleaner vehicles and trucks. Tier 0 and Tier 1 vehicles and trucks only have the short-
term sulfur effects. In addition, the sulfur levels of 30 ppm or less are assumed to have no long-
term sulfur effects.
The short-term sulfur effects from Section 3.2.4.1 and multiplied by the long-term sulfur effects
to produce the variable sulfAdj2 (A2), as shown in Equation 3-7.
A — A xA
2 s,short sjong Equation 3-7
23
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3.2.4.3 Sulfur Irreversibility Effects
In this step, the permanent effects of sulfur on emissions are computed. These effects are
intended to represent the long-term emission impact of past exposure to high sulfur fuels, even
when current fuels have lower sulfur levels. The irreversibility effects apply only to "LEV" and
later (2001+ model year) vehicles, and apply only to target fuel sulfur levels greater than 30 ppm
sulfur. For model years 2000 and earlier and for fuel sulfur levels < 30 ppm, the model does not
calculate permanent effects. The same effects are applied to all three pollutants (HC, CO and
NOx) and processes (start and running).
If the fuel sulfur level is greater than 30 ppm but less than a specified "maxIRFactorSulfur"
(xs,caP), also stored inM6SulfurCoeff Equation 3-8 is used to compute the "irreversible sulfur
effect" 04s,irr, SulflRR). The effect is applied as a function of model year group.
The maxIRFactor Sulfur is applied as a function of model year group, as follows:
Model Year Group Maximum S level
2001 -2003
1,000 ppm
2004 - 2005
303 ppm
2006 - 2007
87 ppm
2008 +
80 ppm
4irr = exP ln xs,cap ) Equation 3-8
If the selected sulfur level is greater than the maximum sulfur level, rather than using the value
of the "cap" as the sulfur level, the actual sulfur level (xs) is input to the Equation 3-8 to
calculate the irreversibility effect. However, sulfur levels above the maximum are not expected
in normal use of the MOVES model.
3.2.4.4 Combining Short-Term, Long-Term and Irreversibility Sulfur Effects
Equation 3-9 combines all the sulfur effects described into a final sulfur effect, designated as
As.3 or "sulfAdj3." The effect is calculated as a multiplicative adjustment, and includes the
short-term effects applied to the fuel basis (Cshort,basis) from Equation 3-3 or Equation 3-5, the
combined short-term and long-term adjustment (A2, Equation 3-7) and the irreversibility effect
^4s,irr (Equation 3-8). The two main terms in the expression are weighted by the factor wir
(irreversibility factor), which takes a value of 0.425.23
4?,3 -i-0+
w
IR
A -C
S.Irr short,basis
A
c,
short, basis
+ (l.0 w1R)A2
Equation 3-9
24
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3.2.4.5 Sulfur Effects in Geographical Phase-In Areas (GPA)
During calendar years 2004-2006, the gasoline sulfur levels in the Sulfur "Geographical Phase-In
Area" (Sulfur GPA) were allowed to remain higher than elsewhere in the nation. MOVES
accounts for this difference with the calculation of "Sulfur GPA Effects." The algorithm applies
a maximum sulfur level of 330 ppm within designated "GPA areas," most of which are located
in the Rocky Mountains and are identified in the database table "county," using the field
"GPAFract."
The sulfur adjustments in GPA are calculated using the same process as for other areas, except
that the variable for the sulfur basis is assigned a different value. A value of 330 ppm,
representing a typical worst case in a GPA scenario (xs,GPAmax), is assigned in Equation 3-10 in
place of the actual sulfur level in the fuel to be evaluated. The result Cshort,GPA is applied in
Equation 3-11 with Cshort,basis to give the adjustment^,short,gpa, as shown below:
Qhort,GPA =exP (ft lnxS,GPAmax ) Equation 3-10
C -C
A short,GPA short,basis ^ ^
s,short,gpa ~~ T, Equation 3-11
short,basis
As with non-GPA areas, the combined short- and long-term effect is calculated by multiplying
the GPA short-term effect and the same long-term coefficient as used outside GPA areas, using
Equation 3-12.
A,GPA = A,short,GPA x 4ong Equation 3-12
Then, the equivalent of the adjustment Asj for the GPA area (A3, gpa) is calculated by applying
Equation 3-13 as shown below.
^3,gpa — 1 • 0 + (wir^2,gpa + (l • 0 — wir )^2) Equation 3-13
For calendar years other than 2004, 2005, and 2006, or in areas where sulfur < 30 ppm, A3,gpa is
set equal to^4s,3. This equivalence is also assigned in cases when the assigned sulfur level is
greater than sulfurGPAMax (i.e., 330 ppm).
To calculate a combined sulfur adjustment, the values of As,3 and A3,gpa are weighted by the
"GPA fraction" (/gpa, GPAFract) in a county being simulated, as shown in Equation 3-14. In the
default values assigned in the database, the fraction is always 0 or 1. However, GPA fraction is a
user input, allowing assignment of alternate values between 0 and 1.
25
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A,combined = (1" ' /gpa )4s,3 /gpa^:
3,GPA
Equation 3-14
3.2.4.6 Weighting for "Normal" and "High " Emitter Fractions
The original M6Sulf algorithm produced separate sulfur corrections for "Normal" and "High"
emitters, as described in Section 3.2.2. However, because MOVES does not attempt to
distinguish "normal" and "high" emitter classes, the sets of model coefficients for "normal" and
"high" emitters were regarded as independent models and assigned equal weights for
consistency with the underlying analyses (i.e., w'normai = n'lugh = 0.50). In the database table
sulfurModelCoeff, the sulfurCoeff field takes different values for "normal" and "high" emitter
classes (denoted by M6emitterID). These calculations shown in Equation 3-2 to Equation 3-9
are applied to both target and base fuels, as shown in Equation 3-15.
4:r=(i - "w ks»„+»'h„h4"s,h
/ \ , , Equation 3-15
4T = (l - Htnonnal + ^4^
Likewise, a composite of normal and high emitter GPAsulf adjustments are calculated using the
same weights.
= (i - >CpA = I1 " ^Mgh Kokno^al +
During a model run, the calculations described to this point (sections 3.2.4.1 through 3.2.4.6) are
repeated and applied for the two base fuels with 90 ppm and 30 ppm sulfur, corresponding to the
two model-year ranges (1960-2000 and 2001-2050), respectively. This step is taken because the
final sulfur fuel adjustment is the ratio of the adjustments for the target and base fuels, as shown
in Equation 3-17 for non-GPA and GPA areas. All calculations described are identical for the
target and base fuels. The sulfur adjustments are calculated independent of the other fuel
properties of the base fuels. A final sulfur adjustment for fuels containing 30 ppm sulfur resolves
to 1.0 because the target fuel level is equal to the base fuel of 30 ppm. The 30 ppm sulfur level is
called the basis because the entire M6Sulf algorithm was developed based on this level. The
calculation result does not equal 1.0 for the 90 ppm base sulfur. As stated earlier, the M6Sulf
model applies to all sulfur levels for model year group 1960-2000, and only to sulfur levels
above 30 ppm for model year groups 2001-2060.
26
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A target
/lc -3
A =
S, final a base
S 3
Equation 3-17
a target
a _ 3,GPA
"^XjPA,final , base
3.GPA
3.2.4.8 Summary of Equations and Variables for M6SulfModel
Table 3-15 provides a glossary and brief description of the variables shown in the calculations
presented in Section 3.2.
27
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Table 3-15. Glossary of Variables and Equations for calculations described in Section 3.2.
Eqn
Eqn(GPA)
Symbol
Name
Type (DB table)
Description
3-2,
3-3,
3-4,
3-5
3-10
P
sulfurCoefficient
DB input
(S ulfurmodelcoefj)
Regression coefficient for short-
term sulfur effects (log-log or
log-linear).
3-2
3-10
Xs
sulfurTarget
DB Input
(FuelFormulation)
"target" sulfur level for
geographic region and time
period covered in a MOVES run.
(in Eqn 3-10 takes value of
-*S.i iI'Amax )-
3-3
Xs,basis
sulfurBasis
DB input
(SulfurBase)
The base sulfur level for all
calculations in MOVES run is
constant at 30 ppm.
3-2,
3-4
3-10
Cshort, target
Short-term
correction for target
sulfur level
Intermediate result
3-3,
3-5
Cshort, basis
Short-term
correction for the
base sulfur level
Intermediate result
3-6
3-11
- 1 S,short
SulfAdj
Intermediate result
Short-term sulfur effect
3-7
- 1 S,long
sulfurLongCoeff
DB input
(M6SulfurCoeff)
Applied to vehicles in LEV and
more recent standards, for S
levels >30 ppm
3-7
3-12
A2
Intermediate result
Adjustment combining short and
long-term sulfur effects.
Calculated as product of ^s,short
and^s.iong.
3-8
Xs,cap
maxIRFactorSulfur
DB input
(M6SulfurCoeff)
Maximum S level for which
"irreversibility effect" is
calculated. Varies by specified
model-year groups.
3-8
sulfurCoefficient
DB input
(S ulfurmodelcoefj)
Equal to /; for TO, LEV or
ULEV vehicles or y for Tier 1
vehicles.
3-8
^4s,Irr
SulflRR
Intermediate result
"irreversible sulfur effect,"
applied for vehicles in model
years 2004+, for S levels >30
ppm but less than xs,caD-
3-9
WlR
sulfurlRFactor
DB input
(M6SulfurCoeff)
3-9
3-13
As, 3
SulfAdj 3
Intermediate result
Combines short-term, long-term
and irreversible S effects.
3-15
3-16
Whigh
Weight for "high-
emitter" class
Assigned constant value of 0.50,
i.e., "normal" and "high" classes
are equally weighted.
3-17
3-17
- 1 S,final
Final Sulfur
adjustment
Intermediate result
Calculated with base sulfur level
at 90 ppm for MY1960-2000
and 30 ppm for MY 2001-2060.
28
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3.3
Tier 2 Low Sulfur Model (T2LowSulJ)
The M6Sulf model, described above, is used in MOVES to model the emission effects for
gasoline fuels with sulfur content greater than 30 ppm. For 2001 and later model year vehicles
operating on sulfur levels equal to or below 30 ppm, a different set of corrections, the "Tier 2
Low Sulfur Model," is used, based on additional data collected since the M6Sulf model was
created.
3.3.1 Background
Following the successful implementation of the Tier 2 sulfur standards, new research has focused
on the emission reduction potential of lowering sulfur levels below 30 ppm, particularly in
vehicles employing Tier 2 and newer technologies, under the hypothesis that increased reliance
on the catalytic converter would result in a higher sensitivity to fuel sulfur content. A 2005 study
conducted jointly by EPA and several automakers on nine Tier 2 vehicles in support of the
Mobile Source Air Toxics (MSAT) rule, found significant reductions in NOx, CO, and HC
emissions when operating on 6 ppm versus 32 ppm sulfur test fuel.22 In particular, the study
found a nearly 50 percent increase in NOx when sulfur was increased from 6 ppm to 32 ppm.
Another study published in 2011 by Umicore Autocat USA examined the impact of sulfur on the
catalyst efficiency during repeated FTP tests using fuels with sulfur levels of 3 and 33 ppm and
observed reductions of 41 percent for NOx and 17 percent for HC on a vehicle certified to the
PZEV standard.24 Both of these studies conducted testing at high and low sulfur levels after
running the test vehicles through test cycles intended to purge the catalyst of the effects of prior
sulfur exposure. Given the preparatory procedures related to catalyst clean-out and loading used
by these studies, these results may represent a "best case" scenario relative to what may be
expected under more typical driving conditions.
Nonetheless, both the MSAT25 and Umicore24 studies showed the emission reduction potential of
lower sulfur fuel on Tier 2 and later technology vehicles over the FTP cycle. However, assessing
the potential for reduction on the in-use fleet requires understanding how sulfur exposure over
time impacts emissions, and what the state of catalyst sulfur loading is for the typical vehicle in
the field.
3.3.2 Data Used in Developing the T2LowSulf Model
To gain further understanding of the effect of fuel sulfur on emissions, EPA conducted a study
assessing the state of sulfur loading (i.e., "poisoning") in typical in-use Tier 2 vehicles, as well as
the effect of fuel sulfur level on these vehicles during subsequent mileage accumulation.26 The
project was designed to take into consideration what was known from prior studies on sulfur
build-up in catalysts over time and the effect of periodic regeneration events that can occur
during higher speed and load operation in day-to-day driving.
The test fleet was chosen to be representative of latest-technology light duty vehicles being sold
at the time the program was launched. The study did not attempt to analyze or model details of
after-treatment design specific to each vehicle model such as catalyst position, precious metal
types and quantities used, or related engine control strategies such as timing advance at cold start
or fuel cut during deceleration. While these things undoubtedly influence the behavior of
29
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emissions and may interact with the fuel sulfur effects being investigated, including them in an
analysis requires correctly assessing and parameterizing them for all vehicles in the study.
Instead, this program's aim was to characterize overall effects of sulfur on emission inventories
by observing the aggregate behavior of a representative fleet of vehicles.
The main and largest group of vehicles was intended to conform on average to the Tier 2/Bin-5
exhaust certification level and employ a variety of emission control technologies. These goals
could be achieved by including a range of vehicle sizes, engine displacements, and
manufacturers. A list of 19 high-sales-volume makes and models based on 2006-8 sales data and
projections had been used for test fleet selection in the EPAct/V2/E-89 study that was launched
shortly before this study.42 Given that we would be targeting recruitment of vehicles 1-3 years
old, this list seemed relevant, with the added benefit that the emission behavior of these same
models would also be characterized in the other study's results. Grouping sales data by engine
family allowed additional transparency and flexibility in choosing test vehicles that represent a
wider group with identical powertrains without targeting one specific make and model. The
resulting target list of 19 vehicle models for recruitment is shown in Table 3-16. The vehicle
sample included in the program 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. While the sample for the main study did not specifically target vehicles certified to the
lowest emissions standards (e.g., Bin 3, Bin 2), the supplemental study acquired additional
vehicles with "Tier 3 like" emission levels and technologies, as discussed in 3.3.3.5.2.
The test fuels used were two non-ethanol gasolines with properties typical of certification fuel,
with sulfur levels of 5 and 28 ppm, with the higher level chosen to represent retail fuel available
to the public in the vehicle recruiting area (see Table 3-17 for detailed fuel properties). All
emissions data was collected using the FTP cycle at a nominal ambient temperature of 75°F.
30
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Table 3-16. Vehicles Targeted for Recruitment.
Model
Year
Make
Brand
Model
Engine
Size
Engine Family
Emissions
Standard Level1
2008
GM
Chevrolet
Cobalt
2.2L 14
8GMXV02.4025
5
2007
GM
Chevrolet
Impala FFV
3.5L V6
8GMXV03.9052
5
2007
GM
Saturn
Outlook
3.6L V6
8GMXT03.6151
5
2007
GM
Chevrolet
Silverado FFV
5.3L V8
8GMXT05.3373
5
>007
Toyota
Toyota
Corolla
1.8L 14
8TYXV01.8BEA
5
2008
Toyota
Toyota
Camry
2.4L 14
8TYXV02.4BEA
5
2007
Toyota
Toyota
Sienna
3.5L V6
8TYXT03.5BEM
5
2007
Toyota
Toyota
Tundra
4.0L V6
8TYXT04.0AES
5
2008
Ford
Ford
Focus
2.0L 14
8FMXV 02.0 VD4
4
2007
Ford
Ford
Taurus
3.5L V6
8FMXV03.5VEP
5
2007
Ford
Ford
Explorer
4.0L V6
8FMXT04.03DB
4
2008
Ford
Ford
F150 FFV
5.4L V8
8FMXT05.44HF
8
2007
Chrysler
Dodge
Caliber
2.4L 14
8CRXB02. 4MEO
5
2007
Chrysler
Dodge
Caravan FFV
3.3L V6
8CRXT03.3NEP
8
2008
Chrysler
Jeep
Liberty
3.7L V6
8CRXT03.7NE0
5
2008
Flonda
Flonda
Civic
1.8L 14
8HNXV01.8LKR
5
2008
Flonda
Flonda
Accord
2.4L 14
8HNXV02.4TKR
5
2007
Flonda
Flonda
Odyssey
3.5L V6
8HNXT03.54KR
5
2007
Nissan
Nissan
Altima
2.5L 14
8NSXV02.5G5A
5
'Certification standard level under the Federal Tier 2 standards.
Table 3-17. Test Fuel Properties.
Fuel Property
ASTM Method
Low S Test Fuel
High S Test Fuel1
Sulfur
D2622
5 ppm
28 ppm
Benzene
D5769
0.34 Vol. %
0.34 Vol. %
Total Aromatics
D5769
31.2 Vol. %
31.2 Vol. %
Olefins
D1319
0.5 Vol. %
0.5 Vol. %
Saturates
D1319
68.3 Vol. %
68.3 Vol. %
Oxygenates
D5599
0.0 Vol. %
0.0 Vol. %
T50
D86
221°F
221°F
T90
D86
317°F
317°F
RVP
D5191
9.0 psi
9.0 psi
'Sulfur content was confirmed for the higher-sulfur test fuel, while other properties were assumed to be the same as
the typical certification fuel given the small amount of dopant added.
The data generated in this program included three distinct but overlapping datasets, designated
as: "clean-out at 28 ppm", "clean-out at 5 ppm", and "mileage accumulation at target sulfur
level." The "sulfur level" data provides the key information for assessing the in-use effect of
target sulfur levels on emissions over time as vehicles accumulated mileage. Only the analyses
pertaining to the "sulfur level" data are discussed in the following section since it's the most
31
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relevant in the context of MOVES. For additional details on the study design, test procedures,
and the complete analyses, see the project report.26
The "sulfur level" data represent the emission measurements from the repeated FTP cycles
following clean-out and include all measurements from vehicles tested on "low" and "high"
sulfur levels. Measurements were completed on a total of 35 vehicles representing 19 engine
families (Table 3-18). The average starting odometer of the 35 vehicles was 31,178 ± 6,351
miles. A total of 322 measurements were taken - 161 measurements each for both high and low
fuel sulfur levels, where a "measurement" represents a completed FTP cycle.
Table 3-18. Description of Tier 2 Vehicles in the "Sulfur Level" Dataset.
Vehicle
Family
ID
Vehicle ID
Make
Model
Model
Year
Tier 2 Bin
Number of
Vehicles
Average
Starting
Odometer
(mi)
M500
0003
Toyota
Corolla
2007
5
1
33,122
M501
0023
Ford
Explorer
2007
4
1
27,562
M502
0026
Dodge
Caliber
2007
5
1
29,097
M503
0194
Honda
Odyssey
2007
5
1
35,816
M504
0021
Saturn
Outlook
2007
5
1
43,733
M505
0031
Chevrolet
Silverado
2007
5
1
27,891
M506
0123
Nissan
Altima
2007
5
1
39,936
M507
0148
Ford
Taurus
2007
5
1
28,802
M508
0075
Dodge
Caravan
2007
8
1
41,117
M509
0046
Chevrolet
Impala
2007
5
1
37,734
N510
0264
Toyota
Sienna
2007
5
1
38,464
N511
0179
Chevrolet
Cobalt
2008
5
1
38,722
N512
0107
Jeep
Liberty
2008
5
1
24,614
N513
0089, 0178
Ford
Focus
2008
4
2
24,726
N514
0010,0101,
0104
Honda
Civic
2008
5
3
32,931
N515
0006, 0007,
0074, 0165
Ford
F150
2008
8
4
29,738
N520
0011,0022,
0026,
Toyota
Tacoma
2009
5
3
28,964
N521
0131,0162,
0179, 0280,
0329
Toyota
Camry
2008
3
5
28,506
P522
0009, 0039,
0146, 0045,
0011
Honda
Accord
2008
3
5
29,601
3.3.3 Data Analysis and Results
The pollutants included in the analysis were total hydrocarbons (THC) as reported by the FID
analyzer, carbon monoxide (CO), oxides of nitrogen (NOx), methane (CH4), as well as
particulate matter (PM) mass. Although each bag, 'Bag 1 minus Bag 3', and the composites
from the FTP test cycle were analyzed separately in the original analysis, only the analyses and
the results for Bag 2 (capturing the running emissions) and 'Bag 1 minus Bag 3' (capturing the
32
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cold-start emissions) are presented in this document. The statistical methodologies described in
the following section were applied consistently in the analysis of all pollutants and all bags.
However, the analysis of nitrogen oxides (NOx) from Bag 2 is presented in greater detail to assist
the reader in understanding the analytical approaches and to illustrate the statistical methods
used.
Note that the design of the experiment and data analysis went through an independent peer-
review process in accordance with EPA's peer review policy. The results of the peer review27'28
largely supported the study design, statistical analyses, and the conclusions from the program
and raised only minor concerns that have not changed the overall conclusions and have
subsequently been addressed in the final version of the project report.26
3.3.3.1 Data Preparation
Prior to proceeding with the statistical analyses, issues associated with very low emissions
measurements and outlying observations were examined. The following sections describe how
these issues were addressed.
3.3.3.2 Imputation of Measurements with Low Concentration
The graphical examination of the "sulfur level" dataset revealed the presence of very low
emission measurements from some pollutants and bags including NOx Bag 2. Since uncertainty
associated with these low measurements could potentially affect the outcome of the analysis, it
was important to understand the measurement process and evaluate the impact of associated
uncertainties.
During emissions testing, the vehicle exhaust stream was collected and diluted with background
air to avoid condensation of water vapor and other factors affecting the stability of the chemical
species. A small sample of this mixture flows into a collection bag for analysis after the test.
The concentration of emission species in the bag is determined by flowing the contents through a
properly calibrated gas analyzer. This method provides a time-weighted result via physical
integration of the emission stream produced over the course of a transient driving cycle.
Uncertainty in the measurement process results from the physics of mixing and sampling from a
gas stream as well as "noise" in analyzer components such as optoelectronic detectors and signal
amplifiers. This presence of these factors means that repeated measurements taken under
identical process conditions will produce a range of results, their average being the true
(intended) response of the instrument and the range around it representing the measurement
variability.
For the analyzers used in this program, the size of the measurement error (in relative terms) is
expected to increase relative to the measured value as the concentration decreases. Moreover,
the dilute-bag method used requires measurement of concentrations in both sample and
background bags, followed by a subtraction between the two, such that the net result contains
variability from both measurements. To assess whether these issues affected this dataset, we
examined plots of the measured concentrations for each test by vehicle by pollutant and bag.
Figure 3-1 shows the Bag 2 NOx dataset for the vehicles providing the "sulfur level" data, which
contains a number of very low values, as well as tests where sample and background are of
similar magnitude (the vehicle codes refer to the Family IDs listed in Table 3-18. Given these
33
-------
findings, we performed sensitivity analyses to evaluate the impact of these low emission
measurements on the study results (presented in 3.3.3.5.3).
Figure 3-1. NOx (Bag 2): Concentrations for Hot-running Emissions by Vehicle.
;»!*•*
f * i
I
* * °*
. j * «
J '
«
o
• %
• 5
i ! i
» ° |
5 * J
: «
• t ?
1 t } ? |
° • 8 _ «
» 8 t 8
o ° 8 5
* 1"
(N (N CD (N
OOO9999999999
OOO t-Ht-HtHtHt—l(N(N(N(N(N
(N(N(N(N(N(N(N(N(N(N(N(N(N
Bag 2 NOx concentration
o Background
» Sample, High Sulfur
* Sample, Low Sulfur
When a dilute emission measurement is lower than the measured background level, the net result
is reported as zero (this calculation is performed on a test-by-test basis). However, as it is
unlikely that tailpipe emissions are truly zero during a test, it was assumed that a "zero" result
indicates that the actual emissions level was smaller than the sum of the measurement errors
occurring on the sample and background measurements. The emission level was thus considered
to be below the limit of quantitation (LOQ), a level below which we are not confident in the
accuracy of quantitative values.
In this situation, the data point can be assigned a value of zero, deleted, or replaced with an
imputed value. However, because it was necessary to apply a natural log-transformation, zero
values were not retained in the data. Table 3-19 summarizes the number of measurements with
zero values, with percentages in parentheses. Given that observations below the LOQ appear to
be randomly distributed across sulfur levels and vehicles, and since excluding such observations
would result in reduced sample size, less statistical power, and larger standard errors,29 they were
imputed in the analysis.
Since an imputation method involving each vehicle's own longitudinal data would be superior to
methods using no information about the vehicle,30 a commonly used single-imputation method,
using half the minimum of a valid measurement from a given mileage bin for the vehicle with
34
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zero values, was performed. This imputation method recognized the fact that emission
measurements below the limit of quantitation must be smaller than any quantified value.
Since vehicle-specific imputation which minimizes the likelihood of artificially reducing the
natural variance of the data was used and the numbers of measurements with imputed values are
less than 20 percent (Table 3-19), we can expect good estimates of the reliability of
measurements.31 Nonetheless, it is important to determine the effect of these imputed values on
the resulting test statistics and corresponding conclusions. Thus, the results from the statistical
analysis with and without the imputed values were compared once the model was finalized to
assess the potential for introducing bias.
Table 3-19. Numbers of Measurements with Zero Values in Sulfur Level Data.
NOx
THC
CO
PM
Bag 2
21 (6.5%)
14 (4.3%)
10(3.1%)
2 (0.9%)
Bag 1 - Bag 3
7 (2.2%)
0
1 (0.3%)
15 (6.5%)
3.3.3.3 Detection of Outliers
Prior to proceeding to the full analysis, preliminary models were fit to detect extreme values or
"outliers." The residual plots were visually inspected for outlying observations and the outliers
were identified using the screening criterion value of ±3.5 for the externally studentized
residuals. Generally, one can expect about 95% of the externally studentized residuals to be
within ±3.5 standard deviations. This criterion has been widely used in statistics. When the
outlying observation represented an actual measurement, it was examined to assess its validity.
Since none of the outliers representing actual measurements showed clear indications of
measurement error, it was assumed that the outlying observations were valid and thus they were
included in the dataset for analysis. However, there were instances where a very low imputed
value was identified as an outlier. In such instances, the imputed values were removed from the
dataset. Table 3-20 summarizes the numbers of outliers as well as numbers of imputed
measurements removed (in parentheses).
Table 3-20. Number of Outliers in Sulfur Level Data (Numbers of Imputed Values removed).
NOx
THC
CO
PM
Bag 2
0(0)
1(1)
4(1)
1(0)
Bag 1 - Bag 3
2(0)
2(0)
6(1)
4(0)
3.3.3.4 Modeling Methodology
The following section describes the statistical approaches and the model-fitting methodologies
applied in the analysis. First, the emission measurements were log-transformed. In the current
study, the distributions of emissions exhibited positive skewness (log-normal), and thus,
transforming emission measurements by the natural logarithm was necessary to stabilize the
variance, to obtain a linear relationship between the mean of the dependent variable and the fixed
and random effects, and to normalize the distributions of residuals. The log-transformation of
emission measurements has been well-established in previous studies analyzing vehicle
emissions data.32'33'34
35
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The "sulfur level" data is a classic example of "repeated measures data" where multiple
measurements were taken on a single vehicle at different accumulated mileages. The
conventional methods for analyzing "repeated measures data" are the univariate and multivariate
analysis of variance. However, the linear mixed model was selected for the analyses of the
"sulfur level" data for the following reasons: The mixed-model approach uses generalized least
squares to estimate the fixed effects, which is considered superior to the ordinary least squares
used by the univariate and multivariate procedure.34 It is a more robust and flexible procedure in
modeling the covariance structures for repeated measurements data and better accounts for
within-vehicle mileage-dependent correlations.32'33 In addition, the mixed model is capable of
including vehicles with missing data and handling irregularly spaced measurements.
