Exhaust Emission Rates for Heavy-Duty
On-road Vehicles in MOVES2014
&EPA
United States
Environmental Protection
Agency
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Exhaust Emission Rates for Heavy-Duty
On-road Vehicles in MOVES2014
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
NOTICE
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.
&EPA
United States
Environmental Protection
Agency
EPA-420-R-15-015
September 2015
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Table of Contents
1 Principles of Modeling Heavy-duty Emissions in MOVES 4
1.1 Heavy-duty Regulatory Classes 5
1.2 Emission Pollutants and Processes 7
1.3 Operating Modes 8
1.4 Vehicle Age 12
2 Heavy-Duty Diesel Emissions 13
2.1 Running Exhaust Emissions 13
2.1.1 Nitrogen Oxides (NOx) 13
2.1.2 Particulate Matter (PM) 43
2.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO) 58
2.1.4 Energy 65
2.2 Start Exhaust Emissions 70
2.2.1 HC, CO, andNOx 70
2.2.2 Particulate Matter 73
2.2.3 Adjusting Start Rates for Soak Time 74
2.2.4 Start Energy Rates 77
2.3 Extended Idling Exhaust Emissions 79
2.3.1 Data Sources 79
2.3.2 Analysis 80
2.3.3 Results 81
2.3.4 MOVES Extended Idle Emission Rates 82
2.3.5 Auxiliary Power Unit Exhaust 83
3 Heavy-Duty Gasoline Vehicles 85
3.1 Running Exhaust Emissions 85
3.1.1 HC, CO, andNOx 85
3.1.2 Particulate Matter Ill
3.1.3 Energy Consumption 115
3.2 Start Emissions 118
3.2.1 Emissions Standards 118
3.2.2 Available Data 119
1
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3.2.3 Estimation of Mean Rates 120
3.2.4 Estimation of Uncertainty 122
3.2.5 Projecting Rates beyond the Available Data 124
3.2.6 Start Energy Rates 132
4 Heavy Duty Compressed Natural Gas Transit Bus Emissions 134
4.1 Transit Bus Driving Cycles and Operating Mode Distributions 134
4.1.1 Heavy-Duty Transit Bus Driving Cycles 134
4.1.2 Transit Bus Operating Mode Distributions 136
4.2 Comparison of Simulated Rates and Real-World Measurements 137
4.2.1 Simulating Cycle Emission Aggregates from MOVES2010b Rates 137
4.2.2 Published Chassis Dynamometer Measurements 138
4.2.3 Plots of Simulated Aggregates and Published Measurements 140
4.3 Development of New Running Exhaust Emission Rates 143
4.3.1 Determining Model Year Groups 144
4.3.2 Scaling Model Years After 2007 144
4.3.3 Creating CNG Running Rates for Future Model Years 147
4.4 Start Exhaust Emission Rates for CNG Buses 148
4.5 Applications to Other Model Years and Age Groups 149
4.6 PMandHC Speciation for CNG Buses 149
4.7 Ammonia and Nitrous Oxide emissions 151
5 Heavy-Duty Crankcase Emissions 152
5.1 Background on Heavy-duty Diesel Crankcase Emissions 152
5.2 Modeling Crankcase Emissions in MOVES 153
5.3 Conventional Heavy-Duty Diesel 154
5.4 2007 + Heavy-Duty Diesel 155
5.5 Heavy-duty Gasoline and CNG Emissions 156
6 Nitrogen Oxide Composition 158
6.1 Heavy-duty Diesel 159
6.2 Heavy-duty Gasoline 159
6.3 Compressed Natural Gas 160
Appendix A Calculation of Accessory Power Requirements 161
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Appendix B Tampering and Mai-maintenance 163
Appendix C Extended Idle Data Summary 176
Appendix D Developing PM emission rates for missing operating modes 181
Appendix E Heavy-duty Diesel EC/PM Fraction Calculation 182
Appendix F Heavy-duty Gasoline Start Emissions Analysis Figures 204
Appendix G Responses to Peer-Review Comments 209
References 222
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1 Principles of Modeling Heavy-duty Emissions in MOVES
This report describes the analyses conducted to generate emission rates and energy rates
representing exhaust emissions and energy consumption for heavy-duty vehicles in MOVES2014.
Heavy-duty vehicles in MOVES are defined as any vehicle with a Gross Vehicle Weight Rating
(GVWR) above 8,500 Ibs. This report discusses the development of emission rates for total
hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOX), and particulate matter (PM).
MOVES reports PM emissions in terms of elemental carbon (EC) and the remaining non-elemental
carbon PM (nonEC). This report covers the derivation of EC/PM fractions used to estimate
elemental carbon (EC), and the remaining non-elemental carbon PM (nonEC).
From HC emissions, MOVES produces other estimates of organic gas emissions, including volatile
organic compounds (VOCs) and total organic gases (TOG). From VOC emission rates and fuel
properties, MOVES estimates individual toxic compounds such as formaldehyde and benzene. The
derivation of the factors used to compute aggregate measures of organic gases and individual toxic
emissions are available in the Speciation42 and Toxics1 MOVES Reports. MOVES estimates PM
emission rates according to 18 subspecies beyond elemental carbon, such as organic carbon, sulfate
and nitrate, through the use of speciation profiles as documented in the Speciation Report42.
This report also documents the energy consumption rates for heavy-duty vehicles. For heavy-duty
diesel vehicles, the energy rates were developed based on a carbon balance method using the
measurements of carbon dioxide (CO2), CO and total hydrocarbons (HC), from the same tests and
measurements used to estimate the MOVES CO and HC emission rates. We developed emission
and energy rates for heavy-duty vehicles powered by both diesel and gasoline fuels, as well as
compressed natural gas (CNG) vehicles, although emissions from the heavy-duty sector
predominantly come from diesel vehicles. As a result, the majority of the data analyzed were from
diesel vehicles.
This report first introduces the principles used to model heavy-duty vehicles in MOVES. Then the
emission rates for heavy-duty diesel, heavy-duty gasoline, and CNG transit buses are documented.
Chapter 5 documents the crankcase emission rates used for each fuel type of heavy-duty vehicles.
Chapter 6 documents the NO, NO2, and HONO ratios that are used to estimate NO, NO2, and
HONO emissions from NOX emissions.
Emission rates for criteria pollutants (HC, CO, NOx, and PM) are stored in the
"EmissionRateByAge" table in the MOVES database. The emission rates in the
EmissionRateByAge table are stored according to
1. MOVES regulatory class
2. Fuel Type (Diesel, Gasoline, and CNG)
3. Model year group
4. Vehicle age
5. Emission process (e.g. running exhaust, start exhaust, crankcase emissions)
6. Vehicle operating mode
Energy emission rates are stored in the "EmissionRate" table, which is similar to the "Emission
RateByAge" table, except emission rates are not differentiated by vehicle age. The MOVES
framework and additional details regarding the "EmissionRateByAge" and "EmissionRate table are
discussed in the report documenting the rates for light-duty vehicles8.
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In the next sections, the following parameters used to classify heavy-duty emissions in MOVES are
discussed in more detail: heavy-duty regulatory classes, vehicle age, emission processes, and
vehicle operating modes. Although not discussed in detail, the model year groupings are designed
to represent major changes in EPA emission standards.
1.1 Heavy-duty Regulatory Classes
The MOVES heavy-duty regulatory classes group vehicles that have similar emission standards
and emission rates. The MOVES heavy-duty regulatory classes are largely determined based on
gross vehicle weight rating (GVWR) classifications, because the heavy-duty emission standards are
based on GVWR. . However, there are additional criteria that define heavy-duty regulatory classes
in MOVES. .For example, Urban Bus engines are distinguished from other heavy heavy-duty
vehicles (GVWR >33,000 Ibs) because they have tighter EPA PM emission standards for the 1994
through 2006 model years2. Urban bus is a regulatory class that is also defined by its intended use,
and not just the GVWR ("heavy heavy-duty diesel-powered passenger-carrying vehicles with a
load capacity of fifteen or more passengers and intended primarily for intra-city operation3").
Regulatory class LHD<=10K (RegClassID 40) and LHD<=14K (RegClassID 41) are also defined
according to additional criteria than GVWR. LHD<=10K is defined as trucks with GVWR between
8,500 and 10,000 Ibs (Class 2b trucks)with only two axles and four tires. Class 2b trucks with two
axles and six tires are classified in regulatory class LHD <=14K, as well as all trucks between 10,000
and 14,000 Ibs (Class 3 trucks).
Unlike Urban Buses, the distinction between LHD<=10K and LHD<=14K in MOVES is not
caused by differences in EPA exhaust emission standards. The reasons for the distinction between
regulatory class LHD<=10K and LHD<=14K is due to (1) available activity information, and (2)
the assignment of operating modes within MOVES source types.
(1) Available Activity Information. As discussed in the Population and Activity Report4, the
FHWA reports vehicle-miles traveled (VMT) of Class 2b trucks with two axles and four
tires in the light-duty vehicle categories, which correspond to MOVES source type
Passenger Trucks (sourceTypelD 31) and Light Commercial Trucks (sourceTypelD 32).
FHWA reports VMT from Class 2b trucks with two axles and six tires, as heavy-duty
vehicles. MOVES2014 includes LHD<=14K trucks within the following vocational heavy-
duty source types: Intercity Buses (sourceTypelD 41), School Buses (sourceTypelD 43),
Refuse Trucks (sourceTypelD 51), Single Unit Short-haul (sourceTypelD 52), Single Unit
Long-Haul (sourceTypelD 53), and Motor Homes (sourceTypelD 54).
(2) Assignment of Operating Modes within MOVES source types. As discussed in the
Population and Activity Report4, MOVES assigns operating modes according to source
type. For light-duty source types (including passenger trucks and light-commercial trucks)
running operating modes as assigned according to Vehicle Specific Power (VSP). For
single-unit source types, operating modes are assigned according to Scaled Tractive Power
(STP). As discussed in subsection 1.3, the emission rates for regulatory class LHD<=10K
(RegClassID 40) use a different scaling factor when computing STP, such that the emission
rates are consistent with VSP-based operating modes. The emission rates for regulatory
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class LHD<=14K (RegClassID 41) are now based on the standard STP scaling factor, to be
consistent with the way MOVES assigns operating modes for heavy-duty source types.
LHD<=10K (RegClassID 40) is a new regulatory class introduced in MOVES2014. Previous
versions of MOVES classified all light-heavy duty trucks with GVWR under 14,000 Ibs as
LHD2b3 (formerly RegClassID 41). In MOVES2010b, the emission rates for LHD2b3 and LHD45
were compatible with VSP-based emission rates.' As discussed in Section 1.3, the emission rates
for LHD<=14K (RegClassID 41) and LHD45 (RegClassID 42) have been changed to be based on
the standard STP scaling factor for heavy-duty trucks. With the addition of LHD<=10K
(RegClassID 40), and the change to the emission rates for LHD<=14K and LHD45, MOVES can
more accurately model the light-heavy duty emission rates that are classified either within the light-
duty truck source types or the vocational heavy-duty source types.
The emission rates for all the heavy-duty sources types are discussed in this report. As discussed
later in the report, the data used to derive the emission rates for regulatory class LHD<=10K
(RegClassID 40) and LHD<=14K (RegClassID 41) trucks are often the same, but analyzed with
appropriate scaling factors to derive separate emission rates for each regulatory class. Occasionally,
the MOVES2010b regulatory class LHD2b3 is used in this report, to refer to all light-heavy duty
trucks with GVWR under 14,000 Ibs. Table 1-1 provides an overview of the regulatory class
definitions in MOVES for Heavy-Duty vehicles. Table 1-1 also indicates whether the emission
rates are developed to be consistent with VSP or STP-based operating modes.
1 In MOVES2010b, LHD2b3 and LHD45 existed only within the light-duty source types (passenger trucks and light-
commercial trucks). In MOVES2010b, the LHD2b3 and LHD45 trucks that existed in vocational source types (buses
and single unit trucks) types were replaced with MHD trucks, to essentially use the MHD emission rates as surrogates
for the light-heavy-duty trucks that existed in the vocational heavy-duty source types. Since 2010, FHWA has updated
the definition of light-duty vehicles in the VM-1 Highway Statistics table to only include vehicles that are less than
10,000 Ibs. MOVES2014 uses this updated definition, so LHD45 trucks are now exclusively classified within heavy-
duty source types, and do not need to be split between VSP and STP based regulatory classes like the LHD2b3 trucks.4
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Table 1-1. Regulatory classes for heavy-duty vehicles
Regulatory Class
Description
Light-heavy duty <
10,000 Ibs. (Class 2b
Trucks with 2 Axles
and 4 Tires.)
Light-heavy duty <
14,000 Ib. Class 2b
(Trucks with 2 Axles
and at least 6 Tires or
Class 3 Trucks.)
Light-heavy duty 4-5
Medium-heavy duty
Heavy-heavy duty
Urban Bus
regClassName
LHD<= 10K
LHD<=14k
LHD45
MHD
HHD
Urban Bus1
regClassID
40
41
42
46
47
48
Gross Vehicle
Weight Rating
(GVWR) [Ib]
8,501 - 10,000
8,501 - 14,000
14,001 - 19,500
19,501-33,000
> 3 3, 000
> 3 3, 000
Source Types
(SourceTypelD)
Passenger
Trucks,(31) Light
Commercial
Trucks(32)
Buses (41, 43),
and Single Unit
Trucks
(51,52,53,54)
Buses (41, 42, 43)
and Single Unit
Trucks
(51,52,53,54)
Buses (41,42,43),
Single Unit Trucks
(51,52,53,54), and
Combination
Trucks (6 1,62)
Buses (41,42,43),
Single Unit Trucks
(51,52,53,54), and
Combination
Trucks (6 1,62)
Transit Bus (42)
Operating
Mode Basis2
VSP
STP
STP
STP
STP
STP
1 see CFR§ 86.091(2).
2 MOVES assigns operating modes based on VSP or STP, depending on source type
1.2
Emission Pollutants and Processes
MOVES models vehicle emissions from fourteen different emission processes as listed in Table
1-2. This report covers the emission rates for the exhaust emission processes (running exhaust, start
exhaust, extended idle exhaust, auxiliary power exhaust, crankcase running exhaust, crankcase start
exhaust, and crankcase extended idle exhaust) for HC, CO, NOx and PM. The 'running' process
occurs as the vehicle is operating on the road either under load or in idle mode. This process is
further delineated by 23 operating modes as discussed in the next subsection. The 'extended idle'
process occurs during an extended period of idling operation such as when a vehicle is parked for
the night and left idling. Extended idle is generally a different mechanism (usually a higher RPM
engine idle to power truck accessories for operator comfort) than the regular 'curb' idle that a
vehicle experiences while it is operating on the road.
Estimation of energy consumption rates for heavy-duty vehicles is also covered in this report.
Energy consumption (in units of kJ) is modeled for running exhaust, start exhaust, extended idle
exhaust, and auxiliary power exhaust. Estimation of the emissions of methane, nitrous oxide (N2O),
and ammonia (NHa) for gasoline and diesel heavy-duty vehicles are described in separate reports.5'
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6 The estimation of emission rates from these pollutants for CNG transit bus vehicles are covered in
this report.
Evaporative and refueling emissions from heavy-duty vehicles are not covered in this report.
Estimation of evaporative hydrocarbon emissions from heavy-duty gasoline vehicles is described in
the evaporative report.7 MOVES does not estimate evaporative emissions for diesel-powered
vehicles, but does estimate fuel spillage emissions which are part of the refueling emissions
documented in the evaporative report.7
Brake and Tire wear emission rates from heavy-duty vehicles are discussed in the Brake and Tire
Wear Report.10
Table 1-2. Emission processes for on-road heavy-duty vehicles
processID
1
2
9
10
11
12
13
15
16
17
18
19
90
91
processName
Running Exhaust
Start Exhaust
Brakewear
Tirewear
Evap Permeation
Evap Fuel Vapor Venting
Evap Fuel Leaks
Crankcase Running Exhaust
Crankcase Start Exhaust
Crankcase Extended Idle Exhaust
Refueling Displacement Vapor Loss
Refueling Spillage Loss
Extended Idle Exhaust
Auxiliary Power Exhaust
Covered in this report?
Y
Y
N
N
N
N
N
Y
Y
Y
N
N
Y
Y
1.3 Operating Modes
Operating modes for heavy-duty vehicles and running exhaust are defined in terms of power output
(with the exception of the idle and braking modes). For light-duty vehicles, the parameter used is
known as vehicle-specific power (VSP), which is calculated by normalizing the continuous power
output for each vehicle to its own weight. Light-duty vehicles are tested on full chassis
dynamometers, and emission standards are in units of grams per mile. Thus, the emission standards
are largely independent of the weight (and other physical characteristics) of the vehicle and depend
on distance (or miles). More in depth discussion of VSP is contained in the light-duty emission rate
report.8
For heavy-duty vehicles, we relate emissions to power output, but in a different way. Heavy-duty
vehicles are regulated using engine dynamometers, and emissions standards are in units of grams
per brake-horsepower-hour (g/bhp-hr). With these work-based emission standards, emission rates
relate strongly to power and are not independent of vehicle mass, so normalizing by mass is not
appropriate. Thus, for heavy-duty modal modeling, the tractive power is used in its natural form
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and simply scaled by a constant to bring its numerical values into the same range as the VSP values
used for light-duty vehicles. We refer to this heavy duty parameter as "scaled-tractive power"
(STP).
The equation for STP is located here, with units in scaled kW or skW. :
STp = _^ Equation 1-1
J scale
Where: Paxie is the power demand at the axle for the heavy-duty truck. As discussed later, Paxie can
be estimated from an engine dynamometer or from an engine control unit (ECU) for on-road or
chassis testing, by measuring the engine power and estimating the accessory loads and power-train
efficiencies for the vehicle.
For on-road tests, measuring power from the ECU is generally more accurate than estimating
power from road load coefficients. Unlike a generic road load equation where vehicle
characteristics, such as aerodynamic drag and rolling resistance are assumed, the ECU measures
engine speed and torque directly during the test. Also, wind speed and wind direction, which can
have a significant effect on aerodynamic drag, are not typically measured in on-road tests.
Additionally, the road load equations may not reflect the actual vehicle test weight, and the tests
may not have accurate grade information for the entire route tested. Thus, for on-road tests we
generally use power calculated from the ECU measurements, because the vehicle and
environmental characteristics determine the axle power (Section 2.1.1.2).
In chassis dynamometer tests, the road load equation works well because it directly determines the
axle power during the test. For data collected on chassis dynamometer tests, with vehicles that do
not have ECU measurements, we use road load equation (Equation 1-2) to estimate power (Section
2.1.2.2.1).
The values offscaie are located in Table 1-3. As mentioned previously, the operating modes for
regulatory class LHD<=10K (RegClassID 40) are VSP-based, because regulatory class
LHD<=10K (RegClassID 40) are modeled as passenger trucks and light commercial trucks, and
MOVES assigns operating modes to these source types using VSP. Thus, for LHD<=10K
(RegClassID 40) ,fscaie is equal to the mean source mass of light-commercial trucks4, to yield
emission rates that are consistent with VSP-based operating modes.
In contrast, all other heavy-duty source types use a constant 17.1 power scaling factor, which is
approximately the average running weight for all heavy-duty vehicles, and yields STP ranges that
are within the same range as the definitions for VSP, as shown in Table 1-4.
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Table 1-3. Power scaling factor/scat
Regulatory Class (RegClassID)
LHD<=10K(40)
LHD<= 14K (41), LHD45 (42), MHD (46), HHD (47), Bus (48)
Power scaling factor (metric tons)
2.06
17.1
In cases where the power is not measured at the engine, it can be estimated from instantaneous
speed, vehicle mass, and road load coefficients, using the following equation:
Avt + Bvl + Cvf + M • vt(at + g • sinO)
STPt = — —— Equation 1-2
Jscale
where
STPt=the scaled tractive power at time t [scaled kW or skW]
A = the rolling resistance coefficient [kW-sec/m],
B = the rotational resistance coefficient [kW-sec2/m2],
C = the aerodynamic drag coefficient [kW-sec3/m3],
m = mass of individual test vehicle [metric ton],
fscaie = fixed mass factor (see Table 1-3),
vt = instantaneous vehicle velocity at time t [m/s],
at = instantaneous vehicle acceleration [m/s2]
g is the acceleration due to gravity [9.8 m/s2]
sin 9 is the (fractional) road grade
The derivation of the load road parameters is discussed in the Population and Activity Report4. This
is the equation used by MOVES to estimate the operating mode distribution from average speed
and second-by-second driving cycles as discussed in the Population and Activity Report. However,
the equation is also used here to estimate the STP-based emission rates from emission tests where a
more direct measure of Paxieis not available.
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Table 1-4. Operating mode definition for running exhaust for heavy-duty vehicles
OpModelD
0
1
11
12
13
14
15
16
21
22
23
24
25
27
28
29
30
33
35
37
38
39
40
Operating Mode
Description
Deceleration/Braking
Idle
Coast
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Coast
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Cruise/ Acceleration
Scaled Tractive
Power (SIP/, skW)
STP(<0
0
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Table 1-5. Operating modes for start emissions (as a function of soak time)
Operating Mode
101
102
103
104
105
106
107
108
Description
Soak Time < 6 minutes
6 minutes <= Soak Time < 3
30 minutes <= Soak Time <
60 minutes <= Soak Time <
90 minutes <= Soak Time <
0 minutes
60 minutes
90 minutes
120 minutes
120 minutes <= Soak Time < 360 minutes
360 minutes <= Soak Time < 720 minutes
720 minutes <= Soak Time
Extended idle exhaust and diesel APU exhaust are each modeled in MOVES with a single
operating mode (opModelDs 200 and 201, respectively)
1.4 Vehicle Age
Emission rates for HC, CO, NOx and PM are differentiated by vehicle age. Currently, start and
running emission rates for HC, CO, NOx and PM are stored in the "emissionRateByAge" table by
age group, meaning that different emission rates can be assigned to different aged vehicles of the
same model year, regulatory class, fuel type and operating mode.
MOVES uses six different age classes to model the age effects, as shown in Table 1-6. The effects
of age on the emission rates are developed separately for gasoline and diesel vehicles. For diesel
vehicles, we estimated the effects of tampering and mal-maintenance on emission rates as a
function of age. We adopted this approach due to the lack of adequate data to directly estimate the
deterioration for heavy-duty vehicles. Based on surveys and studies, we developed estimates of
frequencies and emission impacts of specific emission control component malfunctions, and then
aggregated them to estimate the overall emissions effects for each pollutant (Appendix B). For
gasoline vehicles, the age effects are estimated directly from the emissions data, or are adopted
from light-duty deterioration as discussed in the text (Section 3.1.1.1).
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Table 1-6. MOVES age group definitions
ageGroupID
3
405
607
809
1014
1519
2099
Lower bound
(years)
0
4
6
8
10
15
20
Upper bound
(years)
o
J
5
7
9
14
19
~
Energy rates are stored in the "EmissionRate" table, where rates are not distinguished by age. This
table also includes HC, CO, NOx, PM , and ammonia (NH?) emissions from extended idle and
auxiliary power units (APU), and nitrous oxide (TSbO) from start and running emissions, and tire
and brake wear from running emissions. This report documents the HC, CO, NOx, and PM
emissions from extended idle and APU usage, however the documentation of heavy-duty nitrous
oxide and ammonia9 and tire and brake wear10 emission rates are documented elsewhere.
2 Heavy-Duty Diesel Emissions
This section details our analysis of data to develop emission rates for heavy-duty diesel vehicles.
Four emission processes (running, extended idling, starts, and auxiliary power unit exhaust) are
discussed.
2.1 Running Exh aust Emissions
MOVES running-exhaust emissions analysis requires accurate second-by-second measurements of
emission rates and parameters that can be used to estimate the tractive power exerted by a vehicle.
This section describes how we analyzed continuous "second-by-second' heavy-duty diesel
emissions data to develop emission rates applied within the predefined set of operating modes
(Table 1-4). Stratification of the data sample and generation of the final MOVES emission factors
was done according to the combination of regulatory class (shown in Table 1-1) and model year
group. As mentioned in subsections 1.1 and 1.3, the emission rates were developed using scaled-
tractive power (STP), using the power scaling factors shown in Table 1-3.
2.1.1 Nitrogen Oxides (NOx)
For NOx rates, we stratified heavy-duty vehicles into the model year groups listed in Table 1-6.
These groups were defined based on changes in NOx emissions standards and the outcome of the
Heavy Duty Diesel Consent Decree11, which required additional control of NOx emissions during
highway driving for model years 1999 and later. This measure is referred to as the "Not-to-
Exceed" (NTE) limit.
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Table 2-1. Model year groups for NOx analysis based on emissions standards
Model year group
Pre-1988
1988-1989
1990
1991-1997
1998
1999-2002
2003-2006
2007-2009
2010+
FTP standard
(g/bhp-hr)
None
10.7
6.0
5.0
4.0
4.0
2.4
1.2
0.2
NTE limit (g/bhp-hr)
None
None
None
None
None
7.0 HHD; 5.0 other reg. classes
1.25 times the family emission level
2.1.1.1 Data Sources
In MOVES2010, we relied on two data sources for NOx emissions from HHD, MHD, and urban
buses:
ROVER. This dataset includes measurements collected during on-road operation using the
ROVER system, a portable emissions measurement system (PEMS) developed by the EPA.
The measurements were conducted by the U.S. Army Aberdeen Test Center on behalf of
U.S. EPA12: This ongoing program started in October 2000. Due to time constraints and
data quality issues, we used only data collected from October 2003 through September
2007. The data was compiled and reformatted for MOVES analysis by Sierra Research13.
EPA analyzed the data and developed the emission rates. The data we used represents
approximately 1,400 hours of operation by 124 trucks and buses of model years 1999
through 2007.
The vehicles were driven mainly over two routes:
"Marathon" from Aberdeen, Maryland, to Colorado and back along Interstate 70
• Loop around Aberdeen Proving Grounds in Maryland
Consent Decree Testing. These data were conducted by West Virginia University using
the Mobile Emissions Measurement System (MEMS).14'15'16 This program was initiated as
a result of the consent decree between the several heavy-duty engine manufacturers and the
US government, requiring the manufacturers to test in-use trucks over the road. Data was
collected from 2001 through 2006. The data we used represented approximately 1,100
hours of operation by 188 trucks in model years 1994 through 2003. Trucks were heavily
loaded and tested over numerous routes involving urban, suburban, and rural driving.
Several trucks were re-acquired and tested a second time after 2-3 years. Data were
collected at 5-Hz frequency, which we averaged around each second to convert the data to a
1.0-Hz basis.
However, since the release of MOVES2010, two additional sources of data have become available.
One source comprises data collected during compliance evaluations for the 2004 and 2007 Heavy-
Duty Diesel Motor Vehicle Engines Rule. This dataset includes results for HHD, MHD and LHD
vehicles. The second source includes the results of a study of heavy-duty trucks in drayage service
in and around the port of Houston (Houston Drayage). Both programs are described in detail
below.
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Heavy-Duty Diesel In-Use testing (HDIU). The in-use testing program for heavy-duty
diesel vehicles was promulgated in June 2005 to monitor the emissions performance of the
engines operated under a wide range of real world driving conditions, within the engine's
useful life.17 It requires each manufacturer of heavy-duty highway diesel engines to assess
the in-use exhaust emissions from their engines using onboard, portable emissions
measurement systems (PEMS) during typical operation while on the road. The PEMS unit
must meet the requirements of 40 CFR 1065 subpart J. The in-use testing program began
with a mandatory two-year pilot program for gaseous emissions in calendar years 2005 and
2006. The fully enforceable program began in calendar year 2007 and is ongoing. The
vehicles selected for participation in the program are within the engine's useful life, and
generally, five unique vehicles are selected for a given engine family. The data available
for use in MOVES2014 were collected during calendar years 2005 through 2010 and
represent trucks manufactured in model years 2003 to 2009 (Table 2-2).
Houston Drayage Data. In coordination with the Texas Commission on Environmental
Quality (TCEQ), the Houston-Galveston Area Council (H-GAC), and the Port of Houston
Authority (PHA), EPA conducted a study collecting emissions data from trucks in drayage
service using portable emission measurement systems (PEMS) from December 2009 to
March 2010.18 The trucks studied were diesel-fueled, heavyheavy-duty trucks used to
transport containers, bulk and break-bulk goods to and from ports and intermodal rail yards
to other locations. These trucks conduct the majority of their travel on short-haul runs,
repeatedly moving containers across fixed urban routes. Note that only small fractions of
trucks involved in drayage service are dedicated solely to this function, with most trucks
spending large fractions of their time performing other types of short-haul service. No
specific drive cycles were used and all PEMS testing was based on actual in-use loads and
speeds.
For MOVES2014, the HDIU and Houston Drayage data were analyzed to fulfill two objectives:
(1) to evaluate the rates in MOVES2010 and
(2) to provide a new data source for updating the emission rates
Updating MOVES emission rates currently in use was considered when two conditions were met:
(1) when MOVES2010 rates for a specific regulatory-class and model-year-group combination
were not based on actual data (i.e., due to gaps in the coverage of ROVER and Consent-Decree
testing data") and (2) when the comparisons between MOVES2010 and independent data show that
more than a half of MOVES2010 emission rates are outside the boundary of the 95 percent
confidence intervals of the independent data.
From each data set, we used only tests we determined to be valid. For the ROVER dataset, due to
time constraints, we eliminated all tests that indicated any reported problems, including GPS
11 Specific subsets of rates used in MOVES2010 were forecast by proportioning measured emission rates to emission
standards as described in Section 2.1.1.4
15
-------
malfunctions, PEMS malfunctions, etc., whether or not they affected the actual emissions results.
For HDIU and Houston Drayage, the time-alignment was visually confirmed by comparing
relevant time-series plots, such as exhaust mass-flow rate vs. CCh concentration, and exhaust-mass
flow rate vs. engine speed, as measured by the ECU. Data was generally aligned within one
second. When an issue with the time-alignment was found, efforts were made to realign the data as
much as possible. As our own high-level check on the quality of PEMS and ECU output, we then
eliminated any trip from ROVER, HDIU, and Houston Drayage where the Pearson correlation
coefficient between CCh (from PEMS) and engine power (from ECU) was less than 0.6. The
correlation check removed approximately 7 percent of the ROVER and HDIU data. All the data
from Houston Drayage met the criteria for correlation between CO2 and engine power. In addition,
data were excluded from the analysis when the vehicle speed was not available due to GPS and/or
ECU malfunctions, when no exhaust flow was reported, and when a periodic zero correction was
being performed on gas analyzers. For the WVU MEMS data, WVU itself reported on test
validity under the consent decree procedure and no additional detailed quality checks were
performed by EPA. Table 2-2 shows the total distribution of vehicles by model year group from
the emissions test programs above, following evaluation of the validity of the data.
Table 2-2. Numbers of vehicles by model year group from the ROVER, WVU MEMS, HDIU, and Houston
Drayage programs used for emission rate analysis
Data Source
ROVER and
Consent Decree
Testing
HDIU
Houston Drayage
MYG
1991-1997
1998
1999-2002
2003-2006
2003-2006
2007-2009
1991-1997
1998
1999-2002
2003-2006
Regulatory Class
HHD
19
12
78
91
40
68
8
1
10
8
MHD
-
-
30
32
25
71
-
-
-
-
LHD
-
-
-
-
15
24
-
-
-
-
BUS
2
-
25
19
-
-
-
-
-
-
2.1.1.2 Calculate STP from 1-Hz data
With on-road testing, using vehicle speed and acceleration to estimate tractive power is not
accurate given the effect of road grade and wind speed. As a result, we needed to find an alternate
approach. Therefore, we decided to use tractive power from engine data collected during operation.
We first identified the seconds in the data that the truck was either idling or braking based on
acceleration and speed criteria shown in Table 1-4. For all other operation, engine speed G>eng and
torque Teng from the ECU were used to determine engine power P^g, as shown in Equation 2-1.
Only torque values greater than zero were used so as to only include operation where the engine
was performing work.
eng eng eng
Equation 2-1
16
-------
We then determined the relationship between the power required at the wheels of the vehicle and
the power required by the engine. We first had to account for the losses due to accessory loads
during operation. Some accessories are engine-based and are required for operation. These include
the engine coolant pump, alternator, fuel pump, engine oil pump, and power steering. Other
accessories are required for vehicle operation, such as cooling fans to keep the powertrain cool and
air compressors to improve braking. The third type of accessories is discretionary, such as air
conditioning, lights, and other electrical items used in the cab. None of these power loads are
subtracted in the engine torque values that are output from the engine control unit. The calculation
of the accessory load requirements is derived below.
We grouped the accessories into five categories: cooling fan, air conditioning, engine accessories,
alternator (to run electrical accessories), and air compressor. We identified where the accessories
were predominately used on a vehicle speed versus vehicle load map to properly allocate the loads.
For example, the cooling fan will be on at low vehicle speed where the forced vehicle cooling is
low and at high vehicle loads where the engine requires additional cooling. The air compressor is
used mostly during braking operations; therefore it will have minimal load requirements at
highway, or high, vehicle speeds. Table 2-3 identifies the predominant accessory use within each
of the vehicle speed and load areas.
At this point, we also translated the vehicle speed and engine load map into engine power levels.
The power levels were aggregated into low (green), medium (yellow) and high (red) as identified in
Table 2-3. Low power means the lowest third, medium is the middle third, and high is the highest
third, of the engine's rated power. For example, for an engine rated at 450 hp, the low power
category would include operation between 0 and 150 hp, medium between 150 and 300 hp, and
high between 300 and 450 hp.
Table 2-3. Accessory use as a function of speed and load ranges, coded by power level
\^ Speed
Load ^^\
Low
Mid
High
Low
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Mid
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
High
Air cond.
Engine Access.
Alternator
Air cond.
Engine Access.
Alternator
Cooling Fan
Air cond.
Engine Access.
Alternator
17
-------
We next estimated the power required when the accessory was "on" and percentage of time this
occurred. The majority of the load information and usage rates are based on information from "The
Technology Roadmap for the 21st Century Truck"19
The total accessory load is equal to the power required to operate the accessory multiplied by the
percent of time the accessory is in operation. The total accessory load for a STP bin is equal to the
sum of each accessory load. The calculations are included in Appendix A.
The total accessory loads Pioss,acc listed below in Table 2-4 are subtracted from the engine power
determined from Equation 2-1 to get net engine power available at the engine flywheel. For LHD
vehicles, we assumed negligible accessory losses.
Table 2-4. Estimates of accessory load in kW by power range
Engine power
Low
Mid
High
HDT
8.1
8.8
10.5
MHD
6.6
7.0
7.8
Urban Bus
21.9
22.4
24.0
We then accounted for the driveline efficiency. The driveline efficiency accounts for losses in the
wheel bearings, differential, driveshaft, and transmission. The efficiency values were determined
through literature searches. Driveline efficiency r\dnveime varies with engine speed, vehicle speed,
and vehicle power requirements. Using sources available in the literature, we estimated an average
value for driveline efficiency 20>21>22>23>24>25>26>27>28 Table 2-5 summarizes our findings.
Table 2-5. Driveline efficiencies found through literature research
General truck:
Barth (2005)
Lucic (2001)
80-85%
75-95%
HDT:
Rakha
NREL(1998)
Goodyear Tire Comp.
Ramsay (2003)
21st Century Truck (2000)
SAE J2188 Revised OCT2003:
Single Drive/direct
Single Drive/indirect
Single Drive/double indirect
Tandem Drive/direct
Tandem Drive/indiriect
Tandem Drive/double indirect
75-95%
91%
86%
91%
94%
94%
92%
91%
93%
91%
89%
Bus:
Pritchard (2004): Transmission Eff.
Hedrick (2004)
MIRA
96%
96%
80%
Based on this research, we used a driveline efficiency of 90% for all HD regulatory classes.
Equation 2-2 shows the translation from engine power Peng to axle power Paxie.
18
-------
p =rl (P -P \
axle I driveling ens IOSSMCC'
Equation 2-2
Finally, we scaled the axle power using Equation 1-1, and the the STP-scaling factors fscaie
presented in Table 1-3.
STP =
* axle
J scale
Equation 1-1
We then constructed operating mode bins defined by STP and vehicle speed according to the
methodology outlined earlier in Table 1-4.
2.1.1.3
Calculate emission rates
2.1.1.3.1
Means
Emissions in the data set were reported in grams per second. First, we averaged all the 1-Hz NOx
emissions by vehicle and operating mode because we did not believe the amount of driving done by
each truck was necessarily representative. Then the emission rates were again averaged by
regulatory class and model year group. These data sets were assumed to be representative and each
vehicle received the same weighting. Equation 2-3 summarizes how we calculated the mean
emission rate for each stratification group (i.e. model year group, regulatory class, and operating
mode bin).
IX ^
Equation 2-3
where
«/• = the number of 1-Hz data points for each vehicley,
«veh = the total number of vehicles,
rpj,i = the emission rate of pollutant p for vehicley at second /',
- = the mean emission rate (meanBaseRate) for pollutant/* (for a given model year group,
regulatory class and operating mode bin).
19
-------
We calculated a mean emission rate, denoted as the "meanBaseRate" in the MOVES
emissionRateByAge table, for each combination of regulatory class, model year group, and
operating mode bin combination. Examples of mean emission rates derived using this method are
displayed in Section 2.1.1.4.6, starting with Figure 2-3.
2.1.1.3.2 Statistics
Estimates of uncertainty were calculated for all the emission rates. Because the data represent
subsets of points "clustered" by vehicle, we calculated and combined two variance components,
representing "within-vehicle" and "between-vehicle" variances. First, we calculated the overall
2
within-vehicle variance swuh-
,=1
Equation 2-4
S = the variance within each vehicle, and
where
= the total number of data points for all the vehicles.
2
Then we calculated the between-vehicle variance Sbetw (by source bin, age group, and operating
mode) using the mean emission rates for individual vehicles (T ) as shown in
Equation 2-5.
J=l
Equation 2-5
Then, we estimated the total variance by combining the within-vehicle and between-vehicle
variances to get the standard error S-r (Equation 2-6) and dividing the standard error by the mean
emission rate to get the coefficient-of-variation of the mean (Equation 2-7). We used the
p
standard error to estimate the 95% confidence intervals of the mean emission rate, which are
displayed in Figure 2-3 through Figure 2-19 for a subsample of the NOx heavy-duty emission rates.
For each emission rate the coefficient of variation is stored in the emissionRateByAge table.
20
-------
ri 2
S- = pas- + ^s^.
rP V "> "'<*
Equation 2-6
s^
Equation 2-7
2.1.1.4 Hole-filling Emission Rates
The data included in the emissions analysis does not include all operating modes or vehicle-type
and model year combinations needed for MOVES. In this section we discuss the "hole-filling"
methodology used to fill missing operating mode bins, and and missing vehicle-type and model
year combinations. To do so, we rely on the heavy-duty diesel emission standards, as well as
engineering knowledge and test data of emission control technologies that were forecasted to be
implemented to meet more stringent standards in 2007 and 2010.
2.1.1.4.1 Hole-filling Missing Operating Modes
For MHD and HHD trucks, the maximum operating mode (opModelD = 40) represents a tractive
power greater than 513 kW (STP= 30 skW x 17.1). This value exceeds the capacity of most HHD
vehicles, and MHD vehicles and buses exert even lower levels. As a result, data are very limited in
these modes.
To estimate rates in the modes beyond the ranges of available data, we linearly extrapolated the
rates from the highest operating mode in each speed range where significant data were collected for
each model year group. In most cases, this mode was mode 16 for the lowest speed range, 27 or 28
for the middle speed range, and 37 or 38 for the highest speed range. For each of these operating
modes, work-specific emissions factors (g/kW-hr) were calculated using the midpoint STP (Table
1-4). Then, these emissions factors were multiplied by the midpoint STP of the higher operating
modes (e.g. modes 39 and 40 for speed>50mph) to input emission rates for the modes lacking data.
For the highest bins in each speed range, a "midpoint" STP of 33 skW (564.3 kW) was used.
Equation 2-8 displays an example calculation of the emission rate for opModelD 40, using a mean
emission rate from opModelD 37, for a given regulatory class and model year group.
T-, . . n . T-, . . n . I STPopModeW4Q\ Equation
Emission RateopModeID 40 = Emission RateopModeID 37 x —— 28
V-JJ "opModelD 37 /
2.1.1.4.2 Hole-Filling Missing Regulatory Class and Model Year Combinations
For regulatory class/model year combinations with missing data we proportionally adjusted from
the existing emissions data using certification data or vehicle emission standards. For model year
groups 1988-1989 and 1990, we increased the 1991-1997 model year group emission rates by a
21
-------
factor proportional to the increase of the certification levels. The certification levels came from
analysis conducted for MOBILE629. We applied the 1988-1989 emission rates to model years 1987
and earlier.
For model year 1998, data existed for HHD trucks but not buses. In these cases, the ratio of
emission rates between the Urban Bus regulatory class and HHD regulatory class from the 1999-
2002 model year group was used to calculate rates for the buses by multiplying that ratio by the
existing HHD emission rates for the corresponding model year group, as shown in Equation 2-9.
HHD rates1998
Urban Bus rates1998 = - x Urban Bus rates1999_20o2 Equation 2-9
HHD
As noted in Table 2-2, the ROVER and Consent Decree Testing did not contain any data on LHD
vehicles. We used MHD emission rates as surrogates for the LHD45 and LHD<=14K, because they
use the same mass scaling factor, and are subject to the same emission standards as MHD
vehicles.111 As discussed in Section 2.1.1.8.3 we confirmed that the MHD rates were consistent
with NOx emission rates measured from 2003-2006 and 2007-2009 LHD trucks measured in the
Heavy-Duty In-Use testing program (HDIU).
For LHD<=10K vehicles, the emission rates in 1998 were used as base rates to back-cast emission
rates for 1991-1997 model years, using the ratio of emission standards between these two model
years (5/4 or 1.25% increase in 1991-1997 vs. 1998). Table 2-7 provides a summary of the
assumptions used to estimate emission rates for regulatory class-model year groups with missing
data.
2. 1. 1. 4. 3 Forecasting HHD, MHD, Urban Bus, and LHD 34 and LHD< =14K
Emissions
The 2007 Heavy-duty Rule69 required the use of ultra-low sulfur diesel fuel, necessary for diesel
engines to be equipped with diesel paniculate filters in order to reach the 0.01 g/bhp-hr PM
standard beginning in 2007. In addition, the 2007 Heavy-duty Rule69 established much tighter NOx
emission standards (0.2 g/bhp-hr). While the NOx standard going into effect for MY 2007 is 0.2
g/bhp-hr, it was assigned to be phased in over a three year period ending in 2010. Rather than
phasing in the after-treatment technology needed to meet the new standard, most manufacturers
decided to meet a 1 .2 g/bhp-hr standard for MY2007-2009, which did not require aftertreatment
(down from 2.4 g/bhp-hr in 2006). For the 2007-2009 HHD, we used the data from the HDIU
program as discussed in Section 2.1.1.8.1. For the NOx emission rates within the 2007-2009 model
year group for MHD, Urban Bus, LHD34 and LHD<=14K, we estimated the NOx emission rates
were 50% lower than the corresponding 2003-2006 emissions (proportional to the reduction in the
NOx emission standards mentioned above).
111 In MOVES2010, the LHD45 and LHD2b3 trucks were also based on MHD data, but were analyzed with the 2.06
mass scaling factor. In MOVES2014, the LHD45 and LHD<=14K emission rates were updated to be based on the
MHD rates with the 17.1 mass scaling factor.
