Fuel Effects on Exhaust Emissions from
On-road Vehicles in MOVES2014
Final Report
United States
Environmental Protection
^1 Agency
-------
Fuel Effects on Exhaust Emissions from
On-road Vehicles in MOVES2014
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.
Final Report
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
s>EPA
United States
Environmental Protection
Agency
EPA-420-R-16-001
February 2016
-------
Contents
1 Introduction 3
1.1 Gasoline 3
2 "Base" and "Target" Gasolines 5
2.1 Base Gasolines 6
2.2 Target Gasolines 6
2.2.1 Relevant Database Tables 7
3 Fuel Sulfur Effects 8
3.1 Introduction 8
3.2 The MOBILE6 Sulfur Model (M6Sulf) 9
3.2.1 Data Used in Developing the M6Sulf Model 9
3.2.2 Analysis of Short-Term Sulfur Effects 11
3.2.3 Analysis of Long-Term Sulfur Effects 20
3.2.4 Application in MOVES 22
3.3 Tier 2 Low Sulfur Model (T2LowSulf) 29
3.3.1 Background 29
3.3.2 Data Used in Developing the T2LowSulf Model 30
3.3.3 Data Analysis and Results 34
3.3.4 Application in MOVES 52
3.4 Results: Sulfur Effects in MOVES2014 54
4 Use of the Complex Model (for CO Emissions) 57
4.1 Overview of the Complex Model 57
4.2 Application of the Complex Model 60
5 Use of the EPA Predictive Model (HC and NOx Emissions) 63
5.1 Data Used in Developing the EPA Predictive Model 63
5.2 Analytic Approaches 63
5.2.1 Standardization of Fuel Properties 64
5.3 Application in MOVES 67
6 Gasoline Fuel Effects for Vehicles certified to Tier-2 Standards (EPAct Models: HC, CO,
NOx, PM) 68
6.1 Introduction: the EPAct Project 68
6.2 Analysis and Model Fitting 73
1
-------
6.2.1 Standardizing Fuel Properties 73
6.2.2 Fitting Procedures 75
6.3 Scope and Implementation 83
6.4 Fuel Effect Adjustments 84
6.5 The Database Table "GeneralFuelRatioExpression" 87
6.5.1 Examples 88
7 High-Level Ethanol Blends (E85) 95
7.1 Introduction 95
7.2 Data Analysis and Results 96
7.3 Application in MOVES 102
8 Biodiesel Blends 104
8.1 Pre-2007 Diesel Engines 104
8.2 2007 and later Diesel Engines 104
8.3 Modeling Biodiesel 105
9 Sulfate Emissions 106
9.1 Introduction 106
9.2 Sulfate Calculator Summary 106
9.3 Gasoline Vehicles 109
9.4 Pre-2007 Diesel Vehicles 109
9.5 2007 and Later Technology Diesel Vehicles 110
9.6 Compressed Natural Gas 110
9.7 Example Comparisons Ill
9.8 Sulfur Dioxide Emissions Calculator 113
9.9 Summary 115
Appendix A Derivation of the Sulfate Equation and Parameters 116
Appendix B Peer Review Comments and Responses 130
10 References 174
2
-------
1 Introduction
The MOVES2014 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, "ethanol (E-85)" and "electricity." The "Ethanol" category includes
blends of ethanol and gasoline in which the ethanol fraction exceeds 70 vol.%. Clearly, fully
electrified vehicles do not emit exhaust pollutants, and will not be further discussed in this
report. Note that MOVES2014 applies LPG only for the NONROAD component of the model.
It is visible in the fuelType table and in the GUI due to sharing of tables between the on-road and
NONROAD components of the model.
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. At present, MOVES2014a is intended to estimate emissions from
gasoline blends with ethanol up to 15 vol.%. The treatment for diesel and CNG is much simpler.
This document discusses adjustments or other calculations designed to account for changes in
fuel properties on emissions of THC, CO, NO* or PM. Similar calculations applied to emissions
of air toxics are discussed in a separate report.1
The draft version of this document underwent external peer review.2 The comments of the two
reviewers and the Agency's responses are provided in Appendix B.
1.1 Gasoline
Estimation of emissions from gasoline plays a very important role in MOVES2014. 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.3
Oxygenate requirements reflected in the model include the use of methyl-tertiary-butyl-ether
(MTBE) (as a historical factor) and ethanol mandates, including the renewable fuels standards
(RFS1 and RFS2). The MOVES fuel supply currently reflects the fact that most gasolines in the
U.S. contain approximately 10 vol.% ethanol. In addition, the fuel supply in MOVES2014
incorporates the introduction of gasolines containing up 15 vol.% ethanol, i.e., "El 5" fuels. The
construction and composition of the default fuel supply is described in greater detail in a separate
report.4
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, maximum and average sulfur levels were reduced from 300 to 80 and 120 to 30 ppm
3
-------
from 2004 and 2006, respectively.5 Under the Tier-3 program, further reductions to an average
level of 10 ppm will be realized by 2017.6
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, and "target" emissions, intended to reflect the
set of "target" fuels in the areas and periods covered in a MOVES run. The concept and specific
definitions of base gasolines are discussed below in Section 2.
During a run, MOVES combines emission rates and 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 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 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 < 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 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.
For vehicles applied 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 is described in Chapter 6.
4
-------
Fuel sulfur plays yet another role in that MOVES2014 estimates emissions of sulfate (SO4) as a
component of the non-elemental-carbon component of PM2.5. A refinement introduced in
MOVES2014 is that this 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 so as 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, MOVES2014 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 9-2 (page
114).
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 gasoline or such
"high-level" ethanol blends are designated as flexible-fuel or "flex-fuel" vehicles (FFVs).
MOVES2014 incorporates the capability of modeling 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.
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. The bases and calculations of 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
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.
Several base gasolines are used in MOVES2014. Their properties are defined in the database
table BaseFuel, and are further described in sub-section 2.1.
5
-------
2.1 Base Gasolines
For gasolines, MOVES2014 uses two 'base' fuels for calculation of fuel adjustments for non-
sulfur properties. These two fuels, designated as A and B, differ only in sulfur level. Fuel A is
assigned a lower sulfur level applicable to vehicles in model year 2001 and later. Fuel B is
assigned a higher sulfur level applicable to vehicles in model years 2000 and earlier. Fuels A and
B are used in the calculation of fuel adjustments for HC, CO, NO* and PM emissions.
In terms of properties other than sulfur, Base gasolines A and B are assumed to represent the
"typical" gasoline in the Phoenix metropolitan area between calendar years 1995 and 2005. The
emission rates for gaseous emissions from light-duty vehicles are based on random evaluation
samples from the Phoenix Inspection and Maintenance Program during this time period. The
development of these "I/M reference rates" (meanBaseRatelM) is described in detail in a
separate report.7 Because fuel properties individual vehicles in the I/M lanes are unknown, we
assume that the "averaged" fuel properties, based on fuel surveys in the same area during the
same time period, can be associated with the average emission rates. The properties of each fuel
are shown in Table 2-1 below.
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 gasoline vary by county, year, and month. The MOVES2014 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 2050. 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.4
6
-------
Table 2-1. Properties of MOVES Base Gasolines.
Fuel Property Name
Fuel A
Fuel B
Fuel Sub-Type
Conventional
Conventional
fuelFormulationID
98
99
RVP (psi)
6.9
6.9
Sulfur Level (ppm)
30.0
90.0
Ethanol Volume (%)
0.0
0.0
MTBE Volume (%)'
0.0
0.0
ETBE Volume (%)2
0.0
0.0
TAME Volume (%)3
0.0
0.0
Aromatic Content (%)
26.1
26.1
Olefin Content (%)
5.6
5.6
Benzene Content (%)
1.0
1.0
E200(%)
41.1
41.1
E300 (%)
83.1
83.1
T50 (°F)
218
218
T90 (°F)
329
329
Volume to percent
Oxygen (%)
0.0
0.0
'Methyl tertiary-butyl ether, used as an oxygenate.
2Ethyl tertiary-butyl ether, used as an oxygenate.
3Tertiary amyl-methyl ether, used as an oxygenate.
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 fuels used by MOVES2014 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.8
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.
7
-------
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.
GeneralFuelRatioExpression: this table contains mathematical expressions that calculate some
of the fuel adjustments described in this chapter. It is described in greater detail in 6.5 (page 87).
The additional tables listed below are described in the fuel supply report:4
FuelFormulation,
FuelSupply,
RegionCounty,
FuelEngFraction,
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
8
-------
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 a "clean"
catalysts.
This chapter describes how MOVES2014 adjusts exhaust emissions of hydrocarbons (HC),
carbon monoxide (CO), and nitrogen oxides (NO,;) 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 MOVES2014 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.
MOVES2014 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 MOVES2014.
Previously, in MOVES2010, only the M6Sulf model was used to estimate the effects of fuel
sulfur on emissions.
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
9
-------
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 NO* emissions in this range.
T5o/T9o/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: the 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
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 NO;c 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 NO* 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.
10
-------
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:
• 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
11
-------
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.
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 NO>;. 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 NO>;. or HC, or greater than three times the
emission standard for CO
The algorithm produced separate sulfur corrections for "Normal" and "High" emitters. Because
MOVES2014 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 3.2.2.1, respectively. Table 3-2
shows the numbers of vehicles in each emitter category for the studies included in developing
the M6Sulf model.
12
-------
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
-------
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
NO*
Running
Ln-Ln
0.02083
0.944
HC
Start
Ln-Ln
0.0027436
0.959
CO
Start
Ln-Ln
-0.01792
0.860
NO*
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
14
-------
The Tier 0 analysis summarized in Table 3-3 and Table 3-4 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.
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 NO* 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)
NO* (% 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 one set of data, T50/T90 Sulfur, 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
-------
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
NO*
Running
Ln-Linear
0.0006337
0.853
HC
Start
Ln-Linear
0.00009516
0.941
CO
Start
Ln-Linear
-0.0002338
0.820
NO*
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.
16
-------
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
vehicles. The fractional effect for Tier-1 vehicles at any sulfur level X> 330 ppm (fn. v)is given
by Equation 3-1,
JT\,X JT\, 330
where:
//i.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),
//i.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.
f f \
J T0,X
V /ro,330 J
Equation 3-1
17
-------
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
3.2.2.1 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 for LEV and Tier-
2 "High-Emitting" Vehicles.
Pollutant
Emissions
Process
Type 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
-------
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
NO*
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
NO*
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 Model1
-5.0
1.4
10.0
:CO emissions were not in the original RFG Complex Model. The CO model developed separately (using the same
statistical techniques used to construct the RFG Complex Model) and is discussed in SAE paper 96121413.
19
-------
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 into 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
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
-------
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
-------
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 MOVES2014, 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 (NO.,)
M6emitterID
Identifies the emitter classes. See "sulfurmodelname" 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
-------
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 (Pln JS ) Equation 3-2
Cshort,basis " eXP (P ln *S,basis) Equation 3-3
Qhort, target ®XP (M) Equation 3-4
Qhort,basis w •"'S,basis / Equation 3-5
The Short-term sulfur effect (SulfAdj, As,short) for all groups is computed using Equation 3-6.
23
-------
c.
short,target
C.
short,basis
A
S,short
Equation 3-6
c.
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.
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
MOVES2014 (Table 3-13) are stored in the sulfurLongCoeff variable 04s,long) 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 (At), as shown in Equation 3-7.
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 to only "LEV" and
later (2001+ model year vehicles), and apply only to target fuel sulfur levels greater than 30 ppm
sulfur. For model years 2003 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 "maxIRFactor Sulfur"
(xs,caP), also stored inM6SulfurCoeff Equation 3-8 is used to compute the "irreversible sulfur
effect" 04s,irr, SulfIRR). The effect is applied as a function of model year group.
The maxIRF actor Sulfur is applied as a function of model year group, as follows:
3.2.4.2 Long-Term Sulfur Effects
Equation 3-7
3.2.4.3 Sulfur Irreversibility Effects
24
-------
Model Year Group Maximum S level
2001 -2003
1,000 ppm
2004 - 2005
303 ppm
2006 - 2007
87 ppm
2008 +
80 ppm
4urr=eXP (^lniS.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.
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
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."
3.2.4.4 Combining Short-Term, Long-Term and Irreversibility Sulfur Effects
C ¦ ^
—sh°rt'basls + (l. 0 — ")A2 Equation 3-9
t,basis J
3.2.4.5 Sulfur Effects in Geographical Phase-In Areas (GPA)
25
-------
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's,short,gpa, as shown below:
Qhort, GPA ®XP GPAmax )
Equation 3-10
C -C
A short,GPA short,basis
s,short,gpa - ~ 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.
^2,GPA = 4s,short,GPA x Aong Equation 3-12
Then, the equivalent of the adjustment's,3 for the GPA area (A3, gpa) is calculated by applying
Equation 3-13 as shown below.
^3,GPA = 1 • 0 + (m'ir^2,GPA + (l • 0 _ WIR )y42 ) 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's,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.
26
-------
A,combined - 0 /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 MOVES2014 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 = vi'ingh = 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.
47" = (i - »•«, kK-+"W.4SU
/ \ Equation 3-15
4bJ = (i - KI—+«v
Likewise, a composite of normal and high emitter GPAsulf adjustments are calculated using the
same weights.
= (l
/ \ Equation 3-16
ibase J a base ibase
3,GPA _V high / 3,GPA,normal high 3,GPA,high
3.2.4.7 Computing the Sulfur Adjustment for Base and Target Fuels
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
27
-------
model applies to all sulfur levels for model year group 1960-2000, and only to sulfur levels
above 30 ppm for model year groups 2001-2050.
A,.
A
A target
S,3
S,final j base
S,3
Equation 3-17
A target
3.GPA
GP A,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.
Table 3-15. Glossary of Variables and Equations for calculations described in 3.2.
Eqn
Eqn(GPA)
Symbol
Name
Type (1)B 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 Xs,GPAmax).
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
A S,short
SulfAdj
Intermediate result
Short-term sulfur effect
3-7
¦ I S,long
sulfurLongCoeff
DB input
(M6SulfurCoeff)
Applied to vehicles in LEV
and more recent standards,
for S levels >30 ppm
28
-------
3-7
3-12
Ai
Intermediate result
Adjustment combining short
and long-term sulfur effects.
Calculated as product of
.1 s.shori and. 1 s. long.
3-8
^S,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 ulfurmodelcoeff)
Equal to (1 for TO, LEV or
ULEV vehicles or y for Tier 1
vehicles.
3-8
^4s,Irr
SullIRR
Intermediate result
"irreversible sulfur effect,"
applied for vehicles in model
years 2004+, for S levels >30
ppm but less than xs,cap.
3-9
WlR
sulfurlRFactor
DB input
(M6SulfurCoeff)
3-9
3-13
As, 3
SulfAdj3
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
2001-2050.
3.3 Tier 2 Low Sulfur Model (T2LowSulf)
The M6Sulf model, described above, is used in MOVES2014 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," based on additional data collected since the M6Sulf model was created, is
used.
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
29
-------
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 NO*, 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
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
30
-------
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 3-16 for detailed fuel properties). All
emissions data was collected using the FTP cycle at a nominal ambient temperature of 75°F.
31
-------
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
2007
Toyota
Toyota
Corolla
1.8L 14
8TYXV01.8BEA
5
2008
Toyota
Toyota
Carnry
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
8FMXV02.0VD4
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.
32
-------
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 provide the key information for assessing the in-use effect of
target sulfur levels on emissions over time as vehicles accumulated mileage. These data are the
most relevant in the context of MOVES2014 and therefore, only the analyses pertaining to the
"sulfur level" data are discussed in the following section. 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.
33
-------
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 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
34
-------
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 NO* 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
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).
35
-------
Figure 3-1. N0V (Bag 2): Concentrations for Hot-running Emissions by Vehicle.
• J
( t
S t : • .
; » ,
• • • : .•
:< . * o- . *
4 "#«* \ o J 4 1
8 * $ I * * S *
5 ° 8 • • %
s I J - * 0° 11 o° ; * i
f * i \ Z * * $
.
l *• o° So » 8
• . s i . ' i o o o ° I • : t
1 ° i ^ • i. ° S ! ° * 8 ° ® 1 ! i 4
?»«• ¦*!„ •S*3,00° °o • 8 o * .
• ' * 8
• 0 %
% °
m
¦
0
fotfo T g | 8 ° ° ° ° °
° 8 » o ° 8 «
O
• O
0
o
(N (N CD (N
OOOrHrHrH*HrH(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
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.
36
-------
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
37
-------
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
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:
Kj = Xtp + ZiUi + £j Equation 3-18
where ft 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
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,; o,
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.
38
-------
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.
Figure 3-2. Box-Plot of Individual Vehicle Families by Sulfur Level (NOx Bag 2).
t 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 r
Vehicle
~ Hisfa Sill fur at 28ppm ~ 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.
39
-------
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
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).
veWD = 0003L
vehID = 0006S
vehID = 0007L
velilD - 0009S
vehID = 0010S
vehID = 00211
v el ill) = 0022L
-4-
-10"
+¦
1 o
4-
+¦
0 4+
+
<¦ n
+ + +
vehID = 0023L
vehID = 0026L
veldD = 0026...
vehID = 003IE
vehID = 0039S
vehID = 00461
velilD = 0074S
-4-
-10"
#0 +-K30
+¦ +
o +
+ a
o + + + a
G a
+
vehID = 0075L
vehID = 0089S
velill) = 0101S
velilD = 0104...
velilD = 0107L
vehID = 0123E
veMD = 01.31S
-4-
-10-
%
Of «
>> •;
<
vehID = 0146...
vehID = 0148...
velilD - 0162S
vehID = 0165S
vehID = 0178L
velilD = 01791
velilD = 0179S
-4 -
-10 -
Q+ 0
-------
since cleanout. The dependent variable (Y,) 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
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.
= Varfet) =
a2p
a 2pn~1
a2p
o2pn 2
a2p2
a2p
a2pn~3
a2pn 1
a2pn~2
Equation 3-19
where:
o2 = variance,
41
-------
p = correlation between measurements,
n = number of measurements
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
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.
42
-------
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.
43
-------
Table 3-22. Likelihood Ratio Test for Bag 2 NOx Model.
Fixed effects
-2 Res Log Likelihood
p-value (x2)
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.
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.
44
-------
Figure 3-4. Data vs. Predicted by Vehicle (Log-Transformed Bag 2 NO*).
MD - M500
VID = M300
MD ~ M'301
MD - NBul
MD - Mr>02
sulfur - High
sulfur - Low
sulfur - High
sulfur - Low
^nifiu - Hifih
t * *
t£*j * *
i: « t j
VID = M502
VID = M503
VID = M503
MD = M504
VJD = M504
sulfur = Low
sulfur = High
sulfur = Low
sulfur = High
sulfur = Low
; *, . ± * <
t , * . »
t!
MD = M505
VID = M505
VID = M506
VID = M506
MD = M507
sulfur = High
sulfur = Low
sulfur = High
sulfur = Low
sulfur = High
nt,„ 1
i i • * * *
: t * * * r
\* : : :
Ml)- >1307
VID -• M5U8
MD - M508
MD - M309
\'1D - M309
suil'm low
SllllUT High
sulfur " F ow
sulfur High
sulfur I ow
• r,t» j
, }. t » t »
I 4
i i ri i i I f i i i i r
0 100 200 0 100 200 0 100 200 0 100 200 0 100 200
miles
group + Data + Model
MD - N510
MD = N510
MD~ VU1
MD-N3J1
MD-X3I2
sulfur ^ High
sulfttr = Low
sulfur ^ High
sulfur = I ow
sulfitr - High
•*
,
+ ? * *
t* »*
r
\'1D = N512
MD = N513
VID = N513
VID = N514
MD = N514
sulfur = Low
sulfur = High
suite = Low
sul fur = High
sul ffir = Low
f f «
:r>
tj.* *~
lit :+ *i
It* + * f*
VID - Nil"
MD xni
MD X320
MD - N320
MD N32J
sulfur Hjgih
sulfur- I ow
sullur - High
sulfur Low
sultur High
r*
* r*
:i% t ¦' "
" * Ir
tV It 11
MD - N321
VID - P">22
MD - F322
sullur Low
sulfur High
sulfur - Low
}!«
*» **
t i ri i i i i 1 i i i i i i i
0 100 200 0 100 200 0 100 200 0 100 200 0 10(1 200
miles
group + Data + Model
45
-------
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 NO*.
Figure 3-5. Data vs. Predicted (Log-Transformed NOx Bag 2).
••
- ••V-'.-j
•
• •
-10
-li
-12
-12
-10
-9
-8
-7
-6
5
-3
-11
-4
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 NO*. 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.
46
-------
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.
47
-------
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
(p-value)
THC
(p-value)
CO
(/;-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 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.
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
T2 B4
714
EPA-owned
1 This vehicle was loaned by Umicore Autocat USA, and is the same vehicle used in their 2011 study.
48
-------
The box-plot of the log-transformed emissions from Bag 2 NO* "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
NOi 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 NOv).
-4-
-6-
O
-8-
-10-
T
T
i 1 1 1 r
Honda-Crosstour Chsvy-Malibu Subaru-Outback Chevy-Silverado Ford-Focus
Vehicle
D High Sulfur at 28ppm D Low Sulfur at 5ppm
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
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.
49
-------
Table 3-26 compares the percent reduction in emissions from 28 ppm to 5 ppm fuel sulfur for
Tier 2 vehicle and "Tier-3equivalent" 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 NO* 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.
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.
50
-------
Table 3-27. Results of Sensitivity Analysis of Low Concentration Measurements (Bag 2 NOv)
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 withp-walue <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.
51
-------
Figure 3-7. Influence Diagnostics for Bag 2 NOx.
Restricted Likelihood Distance
4U "'K &<• &<¦ 4?<- 4?< 4r<-
\ % % % % \ % % % '
'& O
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 MO VES
The results shown in Table 3-24 (page 48) were incorporated into MOVES2014 and were
applied to model year 2001-and4ater 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.