The MIXED procedure in the SAS 9.2 software package was used to fit the model. The mixed
model is represented in Equation 3-18 as:
Fj = XifS + Ztut + £j Equation 3-18
where fi and u, are sets of fixed and random effects parameters, respectively, and o, is a set of
random residuals. The mixed model accounts for correlation in the data through the inclusion of
random effects and modeling of the covariance structure. The set of fixed-effect coefficients fi
represent the mean effects of sulfur level across the set of measured vehicles and the set of
random coefficients Ui represent parameters (i.e., slopes or intercepts) allowed to vary by vehicle,
reflecting the natural heterogeneity in the measured fleet. In other words, the model incorporates
differences in the effect of sulfur level on emissions from individual vehicles. The distributional
assumptions for the mixed model are: Ui is normal with mean 0 and variance G,; Si is normal with
mean 0 and variance R,; the random components u, and o, are independent.
In developing the mixed model, a top-down model fitting strategy was used, similar to
previously established methods.35'36 The first step was to start with a "saturated" or full model,
which included all candidate fixed effects to allow unbiased estimation of the random effect
estimates. Next, we selected an optimal covariance structure, which specifies the variation
between vehicles as well as the covariation between emission measurements at different
accumulated mileages on the same vehicle. Finally, the fixed-effects portion of the model was
reduced to fit the final model.
3.3.3.5 Statistical Analysis and Results
3.3.3.5.1 Tier 2 Vehicles
The box-plot of the log-transformed emissions from Bag 2 NOx "sulfur level" data (Figure 3-2)
shows the spread of the data for each vehicle family and sulfur level across all mileages. The
diamond and the line inside the box represent the mean and the median, respectively. The box
represents the interquartile range between 25th and 75th percentile and the error bars show the full
data range. Generally, there is a tendency for the vehicles running on high sulfur fuel to emit
more NOx than the vehicles running on low sulfur fuel. However, the effect of operation on
higher sulfur fuel certainly varies by vehicle family, suggesting the presence of substantial
between-vehicle family variability. For example, the Toyota Corolla, Ford Focus, and Chevrolet
Cobalt clearly show a large effect of fuel sulfur level on emissions while the effect is more
marginal for the Nissan Altima and Honda Civic.
36
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Figure 3-2. Box-Plot of Individual Vehicle Families by Sulfur Level (NO* Bag 2).
Vehicle
~ High Sulfur at 2Sppm ~ Low Sulfur at 5ppm
In the dataset, the numbers of tested vehicles are not the same across vehicle families.
Considering the differences in numbers of unique vehicles in each vehicle family and the
presence of variability among vehicle families illustrated in Figure 3-2, each vehicle family was
considered as a random effect in constructing the statistical model.
Figure 3-3 presents the ln-transformed emissions from individual vehicles by sulfur level. The
plot shows that the increase in emissions as vehicles accumulate mileage for the high sulfur level
is more significant compared to the low sulfur level, contributing to the increased variance for
some vehicles and suggests that the rate of sulfur loading might differ for the two sulfur levels.
Thus, an interaction between sulfur level and the accumulated mileage was included in the
statistical modeling of the data. Thus, these findings from the graphical examination of the data
assisted in formulating the statistical models fit to the data.
We refrained from looking at the simple descriptive statistics, such as means and standard
deviations, to assess the relationship between the sulfur level and emissions even as a
preliminary step, because reaching conclusions from such naive approaches can be very
misleading as they fail to account for such factors as the presence of repeated measurements and
variability both between and within vehicles. In addition, the mileage accumulations varied from
37
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vehicle to vehicle, and simple descriptive statistics would not capture the substantial degree of
variability inherent in the dataset.
Figure 3-3. Log-Transformed Emissions from Individual Vehicles by Sulfur Level (NOx Bag 2).
veMD = 0003L
veMD = 0006S
veMD = 0007L
veMD = 0009S
vehID = 001 OS
veMD = 00211
velilL) = 00221
-4-
-10"
o _ +
® Q
+• *
' 0
Q
a +
s./«
9»»
# «,+»«>++
vehID = 0023L
veMD = 0026L
velilD = 0026...
veMD = 0031E
vehID = 0039S
veMD = 00461
veMD = 0074S
I 1
t 2
a „
+ 4%
a ¦+
, + o +
out O +«jO
V» „
?0+ f i- +
-OCS4
¦+
>. ~*.
+
veMD = 0075L
velilD = 0089S
vehID = 0101S
rp
II
O
O
veMD = 0107L
veMD = 0123E
velilD = 0131S
-4-
-10-
m a +
Of
°+
t *
* # -h»+-
J#'1'-
velilD = 0146
veMD = 0148
veMD = 0162S
veMD = 0165S
vehID = 01781
veMD = '1; ~'>3.
veMD = 01798
-4-
-10-
Q+"
+
¦>v
i"
oo °°
to
4$ Og
+ + ¦*"+-
--
vehID = 0194L
vehID - 0264L
veMD = 0280S
veMD = 0329
veMD = 1011.S
veMD = 1045S
velilD = 201 IS
-4-
-10 -
•0*0+-^ 4
jpO-l-
iXV '<
44'
K
«¦»
t 1 1 I 1 1 n 1 1 i 1 rn 1 rn 1 rn 1 r
0 100 200 0 100 200 0 100 200 0 100 200 0 100 200 0 100 200 0 100 200
miles
sulfmievel ° High Sulfur at 2Spprn + Low Sulfur at 5ppm
In analyzing the "sulfur level" data, a top-down model fitting approach was applied to
characterize the effects of fuel sulfur level on emissions as a function of accumulated mileages
since cleanout. The dependent variable (Yi) was the natural logarithm of emissions. The fixed
effects (Xi) included in the model were sulfur level, accumulated mileage, vehicle type, and the
interaction terms. The random effects (Z,) were each vehicle family in the study. The likelihood
ratio test for the significance of between-vehicle variation was statistically significant for all
pollutants and bags, and thus, the random intercept for each vehicle family was included in the
model. The significance of the between-vehicle variation was observed graphically in Figure
3-3.
All measurements from the same vehicle family were assigned the same between-vehicle family
error variance; their within-vehicle family error variances will differ and can be correlated within
a vehicle family. The measurements from the same vehicle family are assumed to be correlated
because they share common vehicle characteristics and have similar emission profiles. Also,
measurements on the same vehicle close in time are often more highly correlated than
measurements far apart in time as observed in Figure 3-3 - the covariation within vehicles. Both
within- and between-vehicle errors are assumed independent from vehicle to vehicle. Since the
measurements on different vehicles are assumed independent, the structure refers to the
38
-------
covariance pattern of measurements on the same subject. For most of these structures, the
covariance between two measurements on the same vehicle depends only on the differences in
mileage accumulation between measurements, and the variance is constant over mileage.
The covariance structure was modeled by first fitting the "unstructured" (UN) covariance matrix
with a saturated model including all fixed effects, which failed to converge. Next, since
emissions were measured irregularly, where the mileage intervals between measurements are
more or less unique to each vehicle, the spatial covariance structure, which allows for a
continuous representation of mileage, was fit. However, the model failed to converge for the
spatial covariance matrix as well. Thus, we proceeded to fit the compound symmetry (CS)
structure which specifies that measurements at all mileages have the same variance, and that all
measurements on the same vehicle have the same correlation. The Bayesian Information
Criterion (BIC) value for the compound symmetry was 803.36.
Lastly, the first-order autoregressive structure (AR1) was modeled. This structure assumes that
the variances are homogeneous and the correlations decline exponentially with time, i.e., the
error variance in measured emissions is constant for all vehicles at all mileage levels, and sets of
measurements close in time (i.e., mileage) are more highly correlated than the measurements
further apart. The BIC value for the first-order autoregressive structure was 764.90. Since the
BIC value for the first-order autoregressive structure was lower than that for compound
symmetry, the autoregressive structure (Equation 3-19) was selected to model the covariance
structure of the residuals.
Rt = Var(ei) =
o2 = variance,
a2p
a2p
a2p2
a2p
a2pn 1 a2pn 2 a2pn 3
where:
p = correlation between measurements,
n = number of measurements
a 2pn~1
a2pn~2
Equation 3-19
A combination of first-order autoregressive structure within vehicles and a random effect
between vehicles was used to model the covariance structure which specified an inter-vehicle
random effect for differences between vehicles, and a correlation structure within vehicles that
decreases with increasing mileage lag between emission measurements. Furthermore, the error
variance associated with the low sulfur level was permitted to differ from the variance associated
with the high sulfur level. Since the first-order autoregressive structure was selected due to
limited available options, we acknowledge that there might be some limitations inherent in the
assumption of constant distance between two measurements. However, the estimates of fixed
effects, such as the differences between sulfur level means, may be the same for different
covariance structures, differing only in the standard errors of these estimates.
Once the structures for the random effects and the covariance structure for the residuals were
selected, the fixed effects in the model were tested using the approximate /' -test with the
Satterthwaite approximation for denominator degrees of freedom. The step-wise backward
elimination approach was used to remove any non-significant fixed effects (shown in red in
39
-------
Table 3-21), starting with the saturated model. The significance level of 10% (a = 0.1) was used
to test the null hypothesis while keeping statistical hierarchy.
Table 3-21. Type 3 Tests of Fixed Effects (NOx Bag 2).
Model
Effect1
Num DF
Den DF
F Value
Pr >F*
Model 1
slevel
1
254
7.66
0.0061
miles
1
271
0.10
0.7499
vehclass
1
18.2
0.18
0.6761
slevel * miles
1
170
0.79
0.3743
miles * vehclass
1
280
1.20
0.2748
Model 2
slevel
1
259
7.63
0.0062
miles
1
264
17.07
<0.0001
vehclass
1
17
0.40
0.5363
slevel * miles
1
175
0.72
0.3982
Model 3
slevel
1
259
7.66
0.0061
miles
1
264
17.08
<0.0001
slevel * miles
1
174
0.70
0.4028
Model 4
slevel
1
219
18.28
<0.0001
miles
1
270
17.54
<0.0001
1 slevel = sulfur level (high and low); miles = accumulated mileage since clean-out;
vehclass = vehicle types (car and truck); * Pr > F represents the p-value associated with the F statistic.
Finally, a likelihood-ratio test was performed to examine if the model could be reduced further
without compromising the model fit. For example, in comparing model 4 and 5 (Table 3-22), the
result of the likelihood ratio test was not statistically significant, we concluded that accumulated
mileage does not have an effect on Bag 2 NOx, and thus, model 5 was selected as the final
model.
Table 3-22. Likelihood Ratio Test for Bag 2 NOx Model.
Fixed effects
-2 Res Log Likelihood
p-value (y2)
Model 4
slevel, miles
991.6
0.1213
Model 5
slevel
994
The final NOx Bag 2 model (model 5) retains sulfur level as the sole fixed effect. Thus, the
model finds a statistically significant difference in emissions between high and low fuel sulfur
levels. In addition, the sulfur effect does not differ between vehicle types (car vs. truck) as the
the sulfur-level x vehicle type interaction term was not significant. Also, since the mileage term
is not significant, it can be concluded that the mileage accumulation after the clean-out does not
increase emissions independent of the fuel sulfur level in the current analysis. In addition, since
the sulfur level and the accumulated mileage interaction term was not significant, the model
suggests that the rate of sulfur loading does not vary by accumulated mileages after the clean-out
(up to 200 miles under the modified Long procedure) between high and low fuel sulfur levels. In
other words, the effect of high fuel sulfur on Bag 2 NOx exists immediately after clean-out and
remains essentially constant on a percentage basis, during subsequent driving of a vehicle.
40
-------
Figure 3-4 shows the data vs. predicted plots based on the final model for NOx Bag 2. There are
two paired plots next to each other with the same vehicle ID showing emissions from both high
and low sulfur. There are some instances (e.g., VID M502) where the model overestimates the
effect of sulfur by over-predicting the emission levels of high sulfur and under-predicting the
emission levels of low sulfur. In contrast, there are other instances (e.g., VID M513) where the
model underestimates the effect of sulfur by under-predicting the emission levels of high sulfur
and over-predicting the emission levels of low sulfur. However, this is to be expected given the
variability in the emission testing. In general, the model predictions are in agreement with the
data.
41
-------
Figure 3-4. Data vs. Predicted by Vehicle (Log-Transformed Bag 2 NOx).
VID-Nnoo
\TD = M500
VID-Mr>01
vm-\nm
MI)-Mcr>02
sulfur •=¦ High
sulfur = Low
sulfur = High
sulfta I o\\
sulftir = High
, -*•> 4 *
. , - : :
«*• 1 *
, t ! i j +
+ t- + * t *
\TD-\Ril2
VID-ABO?
viD = Nnin
VID - \fi04
\1D-ABI14
sulfur ~ Low
Mil fur - High
sulfur _ Iim
sulfur ~ High
sulfur _ 1 ow
; t , » » «•
t + tt +
:: »~
VID = M505
VID = M505
VID = M506
VID = M506
VID = M507
suite » High
sulfur = Low
sulfur = High
sulfur = Low
sulfur = High
•
* * *
: t t * * ''
,, 1 * < :
\1D - NHOT
VID = M508
VID = M508
VID = M509
VID = M509
sulfur Itm
sulfur = High
sulfur = Low
sulfur = High
sulfur == Low
n «11 J
, j t . » -
: t •:
t r
100 200 0
100
200 0
group
100
miles
t r
200 0
i i
100 200 0
i
200
Model
VID-NtIO
\TD ~ X110
\TD-\i1l
\TD - Xi 11
VID-X512
sulfur = High
sulfur = I ow
sulftir = High
sulftir = Low
sulfur = High
i 4', + 4 1
. t t* '~
»» t- it
r
VID = N512
VID = N513
VID = N513
VID = N514
VID = N514
sulfur = Low
sulfur = High
sulftir = Low
sulfur ~ High
sulftir = Low
"
:r* "
it* *; * *
fii "
i *4 +* 1J
VID = N515
VID = N515
VID = N520
VID = N520
VID = N521
sulfur = High
sulftir = Low
sulftir = High
sulfur = Low
sulftir = High
''' "
tX,
;H f :
!*,„ :: ;:
£14 ti 1-t
VID - N">21
VID - r»22
\'1D - P322
sulfur- Low
sulfur - High
sulfur - Low
tf'.ll ii: **
p., ,»*»
J * * | '* +
C<§
&C
C3 -1() *
:rf~^
100
200 0
200 0
i i
200 0
i i i
100 200 0
miles
Data + Mode!
200
42
-------
Furthermore, the one-to-one plot of data vs. model predictions in Figure 3-5 shows that the
points generally lie close to the 1:1 line. In addition, the model fit has an adjusted R2 of 0.71,
demonstrating reasonable accuracy in model predictions for Bag 2 NOx.
Figure 3-5. Data vs. Predicted (Log-Transformed NOx Bag 2).
Model Prediction
sulfur • • • High Sulfur • • • Low Sulfur
Table 3-23 summarizes the final models selected for all pollutants and bags, applying the same
statistical methodology described for Bag 2 NOx. For all models, the sulfur-level and mileage
interaction terms were not significant, and the change in emissions from reducing the fuel-sulfur
from 28 ppm to 5 ppm was estimated using the differences of least-squares means from the final
model, adjusting for other effects in the model, using a Tukey-Kramer adjustment in calculating
the ^-values for the least squares means. The differences of least-squares means between high
and low fuel-sulfur level were reverse-transformed to estimate the percent reduction in emissions
(Table 3-24). When the sulfur level and mileage interaction term is not significant, the percent
differences in emissions between high- and low fuel-sulfur levels are constant across
accumulated mileage after clean-out (the sulfur loading curves for high and low sulfur are
parallel) and thus, using the least squares means to quantify the reduction in emissions without
considering the as-received in-use sulfur loading was sufficient.
43
-------
Table 3-23. Final Selected Models for All Pollutants.
Pollutant
Bag
Fixed Effects1
NOx
Bag 2
slevel
Bag 1 - Bag 3
-
THC
Bag 2
slevel, miles
Bag 1 - Bag 3
slevel
CO
Bag 2
-
Bag 1 - Bag 3
-
PM
Bag 2
-
Bag 1 - Bag 3
-
1 slevel = sulfur level (high and low); miles = accumulated mileage since clean-out.
Table 3-24 summarizes the percent reduction in emissions from the analysis for NOx, THC, CO,
and PM, which are the most relevant pollutants in the MOVES context. The percent reductions
were estimated for the complete dataset with all Tier 2 standard levels included, and for a dataset
including only the vehicles certified to Tier 2 Bin 8. The ^-values represent the statistics for fuel
sulfur level from the Type III F test. Unlike the gaseous pollutants, there was no effect of sulfur
level found for PM. A plausible explanation is that the majority of PM as measured in this
program (that is, from normal-emitting Tier 2 vehicles operated at low and moderate loads) was
soot produced shortly after cold start (Bag l)37, and the destruction of soot by the catalyst may be
minimal regardless of its relative efficiency. As a result, sulfur would not be expected to have a
significant effect on directly-emitted PM (other than very small amounts of sulfate). Since there
were no analyses of PM composition in this program, we are not able to draw more definitive
conclusions.
Table 3-24. Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur on In-Use Tier 2 Vehicles.
Tier 2 Bin
Process
Pollutant
NOx (/J-value)
THC
(p-value)
CO
(p-value)
PM
B4,B5,B8
Hot-running1
51.9% (< 0.0001)
43.3% (< 0.0001)
-
-
Cold Start2
-
5.9% (0.0074)
-
-
B8 only
Hot-running1
66.3%
(0.0751)
36.8% (<
0.0001)
22.1%
(0.0061)
-
Cold Start2
-
-
-
-
1 Measured on the hot-running Phase of the FTP cycle (Bag 2).
2 Measured as the difference between the cold-start and hot-start phases on the FTP cycle (Bag 1 - Bag 3).
3.3.3.5.2 Tier 3 Equivalent Vehicles
Following the main test program with Tier 2 vehicles, a set of vehicles meeting lower "Tier 3
equivalent" emissions standards were tested to evaluate the effect of sulfur on these newer and
cleaner vehicles. These vehicles were tested using the same fuel and test procedures described
earlier. The "sulfur level" data for this subset of vehicles consisted of all measurements from the
five vehicles tested on both 28 and 5 ppm sulfur fuels. A total of 64 measurements were taken -
33 measurements from high fuel sulfur levels and 31 measurements from low fuel sulfur levels.
The description of the vehicles tested in the supplemental program is shown in Table 3-25.
44
-------
Table 3-25. Description of "Tier 3-like" Vehicles in the "Sulfur Level" Data.
Vehicle
Family
ID
Vehicle
ID
Make
Model
Model
Year
Emission
Standards
Starting
Odometer
Vehicle Origin
P528
0001L
Honda
Crosstour
2011
ULEV
12,827
Recruited
P530
0001
Chevy
Malibu
2010
SULEV
10.285
Manufacturer1
P531
0001L
Subaru
Outback
2008
SULEV
36,635
Recruited
R532
0001L
Ford
Focus
2010
SULEV
28.673
EPA-owned
P532
0001L
Chevy
Silverado
2011
T2B4
714
EPA-owned
1 This vehicle was loaned by Umieore Autocat USA, and is the same vehicle used in their 2011 study.
The box-plot of the log-transformed emissions from Bag 2 NOx "sulfur level" data (Figure 3-6)
shows the spread of the data for each vehicle and sulfur level across all mileages. The diamond
and the line inside the box represent the mean and the median, respectively. The box represents
the interquartile range between 25th and 75th percentile and the error bars show the full data
range. Generally, there is a tendency for the vehicles running on high sulfur fuel to emit more
NOx than the vehicles running on low sulfur fuel. However, the effect of operation on higher
sulfur fuel certainly varies by each vehicle.
Figure 3-6. Box-Plot of "Tier 3-Like" Vehicles by Sulfur Level (Bag 2 NOx).
=^=
6
-8-
-10-
1
Honda-C'rosstour
1
Chevy-Malibu
1
Subaru- Outback
Vehicle
1
Ford-Focus
Chevy-Silverado
D High Sulfur at 2Sppm ~ Low Sulfur at 3ppm
In analyzing the "sulfur level" data for "Tier 3 equivalent" vehicles, a similar top-down model
fitting statistical approach to that described earlier was applied to characterize the effects of fuel
45
-------
sulfur level on emissions as a function of accumulated mileages since cleanout. The dependent
variable (Yi) was the natural logarithm of emissions. The fixed effects (X,) included in the model
were sulfur level, accumulated mileage, vehicle type, and the interaction terms. The random
effects (Zi) were random intercepts for each vehicle in the study. A combination of first-order
autoregressive structure within vehicles and a random effect between vehicles was used to model
the covariance structure which specified an inter-vehicle random effect of differences between
vehicles, and a correlation structure within vehicles that decreases with increasing mileage lag
between emission measurements. The same statistical methodologies utilized for evaluating the
sulfur level effects for Tier 2 vehicles were applied to these vehicles.
Table 3-26 compares the percent reduction in emissions from 28 ppm to 5 ppm fuel sulfur for
Tier 2 vehicle and "Tier 3 equivalent" vehicles. The results suggest that significant reductions in
emissions can be achieved by reducing the fuel sulfur levels from 28 to 5 ppm in the in-use fleet
of "Tier 3 equivalent" vehicles. Furthermore, it shows that the cleaner vehicles are more
sensitivity to the fuel sulfur levels for NOx and CO than what was observed in the analysis of the
Tier 2 vehicles. This is not unexpected since the cleaner vehicles tend to rely more on efficient
catalyst activity sooner in the operation of the vehicle following the cold start. The sulfur
hinders the catalyst from performing at optimal efficiency levels early in running operation,
resulting in a larger penalty to these cleaner vehicles that rely more heavily on the catalyst to
meet the lower emission standards. Overall, we expect lower-emitting Tier 3 vehicles to show
similar or greater sensitivity to the fuel sulfur levels compared to the conventional Tier 2
vehicles.
Table 3-26. Percent Reduction in Emissions from 28 ppm to 5 ppm Fuel Sulfur for Tier 2 and "Tier 3-Like"
Vehicles.
Vehicle
Sample
Pollutant
NOx (p-
value)
THC
(p-value)
CO
(p-value)
PM
Tier 2 Vehicles
14.1%
(0.0008)
15.3%
(< 0.0001)
9.5%
(< 0.0001)
-
"Tier 3
equivalent"
Vehicles
23.9%
(0.0203)
14.6%
(0.0312)
21.0% (<
0.0001)
-
1 Measured on the hot-running Phase of the FTP cycle (Bag 2).
2 Measured as the difference between the cold-start and hot-start phases on the FTP cycle (Bag 1 - Bag 3).
3.3.3.5.3 Sensitivity Analysis
A series of sensitivity analyses of the "sulfur level" data was performed to address some of the
issues that might affect the mixed-model results. They include the impacts of: measurements at
very low concentrations, censoring of measurements with zero values, and influential vehicles.
The sensitivity analyses were conducted only for Bag 2 NOx, since above mentioned issues
pertain the most to Bag 2 NOx. For example, Bag 2 NOx showed a higher percentage of
measurements with zero values than most other pollutant and bag combinations.
46
-------
Effect of Measurements at Low Concentration
The issue of measurements with very low concentration from Bag 2 NOx has been discussed in
3.3.3.2. To address the uncertainty of measurements from these very low-emitting vehicles, we
performed sensitivity analyses using two measurement concentration screening levels: 100 ppb
(based on the lower end of the instrument manufacturer's stated calibration range for the
emission analyzer), and 50 ppb (chosen at half the former limit). In each analysis, the vehicles
with all sample measurements falling below the screening level were removed, and models were
re-fit. Results of these sensitivity analyses are provided in Table 3-27.
Table 3-27. Results of Sensitivity Analysis of Low Concentration Measurements (Bag 2 NOx).
Model Description
No. of Vehicles
No. of Observations
Estimated Reduction
Final Model
35
322
51.9%
50 ppb vehicle screen
28
263
48.4%
100 ppb vehicle screen
19
191
48.2%
In each of these sensitivity analyses, the sulfur level effect remained highly significant with p-
value < 0.004, suggesting a meaningful sulfur effect exists regardless of the removal of lowest-
emitting vehicles. Thus, we conclude that the sulfur effect is considerably larger than the
uncertainty or error associated with the measurements.
Effect of Use of Imputed Values
In order to assess the impact of substituting for censored values, models with and without
imputed values for Bag 2 NOx were compared. For the model without imputed values, the mixed
model was re-fit using a new dataset with all imputed values removed, consisting only of actual
measurements. Based on the examination of the estimates of fixed effects and the standard
errors from both models, we concluded that the imputed values did not significantly bias the
results. The percent reduction in emissions from 28 ppm to 5 ppm fuel sulfur level was changed
from 51.9% in model with imputed values to 50.0% in model without them. The sulfur level
effect remained highly significant with/>value <0.0001 for the model with and without the
imputed values.
Effect of influential vehicles
As an additional test of robustness, we also looked at the impact of removing the influential
vehicles from the dataset. Influence can be broadly defined as the ability of a single or multiple
vehicles to affect the resulting outcome through the presence or absence in the data. The
influential vehicles can be identified by examining the restricted likelihood distance (RLD),
which is calculated after an iterative process of refitting the model with and without each vehicle.
Figure 3-7 shows the restricted likelihood distance from the influence diagnostics where vehicle
family IDs N515, N520, and N521 can be considered influential vehicles affecting both the fixed
effects and covariance parameter estimates based on Cook's I) and COVRATIO estimates.
Although we do not have specific grounds for excluding these vehicles from the mixed model
analysis since the measurements from these vehicles did not fall into the category of either low
concentration measurements or the outlying observations, these influential vehicles were
removed and the model for Bag 2 NOx was re-fit to assess the impacts of these vehicles.
47
-------
Figure 3-7. Influence Diagnostics for Bag 2 NOx.
Restricted Likelihood Distance
4 .
CD
O
W
b
"j8 "*S -£> V7
Deleted VID
The resulting model showed that the percent reduction in emissions from 28 ppm to 5 ppm was
52.1 percent, compared to the reduction of 51.9 percent from the final model. This analysis
demonstrated that even when the influential vehicles are removed from the analysis, the
reduction in emissions from reducing the fuel sulfur level from 28 ppm to 5 ppm is still highly
significant with p-value <0.0001. The sensitivity analyses examining the influential vehicles for
all pollutants and bags are presented in Appendix F of the project report.
3.3.4 Application in MOVES
The results shown in Table 3-24 were incorporated into MOVES3 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. The decision to apply the results from the study of Tier 2 vehicles to
model years as early as 2001 was based on the assumption that NLEV vehicles are more similar
to upcoming Tier 2 vehicles than to Tier 1 vehicles.
The T2LowSulf 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, the M6Sulf model is applied, as described in
Section 3.2.
Equation 3-20 shows the generic form of the calculation of the linear low-sulfur adjustment^.
48
-------
—10 fis{Sbase XS)
Equation 3-20
The Tier 2 Low Sulfur coefficients (/is) were developed by linearly interpolating between the
emission levels at 28 to 5 ppm, corresponding to the reductions in emissions shown in Table
3-24, relative 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. Values of the
coefficients vary among pollutants and processes (i.e., start vs. running, as applicable). The term
Sbase represents a "base" sulfur level of 30 ppm for vehicles in model years after 2000. Finally,
xS represents the sulfur level for the fuel being modeled in a MOVES run.