22
-------
The emission rates for 2010 and later heavy-duty trucks developed in MOVES2010 continue to be
used in MOVES2014. For these rates, we projected that HHD, MHD, Urban Bus, and LHD34
regulatory classes would meet the 2010 standards (0.2 mg/bhrp-hr) through the use of SCR. In the
absence of data, we assumed that we would have a 90 percent NOx reduction efficiency from levels
for MY2006 levels, which is consistent with the drop in NOx emission standards from 2.4 g/bhp-hr
to 0.2 g/bhp-hr. In other words, we estimated the emission rates for regulatory classes HHD, MHD,
Urban Bus, and LHD34 in model year 2010 and later by decreasing MY2003-2006 rates by 90
percent. The NOx emissions are projected to remain constant for 2010 and later vehicles for
regulatory classes HHD, MHD, and Urban Buses. The light heavy-duty trucks are projected to have
a decrease NOx emissions through the implementation of the Tier 3 program as discussed in
Section 2.1.1.4.5.
2.1.1.4.4 Forecasting LHD< =10K Emissions
For LHD<=10K trucks in 2007-2009, we accounted for the penetration of Lean NOx Trap
technology"7. Cummins decided to use Lean NOx Trap (LNT) after-treatment starting in 2007 in
engines designed to meet the 2010 standard and used in vehicles such as the Dodge Ram. This
technology allows for the storage of NOx during fuel-lean operation and conversion of stored NOx
into N2 and H2O during brief periods of fuel-rich operation. In addition, to meet particulate
standards in MY 2007 and later, heavy-duty vehicles are equipped with diesel particulate filters
(DPF). At regular intervals, the DPF must be regenerated to remove and combust accumulated PM
to relieve backpressure and ensure proper engine operation. This step requires high exhaust
temperatures. However, these conditions adversely affect the LNT's NOx storage ability, resulting
in elevated NOx emissions.
In order to determine the fraction of time that DPF's spend in PM regeneration mode, in 2007, EPA
acquired a truck equipped with a LNT and a DPF and performed local on-road measurements using
portable instrumentation and chassis dynamometer tests. We distinguished regimes of PM
regeneration from normal operation based on operating characteristics, such as exhaust
temperature, air-fuel ratio, and ECU signals. During the testing conducted on-road with onboard
emission measurement and on the chassis dynamometer, we observed a PM regeneration frequency
of approximately 10 percent of the operating time.
Emissions from this vehicle were not directly used to calculate emission rates, because only one
vehicle was tested. Rather, adjustments were made from the 2003-2006 model year group to
develop emission rates for this model year group and regulatory class. During PM regeneration, we
assumed that the LNT did not reduce emissions from 2003-2006 levels. During all other times, we
assumed that emissions were reduced by 90 percent from 2003-2006 levels. These assumptions
result in an estimated NOx reduction of 81% for LNT equipped trucks between 2003-2006 and
2007-2009, as shown in Equation 2-10.
1V In MOVES2014, we created a distinction of LHD<= 14K and LHD<= 10K to account for STP and VSP-based
operating modes. LHD<=10K has the same emission rates in MOVES2010b as the old regulatory class LHD2b3
(which includes the Lean-NOx trap assumptions. In MOVES2014, the emission rates for LHD<=14K are set the same
as LHD34, and do not include the Lean-NOx trap assumptions.
23
-------
LNT NOx emissions
Baseline LHD < WK (2003 - 2006) NOx emissions
(LNT normal emissions \ Equation
= (normal op. frequency) x ~> m
V baseline emissions ) •i~lu
/baseline emissions\
+ (DPF reg. frequency) x
V baseline emission )
= (0.90) X (0.10) + (0.10) X (1) = 0.19
Because we assume that LNT-equipped trucks account for about 25 percent of the LHDDT market,
we again weighted the rates for the LHD<=10K regulatory class (RegClassID 40) for model years
2007 and later. For MY 2007-09, we assume that the remaining 75 percent of LHD<=10K diesel
trucks will not have after-treatment and will exhibit the 2007-2009 model year emission rates
described earlier in this section. Overall, these assumptions result in a 58% reduction in NOx
emission rates in 2007-2009 from the MOVES2010 2003-2006 NOx emission rates as shown in
Equation 2-11.
2007 - 2009 LHD < WK NOx emissions
2003 - 2006 LHD < WK NOx emissions
/ LNT NOx emissions \ Equation
= (LNT market share) (20Q3 _ 2QQ6 LHD < WK N0x emissioj 2.n
i 2007 — 2009 emission standerds \
+ (non — LNT market share)
\2003 — 2006 NOx emissions stanaerasJ
= (0.25) x (0.19) + (0.75) x (0.5) = 0.4225
Starting in MY2010, we assume that the remaining 75 percent of LHD<=10K diesel trucks are
equipped with SCR, and exhibit 90 percent NOx reductions from 2006 levels. These assumptions
are outlined in Table 2-7.
2.1.1.4.5 Incorporation of Tier 3 Standards
In addition to regulating light-duty vehicles, the Tier-3 vehicle emission standards30 will affect light
heavy-duty diesel vehicles, i.e., vehicles in regulatory classes LHD<=10k and LHD<=14k
(regClassID = 40, 41, respectively). For these LHD diesel vehicles, reductions in emission rates
attributable to the introduction of Tier 3 standards are applied only to rates for NOx.
For HC and CO emissions, the emission rates currently in MOVES imply that current levels on the
FTP cycle are substantially below the Tier 3 HC and CO standards. For example, when MOVES
rates are combined to estimate a simulated FTP estimate for NMHC, the result is a rate of
approximately 0.05 grams per mile, while the simulated FTP estimate for CO is less than 1.0
gram/mile. Consequently, we assumed that no additional reductions in HC and CO emissions
would be realized through implementation of the Tier 3 standards on LHD diesel vehicles.
By contrast, we estimate that the Tier 3 NOx standard will results in a reduction emissions from
diesel vehicles in regulatory classes LHD<=10K and LHD<=14K. Data on current NO* emissions
24
-------
are limited, so we used a proportional approach to estimate the reductions related to Tier 3,
reducing NO* in proportion to the change in the emission standard. Because emission standards
tend to impact start and running emissions differently, we applied a greater portion of the reduction
to running emissions and a smaller reduction to start emissions. These reductions were phased-in
over the same schedule as for gasoline vehicles, as detailed in Table 2-6.
Table 2-6. Phase-in Assumptions for Tier-3 NOx Standards for light heavy-duty diesel vehicles.
Model Year
2017
2018
2019
2020
2021
2022
Phase-in
fraction (%)
0
38
54
69
85
100
Reduction in
Running Emission
Rate (%)
0
23
33
42
52
61.5
Reduction in Start
Emission Rate (%)
0
9
12
16
19
23
In generating the reduced rates for running operation, the starting point was a subset of rates for
MY2017, extracted from the MOVES2010b EmissionRateByAge table, and taken to represent the
pre-Tier-3 baseline.
The ending point, representing full Tier-3 control, was model year 2022. These rates were
calculated by multiplying the rates for MY2017 by a fraction of 0.3855. This fraction reflects
application of the reduction fraction for running rates in MY2022 as shown in Table 2-6.
Rates in MY 2018 and later were calculated as weighted averages of the values for MY2017 and
MY2022, using the same fractions applied to gasoline vehicles, as shown in Table 3-16 (page 100)
and Equation 3-2 (page 99). Note that these calculations were applied to running rates for the
LHD<=10K regulatory class (based on STP with a fixed mass factor of 2.06) and to those for the
LHD<=14k regulatory class (based on STP with a fixed mass factor of 17.1). Examples of rates for
selected operating modes are shown in Figure 2-1. Note that on the logarithmic scale used, the
parallelism of the trends shows that the proportional reductions are identical for both regulatory
classes.
In addition to tightening emission standards, the Tier 3 regulations require an increase in the
regulatory useful life. An increase in the useful life is interpreted as an improvement in durability,
which is expressed through a delay in deterioration effects. To express this effect, rates estimated
for the 0-3 yr ageGroup are replicated to the 4-5 year ageGroup, i.e., the onset of deterioration is
delayed until the 6-7 year ageGroup. This effect is realized partially for model years 2018-2020
and fully in 2021.
25
-------
Figure 2-1. NO*: Emission rates for running-exhaust operation in selected operating modes vs. model year, for
two light-heavy-duty regulatory classes (LOGARITHMIC SCALE).
100
10-
i 1
CCL
opModelD = 1
opModelD=14
opModelD = 24
opModelD=27
opModelD=28
opModelD =
2017 2019 2021
2017 2019 2021
modelYearlD
2017 2019 2021
regClassName o LHD<=10K o LHD<=14K
100
-10
-1
Figure 2-2. NO*: : Emission rates for running-exhaust operation in a single operating mode (27) vs. age, for
two light-heavy-duty regulatory classes (LINEAR SCALE).
125
100-
75
„ 50
meanBaseRate (
w
LTl
I I I I I
modelYearlD = 201 7 modelYearlD = 2018 modelYearlD = 2019
r~
/" ^ a c
/
^
/
modelYearlD = 2020 modelYearlD = 2021 modelYearlD = 2022
y
& — 3 9 3 O
Cf-^e"^
J
0 «^~
_/" • ' '
e ^ ° ° ° °
125
-100
-75
-25
I I I I I 1 I 1 1 1 1 1
5 10 15 20 5 10 15 20 5 10 15 20
agemid
regClassName o LHD«=10K o LHD<=14K
26
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2.1.1.4.6 Summary
Table 2-7 summarizes the methods used to estimate emission rates for each regulatory-
class/model-year-group combination. The emission rates in MOVES2010 were based on the
analysis of ROVER and Consent Decree testing data. For MOVES2014, we made a decision to
update the emission rates for model year group 2007-2009 for HHD, based on the comparison of
the emission rates in MOVES2010 to HDIU and Houston Drayage data, discussed in Section
2.1.1.8. MOVES2014 also included the impact of the Tier 3 regulations on the LHD<=14K and
LHD<=10K regulatory classes. For all other combinations of regulatory classes and model year
groups, the rates from MOVES2010 were retained in MOVES2014.
27
-------
Table 2-7. Summary of methods for heavy-duty diesel NOx emission rate development for each regulatory class
and model year group
Model
year
group
1960-
1989,
1990
1991-
1997
1998
1999-
2002
2003-
2006
2007-
2009
2010-
2016
2017-
2050
HHD
HHD 1991-
1997 rates
proportioned
to ratio of
certification
levels
Data
analysis13
Data
analysis13
Data
analysis1'3
Data
analysis1'3
Data
analysis2
HHD 2003-
2006 rates
proportioned
to FTP
standards per
Table 2-1
Same as
HHD 20 10-
2016
MHD
Same rates as HHD
Same rates as HHD
Same rates as HHD
Data analysis1
Data analysis1-3
MHD 2003-2006
rates proportioned to
FTP standards per
Table 2-13
MHD 2003-2006
rates proportioned to
FTP standards per
Table 2-1
Same as MHD 20 10-
2016
Urban Bus
Urban Bus 1991-1997
rates proportioned
using ratio of HHD
certification levels
Data analysis1
Urban Bus 1999-2002
rates proportioned
using ratio of HHD
1998 rates to HHD
1999-2002 rates
Data analysis1
Data analysis1
Urban Bus 2003 -2006
rates proportioned to
FTP standards per
Table 2-1
Urban Bus 2003 -2006
rates proportioned to
FTP standards per
Table 2-1
Same as Urban Bus
2010-2016
LHD34 and
LHD<=14K
Same rates as
HHD
Same rates as
HHD
Same rates as
HHD
Same rates as
MHD
Same rates as
MHD
Same rates as
MHD
Same rates as
MHD
MHD rates
proportioned to
Tier 3 standards
LHD<=10K
LHD <=10K 1991-
1993 rates
proportioned to
LHD certification
levels
Proportioned to
1998 FTP
standards per
Table 2-1
Same rates as
1999-2002
MHD engine data
with 2. 06 mass
factor
Data analysis with
2.06 mass factor2
LNT specific
reductions from
theMOVES2010
2003-2006 rates,
and same rates as
2003-2006 (non-
LNT)3
MOVES2010
LHD<=10K2003-
2006 rates
proportioned to
FTP standards per
Table 2-1
MOVES2010
LHD<=10K2003-
2006 rates
proportioned to
Tier 3 standards
lAnalysis based on ROVER and Consent Decree testing data; 2 Analysis based on HDIU data; 3 Confirmed by
HDIU and Houston Drayage data
2.1.1.5 Tampering and Mai-maintenance
Table 2-8 shows the estimated aggregate NOx emissions increases due to Tampering and Mai-
maintenance (T&M) by regulatory class and model year group. As described in Appendix B, the
T&M emission increases in Table 2-8 are calculated by combining information regarding the
assumed frequency rate of an equipment failure at the useful life of the engine, combined with the
28
-------
estimated emission impact of the equipment failure. The emission increases are reduced for ages
that are below the useful life of the engine, as shown in Table B-2 (Appendix B. 1), and the
emission increases by age differ for the LHD, MHD, HHD and Bus regulatory classes. Thus, the
aged emission rates for regulatory classes with the same zero-mile emission rates (Table 2-7) may
be the different due to the T&M NOx effects (Table 2-8) and phase-in of T&M effects by age
(Table B-2) that differ according to regulatory classes.
The LHD<=10K trucks have different T&M NOx increases than LHD<=14K trucks, due to the
assumed penetration of lean NOx trap (LNT) aftertreatment which was assumed to penetrate 25%
of LHD<=10K trucks starting in 2007, consistent with the assumptions previously made in Section
2.1.1.4.4.
The T&M values for 2010 and later vehicles include the impact of the implementation of heavy-
duty on-board diagnostics (OBD). For LHD2b/3 trucks, OBD systems were assumed to be fully
implemented in MY 2010 and onward. For Class 4 through 8 trucks, (LHD45, MHD, HHD) we
assumed there would be a phase-in period from MY 2010 to 2012 where we one-third of those
trucks were equipped with OBD systems. In MY 2013 and later, all trucks have OBD systems.
These OBD adoption rates have been incorporated into the in the tampering and mal-maintenance
emission increases in Table 2-8 with the assumptions and calculations detailed in Appendix B.
Table 2-8. Fleet-average NOx emissions increases in MOVES from zero-mile levels over the useful life due to
tampering and mal-maintenance (T&M)
Model
years
1994-
1997
1998-
2002
2003-
2006
2007-
2009
2010-
2012
2013+
NOx increase (TMNOx) for
LHD<=10K trucks [%]
0
0
0
18
56
56
NOx increase (TMNox) for
LHD<=14K trucks [%]
0
0
0
0
58
58
NOx increase (TMNOx) for all
other HD trucks [%]
0
0
0
0
77
58
Using the assumptions included in Appendix B (Table B-4), we originally calculated small (9-14%)
T&M NOx emission increases for model year groups before 2010. However, we did not implement
these increases in MOVES because we assumed that NOx increases due to T&M only occurred in
engines equipped with NOx aftertreatment technologies, (largely 2009 model year and earlier).
This is due to a few reasons:
• The WVU MEMS data did not show an increase in NOx emissions with odometer (and
consequently, age) during or following the regulatory useful life31. Since the trucks in this program
were collected from in-use fleets, we do not believe that these trucks were necessarily biased toward
cleaner engines.
• Manufacturers often certify zero or low deterioration factors for these engines.
29
-------
• Starting with MY 2010, we expect tampering and mal-maintenance to substantially increase
emissions over time compared to the zero-mile level, because these engines rely on the use of an
aftertreatment emission control systems, to meet 2010 and later emission standards, and a control
system failure will substantially increase emissions.
The NOx deterioration value for SCR-equipped heavy-duty diesel vehicles in 2010-2012 is a 77%
increase. Though 77% may appear to be a large increase in fleet-average emissions over time, it
should be noted that the 2010 model year standard (0.2 g/bhp-hr) is about 83% lower than the 2009
model year effective standard (1.2 g/bhp-hr). This still yields a substantial reduction of about 71%
from 2009 zero-mile levels to 2010 fully deteriorated levels.
As more data becomes available for future model years, we plan to update these tampering and
mal-maintenance and overall aging effects.
2.1.1.6 Defeat Device andLow-NOx Rebuilds
The default emission rates in MOVES for model years 1991 through 1998 are intended to include
the effects of defeat devices as well as the benefits of heavy-duty low-NOx rebuilds (commonly
called reflash) that occurred as the result of the heavy-duty diesel consent decree. Reflashes reduce
NOx emissions from these engines by reconfiguring certain engine calibrations, such as fuel
injection timing. The MOVES database also includes a set of alternate emission rates for model
years 1991 through 1998 assuming a hypothetical fully reflashed fleet.
Since defeat devices were in effect mostly during highway or steady cruising operation, we
assumed that NOX emissions were elevated for only the top two speed ranges in the running exhaust
operating modes (>25mph). To modify the relevant emission rates to represent reflash programs,
we first calculated the ratios from the emission rates in modes 27 and 37 to that for opMode 16, for
model year 1999 (the first model year with not-to-exceed emission limits). We then multiplied the
MY 1999 ratios by the emission rates in mode 16 for model years 1991 through 1998, to get
estimated "reflashed" emission rates for operating modes 27 and 37. This step is described in
Equation 2-12 and Equation 2-14. To estimate "reflashed" rates in the remaining operating modes,
we multiplied the reflashed rates by ratios of the remaining operating modes to mode 27 for
MY 1991-98, as shown in Equation 2-13 and Equation 2-15.
reflash ,91-98,27 91-98,16 | _
Operating modes
(OM)21-30
_ ri999.27 I Equation 2-12
*ni "" - - ' '
= r
T
91-98,OMi
'reflash,91-98,OMx f reflash ,91-98,27 Equation 2-13
^91-98,27
30
-------
— — i yyy , J /
reflash ,91-98,37 ~ r91-98,16| ~ | EqiiatlOIl 2-14
Operating modes
(OM)31-40
I r
^rf,M71991-1998,0M, = ^,91-98,37 | -^^ I Equation 2-15
^91-98,37
The default emission rates were also slightly adjusted for age for the consent decree model years.
An EPA assessment shows that about 20 percent of all vehicles eligible for reflash had been
reflashed by the end of 2008.32 We assumed that vehicles were receiving the reflashes after the
heavy-duty diesel consent decree (post 1999/2000 calendar year) steadily, such that in 2008, about
20 percent had been reflashed. We approximated a linear increase in reflash rate from age zero.
2.1.1.7 Sample results
The charts in this sub-section show examples of the emission rates that resulted from the analysis
of the data described in Section 2.1.1.1. Not all rates are shown; the intention is to illustrate the
most common trends and hole-filling results.
Figure 2-3 and Figure 2-4 show that NOx emission rates increase with STP for FfflD trucks.
Figure 2-5 adds the MHD and bus regulatory classes, with the error bars removed for clarity. As
expected, the emissions increase with power, with the lowest emissions occurring in the
idling/coasting/braking bins.
31
-------
Figure 2-3. Trends in NOx Emissions by operating mode from HHD trucks for model year 2002. Error bars
represent the 95% confidence interval of the mean.
4UUU
3500
Is
72500 -
E
x
°2000 -
c
ra
01
§1500
1000
500 -
n .
t
I
I
H
i
* S *
* *
5 5
1
* • * *
0 I 1112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
Figure 2-4. Trends in NOx Emissions by operating mode from HHD trucks for model year 2007. Error bars
represent the 95% confidence interval of the mean.
4000
3500
3000
2500 -
2000 -
1500 -
1000
500 -
n .
....-''
1
*
j I
I
l*]
I
i
t 5
I
x 1
5
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
32
-------
The highest operating modes in each speed range will rarely be attained due to the power
limitations of heavy-duty vehicles, but are included in the figures (and in MOVES) for
completeness. Nearly all of the activity occurs in modes 0, 1, 11-16, 21-28, and 33-38, with
activity for buses and MHD vehicles usually occurring over an even smaller range. In some model
year groups, the MHD and HHD classes use the same rates, based on lack of significant differences
between those two classes' emission rates.
Figure 2-5. Trends in NOx emissions by operating mode from LHD<=14K, LHD45, MHD, HHD, and bus
regulatory classes for model year 2002. LHD<=14K, LHD45, and MHD have the same NOx zero-mile NOx
emission rates.
6000
5000
§ 4000
01
E
x 3000
ra
01
2000
1000
• MHD
A Bus
• HHD
* •
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
The effects of model year, representing a rough surrogate for technology or standards, can be seen
in Figure 2-6, which shows decreasing NOx rates by model year group for a sample operating
mode (opModeID24) for HHD trucks. Other regulatory classes show similar trends. The rates in
this chart were derived with a combination of data analysis (model years 1991 through 2009) and
hole-filling. The trends in the data are expected, since the model year groups were formed on the
basis of NOx standards. Increasingly stringent emissions standards have caused NOx emissions to
decrease significantly.
33
-------
Figure 2-6. Trends in NOx by model year for HHD trucks in operating mode 24. Error bars represent the 95%
confidence interval of the mean.
2500
T
2000 -
*C*
s
a, 1500
4-»
as
c
ra
01
1000
500 -
•&
&
V
Model year group
Age effects were implemented for after-treatment-equipped trucks only (mostly model year 2010
and later) based on an analysis of tampering and mal-maintenance effects. Due to faster mileage
accumulation, the heavy-heavy duty trucks reach their maximum emission at the youngest ages, as
shown in Figure 2-7. Relative Standard Errors (based on coefficients-of-variation for means) from
previous model year groups were used to estimate uncertainties for MY 2010.
34
-------
Figure 2-7. Modeled NOx trends by age for model year 2010 for operating mode 24 for MHD, HHD, and Urban
Bus regulatory classes for model year 2002. Error bars represent the 95% confidence interval of the mean.
14U -
120 -
T- 100
i
Si 80 -
- ' L [
Birr irr
I 11
E H 1
X
% 60
ra
01
§ 40
20 -
n -
4 •"•
fe
"5"
-IT
11
• HHD
• MHD
A Bus
0-3 4-5 6-7 8-9 10-14 15-19
Age group [years]
20+
Figure 2-8 and Figure 2-9 shows the mean emission rates for LFID<= 10K trucks for model years
2003-2006 and 2007-2009, respectively. The estimated uncertainties are greater than for the other
heavy-duty regulatory classes, since there were fewer vehicles in our test data. As described
previously, model years 2007-2009 vehicles includes vehicles with LNTs (with NOX increases
during PM regeneration) and vehicles without any aftertreatment.
Figure 2-8. Mean NOx rates by operating mode for model years 2003-2006 LHD<=10K (RegClassID 40) trucks
age 0-3. Error bars represent the 95% confidence interval of the mean.
600 n
500 -
400
300
200
100 -
1
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
35
-------
Figure 2-9. Mean NOx rates by operating mode for model years 2007-2009 LHD<=10K trucks age 0-3. Error
bars represent the 95% confidence interval of the mean.
600 -i
500 -
400 -
300 -
8 200
100 -
*
.**•
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
2.1.1.8 Evaluation of NOx Emission Rates in MOVES2010
This section presents the comparisons of NOx rates in MOVES2010 to the emissions data from the
Heavy Duty In-Use (HDIU) and Houston Drayage programs. The HDIU data includes HHD,
MHD, and LHD trucks. The Houston Drayage only includes HHD trucks (Table 2-2).
The purpose of the evaluation was to examine the need for updating the NOx rates in MOVES2010
based on the analysis of the newly acquired independent data. As discussed in Section 2.1.1.1,
HDIU and Houston Drayage data became available after the MOVES2010 release and have served
two purposes - to evaluate the rates in MOVES2010 and to provide data for updating existing
emission rates. The emission rates for a regulatory class and model year group combination were
considered for an update if:
1) MOVES2010 rates were not based on actual data, and
2) the comparison to independent data shows that more than a half of MOVES2010 emission
rates are outside the boundary of the 95 percent confidence intervals of the independent
data.
2.1.1.8.1 Heavy-Heavy Duty Trucks
Figure 2-10 through Figure 2-12 show that MOVES2010 rates for pre-2003 model years are
generally in good agreement with the Houston Drayage data and within the range of uncertainty of
means calculated from these data. The error bars represent the 95% confidence intervals of the
mean. The MOVES2010 rates for 1998 HHD trucks are lower in the high-speed operating modes
(33 and above) compared to the Houston Drayage data (Figure 2-11), but only a single truck is
represented in the comparison. As expected, the drayage fleet typically did not reach the high-
speed/high-power operating modes (operating modes 28-30 and 38-40) during normal operation.
36
-------
Figure 2-10. Comparison of Means: MOVES2010 emission rates vs. Houston Drayage Data (n=8) for model
years 1991-1997 HHD trucks. Error bars represent the 95% confidence interval of the mean.
6000
5000
0)
4-1
£
O
£
0)
3000
2000
1000
AMOVES • Drayage
«'*
.
*}
* * »
,
{
0 1 1112 13 14 15 16 2122 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
Figure 2-11. Comparison of Means: MOVES2010 emission rates vs. Houston Drayage Data (n=l) for model year
1998 HHD trucks. Error bars represent the 95% confidence interval of the mean.
5000
AMOVES • Drayage
4500
4000
4. 3500
00
CD 3000
4-1
5
x 2500
O
Z 2000
c
S 1500
1000
500
n
4
^
^
f •
* J
J* " J
. * *
ft * f 5
j
•«
1 1 ^
; _
0 1 111213141516212223242527282930333537383940
Operating Mode
37
-------
Figure 2-12. Comparison of Means: MOVES2010 emission rates vs. Houston Drayage Data (n=10) for model
year 1999-2002 HHD trucks. Error bars represent the 95% confidence interval of the mean.
3500
3000
4. 2500
S 200°
^
X
g 1500
c
m
S 1000
500
n
AMOVES • Drayage ^
•
A
<
T
• 1
* 1
> T
¥
1 i
I
\
^
*ft *
0 11112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
In Figure 2-13 and Figure 2-14, MOVES2010 rates for model years 2003-2006 are compared to
results from the Houston Drayage and FIDIU datasets, respectively. Although MOVES' rates for
middle and high speed operating modes are lower, they are within the 95% confidence intervals of
the mean of Houston Drayage data in Figure 2-13. When compared to HDIU data in Figure 2-14,
MOVES2010 is generally within the variability of the data except for the low speed operating
modes. Although both comparisons showed that MOVES2010 rates were slightly lower, since the
rates in MOVES2010 for model years 2003-2006 were based on a larger sample of actual test data
from ROVER and Consent Decree Testing (n=91), no change was made to the rates in
MOVES2014.
38
-------
Figure 2-13. Comparison of Means: MOVES2010 emission rates vs. Houston Drayage Data (n=8) for model
year 2003-2006 HHD trucks. Error bars represent the 95% confidence interval of the mean.
3UUU
2500
[So 2000
0)
ro
x 1500
O
i 1000
0)
500
n .
• MOVES • Drayage
..,"'1
- i
[ili
I
I <
> .
i <
>
• *
•
"
I
I <
I
>
•
.
0 11112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
Figure 2-14. Comparison of Means: MOVES2010 rates vs. HDIU (n=40) for model years 2003-2006 HHD
trucks. Error bars represent the 95% confidence interval of the mean.
1800
1600
1400
^ 1200
| 1000
O 800
£ 600
0)
S 400
200
0
• MOVES BHDIUD
' J-
•«!* ,'* '
i i
II •
0 1 1112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
39
-------
In MOVES2010, the rates for model years 2007-2009 were forecast from those for model year
group 2003-2006 based on the ratio of emissions standards for these two model-year groups, as
described in Section 0. This approach was adopted in view of the fact that neither of the two
datasets used at the time (ROVER and Consent Decree) included data for trucks in this model-year
group. However, for MOVES2014, the availability of the HDIU dataset makes it possible to
compare the projected rates to a set of relevant measurements. Figure 2-15 shows that the
MOVES2010 rates are lower than the corresponding means from the HDIU data and are generally
outside the uncertainty of these means across operating modes. Because the rates for this model
year group met the two conditions described above in Section 2.1.1.8, this subset of rates was
updated in MOVES2014 on the basis of HDIU data.
Figure 2-15. Comparison of Means: MOVES rates vs. HDIU (n=68) for model years 2007-2009 HHD trucks.
Error bars represent the 95% confidence interval of the mean.
900
800
,..700
.c
"So 600
0)
15 500 -
^
X
0 400 -
z
ra 300 -
0)
S 200 -
100
0 -
AMOVES BHDIUD
1-
1
* ,
. .i*1' i!
• f •
4
*
i
i 1
<
•
4
> 1
«
1 •
>
*
1
' <
<
i
i
4
*
•
*
•
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
2.1.1.8.2 Medium-Heavy Duty Trucks
Figure 2-16 and Figure 2-17 show that MOVES2010 rates for MHD trucks compare well with the
HDIU data for both model years groups 2003-2006 and 2007-2009. The data is generally scarce in
high-power operation modes, and thus, no 95% confidence interval was calculated. The
comparisons validated the MOVES2010 rates for MHD trucks, and no change was made in
MOVES2014.
40
-------
Figure 2-16. Comparison of Means: MOVES2010 rates vs. HDIU (n=25) for model years 2003-2006 MHD
trucks. Error bars represent the 95% confidence interval of the mean.
1DUU
1400
•C- 1200
•— 1000
to
x 800
0
c 600
to
0)
^ 400
200
n .
AMOVES BHDIUD •
j 4
T I
^ f '
• * *
^
•
<
T
4 1
T f
}*
"4
^
• •
• •
> • T
1 1
. i
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
Figure 2-17. Comparison of Means: MOVES2010 rates vs. HDIU (n=71) for model years 2007-2009 MHD
trucks. Error bars represent the 95% confidence interval of the mean.
800
AMOVES BHDIUD
700
•^r 600 -
.c
^^
— 500
0)
ro
x 400 -
O
1 300 -
to
0)
^ 200 -
100
n .
"
,1*1
_
i
B
• * f ~
*
i '
*
u
v
^
9
•
- t '
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
41
-------
2.1.1.8.3 Light-Heavy Duty Trucks
The comparisons of the MOVES2010 LHD34 rates to the corresponding LHD34 HDIU trucks for
model years 2003-2006 (Figure 2-18) and 2007-2009 (Figure 2-19) show that MOVES2010 rates
compare well with the HDIU data. Therefore, MOVES2010 rates for these model year groups
were retained in MOVES2014.
Figure 2-18. Comparison of Means: MOVES2010 rates vs. HDIU (n=15) for model years 2003-2006 LHD34
trucks. Error bars represent the 95% confidence interval of the mean.
1600
1400
TT1200
— 1000
-------
Figure 2-19. Comparison of Means: MOVES2010 rates vs. HDIU (n=24) for model years 2007-2009 LHD34
trucks. Error bars represent the 95% confidence interval of the mean.
800
700
— 600 -
AMOVES BHDIUD
— 500
to
* 400 -
0
1 300 -
to
0)
^ 200 -
100
n
•
t*
A -
<
T 1
i,,!'1
•
^ ' ' j
B _
1 1 II T
I J^ *'
9
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
2.1.2 Paniculate Matter (PM)
In this section, particulate matter refers to particles emitted from heavy-duty engines which have a
mean diameter less than 2.5 microns, known as PIVh.s. Conventional diesel particulate matter is
primarily carbonaceous, measured as elemental carbon (EC) and organic carbon (OC). Particles
also contain a complex mixture of metals, elements, and other ions, including sulfate.
Measurements of total PIVh.s emission rates are typically filter-based, including the mass of all the
chemical components in the particle-phase. As described above for NOx, the heavy-duty diesel PM
emission rates in MOVES are a function of: (1) source bin, (2) operating mode, and (3) age group.
We classified heavy-duty PM emission data into the following model year groups for purposes of
emission rate development. These groups are generally based on the introduction of emissions
standards for heavy-duty diesel engines. They also serve as a surrogate for continually advancing
emission control technology on heavy-duty engines. Table 2-9 shows the model year group ranges
and the applicable brake-specific emissions standards.
43
-------
Table 2-9. Model year groups used for analysis based on the PM emissions standard
Model Year Group Range
1960-1987
1988-1990
1991-1993
1994-1997
1998-2006
2007+
PM Standard [g/bhp-hr]
No transient cycle standard
0.60
0.25
0.10
0.10
0.01
2.1.2.1 Data Sources
All of the data used to develop the MOVES2014 PIVh.s emission rates was generated in the CRC E-
55/59 research program33v. The following description by Dr. Ying Hsu and Maureen Mullen of E.
H. Pechan, in the "Compilation of Diesel Emissions Speciation Data - Final Report" provides a
good summary of the program:
The objective of the CRC E55/59 test program was to improve the understanding of the
California heavy-duty vehicle emissions inventory by obtaining emissions from a
representative vehicle fleet, and to include unregulated emissions measured for a subset of
the tested fleet. The sponsors of this project include CARB, EPA, Engine Manufacturers
Association, DOE/NREL, and SCAQMD. The project consisted of four segments,
designated as Phases 1, 1.5, 2, and 3. Seventy-five vehicles were recruited in total for the
program, and recruitment covered the model year range of 1974 through 2004. The number
and types of vehicles tested in each phase are as follows:
• Phase 1: 25 heavy heavy-duty (HHD) diesel trucks
• Phase 1.5: 13 HHD diesel trucks
• Phase 2: 10 HHD diesel trucks, 7 medium heavy-duty (MHD) diesel trucks,
2 MHD gasoline trucks
• Phase 3: 9 MHD diesel, 8 HHD diesel, and 2 MHD gasoline
The vehicles tested in this study were procured in the Los Angeles area, based on model
years specified by the sponsors and by engine types determined from a survey. WVU
measured regulated emissions data from these vehicles and gathered emissions samples.
Emission samples from a subset of the vehicles were analyzed by Desert Research Institute
for chemical species detail. The California Trucking Association assisted in the selection of
v The MOVES2014 PM2s emission rates were originally developed in MOVES2010, and are largely unchanged for
heavy-duty diesel vehicles.
44
-------
vehicles to be included in this study. Speciation data were obtained from a total of nine
different vehicles. Emissions were measured using WVU's Transportable Heavy-Duty
Vehicle Emissions Testing Laboratory. The laboratory employed a chassis dynamometer,
with flywheels and eddy-current power absorbers, a full-scale dilution tunnel, heated probes
and sample lines and research grade gas analyzers. PM was measured gravimetrically.
Additional sampling ports on the dilution tunnel supplied dilute exhaust for capturing
unregulated species and PM size fractions. Background data for gaseous emissions were
gathered for each vehicle test and separate tests were performed to capture background
samples of PM and unregulated species. In addition, a sample of the vehicles received
Tapered Element Oscillating Microbalance (TEOM) measurement of real time paniculate
emissions.
The HHDDTs were tested under unladen, 56,000 Ib, and 30,000 Ib truck load weights. The
driving cycles used for the HHDDT testing included:
• AC50/80;
•HDDS;
• Five modes of an HHDDT test schedule proposed by CARB: Idle, Creep, Transient, Cruise,
and HHDDT_S (a high speed cruise mode of shortened duration)
• The U.S. EPA transient test
The proposed CARB HHDDT test cycle is based on California truck activity data, and was
developed to improve the accuracy of emissions inventories. It should be noted that the
transient portion of this proposed CARB test schedule is similar but not the same as the
EPA certification transient test.
The tables below provide a greater detail on the data used in the analysis. Vehicles counts are
provided by number of vehicles, number of tests, model year group and regulatory class (46 =
MHD, 47=HHD) in Table 2-10.
45
-------
Table 2-10. Vehicle and test counts by regulatory class and model year group
Regulatory Class
MHD
HHD
Model Year
Group
1960 - 1987
1988 - 1990
1991 - 1993
1994 - 1997
1998 - 2006
2007 +
1960 - 1987
1988 - 1990
1991 - 1993
1994 - 1997
1998 - 2006
2007 +
Number of tests
82
39
22
39
43
0
31
7
14
22
171
0
Number of vehicles
7
5
2
4
5
0
6
2
2
5
18
0
Counts of tests are provided by test cycle in Table 2-11.
Table 2-11. Vehicle test counts by test cycle
Test Cycle
CARB-T
CARB-R
CARB-I
UDDS_W
AC5080
CARB-C
CARBCL
MHDTCS
MHDTLO
MHDTHI
MHDTCR
Number of tests
71
66
42
65
42
24
34
63
23
24
29
2.1.2.2 Analysis
The PM2.5 data from CRC E55/59 was analyzed in several steps to obtain MOVES PM2.5 emission
rates. First, STP operating mode bins were calculated from the chassis dynamometer data. Second,
continuous PIVh.s data measured by the TEOM was normalized to gravimetric PM filters. Third,
MOVES PM2.5 emission rates were calculated for the STP operating mode bins for the available
regulatory class and model year combinations. These steps are explained in detail in the following
subsections.
46
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2.1.2.2.1 Calculate STP in 1-hz data
For each second of operation on the chassis-dynamometer the instantaneous scaled tractive power
(STPt) was calculated using Equation 1-2, and then subsequently classified to one of the 23
operating modes defined above in Table 1-4.
The values of coefficients A, B, and C are the road load coefficients pertaining to the heavy-duty
vehicles34 as determined through previous analyses for EPA's Physical Emission Rate Estimator
(PERE). The chassis dynamometer cycles used in E55/59 include the impact of speed, acceleration,
and loaded weight on the vehicle load, but grade effects are not included and the grade value is set
equal to zero in Equation 1-2.
Note that this approach differs from that the NOx emission rates analysis described in Section
2.1.1.2, since the particulate data was collected on a chassis dynamometer from vehicles lacking
electronic control units (ECU). We have not formally compared the results of the two methods of
calculating STP. However, on average, we did find the operating-mode distributions to be similar
between the two calculation methods for a given vehicle type. For example, we found that the
maximum STP in each speed range was approximately the same.
2.1.2.2.2 Compute Normalized TEOM Readings
The TEOM readings were obtained for a subset of tests in the E-55/59 test program. Only 29
vehicles had a full complement of 1-hz TEOM measurements. However, the continuous particulate
values were modeled for the remaining vehicles by West Virginia University, and results were
provided to EPA. In the end, a total of 56 vehicles (out of a total of 75) and 470 tests were used in
the analysis out of a possible 75 vehicles. Vehicles and tests were excluded if the total TEOM
PM2.5 reading was negative or zero, or if corresponding full-cycle filter masses were not available.
Table 2-12 provides vehicle and test counts by vehicle class and model year. The HDD Class 6
and Class 7 trucks were combined in the table because there were only seven HDD Class 6
vehicles in the study.
47
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Table 2-12. Vehicle and test counts by heavy-duty class and model year
Model Year
1969
1974
1975
1978
1982
1983
1985
1986
1989
1990
1992
1993
1994
1995
1998
1999
2000
2001
2004
2005
HDD Class 6/7(MHD)
No. Vehicles
-
1
-
-
1
1
1
1
2
1
1
1
1
2
2
-
2
1
-
-
No. Tests
-
10
-
-
5
10
28
3
11
12
11
11
9
24
20
-
18
5
-
-
HDD Class 8 (HHD)
No. Vehicles
1
-
2
1
-
1
1
1
1
1
1
1
3
o
J
3
3
5
2
4
1
No. Tests
6
-
10
5
-
6
10
4
4
3
11
3
15
13
28
43
44
21
29
6
Since the development of MOVES emission rates is cycle independent, all available cycles/tests
which met the above requirements were utilized. As a result, 488,881 seconds of TEOM data were
used. The process required that each individual second by second TEOM rate be normalized to its
corresponding full-cycle filter mass, available for each combination of vehicle and test. This step
was necessary because individual TEOM measurements are highly uncertain and vary widely in
terms of magnitude (extreme positive and negative absolute readings can occur). The equation
below shows the normalization process for a particular one second TEOM measurement.
PM
PM,
filter, j
normalized,!,/ /
TEOM,/,!
Equation 2-16
TEOM, i
Where
/' = an individual 1-Hz measurement (g/sec),
j = an individual test on an individual vehicle,
PMiEou,j,i = an individual TEOM measurement on vehicley at second /',
48
-------
j = the Total PIVh.s filter mass on j,
aiized.ij = an estimated continuous emission result (PIVh.s) emission result on vehicley
at second /'.
Kinsey et al. (2006)35 demonstrated that time-integrated TEOM measurements compare well with
gravimetric filter measurements of diesel-generated particulate matter.
2.1.2.2.3 Compute Average Normalized TEOM measures by MOVES Bin
After normalization, the data were classified by regulatory class, model-year group and the 23
operating modes. Mean average results, sample sizes and standard deviation statistics for PIVh.s
emission values were computed in terms of g/hour for each mode. In cases where the vehicle and
TEOM samples were sufficient for a given mode (based on the number of points within each
operating mode bin), these mean values were adopted as the MOVES emission rates for total
PM2.5. In cases of insufficient data for particular modes, a regression technique was utilized to
impute missing values.
2.1.2.3 Hole filling and Forecasting
2.1.2.3.1 Missing operating modes
Detailed in Appendix D, a log-linear regression was performed on the existing PM data against
STP to fill in emission rates for missing operating mode bins. Similar to the NOx rates, emission
rates were extrapolated for the highest STP operating modes.
2.1.2.3.2 Other Regulatory Classes
The TEOM data was only available in quantity for MHD and HHD classes. There were no data
available for the LHD or bus classes. The Urban Bus (regulatory class 48) emission rates were
proportioned to HHD rates according to differences in the PM standards.
Because the certification standards in terms of brake horsepower-hour (bhp-hr) are the same for all
of the heavy-duty engines, the emission rate of LHD<=14K and LHD45 is assumed to be equivalent
to the MHD emission rate.vl
The emission rates of LHD<= 10K (regClassID 40) need to be compatible with VSP-based operating
modes as discussed in Section 1.1. In Draft MOVES2009, heavy-duty emission rates were VSP-
based.36 The PM emission rates for LHD<=10K in MOVES2014 are equivalent to the PM emission
rates for LHD2b3 from MOVES2009. The LHD2b3 emission rates in MOVES2009 were derived by
applying a factor to the VSP-based MHD PM emission factors derived from the E55/59 TEOM data.
A factor of 0.46 was obtained from the MOBILE6.2 heavy-duty conversion factors37, which accounts
for the lower power requirements per mile (bhp-hr/mile) of light-heavy duty trucks versus MHD
V1 In MOVES2010, the LHD45 and LHD2b3 were both estimated based on VSP-based emission rates, using a similar
methodology as the current LHD<=10K emission rates. In MOVES2014, we replaced the LHD<=14K and LHD45
emission rates with MHD emission rates because they now use the same mass scaling factor.
49
-------
trucks. The equation used to derive the PM emission rates for regulatory class LHD<=10K
(RegClassID 40) is shown below:
LHD < WK emission rate = 0.46 x MHD (VSP_based~)emission rate Equation 2-17
Where the MHD VSP-based emission rate is obtained from MOVES2009.36
Urban Bus (RegClassID 48) emission rates are assumed to be either the same as the HHD emission
rates, or for some selected model year groups, to be a ratio of the EPA certification standards. Table
2-13 displays the model years for which the Urban Bus regulatory class has different PM emission
standards from other heavy-duty compression-ignition engines. For these model years (1991-2006),
the Urban Bus PM emission standards are equal to the HHD emission rates multiplied by the ratio in
emission standards. In addition, the Urban Bus emissions have different emission deterioration
effects as discussed in Appendix B.6.