52
-------
Equation 3-20 shows the generic form of the calculation of the linear low-sulfur adjustment^.
As =1-0-/1 Sybase ~xs) Equation 3-20
The Tier 2 Low Sulfur coefficients (fis) 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. However, for model year
group 2017-2050, since the Tier 3 emission rates in MOVES assume that Tier 3 vehicles run on
10 ppm sulfur, the sulfur base is set at 10 ppm to prevent double-counting of the impacts of low
levels of sulfur in fuels for Tier 3 vehicles. xs represents the actual in-use sulfur levels in the
region being modeled.
Table 3-28. Interpolated Coefficients by Vehicle Type, Process and Pollutant, applied for sulfur levels < 30
ppm
Vehicle Type
THC
CO
NO*
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
53
-------
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 database ("generalFuelRatioExpression"). This
table consolidates the two sulfur models (M6Sulf and T2LowSulf) for MYG 2001-2016 and
2017-2050, 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 shown in Section 6.5 (page 87).
3.4 Results: Sulfur Effects in MOVES2014
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.
54
-------
Figure 3-8. Relative Fuel Sulfur Effects for Running-Exhaust Emissions for MY 2017 and later, normalized
to a sulfur level of 10 ppm.
s 1.0
s 0.6
a 0.4
30 40 50 60 70
Sulfur Level (ppm)
100
Figure 3-9. Relative Fuel Sulfur Effect for Running Exhaust Emissions for MYs 2001 to 2016, normalized to
a sulfur level of 30 ppm.
S 0.6
S 0.4
0 50 100 150 200 250 300 350 400 450 500 550
Sulfur Level (ppm)
55
-------
Figure 3-10. Relative Fuel Sulfur Effect for Running Exhaust Emissions for MY 1996, normalized to a sulfur
level of 90 ppm.
Sulfur Level (ppm)
Figure 3-11. Relative Fuel Sulfur Effect for Running Exhaust Emissions for MY 1988, normalized to a sulfur
level of 90 ppm.
1.4
1.2
1.0
5 0.8
(fl
S
-a5
-------
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 th e 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 centered 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.
^ ^ /^0 /^oxy ("^oxy,! ^"oxy /^arom (•^aroni./ ¦^arom)-'- " ' /^RVP ("^RVP,z ^RVp)-'
-+B (x -x Yt -x ) Equation 4-1
+ PE300OLE \ E300,z XE300 AxOLE,; xOLE )
57
-------
The mean values used for centering all individual fuel-property values are presented in Table
4-3. The set of coefficients (J) 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 ComplexModelPammeters, 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 coeff2 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.
58
-------
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
59
-------
Table 4-5. Complex Model Coefficients for 2nd-0rder 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
4.2 Application of the Complex Model
For each compound, the model equations are evaluated for a "base" and a "target" fuel (See
Section 1). 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
./ad I
exp(xptarget) 1 Q
exp(xpbase)
Equation 4-2
The expression in Equation 4-2 is evaluated for 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.
60
-------
10
10
adj,mean
Group=1
2 WGroup/a
Group-/ adj,Group
Group=1
2 WG«»^=1-0
Equation 4-3
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.
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 (page 9).
Note that 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.
F . = F 1+ f ¦
relative base \ J adj,mean
Equation 4-4
61
-------
Table 4-6. Weights Applied to Complex Model coefficients for Technology Groups, by Age (Vehicle Age 0
represents model year 2000).a
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
a Note that in the MOVES database, these weights are stored in the table FuelModelWtFactor.
62
-------
5 Use of the EPA Predictive Model (HC and NO* Emissions)
For hydrocarbon and NO* 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 NO* 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, 2nd-order terms with individual p-
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.
63
-------
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
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 ComplexModelParameters, 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 _ V^ARO,/ ^ARO/ „ . „
aro liquation 5~1
^ARO
64
-------
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).
^T50SQR, i ~ ^T50,i^T50,i
T 50,i
-X'
T 50
Equation 5-2
3T50
rj rj I V " AK.U,! AKO )¥(¦» i yu,z i yu / | r _ „
AROT90,/ — ^aro,/^t9o,/ — 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 NO*, 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 2nd -order quadratic and
interaction terms, respectively.
In Y /?0 +/?0XYZ0XY +/?AR0ZAR0 H +/?rvpZrvph
Equation 5-4
I" /?T50SQR"Zt50SQR + PaROT90^AROT90
The sets of coefficients (fi values in the equation) for the NO* 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 NO;.: and three for HC. When the models are applied, an unweighted average of results for
65
-------
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.
Table 5-3. NO*: Predictive Model Coefficients for Linear Effects for Six Candidate Models.
Candidate
Model
Fuel Property
Intercept1
a
Oxygen
Aromatic s
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. NO*: Predictive Model Coefficients for 2nd-0rder Effects for Six Candidate Models.
Candidate
Fuel Properly
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
66
-------
Table 5-5. HC: Predictive Model Coefficients for Linear Effects for Three Candidate Models.
Candidate
Model
Fuel Properly
Intercept1
a
Oxygen
Aromatic s
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.
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 1 above (page 3)). 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.
67
-------
exp(xp target)
exp(Xpbase)
/adj - t~\7narge \ Equation 5-5
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 (page 9). 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.
6 Gasoline Fuel Effects for Vehicles certified to Tier-2 Standards
(EPAct Models: HC, CO, NO*, 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.
68
-------
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 so as to represent
the latest technologies on the market at the time the program was launched (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.
Table 6-1. Test Vehicles for the Phase-3 EPAct Program (all vehicles in MY2008).
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
69
-------
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.
Y = f30+ yfljetOH + P2 Arom + /?3RVP + /?4T50 + /?5T90 +
/?6T502 + yCLetOH2
Equation 6-1
/?7etOH x Arom + /?setOH x RVP + /?,etOH x T50 + /?|f,etOH x T90 +
s
In the equation, the linear terms (e.g., /?ietOH, etc.) describe linear associations between
emissions (7) 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 6-1(a) shows InNOx, 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 InNOx 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.
70
-------
Figure 6-1. NOx (Bag 1): Mean emissions levels, averaged by three ethanol and two Aromatics Levels,
depicting an etOHxArom interaction.
(b)Aromx EtOH
_ -2.80 (a) EtOH x Arom
5 -2.85
z
£ -2.90 1 r"""' —
c
S -2.95
£ -2.90
v -2.95
:15
=35
Linear Effect -
-3.10 , f
Linear Effect "
25
A romatics (vol. %)
Ethanol (vol.%)
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.
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 (MX), 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
71
-------
statistical models developed during the EPAct study are applied in the MOVES model
(MOVES2014).
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.
72
-------
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
Z; = — Equation 6-2
5
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.
Y y
V _ arom arom ^ ^ ^
arom — hquatlOIl 0-3
c
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 fe.oHZe.oHare the mean and
standard deviation of the quadratic term constructed from the Z score for the linear effect.
VV "^etOH^etOH ^^etOH^etOH ^ ,
zz etOHxetOH Equation 6-4
^etOH^etOH
73
-------
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 — mi
77 etOH Arom ZetOUZArom ^ ^ ^
etOHxeArom ~ EqUatlOIl 6-5
$
^etOH^Arom
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
Project.1
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
T50x 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.
74
-------
Table 6-5. Example of One-Stage and Two-Stage Standardization for Tier-3 Certification fuel.1
Fuel
etOH
Arorn
RVP
T50 (°F)
T90 (°F)
ctOH x
T50 x
etOHx
ctOH x
ctOH *
ctOH x
(vol.%)
(vol.%)
(pa)
etOH
T50
Arom
RVP
T50
T90
Fuel properties
T3
9 8
23
8.95
200
325
Mean*
10 J1
25.63
S..51S
190.6
320.5
Std. Dev.3
7.S80
10.02
1.611
25.58
19.48
Orte-Stage standardized values (Z) (Equation 6-3)
ze
2,
zr
z,
Zty
T3
-0.06519
-0.2626
0.2682
0.3285
0.2293
Mean"
0.9630
0.9630
-0.03674
-0.09924
-0.5413
0.01633
Std. Dev.3
0.8028
0 7398
0.9785
0.9996
0.7692
0.9728
Two-stage standardized values (ZZ) (Equation 6-4, Equation 6-5}
ZZm
ZZ55
ZZm
ZZr
ZZt<
ZZ*
T3
-1.281
-1.657
0.3117
0.427923
1.001927
-0.01678
1 See 19 FR 23525. Values assigned as midpoints of ranges: -with RW values for "General Testing."
"Mean and standard deviations of fuel properties for the entire fuel set. See Table 6-4.
3Mesns 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 (In7), 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 7 = /?0 +
P\Ze + + PlPr + Pa^5 + +
/?6ZZ55 + /?7ZZee + Equation 6-6
PJ-7-e* + P'7-7-c, + PwZZe5 + /3uZZe9 +
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/NO*) 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
75
-------
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
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 se, 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 NO* 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 the 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,
76
-------
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, p 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.
77
-------
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:! Mode! fsuperzetl
Estimate
Std Err
d.f
?-value
.-I-;
Intercept
-3.S653
3 w-u
15
-9.18
0 33-i-3
3 3555
¦N •"(
941
4.36
0.00001
Z a
G.05~S
G-.vOS!?
7.64
0.00000
z,
-0 CH3S
3-1
-4.33
% ah-v*!
-
Z j
0.1298
:
3-1
10.14
Zg
3.3 rs
3 33-SS
9-1
2.0!
3.344S 1
ZZ
; :-r:
34:
2.64
3.3353-1
ZZ;:
:
;.;;2S
8-1
3.80
:: 33;-:
ZZ
0.0183
3 CCS""
94!
2,11
.. .
ZZ.;-
3 33-4
? CCS3
0.50
C3i~25
ZZ;;
:
3 3.; S3
2.51
0.0122-
ZZio
3 323S
:
s::
2J8
: ::_33
Reduced Modei f'SMU
Estimate
0 S5t-
3 35-S
I
3 01S3
a.o-
c .i:-
Std. En.
r
C C IdS
: ::c
d.f.
15
941
t -value
-? IS
4,33
J.62
-4.43
10 13
2.0"
2.4-6
?r>t
0.1325
3 3SS7S
0.1.325
C.0"68~2
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.
Intercept
Z,
Ze
ZZ,„
12..
ZZt:
zz,
Full Mods! fsnperse:
Estimate
¦ 55-3
: :.3:
f.OlS-
3.333-t
-3.011
Std. Ejt.
OOP:
0:25
D334
d.f
13
sis
raiue
3 <59
5.39
3.53
1.33
-1.31
-1.2600
Pr>?
0.0051
0,037
0.1839
0.1914
Ridu.'idMod*! iSMSi
Estimate
Std Eit
d.f
(-value
Ppr
-- ; 533
GZ5i;
13
-18 J1
. .'.'...
: 332"
-•
. _ •.
S19
2,73
0,0066
- :.35
3 -v 33.3
819
-2.10
0.0360
335:
3 3 *
819
-3 36
3 ]33S3
n *
3 3*23
819
3 S3
. -3
S19
3 03033
—
0 335"
C 3094
819
3.59
3.33036
J 84?:
} 3e**>
C.S3S4
of
78
-------
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
models, as ranked by BIC.
Effect
Intercept
Ze
Z5
Zp_
zzee
zz55
ea
zzer
ZZeS
ZZsg
Full Model (Superset)
Estimate
Std.Err.
d.f.
t-
value
Pr>?
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.
lvalue
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
'veh
0.3917
0.07212
0.3920
0.07214
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.
Effect
Pull Mo del (superset)
Estimate
Std. Err.
d.f.
7-value
Pr>?
Intercept
-13S99
0.3578
15
-3.88
0.0015
z,
0.01949
0.01567
941
1.24
0.21
Za
0.09453
0.01 If5
941
7.91
o.ooooo
Zr
a r<;-sc
a. «.o y >
O.OI35I
941
2.19
0.0054
z<
C.33535
0.01655
941
2.3S
0.018
Z9
n o • t f
..
0.0119®
941
3.34
0.00042
zzm
:
0.01220
941
140
0.16
ZZS3
-0.003339
0,01205
94!
-0.277
0.7S
zzta
_
_
_
—
—
,r
—
—
—
—
—
ZZ,s
—
—
—
—
—
ZZ,i
-#.01487
00116!
941
-1.28
0.20
Reduced Model (SM4)
Estimate
Std. En.
d.f.
7-value
Pr>*
-1.3193
0.3578
15
-3.88
0 0015
0.0913
0.0I1S
941
7.76
0.0000
0.0122
941
2.45
!' ! 1 —
0.0123
941
2.12
: .3-:
0.0118
941
3.73
C C331
—2
vth
1.9182
2
0.1250
_2
irth
15187
2
0.1256
79
-------
Table 6-10. NO* (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 include those in the top six candidate models, as ranked by BIC.
Effect
Intercept
Z,
ZZee
ZZ55
ZZea
ZZer
ZZe.5
ZZeg
Full Model (Superset)
Estimate
Std.Err.
d.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
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
veh
0.5926
0.5925
0.1454
0.1458
80
-------
Table 6-11. N0 V (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
1e
0.05813
0.01952
879
2.98
0.0030
la
0.04469
0.01492
879
3.00
0.0028
Zr
-0.01729
0.01653
879
-1.05
0.30
Is
-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
ZZ55
—
—
—
—
—
1Zm
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
81
-------
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.
X2- value
Pr>X2
Intercept1
1e
0.1365
0.05030
1
7.35
0.0067
Za
0.3840
0.03510
1
119.96
<.0001
Zr
-0.0227
0.04000
1
0.32
0.57
Is
0.0338
0.05050
1
0.45
0.50
Z9
0.2965
0.03510
1
71.48
<.0001
zzee
-0.0401
0.06750
1
0.35
0.55
zz55
0.0700
0.05050
1
1.92
0.166
zzea
0.0508
0.03430
1
2.19
0.139
zz„
0.0295
0.03500
1
0.71
0.40
zze5
-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.
X2- value
Pr>X2
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
2 1
veh
0.4251
2
1.0321
1.0359
1 Not fit by Tobit model; calculated manually from individual vehicle intercepts.
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
Za
0.1619
0.0384
1
17.8
<.0001
Zr
-0.0615
0.0438
1
1.97
0.16
z5
-0.0725
0.0553
1
1.72
0.19
z,
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
zzea
0.0210
0.0375
1
0.31
0.58
zz„
-0.0272
0.0383
1
0.50
0.48
ZZe5
-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
1.1294
1.1337
1 Not fit by Tobit model; calculated manually from individual vehicle intercepts.
82
-------
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). The adjustments described in this
document are applied to gasoline blends containing 0-15 vol.% ethanol.
Engine technology. For MOVES2014, these adjustments will 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.
Table 6-14. Pollutants Modified by Fuel Adjustments
pollutantID
pollutantName
Acronym
1
Total Gaseous Hydrocarbons
THC
2
Carbon Monoxide
CO
3
Oxides of Nitrogen (NO*)
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
generically as "PM" in this document.
Database Table: MOVES2014 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).
83
-------
6.4 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.48 The base rates are assumed to represent emissions on a "base fuel"
which are multiplied by a ratio adjustment 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.
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 6-7
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."
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 6-8.
The sets of coefficients for four individual pollutants, including total hydrocarbons (THC) and
the criteria pollutants CO, NOx, and PM have been presented in Table 6-6 through Table 6-13.
The application of these models has been integrated into MOVES2014. For implementation in
MOVES, this calculation is input directly into the GeneralFuelRatioExpression table.
Equation 6-7
Fuel Effect =
Equation 6-8
84
-------
The table presents two sets of coefficients for each pollutant, representing the effects of the fuel
properties on start and running exhaust emissions, respectively.13 In some cases fuel effects
estimated for these two processes differed substantially, as the effects of fuel properties on start
emissions are dominated by changes in combustion and catalyst warm-up, while the effects on
running emissions are dictated by catalyst efficiency when fully operational. These results are
depicted graphically in Figure 6-2 through Figure 6-5, with the lengths and directions of the bars
representing the magnitude and sign of coefficients, respectively.
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 standard deviation, and assuming
that the other fuel properties remain constant. Note that "standard deviation" refers to values
defined for the fuel matrix used in the study (see Table 6-4). Because these coefficients apply to
"standardized" fuel properties, as mentioned above, the magnitude and signs of the terms are
comparable, giving a sense of the influence of each term in the estimation of that pollutant
relative to the others. For example, for PM start emissions, changes in aromatics and T90 are
very influential, and are positively related with PM emissions (e.g., when aromatics increase, PM
increases). For THC start emissions, on the other hand, the negative sign of the RVP coefficient
indicates that the relationship is inverse, i.e., increases in RVP are associated with decreases in
THC.
Figure 6-2. THC: Standardized coefficients for models representing start and running emissions. (Error
bars represent 90% confidence intervals for the coefficients).
0.20
0.15
b
at
-D 0.10
1 0.05
u
¦g 0.00
N
^ -0.05
e«
B -0.10
e«
^ -0.15
-0.20
¦ Start (Bag 1)
Running (Bag 2)
¦ I
b For all models, "start" and "running" emissions are represented by results measured on Bags 1 and 2 of the LA92
cycle, respectively.
85
-------
Figure 6-3. CO: Standardized coefficients for models representing start and running Emissions. (Error bars
represent 90% confidence intervals for the coefficients).
.2 0.10
I Start (Bag 1)
Running (Bag 2)
tu 0.05
V, o.oo
"2 -0.05
«
¦c
§ -0.10
-0.15
-0.20
Figure 6-4. NOx: Standardized Coefficients for Models representing Start and Running Emissions. (Error
bars represent 90% confidence intervals for the coefficients).
0.20
« 0.15
e
0)
¦Q 0.10
01
0
u
"B
01
N
-3
¦—
re
"B
e
re
0.05
0.00
-0.05
-0.10
-0.15
-0.20
¦ Start (Bag 1)
Running (Bag 2)
:fc
'c?
; N
1
|1 T |
rt ^ in o>-*- « in LI flj-J- u Ln
N N N N Lfi O <0 « «
'—' —' w w N N N N
X
o
I 03
S S i £ S S
¦e H «3 X X X
° x X X X
X o o o
O qj gj
a
86
-------
Figure 6-5. PM: Standardized Coefficients for Models representing Start and Running Emissions. (Error
bars represent 90% confidence intervals for the coefficients).
0.50
Start [Bag 1)
Running [Bag 2)
,= 0.30
U 0.20
.2 0.10
T3 0.00
1/5 -0.10
-0.20
6.5 The Database Table "GeneralFuelRatioExpression "
As mentioned, the models shown in Equations Equation 6-7 and Equation 6-8, applying
coefficients as shown in Table 6-6 to Table 6-13, and in Figure 6-2 to Figure 6-5, are stored in
the database table "GeneralFuelRatioExpression." A description of this table is shown in Table
6-15.
87
-------
Table 6-15. Description of the DatabaseTable "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.
minModel Y earlD
The earliest model year to which a specific value of
fuelEffectRatioExpression is applied.
e.g., 2001
maxModel Y earlD
The latest model year to which a specific value of
fuelEffectRatioExpression is applied.
e.g., 2050
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
31=passenger
truck
32=light
commercial truck,
etc.
fuelEffectRatioExpression
A mathematical expression containing up to 32,000
characters.
6.5.1 Examples
We show an examples of the calculation of fuel adjustment for start NO* 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 in this document. The
entire expression is shown below in Table 6-16. Due to its length, the whole is divided into
terms and segments, which, along with descriptions, are presented in Table 6-17.
The calculations in the expression shown in Table 6-16 and Table 6-17 are shown in Table 6-18,
which 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. Calculation of standardized fuel properties for the
test fuels is illustrated in Table 6-5. The lower segments of the table present the model
predictions, as ln(NOx), NOx 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
88
-------
fuel. As mentioned, note that the rates represent aggregate results on Bags 1 and 2 of the LA92
cycle, respectively.
For NOx, the calculations predict increases of approximately 1.0% and 6.6% for start and running
emissions, respectively. For THC, corresponding reductions for start and running emissions are
16.7%) and 7.5%, respectively, as shown in Table 6-19 and Figure 6-7.
To understand how positive or negative changes in all five fuel properties affect the adjustment,
it is useful to view the contributions of each model term. This step can be achieved by
rearranging the calculation (for start NOx) as shown in Equation 6-9 below. The rearrangement
of terms shows that the adjustment can be calculated by applying the coefficients to the
differences in standardized properties for the fuels to be compared. Note that the intercepts and
variances drop out of the calculation.
Fuel Adjustment = exp(Xpm_use - Xpbase)
= exp
= exp
(A> /^etOH^etOH,in-use /^arom^arom,in-use /^TSO^TSCUn-use /^etOHxArom^^etOHxArom,in-use + 0.5cJ ) —
(A) /^etOH^etOH,base /^arom^arom,base /^TSO^TSCXbase /^etOHxArom^^etOHxArom,base + 0.5cJ ) ^
T50,base >
^/^etOH (^etOH,in-use ^etOH,base ) /^arom (^aromjn-use ^arom,base ) + /?T50 (-^T50,m-use -^T50,base )
Pi
etOHxArom (^^etOHxArom,in-use ^^etOHxArom,base )
Equation 6-9
Term-by-term results for the NOx adjustments shown in Table 6-18 are presented graphically in
Figure 6-6. Not surprisingly, these examples display the importance of the aromatics linear term.
For the Tier-3 fuel, the ethanol term is positive, showing the effect of the increase in ethanol to
10 vol.%> for the test fuel, relative to the base fuel. However, the positive ethanol term is almost
entirely offset by the reductions in aromatics and T50.