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 below 100 ppm (e.g., CRC E-60
and 2001 AAM/AIAM programs). The emission reductions from FTP bag 2 and FTP bagl-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 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, as described earlier, and
the resulting estimates of percent reduction (Table 3-24) were used to develop the coefficients for
heavy-duty gasoline vehicles, assuming that the catalyst response of heavier gasoline trucks to
fuel sulfur level is closer to Tier 2 Bin 8 vehicles than to lower standard levels. Due to a lack of
data, we assumed no impact of sulfur on emissions for 2001-and-later motorcycles. Table 3-28
shows the specific values of the sulfur coefficients used in T2LowSulf model by pollutant,
process, and vehicle type.
The sulfur base (Sbase) in the T2LowSulf model varies as a function of model year group. For
model year group 2001-2016, the sulfur base is unchanged at 30 ppm. Subsequently, for light-
duty passenger cars (sourceType 21) in model year group 2017-2060, the sulfur base is set at 10
ppm due to the assumption that Tier 3 Vehicle Standard is enabled by Tier 3 vehicles running on
10 ppm sulfur. This prevents double-counting of the impacts of low levels of sulfur in fuels for
Tier 3 vehicles. Similarly, light-duty trucks (sourceType 31 and 32) in model year group 2018-
2060 are also set to a sulfur base of 10 ppm, with the additional year accounting for a lag in low
sulfur phase-in for these vehicles. Vehicles in heavier weight classes (sourceType 41 and above)
continue through 2060 with a sulfur base of 30 ppm. xs represents the actual in-use sulfur levels
in the region being modeled.
49
-------
Table 3-28. Interpolated Coefficients by Vehicle Type, Process and Pollutant, applied for sulfur levels < 30
IH1HL
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
0.002568
0.018126
0
0
0
0.021582
0
0
All other Vehicle
Types
0
0.015488
0
0.009436
0
0.027266
0
0
Equation 3-20 has been applied using the coefficients in Table 3-28 in the database table that
stores the fuel effect equations in the MOVES ("generalFuelRatioExpression"). This table
consolidates the two sulfur models (M6Sulf and T2LowSulf) for MYG 2001-2016 and 2017-
2060, and the other fuel-effect models (i.e., EPAct model, discussed later), and allows the
MOVES model to compute the fuel effects based on the fuel properties of any fuel contained in
the ''fuelSupply" and''fuelFormulation" database tables. A detailed description of the
"generalFuelRatioExpression" table is provided in Section 6.6..
3.4 Results: Sulfur Effects in MOVES3
The trends in emissions in relation to fuel sulfur levels are shown in Figure 3-8 through Figure
3-11 for the 2017+, 2001-2016, 1996 and 1988 model years, respectively, for passenger cars,
passenger and light commercial trucks. The effects are 'net fuel effects' for running-exhaust
emissions from the MOVES model. They were produced by compiling results from eight
separate MOVES runs using a constant fuel formulation and varying the fuel sulfur level from 4
ppm sulfur to 500 ppm sulfur. The 1988 model year represents the fuel effects on Tier 0
vehicles, and the 1996 model year represents the Tierl and LEV standards, applying log-log and
log-linear relationships within the M6Sulf model, as previously described; the 2001-2016 model
year represent Tier 2 vehicles, and the 2017+ model years represent Tier 3 vehicles.
The fuel effects are normalized to 90 ppm sulfur for model years 1988 to 1996, to 30 ppm sulfur
for model years 2001 to 2016, and to 10 ppm sulfur for model years 2017 and later. In this
context, 'normalization' means the correction factor is set to 1.0 at the specified level. For these
examples, the other fuel parameters were set at Base-Fuel levels (RVP at 6.9 psi, 0% Ethanol
volume, 26.1% aromatic content, 5.6% olefin content, 1.0% benzene content, T50 at 218°F and
T90 at 329°F).
It is worth noting that, in contrast to NOx and THC, the fuel sulfur adjustment for running CO for
MY 2001 and later is equal to 1.0 for all fuel sulfur levels less than 30 ppm (Figure 3-8 and
Figure 3-9). This pattern is applied because the sulfur coefficient for running CO (Table 3-28) is
zero in the T2Sulfur model for passenger cars, passenger and light commercial trucks.
50
-------
Figure 3-8. Relative Fuel Sulfur Effects for Running-Exhaust Emissions for MY 2017 and later, normalized
to a sulfur level of 10 ppm.
Sulfur Level (ppm)
50 100 150 200 250 300 350
Sulfur Level (ppm)
Figure 3-9. Relative Fuel Sulfur Effect for Running Exhaust Emissions for MYs 2001 to 2016, normalized to a
sulfur level of 30 ppm.
51
-------
Figure 3-10. Relative Fuel Sulfur Effect for Running Exhaust Emissions for MY 1996, normalized to a sulfur
level of 90 ppm.
Figure 3-11. Relative Fuel Sulfur Effect for Running Exhaust Emissions for MY 1988, normalized to a sulfur
level of 90 ppm.
S
1.4
1.2
1.0
0.S
•e
-< 0.6
i 0A
"3
s
0.2
0.0
;
¦
¦
¦
-~-NC
X
-¦-THC
;
-A-CO
;
0 50 100 150 200 250 300 350 400 450 500 550
Sulfur Level (ppm)
52
-------
4 Use of the Complex Model (for CO Emissions)
For carbon monoxide, fuel adjustments were estimated through application of equations
developed for the Complex Model for Reformulated Gasoline.38 The "Complex Model" is so
called because it was designed to model the "complex" behavior of selected pollutants in relation
to changes in a set of selected fuel properties. By contrast, a "simple model" is a uniform ratio or
fraction that does not vary in response to fuel properties.
The Complex Model equations are applied to running, start and extended-idle emissions for
gasoline-fueled vehicles for all 2000 and earlier model years. For 1974 and earlier model years,
1975 weightings are used. In addition, while MOBILE6.2 relied on very limited data from
heavy-duty gasoline vehicles, MOVES applies Complex Model equations to both light-duty and
heavy-duty gasoline vehicles. This step was taken because the very limited data specific to
heavy-duty gasoline vehicles are not adequate to account for effects of fuel properties.
4.1 Overview of the Complex Model
The underlying dataset included measurements collected on sample of vehicles manufactured in
MY1990 or earlier, and reflecting "Tier 0" standards, over a variety of gasoline formulations.
The complex model is composed of sets of models for each pollutant. The models are statistical
models fit to sets of emissions measurements on a set of fuels with widely varying properties.
For CO, the model includes linear terms for six properties, quadratic terms for four properties,
and one interaction term, as shown in Table 4-1. Note that in the database table
ComplexModelPammeters, model terms are represented by a cmpID, which is defined in the
translation table ComplexModelParameterName. For convenience, relevant values of cmpID are
also translated in Table 4-4 below.
For CO, 10 models were fit, with each representing a specific combination of fuel-delivery,
catalyst, air injection and emissions-control technology. The technology groups are described in
Table 4-2. As an aggregate, these sets of models are referred to as the "unconsolidated complex
model."
In fitting the complex models, the measurements for all fuel properties were "centered,"
meaning that the mean of all measurements for the property was subtracted from each individual
measurement. This step aids in scaling the dataset so that each fuel property is centered11 on a
mean of 0.0. Thus, if ln7 is the natural logarithm of a emissions, the model is fit as shown in
Equation 4-1, using terms for oxygenate (wt.%), aromatics (vol.%) and RVP (psi) as examples
for linear terms, and E300xOLE as an example of a 2nd-order interaction term. Note that squared
(quadratic) terms are centered similarly to the interaction term.
ln^ — Pq + /^oxy ("^oxjy ^Oxy /^arom (^aioni./ ^aiom )+'"' + /^RYP ^RVP )~'
-+B (x -x Yx -x ) Equation 4-1
+ ^E300OLE VXE300,; XE300 AxOLE,; xOLE /
The mean values used for centering all individual fuel property values are presented in
d For additional details on the mean fuel property values used for centering the terms in the complex model, see Air
Toxic Emissions from On-road Vehicles in MOVES3.1
53
-------
Table 4-3. The set of coefficients (fi values in Equation 4-1) for the CO models by technology
group, are presented in Table 4-4 and Table 4-5, which contain linear and 2nd-order terms,
respectively. Note that in the database table ComplexModelParameters, the values are stored in
two fields, coeffl and coeff2. The values in the tables below are the sums of these two fields. In
the model fitting, coeffl represents a coefficient for all 11 groups as an aggregate, and coeffl
represents an adjustment to the aggregate term to represent a difference between the main model
(for all groups) and the model specific to a group.
It should be noted that the sulfur effects terms in the original complex model were not included
when the model was adapted for inclusion in MOVES. Rather, the effects of fuel sulfur are
estimated independently, due to the propensity of sulfur to reduce catalyst efficiency and
confound the effects of other fuel properties.
Table 4-1. Definition and Description
of Terms included in the Complex Model for CO.
cmpID
cmpName
Description
1
OXYGEN
Oxygenate
6
AROMATIC
Aromatics Content
7
OLEFINS
Olefin content
3
RVP
Reid Vapor Pressure
4
E200
Percent Fuel evaporated at 200 °F
5
E300
Percent Fuel evaporated at 300 °F
15
OLESQR
Olefin x Olefin
11
RVPSQR
RVP x RVP
12
E200SQR
E200 x E200
13
E300SQR
E300x E300
22
E300OLE
E300 x Olefins
Table 4-2. Technology Groups included in the Complex Model.
Technology Group
Fuel System1
Catalyst2
Air Injection
Exhaust-gas
Recirculation
1
PFI
3-Way
No
Yes
2
PFI
3-Way
No
No
3
TBI
3-Way
No
Yes
4
PFI
3-Way + Oxy
Yes
Yes
5
PFI
3-Way
Yes
Yes
6
TBI
3-Way
Yes
Yes
7
TBI
3-Way + Oxy
Yes
Yes
8
TBI
3-Way
No
No
9
carburetor
3-Way + Oxy
Yes
Yes
10 ("High Emitters")
ALL
ALL
ALL
ALL
1 Fuel System: PFI = port fuel Injection, TBI = throttle body injection.
2 Catalyst type: "3-way" = three-way catalyst, "Oxy" = oxidation catalyst.
54
-------
Table 4-3. Mean Fuel-Property Values used for centering Terms in the Complex Model for CO.1
Property
fuelParameterlD
Units
Base Value2
Mean Value
Aromatics
6
Vol. %
32
28.26110
Olefins
7
Vol. %
9.2
7.318716
Oxygenate
1
Wt.%
0
1.774834
RVP
3
psi
8.7
8.611478
E200
4
%
41
46.72577
E300
5
%
83
85.89620
1 Stored in database table meanFuelPammeters where polprocessid = 201 or 202.
2 Value for base fuel.
Table 4-4. Complex Model Coefficients for Linear Effects, for Carbon Monoxide, by Technology Group
Technology
Group
Fuel Property
Oxygen
Aromatics
Olefins
RVP
E200
E300
1
-0.032584
0.007795
0.000507
0.043314
-0.002335
0.002372
2
-0.019006
0.00547
0.000507
0.003448
-0.002335
0.002372
3
-0.019006
0.00547
0.000507
0.003448
-0.002335
-0.009238
4
-0.095314
0.00547
0.000507
0.003448
0.005751
0.002372
5
-0.019006
0.000365
0.000507
0.003448
-0.002335
0.002372
6
-0.019006
0.00547
0.000507
0.003448
-0.002335
-0.002211
7
-0.019006
0.00547
0.000507
0.003448
-0.002335
0.002372
8
-0.019006
0.00547
0.000507
0.003448
-0.002335
0.002372
9
-0.019006
0.00547
0.000507
0.003448
-0.002335
0.002372
10
-0.019006
0.00547
0.000507
0.003448
-0.002335
0.002372
11
-0.032584
0.007795
0.000507
0.043314
-0.002335
0.002372
Table 4-5. Complex Model Coefficients for 2nd-Order Effects, for Carbon Monoxide, by Technology Group
Technology
Group
Fuel Property
OLESQR
RVPSQR
E200SQR
E300SQR
E300OLE
1
0.000291
0.017288
0.000078
0.000515
0.000362
2
-0.000104
0.007093
0.000078
0.000515
0.000362
3
-0.000104
0.007093
0.000217
0.000515
-0.000511
4
0.000605
0.007093
0.000078
0.000515
0.000362
5
-0.000104
0.007093
0.000078
0.000515
0.000362
6
-0.000104
0.007093
0.000078
0.000515
-0.000244
7
-0.000104
0.007093
0.000078
0.000515
0.000362
8
-0.000104
0.007093
0.000078
0.000515
0.000362
9
-0.000104
0.007093
0.000078
0.000515
0.000362
10
-0.000104
0.007093
0.000078
0.000515
0.000362
11
0.000291
0.017288
0.000078
0.000515
0.000362
55
-------
4.2 Application of the Complex Model
For each compound, the model equations are evaluated for a "base" and a "target" fuel (See
Section 2). The base fuel represents a fuel assumed to be that reflected in the base emission rates
and which serves as a basis for fuel adjustments. The target fuel is represented by a specific set
of properties and which represents a fuel "in-use" in the geographic area(s) and season(s) being
modeled.
Initially, an adjustment for the difference in emissions of the compound modeled on the target
fuel relative to the base fuel is calculated. If the model, as shown above, can be conveniently
expressed, using matrix notation, as XPtarget and XPbase for estimates on the target and base fuels,
then the fractional difference in emissions is given by
eXp(,XPtarget)
exp(xpbase)
/adj = —^—1.0 Equation 4-2
The expression in Equation 4-2 is evaluated for the same target and base fuels for each of the ten
technology groups. A mean value of the adjustment is then calculated for each model year from
2000 to 1970, as a weighted average of the fraction of sales in each group in each model year, for
the groups, as shown in Equation 4-3. The weights are shown in Table 4-6 and represent the
sales fractions for the ten vehicle technologies at each age.
Note that the use of varying weights in applying the complex model in MOVES differs from the
original application in which the weights were invariant. The application of Equation 4-3 to each
of the 30 model years gives a set of 30 adjustments, with each applied to its respective model
year.
10 10
/id,me;in S ^Group/adj,Group > S ^Group ^*0 E(|Uilti()n 4-3
Group-1 Group-1
The mean adjustments calculated in Equation 4-3 are then applied to estimate emissions of CO
on the target fuel (Ereiative), representing the effect on the emissions of CO due to the changes in
fuel properties between the target and base fuels (Equation 4-4). If the target and base fuels were
identical, the values of/adj ,mean would be 0.0.
^relative — ^basel^ ./adj,mean,) Equation 4-4
Note that the weights used in MOVES differ from those originally used in the Complex model
for purposes of fuel certification. They now vary by age to reflect the changing importance of
technology groups (weights in the original do not vary). There is now less emphasis on so called
"High emitters". The original Complex model gave a 55 percent weighting to high emitters (i.e.,
fuel model = 10). Group 10 now receives a weighting ranging from 0.01 percent at age zero to
32.8 percent at age 30.
The final adjustment for non-sulfur properties, calculated as described in this section, is then
multiplied by the adjustment for sulfur, calculated as described above in Section 3.2. Note that
56
-------
the fuel adjustment for CO is applied only to vehicles in model years 1975 to 2003. For model
years 1974 and earlier, the adjustment is reset to 1.0, i.e., no adjustment is applied.
Table 4-6. Weights Applied to Complex Model coefficients for Technology Groups, by Age (Vehicle Age 0
represents model year 2000).e
Age
Technology Group
1
2
3
4
5
6
7
8
9
10
0
0.2360
0.2829
0.1806
0.1814
0.0290
0.0042
0.0556
0.0
0.0203
0.0100
1
0.2339
0.2803
0.1789
0.1797
0.0287
0.0042
0.0551
0.0
0.0201
0.0190
2
0.2315
0.2774
0.1771
0.1779
0.0284
0.0041
0.0546
0.0
0.0199
0.0290
3
0.2272
0.2723
0.1738
0.1746
0.0279
0.0041
0.0536
0.0
0.0196
0.0470
4
0.2229
0.2672
0.1706
0.1713
0.0274
0.0040
0.0525
0.0
0.0192
0.0650
5
0.2189
0.2623
0.1675
0.1682
0.0269
0.0039
0.0516
0.0
0.0188
0.0820
6
0.2148
0.2574
0.1644
0.1651
0.0264
0.0038
0.0506
0.0
0.0185
0.0990
7
0.2110
0.2529
0.1614
0.1621
0.0259
0.0038
0.0497
0.0
0.0182
0.1150
8
0.2072
0.2483
0.1585
0.1592
0.0254
0.0037
0.0488
0.0
0.0178
0.1310
9
0.2036
0.2440
0.1558
0.1565
0.0250
0.0036
0.0480
0.0
0.0175
0.1460
10
0.2000
0.2397
0.1530
0.1537
0.0246
0.0036
0.0471
0.0
0.0172
0.1610
11
0.1967
0.2357
0.1505
0.1512
0.0241
0.0035
0.0464
0.0
0.0169
0.1750
12
0.1934
0.2317
0.1479
0.1486
0.0237
0.0035
0.0456
0.0
0.0166
0.1890
13
0.1903
0.2280
0.1456
0.1462
0.0234
0.0034
0.0448
0.0
0.0164
0.2020
14
0.1872
0.2243
0.1432
0.1438
0.0230
0.0033
0.0441
0.0
0.0161
0.2150
15
0.1843
0.2209
0.1410
0.1416
0.0226
0.0033
0.0434
0.0
0.0159
0.2270
16
0.1814
0.2174
0.1388
0.1394
0.0223
0.0032
0.0428
0.0
0.0156
0.2390
17
0.1786
0.2140
0.1366
0.1372
0.0219
0.0032
0.0421
0.0
0.0154
0.2510
18
0.1760
0.2109
0.1346
0.1352
0.0216
0.0031
0.0415
0.0
0.0151
0.2620
19
0.1736
0.2080
0.1328
0.1334
0.0213
0.0031
0.0409
0.0
0.0149
0.2720
20
0.1712
0.2052
0.1310
0.1315
0.0210
0.0031
0.0403
0.0
0.0147
0.2820
21
0.1688
0.2023
0.1291
0.1297
0.0207
0.0030
0.0398
0.0
0.0145
0.2920
22
0.1664
0.1994
0.1273
0.1279
0.0204
0.0030
0.0392
0.0
0.0143
0.3020
23
0.1643
0.1969
0.1257
0.1262
0.0202
0.0029
0.0387
0.0
0.0141
0.3110
24
0.1624
0.1946
0.1242
0.1248
0.0199
0.0029
0.0383
0.0
0.0140
0.3190
25
0.1602
0.1920
0.1226
0.1231
0.0197
0.0029
0.0378
0.0
0.0138
0.3280
26
0.1602
0.1920
0.1226
0.1231
0.0197
0.0029
0.0378
0.0
0.0138
0.3280
27
0.1602
0.1920
0.1226
0.1231
0.0197
0.0029
0.0378
0.0
0.0138
0.3280
28
0.1602
0.1920
0.1226
0.1231
0.0197
0.0029
0.0378
0.0
0.0138
0.3280
29
0.1602
0.1920
0.1226
0.1231
0.0197
0.0029
0.0378
0.0
0.0138
0.3280
30
0.1602
0.1920
0.1226
0.1231
0.0197
0.0029
0.0378
0.0
0.0138
0.3280
e Note that in the MOVES database, these weights are stored in the table FuelModelWtFactor.
57
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5 Use of the EPA Predictive Model (HC and NOx Emissions)
For hydrocarbon and NOx emissions, "complex" fuel adjustments are estimated not through
application of the Complex Model, but rather through application of equations developed for the
"EPA Predictive Model" (EPM). The Predictive Model was applied for these two pollutants
because it represents more recent work than the Complex Model, although modeling for CO was
not included in the effort.
The EPM is composed of sets of statistical models developed during evaluation of a petition by
the State of California for a waiver of the Federal oxygenate requirement for reformulated
gasoline.39 The EPM predicts changes in NOx and HC emissions from changes in fuel
properties, and was initially developed in the course of EPA's evaluation of the "CARB Phase-3
Predictive Model," developed to "allow evaluation of gasoline specifications, ... , as alternatives
to the flat and average property limits on gasoline specifications in California's regulations."
After reviewing technical analyses submitted by the State of California, EPA elected to pursue
an independent modeling effort, in large part due to "a substantial disparity between the NOx -
oxygen relationship that emerges from the Phase 3 Model andfrom the other two major
modeling efforts - the EPA Complex Model and the CARB Phase 2 model."
5.1 Data Used in Developing the EPA Predictive Model
In developing the EPM, EPA used the same dataset used by California, with some additions,
modifications and exclusions. Specifically, EPA confined its efforts to sets of data for vehicles
manufactured in model years 1986 to 1993, designated as "Tech 4" vehicles. EPA elected not to
revisit models for vehicles manufactured prior to 1986 ("Tech 3") or in 1996 and later ("Tech
5"), which were included in the CARB models. As the analysis concerned application of
regulations in California, only vehicles certified to California standards were included.
Additionally, observations with "extreme" fuel-property values or measured at ambient
temperatures outside the range of 68-95 °F were excluded. Finally, observations missing values
for any of the fuel properties under study were removed.
5.2 Analytic Approaches
As in the Complex Model, models were fit to the natural logarithm of emissions results, applied
as a normalizing and variance-stabilizing transformation. The models were fit as "mixed"
models, with fuel properties as "fixed" and vehicles as "random" effects. In a departure from the
approach used by CARB, EPA chose to include separate terms for "high emitters," as in the
Complex model, whereas CARB had not distinguished "high emitters" in its Phase-3 model.
Model fitting was performed in a series of steps. In the first step, all linear effects were included
in an initial model, and second-order quadratic and interaction terms were selected for inclusion
through a forward stepwise process. During stepwise fitting, second-order terms with individual
/(-values increasing to levels exceeding a 5.0% confidence level upon the addition of subsequent
terms were removed. Again, all linear terms were retained at this stage, regardless of their
individual confidence levels.
Models developed in the first step were further evaluated using two information criteria (AIC
and BIC). At the culmination of model fitting, single models were not selected for each
58
-------
pollutant. Rather sets of models were retained for application, with overall results to be obtained
by averaging the results for individual models.
The MOVES database contains sets of coefficients for NOx and THC. The models include linear
terms for six properties, with additional quadratic or interaction terms, as shown in Table 5-1.
Note that in the database table Comp/exMode/Parameters^ model terms are represented by a
cmpID, which is defined in the translation table ComplexModelParameterName. For
convenience, relevant values of cmpID are also translated in Table 5-1.
Table 5-1. Definition and Description of Terms included in the Predictive Model for NOx and THC
cmpID
cmpName
Description
52
Intercept
Intercept term
1
OXYGEN
Oxygenate
6
AROMATIC
Aromatics Content
7
OLEFINS
Olefin content
3
RVP
Reid Vapor Pressure
54
T50
T50 (°F)
55
T90
T90 (°F)
57
T50SQR
Quadratic term for T50
56
T90SQR
Quadratic term for T90
63
OXYT50
Oxygenate x T90 interaction
58
OXYT90
Oxygenate x T90 interaction
60
AROT90
Aromatics * T90 interaction
61
T50HI
Distinct T50 slope for "high emitters"
53
HI
Distinct intercept for "high emitters"
5.2.1 Standardization of Fuel Properties
In fitting the predictive models, the measurements for all fuel properties were "centered,"
meaning that the mean of all measurements for the property was subtracted from each individual
measurement. The centered measurement, representing the distance between the measurement
and its mean (positive or negative) was then "scaled" by dividing it by the standard deviation of
all measurements. These steps, known as "standardization," aided in scaling the dataset so that
each fuel property is centered on a mean of 0.0 and expressed in units of its own standard
deviation, which places the various fuel properties into a common "space" for purposes of
analysis. The result, designated as "Z" was calculated as shown in Equation 5-1, using the
aromatics term as an example. Means and standard deviations for the properties used in
standardization are shown in Table 5-2.
ry (-^ARCy _ ^ARO )
aaro = Equation 5-1
^ARO
The standardization for a 2nd-order term, i.e., a quadratic or interaction term is calculated by
multiplying the individual standardized terms, as shown in Equation 5-2 for a squared term
(T50xT50), and in Equation 5-3 for an interaction term (AROXT90).
59
-------
^T50SQR, i ~ ^T50yi^T50,i
(xT50,i XT50
Jr 50
Equation 5-2
7 -77
AROT90,/ ~~ ^ ARO,i^T90,i
(^ARO.i ¦*ARC>)>)f (-*T90,i -*T90)
Equation 5-3
Table 5-2. Fuel-Property Values used to Standardize Terms in the Predictive Model.1
Property
fuelParameterlD
Units
Base Value2
Mean Value
Std. Dev.
Aromatics
6
Vol. %
26.1
28.0828
7.38317
Olefins
7
Vol. %
5.6
6.97437
4.93287
Oxygenate
1
Wt.%
0
1.34763
1.25188
RVP
3
psi
6.9
8.44534
0.780184
T50
9
°F
218
206.816
17.9063
T90
10
°F
329
312.126
22.0993
1 Stored in database table meanFuelPammeters where polprocessid = 101,102, 301 or 302.
2 Value for base fuel.
Thus, if ln7 is the natural logarithm of a species such as NOx, the model is fit as shown in
Equation 5-4, using terms for oxygenate (wt.%), aromatics (vol.%) and RVP (psi) as examples
for linear terms, and T50SQR and AROT90 terms as examples of second-order quadratic and
interaction terms, respectively.
In7 - P0+ /?0XYZ0XY + /^ARO^ARO ^ /Wp^RVP
Equation 5-4
I" /^T50SQR T50SQR + "aROT90^AROT90
The sets of coefficients (fi values in the equation) for the NOx models are presented in Table 5-3
and Table 5-4, which contain linear and 2nd-order terms, respectively. Corresponding terms for
the HC models are presented in Table 5-5 and Table 5-6. The tables include six candidate model
fits for NOx and three for HC. When the models are applied, an unweighted average of results for
all candidate models is calculated and used to calculate fuel effects. Note that in the database
table ComplexModelParameters, the values are stored in a single field coeffl.
It should be noted that the sulfur effects terms in the original Predictive Model were not included
when the model was adapted for inclusion in MOVES; rather, the effects of fuel sulfur are
estimated independently, due to the propensity of sulfur to reduce catalyst efficiency and
confound the effects of other fuel properties. The effects of fuel sulfur are discussed in Chapter
3.
60
-------
Table 5-3. NOx: Predictive Model Coefficients for Linear Effects for Six Candidate Models.
Candidate
Model
Fuel Property
Intercept1
a
Oxygen
Aromatics
Olefins
RVP
T50
061
302 (Step-2)
-0.6603
0.396
0.0124
0.01587
0.01988
0.009093
-0.00245
0.00719
303 ( 3 )
-0.6606
0.3963
0.01728
0.01431
0.01949
0.01172
0.000084
0.007879
304 (Step-3)
-0.6656
0.3965
0.01333
0.01524
0.0194
0.009694
0.001804
0.005543
305 ( 5 )
-0.6651
0.396
0.01371
0.01407
0.01966
0.007673
0.001173
0.006239
306 (6)
-0.6624
0.3961
0.01351
0.01501
0.0199
0.00839
0.000312
0.006213
307 (7)
-0.6737
0.3969
0.008245
0.01209
0.01969
0.006188
-0.00475
0.007587
1 The original values from model fitting are presented in the table; in the MOVES application, this term is reset to
1.0.
Table 5-4. NOx: Predictive Model Coefficients for 2nd-Order Effects for Six Candidate Models.
Candidate
Fuel Property
Model
OXYSQR
T50SQR
OXYARO
OXYT50
OXYT90
302 (Step-2)
303 ( 3 )
-0.0051
304 (Step-3)
0.006974
305 ( 5 )
-0.0083
306 (6)
-0.00547
307 (7)
0.0112
Table 5-5. HC: Predictive Model Coefficients for Linear Effects for Three Candidate Models.