Table 2-13. Urban Bus PM standards in comparison to heavy-duty highway compression engine standards
Engine
Model Year
1991-19933
1994-1995
1996-2006
Heavy-duty Highway
Compression-Ignition
Engines
0.25
0.1
0.1
Urban Buses
0.1
0.07
0.05
Ratio in
standards
0.4
0.7
0.5
aThe 0. 1 g/bhp-hr US EPA Urban Bus standard began with model year 1 993 . In
California, the 0.1 g/bhp-hr Urban Bus standard began in 1991. MOVES assumes all
Urban Buses met the stricter CA standard beginning in 1 99 1 .
2.7.2.3.3
Model year 2007 and later trucks (with dieselpaniculate filters)
EPA heavy-duty diesel emission regulations were made considerably more stringent for total PM2.5
emissions starting in model year 2007. Ignoring phase-ins and banking and trading issues, the basic
emission standard fell from 0.1 g/bhp-hr to 0.01 g/bhp-hr. This increase by a factor often in the
level of regulatory stringency required the use of particulate trap systems on heavy-duty diesels. As
a result, we expect the emission performance of diesel vehicles has changed dramatically.
At the time of analysis, no continuous PM emissions data were available for analysis on the 2007
and later model-year vehicles. However, heavy and medium heavy-duty diesel PM2.5 data are
available from the EPA engine certification program on model years 2003 through 2007. These
data provide a snapshot of new engine emission performance before and after the introduction of
parti culate trap technology in 2007. The existence of these data makes it possible to determine the
relative improvement in PM emissions from model years 2003 through 2006 to model year 2007.
This same relative improvement can then be applied to the existing, modal based, 1998-2006 model
year PM emission ratesto estimate in-use rates for 2007 and later vehicles.
An analysis of the available certification data is shown in Table 2-14 below. It suggests that the
actual ratio of improvement due to the paniculate trap is reduction of a factor of 27.7. This factor
is considerably higher than the relative change in the certification standards, i.e., a factor of 10.
50
-------
The reason for the difference is that the new trap equipped vehicles certify at emission levels which
are much lower than the standard and thus create a much larger 'margin of safety' than previous
technologies could achieve.
As an additional check on the effectiveness of the trap technology, EPA conducted some limited in-
house testing of a Dodge Ram truck, and carefully reviewed the test results from the CRC
Advanced Collaborative Emission Study (ACES) phase-one program, designed to characterize
emissions from diesel engines meeting 2007 standards. The limited results from these studies
demonstrate that the effectiveness of working paniculate traps is very high. The interested reader
can review the ACES report.38
Table 2-14. The average certification results for model years 2003-2007. Average ratio from MYs 2003-2006 to
MY 2007 is 27.7
Certification Model
Year
2003
2004
2005
2006
2007
Mean
(g/bhp-hr)
0.08369
0.08783
0.08543
0.08530
0.00308
St. Dev.
0.01385
0.01301
0.01440
0.01374
0.00228
n
91
59
60
60
21
2.1.2.3.4 Tampering and Mai-maintenance
The MOVES model contains assumptions for the frequency and emissions effect of tampering and
mal-maintenance on heavy-duty diesel trucks and buses. The assumption of tampering and mal-
maintenance (T&M) of heavy-duty diesel vehicles is a departure from the MOBILE6.2 model
which assumed such vehicles operated from build to final scrappage at a design emission level
which was lower than the prevailing EPA emission standards. Both long term anecdotal data
sources and more comprehensive studies now suggest that the assumption of no natural
deterioration and/or no deliberate tampering of emission control components in the heavy-duty
diesel fleet was likely an unrealistic assumption, particularly with the transition to emission
aftertreatment devices with the 2007/2010 standards
The primary data set was collected during a limited calendar year period, yet MOVES requires data
from a complete range of model year/age combinations. As a result, the T&M factors shown below
in Table 2-15 were used to forecast or back-cast the basic PM emission rates to predict model year
group and age group combinations not covered by the primary data set. For example, for the 1981
through 1983 model year group, the primary dataset contained data which was in either the 15 to 19
or the 20+ age groups. However, for completeness, MOVES must have emission rates for these
model years for ageGroups 0-3, 4-5, 6-7, etc. As a result, unless we assume that the higher
emission rates which are were measured on the older model year vehicles have always prevailed -
even when they were young, a modeling approach such as T&M must be employed. Likewise,
more recent model years could only be tested at younger ages. The T&M methodology used in the
51
-------
MOVES analysis allows for the filling of age - model year group combinations for which no data
is available.
One criticism of the T&M approach is that it may double count the effect of T&M on the fleet
because the primary emission measurements, and base emission rates, were made on in-use
vehicles that may have had some maintenance issues during the testing period. This issue would be
most acute for the 2007 and later model year vehicles where all of the deterioration is subject to
projection. However, for this model year group of vehicles, the base emission rates start at low
levels, and represent vehicles that are virtually free from T&M.
We followed the same tampering and mal-maintenance methodology and analysis for PM as we did
forNOx, as described in Appendix B.8. The overall MOVES tampering and mal-maintenance effects
on PM emissions over the fleet's useful life are shown in Table 2-15. The value of 89 percent for
2010-2012 model years reflects the projected effect of heavy-duty on-board diagnostic
deterrence/early repair of Tampering and Mal-maintenance effects. It is an eleven percent
improvement from model years which do not have OBD (i.e., 2007-2009). The 67% value for 2013+
is driven by the assumed full-implementation of the OBD in 2013 and later trucks, which assumes a
33% decrease in tampering and mal-maintenance emission effects.
Table 2-15. Estimated increases in PM emissions attributed to tampering and mal-maintenance over the useful
life of heavy-duty vehicles
Model Year Group
Pre-1998
1998-2002
2003 - 2006
2007 - 2009
2010-2012
2013+
Percent increase in
T&M
PM due to
85
74
48
100
89
67
2.1.2.3.5 Computation of Elemental Carbon and Non-Elemental Carbon
Emission Factors
Particulate matter from conventional diesel engines is dominantly composed of elemental carbon
emissions. Elemental carbon emissions are often uses synonymously with soot and black carbon
emissions. Black carbon is important because of its negative-health effects and to its environmental
impacts as a climate forcer39. Elemental carbon from vehicle exhaust is measured with filter-based
measurements using thermal optical methods. Continuous surrogate measures of elemental carbon
can also be made with available photoacoustic instruments.
MOVES models EC emissions explicitly at the operating mode level, because of the availability of
EC emission measurements at the operating mode level, and the importance of mode in
determining the composition of PM emissions.
MOVES models Total PM2.5 emissions by vehicle operating mode using elemental carbon (EC)
and non-elemental particulate matter carbon (NonECPM), as shown in Equation 2-18.
52
-------
PM 2 5 = EC + NonECPM
Equation 2-18
The EC fractions used in MOVES for pre-2007 model year trucks (i.e. before diesel paniculate
filters (DPFs) were standard) are shown in Figure 2-20. These vary according to regulatory class
and MOVES operating mode. They typically range from 25 percent at low loads (low STP) to over
90 percent at highly loaded modes. All of the EC fractions were developed in a separate analysis
from which the Total PM2.5 emission rates were developed, and are documented in Appendix E.
The primary dataset used in the analysis came from Kweon et al. (2004) where particulate
composition and mass rate data were collected on a Cummins N14 series test engine over the
CARB eight-mode engine test cycle. The EPA PERE model and a Monte Carlo approach were
used to simulate and develop operating mode-specific EC/PM fractions. The EC and NonECPM
emission rates in the MOVES database were calculated by multiplying the Total PM2.5 emission
rates developed from E-55/59 by the EC/PM and NonECPM/PM2.s fractions developed in
Appendix E. The NonECPM fraction of PM is simply calculated as the remainder of PM2.5 that is
not EC as shown in Equation 2-19.
NonECPM EC
- = 1.0 —
PM
-2.5
PM
-2.5
Equation 2-19
Figure 2-20. Elemental Carbon fraction by operating mode for pre-DPF-equipped trucks
£ n R
m n ^
0 04
QJ u-4
n -
• • *
-*
•
•
•
• • 1
• • •
• * : • • •
•
•
9
• • • •
^
• ^
• • •
+
»HHD
• MHD
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode bin
For 2007 and later model year DPF-equipped diesel engines, we used the elemental carbon fraction
of 9.98% measured in Phase 1 of the Advanced Collaborative Emissions Study (ACES) Report40.
Diesel particulate filters preferentially reduce elemental carbon emissions, resulting in the low
percentage of elemental carbon emissions. The average EC/PM fraction is based on the 16-hour
cycle which composes several different operating cycles. Because the fraction is based upon a
53
-------
range of driving conditions, we applied the constant 9.98% EC/PM fraction across all operating
modes for the 2007+ diesel emissions rates.
The nonECPM fraction of emissions contains organic carbon (OC), sulfate, and other trace
elements and ions. MOVES uses the fuel sulfur content to adjust the sulfate emission contribution
to NonECPM as discussed in the MOVES2014 Fuel Adjustment Report41. MOVES uses speciation
profiles to estimate the composition of organic carbon, ions, and elements in NonECPM as
discussed in the MOVES2014 TOG and PM Speciation Report42.
2.1.2.4 Sample results
Figure 2-21 and Figure 2-22 show the trend of increasing PM rates with STP. As with NOX, the
highest operating modes in each speed range will rarely be attained due to the power limitations of
heavy-duty vehicles, but are included in the figures for completeness. At high speeds (greater than
50 mph; operating modes > 30), the overall PM rates are lower than the other speed ranges. For
pre-2007 model years the PM rates are dominated by EC. With the introduction of DPFs in model
year 2007, we model the large reductions in overall PM rates and the smaller relative EC
contribution to PM emissions.
Figure 2-21. Participate matter rates by operating mode representing heavy heavy-duty vehicles (model year
2002 at age 0-3 years)
160 -
140
120 -
3 100
I
•- 80 H
Z
a.
Z 60 -
40 -
20 -
EC
Non-EC
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
54
-------
Figure 2-22. Participate matter rates by operating mode for heavy heavy-duty vehicles (model year 2007 at age
0-3 years)
I
1
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
Figure 2-23 shows an example of how tampering and mal-maintenance estimates increase PM with
age. The EC/PM proportion does not change by age, but the overall rate increases and levels off
after the end of useful life. This figure shows the age effect for MHD. The rate at which emissions
increase toward their maximum depends on regulatory class.
55
-------
Figure 2-23. Participate matter rates by age group for medium heavy-duty vehicles (model year 2002, operating
mode 24)
0-3 4-5 6-7 8-9 10-14
Age group [years]
20+
Figure 2-24. shows the effect of model year on emission rates. Emissions generally decrease with
new PM standards. The EC fraction stays constant until model year 2007, when it is reduced to
less than -10% due the implementation of diesel particle filters. The overall PM level is
substantially lower starting in model year 2007. The emission rates shown here for earlier model
years are an extrapolation of the T&M analysis since young-age engines from early model years
could not be tested in the E-55 program.
56
-------
Figure 2-24. Participate matter rates for heavy heavy-duty vehicles by model year group (age 0-3 years,
operating mode 24)
EC
Non-EC
1960-1987 198S-1990 1991-1993 1994-1997 199S-2006 2007-2009 2010-2012 2013-2050
Model year group
57
-------
2.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO)
Diesel engines account for a substantial portion of the mobile source HC and CO emission
inventories. Recent regulations on non-methane hydrocarbons (NMHC) (sometimes in conjunction
with NOx) combined with the common use of diesel oxidation catalysts will yield reductions in
both HC and CO emissions from heavy-duty diesel engines. As a result, data collection efforts do
not focus on HC or CO from heavy-duty engines. In this report, hydrocarbons are sometimes
referred to as total hydrocarbons (THC).
We used certification levels combined with emissions standards to develop appropriate model year
groups. Since standards did not change frequently in the past for either HC or CO, we created
fewer model year groups than we did from NOx and PM. The HC/CO model year groups are:
• 1960-1989
• 1990-2006
• 2007+
2.1.3.1 Data Sources
The heavy-duty diesel HC and CO emission rate development followed a methodology that
resembles the light-duty methodology, where emission rates were calculated from 1-hz data
produced from chassis dynamometer testing. Data sources were all heavy-duty chassis test
programs:
1. CRC E-55/5933: Mentioned earlier, this program represents the largest volume of heavy-
duty emissions data collected from chassis dynamometer tests. All tests were used, not just
those using the TEOM. Overall, 75 trucks were tested on a variety of drive cycles. Model
years ranged from 1969 to 2005, with testing conducted by West Virginia University from
2001 to 2005.
2. Northern Front Range Air Quality Study (NFRAQS)43: This study was performed by
the Colorado Institute for Fuels and High-Altitude Engine Research in 1997. Twenty-one
HD diesel vehicles from model years 1981 to 1995 selected to be representative of the in-
use fleet in the Northern Front Range of Colorado were tested over three different transient
drive cycles.
3 New York Department of Environmental Conservation (NYSDEC)44: NYSDEC
sponsored this study to investigate the nature and extent of heavy-duty diesel vehicle
emissions in the New York Metropolitan Area. West Virginia University tested 25 heavy-
heavy and 12 medium-heavy duty diesel trucks under transient and steady-state drive
cycles.
4. West Virginia University: Additional historical data collected on chassis dynamometers
by WVU is available in the EPA Mobile Source Observation Database.
The on-road data used for the NOx analysis was not used since HC and CO were not collected in
the MEMS program, and the ROVER program used the less accurate non-dispersive infrared
(NDIR) technology instead of flame-ionization detection (FID) to measure HC. To keep HC and
CO data sources consistent, we used chassis test programs exclusively for the analysis of these two
58
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pollutants. Time-series alignment was performed using a method similar to that used for light-duty
chassis test data. The numbers of vehicles in the data sets are shown in Table 2-16.
Table 2-16. Numbers of vehicles by model year group, regulatory class, and age group
Model year group
1960-2002
2003-2006
Regulatory class
HHD
MHD
Bus
LHD45
LHD2b3
HHD
Age group
0-3
58
9
26
2
6
6
4-5
19
6
6-7
16
5
8-9
9
4
1
1
10-14
16
12
3
15-19
6
15
20+
7
6
2.1.3.2 Analysis
As for PM emission rates, for each second of operation on the chassis-dynamometer the
instantaneous scaled tractive power (STPt) was calculated using Equation 1-2, and then
subsequently classified to one of the 23 operating modes defined in Table 1-4. We used the same
track-load coefficients, A, B, and C pertaining to heavy-duty vehicles4 that were used in the PM
analysis.
Using a method similar to that used in the NOx and PM analysis, we averaged emissions by vehicle
and operating mode. We then averaged across all vehicles by model year group, age group, and
operating mode. Estimates of uncertainty for each mean rate were calculated using the same
equations and methods described in 2.1.1.3.2 Instead of using our results to directly populate all
the emission rates, we directly populated only the age group that was most prevalent in each
regulatory class and model year group combination. These age groups are shown in Table 2-17.
We used the MHD to represent the LHD45 and LHD<=14K emission rates.vii
V11 MOVES2010 had LHD45 and LHD2b3 emission rates estimated from the data with a fixed mass factor of 2.06. In
MOVES2014, we applied the MHD emission rates to the LHD45 and LHD<=14K, so they would have emission rates
based on the fixed mass factor of 17.1.
59
-------
Table 2-17. Age groups used directly in MOVES emission rate inputs for each regulatory class and model year
group present in the data
Regulatory class
HHD
HHD
MHD
BUS
LHD <= 10K
Model year group
1960-2002
2003-2006
1960-2002
1960-2002
1960-2002
Age group
0-3
0-3
15-19
0-3
0-3
We then applied tampering and mal-maintenance effects through that age point, either lowering
emissions for younger ages or raising them for older ages, using the methodology described in
Appendix B.9. We applied the same tampering and mal-maintenance effects for CO as HC, which
are shown in Table 2-18.
Table 2-18. Tampering and mal-maintenance effects for HC and CO over the useful life of trucks
Model years
Pre-2003
2003 - 2006
2007 - 2009
2010-2012
2013+
Increase in HC and CO
Emissions (%)
300
150
150
29
22
We multiplied these increases by the T&M adjustment factors from the zero-mile emissions level
due to deterioration in Table B-2 in Appendix B.6 to get the emissions by age group. While
LHD<=14K and LHD45 and MHD vehicles share the same fully deteriorated emission rates for
HC and CO, they deteriorate differently as they age. Table B-2 estimates the degree of T&M that
occurs by age by using the warranty and full useful life requirements for each heavy-duty
regulatory class with the average mileage accumulation rates.
We did not analyze emissions data on 2007 and later heavy-duty trucks. With the increased use of
diesel oxidation catalysts (DOCs) in conjunction with DPFs, we assumed an 80 percent reduction
in zero-mile emission rates for both HC and CO starting with model year 2007. The derivation of
the T&M effects for 2007 and later trucks presented in Table 2-19 are discussed in Appendix B.9.
2.1.3.3 Sample results
The charts in this sub-section show examples of the emission rates that are derived from the
analysis described above. Not all rates are shown; the intent is to illustrate the most common
trends and hole-filling results. For simplicity, the light-heavy duty regulatory classes are not
shown, but since the medium-heavy data were used for much of the light-heavy duty emission rate
development, the light-heavy duty rates follow similar trends. Uncertainties were calculated as for
NOx.
In Figure 2-25 and Figure 2-26, we see that HC and CO mean emission rates increase with STP,
though there is much higher uncertainty than for the NOx rates. This pattern could be due to the
60
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smaller data set or may truly reflect a less direct correlation between HC, CO and STP. In these
figures, the data for HHD and bus classes were combined to generate one set of rates for HHD and
buses.
Figure 2-25. THC emission rates [g/hr] by operating mode for model year 2002 and age group 0-3. Error bars
represent the 95% confidence interval of the mean.
100 -,
90 -
80 -
u
70 -
60 -
50 -
5 40
01
30 -
20 -
10 -
MHD
HMD/Bus
i i «
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
61
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Figure 2-26. CO emission rates [g/hr] by operating mode for model year 2002 and age group 0-3. Error bars
represent the 95% confidence interval of the mean.
600 -,
500 -
T 400
0)
+•»
E
8
300 -
ro
01
200 -
100 -
• MHD
• HMD/Bus
!{::
* *
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
Figure 2-27 and Figure 2-28 show HC and CO emission rates by age group. Due to our projections
of T&M effects, there are large increases as a function of age. Additional data collection would be
valuable to determine if real-world deterioration effects are consistent with those in the model,
especially in model years where diesel oxidation catalysts are most prevalent (2007 and later).
62
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Figure 2-27. THC emission rates [g/hr] by age group for model year 2002 and operating mode 24. Error bars
represent the 95% confidence interval of the mean.
100 n
90 -
80 -
-. 70
3§ 60
Ol
E 50
u
E 40
as
01
^ 30
20 -
10 -
n -
T
i
I
n "
11
1
,,
-.
11 IHHD
*MHD
..
A Bus
0-3 4-5 6-7 8-9 10-14
Age group [years]
15-19
20+
Figure 2-28. CO emission rates [g/hr] by age group for model year 2002 and operating mode 24. Error bars
represent the 95% confidence interval of the mean.
600 -,
500 -
™ 400
i
0)
+•*
E
8
300 -
ra
01
200 -
100 -
• HMD
• MHD
A Bus
0-3
4-5 6-7 8-9
10-14 15-19
Age group [years]
20+
Figure 2-29 and Figure 2-30 show sample HC and CO emission rates by model year group. The
two earlier model year groups are relatively similar. The rates in the 2007-2050 model year group
reflect the use of diesel oxidation catalysts. Due to the sparseness of the data and the fact that HC
63
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and CO emissions do not correlate as well with STP (or power) as NOx and PM do, uncertainties
are much greater.
We only analyzed data from vehicles within the HHD regulatory class within model year group
2003-2006. The zero-mile emission rates derived from for HHD regulatory class are used as the
basis for the zero-mile emission rates for the other HD regulatory classes. As mentioned earlier, the
2007 and later emission rates are derived by reducing the CO and HC emissions in 2003-2006 by
80% and applying the model-year and regulatory class specific T&M adjustment factors.
Figure 2-29. THC emission rates by model year group for operating mode 24 and age group 0-3. Error bars
represent the 95% confidence interval of the mean.
30 -,
25 -
;= 20
01
2 15
u
JS 10
5 -
• MHD
• HMD/Bus
1960-2002
2003-2006
Model year group
2007-2050
64
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Figure 2-30. CO emission rates by model year group for operating mode 24 and age group 0-3. Error bars
represent the 95% confidence interval of the mean.
0)
+•*
E
8
200 -|
180 -
160 -
140 -
120 -
100 -
80 -
60 -
40 -
20 -
n .
i
i
• MHD
• HMD/Bus
i
1
1960-2002
2003-2006
Model year group
2007-2050
2.1.4 Energy
2.1 A.I LHD< =10k Energy Rates for Model Years 1960-2013
In MOVES2014, the energy rates for LHD<=10k for pre-2007 diesel energy rates are unchanged
from the LHD2b3 regulatory class rates in MOVES2010a. In MOVES2010a, the energy rates for
this regulatory class, along with the light-duty regulatory classes, were consolidated across weight
classes and engine technologies, as discussed in the MOVES2010 energy updates report.45 As
explained in the 2010 energy update report, the approach for modeling energy emission rates
changed significantly in MOVES2010a. Earlier MOVES versions significantly more detail in the
energy rates, which varied by engine technologies, engine size and more refined loaded weight
classes. For MOVES2010a, the energy rates were simplified to be single energy rates for regulatory
class, fuel type and model year combinations. This was done by aggregating the MOVES2010
energy rates using weighted across engine size, engine technology, and vehicle weight according to
the default population in the MOVES2010 sample vehicle population table. Because this approach
uses highly detailed data, coupled with information on the vehicle fleet that varies for each model
year, variability was introduced into the aggregated energy rates used in MOVES2010a and now in
MOVES2014. The emission rates for these model years are shown in Figure 2-31, although not
entirely shown, the emission rates from 1960-1983 are constant.
2.1.4.2 LHD< =10k Energy Rates for Model Years 2014-2050
For model years 2014 and later, lower energy consumption rates for LHD<=10k vehicles are
expected due to the Phase 1 Medium and Heavy Duty Greenhouse Gas Rule, as discussed in more
detail in Section 2.1.4.4. The CCh emission reductions for diesel 2b-3 trucks in Table 2-20 were
applied equally to the 2013 model year energy consumption rates in each running operating mode
bins to derive 2014 and later energy consumption rates. Figure 2-31 displays the average energy
65
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consumption (across all running operating modes) for model years 1970 through 2030. The energy
rates are constant going forward from 2018 to 2050.
Figure 2-31. Average Energy Consumption Rates for LHD<=10K diesel vehicles across all running operating
modes
1.2e+07 -
regClassName
LHD<= 10k
O.Oe+00-
1970 1980 1990 2000 2010
modelYearlD
2020 2030
2.1.4.3 LHD< =14k, LHD45, MHD, Urban Bus, andHHD Energy Rates for Model
Years 1960-2013
The data used to develop NOX rates was also used to develop running-exhaust energy rates for most
of the heavy-duty source types. The energy rates were based on the same data, STP structure and
calculation steps as in the NOx analysis; however, unlike NOX, we did not classify the energy rates
by model year or by age, because neither variable had a significant impact on energy rates or CCh.
As for previous versions of MOVES, CCh emissions were used as the basis for calculating energy
rates. To calculate energy rates [kJ/hour] from CCh emissions, we used a heating value (HV) of
138,451 kJ/gallon and CCh fuel-specific emission factor (fcoi) of 10,180 g/gallon46 for diesel fuel,
using Equation 2-20.
_ HV
r = r
'energy ' CO2
Equation 2-20
J CO,
66
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Figure 2-32. Diesel running exhaust energy rates for LHD<=14K, LHD45 , MHD, HHD, and Urban Buses for
1960-2013 model years. Error bars represent the 95% confidence interval of the mean.
V)
c
O
6 -i
5 -
4 -
01
4-»
as
SS3 -
8 2
1 -
0 I 1112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
The energy rates for these heavy-duty diesel vehicle classes are shown in Figure 2-32. Compared to
other emissions, the uncertainties in the energy rates are smaller in part because there is no
classification by age, model year, or regulatory class. Thus, the number of vehicles used to
determine each rate is larger, providing for a greater certainty of the mean energy rate.
2.1.4.4 LHD< =14K, LHD45, MHD, Urban Bus, and HHD Energy Rates for Model
Years 2014-2050
The energy rates are revised for 2014 and later model years, to reflect the impact of the 2014
Medium and Heavy Duty Greenhouse Gas Rule.47 The medium and heavy duty greenhouse gas
program begins with 2014 model year and increases in stringency through 2018. The standards
continue indefinitely after 2018. The program breaks the diverse truck sector into 3 distinct
categories, including
• Line haul tractors (largest heavy-duty tractors used to pull trailers, combination trucks in
MOVES)
• Heavy-duty pickups and vans (3/4 and 1 ton trucks and vans)
• Vocational trucks (buses, refuse trucks, motorhomes, single-unit trucks)
67
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The program set separate standards for engines and vehicles and ensures improvements in both. It
also sets separate standards for fuel consumption, CCh, N2O, CH4 and HFCs.vm
In MOVES, the improved fuel consumption from the HD GHG Rule is implemented in two ways.
First, the running emission rates for total energy are reduced. Second, the truck weights and road
load coefficients are updated to reflect the lower vehicle weights, lower resistance tires, and
improved aerodynamics of the vehicle chassis. The discussion of the vehicle weights and road load
coefficients is included in the Population and Activity Report.4
The revised running emission rates for total energy are drawn from the HDGHG rulemaking
modeling.47 The estimated reductions for heavy-duty diesel vehicles, including all rates are for
include new running, start and extended idle rates, are shown in Table 2-19 . These rates are for
the 2014 and later model years, and reflect the improvements expected from improved energy
efficiency in the powertrain. The reductions from the baseline were applied to the appropriate
regulatory classes and model years in the MOVES emissionRate table.
Table 2-19 Estimated reductions in diesel and gasoline engine CCh Emission rate reductions from the HD GHG
Program Phase 1
GVWR Class
HHD (8a-8b)
LHD(4-5) and
MHD (6-7)
Fuel
Diesel
Diesel
Gasoline
Model Years
2014-2016
2017+
2014-2016
2017+
2016+
COi Reduction From
Baseline
3%
6%
5%
9%
5%
Unlike the HHD standards, the HD pickup truck/van standards are evaluated in terms of grams of
CO2 per mile or gallons of fuel per 100 miles. Table 2-22 describes the estimated expected changes
in CO2 emissions due to improved engine and vehicle technologies. Since nearly all HD pickup
trucks and vans will be certified on a chassis dynamometer, the CO2 reductions for these vehicles
are not represented as engine and road load reduction components, but total vehicle CO2
reductions. MOVES2014 models the HD pickup truck/van standards by lowering the energy rates
stored in the emissionrate table. No change is made to the road-load coefficients or weights of
passenger or light-duty truck source types. The energy consumption rates for LHD<=10 and
LHD<=14K were lowered by the percentages shown in Table 2-22 for the corresponding model
years.
vm HFCs are not modeled in MOVES, and the N2O and CH4 standards are not considered forcing on emissions.
68
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Table 2-20 Estimated total vehicle CCh reductions for HD diesel and gasoline pickup trucks and vans
GVWR class
LHD 2b-3
Fuel
Gasoline
Diesel
Model years
2014
2015
2016
2017
2018+
2014
2015
2016
2017
2018+
CCh Reduction from
baseline
1.5%
2%
4%
6%
10%
2.3%
3%
6%
9%
15%
Figure 2-33 displays the average energy consumption rates for the heavy-duty diesel source types
that are modeled using Scaled Tractive Power (STP) with a fixed mass factor of 17.1. The energy
rates for all these source types are equivalent for model years 1960-2013. The reduction in the
average energy consumption rates is displayed in Figure 2-33, with separate reductions for the class
2b and 3 trucks (LHD<=14k), class 4-7 trucks (LHD45, MHD), and class 8 trucks (HHD). The
urban bus regulatory is by definition a heavy heavy-duty vehicle, and is treated the same as the
other heavy-heavy duty vehicles (HHD). For LFID<=14k the energy rates are constant from 2018
going forward, for the other categories (LHD45, MHD, Urban Bus, HHD) the energy rates are
constant going forward starting in model year 2017.
69
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Figure 2-33. Average Energy Consumption Rates for LHD<=14k (41), LHD45 (42), MHD (46), Urban Bus (48),
and HHD (47) diesel vehicles across all running operating modes.
,_ 4e+07 -
o
O
'co
tn
£ 2e+07
01
720
minutes of soak time) and a hot start FTP (< 6 minutes of soak time). More details on how start
emission rates are calculated as a function of soak time, can be found later in this section and in the
MOVES light-duty emission ratedocument8. The impact of ambient temperature on cold starts is
discussed in the Emission Adjustments MOVES report48.
2.2.1 HC, CO, andNOx
For light-duty diesel vehicles, start emissions are estimated by subtracting FTP bag 3 emissions
from FTP bag 1 emissions. Bag 3 and Bag 1 are the same dynamometer cycle, except that Bag 1
starts with a cold start, and Bag 3 begins with a hot start. A similar approach was applied for LHD
vehicles tested on the FTP and ST01 cycles, which also have separate bags containing cold and hot
start emissions over identical drive cycles. Data from 21 LFtD diesel vehicles, ranging from model
years 1988 to 2000, were analyzed. No classifications were made for model year or age due to the
limited number of vehicles. The results of this analysis for HC, CO, and NOx are shown in Table
2-21.
70
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Table 2-21. The average start emissions increases for light-heavy duty diesel vehicles (g) for regulatory class
LHD<=10K, LHD<=14K, and LHD45 (RegClassIDs 40,41, and 42). No differentiation by model year or age.
Cold start emission increase in grams
HC
0.13
CO
1.38
NOx
1.68
For HHD and MHD trucks, data were unavailable. To provide at least a minimal amount of
information, we measured emissions from a 2007 Cummins ISB on an engine dynamometer at the
EPA National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan. Among other idle
tests, we performed a cold start idle test at 1,100 RPM lasting four hours, long enough for the
engine to warm up. Essentially, the "drive cycle" we used to compare cold start and warm
emissions was the idle cycle, analogous to the FTP and ST01 cycles used for LHD vehicles.
Emissions and temperature stabilized about 25 minutes into the test. The emission rates through
time are shown in Figure 2-34. The biggest drop in emission rate through the test was with CO,
whereas there was a slight increase in NOx (implying that cold start NOx is lower than
runningNOx), and an insignificant change in HC.
Figure 2-34. Trends in the stabilization of idle emissions from a diesel engine following a cold start. Data were
collected from a 2007 Cummins ISB measured on an engine dynamometer
20 -
-1C _
10 -
b -
0.
f
DO 1.00 2.00 3.00 4.00
time [hrs]
-»— NOx
— HC
-*-CO
c
We calculated the area under each trend for the first 25 minutes and divided by 25 minutes to get
the average emission rate during the cold start idle portion. Then, we averaged the data for the
remaining portion of the test, or the warm idle portion. The difference between cold start and
warm idle is in Table 2-22. The measured HC increment is zero. The NOx increment is negative
since cold start emissions are lower than warm idle emissions.
71
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Table 2-22. Cold-start emissions increases in grams on the 2007 Cummins ISB
HC
0.0
CO
16.0
NOx
-2.3
We also considered NOx data from University of Tennessee49, which tested 24 trucks with PEMS
at different load levels during idling. Each truck was tested with a cold start going into low-RPM
idle with air-conditioning on. We integrated the emissions over the warm-up period to get the total
cold start idling emissions. We calculated the warm idling emissions by multiplying the reported
warm idling rate by the stabilization time. We used the stabilization period from our engine
dynamometer tests (25 minutes). Then we subtracted the cold start-idle emissions from the warm
idle emissions to estimate the cold start increment. We found that several trucks produced lower
NOx emissions during cold start (similar to our own work described above), and several trucks
produced higher NOx emissions during cold start. Due to these conflicting results, and the
recognition that many factors affect NOx emission during start (e.g. air-fuel ratio, injection timing,
etc), we set the default NOx cold-start increment to zero. Table 2-23 shows our final MOVES
inputs for HHD and MHD diesel start emissions increases from our 2007 MY in-house testing. Due
to the limited data, the emission rate is constant for all model years and ages.
Table 2-23. MOVES inputs for HHD and MHD diesel start emissions (grams/start) for regulatory class 46,47,
and 48. No differentiation by model year or age.
HC
0.0
CO
16.0
NOx
0.0
As discussed in the Emission Adjustments Report48, MOVES2014 applies an additive adjustment
to HC cold-start emissions to the diesel start emissions for ambient temperatures below 72 F. Thus,
despite a baseline HC start emission rate of zero, MOVES2014 estimates positive HC start
emissions from heavy-duty diesel vehicles at ambient temperatures below 72 F. No temperature
adjustments are applied to CO, PM or NOx diesel start emissions.
2.2.1.1 Incorporation of Tier-3 Standards for Light Heavy-Duty Diesel
The Tier-3 exhaust emission standards affect light heavy-duty diesel vehicles in the LHD<=10K
and LHD<=14K categories (regClassID = 40, 41, respectively). Reductions are applied to rates for
NOx only starting in MY2018 and culminating in MY2021. No reductions are applied to HC and
CO rates.
For NOx, reductions for start emissions are applied as previously described for running emissions in
Section 2.1.1.4.5. Examples of rates during the phase-in period are shown in Figure 2-35. Note
that start rates are identical for the two regulatory classes.
72
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Figure 2-35. NO*: Start emission rates in selected operating modes vs. model year for the two light-heavy duty
regulatory classes (LOGARITHMIC SCALE).
1 -
0.5-
meanBaseRate (g/hr)
P
-
-
opModelD = 101 opModelD = 102
-—- —
--— — - .
opModelD = 106 opModelD= 107
•-•— ,
-~_
opModelD= 104
-— — .
opModelD = 108
-— —
2017 2019 2021 2017 2019 2021 2017 2019 2021
-
-1
-0.5
-0.1
modelYearlD
regClassName o LHD<=10K o LHD<=14K
2.2.2 Paniculate Matter
Data for particulate matter start emissions from heavy-duty vehicles are rare. Typically, heavy-
duty vehicle emission measurements are performed on fully warmed up vehicles. These
procedures bypass the engine crank and early operating periods when the vehicle is not fully
warmed up.
Data was available from engine dynamometer testing performed on one heavy-heavy-duty diesel
engine, using the FTP cycle with particulate mass collected on filters. The engine was
manufactured in MY2004. The cycle was repeated six times, under both hot and cold start
conditions (two tests for cold start and four replicate tests for hot start). The average difference in
PM2.5 emissions (filter measurement - FTP cycle) was 0.10985 grams. The data are shown here:
Cold start FTP average = 1.9314gPM2.5
Warm start FTP average
Cold start - warm start
1.8215gPM2.5
0.1099gPM2.5
We applied this value to 1960 through 2006 model year vehicles. For 2007 and later model years,
we applied a 90 percent reduction to account for the expected use of DPFs, leading to a
corresponding value of 0.01099 g. The value is the same for all heavy-duty diesel regulatory class
vehicles.We plan to update this value when more data becomes available.
73
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223 Adjusting Start Rates for Soak Time
The discussion to this point has concerned the development of rates for cold-start emissions from
heavy duty diesel vehicles. In addition, it was necessary to derive rates for additional operating
modes that account for shorter soak times. As with light-duty vehicles, we accomplished this step
by applying soak fractions. As no data are available for heavy-duty vehicles, we applied the same
fractions used for light-duty emissions. Table 2-24 describes the different start-related operating
modes in MOVES as a function of soak time. The value at 720 min (12 hours) represents cold
start. These modes are not related to the operating modes defined in Table 1-4 which are for
running exhaust emissions.
Table 2-24. Operating modes for start emissions (as a function of soak time)
Operating Mode
101
102
103
104
105
106
107
108
Description
Soak Time < 6 minutes
6 minutes <= Soak Time <
30 minutes
30 minutes <= Soak Time < 60 minutes
60 minutes <= Soak Time < 90 minutes
90 minutes <= Soak Time < 120 minutes
120 minutes <= Soak Time
360 minutes <= Soak Time
< 360 minutes
< 720 minutes
720 minutes <= Soak Time
The soak fractions we used for HC, CO, and NOx are illustrated in Figure 2-36 below. Due to
limited data, we applied the same soak fractions that we applied to 1996+ MY light-duty gasoline
vehicle as documented in the light-duty emission rate report8. The soak fractions are taken from the
non-catalyst soak fractions derived in a CARB report50 and reproduced in a MOBILE6 report51.
For light-heavy duty vehicles (regulatory classes LHD<=10K, LHD<=14K, and LHD34), the soak
distributions apply to the cold starts for HC, CO and NOx. For medium and heavy-heavy duty
vehicles (regulatory classes MHD, HHD, and Urban Bus) only the CO soak fractions in Figure
2-36 are applied to the cold-start emissions, because the base cold start HC and NOx emission rates
for medium and heavy-heavy duty emission rates are zero.
74
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Figure 2-36. Soak Fractions Applied to Cold-Start Emissions (opModelD = 108) to Estimate Emissions for
shorter Soak Periods (operating modes 101-107). This Figure is reproduced the Light-duty emissions Report8
1.20
120 240 360 480
Soak Time (minutes)
600
720
The start emission rates used for heavy-duty vehicles, derived from applying the soak fractions are
displayed in Table 2-25 for HC, CO, and NOx.
Table 2-25. Heavy-duty diesel HC, CO, and NOx Start emissions (g/start) by operating mode for all model year
and all ages in MOVES.
opModelD
101
102
103
104
105
106
107
108
HC
LHD1
0.0052
0.0273
0.0572
0.0780
0.0832
0.0949
0.1183
0.1300
Other HD2
0
0
0
0
0
0
0
0
CO
LHD
0.055
0.276
0.607
0.869
1.007
1.090
1.256
1.380
Other HD
0.64
3.2
7.04
10.08
11.68
12.64
14.56
16
NOx
LHD
0.275
0.760
1.350
1.481
1.481
1.468
1.376
1.298
Other HD
0
0
0
0
0
0
0
0
'LHD refers to regClassIDs 40, 41, and 42
2 Other HD refers to the Medium-heavy duty, heavy -heavy duty, and Urban Bus Regulatory
classes (46, 47, and 48)
The PM start rates by operating mode are given in Table 2-26 below. They are estimated by
assuming a linear decrease in emissions with time between a full cold start (>720 minutes) and zero
emissions at a short soak time (< 6 minutes).
75
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Table 2-26. Particulate Matter Start Emission Rates by Operating Mode (soak fraction) for all HD
vehicles (regClass ID 40 through 48)
Operating Mode
101
102
103
104
105
106
107
108
PM2.s (grams per start)
1960-2006 MY
0.0000
0.0009
0.0046
0.0092
0.0138
0.0183
0.0549
0.1099
PM2.s (grams per start)
2007+ MY
0.00000
0.00009
0.00046
0.00092
0.00138
0.00183
0.00549
0.01099
2.2.3.1 Adjusting Start Rates for Ambient Temperature
The emission adjustments report discusses the impact of ambient temperature on cold start
emission rates (opModelD 108)48. The ambient temperature effects in MOVES model the impact
ambient temperature has on cooling the engine and aftertreatment system on vehicle emissions. The
temperature effect is greatest for a vehicle that has been soaking for a long period of time, such that
the vehicle is at ambient temperature. Accordingly, the impact of ambient temperature should be
less for vehicles that are still warm from driving.
However, because the HC temperature effects in MOVES are modeled as additive adjustments, the
adjustment calculated for cold starts needs to be reduced for warm and hot starts. Due to lack of
data, we applied the same soak fractions described in Section 2.2.3 to obtain cold start temperature
adjustments for opModelD 101 through 107. The additive cold start adjustment for HC emission
factors are displayed in Table 2-27, along with the soak fractions applied. These additive HC starts
are applied to all diesel sources in MOVES, including light-duty diesel (regulatory class 20 and
30).
There are currently no diesel temperature effects in MOVES for PM, CO, and NOx.
76
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Table 2-27. HC Diesel Start Temperature Adjustment by opModelD.
opModelD
101
102
103
104
105
106
107
108
Start Temp Adjustment
-0.0153x(Temp-75)
-0.0152x(Temp-75)
-0.0180x(Temp-75)
-0.020 lx(Temp- 75)
-0.021 lx(Temp- 75)
-0.0254x(Temp - 75)
-0.0349x(Temp - 75)
-0.0406x(Temp - 75)
Soak fraction
0.38
0.37
0.44
0.50
0.52
0.62
0.86
1.00
2.2.4 Start Energy Rates
The MOVES start energy rates for the heavy-duty diesel regulatory classes are shown in Figure
2-37. The energy start rates were developed for MOVES200452, and updated in MOVES2010 as
documented in the MOVES2010a energy updates report45. As shown, there is more detail in the
pre-2000 emission rates. The spike in fuel economy at 1984-1985 reflects variability in the data
used to derive starts, which was consistent with the more detailed approach used to derive the pre-
2000 energy rates in MOVES2004. The only updates to the energy rates post-2000 is the impact of
the Phase 1 Heavy-duty GHG standards, which begin phase-in in 2014 and have the same
reductions as the running energy rates as presented in Table 2-19.
Figure 2-37. Heayv-duty energy cold start energy rates (opMode 108) by model year and regulatory class.
•c
ro
14000-
ro
o
'{/)
LU
0-
1960
1980
2000
modelYearlD
2020
regClassName
LHD<= 10k
-^- LHD <= 14k
—- LHD45
-+- MHD67
HMDS
Urban Bus
77
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The start energy rates are adjusted in MOVES for increased fuel consumption required to start a
vehicle at cold ambient temperatures. The temperature effects are documented in the 2004 Energy
Report.52 Additionally, the energy consumption is reduced for starts that occur when the vehicles is
soaking for a short period of time. The soak fractions used to reduce the energy consumption
emission rates at cold start are provided in Table 2-28. These fractions are used for all model years
and regulatory classes of diesel vehicles.
Table 2-28. Fraction of energy consumed at start of varying soak lengths compared to the energy consumed at a
full cold start (operating mode 108).
Operating
Mode
101
102
103
104
105
106
107
108
Description
Soak Time < 6 minutes
6 minutes <= Soak Time < 30 minutes
30 minutes <= Soak Time < 60 minutes
60 minutes <= Soak Time < 90 minutes
90 minutes <= Soak Time < 120 minutes
120 minutes <= Soak Time < 360 minutes
360 minutes <= Soak Time < 720 minutes
720 minutes <= Soak Time
Fraction of energy
consumption
compared to cold
start
0.013
0.0773
0.1903
0.3118
0.4078
0.5786
0.8751
1
One of the reasons that energy rates for heavy-duty starts has not been updated is the relatively
small contribution the starts have on the energy inventory. Table 2-29 displays the relative
contribution of total energy consumption estimated from a national run of MOVES for calendar
year 2011, using MOVES2014. As shown, the estimated energy consumed due to starts is minor in
comparison to the energy use of running activity.
Table 2-29. Relative contribution of total energy consumption from each pollutant process by regulatory class
for heavy-duty diesel vehicles in calendar year 2011.
processID
1
2
90
91
processName
Running Exhaust
Start Exhaust
Extended Idle Exhaust
Auxiliary Power
Exhaust
LHD<=10K
97.4%
2.6%
LHD<=14K
99.2%
0.8%
LHD45
99.3%
0.7%
MHD
98.1%
0.6%
1.3%
0.01%
HHD
95.1%
0.1%
4.7%
0.04%
Urban
Bus
99.7%
0.3%
78
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2.3 Exten ded Idling Exh aust Emissions
In the MOVES model, extended idling is idle operation characterized by idle periods more than an
hour in duration, typically overnight, including higher engine speed settings and extensive use of
accessories by the vehicle operator. Extended idling most often occurs during long layovers
between trips by long-haul trucking operators where the truck is used as a residence, and is
sometimes referred to as "hotelling." The use of accessories such as air conditioning systems or
heating systems will affect emissions emitted by the engine during idling. Extended idling by
vehicles also allows cool-down of the vehicle's catalytic converter system or other exhaust
emission after-treatments, when these controls are present. Extended idle is treated as a separate
emission process in MOVES.