Table 6-19 and Figure 6-7 show adjustments for start THC. The linear terms for ethanol,
aromatics and T50 are directionally similar to those for NOx in the previous example, but with
the terms taking different sizes. In contrast to the picture for NOx, the T50 linear term in
combination with the two quadratic terms and the etOH>
-------
Table 6-16. Example Value for Field "fuelEffectRatioExpression" in Database Table
"GeneralFuelRatioExpression" (NOTE: this example calculates an adjustment for cold-start NO*, accounting
for the fuel properties: ethanol, aromatics, vapor pressure, T50, T90 and sulfur).
if(sulfurLevel > 30,(exp(-2.8593506+(0.0675016*((ETOHVolume-
10.313704)/(7.879557)))+(0.1339309* ((aromaticContent-
25.629630)/(10.015366)))+(0.0478207*((T50-190.611111)/(28.579112)))+(-
0.0236855*(((((ETOHVolume-10.313704)/(7.879557))*((aromaticContent-25.629630)/(10.015366)))-
(-0.036738))/(0.97846 l))))*((l+((0.425*(exp(0.351 *ln(303))-
exp(0.351*ln(30)))/exp(0.351*ln(30)))+0.575*(1.47*(exp(0.351*ln(sulfurLevel))-
exp(0.351 *ln(30)))/exp(0.351 *ln(30)))))/l.53198632576)/0.0552997544579),((exp(-
2.8593506+(0.0675016*((ETOHVolume-10.313704)/(7.879557)))+(0.1339309*((aromaticContent-
25.629630)/(10.015366)))+(0.0478207*((T50-190.611111)/(28.579112)))+(-
0.0236855*(((((ETOHVolume-10.313704)/(7.879557))*((aromaticContent-25.629630)/(10.015366)))-
(-0.036738))/(0.97846 l))))/0.055299754458)*(l-0.0*(30-sulfiirLevel))))
90
-------
Table 6-17. Expression stored in the Field "fuelEffectRatioExpression" in the Table
" GeneralF uelRatio Expression."
Segment of Expression
Comments
if(sulfurLevel > 30,
Initiate condition to be
applied for sulfur level
> 30 ppm
(exp(-2.8593506 +
Initiate exponential
expression, enter
intercept for EPAct
model.
(0.0675016*((ETOH Volume -10.313704)/(7.879557))) +
Enter standardized
linear term for ethanol
(0.1339309*((aromaticContent-25.629630)/(10.015366))) +
Enter standardized
linear term for
aromatics
(0.0478207*((T50-190.611111)/(28.579112))) +
Enter standardized
linear term for T50
(-0.0236855*(((((ETOHVolume-10.313704)/(7.879557))*((aromaticContent-
25.629630)/(10.015366)))-(-0.036738))/(0.978461)))) *
Enter standardized
interaction term for
ethanolxaromatics
((l+((0.425*(exp(0.351*ln(303))-
exp(0.351*ln(30)))/exp(0.351*ln(30)))+0.575*(1.47*(exp(0.351*ln(sulfurLevel))-
exp(0.351*ln(30)))/exp(0.351*ln(30)))))/l.53198632576)/0.0552997544579)
Apply expression to
calculate sulfur effect
(application of
M6Sulf model).
?
Initiate else condition
for sulfur Level <=30
ppm. (NOTE:
following comma,
condition is implicit).
(exp(-2.8593506 +
Initiate exponential
expression, enter
intercept for EPAct
model.
(0.0675016*((ETOH Volume -10.313704)/(7.879557))) +
Enter standardized
linear term for ethanol
(0.1339309*((aromaticContent-25.629630)/(10.015366))) +
Enter standardized
linear term for
aromatics
(0.0478207*((T50-190.611111)/(28.579112))) +
Enter standardized
linear term for T50
(-0.0236855*(((((ETOHVolume-10.313704)/(7.879557))*((aromaticContent-
25.629630)/(10.015366)))-(-0.036738))/(0.978461)))) *
Enter standardized
interaction term for
ethanolxaromatics
/0.055299754458)*(l-0.0*(30-sulfurLevel))))
Apply expression to
estimate sulfur effect
(T2 sulfur model).
91
-------
Table 6-18. N0V: Application of Models for Tier-3 Certification Fuel and a MOVES Base Fuel, with
Standardized properties Models
Calculation of Fuel Adjustments.
Fuel properties
Property
Fuel
base
T3
etOH (vol.%)
0
9.8
Arom (vol.%)
26.1
23
RVP (psi)
6.9
8.95
T50 (°F)
218
200
T90 (°F)
329
325
Model Term
Fuel
base
T3
Ze
-1.309
-0.06519
0.04696
-0.2626
Zr
-1.004
0.2682
z5
0.9584
0.3285
z9
0.4346
0.2293
ZZee
0.9346
-1.194
ZZ55
-0.0602
-1.156
-0.02528
0.0550
ZZff
1.414
0.08178
ZZe5
-0.9271
0.6760
ZZe 9
-0.6016
-0.03215
Intercept
1
1
Variance
Results: start model
ln(NOx)
-2.895
-2.884
NOx (g/mi)
0.08002
0.08086
Adjustment
1.000
1.011
Results: runnin
g model
ln(NOx)
-4.649
-4.585
NOx (g/mi)
0.01328
0.01417
Adjustment
1.000
1.067
Coefficients
Start
Running
0.067502
0.062989
0.133931
0.044062
0
0
0.047821
0
0
0
0
0
0
0
-0.02369
0
0
0
0
0
0
0
-2.859
-4.569
0.7383
0.6556
92
-------
Figure 6-6. N0V (Bag 1): Fuel Adjustment for Tier-3 Certification Fuel, Displayed by Individual Logarithmic
Model Terms.
0.10
o.os
0.06
0.04
| 0.02
Cs
'¦z 0.00
=
N
W -0.02
ea.
-0.04
-0.06
-O.OS
-0.10
93
-------
Table 6-19. THC: Application of Models for Tier-3 Certification Fuel and a MOVES Base Fuel, with
Calculation of Fuel Adjustments.
Fuel properties
Property
Fuel
base
T3
etOH (vol.%)
0
9.8
Arom (vol.%)
26.1
23
RVP (psi)
6.9
8.95
T50 (°F)
218
200
T90 (°F)
329
325
Standardized properties
Model Term
Fuel
base
T3
Ze
-1.309
-0.06519
0.04696
-0.2626
Zr
-1.004
0.2682
z5
0.9584
0.3285
z9
0.4346
0.2293
ZZee
0.9346
-1.194
ZZ55
-0.0602
-1.156
ZZea
-0.02528
0.0550
zzer
1.414
0.08178
ZZe5
-0.9271
0.6760
zze9
-0.6016
-0.03215
Intercept
1
1
Variance
Results: start model
ln(THC)
-0.7771
-0.9600
THC (g/mi)
0.5084
0.4234
Adjustment
1.000
0.833
Results: runnin
g model
ln(THC)
-4.593
-4.670
THC (g/mi)
0.01592
0.01474
Adjustment
1.000
0.925
Models
Coefficients
Start
Running
0.05482
0.03268
0.06758
-0.01953
-0.04453
-0.03553
0.1288
0.05008
0.01827
0.05136
0.04361
0
0.07364
0.03373
0.01792
0
0
0
0.04446
0
0.02145
0
-0.8664
-4.653
0.2012
0.9056
94
-------
Figure 6-7. THC (Bag 1): Fuel Adjustment for Tier-3 Certification Fuel, Displayed by Individual
Logarithmic Model Terms.
0.10
0.08
0.06
0.04
0.02
, 0,00
J -0.02
^ -0.04 3-
j| -0.06
N
w -O.OS
-o.io
-0.12 :-
-0.14
-0.16 :-
-0.1 S
-0.20
I
. ¦
e
cC
1 -
-u
I
w
o
H
¦ ¦
s
EL
Q
S3
5
>
LTj
&
Cfi
_H
_H
X
X
X
*
"ST"
£
DC
x
o
o
o
o
4-1
4-1
u
.
o
4-1
V
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),49 its implementation in the Renewable Fuel Standard
(RFS)50 and passage of the Energy Independence and Security Act of 2007 (EISA).51 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.
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.52"53"54
In MOVES2014, we have incorporated the capability of modeling emissions from vehicles
running on E85. In MOVES2014, users can model E85 by selecting the "ethanol (E-85)"
category55 which includes fuels containing 70% or more ethanol by volume. MOVES2014
allows E85 use for the following sourceTypes only: passenger cars, passenger trucks, and light
commercial trucks.56
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 (NO*), and particulate matter
95
-------
(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 MOVES2014.
The MOVES2014 algorithms for estimating the effects of E85 on air toxics or evaporative
hydrocarbons are discussed in separate reports.1'3
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,57 National
Renewable Energy Laboratory (NREL) E40,58 Coordinating Research Council (CRC) E-80,59
and the EPA NRMRL Test Program.60 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, cold start test similar to the FTP.
Table 7-1. Description of the Vehicles Tested in EPAct Program.
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.
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
96
-------
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
Uplander LS
17,898
2006
Chevrolet
Monte Carlo
48,761
EPANRMRL Test Program C'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.
Table 7-5. Fuel Properties of the Fuels Used in Each Program.
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)
OO
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
97
-------
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 /-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.
98
-------
Figure 7-1. Mean THC Emissions from Vehicles Running on E10 and E85.
120
100
80
bjo
_£ 60
u
I
I—
40
20
E10
E85
Figure 7-2. Mean NMOG Emissions from Vehicles Running on E10 and E85.
80
70
60
£ 50
"SB
— 40
15
O
S 30
Z
20
10
0
E10
E85
99
-------
Figure 7-3. Mean NMHC Emissions from Vehicles Running on E10 and E85.
45
40
35
— 30
E
25
^ 20
§
z 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
100
-------
Figure 7-5. Mean NOx Emissions from Vehicles Running on E10 and E85.
0.2
0.18
0.16
0.14
=¦ 0.12
0.1
x
O
z 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.
2
1.8
1.6
1.4
£ 1.2
"SB
£ 1
in
rsi
§ 0.8
Cl
0.6
0.4
0.2
0
E10
E85
101
-------
Figure 7-7. Mean CO Emissions from Vehicles Running on E10 and E85.
1.8 -|
1.6
L4 «~
^ "
£ 1 1
"SB
O 0.8
u
0.6
0.4
0.2
0 -I 1 1
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 NO*. 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
/?-\aluc
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, NO* and PM2.5 are
replicated for high-level ethanol blends (E85) in the database table, emissionRateByAge, for
vehicles allowed to use E85 - passenger cars, passenger trucks, and light commercial trucks.
102
-------
Similarly, the fuel effect adjustments for E10, including the effect of fuel sulfur, are applied to
E85 in the generalI'lielRatioIixpression table, described in Section 6.5 (page 87).
In MOVES, the estimation of the other hydrocarbon emissions starts with THC. MOVES
calculates both methane and NMHC from THC emissions using methane/total hydrocarbon
ratios (CH4THCRatio in the database table methaneTHCRatio). Consistent with the results,
FFVs fueled with E85 are projected to produce higher methane emissions than E10 and
therefore, correspondingly lower levels of NMHC compared to E10. The development of the
methane/total hydrocarbon ratios for E85 is documented in the MOVES2014 Greenhouse Gas
and Energy Consumption Rates Report.61
For calculation of NMOG emissions for model year 2001 and later FFVs using E85, the exhaust
speciation factors for E10 were used, because no statistically significant difference was observed
between E10 and E85. Although volatile organic compounds (VOC) were not analyzed, due to a
lack of speciated data, it was assumed that VOC would behave similarly to NMOG in terms of
response to high-level ethanol blends for model year 2001 and later FFVs, since the only
differences between NMOG and VOC are the presence of ethane and acetone. Therefore, the
VOC/NMHC ratios used for E10 are also used for FFVs fueled with E85. For a detailed
description of the algorithm used to estimate NMOG and VOC emissions from E85, see
Appendix D of the MOVES2014a software design reference manual.62
103
-------
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 HC
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.63
As for sulfur, separate effects are modeled for pre-2007 and post-2007 technology diesel
engines.
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.64 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 (201064))
Pollutant Name
Percent Change in
Emissions
HC
-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.65'66
104
-------
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
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 the change at 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
HC
-0.705
0
CO
-0.690
0
NO*
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.
. \Q&st(bioDieselEsterVolume. 20) , _ ^ -
ruel Adjustment = 1h -x bioDieselbactor Equation 8-1
To estimate 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 MOVES2014 default fuel supply. For 2007+ and later
diesel, the biodiesel fuel adjustment factor is set equal to 0, consistent with the literature review in
Section 8.2.
105
-------
9 Sulfate 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 run 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.67 For diesel
engines equipped with catalyzed diesel particulate filters, the sulfate contribution from
lubricating oil can also make up a substantial fraction of the PM2.5 exhaust emissions.68
Maintaining the capability to model high fuel-sulfur levels is important for MOVES. The
particulate emission rates used in MOVES for gasoline and pre-2007 diesel were derived from
sets of measurements on higher fuel-sulfur levels; it is thus important that MOVES be able to
account for changes in fuel sulfur content in estimating particulate emissions. In addition,
MOVES is used by international users, in regions where fuel sulfur levels can be much higher
than current US levels.
MOVES2014 includes two major changes to improve the modeling of sulfate emissions. First,
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. Second, MOVES2014 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 MOVES2014 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
MOVES2014 algorithm for estimating sulfur dioxides is included in this chapter for consistency.
The algorithm for gaseous sulfur-dioxide (SO2) emissions remains the same as in MOVES2010b
and is based on fuel consumption, but the parameters have been updated in MOVES2014 to be
consistent with the changes to the sulfate emission factors.
9.2 Sulfate Calculator Summary
The MOVES2014 sulfate calculator adjusts the reference sulfate emissions using the following
assumptions:
1. Sulfate emissions from the lubricating oil are constant regardless of the fuel sulfur level.
2. 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.69 and Kittelson et al.67 treated the sulfate
contribution from the lubricating oil independently of the fuel sulfur level from diesel engines.
106
-------
Wall et al.70 demonstrated that sulfate emissions from diesel engines decrease linearly with
decreases in the diesel fuel sulfur level down to 100 ppm and 0 ppm. Baranescu71 and
Hochhauser72 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.67 also assumed a constant relationship between fuel sulfur level and particle number
emissions from modern trap-equipped diesel engines.
Figure 9-1. Schematic of Fuel and Lubricating Oil Contributions in MOVES2014.
S04, Sulfate
emissions
F„ = % of Sulfate emissions from Fuel at the Base Case
Fuel Contribution
Lubricating Oil Contribution
0
X, Fuel sulfur level
(ppm)
The sulfate calculator uses the concept of reference emission rates and sulfate fractions.
MOVES2014 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
MOVES2014, the base PM2.5 rates are divided between elemental carbon (EC) and the remaining
PM that is not elemental carbon (NonECPM). MOVES2014 incorporates these modeling
assumptions into Equation 9-1, the derivation of which is included in Appendix A:
SO= NonECPMg x SB x
1+ Fr x
£-*)
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
107
-------
sulfur in the reference case, and S04x = 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 MOVES2014
default fuel formulation and fuel supply table which specifies 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
"sulfateFractions." 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 MOVES2014 Speciation
report.73
Sulfate-bound water (H2O aerosol) is a new pollutant in MOVES2014. Currently, the value of
H20b in MOVES2014 is 0 for all on-road source types, as derived from the PM2.5 speciation
profiles (cite Speciation report). If 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 = NonECPM B X (H20)B X [l + FB X (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 MOVES2014
Source
Process
Reference Fractions
Reference fuel
sulfur Level, ppm
(xB)
Reference
estimated
fraction from
fuel sulfur
(Fb)
SO4/PM25
SOz/NonEC
PM (,S'«)
Gasoline
running exhaust
7.2%
8.4%
161.2
68.7%
start exhaust
0.9%
1.7%
Pre-2007 diesel
running exhaust
1.0%
4.9%
172.0
72.6%
start, extended idle
and apu
5.3%
9.8%
2007+ diesel
running, extended
idle, start
67.6%
73.6%
11.0
48.3%
Pre-2002,
compressed
natural gas
running, extended
idle, start
0.6%
0.7%
5.0
0.0%
2002+
Compressed
natural gas
running, extended
idle, start
1.0%
1.2%
5.0
0.0%
108
-------
The following section discusses 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
The reference sulfate fractions and the reference fuel sulfur level for gasoline vehicles are
estimated from the Kansas City Light-Duty Vehicle Emissions Study (KCVES). The use of the
KCVES for estimating PM2.5 emission rates is documented in the MOVES2014 Light-duty
Vehicle Emission Rate report,48 and the derivation of the sulfate emission factor is documented
in the MOVES 2014 TOG and PM Speciation Report.73 The 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.74 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 in Table 9-1 are used for all gasoline sourceTypes in MOVES,
including motorcycles, light-duty passenger-cars and trucks, medium-duty and heavy-duty
gasoline trucks, and gasoline-powered buses. Applicable sourceType identifiers include 11, 21,
31, 32, 42, 43, 52, 52, 53, 54, and 61.
9.4 Pre-2007Diesel 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).75 The E55/59 study is also used to derive the
PM2.5 emission rates for medium and heavy-duty diesel in MOVES2014 (MOVES2014 heavy-
duty report). 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 analysis0.
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.76
The DECSE project was conducted to investigate the impact of low-sulfur diesel fuel standards
0 See Table 11 in Clark et al.75
109
-------
on diesel emissions. Specifically, the DESCE conducted testing of two engines at four sulfur
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 MOVES2014. 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.
9.5 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.67 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 as the reference fuel-
sulfur content used in MOVES2014. 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.77 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).78 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 MOVES2014 is
based on data acquired from these four engines. The fuel-sulfur level tested in the ACES
program is 4.5 ppm.67 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 MOVES2014 as shown in
Table 9-1. Additional details on the analysis are included in Appendix A.4.
9.6 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
110
-------
engine with and without an oxidation catalyst as documented in the MOVES Speciation
Report.73 We set Fb coefficient to 0, so that MOVES estimates the same sulfate emissions
regardless of the sulfur level in the CNG fuel.
9.7 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 emission rates calculated from Equation 9-1 and the parameters in Table
9-1 across a range of fuel sulfur levels.
'Gasoline Running
'Gasoline Start
Pre-2007 Diesel Running
Pre-2007 Diesel idle
Pre-2002 CNG
2002+CNG
200.00 300.1
Sulfur level, ppm
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 gasoline passenger cars, heavy-duty diesel
long-haul combination trucks, and CNG transit bus emissions estimated using MOVES2014 for
a state-wide (Michigan) run in calendar year 2011.
The base NonECPM emission rates in MOVES2014 for pre-2007 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)]. At 2007+ diesel sulfur
levels (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
111
-------
PM (and total PM2.5 emission rates) from the reference pre-2007 diesel PM emission rates by ~
13 mg/mile.
Similarly, the reference sulfur level for 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) estimated from a
MOVES2014 for the state of Michigan, in calendar year 2011 run for Michigan using national default data.
30
25
20
15
10
5
0
0.00
100.00
200.00
300.00
400.00
500.00
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 has 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. 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.
112
-------
Figure 9-4. Example S04 emission rates as a function of fuel sulfur level (0 to 30 ppm) estimated from a
MOVES2014 for the state of Michigan, in calendar year 2011 run for Michigan using national default data.
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.
9.8 Sulfur Dioxide Emissions Calculator
The sulfur dioxide (SO2) emissions algorithm is unchanged from MOVES2010b, but the
parameters are updated to be consistent with the updated analysis on sulfate emissions. 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 on a mass-balance basis most of
the sulfur originates from the fuel, 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.67
SO2 emissions are calculated using Equation 9-3:
MW_S02 /10"6\
S02(g) = FC(g) x [S] (ppm) x mw ^ x fS02 x ) Equation 9-3
113
-------
where
FC(g)= fuel consumption (g), and
[S] (ppm)= relative fuel-sulfur concentration (ppm)
MW_S02 ;
MW_S
is the ratio of the molecular weight of sulfur dioxide as defined in Equation 9-4.
MWS02 32 + (2 X 16)
£ = moi = 2.0 Equation 9-4
MW S 32-%-
mol
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 MOVES2014, 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 9-3 including the ratio of molecular masses Equation 9-4.
The SO2 conversion values and resulting SO2 emission factors for use in MOVES2014 are
displayed in Table 9-2.
Table 9-2. SO2 conversion efficiencies and MOVES SO2 emission factors.
Source
S02
conversion
efficiency (%)
SO2 EF (1/ppm)
Gasoline
99.69%
1.994E-06
Pre-2007 Diesel
94.87%
1.897E-06
2007 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.
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
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). MOVES2014 thus assumes a larger percentage of fuel
sulfur forms sulfate emissions in conventional diesel engines. The 2007 diesel SO2 emissions
114
-------
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%) 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 MOVES2014. 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.79 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. 80 reported that the maximum allowable fuel sulfur content for use in CNG motor vehicles is
16 ppmv. The Energy Information Administration reports that the fuel sulfur content of natural
gas at the burner tip is less than 5 ppm.81 For use in MOVES, we selected the default sulfur level
of CNG to be 7.6 ppm, to be consistent with the sulfur dioxide measurements conducted by
Lanni et al.79
9.9 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 less than 30 ppm, with further reduction in gasoline sulfur to 10 ppm required
by 2017. When modeling these lower fuel sulfur levels, MOVES reduces the reference sulfate
emission rates by ~ 10 mg/mile for pre-2007 diesel trucks, and ~ 1 mg/mile for gasoline cars.
While the sulfate calculator is important to adjust 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 sulfates 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.
Additionally work is needed to quantity the sulfate emissions from advanced engine and
emission control technologies in MOVES, including: 2010 DPF/selective-reduction-catalyst
equipped diesel engines, light-duty diesel engines, lean-burn gasoline, and direct injection
gasoline vehicles. Because the inputs to the sulfate calculator are table-driven, these values can
be updated as new data become available.
115
-------
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 Fb = % 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 |^1 + FB x ^— 1 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
116
-------
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 (200882) and Fulper et al. (201083).
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 Reportd. 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.
d The fuel sulfur content from 87 vehicles is reported in Tables 4-11 and 4-15 from the KC PM Characterization
Report82. 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.
117
-------
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:
ft ¦ 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).74
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.
118
-------
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).