Candidate
Model
Fuel Property
Intercept1
a
Oxygen
Aromatics
Olefins
RVP
T50
061
107
-1.5957
1.6909
-0.01329
0.008729
-0.01426
0.008474
0.06125
0.02084
108
-1.598
1.6935
-0.01378
0.008465
-0.0143
0.008971
0.06499
0.02104
112
-1.6012
1.7091
-0.01391
0.008759
-0.01457
0.007973
0.06046
0.02133
1 The original values from model fitting are presented in the table; in the MOVES application, this term is reset to
1.0.
61
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Table 5-6. HC: Predictive Model Coefficients for 2nd-Order Effects for Three Candidate Models.
Candidate
Fuel Property
Model
OXYSQR
T50SQR
T90SQR
OXYT90
AROT90
T50HI
107
0.01256
0.02494
0.01617
0.01589
0.006908
108
0.01353
0.02477
0.01604
0.01576
0.007013
-0.02609
112
0.01288
0.02469
0.01633
0.01552
0.006814
5.3 Application in MOVES
The application of the EPM in MOVES differs from its regulatory application in certain respects,
as described below. The Predictive Model equations are applied to running, start and extended
idle emissions for gasoline-fueled vehicles over MY range 1960-2000. In addition, while
MOBILE6.2 relied on very limited data from heavy-duty gasoline vehicles, MOVES applies
Predictive Model equations to both light-duty and heavy-duty gasoline vehicles. This step was
taken because the very limited data specific to heavy-duty gasoline vehicles are not adequate to
account for effects of fuel properties.
For each compound, the model equations as shown in the tables above, are evaluated for "base"
and "target" fuels (as defined in Chapter 0 above). The base fuel represents a fuel assumed to be
reflected in the base emission rates and which serves as a basis for fuel adjustments. The target
fuel is represented by a specific set of properties and which represents a fuel "in-use" in the
geographic area(s) and season(s) being modeled.
Initially, an adjustment for the difference in emissions of the compound modeled on the target
fuel relative to the base fuel is calculated. If the model, as shown in Equation 5-4, can be
conveniently expressed, using matrix notation, as XPtarget and XPbase for estimates on the target
and base fuels, then the ratio difference in emissions is given by Equation 5-5.
exp(XP target)
*0$ ~ V Equation 5-5
exp(XpbaJ
The adjustment for the non-sulfur properties developed as described in this chapter is multiplied
by the adjustment for sulfur, which is derived as described above in Section 3.2. Note that the
fuel adjustments for HC and NOx are applied only to vehicles in model years 1975 to 2003. For
model years 1974 and earlier, the adjustment is reset to 1.0, i.e., no adjustment is applied.
62
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6 Gasoline Fuel Effects for Vehicles certified to Tier 2 Standards
(EPAct Models: HC, CO, NOx, PM)
6.1 Introduction: the EPAct Project
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 previously 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, with emissions levels an order of magnitude higher than
those for currently manufactured vehicles compliant with Federal Tier 2 or equivalent LEV-II
standards. These models are still in use in MOVES to estimate fuel effects for vehicles
manufactured prior to model year 2001, as described in the previous two chapters. 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
carbon monoxide and air toxics, such as benzene and the aldehydes.40 More recently, the
applicability of older models 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 provided 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"), 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. The vehicles were selected to represent the
latest technologies on the market at the time the program was launched in 2008. The vehicles
were to reflect a majority of sales for model year 2008. In addition, the vehicles were to conform
primarily to Tier 2 Bin-5 exhaust standards, and to reflect a variety of emission-control
technologies, as realized through the selection of a range of vehicles sizes and manufacturers.
The vehicle sample is summarized in Table 6-1.
63
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Make
Brand
Model
Engine Size
Tier 2 Bin
LEVTI
Std
Odometer
GM
Chevrolet
Cobalt
2.2L 14
5
NA
4,841
GM
Chevrolet
Impala FFV
3.5L V6
5
L2
5,048
GM
Saturn
Outlook
3.6L V6
5
L2
5,212
GM
Chevrolet
Silverado FFV
5.3L V8
5
NA
5,347
Toyota
Toyota
Corolla
1.8L 14
5
U2
5,019
Toyota
Toyota
Camry
2.4L 14
5
U2
4,974
Toyota
Toyota
Sienna
3.5L V6
5
U2
4,997
Ford
Ford
Focus
2.0L 14
4
U2
5,150
Ford
Ford
Explorer
4.0L V6
4
NA
6,799
Ford
Ford
F150 FFV
5.4L V8
8
NA
5,523
Chrysler
Dodge
Caliber
2.4L 14
5
NA
4,959
Chrysler
Jeep
Liberty
3.7L V6
5
NA
4,785
Honda
Honda
Civic
1.8L 14
5
U2
4,765
Honda
Honda
Odyssey
3.5L V6
5
U2
4,850
Nissan
Nissan
Altima
2.5L 14
5
L2
5,211
The study used a set of twenty-seven test fuels spanning wide ranges of five fuel properties
(ethanol, aromatics, vapor pressure, and two distillation parameters: T50 and T90). The numbers
of test points and values of each property are shown in Table 6-2. The properties of the test fuels
were not assigned to 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 commercially available summer fuels in the U.S. (5th to 95th percentiles
for each property).
Table 6-2. Levels assigned to Experimental Factors (Fuel parameters) for the Phase-3 EPAct program
Factor
No. Levels
Levels
Low
Middle
High
Ethanol (vol.%)
4
0
10, 15
20
Aromatics (vol.%)
2
15
35
RVP (psi)
2
7
10
T50 (°F)
5
150
165, 190, 220
240
T90 (°F)
3
300
340
The experimental design embodied in the fuel set is the product of an iterative process involving
balancing among research goals, fuel-blending feasibility and experimental design. As fuel
properties tend to be moderately to strongly correlated, and as the goal was to enable analysis of
fuel effects as though the properties were independent (uncorrelated), it was necessary to address
these issues in design and analysis. Accordingly, the fuel set was designed using a computer-
generated optimal design, as modified by additional requirements such as the total number of
fuels and specific properties for subsets of fuels. In addition, to generate the design, it was
necessary to specify the fuel effects to be estimated by the resulting model. The fuel set was
designed to allow estimation of linear effects for the five properties shown in Table 6-2, plus
two-way interactions of ethanol and the other five properties, as shown in Equation 6-1, in which
fi represents a linear coefficient for each effect.
64
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Y = /3, + ^etOH + p2 Arom + /?3RVP + /?4T50 + J35 T90 +
/?6T502 +/?1ietOH2
Equation 6-1
/?7etOH x Arom + /?setOH x RVP + /?9etOH x T50 + AoetOH x T90 +
s
In the equation, the linear terms (e.g., /?ietOH, etc.) describe linear associations between
emissions (Y) and the value of the fuel property. The quadratic terms are used to describe some
degree of curvature in the relationship between emissions and the fuel property. Note that a
minimum of 3 test levels for a property is needed to assess curvilinear relationships and that the
design included such effects only for ethanol and T50. Two-way interaction terms indicate that
the relationship between emissions and the first fuel property is dependent on the level of the
second fuel property. For example, if an etOHx Arom interaction is included in a model, it
implies that the effect of ethanol on the emission Y cannot be estimated without accounting for
the aromatics level, and vice versa.
Using start NOx as an example, we can use the relationship between emissions, ethanol and
aromatics levels as an example. Figure 6-l(a) shows lnNOx, averaged by nominal ethanol levels.
Different series are shown for means at the high and low aromatics levels and across both levels
("linear effect"). The linear effect would suggest a small but positive coefficient for ethanol.
However, accounting for the ethanol level shows a more complex relation in which the trend at
low aromatics is steeper than the linear effect, and that the effect at high aromatics inverts from a
positive to negative slope. Similarly, in Figure 6-1(b), the complementary view is shown, with
mean lnNOx vs. aromatics levels, and with separate series for the three ethanol levels and across
all levels. The trends are marked and positive in all cases, but with steepness decreasing with
increasing ethanol level. The overall mean or "linear effect" is very close to the middle ethanol
level (10 vol.%). Overall, this relationship can be characterized as an "interference interaction"
in that increasing the level of aromatics reduces the steepness of the trend with ethanol, and vice
versa. Note also that in (a), a slight curvature in the trends suggests that a quadratic term for
ethanol could be appropriate. In fact, the quadratic term is not significant in fitting this model,
whereas the interaction is found to be significant.
Figure 6-1. NOx (Bag 1): Mean emissions levels, averaged by three ethanol and two Aromatics Levels,
Note that inclusion of the 11 effects in the design does not imply that all effects will be retained
in all models following the fitting process. Properties for each of the test fuels are shown in
Table 6-3.
65
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In conducting measurements, the LA92 "Unified" 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. The three phases ("bags") of the cycle are characterized as "cold-start" (bag
1), "hot-running" (bag 2) and "hot-start" (bag 3). In the discussion that follows, the terms "cold-
start," "start" and "bag 1" will be treated as effectively synonymous. Similarly, the terms "hot-
running," "running" and "bag 2" will be treated as synonymous.
Note that 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 was iterative, with some steps triggering 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
process is briefly described in the following section.
The EPAct exhaust research program and analysis are extensively documented in the "EPAct
Test Program Report" 41 and "EPAct Analysis Report."42 This chapter describes how the
statistical models developed during the EPAct study are applied in the MOVES model.
66
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Table 6-3. Measured Parameters for Fuels in the Phase-3 EPAct Program.
Fuel1
etOH (vol.%)
Aromatics (vol.%)
RVP (psi)2
T50 (°F)
T90 (°F)
1
10.03
15.4
10.07
148.9
300.2
2
0
14.1
10.2
236.7
340.1
3
10.36
15.0
6.93
217.5
295.9
4
9.94
15.5
10.01
221.9
337.5
5
0
34.7
6.95
237.0
300.0
6
10.56
15.0
7.24
188.5
340.4
7
0
17.0
7.15
193.1
298.4
8
0
15.7
10.2
221.1
303.1
9
0
35.8
10.30
192.8
341.8
10
9.82
34.0
7.11
217.1
340.2
11
10.30
35.0
9.93
189.3
298.6
12
9.83
34.8
10.13
152.2
339.8
13
0
34.1
6.92
222.5
337.9
14
0
16.9
7.14
192.8
338.5
15
0
35.3
10.23
189.7
299.4
16
10.76
35.6
7.12
218.8
300.6
20
20.31
15.2
6.70
162.7
298.7
21
21.14
35.5
7.06
167.6
305.0
22
20.51
15.0
10.21
163.2
297.3
23
20.32
15.9
6.84
162.5
338.2
24
20.51
15.3
10.12
165.1
338.1
25
20.03
35.2
10.16
166.9
337.9
26
15.24
35.6
10.21
160.3
338.7
27
14.91
14.9
6.97
221.5
340.3
28
14.98
34.5
6.87
216.6
298.8
30
9.81
35.5
10.23
152.9
323.8
31
20.11
35.5
6.98
167.3
325.2
^ Note that numbering of fuels
is not entirely sequential throughout.
This parameter was measured as "DVPE," but for simplicity, will be referred to as "RVP" in this document.
-------
6.2 Analysis and Model Fitting
This chapter concerns the development and application of models for four pollutants (THC, CO,
NOx and PM) and two test phases, i.e., start (bagl) and running (bag 2). For all models, the
response variable was the natural logarithm of cycle aggregate emissions on a single test phase
of the LA92 cycle (g/mi for gaseous emissions, mg/mi for particulate). The predictor variables
were the 11 fuel properties terms, "centered" and "scaled" as described in the next sub-section.
6.2.1 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, a process 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 means and standard deviations calculated from the
fuel set used in the study), as shown in Equation 6-1. The result is a "Z score," representing a
"standard normal distribution" with a mean of 0.0 and a standard deviation of 1.0.43
x — X
Z. = — Equation 6-2
s
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 6-3.
x — X
arom arom . _
arom — Equation 6-3
e
arom
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. This step is taken to neutralize correlations
between second-order terms and the linear terms from which they were constructed. Using the
quadratic term for ethanol as an example (etOHxetOH), the standardized value, denoted by
ZZetoHxetoH, is calculated as shown in Equation 6-4, where mz^z^ and Sz^z^are the mean and
standard deviation of the quadratic term constructed from the Z score for the linear effect.
ryry ^etOH^etOH ~~ OTZetOHZetOH
etoHxetoH = Equation 6-4
s z z
^ etOHxetOH
Standardized terms for interaction effects are constructed similarly. For example, Equation 6-5
shows the standardization of an interaction term between ethanol and aromatics.
7 7 — m
77 etOH Arom 2etOHZArom ^ ^ _
etoHxeArom - Equation 6-5
S7 7
^etOH^Arom
68
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Means and standard deviations for relevant model terms designs are shown in Table 6-4. Note
that the means and standard deviations shown in the table are calculated from the fuel set itself as
shown in Table 6-3; in this calculation the properties are not weighted for numbers of replicates
on each fuel and emission combination. In this way, the process is simplified by using the same
standardization in fitting all models, as well as in subsequent application of the models.
The process of standardization is illustrated for a fuel in Table 6-5, taking Tier 3 Certification
fuel as an example. Overall, the process applied here is similar to the "correlation
transformation" sometimes applied in multiple regression. One difference in this case is that the
standardization is applied only to the predictor variables, whereas it is also possible to apply it to
the response variable.44
Table 6-4. Means and Standard deviations for Fuel Properties, based on Fuel Set for the EPAct Phase-3
Model Term
Mean
Standard
deviation
Ethanol
(vol.%)
10.3137
7.87956
Aromatics
(vol.%)
25.6296
10.0154
RVP (psi)
8.5178
1.61137
T50 (°F)
190.611
28.5791
T90 (°F)
320.533
19.4801
etOH x etOH
0.962963
0.802769
T50 x T50
0.962963
0.739766
etOH x Arom
-0.03674
0.978461
etOH x RVP
-0.0992352
0.999615
etOH x T50
-0.541342
0.769153
etOH x T90
0.0163277
0.972825
1 Applies to models fit with data from 13-15 vehicles
measured on 27 fuels.
69
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Table 6-5. Example of One-Stage and Two-Stage Standardization for Tier 3 Certification fuel.1
Fuel
etQH
Aram
RVP
T50 (°F)
T90 (°F)
etOH x
T50 x
etOH x
etOH x
etOH*
rtOH x
(vol.%)
(vol.%)
(pst)
etOH
T50
Arom
RVP
T50
T90
Fuel properties
T3
9.8
23
8 95
200
325
Mean3
10.31
25.63
8.518
190.6
320.5
Std Dtv.5
7.8®
10.02
1 611
28.58
19.48
One-Stage standardized values (Z) (Equation 6-3)
z.
Zf
Zj
Zs
T3
-0.06519
-0.2626
0.2682
0.3285
0.2293
Mean1
0.9630
0.9630
-0.03674
-0.09924
-0.5413
0.01633
Std. Dev."
0.8028
0.739S
0.9785
0.9996
0.7692
0.9728
Two-stage standardized values (ZZ) (Equation 6-4, Equation 6-5}
ZZ55
ZZ*i
ZZ*
T3
-1.281
-1.657
0.3117
0.427923
1.001927
-0.01678
1 See 79 FR 2352S, Values assigned as midpoints of ranges: with RYP values for "General Testing."
"Mean .and standard deviations of fuel properties for the entire fuel set. See Table 6-4.
3Means and standard deviations of 2nd-order terms for the entire fuel set.
6.2.2 Fitting Procedures
Throughout model fitting, the response variable was the natural logarithm transformation of the
emissions results (InY), and the predictor variables were the one- or two-stage standardized fuel
properties, as shown in Table 6-5. Thus, the model to be fit includes some subset of the 11
candidate terms, as shown in Equation 6-6.
In Y = /30 +
P\ZC + P-7-a + P:7-r + + Ps^9 +
/?6ZZ55 + /?7ZZCC + Equation 6-6
A^Zea +/?9ZZer +/?! 0ZZe5 + /?, 1 zze„ +
s
A model containing all 11 candidate terms is referred to as a "full model," whereas a model
containing a subset of the 11 terms is referred to as a "reduced model." The goal of model fitting
is to identify a reduced model by removing terms from the full model that do not contribute to
fit.
Models for gaseous emissions (HC/CO/ NOx) were fit as "mixed models," in which the terms
listed in Equation 6-6 were included as "fixed" terms. In addition, a "random intercept" was fit
for each vehicle, which represents the high degree of variability contributed to the dataset by
variability among the vehicles measured. One way of understanding this distinction that the fuel
properties are "fixed" because the goal of the analysis is to estimate the effect of these
parameters on the mean levels of emissions for the entire fleet. On the other hand, "vehicle" is
70
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treated as a "random" factor because the sample of vehicles measured is but one of many
samples that could have been measured. In the analysis, the emission levels of the specific
vehicles are not of interest per sc\ but rather the degree of variability contributed to the analysis
by the different vehicles. Analyses were performed using the MIXED procedure in the Statistical
Analysis System (SAS®), version 9.2.45
Models for particulate matter were fit by "Tobit regression." This technique was used because
the datasets for PM were affected by low-end "censoring." For measurements with low masses
on the filter, the mass ostensibly obtained from the vehicle exhaust was lower than that
accumulated from levels of background PM. In these cases, we assumed that a small but
detectable PM mass was not measured accurately due to limitations in the sampling technique. In
the Tobit technique, the fitting method (maximum likelihood) is modified so as to compensate
for the absence of the censored measurements. As with the mixed models, individual intercepts
were fit for each vehicle, however, that Tobit procedure does not distinguish "fixed" and
"random" factors, vehicles were entered into the model as fixed factors. The Tobit models were
fit using the LIFEREG procedure in SAS 9.2.46
The process of model fitting proceeded through several iterations. An initial round of fitting was
performed to identify influential observations. For this purpose, full models were used, with no
model fitting performed. Observations were identified as "influential" if their "externally-
deleted" residual was greater than 3.5 or less than -3.5.47 This analysis is described in Section 5.2
of the Project Report.
A second round was then performed to identify sets of preliminary "reduced" models, i.e.,
models containing subsets of the 11 terms included in the design, identified as contributing to the
fit to the dataset for specific pollutantxbag combinations. This process is described in Section 5.3
of the Project Report.
The results of the second round were designated as "preliminary reduced models." These models
were then used to identify influential vehicles, as described in Section 5.5 of the Project Report.
Subsequent review of data for vehicles found to be highly influential for specific models led to
additional scrutiny of these subsets of data and eventual exclusion of data for selected vehicles
for specific models. The criterion for exclusion was that most measurements for a given vehicle
were below levels of background contamination for the pollutant under consideration. Models
thus affected were Bag-1 NOx, Bag-2 NOx and Bag-2 THC. The additional data review following
influence analysis is described in Chapter 6 of the Project Report.
In a third and final round of model-fitting, final reduced or "best-fit" models were obtained,
incorporating the results of the previous rounds and following the procedures described below.
The outcome of the process was a set of "best fit" models, summarized in Chapter 7 of the
project report and applied in MOVES (as described in sub-section 9.2.2 and the Executive
Summary).
Models for the gaseous emissions (THC, CO, NOx) were fit by following a series of steps: (1)
all possible candidate models were fit, and ranked by a goodness-of-fit criterion known as the
"Bayesian Information Criterion" (BIC). (2) From the top set of 5-9 leading candidate models,
71
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all terms were pooled, to form a starting model for next step. (3) a final fitting process was
conducted by backwards elimination, in which all terms in the pool were included at the outset.
In fitting successive models, terms not contributing to fit were removed based on results of
likelihood-ratio tests.44 Note that the BIC and LRT were used for model ranking and selection
because all models were fit using "maximum-likelihood" (rather than "least-squares") methods.
Results for full and reduced models are shown in Table 6-6 through Table 6-11 for HC, CO and
NOx, respectively. In the tables,/* denotes the number of parameters in the model, including the
intercept, and BIC denotes the Bayesian Information Criterion. The models are ranked by BIC,
with smaller values indicating a better fit to the data.
Models for particulate matter were not fit by the process described in the previous paragraph but
simply by backwards elimination starting with the 11 terms in the study design. Results for these
models are shown in Table 6-12 and Table 6-13.
Table 6-6. THC (Bag 1): Coefficients and Tests of Effect for the Full and Reduced Models, with BIC = 263.09
and 260.63, respectively. Note that the 11 terms in the Full Model include those in the top five candidate
models, as ranked by BIC.
Effect
Fit!! Model (zupentU
Estimate
Std. Etr.
d.f.
t - value
Pr>r
Intercept
-Q. £663
C.C34-4
15
-9.18
0.00000
I.
0.0555
3.3127
Ml
436
0.0000!
Z3
3.30
: ::s?
Ml
7.64
Zr
-0.3-39
- - -
941
4.33
1 f, f. -1
_
2s
3.1296
. .y.
Ml
10.14
Zs
o.ors
? :os^
941
2,01
:
zz„
3.3-52
0.017!
Ml
2.64
] ::s54
ZZss
o.o"-:
0.0128
941
5.SO
2Za
3.31 S3
: ::s~
Ml
2.11
: :35-2
ZZgr
;
-
941
0.50
3 srif
ZZgJ
3 3-s]
" " - o ;
Ml
2.51
ZZgp
0.0208
: 70S""
941
2,38
: :
0.1325
of
0.06870
RkJuciii Modi' i SMI >
Estimate
Std En
d.f
! -1 alue
Pr>?
-0 SsS
?
15
-9.18
0.00000
i 05-S
Q QP"
Ml
4.33
0.00002
3.33S 9
Ml
7.62
000000
-1 J--C
O Q •; f! *
941
-4.43
? 1255
3.312"
941
10.15
3.33333
j ::S3
C-CCSS
941
2,07
0.33SS8
0.0I6S
94!
2,60
000959
3 T3S
0.0128
94!
5.77
0.00000
;
0.#0S7
94!
2,07
0.03857
0,0445
C.C1SI
941
2,46
0.01409
0.0214
0.3385
94!
2.49
0.01294
0.1325
of
0.06872
72
-------
Table 6-7. THC (Bag 2): Coefficients and Tests of Effect for the Full and Reduced Models, with BIC = 226.11
and 224.30, respectively. Note that the 10 terms in the Full Model include those in the top five candidate
models, as ranked by BIC. These models were fit without the Siena and Odyssey.
Effect
Full Model (superset)
Estimate
Std En.
d.f.
lvalue
Pt>?
Intercept
--.55-5
0 25-^5
13
-18.29
0.0000
Zt
C 1
3 3123
819
2.77
0-0057
Z a
-0.019-
10093
Si?
-2.09
0.0370
Zr
!35-
::.::
819
-3.33
00009
Zj
O.Or'S
3.3129
819
3.69
0.0002
Zg
0.051)6
0.0094
S19
5.39
0.0000
ZZSS
—
ZZjj
0.0334
00094
819
3.55
0.0004
zzm
c.c:::
: cc-:
819
1.33
0.1839
ZZ
.
•J ji. _
S19
-1.31
0.1914
ZZsj
—
ZZ,?
-0.0116
0.0092
819
-1.2600
02080
4
0 8+06
006669
Reduced Model (SM3)
Estimate
Std. Eti.
df.
r-value
Pt>?
-1.5533
13
-1S.31
o.ooooo
3.3327
:::::
819
2.73
0.0066
-3.0155
? 0" 9 3
819
-2.10
0.0360
-3.3355
0.3136
819
-3.36
C.D33SC
0.0501
C.C 125
819
3.89
0.0001
0.0514
I....-:-
819
5.54
oooooo
0.0337
0.0094
819
3.59
0.00036
0.8384
0.06717
Table 6-8. CO (Bag 1): Coefficients and Tests of Effect for the Full and Reduced Models, with BIC = 324.99
and 322.48, respectively. Note that the 11 terms in the Full Model include those in the top six candidate
Effect
Intercept
Z,
zzei
zz„
zze<
zze,
ZZe.5
ZZeff
Full Model (Superset)
Estimate
Std.Err.
d.f.
t-
value
Pr> t
1.3467
0.1618
15
8.32
<0.0001
-0.1051
0.01305
941
-8.06
<0.0001
-0.01248
0.009092
941
-1.37
0.170
-0.0081
0.01038
941
0.780
0.436
-0.03285
0.01310
941
-2.51
0.0123
-0.1565
0.009095
941
17.20
<0.0001
0.07290
0.01751
941
4.16
<0.0001
0.05362
0.01311
941
4.09
<0.0001
0.02074
0.008894
941
2.33
0.0199
0.01535
0.009073
941
1.69
0.0911
0.1062
0.01879
941
5.65
<0.0001
0.003963
0.008928
941
0.444
0.657
Reduced Model (SMI)
Estimate
Std.Err.
d.f.
f-value
Pr> t
1.3466
0.1619
15
8.32
<0.0001
-0.1049
0.01304
941
-8.05
<0.0001
-0.01242
0.009092
941
-1.37
0.172
-0.00762
0.01033
941
-0.737
0.461
-0.03273
0.01310
941
-2.50
0.0126
-0.1571
0.008992
941
-17.47
<0.0001
0.07304
0.01750
941
4.17
<0.0001
0.05358
0.01311
941
4.09
<0.0001
0.02086
0.008891
941
2.35
0.0192
0.01596
0.008967
941
1.78
0.0753
0.1064
0.01878
941
5.67
<0.0001
941
<7
veh
(T„
0.3917
0.07212
0.3920
0.07214
73
-------
Table 6-9. CO (Bag 2): Coefficients and Tests of Effect for the Full and Reduced Models, with BIC = 857.84
and 851.62, respectively. Note that the Eight terms in the Full Model include those in the top 8 candidate
models, as ranked by BIC.
o,
veh
Effect
Full Model (suviistt1
Estimate
Std. Err.
d.f.
i-value
Pr>f
Intercept
-13 899
0.3578
15
-3.8-8
0.0015
z.
0.01949
0.01567
Ml
1.24
0.21
z3
0.09433
MI its
941
7.91
0.00000
Zr
0.03769
0,01351
941
2.79
0.0054
Zs
0.03936
0.01655
941
2.38
0.018
Z9
0.04214
0.01190
941
3.54
0.00042
zzm
3,or;3
0.01220
941
1.4-0
0.16
ZZjj
-0.303539
0.01203
941
-0.277
0.7S
zzm
—
—
—
—
—
zz„
—
—
—
—
—
zzs5
—
__
_
_
_
ZZ,s
-0.01-ST
0.0II6I
§41
-1.28
0.20
! 9182
0.1250
Reduced Modei (SM4>
Estimate
Std. Err.
d.f.