Extended idling does not include the vehicle idle operation which occurs during normal road
operation, such as the idle operation which a vehicle experiences while waiting at a traffic signal or
during a relatively short stop, such as idle operation during a delivery. Although frequent stops and
idling can contribute to overall emissions, these modes are already included in the normal vehicle
hours of operation. Extended idling is characterized by idling periods that last hours, rather than
minutes.
In the MOVES model, diesel long-haul combination trucks are the only source type assumed to
have any significant extended idling activity. As a result, an estimate for the extended idling
emission rate has not been made for any of the other source types modeled in MOVES.
While the MOVES2014 emission rates for extended idling are the same as the rates in
MOVES2010, the extended idling activity in MOVES2014 has been reduced to account for the
anticipated growing use of Auxiliary Power Units and the impact of the HDGHG rule as discussed
below in Section 2.3.5.
2.3.1 Data Sources
The data used in the analysis of extended idling emission rates includes idle emission results from
several test programs conducted by a variety of researchers at different times. Not all of the studies
included all the pollutants of interest. The references contain more detailed descriptions of the data
and how the data was obtained.
Testing was conducted on 12 heavy-duty diesel trucks and 12 transit buses in Colorado by
McCormick et al.53. Ten of the trucks were Class 8 heavy-duty axle semi-tractors, one was a Class
7 truck, and one of the vehicles was a school bus. The model year ranged from 1990 through 1998.
A typical Denver area wintertime diesel fuel (NFRAQS) was used in all tests. Idle measurements
were collected during a 20 minute time period. All testing was done at 1,609 meters above sea
level (high altitude).
Testing was conducted by EPA on five trucks in May 2002 (Lim et al.)54. The model years ranged
from 1985 through 2001. The vehicles were put through a battery of tests including a variety of
discretionary and non-discretionary idling conditions.
Testing was conducted on 42 diesel trucks in parallel with roadside smoke opacity testing in
California (Lambert)55. All tests were conducted by the California Air Resources Board (CARB) at
a rest area near Tulare, California in April 2002. Data collected during this study were included in
the data provided by IdleAire Technologies (below) that was used in the analysis.
79
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A total of 63 trucks (nine in Tennessee, 12 in New York and 42 in California) were tested over a
battery of idle test conditions including with and without air conditioning (Irick et al.)56. Not all
trucks were tested under all conditions. Only results from the testing in Tennessee and New York
are described in the IdleAire report. The Tulare, California, data are described in the Clean Air
Study cited above. All analytical equipment for all testing at all locations was operated by Clean
Air Technologies.
Fourteen trucks were tested as part of the E-55/59 Coordinating Research Council (CRC) study of
heavy duty diesel trucks with idling times either 900 or 1,800 seconds long 57.
The National Cooperative Highway Research Program (NCHRP)58 obtained the idling portion of
continuous sampling during transient testing was used to determine idling emission rates on two
trucks.
A total of 33 heavy-duty diesel trucks were tested in an internal study by the City of New York
(Tang et al.)59. The model years ranged from 1984 through 1999. One hundred seconds of idling
were added at the end of the WVU five-mile transient test driving cycle.
A Class 8 Freightliner Century with a 1999 engine was tested using EPA's on-road emissions
testing trailer based in Research Triangle Park, North Carolina (Broderick)60. Both short (10
minute) and longer (five hour) measurements were made during idling. Some testing was also
done on three older trucks.
Five heavy-duty trucks were tested for particulate and NOx emissions under a variety of conditions
at Oak Ridge Laboratories (Story et al.)61. These are the same trucks used in the EPA study (Lim
et al.).
The University of Tennessee (Calcagno et al.) tested 24 1992 through 2006 model year heavy duty
diesel trucks using a variety of idling conditions including variations of engine idle speed and load
(air conditioning)49.
2.3.2 Analysis
EPA estimated mean emission rates during extended idling operation for particulate matter (PM),
oxides of nitrogen (NOx), hydrocarbons (HC), and carbon monoxide (CO using all the data sources
referenced above. . The data was grouped by truck and bus and by idle speed and accessory usage
to develop emission rates more representative of extended idle emissions.
The important conclusion from theanalysis was that factors affecting engine load, such as accessory
use, and engine idle speed are the important parameters in estimating the emission rates of
extended idling. The impacts of most other factors, such as engine size, altitude, model year within
MOVES groups, and test cycle are negligible. This makes the behavior of truck operators very
important in estimating the emission rates to assign to periods of extended idling.
The use of accessories (air conditioners, heaters, televisions, etc.) provides recreation and comfort
to the operator and increases load on the engine. There is also a tendency to increase idle speed
during long idle periods for engine durability. The emission rates estimated for the extended idle
pollutant process assume both accessory use and engine idle speeds set higher than used for "curb"
(non-discretionary) idling.
80
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The studies focused on three types of idle conditions. The first is considered a curb idle, with low
engine speed (<1,000 rpm) and no air conditioning. The second is representative of an extended
idle condition with higher engine speed (>1,000 rpm) and no air conditioning. The third represents
an extended idle condition with higher engine speed (> 1,000 rpm) and air conditioning.
The idle emission rates for heavy duty diesel trucks prior to the 1990 model year are based on the
analysis of the 18 trucks from 1975-1990 model years used in the CRC E-55/59 study and one
1985 truck from the Lim study. The only data available represents a curb idle condition. No data
was available to develop the elevated NOx emission rates characteristic of higher engine speed and
accessory loading, therefore, the percent increase developed from the 1991-2006 trucks was used.
Extended idle emission rates for 1991-2006 model year heavy duty diesel trucks are based on
several studies and 184 tests detailed in Appendix C. The increase in NOx emissions due to higher
idle speed and air conditioning was estimated based on three studies that included 26 tests. The
average emissions from these trucks using the high idle engine speed and with accessory loading
was used for the emission rates for extended idling.
The rates for 2007-and-later were calculated before these vehicles were available and have not been
updated for MOVES2014. The 2007 heavy duty diesel emission standards were expected to result
in the widespread use of PM filters and exhaust gas recirculation (EGR) and 2010 standards to
result in after-treatment technologies. However, since there is no requirement to address extended
idling emissions in the emission certification procedure, EPA anticipated little effect on HC, CO,
and NOx emissions after hours of idling due to cool-down effects on EGR and most aftertreatment
systems. However, we did not expect DPFs to lose much effectiveness during extended idling. As
a result, we projected that idle NOx emissions would be reduced 12 percent and HC and CO
emissions will be reduced 9 percent from the extended idle emission rates used for 1988-2006
model year trucks. The reduction estimates are based on a ratio of the 2007 standard to the
previous standard and assuming that the emission control of the new standard will only last for the
first hour of an eight hour idle. For PM, we assumed an extended idling emission rate equal to the
curb idling rate (operating mode 1 from the running exhaust analysis). Detailed equations are
included in Appendix C.
2.3.3 Results
Table 2-30 shows the resulting NOx, HC, and CO emission rates estimated for heavy-duty diesel
trucks from the data analysis. Extended idling measurements have large variability due to low
engine loads.
Table 2-30. Mean extended idle emission rates from data analysis (g/hour)
Model Year
Groups
Pre-1990
1990-2006
2007 and later
NOx
112
227
201
HC
108
56
53
CO
84
91
91
PM
8.4
4.0
0.2
81
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2.3.4 MO VES Extended Idle Emission Rates
Table 2-31 shows the emission rates used in MOVES for extended idle for diesel MHD and HHD
trucks. These are the only regulatory classes in MOVES for diesel combination trucks, which are
the only types of trucks with extended idle vehicle activity in MOVES. The emission rates for
regulatory class HHD (RegClassID 47) are equal to the mean extended emission rates from Table
2-30 for HC, CO, and NOx. Due to limited data we calculated the MHD (regClassID 46) extended
idle emission rates as half of the extended idle emission rates of the HHD emission rates for HC,
CO and NOx. There are no age effects modeled for extended idle emissions in MOVES.
Table 2-31. Extended idle emission rates in MOVES by pollutant and regulatory class (g/hour)
Model Year
Groups
1960-1990
1991-2006
2007+
HC
MHD
54
28
26.5
HHD
108
56
53
CO
MHD
42
45.5
45.5
HHD
84
91
91
NOx
MHD
56
113.5
100.5
HHD
112
227
201
Table 2-32 shows the extended idle PM emission rates in MOVES. MOVES stores PM emission
rates according to EC and NonECPM, but the total PM, and EC/PM fraction are reported in Table
2-32 as well. As mentioned previously, the PM2.5 extended idle emission rates are based on curb
idle emission rate (operating mode 1 from the running process). Thus, the model year groups for
PM are the same model year groups used for running PM emission rates. However, despite the
different sources, the PM emission rates used in MOVES are similar in magnitude to the mean PM
emission rates calculated from the extended idle studies shown in in Table 2-30.
Table 2-32. Particulate matter emission rates for extended idle emissions
Model Year
Groups
1960 - 1993
1994 - 1997
1998 - 2002
2003 - 2006
2007+
1960 - 1993
1994 - 1997
1998 - 2002
2003 - 2006
2007+
Regulatory Class MHD
EC
1.77
3.07
2.91
2.63
0.032
NonECPM
2.44
4.21
4.00
3.60
0.288
PM
4.21
7.28
6.91
6.23
0.32
EC/PM
42.1%
42.1%
42.1%
42.1%
9.98%
Regulatory Class HHD
EC
1.08
1.66
1.58
1.43
0.03
NonECPM
3.13
4.78
4.57
4.13
0.31
PM
4.21
6.44
6.16
5.56
0.35
EC/PM
25.7%
25.7%
25.7%
25.7%
9.98%
82
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The extended idle energy emission rates are unchanged from those originally developed for
MOVES2004 and are documented in the Energy and Emissions Report52, and are displayed in
Figure 2-38. The extended idle energy consumption rates are the same for both regulatory class
MHD67 and HHD diesel vehicles.
Figure 2-38. Extended idle energy emission rates for regulatory class HHD and MHD diesel trucks.
150000-
o
^
100000 -
-------
The APUs in MOVES are assumed to be Tier 4-compliant, small (<8 kW) nonroad compression-
ignition engines. We use the THC, CO, NOx, and PIVh.s emission rates from the NONROAD2008
model for this category of nonroad engine to develop the APU emissions rates, as was done in the
2014 Medium and Heavy Duty Greenhouse Gas Rule47. The PM2.5 emissions were divided into EC
(25%) and 75% (nonEC) using fractions similar to the EC/PM split for conventional extended
idling exhaust from HHD trucks (Table 2-32). The APU emission rates are displayed in Table 2-33.
The APU energy usage (per hour) is 22% of the MOVES extended idle emission rate for 2002 and
later trucks, demonstrating the potential energy savings from using an auxiliary power unit.
Table 2-33 - APU emission rates
Pollutant
THC
CO
NOx
EC
NonEC
EC/PM25
Total Energy
Emission
Rate
6.72
36
26.88
0.45
1.35
25%
27171.336
Units
g/hr
g/hr
g/hr
g/hr
g/hr
%
KJ/hr
84
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3 Heavy-Duty Gasoline Vehicles
In MOVES2014, the exhaust emission rates for the MHD and HHD regulatory classes of heavy-
duty gasoline vehicles were largely unchanged from MOVES2010. The exhaust emission rates
changed for LHD emission rates, to incorporate new data, as well as to develop the appropriate
emission rates for the new regulatory classes LHD<=10K (RegClassID 40) and LHD<=14K
(RegClassID 41). Also, we updated exhaust and energy rates to account for the impact of new Tier
3 and Heavy-Duty Greenhouse Gas regulations which impacted all the heavy-duty gasoline source
types
3.1
Running Exhaust Emissions
3.1.1 HC, CO, andNOx
3.1.1.1 Data and Analysis for 1960-200 7 Model Year Trucks
As gasoline-fueled vehicles are a small percentage of the heavy-duty vehicle fleet, the amount of
data available for analysis was small. We relied on four medium-heavy duty gasoline trucks from
the CRC E-55 program and historical data from EPA's Mobile Source Observation Database
(MSOD), which has results from chassis tests performed by EPA, contractors and outside parties.
The heavy-duty gasoline data in the MSOD is mostly from pickup trucks which fall mainly in the
LHD2b3 regulatory class. Table 3-1 shows the total number of vehicles in these data sets. In the
real world, most heavy-duty gasoline vehicles fall in either the LHD2b3 or LHD45 class, with a
smaller percentage in the MHD class. There are very few HHD gasoline trucks now in use.
Table 3-1. Distribution of vehicles in the data sets by model-year group, regulatory class and age group
Model year group
1960-1989
1990-1997
1998-2002
Regulatory class
MHD
LHD2b3
MHD
LHD2b3
MHD
LHD2b3
Age group
0-5
33
1
1
6-9
2
10
1
19
Similar to the HD diesel PM, HC, and CO analysis, the chassis vehicle speed and acceleration,
coupled with the average weight for each regulatory class, were used to calculate STP (Equation
1-2). To supplement the meager data available, we examined certification data as a guide to
developing model year groups for analysis. Figure 3-1 shows averages of certification results by
model year.
85
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Figure 3-1. Brake-specific certification emission rates by model year for heavy-duty gasoline engines
O
o
n -
••
..
• CO
A Nox
•I- --f ------- .---T • HC
i •
i , ' i i H;i,"*H]
- i :; i f i f E E '
, .
i
h 4
.
f J
- 1.2
- 1
- 0.8
- 0.4
- 0.2
- 0
no
1980
1985
1990
1995
Model year
2000
2005
2010
Based on these certification results, we decided to classify the data into the coarse model year
groups listed below.
• 1960-1989
• 1990-1997
• 1998-2007
Unlike the analysis for HD diesel vehicles, we used the age effects present in the data itself. We did
not incorporate external tampering and mal-maintenance assumptions into the HD gasoline rates.
Due to sparseness of data we used only the two age groups listed in Table 3-1. We also did not
classify by regulatory class since there was not sufficient data to estimate emission rates by
separate regulatory classes. The derivation of the model year 2008 and later emission rates are
discussed in Sections 3.1.1.3 and 3.1.1.6.
3.1.1.2 Emission Rates for Regulatory Class LED <=10K (RegClassID 40)
The emission rates were initially analyzed by binning the emission rates using the STP with a fixed
mass factor of 2.06, to bring the emission rates into VSP-equivalent space, used for modeling
emissions for regulatory class LHD<=10K. Figure 3-2 shows all three pollutants vs. operating
mode for the LHD<=10K. In general, emissions follow the expected trend with STP, though the
trend is most pronounced for NOX. As expected, NOx emissions for light-heavy-duty gasoline
vehicles are much lower than for light-heavy-duty diesel vehicles.
86
-------
Figure 3-2. Emission Rates by operating mode for MY groups 1960-1989,1990-1997, and 1998-2007 at age 0-3
years for regulatory class LHD <= 10K
4000-
3000-
2000-
1000-
o-
,_ 150-
D
O
~0)
•inn —
I UU
en
c:
.2 50-
w
E
^ 0
500-
400-
300-
200-
100-
o-
A
A
4
+ A
fflffl ::
1 ^ • * I ^ • '
*_M_M* * A
j
4
k
1
4
*'
k
4
A
1
i.
1
1
k
>
1
>
'
A
•
i
k
1
1
A
k
<
4
1 '
^ J
A
*
— — — — 1—
M
JTT]
*
*
ra
:**•
k
o
0
0
X
Model. Years
* 1960-1989
A 1990-1997
• 1 1998-2007
I I I I I I I I I I I I I I I I I I I I I I I
0 1 111213141516212223242527282930333537383940
opModelD
Figure 3-3 shows the emissions trends by age group. Since we did not use the tampering and mal-
maintenance methodology as we did for diesels, the age trends reflect our coarse binning with age.
For each pollutant, only two distinct rates exist - one for ages 0-5 and another for age 6 and older.
87
-------
Figure 3-3. Emission rates by operating mode and age group for MY 1998-2007 vehicles in regulatory class LHD
<=10K
2500-
2000-
1500-
1000-
500-
o-
^ 100-
o
o) 75-
oT
^ 50-
c
o
CO oi
LJJ
o-
200-
150-
100-
50-
o-
*
A A A A
A A A ^ * + *
4 AiU44_ 11444444
.
A
n
age
* i
it***
TTTTl
i i i i i i i i i i i i i i i i i i i i i i i
0 1 111213141516212223242527282930333537383940
opModelD
Table 3-2 displays the multiplicative age effects by operating mode for Regulatory Class
LHD<=10K vehicles. The relative age effects are derived from the sample of vehicle tests
summarized in Table 3-1. The multiplicative age effects are used to estimate the aged emission
rates (ages 6+) years from the base emission rates (ages 0-5) for HC, CO, and NOx. These
multiplicative age effects apply to all model year groups between 1960-2007. As discussed earlier,
we derived multiplicative age effects from the pooled data across the three model year groups and
regulatory classes to develop the multiplicative age effects due to the limited data set. The relative
age effects were derived for each OpModelD defined using Scaled Tractive Power with the fscaie =
2.06, to be consistent with LHD<=10K (RegClassID 40).
88
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Table 3-2. Relative age effect on emission rates between age 6+ and age 0-5 for LHD<=10K gasoline vehicles in
model years 1960-2007.
OpModelD
0
1
11
12
13
14
15
16
21
22
23
24
25
27
28
29
30
33
35
37
38
39
40
HC
2.85
2.43
3.12
2.85
3.55
3.43
3.37
3.76
2.78
2.64
2.96
2.83
3.23
3.21
3.20
3.00
2.55
1.95
2.67
2.80
2.46
2.46
2.47
CO
1.45
1.79
1.66
2.05
2.68
2.84
3.03
3.88
1.67
1.64
1.67
1.62
2.79
3.20
4.04
3.90
2.56
2.00
2.20
2.24
2.06
2.30
2.59
NOx
1.67
1.85
1.88
1.69
1.48
1.46
1.26
1.06
1.42
1.36
1.32
1.21
1.43
1.21
1.11
1.05
1.05
1.77
1.59
1.42
1.34
1.27
1.17
3.1.1.3 Emission Rates for RegClass 40 for 2008 through 2017 model years
In MOVES2014, we introduced a new regulatory class, LHD<=10K (RegClassID 40) that applies
to LHD2b trucks that are classified as passenger or light-commercial trucks. Regulatory class
LHD<=14K (RegClassID 41) also contains LHD2b trucks, but only vehicles that are classified as
single-unit trucks. The distinction was made in MOVES2014 because passenger and light-
commercial trucks assign operating modes using VSP, and MOVES assigns STP-based operating
modes to single-unit trucks. In previous versions of MOVES (201 Ob and earlier), regulatory class
LHD2b3 (Previously RegClasID 41) was used to model all Class 2b and 3 trucks.
Most of the analysis conducted in this section was conducted assuming that there would be a single
regulatory class to represent all Class 2b and 3 trucks (LHD2b3). We thus used the term LHD2b3
trucks to refer to trucks in both regulatory class LHD<=10K (RegClassID 40) and LHD<=14K
(RegClassID 41). However, we used the data in this section only to update the emission rates for
89
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regulatory class LHD<=10K (RegClassID 40). Emission rates for regulatory class
LHDLHD<=14K (RegClassID 41) for 2008+ vehicles are discussed in the following section.
3.1.1.3.1 Comparison ofLHD2b3 emission rates in MOVES2010 with relevant
emission standards
Gasoline vehicles in MOVES2010 regulatory class LHD2b3 are a mixture of engine certified
Heavy-duty vehicles, chassis certified Heavy-duty vehicles, and medium duty passenger vehicles
(MDPVs). Each group has a separate set of regulations governing their emissions. These emission
standards are summarized below (Table 3-3).x
Table 3-3. Useful Life FTP Standards from the Tier 2s3 and 2007 Heavy-Duty Highway64 Rules
Units
Fully Phased in MY
HC
CO
NOx
MDPV
(Tier 2 Bin 5)
g/mile
2009
0.09 NMOG
4.2
0.07
8.5k -10k
(Class 2B)
g/mile
2009
0.195NMHC
7.3
0.2
10k-14k
(Class 3)
g/mile
2009
0.230 NMHC
8.1
0.4
Engine
Certified"1
g/bhp-hr
2010
0.14 NMHC
14.4
0.2
The relative proportions of the vehicles within the MOVES2010 LHD2b3 regulatory class vary
each year depending on demand. Consequently, we estimated proportions based on recent model
year data and engineering judgment. MOBILE6 documentation from 2003 indicates that MDPVs
were approximately 16% of the gasoline 8,500 to 10,000 truck class.65 In MOVES2014, we project
that MDPVs are 15% of total MOVES LHD2b3 regulatory class in MYs 2008 and later. The
MOBILE6 document also states that more than 95% of class 2B trucks are chassis certified.65
Thus, we estimate that 5% of all vehicles in the LHD2b3 regulatory class are engine certified.
Based on analysis from the recent medium and heavy duty greenhouse gas rulemaking, we assume
that sales of 2B class trucks vehicles were triple that of 3 class trucks.66 This is roughly consistent
with recent model year sales totals.67 Combining these assumptions, we get the sales fractions
shown below (Table 3-4).
x This mixture of vehicles was not explicitly considered during the development of MOVES2010.
X1 The FTP differs between engine and chassis certified vehicles. We used adjustment factors described in the
MOBILE 6 documentation to convert from g/bhp-hr to g/mile (1.2x), but these adjustment factors may vary in their
utility. The small proportion of engine certified vehicles within the population of LHD2b3 trucks dilutes their impact.
90
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Table 3-4. Population percentage of LHD2b3 trucks
MDPV
Class 2B
Class 3
Engine Certified
% of Reg Class
15%
60%
20%
5%
To generate an aggregate FTP standard for LHD2b3 regulatory class, we weighted the individual
certification standards shown in Table 3-3 using the proportions shown in Table 3-4.xu While the
model produces estimates of on-road emissions rather than certification emissions, the weighted
certification standard is a useful benchmark for the modeled rates (Table 3-5).xm
Table 3-5. Aggregate useful life FTP for LHD2b3 trucks
NMOG
CO
NOX
g/mile
0.18
7.49
0.22
As a benchmark, we compared the calculated aggregate FTP standard to an FTP calculated using
the emission rates in the MOVES2010a database. The Physical Emission Rate Estimator
(PERE),34 modified to produce Scaled Tractive Power (STP) distributions, was used to generate the
operating mode mix of a LHD2b3 regulatory class vehicle on the Federal Test Procedure drive
cycle. For the STP modification, we changed the vehicle weight in PERE to match the
sourceTypelD 32 (Light Commercial Truck) in MOVES (2.06 Tons). We incorporated emission
rates from the MOVES database for the age 0-3 group, and added in a cold start (operating mode
108) and a hot start (operating mode 102) from the MOVES database. The modified version of
PERE produced the operating mode distribution shown in Table 3-6.
X11 The engine standard was converted to a g/mile standard using a factor of 1.2 as described in the MOBILE6 report
xm Several simplifications were made in calculating this aggregate useful life FTP. The distinction between NMHC
and NMOG was ignored in calculating the aggregate FTP, and would have yielded only minor variation in the
aggregate certification standard. The engine standard was also converted to a chassis equivalent as discussed above.
91
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Table 3-6. Operating mode bin distribution for a light-commercial truck on the Federal Test Procedure (FTP)
OpModelD
0
1
11
12
13
14
15
16
21
22
23
24
N
160
258
94
68
70
36
48
141
68
44
97
77
%
12%
19%
7%
5%
5%
3%
3%
10%
5%
3%
7%
6%
OpModelD
25
27
28
29
30
33
35
37
38
39
40
Total
N
41
49
17
13
15
13
12
13
17
15
6
1372
%
3%
4%
1%
1%
1%
1%
1%
1%
1%
1%
0%
100%
Using this operating mode distribution, we constructed a simulated FTP out of four components
(bag 1/3 running,xlv cold start, hot start, and bag 2 running). We constructed bag 1 (cold start + bag
1 running) and bag 3 (hot start + bag 3 running) and weighted the resulting components together
according to the FTP formula,xv and compared the 2008 and later rates in MOVES to the aggregate
standard calculated above (Table 3-7). MOVES2010a estimates at age 0-3 were two to ten times
larger than the standard, which indicates that the average vehicle HD gas vehicle in MOVES2010a
is modeled as significantly out of compliance with the relevant emission standards.
Table 3-7. Comparison between MOVES DB FTP and aggregate FTP for LHD2b3 trucks
NMOG
CO
NOx
MOVES2010
FTP for
LHD2b3
Trucks
(g/mile)
0.36
14.54
2.04
LHD2b3
Aggregate
FTP
Standard
(g/mile)
0.18
7.49
0.22
Ratio -MOVES
to Aggregate
Standard
1.93
1.94
9.28
X1V Bag 1 and Bag 3 are considered to have the same emission rate.
xv FTp =(- (Bag l + Bag 2)*o.43+ (Bag 3+ Bag 2)*0.57)/ 7.45
92
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3.1.1.3.2 Validation against In-Use Verification Program Data
We reviewed In Use Verification Program (IUVP) data for MYs 2004-2008 vehicles (estimated
test weights of 7,500 pounds to 10,000 pounds) to determine the appropriateness of the
MOVES2010 emission rates.™ We evaluated whether vehicles during these MYGS were
achieving the standard, or if alternate methods were being used for compliance. While the IUVP
data is not fully representative of the in-use fleet, it provides a reasonable snap-shot. Without
weighting for sales or accounting for the standards applicable to each vehicle, we calculate average
ratios of test value to the aggregate standard (Table 3-5) of 0.42 (NMOG) and 0.23 (NOx) in Table
3-8 & Figure 3-4. These ratios indicate that vehicles typically comply with the standard, with a
significant amount of headroom.
Table 3-8. Average compliance margin and headroom for LHD2b3 trucks
NMOG
NOx
Average
Ratio Certification
FTP/Aggregate
Standard
0.42
0.23
Average
Headroom
0.58
0.77
V1 While this population of vehicles is not identical, these test weights significantly overlap with these GVWR classes.
93
-------
Figure 3-4. Distribution of IUVP FTP tests for LHD2b3 trucks
MD NOX and NMOG IUVP FTP
2004 2005 200B 2007 20DS 2004 2005 200B 2007 20DB
MY
The emission rates in MOVES include all vehicles, and consequently represent a broader sample
than the IUVP data. As a result, we expect that the onroad vehicles would have higher emission
rates than vehicles in the IUVP program.™1 However, the emission rates represented by
MOVES2010 are higher than those that would be expected from vehicles compliant with the
standards in place in MY 2008 and later.
3.1.1.3.3
Emission Rates
Given that (a) the MOVES2010 LHD2b3 emission rates are significantly above the calculated
aggregate standard, and (b) the IUVP data shows that most light-heavy 2b trucks achieve the
standard, we calculated new MOVES2014 HC/CO/NOx emission rates for regulatory Class
LHD<=10K (RegClassID 40) vehicles in 2008 and later MYs.
In conducting this analysis, we lacked any modal data on regulatory class LHD<=10K (RegClassID
40) vehicles. As such, we conducted the analysis using a method that we have used repeatedly on
the light duty side, which is ratioing the modal emission profile by the difference in standards.8 By
MY 2008, the medium duty vehicles are nearing the emission levels of Tier 2 Bin 8 vehicles.
Consequently, we relied on the analysis of in-use Tier 2 Bin 8 vehicles conducted for the light duty
vehicle emission rates.8 Because we are basing the emission rates on light-duty emission rates
XV11 Even in the absence of emission equipment deterioration, tampering and mal-maintenance will increase the
emissions from an on-road vehicle.
94
-------
(which are also VSP-based), the emission rate update is limited to regulatory class LHD<=10K
(RegClassID 40) vehicles.
We scaled the modal data from Tier 2 Bin 8 vehicles by the ratio of FTP standards™11 so that the
rates would be consistent with the higher emission rates of regulatory class LHD<=10K
(RegClassID 40) vehicles.
Table 3-9. Aggregate LHD2b3 standard ratios against Bin 8 modal rates
NMOG
CO
NOx
Aggregate
LHD2b3 FTP
standard
0.18
7.49
0.22
Bin 8
FTP
standard
0.1
3.4
0.14
Aggregate/Bin 8
1.8
2.2
1.6
We converted this ratio into a "split" ratio, where the running rates increased twice as much as the
start rates, but the same overall emissions were simulated on the FTP. This split ratio is consistent
with typical emission reduction trends, where running emissions are reduced about twice as much
as start emissions.8 The "split" ratios for running and start, which were applied to the light-duty
Tier 2 Bin 8 vehicle emission rates are shown in Table 3-10.
Table 3-10. Ratio applied to light-duty Tier 2 Bin 8 emission rates to estimate regulatory class LHD<=10K
(RegClassID 40) emission rates for 2008-2017 MY.
Running
Start
HC
2.73
1.37
CO
2.73
1.37
NOx
1.95
1.00
We also adopted the light-duty deterioration effects and applied them to the 2009 and later
regulatory class LFID<=10K (RegClassID 40) emission rates. The light-duty emission rates have
age effects that change with each of the 6 age groups in MOVES, as shown in Table 3-11.
xvm The aggregate FTP standards used include both Class 2b and 3 trucks. However, the ratio is only applied to develop
updated regulatory class LHD<=10K (RegClassID 40) emission rates (which only contain 2b trucks). The LHD2b3
aggregate emission factors are 2%, 28%, and 25% higher than aggregate emission factors based on 2b trucks only for
NMOG, CO, and NOx. However, as discussed later, the final emission rates are still below the aggregate standard. So,
we believe using the LHD2b3 aggregate standard is appropriate.
95
-------
Table 3-11. Multiplicative age effect used for running emissions for regulatory class LHD<=10K (RegClassID
40) 2008+ model years.
ageGroupID
3
405
607
809
1014
1519
2099
HC
1
1.95
2.80
3.71
4.94
5.97
7.20
CO
1
2.31
3.08
3.62
4.63
5.62
6.81
NOx
1
1.73
2.21
2.76
3.20
3.63
4.11
After applying the above mentioned steps (scaling the emission factors by ratio of FTP standards,
and applying light-duty deterioration trends), we restricted the scaled data so that the individual
emission rates by operating mode were never scaled to be higher than MY 2006 regulatory class
LHD<=10K (RegClassID 40) rates. This essentially capped the emission rates, such that none of
the operating mode, or age-specific emission rates for 2009 and later model year vehicles are
higher than the 2007 and earlier model year emission rates.
This final step capped emission rates in the highest operating modes. For HC, emission rates in
operating modes 28-30 and 38-40 were capped for some or all age groups by the pre-2007 emission
rates. For CO, emission rates in 12 of the 23 running operating modes (1, 16, 23-24, 27-30, 35-40)
were capped by the pre-2007 rates. None of the NOx emission rates were impacted by this step.
Figure 3-5 shows the regulatory class LHD<=10K (RegClassID 40) model year 2008-2017
emission rates for CO, HC, and NOx. Emission rates that exhibit the start-step deterioration trend
are the emission rates that were capped with the pre-2007 emission rates. Even with the capped
emission rates, the regulatory class LHD<=10K (RegClassID 40) emission rates are higher than the
Light Duty Trucks (RegClassID 30) emission rates with a few exceptions. The few exceptions are
some of the age-dependent HC and or CO emission rates in operating modes 1, 30, 38, 39, and 40.
However, the majority of emission rates are significantly higher in regulatory class LHD<=10K
than regulatory class Light Duty Trucks and when used in MOVES, the simulated FTP emission
rates are significantly higher for regulatory class LHD<=10K vehicles.
96
-------
Figure 3-5. Age Effects for CO, HC, and NOx emission rates for regulatory class LHD<=10K (RegClassID 40)
vehicles in running operating modes for MY 2008-2017.
After calculating new regulatory class LHD<=10K (RegClassID 40) emission rates, we used the
emission rates to simulate an FTP cycle, as shown in Table 3-12. We compared these emission
rates to the the calculated aggregate standard. The calculated headroom for NOx is less than that
shown in the IUVP data, and the calculated headroom for NMOG is greater than that shown in the
IUVP data (Table 3-8). For NOx, this difference is more significant. However, as stated above, the
IUVP data is not fully representative of in-use vehicles. By contrast, the Tier 2 Bin 8 rates are
based on extensive I/M testing, and are considered more representative of the entire fleet.
97
-------
Table 3-12. Ratio of final rates against standards
NMOG
CO
NOx
Simulated
regulatory class
class 40 2008+
FTP (g/mile)
0.06
3.08
0.18
Aggregate 20 10+
LHD2b3
FTP Standard
(g/mile)
0.18
7.49
0.22
Simulated
FTP
emissions/
Aggregate
FTP Standard
33%
41%
84%
In terms of the phase-in, we assumed that the regulatory class LHD<=10K (RegClassID 40) rates
phase in at a rate of 50% in MY2008 and considered fully phased in MY2009. The MY2008
running emission rates are interpolated values between the 2007 and 2009 emission rates by
operating mode and age group.
3.1.1.4 Running Emission Rates for RegClass LHD< =10K (RegClassID 40)
Vehicles for 2018 and later
The Tier 3 program will affect not only light-duty vehicles (below 8,500 pounds GVWR), but also
chassis-certified vehicles between 8,500 and 14,000 pounds GVWR. This class of vehicles is
referred to "light-heavy-duty" or "medium-duty" vehicles.
This regulatory class comprises several classes of vehicles, including Class 2b and Class 3 trucks,
medium-duty passenger vehicles (MDPV) and engine-certified trucks. However, the latter two
groups of vehicles are not regulated under the medium-duty standards described here. However, for
completeness, they are reflected in the emission rates.
During the phase-in period, we assumed that Class 2b and 3 vehicles would be certified to four
standard levels. Composite FTP values for these standard levels are shown in Table 3-13. Phase-in
fractions for each standard level are also shown in Table 3-14. The phase-in fractions were applied
to the FTP values to calculate weighted average FTP values for these two truck classes for each
model year during the phase-in, as shown in Table 3-15.
In addition to the 2b and 3 vehicles regulated under Tier 3, light-heavy duty vehicles also include
MDPV and engine-certified vehicles. Composite FTP values were estimated for these classes as
well. The levels for MDPV were assumed to be equivalent to Tier 2 Bin 8 vehicles in 2017 and to
light-duty vehicles in 2022 (30 mg/mi). Interim values were calculated for each model year during
the phase-in by assuming a linear decrease over each year between the initial and final values. The
FTP values for the engine-certified vehicles were assumed to be unaffected by the Tier 3 standards
and to therefore remain constant throughout. The projected averaged FTP values for these two
vehicle classes are also shown in Table 3-15.
Finally, weighted average values for all four vehicle classes were calculated as shown in Equation
3-1. Note that the weights assigned to each vehicle class are equivalent to those previously shown
in Table 3-4. Values of the weighted means by model year are shown in Table 3-15.
98
-------
fcd = °-8 (°-75 FTP2b + 0-25 FTP3)+ 0.05 FTPEngme.ceitified + 0.15 FTPMDPV Equation 3-1
Table 3-13. Composite FTP NMOG+NOx standards for Class 2b and 3 vehicles (mg/mi).
Vehicle Class
2b
3
LEV
395
630
ULEV34
340
570
ULEV25
250
400
SULEV17
170
230
Table 3-14. Phase-in fractions by standard level for Class 2b and 3 vehicles.
Model Year
2017
2018
2019
2020
2021
2022
LEV
0.10
0.0
0.0
0.0
0.0
0.0
ULEV34
0.50
0.40
0.30
0.20
0.10
0.0
ULEV25
0.40
0.50
0.40
0.30
0.20
0.10
SULEV17
0.0
0.10
0.30
0.50
0.70
0.90
Table 3-15. Projected FTP composite values for four vehicle classes (mg/mi), plus weighted means, for 2017
(pre-Tier 3) and 2022 (full phase-in of Tier 3)
Model Year
2017
2022
Vehicle Class
2b
3
MDPV
Engine-Certified
178
247
30
408
Weighted Mean
400
181
If we take the initial value before onset of the phase-in (400 mg/mi) and the final value when the
phase-in is complete (181 mg/mi), and treat these two values as references, we can calculate the
phase-in fractions that correspond to the weighted means in each intervening model year from 2018
to 2021 inclusive, as shown in Equation 3-2. Resulting phase-in fractions so calculated are shown
in Table 3-16.
FTP
weighted
= 181 fT3 + 400 (1 - /r3)
Equation 3-2
99
-------
Table 3-16. Phase-in fractions applied to rates in model years 2018 and later to represent partial and full Tier-3
control.
Model Year
2017
2018
2019
2020
2021
20221
/T3
0.00
0.49
0.62
0.75
0.87
1.00
1-/T3
1.00
0.51
0.38
0.25
0.13
0.00
1 Also applicable to model years 2022 and later.
To calculate modal emission rates in MY2018 and later, we applied the fractions shown in Table
3-16 above to sets of modal rates representing MY 2017 and MY2022.
The rates for MY2017 were extracting from a previous version of the MOVES database used in
analyses supporting the Tier-3 Rulemaking, and represented existing rates prior to the adoption of
Tier-3 standards.X1X The rates for MY2022 were estimated as equivalent to light-duty rates,
assuming a fleet composition of 10% Bin 8 and 90% Bin 5 standards. These rates were designed to
represent full Tier-3 control.
Thus, starting with these subsets of rates for MY2017 and MY2022, the calculation shown in
Equation 3-2 was performed for all rates across all operating modes and ageGroups.
Resulting rates for HC, CO and NOx are shown in Figure 3-6, Figure 3-7, and Figure 3-8
respectively.
: The database version used was MOVESt3DB20110331.
100
-------
Figure 3-6. THC: running-exhaust emission rates for vehicles in the LHD<=10k regulatory class (regClassID
40), during the Tier-3 phase-in.
60-
40 -
20 -
D-
tf
-------
Figure 3-7. CO: running-exhaust emission rates for vehicles in the LHD<=10k regulatory class (regClassID 40),
during the Tier-3 phase-in.
ageGrouplD = 3
ageGrouplD= 405
ageGrouplD = 607
2500-
2000 -
1500-
1000-
500 -
0
ageOroup!D= 809
ageGrouplD =
ageGrouplD= 1519
2500
2000
-1500
1000
500
-D
ageGrouplD =
2500 -
2000 -
1500-
1000-
500 -
0-
2017 2019 2021
2017
2019
2021
2017
2019
modelYearlD
opModelD
o 0 o 1 o 11 o 12 o 13 ° 14 o 15 ° 16 o 21 o 22 •-• 23
+ 25 +27 +28 +29 +30 +33 +35 +37 +38 +39 40
24
102
-------
Figure 3-8. NO*: running-exhaust emission rates for vehicles in the LHD<=10k regulatory class
(regClassID=40), during the Tier-3 phase-in.
ageGrouplD = 3
ageGrouplD =
ageGrouplD =
150-
100
50-
0
TT-
ageGrouplD = 809
ageGrouplD = 1014
3S^t
ageGrouplD = 1519
-150
-100
50
-0
ageGrouplD = 2099
150
100-
50-
0-
2017
2019
2021
2017
2019
2021
2017
2019
2021
modelYearlD
opModelD
o 0 o 1 o 11 o 12 o 13 o 14 c. 15 .1. 16 o 21 o 22 o 23 24
+ 25 +27 +28 +29 +30 i 33 + 35 + 37 + 38 + 39 40
Figure 3-9 summarizes the decreasing trend in emissions from the analysis documented in this
chapter, showing the average emission rates (across all operating modes) for CO, HC, and NOx for
the 1980 to 2007 model years for LHD<=10k vehicles. Note that the 1980 rates are used for all
model years 1960-1980, and the 2030 rates are used for all model years beyond 2030.
103
-------
Figure 3-9. Average emission rate (across all operating modes) for regulatory class LHD<=10K (RegClassID 40)
trucks for CO, HC, and NOx. The 1960-2007 emission rates only differ according to two broad age groups (0-5)
and (6+). For 2008 and later emission rates, the emissions differ according to the age groups shown in the
legend.
1500
ageGroupName
20 or more years old
15 to 19 years old
10 to 14 years old
8 or 9 years old
6 or 7 years old
4 or 5 years old
0 to 3 years old
1980 1990 2000 2010
modelYearlD
2020
2030
3.1.1.5 Running Emission Rates for Regulatory Class LHD<=14K, LHD45, and
MHD, andHHDfor 1960-2007 model years
Emission rates are equivalent across all the heavy-duty gasoline regulatory classes:LHD<14 ,
LHD45, MHD, and HHD . Like the regulatory class LHD<=10K rates described above, the heavy-
duty gasoline rates are based on emissions data from the mix of LHD2b3 and MHD vehicles
outlined in Table 3-1. The same model year groups are used to classify the emission rates: 1960-
1989, 1990-1997, and 1998-2007. Also, we use the same relative increase in emission rates for the
age effect. The only difference from the analysis of regulatory class LHD<=10K emission rates is
that the regulatory class LHD<=14K, LHD45, MHD, and HHD emission rates were analyzed using
STP operating modes with a fixed mass factor of 17.1. Sample emission rates for HC, CO, and
NOx for the 1994 MY Group are presented in Figure 3-10 for these source types.
104
-------
Figure 3-10. Emission rates by STP operating mode for MY 1994 at age 0-3 years for regulatory classes LHD <
14K, LHD45, MHD, and HHD
30000-
20000-
10000-
o-
o 600-
O)
0) 400-
c
.2 200-
E
LJJ 0-
2000-
1500-
1000-
500-
o-
fl
A-: ** .-- [ft*
* * * 4!:i•* * * • •" *:•••
Model.Years
* 1960-1989
1990-1997
1998-2007
*
* A
^4:
,'•••' •>'••
.
..:J-' »i-
0 1 111213141516212223242527282930333537383940
opModelD
Table 3-17 displays the multiplicative age effects by operating mode for LHD<14K, LHD45,
MHD, and HHD gasoline vehicles. While these age effects were derived from the same data as
those for the LHD<=10K vehicles, these heavy-duty age effects are slightly different for these
vehicles, because the operating modes are defined with the STP scaling factor of 17.1. For
operating modes that do not depend on the scaling factor (opModelD 0, 1, 11, and 21) the age
effects are the same as the LHD<=10K age effects. Also, because the vehicles tested were
LHD2b/3 and MHD vehicles, no data were available in the high STP power modes (typically only
a HHD truck would reach these). Thus, the higher operating modes (opModelD 13-16, 24-30, and
35-40 use the same values as the closest operating mode bin with data).
105
-------
Table 3-17 Relative age effect on emission rates between age 6+ and age 0-5 for LHD<14K, LHD45, MHD, and
HHD gasoline vehicles in all model years 1960-2050.
OpModelD
0
1
11
12
13
14
15
16
21
22
23
24
25
27
28
29
30
33
35
37
38
39
40
HC
2.85
2.43
3.12
3.36
3.53
3.53
3.53
3.53
2.78
3.08
2.97
1.80
1.80
1.80
1.80
1.80
1.80
2.45
2.16
2.16
2.16
2.16
2.16
CO
1.45
1.79
1.66
3.12
3.16
3.16
3.16
3.16
1.67
2.59
3.31
1.54
1.54
1.54
1.54
1.54
1.54
2.41
2.41
2.41
2.41
2.41
2.41
NOx
1.67
1.85
1.88
1.13
1.11
1.11
1.11
1.11
1.42
1.23
1.05
1.03
1.03
1.03
1.03
1.03
1.03
1.33
1.19
1.19
1.19
1.19
1.19
Figure 3-11 displays the resulting emission rates by operating mode bin and age group for the
LHD<14K, LHD45, MHD, and HHD gasoline vehicles, which were calculated by applying the
multiplicative age effects in Table 3-17.