O)
3
Q)
CD
(Y
c
o
'(/)
c
0)
MO
IttT.I
PH
S04
SU
ZN
data
FUL.Cold
FUL.Hot
FUL.US06
KC.LA92
Figure A-l contains the oil-derived metals (calcium, molybdenum, phosphorous, zinc), sulfate
and sulfur emission rates from the Full Useful Life Program, from the newest vehicles from the
Kansas City study (1996-2004) that are tested in the summer round. Calcium is the dominant
element emitted in the exhaust, as well as the dominant metal component of lubricating oil. As
shown, the calcium emissions on the FUL UDDS tests are comparable to the calcium emissions
on the Kansas City LA-92 tests. The calcium emission rates from KCVES are slightly higher,
which would be expected due to the slightly more aggressive LA-92 cycle compared to the FTP.
In contrast, the US06 has very high oil element emissions in the FUL which is a very aggressive
cycle, which could lead to high oil consumption/and or burn-off of particles on the catalyst and
exhaust system. Overall, the oil consumption based on the element emission rates, appears to be
comparable between the FUL and newest KCVES vehicles. The KCVES vehicles have much
higher sulfate emission rates, which is expected due to the higher sulfur content in the fuel.
The two data sets (FUL vehicles, and the newest vehicles from KCVES) were combined to
estimate the relative contribution of sulfate from the lubricating oil and the fuel. In combining
the data sets, the 4 gasoline-direct injected vehicles are excluded from the FUL program to
provide a comparison of port-fuel injection technology. Also the composite FTP values were
used from the FUL test program (0.43*Cold UDDS + 0.57* Hot UDDS). Only the KCVES
vehicles tested in the summer are included to minimize any confounding effects of temperature
on sulfate and oil emissions. The following assumptions regarding the two sets of vehicles are
made to estimate the sulfate contributions:
119
-------
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:
Assumption 1 implies p1 = /?l5 and assumption 2 implies /?2 = /?2- With two unknowns, and two
equations, p1 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.
For Kansas City:
For the Full Useful Life Program:
Pi' OSEkc + /?2 1 FSCkc — SESkc
Pi ' OSEFUL + p2 ' FSCpui — SESFUL
120
-------
Table A-2. Data, estimated coefficients, and estimated contributions of sulfate from the lubricating oil and
fuel from the FUL and Kansas City studies.
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 MOVES2014, 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.
121
-------
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.
a ¦ pi ¦ OSEkc + a ¦ p2' 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 MOVES2014, the fuel sulfate contribution (68.7%) scales linearly with
changes in fuel sulfur level, but the MOVES2014 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 MOVES2014 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. (200484). 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. (200484) had a sulfur value
of 293 ppm (obtained from the MOVES2014 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. (200785) 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. (200785). We estimated the sulfur content for California fuels in
2001, from MOVES default database as 36 ppm.
122
-------
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).
c 0.08
tt 0.05
0.04
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 MYGasoline at 30F
+ 1999 Gasoline Vehicle
- Gasoline high EC Cold
— Gasoline high EC warm
~ Gasline high emitters cold
¦ Gasoline high emitters warm
A 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. (200786) 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.
(200987) 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.88
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 MOVES2014 still
provides results that are within the range of results from the literature.
123
-------
A3 Derivation of the Sulfate Calculator Parameters for
Conventional Diesel Vehicles
In Phase 1 of the DECSE76, 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.
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 Study76
4-Mode
FTP Hot-Cycle
o
Engine
Cummins
Navistar
0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350
Sulfur, ppm
124
-------
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.76
—i—
50
—i 1 1 1 1 1—
100 150 200 250 300 350
—i—
50
100
—i 1 1 1 r
150 200 250 300 350
Navistar, 4-Mode
Navistar, FTP Hot-Cycle
Cummins, FTP Hot-Cycle
Engine
Cummins
Navistar
Cummins, 4-Mode
10.0 -
0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350
Sulfur, pprn
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.
125
-------
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.
y = 0.5499 +
~ .~~849- x,
r - 0.76
~
i
~
50
100
150 200
Sulfur, pprn
250
300
350
Engine
* Cummins
~ Navistar
Table 3-1.
Table A-3. Estimated linear regression parameters of the engine-out sulfate emissions and fuel sulfur level
data for the data displayed in Figure A-5.
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.69
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.
126
-------
Table A-4. Estimated oil and fuel sulfate contributions to the model.
Component
Sulfur leve
, 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.
(200484), and from heavy and medium-duty diesel trucks tested as part of the DOE
Gasoline/Diesel PM Split Study reported by Fujita et al. (200785). 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 time-frame, 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.89
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 a 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.
127
-------
Figure A-6. Sulfate/PM fractions estimated by MOVES for gasoline vehicles compared to values reported by
SPECIATE Profile #91106 (NFRAQS), Zielinska et al. (200484), and Fujita et al. (200785).
0.35
0.3
0.25
c
o
tj
re
5
Q.
« 0.15
3
I/)
0.1
0.05
0
50
100
150
200
250
300
350
Sulfur Level, ppm
Pre-2007 Diesel Running
Pre-2007 Diesel idle
A 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)
128
-------
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
lubricating oil from Kittelson et al. (200867)
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
MOVES2014. 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.
129
-------
Appendix B Peer Review Comments and Responses
Prior to the release of MOVES2014, this report underwent external peer-review at the end of
2013. We received the compiled comments from the peer-reviewers in early February, 2014.
The two peer-reviewers were:
Tom Durbin, PhD.
Research Engineer
Bournes College of Engineering
Center for Environmental Research and Technology (CE-CERT)
University of California, Riverside
Allen Robinson, PhD.
Raymond J. Lane Distinguished Professor and Head, Department of Mechanical Engineering
Professor, Department of Engineering and Public Policy
Carnegie Mellon University
The peer-reviewers were charged with peer-reviewing chapters from the MOVES2014 Fuel
Effects Report that included algorithm and data updates, these document sections included:
• Chapter 3: Fuel Sulfur Effects
• Chapter 6: Gasoline Fuel Effects for Vehicles certified to Tier-2 Standards
• Chapter 9: Sulfate Emissions
An additional chapter (Chapter 7: High-Lev el Ethanol Blends) describes new data and methods
for modeling emissions from flex-fuel vehicles. However, this this chapter was not included in
the peer-review. The content in this chapter was subject to public review and comment during
the Tier 3 Vehicle Emission and Fuel Standards Program.®
In addition to peer-reviewing updated chapters from Fuel Effects Reports, the peer-reviewers
were charged with peer-reviewing two other MOVES2014 Reports:
• Air Toxic Emissions from On-road Vehicles in MOVES2014 Air Toxic Emissions from
On-road Vehicles in MOVES2014f
e U SEPA Office of Transportation and Air Quality. Updates to MOVES for Tier 3 FRM Analyses. Memorandum to
Docket EPA-HQ-OAR-2011-0135, Item No. 5063. Assessment and Standards Division, Ann Arbor, MI. February
27, 2014.
f USEPA Office of Transportation and Air Quality. Air Toxic Emissions from On-road Vehicles in MOVES2014.
EPA-420-R-15-021. Assessment and Standards Divison, Ann Arbor, MI. November, 2015.
130
-------
• Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles
in MOVES2014g
The peer-reviewers were also charged to peer-review sections in the following two reports that
pertained to speciation updates:
¦ Exhaust Emission Rates for Light-Duty On-road Vehicles in MOVES2014
¦ Section 2.3 Estimating Elemental Carbon Fractions
¦ Section 2.5 Updates to PM2.5 emission rates in MOVES2014
¦ Exhaust Emission Rates for Heavy-Duty On-road Vehicles in MOVES2014
¦ Section 2.1.2.3.5 Computation of Elemental Carbon and Non-Elemental Carbon Emission Factors
This Appendix provides the list of peer reviewer comments pertaining to the Fuel Effects
chapters. Additionally, the peer-reviewers provided 'general/catch-all' comments pertaining to
all the documents they reviewed. Those general comments are contained in this peer-review,
even though they may refer to content in the other MOVES2014 reports. The peer-review
comments given specifically with regard to the other MOVES2014 reports are addressed in the
appendices of the respective reports.
Reviewers' Responses to Charge Questions
B.l All Documents Reviewed
This section provides a verbatim list of peer reviewer comments submitted in response to the
general/catch-all review charge question for all documents reviewed as a part of the Fuel Effects,
Toxics Emissions, Total Organic Gases (TOG) and Particulate Matter (PM) Speciation Analysis.
B.l.l Adequacy of Selected Data Sources
Does the presentation give a description of selected data sources sufficient to allow the reader to
form a general view of the quantity, quality and representativeness of data used in the
development of emission rates? Are you able to recommend alternate data sources might better
allow the model to estimate national or regional default values?
g USEPA Office of Transportation and Air Quality. Speciation of Total Organic Gas and Particulate Matter
Emissions from On-road Vehicles in MOVES2014. EPA-420-R-15-022. Assessment and Standards Divison, Ann
Arbor, MI. November, 2015.
131
-------
B.1.1.1
Dr. Tom Durbin
This particular question I will address globally for all of the reports, as many of the datasets being
recommended apply to more than one report. This is also the area of my significant criticisms. The data
sets selected for the MOVES2014 development are large, relatively comprehensive, representative, and
generally well conducted, and as such represent a good basis in the model development for
MOVES2014. These data sets focus predominantly on the EPA Kansas City study, the E-55/59 study, the
ACES Phase 1 study, and the EPAct study for fuel effects.
On the other hand, EPA coverage of data is relatively narrow in terms of the larger body of literature,
and in particular doesn't consider the relatively significant work being carried out in California. As the
MOVES model continues to develop into future years, it is suggested that EPA broadens its coverage of
data being collected around the country. Many of the California datasets are just being completed and
should be available in time for the next MOVES update.
The issue with the silicone in the Kansas City study for the hot running is another point of consideration.
While some corrections can be applied to species profiles that may be reasonable, it also reinforces the
idea that a broader range of data sources should be considered.
RESPONSE: We are eager to acquire additional data that would shed light on the
behavior of emissions in the model.
Some of the areas where additional data could be particularly useful is for vehicle categories for
which data is still relatively limited. In particular, gasoline direct injection engines (GDI) are
rapidly expanding into the in-use fleet, have considerably different characteristics compared to
more traditional gasoline vehicles, and are not included in the data sets currently being used for
MOVES2014. Data for heavy-duty vehicles/engines with newer 2007+ and 2010+ are also still
relatively limited. Finally, data on natural gas vehicles/engines are relatively limited.
RESPONSE: Considering the anticipated importance of GDI vehicles in the next
decade, research is planned to characterize the effects of fuel properties in vehicles
using this technology.
It's difficult to determine how recent the Predictive/Complex model are. In one of the document that
discusses fuel effects for sulfur its seems to rely heavily on studies conducted in the early 1990s by CRC
and EPA and then goes to the EPAct Study with almost no consideration of anything done in between.
CARB, on the other hand, considered a number of additional and robust dataset in its 2007 update of its
predictive model.
RESPONSE: see response on page 143 below.
132
-------
Another important consideration is that the heavy-duty pre-2007 data does not seem to include any
data from retrofit DPFs, which tend to be more passive in nature and can vary from the OEM DPFs for
2007+ engines.
RESPONSE: The reviewer is correct in that heavy-duty pre-2007 emission rates and
accompanying speciation profiles in MOVES are not based on vehicles retrofit with
diesel particle filters. MOVES considers pre-2007 vehicles without retrofit technology
as the baseline from which users can estimate the benefit of retrofits with knowledge of
the retrofit penetration (or expected penetration). The technical guidance regarding
modeling retrofits in MOVES is located in the report: Diesel Retrofits: Quantifying and
Using Their Emission Benefits in SIPs and Conformity - Guidance for State and Local
Air and Transportation Agencies, posted here:
http://www.epa. gov/otaq/stateresources/transconf/policv.htm. Incorporating a default
retrofit penetration in MOVES, along with accompanying emission rate and speciation
profiles for pre-2007 vehicles could be considered for a future update.
For the "Gasoline Fuel Effects for Vehicles Certified to Tier-2 Standards" report, there are several other
data sets should be considered for inclusion in the fuel effects part of the model as the model continues
to be developed. These include the CRC E-83 project, which utilizes the same vehicle fleet as the main
EPAct study, but evaluated fuel olefin levels. UC Riverside is also conducted an extensive study of
ethanol/butanol blends that is nearing completion. In particular, this study includes GDI vehicles that are
not covered in EPAct study. This study has some emphasis on California fuels, but should also have more
general applicability for evaluated fuels at a national level.
RESPONSE: The studies cited lack sufficient numbers of fuels or an experimental
design that would allow them to be directly incorporated into the statistical modeling
used to develop the fuel adjustments. Specifically, the CRC E-83 project incorporated
only two fuels, with high and low olefins, respectively. It is not possible to simply fold
additional datasets into the statistical modeling performed for EPAct without obviating
the experimental design that enables the statistical modeling to be performed in a
meaningful way. However, additional studies such as these can be analyzed and
evaluated independently for purposes of comparison or verification.
For the "MOVES2014 Sulfate and Sulfur Dioxide Emissions Calculator" report, there are several
other data sets should be considered for inclusion in the model as the model continues to be
developed. There are several other datasets that are coming out that would be worth EPA
considering or at least evaluating with respect to the model, especially on the diesel vehicle side.
The California Air Resources Board has been looking at the toxicity of advanced technology
vehicles, and some of this data has sulfate emissions that could be of relevance here. The South
Coast Air Quality Management District has also conducted a study to evaluate the in-use
emission rates of 2007+ technology, heavy-duty diesel and natural gas vehicles. These data will
probably not be available until the first part of next year, but they could be considered for future
application to the model. Phase 2 of the ACES program is another data set that could be of value
for future model revisions.
133
-------
RESPONSE: As these data will not be available in a timely way prior to model release,
it is not feasible to incorporate them into MOVES2014. However, it may be possible to
consider them in development of inputs for future releases.
For the "Calculating the Effects of Gasoline Sulfur on Exhaust Emissions" report, there are
several other data sets should be considered for inclusion in the model as the model continues to
be developed. Even though M6Sulf is supposed to model Tier 1, LEV, and ULEV vehicles, the
majority of the datasets listed are from studies conducted in the early 1990s. Given that early
1990s technologies are not very representative of Tier 1, LEV, and ULEV vehicles,
consideration should be given to incorporating more data here. Example data sets include the
CRC E-60 program.
RESPONSE: In fact, the updated T2Sulf adjustments are applied to gasoline vehicles in
model years 2001 and later. Thus, the updated adjustments are applied to LEV and
ULEV vehicles, although not to Tier 1 vehicles. Given that it is adapted from the
MOBILE model and that it is very dated, and given its complexity, we do not
anticipated devoted additional effort or resources to updating the M6Sulf model.
General/Catch-All Reviewer Comments
Please provide any additional thoughts or review of the material you feel important to note that is not
captured by the preceding questions.
B.l.1.2 Dr. Allen Robinson
Overall I think that EPA has done a good job of developing MOVES2014 and that these chapters
provide the reader/user a reasonable description of the model. The models are statistical fits of
data; that is probably the best approach given the limitations in our quantitative understanding in
the underlying physical and chemical processes that control the emissions. For the most part the
models seem to be based on the best available datasets, but there are inevitably gaps. In certain
instance there appear to be important data that are not incorporated into the analysis. I have
provided many comments on individual chapters. The majority of the comments are focused on
improving the usability of the materials. However, there are some important scientific
shortcomings (treatment of uncertainty, semivolatile PM, and SOA precursors).
Here are the major comments that apply across most if not all of the sections that I read:
Presentation related:
1. Data sources - the various chapters and report often provide references to the underlying
data. However, these references often point to large reports (e.g. the EPAct data analysis),
which means that the reader may not be able to figure out what specific data were used. I
would encourage EPA to be as specific as possible about what data are used. I have often
been frustrated trying to figure out the exact data underlying models like MOVES and
MOBILE.
134
-------
RESPONSE: For studies generating very small datasets, it may be possible to list the
entire dataset in a table in the report body or in an appendix. Datasets too large to list
on a single page, can often be summarized in an aggregate way in tables or in graphs
or charts. However, in the MOVES project, many datasets are too large to present
directly even as summaries. In such cases, only subsets or examples can be directly
presented without making documents unduly lengthy. However, this point is not unique
to MOVES reports, but is equally true of articles in the peer-reviewed literature. In the
case of the EPAct project, the dataset used to develop the fuel adjustments is publicly
available for download at: http://www3.epa.gov/otaq/models/moves/epact.htm.
2. Examples -1 think quantitative examples really help the reader understand the model. These
exist in a few chapters but not in most. I would encourage EPA to include more examples
which will help the reader understand what MOVES is doing. Pointing the reader to online
tools, such as the fuel effects spreadsheet are also useful.
RESPONSE: For the fuel adjustments based on the EPAct models, we have extended
the discussion of the example to add text, tables and figures to illustrate and explain the
application of the models (absent the sulfur adjustment) and calculation offuel
adjustments for NOx and THC.
Tables defining all variables - in some chapter many variables are not defined making it difficult
for the reader to understand the model. These tables should also indicate which variables are
user inputs and which are derived from existing data. For the user inputs, default values
should be clearly defined.
RESPONSE: In a model such as MOVES, discussions in the technical reports describe
development of inputs stored in the default database, rather than to inputs provided
directly by users through the GUI or through run specifications. However, the
variables in the input tables often take hundreds to thousands of values, too numerous
to simply list in a report table. Nonetheless, in several chapters of this report, we have
attempted to be more thorough in identifying specific input tables involved, listing and
defining the fields in the tables, and giving readers some sense of the values or ranges
of values taken by the inputs.
3. Example results - For the reader it would be useful to provide some sample output from the
model to understand the effects. Ideally this would be graphical presentation.
RESPONSE: We agree with the reviewers comment. Graphical presentation of results
of the MOVES model or components of the model has been added to some chapters of
this report, including Chapter 3 (sulfur effects) and Chapter 6 (Fuel effects for Tier 2
Vehicles).
135
-------
Content related:
4. Goodness of fit - Given that the models are statistical fits of data, some description is needed
in each chapter on how well the model(s) fit the underlying data is important. These could be
some sort of statistical measure and/or scatter plots of model predictions versus underlying
data.
RESPONSE: Substantial information concerning goodness of fit has been added to the
revised report, particularly for the chapters describing effects of sulfur and other fuel
properties on Tier 2 vehicles, i.e., Chapters 3.3 and 6, respectively.
5. Uncertainty - There is no discussion of uncertainty of the model predictions. This is my
largest substantive concern with the reports. One measure of the uncertainty is the quality of
the statistical fit. A better measure is how well the model performs against data that were not
used to derive the fitting parameters. I strongly encourage EPA to quantify the uncertainty in
the MOVES2014 predictions. Every prediction should be accompanied by a quantitative
uncertainty estimate.
RESPONSE: Given the scale and complexity of MOVES, incorporating such
calculations in a MOVES run is intractable at present, as they would require additional
inputs and complex calculations employing matrix algebra to existing code. In addition,
for the uncertainties to be meaningful in a broad context, it would be necessary to
propagate the uncertainties in fuel adjustments with the numerous other uncertainties
in the calculations. Unfortunately, while the availability of such uncertainty estimates
would be valuable, the additional computational burdens involved in multiple
propagations of uncertainty would in all likelihood make the use of MOVES infeasible
for most users.
6. Data limitations - EPA has done a good job utilizing existing data. However, there are
inevitably gaps. Obvious gaps are GDI, higher mileage vehicles, high emitters, etc. The
reader should be made aware of these limitations and guidance should be given about how to
address.
RESPONSE: The reviewer raises important questions. See discussion on page 144 ff
B.2 Gasoline Fuel Effects for Vehicles Certified to Tier-2 Standards
This section provides a verbatim list of peer reviewer comments submitted in response to the
charge questions for the chapter Gasoline Fuel Effects for Vehicles Certified to Tier-2 Standards,
IN: Modeling Effects of Fuel Properties in the Motor Vehicle Emissions Simulator
(MOVES2014).
136
-------
B.2.1 Adequacy of Selected Data Sources
Does the presentation give a description of selected data sources sufficient to allow the reader to form a
general view of the quantity, quality and representativeness of data used in the development of emission
rates? Are you able to recommend alternate data sources might better allow the model to estimate
national or regional default values?
B.2.1.1 Dr. Tom Durbin
Refer to response to All Documents Reviewed in Section B.l.
B.2.1.2 Dr. Allen Robinson
I think that the presentation of the data sources (specifically test fleet, and fuel composition)
could be improved. There is a lot of detailed information in the main EPAct report, which I
download and skimmed parts of, but it would helpful for the reader if a bit more information (a
few more paragraphs) was provided in the intro about this test program. Here are some
examples of the sort of information that would be useful to provide the reader: Were these all
relatively new, low-mileage vehicles? What was the variety of emission control technologies?
Were the vehicles all port fuel injected? Were all the vehicles 2008 MY? How were the
vehicles procured? Recruited from the in-use fleet - if so where? What was the range of each
property of the fuels tested in EPAct? What are typical values for each of these properties in
actual in-use fuels (summer and winter)?
If all the vehicles were port fuel injected then what is the guidance for gasoline direct injection
vehicles which are becoming more prevalent? That seems like the most significant gap in the
information.
All of the EPAct vehicles were low mileage, what are the recommendations for higher mileage
tier 2 vehicles?
These things seem like important data limitations. Although these issues probably cannot be
addressed (these types of vehicles were not in the EPAct test fleet), the document should clearly
describe potential limitations of the model so that the reader is aware of them.
RESPONSE: The reviewer's comment is well taken. To address these points we have
condensed additional information from the project report and added it to the MOVES
report. Among other topics, we have added descriptions of the vehicle sample, test
fuels and the study design.
B.2.2 Clarity of Analytical Methods and Procedures
Is the description of analytic methods and procedures clear and detailed enough to allow the reader to
develop an adequate understanding of the steps taken and assumptions made by EPA to develop the
model inputs? Are examples selected for tables and figures well chosen and designed to assist the reader
in understanding approaches and methods?