! -value
Pz>t
-13893
0.3578
15
-3.88
0.0015
0.0913
0.0118
941
7.76
o.oooo
0.029-9
0,0122
941
2.45
C.Cl-i-
3.0251
0.012.3
941
2.12
C.3342
0.3--3
0.0118
941
3.7.3
r>
IJ157
0.1256
Table 6-10. NOx (Bag 1): Coefficients and Tests of Effect for the Full and Reduced Models, with BIC = 914.04
and 911.00, respectively. Models were fit without the Ford Focus. Note that the six terms in the Full Model
Effect
Intercept
Z,
ZZee
ZZ„
Ilea
ZZer
ZZe.5
ZZep
Full Model (Superset)
Estimate
Std. Err.
d.f.
f-value
Pr> t
-2.8598
0.2061
14
-13.87
<0.0001
0.06830
0.01688
879
4.05
<0.0001
0.1368
0.01333
879
10.27
<0.0001
—
—
—
—
—
0.04678
0.01688
879
2.77
0.0057
—
—
—
—
—
0.00634
0.01899
879
0.334
0.74
—
—
—
—
—
-0.02343
0.01302
879
-1.80
0.072
—
—
—
—
—
-0.01495
0.01857
879
-0.805
0.42
—
—
—
—
—
Reduced Model (SM2)
Estimate
Std.Err.
d.f.
f-value
Pr> t
-2.8594
0.2061
14
-13.87
<0.0001
0.06750
0.01568
879
4.30
<0.0001
0.1339
0.01320
879
10.15
<0.0001
—
—
—
—
—
0.04783
0.01619
879
2.95
0.0032
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
-0.02369
0.01290
879
-1.84
0.067
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
<7
veh
0.5926
0.5925
(7
0.1454
0.1458
74
-------
Table 6-11. NOx (Bag 2): Coefficients and Tests of Effect for the Full and Reduced Models, with BIC =
1118.40 and 1105.17, respectively. Models fit without the Chevrolet Cobalt. Note that the eight terms in the
Full Model include those in the top 10 candidate models, as ranked by BIC.
Effect
Full Model (superset)
Estimate
Std. Err.
d.f.
/-value
Pr>?
Intercept
-4.5680
0.1844
14
-24.8
0.00000
0.05813
0.01952
879
2.98
0.0030
0.04469
0.01492
879
3.00
0.0028
Zr
-0.01729
0.01653
879
-1.05
0.30
15
-0.00245
0.02024
879
-0.12
0.90
Z9
0.00491
0.01481
879
0.33
0.74
zzee
-0.00447
0.01525
879
-0.29
0.77
ZZ.55
—
—
—
—
—
1Zea
0.00478
0.01455
879
0.33
0.74
zz„
0.01418
0.01455
879
0.97
0.33
ZZe5
—
—
—
—
—
zze9
...
...
...
...
...
°"veh
0.4730
0.1830
Reduced Model (SM6)
Estimate
Std. Err.
d.f.
/-value
Pr>?
-4.5692
0.1842
14
-24.8
0.000000
0.06299
0.01444
879
4.36
0.000014
0.04407
0.01465
879
3.01
0.0027
—
—
—
—
—
—
—
—
—
—
...
...
...
...
...
°"veh
0.4720
0.1836
Table 6-12. PM (Bag 1): Coefficients and Tests of Effect for the Full and Reduced Models.
Effect
Full Model
Estimate
Std. Err.
d.f.
%- value
Pr>x2
Intercept1
Ze
0.1365
0.05030
1
7.35
0.0067
la
0.3840
0.03510
1
119.96
<.0001
Zr
-0.0227
0.04000
1
0.32
0.57
0.0338
0.05050
1
0.45
0.50
19
0.2965
0.03510
1
71.48
<.0001
zz ee
-0.0401
0.06750
1
0.35
0.55
ZZ55
0.0700
0.05050
1
1.92
0.166
zz m
0.0508
0.03430
1
2.19
0.139
ZZ „
0.0295
0.03500
1
0.71
0.40
ZZ e5
-0.0482
0.07230
1
0.44
0.51
ZZ
0.0503
0.03440
1
2.14
0.14
Reduced Model (FM6)
Estimate
Std. Err.
d.f.
%- value
Pr>*2
0.6559
0.1582
0.04130
1
14.7
0.00010
0.3833
0.03480
1
121
<.0001
0.0550
0.04310
1
1.63
0.20
0.2923
0.03440
1
72.2
<.0001
0.0935
0.03420
1
7.46
0.0063
0.4251
1.0321
1.0359
1 Not fit by Tobit model; calculated manually from individual vehicle intercepts.
75
-------
Table 6-13. PM (Bag 2): Coefficients and Tests of Effect for the Full and Reduced Models.
Effect
Full Model
Estimate
Std.Err.
d.f.
X2- value
Pr>X2
Intercept1
ze
0.0390
0.0552
1
0.500
0.48
la
0.1619
0.0384
1
17.8
<.0001
Zr
-0.0615
0.0438
1
1.97
0.16
15
-0.0725
0.0553
1
1.72
0.19
Z9
0.1064
0.0384
1
7.69
0.0055
zzee
-0.1380
0.0739
1
3.48
0.062
ZZ55
-0.0143
0.0553
1
0.0700
0.80
11m
0.0210
0.0375
1
0.31
0.58
ZZ„
-0.0272
0.0383
1
0.50
0.48
zzei
-0.1109
0.0795
1
1.95
0.16
zze9
-0.0135
0.0377
1
0.13
0.72
Reduced Model (FM8)
Estimate
Std. Err.
d.f.
X2- value
Pr>X2
-1.3107
0.1126
0.0370
1
9.27
0.0023
0.1662
0.0376
1
19.6
<.0001
0.1072
0.0376
1
8.14
0.0043
^veh'
0.7827
*1
1.1294
1.1337
1 Not fit by Tobit model; calculated manually from individual vehicle intercepts.
6.3 Scope and Implementation
Within MOVES, the steps described in this document are applied within the scope listed below.
Fuels: The adjustments apply to gasoline (fueltypelD = 1) and E85 (fuelTypelD = 5). The
adjustments described in this document are applied to gasoline blends containing 0-15 vol.%
ethanol and high-level ethanol blends containing 70 vol.% or more ethanol.
Engine technology. For MOVES, these adjustments apply to all engine technologies other than
purely electric vehicles.
Model Years: Adjustments apply to model year 2001 and later.
SourceType: The adjustments apply to all sourceTypes.
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 6-14.
76
-------
Table 6-14. Pollutants Modified by Fuel Adjustments.
pollutantID
pollutantName
Acronym
1
Total Gaseous Hydrocarbons
THC
2
Carbon Monoxide
CO
3
Oxides of Nitrogen
NOx
112
Primary PM2 5 - Elemental Carbon
PM (EC)2
118
Primary PM2 5 - non-Elemental Carbon
PM (nonEC)2
20
Benzene
21
Ethanol
24
1,3-Butadiene
25
Formaldehyde
26
Acetaldehyde
27
Acrolein
2As the same adjustments are applied to OC and EC, they will be referred to more
genetically 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).
6.4 De-standardization of Model Coefficients
As described above in 6.2.1, the values for the fuel property predictors are 'standardized' before
fitting the models. In MOVES2014, the expressions used to calculate the fuel adjustments have
included the standardization of fuel properties for base and target fuels. The additional
calculations make the corresponding GFRE expressions extremely complicated.
For application of selected models in MOVES3, we have reversed this process to develop sets of
"de-standardized" model coefficients. In applying the models, the benefit of de-standardization
is that it simplifies the process by enabling the entry of fuel properties in their native units, e.g.,
vol.%, psi, °F, etc. In the MOVES GFRE table, the resulting expressions are shorter, simpler and
easier to follow.
6.4.1 De-standardizing Linear Terms
For the simple linear terms, the process is straightforward. We can illustrate an example for the
emission^ so that the response variable is ln>\
If we include the model intercept and a single linear term, as in Equation 6-7, we see
77
-------
In y = p0 + faZ
Equation 6-7
where as in Equation 6-2, Z is the "first-stage" standardized value for the fuel property x, for
which, if we substitute the definition of Z in terms of x and its mean and standard deviation, we
obtain the expression in Equation 6-8.
(x — x\
—-—J Equation 6-8
We can distribute the slope coefficient [h and recollect terms to obtain Equation 6-9,
BiX BiX
Iny = Bq H Equation 6-9
s s
which we can rearrange to show the modified slope and intercept terms, i.e., the de-standardized
slope term is simply /?iIs, as shown in Equation 6-10.
/ PiX\ Pi* Equation 6-10
y= [P*-—)+—
6.4.2 De-standardizing 2nd-order Terms
For interaction terms, involving "second-stage" standardized terms, the process is similar but
more detailed. We'll consider an example of an interaction term between fuel properties 1 and 2,
in which "1" would be ethanol and the other could be any of the other four fuel properties, e.g.,
aromatics, RVP, T50 or T90. These terms are denoted by the notation 'ZZ12.' In model fitting,
we respected 'hierarchy,' meaning that if the term ZZ12 was included in the model, both linear
terms Zi and Z2 were also included, as shown in Equation 6-11.
lny = /?o + P1Z1 + P2Z2 + P12ZZ12 Equation 6-11
The two linear terms would be de-standardized as shown above. Limiting attention to the
interaction term, Equation 6-12 shows the 'second-stage' standardized term as constructed from
the 'first-stage' terms for the two predictors.
/ZiZ? 771i
/?12ZZ12 = P12 ( ^ ) Equation 6-12
78
-------
In this expression, mn and .V12 are the mean and standard deviation of the product Z1Z2 across the
fuel set, as shown in Equation 6-5 (page 68). Disassembling the 'first-stage' terms yields
Equation 6-13, and
P12 (
Z]Z2 ^12\
s12
J ~ Pl2
Xi - Xi^j (X2 -x2^
Si
S2
™12
V
s12
Equation 6-13
distributing across terms in the numerator gives the expression in Equation 6-14.
P12
V
5X S1J\S2 S2) 12
512
Equation 6-14
Multiplying through and rearranging gives Equation 6-15.
ft2 (J_) (&* _ +£s£i) _ \ M5
\Si2/\\SiS2 5XS2 S!S2 SiSz/ /
In the final step, we can distribute through to obtain the final set of terms shown in Equation
6-16.
Pl2xlx2 Pl2xlx2 Pl2x2xl Pl2x2xl Pl2m12
1 Equation 6-16
5152512 5152512 5152512 5152512 512
In this final degree of separation, the individual terms can be applied as appropriate. The first
term represents the de-standardized coefficient for the interaction slope term X1X2. The second
term modifies the slope term for the first fuel property xi. The third term modifies the slope term
for the second fuel property X2. The fourth and fifth terms, including only constants, modify the
intercept term for the model. Note that in applying these terms, the signs of each must be
maintained. Thus, signs for the first and fourth terms are positive, and those for the second, third
and fifth terms are negative.
The process described above is repeated for the additional interaction terms X1X3, X1X4 and X1X5.
Additionally, the models can contain up to two quadratic terms, such as xi2, denoted in the
EPAct models as ZZ11 and paired with coefficient /? 11. The process of de-standardizing a
quadratic term is a more specific case of de-standardizing an interaction term, i.e., the predictor
has an 'interaction' with itself. The final terms for the quadratic for predictor xi resolve to the
following expression in Equation 6-17.
79
-------
011*1 n 011*1*1 , 011*1 011™12 ^ ^
— 2 —= 1 = Equation 6-17
51512 51512 51512 512
In this final expression, the first term is the de-standardized term for the quadratic term xi2 The
second term modifies the linear slope term for predictor xi. As before, the third and fourth terms
modify the intercept term for the model.
Sets of standardized coefficients for the models for THC, CO, NOx and PM have been presented
above in Table 6-6 through Table 6-13. Corresponding de-standardized coefficients are
presented in Table 6-15 and Table 6-16.
The de-standardized coefficients are useful only for application of the models to generate
predictions. We need to emphasize that they are not useful for interpretation or comparison. As
the de-standardized coefficients are expressed in original units for the respective fuel properties,
it is not appropriate to compare coefficients for different properties to assess magnitudes of
effects. It is also very important to avoid interpreting any individual coefficient as the "effect"
of a fuel property. As with the standardized coefficients, it is critical that ensembles of
coefficients containing the same predictor must be taken as packages, never individually.
Note that the two variance terms for the models are not affected by the process, as the de-
standardization involves only manipulation of the coefficients, as described above.
80
-------
Table 6-15. De-standardized Models representing "Cold-start" Emissions for the Regulated Pollutants.
Model term
THC
CO
NOx
PM
Intercept
3.4101
9.0464
-3.6914
-0.2078
etOH (v.%)
-0.1120
-0.17827
0.01643
0.02008
Arom (v.%)
0.00435
-0.00403
0.01654
0.03827
RVP (psi)
-0.02763
-0.01770
...
...
T50 (°F)
-0.04460
-0.04129
0.00167
-0.05707
T90 (°F)
-0.00054
-0.00806
0.01501
etOH x etOH
0.00087
0.00147
—
—
T50x T50
0.00012
0.00009
0.00015
etOH x Arom
0.00023
0.00027
-0.00031
—
etOH x RVP
—
0.00126
...
...
etOH x T50
0.00026
0.00061
etOH x T90
0.00014
Vehicle variance
0.1325
0.3920
0.5925
0.4251
Residual error
0.06872
0.07214
0.1458
1.0359
Table 6-16. De-standardized Models representing "Hot-running" Emissions for the Regulated Pollutants.
Model term
THC
CO
NOx
PM
Intercept
-3.6528
-2.6790
-4.7644
-3.6473
Ethanol (v.%)
0.00415
—
0.00799
0.01429
Aromatics (v.%)
-0.00195
0.00911
0.00440
0.01659
RVP (psi)
-0.02205
0.01855
—
—
T50 (°F)
-0.01953
0.00091
—
—
T90 (°F)
0.00264
0.00226
—
0.00550
etOH x etOH
—
—
—
—
T50x T50
0.000056
—
—
—
etOH x Arom
—
—
—
—
etOH x RVP
—
—
—
—
etOH x T50
—
—
—
—
etOH x T90
—
—
—
—
Vehicle variance
0.8384
1.9187
0.4720
0.7827
Residual error
0.06717
0.1256
0.1836
1.1337
6.5 Fuel Effect Adjustments
In MOVES, emissions of the pollutants THC, CO, NOx and PM are calculated starting with
"base emission rates" (meanBaseRate, meanBaseRatelM) stored in the database table,
emissionRateByAge.8 The base rates are assumed to represent emissions on a "base fuel" which
are adjusted by a ratio to represent emissions on a selected 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. This chapter describes the application of the
EPAct study results to derive fuel adjustments for the subsets of vehicles and model years
described above.
81
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The models generated using EPAct results allow estimation of emissions effects related to five
fuel properties: ethanol content (vol.%), aromatics content (vol.%), 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 6-18
below. Using the reduced model for cold-start NOx as an example (see Table 6-10), /? denotes a
model coefficient, ZetoH denotes a "standardized" fuel term for this property, and ZetoHxArom
denotes a "standardized" etOHxArom interaction term. Finally, the term s£2 represents the total
error or "mean square error" for the model. Note that the model expression can be represented
conveniently using the matrix notation "Xp."
Emissions (g/mi) =
exp(^0 + AtOH^etOH + ^Arom^Arom + ^T50^T50 + AtOHxArom^etOHxArom + 0.5ss ) Equation 6-18
The equivalent expression of the same model using "de-standardized" coefficients, denoted as pd,
with fuel properties x in their respective units is shown in Equation 6-19.
Emissions (g/mi)
Relative fuel effects are calculated by applying the models to specific "in-use" fuels and pre-
defined "base fuels" and by calculating the ratio of the emissions on the in-use fuel to those on
the base fuel, as shown in Equation 6-20. Please note that this calculation does not pull the base
fuel characteristics from the baseFuels database table; instead, the base fuel properties are
directly included in the equations in the GeneralFuelRatioExpression database table.
The sets of de-standardized coefficients for four individual pollutants, including THC, CO, NOx,
and PM have been presented in Table 6-15 and Table 6-16 above. These models are applied in
MOVES through the GeneralFuelRatioExpression table, described in detail in Section 6.6.
The table includes two sets of coefficients for each pollutant, representing the effects of the fuel
properties on start and running exhaust emissions, respectively/In some cases, the 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 effects on
running emissions are dictated by catalyst efficiency when fully operational.
exp(Xp)
Equation 6-19
Fuel Effect =
Equation 6-20
f For all models, "start" and "running" emissions are represented by results measured on Bags 1 and 2 of the LA92
cycle, respectively.
82
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The coefficients can be understood as the change in the natural logarithm of emissions (e.g.,
AlnCO) associated with a change in the fuel property of 1.0 units, and assuming that the other
fuel properties remain constant.
6.6 The Database Table "GeneralFuelRatioExpression"
A detailed description of the "generalFuelRatioExpression" table is shown below in Table 6-17.
Table 6-17. Description of the Database Table "GeneralFuelRatioExpression."
Field
Description
Values
fuelTypelD
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 = hot-running NOx,
etc.
minModelY earlD
The earliest model year to which a specific value
of fuelEffectRatioExpression is applied.
e.g., 2001
maxModelY earlD
The latest model year to which a specific value of
fuelEffectRatioExpression is applied.
e.g., 2060
minAgelD
The minimum vehicle age at which the value of
fuelEffectRatioExpression is applied.
e.g., 0 years
maxAgelD
The maximum vehicle age at which the value of
fuelEffectRatioExpression is applied.
e.g., 30 years
sourceTypelD
Identifies vehicles by functional type. See table
"sourceUseType."
11= motorcycle
21= passenger car
3 l=passenger truck
32=light commercial truck, etc.
fuelEffectRatioExpression
A mathematical expression containing up to
32,000 characters.
6.6.1 Examples
We show an example of the calculation of fuel adjustment for start NOx applied in conjunction
with the adjustment for fuel sulfur. Note that the adjustment for sulfur is calculated
independently of that for the other properties: ethanol, aromatics, vapor pressure, T50 and T90.
The calculation of adjustments for sulfur content is described in Chapter 3. The entire
expression for NOx starts is shown below in Table 6-18. Due to its length, the whole is divided
into terms and segments, which, along with descriptions, are presented in Table 6-19.
83
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Table 6-18. Example Value for Field "fuelEffectRatioExpression" in Database Table
"GeneralFuelRatioExpression" (NOTE: this example calculates an adjustment for cold-start NOx, accounting
for the fuel properties: ethanol, aromatics, reid vapor pressure, T50, T90 and sulfur).
if(sulfurLevel>30,((exp(-3.69135462091143-
0.000306740025914599*ETOHVolume*aromaticContent+0.016428307360575 l*ETOHVolume+0.0165361679107306*ar
omaticContent+0.00167327452473912*T50))*(l+(0.425*((exp(0.351*ln(303))-exp(0.351*ln(30)))/exp(0.351*ln(30)))+(1.0-
0.425)*(1.47*(exp(0.351*ln(sulfurLevel))-
exp(0.35 l*ln(30)))/(exp(0.35 l*ln(30)))))))/(0.05932198247482521 *1.53198632575613),((exp(-3.69135462091143-
0.000306740025914599*ETOHVolume*aromaticContent+0.016428307360575 l*ETOHVolume+0.0165361679107306*ar
omaticContent+0.00167327452473912*T50))/0.05932198247482521 )*(1.0-0*(30-sulfurLevel)))
Table 6-19. Expression stored in the Field "fuelEffectRatioExpression" in the Table
"GeneralFuelRatioExpression," illustrating the combined application of non-sulfur and sulfur fuel
adjustments for start NOx emissions.
if(sulfurLevel>30,
Initiate condition to be applied
for sulfur level > 30 ppm
(exp(-3.69135462091143
Initiate exponential expression,
enter intercept for EPAct
model.
-0.000306740025914599*ETOHVolume*aromaticContent
enter interaction term for
ethanolxaromatics
+0.016428307360575 l*ETOHVolume
enter linear term for etahanol
+0.0165361679107306*aromaticContent
enter linear term for aromatics
+0.00167327452473912*T50))
enter linear term for T 50,
close exponential term
*(l+(0.425*((exp(0.351*ln(303))-exp(0.351*ln(30)))/exp(0.351*ln(30)))+(1.0-
0.425)*(1.47*(exp(0.351*ln(sulfurLevel))-
exp(0.351*ln(30)))/(exp(0.351*ln(30)))))))/(0.05932198247482521*1.53198632575613)
enter expression to calculate
sulfur effect (application of
M6Sulf model).
5
Initiate condition for sulfur
level < 30 ppm (NOTE:
following comma, condition is
implicit).
(exp(-3.69135462091143
Initiate exponential expression,
enter intercept for EPAct
model.
-0.000306740025914599*ETOHVolume*aromaticContent
enter interaction term for
ethanolxaromatics
+0.016428307360575 l*ETOHVolume
enter linear term for etahanol
+0.0165361679107306*aromaticContent
enter linear term for aromatics
+0.00167327452473912*T50))
enter linear term for T 50,
close exponential term
/0.05932198247482521)
enter ratio to emissions on
base fuel
*(1.0-0*(30-sulfurLevel)))
enter expression to estmate
sulfur effect (T2 sulfur
model).
Table 6-20 illustrates calculation of NOx fuel adjustments for Tier 3 certification fuel, relative to
a MOVES base fuel, although without the inclusion of the sulfur adjustment. Results are
presented for both start and running models, using the de-standardized coefficients as presented
in Section 6.4. The lower segments of the table present the model predictions, as ln(NOx), NOx
84
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rates as g/mi, obtained by exponentiating the logarithmic results, and the fuel adjustments,
calculated as ratios of the rates for the test fuels to those for the base fuel. As mentioned, note
that the start and running rates represent aggregate results on Bags 1 and 2 of the LA92 cycle,
respectively.
For NOx, the calculations predict decreases of approximately 6% and 1.4% for start and running
emissions, respectively. For THC, corresponding reductions for start and running emissions are
11% and 3.3%, respectively, as shown in Table 6-21.
Table 6-20. NOx: Application of Models for Tier 3 Certification Fuel and a MOVES Base Fuel, with
Calculation of Fuel Adjustments.
Models
Fuel properties
Property
Fuel
base
T3
etOH (vol.%)
10
9.8
Arom (vol.%)
25.77
23
RVP (psi)
8.8
8.95
T50 (°F)
212.3
200
T90 (°F)
321.7
325
etOH x etOH
100
96.04
T50x T50
45,071
40,000
etOH x Arom
257.7
225.4
etOH x RVP
88
87.71
etOH x T50
2123
1960
etOH x T90
3217
3185
Intercept
1
1
Variance
Coefficients
Start
Running
0.01643
0.00799
0.01654
0.0044
0
0
0.00167
0
0
0
0
0
0
0
-0.00031
0
0
0
0
0
0
0
-3.6914
-4.7644
0.7383
0.6556
Results: start model
ln(NOx)
-2.8262
-2.886
NOx (g/mi)
0.08569
0.08073
Adjustment
1.000
0.942
Results: running model
ln(NOx)
-4.571
-4.585
NOx (g/mi)
0.01436
0.01416
Adjustment
1.000
0.986
85
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Table 6-21. THC: Application of Models for Tier 3 Certification Fuel and a MOVES Base Fuel, with
Calculation of Fuel Adjustments.
Models
Fuel properties
Property
Fuel
base
T3
etOH (vol.%)
10
9.8
Arom (vol.%)
25.77
23
RVP (psi)
8.8
8.95
T50 (°F)
212.3
200
T90 (°F)
321.7
325
etOH x etOH
100
96.04
T50x T50
45,071
40,000
etOH x Arom
257.7
225.4
etOH x RVP
88
87.71
etOH x T50
2123
1960
etOH x T90
3217
3185
Intercept
1
1
Variance
Coefficients
Start
Running
-0.112
0.00415
0.00435
-0.00195
-0.02763
-0.02205
-0.0446
-0.01953
-0.00054
0.00264
0.00087
0
0.00012
0.000056
0.00023
0
0
0
0.00026
0
0.00014
0
3.4101
-3.6528
0.20122
0.90557
Results: start model
ln(THC)
-0.9261
-1.039
THC (g/mi)
0.43804
0.39112
Adjustment
1.000
0.893
Results: running model
ln(THC)
-4.629
-4.662
THC (g/mi)
0.01536
0.01485
Adjustment
1.000
0.967
7 High-Level Ethanol Blends (E85)
7.1 Introduction
Fuels containing 70 to 85 vol.% ethanol combined with hydrocarbon blendstocks ("E85") have
been available for many years and their use as transportation fuels has grown since passage of
the Energy Policy Act of 2005 (EPAct),48 its implementation in the Renewable Fuel Standard
(RFS)49 and passage of the Energy Independence and Security Act of 2007 (EISA).50 To be
consistent with these policies, calendar year 2010 is the first year that E85 usage is modeled and
included in the default MOVES database (see Section 7.3).3 Vehicles designed to run on gasoline
or such "high-level" ethanol blends are designated as flexible-fuel or "flex-fuel" vehicles
(FFVs). In the U.S., the highest ethanol blend that existing flex-fuel vehicles can use is E85.
86
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With the increased use of E85 in the fleet, numerous studies have examined the differences in
emissions of FFVs operated on E85 versus gasoline, typically E10. However, the numbers of
vehicles included in these studies typically have been small and the results have been mixed in
terms of the effects of E85 on emissions of gaseous or criteria-pollutant emissions from
FFVs.51'52'53
In MOVES, the "ethanol (E-85)" category includes fuels containing 70% or more ethanol by
volume. MOVES allows E85 use for the following sourcetypes only: passenger cars, passenger
trucks, and light commercial trucks.54
This chapter describes the analysis conducted to estimate the effects of E85 use on exhaust
emissions of total hydrocarbons (THC), non-methane hydrocarbons (NMHC), non-methane
organic gases (NMOG), carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter
(PM2.5) from flex-fuel vehicles. The chapter also describes the underlying data used in the
analysis, and the algorithms used to model the emissions from FFVs using E85 in MOVES. The
MOVES algorithms for estimating the effects of E85 on air toxics1 and evaporative2
hydrocarbons are discussed in their respective reports.
7.2 Data Analysis and Results
The impacts of E85 on emissions on THC, NMHC, NMOG, CO, NOx, and PM2.5 were examined
using the results from four test programs, namely, the EPAct Phase 3 program,55 National
Renewable Energy Laboratory (NREL) E40,56 Coordinating Research Council (CRC) E-80,57
and the EPA NRMRL Test Program.58 The details of each program are described below.
Energy Policy Act (EPAct) Program - Phase 3 of the EPAct program included testing of four
flexible-fuel vehicles on both E10 and E85 fuels. Table 7-1 shows the description of the tested
vehicles. The vehicles were tested using the California Unified Cycle, also known as the LA92.
The LA92 was conducted as a three-phase, including a cold-start test similar to the FTP.
Model Year
Make
Model
Odometer
2008
Chevrolet
Impala
5,048
2008
Chevrolet
Silverado
5,347
2008
Ford
F150
5,523
2008
Dodge
Caravan1
5,282
1 Dodge Caravan was tested only on E85 fuel, and thus, was excluded from the analysis.
National Renewable Energy Laboratory (NREL) E40 - Nine flex-fuel vehicles aged between one
and ten years were tested on three-phase LA92 cycles with a minimum of two replicates. Table
7-2 shows the description of the tested vehicles. The fuels examined in the study were a retail
E10 meeting ASTM D4814 Class A-2 standards, a flex fuel containing 76 percent ethanol by
volume, and a mid-level ethanol blend, E40. For the current analysis, only the data from
vehicles running on E10 and E85 was included.
87
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Table 7-2. Description of the Vehicles Tested in NREL E40 Program.
Model Year
Make
Model
Odometer
2011
GMC
Terrain
10,000
2010
Chrysler
Town & Country
28,000
2010
Toyota
Tundra
17,000
2009
Nissan
Titan
21,000
2011
Ford
Fusion
11,000
2007
Chevrolet
Silverado
10,000
2002
Ford
Taurus
115,000
2002
Dodge
Caravan
110,000
2002
Chevrolet
Tahoe
118,000
Coordinating Research Council (CRC) E-80 Project - This study conducted by the Coordinating
Research Council tested seven flex-fuel vehicles running on four different ethanol blends (E6,
E32, E59, and E85). The test vehicles (see Table 7-3) were driven under various test cycles -
Cold Start Federal Test Procedure (FTP), Hot Start High Speed/Load Driving Cycle (US06), and
Cold Start Unified Driving Cycle (LA92). Each vehicle, fuel, and test condition was measured
only once. For the current analysis, only the data from vehicles running on E6 and E85 under
LA92 cycle was included.