106
-------
Figure 3-11. Emission rates by operating mode and age group for MY 1998-2007 vehicles in regulatory class
LHD <=14K, LHD45, MHD, and HHD gasoline vehicles.
7500-
5000-
2500-
o-
i 500"
"3) 400-
i 300-
8 100-
E
LJJ 0-
1000-
750-
500-
250-
A
:T
.A *
i *
A A
**
age
6+
0-5
A *
*
A
*
A
4 A
i i i i i i i i i i i i i i i i i i i i i i i
0 1 111213141516212223242527282930333537383940
opModelD
107
-------
3.1.1.6 Running Emission Rates for Regulatory Class LHD<=14 K, LHD45, and
MHD, andHHDfor 2008 and later model years
Of the on-road heavy duty vehicles GVW class 4 and above, a relatively small fraction are powered
by gasoline: about 15% are gasoline, as opposed to 85% diesel.xx The gasoline percentage
decreases as the GVW class increases. Since these vehicles are a small portion of the fleet, there is
relatively little data on these vehicles, and we did not update the 2008 and later model year
emission rates from MOVES201068 The 2008 and later model years are modeled with a 70%
reduction in the running rates starting in MY 2008, which is consistent with the emission standard
reduction with the "Heavy-duty 2007 Rule".69 The 2008 and later model year emission rates have
two age groups (0-5, and 6+) and the same relative multiplicative age effects as the pre-2007
emission rates, as shown in Figure 3-14. The analysis of regulatory class LHD<=10K (RegClassID
40) emission rates for 2008 and later model years is based on light-duty truck VSP-based emission
rates. We did not have load-based data on class 2b and 3 trucks to derive STP-based emission rates
specific for regulatory class LHD<=14K (RegClassID 41) trucks. As such, we estimate regulatory
class LHD<=14K trucks for 2008 -2017, using the relatively simple 70% reduction from the 1998-
2007 baseline
3.1.1.7 Running Emission Rates for Regulatory Class LHD<=14K for 2018 and
later model years
As discussed earlier, regulatory class LHD<=14K (regClassID 41) includes Class 2b and 3 trucks;
as such, the Tier 3 Vehicle Emission standards apply to 2b portion of this category. Rates for
vehicles in this regulatory class were developed in the same way as those for the LHD<=10k
regulatory class, as described in 3.1.1.4.
However, for these two classes, the rates for running operation differ in that those for regulatory
class LHD<=10K (RegClassID 40) are based on STP with a fixed mass factor of 2.06, whereas
those for regulatory class LHD<=14K (RegClassID 41) are based on STP with the same fixed mass
factor (17.1) used for the other heavy-duty regulatory classes.
For these two sets of rates, the absolute values of the running rates differ but the relative reductions
representing Tier-3 control in each model year are applied in the same proportions. These patterns
are shown in Figure 3-12 and Figure 3-13, which show rates for regulatory classes LHD<=10K and
LHD<=14K in selected operating modes for running emissions. Note that the results are shown on
logarithmic scales, and that the parallelism in the trends indicates that the proportional reductions
are identical for both the LHD<=10K (regClassID 40) and the LHD<=14K (regClassID 41) rates.
Note also that start rates for the two regulatory classes are identical, as they are not defined in terms
of STP.
[ Negligible portions are ran on other fuels. The figures are aggregated from data supplied by Polk.
108
-------
Figure 3-12. THC emission rates vs. model year for regulatory classes LHD<=10K and LHD<14K, showing
selected operating modes for the running-exhaust process (Note the logarithmic scale).
opModelD =
opModelD= 14
opMode!D =
10
1
0.1
•g- 0.01
3
o> 0.001
"ro
cc
0}
(fl
n
in
opModelD =
opModelD=28
opModelD =
-10
-1
0.1
0.01
-0.001
2017 2019 2021
2017 2019 2021
modelYearlD
2017
2019 2021
regClassName o LHD<=10K o LHD<=14K
109
-------
Figure 3-13. NO* emission rates vs. model year for regulatory classes LHD<=10K and LHD<14K, showing
selected operating modes for the running exhaust process (Note the logarithmic scale).
opModelD =
opModelD = 14
opModelD= 24
100 -
10
1
I" °-1 -
(31
-S 0.01
ct
01
to
03
I
ro
opModelD=27
opModelD= 28
opModeiD= 35
-100
-10
-1
-0.1
-0.01
2017 2019 2021
2017
2019 2021
modelYearlD
regClassName o LHD<=10K o LHD<=14K
2017 2019
2021
110
-------
Figure 3-14. Average emission rate (across all operating modes) for regulatory class LHD<=14K, LHD34 MHD
and HHD (RegClassIDs 41,42,46 and 47) for CO, HC, and NOx. Emission rates for 1960-1989, and 2022 - 2050
are constant.
15000-
10000-
,_ 5000-
0
-£= 0"
0)~ 400 -
CD
j- 300
o
CD 100-
CD
0) n_
CD U
CD
< 750-
500-
250
o-
41
.
~~\
"1
~^
J;
|
L
f--
1
'
»
*-
L.
•^
42,46,47
;
\
-i
- j
"H
L
\t
1
8
O
O
X
pollutant
- NOx
age
— o-5
1980 1990 2000 2010 2020 1980 1990 2000 2010 2020
modelYearlD
3.7.2 Paniculate Matter
Unfortunately, the available PM2.5 emission data from heavy-duty gasoline trucks were too sparse
to develop the detailed emission rates for which the MOVES model is designed at the time of
analysis. As a result, only a very limited analysis could be done. EPA will likely revisit and update
these emission rates when sufficient additional data on PM2.5 emissions from heavy-duty gasoline
vehicles become available.
In MOVES2010 and MOVES2014, the heavy-duty gas PM2.s emission rates are calculated by
multiplying the MOVES2010 light-duty gasoline truck PM2.5 emission rates by a factor of 1.40, as
explained below. Since the MOVES light-duty gasoline PM2.5 emission rates comprise a complete
set of factors classified by paniculate sub-type (EC and nonECPM), operating mode, model year
and regulatory class, the heavy-duty PM2.5 emission factors are also a complete set. No change to
the PM emission rates are made, because the HD 2007 Rule PM standards are not expected to
111
-------
change in-use emissions for medium and heavy-duty gasoline vehicles. As presented in the next
section, the MOVES2014 PM rates for 2008+ vehicles is based on HDDS results of 2.7 mg/mile,
while the standard for 2008+ spark-ignition vehicles is 20 mg/mile69.
3.1.2.1 Data Sources
The factor of 1.4 used to convert light-duty gasoline PM rates to heavy-duty rates was developed
based on PIVh.s emission test results from the four gasoline trucks tested in the CRC E55-E59 test
program. The specific data used were collected on the UDDS test cycle. Each of the four vehicles
in the sample received two HDDS tests, conducted at different test weights. Other emission tests
using different cycles were also available on the same vehicles, but were not used in the
calculation. The use of the UDDS data enabled the analysis to have a consistent driving cycle. The
trucks and tests are described in Table 3-18.
Table 3-18. Summary of data used in HD gasoline PM emission rate analysis
Vehicle
1
2
3
4
MY
2001
2001
1983
1983
1993
1993
1987
1987
Age
o
J
3
21
21
12
12
18
18
Test cycle
UDDS
UDDS
UDDS
UDDS
UDDS
UDDS
UDDS
UDDS
GVWR
[Ib]
12,975
19,463
9,850
14,775
13,000
19,500
10,600
15,900
PMi.s mg/mi
1.81
3.61
43.3
54.3
67.1
108.3
96.7
21.5
The table shows only four vehicles, two of which are quite old and certified to fairly lenient standards.
A third truck is also fairly old at twelve years and certified to an intermediate standard. The fourth
is a relatively new truck at age three and certified to a more stringent standard. No trucks in the
sample are certified to the Tier 2 or equivalent standards.
Examination of the heavy-duty data shows two distinct levels: vehicle #1 (MY 2001) and the other
three vehicles. Because of its lower age (3 years old) and newer model year status, this vehicle has
substantially lower PM emission levels than the others, and initially was separated in the analysis.
The emissions of the other three vehicles were averaged together to produce these mean results:
Mean for Vehicles 2 through 4: 65.22 mg/mi
Mean for Vehicle 1: 2.71 mg/mi
Older Group
Newer Group
3.1.2.2 Emission Rates for Regulatory Class LHD< =10K
To compare these rates with rates from light-duty gasoline vehicles, we simulated UDDS cycle
emission rates based on MOVES light-duty gas PM2.5 emission rates (with normal deterioration
assumptions) for light-duty gasoline trucks (regulatory class LOT. The UDDS cycle represents
standardized operation for the heavy-duty vehicles.
112
-------
To make the comparisons appropriate, the simulated light-duty UDDS results were matched to the
results from the four heavy-duty gas trucks in the sample. This comparison meant that the
emission rates from the following MOVES model year groups and age groups for light-duty trucks
were used:
• MY group 1983-1984, age 20+
• MY group 1986-1987, age 15-19
• MY group 1991-1993, age 10-14
• MY group 2001, age 0-3
The simulated PM2.5 HDDS emission factors for the older light-duty gas truck group using
MOVES2010b are 38.84 mg/mi 2.s(Ignoring sulfate emissions which are on the order of IxlO"4
. _
mg/mile for low sulfur fuels), This value leads to the computation of the ratio: - ^7 = 1.679.
38.84 rr-
mile
The simulated PM2.5 HDDS emission rates for the newer light-duty gas truck group are 4.687
mg/mi using MOVES2010b (Ignoring sulfate emissions(which are in the order of IxlO"5 mg/mile
for low sulfur fuels),
2.7 1^
This value leads to the computation of the ratio: - 21LS- = 0.578.
The newer model year group produces a ratio which is less than one and implied that large trucks
produce less PM2.5 emissions than smaller trucks. This result was intuitively inconsistent, and is the
likely result of a very small sample and a large natural variability in emission results.
Thus, all four data points were retained and averaged together by giving the older model year group
a 75 percent weighting and the newer model year group (MY 2001) a 25 percent weighting. This is
consistent with the underlying data sample. It produces a final ratio of:
RatiOfinal = Ratio olderWtFrac + Rationewer(l — WtFrac)
= 1.679x0.75 + 0.578x0.25 = 1.40
We then multiplied this final ratio of 1.40 by the light-duty gasoline truck PM rates to calculate the
input emission rates for heavy-duty gasoline PM rates. This approach works for regulatory class LHD
<= 40 (RegClassID 40) because the emission rates for both regulatory class LOT and LHD<=10K
are normalized to vehicle mass (or VSP-based emission rates).
As documented in the light-duty report8, the PM emission rates for light-duty vehicles were
revised in MOVES2014. This analysis used the light-duty truck PM emission rates from
MOVES2010b PM emission rates to derive the 1.4 ratio, and the subsequent heavy-duty gasoline
113
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PM emission rates. Hence, a comparison of PM emission rates in MOVES2014 between light-duty,
and LHD<= 10K, will yield a different ratio than the 1.4 derived for MOVES2010b.
3.1.23 Emission Rates for Regulatory Class LHD< =14 K, LHD45, MHD, andHHD
For the larger heavy-duty gasoline emission rates, the emission rates are STP-based with a fixed
mass factor of 17.1. Unlike the gaseous emission rates, we do not have sec/sec emission rates
associated with power output that would enable us to calculate a 17.1 metric ton STP-based PM
emission rates directly.
We used an indirect approach to derive STP-based PM emission rates from the emission rates
derived for the LHD <= 10K regulatory class. We assume that the relationship of HC between STP
and VSP based emission rates is a reasonable surrogate to map PM emission rates to STP-based
emission rates. To do so, we first calculated the emission rate ratio for HC emissions for each
operating mode between regulatory class LHD<=14K (RegClassID 41) and LHD<=10K
(RegClassID 40). We then multiplied this ratio to the PM emission rates in regulatory class
LHD<=10K (RegClassID 40) to obtain STP-based PM emission rates in the heavier regulatory
classes (RegClass IDs 41, 42, 46 and 47). An example of the regulatory class LHD<=10K PM
emission rates, STP/VSP HC ratios, and the calculated STP-based PM2.5 emission rates are
displayed in Table 3-19.
114
-------
Table 3-19. Derivation of STP-based PM emission rates from VSP-based rates using the ratio of HC VSP to STP
emission rates as a surrogate, using model year 2001 as an example.
opModelD
0
1
11
12
13
14
15
16
21
22
23
24
25
27
28
29
30
33
35
37
38
39
40
RegClassID 40
EC emission
rates (mg/hr)
0.59
0.54
0.60
0.79
1.38
2.62
5.55
64.52
8.38
2.92
2.08
2.92
10.94
20.50
126.42
523.16
2,366.75
26.59
10.76
13.29
43.61
75.73
74.96
HC STP to
VSP Ratio
1.000
1.000
1.000
2.263
3.677
5.095
5.443
5.427
1.000
1.154
2.173
2.825
4.842
7.906
8.796
6.471
7.102
2.121
4.780
4.010
8.979
9.522
5.300
RegClassID 41, 42, 46, 47 EC
emission rates (mg/hr)
0.59
0.54
0.60
1.78
5.08
13.37
30.22
350.13
8.38
3.37
4.52
8.24
52.95
162.10
1,112.05
3,385.32
16,809.50
56.40
51.42
53.28
391.56
721.06
397.26
3.1.3 Energy Consumption
3.1.3.1 LHD< =10K Energy Rates for Model Years 1960-2013
The energy rates for LHD<=10K gasoline pre-2007 energy rates are unchanged from the rates for
the LHD2b3 regulatory class in MOVES2010a. In MOVES2010a, the energy rates for this
regulatory class, along with the light-duty regulatory classes, were consolidated across weight
classes, engine size and engine technologies, as discussed in the MOVES2010a energy updates
report45.
115
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3.1.3.2 LHD< =10K Energy Rates for Model Years 2014-2050
For model years 2014 and later, lower energy consumption rates for LHD<=10K vehicles are
expected due to the Phase 1 Medium and Heavy Duty Greenhouse Gas Rule, as discussed in more
detail in Section 2.1.4.4. The CCh emission reductions for gasoline 2b trucks in Table 2-20 were
applied to the 2013 model year energy consumption rates in each running operating mode bin to
derive 2014 and later energy consumption rates. Figure 2-31 displays the average energy
consumption (across all running operating modes) for model years 1970 through 2030. The rates
are constant between 1960 to 1973, and from 2018 to 2050.
Figure 3-15. Average Energy Consumption Rates for LHD<=10K gasoline vehicles across all running operating
modes
regClassName
LHD<= 10k
O.Oe+OO-
1970
1980
1990
2000
modelYearlD
2010
2020
2030
116
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3.1.3.3 Energy Rates for LHD<=14K (Model Years 1960-2013), LHD45, MHD, and
HHD (Model Years 1960-2015)
The data used to develop heavy-duty running exhaust gasoline rates were the same as those used
for HC, CO, and NOX. However, new energy rates were only developed for LHD<=14K, LHD45,
MHD, and HHD regulatory classes. Similar to the diesel running exhaust energy rates, we made no
distinction in rates by model year(within the 1960-2013 range for LHD<=14K and within the 1960-
2015 range for the other heavy-duty regulatory classes), age, or regulatory class. To calculate
energy rates (kJ/hour) from CO2 emissions, we used a heating value (HV) of 122,893 kJ/gallon and
CO2 fuel-specific emission factor (fcoi) of 8,788 g/gallon for gasoline (see Equation 2-20). STP
was calculated using Equation 1-2. Figure 3-16 summarizes the gasoline running exhaust energy
rates stored in MOVES for the STP-based regulatory classes (LHD= <14K, MHD, and HHD).
Figure 3-16. Gasoline running exhaust energy rates for LHD<=14K (1960-2013), LHD45 (1960-2015), MHD
(1960-2015), and HHD (1960-2015)
V)
O
6 -i
5 -
4 -
01
4-»
3 -
ro
01
1 -
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating mode
A linear extrapolation to determine rates at the highest operating modes in each speed range was
performed analogously to diesel energy and NOX rates (see Section 2.1.1.4.1).
3.1.3.4 Energy Rates for LHD<=14k (2014-2050), LHD45, MHD, and HHD (2016-
2050)
Updates to the rates displayed in Figure 3-16 were made to the heavy-duty gasoline energy rates for
model years 2014+ based on the 2014 Medium and Heavy-duty Greenhouse Gas Rule47 as
discussed in Section 2.1.4.4. Figure 3-17 displays the average energy consumption rates for the
heavy-duty gasoline sources. The energy rates for all these source types are equivalent for model
117
-------
years 1960-2013. The reduction in the average energy consumption rates is displayed in Figure
3-17, with separate reductions for the class 2b and 3 trucks (LHD<=14k), class 4-7 trucks (LHD45,
MHD), and class 8 trucks (HHD). For LFID<=14k the energy rates are constant 2018 going
forward, for the other categories (LHD45, MHD, HHD) the energy rates are constant going
forward starting in model year 2017.
Figure 3-17. Average energy consumption rates for LHD<=14k (RegClassID 41), LHD45 (RegClassID 42), MHD
(RegClassID 46) and HHD (RegClassID 47) gasoline vehicles across all running operating modes.
4e+07-
03
c
0
'w
tn
;2e+07-
CD
c
CD
cDle+07-
O)
CD
>
Oe+00'
RegulatoryClass
41
42,46,47
2012
2014 2016
modelYearlD
2018
2020
3.2 Start Emissions
3.2.1 Emissions Standards
Emissions standards for the Federal Test Procedure (FTP) are shown in Table 3-20 for the two
applicable regulatory classes, LHD<14 and LHD>=14. These standards cover the model years
1990 through 2004. Note that the standards for CO and THC vary by regulatory class (LfflKlOk,
LHD<14k, LHD45) but not by model year, whereas those for NO* vary by model year but not by
regulatory class. Note that for model years 2005-2007 a single standard was applied for
NMHC+NOx, but that by 2008 separate but lower standards were again in effect. Note also that by
model year 2008, the standards for all three regulatory classes were uniform for the three gaseous
pollutants.
118
-------
Table 3-20. FTP Standards (g/hp-hr) for heavy-duty gasoline engines for Model years 1990-2016.
Model-Year
Group
1990
1991-1997
1998-2004
2005-2007
2008-2016
GVWR< 14,000 Ib
CO
14.4
14.4
14.4
14.4
14.4
HC1
1.1
1.1
1.1
NO*
6.0
5.0
4.0
l.O2
0.14
0.20
GVWR> 14,000 Ib
CO
37.1
37.1
37.1
37.1
14.4
HC1
1.9
1.9
1.9
NO*
6.0
5.0
4.0
l.O2
0.14
0.20
1 Expressed as non-methane hydrocarbons (NMHC).
2 Standard expressed as NMHC + NO*.
3.2.2 Available Data
To develop start emission rates for heavy-duty gasoline-fueled vehicles, we extracted data available
in the USEPA Mobile-Source Observation Database (MSOD). These data represent aggregate test
results for heavy-duty spark-ignition (gasoline powered) engines measured on the Federal Test
Procedure (FTP) cycle. The GVWR for all trucks was between 8,500 and 14,000 Ib, placing all
trucks in the MOVES2010b LHD2b3 regulatory class. In MOVES2014, LHD<=10K and
LHD<=14K have identical start rates that are unchanged (except for the implementation of the Tier
3 rule) from LHD2b3 start emission rates from MOVES2010b.
Table 3-21 shows the model-year by age classification for the data. The model year groups in the
table were designed based on the progression in NOx standards between MY 1990 and 2004.
Standards for CO and HC are stable over this period, until MY 2004, when a combined NMHC+
NOx standard was introduced. However, no measurements for gasoline HD trucks were available
for MY2004 or later.
Start emissions are not dependent on power, and the emission rates do not need to be calculated
differently to distinguish VSP/STP or different scaling as was done for running exhaust rates. As
discussed later, start emission rates are separated by regulatory classes to account for differences in
the emission standards and/or available test data.
119
-------
Table 3-21. Availability of emissions start data by model-year group and age group. NOTE: this table represents
vehicles with GVWR < 14,000 Ib.
Model-year Group
1960-1989
1990
1991-1997
1998-2004
Total
Age Group (Years)
0-3
73
8
81
4-5
59
59
6-7
1
32
33
8-9
19
29
4
52
10-14
22
22
Total
41
30
168
8
247
3.2.3 Estimation of Mean Rates
As with light-duty vehicles, we estimated the "cold-start" as the mass from the cold-start phase of
the FTP (bag 1) less the "hot-start" phase (Bag 3). As a preliminary exploration of the data, we
averaged by model year group and age group and produced the graphs shown in Appendix F.
Sample sizes are small overall and very small in some cases (e.g. 1990, age 6-7) and the behavior
of the averages is somewhat erratic. In contrast to light-duty vehicle emissions, strong model-year
effects are not apparent. This may not be surprising for CO or HC, given the uniformity of
standards throughout. This result is more surprising for NOx but model year trends are no more
evident for NOx than for the other two. Broadly speaking, it appears that an age trend may be
evident.
If we assume that the underlying population distributions are approximately log-normal, we can
visualize the data in ways that illustrate underlying relationships. As a first step, we calculated
geometric mean emissions, for purposes of comparison to the arithmetic means calculated by
simply averaging the data. Based on the assumption of log-normality, the geometric mean (xg) was
calculated in terms of the logarithmic mean (xi) as
Equation 3-3
This measure is not appropriate for use as an emission rate, but is useful in that it represents the
"center" of the skewed parent distribution. As such, it is less strongly influenced by unusually high
or outlying measurements than the arithmetic means. In general, the small differences between
geometric means and arithmetic means suggest that the distributions represented by the data do not
show strong skew in most cases. Because evidence from light-duty vehicles suggests that
emissions distributions should be strongly skewed, this result implies that these data are not
representative of "real-world" emissions for these vehicles. This conclusion appears to be
reinforced by the values in Figure F-3 which represent the "logarithmic standard deviation"
calculated by model-year and age groups. This measure (si), is the standard deviation of natural
logarithm of emissions (xi). The values of 5; are highly variable, and generally less than 0.8,
showing that the degree of skew in the data is also highly variable as well as generally low for
120
-------
emissions data; e.g., corresponding values for light-duty running emissions are generally 1.0 or
greater. Overall, review of the geometric means confirms the impression of age trends in the CO
and HC results, and the general lack of an age trend in the NO* results.
Given the conclusion that the data as such are probably unrepresentative, assuming the log-normal
parent distributions allows us to re-estimate the arithmetic mean after assuming reasonable values
for 5;. For this calculation we assumed values of 0.9 for CO and HC and 1.2 for NO*. These values
approximate the maxima seen in these data and are broadly comparable to rates observed for light-
duty vehicles.
The re-estimated arithmetic means are calculated from the geometric means, by adding a term that
represents the influence of the "dirtier" or "higher-emitting" vehicles, or the "upper tail of the
distribution," as shown in Figure F-4.
v _ v „ 2 Equation 3-4
•*a--*ge
For purposes of rate development using these data, we concluded that a model-year group effect was
not evident and re-averaged all data by age group alone. Results of the coarser averaging are
presented in Figure 3-18 with the arithmetic mean (directly calculated and re-estimated) and
geometric means shown separately.
We then addressed the question of the projection of age trends. As a general principle, we did not
allow emissions to decline with age. We implemented this assumption by stabilizing emissions at the
maximum level reached between the 6-7 and 10-14 age groups.
121
-------
Figure 3-18. Cold-start FTP Emissions for heavy-duty gasoline trucks, averaged by age group only (g:
geometric mean, a= arithmetic mean recalculated from xi and si)
Age (years)
Age (years)
Age (years)
3.2.4 Estimation of Uncertainty
We calculated standard errors for each mean in a manner consistent with the re-calculation of the
arithmetic means. Because the (arithmetic) means were recalculated with assumed values of si, it was
necessary to re-estimate corresponding standard deviations for the parent distribution s, as shown in
Equation 3-5.
122
-------
Equation 3-5
After recalculating the standard deviations, the calculation of corresponding standard errors was
simple. Because each vehicle is represented by only one data point, there was no within-vehicle
variability to consider, and the standard error could be calculated as Sl\n. We divided the standard
errors by their respective means to obtain CV-of-the-mean or "relative standard error." Means,
standard deviations and uncertainties are presented in Table 3-22 and in Figure 3-19. Note that these
results represent only "cold-start" rates (opModelD 108).
Table 3-22. Cold-start emission rates (g) for heavy-duty gasoline trucks, by age group (italicized values
replicated from previous age groups)
Age Group
n
Pollutant
CO
THC
NOx
Means
0-3
4-5
6-7
8-9
10-14
81
59
33
52
22
101.2
133.0
155.9
190.3
189.1
6.39
7.40
11.21
11.21
11.21
4.23
5.18
6.12
7.08
7.08
Standard Deviations
0-3
4-5
6-7
8-9
10-14
108.1
142.0
166.5
203.2
202.0
6.82
7.90
11.98
11.98
11.98
8.55
12.39
14.32
14.32
Standard Errors
0-3
4-5
6-7
8-9
10-14
12.01
18.49
28.98
28.18
43.06
0.758
1.03
2.08
2.08
2.08
0.951
1.18
2.16
1.99
1.99
123
-------
Figure 3-19. Cold-start emission rates for heavy-duty gasoline trucks, with 95% confidence intervals
•3 200
•K
JS icn
3
O 100
^ 50
(a) CO 1
j.
I^^T
S
r -|
*
0 5 10 15 20 2
Age (yea is)
10
fi
4
2
/l->\ Tl If*
(a) THC
-
^^
/
-
D
f 1 1 1 1 1
0 5
10 15 20 2
Age (years)
T
n
(c
)NOx
4
^
^
^'
-,
J
>
0 5 10 15 20 2
Age (years)
3.2.5 Projecting Rates beyond the Available Data
The steps described so far involved reduction and analysis of the available emissions data. In the next
step, we describe approaches used to impute rates for model years not represented in these data. For
purposes of analysis we delineated four model year groups: 1960-2004, 2005-2007, 2008-2017 and
2018 and later. The rates above were used for the 1960-2004 model year group. We describe the
derivation of rates for the remaining groups below.
124
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3.2.5.1 Regulatory class LHD<=1 OK and LHD<=14K (RegClassID 40 and 41)
For CO the approach was simple. We applied the values in Table 3-22 to all model -year groups. The
rationale for this approach is that the CO standards do not change over the full range of model years
considered.
For HC and NOx we imputed values for the 2005-07 and 2008-2017 model -year groups by
multiplying the values in Table 3-22 by ratios expressed in terms of the applicable standards. Starting
in 2005, a combined HC+NOx standard was introduced. It was necessary for modeling purposes to
partition the standard into HC and NOx components. We assumed that the proportions of NMHC
and NOx would be similar to those in the 2008 standards, which separate NMHC and NOx while
reducing both.
We calculated the HC value by multiplying the 1960-2004 value by the fraction^kc, where
(l.0g/hp-hr)
v
.
_ v (0.14 +0.20) g/hp -hr _ Equation 3-6
/ ur1 — — \J . 3 I
l.lg/hp-hr
This ratio represents the component of the 2005 combined standard attributed to NMHC. We
calculated the corresponding value for NOx as
0.20 g/hp - hr ,
1.0 g/hp -hr
_ v(0.14 +0.20) g/hp -hr ) _ n 1A7 Equation 3-7
/NOX ~~77; ~n ;—0.147
4.0 g/hp -hr
For these heavy-duty rates we neglected the THC/NMHC conversions, to which we gave attention
for light-duty.
For the 2008-2017 model years, the approach to projecting rates was modified to adopt two
refinements developed for light-duty rates. First, start emission rates from the LHDH<10K and
LHD<14K gasoline vehicles were estimated by applying the "start split-ratio" shown in Table 3-10
to a set of rates representing light-duty vehicles in Tier-2/Bin 8. Second, start emission rates
adopted the same age effects as the light-duty start emission rates. The multiplicative age effects
for start emission rates for vehicles in model years 2008-2017 are shown in Table 3-23.
125
-------
Table 3-23. Multiplicative age effect used for start emissions for LHD<=10K and LHD<=14K vehicles for 2008-
2017 model years. Adopted from the deterioration effects for Light Duty Trucks vehicles from the Light-Duty
Emission Rate Report.8
ageGroupID
3
405
607
809
1014
1519
2099
HC
1
1.65
2.20
2.68
3.30
3.66
4.42
CO
1
1.93
2.36
2.54
3.00
3.35
4.06
NO*
1
1.73
2.21
2.76
3.20
3.63
4.11
3.2.5.2 Incorporating Tier-3 Standards: Model years 2018 and later.
Emission rates for the start-exhaust process were developed employing the techniques described for
running-exhaust emissions, as described above in 3.1.1.4. Start rates for HC, CO and NO* during
the Tier-3 phase-in (2018-2022) are shown below in Figure 3-20 to Figure 3-22. Note that start
rates are identical for both the LHD<10K and LHD<14K regulatory classes (regClassID = 40 and
41, respectively).
126
-------
Figure 3-20. THC: Emission rates for the cold start-exhaust process, for the LHD<=10k (RegClassID 40) and
the LHD<=14k (RegClassID 41) regulatory classes, by operating mode and age group, during the Tier-3 phase-
in.
o
CO
c
'^
O)
1
ageGrouplD = 3
ageGrouplD = 405
ageGrouplD = 809
ageGrouplD = 1014
ageGroup!D= 2099
ageGrouplD = 607
ageGroup!D = 1519
2017
2019
2021
2017
2019
2017 2019 2021
modelYeailD
opModelD o 101 o 102 a 103 o 104 ^~inS o~106 o 107 o 108
2021
127
-------
Figure 3-21. CO: Emission rates for the cold start-exhaust process, for the LHD<=10k (RegClassID 40) and the
LHD<=14k (RegClassID 41) regulatory classes, by operating mode and age group, during the Tier-3 phase-in.
ageOrouplD = 3
40
30-
20-
10-
0
40
30
20
10
0
ageGrouplD= 809
ageGrouplD = 2099
ageGrouplD = 405
ageGrouplD = 1014
ageGrouplD= 607
ageGrouplD = 1519
-40
-3D
-20
-10
2017 2019 2021
2017 2019 2021
modelYearlD
— e — Q
2021
D
o 105 o
1
2017 2019
06 a 107 o 108
• e o
0 0
2021
-0
-
128
-------
Figure 3-22. NO*: Emission rates for the cold start-exhaust process, for the LHD<=10k (RegClassID 40) and
the LHD<=14k (RegClassID 41) regulatory classes, by operating mode and age group, during the Tier-3 phase-
in.
ageGrouplD = 3
ageGrouplD = 405
ageGrouplD = 607
2.0-
1.5-
1.0-
0.5
0.0-
f
ageGrouplD= 809
ageGrouplD = 1014
ageGrouplD= 2099
2.0-
1.5-
1.0-
0.5-
0.0
ageGrouplD =
-2.0
-1.5
-1.0
0.5
-0.0
2017 2019
2021
2017 2019 2021 2017 2019
modelYearlD
opModelD o 101 o 102 o 103 o 104 o 105 o 106 o 107 o 108
2021
3.2.5.3 Regulatory classes LHD45, MHD, and HHD
Since continuous data were lacking for vehicles in classes LHD45 and MHD, we estimated cold
start values relative to the LHD2b3 start emission rates estimated in MOVES2010.
For CO and HC, we estimated rates for the heavier vehicles by multiplying them by ratios of
standards for the heavier class to those for the lighter class.
The value of the ratio for CO based on 1990-2004 model year standards is
/CO ~
37.1g/hp -hr
14.4 g/hp - hr
= 2.58
Equation 3-8
and the corresponding ratio for HC for 1990-2004 model year vehicles is 1.73.
129
-------
/HC ~~
1.9g/hp -hr
1.1 g/hp - hr
= 1.73
Equation 3-9
The ratios derived in the previous two equations (2.58 and 1.73) are applied to estimate the start
emission rates for the first three model year groups for the LHD45, MHD, and HHD gasoline
vehicles (Table 3-24). Note that the ratios for CO and HC do not vary by model year group because
the standards do not; See Table 3-20 (page 119).
For NO*, all MOVES2014 start emissions for medium and heavy-duty vehicles are equal to the
MOVES2010 LHD2b3 start emission rates, because the same standards apply to both classes
throughout. The approaches for all three regulatory classes in all three model years are summarized
in Table 3-24.
The outcomes of the methods described in the table are summarized graphically in Figure 3-24 for
cold-start emissions. The decline in start emissions with the adoption of more stringent standards is
shown over the period between model years 1990 and 2022, at the completion of the phase-in of
Tier 3 standards for vehicles with GVWR <14,000 Ib.
Table 3-24. Methods used to calculate start emission rates for heavy-duty spark-ignition engines
Regulatory Class
LHD<= 10K and
LHD < 14K
LHD45, MHD, HHD
Model-year
Group
1960-2004
2005-2007
2008 - 2017
2018 +
1960-2004
2005-2007
2008 +
Method
CO
Values from
Table 3-22
Values from
Table 3-22
Values from
Table 3-22
Section 3.2.5.2
Increase in proportion
to standards
Increase in proportion
to standards
Increase in proportion
to standards from
LHD2b3
THC
Values from
Table 3-22
Reduce in
proportion
to standards from 1960-
2004
Section 3.2. 5.1
Section 3.2. 5. 2
Increase
in proportion
to standards from
LHD2b3
Increase in proportion
to standards from
LHD2b3
Increase in proportion
to standards from
LHD2b3
NO*
Values from
Table 3-22
Reduce in proportion
to standards from 1960-
2004
Section 3. 2. 5.1
Section 3. 2. 5. 2
Same values as
LHD2b3
Same values as
LHD2b3
Same values as
LHD2b3
130
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Figure 3-23. Cold-Start rates (opmodeID=108) vs. model year, by pollutant, for heavy-duty gasoline vehicles in
two regulatory classes. NOTE: the reference lines indicate the model years 2004,2005,2007,2008 and 2017,
respectively.
250
200-
150-
100-
50-
0-
Cfi
"S 8-
ce
CD
S 6-
CD
to i _
CD *
E
2-
4-
3-
2-
1
0-
Pollutant =
CO
\
\
Pollutant=THC
I
1
\
-
Pollutant =
\
\
i
N
NOx
1
\
i i i i
1990 2000 2010 2020
mo<
JelYearl
<=14K -
3
As we did for for heavy-duty diesel and light-duty vehicles we applied the curve in Figure 2-36 to
adjust the start emission rates for varying soak times. The rates described in this section were for
cold starts (soak time > 720 minutes).
3.2.5.4 Paniculate Matter
Data on PM start emissions from heavy-duty gasoline vehicles were unavailable. As a result, we
used the multiplication factor from the running exhaust emissions analysis of 1.40 to scale up start
emission rates for light-duty trucks (regClassID 30) for model years 1960-2017 (Section 3.1.2.2).
For 2018 and later model years, the start PM emissions for heavy-duty gasoline are estimated to be
131
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the same as the rates in 2017.XX1 As such, the start PM emission rates for heavy-duty gasoline
vehicles exhibit the same relative effects of soak time, and deterioration as the light-duty PM start
emission rates.
3.2.6 Start Energy Rates
The MOVES2014 energy rates are displayed in Figure 3-24. The heavy-duty gasoline start energy
rates were originally derived in MOVES2004, and updated in MOVES2010a as described in the
corresponding reports.45 As shown, there is substantial variability in the start rates between 1974
and 2000. As discussed in Section 2.1.4.1, the detailed methodology used in MOVES2004 (which
modeled different emission rates according to vehicle weights, engine technologies, and engine
sizes) introduced variability into the energy rate within the current MOVES regulatory class
emission rates.
Table 3-25 displays the relative contribution of running and start operation to total energy
consumption from the heavy-duty gasoline regulatory classes from a national MOVES run for
calendar year 2011. As for diesel vehicles, starts are estimated to be a relatively small contributor
to the total energy demand of vehicle operation. Due to the small contribution to the total energy
inventory, we have not prioritized updating the heavy-duty gasoline start emissions rates.
Table 3-25. Relative contribution of total energy consumption from each pollutant process by regulatory class
for heavy-duty gasoline vehicles in calendar year 2011.
processID
1
2
processName
Running
Exhaust
Start Exhaust
LHD<=10K
96.3%
3.7%
LHD<=14K
98.9%
1.1%
LHD45
99.0%
1.0%
MHD
98.1%
1.9%
HHD
98.1%
1.9%
The start energy rates are reduced for shorter soak times using the same factors for diesel vehicles,
as presented in Table 2-28. The energy rates also increase with cold temperatures using the
temperature effects documented in the 2004 Energy Report.52
The only changes to the start energy rates between MOVES2010b and MOVES2014 is the
projected impact of the Phase 1 Heavy-duty GHG standards, which begin phase-in in 2014 and
have the same reductions as the running energy rates, as presented in Table 2-19.
XX1 The light-duty PM start rates are projected to decrease in model year 2018 with the implementation of the Tier-
3Vehicle Emissions and Fuel Standards Program. As discussed in Section 3.1.2, Tier 3 is not expected to impact the
PM emissions of heavy-duty gasoline vehicles. MOVES2014 does not model a reduction in HD PM start emissions
from the Tier 3 program, so the 1.4 scaled ratio is not applicable for 2018 and later model years.
132
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Figure 3-24. Heavy-duty gasoline cold start energy rates (opMode 108) by model year and regulatory class.
6000-
-c
£
~
=5
0)
U>
|^000
(5
c
LJ
o-
regClassName
LHD<= 10k
-^ LHD<= 14k
— LHD45
-+- MHD67
-B- HHD8
1960
1980
2000
modelYearlD
2020
133
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4 Heavy Duty Compressed Natural Gas Transit Bus Emissions
While natural gas lacks the ubiquitous fueling infrastructure of gasoline, compressed natural gas
(CNG) has grown as a transportation fuel for public transit, government, and corporate fleets. Such
fleets typically utilize centralized, privately-owned refueling stations. Within this segment, some of
the most rapid growth in CNG vehicles over the last 15 years has occurred among city transit bus
fleets, as seen in Figure 4-1.70
Figure 4-1. US natural gas bus population by year and fuel type for 1996-2011 (APTA)71
100%
90%
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
• DIESEL • ELECTRICITY & HYBRIDS • GASOLINE • NATURAL GAS BOTHER
MOVES2010b and earlier versions can model emissions from CNG bus fleets. However, in
absence of better data, MOVES2010b used the emission rates originally developed for medium
heavy-duty gasoline trucks (regulatory class 46). These rates were used for hydrocarbon (HC),
nitrogen oxides (NOx), carbon monoxide (CO), and particulate matter (PM) emission rates.72
Medium HD gasoline trucks are reasonable proxies in terms of vehicle weight and engine size, but
as this report shows, there are substantial differences between the MOVES2010b emissions rates
and real-world measurements of CNG transit buses. This section describes MOVES2014 updates
to the CNG bus emission rates in MOVES based on measurements from CNG buses and future
projections.
4.1 Transit Bus Driving Cycles and Operating Mode Distributions
4.1.1 Heavy-Duty Transit Bus Driving Cycles
To evaluate whether the existing MOVES2010b rates for gasoline vehicles were appropriate
surrogates for buses powered with CNG, we generated test cycle simulations using MOVES and
compared the simulated results against chassis dynamometer measurements from published test
programs. This process involved using MOVES to determine the distribution of operating modes
134
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for each drive cycle, and then multiplying the time spent in each mode by the corresponding
emission rates in the EmissionRateByAge table. As in a transient emissions test, the sum of the
emissions at each second over the duration of the test yields the total mass of emissions over the
test cycle. Dividing the total by distance yields the emission rate over the test. These test programs
included only running emissions and were based on a variety of heavy-duty and transit bus driving
cycles. We configured MOVES to simulate the drive cycles by importing each drive cycle into
MOVES using the Link Driving Schedules template in the Project Data Manager tool. As these
were dynamometer measurements, we set the grade to "0" over the duration of each cycle. We
imported two driving cycles: 1) the Central Business District (CBD), and 2) Washington
Metropolitan Area Transit Authority (WMATA).
The CBD cycle is defined as a driving pattern with constant acceleration from rest to 20 mph, a
short cruise period at 20 mph, and constant deceleration back to rest, repeated for 600 seconds (see
Figure 4-2).73 The WMATA cycle was developed using GPS data from city buses in Washington,
DC, and has higher speeds and greater periods of acceleration than the CBD cycle (see Figure
4-3).75
Figure 4-2. Driving schedule trace of the Central Business District (CBD) cycle 74
'It
•
0 -
|
CBD
0 100 200 300 400 500 60
Time, s
135
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Figure 4-3 Driving schedule trace of the Washington Metropolitan Area Transit Authority (WMATA) cycle
CO
i
L
I20
10
1 1 111 ' I
./. I'll i '
111.11, lit
1 1
0 500 1000
Time (sec)
1
1
i
.
I
1
1
T
1500 2000
4.1.2 Transit Bus Operating Mode Distributions
The MOVES project level importer was used to input the second-by-second drive cycle. A single
link was created, with the test cycle entered as a drive trace. Running MOVES generated the
operating mode distribution, which is created by allocating the time spent in each operating mode
according to the cycle speed and acceleration, as shown in Figure 4-4 and Figure 4-5. The
derivation of scaled tractive power (STP) and operating mode attribution for heavy-duty vehicles
are discussed earlier in this report, in Section 1.3.
Since STP is dependent on mass (among other factors), the average vehicle inertial test mass for
each cycle was inserted into the MOVES2010b sourceUseType table in place of the default transit
bus mass to ensure a more accurate simulation. Using the measured vehicle masses across all the
test programs reviewed, the CBD cycle had an average test mass of 14.957 metric tons and the
WMATA cycle had an average mass of 16.308 metric tons, compared to the MOVES2010b default
of 16.556 metric tons. We used the road load coefficients from MOVES2010b for transit buses, and
any changes in the coefficients (A, B, and C) with the tested buses were assumed to be negligible.
Figure 4-4. Operating mode distribution for the CBD cycle
350
7 SO
700 -
1 RO -
mn
so -
O -
.1
ll
1
0 I 1112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
136
-------
Figure 4-5. Operating mode distribution for the WMATA cycle
900
800
700
-. 600
J2. 500
j= 400
^ 300
200
100
t
ffi
0 I 1112 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
Operating Mode
4.2 Comparison of Simulated Rates and Real- World Measurements
4.2.1 Simulating Cycle Emission Aggregates from MO VES201 Ob Rates
Having determined the total amount time spent in each operating mode over the course of each
drive cycle, using the emission rates in the MOVES database (DB), we were able to simulate
emissions over each cycle. Using this method, the simulated cycle emission aggregates were
calculated as a function of the following parameters:
• fuel type,
• driving cycle,
• age group,
• regulatory class,
• model year, and
• pollutant and process.
We simulated a distance-specific emission factor (EFsim, g/mile) for each pollutant for each cycle
based on the operating mode distributions, existing MOVES emission rates, and the distance of the
drive cycle, using the equation below:
V t r
/ , l OM .cycle ' p,OM
OM_ _
Equation 4-1
sim ,p, cycle
where
toM.cyde = cycle's total time spend in operating mode OM,
dcyde = distance of the cycle,
rP,oM = time-specific emission rate of pollutantp in operating mode OM.