137
-------
B.2.2.1 Dr. Tom Durbin
The description of the methods and procedures is reasonable. The following are some
suggestions in this area.
Section 2.1 should have a reference to a more basic description of the "Z factor" and other
elements of the discussion for those looking for a more fundamental discussion of the method.
RESPONSE: Standardization is one of the most common techniques in statistical
analysis, serving, for example, as the basis for the Z test between means and t tests of
significance for regression coefficients. Nonetheless, we have cited several sources that
give descriptions of standardization and some of its applications to allow interested
readers to better understand the background of the approaches used in the EPAct
analysis.
The first example on page 6 is for aromatics, and then the examples switch to ethanol.
RESPONSE: Yes. The examples are provided not to exhaust all possibilities but rather
to illustrate the process more concretely.
Tables 2 and 3 provide a good description of the different coefficients. It is worth noting that
because Table 3 is in log scale it, it is not necessarily straightforward to determine the magnitude
of the effects that might be seen for different in arithmetic space. It would be interesting to see
what the coefficients would be when they are transformed to arithmetic space, although this is
not how they are used in the model. Also, the blanks in table 3 are not explained. Tables 5 and 6
are good, especially Table 6 that goes into detail on each of the terms.
RESPONSE: Since the response variable is always the natural logarithm of emissions,
it would not be meaningful to attempt to represent the model coefficients themselves in
"arithmetic space. " However, model results can be expressed in arithmetic space by
applying the reverse exponential transformation. Note that the logarithmic form is
convenient in that the arithmetic difference in model results for two fuels represents a
ratio difference. The blanks in Table 3 represent terms that were not retained in
specific models following model fitting. In the revision we have added a description of
the model fitting process and replaced Table 3 with a set of tables that shows the full
and reduced models for each combination ofpollutant and process (i.e., start, running).
For the means in Table 2, are these based on just a mean for the fuels in the test matrix, or are
they weighted based on the number of tests run on each fuel for the dataset being used.
RESPONSE: The means in Table 2 represent the fuel matrix itself and do not
incorporate weighting by numbers of replicates on combinations of vehicle and fuel.
This approach simplifies both the fitting and application of the models by allowing the
same standardization to be used in all cases.
138
-------
The first example on page 6 is for aromatics, and then the example switches to the quadratic term
for ethanol.
RESPONSE: Yes. The examples are provided not to exhaust all possibilities but rather
to illustrate the process more concretely.
How are start and running emissions calculated? Based on bag 1 for start and bag 2 for running?
RESPONSE: Correct. "Start" emissions represent Bag 1 of the LA92, and "running"
emissions represent Bag 2. Not also that "start" and "cold start" are treated as
synonymous, as are the terms "running " and "hot-running. " We have added text to the
revised chapter to make these points more explicit.
B.2.2.2 Dr. Allen Robinson
The core statistical model/parameterizations appears to have been derived by the EPAct project
and appears to be described in the final report for that project (Assessing the Effect of Five
Gasoline Properties on Exhaust Emissions from Light-Duty Vehicles Certified to Tier 2
Standards: Analysis of Data from EPAct Phase 3 (EPAct/V2/E-89) Final Report (EPA-420-R-
13-002)). In that (EPAct) report they describe multiple models, but the set of parameters that
will be used in MOVES2014 appear to be the same as what is listed in Table ES-1 and ES-2 of
the EPAct report (the only exception appears to be the value of the variance listed in Table 3 -
why are those different?). This was not clear from reading the fuel effects document. If that is
the case (the models were taken directly from the EPAct report), then this document needs to
have a short declarative sentencing stating so. "The models used here were derived and
described in the EPAct final report (ref)." Right now the introduction only provides a very
qualitative discussion of the EPAct process, but does not explicitly say that the analysis was used
here. If the model is different than one of the models derived in the EPAct report then this report
needs a lot more discussion of the derivation of the model.
RESPONSE: It is correct that the models used are those listed in the Executive
Summary of the Project Report. We have added text to make this point explicit.
Without reading the EPAct report the reader has essentially no "understanding of the steps taken
and assumptions made by EPA to develop the model inputs." The EPAct report is very long and
detailed. In addition, they fit multiple models to the data. This chapter would benefit if it
provided some more discussion of the EPAct modeling process and why this particular model
was chosen (as opposed to one of the other models fit by the EPAct team). This would be a page
or so of text. This would give every reader a basic understanding of the model; interested
readers could then be referred to the EPAct report for more details. I thought that the air toxics
report did a much better job of describing the underlying model(s) than this chapter.
RESPONSE: The reviewer makes a good observation. To address this deficiency we
have added material to give a relatively brief description of the iterative model-fitting
processes employed in the project. We believe that the revised text will give general
139
-------
readers a much better idea of how the models were developed. However, readers
desiring in-depth understanding of how the EPAct analyses were conducted will still
need to consult the project reports.
Another shortcoming of this document is that it does not provide some description of the
goodness of fit of the model to the original data (part of this should be providing some physical
description of what the variance values in Table 3). I skimmed through multiple sections of the
EPAct report and could not find that succinctly summarized. A few paragraph (up to a page or
two) description of the goodness of fit of the model to EPAct data should be provided as the
ability of MOVES2014 to predict fuel effects ultimately depends on the model and how well it
describes the data.
RESPONSE: In addition to describing the model-fitting process, we have added tables
summarizing model coefficients, standard errors and associated t-tests (or y2 tests) for
individual coefficients. Reviewing these statistics gives a clear sense of how model
terms not contributing to fit are dropped during model fitting. Unfortunately,
commonly used and easily interpreted goodness-of-fit measures, such as R2, are of little
use in this context, as the vast majority of variability in the dataset is attributable to
differences among the test vehicles. However, the goal of the analysis was to account
for the much smaller fraction of variability attributable to differences in fuel properties.
Were any exercises performed to test the model with independent data (data not used to fit the
model)? Standard techniques such as "leave-one-out" can be used. Alternatively one could use
speciated data from other test campaigns to test the model? For example, ARB has extensive
data from their surveillance program. This sort of independent evaluation of the model with real
world data seems extremely important. This analysis should be performed and described in the
report to provide the user confidence in the model.
RESPONSE: Two projects sponsored by the Coordinating Research Council are
playing this role. The first is E-98 (Exhaust Emissions of Average Fuel Composition), in
which additional measurements were performed on the sample of vehicles used in
EPAct/V2/E-89, using fuels representing properties in the "envelope ofproperties
defined by the EPAct fuels. " In addition, the results of E-98 have been applied in E-
101, a project designed as a comprehensive evaluation of the MOVES2014 model. A
final report for E-101 is expected during 2016.
What was the basis for the assumption "that effects for fuels and temperature are independent
and multiplicative."
RESPONSE: The commenter notes correctly that the datasets used to estimate fuel
effects did not incorporate the effect of temperature.
At the outset, we can point out that MOVES does not apply temperature adjustments to
hot-running CO, THC or NOx emissions, thus obviating the need to consider
interactions between temperature and fuel effects. That the effect of temperature on hot-
running gaseous emissions is negligible is widely accepted.
140
-------
For start emissions of CO, THC and NOx, however, MOVES applies both temperature
and fuel effects, under an assumption that they can be applied multiplicatively and
independently. However, when multiplicative effects are jointly applied to mass
emissions (in "linear" as opposed to logarithmic space), the results appear
"interactive " in that the different effects either reinforce or damp each other. For
example, start emissions increase substantially as temperature declines, with the
implication that fuel effects are amplified at lower temperature, whether positive or
negative. The net result can be either increased or decreased emissions, depending on
the nature of the fuel effects.
We can focus on start CO as an example. For CO (HC and NOx) fuel effects are
calculated using the Complex model for 2000 and earlier model years, and using the
EPAct models for 2001 and later model years. In both cases, the results applied are
broadly consistent with those of a past study (CRC E-74b). As the two studies applied
differing approaches to statistical analysis, the respective model coefficients cannot be
compared in terms of magnitude. However, they can presumably be compared
qualitatively in terms of sign.
For cold-start CO emissions (Bag 1), the Complex Model has a small but positive
linear-effect coefficient for RVP. This result is directionally similar to the E-74b
Composite CO model, which also has positive RVP coefficients, as well as a positive
interaction between RVP and temperature. This result implies that for "cold"
temperatures below 50°F, increasing RVP should increase CO, with the effect amplified
by decreasing temperature. The application of the Complex Model in MOVES gives
qualitatively similar results with a positive RVP coefficient amplified by the
multiplicative temperature adjustment.
For MY2001 and later, the EPAct models can be applied to start emissions specifically.
The CO start model has a negative linear coefficient for RVP, (meaning that emissions
decline as RVP increases). In E-74b, a piece-wise fit was used, giving negative and
positive coefficients for RVP < 9 and > 9 psi, respectively. In addition, a positive
interaction term was included in the reduced model. As expected, the temperature
coefficient is negative, suggesting that an "interference" interaction obtains, i.e., that
the combined effects of RVP and temperature would have a mutual "damping" effect.
The net results of the E-74b model are shown in Figure 5-1 (page 76). At temperatures
below 50 °F, the trends portrayed show an "interference" effect, i.e,, that increasing
RVP decreases CO start emissions, with the absolute margin (in g/mi) increasing with
declining temperature. As mentioned, despite the differences in underlying data and
modeling approaches, the application of independent RVP and temperature effects in
MOVES gives similar results. This outcome results from the multiplicative combination
of a negative temperature effect (CO increases and T decreases) with a negative RVP
effect (CO decreases as RVP increases). The net result is that the temperature effect is
reduced by increasing RVP at lower temperatures, which is directionally similar to the
result obtained in E-74b, and suggested by the commenter as a correct representation
of CO behavior in relation to RVP and "cold" temperature.
Results for THC and NOx are similar. The models applied in MOVES do not contain
temperature effects, but multiplicative combinations of the fuel and temperature effects
results in interaction effects in the projected emission volumes. Thus, on the whole, we
conclude that the multiplicative combination of temperature and fuel effects as applied
in MOVES does allow for interactions between these effects.
141
-------
B.2.3 Appropriateness of Technical Approach
Are the methods and procedures employed technically appropriate and reasonable, with respect to the
relevant disciplines, including physics, chemistry, engineering, mathematics and statistics? Are you able
to suggest or recommend alternate approaches that might better achieve the goal of developing
accurate and representative model inputs? In making recommendations please distinguish between
cases involving reasonable disagreement in adoption of methods as opposed to cases where you
conclude that current methods involve specific technical errors.
B.2.3.1 Dr. Tom Durbin
The equations for this report appear to trace back to methods used and reviewed previously. The
current application of these methods appears to be appropriate in that context. Comments to
consider on the presentation of the methods are provided above.
RESPONSE: In the revision, we have incorporated material from the EPAct project
report to briefly summarize the EPAct project and analyses, including an overview of
the test vehicles, test fuels and the model fitting process.
It's difficult to determine how recent the Predictive/Complex model are. In another document
that discusses fuel effects for sulfur its seems to rely heavily on studies conducted in the early
1990s by CRC and EPA and then goes to the EPAct Study with almost no consideration of
anything done in between. CARB, on the other hand, considered a number of additional and
robust dataset in its 2007 update of its predictive model.
RESPONSE: The Complex model has not been updated since it was originally
developed in the early 1990's. The "EPA Predictive Model, " however, was updated in
2001 in the context of EPA 's review of California's Request for Waiver of the
Reformulated Gasoline Requirement. At that time, additional data was incorporated in
the EPA analysis. However, it is not clear that combining other datasets with the EPAct
dataset would improve the analysis. Incorporating additional data would obviate one of
the main advantages of the EPAct project, namely, the carefully optimized randomized-
block design, which allows substantial vehicle variability to be neutralized in the
analysis offuel effects. In our view, it could be a better use of other datasets to analyze
them independently with respect to their own designs and data structures, and to make
comparisons to the EPAct results.
B.2.3.2 Dr. Allen Robinson
This sort of statistical fitting is commonly done to create "models" to describe fuel effects. The
parameters included in the model are known to influence emissions. However, I am not aware of
any scientific basis for the underlying mathematical form of the model. If there is one the report
would benefit from a description of it. In addition, without the information on goodness of fit
142
-------
and evaluation of model with independent data as described in the previous section it is
impossible to answer these questions.
RESPONSE: The models developed in the project are statistical models in the
commonly used sense of the term, and as such, describe associations between fuel
properties and emissions. The mathematical forms of the models do not attempt to
represent physical processes or relationships. To our knowledge, such forms have
never been proposed for the multi-dimensional relationships that were the subjects of
study in the EPAct project. It is important to add that assignment of cause and effect
cannot be inferred from the associations themselves and must be supplied through
interpretation. (Note that such models are called "linear" because they are linear in
their coefficients, not because they necessarily describe straight-line trends). The linear
forms of the models are very commonly used in empirical studies and provide a
framework within which to analyze and express degrees of association.
Beyond a description of the goodness of fit, the major shortcoming of the model is there is no
treatment of uncertainty. I would advocate that the model should provide uncertainty estimates
(confidence intervals) for every output/prediction. One simple way to provide an estimate would
be to use the statistical uncertainty of the fit. This is reasonably straightforward. A more robust
approach would also be to try to account for the limitations in the underlying dataset (e.g. lack of
GDI). Providing a robust treatment of uncertainty is not easy but it seems essential to ensure that
the data are used appropriately. Including uncertainty estimates would be a major upgrade of the
model, which may not be possible for this release of MOVES. However, I would strongly
encourage EPA to make starting implementing uncertainty a high priority for future releases.
RESPONSE: Calculations of uncertainty for model predictions can be readily
performed, by combining sets of covariances for the model coefficients with sets of
properties for the fuel(s) under consideration. Incorporating such calculations in a
MOVES run is intractable at present, as they would require additional inputs and
complex modifications employing matrix algebra to existing code. In addition, for the
uncertainties to be meaningful in a broad context, it would be necessary to propagate
the uncertainties in the fuel adjustments with the numerous other uncertainties in the
calculations. Unfortunately, while the availability of such uncertainty estimates would
be valuable, the additional computational burdens involved in multiple propagations of
uncertainty would in all likelihood make the use of MOVES infeasible for most users.
B.2.4 Appropriateness of Assumptions
In areas where EPA has concluded that applicable data is meager or unavailable, and consequently has
made assumptions to frame approaches and arrive at solutions, do you agree that the assumptions
made are appropriate and reasonable? If not, and you are so able, please suggest alternative sets of
assumptions that might lead to more reasonable or accurate model inputs while allowing a reasonable
margin of environmental protection.
B.2.4.1 Dr. Tom Durbin
143
-------
This report does not deal extensively on data sets where data is meager. On the other hand, the
data set being used does not contain any GDI vehicles, which will represent a growing and
important segment of the in-use fleet going into the future.
B.2.4.2 Dr. Allen Robinson
I don't think that there are any statements about data limitations in this section. However, there
are some critical data gaps, such as for GDI, higher mileage vehicles, and malfunctioning (gross
emitting) Tier2 emitting vehicles. It would be good to at least specifically mention these gaps.
RESPONSE: The importance of these assumptions justifies spending some effort to
examine them, and to review available data that has the potential to falsify them.
It is correct that the EPAct models were developed using a set of low-mileage Tier-2
vehicles, and that in MOVES the models are applied to Tier-2 vehicles as they age and
acquire mileage. Nonetheless, we argue that these extrapolations are reasonable and
appropriate given the ways in which the models were developed and the fuel
adjustments applied.
The comment itself is premised on an assumption that fuel effects should differ in some
way between vehicles of differing technologies, ages or "high-emitter " status. Fuel
effects are represented in MOVES as multiplicative effects that are proportional to base
emission levels. We agree with the reviewer in that when fuel effects are expressed as
absolute changes in mass (e.g., g, g/mi, g/kg, etc.), vehicles of different technologies or
ages will differ strongly. We do, however, assume that proportional, or relative fuel
effects (expressed as fractions, ratios or logarithmic differences), can be seen as similar
and thus transportable on average across technology groups and ages.
The data available to directly evaluate this assumption are limited. Nonetheless, some
relevant data can be compiled from the results of the EPAct program. The models
applied in MOVES were developed from the results of EPAct Phase 3, in which 15 Tier-
2 vehicles were measured on 27 fuels. In addition, in EPAct Phase 5, three vehicles
manufactured in the 1990's were measured on three fuels used in Phase 3. Thus, using
these results, it is possible to make a direct comparison of emissions for sets ofpre-T2
and Tier-2 vehicles measured on the same fuels (at 75°F).
The three 1990's vehicles are briefly described in Table R-l. Note that these vehicles
range from 10-17 years in age and that all had presumably accumulated over 150,000
mi.
In terms of fuels, we limited the comparison to 2 fuels with 0 and 10% ethanol content,
respectively. These two fuels are closely matched in terms of RVP and aromatics levels,
and differ slightly in T50. However, they differ widely in T90. The properties of these
fuels are summarized in Table R-2. Note that emissions on both fuels were acquired for
only two of the three vehicles.
Table R-l. Characteristics of three "High-mileage" pre-Tier-2 Vehicles measured in EPAct
(Phase 5).
Make/Model
Engine
Model Year
Odometer (mi)
Chevrolet Tahoe
V8-5.7L
1997
221,000
144
-------
Ford Taurus
V6-3.0L
1990
>90,400(?)
Dodge Dakota
1993
229,000
Table R-2. Selected Properties of Two Fuels measured in EPAct (Phase 5).
Fuel No.
Ethanol
(vol.%)
Aromatics
(vol. %)
RVP (psi)
T50 (°F)
T90 (°F)
6
10.56
15.0
7.24
188.5
340.4
7
<0.10
17.0
7.15
193.1
298.4
At the outset, we averaged the results by vehicle and fuel, and plotted the results for
both cold-start and hot-running phases of the LA92 cycle. Results for NOx, THC and
PM are shown below in Figures R-l to R-6. Note that the results are shown on a
logarithmic scale (base 10). This view of the data facilitates showing the results for all
the vehicles in one plot. In addition, differences in logarithms can be interpreted as
proportional or relative differences between the fuels for the various vehicles.
Aside from the fact that their emissions are higher, the differences in logarithms
between the two fuels are not obvious for the older "high-mileage " vehicles. Although
the sample of older vehicles is small, a qualitative view of the plots suggests that no
clear and obvious differences between TO, Tl and T2 vehicles are evident.
Figure R-l. NOx (Bag 1): Mean Emissions for Low mileage Tier-2 Vehicles and high mileage TO and Tl
vehicles on two Fuels.
constituent=NOx
10.000
E
*g> 1.000
a>
v*
Q. 0.100
+-
hm
TO
+*
t/t
2 0.010
o
u
0.001
Fuel 6
Fuel 7
Fuel
Vehicle i-wALTIMA
^COBALT
bhhODYSSEY
»»eTahoe(97)
'CALIBER
tCOROLLACE
fFOCUS
*OUTLOOKXE
3Taurus(90)
hhhCAMRY
Dakota(93)
fwlMPALALS
** SIENNA
f CIVIC
EXPLORERXL
s LIBERTY
SILVERADO
145
-------
Figure R-2. NOx (Bag 2): Mean Emissions for Low mileage Tier-2 Vehicles and high mileage TO and T1
vehicles on two Fuels.
constituent=NOx
10.0000
E
o>
1.0000
a>
to
2 0.1000
o>
n
c
c
3
0
1
0.0100
0.0010
0.0001
Fuel 6
Fuel 7
Fuel
Vehicle +++ALTIMA
*** COBALT
wt
t/>
(U
.c
£L
v>
2
o
u
10.0
1.0
0.1
Fuel 6
Fuel 7
Fuel
Vehicle fw-ALTIMA
COBALT
>M*F150
see ODYSSEY
»s«Tahoe(97)
CALIBER
COROLLACE
FOCUS
wOUTLOOKXE
se«Taurus(90)
bhh CAMRY
Dakota(93)
«IMPALALS
SIENNA
* CIVIC
EXPLORERXL
'LIBERTY
SILVERADO
146
-------
Figure R-4. THC (Bag 2): Mean Emissions for Low mileage Tier-2 Vehicles and high mileage TO and T1
vehicles on two Fuels.
constituenl^THC
100.000
10.000
o>
4>
J2 1.000
a.
a>
c
c
c
3
0
1
0.100
0.010
0.001
Fuel 6
Fuel 7
Fuel
Vehicle +++altima
««COBALT
M"*F150
™ODYSSEY
»®®Tahoe(97)
CALIBER
•"-fCOROLLACE
FOCUS
«-*OUTLOOKXE
eeeTaurus(90)
see CAMRY
Dakota(93)
«IMPALALS
SIENNA
+*+ CIVIC
EXPLORERXL
LIBERTY
++SILVERADO
Figure R-5. PM (Bag 1): Mean Emissions for Low mileage Tier-2 Vehicles and high mileage TO and T1
vehicles on two Fuels.
constituent=PM
1000.0
E
"5> 100.0
v»
2 1.0
o
O
0.1
Fuel 6
Fuel 7
Fuel
Vehicle +++ALTIMA
^COBALT
>H**F150
hhhoDYSSEY
®^®Tahoe(97)
s CALIBER
~COROLLACE
sFOCUS
fOUTLOOKXE
3Taurus(90)
hhbCAMRY
»«Dakota(93)
+++ IMPALALS
SIENNA
CIVIC
EXPLORERXL
^LIBERTY
SILVERADO
147
-------
Figure R-6. PM (Bag 2): Mean Emissions for Low mileage Tier-2 Vehicles and high mileage TO and T1
vehicles on two Fuels.
constituent^PM
1000.00
2
<13
JZ
a.
a>
C
*C
c
3
0
1
100.00
10.00
1.00
0.10
0.01
Fuel 6
Fuel 7
Fuel
Vehicle *++ALTIMA
^COBALT
bhbODYSSEY
»®®Tahoe(97)
s CALIBER
^COROLLACE
sFOCUS
*OUTLOOKXE
^Taurus(90)
iCAMRY
Dakota(93)
~ IMPALALS
SIENNA
>w»* CIVIC
EXPLORERXL
LIBERTY
SILVERADO
148
-------
It is helpful to follow up with a closer examination of this subset of results. As
mentioned, differences in logarithms represent ratios, e.g., log a - log b = log(a/b).