Table 7-3. Description of the Vehicles Tested in CRC E-80 Project.
Model Year
Make
Model
Odometer
2007
Dodge
Grand Caravan
30,514
2007
Ford
F-150 XLT
12,646
2007
Ford
Crown Victoria
16,345
2007
Chevrolet
Tahoe LS
18,555
2007
Chevrolet
Silverado LT
22,008
2007
Chevrolet
UplanderLS
17,898
2006
Chevrolet
Monte Carlo
48,761
EPANRMRL Test Program ("PM Speciation Program") - As part of a coordinated program
between EPA/ORD/NRMRL (Research Triangle Park, NC) and EPA/OAR/OTAQ (Ann Arbor,
MI), the study tested Tier 2 flex-fuel vehicles (see Table 7-4) running on EO, E10, and E85
driven under LA92 cycle run as a 4-phase test. The test cycle was repeated for each ethanol
blend and vehicle. For the current analysis, only the data from vehicles running on E10 and E85
were included.
Table 7-4. Description of the Vehicles Tested in EPA NRMRL Test Program.
Model Year
Make
Model
Odometer
2008
Chevrolet
Impala
50,000
2008
Chrysler
Town & Country
50,000
All programs measured emissions on the LA92 cycle running on both E10 and E85 blends,
except CRC E-80, which measured E6 and E85 blends. Table 7-5 describes the properties of the
fuels used in each of the programs included in the current analysis. Only the vehicles tested on
both E10 (E6) and E85 were included in the analysis. The composite emissions were calculated
using the same weighting factors as specified for the FTP.
88
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Property
EPAct
NRELE40
CRC E-80
EPANRMRL
E10
E85
E10
E85
E6
E85
E10
E85
EtOH
(vol.%)
10
77
10.6
75.5
6
82.9
9.3
80.5
Aromatics
(vol.%)
26.2
5.9
20.8
7.1
11.9
2.0
21.8
5.7
RVP (psi)
00
00
8.9
8.4
5.8
7.3
7.3
9.2
8.9
T50 (°F)
189.7
171.8
160.0
172.2
204.2
171.3
221.0
171.5
T90 (°F)
319.7
173.9
307.8
174.2
307.8
172.5
325.2
173.5
Initially, each dataset was analyzed separately to examine the differences in emissions between
E10 and E85. However, because the preliminary results showed directionally consistent
emission trends across datasets and similar percent changes in emission between E10 and E85,
all available datasets were pooled to examine the effect of E85 on emissions, relative to E10.
We acknowledge that fuel properties other than ethanol are confounders and therefore, they may
introduce bias to the extent that fuel properties of E10 and E85 vary between programs.
However, considering the small sample size in each dataset, we believe performing Student's
paired t-test on a pooled dataset increases the statistical power and reduces the effects of
confounders, compared to an analysis that examines the effect of E85 on emissions compared to
E10 for each test program. Because not all programs measured the same set of pollutants, the
numbers of test vehicles included in the analysis are different for each pollutant (Table 7-6).
Table 7-6. Number of Vehicles for Analysis of Each Pollutant
Pollutant
Number of Vehicles
THC
12
NMOG
19
NMHC
7
ch4
5
NOx
21
pm25
5
CO
21
Figure 7-1 through Figure 7-7 show the mean measured emissions for E10 and E85. The error
bars represent the 95% confidence intervals.
89
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Figure 7-1. Mean THC Emissions from Vehicles Running on E10 and E85.
120
100
80
M
_£ 60
u
I
I—
40
20
E10
E85
Figure 7-2. Mean NMOG Emissions from Vehicles Running on E10 and E85.
80
70
60
E
50
^
two
£
40
15
O
30
Z
20
10
0
E10
E85
90
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Figure 7-3. Mean NMHC Emissions from Vehicles Running on E10 and E85.
45
40
35
30
m 25
u
X
20
15
10
5
0
E10
E85
Figure 7-4. Mean CH4 Emissions from Vehicles Running on E10 and E85.
60
50
40
hn
£ 30
^F
x
u
20
10
E10
E85
91
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Figure 7-5. Mean NOx Emissions from Vehicles Running on E10 and E85.
E
j2j
x
O
0.2
0.18
0.16
0.14
0.12
m 0.1
x
0.08
0.06
0.04
0.02
0
E10
E85
Figure 7-6. Mean PM2.5 Emissions from Vehicles Running on E10 and E85.
E10
E85
92
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Figure 7-7. Mean CO Emissions from Vehicles Running on E10 and E85.
1.8
1.6
1.4
1.2
f 1
"SB
O 0.8
u
0.6
0.4
0.2
0
E10
E85
Although the 95% confidence intervals of the mean overlapped between E10 and E85 for all
pollutants, to assess whether their population means differ statistically, the tests of significance
between E10 and E85 were performed using Student's paired /-tests for the pooled dataset.
As shown in Table 7-7, the emissions for E10 and E85 did not result in statistically significant
differences for THC, NMOG, and NOx. For PM2.5, although a couple of vehicles showed
reduction in emission between E10 and E85, the paired /-test showed no statically significant
difference. The difference in CO emissions was nearly statistically significant. Only NMHC
and CH4 emissions showed statistically significant differences between E10 and E85.
Table 7-7. Tests of Significance
using Student's Paired T-Tests,
Pollutant
p-value
THC
0.7968
NMOG
0.3056
NMHC
0.0046
ch4
0.0226
NOx
0.1667
PM25
0.2797
CO
0.0665
7.3 Application in MOVES
Based on the analysis in Section 7.2, the gasoline rates for THC, CO, NOx and PM2.5 are
replicated for high-level ethanol blends (E85) in the database table, emissionRateByAge, for
vehicle regulatory classes capable of running in E85 - light-duty vehicles (LDV) and light-duty
trucks (LDT). No fuel adjustments are applied to the pre-2001 MY E85-fueled vehicles.8 The
g MOVES default activity information estimates E85 capable vehicles (flex-fuel vehicles) entered the fleet starting
in with model year 1998 vehicles.54
93
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Tier 2 fuel effect adjustments equations for gasoline, including the effect of fuel sulfur, are
applied to emissions of THC, CO, NOx, and PM2.5 from MY 2001 and later E85-fueled vehicles
in passenger cars, passenger trucks, and light commercial trucks source types in the
generalFuelRatioExpression table (described in Section 6.6).
For E85 fuel effects, MOVES uses fuel properties from both representative E10 fuels and E85
fuels. As shown in Table 7-8, the fuel properties of E10 (e.g., ethanol volume, aromatic content,
T50, and T90) are used in estimating E85 fuel effects, instead of directly using E85-specific fuel
properties. MOVES uses E10 fuel as the source of these fuel properties for two reasons:
1. Based on our literature review, the emission rates for THC, CO, NOx, PM2.5, NMOG and
VOC emissions from E10 and E85-fueled FFVs are not statistically significantly
different. By using the E10 fuel properties, MOVES adjusts the E85 vehicle emissions
using the same fuel adjustments that are applied to the E10 vehicle emissions.
2. The EPAct fuel program included ethanol volume as a factor in the sample design for
ethanol levels only between 0% and 15% by volume. Therefore, the fuel effects based on
the EPAct program should not be applied to values of ethanol volume, aromatic content,
T50 and T90 from E85 fuels which fall outside of the range of values included in the
sample design.
The representative E10 fuel properties used to estimate emissions from E85 fuel-vehicles are
stored in the ElOFuelProperties table by fuel region, calendar year and month for the fuel
properties shown in Table 7-8 (except ethanol volume, sulfur level, and benzene content).
MOVES sets the ethanol volume to be 10% in the fuel effects equation for E85 in the
generalFuelRatioExpression tab 1 e.
RVP is handled differently than the other fuel properties that are used in the EPAct fuel
equations. The RVP levels of E85 fuels (Table 7-9) fall approximately within the range of RVP
values included in the EPAct sample design (7 and 10 psi, Table 6-2). As such, for estimating
THC, CO, NOx, and PM2.5 emissions, MOVES uses the RVP from E85 fuels, which tend to be
lower than comparable E10 fuels. For NMOG and VOC emissions, MOVES uses the RVP from
E10 as shown in Table 7-8.
Table 7-8. Source of Fuel Properties (E85 or E10) used to Estimate Fuel Effects for MY 2001 and Later E85
Vehicles by Pollutant.
Pollutant
Ethanol
Volume
RVP
Sulfur
Level
Benzene
Content
Aromatic
Content
Olefin
Content
T50
T90
THC, CO, NOx, PM2.5
E10 (10%)
E85
E85
E85
E10
E10
E10
E10
altTHC1, altNMHC1,
NMOG, VOC
E10
Note:
1 altTHC and altNMHC are the temporary intermediate values from which MOVES calculates NMOG and VOC.
As shown in Table 7-8 and Table 7-9, MOVES uses the sulfur level and benzene content from
E85 fuels since the fuel effects for sulfur and benzene content were derived without
consideration of the ethanol volume of the fuel. The Tier 2 sulfur model discussed in Section 3.3
has emission adjustments for THC, CO and NOx emissions. Sulfur level also has an impact on
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the sulfate (SO4, a component of PM2.5) and sulfur dioxide (SO2) as discussed in Section 9.
Benzene content influences benzene emissions as discussed in the air toxics report.1 Therefore,
we use the E85-specific sulfur level and benzene content to capture the expected emissions
benefit from having lower sulfur and benzene content than comparable E10 fuels.
In summary, MOVES uses the E85 fuel properties for RVP (for THC, CO, NOx, and PM2.5),
sulfur level and benzene content from the corresponding E85 fuel assigned to the specific fuel
region, calendar year, and month stored in the fuelFormulation table. MOVES currently assumes
two distinct E85 fuel formulations for all fuel regions and calendar years3, with the relevant fuel
properties used by MOVES for E85 fuel effects shown in Table 7-9.
Table 7-9. Relevant Fuel Properties used for the E85 Fuel Effects.
E85 Fuel
Formulation ID
Month Applied
RVP
(psi)
Sulfur Level
(ppm)
Benzene Content
(% volume)
27001
October through April
10.5
8
0.16%
27002
May through September
7.7
MOVES uses the olefin content from E10 representative fuels to estimate 1,3-butadiene
emissions from E85-fueled vehicles, because E85 fuels have much lower olefin content than the
gasoline fuels used to develop the adjustments from the Complex Model. MOVES accounts for
the lower 1,3-butadiene emissions in vehicles fueled by E85 compared to E10 using E85/E10
ratios as discussed in the air toxics report.1
In MOVES, the estimation of the other organic gas emissions starts with emissions of THC. As
discussed above, the THC fuel effects for MY 2001 and later vehicles using E85 are estimated
using the fuel properties of E10, with the exception of RVP and sulfur level where E85-specific
fuel properties are used. Next, MOVES calculates both methane and NMHC from THC
emissions using methane/total hydrocarbon ratios (CH4THCRatio in the database table
methaneTHCRatio). MOVES applies an E85-specific methane ratio (82% methane for running
and 27% for starts) which is directionally consistent with the results shown in Section 7.2, where
FFVs fueled with E85 emit higher methane emissions than E10 and correspondingly lower levels
of NMHC. The development of the methane/total hydrocarbon ratios for E85-fueled vehicles is
documented in the MOVES speciation report.59
For calculation of NMOG emissions for model year 2001 and later vehicles using E85, MOVES
starts by calculating an intermediate value called "alternative THC (altTHC)" that is never
reported to the user. The altTHC value represents THC emissions calculated using the fuel
properties from E10 fuels (including RVP), except the sulfur level (Table 7-8). Next, MOVES
calculates altNMHC from altTHC using the CH4/THC ratios from ElO-fueled vehicles. Then,
MOVES calculates NMOG emissions from the altNMHC emissions using the ElO-specific
NMOG/NMHC exhaust speciation factors as summarized in Table 7-10. MOVES uses this
method because no statistically significant difference was observed in the NMOG emissions
rates between E10 and E85-fueled vehicles in Section 7.2.
Although volatile organic compounds (VOC) were not analyzed in Section 7.2, due to a lack of
speciated data from the larger study, it was assumed that VOC would behave similarly to NMOG
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in terms of response to high-level ethanol fuels for model year 2001 and later FFVs, since the
only differences between NMOG and VOC are the presence of ethane and acetone. Therefore,
we used the altNMHC emissions and the ElO-specific VOC/NMHC speciation ratios to estimate
VOC emissions in a similar method as was done for NMOG as summarized in Table 7-10. For a
detailed description of the algorithm used to estimate NMOG and VOC emissions from MY
2001 and later E85-fueled vehicles, see Appendix C.
Table 7-10. Calculation of THC, CH4, NMOG, VOC, and TOG emissions from E85-fueled Vehicles in
MOVES
Model
Year
THC
ch4
NMOG
VOC
TOG
1960-2000
ElObase THC
rates with no fuel
effect adjustments
Calculated from
THC emissions
using E85
CH4/THC ratio
Calculated from
NMHC emissions
using E85
NMOG/NMHC
ratios
Calculated from
NMHC emissions
using E85
VOC/NMHC
ratios
NMOG + CH4
2001-2060
E10 base THC
rates with fuel
effect adjustments
using E10 fuel
properties with the
exception of RVP
and sulfur level
from E85
Calculated from
THC emissions
using E85
CH4/THC ratios
Calculated from
altNMHC
emissions using
E10
NMOG/NMHC
ratios
Calculated from
altNMHC
emissions using
E10 VOC/NMHC
ratios
NMOG + CH4
Because the supporting data for the NMOG comparison in Section 7.2 was based on Tier 2
vehicles, we did not apply the same logic for the pre-2001 MY year vehicles. For those vehicles,
we apply the E85-specific speciation factors to calculate NMOG and VOC emissions from the
baseline NMHC values as shown in Table 7-10.
7.3.1 Example MO VES Results
Figure 7-8 and Figure 7-9 display the running emission rates (g/mile) for gasoline and E85-
fueled light-duty vehicle (LDV) by model year for select pollutants estimated from calendar year
2010 and 2020 national MOVES3 runs. Figure 7-8 through Figure 7-9 display the percent
differences between E85 and E10 emission rates by pollutant and model year group for running
and start emissions. The percent differences are the same within the outlined model year groups
for light-duty vehicles and light-duty trucks.
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Figure 7-8 Gasoline and E85-fueled LDV Running Emission Rates (g/mile) by Model Year and Pollutant
Estimated from a 2010 MOVES National Run
Model Year
fuelTypeDesc — Gasoline -¦ Ethanol (E-85)
Figure 7-9. Gasoline and E85-fueled LDV Running Emission Rates (g/mile) by Model Year and Pollutant
Estimated from a 2020 MOVES National Run
Model Year
fuelTypeDesc — Gasoline -- Ethanol (E-85)
The largest differences in emission rates between gasoline and E85 are due to differences in
methane emissions, and the other pollutants which are calculated from methane (NMHC and
TOG for all model years, and NMOG and VOC for pre-2001 model years). Methane emissions
are significantly higher from E85 vehicles, particularly for the pre-2001 model years, due to the
different methane/THC ratios used by MOVES. For example, E85 vehicles use a methane ratio
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of 82% in all model years for running exhaust, and E10 vehicles use methane ratio of 15% for
pre-2001 model years, and 11% for 2001+ MY vehicles.59 Significantly higher methane
emissions from E85-fueled vehicles compared to gasoline for 2001 and later model year are
consistent with the analysis presented in Figure 7-4.
In general, the emission rates for THC, CO, NOx, VOC and PM2.5 emissions from E10 and E85-
fueled vehicles are relatively similar to each other across all model years. Because no fuel effects
are applied to the pre-2001 model year E85 emission rates, the small observed differences in
THC, CO and NOx emissions are due to the Complex and Predictive Model fuel effects applied
to the pre-2001 model year ElO-fueled vehicles.
The emission rates of THC, CO, and NOx for MY 2001+ E85-fueled vehicles are generally lower
than the comparable model year E10 emission rates, primarily because of the lower sulfur
content and lower RVP properties in E85 fuels compared to E10 fuels. In calendar year 2010, the
national average sulfur level for E10 fuels in MOVES is approximately 36 ppm which decreases
to 10 ppm by CY 2020. In contrast, the sulfur level of the E85 fuels in MOVES is assumed to be
8 ppm in all years. As such, larger differences in the emission rates are observed between E85
and ElO-fueled vehicles in calendar year 2010 (Figure 7-8), when there is a large difference in
the sulfur levels. In contrast, when sulfur levels of E85 and 10 fuels are similar (8 ppm and 10
ppm, respectively) in 2020, the emission rates for MY 2001+ vehicles are roughly equivalent for
THC, CO, NOx, VOC and PM2.5 (Figure 7-9).
The MY 2001-2016 LDV, and MY 2001-2017 LDT vehicles use the Tier 2 base sulfur level of
30 ppm in the sulfur equation stored in the generalFuelRatioExpression (GFRE) table. For MY
2017 and later LDVs and 2018 and later LDTs, the sulfur equations use the Tier 3 base sulfur
level of 10 ppm, as described in Section 3.3.4. This change in the base sulfur level explains the
small change in the percent differences of E85 and E10 emission rates between the 2001-2016
and 2017-2020 model years for LDV vehicles seen in Figure 7-9.
The PM2.5 emission rates are equivalent for the pre-2001 model year vehicles because neither
E85 nor gasoline vehicles have fuel effects for these model years, with the exception of sulfate
emissions, which are a minor portion of the total PM2.5 gasoline emissions. For 2001 and later
model years, the emission rates are roughly equivalent for running emissions, because the EPAct
PM2.5 equation uses the E10 fuel properties to adjust the PM2.5 emission rates for both E85 and
ElO-fueled vehicles. We attribute some of the minor differences between the PM2.5 emissions
rates of E85 and ElO-fueled vehicles for MY 2001 and later vehicles to differences in the fuel
properties estimated from the national average of E10 fuel properties and the national average of
E10 fuel properties estimated in the ElOfuelproperties table.
Emission rates for pollutants that are not adjusted by the equations in the
generalFuelRatioExpression table, such as CO2, SO2, SO4, are discussed in Section 9.
We recognize that additional data and analysis could improve how vehicles running on high
ethanol blends are modeled in MOVES. This could be improved in future versions of MOVES as
more data become available.
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8 Biodiesel Blends
MOVES contains two fuel effects for diesel, based on the sulfur and biodiesel content of the fuel.
For diesel vehicles, MOVES has fuel sulfur effects for particulate sulfate and gaseous sulfur
dioxide emissions as described in Chapter 9 below. Unlike gasoline, no relationships between
sulfur and HC, CO, and NOx emissions are estimated in MOVES for diesel vehicles.
MOVES contains biodiesel effects that are applied to HC, CO, NOx and PM. The biodiesel
effects also affect the speciated hydrocarbon and particulate species that are derived from THC
and PM emissions, even though the same toxic fractions (e.g., benzene/VOC) are applied to
estimate toxic emissions from conventional diesel and biodiesel fueled vehicles.1
As for sulfur, separate effects are modeled for pre-2007 and post-2007 technology diesel
engines, as described below.
8.1 Pre-2007 Diesel Engines
The biodiesel effects implemented in MOVES are obtained from an analysis conducted in the
course of the 2010 Renewable Fuel Standard Program.60 The biodiesel effects were derived from
an analysis of publicly available datasets on the effect of biodiesel on emissions from medium-
duty and heavy-duty diesel engines that are representative of the in-use US fleet. The effect of a
blend containing 20% biodiesel (B20) derived from this study is presented in Table 8-1.
Additional analysis and discussion of the results are contained in EPA (2010).
Table 8-1. Emission impacts for all cycles tested on 20 vol% soybean-based biodiesel fuel relative to an
average base fuel. (Reproduced from Table ES-A from the EPA (2Q1Q60))
Pollutant Name
Percent Change in
Emissions
THC
-14.1%
CO
-13.8%
NOx
+2.2%
PM2.5
-15.6%
This analysis evaluated only the impact of B20 on diesel emissions. The study did not evaluate
the impact on gaseous emissions beyond the 20% biodiesel volumes.
8.2 2007 and later Diesel Engines
The analysis conducted by the Renewable Fuel Standard did not include 2007+ diesel engines or
associated emission control technologies (including diesel particulate filters and selective
catalytic reduction). Consistent and significant biodiesel effects have not been observed for
2007+ engines.61'62
8.3 Modeling Biodiesel
The fuelFormulation table contains a parameter, "bioDieselEsterVolume," that represents the
volume percentage of biodiesel ester in a target fuel. The default fuel supply contains estimates
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of biodiesel volumes for fuel regions in the United States, as described in the fuel supply report.
However, users can also enter local information about biodiesel fuels.
Mathematically, the bioDieselEsterVolume is used with a set of "biodiesel factors" (presented in
Table 8-2). For pre-2007 engines, these factors are designed to give the fractional changes shown
in Table 8-1, for a bioDieselEsterVolume of 20 vol.%. For volumes less than 20 vol.%, the
fractional change is linearly interpolated between 0% and 20 vol.%; for volumes greater than 20
vol.%, the fractional change for 20 vol.% is applied. In combination, these two parameters produce
an overall fuel adjustment for biodiesel fuels.
Table 8-2. Biodiesel Fuel Adjustment Factors
Pollutant Name
BioDiesel Factor
Pre-2007 Diesel
2007+ Diesel
THC
-0.705
0
CO
-0.690
0
NOx
0.110
0
PM2.5
-0.780
0
These fuel adjustments give the relative change in emissions associated with adding biodiesel to
petroleum diesel fuel. The formulation for the fuel adjustment is shown in Equation 8-1.
„ , . , \east(bioDieselEsterVolume,20) , ,,
ruei Adiustment = 1H x bioDieselractor Equation 8-1
100
To estimate the adjusted emissions, the fuel adjustment is multiplied to the base emissions
estimate, which represents operation on petroleum diesel. Note that, currently, there are no diesel
fuels with biodiesel levels above 20 vol.% in the MOVES3 default fuel supply. The pre-2007
biodiesel factors apply to all diesel tailpipe exhaust processes (running exhaust, start exhaust,
extended idle exhaust). The pre-2007 biodiesel factors also apply to all model years (1960-2060)
of auxiliary power unit exhaust, because they are projected to have more limited emission controls
until 2024.71 For 2007 and later diesel, the biodiesel fuel adjustment factor for diesel tailpipe
exhaust processes is set equal to 0, consistent with the literature review in Section 8.2.
9 Sulfate and Sulfur Dioxide Emissions
9.1 Introduction
Sulfate (SO4) is an important contributor to primary exhaust particulate matter emissions from
motor vehicles. The formation of sulfate from motor vehicles is a function of the engine
combustion, emission control technology conditions, and the sulfur content in the fuel and the
lubricating oil. MOVES2010b assumed that all sulfate emissions originated from the fuel sulfur
and based the sulfate calculations entirely from fuel consumption. Research on current
technology diesel engines running on ultra-low sulfur diesel has shown that the sulfur
contribution of lubricating oil can be more important than that of fuel in forming sulfate
emissions.63 For diesel engines equipped with catalyzed diesel particulate filters, the sulfate
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contribution from lubricating oil can also make up a substantial fraction of the PM2.5 exhaust
emissions.64
Maintaining the capability to model emission changes to fuel sulfur content is important for
MOVES. The MOVES particulate emission rates for pre-2004 gasoline and pre-2007 diesel
vehicles were derived from sets of measurements on higher fuel-sulfur levels than current fuels;
it is thus important that MOVES be able to account for changes in fuel sulfur content in
estimating particulate emissions.
In MOVES, sulfate emissions are estimated from PM2.5 emissions rather than from fuel
consumption. This approach assures that the reference fraction of sulfate is consistent with the
PM2.5 emissions profile. MOVES also accounts for sulfate contributions from both the
lubricating oil and the fuel. Using particulate matter test programs conducted by the US EPA and
reported in the literature, the relative contribution of sulfate emissions from lubricating oil and
fuel is estimated.
This chapter includes an overview of the MOVES sulfate calculator, and analysis conducted to
determine the necessary inputs for 1) gasoline engines, 2) conventional diesel engines, 3) 2007
technology diesel engines, and 4) compressed natural gas engines. Additionally, the MOVES
algorithm for estimating sulfur dioxides is included in this chapter for consistency. The algorithm
for gaseous sulfur-dioxide (SO2) emissions is based on fuel consumption, but the parameters
have been updated in MOVES3 to be consistent with the changes to the PM2.5 emission factors.
9.2 Sulfate Calculator Summary
The MOVES sulfate calculator adjusts the reference sulfate emissions using the following
assumptions:
• Sulfate emissions from the lubricating oil are constant regardless of the fuel sulfur level.
• Sulfate emissions originating from the fuel scale linearly with changes in fuel sulfur
level.
These assumptions are illustrated in Figure 9-1. Research on sulfur levels in lubricating oil and
diesel fuel support these assumptions. Allansoon et al.65 and Kittelson et al.63 treated the sulfate
contribution from the lubricating oil independently of the fuel sulfur level from diesel engines.
Wall et al.66 demonstrated that sulfate emissions from diesel engines decrease linearly with
decreases in the diesel fuel sulfur level down to 100 ppm and 0 ppm. Baranescu67 and
Hochhauser68 affirmed that changes in diesel fuel sulfur did not affect the sulfur to sulfate
conversion rate from conventional diesel engines operating on different driving cycles. Kittelson
et al.63 also assumed a constant relationship between fuel sulfur level and particle number
emissions from modern trap-equipped diesel engines.
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Figure 9-1. Schematic of Fuel and Lubricating Oil Contributions in MOVES.
S04, Sulfate
emissions
Fb = % of Sulfate emissions from Fuel at the Base Case
S04„
S04n
The sulfate calculator uses the concept of reference emission rates and sulfate fractions. MOVES
adjusts the sulfate emissions based on differences between the sulfur content of the reference test
program, and the user-supplied fuel sulfur content in a MOVES run. In MOVES, the base PM2.5
rates are divided between elemental carbon (EC) and the remaining PM that is not elemental
carbon (NonECPM). MOVES incorporates these modeling assumptions into Equation 9-1, the
derivation of which is included in Appendix A:
S04r = NonECPMR x 5r x
1 + Fr x
(f-1)
Equation 9-1
where: NonECPMB is the reference non-elemental carbon PM2 5 emission rate, Sb = the reference sulfate
fraction, x = the user-supplied or default fuel sulfur level for the MOVES run, xb = the reference fuel
sulfur level, and F,-; = the percentage of sulfate originating from the fuel sulfur in the reference case, and
SO4,: = sulfate emissions at the fuel sulfur content for the MOVES run.
The Sb, Fb, and xb, parameters vary by vehicle sourceType, model year group, and emission
process as shown in Table 9-1. The only value that changes across moves runs, is the actual fuel
sulfur level, x, which is either specified by the MOVES user, or is drawn from the MOVES3
default fuelForumulation and fuel Supply table which specify fuel properties and usage according
to the geographic fuel region and calendar year. Each of the needed parameters for the sulfate
calculator (Sb, Fb, xb) are provided in Table 9-1, which is stored in the MOVES table
"salfateFr actions" The sulfate calculator works in concert with other calculators in MOVES to
estimate PM2.5 emissions. A flow chart which illustrates the context in which the sulfate
calculator is involved in estimating PM2.5 emission rates is shown in the MOVES3 Speciation
report.59
Sulfate-bound water (H2O aerosol) was added in MOVES2014. Currently, the value of H20b in
MOVES is 0 for all on-road source types, as derived from the PM2.5 speciation profiles.59 If
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included in the PM2.5 speciation profile, the H2O aerosol is assumed to be associated with sulfate,
and is scaled using the same relationship with fuel sulfur level, as shown in equation:
(H20)x = NonECPMg x (H20)B x [l + FBx(j-~ l)] Equation 9-2
where (H20)B is the fraction of water-bound sulfate in the NonECPM.