137
-------
We compared the published test measurements to simulations using the MOVES2010b CNG transit
bus rates from Equation 4-1. We also specified the age group and model year to match individual
vehicles in the testing programs from the literature on CNG transit buses.
4.2.2 Published Chassis Dynamometer Measurements
The real-world data was collected from programs that were conducted at several research locations
around the country on different heavy-duty chassis dynamometer equipment. In our analysis, we
collected 35 unique dynamometer measurements—which consisted of running emissions rates in
mass per unit distance for each of the pollutants and total energy below:
1. total hydrocarbons (THC),
2. methane (CH4),
3. carbon monoxide (CO),
4. oxides of nitrogen (NOx),
5. particulate matter (EC + non-EC), and
6. total energy consumption.
Note that, in MOVES, methane emissions are not estimated using emission rates, as are the other
pollutants listed above. Rather, methane is estimated in relation to THC, using ratios stored in the
MethaneTHCratio table. The ratios are categorized by fuel type, pollutant process, source type,
model-year group, and age group. MOVES multiplies the THC rate by the corresponding ratio
from the "methanethcratio" table to calculate the CH^ate.
All criteria emission rates are dependent on vehicle age, and thus are stored in the
emissionRateByAge table. Total energy consumption is age independent, and therefore stored in
the EmissionRate table. Some of the published studies did not report total energy consumption
directly, so it was necessary to compute energy from a stoichiometric equation based on the carbon
content in the emitted pollutants or from reported values of miles per gallon equivalent of diesel
fuel. In the former case, we used 0.8037 as the carbon fraction coefficient for non-methane
hydrocarbons (NMHC) when the bus was equipped with an oxidation catalyst and 0.835 without
due to high ethene levels, using speciation profiles from Ayala et al. (2003)76 discussed later in this
section. All other conversion factors to energy were taken from Melendez et al. (2005).75
On a similar note, MOVES does not report paniculate matter (PM) as a single rate; it reports one
rate for PM from elemental carbon (EC) of 2.5 microns or less, and another rate for non-elemental
carbon of 2.5 microns or less. These separate rates for PM (EC) and PM (NonEC) from the
emissionRateByAge table are added together for a total PM rate used for comparison to the
measurements.
Table 4-1 shows a summary of the number of unique CNG bus measurements by driving cycle for
each study. Navistar published a similar study of CNG and diesel buses in 2008, and this analysis
shares many of the same sources.77All of the vehicles were in service with a transit agency at the
time of testing. The number of unique measurements are typically equal to the number of vehicles
tested and the measurements were typically reported as averages based on multiple runs with the
same vehicle and configuration over a specific driving cycle with the exception of measurements
reported by Ayala et al. (2002)78 and Ayala et al. (2003).76 In the Ayala et al. (2002) study the
2000 model year CNG bus was tested and then retested after approximately two months of
138
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service,78 which we treated as independent measurements. Ayala et al. (2003) again retested the
same 2000 CNG bus in their previous study; however, the bus had accumulated an additional
35,000 miles and was serviced by the OEM to be equipped with an oxidation catalyst that was later
removed for baseline testing. Ayala et al. (2003) conducted duplicate tests under each
vehicle/aftertreatment configuration, which we considered four independent measurements.
Table 4-1. Summary of external emissions testing programs by driving cycle and number of unique
measurements and their corresponding model years
Paper/Article
Melendez 200575
Ayala 200278
Ayala 200376
Lanni 200379
McCormick
199980
LaTavec 200281
McKain 200082
Clark 199783
TOTAL
Lead Research Unit
National Renewable Energy Laboratory (NREL)
California Air Resources Board (CARB)
CARB
New York Department of Environmental
Conservation
Colorado School of Mines
ARCO (a BP Company)
WVU
wvu
Driving
Cycle(s)
WMATA
CBD
CBD
CBD
CBD
CBD
CBD
CBD
Model Year
(Number of
Measurements)
2001(4), 2004(3)
2000 (2)
2000 (4), 2001 (2)
1999 (3)
1994 (2)
2001 (1)
1999 (3)
1996 (10)
34
As seen above, the CBD driving cycle was applied in each study except for one. Since this cycle (a)
had the largest sample size and (b) appeared to be representative of the data from other cycles, we
focused our MOVES2010b comparisons on the CBD cycle results.
We approximated the vehicle's age by subtracting the year the study was conducted from the
model year of the vehicle. Most vehicles tested were less than three years old (ageGroupID "3"),
whereas 9 vehicles fell into the four to five year-old age group (ageGroupID "405"). In the CBD
cycle, 5 out of 28 vehicles were in ageGroupID "405", and their performance was generally similar
to the 0-3 age vehicle results. Consequently, we combined the vehicles from age group 405 with
the vehicles from group 3.xxu Vehicle model years ranged from MY 2001 to MY 2004 for the
WMATA cycle and from MY 1994 to MY 2001 for the CBD cycle.
XX11 Note that for MY 1994 in Figure 4-6 through Figure 4-10, CNG (MHD gasoline) MOVES2010b rates are based on
age group 405. All other MOVES2010b rates are based on age group 3.
139
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4.2.3 Plots of Simulated Aggregates and Published Measurements
Below are graphs of the CBD measurements by model year for each pollutant compared to
simulated MOVES2010b CNG (MHD gasoline) rates.
Figure 4-6. NOx emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b
simulated aggregates on the CBD cycle.
"** An
,e
p
ro
k.
*52
x ^n
n -
A
A
A
* *
; « 1 .
^ Measurements CNG
(0-3 Age Group)
A Measurements CNG
(4-5 Age Group)
-•-MOVES 2010b CNG
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Model Year
Figure 4-7. CO emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b
simulated aggregates on the CBD cycle.
70
60
50
.=. 40
U)
|
_M 30
8
20
10
0
^ Measurements CNG
(0-3 Age Group)
A Measurements CNG
(4-5 Age Group)
-•-MOVES 2010b CNG
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Model Year
140
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Figure 4-8. PM emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b
simulated aggregates on the CBD cycle.
0.3
0.25
v 0.2
1
-------
Figure 4-10. CH4 emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b
simulated aggregates on the CBD cycle.
70 -
5"
i
CD
ClO -I/-)
u
c .
°
+ •
I * I
o
A ^
•
A
• Measurements CNG (0-3
Age Group)
A Measurements CNG (4-5
Age Group)
-•-MOVES 2010b CNG
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Model Year
142
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Figure 4-11. Total energy consumption comparisons of CNG transit bus dynamometer measurements and
MOVES2010b simulated aggregates on the CBD cycle.
60000
50000
-• 40000
30000
20000
10000
0
-I-
^ Measurements CNG (0-3
Age Group)
A Measurements CNG (4-5
Age Group)
-•-MOVES 2010b CNG
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Model Year
In Figure 4-6, the MOVES2010b CNG rates slightly under-predict the bus NOx measurements. As
shown in Figure 4-7, MOVES2010b predictions for CO emissions are similar to the CNG
measurements, particularly after 1999. Figure 4-8 shows that the MOVES2010b CNG predictions
are lower for PM. As seen in Figure 4-9, MOVES2010b CNG predictions for THC emissions are
lower than the measurements by an order of magnitude. As seen in Figure 4-10, this underestimate
of THC is largely attributable to a significant underestimate of CNG related CFLt in MOVES2010b.
These relatively high CFLt emissions from CNG buses compared to gasoline or diesel buses are
likely from the exhaust of un-combusted natural gas, but further study is warranted.
Figure 4-11 shows that MOVES2010b under-predicts the total energy consumption seen in the
literature. Thus, we concluded that the MOVES2010b CNG rates based on MHD gasoline truck
rates were not adequate. As discussed in the next section, we developed new rates based on cycle
averages from the dynamometer measurements.
4.3 Development of New Running Exhaust Emission Rates
Ideally, new MOVES emission rates are developed through analysis of second-by-second data of
vehicles of the appropriate regulatory class, model year, and age. Unfortunately, such modal data
was not readily available in this case. However, we substantially improved the CNG bus emission
rates in MOVES2014 relative to MOVES2010b by raising or lowering the MY emission rates as a
group (as opposed to individual adjustments by operating mode).
143
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4.3.1 Determining Model Year Groups
First we evaluated the measured criteria pollutant rates (THC, CO, NOx and PM) to establish
model year groups. Initially, we separated CNG buses equipped with aftertreatment (oxidation
catalysts) and those not equipped, to determine if this was a reasonable distinction, and to see if
these vehicles' emission rates for criteria pollutants varied by model year and by age. For some
model years, there are both after-treatment equipped and non-equipped vehicles. Criteria emission
improvements between the vehicles with after-treatment (AT) equipment and those without were
primarily inconclusive and did not exhibit any clear trends.xxm Therefore, we chose to group all the
CBD measurements from the literature into one model year group, spanning from MY 1994 to MY
2001. No data on CNG buses equipped with three-way catalysts (TWC) was readily available at the
time of this analysis; we will look to incorporate data from buses that have TWCs and spark
ignited, stoichiometric-burn engine technology as it becomes available.
Of the surveyed data, only one study had any vehicles newer than MY 2001.xxiv'84 This paper, a
joint study between NREL and WMATA, had a small sample of vehicles from MY 2004. These
vehicles have a visibly different emissions profile than the other vehicles.75 While these buses were
only tested on the WMATA driving cycle, they were all equipped with oxidation catalysts and had
substantially lower emissions from the 1994-2001 buses, particularly for PM emissions. As a result,
we created a second model year group from MY 2002 to MY 2006 based on the MY 2004 John
Deere WMATA buses. This MY group ends before MY 2007 when a new series of stringent
emission standards went into effect, as described below in Section 4.3.2.85
Note that the measured CO rate from the WMATA study for MY group 2002-2006 was not used.
This vehicle's certification rate was a full order of magnitude lower than data from other 2004
certified models, and was not supported by additional test results. We adjusted the WMATA rate
by the ratio between the sales-weighted average of the MY 2004 certification levels of all models
and the certification level for that particular MY 2004 John Deere bus with the low CO rate.
4.3.2 Scaling Model Years After 2007
Without published data on in-use vehicles past MY 2004, we use emission certification levels as a
proxies to estimate running emission rate changes since then. Certification levels are reported in
grams per brake horsepower-hour and are not directly used in formulating MOVES emission rates
because they do not include real-world effects such as deterioration.86'xxv These real-world effects
studies do show that after-treatment has a large impact on several of the unregulated pollutants (e.g.
formaldehyde). This impact is discussed later with PM and HC speciation in Section 4.6.
XX1V A number of papers have discussed more recent vehicles. Examples include Clark et al. (2007).84 Data from these
newer studies would provide further validation and refinement to the rates discussed in this report, however it was not
available in time.
xxv As with other MOVES emission rate projections, we have used ratios to real-world measurements on tested
technologies to estimate the real-world performance of new technologies. For diesel vehicles, we created ratios using
emission standards as described above in Table 2-7. However, since standards for CNG and diesel buses are shared,
144
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were present in the testing programs, so we created scaling factors that we could apply to the
measured data from the testing programs to estimate rates after MY 2004.
Certification emission data for natural gas transit buses are publicly available by model year on the
EPA's Office of Transportation and Air Quality website.87 Analysis of these data showed that from
MY 2002 to MY 2012 there have been changes in average certification levels for all the pollutants
considered in this report. In particular, NOx and PM levels have dropped dramatically over the past
decade. This effect is largely attributable to increasingly strict emission standards, which have
affected both diesel and CNG buses. To improve the accuracy of the scaling factor we weighted the
emission levels with projected US sales figures for the certified CNG buses. These figures are
confidential business information and cannot be shared publicly but have been incorporated as
ratios to calculate the MY group 2007-2012 emission rates. The sales weighted average
certification levels for MY group 2002-2006 and MY group 2007-2012 are shown in Table 4-2
below.
Table 4-2. Model year group 2002-2006 and 2007-2012 certification levels for CNG buses used for scaling of
measured emission rate data
Certification
(g/bhp-hr)
Certification
(g/bhp-hr)
Model Year Group
2002-2006
2007-2012
NOx
1.208
0.2902
CO
1.355
3.032
PM
0.0078
0.0033
NMHC1
0.147
0.057
Certification data has measurements of organic material non-methane hydrocarbon equivalent
(OMNMHCE). For this analysis they were treated as NMHC values.88
While the sales figures cannot be shared, Table 4-3 below gives a sense of the CNG engine market
by indicating the number of CNG transit bus models certified for each model year.
and the standards were primarily designed for diesel buses, we think ratios of certification levels are a better indicator
for new CNG bus emissions.
145
-------
Table 4-3. A summary of the number of certified CNG transit bus models by model year used in the sales-
weighted calculations (USEPA OTAQ).
Model Year
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
TOTAL
Number of
Vehicle Models
4
4
4
6
4
3
1
1
2
2
2
33
Methane levels are not reported in the certification data, so we estimate CH4 rates for MY group
2007-2012 through an analysis of the CH4 to THC ratio by model year from the dynamometer
measurements from the WMATA study. The CHVTHC ratio for every model year fell within one
standard deviation of the average ratio across all model years. The CH/t/THC ratios are calculated
from averaged CH4 and THC measurements on the respective CBD and WMATA cycles, as
displayed in Table 4-4. We kept the CH4/THC ratio constant from MY group 2002-2006 to MY
group 2007-2012 and estimated the new CJLt rate (given in Table 4-5) using this ratio.
Table 4-4 Summary of CH4/THC ratios for MOVES2014
Model Year
1994-2001
2002-2006
2007-2012
Age Group
0-3
0-3
0-3
CH4/THC Ratio
0.917
0.950
0.950
To summarize, we scaled the newer model year rates rp based off the measurements in the MY
group 2002-2006 in proportion to the ratio of certification levels CLp from MY group 2007-2012 to
MY group 2002-2006. In this case,
CL
rp,MY2007-2012 ~ rp,MY2002-2006
'P.MY2007-2Q-L2
Jp,MY2002-2006
Equation 4-2
146
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The estimated CO rate for MY group 2007-2012 is notably greater than the previous MY group,
but this change was reflected in our certification level proxies, and has been observed in more
recent testing with three-way catalyst, stoichiometric CNG buses.89 Note that the increase in the
CO rate from MY group 2002-2006 to MY 2007-2012 seems to be an outcome of transit!oning
from lean-burn CNG engines to stoichiometric-burn engines.90
Note that there was limited data on older vehicles in the literature, so the ratios from certification
level to in-use rate that were developed using vehicles in the 0-3 age group have been applied to all
other age groups. In addition, we are assuming that CNG buses exhibit deterioration rates in control
equipment proportional to medium heavy-duty gasoline trucks (Sections 3.1.1.5 and 3.1.2.3).
Since there is no certification data on carbon dioxide (CO2) or other greenhouse gases until 2011,
we maintained the same total energy consumption rate from MY group 2002-2006 to MY 2007-
2012.
4.3.3 Creating CNG Running Rates for Future Model Years
Table 4-5 shows CNG transit bus emissions on each drive cycle calculated using MOVES2010b
rates for each MY group. These calculations are shown using a single model year within the group.
The table also shows the emission rates estimated from our meta-analysis of the literature above.
We converted MOVES default op mode rates (g/hr) to distance-based rates (g/mi) in order to
compare them to the literature. When creating the new op mode rates, we simply multiplied the
MOVES2010b rates by the ratio between the literature and the existing rates. These ratios were
applied to the 1997, 2004 and 2009 MOVES2010b CNG bus rates in order to calculate the
MOVES2014 rates by operating mode.
147
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Table 4-5 Summary of MOVES2010b distance-dependent running emission rates for CNG transit buses and the
ratios to be applied to the MOVES2010b STP-based operating mode rates to compute rates for MOVES2014.
MY
1997
2004 and
2009
MY
1994-2001
2002-2006
2007-2013
MY
1994-2001
2002-2006
2007-2013
MOVES2010b CNG Rates (g/mile)
Age
Group
0-3
0-3
Cycle
CBD
WMATA
NOx
9.63
5.45
CO
62.4
18.9
PM Non
EC
0.0024
0.0035
PM EC
0.0002
0.0003
TOTAL
ENERGY
(BTU/mi)
31137
35489
THC
1.84
1.43
CH4
0.049
0.032
MOVES2014 CNG Rates (g/mile - measured/estimated from analysis)
Age
Group
0-3
0-3
0-3
Cycle
CBD
WMATA
WMATA
NOx
20.8
9.08
2.18
Ratios Ap]
Age
Group
all
all
all
Cycle
ratioed
CBD
WMATA
WMATA
NOx
2.16
1.67
0.40
CO
9.97
2.171
5.93
PM Non
EC
0.037
0.0039
0.0016
PM EC
0.0038
0.0005
0.0002
TOTAL
ENERGY
(BTU/mi)
42782
40900
40900
THC
13.2
11.2
4.33
CH4
12.1
10.6
4.12
jlied to the STP-Based MOVES2014 Rates
CO
0.16
0.11
0.31
PM Non
EC
15.5
1.09
0.46
PM EC
21.6
1.87
0.78
TOTAL
ENERGY
1.37
1.15
1.15
THC
7.17
7.79
3.02
CH4
250
330
128
1. The raw measured CO rate was uncharacteristically low (0.14 g/mi), determined to be an outlier, and has
been adjusted using sales-weighted certification data, as described in more detail in the text above.
For each model year group, a central model year was selected for scaling. We chose to use MY
1997 for MY group 1994-2001 due to it being a median year in the group. For MY group 2002-
2006, we selected MY 2004 because that was the year all the measured vehicles in that group were
manufactured. As for MY group 2007-2012, MY 2009 was chosen as one of the two model years
near the median for the group.
For MY 2014 and later, the CNG energy consumption rates are reduced by the same percentage
reduction as diesel urban buses (F£HD vehicles), in response to the 2014 Medium and Heavy Duty
Greenhouse Gas Rule as documented in Table 2-19.
4.4 Start Exh aust Emission Rates for CNG Buses
In the absence of any measured start exhaust emissions from CNG transit buses, their start rates are
copied from the heavy-duty diesel start rates for all pollutants including energy rates. We believe
this is an environmentally conservative approach, rather than assuming zero CNG start emissions.
MOVES still estimates that the majority of emissions from CNG buses are from running emissions,
which are based on CNG test programs. We readily acknowledge that the diesel start rates may not
accurately represent CNG start rates. This assumption will be revisited for future releases of
MOVES if new data on CNG start rates becomes available.
148
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4.5 Applications to Other Model Years and Age Groups
We applied the ratios in Table 4-5 to all ages of CNG bus emission running rates in MOVES2010b.
In this way, the deterioration assumptions for criteria pollutants in the MOVES2010b running rates
are preserved in the MOVES2014 CNG bus rates. For completeness, CNG buses prior to MY 1994
use the same emission rates as MY group 1994-2001. Rates for buses built after MY 2012 use the
same emission rate as MY group 2007-2012. We hope revisit these rates in future MOVES releases
as more data becomes available.
4.6 PMand HC Spedation for CNG Buses
MOVES estimates methane and nonmethane hydrocarbons (NMHC) through the use of CFLt /THC
ratios, as shown in Table 4-4. The MOVES2014 CHVTHC ratios are binned by model year group
and are constant across all age groups. For CNG buses, we set the start CHVTHC ratios equal to the
running ratios.
MOVES calculates emissions of total organic gases (TOG), nonmethane organic gases (NMOG)
and volatile organic carbons (VOC) using information regarding the hydrocarbon speciation of
emissions. Studies have shown that the speciation of hydrocarbon can be drastically different
between uncontrolled CNG buses and CNG buses with oxidation catalysts. For example,
formaldehyde emissions can be quite large from uncontrolled CNG buses77'91, but are significantly
reduced with oxidation catalysts.76 Large formaldehyde emissions have a large impact on the
NMOG and VOC emissions estimated from THC emissions from CNG buses because THC-FID
measurements have a small response to formaldehyde concentrations.42'92
We used hydrocarbon speciation data from the Ayala et al. (2003)76 measurements of a 2000 MY
transit bus with a Detroit Diesel Series 50G engine with and without an oxidation catalyst collected
on the CBD cycle.76 This data allows us to isolate the impact of the oxidation catalyst. We used the
CBD test cycle to be consistent with our analysis of the criteria emission rates. The NMOG and
VOC conversion factors are listed in Table 4-6. The NMOG values are calculated following EPA's
regulation requirements using Equation 9 from the MOVES2014 Speciation Report42.
The VOC emissions are calculated from subtracting the ethane from the NMOG values. The
MOVES definition of VOC emissions from mobile-sources is NMOG minus ethane and acetone
XXV1. The emissions of hazardous air pollutants, including formaldehyde and acetaldehyde, are also
estimated from this study as documented in the MOVES2014 Toxics Emissions Report93.
XXV1 In the original analysis of the CNG emissions, acetone was not considered in the VOC calculation. Upon realization
of the oversight, the emission values were not recalculated, due to the small fraction of acetone measured in the
exhaust. The VOC results for CNG vehicles without control are negligible. VOC emissions for CNG buses with
oxidation catalysts are impacted by less than 2.5%.
149
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Table 4-6 Hydrocarbon speciation values for CNG transit emissions with no control and with oxidation catalyst
from Ayala et al. (2003)76
Measured values (nig/mile)
THC
Methane
Ethane
Acetone
Formaldehyde
Acetaldehyde
Calculated values (nig/mile)
NMHC
NMOG
VOC
Ratios
NMOG/NMHC
VOC/NMHC
No Control
8660
7670
217
4.67
860
50.7
990
1881.0
1664.0
1.90
1.68
Oxidation Catalyst
6150
5900
72.2
5.51
38.4
32.6
250
309.0
236.8
1.24
0.95
As discussed in Section 4.3.1, the emission rates for the MY 2002-2006 CNG transit bus emission
were based on vehicles that were all equipped with oxidation catalysts. The earlier emission rates
(1996-2001 MY) emission rates were based on a mix of transit buses with and without oxidation
catalysts. To be consistent with our emission rates, we used the NMOG and VOC for the 'No
Control' emission factors for 2001 and earlier model yearsxxvu. We used the NMOG and VOC
ratios for 'Oxidation Catalyst for the 2004 and later model years. We did not have information on
2007 and later CNG buses, so we also applied the oxidation catalyst from the lean-burn engine
results to 2007 and later groups.
The composition of PM2.5 emissions are estimated from CARB's measurements94 on the 2000 MY
Detroit Diesel Series 50G with and without the oxidation catalyst. The EC/PM2.5 fractions are
reported in Table 4-7 and are used to estimate the base PM components in MOVES: elemental
carbon (EC) and non-elemental carbon (nonECPM) rates. By using the single bus, we again isolate
the impact of the control, without confounding differences in different engine technologies. Similar
for the HC speciation, we apply the uncontrolled EC/PM fraction to the 2001 and earlier MY CNG
buses, and the oxidation catalyst equipped EC/PM profile for the 2002 and later MY buses.
Table 4-7 MOVES2014 EC/PM Fraction for CNG transit bus emissions by use of aftertreatment94
No Control
9.25%
Oxidation Catalyst
11.12%
xxvu within the hcSpeciation table, MOVES combines 2001-2003 model years into a single model year group. So, the
2001-2003 model years all use the NMOG and VOC ratios from the 'No Control' case, and the 'Oxidation Catalyst'
values do not begin until MY 2004.
150
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The CARB measurements were also used to estimate the more detailed PIVh.s composition,
including organic carbon, elements, and sulfate as discussed in the TOG and PIVh.s speciation
report. Future work should be done to improve the emission rates and speciation profiles used in
MOVES to represent emissions from recent technologies such as the stoichiometric-burn spark
ignition CNG engines with three-way catalysts that have been introduced in 2007 and later CNG
buses.
4.7 Ammonia and Nitrous Oxide emissions
No data were available on ammonia emissions rates from CNG buses. We used the ammonia
emissions for heavy-duty gasoline vehicles, which are documented in a separate report6.
We did not update the nitrous oxide emission rates for CNG in MOVES2014, and they remain
unchanged from MOVES2009 and later versions. The rates are based on CNG-specific values as
documented in a separate MOVES report5.
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5 Heavy-Duty Crankcase Emissions
Crankcase emissions, also referred to as crankcase blowby, are combustion gases that pass the
piston rings into the crankcase, and are subsequently vented to the atmosphere. Crankcase blowby
includes oil-enriched air from the turbocharger shaft, air compressors, and valve stems that enters
the crankcase. The crankcase blowby contains combustion generated pollutants, as well as oil
droplets from the engine components and engine crankcase.95
5.7 Background on Heavy-duty Diesel Crankcase Emissions
Federal regulations permit 2006 and earlier heavy-duty diesel-fueled engines equipped with
"turbochargers, pumps, blowers, or superchargers" to vent crankcase emissions to the
atmosphere.96 Crankcase emissions from pre-2007 diesel engines were typically vented to the
atmosphere, using an open unfiltered crankcase system, referred to as a 'road draft tube'.95
Researchers have found that crankcase emissions vented to the atmosphere can be the dominant
source of diesel particulate matter concentrations measured within the vehicle cabin 97 98 99.
Beginning with 2007 model year heavy-duty diesel vehicles, federal regulations no longer permit
crankcase emissions to be vented to the atmosphere, unless they are included in the certification
exhaust measurements.100 Most manufacturers have adopted open crankcase filtration systems.95
These systems vent the exhaust gases to the atmosphere after the gases have passed a coalescing
filter which removes oil and a substantial fraction of the particles in the crankcase blowby.95 In the
ACES Phase 1 program, four MY2007 diesel engines from major diesel engine manufactures
(Caterpillar, Cummins, Detroit Diesel, and Volvo) all employed filtered crankcase ventilation
systems.101
A summary of published estimates of diesel crankcase emissions as percentages of the total
emissions (exhaust + crankcase) are provided in Table 5-1. For the conventional diesel
technologies, hydrocarbon and particulate matter emissions have the largest contributions from
crankcase emissions. There is a substantial decrease in PM emissions beginning with the 2007
model year diesel engines. The 2007 diesel technology reduces the tailpipe emissions more than the
crankcase emissions, resulting in an increase in the relative crankcase contribution for HC, CO, and
PM emissions. NOx emissions for the 2007 and later are reported as a negative number. In reality,
the crankcase emission contribution cannot be negative, and the negative number is attributed to
sampling variability.
152
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Table 5-1 Literature review on the contribution of crankcase emissions to diesel exhaust.
Study
Hare and Baines, 1977104
Zielinska et al. 200897,
Iresonetal. 20 II98
Clark etal. 2006 103,
Clark etal.2006102
Khalek et al. 2009
Model
Year
1966,
1973
2000,
2003
2006
2007
Type
Conv.
Diesel
Conv.
Diesel
Conv.
Diesel
DPF-
equipped
#
Engines/
Vehicles
2
2
1
4
HC
0.2%-
3.9%
3.6%
95.6%
CO
0.01-
0.4%
1.3%
27.2%
NOx
0.01%-
0.1%
0.1%
-0.2%
PM
0.9%-
2.8%
13.5%
41.4%
5.9%
38.2%
5.2 Modeling Crankcase Emissions in MOVES
MOVES2014 calculates THC, CO, NOX, and PIVh.s using a gaseous and a particulate crankcase
emission calculator. Within the calculator, crankcase emissions are calculated as a fraction of
tailpipe exhaust emissions, including start, running, and extended-idle. As discussed in the
background section above, the 2007 heavy-duty diesel emission regulations impacted the
technologies used to control exhaust and crankcase emissions. The regulations also expanded the
types of emissions data included in certification tests, by including crankcase emissions in the
regulatory standards, which previously included only tailpipe emissions. Because heavy-duty diesel
engine manufacturers are using open-filtration crankcase systems, the crankcase emissions are
included in the emission certification results. In MOVES2014, the base exhaust rates for 2007 and
later diesel engines are based on certification levels.
In response to the changes in certification testing, we changed the data and the methodology with
which crankcase emissions are modeled in MOVES. For 2007 and later diesel engines, the
crankcase emissions are included in the base exhaust emission rates. A new crankcase calculator in
MOVES2014 divides the base exhaust emission rates into components representing the
contributions from exhaust and crankcase emissions. The exhaust emission ratio is equal to 1.0 for
all pre-2007 diesel engines, and less than 1.0 for all 2007 and later diesel engines, to account for
the inclusion of crankcase emissions in the base rates. Unfortunately, due to budget and time
constraints, only the PIVh.s species are incorporated using the new crankcase calculator in
MOVES2014. More details on the crankcase calculator is provided in the MOVES2014 Speciation
Report.42
MOVES2014 continues to use the same calculator as MOVES2010 for the gaseous crankcase
pollutants, THC, CO, and NOx. The gaseous crankcase calculator chains the crankcase emission
rates to the base exhaust emissions, but it does not reduce the exhaust emission contribution, which
is desired for the 2007+ diesel technologies. The 2007+ diesel subsection discusses how
MOVES2014 handles THC, CO, and NOx to avoid double-counting crankcase emissions. We
anticipate that future versions of MOVES will include the updated crankcase calculator for all
crankcase emission pollutants, including THC, CO, and NOx.
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5.3 Conventional Heavy-Duty Diesel
Table 5-2 includes the crankcase/tail-pipe emission ratios used for conventional diesel exhaust. For
HC, CO, and NOx, we selected the values measured on the MY2006 diesel engine reported by
Clark et al. 2006103. These values compare well with the previous HC, CO, NOx values reported
much earlier by Hare and Baines (1977), 104 which represent much older diesel technology. The
similarity of the crankcase emission ratios across several decades of diesel engines, suggests that
for conventional diesel engines, crankcase emissions can be well represented as a fraction of the
exhaust emissions.
For PM2.5 emissions, we use the same crankcase/tail-pipe ratio of 20% used in MOVES2010. The
20% ratio falls within the range of observations from the literature on diesel PM emissions.
Zielinska et al. 200897 and Ireson et al. 201198 reported crankcase contributions to total PIVh.s
emissions as high as 40%. Jaaskelainen (2012)95 reported that crankcase can contribute as much as
20% of the total emissions from a review of six diesel crankcase studies. Similarly, an industry
report estimated that crankcase emissions contributed 20% of total particulate emissions from
1994-2006 diesel engines105.
Table 5-2 MOVES2014 conventional diesel crankcase/tail-pipe ratios for HC, CO, NOx and PM2.s
Pollutant
HC
CO
NOx
PM25
crankcase/tailpipe ratio
0.037
0.013
0.001
0.200
crankcase/(crankcase +
tailpipe) ratio
0.036
0.013
0.001
0.167
As outlined in the MOVES 2014 TOG and PM Speciation Report, MOVES does not apply the
crankcase/tailpipe emission ratio in Table 5-4 to the total exhaust PM2.5 emissions. MOVES applies
the crankcase/tailpipe emission ratios to PM2.5 subspecies: elemental carbon PM2.5, sulfate PM2.5,
aerosol water PM2.5, and the remaining PM (nonECnonSO4PM). This allows MOVES to account
for important differences in the PM speciation between tailpipe and crankcase emissions.
The pre-2007 diesel ratios are derived such that the total crankcase PM2.s/exhaust PM2.5 ratio is
20%, and the crankcase emissions EC/PM fraction reflects measurements from in-use crankcase
emissions. Zielinska et al. 200897 reported that the EC/PM fraction of crankcase emissions from
two conventional diesel buses is 1.57%. Tailpipe exhaust from conventional diesel engines is
dominated by elemental carbon emissions from combustion of the diesel fuel, while crankcase
emissions are dominated by organic carbon emissions largely contributed from the lubricating oil.
97>98. The crankcase emission factors shown in Table 5-3 are derived such that the crankcase PM2.5
emissions are 20% of the PM2.5 exhaust measurements, and have an EC/PM split of 1.57%.
The PMio emission rates are subsequently estimated from the PM2.5 exhaust and crankcase
emission rates using PMio/PM2.s emission ratios as documented in the MOVES2014 TOG and PM
Speciation Report.
154
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Table 5-3. MOVES2014 exhaust and crankcase ratios for pre-2007 diesel by pollutant, process, and model year
group for PM2.5 species.
Pollutant
EC
nonECnonSO4PM
S04
H2O
EC
nonECnonSO4PM
S04
H2O
Process
Exhaust
Crank-
case
Start
1
1
1
1
0.009
0.295
0.295
0.295
Running
1
1
1
1
0.004
0.954
0.954
0.954
Extended Idle
1
1
1
1
0.012
0.268
0.268
0.268
5.4 2007 + Heavy-Duty Diesel
The 2007+ heavy-duty diesel THC, CO, and NOx crankcase emissions are included in the exhaust
emissions. However, with the current gaseous crankcaseemission calculator code, the crankcase
contribution of THC, CO, and NOx to the base exhaust emission rates cannot be properly
accounted. For MOVES2014, the crankcase to tailpipe emission ratios for THC, CO, and NOx are
set to zero as shown in Table 5-4, and MOVES2014 produces no crankcase emissions for each of
the pollutants. Table 5-4 also lists the crankcase to tailpipe emission ratios based on ACES Phase 1
tests. Based on the ACES Phase 1 program, the MOVES2014 estimate of no crankcase emissions is
reasonable for NOx, but not for THC and CO emissions. MOVES2014 does not report separate
crankcase emissions for THC and CO because they are included in the exhaust emission rates for
2007 and later model years from heavy-duty diesel vehicles. Users can use the ratios listed in Table
5-4 to post-process the exhaust emission rates if separate estimates of crankcase emissions of THC
and CO emissions are desired.
Table 5-4 MOVES2014 2007 and later diesel crankcase/tailpipe ratio for HC, CO, and NOx.
Pollutant
HC
CO
NOx
MOVES2014
crankcase/tailpipe ratio
0
0
0
ACES Phase 1
crankcase/tail-pipe ratio
21.95
0.37
0.00
ACES Phase 1
crankcase/(crankcase
+ tail-pipe) ratio
95.6%
27.2%
0.0%
For PM2.5 emissions, we used data from the ACES Phase 1 test program to inform the crankcase
and exhaust ratios for the updated PIVb.s crankcase emissions calculator. The crankcase emissions
measured in the ACES Phase 1 test program contributed 38% of the total PIVh.s emissions on the
hot-FTP driving cycle. Other tests suggest that the crankcase emissions can contribute to over 50%
of the particulate matter emissions from 2007 and later diesel technologies105.
155
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For PM2.5 emissions, MOVES applies crankcase ratios to each of the intermediate PIVh.s species
(EC, nonECnonSO4PM, SO4, and H2O). For 2007+ heavy-duty diesel engines, the same crankcase
ratio is applied to each of the intermediate species (0.62 for exhaust and 0.38 for crankcase). The
MOVES PM2.5 speciation profile developed from the ACES Phase 1 study combined the crankcase
and tailpipe emissions. As such, MOVES2014 uses the same speciation profile for both crankcase
and tailpipe emissions. The resulting exhaust and crankcase emission ratios for 2007 and later
heavy-duty diesel are provided in Table 5-5. As explained in Section 5.2, the exhaust crankcase
emission factor is less than one for 2007+ diesel vehicles to account for the contribution of
crankcase emissions in the base exhaust emission rates.
Table 5-5 MOVES2014 exhaust and crankcase emission factors for 2007 + heavy-duty diesel by pollutant,
process, and model year group for PM2.s species.
Pollutant
EC
nonECnonSO4PM
SO4
H2O
EC
nonECnonSO4PM
SO4
H2O
Process
Exhaust
Crank-
case
All processes
0.62
0.62
0.62
0.62
0.38
0.38
0.38
0.38
5.5 Heavy-duty Gasoline and CNG Emissions
The data on heavy-duty gasoline and CNG crankcase emissions are limited. All 1969 and later
spark ignition heavy-duty engines are required to control crankcase emissions. All gasoline engines
are assumed to use positive crankcase ventilation (PCV) systems, which route the crankcase gases
into the intake manifold. For heavy-duty gasoline engines we use the same values of crankcase
emission ratios as light-duty gasoline, which are documented in the MOVES2014 light-duty
emission rates report.8 We assume 4% of PCV systems fail, resulting in the small crankcase to
exhaust emission ratios shown in Table 5-6 for 1969 and later gasoline engines. Due to limited
information, we used the gasoline heavy-duty crankcase emission factors for heavy-duty CNG
engines because they have low crankcase PM emissions.
Table 5-6 Crankcase to tailpipe exhaust emission tatio for heavy-duty gasoline and CNG vehicles for HC, CO,
NOx and PM2 s
Pollutant
HC
CO
NOX
PM (all species)
pre-1969
0.33
0.013
0.001
0.20
1969 and later
0.013
0.00052
0.00004
0.008
156
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The crankcase and exhaust ratios used by the crankcase calculator for PIVh.s emissions from heavy-
duty gasoline and compressed natural gas vehicles are provided in Table 5-7. No information is
available to estimate separate speciation between exhaust and crankcase, so the factors are the same
between the PM subspecies.
Table 5-7 MOVES2014 exhaust and crankcase ratios by pollutant, process, model year group, and fuel type, and
source type for PMi.s species
Pollutant
EC
nonECnonSO4PM
SO4
H2O
EC
nonECnonSO4PM
SO4
H2O
Process
Exhaust
Crankcase
1960-1968
Gasoline
Vehicles
All
processes
1
1
1
1
0.2
0.2
0.2
0.2
1969-2050
Gasoline/
CNG
All
processes
1
1
1
1
0.008
0.008
0.008
0.008
157
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6 Nitrogen Oxide Composition
This section discusses the values used to estimate nitric oxide (NO), nitrogen dioxide (NCh) and
nitrous acid (HONO) from nitrogen oxide (NOX) emissions from heavy-duty vehicles. A similar
section on NOx composition from light-duty emissions is included in the light-duty emissions
report.
Nitrogen oxides (NOX) are defined as NO + NO2.106'107 NOX is considered a subset of reactive
nitrogen species (NOy) with an nitrogen oxidation state of+2 or greater which contain other
nitrogen containing species (NOZ), thus NOy = NOX + NOZ.106 NOZ compounds are formed in the
atmosphere as oxidation products of NOX107.
Chemiluminescent analyzers used for exhaust NOX measurements directly measure NO, as NO is
oxidized by ozone to form NO2 and produces florescent light. Chemiluminescent analyzers
measure NOX (NO + NO2) by using a catalyst that reduces the NO2 to NO in the sample air stream
before measurement. NO2 is calculated as the difference between NOX and NO measurements. The
NOX converter within Chemiluminescent analyzers can also reduce other reactive nitrogen species
(NOZ), including HONO to NO. If the concentrations of NOZ interfering species in the sample
stream are significant relative to NO2 concentrations, than they can bias the NO2 measurements
high.108
MOVES produces estimates of NO and NO2 by applying an NO/NOX or NO2/NOX fraction to the
NOX emission rates. The NO/NO2 and NO2/NOX fractions are stored in a MOVES table called
nono2ratio. The nono2ratio enables the nitrogen oxide composition to vary according to source
type, fuel type, model year, and pollutant process. For the heavy-duty vehicle source types, the NOX
fractions only vary according to fuel type, model year, and emission process. The NOX fractions in
MOVES were developed from a literature review reported by Sierra Research to the EPA, from
emission test programs conducted in the laboratory with constant volume sampling dilution
tunnels.6
MOVES also produces estimates of one important NOZ species, nitrous acid (HONO), from the
NOX values. HONO emissions are estimated as a fraction (0.8%) of NOX emissions from all vehicle
types in MOVES, based on HONO and NOX measurements made at a road tunnel in Europe.109 In
MOVES, we assume HONO contributes to the NOX values, because either (1) the
Chemiluminescent analyzers are biased slightly high by HONO in the exhaust stream, or (2) HONO
is formed almost immediately upon dilution into the roadway environment from NO2 emissions.
To avoid overcounting reactive nitrogen formation, we include HONO in the sum of NOX in
MOVES. HONO emissions are also estimated using the non2ratio MOVES table. For each source
type, fuel type, and emission process, the NO, NO2, and HONO values in the non2ratio sum to
unity.
MOVES users should be aware that the definition of NOX in MOVES (NO+NO2+HONO) is
different than the standard NOX definition of NOX (NO + NO2).This is because we are correcting
the exhaust NOX emission in MOVES for potential interference with HONO measurements.
MOVES users should consider which measure they would like to use depending on their use-case.
For example, for comparing NOX results with a vehicle emission test program, MOVES users may
want to simply use NOX (pollutantID 3), whereas a MOVES users developing air quality inputs of
NO, NO2, and HONO, should estimate NOX as the sum of NO + NO2 (pollutanflDs 32 and 33),
rather than using the direct NOX output in MOVES (polluantID 3).
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Future work is needed to (1) update the NOX and HONO fractions in MOVES based on more recent
measurements, (2) reconcile the definition of NOX in MOVES, while also correctly accounting for
the emissions of NOZ species that may impact NOX measurements and (3) reconcile measurement
differences that may occur between NOy species measured at the tailpipe, with NOy species
measured on road side measurements.110
6.1 Heavy-duty Diesel
The conventional diesel (1960-2006 model year) NOX fractions were estimated as the average
reported fraction from three studies of heavy-duty vehicles not equipped with diesel particulate
filters.6 The 2010+NO2 fractions are based on the average of three diesel programs of diesel
vehicles measured with diesel parti culate filters. The 2007-2009 values are an average of the 1960-
2006 and 2010-2050 values, which assumes that the NOX fractions changed incrementally, as
trucks equipped with catalyzed diesel particulate filters were phased-into the fleet. The NOX
fractions are the same across all diesel source types (including light-duty) and across all emission
processes (running, start, extended idle), except for auxiliary power units, which use the
conventional NOX fractions (1960-2006) for all model years because it is assumed that the APUs
are not fitted with diesel particulate filters. The NO2 fractions originally developed from the Sierra
report6 were reduced by 0.008 to account for the HONO emissions.
Table 6-1. NOx and HONO fractions for heavy-duty diesel vehicles
Model Year
1960-2006*
2007-2009
2010-2050
NO
0.935
0.764
0.594
NO2
0.057
0.228
0.398
HONO
0.008
0.008
0.008
* All Model Year of Auxiliary Power Units (APUs) use the 1960-2006 NOX and HONO fractions.
6.2 Heavy-duty Gasoline
The NOX fractions for heavy-duty gasoline are based on the MOVES values used for light-duty
gasoline measurements. Separate values are used for running and start emission processes. As
stated in the Sierra Report,6 the values are shifted to later model year groups to be consistent with
emission standards and emission control technologies. These values are shown in Table 6-2 for
both light-duty and heavy-duty gasoline vehicles. The NO2 fractions originally developed from the
Sierra report6 were reduced by 0.008 to account for the HONO emissions.
Table 6-2. NOx and HONO fractions for light-duty (sourceTypelD 21,31,32) and heavy-duty gasoline vehicles
(sourceTypelD 41,42,43,51,52,53,54,61, and 62)
Light-duty gasoline
model year groups
1960-1980
1981-1990
1991-1995
1996-2050
Heavy-duty gasoline
model year groups
1960-1987
1988-2004
2005-2007
2008-2050
Running
NO
0.975
0.932
0.954
0.836
NO2
0.017
0.06
0.038
0.156
HONO
0.008
0.008
0.008
0.008
Start
NO
0.975
0.932
0.987
0.951
NO2
0.017
0.031
0.005
0.041
HONO
0.008
0.008
0.008
0.008
159
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6.3 Compressed Natural Gas
We used the average of three NCh/NOx fraction reported on three CNG transit buses with DDC
Series 50 G engines by Lanni et al. (2003)79 along with the 0.008 HONO fraction assumed for
other source types, to estimate the NOX fractions of NO, NO2, and the HONO fraction. These
assumptions yield the NOX and HONO fractions in Table 6-3, which are used for all model year
CNG transit buses.