However, for purposes of summary, it is more intuitive to express the results as percent
differences between fuels 6 and 7 (relative to fuel 7). Accordingly, mean percent
differences for the set of vehicles are presented below in Figure R-7 to R-9 for NOx,
THC and PM, respectively. In each plot, the differences are ranked from smallest to
largest, meaning that the ordering of the vehicles differs in each chart.
In reviewing the charts, it is clear that when the differences in emissions between the
two fuels are viewed as fractions, there are no clear or obvious differences between the
1990's vintage high-mileage vehicles and the MY 2008 Tier-2 compliant low-mileage
vehicles. Generally, the two high-mileage vehicles differ in the signs of their effects,
with one vehicle showing a positive and the other a negative change. Cold-start PM is
the only case in which both vehicles have negative effects and have low rankings. Hot-
running THC also stands out as the only in which the two older vehicles have the
largest and smallest fractional effects. In the remaining cases the older vehicles are
distributed evenly across the rank order.
Overall, the available evidence suggests that when fuel effects are expressed as relative
multiplicative factors, as they are in both the EPAct analyses, and in their applications
in MOVES, it is reasonable to assume that the proportional effects are transportable
across different vehicle technologies, as well as across other factors such as age,
mileage or "high-emitter " status.
149
-------
Figure R-7. Mean Percent Difference in NOx Cold-Start (top) and Hot-running
(bottom) Emissions from 14 Tier-2 and 2 pre-T2 vehicles measured on two Fuels.
/"
CIVIC
SIENNA
ii iti nni/vc
L
)U 1 LlJLllxAt
Taurus(90)
CAMRY
ALTIMA
EX
PLORERXLT
SILVERADO
CALIBER
¦
IMPALALS
¦
F1501
Tahoe(97p
COBALT
COROLLACE
LIBERTY
-100 -50 0 50 100 150 200
Mean Difference (%)
CIVIC
FOCUS
COROLLACE
ODYSSEY
Taurus(90)
SILVERADO
SIENNA
F150
CAMRY
ALTIMA
Tahoe(97)
IMPALAL5
LIBERTY
OUTLOOKXE
-100
-50
0 50 100
Mean Difference (%)
150
200
150
-------
Figure R-8. Mean Percent Difference in THC Cold-Start (top) and Hot-running
(bottom) Emissions from 14 Tier-2 and 2 pre- T2 vehicles measured on two Fuels.
ALTIMA
CAMRY
LIBERTY
Tahoe(97)
FOCUS
ODYSSEY
COBALT
IMPAL
Taurus(90)
CALI
SIENNA
EXPLOR
OUTLOOKXE
COROLLACE
-50
50 100
Mean Difference (%)
150
Tahoe(97)
FOCUS
CIVIC
LIBERTY
CAMRY
F150
OUTLOOKXE
COROLLACE
IMPALALS
SILVERADO
COBALT
EXPLORERXLT
CALIBER
ALTIMA
Taurus
-50
50 100
Mean Difference (%)
150
151
-------
Figure R-9. Mean Percent Difference in PM Cold-Start (top) and Hot-running
(bottom) Emissions from 14 Tier-2 and 2 pre-T2 vehicles measured on two Fuels.
ou
EXP
COBALT
TLOOKXE
CALIBER
ALTIMA
ODYSSEY
CIVIC
F150
LIBERTY
ORERXLT
FOCUS
IMPALALS
ILVERADO
ahoe(97)
SIENNA
aurus(90)
COROLI
-200 -100 0 100 200 300
Mean Difference (%)
400
500
EXP
LIBERTY
ahoe 97
LVERADO
ODYSSEY
ORERXLT
MPALALS
S ENNA
cvc
CAMRY
aurus(90)
TLOOKXE
COROL
-200 -100 0 100 200 300
Mean Difference (%)
400
500
152
-------
B.2.5 Consistency with Existing Body of Data and Literature
Are the resulting model inputs appropriate, and to the best of your knowledge and experience,
reasonably consistent with physical and chemical processes involved in emissions formation and control?
Are the resulting model inputs empirically consistent with the body of data and literature that has come
to your attention ?
B.2.5.1 Dr. Tom Durbin
The paragraph at the bottom of page 8 provides some sense of what the model outputs would be
and how fuel properties would influence emission rates. Interpreting these results in terms of
natural log of the emissions is not necessarily straightforward to a more casual reader.
RESPONSE: We agree that the models are abstract and not necessarily intuitive. In the
revised document, we have added a concrete example to illustrate application of the
models for a test fuel, in relation to a MOVES base fuel, including calculation offuel
adjustments to start NOx and start THC.
B.2.5.2 Dr. Allen Robinson
This is not covered in this chapter. The trends as report in the EPAct final report seem consistent
with expectations.
B.2.6 General/Catch-All Reviewer Comments
Please provide any additional thoughts or review of the material you feel important to note that is not
captured by the preceding questions.
B.2.6.1 Dr. Tom Durbin
¦ p. 6 . The description of the LA92 should explicitly note that is has a cold start phase, since this is one
of the process categories included in the modeling, and how the start emissions are obtained.
RESPONSE: We have added text to make this point explicit.
¦ The abbreviations CO, THC, are given on page 4, instead of when they are first use in the 1st
paragraph of the document.
¦ There are lots of extra spaces in the text. P. 3 last paragraph 2nd sentence was launched; p. 4
"EPAct Test Program Report" 2 and (fueltypelD = 1).; p 8 1st sentence etOHxArom interaction
¦ Superscripted numbers are used for both references and footnotes, which takes away from the
presentation.
153
-------
RESPONSE: In compiling the revised report, we have used numbers for references and
letters for footnotes, to avoid confusion in this regard.
¦ Introduction - 3rd sentence is very long. Suggest splitting into 3 sentences.
RESPONSE: We believe that the comment refers to the fourth sentence rather than the
third, but in any case, we have split the long sentence into several shorter ones.
¦ p. 4 3rd full paragraph "The analysis involved several iterations between analysis and additional
physical and chemical review of data." The part about physical and chemical review of data is
unclear. Same paragraph add commas including subsets of terms/'
¦ page 5 Emissions Process: add evap reference.
¦ page 8 1st full paragraph "while the impacts of fuel properties on running isare dictated ...1st and
second part of sentence should match
B.2.6.2 Dr. Allen Robinson
Section 2.1
¦ It would be good to list the fuel properties that are used in the model (or at least considered in the
modeling, since some were dropped out in the analysis) in section 2.1 so that it is clear to the reader
what they are. The properties are listed in the intro but it was not clear those were the properties
used in the model.
RESPONSE: The complete list of properties included in the study and the subsets
retained in individual models are presented in several tables added to the revised
chapter.
¦ Readers may not know what you mean by second-order and linear terms as these are never defined.
RESPONSE: We have added text to define and explain these terms, in the senses used in
experimental design and statistical modeling. We have also provided a figure showing
an interaction apparent in test results between ethanol and aromatics.
Table 2
Units this is % buy vol or mass. Same comment for aromatics.
RESPONSE: For ethanol and aromatics, we have replaced "% " with "vol. % "
throughout.
154
-------
¦
The terms like "etOH x etOH" terms are not defined. Presumably this is the ZZetOHxetOH listed in
Equation 3. If so then the table should use the same nomenclature. If not then these terms need to
be defined.
RESPONSE: These terms have been defined and illustrated as mentioned above.
¦ The document frequently uses the term "start." Presumably this is actually "cold start" (bag 1) as
opposed to "hot start" (bag 3). The term start should always be defined.
RESPONSE: Text has been added to define how the term is used in the context of this
chapter. Throughout, "start" is treated as synonymous with "cold start" which refers
to the first phase, or "Bag 1" of the LA92 cycle.
Section 3. Fuel effect adjustments
¦ It seems like the key here is equation 6 because that is what is actually used by MOVES. You are
calculating a scaling factor (equation 6) to apply to the base MOVES emission rate. If that is correct
then that should be explicitly stated.
RESPONSE: This point is clearly stated in the paragraph immediately preceding the
relevant equation.
Equations 5—1 think that it would be useful to list out all the terms.
RESPONSE: We have listed all terms using the model for cold-start NOx as an
example.
¦ Equation 6—X (bold) and Beta_in-use are not defined. These are some sort of vector?
RESPONSE: These symbols are commonly used matrix notation for the regression
model. We have added a sentence to define this notation immediately before using it.
Table 3
¦ It would be helpful if you included a column that had the actual model nomenclature (e.g. ZetOH) as
opposed to what you currently list as model terms. Right now the reader may be confused trying to
relate the information in Table 3 with the equation (this applies especially to the cross terms).
RESPONSE: We have included this notation in a new equation, and in the new tables
showing the model coefficients and statistical tests (Tables 6-13).
155
-------
¦ Why are the variance values in Table 3 different than those in the EPAct report for the same set of
model parameters? (This is based on comparing with values in Tables ES-1 and ES2 in EPAct report).
RESPONSE: The single value listed in the table in the MOVES report is the sum of the
two values listed in the Executive Summary. The variance for vehicle represents
variance of individual vehicle intercepts and the variance for error represents the
"random error " remaining after the variability due to "vehicle " has been accounted
for. Revised tables present the two values separately, as in the project report.
Some discussion of the meaning of the values in Table 3 would be useful to provide the reader
some understanding of the actual model. From reading the EPAct report it appears that the sign
on the coefficient indicates that it is positively or negatively correlated. The magnitude indicates
the size of the dependence?
RESPONSE: We have included a paragraph giving a brief discussion of how the
coefficients can be understood and interpreted.
Section 4
¦ This table only defines selective values of parameters. It would be useful to have a footnote to a
reference where all of the values of each parameter are defined (this would include report and page
number).
RESPONSE: A complete listing of values in all fields of the table would be lengthy.
While it could be helpful to readers we don 't find this chapter as the appropriate
location to provide this depth of information.
Table 6
¦ When you write something like ETOHVolume presumably this actual the Z value for this parameter.
Should probably try to make this clear in the table in comments column?
RESPONSE: No. The Z value is not the ETOHVolume itself but the whole term
"((ETOHVolume-10.313704)/(7.879557)), i.e., the ethanol volume minus the mean
value and divided by the standard deviation.
¦ Fuel sulfur - ppm volume or mass?
RESPONSE: Sulfur content is expressed as ppm by mass. This value is numerically
equivalent to a mass content expressed in mg S/kg fuel.
Section 4.1 example
156
-------
¦ I really like including an example because it can help people understand the model. In this particular
chapter, it would be very useful if you actually complete the sample calculation. Provide the reader
with a table of input values (actual fuel values and then presumably the Z values for each parameter
calculated using the parameters Table 2 - my understanding is the Z values are what is actually used
in the model) and the numerical value of what the model predicts. Having the answer will allow the
reader to verify that they understand how to use the model. I would encourage EPA to include this
sort of calculation in each of the documents.
RESPONSE: We have extended the discussion of the example to add text, tables and
figures to illustrate and explain the application of the models (absent the sulfur
adjustment) and calculation offuel adjustments for NOx and THC.
¦ There are few places in report where the text is not complete e.g. "add reference to evap report"
"Chapter X.X"
RESPONSE: We have updated these references.
157
-------
B.3 MOVES2014 Sulfate and Sulfur Dioxide Emissions Calculator
This section provides a verbatim list of peer reviewer comments submitted in response to the charge
questions for the chapter MOVES2014 Sulfate and Sulfur Dioxide Emissions Calculator, IN: Modelling
Effects of Fuel Properties in the Motor Vehicle Emissions Simulator (MOVES2014).
B.3.1 Adequacy of Selected Data Sources
Does the presentation give a description of selected data sources sufficient to allow the reader to form a
general view of the quantity, quality and representativeness of data used in the development of emission
rates? Are you able to recommend alternate data sources might better allow the model to estimate
national or regional default values?
B.3.1.1 Dr. Tom Durbin
Refer to response to All Documents Reviewed in Section 0.
B.3.1.2 Dr. Allen Robinson
I think that the paper gives a good description of the underlying datasets used to derive the model
(in fact I think that these descriptions are better in this document then in some of the other
documents).
The models (gas, old diesel, new diesel, CNG) are based on a relatively limited amount of data
(one or two studies). The selected studies are relevant because some of them systematically
varied key parameters such as fuel sulfur levels (e.g. FUL and DECSE). I am not aware of other
studies that have systematically varied these properties.
It seems concerning that some of the core studies (e.g. the KCVES) used gasoline with much
higher sulfur content gasoline compare to Tier 2 gas. This means the model has to extrapolate a
long way from the reference case. I understand the FUL dataset help do this extrapolation, but it
seems strange to have the reference be so far from the current norm on fuel sulfur content.
RESPONSE: As the average sulfate emission rate from the FUL program was only
0.024 mg/mile, it would be difficult to observe a consistent trend of sulfate emissions
and fuel sulfur content over the lower fuel-sulfur concentration range. We feel more
confident in the results of our calculations that the high and low sulfur endpoints are
anchored in actual data, rather than extrapolations from two low sulfur samples to a
high fuel sulfur fuel. Regardless, we used the only data available to us at the time to
estimate the effect of sulfur emissions on sulfate emissions.
A major shortcoming of this report is that they show no model evaluation and only limited
discussion of goodness of fit. This sort of quality assurance seems essential in an application like
MOVES. The model can be evaluated by the many other studies have measured sulfate
emissions (e.g. PM characterization by Kleeman group, gasoline component of the gasoline
158
-------
diesel split study, etc.). If some of the parameters are not available in these studies (e.g. fuel
sulfur content) the comparison will still provide insight into the suitability of default values. The
model should be tested against at least some of these other data to evaluate its robustness. This
analysis should be performed and described in the report to provide the user confidence in the
model.
RESPONSE: To better describe goodness-of-fit in the revised chapter, we have added
the estimates, standard errors, and confidence intervals for the linear model
coefficients used to estimate the pre-2007 diesel sulfate values.
In addition, we added discussion of model evaluation in the gasoline and diesel
sections. In the gasoline section, we compared the results from our model to results
reported from the DOE gasoline/diesel "PM split study" (Fujita et al. 2007), and three
other studies, including Zielinska et al. (2004), a paper from Kleeman's group (Robert
et al. 2007), Chueng et al. (2009), and a paper from Robinson's group (May et al.
2014).
For pre-2007 diesel, we compared our estimates to results from the DOE
gasoline/diesel "PM split study" (Fujita et al. 2007), Zielinska et al. (2004), and from
the Northern Front Range Air Quality Study (Zielinska, 1998).
In both cases, the data in the literature bounded the projected sulfate/PM values as
discussed in these sections. We did not do model comparisons with the diesel 2007+
because we do not have available speciated data on these engines for comparison at the
time of the analysis, as we state in that section.
B.3.2 Clarity of Analytical Methods and Procedures
Is the description of analytic methods and procedures clear and detailed enough to allow the reader to
develop an adequate understanding of the steps taken and assumptions made by EPA to develop the
model inputs? Are examples selected for tables and figures well chosen and designed to assist the reader
in understanding approaches and methods?
B.3.2.1 Dr. Tom Durbin
The description of the methods and procedures is reasonable. The following are some
suggestions in this area.
As equations 1 and 2 are described, it should be noted that the derivation of these formulas is
provided in Appendix 1.
RESPONSE: The following text is located in the first section, just before we present
Equation 1. "the derivation of which is included in the Appendix X. " We have clarified
the Appendix by updating the Appendix number.
What are typical value for (H20)b?
159
-------
RESPONSE: We added text after we introduce (H20)b , stating: "Currently, the value
o/H20b inMOVES2014 is 0 for all on-road source types, as derived from the PM2.5
speciation profiles. "
Were any measurements made of the oil sulfur levels in the Kansas City study. Can EPA provide
an estimate of what the oil sulfur levels might have been in Kansas City based on typical levels
in oils of the time.
RESPONSE: The sulfur level was analyzed by Fujita et al. (2006 E-69A) for 9
composite used oil samples (from 15 vehicles) in the Kansas City study, and 3 unused
oil samples. The average sulfur content in the 12 samples from the KCVES was 3,006
ppm. However, none of the oil samples came from the vehicles that were analyzed in
this report (summer round, 1996-2004 model year group).
The average sulfur content from 18 post-test oil samples from the full useful-life (FUL)
study was 1,714 ppm. We did not present this information in the report, because we do
not use the lubricating oil sulfur concentration in our calculations. We did use calcium
as a tracer for lubricating oil consumption, as discussed in the report, and assume that
the sulfur/calcium concentrations is the lubricating oil is the same between the FUL
vehicles and the KCVES vehicles. We also report the measured sulfur/calcium ratio
from the lubricating oil.
Pre-2007 Vehicles section. It would be worth noting how many samples the 172 ppm is based
on.
RESPONSE: We added text to the section stating that "based on in-tankfuel samples
from three vehicles in the program that were selected for standard fuel analysis'1... "
The examples in the Appendices provide a good description of how the sulfate contribution is
determined for each of the different vehicle/engine categories. They are a nice contribution to the
report.
B.3.2.2 Dr. Allen Robinson
The basic approach is reasonably well described. I also think that the basic approach of linking
sulfate emissions to nonECPM makes sense (and is an improvement from the old approach of
linking to fuel S) because it avoids the potentially absurd result if you make assumptions about
fuel sulfur content conversion to SO4.
Equation 1 is the core of the model. It was not totally clear how this is implemented in practice.
It appears that NonECPM is an output from another part of MOVES2014 and that this model
simply scales that fraction using the actual fuel sulfur concentration. Therefore the only
independent input into the model is the fuel sulfur concentration (x). All of the rest of the
parameters are determined by the reference (listed in Table 1 of main text). If this is the case
then it should be clarified in the text.
h See Table 11 in Clark et al. 2007.
160
-------
Presumably there is a default value for this if the user does not know the fuel sulfur content. It
would be good to define that value.
RESPONSE: We added a paragraph, which clarifies that the only value that changes is
x (the actual fuel sulfur level), during MOVES runs. We also discuss that the default
values for the fuel sulfur level are provided with the default fuel formulation and fuel
supply tables in MOVES. We also discuss the context of which the sulfate calculator is
used within MOVES.
It seems like a key assumption is sulfate emission rate from lube oil (SO40) is fixed for different
types of vehicles. Is there evidence to support this assumption? If so it was not adequately
discussed in the report. The second assumption is the parameter that describes the conversion of
fuel sulfur to sulfate.
RESPONSE: We assume that the fractional contribution of sulfate from the fuel/or
lubricating oil varies according to fuel type. As stated, for gasoline vehicles, we apply
these assumptions to "all gasoline sources in MOVES, including motorcycles, light-
duty, medium-duty and heavy-duty gasoline trucks, and gasoline-powered buses. "
Regarding the conversion of fuel sulfur to sulfate, we do not use a coefficient to relate
fuel sulfur to sulfate emissions (as in MOVES2010). However, we scale the estimated
contribution offuel sulfur (up and down) linearly with respect to the fuel sulfur level. If
the MOVES user is interested in this value, they can calculate the number from the
sulfate emissions produced in MOVES.
I do not understand the treatment of particulate water (Appendix 1 equation 2). Aerosol water
depends on the composition of the aerosol and the relative humidity of the exhaust. This can be
easily calculated using thermodynamic model such as ISOROPIA. I am not sure how this
equation relates to the underlying theory.
RESPONSE: The sulfate adjustments are based on the PM speciation profile. Equation
2 enables MOVES to adjust sulfate-bound water if the underlying PM speciation profile
suggests water-bound sulfate should be included.
A thermodynamic model (E-AIM,
http://www.aim. env. uea. ac. uk/aim/tutorial/tutorial.php) was evaluated for use in
estimating the fraction of sulfate-bound water during development of the PM2.5
speciation profile from the Kansas City Study. However, at the levels ofNH4 measured,
the fraction of water-bound sulfate was estimated at 0.0, i.e., all sulfate was estimated
to be in the form of ammonium sulfate.
At this time, all underlying PM speciation profiles in MOVES assume that the water
fraction is aerosol so this equation is not used. We added text to make this point
explicit.
A table of variables and definitions would be useful. This is general comment that applies to all
chapters.
RESPONSE: In the revised report, we have added text to ensure that variables are
defined when used.
161
-------
B.3.3 Appropriateness of Technical Approach
Are the methods and procedures employed technically appropriate and reasonable, with respect to the
relevant disciplines, including physics, chemistry, engineering, mathematics and statistics? Are you able
to suggest or recommend alternate approaches that might better achieve the goal of developing
accurate and representative model inputs? In making recommendations please distinguish between
cases involving reasonable disagreement in adoption of methods as opposed to cases where you
conclude that current methods involve specific technical errors.
B.3.3.1 Dr. Tom Durbin
The inclusion of both sulfur for fuel and lubricating oil is an important advancement, especially
as fuel sulfur level have been reduced. Overall, the methodology appears to be reasonable based
on the data available.
Data for pre-2007 heavy-duty engines/vehicles appears to be lacking. One consideration with
sulfate emissions for diesel engines equipped with such DPFs is that the formation of sulfate
emissions is highly nonlinear. Nucleation particles comprised of sulfate increase substantially
above a certain temperature threshold (~350°C). This phenomena is likely too complex to
incorporate into the current model, but is worth considering in future versions of the model.
For the light-duty gasoline vehicle, the expanded use of gasoline direct injection engine is an
important consideration in model future fleets. Little data on sulfate emissions is available for
these types of vehicles, but EPA should keep this in mind in the development of future versions
of the model. UC Riverside is collected some data that might be of interest as part of a mixed
alcohol program being funded by the California Energy Commission.
RESPONSE: We agree that measuring the sulfate contribution from lubricating oil
from new technologies, such as gasoline direct injection, is valuable information to
collect from emission test programs.