Table 9-1. Coefficients for the Sulfate Calculator in MOVES.
Source
Process
Reference Fractions
Reference fuel
sulfur Level, ppm
(xB)
Reference
estimated
fraction from
fuel sulfur
(Fb)
SO4/PM25
SOz/NonEC
PM (,S«)
Pre-2004 light-
duty gasoline and
E85 (passenger
cars and trucks
and light-
commercial
trucks)
running exhaust
7.2%
8.4%
161.2
68.7%
start exhaust
0.9%
1.7%
2004+ light-duty
gasoline and E85
running exhaust
Varies by
model year3
(7% to 3%)
8.4%
23.5
24.2%
start exhaust
Varies by
model year3
(l%to 0.5%)
1.7%
Motorcycles and
heavy-duty
gasoline
sourcetypes
(all model years)
running exhaust
7.2%
8.4%
161.2
68.7%
start exhaust
0.9%
1.7%
Pre-2007 diesel
(all sourcetypes)
running exhaust
1.0%
4.9%
172.0
72.6%
start, extended idle
and APU
5.3%
9.8%
2007+ diesel
(all sourcetypes)
running, extended
idle, start
67.6%
73.6%
11.0
48.3%
Pre-2002
compressed
natural gas
(all sourcetypes)
running, extended
idle, start
0.6%
0.7%
5.0
0.0%
2002+
Compressed
natural gas
(all sourcetypes)
running, extended
idle, start
1.0%
1.2%
5.0
0.0%
o
a The EC/PM fraction varies by model year as discussed in the light-duty exhaust report , thus the sulfate/PM
fraction also varies by model year.
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The following sections discuss the derivation of the parameters displayed in Table 9-1 for 1)
gasoline vehicles, 2) conventional diesel vehicles, 3) 2007 technology diesel vehicles, and 4)
compressed natural gas vehicles.
9.3 Gasoline Vehicles
9.3.1 Pre-2004 Light-duty Gasoline Vehicles
The reference sulfate fractions and the reference fuel sulfur level for pre-2004 light-duty gasoline
vehicles are estimated from the Kansas City Light-Duty Vehicle Emissions Study (KCVES).37
The use of the KCVES for estimating PM2.5 emission rates is documented in the MOVES3
Light-duty Vehicle Emission Rate report,8 and the derivation of the sulfate emission factor is
documented in the TOG and PM Speciation Report.59 The pre-2004 light-duty gasoline reference
fuel sulfur content (161.2 ppm) was calculated using 171 fuel analysis samples from the KCVES.
The high sulfur content of the fuels tested in KCVES is a limitation when applying the speciation
profile to Tier 2 and Tier 3 gasoline. But, as discussed in the Speciation report, the KCVES
PM2.5 speciation profile is the most representative profile available to EPA at this time to
represent PM emissions from in-use light-duty gasoline vehicles.
The reference contribution of fuel sulfur to the sulfate emissions (68.7%) is estimated from an
analysis that combined data from the KCVES, which tested vehicles using high fuel-sulfur
content, with light-duty gasoline vehicles tested at a low fuel-sulfur content (6 ppm) as part of
the Full Useful Life (FUL) Test Program.69 The FUL program was the most relevant study
available to the US EPA that measured sulfate emissions from low-sulfur gasoline available that
could be used to evaluate the impact of low-sulfur gasoline fuel on light-duty engines. By using
the FUL test program in the analysis, the sulfate fraction estimated by MOVES is based on
actual data tested on Tier 2 vehicles on low-sulfur fuels. An overview of the data and the analysis
performed to calculate the reference contribution of fuel sulfur to sulfate emissions is provided in
Appendix A.
The sulfate values derived for pre-2004 light-duty gasoline sourceTypes: passenger cars and
trucks and light-commercial trucks (sourceTypelD 21,31,32) are displayed in Table 9-2.
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Kansas City Light-Duty
Vehicle Emissions
Study (KCVES)
Tier 2 PM update
Model Year Range of Measured
Vehicles
1968-2004 (VMT
weighted)
2007-2014
Model Year Range Applied in
MOVES
1960-2003
2004-2060
Sulfur (ppm) (xb)
161.2
23.5
Oil Sulfate Contribution (mg/mi)
0.106
0.106
Fuel Sulfate Contribution (mg/mi)
0.233
0.034
Oil Sulfate Contribution %
31.3%
75.8%
Fuel Sulfate Contribution % (Fb)
68.7%
24.2%
9.3.2 2004 and later Light-duty Gasoline Vehicles
We updated the MY 2004 and later light-duty PM emission rates in MOVES3 using data from
six different studies as documented in the light-duty vehicle emission rate report8. The updated
base sulfur level for the MY 2004+ emission rates (calculated by averaging each vehicle test by
its associated fuel sulfur level) is 23.5 ppm as shown in Table 9-2.
We also updated the fraction of the fuel sulfate contribution (Fb). Table 9-2 contains the
estimated oil and fuel sulfate contributions estimated from the Kansas City Light-Duty Vehicle
Emissions Study (KCVES) derived in Appendix A.2. For the Tier 2 PM update, we assumed that
the sulfate contribution from lubricating oil is unchanged (0.106 mg/mile), but that the sulfate
from the fuel is reduced proportionally to the fuel sulfur level, as consistent with the assumptions
used to derive the sulfate calculator in Appendix A, and shown in Equation 9-3.
xkcves' Equation 9-3
= 0.233 (^A X
23. 5 ppm \
^mileJ V161.2 ppm)
= 0.034
( mg \
\mile
:)
We then calculated the fuel sulfate fraction, (Fb), by re-calculating the relative sulfate emissions
contributed from the lubricating oil and the gasoline using Equation 9-4. We estimate that the
gasoline fuel contributes to less than a quarter of the sulfate emissions from 2004 and later
vehicles, while the gasoline fuel contributes to over two-thirds of sulfate emissions estimated
from 2003 and earlier model year vehicles as shown in Table 9-2.
B Tier 2 —
/ Sulfate from fuel \
\Sulfate from oil and fuel)
( 0.034 \
~ (o. 106+ 0.034/ ~ 24' 2%
Equation 9-4
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The 2004 and later model year emission rates have updated fractions of elemental carbon to
particulate matter (EC/PM), which vary by model year according to the fraction of port-fuel
injection (PFI) and gasoline direct injection (GDI) vehicles.8 Despite the updated elemental
carbon fractions, we continue to use the same PM2.5 speciation profile59 derived from the KCVES
to estimate the components of the non-elemental carbon particulate matter (nonECPM) for all
model year gasoline vehicles. As such, we continue to apply the same reference sulfate fraction,
SOVNonECPM (Sii), derived from the KCVES, to the 2004 and later model year gasoline
vehicles. Because the EC/PM fraction varies by model year for the 2004 and later vehicles, the
sulfate/PM fraction also varies for the 2004+ model year vehicles as noted in Table 9-1.
9.3.3 High Ethanol Blend (E85) Gasoline Vehicles
The sulfate values derived for light-duty gasoline vehicles are also applied to flex-fuel vehicles
fueled on E85. Flex-fuel vehicles use the same emission rates, EC/PM fractions and speciation
fractions as light-duty gasoline vehicles. Because E85 has lower sulfur content than gasoline, the
E85-fueled vehicles are estimated to have lower sulfate emissions compared to those fueled with
gasoline.
9.3.4 Motorcycles Heavy-duty Gasoline Vehicles
The sulfate values derived for pre-2004 light-duty gasoline vehicles are also applied to all model
years of the other gasoline-sourcetypes including motorcycles, heavy-duty gasoline trucks and
gasoline-powered buses (sourceTypelD 11, 42, 43, 52, 52, 53, 54, and 61). These sourcetypes
also use the same EC/PM fractions71 and PM speciation profiles59 derived from the Kansas City
Light-duty Vehicle Emissions Study. In MOVES3, we have updated the 2010 and later HD
PM2.5 emission rates for gasoline vehicle to be set equal to heavy-duty diesel PM2.5 emission
rates. However, we have not updated the gasoline PM speciation data, and we continue to
assume that the PM2.5 rates are still based on a 161.2 ppm sulfur fuel.
9.4 Diesel Vehicles
9.4.1 Pre-2007 Diesel Vehicles
The reference sulfate fraction of PM2.5 is derived from the Heavy-Duty Vehicle Chassis
Dynamometer Testing for Emissions Inventory, Air Quality Modeling, Source Apportionment
and Air Toxics Emissions Inventory (E55/59).70 The E55/59 study is also used to derive the
PM2.5 emission rates for medium- and heavy-duty diesel in MOVES3.71 The estimated fuel sulfur
content of diesel trucks tested in E55/59 is 172 ppm, based on in-tank fuel samples from three
vehicles in the program that were selected for standard fuel analysis11.
To estimate the relative contribution of lubricating oil and fuel from conventional diesel engines,
data collected from the Diesel Emissions-Control Sulfur Effects Project (DECSE) was used.72
The DECSE project was conducted to investigate the impact of low-sulfur diesel fuel standards
on diesel emissions. Specifically, the DESCE conducted testing of two engines at four sulfur
h See Table 11 in Clark et al.70
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levels: 3, 30, 150, and 350 ppm. Sulfate emissions were measured at each of the levels. These
data were used to calculate the 72.6% contribution of the fuel to sulfate emissions at the
reference fuel-sulfur level (172 ppm) for the base pre-2007 diesel rates in MOVES. The sulfate
emissions estimated from the fuel-sulfur (72.6%) are then scaled linearly with changes in fuel-
sulfur relative to the reference fuel sulfur level (172 ppm) using Equation 9-1. Details on the
analysis used to derive the relative fuel contribution to pre-2007 diesel sulfate emissions from the
DESCE data are provided in the appendix.
9.4.2 2007 and Later Technology Diesel Vehicles
The sulfate contribution of the fuel and lubricating oil for 2007 and later diesel vehicles is based
on a study designed and conducted by Kittelson et al.63 The study evaluated the contribution of
lubricating oil and diesel fuel to ultrafine particle emissions from a modern diesel engine
equipped with a catalyzed diesel-particulate filter (C-DPF). The researchers estimated a linear
model that predicts the ultrafine particle-number emissions from the sulfur content in the
lubricating oil and the fuel. We adapted this analysis by assuming that the relative contribution of
lubricating oil and fuel to sulfate emissions is the same as their relative contribution to the
ultrafine particle emissions. We applied the coefficients developed by Kittelson et al. to estimate
the relative contributions of lubricating oil and fuel to sulfate emissions at fuel-sulfur levels in
fuel and lubricating oil of 11 ppm and 3,000 ppm. Eleven ppm is selected because it is the sulfur
level of the reference conventional low-sulfur diesel (fuelFormulationID 20) in MOVES3. The
sulfur level in oil (3,000 ppm) is the sulfur content assumed by Kittelson et al. for trap-equipped
diesel engines, lower than 4,000 ppm limit specified by API category CJ-4 lubricating oil used
for 2006 and later diesel engines.73 Using these assumptions, the lubricating oil is estimated to
contribute the majority of the sulfate emissions (51.7%) when the fuel-sulfur is 11 ppm.
The reference sulfate fraction is based on the PM2.5 speciation profile for 2007 and newer on-
highway diesel technology, based on Phase 1 of the Advanced Collaborative Emissions Study
(ACES).74 The Phase 1 study tested four heavy-duty diesel engines, each equipped with a
catalyzed diesel-particulate filter (C-DPF), over a 16-hour cycle specifically developed for this
purpose. The PM2.5 speciation profile for 2007 and later diesel engines used in MOVES3 is
based on data acquired from these four engines. The fuel-sulfur level tested in the ACES
program is 4.5 ppm.63 The sulfate fraction from the ACES Phase 1 project is adjusted to account
for a level of 11 ppm assumed to apply to base PM2.5 emission rates for engines manufactured in
2007 and later. Using Equation 9-1 and the derived parameters in Table 9-1, a SO4/PM2.5 fraction
for 11 ppm fuel is estimated to be 67.6% (as compared to 59.1% at 4.5 ppm). This fraction is
used as the reference sulfate fraction for 2007 and later diesels in MOVES3 as shown in Table
9-1. Additional details on the analysis are included in Appendix A.4.
9.5 Compressed Natural Gas
We had limited data on sulfate emissions from engines running on compressed natural gas,
especially regarding the relative contribution of the lubricating oil and CNG fuel to sulfate
emissions. As such, we do not adjust the sulfate emissions according to fuel sulfur level. We
derived a constant fraction of sulfate emissions from elemental sulfur emissions measured by the
California Air Resources Board on a CNG transit bus with a 2000 MY Detroit Diesel Series 50
engine with and without an oxidation catalyst as documented in the MOVES Speciation
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Report.59 We set Fb coefficient to 0, so that MOVES estimates the same sulfate emissions
regardless of the sulfur level in the CNG fuel.
9.6 Example Comparisons
Figure 9-2 plots the sulfate/nonECPM ratios calculated from the parameters in Table 9-1 across a
range of sulfur levels from 0 to 500 ppm. We excluded the 2007+ diesel values from this plot,
because those vehicles have much higher sulfate PM fractions, and 2007+ diesel vehicles operate
only on ultra-low-sulfur diesel (sulfur concentration <15 ppm). This figure demonstrates that
sulfate levels can range from less than 4% of nonECPM at low sulfur levels, to over 20% of
nonECPM at high fuel-sulfur levels.
Figure 9-2. Sulfate/Base nonECPM ratio across a range of fuel sulfur levels.
In Figure 9-3, we show estimated SO4 mass emission rates by combining the estimates in Figure
9-2 with estimates of nonECPM emission rates from pre-2004 gasoline passenger cars, heavy-
duty diesel long-haul combination trucks, and CNG transit bus emissions estimated using
MOVES20141 for a state-wide (Michigan) run in calendar year 2011.
The base nonECPM emission rates in MOVES for pre-2007 MY diesels are based on a reference
sulfur level of 172 ppm (Section 9.4). At this level, the sulfate emission rate across all processes
is ~ 20 mg/mile [12 mg/mile (running) + 8 mg/mile (idle/start)]. For diesel sulfur level of 15
ppm, MOVES estimates sulfate emissions of ~ 7 mg/mile [4 mg (running) + 3 mg/mile
(idle/start)]. For this MOVES scenario, the sulfate calculator in MOVES reduces sulfate PM (and
1 While the intercept (0 ppm) values are updated in MOVES3 with the updated PM2.5 emission rates, the adjustments
to the sulfate emission rate according to the fuel sulfur level shown in this analysis are unchanged from
MOVES2014.
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total PM2.5 emission rates) from the reference pre-2007 diesel PM emission rates by ~ 13
mg/mile.
Similarly, the reference sulfur level for pre-2004 model year gasoline vehicles in MOVES is
161.2 ppm (Section 9.3). Reducing the sulfur levels to Tier 3 gasoline sulfur levels (10 ppm),
reduces the sulfate emissions by approximately 1 mg/mile, from 1.2 mg/mile to 0.4 mg/mile.
Figure 9-3. Example S04 emission rates as a function of fuel sulfur level (0 to 500 ppm) in calendar year 2011
estimated using national default data in MOVES2014.
Sulfur, ppm
Gasoline Running
Gasoline Start
Pre-2007 Diesel Running
Pre-2007 Diesel idle
Pre-2002 CNG
2002+CNG
Figure 9-4 plots the estimated sulfate emissions (including 2007+ diesel engines) across a
smaller range of fuel sulfur levels (0 to 30 ppm). The 2007+ diesel engines clearly have the
largest sulfate emission rates (mg/mile) across all sulfur levels, and also have the largest
sensitivity to fuel sulfur levels. This is not surprising, because the 2007+ trucks have a large
sulfate fraction in the reference rates, coupled with a low reference sulfur level (11 ppm). The
2007+ diesel engines are estimated to emit ~ 20 mg/mile sulfate at 11 ppm (from running, start,
and idle processes). This level is comparable to the estimated volume of sulfate emitted from the
pre-2007 diesel trucks at 172 ppm.
The gasoline and pre-2007 diesel sulfate emission rates are relatively insensitive to sulfur
changes within this range (0-30 ppm) of fuel sulfur content. The sulfate emissions from gasoline
and pre-2007 diesel trucks contribute less than 4% of the nonECPM emission rates (and less than
3% of total PM2.5 emission rates), and the contribution changes by only -1% between 0 and 30
ppm.
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Figure 9-4. Example S04 emission rates as a function of fuel sulfur level (0 to 30 ppm)
in calendar year 2011 estimated using national default data in MOVES2014.
40
0
0.00 5.00 10.00 15.00 20.00 25.00 30.00
Sulfur, ppm
Gasoline Running
Gasoline Start
Pre-2007 Diesel Running
Pre-2007 Diesel idle
— — — 2007+ Diesel
— Pre-2002 CNG
2002+CNG
Comparisons of the sulfate calculator to other reported values in the literature for gasoline and
pre-2007 diesel are presented in Appendix A.
9.7 Sulfur Dioxide Emissions Calculator
The MOVES SO2 algorithm calculates SO2 emissions using three parameters (1) total fuel
consumption, (2) fuel sulfur level, and (3) the fraction (%) of fuel sulfur emitted as sulfate
emissions.
Unlike the sulfate calculator, the SO2 calculator assumes that all of the sulfur in SO2 emissions
originate from the fuel. This assumption is reasonable because most of the sulfur originates from
the fuel on a mass-balance basis, even at low fuel-sulfur levels. The reason that sulfur in the
lubricating oil has a large impact on sulfate emissions is that the sulfur in the lubricating oil has a
much high propensity to form sulfate than sulfur burned in the fuel.63
SO2 emissions are calculated using Equation 9-5:
MW_S02 (10"6\
S02(g) = FC(g) x [S] (ppm) x ^ g x fS02 x \—^J Equation 9-5
where
FC(g)= fuel consumption (g), and
[S] (ppm)= relative fuel-sulfur concentration (ppm)
MW S°2 is the ratio of the molecular weight of sulfur dioxide as defined in Equation 9-6.
mw_s ° 1
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MW S02 32 + (2 X 16) -^-r
—7777rrr~ = 5—=2.0 Equation 9-6
MW S ^2-i-
Jzmol
fS02 = Fraction of fuel sulfur that is converted to gaseous SO2 emissions. The SO2 conversion
fraction is calculated as the fraction of fuel sulfur not converted to sulfate.
In MOVES3, the SO2 calculator first calculates the product of FC(g) x [S] (ppm). Then, it
multiplies the product by the SO2 emission factor which combines the last three terms of
Equation 9-5 including the ratio of molecular masses Equation 9-6.
The SO2 conversion values and resulting SO2 emission factors for use in MOVES3 are displayed
in Table 9-3 and stored in the sulfateemissionrate tabled
Table 9-3. SO2 conversion efficiencies and MOVES SO2 emission factors.
Source
SO2 conversion efficiency (%)
SO2 EF (1/ppm)
Gasoline
99.69%
1.994E-06
High Ethanol Blends (E85)
99.69%
1.994E-06
Pre-2007 MY Diesel
94.87%
1.897E-06
2007 and later MY Diesel
88.15%
1.763E-06
CNG
100%
2.000E-06
The SO2 conversion factors for gasoline are based on the VMT-weighted values from the Kansas
City study. The updated SO2 conversion values (99.69%) for gasoline engines are slightly lower
than the previous values used in MOVES2010b (99.84%), which is required to provide
consistent rates with the updated sulfate emission rates. These values are used for all highway
gasoline sources.
As for other pollutants, we model E85 fueled-vehicles using the same emission rates and
adjustment equations as gasoline vehicles. We set the SO2 EF derived for gasoline vehicles to
E85 vehicles. This can result in higher estimates of SO2 from E85 vehicles, because the energy
density of E85 in MOVES is estimated to be 30% lower than E10 gasoline. Thus, even though
MOVES assumes the same energy consumption for E85 and gasoline vehicles, E85 vehicles are
estimated to consume 43% more grams of fuel per mile.
Fuel consumption data were not available from the E55/59 study which was used as the source of
the sulfate emission rates for diesel engines. The updated SO2 conversion values for the pre-2007
diesel were calculated by achieving sulfur balance with the estimated fuel sulfur consumed and
sulfate emissions from pre-2007 diesel trucks, with both quantities estimated using MOVES. A
2014 national MOVES inventory was calculated for pre-2007 single and combination diesel
trucks, with the fuel sulfur assigned to a level derived from the E55/59 study (172 ppm). The
sulfate speciation factor and percentage of sulfate coming from the fuel were taken from Table
9-1. The analysis estimated that 5.13% of the fuel sulfur forms sulfate emissions, leaving an
J The sulfateEmissionRate table stored sulfate and sulfur dioxide emission factors inMOVES2010. InMOVES2014
and later, it only contains sulfur dioxide emission factors.
Ill
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estimated SO2 conversion value of 94.87%. MOVES2010 assumed that 2% of fuel sulfur formed
sulfate emissions, which was taken from the US EPA PART5 model used in previous versions of
MOVES and MOBILE (EPA, 2003). MOVES3, thus, assumes a larger percentage of fuel sulfur
forms sulfate emissions in conventional diesel engines. The 2007 and later MY diesel SO2
emissions factor is based on calculations using the reported fuel consumption and sulfate
emissions from the ACES Phase 1 report, along with the data from the sulfate calculator for
sulfate emissions. The SO2 conversion factor for 2007 and later diesel (88.15%) in MOVES3 is
considerably larger than the SO2 assumed in MOVES2010b (54.16%). The reason for the large
shift is the large contribution of lubricating oil to sulfate emissions accounted for in MOVES3.
The diesel values are used for all on-highway diesel sources for 2007 and later.
In the absence of other data, we assume that 100% of the sulfur in the CNG fuel forms SO2
emissions. This is a reasonable simplification because the sulfur content of CNG is low in
comparison to diesel and gasoline, and because lubricating oil also contributes to SO2 emissions.
This assumption is also consistent with our assumption for the formation of sulfate emissions
from CNG engines. Lanni et al.75 measured SO2 and SO4 emissions from three CNG transit
buses. The sulfur content of the CNG fuel was not reported, but by assuming that all of the fuel
sulfur is converted to SO2 emissions we estimated a CNG sulfur content of 7.6 ppm. Ayala et al.
76 reported that the maximum allowable fuel sulfur content for use in CNG motor vehicles is 16
ppm. The Energy Information Administration reports that the fuel sulfur content of natural gas at
the burner tip is less than 5 ppm.77 In MOVES, the default sulfur level of 7.6 ppm is assumped
for CNG, to be consistent with the sulfur dioxide measurements conducted by Lanni et al.75
9.8 Summary
The sulfate calculator is used to adjust sulfate (and thus, the total PM2.5 emission rates) for
gasoline and pre-2007 diesel trucks across a wide range of sulfur values. The reference sulfate
emission rates for gasoline and pre-2007 diesel are based on reference fuel sulfur levels of 161
and 172 ppm, respectively. Current regulations require diesel sulfur levels to be less than 15
ppm, and gasoline sulfur levels to be at or below 10 ppm. When modeling these lower fuel sulfur
levels, MOVES reduces the reference sulfate emission rates by ~ 10 mg/mile for pre-2007
heavy-duty diesel trucks, and ~ 1 mg/mile for light-duty gasoline vehicles.
While the sulfate calculator is important in adjusting the pre-2007 diesel and gasoline emission
rates for large fuel sulfur changes, the sulfate calculator has a minimal impact on the sulfate
emissions for small sulfur changes (e.g., less than 30 ppm changes), which reflect the large
contribution of lubricating oil to sulfate emissions at low fuel sulfur levels. In contrast, sulfate
emissions from 2007+ diesel technology engines are highly sensitive to the fuel sulfur level,
because these engines produce a high amount of sulfate even at very low fuel sulfur levels.
Because PM2.5 and sulfate emissions are relatively low from CNG vehicles, we maintained a
simple sulfate emission rates in MOVES for these vehicle types that do not adjust the sulfate
emissions to the sulfur-content of the CNG fuel.
We also updated the values in the MOVES SO2 calculator, such that the SO2 and sulfate
emissions approximately achieve a mass balance with the sulfur consumed in the fuel.
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Additionally, work is needed to quantity the sulfate emissions from advanced engines and
emission control technologies in MOVES, including from 2010 DPF/selective-reduction-catalyst
equipped diesel engines including during diesel particulate filter regeneration (see sulfate
discussion in the MOVES3 Speciation report59), and from light-duty diesel engines, lean-burn
gasoline, and direct injection gasoline vehicles. These values can be updated as data become
available.
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Appendix A Derivation of the Sulfate Equation and Parameters
A.1 Derivation of Calculations Performed in the Sulfate Calculator
The following equation is used to model the Sulfate emissions:
S04x = (Sulfate from oil) + (Sulfate from Fuel) Equation A-l
S04x = NonECPM x (sB g) + (SB - S0) {£)
Where: S04x = Sulfate level at fuel sulfur x, So =Fraction of sulfate emissions from lubricating
oil, Sb = Sulfate fraction in the reference case, xb = fuel sulfur level in the reference case.
Let /•'/>' = % of sulfate from the fuel sulfur in the baseline case:
(SB - S0)
Fb = Equation A-2
Substituting Equation A-2 into Equation A-l yields Equation A-3:
S04x = NonECPM x ^SB x (1 - FB) + SB x FB x
= NonECPM x (sB x [l - FB + FB x
= NonECPM x (sB x |l + FBx — ljj j Equation A-3
Using Equation A-3, the sulfate emissions can be modeled, with the user supplied values of x
(fuel sulfur level), and model parameters, SB, FB and xb.
Similarly, the particulate water (H2O) depends on the amount of sulfate in the exhaust, and thus
the amount of fuel sulfur. The same adjustment to the sulfate-bound water will be applied to the
reference water emission rate as shown in Equation A-4:
CH20)x = NonECPM X ((//20)B X [l + FBx(^~- l)]) Equation A-4
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A. 2 Derivation of the Sulfate Calculator Parameters for Light-duty
Gasoline Vehicles
The KCVES collected PM2.5 measurements from a statistically representative sample of vehicles
in the Kansas City Metropolitan Area. The study was conducted in the summer of 2004 (Phase 1)
and winter of 2004/2005 (Phase 2). In total, 496 vehicles were measured over both phases of the
program. Chemical speciation was estimated from a subset of 99 vehicles from the initial 496
vehicles. The vehicles were tested on the LA-92 cycle. The details of the KCVES are located in
US EPA (200878) and Fulper et al. (201079).
A. 2.1 Fuel Sulfur Content
The first step is to determine the sulfur content for the Kansas City vehicles from which the
reference sulfate emission rates are derived. Analysis of the fuel properties was conducted on a
subset of vehicles in KCVES. One hundred seventy-one vehicle tests in the KCVES were
matched with a fuel analysis reported in the Kansas City PM Characterization Reportk. The
average fuel sulfur content is shown in Table 2-1, with associated 95% confidence intervals. The
mean sulfur content is significantly lower in the summer, as shown by the 95% confidence
intervals. Interestingly, the winter measurements had higher sulfur content, although they were
closer to the phase-in of the Tier 2 low-sulfur standards.
Table A-l. Mean Fuel Sulfur content by Season.