Table 6-3 NOx and HONO fractions CNG transit buses
Model Year
1960-2050
NO
0.865
NO2
0.127
HONO
0.008
160
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Appendix A Calculation of Accessory Power Requirements
Table A-l. Accessory load estimates for HHD trucks
VSP
Low
Power (kw)
% time on
Total (kW)
Mid
Power (kw)
% time on
Total (kW)
High
Power (kw)
% time on
Total (kW)
Cooling Fan
19.0
10%
1.9
19.0
20%
3.8
19.0
30%
5.7
Air cond
2.3
50%
1.2
2.3
50%
1.2
2.3
50%
1.2
Air comp
Off = 0.5 kW
3.0
60%
2.0
Off = 0.5 kW
2.3
20%
0.9
Off = 0.5 kW
2.3
10%
0.7
Alternator
1.5
100%
1.5
1.5
100%
1.5
1.5
100%
1.5
Engine
Accessories
1.5
100%
1.5
1.5
100%
1.5
1.5
100%
1.5
Total Accessory Load (kW)
8.1
8.8
10.5
Table A-2. Accessory load estimates for MHD trucks
VSP
Low
Power (kw)
% time on
Total (kW)
Mid
Power (kw)
% time on
Total (kW)
High
Power (kw)
% time on
Total (kW)
Cooling Fan
10.0
10%
1.0
10.0
20%
2.0
10.0
30%
3.0
Air cond
2.3
50%
1.2
2.3
50%
1.2
2.3
50%
1.2
Air comp
Off = 0.5 kW
2.0
60%
1.4
Off = 0.5 kW
2.0
20%
0.8
Off = 0.5 kW
2.0
10%
0.7
Alternator
1.5
100%
1.5
1.5
100%
1.5
1.5
100%
1.5
Engine
Accessories
1.5
100%
1.5
1.5
100%
1.5
1.5
100%
1.5
Total Accessory Load (kW)
6.6
7.0
7.8
161
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Table A-3. Accessory load estimates for buses
VSP
Low
Power (kw)
% time on
Total (kW)
Mid
Power (kw)
% time on
Total (kW)
High
Power (kw)
% time on
Total (kW)
Cooling Fan
19.0
10%
1.9
19.0
20%
3.8
19.0
30%
5.7
Air cond
18.0
80%
14.4
18.0
80%
14.4
18.0
80%
14.4
Air comp
Off = 0.5 kW
4.0
60%
2.6
Off = 0.5 kW
4.0
20%
1.2
Off = 0.5 kW
4.0
10%
0.9
Alternator
1.5
100%
1.5
1.5
100%
1.5
1.5
100%
1.5
Engine
Accessories
1.5
100%
1.5
1.5
100%
1.5
1.5
100%
1.5
Total Accessory Load (kW)
21.9
22.4
24.0
162
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Appendix B Tampering and Mai-maintenance
Tampering and mal-maintenance (T&M) effects represent the fleet-wide average increase in
emissions over the useful life of the engines. In laboratory testing, properly maintained engines
often yield very small rates of emissions deterioration through time. However, we assume that in
real-world use, tampering and mal-maintenance yield higher rates of emissions deterioration over
time. As a result, we feel it is important to model the amount of deterioration we expect from this
tampering and mal-maintenance. We estimated these fleet-wide emissions effects by multiplying
the frequencies of engine component failures by the emissions impacts related to those failures for
each pollutant. Details of this analysis appear later in this section.
B.I Modeling Tampering and Mal-maintenance
As T&M affects emissions through age, we developed a simple function of emission deterioration
with age. We applied the zero-age rates through the emissions warranty period (5 years/100,000
miles), then increased the rates linearly up to the useful life. Then we assumed that all the rates
level off beyond the useful life age. Figure B-l shows this relationship. The actual emission levels
were determined through data analysis detailed below.
Figure B-l. Qualitative Depiction of the implementation of age effects.
Emission rate
Final emission rate
due to T&M
Zero-mile
emission
End of warranty
period
End of useful life
Age
The useful life refers to the length of time that engines are required to meet emissions standards.
We incorporated this age relationship by averaging emissions rates across the ages in each age
group. Mileage was converted to age with VIUS111 (Vehicle Inventory and Use Survey) data,
which contains data on how quickly trucks of different regulatory classes accumulate mileage.
Table B-l shows the emissions warranty period and approximate useful life requirement period for
each of the regulatory classes.
163
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Table B-l. Warranty and useful life requirements by regulatory class
Regulatory class
HHD
MHD
LHD45
LHD2b3
BUS
Warranty age
(Requirement:
100,000 miles or 5 years)
1
2
4
4
2
Useful life
mileage/age
requirement
435,000/10
185,000/10
110,000/10
110,000/10
435,000/10
Useful
life age
4
5
4
4
10
While both age and mileage metrics are given for these periods, whichever comes first determines
the applicability of the warranty. As a result, since MOVES deals with age and not mileage, we
needed to convert all the mileage values to age equivalents, as the mileage limit is usually reached
before the age limit. The data show that on average, heavy-heavy-duty trucks accumulate mileage
much more quickly than other regulatory classes. Therefore, deterioration in heavy-heavy-duty
truck emissions will presumably happen at younger ages than for other regulatory classes. Buses,
on average, do not accumulate mileage quickly. Therefore, their useful life period is governed by
the age requirement, not the mileage requirement.
Since MOVES deals with age groups and not individual ages, the increase in emissions by age
must be calculated by age group. We assumed that there is an even age distribution within each
age group (e.g. ages 0, 1,2, and 3 are equally represented in the 0-3 age group). This is important
since, for example, HHD trucks reach their useful life at four years, which means they will increase
emissions through the 0-3 age group. As a result, the 0-3 age group emission rate will be higher
than the zero-mile emission rate for HHD trucks. Table B-2 shows the multiplicative T&M
adjustment factor by age. We determined this factor using the mileage-age data from Table B-l
and the emissions-age relationship that we described in Figure B-l. We multiplied this factor by
the emissions increase of each pollutant over the useful life of the engine, which we determined
from the analysis in sections B.7 through B.9.
164
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Table B-2. T&M multiplicative adjustment factor by age (fTM,age group).
Age Group
0-3
4-5
6-7
8-9
10-14
15-19
20+
LHD
0
1
1
1
1
1
1
MHD
0.083
0.833
1
1
1
1
1
HHD
0.25
1
1
1
1
1
1
Bus
0.03125
0.3125
0.5625
0.8125
1
1
1
In this table, a value of 0 indicates no deterioration, or zero-mile emissions level (ZML), and a value
of 1 indicates a fully deteriorated engine, or maximum emissions level, at or beyond useful life (UL).
The calculation of emission rate by age group is described in the equation below. TMpoi represents
the estimated emissions rate increase through the useful life for a given pollutant.
polagegrp
vID^ ^JniagegroflpMpol)
Equation B-l
B.2
Data Sources
EPA used the following information to develop the tamper and mal-maintenance occurrence rates
used to develop emission rates used in MOVES:
• California's ARE EMFAC2007 Modeling Change Technical Memo112 (2006). The basic
EMFAC occurrence rates for tampering and mal-maintenance were developed from
Radian and EFEE reports and internal CARB engineering judgment.
• Radian Study (1988). The report estimated the malfunction rates based on survey and
observation. The data may be questionable for current heavy-duty trucks due to
advancements such as electronic controls, injection systems, and exhaust aftertreatment.
• EFEE report (1998) on PM emission deterioration rates for in-use vehicles. Their work
included heavy-duty diesel vehicle chassis dynamometer testing at Southwest Research
Institute.
• EMFAC2000 (2000) Tampering and Mal-maintenance Rates
• EMA's comments on ARB's Tampering, Malfunction, and Mal-maintenance
Assumptions for EMFAC 2007
• University of California -Riverside (UCR) "Incidence of Malfunctions and Tampering in
Heavy-Duty Vehicles"
165
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• Air Improvement Resources, Inc.'s Comments on Heavy-Duty Tampering and Mai-
maintenance Symposium
• EPA internal engineering judgment
B.3 T &M Categories
EPA generally adopted the categories developed by CARB, with a few exceptions. The high fuel
pressure category was removed. We added a category for misfueling to represent the use of
nonroad diesel in cases when ULSD onroad diesel is required. We combined the injector
categories into a single group. We reorganized the EGR categories into "Stuck Open" and
"Disabled/Low Flow'' We included the PM regeneration system, including the igniter, injector,
and combustion air system in the PM filter leak category.
EPA grouped the LHDD, MHDD, HHDD, and Diesel bus groups together, except for model years
2010 and beyond. We assumed that the LHDD group will primarily use Lean NOx Traps (LNT) for
the NOx control in 2010 and beyond. On the other hand, we also assumed that Selective Catalyst
Reduction (SCR) systems will be the primary NOx aftertreatment system for HHDD. Therefore, the
occurrence rates and emission impacts will vary in 2010 and beyond depending on the regulatory
class of the vehicles.
B.4 T&M Model Year Groups
EPA developed the model year groups based on regulation and technology changes.
• Pre-1994 represents non-electronic fuel control.
• 1998-2002 represents the time period with consent decree issues.
• 2003 represents early use of EGR.
• 2007 and 2010 contain significant PM and NOx regulation changes.
• 2010-and later represent heavy-duty trucks with required OBD. This rule began in MY
2010 with complete phase-in by MY 2013. The OBD impacts are discussed in Section
B.10.
B. 5 T &M Occurrence Rates and Differences from EMFAC2007
EPA adopted the CARB EMFAC2007 occurrence rates, except as noted below.
Clogged Air Filter: EPA reduced the frequency rate from EMFAC's 15 percent to 8 percent.
EPA reduced this value based on the UCR results, the Radian study, and EMA's comments that air
filters are a maintenance item. Many trucks contain indicators to notify the driver of dirty air filters
and the drivers have incentive to replace the filters for other performance reasons.
Other Air Problems: EPA reduced the frequency rate from 8 percent to 6 percent based on the
UCR results.
Electronics Failed: EPA continued to use the 3 percent frequency rate for all model years beyond
2010. We projected that the hardware would evolve through 2010, rather than be replaced with
completely new systems that would justify a higher rate of failure. We assumed that many of the
2010 changes would occur with the aftertreatment systems which are accounted for separately.
166
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EGR Stuck Open: EPA believes the failure frequency of this item is rare and therefore set the
level at 0.2 percent. This failure will lead to drivability issues that will be noticeable to the driver
and serve as an incentive to repair.
EGR Disabled/Low Flow: EPA estimates the ERG failure rate at 10 percent. All but one major
engine manufacturer had EGR previous to the 2007 model year and all have it after 2007, so a large
increase in rates seem unwarranted. However, the Illinois EPA stated that "EGR flow insufficient"
is the top OBD issue found in their LDV I/M program113 so it cannot be ignored.
NOX Aftertreatment malfunction: EPA developed a NOx aftertreatment malfunction rate that is
dependent on the type of system used. We assumed that HHDD will use primarily SCR systems
and LHDD will primarily use LNT systems. We estimated the failure rates of the various
components within each system to develop a composite malfunction rate.
The individual failure rates were developed considering the experience in agriculture and stationary
industries of NOx aftertreatment systems and similar component applications. Details are included
in the chart below. We assumed that tank heaters had a five percent failure rate, but were only
required in one third of the country during one fifth of the year. The injector failure rate is lower
than fuel injectors, even though they have similar technology, because there is only one required in
each system and it is operating in less severe environment of pressure and temperature. We believe
the compressed air delivery system is very mature based on a similar use in air brakes. We also
believe that manufacturers will initiate engine power de-rate as incentive to keep the urea supply
sufficient.
Table B-3. NOx Aftertreatment Failure Rates
Occurrence Rate
SCR
Urea tank
Tank heaters
In-exhaust injectors
Compressed air delivery to injector
Urea supply pump
Control system
Exhaust temperature sensor
Urea supply
0.5%
1%
2%
1%
1%
5%
1%
1%
Overall
13%
LNT
Adsorber
In-exhaust injectors
Control system
Exhaust temperature sensor
7%
2%
5%
1%
Overall
16%
NOx aftertreatment sensor: EPA will assume a 10 percent failure mode for the aftertreatment
sensor. We developed the occurrence rate based on the following assumptions:
167
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• Population: HHDD: vast majority of heavy-duty applications will use selective catalytic
reduction (SCR) technology with a maximum of one NOx sensor. NOx sensors are not
required for SCR - manufacturers can use models or run open loop. Several engine
manufacturers representing 30 percent of the market plan to delay the use of NOx
aftertreatment devices through the use of improved engine-out emissions and emission
credits.
• Durability expectations: SwRI completed 6000 hours of the European Stationary Cycle
(ESC) cycling with NOx sensor. Internal testing supports longer life durability. Discussions
with OEMs in 2007 indicate longer life expected by 2010.
• Forward looking assumptions: Manufacturers have a strong incentive to improve the
reliability and durability of the sensors because of the high cost associated with frequent
replacements.
PM Filter Leak: EPA will use 5 percent PM filter leak and system failure rate. They discounted
high failure rates currently seen in the field.
PM Filter Disable: EPA agrees with CARB's 2 percent tamper rate of the PM filter. The filter
causes a fuel economy penalty so the drivers have an incentive to remove it.
Oxidation Catalyst Malfunction/Remove: EPA believes most manufacturers will install
oxidation catalysts initially in the 2007 model year and agrees with CARB's assessment of 5
percent failure rate. This rate consists of an approximate 2 percent tampering rate and 3 percent
malfunction rate. The catalysts are more robust than PM filters, but have the potential to
experience degradation when exposed to high temperatures.
Misfuel: EPA estimated that operators will use the wrong type of fuel, such as agricultural diesel
fuel with higher sulfur levels, approximately 0.1 percent of the time.
168
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B. 6 Tampering & Mai-maintenance Occurrence Rate Summary
Tamper & Malmaintenance
Frequency of Occurrence: Average rate over life of vehicle
Timing Advanced
Timing Retarded
Injector Problem (all)
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter - EPA
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem - EPA
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed - EPA
Electronics Tampered
EGR Stuck Open
EGR Disabled/Low Flow - EPA
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction - EPA
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove - EPA
Mis-fuel - EPA
Frequency Rates
1994-97
5%
3%
28%
4%
4%
3%
8%
5%
5%
6%
2%
5%
3%
10%
0%
0%
0%
0%
0%
0%
0%
0%
0.1%
1998-2002
2%
2%
28%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
15%
0%
0%
0%
0%
0%
0%
0%
0%
0.1%
2003-2006
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
0%
0%
0%
0%
0%
0%
0.1%
2007-2009
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
0%
0%
0%
5%
2%
5%
0.1%
2010+ HHDT
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
10%
1%
13%
5%
2%
5%
0.1%
2010+ LHDT
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
10%
1%
16%
5%
2%
5%
0.1%
B. 7 NOx Emission Effects
EPA developed the emission effect from each tampering and mal-maintenance incident from
CARB's EMFAC, Radian's dynamometer testing with and without the malfunction present,
Engine, Fuel, and Emissions Engineering Inc. (EFEE) results, and internal testing experience.
EPA estimated that the lean NOx traps (LNT) in LHDD are 80 percent efficient and the selective
catalyst reduction (SCR) systems in HHDD are 90 percent efficient at reducing NOx.
EPA developed the NOx emission factors of the NOx sensors based on SCR systems' ability to run
in open-loop mode and still achieve NOx reductions. The Manufacturers of Emission Controls
Association (MECA) has stated that a 75-90 percent NOx reduction should occur with open loop
control and >95 percent reduction should occur with closed loop control.114 Visteon reports a 60-
80 percent NOxreduction with open loop control.115
In testing, the failure of the NOx aftertreatment system had a different impact on the NOx
emissions depending on the type of aftertreatment. The HHDD vehicles with SCR systems would
experience a 1000 percent increase in NOx during a complete failure, therefore we estimated a 500
percent increase as a midpoint between normal operation and a complete failure. The LHDD
vehicles with LNT systems would experience a 500 percent increase in NOx during a complete
failure. We estimated a 300 percent increase as a value between a complete failure and normal
system operation.
The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.
169
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Tamper & Malmaintenance
NOX Emission Effect
Federal Emission Standard
Timing Advanced
Timing Retarded
Injector Problem (all)
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed
Electronics Tampered
EGR Stuck Open
EGR Disabled / Low Flow
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove
Mis-fuel
1 994-97
5.0
60%
-20%
-5%
0%
0%
10%
0%
0%
25%
0%
-10%
0%
0%
80%
0%
0%
0%
0%
0%
0%
0%
0%
1 998-2002
5.0
60%
-20%
-1%
0%
0%
0%
0%
0%
25%
0%
-10%
0%
0%
80%
0%
0%
0%
0%
0%
0%
0%
0%
2003-2006
4.0
60%
-20%
-1%
0%
0%
0%
0%
0%
25%
0%
-10%
0%
0%
80%
-20%
30%
0%
0%
0%
0%
0%
0%
2007-2009
2.0
60%
-20%
-1%
0%
0%
0%
0%
0%
25%
0%
-10%
0%
0%
80%
-20%
50%
0%
0%
0%
0%
0%
0%
2010+ HHDT
0.2
6%
-20%
-1%
0%
0%
0%
0%
0%
3%
0%
-10%
0%
0%
8%
-20%
5%
200%
200%
500%
0%
0%
0%
2010LHDT
0.2
12%
-20%
-1%
0%
0%
0%
0%
0%
5%
0%
-10%
0%
0%
16%
-20%
10%
200%
200%
300%
0%
0%
0%
Combining the NOx emission effects with the frequency results in the initial Tampering & Mai-maintenance
(T&M) effects shown in the
Table B-4 below. As noted in section 2.1.1.5, MOVES does not use the estimate NOx increase
from T&M for 2009 and earlier model years, and assumes no NOx increase. This is incorporated
into the 3rd column of Table B-4 labeled with (Remove 2009 and earlier)
Table B-4. Fleet-average Tampering & Mai-maintenance (TM) NOx emission increases (%) from zero-mile
levels calculated over the useful life s. TMNOx,nonOBD are calculated using the NOx emission effects and
frequencies shown above. TMNOX,OBD incorporate the OBD assumptions discussed in Section B.10, including the
assumed penetration of OBD (foBD)
Model years
1994-1997
1998-2002
2003-2006
2007-2009
2010-2012 SCR
20 10-20 12 LNT
2013+ SCR
TMNOx.nonOBD
(Initial)
10
14
9
11
87
72
87
TMNOx.nonOBD
(Remove 2009 and earlier)
0
0
0
0
87
72
87
foBD
0
0
0
0
0.33
1
1
TMNOx.OBD
-
-
-
-
77
48
58
The LHD<=10K trucks have different T&M NOx increases than LHD<=14K trucks, due to the
assumed penetration of lean NOx trap (LNT) aftertreatment which was assumed to penetrate 25%
of LHD<=10K trucks starting in 2007, consistent with the assumptions previously made in Section
2.1.1.4.4.
170
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The T&M rates for LHD<=10K in 2007-2009 are calculated by adjusting Equation 2-10 to account
for T&M of LNT aftertreatment, as shown in Equation B-2 :
2007 - 2009 LNT NOx emissions (T&M) _
2003 - 2006 LHD < WK NOx emissions ~
/LNT normal emissions \
= (normal op. frequency) x x (T&M effect) Equation B-2
V baseline emissions )
/baseline emissions\
+ (DPF reg. frequency) x
V baseline emission )
= (0.90) x (0.10) x (1.72) + (0.10) x (1) x (1) = 0.2548
The ratio of 2007-2009 LHD<= 10K (with T&M) over the baseline 2003-2006 NOx rates is
calculated by adjusting Equation 2-11 to account for the T&M effects of LNT, as shown in
EquationB-3.
2007 - 2009 LHD < WK NOx emissions (T&M) _
2003 - 2006 LHD < WK NOx emissions ~
/2007 - 2009 LNT NOx emissions (T&M)\
= (LNT market share) - Equation B-3
v } V2003 - 2006 LHD < WK NOx emissions/
/ 2007 — 2009 emission standards \
+ (non — LNT market share)
\2003 - 2006 NOx emissions standards;
=0.25x0.2548 +0.75x0.5=0.4225
Then, the overall T&M effect for 2007-2009 LHD<= 10K is then calculated in Equation B-4, by
dividing Equation B-2 by Equation 2-11.
2007 - 2009 LHD < WK NOx emissions (T&M)
2007 - 2009 LHD < WK NOx emissions (zero mile) Equation
2007 - 2009 LHD < WK NOx (T&M) \ //2007 - 2009 LHD < WK NOx (zero mile)\ B-4
2003 - 2006 LHD < WK NOx emissions )/ \ 2003 - 2006 LHD < WK NOx emissions I
= (Equation B-2)/(Equation 2-11)
= 0.4387/0.4225 = 1.04 = 4% increase due to T&M
For 2010+, LHD<=14K, we assume that both LNT and SCR equipped vehicles will provide the
same level of control with a 90% reduction from 2003-2006 levels (ignoring the PM regeneration
NOx benefit for LNT aftertreatment). Thus, for calculating the T&M NOx effects for 2010-2012,
we weighted the LNT-specific and 2013+SCR-specific T&M effects (from Table B-4) according to
the market shares, as shown in Equation B-5:
171
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2010+ LHD
-------
Tamper & Malmaintenance
PM Emission Effect
Federal Emission Standard
Timing Advanced
Timing Retarded
Injector Problem
Puff Limiter Mis-set
Puff Limiter Dsabled
Max Fuel High
Clogged Air Filter
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed
Electronics Tampered
EGR Stuck Open
EGR Disabled/Low Flow
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove
Mis-fuel - EPA
1994-1997
0.1
-10%
25%
100%
20%
50%
20%
50%
50%
50%
40%
500%
500%
60%
50%
0%
0%
0%
0%
0%
0%
0%
0%
30%
1998-2002
0.1
-10%
25%
100%
0%
0%
0%
50%
50%
50%
40%
500%
500%
60%
50%
0%
0%
0%
0%
0%
0%
0%
0%
30%
2003-2006
0.1
-10%
25%
100%
0%
0%
0%
30%
50%
30%
30%
500%
500%
60%
50%
100%
-30%
0%
0%
0%
0%
0%
0%
30%
2007-2009
0.01
0%
1%
5%
0%
0%
0%
2%
3%
2%
2%
25%
25%
3%
3%
5%
-30%
0%
0%
0%
935%
2670%
0%
100%
2010
0.01
0%
1%
5%
0%
0%
0%
2%
3%
2%
2%
25%
25%
3%
3%
5%
-30%
0%
0%
0%
935%
2670%
0%
100%
B. 9 HC Emission Effects
EPA estimated oxidation catalysts are 80 percent effective at reducing hydrocarbons. All
manufacturers will utilize oxidation catalysts in 2007, but only a negligible number were installed
prior to the PM regulation reduction in 2007. We assumed that with Tampering and Mai-
maintenance, the HC zero level emissions will increase by 50%. This still represents a 70%
reduction in HC emissions between zero-mile 2006 emissions and fully deteriorated 2007 vehicles.
We reduced CARB's HC emission effect for timing advanced because earlier timing should reduce
HC, not increase them. The effect of injector problems was reduced to 1000 percent based on
EPA's engineering staff experience. We increased the HC emission effect of high fuel pressure
(labeled as Max Fuel High) to 10 percent in 1994-1997 years because the higher pressure will lead
to extra fuel in early model years and therefore increased HC. Lastly, we used the HC emission
effect of advanced timing for the electronics tampering (0%) for all model years.
173
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The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.
Tamper & Malmaintenance
HC Emission Effect
Federal Emission Standard
Timing Advanced
Timing Retarded
Injector Problem (all)
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed
Electronics Tampered
EGR Stuck Open
EGR Disabled / Low Flow
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove
Mis-fuel
1994-97
1.3
0%
50%
1000%
0%
0%
10%
0%
0%
0%
0%
500%
300%
50%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1998-2002
1.3
0%
50%
1 000%
0%
0%
0%
0%
0%
0%
0%
500%
300%
50%
0%
0%
0%
0%
0%
0%
0%
0%
0%
2003-2006
1.3
0%
50%
1 000%
0%
0%
0%
0%
0%
0%
0%
500%
300%
50%
0%
100%
0%
0%
0%
0%
0%
0%
0%
2007-2009
0.2
0%
50%
1000%
0%
0%
0%
0%
0%
0%
0%
500%
300%
50%
0%
100%
0%
0%
0%
0%
0%
0%
50%
2010+ HHDT
0.14
0%
10%
200%
0%
0%
0%
0%
0%
0%
0%
100%
60%
10%
0%
20%
0%
0%
0%
0%
0%
0%
50%
2010 LHDT
0.14
0%
10%
200%
0%
0%
0%
0%
0%
0%
0%
100%
60%
10%
0%
20%
0%
0%
0%
0%
0%
0%
50%
A separate tampering analysis was not performed for CO; rather, the HC effects were assumed to
apply for CO.
Combining all of the emissions effects and failure frequencies discussed in this section, we
summarized the aggregate emissions impacts over the useful life of the fleet in the main body of
the document in Table 2-8 (NOx), Table 2-15 (PM), and Table 2-18 (HC and CO).
B.10 HD OBD impacts
With the fmalization of the heavy-duty onboard diagnostics (HD OBD) rule, we made adjustments
to 2010 and later model years to reflect the rule's implementation.
Specifically, we reduced the emissions increases for all pollutants due to tampering and mal-
maintenance by 33 percent. Data were not available for heavy-duty trucks equipped with OBD,
and this number is probably a conservative estimate. Still, due to the implementation of other
standards, PM and NOX reductions from 2010 and later model year vehicles will be substantial
compared to prior model years regardless of the additional incremental benefit from OBD. We
assumed, since the rule phased-in OBD implementation, that 33 percent of all engines would have
OBD in 2010, 2011, and 2012 model years, and 100 percent would have OBD by 2013 model year
and later. Equation B-6 describes the calculation of TMpoi, the increase in emission rate through
useful life, where/OBD represents the fraction of the fleet equipped with OBD (0 percent for model
years 2009 and earlier, 33 percent for model years 2010-2012, and 100 percent for model years
174
-------
2013 and later). The result from this equation can be plugged into Equation B-l to determine the
emission rate for any age group.
Equation B-6
These OBD impacts apply to any truck in GVWR Class 4 and above. Lighter trucks are assumed to
follow light-duty OBD impacts and will be fully phased in starting in model year 2010. As data for
current and future model years become available, we may consider refining these estimates and
methodology.
175
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Appendix C Extended Idle Data Summary
Idle HC Rates (gram/hour) Summary
Program |Condition
# Samples
Mean HC Emiss Rate
1991-2006 Low Speed Idle, A/C Off - HOT
McCormick, High Altitude, HOT
WVU- 1991 -2004
Storey
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
12
48
4
10.2
9.5
28
Overall|| 64 || 10.8
1991-2006 High Speed Idle, A/C On - HOT
Broderick DC Davis
Storey
High Idle, AC On
High Idle, AC On
1
4
86
48
Overall)) | II 55.6
1975-1990 MY Low Speed Idle, A/C Off - HOT
Program
WVU -1975-1 990
Condition
Low Idle, AC Off
Samples
18
Mean
21
Overall)) 18 || 21.0
1991-2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus
Condition
Low Idle, AC Off
Samples
12
Mean
8.2
Overall!! 12 || 8.2
176
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Idle CO Rates (gram/hour) Summary
Program (Condition
# Samples
Mean CO Emiss Rate
1991-2006 Low Speed Idle, A/C Off - HOT
McCormick, High Altitude, HOT
Calcagno
WVU- 199 1-2004
Storey
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
12
27
48
4
71
37
23
25
Overall 91 || 33.6
1991-2006 High Speed Idle, A/C On - HOT
Calcagno
Broderick DC Davis
Storey
High Idle, AC On
High Idle, AC On
High Idle, AC On
21
1
4
99
190
73
OveraM]| 26 || 91.2
1975-1990 MY Low Speed Idle, A/C Off - HOT
Program
WVU- 1975-1990
Condition
Low Idle, AC Off
Samples
18
Mean
31
Overall)) H II 31-°
1991-2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus
Condition
Low Idle, AC Off
Samples
12
Mean
79.6
OyeraM]| 12 || 79.6
177
-------
Idle PM Rates (gram/hour) Summary
Program | Condition
# Samples
Mean PM Emiss Rate
1991-2006 Low Speed Idle, A/C Off - HOT
McCormick, High Altitude, HOT
Calcagno
WVU- 1991-2004
Storey
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
12
27
48
4
1.8
2.55
1.4
1.3
Overall 91 || 1.8
1991-2006 High Speed Idle, A/C On - HOT
Calcagno
Storey
High Idle, AC On
High Idle, AC On
21
4
4.11
3.2
OveraM]| 25 || 4.0
1975-1990 MY Low Speed Idle, A/C Off - HOT
Program
WVU -1975-1 990
Condition
Low Idle, AC Off
Samples
18
Mean
3.8
Overall)) H II O
1991-2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus
Condition
Low Idle, AC Off
Samples
12
Mean
2.88
OveraM]| 12 || 2.9
178
-------
Idle Nox Rates (gram/hour) Summary
Program [Condition
# Samples
Mean NOX Emiss Rate
1991-2006 Low Speed Idle, A/C Off
McCormick, High Altitude, HOT
Lim, EPA
Irick, Clean Air Tech & IdleAire
WVU- 1991 -2004
WVU, NCHRP
Tang, Metro NY, 1984-1999
Calcagno
Broderick DC Davis
Storey
Low RPM, AC Off
Low RPM, No access
Low RPM, AC Off
Low RPM, AC Off
Low RPM, AC Off
Low RPM, AC Off
12
12
49
48
2
33
27
1
4
85
109
87
83
47
81
120
104
126
Overall|| 188 II 94
1991-2006 High Speed Idle, A/C Off
Lim, EPA CCD
Calcagno
High RPM, No access
High RPM, AC Off
5
21
169
164
Overall)) H II 165
Lim, EPA CCD
Broderick DC Davis
Calcagno
Storey
High RPM, AC On
High RPM, AC On
High RPM, AC On
High RPM, AC On
5
1
21
4
212
240
223
262
Overall)) 31 || 227
Program
WVU -1975-1 990
Lim, EPA CCD, 1985 MY
Condition
Low RPM, AC Off
Low RPM, AC Off
Samples
18
1
Mean
48
20
Overall)) 19 || 47
1991-2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus
Condition
Low Idle, AC Off
Samples
12
Mean
121
Overall)) 12 || 121.0
2007 Extended Idle Emissions calculation:
• Assumed 8 hour idle period where the emissions controls, such as EGR, oxidation catalyst, and
NOx aftertreatment, are still active for the first hour.
• HC emissions standards:
o Pre-2007: 0.50 g/bhp-hr
o 2007: 0.14 g/bhp-hr
• NOx emissions standards:
o Pre-2010: 5.0 g/bhp-hr
179
-------
o 2010: 0.2g/bhp-hr
Idle HC Rate Reduction = 1 - [(1/8 * 0.14 g/bhp-hr + 7/8 * 0.5 g/bhp-hr) / 0.5 g/bhp-hr] = 9%
Idle NOx Rate Reduction = 1 - [(1/8 * 0.2 g/bhp-hr + 7/8 * 5.0 g/bhp-hr) / 5.0 g/bhp-hr] = 12%
180
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Appendix D Developing PM emission rates for missing operating
modes
In cases where an estimated rate could not be directly calculated from data, we imputed the missing
value using a log-linear least-squares regression procedure. Regulatory class, model year group
and speed class (0-25 mph, 25-50 mph and 50+ mph ) were represented by dummy variables in the
regression. The natural logarithm of emissions was regressed versus scaled tractive power (STP) to
represent the operating mode bins. The regression assumed a constant slope versus STP for each
regulatory class. Logarithmic transformation factors (mean square error of the regression squared /
2) were used to transform the regression results from a log based form to a linear form. Due to the
huge number of individual second-by-second data points, all of the regression relationships were
statistically significant at a high level (99% confident level). The table below shows the regression
statistics, and the equation shows the form of the resulting regression equation.
Table D-l. Regression Coefficients for PM Emission Factor Model
Model-year
group
1960-87
1988-90
1991-93
1994-97
1998-2006
Speed Class (mph)
1-25
25-50
50+
1-25
25-50
50+
1-25
25-50
50+
1-25
25-50
50+
1-25
25-50
50+
STP
Type
Intercept (/?o)
Slope 08i)
Transformation
Coefficient
(0.5o2)
Medium
Heavy-Duty
-5.419
-4.942
-4.765
-5.366
-4.929
-4.785
-5.936
-5.504
-5.574
-5.927
-5.708
-5.933
-6.608
-6.369
-6.305
0.02821
0.5864
Heavy Heavy-
Duty
-5.143
-4.564
-4.678
-5.847
-5.287
-5.480
-5.494
-5.269
-5.133
-6.242
-5.923
-6.368
-6.067
-5.754
-6.154
0.0968
0.84035
Where :
flo = an intercept term for a speed class within a model year group, as shown in the table above,
/?i = a slope term for STP, and
a2 = the mean-square error or residual error for the model fit,
STP = the midpoint value for each operating mode (kW/metric ton, see Table 1-4).
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Appendix E Heavy-duty Diesel EC/PM Fraction Calculation
E.I Introduction
This appendix describes the development and application of a simple emission model for
estimating elemental and organic carbonaceous material (EC and OM) emission rates (or EC/OM
ratios) for MOVES. The appendix describes the following steps involved in predicting EC/OM
ratios. The appendix also briefly describes comparisons with independent emission data collected
using the "Mobile Emission Laboratory," operated by the University of California Riverside.
The subsequent sections of the appendix describe the following topics:
• the extension of Physical Emission Rate Simulator (PERE) to estimate heavy-duty
fleet-average emission factors for any specified driving cycle;
• the acquisition of data used in estimating EC/OC rates as a function of engine operating
mode and the fitting of simple empirical models to them;
• the application of PERE to estimate EC and OC emission rates for different test cycles;
and,
• the comparison of PERE-based EC and OC emission rates to those measured by
independent researchers in HD trucks.
E.2 PERE for Heavy-duty Vehicles (PERE-HD) and Its Extensions
The Physical Emission Rate Estimator (PERE) is a model employed by EPA in early development
of MOVES.34 In particular, the MOVES team employed it in development of MOVES2004 to
impute greenhouse gas emission rates for combinations of SourceBin and Operating Mode for
which data was unavailable or of insufficient quality.
The underlying theory behind PERE and its comparison with measured fuel consumption data is
described by Nam and Giannelli (2005).34 Briefly, PERE estimates fuel consumption and emission
rates on the basis of fundamental physical and mathematical relationships describing the road load
that a vehicle meets when driving a particular speed trace. Accessory loads are handled by addition
of an accessory power term. In the heavy-duty version of PERE (hereafter, "PERE-HD"),
accessory loads were described by a single value.
For the current project, PERE was modified to incorporate several "extensions" that allowed it to
estimate fleet-average emission rates, simulate a variety of accessory load conditions, and predict
EC rates for any given driving cycle.
E.2.1 PERE-HD Fleet-wide Average Emission Rate Estimator
PERE-HD requires a number of user-specified inputs, including:
• vehicle-level descriptors (model year, running weight, track road-load coefficients
(A,B,C), transmission type, class [MDT/HDT/bus]);
• engine parameters (fuel type, displacement); and
• driving cycle (expressed through a speed trace).
182
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The specification of these inputs allows PERE to model the engine operation, fuel consumption,
and GHG emissions for a HDV on a specified driving cycle.
However, the baseline PERE-HD provides output for only one combination of these parameters at
once. To estimate fleet-wide average a large number of PERE-HD runs would be required.
Furthermore, the specification of only fleet-wide average coefficients is likely to substantially
underestimate variability in fuel consumption and emissions. Emissions data from a large number
of laboratory and field studies suggest that a very large fraction of total emissions from all vehicles
derives from a small fraction of the study fleet. Therefore, it is desirable to develop an approach
that comes closer to spanning the range of likely combinations of inputs than using a small
selection of "average" or "typical" values.
For the current application, PERE-HD (built within Microsoft Excel) was expanded to allow for a
representative sample of [running weight] x [engine displacement] x [model year] combinations.
A third-party add-on package to Excel, @Risk 4.5 (Palisade Corporation, 2004), allows users to
supplement deterministic inputs within spreadsheet models with selected continuous probability
distributions, sample input values from each input distribution, and re-run the spreadsheet model
with sets of selected inputs over a specified number of iterations. This type of procedure is
commonly referred to as "Monte Carlo" simulation.
E.2.2 Monte Carlo Simulation in PERE-HD
To illustrate how @Risk performs this process, we illustrate the application of a simple model,
employing both deterministic calculations and stochastic Monte Carlo simulation:
EMI-™
./__/
This equation defines the body mass index for humans, a simple surrogate indicating overweight
and underweight conditions. According to the Centers for Disease Control and Prevention (CDC),
the average U.S. woman weighed 164.3 Ib (74.5 kg) in 2002 and was 5'4" (1.6 m) tall. This result
corresponds to a BMI of 28, suggesting that the average U.S. woman is overweight. While this is
useful information from a public health perspective, it does not provide any indication as to which
individuals are likely to experience the adverse effects of being overweight and obese. However, if
we were to assume (arbitrarily) that the range of weight and height within the U.S. population was
+/-50% of the mean, distributed uniformly, and perform a Monte Carlo simulation (5,000
iterations) using @Risk, we would predict a probability distribution of BMI in the population as
follows:
183
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Distribution of BMI in Simulated Population
0.060-
-~ c
_CD o
11
— ^
C/J Q.
«- O
O Q_
0.000
BM
In contrast, here is the BMI distribution in the entire U.S. population, according to the CDC's
National Health and Nutrition Examination Survey (NHANES):
201-
15 -
NHANES 1976-1980
NHANES 2005-2006
BMI
SOURCE: COC/NCHS, National Health and Nutritian Examination Survey (NHANES).
These graphs illustrate how Monte Carlo simulation can be used to provide meaningful information
about the variability in a population. Although the model example is very simple, it illustrates the
point that a model with "typical" inputs provides much less information than Monte Carlo
simulation does with variable inputs.
For emission modeling purposes using PERE-HD, several key inputs were modeled as probability
distributions.
E.2.3 Model Year
Model year is an important factor in PERE, as the frictional losses in the model, expressed as
"friction mean effective pressure" (FMEP), vary by model year, improving with later model years.
As such, model year was simulated as a probability distribution, based on data from the Census
Bureau's 1997 Vehicle Inventory and Use Survey (VIUS), which reports "vehicle miles traveled"
184
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(VMT) by model year. Accordingly these data were normalized to total VMT to develop a
probability distribution. Model year distributions in 1997 were normalized to the current calendar
year (2008).xxviii For instance, the fraction of 1996 vehicles reported in the 1997 VIUS is treated as
the fraction of 2002 vehicles in the 2003 calendar year. Although a 2002 VIUS is available,
previous analyses (unpublished) have shown the "relative" model year distribution of trucks to
have changed little between 1997 and 2002, though this assumption is one limitation of this
analysis.
The model year distribution for PERE-HD was represented as a discrete probability distribution, as
shown below:
Probability and Cumulative Probability Distributions of Model Years in PERE-HD
-Probability by Age
-Cumulative Probability by Age
012345678 910111213141516171819202122232425
Relative Age (Calendar Year - Model Year)
E.2.4 Vehicle Weight and Engine Displacement
Vehicle running weights and engine displacements were modeled as a two-way probability
distribution with engine displacement depending on running weight. These data were derived from
VIUS micro data obtained from the Census Bureau.116 A two-way table was constructed to
estimate VMT classified by combinations of [weight class] x [displacement class]. Analyses were
restricted to diesel-powered trucks only.
As a first step, @Risk selects a running weight from a probability distribution representing the
fraction of truck VMT occurring at a given running weight:
reports model years 11 years old and greater as a single number. For the current analysis, the fraction of
vehicles within each model year older than 10 years of age through 25 years was estimated using an exponential decay
of the form p(x) = A *exp[-B*(x-10)]. Coefficients representing the A and B parameters were estimated by minimizing
least squares of the residuals. The sum of probabilities for model years older than 10 years was constrained the fraction
of VMT driven by trucks older than 10 years in VIUS.
185
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Probability Distribution of Vehicle Running Weight based on VIUS
0.4 -,
0.35 -
0.3 -
£• 0-25 -
| 0.2-
a. 0.15 -
0.1 -
0.05 -
0 -
-•—i—»—i—»—i—•—i—»—i—•—i—»-
a 8 § § § §
in o in o in
t- (N
Weight Range
Because VIUS reports classes defined as ranges in running weight, any value of weight within each
VlUS-specified class was considered equally likely and modeled as a uniform probability
distribution within the class. For the upper and lower bounds of the distribution the minimum and
maximum running weights were assumed to be 7,000 and 240,000 Ib, respectively.
After @Risk selects a running weight, it selects an engine displacement based on a discrete
distribution assigned to every weight class in VIUS, represented below:
Distribution of Displacement (cu. in.) by Running Weight (Ib) in 71US
o
Running Weight
D850+
• 800-849
• 750-799
• 700-749
• 650-699
D 600-649
D 550-599
• 500-549
• 470-499
D 350-469
• 430-449
D 400-429
• 370-399
D 350-369
D 300-349
• 250-299
D 1-2 49
Again, because VIUS describes ranges of values for displacement, all values within each range
were given uniform weight and assigned a uniform distribution. For the extreme classes, the
minimum and maximum engine displacements were assumed to be 100 in3 and 915 in3,
respectively.
186
-------
This procedure reflects the range in running weights present among HDV in operation, and
constrains the combinations of weight and displacement to plausible pairs of values based on
surveyed truck operator responses. These steps allow for plausible variability in weight-engine
pairings, which translates into differences in engine parameters influencing EC and OC emissions.
For use in PERE-HD, all units were converted to SI units (kg and L).
E.2.5 Accessory Load
The original PERE-HD treats accessory load as a fixed value, which may be varied by the user. It
is set at 0.75, and used in calculating fuel rate and total power demand at each second of driving.
Following the development of PERE-HD, a more detailed set of accessory load estimates was
developed based on several accessories' power demand while in use and the fraction of time each
accessory is in use (see Table 2-4).117 High, medium, and low accessory use categories were
estimated for three vehicle classes: HDT, MDT, and buses. For the current version of the model,
only the HDT accessory load estimates were employed, though a sensitivity analysis indicated that
mean EC/OM ratios were most sensitive to accessory load during idle and creep driving cycles. In
the "base case," a mean ratio of 0.54 was predicted, while in the sensitivity case, a mean ratio of
0.50 was predicted. This issue may be revisited at some point, although the limited sensitivity of
total results limits the importance of the accessory terms within the current exercise.
Within @Risk, the variable in PERE-HD, Pacc for accessory use was substituted with a variable
representing the distribution (in time) of accessory loads as estimated as the sum of a number of
discrete probability distributions.
Depending on the assumption of high, medium or low use, the power demand for these accessories
is distributed in time as follows:
Comparison of High / Medium / Low Accessory
Load Cases
1.000-
0.800--
0.600- -
0.400- -
0.200- -
0.000
00
51
CO
51
00
CL
0
10
20
30
E.2.6 Driving Cycle
For purposes of this exercise, the four phases of the California Air Resources Board's Heavy
Heavy-Duty Diesel Truck (HHDDT) chassis dynamometer testing cycle were used to reflect
variability in vehicle operations for PERE-HD.