B.3.3.2 Dr. Allen Robinson
The model is empirical with the constraint of conservation of mass. This seems like a reasonable
approach given the complexity of the system.
B.3.4 Appropriateness of Assumptions
In areas where EPA has concluded that applicable data is meager or unavailable, and consequently has
made assumptions to frame approaches and arrive at solutions, do you agree that the assumptions
made are appropriate and reasonable? If not, and you are so able, please suggest alternative sets of
assumptions that might lead to more reasonable or accurate model inputs while allowing a reasonable
margin of environmental protection.
162
-------
B.3.4.1 Dr. Tom Durbin
Page 7 2nd paragraph - It indicates that fuel consumption data was not available for E55/59. If the C02,
CO, and THC emissions are available using standard carbon balance equations using assumptions for the
properties of typical diesel fuel.
RESPONSE: We chose to continue to use the MOVES national average fuel
consumption rather than estimate fuel consumption from the E55/59 transient cycles,
because the current approach preserves a sulfur balance in the MOVES output.
Following the suggestion would entail additional effort of analysis, but without any
improvement in the total sulfur balance. However, such an approach could be
implemented in a future version of the model.
For the CNG measurements, EPA should consider data from CARB's latest round of studies on
CNG vehicles.
RESPONSE: These data will be considered for future updates to MOVES. They were
not available to EPA during the development of the emission rates, as mentioned in
responses to peer review the 2014 Heavy-duty Emission Rate Report.
B.3.4.2 Dr. Allen Robinson
A limitation that is not discussed is that the sulfur levels of the fuels used in the KCVES are
much higher than they are in current Tier 2 gasoline.
RESPONSE: We added text to discuss this limitation in the section covering gasoline
vehicles:
"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 the time to represent PM emissions from in-use light-duty gasoline vehicles. "
We also added a sentence in the next paragraph, explaining that the sulfate fraction is
anchored on real-data measured on Tier 2 vehicles on low-sulfur fuel.
"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. "
Another limitation is the lack of GDI vehicles - as the report states the sulfate emissions depend
on sulfur content of the oil/fuel but also combustion conditions. Presumably the differences
between combustion in a GDI versus PFI may influence sulfate emission rates.
RESPONSE: The increasing prevalence of GDI vehicles is an important development to
be considered in the future when additional data becomes available for these engines.
163
-------
The major shortcoming of the model is there is no treatment of uncertainty. I would advocate
that the model should provide uncertainty estimates (confidence intervals) for every
output/prediction. One simple way to provide an estimate would be to use the statistical
uncertainty of the fit. This is reasonably straightforward. It appears to have been done in
Figures 3-1 and 3-3, which shows the results for the conventional diesel. This needs to be
transferred into the core model. Uncertainties should be listed for each of the parameters in
Table 1.
A more robust approach would also be to try to account for the limitations in the underlying
dataset (e.g. lack of GDI). Providing a robust treatment of uncertainty is not easy but it seems
essential to ensure that the data are used appropriately. One way to define this uncertainty would
be to challenge the model with additional data that were not used to derive the parameters listed
in Table 1. Including uncertainty estimates would be a major upgrade of the model, which may
not be possible for this release of MOVES. However, I would strongly encourage EPA to make
starting implementing uncertainty a high priority for future releases.
RESPONSE: MOVES is intended to provide the best estimations of mobile-source
emissions available to us. However, considering the model's scope and complexity,
estimating uncertainty for MOVES outputs is currently outside the scope of the model's
capabilities for most applications. In the MOVES technical reports, we provide
estimates of statistical estimates of variability and discuss sources of uncertainty (e.g.
using limited studies to represent emissions for different technologies) to guide
reasonable choices of numbers and equations used within MOVES.
B.3.5 Consistency with Existing Body of Data and Literature
Are the resulting model inputs appropriate, and to the best of your knowledge and experience,
reasonably consistent with physical and chemical processes involved in emissions formation and control?
Are the resulting model inputs empirically consistent with the body of data and literature that has come
to your attention ?
B.3.5.1 Dr. Tom Durbin
It would be useful to bring some of the information from the Appendix into the main part of the
text. In particular, it would be useful to provide oil and fuel contributions in mg/mi and oil and
fuel sulfate contributions for both the fuel sulfur = 0 case and for the fuel sulfur = reference
level. This would immediately give the reader a feel for what the model inputs would be.
B.3.5.2 Dr. Allen Robinson
There were not sample calculations presented in the chapter. Adding a simple figure that plots
sulfate fraction of non-ECPM for a range of reasonable fuel sulfur contents would help the
reader understand the model predictions. I suspect that the results will be reasonable a few
percent of the PM is sulfate.
RESPONSE: We responded to these two comments jointly in the Sulfate Calculator
chapter by including a new section that presents example comparisons. In this section,
164
-------
we provide graphical comparisons of the PM sulfate contribution and total sulfate
emission rates for each vehicle/fuel technology in MOVES, and across a range offuel
sulfur levels. These cases demonstrate the magnitude of sulfate adjustments and
contributions based a range of fuel sulfur, including no fuel sulfur, the reference level,
and "typical" values.
B.3.6 General/Catch-All Reviewer Comments
Please provide any additional thoughts or review of the material you feel important to note that is not
captured by the preceding questions.
B.3.6.1 Dr. Tom Durbin
¦ Document needs page numbers.
¦ Page 1 paragraph 1 final sentence - change "consist of" to "make up".
¦ Page 2 1st paragraph - 1st sentence ...shown in schematically in Figure 1.; 2nd sentence ...has
supporte4s; 3rd sentence ....treated that the; 4th sentence ....engines decreases
¦ page 3 1st paragraph - last sentence "If included in the PM2.5 speciation profile..." is somewhat
unclear.
¦ Several sentences begin with a number; page 5 1st paragraph 11 ppm; Appendix 2 2nd paragraph
171; Appendix 3 3rd page 11 ppm and 172 ppm; Appendix 4 page 1 15 ppm and 11 ppm.
¦ Appendix 2 2nd paragraph - mean sulfur level is significantly smallorlower in the summer,; 2nd page
of Appendix 2 last sentence - need space before last sentence; 3rd page of Appendix 2 last sentence
bashave
¦ Appendix 1 - 5th line - eliminate space ....reference case . xB
RESPONSE: We have addressed these editorial suggestions.
B.3.6.2 Dr. Allen Robinson
Table 1 -
¦ In headers I would add the word "reference" to the last three columns. For example, xB is the
reference fuel sulfur level not just the fuel sulfur level.
Table 2-1
¦ Units for sulfur content
165
-------
RESPONSE: We added the suggestions.
¦ Definition of SES variable - sulfur emitted as sulfate suggests that this is ratio or fraction. However
this appears to be an absolute emission rate. Why not just call it a sulfate emission rate?
RESPONSE: We added text after we present SES to clarify the difference between SES
and the sulfate emission rate. "SES is 1/3 the value of the sulfate emission rate, to
account for only the mass of sulfur in sulfate molecules (SO 4). "
¦ Equations before Table 2-2 - It seems like the Betal and Beta2 parameters in this equation are test
specific (KC or FUL) and then you make the assumption that they are equivalent.
RESPONSE: Yes, values for these coefficients may be specific to the emission test
program, but in order to estimate a value for use in MOVES, we assume that the values
are the same between the emission test programs.
Table 2-2
¦ Did FUL use FTP or UDDS? In text I thought you said UDDS.
RESPONSE: We added a sentence in Appendix 2 that clarifies that the FTP used in
Table 2 is computed as a composite of the Cold UDDS and Hot UDDS.
B.4 Calculating the Effects of Gasoline Sulfur on Exhaust Emissions
This section provides a verbatim list of peer reviewer comments submitted in response to the
charge questions for the chapter "Calculating the Effects of Gasoline Sulfur on Exhaust
Emissions," IN: Modelling Effects of Fuel Properties in the Motor Vehicle Emissions Simulator
(MOVES2014).
B.4.1 Adequacy of Selected Data Sources
Does the presentation give a description of selected data sources sufficient to allow the reader to form a
general view of the quantity, quality and representativeness of data used in the development of emission
rates? Are you able to recommend alternate data sources might better allow the model to estimate
national or regional default values?
B.4.1.1 Dr. Tom Durbin
Refer to response to All Documents Reviewed in Section B.l.
166
-------
B.4.1.2 Dr. Allen Robinson
The data sources for the Tier 2 models are poorly described. They seem to be contained in
references 10-12. Were all these data weighted equally for the modeling? How were the data
from different studies that had different sulfur contents included in the interpolation? It is not
clear which study the paragraph starting with "The study .." refers to. I assume study 12.
RESPONSE: The T2LowSulf model was based solely on the study conducted by EPA,
with the primary analyses documented in a separate report. "The Effects of Ultra-Low
Sulfur Gasoline on Emissions from Tier 2 Vehicles in the In-Use Fleet. Final Report.
We have modified the text to clarify this point.
B.4.2 Clarity of Analytical Methods and Procedures
Is the description of analytic methods and procedures clear and detailed enough to allow the reader to
develop an adequate understanding of the steps taken and assumptions made by EPA to develop the
model inputs? Are examples selected for tables and figures well chosen and designed to assist the reader
in understanding approaches and methods?
B.4.2.1 Dr. Tom Durbin
The description of the methods and procedures is reasonable. The following are some
suggestions in this area.
Top of page 2. Would like to see some explanation as to why the weighting of high and normal
emitters is 50/50.
RESPONSE: Text was added in Section 3.2.2 that states "Because MOVES2014 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. "
There should be some discussion of why the Tier 2 Low Sulfur Model applies to 2001 and later
vehicles, and how this relates to the NLEV and other phase in transitions.
RESPONSE: Additional text was added in Section 3.3.4 that states "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. "
1USEPA Office of Transportation and Air Quality. The Effects of Ultra-Low Sulfur Gasoline on Emissions from
Tier 2 Vehicles in the In-Use Fleet. Final Report. EPA-420-R-14-002. Assessment and Standards Division, Ann
Arbor, MI. March, 2014.
167
-------
Section 2.1 - This section could be improved in terms of provided an overview of the model. A
table should be added defining the elements in the table structure. There should be an
explanation as to why the model in the log-log form or log-linear form is applied in one case but
not the other. Why is log-log used for Tier 0 and LEV+ vehicles, whereas log-linear is used for
the in between Tier 1 vehicles? Beta is not defined.
RESPONSE: A table defining the elements in the "sulfurmodelcoeff" table was added
in Section 3.2.4. For Tier 0 vehicles, the log-log fit was applied because it provided
consistently better fit than the log-linear fit. For Tier 1 vehicles, because only two
sulfur levels were available, the log-linear fit was chosen to represent the data. Text
was added to describe what beta represents.
Section 2.2 - This section says even less than section 2.1. Does this use the same table structure
as for the short term fuel effects? What is the basis of the different factors for HC, CO, and NOx
and what is the source of their derivations (maybe a couple sentences).
RESPONSE: The values used inMOVES2014 for the long-term sulfur effects are stored
in "M6SulfurCoeff" table, as described in Section 3.2.4.2. The methodology used to
develop the long-term sulfur effects was added in Section 3.2.3.
Section 2.3.1 - Would be useful to add a sentence on why w\r is 0.425 or where it came from.
RESPONSE: Please refer to the original analysis referenced in the report.
Section 2.4 - Would be useful to add a sentence on why the numerator is multiplied by 0.608 for
high NOx emitters. Is this not applied for other pollutants.
RESPONSE: A sentence was added that states 0.60 for NOx high emitters is "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. "
Section 2.6 - Last sentence - Would the calculation be greater than 1 for 90 ppm.
RESPONSE: The result should be greater than 1.0 for the 90 ppm base fuel, as the
underlying research was performed using a reference level of 30 ppm.
It should be noted somewhere in section 2 examples of the model output are provided in section
4.
Section 3 - The coefficients in Table 2 represent the slope but not sure how to interpret them
without understanding the intercept.
RESPONSE: For purposes of this step, as 5 ppm is the lower bound of the interval
under consideration, the emission level at 5ppm acts as the "intercept" in relation to
the "coefficient, " which acts as a "slope, " and is used simply to infer the emissions
level at any sulfur level between 5 and 28ppm, by linear interpolation.
168
-------
Section 4 - The graphs in section 4 are very informative.
B.4.2.2 Dr. Allen Robinson
The model is based on statistical analysis of emission testing performed with gasoline that had
two different sulfur levels. The report refers to this analysis as "mixed-model analysis." I am
not sure what that means - presumably this is some sort of multivariate model. The chapter
needs to describe what the mixed model analysis is. On page 8 the document states that details
"can be found in the report." There is not reference provided for this report.
Presumably the mixed model analysis is used to derive the beta values in equation 17? The
report discussing using interpolation for this analysis?
Equation 17 - This needs to be much better described.
What is A? A scaling factor? How is it used? Presumably there are different values of A for
different pollutants (e.g. NOx, CO, THC)?
RESPONSE: As is a linear scaling factor used to interpolate the emissions level for fuel
sulfur levels between 5 and 28 ppm. It does vary by pollutant. We have added text to
define these terms in the paragraph immediately following the equation.
I do not understand how the betaS were derived. The text says they were developed by linearly
interpolating? However you have many vehicles so presumably you get a whole bunch of betaS
values (one for each vehicle tested at the two fuel S levels). In addition the different studies used
different fuel S levels? How do you combine the betaS values for different vehicles and different
studies? Lumping them together and then averaging? Presumably the data are stratified by
pollutant, model year? What is the uncertainty in these values? How did the values of betaS
vary across the vehicle fleet?
RESPONSE: The /3s were derived from the percent changes in emissions presented in
the preceding table (Table 1 in the draft, Tables 3-23 and 3-25 in the revised report).
As such they represent mean values calculated from the statistical models derivedfrom
the vehicle sample. The percent reduction, represents the "rise " (Ay), and the
difference in the two emission levels (28 -5 = 23 ppm) represents the "run " (Ax). The
"rise " divided by the "run" represents the "slope "from 5 to 28 ppm for the pollutant,
or the value of /3s.
What is listed in Table 2? The BetaS values?
RESPONSE: Yes. The values in Table 2 (Table 3-27 in the final report) represent the
values of /3s, by pollutant and vehicle type.
B.4.3 Appropriateness of Technical Approach
Are the methods and procedures employed technically appropriate and reasonable, with respect to the
relevant disciplines, including physics, chemistry, engineering, mathematics and statistics? Are you able
169
-------
to suggest or recommend alternate approaches that might better achieve the goal of developing
accurate and representative model inputs? In making recommendations please distinguish between
cases involving reasonable disagreement in adoption of methods as opposed to cases where you
conclude that current methods involve specific technical errors.
B.4.3.1 Dr. Tom Durbin
The methods and procedures for the M6Sulf is an already developed model, with developed
methods, so most of the comments in this regard are related to the presentation of the model
methodology and if it is clear, as discussed under point 2.
The discussion on the Tier Low Sulfur Model is somewhat short, but appears to be sufficient
based on the fact that the data sources and analysis have been reviewed as part of another report.
B.4.3.2 Dr. Allen Robinson
I do not understand the methods or analysis ("mixed model analysis"). This appears to be a
purely statistical model as opposed to something based on the underlying physics and chemistry.
RESPONSE: The reviewer points out correctly that the "mixed model" is a technique
used to develop statistical models. The mixed model is an approach that distinguishes
variables as "fixed' or "random "factors. Fixed factors are variables ofprimary
intrinsic interest in terms of their effects on mean emission levels, e.g., fuel sulfur level.
Random factors are not of intrinsic interest but are designated so as to account for the
variability they introduce while not allowing it to confound the effects of the fixed
factors. For example, in the low-sulfur study, the test vehicles are designated as
"random factors. " In the analysis, the behavior of each vehicle is treated as a case of
random variation around the mean behavior of all vehicles. This approach allows the
considerable variability among vehicles to be neutralized in estimation of the effect of
the main variable of interest, the fuel sulfur level.
Uncertainty is a key issue that is completely neglected in this chapter. For example, table 1 lists
sulfur reduction with 3 significant figures. These values need uncertainty estimates. Uncertainty
estimates on these parameters can be derived from the statistical analysis. A better approach
would be to challenge the model by performing leave one out cross validation. Ideally both of
these approaches would be taken. The complete lack of uncertainty seems like a major weakness
of the entire report.
RESPONSE: Considering the scope and complexity of the MOVES model, estimating
uncertainty for MOVES outputs is currently outside the scope of the model's
capabilities for most applications. In technical reports or underlying project reports,
we generally provide estimates of statistical estimates of variability and discuss sources
of uncertainty (e.g. using limited studies to represent emissions for different
technologies) to guide reasonable choices of numbers and equations used within
MOVES.
170
-------
B.4.4 Appropriateness of Assumptions
In areas where EPA has concluded that applicable data is meager or unavailable, and consequently has
made assumptions to frame approaches and arrive at solutions, do you agree that the assumptions
made are appropriate and reasonable? If not, and you are so able, please suggest alternative sets of
assumptions that might lead to more reasonable or accurate model inputs while allowing a reasonable
margin of environmental protection.
B.4.4.1 Dr. Tom Durbin
Even though M6Sulf is supposed to model Tier 1, LEV, and ULEV vehicles, the majority of the
datasets listed are from studies conducted in the early 1990s. Given that early 1990s technologies
are not very representative of Tier 1, LEV, and ULEV vehicles, consideration should be given to
incorporating more data here. Example data sets include the CRC E-60 program.
The assumption on page 9 under Table 1 that NLEV vehicles are more similar to upcoming Tier
2 vehicles than Tier 1 vehicles is reasonable. This detail and how it related to the 2001+ vehicles
should be discussed earlier, however.
B.4.4.2 Dr. Allen Robinson
The data seem reasonable. I am not aware of other data.
B.4.5 Consistency with Existing Body of Data and Literature
Are the resulting model inputs appropriate, and to the best of your knowledge and experience,
reasonably consistent with physical and chemical processes involved in emissions formation and control?
Are the resulting model inputs empirically consistent with the body of data and literature that has come
to your attention?
B.4.5.1 Dr. Tom Durbin
The presentation of model results in section 4 provide good information on how sulfur effects are
implemented in MOVES. The results appear to be reasonably representative of sulfur effects over the
range of different vehicle technologies being evaluated.
B.4.5.2 Dr. Allen Robinson
The chapter presents no data that demonstrates the model provides reasonable results. For
example data could be added to Figures 1-4 to help the reader evaluate the model.
RESPONSE: A project designed to review the MOVES2014 model (CRC E-101)
devoted some effort to evaluating the fuel adjustments for sulfur. As of this writing, the
report is in draft but not yet released.
171
-------
B.4.6 General/Catch-All Reviewer Comments
Please provide any additional thoughts or review of the material you feel important to note that is
not captured by the preceding questions.
The "x" in NOx should be subscripted.
Page 1 paragraph 2 - impair the effectiveness of the catalyst into converting the products of
combustion, leading to increases; last sentence ... as though they are independent
Page 1 paragraph 5 - Add section number for Tier 2 gasoline vehicles
page 4 Section 2.3 1st paragraph - .. .represent the long-term., only to target fuel sulfur levels
page 9 paragraph below Table 1 2nd sentence - model years as early as ..
there is an extra space... bottom of page 6 Equation 14; section 2.6 Equation 16; Last
paragraph section 3 Equation 17; section 4 Equation 1 to Equation 16)
section 3 - 1st sentence greater 30 ppm, and for all vehicles older than 2001. 2nd sentence ..For
sulfur contents; 2nd paragraph catalytic convertefer; 4th paragraph 29 ppm, the higher level
was...
Appendix 1 - 5th line - eliminate extra space ... .reference case . xb
B.4.6.2 Dr. Allen Robinson
I found this document to be very difficult to follow. The model was poorly described with many
variables not even defined. It was also not clear how the model would be used. It would be
impossible for the reader to reproduce the calculations shown in Figures 1-4.
RESPONSE: The content in these figures represents output from MOVES runs. As such
they reflect the net effect of all calculations described in Section 3.1. Interested readers
can replicate these results by performing and summarizing similar MOVES runs.
Figures 1-4. These appear to summarize the output from the sulfur model. What is the "fuel
sulfur adjustment" (which variable, some version of A?)? How is it used? Simply as a scaling
parameter on the base emissions? These details need to be clarified.
RESPONSE: The figures present summary results for running-exhaust emissions
reflecting the calculation of the model's sulfur adjustments over a range of sulfur levels.
The adjustment represents either the result of Equation 3-9 or Equation 3-20.
Equation 3-9 applies for all model years prior to 2001, and to model years after 2000
for sulfur levels > 30 ppm. Equation 3-20 applies to model years after 2000for sulfur
levels of 30ppm and lower. See 3.3.4.
The review is focusing on the Tier 2 model which applies up to fuel sulfur level of 30 ppmv. It
is hard to see the predictions of this model in Figure 1-4 because the x-axis scale goes to 600
ppm. Less than 30 ppmv is less than 5% of this scale. Given the Tier 2 standard for fuel sulfur
the report should focus more on the model behavior at current and future sulfur levels (< 30
ppm). For retrospective analyses showing such high fuel sulfur levels may be useful (how long
172
-------
ago were fuel-S levels greater than 400 ppm?). Bottom line is that these figures or a comparable
set such focus on performance of the models over the range of current fuel-S levels. Does it
even make sense to plot MY 2017 vehicle out at such high fuel S levels?
RESPONSE: The reviewer raises a good point regarding the scaling on the figure for
the most recent vehicles. Accordingly, we have rescaled the figure to better focus on
sulfur levels more relevant during the time period represented.
Although I realize we were not supposed to review the older M6Sulf model, I found the
description of the model to be impossible to follow. It is clear that the model is simply a curve
fit of the underlying dataset. However, many of the variables in this section are not defined. For
example what is A? What do the M6SulfurCoeff values listed on the bottom of page 3
represent? Without more description it is essentially impossible to understand how to apply the
model.
It would be useful if this chapter listed the parameterization developed for the M6Sulf model.
Presumably these are the wIR, betas's etc. A table defining each variable and listing its value
would be very helpful.