Season
n
Mean sulfur
content, ppm
sd
95% Lower 95% Upper
Confidence Confidence
level level
summer
98
138.8
83.0
122.1 155.4
winter
73
183.6
87.4
163.2 204.0
Because most of the vehicles that had a chemical analysis of the emissions did not have the fuel
analysis conducted, the average fuel sulfur content from all the tests is used to represent the
reference case fuel sulfur level. An equally weighted average of the summer and winter is used
of 161.2 ppm.
k The fuel sulfur content from 87 vehicles is reported in Tables 4-11 and 4-15 from the KC PM Characterization
Report78. An additional 84 fuel samples were transcribed from the fuel analysis reports in Appendix ff, because the
tests were not complete by the release of the initial report.
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A.2.2 Fuel Sulfur Contribution Analysis
The sulfate-adjustments in MOVES 2014 consider the sulfate contribution from both the fuel and
the lubricating oil. The following equation (A-4) is used to estimate the fuel and lubricating oil
contribution for the gasoline engines:
Pi ' OSE + p2 " FSC = SES Equation A-4
Where: /?x = Fraction of oil sulfur converted to sulfate, OSE = Oil-sulfur emissions in mg/mi,
/?2 = Fraction of fuel sulfur converted to sulfate, FSC = fuel-sulfur consumption in mg/mi, SES
= Sulfur- emitted as sulfate (mg/mi). SES is 1/3 the value of the sulfate emission rate, to only
account for the mass of sulfur in the sulfate molecule (SO4). To estimate parameters in Equation
A requires at least two data points, ideally one data point at a high fuel sulfur level, and another
at a low fuel sulfur level.
We used the KCVES as our data source from gasoline testing at a high fuel sulfur level. And we
used a recent gasoline test program, the Full Useful Life (FUL) Test Program conducted at the
National Vehicle Fuels & Emissions Laboratory in 2011 as our test program on low fuel sulfur.
The Full Useful Life (FUL) Test Program conducted at the National Vehicle Fuels & Emissions
Laboratory in 2011 evaluated light-duty gasoline Tier 2 vehicles (model year 2005 - 2009
vehicles) at ~ 120,000 miles. The FUL vehicles were tested at low fuel sulfur content (6 ppm),
and sulfate measurements are made from the samples, on cold UDDS (bag 1 + bag 2 of the FTP),
hot UDDS cycles, and hot US06 cycles. Documentation of the FUL test program is located in
Sobotowski (2013).69
Unfortunately, different vehicles were tested between the two studies. To best match the vehicle
technologies and testing conditions, we only used the emissions data collected from the 1996-
2004 vehicles in the KCVES, and only used the summer round data. Because the fuel sulfur
content was not measured for each of the KCVES vehicles, we assumed that the fuel sulfur
content is the mean fuel sulfur level measured in the summer (138.8 ppm). Comparisons of the
particulate measurements of the elements are compared for the newest vehicles from Kansas City
LA-92 cycle, with the three cycles measured in the FUL program in Figure A-l.
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Figure A-l. Oil-derived metals (calcium, molybdenum, phosphorous, zinc), and sulfate and sulfur emission
rates from the Full Useful Life Program, and the newest vehicles from the Kansas City study (1996-2004).
E
O)
=5
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1. Sulfur that is consumed with the lubricating oil in the engine forms sulfate emissions with
the same propensity between the FUL and KCVES vehicles. Oil consumption is not
measured on the vehicles over each cycle. The sulfur emitted in the oil is estimated using
the measured calcium emission rates, and the average sulfur to calcium concentration
measured in the lubricating oil from the FUL test program. The ratio between calcium to
sulfur concentration in the lubricating oil is assumed to be equal between the 1996-2004
KCVES vehicles and the FUL program vehicles.
2. The fraction of fuel sulfur converted to sulfate is the same between the FUL and 1996-
2004 Kansas City vehicles. Both set of vehicles have port-fuel injected, closed looped
engines with three-way catalysts emission control technologies.
The mean values from the KCVES (1996-2004) and the FUL vehicles are used to estimate the
parameters in Table A-2. Weighted means were calculating, using the distribution of the cars and
trucks from the KCVES for the 1996-2004 model years (57% cars, 43% light-duty trucks). The
following data were used with Equation A:
For Kansas City: /?x ¦ OSEKC + /?2 1 FSCkc = SESKC
For the Full Useful Life Program: /?x ¦ OSEFUL + (i2 1 FSCful = SESFUL
Assumption 1 implies /?x = /?x, and assumption 2 implies /?2 = /?2- With two unknowns, and two
equations, /?x and /?2 are estimated, and the model parameters are displayed in Table A-2. The
fuel is estimated to contribute -20% of the sulfate emissions for the FUL program vehicles, and
over 70%) of the sulfate emissions for the Kansas City vehicles.
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Table A-2. Data, estimated coefficients, and estimated contributions of sulfate from the lubricating oil and
Parameter
FUL (FTP)
Kansas City (LA-
92)
Kansas City (LA-92)
Vehicle Model Year Range
2005-2009
1996-2004
(Summer only)
1968-2004 (VMT
weighted)
Sulfur, ppm (xB)
6
138.8
161.2
Calcium emissions, mg/mi
0.028
0.067
0.089
Sulfur/Calcium lubricant concentration
ratio
0.697
Estimated oil sulfur emission, mg/mi
(OSE)
0.020
0.047
0.062
Estimated fuel sulfur consumption, mg/mi
(FSC)
0.849
21.648
25.033
Sulfate emissions, mg/mi
0.024
0.163
0.340
Fraction of Oil Sulfur Converted to
Sulfate Emissions ((3i)
0.333
0.333
0.575
Fraction of Fuel Sulfur Converted to
Sulfate Emissions ((fe)
0.0018
0.0018
0.003
Sulfate conversion adjustment (a)
1
1
1.726
Oil Sulfate Contribution, mg/mi
0.020
0.047
0.106
Fuel Sulfate Contribution, mg/mi
0.005
0.117
0.233
Oil Sulfate Contribution %
81.1%
28.5%
31.3%
Fuel Sulfate Contribution % (FB)
18.9%
71.5%
68.7%
The sulfate PM speciation factors needed for MOVES 2014 gasoline vehicles were based on a
fleet-average of the both the summer and winter tests. The model parameters were adjusted to be
applicable for the fleet of vehicles measured in Kansas City. As stated earlier, the winter tests
had significantly higher sulfur contents in than the summer tests. For modeling the fleet sulfate
contributions in MOVES, the fuel contribution to sulfate emissions was estimated from the mean
fuel sulfur level of both the summer and winter sulfur levels: 161.2 ppm. The average calcium
emissions and fuel consumption were calculated using all 99 vehicles selected for chemical
analysis in the Kansas City study. The means were calculated using a VMT-weighting, and an
equal weight to both the summer and winter data. The VMT weighting places most of the weight
on the 1996-2004 vehicles.
To estimate the relative oil and fuel contribution from fleet-average emissions, the model
coefficients were adjusted to account for different sulfate formation rates. Both the parameters
(/?i,/?2) were adjusted equally with a sulfate conversion adjustment, (a), such that estimated the
fleet-weighted sulfate emissions data.
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a ¦ ft ¦ OSEkc + a ¦ /?2 ¦ FSCkc = SESKC Equation A-3
An adjustment value of 1.726 was estimated to fit the VMT-weighted average, meaning that the
sulfur in the fuel and oil is 1.7 times as likely to form sulfate emissions using the fleet-average
KCVES data set compared to only the summer 1996-2004 vehicles. The increase could be due to
increase in oil emissions with older vehicles and the use of oxidation catalysts in older vehicles
which increase the formation of sulfate emissions. Table 2-2 displays the estimated fuel sulfate
contribution and oil contribution for the VMT-weighted KCVES data. In the KCVES study,
68.7% of the sulfate emissions are estimated to be originating from the gasoline fuel at the
observed sulfur level. In MOVES, the fuel sulfate contribution (68.7%) scales linearly with
changes in fuel sulfur level, but the MOVES retains the lubricating oil sulfate contribution
regardless of the fuel sulfur level. The sulfur levels (xb), and the fuel sulfate contribution values
(Fb) in Table A-2 for the fleet results are the parameters that are used in MOVES to adjust the
gasoline sulfate emissions (Table 9-1.).
A. 2.3 Gasoline Model Evaluation
Figure A-2 contains the sulfate models sulfate/PM predictions for gasoline start and running
conditions compared to values observed in the literature. The values at 293 ppm sulfur level are
obtained from Zielinska et al. (200480). The vehicles were tested in San Antonio, Texas around
1999-2000, with the lubricating oil and commercial fuel "as received." For comparison with the
sulfate values, we assumed that the tested vehicles by Zielinksa et al. (200480) had a sulfur value
of 293 ppm (obtained from the MOVES3 default gasoline fuel formulation for San Antonio for
calendar years 1999-2000).
The values at 36 ppm sulfur level are obtained from Fujita et al. (200781) from testing of 57
light-duty gasoline vehicles in the DOE Gasoline/Diesel PM Split study, conducted in the
summer of 2001. The vehicles were also tested "as received" and gasoline sulfur level was not
reported for by Fujita et al. (200781). We estimated the sulfur content for California fuels in
2001, from MOVES default database as 36 ppm.
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Figure A-2. Sulfate/PM fractions estimated by MOVES for gasoline vehicles compared to values reported by
Zielinska et al. (2004) and Fujita et al. (2007)
0.00 50.00 100.00 150.00 200.00
Sulfur level, ppm
250.00
300.00
^—Gasoline Running
^—Gasoline Start
A 1982-1996 Gasoline Vehicles
X 1976 Gasoline Black Smoker
X 1990 Gasoline White Smoker
• 1982-1996 MY Gasoline at 30F
+ 1999 Gasoline Vehicle
- Gasoline high EC Cold
— Gasoline high EC warm
~ Gasline high emitters cold
¦ Gasoline high emitters warm
~ Gasoline low emitters cold
Gasoline low emitters warm
We reviewed newer data sources that reported sulfate and PM emission rates from gasoline
vehicles. Robert et al. (200782) reported sulfate emission rates from different gasoline
technologies ranging from 0.06 ug/km to 3 ug/km fueled on 35 ppm sulfur fuel, which comprised
less than 0.0004 as a fraction of the PMi.8 emission rates. On the other hand, Cheung et al.
(200983) reported sulfate emissions from a Toyota Corolla which had sulfate emissions of 990
ug/km, which composed as a fraction 0.41 of the measured PM emissions. The sulfate values
from the FUL were 25 ug/mile, and the fleet-averaged Kansas City Study were 340 ug/mile.
Recent testing of sixty-four LDGV vehicles tested at CARB on 1987-2012 model year vehicles
indicate that a significant fraction of the PM emissions is composed of ions (<20%) but the
sulfate ion fraction of the PM was not reported.84
There is a large variation of sulfate emissions reported in the literature (values of sulfate
emission rates ranging 4 orders of magnitude). Differences in measurement methods between
laboratories on particulate matter and sulfate measurements, and variability in emissions from
vehicles appear to contribute to significant variability between the sulfate measurements between
the two laboratories. Given the uncertainty, the sulfate model implemented in MOVES still
provides results that are within the range of results from the literature.
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A3 Derivation of the Sulfate Calculator Parameters for
Conventional Diesel Vehicles
In Phase 1 of the DECSE72, two engines were tested with diesel oxidation catalysts: a 1999
Cummins ISM370 and a 1999 Navistar T443 engine. The Cummins is a heavy-duty diesel
engine, and the Navistar is a medium-duty engine used in light duty trucks. The engines were
tested on steady-state 4-mode test cycles, as well as a transient FTP hot-cycle test. The engines
were tested at 4 sulfur fuel levels: 3, 30, 150, and 350 ppm. The lubricating oil used in the study
was Shell Rotella T15W40, which is a commercially available CH-4 diesel lubricating oil
specified for use in diesel trucks running on sulfur fuel <500 ppm, and engines that comply with
the 1998 US EPA engine standards. The sulfur content of the engine oil was measured at 3520
ppm (DECSE phase 1). The PM and sulfate emissions were measured engine-out, and post-
catalyst to examine the impact of the diesel oxidation catalyst on emissions. The engine-out and
post-catalyst SO4 emissions are plotted at the four sulfur levels in Figure A-3 and Figure A-4.
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Figure A-3. Engine-out sulfate emissions at four fuel sulfur levels (3,30,150,350) measured on a 4-mode and
FTP engine test cycle, from a heavy-duty engine (Cummins) and a medium-duty engine (Navistar) from the
" DESCE Phase 1 Study72
Engine
Cummins
Navistar
4-Mode
FTP Hut-Cycle
1 1 1 1 1 1 r^l 1 1 1 1 1 1 r~
0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350
Sulfur, pprn
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Figure A-4. Post-catalyst sulfate emissions at four fuel sulfur levels (3,30,150,350) measured on a 4-mode
and FTP engine test cycle, from a heavy-duty engine (Cummins) and a medium-duty engine (Navistar) from
the DESCE Phase 1 Study.72
Sulfur, ppm
The post-catalyst results produced much more variable results with respect to fuel sulfur. On the
steady-state cycle, the medium-duty engine was very sensitive to fuels sulfur level and produced
over 90 mg/mile of Sulfur at the elevated fuel sulfur level. The engine-out results (Figure A-3)
produced more consistent results between driving cycles and between the heavy-duty and
medium-duty engines. Because this data produced more consistent results, the engine-out sulfate
data is used to estimate the relative contribution of lubricating oil and fuel to the sulfate
emissions for diesel engines in MOVES. Figure A-5 plots the engine-out sulfate results with
respect to fuel sulfur level for the two engines and two driving cycles.
124
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Figure A-5. Simple linear regression fit of the engine-out sulfate emissions and fuel sulfur level data. This
includes the medium and heavy-duty engine, and both the steady-state 4-mode cycles and the FTP cycles. The
shaded areas are the 95% confidence intervals of the mean-value of the regression.
Engine
* Cummins
* Navistar
1 1 1 1 1 1 1—
0 50 100 150 200 250 300 350
Sulfur, pprn
~
y = 0.5499 + 0.00849-x, r2 = 0.76 .
Table A-3. Estimated linear regression parameters of the engine-out sulfate emissions and fuel sulfur level
Parameter
Estimate
Std. Error
Lower 95%
CI
Upper 95%
CI
t-value
p-value
Intercept
0.549904
0.241605
0.0317
1.068
2.276
0.0391
Fuel Sulfur
0.00849
0.001265
0.0058
0.011
6.712
9.92E-06
Using the simple linear regression fit, the relationship between sulfur content and fuel is
estimated. The intercept can be interpreted as the sulfate contribution from the lubricating oil.65
Using the model coefficients in Figure A-5, the fuel sulfate and oil sulfate contributions are
calculated for four sulfur levels in Table A-3 (0, 11, 172, and 350). At 0 ppm sulfur, the fuel
sulfate contribution is 0, and all the estimated sulfur is from the lubricating oil. At 350 ppm fuel
sulfur, most of the estimated sulfate is from the fuel sulfur. The national default fuel sulfur level
in MOVES for heavy-duty trucks is 11 ppm. The estimated sulfur content for the base PM rates
for pre-2007 model year diesel vehicles in MOVES is 127 ppm, which is based on the E55/59
study. In MOVES runs, the estimated fuel sulfate contribution from the E55/59 (72.6%) is scaled
linearly with changes in fuel sulfur from 172 ppm. We provided the linear model parameter
estimates in Table A-3 so that a measure of uncertainty of the derived model coefficients in
Table A-4 can be estimated.
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Table A-4. Estimated oil and fuel sulfate contributions to the model.
Component
Sulfur leve
1, ppm (x)
0
11
172
350
Oil Sulfate Contribution (mg/bhp-hr)
0.55
0.55
0.55
0.55
Fuel Sulfate Contribution (mg/bhp-hr)
0.00
0.09
1.46
2.97
Oil Sulfate Contribution (%)
100.0%
85.5%
27.4%
15.6%
Fuel Sulfate Contribution (%)
0.0%
14.5%
72.6%
84.4%
A.3.1 Pre-2007 Diesel Model Evaluation
In Figure A-6 we compared the estimated sulfate/PM fractions obtained from applying the
sulfate calculator in conjunction with the pre-2007 PM speciation profile used in MOVES. We
compared these values to values reported for light-duty diesel trucks reported by Zielinska et al.
(200480), and from heavy and medium-duty diesel trucks tested as part of the DOE
Gasoline/Diesel PM Split Study reported by Fujita et al. (200781). In both of these test programs,
the fuel sulfur level was not reported, and the vehicles were tested with the fuel "as received."
For these test programs conducted in 1999-2001 timeframe, the MOVES default sulfur level is
130 ppm for these locations (San Antonio, TX and Riverside CA, respectively). We also
compared these values to the sulfate fraction reported in PM2.5 SPECIATE profile # 91106 based
on the Northern Front Range Air Quality Study (NFRAQS). The diesel fuel sulfur was estimated
to be around ~ 340 ppm from three diesel samples taken from three nearby fueling stations.85
The sulfate/PM fractions from the literature bound the sulfate calculator predictions in MOVES.
Two of the three light-duty diesel sulfate/PM fractions are much higher than the medium-duty
and heavy-duty diesel emission rates and from the values predicted from the sulfate calculator
and the pre-2007 PM2.5 speciation profiles. This may be indicative of significant differences
between light-duty and heavy-duty PM2.5 speciation. This could be an area for future research.
For now, the sulfate calculator appears to provide a reasonable sulfate/PM fractions compared to
the available sources in the literature.
126
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Figure A-6. Sulfate/PM fractions estimated by MOVES for gasoline vehicles compared to values reported by
SPECIATE Profile #91106 (NFRAQS), Zielinska et aL (200480), and Fujita et aL (200781).
Sulfur Level, ppm
Pre-2007 Diesel Running
Pre-2007 Diesel idle
~ Heavy-duty Diesel Comp (Fujita etal.
2007)
X Medium-duty diesel trucks (Fujita et
al. 2007)
X Medium and Heavy-Diesel (NFRAQS)
• Light-duty diesel 1998-2000 (Zielinska
etal. 2004)
+ Light-duty diesel 1991 (Zielinska et al.
2004)
- Light-duty diesel at 30 F (Zielinska et
al. 2004)
127
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A.4 Derivation of the Sulfate Calculator Parameters for 2007 and
later Diesel Vehicles
Table A-5. Model Parameters for predicting particle number contribution from sulfur in the fuel and the
111 (a in All f 1'AIYI ITlMalciAtt A+ 111 (
Parameter
Estimate
90% Confidence
Intervals
Fuel sulfur
concentration
36.2
(24.3 to 48.1)
Lubricating Oil
concentration
0.142
(0.054 to 0.23)
The relative contributions of sulfate emissions are computed using the contributions from fuel
and oil parameters from Table A-5. Table A-6 displays the contributions from lubricating oil,
assuming 3,000 ppm sulfur content, and varying levels of sulfur content in the diesel fuel. 4.5
ppm is selected because it is the fuel sulfur level used in the ACES phase 1 program, from which
the sulfate emissions for post-2007 emissions are derived. Fifteen ppm is the sulfur limit
mandated by the 2007 ultra-low fuel sulfur. The current default sulfur content is 11 ppm used in
MOVES3. As shown in Table A-6, the lubricating oil is estimated to contribute the majority of
sulfate emissions when the fuel sulfur level is below 12 ppm.
Table A-6. Estimation of the relative contribution of fuel sulfur and lubricating oil sulfur on sulfate emissions
Parameter
Sulfur level (x) ppm
4.5
11
15
Oil Particle Number Contribution
(CPC/cm3)/106
426.00
426.00
426.00
Fuel Particle Number Contribution
(CPC/cm3)/106
162.90
398.20
543.00
Oil Sulfate Contribution (%)
72.3%
51.7%
44.0%
Fuel Sulfate Contribution (%)
27.7%
48.3%
56.0%
No additional studies were available at the time of this analysis to validate the sulfate model with
2007+ technology diesel engines.
128
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Appendix B Estimation of Weight % Oxygenates for the
Complex and Predictive Models
The complex model for carbon monoxide (Section 4.1), benzene, and 1,3-butadiene (documented
in the air toxics report1) and has a term for wt% of oxygenate. The oxygenate wt% is calculated
from the volume percent using Equation B-l.
Oxygenate (wt%)i = Oxygenate (yolume%)i x volToWtPercentOxyi
Equation B-l
Where:
Oxygenate (volume%) = is the volume percent of a fuel oxygenate. Oxygenate fuel volumes are
mainly reported as % volume. For example, E10 fuel refers to a gasoline-ethanol blend fuel with
approximately 10% ethanol by volume.
volToWtPercentOxyi = term used to convert from the oxygenate percentage by volume (vol%) to
the mass percentage of oxygen in the fuel(mass%). volToWtPercentOxy is calculated using
Equation B-2 and the values provided in Table B-l.
volToWtPercentOxyi = Mass Fraction of Oxygeni x —
Pf
Equation B-2
Where:
Pi = the density of the oxygenate (g/cm3)
pF = the density of the gasoline fuel, assume to be 0.75 g/cm3
The mass fraction of oxygen, densities of the oxygenates, and calculated volToWtPercentOxy
values are shown in Table B-l.
Oxygenate
Name
Mass Fraction of
Oxygen
Density of the
Oxygenate
(g/cm3)
Volume to Weight Percent
Oxygen (volToWtPercentOxy),
assuming gasoline fuel density
of 0.75 g/cm3
Ethanol
0.3473
0.789
0.3653
MTBE
0.1815
0.7404
0.1792
ETBE
0.1566
0.7364
0.1537
TAME
0.1566
0.791
0.1651
Table B-l above contains values for Ethanol, MTBE, ETBE, and TAME. In MOVES3, we have
removed MTBE, ETBE, and TAME from the fuel formulation table, and MOVES3 assumes all
input fuels have zero volume of MTBE, ETBE, and TAME. However, for the complex model
fuel adjustments, MOVES3 assumes the base fuel does contain volumes of these fuels, as
documented in the Air Toxics Report.1
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Appendix C High Ethanol (E85) Fuel Adjustments and HC
species VOC and NMOG
As explained in Speciation of Total Organic Gas and Particulate Matter Emissions from
Onroad Vehicles in MOVE S3,5 9 VOC and NMOG in MOVES are calculated from non-methane
hydrocarbons (NMHC), which, in turn, are calculated from total hydrocarbons (THC). EPA
analysis of emissions using high ethanol gasoline blends (E85 and similar) found that NMHC
emissions from vehicles using these fuels were statistically significantly different from vehicles
using E10 due to statistically significant differences in methane (CH4) emissions. However, no
such differences were seen for VOC or NMOG emissions (Section 7.2). Thus, MOVES has
special algorithms to calculate start and running VOC and NMOG for vehicles using E85 and
E70 fuels.
As depicted in Figure C-l, this algorithm requires developing a set of substitute fuel
formulations that have the fuel properties needed to calculate the actual emission effects. The
detailed steps of this process are described below.
Figure C-l. VOC and NMOG Calculation with E85 and E70
Ethanol Fuel Adjustments
_ , Swap Values &
Copy Fuel
. . Create Substitute Fuel Generator Criteria Calculator HC Speciation Calculator
Formulations
Fuels
130
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C.l Copy fuel formulations
First, the high ethanol fuel formulations in the fuelFormulation table are duplicated. New fuel
formulation entries are added with identical properties and new fuelFormulationID values. The
new formulations will be the only formulations used in subsequent steps. The original
formulations are preserved in case future MOVES algorithms require the data.
The fuelSupply table is searched for all high ethanol fuel formulations (fuel subtypes 51 and 52)
and their region, fuel year, and month group noted. The matching formulations are duplicated
and fuelSupply entries changed to use the new formulations. A new fuel formulation entry is
created for each unique combination of fuel formulation, region, fuel year, and month group.
This is done to guarantee formulation independence required by the next step.
C.2 Swap values to create substitute fuels
The ElOFuelProperties table provides E10 properties by region, fuel year, and month group.
Each new formulation and its associated region, fuel year, and month group is matched with an
entry in the ElOFuelProperties table and the new formulation's properties updated. The
ElOFuelProperties table contains several NULL values to use E85-specific fuel properties in
estimating emissions - they are ETOHVolume, sulfurLevel, and benzeneContent and for these
fuel properties, the values in the new formulation is left unaltered.
In addition, the new fuelFormulation's RVP is not altered, remaining at the high ethanol RVP for
calculation of THC. In parallel, a field named "altRVP" is added to the new fuelFormulation
table and the formulation's altRVP is set to the E10 fuel's RVP. The E10 altRVP will be used to
estimate intermediate "altTHC" that is not reported to the user but is needed for subsequent
calculations in Step 5.
C.3 Step 3 Fuel Generator
The FuelEffectsGenerator populates the CriteriaRatio table that is used to calculate THC. For
ethanol fuels (fuel type 5), the data comes from equations in the GeneralFuelRatioExpression
("GFRE") table. In addition to normal THC CriteriaRatio entries, entries for THC equivalent to
E10 fuel must be calculated for high ethanol fuel formulations.
Within FuelEffectsGenerator, this is accomplished by duplicating and altering the GFRE entries
for fueltypeid = 5. The GFRE table is read into memory as a collection of Java objects. These
objects are searched for entries that are applicable for fueltypes, THC pollutant, Running
Exhaust (1) or Start Exhaust (2), and model years 2001 and later. Matching entries are
duplicated. The duplicates are altered to apply to pollutant "altTHC" (10001), restricted to only
fuel subtypes 51 and 52, and to begin no earlier than the 2001 model year. Further, the equations
in the duplicates are altered to use "altRVP" instead of "RVP", thus making the equations
generate altTHC based upon E10 RVP not E70/E85 RVP.
131
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The GFRE objects are then processed as normal, populating the CriteriaRatio table. In addition,
altTHC entries are used to populate the altCriteriaRatio table. This table has the same schema as
CriteriaRatio and is used to ratio altTHC to THC in the next step.
C.4 Step 4 Criteria Calculator
Within BaseRateCalculator.sql, THC, but not altTHC, calculations proceed normally based upon
data within the CriteriaRatio table. After all emission rates and adjustments to THC have been
performed, altTHC is calculated using the ratio between altCriteriaRatio and CriteriaRatio:
altTHC = THC * (altCriteriaRatio.ratio for altTHC) / (CriteriaRatio.ratio for THC)
At this point, altTHC inventory or rates exist for the new high ethanol fuel formulations, running
and start exhausts, and model years 2001 and later. Further, these altTHC values were derived
using E10 RVP.
C. 5 Step 5 HC Speciation Calculator
Within HCSpeciationCalculator.sql, methane (5) and NMHC (79) are calculated from THC for
all fuel types, as normal, using speciation factors that match the fuel type. That is, for E85
emissions, methane and NMHC are calculated using the E85 methane/THC ratios.
Pollutant altNMHC (10079) is calculated from altTHC (10001) for high ethanol fuels using
methane ratios for E10 fuels. AltNMHC is needed for the calculation of VOC and NMOG for
high ethanol fuels. It is never reported to the user.
Pollutants VOC (87) and NMOG (80) are created from altNMHC for high ethanol fuels, running
and start exhausts, and model years 2001 and later, using HC speciation factors and ethanol level
for E10 fuels. VOC and NMOG are the only pollutants from E85 that are assumed to be the
same as those from E10 fuels.
C.6 Step 6 Toxics and other pollutants
The calculation of toxics from E85 that are chained to VOC proceed normally, using the
adjustments specific for the high ethanol fuels.
All other pollutants, including CO, NOx, Total Energy, and PM, use the new "alt" fuel
formulations that contain the input for high ethanol's RVP, sulfur, and benzene, combined with
E10 values for the remaining fuel properties.
132
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