You need to define all variables - a short table would be very helpful. What is A2, As,short,
As,long, As,Irr ((), etc. A is clearly an important symbol. What does it represent? It appears to
be some sort of adjustment factor. Is this multiplied with the base emissions to estimate the
effects of sulfur? In order for someone to figure out the model, these details need to be much
more clearly spelled out.
Equation 8??? I have no idea of the basis for this equation. It is doing some sort of weighting of
undefined terms. What is the basis for the irreversibility factor (a sentence to help the reader so
that they don't have to look up that grey literature reference).
RESPONSE: In the section describing the M6SulfModel, we have added substantial
content and detail to better describe the underlying source data, the primary analyses
yielding the basis effects applied in the MOVES calculations, and the model
calculations themselves. In addition, we have added a glossary to list, define and
describe the variables used in these calculations.
30 ppmv is the boundary between the two models (Mobile and new Tier 2). Do the two models
predict the same effect at 30 ppm? Figures 1-4 suggests that the models link up. What is the
basis for the 30 ppm cut - just that it is the tier 2 fuel standard?
RESPONSE: Yes. The basis for the 30 ppm cut is the introduction of the Tier-2 fuel
standard.
173
-------
10 References
1 USEPA Office of Transportation and Air Quality. Air Toxic Emissions from On-road Vehicles in MOVES2014.
EPA-420-R-14-021. Assessment and Standards Division, Ann Arbor, MI. December, 2014.
2 USEPA Office of Transportation and Air Quality. MOVES2014: Fuel Effects, Toxics Emissions, Total Organic
Gases (TOG) andPMSpeciation Analysis. EPA Science Inventory Record 26365.
http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=263653
3 USEPA Office of Transportation and Air Quality. Evaporative Emissions from On-road Vehicles in MOVES2014.
EPA-420-R-14-014. Assessment and Standards Division, Ann Arbor, MI. September, 2014.
4 USEPA Office of Transportation and Air Quality. Fuel Supply Defaults: Regional Fuels and the Fuel Wizard in
MOVES2014. EPA-420-R-16-002. Assessment and Standards Division, Ann Arbor, MI.
5 USEPA Office of Mobile Sources. Regulatory Announcement: EPA's Program for Cleaner Vehicles and Cleaner
Gasoline. EPA-420-F-99-051. Office of Air and Radiation. December, 1999.
http://www3 .epa.gov/tier2/documents/f99051 .pdf.
6 USEPA Office of Transportation and Air Quality. Regulatory Announcement: EPA Sets Tier 3 Tailpipe and
Evaporative Emission and Vehicle Fuel Standards. EPA-420-F-008. Office of Air and Radiation. March, 2014.
http://www3.epa.gov/otaq/documents/tier3/420fl4008.pdf
7 USEPA Office of Transportation and Air Quality. Exhaust Emission Rates for Light-Duty On-road Vehicles in
MOVES2014. EPA-420-R-15-005. Assessment and Standards Division, Ann Arbor, MI. October, 2015.
8 USEPA Office of Transportation and Air Quality. Air Toxic Emissions from On-road Vehicles in MOVES2014.
EPA-420-R-14-021. Assessment and Standards Division, Ann Arbor, MI. December, 2014.
9 Heck, R.; Farrauto, R.J. Catalytic Air Pollution Control: Commercial Technology. 3rd Edition. John Wiley &
Sons, Hoboken, NJ, 2009.
10 Eastwood, P. Critical Topics in Exhaust Gas Aftertreatment (Engineering Design). Research Studies Press Ltd.
Hertfordshire, England. 2000.
11 USEPA Office of Transportation and Air Quality. Fuel Sulfur Effects on Exhaust Emissions - Recommendations
forMOBILE6. EPA420-R-01-039. Assessment and Standards Division, Ann Arbor, MI. July, 2001.
12 Benson, J. D., et al. Effects of Gasoline Sulfur Level on Mass Exhaust Emissions. Auto/Oil Air Quality
Improvement Research Program. SAE Paper No. 912323. 1991.
13 Koehl, W. J., et al. Effects of Gasoline Sulfur Level on Exhaust Mass and Speciated Emissions: The Question of
Linearity. Auto/Oil Air Quality Improvement Research Program. SAE Paper No. 932727. 1993.
14 Rutherford, J. A., et al. Effects of Gasoline Properties on Emissions of Current and Future Vehicles-Tso, T90, and
Sulfur Effects. Auto/Oil Air Quality Improvement Research Program. SAE Paper No. 952510. 1995.
15 American Petroleum Institute. Sulfur "Extension " Study. Unpublished Data Transmitted to EPA in electronic
format.
16 Mayotte, S. C., et al. Reformulated Gasoline Effects on Exhaust Emissions: Phase L: Lnitial Lnvestigation of
Oxygenate, Volatility, Distillation and Sulfur Effects. SAE Paper No. 941973. 1994.
17 Mayotte, S. C., et al. Reformulated Gasoline Effects on Exhaust Emissions: Phase LL: Continued Lnvestigation of
the Effects of Fuel Oxygenate Content, Oxygenate Type, Volatility, Sulfur, Olefin and Distillation Parameters. SAE
Paper No. 941974. 1995.
18 American Petroleum Institute. Sulfur "Reversibility" Study on LEV-certified Vehicles. David Lax, Private
communication.
174
-------
19 Coordinating Research Council. Summary: CRC Sulfur/LEVProgram. CEC Project No. E-42. December 22,
1997.
20 American Automobile Manufacturers Association (AAMA)/Association of International Automobile
Manufacturers (AIAM). Effects of Fuel Sulfur on Low Emission Vehicle Criteria Pollutants. December 1997.
21 Rao, V. Development of an Exhaust Carbon Monoxide Emissions Model. SAE Technical Paper 961214. 1996.
22 U SEPA Office of Transportation and Air Quality. Regulatory Impact Analysis: Tier2/Sulfur Final Rule. EPA-
420-R-99-023. Assessment and Standards Division, Ann Arbor, MI. (Chapter III and Appendix B).
23 USEPA Office of Mobile Sources. Regulatory Impact Analysis - Control of Air Pollution from New Motor
Vehicles: Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements. Appendix B.
EPA420-R-99-023. December, 1999.
24 Ball D.; Clark D.; Moser D. Effects of Fuel Sulfur on FTP NOx Emissions from a PZEV 4 Cylinder Application.
SAE 2011 World Congress. 2011-01-0300. SAE International. Warrendale, PA. 2011.
25 USEPA Office of Transportation and Air Quality. Regulatory Impact Analysis: Control of Hazardous Air
Pollutants from Mobile Sources. Final Rule. EPA 420-R-07-002. Assessment and Standards Division, Ann Arbor,
MI. February, 2007. (Chapter 6).
26 U SEPA Office of Transportation and Air Quality. The Effects of Ultra-Low Sulfur Gasoline on Emissions from
Tier 2 Vehicles in the In-Use Fleet. Final Report. EPA-420-R-14-002. Assessment and Standards Division, Ann
Arbor, MI. March, 2014.
27 Systems Research and Application Corporation. Peer Review of the Effects of Fuel Sulfur Level on Emissions
from the In-Use Tier 2 Vehicles. Docket EPA-HQ-OAR-2011-0135, Item 1847. Charlottesville, VA. February,
2012. (available at www.regulations.gov. EPA-HQ-OAR-2011-0135-1847).
28 USEPA Office of Transportation and Air Quality. EPA Response to Comments on the peer review of "The Effects
of Fuel Sulfur Level on Emissions from the In-Use Tier-2 Vehicles" (Fuel Sulfur Effects Report). Memorandum to
Docket EPA-HQ-OAR-2011-0135, Item 1848. USEPA Assessment and Standards Division, Ann Arbor, MI. April
11, 2013. (available at www.regulations.gov. EPA-HQ-OAR-2011-0135-1848).
29 Cohen, J., & Cohen, P. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 2nd Ed.
Lawrence Erlbaum, Hillsdale, NJ. 1983.
30Engels, J.M.; Diehr, P. 2003. Imputation of missing longitudinal data: a comparison of methods. Journal of
Clinical Epidemiology, 56:968-976.
31 Downey R.G.; King C.V. 1998. Missing data in Likert ratings: a comparison of replacement methods. J Gen.
Psychol. 125:175-191.
32 Stephens, R.D. 1994. Remote sensing data and a potential model of vehicle exhaust emissions. J Air Waste Man
Assoc. 44:1284-1292.
33 Holmen, B.A.; Niemeier, D.A. 1998. Characterizing the effects of driver variability on real-world vehicle
emissions. Transportation Research Part D: Transport and Environment. 3(2): 117-128.
34 Beydoun, M., & Guldmann, J. 2006. Vehicle characteristics and emissions: Logit and regression analyses of I/M
data from Massachusetts, Maryland, and Illinois. Transportation Research Part D: Transport and Environment,
11(1), 59-76.
35 Diggle P.J. 1988. An approach to the analysis of repeated measures. Biometrics. 44:959-971.
36 Wolfinger, R.D. 1993. Covariance structure selection in general mixed models. Communications in Statistics,
Simulation and Computation. 22(4): 1079-1106.
U SEPA Office of Transportation and Air Quality. Analysis of Particulate Matter Emissions from Light-Duty
Gasoline Vehicles in Kansas City. EPA-420-R-08-010. Assessment and Standards Division, Ann Arbor, MI. April,
2008. (Chapter 8).
175
-------
38 U SEPA Office of Mobile Sources. Final Regulatory Impact Assessment for Reformulated Gasoline. EPA-420-R-
93-017. Ann Arbor, MI. December, 1993.
39 U SEPA Office of Transportation and Air Quality. Technical Support Document: Analysis of California's Request
for Waiver of the Reformulated Gasoline Oxygen Content Requirement for California Covered Areas. EPA420-R-
01-016 (Docket A-2000-10, Document No. II-B-2). Transportation and Regional Programs Division, Ann Arbor,
MI. June 2001.
40 USEPA Office of Transportation and Air Quality. MOVES2010 Fuel Adjustment and Air Toxic Emission
Calculation Algorithm - Development and Results. EPA-420-R-11-009. Assessment and Standards Division, Ann
Arbor, MI. July, 2011. http://www.epa.gov/otaq/models/moves/documents/420rll009.pdf
41 USEPA Office of Transportation and Air Quality. EPAct/V2/E-89: Assessing the Effect of Five Gasoline
Properties on Exhaust Emissions from Light-Duty Vehicles certified to Tier-2 Standards: Final Report on Program
Design and Data Collection. EPA-420-R-13-004. Assessment and Standards Division,, Ann Arbor, MI; National
Renewable Energy Laboratory, Golden, CO; Coordinating Research Council, Alpharetta, GA. April, 2013.
42 USEPA Office of Transportation and Air Quality. Assessing the Effect of Five Gasoline Properties on Exhaust
Emissions from Light-Duty Vehicles certified to Tier-2 Standards: Analysis of Data from EPAct Phase 3
(EPAct/V2/E-89). Final Report. EPA-420-R-13-002. Assessment and Standards Division, Ann Arbor, MI. April,
2013.
43 Snedecor, George W.; Cochran, William G. Statistical Methods. Iowa State University Press, Ames, Iowa. 1967.
(page 35.).
44 Neter, J.: Kutner, M.H.; Nachtsheim, C.J.; Wasserman, W. Applied Linear Statistical Models. Fourth Ed., Irwin,
Chicago. 1996. (Section 7.5).
45 West, Brady T.; Welch, KathleenB.; Galecki, Andrzej T. Linear Mixed Models: A Practical Guide Using
Statistical Software. Taylor and Francis CRC Press. 376 pp. 2007.
46 S AS Institute Inc. SAS/STAT User's Guide, Version 6. Fourth Ed., Vol. 2. Chapter 25, The LIFEREG Procedure.
Pp. 997-1026. Cary, NC. 1990.
47 Schabenberger, O. Mixed Model Influence Diagnostics. Paper 189-29 In: S AS Institute, Inc. Proceedings,
Twenty-Ninth Annual SAS Users Group International Conference (SUGI 29), Montreal, Canada May 9-12, 2004.
48 U SEPA Office of Transportation and Air Quality. Exhaust Emission Rates for Light-Duty On-road Vehicles in
MOVES2014. EPA-420-R-15-005. Assessment and Standards Division, Ann Arbor, MI. October, 2015.
49 Energy Policy Act of 2005. Public Law 109-58, 2005; http:// www.gpo.gov/fdsys/pkg/BILLS-
109hr6enr/pdf/B ILLS-109hr6enr.pdf.
50 U.S. Environmental Protection Agency. Fuels and Fuel Additives, Renewable Fuel Standard;
http://www.epa.gov/otaq/fuels/renewablefuels.
51 Energy Independence and Security Act of 2007. Public Law 110-140, 2007;
http://www.gpo.gov/fdsys/pkg/BILLS-l 10hr6enr/pdf/ BILLS-110hr6enr.pdf.
52 Kelly, K., Eudy, L., and Coburn, T. Light-Duty Alternative Fuel Vehicles: Federal Test Procedure Emissions
Results. Technical Report. National Renewable Energy Laboratory, NREL/TP-540-25818, September, 1999.
53 Karavalakis, G., Durbin, T., Shrivastava, M., Zheng, Z., Villela, M., and Jung, H. Impacts of ethanol fuel level on
emissions of regulated and unregulated pollutants from a fleet of gasoline light-duty vehicles. Fuel. 93:549-558.
March, 2012.
54 Haibo Zhai, H. Christopher Frey , Nagui M. Rouphail, Goncalo A. Goncalvcs & Tiago L. Farias (2009)
Comparison of Flexible Fuel Vehicle and Life-Cycle Fuel Consumption and Emissions of Selected Pollutants and
Greenhouse Gases for Ethanol 85 Versus Gasoline, Journal of the Air & Waste Management Association, 59:8,
912-924.
176
-------
55USEPA (2015). MOVES2014a User Interface Reference Manual. EPA-420-B-15-094. Assessment and Standards
Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 2015.
http://www.epa.gov/otaq/models/moves.
56 USEPA (2015). Population and Activity of On-road Vehicles in MOVES2014. Ann Arbor, MI, Assessment and
Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency.
57 USEPA (2013). Assessing the Effect of Five Gasoline Properties on Exhaust Emissions from Light-Duty Vehicles
certified to Tier-2 Standards: Analysis of Data from EPAct Phase 3 (EPActA/2/E-89). Final Report. EPA-420-R-
13-002. Assessment and Standards Division, Office of Transportation and Air Quality, Ann Arbor, MI. April,
2013.
58 Yanowitz, J., Knoll, K., Kemper, J., Luecke, J., and McCormick R. Impact of Adaptation on Flex-Fuel Vehicle
Emissions When Fueled with E40. Environmental Science & Technology. 2013, 47(6), 2990-2997.
59 Haskew, H.M., and Liberty, T.F. Exhaust and Evaporative Emissions Testing of Flexible-Fuel Vehicles. Final
Report. Coordinating Research Council. CRC Report No. E-80, August, 2011. Available at www.crcao.org.
60 Long, T., Herrington, J., Hays, M., Baldauf, R., and Snow, R. Air Toxic Emission from Passenger Cars
Operating on Ethanol Blend Gasoline. 104th Air and Waste Management Association Annual Conference and
Exhibition, 2011, #118, 3053-3058.
61 USEPA (2015). Greenhouse Gas and Energy Consumption Rates for On-road Vehicles: Updates for
MOVES2014. Ann Arbor, MI, Assessment and Standards Division. Office of Transportation and Air Quality. US
Environmental Protection Agency.
62 USEPA (2015). MOVES2014a Software Design Reference Manual. EPA-420-B-15-096. Assessment and
Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor,
MI. 2015. http://www.epa.gov/otaq/models/moves
63 USEPA (2014). Air Toxic Emissions from On-road Vehicles in MOVES2014. Ann Arbor, MI, Assessment and
Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency.
64 USEPA Office of Transportation and Air Quality. Regulatory Impact Analysis: Renewable Fuel Standard
Program (RFS2). EPA-420-R-10-006. Assessment and Standards Division, Ann Arbor, MI. February, 2010.
(Appendix A).
65 McCormick, R.; Williams, A. Impact of Biodiesel on Modern Diesel Engine Emissions. Project ID: FT011.
National Renewable Energy Laboratory, Golden, CO. May 9, 2011.
http://energy.gov/eere/vehicles/downloads/impact-biodiesel-modern-diesel-engine-emissions.
66 Durbin, T., et al. (2011). Final Report for the CE-CERT Engine Testing Portion for the CARB Assessment of the
Emissions from the Use of Biodiesel as a Motor Vehicle Fuel in California Biodiesel Characterization and NOx
Mitigation Study. Final Report Prepared for CARB. .
67 Kittelson, D. B., et al. (2008). Effect of fuel and lube oil sulfur on the performance of a diesel exhaust gas
continuously regenerating trap. Environmental Science & Technology 42(24): 9276-9282.
68 Khalek, I. A., et al. (2011). Regulated and unregulated emissions from highway heavy-duty diesel engines
complying with U.S. Environmental Protection Agency 2007 Emissions Standards. Journal of the Air & Waste
Management Association 61(4): 427-442.
69 Allansson, R.; Maloney, C.A.; Walker, A.P.; Warren, J.P. Sulphate Production Over the CRT: What Fuel Sulphur
Level is Required to Enable the EU 4 and the EU 5 PMStandards to be Met?. SAE Technical Paper Series. 2000-
01-1875. SAE International, Warren, PA.
70 Wall, J.; Shimpi, S.; Yu, M. Fuel Sulfur Reduction for Control of Diesel Particulate Emissions. SAE Technical
Paper 872139. 1987. SAE International, Warren, PA.
71 Baranescu, R. Influence of Fuel Sulfur on Diesel Particulate Emissions. SAE Technical Paper 881174. 1988.
72 Hochhauser, A.; Schleyer, C.; Yeh, L.; Rickeard, D. Impact of Fuel Sulfur on Gasoline and Diesel Vehicle
Emissions. SAE Technical Paper 2006-01-3370. 2006.
Ill
-------
73 USEPA Office of Transportation and Air Quality. Speciation of Total Organic Gas and Particulate Matter
Emissions from On-road Vehicles in MOVES2014. EPA-420-R-15-022. Assessment and Standards Division, Ann
Arbor, MI. November, 2015.
74 Sobotowski, R.; Test Program to establish LDVFull Useful Life PMPerformance. Memorandum to Docket EPA-
HQ-OAR-2011-0135, Item 0428. USEPA Office of Transportation and Air Quality, Assessment and Standards
Division, Ann Arbor, MI. March 1, 2013. (www.regulations.gov. EPA-HQ-OAR-2011-0135-0428).
75 Clark, N.N.; Gautam, M. Heavy-Duty Vehicle Chassis Dynamometer Testing for Emissions Inventory, Air Quality
Modeling, Source Apportionment and Air Toxics Emissions Inventory. CRC Report No. E55/59. West Virginia
University Research Corporation, Morgantown, WV. August, 2007.
76 US Department of Energy. Diesel Oxidation Catalysts and Lean-NOx Catalysts. Final Report. Diesel Emission
Control - Sulfur Effects (DECSE) Program. National Renewable Energy Laboratory, Golden, CO. June 2001.
77 Jaaskelainen, H.; Majewski, W. A. Diesel Engine Lubricants. www.DieselNet.com. Copyright © Ecopoint Inc.
Revision 2013.01.
78 Khalek, Imad. A.; Bougher, T. L; Merritt, P. M. Phase 1 of the Advanced Collaborative Emissions Study. CRC
Report: ACES Phase 1. Coordinating Research Council, Alpharetta, GA. June, 2009.
79 Lanni, T., et al. (2003). "Performance and Emissions Evaluation of Compressed Natural Gas and Clean Diesel
Buses at New York City's Metropolitan Transit Authority." Society of Automotive EngineersfSAE Technical Paper
2003-01-0300).
80 Ayala, A., et al. "Oxidation Catalyst Effect on CNG Transit Bus Emissions." (SAE Technical Paper 2003-01-
1900).
81 Energy Information Administration. Natural Gas 1998: Issues and Trends. Chapter 2. Natural Gas and the
Environment. April 1998. Available at:
http://www.eia.doe.gov/oil_gas/natural_gas/analysis_publications/natural_gas_1998_issues_and_trends/it98.html.
82 USEPA Office of Transportation and Air Quality. Kansas City PM Characterization Study. Final Report.
EPA420-R-08-009. Assessment and Standards Division, Ann Arbor, MI. Revised by EPA Staff, April, 2008.
83 Fulper, C. R., et al. (2010). "Methods of characterizing the distribution of exhaust emissions from light-duty,
gasoline-powered motor vehicles in the US fleet." Journal of the Air & Waste Management Association 60(111:
1376-1387.
84 Zielinska, B., et al. (2004). "Emission Rates and Comparative Chemical Composition from Selected In-Use Diesel
and Gasoline-Fueled Vehicles." Ibid. 54(9): 1138-1150.
85 Fujita, E. M., et al. (2007). "Variations in Speciated Emissions from Spark-Ignition and Compression-Ignition
Motor Vehicles in California's South Coast Air Basin." Ibid. 57(6): 705-720.
86 Robert, M. A., et al. Ibid."Size and Composition Distributions of Particulate Matter Emissions: Part 1—Light-
Duty Gasoline Vehicles." (12): 1414-1428.
87 Cheung, K. L., et al. (2009). "Chemical Characteristics and Oxidative Potential of Particulate Matter Emissions
from Gasoline, Diesel, and Biodiesel Cars." Environmental Science & Technology 43(16): 6334-6340.
88 May, A. A., et al. (2014). "Gas- and particle-phase primary emissions from in-use, on-road gasoline and diesel
vehicles." Atmospheric Environment 88(0): 247-260.
89 Zielinska, B.; McDonald, J.; Hayes, T.; Chow, Fujita, J.E.; Watson, J. 1998. Northern Front Range Air Quality
Study: Final Report. Volume B: Source Measurements. Desert Research Institute, Reno NV. June, 1998.
178
------- |