187
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E.2.7 Other Factors
Some elements of variability were not examined as part of this study. Hybrid-electric
transmissions and fuel cell power plants were excluded from the analysis, due to their low
prevalence within the current truck fleet.
One important source of variability that was not examined in this analysis is the variation in
resistive forces among vehicles with identical running weights. This exclusion is important, given
the potential role for aerodynamic improvements, low rolling resistance tires, and other
technologies in saving fuel for long-distance trucking firms and drivers. Such considerations could
be incorporated into PERE-HD in the future as a means of estimating the emission benefits of fuel-
saving technologies.
E.3 Prediction of Elemental Carbon and Organic Mass based on
PERE-HD
E.3.1 Definition of Elemental and Organic Carbon and Organic Mass
In motor vehicle exhaust, the terms "EC," "elemental carbon," and "black carbon" refer to the
fraction of total carbonaceous mass within a particle sample that consists of light-absorbing carbon.
Alternatively, they refer to the portion of carbonaceous mass that has a graphitic crystalline
structure. Further, one can define EC as the portion of carbonaceous mass that has been altered by
pyrolysis, that is, the chemical transformation that occurs in high temperature in the absence of
oxygen.
EC forms in diesel engines as a result of the stratified combustion process within a cylinder. Fuel
injectors spray aerosolized fuel into the cylinder during the compression stroke. The high-pressure
and high temperature during the cylinder cause spontaneous ignition of the fuel vaporizing from the
injected droplets. Because temperature can rise more quickly than oxygen can diffuse to the fuel at
the center of each droplets, pyrolysis can occur as hydrogen and other atoms are removed from the
carbonaceous fuel, resulting in extensive C-C bond interlinking. As a result, pyrolyzed carbon is
produced in a crystalline form similar to graphite.
"Organic carbon" or "organic mass" (OC or OM) is used to denote the portion of carbonaceous
material in exhaust that is not graphitic. Chemical analysis of this non-graphitic carbon mass
indicates that it is composed of an extensive mixture of different organic molecules, including CIS
to C44 alkanes, polycyclic aromatic hydrocarbons, lubricating oil constituents (hopanes, steranes,
and carpanes), and a sizeable fraction of uncharacterized material. This component of exhaust can
derive from numerous processes inside the engine involving both fuel and oil. Because of the
complex chemical mixture that comprises this mass, its measurement is highly dependent on
sampling conditions. The wide range of organics that compose it undergo evaporation and
condensation at different temperatures, and the phase-partitioning behavior of each molecule is
dependent on other factors, such as the sorption of vapor-phase organics to available surface area in
a dilution tunnel or background aerosol.
188
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E.3.2 EPA Carbon Analysis Techniques in Ambient Air
The definitions of EC and OM are critical, as different groups use different techniques for
quantifying their concentrations within a given medium. For purposes of this document, it is
assumed that EC, OC, and OM are operationally defined quantities, meaning that they are defined
by the measurement technique used to quantify their concentrations on a filter or in air.
The different types of commonly used approaches for carbon include:
• Thermal/optical techniques, where the evaporation and oxidation of carbon are used in
conjunction with a laser to measure optical properties of a particle sample. The major
methods used for this type of analysis include:
o Thermal/optical reflectance (TOR). EPA is adopting this technique for the
PM2.5 speciation monitoring network nationwide. It is also employed by the
IMPROVE program (Interagency Monitoring of Protected Visual Environments)
in national parks. This technique heats a punch from a quartz fiber filter
according to a certain schedule. A Helium gas atmosphere is first employed
within the oven, and the evolved carbon is measured with a FID as temperatures
are increased in steps up to 580°C. All carbon evolved in this way is assumed to
be volatilized organic material. Next, 2% oxygen gas is added to the
atmosphere, and temperatures are stepped up a number of times to a maximum
of 840°C. All carbon evolved after the introduction of oxygen is assumed to be
elemental carbon. The reflection of light from a laser by the filter is employed
to account for the pyrolysis of organic carbon that occurs during the warm-up
process.
o Thermal/optical transmission (TOT). The National Institute of Occupational
Safety and Health (NIOSH) uses this technique for measuring EC concentrations
in occupational environments. It is based on similar principles to TOR, but
employs a different heating schedule and transmission of light as opposed to
reflectance.
• Radiation absorption techniques
o Aethalometer® - This instrument reports "black carbon" (BC) concentrations
based the extent of light absorption by a "filter tape," that allows for a time
series of BC concentrations to be estimated. It has a time resolution of several
minutes.
o Photoacoustic Spectrometer (PAS) - This instrument irradiates an air sample
with a laser. The resulting heat that occurs from the absorption of the laser light
by light-absorbing carbon in the air sample produces a pressure wave that is
measured by the device. The signal from this pressure wave is proportional to
the light-absorbing carbon content in exhaust.
• Thermogravimetric techniques, where the "volatile organic fraction" (VOF) is separated
by heat from the non-volatile refractory component of a particle sample.
• Chemical extraction, where solvents are used to separate the soluble and insoluble
components of exhaust.
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A number of additional techniques are also described in the published literature, but the above
techniques have been most commonly applied in emissions and routine ambient PM measurement.
Among the available techniques, it has been a point of controversy among academics as to which
method provides the "correct" carbon signal. Rather than addressing these arguments in detail, this
analysis adopts the technique employed by the EPA ambient speciation monitoring network, TOR.
Needless to say, different researchers employ different sampling, measurement and analysis
techniques. Desert Research Institute (DRI) employed TOR in analyzing the Kansas City gasoline
PM emission study samples118, while other prominent academics employ TOT, notably the
University of California Riverside College of Engineering Center for Environmental Research and
Technology (CE-CERT) and the University of Wisconsin-Madison (UWM) State Hygiene
Laboratory. As research results from these groups is employed throughout this analysis, an inter-
comparison of the methods of TOT/TOR is necessary to "recalibrate" various datasets with respect
to each other.
EPA defines measurement techniques for dynamometer-based sampling and analysis of particulate
matter, in addition to techniques for sampling and analyzing particles in ambient air. Inventories
estimated for EC and OM can be considered to reflect both broad categories of measurement
techniques, depending on context.
The user community for MOVES is predominantly concerned with emissions that occur into
ambient air. EPA regulations for demonstration of attainment of state implementation plans (SIPs)
are based on monitored ambient particulate matter using Federal Reference Methods (FRM) for
ambient air. FRM monitors for particle speciation in ambient air undergo analysis for EC and OC
according to a defined standard operating procedure.119 That standard operating procedure defines
thermal/optical reflectance (TOR) as the desired method for analysis of ambient carbon PM.
E.3.3 TOR - TOR Calibration Curve
In the course of the Gasoline/Diesel PM Split Study funded by the Department of Energy (DOE),
researchers from DRI analyzed filter samples using both TOR and TOT methods[cite]. These data
were obtained and analyzed in the SPSS 9.0 statistical package.
Briefly, the DOE study included emissions characterizations of 57 light-duty gasoline vehicles
(LDGV) and 34 HD diesel vehicles (HDDV). The vehicles were operated on a number of different
test cycles including cold-start and warm-start cycles. The data set employed in this study was
generated by DRI and obtained from the DOE study web site.120 Both EC and OC were analyzed
using the same approach. All data from all vehicles were compiled.
First, EC and OC measured by TOR (denoted EC-TOR and OC-TOR) were regressed on EC-TOT
and OC-TOT. Studentized residuals from these regressions were noted, and those with Studentized
residuals >3 were excluded from further analysis.
Second, each test in the reduced data set was assigned a random number (RAND) on the range
[0,1]. Those cases with RAND > 0.95 were set aside as a cross-validation data set, and excluded
from additional regression analyses.
Third, those cases with RAND < 0.95 were regressed again, this time using an inverse uncertainty
weighting procedure for each data point. When DRI analyzes a filter sample, it reports an
analytical uncertainty associated with the primary estimate of EC and OC. Accordingly, the quality
190
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of each datum depends on the level of analytical uncertainty reported. The inverse of the DRI-
reported uncertainty (I/a) associated with the TOR-based measurement was used to weight each
point in the weighted regression.
It should be noted that for each regression, the intercept term was set to zero. Models including
intercepts did not have intercept terms that reached statistical significance. As such, R2 values are
not considered valid.
Coefficients from the weighted regression for EC and OC are reported below:
Slope
EC-TOR
OC-TOR
Beta
1.047
1.014
Std. Error
0.011
0.007
t-value
91.331
153.923
Sig.
<0.0001
<0.0001
To evaluate the quality of predictions resulting from these statistically-based adjustment factors,
they were used to predict EC-TOR and OC-TOR values for the subset of data with RAND > 0.95.
Scatter plots of the statistical fits are illustrated below (note logarithmic scaling).
1000
W
CD
CD
o:
p
6
LU
30
200-
100-
20-
10-
2-
1-
.2-
.1
EC-TOR Predicted
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1
w
CD
CD
^
or
O
i —
8
i uuu •
300-
200-
100-
20-
10-
5-
3-
2-
1-
•§"
:s:
.2-
.1
D
D
D
D
D
D
D
D
OC TOR-Predicted
When measured values are regressed against predicted values, the following statistical
estimates of fit are obtained:
Prediction
EC
OC
Slope
1.080
1.092
Std. Error
0.009
0.069
Intercept
3.737
-4.417
Std. Error
3.173
16.188
As shown, the prediction vs. observed comparison yields a slope near unity for both EC-TOR and
OC-TOR, with nonsignificant intercepts. On this basis, the "calibration" factors for converting EC-
TOT and OC-TOT into their respective TOR-based metrics appear reasonable.
It remains an unverified assumption that the "calibration" factors derived from the emissions data
derived from DRI as part of the DOE Gasoline / Diesel PM Split Study are general enough to apply
to EC-TOT measurements obtained by other research groups.
E.3.4 EC and OC Emission Rates
Selection of Engine Parameters for Predictive Modeling
PERE-HD produces estimates of engine operating conditions and fuel consumption for a given
driving cycle. Prediction of EC and OM emissions requires information on the composition of
particulate matter as a function of some factor that may be related back to MOVES' activity basis,
the time spent in a particular operating mode (opModelD).
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It should be noted that continuous ("second-by-second, or "real time") measurement of EC and
OM is an exceptionally complicated endeavor. While measurement techniques for EC have been
developed that produce apparently good correlation with traditional filter-based methods,
While numerous publications report the EC and OM (or OC) exhaust emission rates across an
entire driving cycle, it is not clear which parameter of a particular driving cycle, such as average
speed (or power), might be applicable to the extrapolation of the observed rates to other vehicles or
driving conditions. As a result, identifying one or more engine parameters that explain the
observed variation in driving cycle-based emission rates for EC and OM is desirable. Such
parameter(s) will assist in estimating emission associated with short-term variations in driving.
One good candidate for establishing an engine-based emission model is mean effective pressure
(MEP). MEP is defined as:
MEP=Pn*
VdN
Here, P is the power (in kW or hp), YIR is the number of crank revolutions per power stroke per
cylinder (2 for four-stroke engines, 1 for two-strokes), Vd is the engine displacement, and TV is the
engine speed. In other words, MEP is the engine torque normalized by volume.
MEP can be broken into various components. "Indicated MEP" or IMEP refers to the sum of
BMEP (brake MEP) and FMEP (friction MEP). Heywood (1988) writes that maximum BMEP is
an indicator of good engine design and "essentially constant over a wide range of engine
sizes, [cite]" Nam and Giannelli (2004) note that it can be related to fuel MEP multiplied by the
indicated or thermal efficiency of an engine, and have developed trend lines in FMEP by model
year. As such, since maximum BMEP is comparable across well-designed engines and FMEP can
be well-predicted by Nam and Giannelli's trends within PERE, IMEP should be an appropriate
metric for building an engine emission model that can be applied across vehicles with different
loads and engine displacements.
Emission Data
Kweon et al. (2004) measured particle composition and mass emission rates from a single-cylinder
research engine based on an in-line 2.333 liter turbo-charged direct-injection six cylinder Cummins
N14-series engine, with a quiescent, shallow dish piston chamber and a quiescent combustion
chamber. Emission data were obtained from all eight modes of the CARB 8-mode engine test
cycle:
Speed
Load%
Equiv.
Ratio (cp)
IMEP
(MPa)
Mode 1
1800
100
0.69
1.083
Mode 2
1800
75
0.50
0.922
Mode 3
1800
50
0.34
0.671
Mode 4
1200
25
0.21
0.524
Mode 5
1200
100
0.82
1.491
Mode 6
1200
75
0.69
1.225
Mode 7
1200
50
0.41
0.878
ModeS
700
10 (idle)
0.09
0.150
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The study reports exhaust mass composition, including PIVh.s, EC, and organic mass (OM,
estimated as 1.2 x OC) measured with TOT (denoted here as EC-TOT and OC-TOT). In the main
study, the authors report that EC and OC are highly sensitive to the equivalence ratio. However,
IMEP is highly correlated with the measured equivalence ratio (R2 = 0.96). As such, it is
reasonable to report the data as a function of IMEP, expecting it to have approximately equal
explanatory power as has the equivalence ratio variable. The figure below plots the emission data
from Kweon et al. (2002) as a function of EVIEP.
Particle Mass, EC-TOT, and OC-TOT vs. IMEP
As shown in the figure, the EC-TOT work-specific emission rate is relatively insensitive to IMEP
except between IMEP of approximately 0.85 and 1.1, where it undergoes a rapid increase. Overall,
the EC-TOR/EVTEP curve is S-shaped, similar to a logistic curve or growth curve. OC-TOT work-
specific emissions are highest at low IMEP (i.e. idle) and are monotonically lower with higher
IMEP. Total work-specific PM2.5 is not monotonic, but appears to be described by a single global
minimum around IMEP ~ 0.9 and two local maxima around IMEP of 0.2 and 1.2, respectively.
The oppositely signed slopes of the emission-IMEP curves for EC-TOT and OC-TOT suggest that
there are different underlying physical processes. It is not the intent of this document to explicitly
describe the particle-formation mechanisms in a diesel engine. However, the use of two separate
functions to predict EC-TOT and OC-TOT separately is warranted. This implies that the EC/OC
ratio will vary by engine operating mode. The following figure depicts the EC/OC ratio as a
function of EVIEP.
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EC/OC Ratio vs. IMEP from Kweon et al. (2004)
Note Logarithmic Scale
100 n
O
Q
O
LU
10 -
0.1 J
0.2
0.4
0.6
0.8 1
IMEP
1.2
1.4
1.6
Estimation of IMEP-based Emissions of EC and OC
To produce a relationship that generalizes the implied relationship between EC-TOT and OC-TOT
work-specific emissions and IMEP in the data presented by Kweon et al. (2004), it is necessary to
specify some functional form of a relationship between the two.
A priori, on the basis of visual inspection of the data, a flexible logistic-type curve was fit to the
data by a least-squares minimization procedure using the Microsoft Excel "Solver" tool, which
employs the GRG2 optimization approach.
The functional form of the logistic-type curves fit to both the EC-TOT and OC-TOT data from
Kweon et al. (2004) is as follows:
7 =
A
,-Bx
+c
A least-squared error approach was implemented within Microsoft Excel to derive the coefficients
for the logistic curves for EC-TOT and OC-TOT. The solutions to the fits are as follows:
Y
EC-TOT
OC-TOT
A
2.12 x 10-5
0.155
B
-9.79
-2.275
C
4.67x 10-5
-0.859
Graphically, in comparison to observed values of EC-TOT and OC-TOT, the fitted curves result in
predictions reasonably close to the observed values. Furthermore, when compared to the observed
PM2.5 values, the sum of predicted EC-TOT and OC-TOT values predict the lack of monotonicity
and patterns of maxima and minimum seen in the PM2.5 data.
However, as a result of the values predicted by these sigmoid-type curves at high and low IMEP
values, extreme patterns in the EC-TOT/OC-TOT ratios predicted occur. These extreme values are
195
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artifacts that result solely from the behavior of simplistic logistic curves at the bounds of IMEP in
the observed data sets. As a result, for predictive purposes, the maximum and minimum observed
EC-TOT and OC-TOT values observed in the data set were set as the artificial limits of predicted
EC-TOT and OC-TOT, respectively. While this approach is arbitrary, it does ensure that extreme
predictions resulting from the selection of the logistic functional form do not occur.
The following graph (log-scale) depicts the behavior of the TOT-based EC/OC ratio as a function
of EVIEP. As demonstrated on the graph, without the max/min constraints on predicted EC-TOT
and OC-TOT, the predicted ratio assumes values with a much broader range than found in the data.
Comparison of EC/OC Ratio (TOT) by IMEP
With and Without Max/Min Constraints
100
I
O
UJ
-EC/OC(TOT-Predicted)
- - - - EC/OC(TOT-Predicted) with
Constraints
EC/OC(TOT-Measured)
0.01
IMEP
The approach of constraining predictions to the maximum and minimum values observed in the
measured data set is not grounded in any theoretical basis, but is a "brute force" approach. Future
revisions to this analysis may consider alternative approaches more grounded in accepted
theoretical or statistical methodology.
The logistic curves described above receive IMEP predictions from PERE to predict EC-TOT and
OC-TOT emission rates (g/bhp-hr) for every second of a driving cycle. Combined with real-time
work estimates from PERE, emissions are expressed in g/s, the same units required for MOVES.
EC-TOT and OC-TOT emission rates are converted to TOR-equivalent rates for use in MOVES,
using the TOT-TOR "calibration" relationships described above. Alternatively, TOT-equivalent
rates can be used to compare with data from studies employing TOT for carbon analysis.
It should be noted that these emission estimates are based on a single engine. Therefore,
predictions of EC and OC emission rates based on these relationships are insensitive to model year,
although PERE-HD does vary frictional MEP as a function of model year.
Organic Carbon to Organic Mass Conversion
Carbon is only one component of the organic material found in PM emission samples. Hydrogen,
oxygen, and nitrogen are also components of organic molecules found in exhaust PM. For this
study, a simple set of OC/OM conversion ratios were employed.
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Heywood (1988) presents data on the chemical composition of diesel exhaust PM, presenting
characterization of both the "extractable composition" and "dry soot" components of PM measured
at idle and at 48 km/h.121 The composition data is as follows:
Idle
48 km/h
Atomic formula
C24H3oO2.eNo.
o.18
OM/OC Ratio
1.39
1.26
The data for the "extractable composition" is assumed to represent the organic mass of particles.
The total molar weight to carbon molar weight ratio was used to convert OC to OM. The idle data
from Heywood were used when engine IMEP was 0.15 or under, corresponding to the idle mode of
the cycle employed by Kweon et al. (2004). All other engine conditions employed the ratio based
on the 48 km/h sample in Heywood.
E.4 Comparison of Predicted Emissions with Independent
Measurements
To ensure that predicted EC and OC emission rates from this approach are reasonable prior to any
application for MOVES, PERE-HD based EC and OC emission factors were compared with
measured emission factors from an independent study. Shah et al. (2004) report EC and OC
emission factor and rates for a series of heavy heavy-duty diesel trucks (HHDT) in California.122
Shah et al. report the results of emission testing using the CE-CERT Mobile Emissions Laboratory
(MEL), a 53-foot combination truck trailer containing a full-scale dilution tunnel designed to meet
Code of Federal Register (CFR) requirements. The primary dilution tunnel is a full-flow constant
volume sampler, with a double-wall insulated stainless steel snorkel that connects the MEL directly
to the exhaust system of a diesel truck. PM collection systems were designed to meet 2007 CFR
specification, including a secondary dilution system (SDS).
The 11 trucks sampled in this study were all large HHDDTs with engine model years 1996-2000,
odometers between approximately 9,000 and 547,000 miles, and rated powers from 360-475 hp. It
should be noted that these trucks, on average, have larger engines and higher rated power than
"typical" trucks on the road. Furthermore, they were loaded with only the MEL, which weighs
20,400 kg. As a result, the emissions from these trucks do not reflect the expected variability in
truck running weight described above and used in the PERE-HD runs for this study.
Shah et al. (2004) report emission data for each of the four modes of the CARB HHDDT cycle,
including cold start/idle, creep, transient, and cruise. The test cycle represents a wide range of
driving patterns, as suggested in the table below. Note that these test cycles are trip-based, so each
begins and ends with the vehicle at stop.
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Cycle
Cold start/idle
Creep
Transient
Cruise
Distance (mi)
0
0.124
2.85
23.1
Duration (s)
600
253
668
2083
Average
Speed (mph)
0
1.77
15.4
39.9
Maximum
Speed (mph)
0
8.24
47.5
59.3
Maximum
Acceleration
(mph/s)
0
2.3
3.0
2.3
The following table presents the EC-TOT and OC-TOT emission rates reported in Table 6 of the
study:
Rate
EC (mg/mi)
OC (mg/mi)
EC (mg/min)
OC (mg/min)
Idle
4.10±2.38
20.9±11.6
Creep
340±140
607±329
10.4±4.8
17.0±6.4
Transient
446±115
182.9±51.2
110.7±27.0
45.5±13.2
Cruise
175±172
74.7±56.3
93.0±68.3
42.3±26.8
The following graph illustrates the comparison between predicted EC-TOT and OC-TOT emission
factors predicted by PERE-HD and those reported by Shah et al. (2004). The letters "H," "M," and
"L" refer to high, medium, and low accessory loads employed in the PERE-HD runs with IMEP-
based emission rates. As shown in the graph, it appears that for transient and cruise conditions,
PERE-HD predicts the general between-cycle trends in EC-TOT and OC-TOT emission factors. It
appears that for the low-speed "creep cycle," PERE-HD or the IMEP-based emission rates
underpredict total carbon (EC+OC) emission factors, but that the general trend in the EC/OC ratio
is directionally correct.
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Predicted EC and OC Emission Factors(g/mi) vs. Measured Values in Shah et al. (2004)
D Average of OC g/mi
• Average of EC g/mi
E. 5 Variability in Predicted EC and OC Emission Rates
Through the modeling approach used here the influence of variability in vehicle weight and engine
displacement on heavy-duty EC and OC emission rates can be assessed. It should be noted that
these relationships are contingent on the particular algorithms employed in PERE-HD for
estimating power and EVIEP, as well as on the functional form of the IMEP-based emission
relationship described above. As such, the analysis of variability in EC and OC emission rates is
constrained within the functional forms of all models employed.
The graph below depicts the TOR-specific ratios of the total amount of EC and OM emitted across
the transient driving cycle. As is apparent, increasing running weight per unit of engine
displacement is associated with an increased EC/OC ratio. The highest EC/OM ratios, located in
the upper right-hand-quadrant of the graph, correspond to vehicles loaded with extreme weight
relative to the total available engine displacement.
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25 -,
20 -
15 -
O
D 10 -
LU
5 -
EC/CM Ratio (TOR-Spacific) versus Weight/Displacement Ratio for Individual Truck Samples
Transient Driving Cycle, High Accessory Load
1000 2000 3000 4000 5000 6000
Running Weight/ Displacement (kg/I)
7000
8000
In general, these results reflect the role that running weight has on IMEP in a truck. Since IMEP
correlates highly with the air/fuel ratio (or equivalence ratio (p), the data suggest that EC/OC
partitioning is driven by the pyrolysis that occurs in engines under load.
Very few weight/displacement pairings are greater than 3,300 kg/L. The following graph depicts
the cumulative frequency distribution (CFD) of simulated weight/displacement ratios in PERE-HD.
Distribution of kg/I Ratios in Transient, High Accessory Load Simulation
7000
6000
4000
3000
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Percentile
For a 12 L engine, 3,000 kg/L would correspond to a running weight of 39600 kg (87,302 Ib).
Such vehicle loadings are infrequent, as they exceed Federal and state limits for vehicle weights on
highways. The graph below presents the cumulative distribution of simulated weights, based on
200
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the VIUS microdata. Furthermore, the graph presents cumulative frequency distributions for
several broad weight categories reported by Ahanotu (1999) for trucks in the Atlanta metropolitan
area.123 Note that in the graph, the highest weight category reported by Ahanotu (1999) is
represented as 100%, although the actual maxima of observed trucks are unknown.
Comparison of Simulated Weights with Atlanta Area Truck Measurements
• Simulated Weight
GA Tech Class 9 Monday Midday
GA Tech Class 9 Monday 3-7 PM
GA Tech Class 8 Trucks M-F Midday
GA Tech Class 10-13 Trucks Daytime
GA Tech Class 10-13 Trucks
Nighttime
20000 40000 60000 80000
Weight (Ib)
100000 120000
In general, the sensitivity of EC/OM ratios to the weight/displacement ratio suggest that properly
capturing the variability in both inputs is key to developing representative inputs for MOVES.
E. 6 Calculating EC/OC fraction by Operating Mode
The modeling described in the previous sections has been employed to create second-by-second
estimates of EC-TOR and OC-TOR emission factors for use in the MOVES emissionRateByAge
table. The next step of consists of appropriately binning the outputs to fit the MOVES operating-
mode structure. EC and nonECPM emission rates, , are the inputs to the MOVES model for PM
inventory calculations. To convert the total PM rates calculated from heavy-duty emissions
analysis into EC and nonECPM rates, we must calculate EC and nonECPM fractions by operating
modes. Then, the total PM rate can be multiplied by the EC and nonECPM fractions to obtain EC
and NonECPM input emission rates.
PM emissions contain additional inorganic species. However, the total carbon (TC =EC + OC)
composes almost all the PM2.5 emissions from conventional diesel emissions. As such, we use the
EC/TC as a surrogate for the EC/PM emissions in MOVES.
One of PERE's outputs for heavy-duty vehicles is the track road-load coefficients. For each
individual weight in the distribution, PERE outputs a set ofA/B/C coefficients similar to the ones
used to calculate VSP in the HC, CO, and PM emission rate analysis. We used these coefficients
and weights to calculate VSP for each second using the equation below.
201
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• Bvt + Cv:
mvta
t*"t
m
This equation is implemented slightly differently than the one used for analysis of the chassis
dynamometer testing for PM, HC, and CO since the road load coefficients (A, B, and C) and weight
(or mass) m were specific to each individual vehicle, not general to the regulatory class. In the PM,
HC, and CO equation, the road load coefficients and denominator mass were not specific to the
vehicle and the numerator mass was specific to the vehicle. We felt confident in using vehicle-
specific numbers because we performed the analysis using a full representative distribution of
weights and displacements. Also, since we are interested in the EC and nonECPM fractions rather
than the actual rates themselves, normalizing by the actual weight provides a more accurate picture.
For example, a large engine operating at 90% of rated power (high VSP) would have a similar EC
fraction as a smaller engine operating at 90% of rated power, even though the large engine would
likely be hauling a proportionally greater amount of weight. This is also supported by the previous
research and analysis that relates EC fraction to IMEP and not power itself. The large engine
would, however, emit a larger EC rate than the smaller engine, but this difference in rates is
captured by our PM emission rate analysis.
We separated vehicles into two different regulatory classes based on running weight (we did not
have GVWR information). The weight distribution used in the analysis is shown below.
Representative distribution of weights used in the EC/OC analysis.
15000 25000 35000 45OOO 55OOO 65OOO 75OOO 85OOO 95OOO 105OOO 115OOO 125OOO 135OOO 145OOO
Based on this weight distribution, we considered all vehicles weighing more than 40,000 Ib to be
HHD vehicles and all vehicles less than 40,000 to be MHD vehicles. This was a very simple
approach to stratifying by regulatory class.
As EC and nonECPM rates were also computed for each second during each cycle, we were able to
average the EC and nonECPM rates by operating mode. Then, we calculated the fractions of EC
and nonECPM for each operating mode. For the LHD classes, we used the MHD fractions, and for
buses, we used the HHD fractions.
202
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roc
The resulting EC fractions by operating mode are shown in Figure 2-20 in the main body of this
report.
203
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Appendix F Heavy-duty Gasoline Start Emissions Analysis
Figures
Figure F-l. Cold-Start Emissions (FTP, g) for Heavy-Duty Gasoline Vehicles, averaged by Model-year and
Age Groups
FTP Cold-Starts (g), HD SI (HD< = 14K)
CO starts IB, Age by BYE
(a) CO
FTP Cold—Starts (g), HD SI (HD< = 14K)
THC starts vs. flge by MYG
(b)THC
-t 19901990 -\—I—h 13311997
FTP Cold-Starts (g), HD SI (HD< = 14 K)
HOx starts vs. figs by MYG
(c) NOx
10 II 12 13
-t 19901990 I ' I 19911997
204
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Figure F-2. Cold-Start FTP Emissions for Heavy-Duty Gasoline Vehicles, GEOMETRIC MEANS by Model-
year and Age Groups
FTP Cold—Starts (g) HD SI (HD< = 14K)
CO CEO-mean starts vs. Age by KVG
(a) CO
modelyeargroup
19911997 Bnf!-i 19932004
FTP Cold-Starts (g), HD SI (HD< = 14K)
THC GEQ-mean starts vs. Ags by MYG
(b) THC
mode 1 yeargroup O O O 1960198
agem i d
a O HI 19901990
FTP Cold-Starts (g), HD SI (HD< = 14K)
NOx GEO-mean start s vs. Age by MY
(c)NOx
mode1yeargroup
12 13
205
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Figure F-3. Cold-start FTP Emissions for Heavy-Duty Gasoline Trucks: LOGARITHMIC STANDARD
DEVIATION by Model-year and Age Groups.
(a) CO
CO I n_SD is. Age by UYG
lodelyeargroup P O C 19601389 n n n 19901990 -1—I—t- 19911997
FTP Cold-Starts (g), HD SI (HD< = 14K)
THC ln_SD vi. Age by HVG
(b)THC
nodelyeargroLip O O O 19601989 a-B-B 19901990 "I—I—I- 19911997
(c) NOx
FTP Cold-Starts (g), HD SI (hD< = M
NGx ln_SD vs. Age by MVG
i.o ;
0.9 ::
....i;
0.7 - .
O.G : :•
0.5 -:
0.4
0.3:;
0.2 -L
I9GOI989 B-B-B 19901990
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Figure F-4. Cold-Start Emissions for Heavy-Duty Gasoline Trucks: RECALCULATED ARITHMETIC
MEANS by Model-year and Age Groups.
(a) CO
nodelyearoroLip o o c I9GOI989 B-e-B 19901990 H—I—H 19311997 a-A-Tfe 19992004
FTP Cold-Starts (g), HD SI (HD< - MK)
THC ARITH-meon starts »s. Age by UYG
(b) THC
modelyeargroup B-9-e 19B01 989 E^B-B 1 990! 990 I I I 1991199?
FTP Cold-Starts (g), HD SI (HD<-
NDx ARITH-meon starts vs. Age
14K)
by HVG
(G)NOx
mode1yeargroup
19601989 B-Er-B 19901990 I I I 199)1997
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Table F-l. Emission Standards for Heavy-Duty Spark-Ignition On-road Engines
Regulatory Class
LHD2b3
LHD45, MHD
Model Year
1990
1991-1997
1998-2004
2005-2007
2008+
1990
1991-1997
1998-2004
2005-2007
2008+
Emissions Standards (g/hp-hr)
CO
14.4
14.4
14.4
14.4
14.4
37.1
37.1
37.1
37.1
14.4
THC
1.1
1.1
1.1
1.9
1.9
1.9
NMHC
0.14
0.14
NOx
6.0
5.0
4.0
0.20
6.0
5.0
4.0
0.20
NMHC + NOx
1.0
1.0
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Appendix G Responses to Peer-Review Comments
This section provides the list of peer reviewer comments submitted in response to the updates made
to the draft MOVES2014 Heavy-Duty Emission Rates Report received February 5, 2014.
The peer-reviewers were charged with reviewing the following sections of the report.
• Updates to Chapter 2.1.1: Heavy Duty Diesel, Running NOx Emissions
• Section 3.3 "Updates to Emission Rates in MOVES2014" [material in Sections 3.1.1.3 and
3.1.1.4 in the current report]
• Chapter 4 Heavy-Duty Compressed Natural Gas Transit Bus Emissions [
• Chapter 5 Heavy-Duty Crankcase Emissions
After the peer-review, additional changes were made to the report in response to the peer-review
comments, internal EPA review, and updates made to the MOVES2014 heavy-duty emission rates
that occurred after the peer-review (Such as the addition of regulatory class LHD<=10K and
LHD<=14K, and the redefinition of regulatory class LHD45 described in Section 1.1).The edits
have results in section changes and page number changes. In the responses to comments, we have
updated section number, table, figure, equation, and page number references [in brackets] to be
current with the released report.
G. 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 which might better allow the
model to estimate national or regional default values?
G.I.I Dr. Mohamadreza Farzaneh
In general, the authors adequately described the data sources, data gaps and limitations, and
assumptions and methodologies they used to address these limitations. The list below shows a few
instances that they can improve the presentation by providing more details on their assumptions:
Page 14 [Section 2.1.1.4.4, Page 23], last line - A temperature threshold of 300C is assumed for
PM regeneration. No reference is provided to support this assumption.
RESPONSE: Temperature, along with air-fuel ratio, and ECU signals, was used to estimate
the state of the engine/emission control system (in PM regeneration, or normal operation).
We have clarified the text that additional variables were used then temperature alone.
Page 15 [Section 2.1.1.4.4, Page 23] line 2 - it is assumed that 10% of VMT for PM regeneration
frequency. No reference listed for to the data used for this assumption.
RESPONSE: The approximate 10% frequency of PM regeneration was observed from the
EPA data set on the LNT/DPF equipped vehicle. We have clarified that the 10% assumption
is obtained from the LNT data set.
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Page 37,[Page 49] section 2.1.2.3.2 -the values "0.46" and "0.60" are taken from MOBILE6.2; is
there any data to confirm they are still valid?
RESPONSE: The "0.60" value is removed from the report, since the updated LHD<=14K
andLHD45 emission rates for MOVES2014 s use the same STP-based PM emission rates
asMHD vehicles. We added a footnote in Section 2.1.2.3.2 that discusses this change
between MOVES2010 andMOVES2014. Presently we do not have second-by-secondPM
emissions from regulatory class LHD< =10K diesel vehicles to evaluate whether the "0.46 "
value is still a reasonable value.
Page 37. [Page 50] Section 2.1.2.3.2 - Where [are] the coefficient values "0.40, 0.70, and .50"
coming from?
RESPONSE: We added Table 2-13 which displays the heavy-duty Cl and Urban Bus PM
emission standards for model year groups 1991-1993, 1994-1995, and 1996-2006. The
ratio in standards shows the derivation of 0.40, 0.70, and 0.50.
Page 104 [Page 160]— NO2/NOx fractions are based on a single 2003 study (three CNG transit
buses and the same engine make/model). A 12.7% seems to be too low (based on a limited data for
CNG refuse trucks collected by TTI). Although, I should admit that this fraction for CNG engines
is sensitive to the drive cycle and can vary significantly for different vehicle types. Further data is
definitely needed and TTI will be happy to share the mentioned CNG refuse trucks data with EPA.
RESPONSE: We added Chapter 6: Nitrogen Oxide Composition, which presents the NOx
fractions for all vehicle types, including diesel and gasoline, which were previously not
located in this report. We agree that further data is needed to evaluate all the NOx
fractions, including from CNG transit buses. By clearly stating the fractions, we intend that
the MOVES rates can be more easily evaluated by making comparisons to NO and NO 2
emission measurements.
TTI's Air Quality Program has performed quite a few studies using mostly PEMS equipment that
could enhance the database used for this analysis. We will be happy to share any information
gathered during these studies. Specifically, TTI collected second by second data from class 8b
HHDVs driving at speeds as high as 85 mph which can be used to improve the rates for the high
power/high speed bins.
Expanding MOBILE6 Rates to Accommodate High Speeds
Sponsor: Houston Advanced Research Center and Center for International Intelligent
Transportation Research
Budget: $150,000
Description: PEMS testing of 3 long haul HHD trucks under different acceleration and speed
conditions including speeds up to 85mph.
Location: Study performed at TTI's High Speed Test Track in Pecos, Texas
RESPONSE: We intend to use this study and others to evaluate MOVES and improve
MOVES data in future versions.
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G.I.2 Dr. Janet Yanowitz
p. 1 [Page 1] - "From each data set, we used only tests we determined to be valid." Specify what
proportion of each data set was discarded for time alignment and other issues.
RESPONSE: We added a sentence specifying that approximately 7% of the ROVER and
HDIU data were removed due to the correlation checks. No data was removed from the
WVU MEMS data, as is stated in the paragraph. All the data from Houston Drayage met
the criteria for correlation between CO2 and engine power. Table 2-2 specifies the number
of vehicles that were included in the final data set.
p. 19, Figure 1 [Page 32, Figure 2-3]- it would be useful to include the number of data points
available for each operating mode, as perhaps that explains why the error bars are so large for
certain operating modes. If not, perhaps the authors could suggest another reason for the large error
bars (does your hole-filling technique result in these large error bars? Why?).
RESPONSE: As discussed in Section G.2.2. (4th Comment), the error bars represent 95%
confidence intervals of the mean, which increase with smaller number of sample points and
with the standard deviation. Also, as stated in Section G.2.2, and Section 2.1.1.8 of the
report, if no data were available, the relative standard error, was applied to the forecasted
to estimate confidence intervals for the operating modes, age groups, or regulatory class
with missing data.
G. 2 Clarity of Analytical Meth ods 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?
G.2.1 Dr. Mohamadreza Farzaneh
The descriptions are clear and I was able to develop an adequate understanding of the steps taken
and assumptions made. The following are a few questions I had on the procedures:
Page 36, eq. 14 [Page 48, Equation 2-16] - Is there any study on the validity of this normalization
approach? The main concern is that in the current form, it assumes that all the changes are
essentially linear; which might not be necessarily true. An empirical comparison will show how
this assumption is representative of the actual behavior of the data.
RESPONSE: We added a reference Kinsey et al. (2006) that showed that time-integrated
TEOM measurements have good correlation with gravimetric filter measurements.
Page 37 [Page 50], first paragraph under 2.1.2.2.3, line 4 - what is the criteria/definition for
"sufficient"?
RESPONSE: The material in this section was peer-reviewed for MOVES2010, and was
outside the scope of the review for MOVES2014. The details on the original analysis are no
longer available, but the original author believes that 'sufficient' meant that there were less
than ~ 25 points within each operating mode bin.
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The examples for tables and figures provide adequate information are on the methodologies used.
The presentation of figures and tables can be improved as follows:
Some of the tables and figures that use colors are not easy to follow in black and white print. For
example, in figures 47 [Figure 4-6] to 52 [Figure 4-11], the same symbol is used for 0-3 and 4-5
age groups while their colors are not different in BW print. The same is true for figure 42 [45]. In
general, when using colors in tables and charts, they should be selected in a way so they could be
differentiated in BW print.
RESPONSE: The two age groups of measurements are now represented by different
symbols, blue diamonds correspond to the 0-3 age group and purple triangles correspond
to the 4-5 age group.
I suggest including the pollutant name on all graphs dealing with the rates; e.g. figures 47-58. [Now
Figure 4-6 through Figure 4-11].
Tables 51 to 55 [Table 5-2 to Table 5-7]. Table captions need to mention what pollutant they cover.
RESPONSE: These pollutant names have been added to the y-axis labels for Figure 4-6
through Figure 4-11, and the pollutant names have been added to Table 5-2 through Table
5-7.
G.2.2 Dr. Janet Yanowitz
Add a list of acronyms, with their meanings spelled out.
RESPONSE: We have revised the report to define the acronym when it is first used in a new
section, and to be consistent with their use (for example using the regulatory class name
consistently,, e.g. LHD<=10K, rather than intermixing references to the regClassID, e.g.
40.
p. 1 [Page 1]- first paragraph, "exhaust rate inputs" is a confusing way to refer to "emission
factors" based on various inputs such as model year, engine type, etc. Emission factors were also
developed for organic species (including formaldehyde and acetaldehyde) and PM components.
RESPONSE: We removed the terminology "exhaust rate inputs", and replaced it with
emission rates, which is the terminology we use to express emissions/distance or
emissions/time in the report. We also added several sentences to the introductory
paragraphs of the report that reference the Toxics and Speciation report to the places
where aggregate measures of organic gases (e.g. VOC, NMOG, TOG) and individual
compounds (e.g. formaldehyde and acetaldehyde) are located.
p. 15 - Table 10 [Page 25, Table 2-6] is a very useful table, but it could be made better by ensuring
that each box representing an estimate detailed the base case upon which the estimate was made
and the whether it was done by proportioning by emissions standards or certification data (e.g.
"proportioned to 1990 LHD by certification levels", or "proportioned to 1991-1997 HHD by
emissions standards" instead of just "proportioned to certification levels" or "proportioned to
HHD").
RESPONSE: We have updated the table so that the source the data used as the 'base data',
and what data is being used to ratio the 'base' data is clear from the table. We also added
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Equation 2-8, Equation 2-9, Equation 2-10, and Equation 2-11 were added to clarify the
adjustments made with limited data.
p. 19 through p. 31, Figures 1-3, 6-17 [Pages 32-43, Figure 2-3-Figure 2-19] It would be beneficial
to show which graph points were developed from "hole-filling" estimation techniques for
individual mode and which reflect actual data, so that the reader could better judge how well the
estimation techniques work. This could be accomplished by showing all the estimated data using a
different symbol than the measured data. How were the error bars for operating modes in which
there was no data calculated?
RESPONSE: Where data existed, error bars were estimated using a 95 percent confidence
interval of the mean emission rate. The standard error was the statistic used for this. Where
holes were filled, the relative standard error of the emission rate (i.e. standard error
normalized by the mean) was kept constant with that of the data from which any missing
rates were proportioned. We have added text in the Figure with error bars: "Error bars
represent the 95% confidence interval of the mean. "
We agree that labeling the 'hole-filled' data differently from the actual data on the emission
rate figures would be beneficial to judge the quality of the 'hole-filled' data. We will
consider doing this for future updates to the emission rates.
p. 112, Table 54 [Page 156 Table 5-6]- If I understand the text correctly, the emission factors for
CNG and gasoline (1969 and later) are the same. If so the title of the table and the top of the second
column should state that Table 54 applies also to CNG. If not please explain how the CNG criteria
emissions from the crankcase are estimated
RESPONSE: We added CNG to the Table heading for Table 5-6.
G.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.
G.3.1 Dr. Mohamadreza Farzaneh
Yes, I found the methodologies sound and reasonable given the data available. Below are a few
questions and comments on the analyses.
Page 8, first paragraph [Page 9]- it is implied that using ECU data will be more accurate than using
speed and acceleration. Is this a fact supported by data (any references)? The methodology based
on the ECU data uses a few simplifying assumptions that can introduce significant uncertainty into
the process. A quantitative comparison would validate the implied assumption for this section.
RESPONSE: We added discussion in Section 1.3 why we regard ECU data as more
accurate for on-road tests, than using generic road-load coefficients. However, we are not
aware of studies that have compared power estimates from ECU to estimates from
speed/acceleration data from road load coefficients.
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Figures 9 through 11 [Figure 2-10 through Figure 2-12] - opMode 33 seems to have a high
discrepancy between MOVES and data. Is there any explanation for this trend.
RESPONSE: Considering the small number of vehicles in the Houston Drayage data and
the fleet characteristics, such as driving pattern and the level of maintenance, of the
drayage fleet, we believe that much higher NOx rates from Houston Drayage seen for
opMode 33 may not be representative of the general heavy-heavy duty fleet. However, we
agree that it is an interesting observation and we plan to look into this issue when we
validate the MOVES rates compared to other independent data.
G.3.2 Dr. Janet Yanowitz
p. 12 Eq. 4 [Equation 2-3]- rather than k=l underneath the leftmost sigma it should be j=l
p. 12 Eq. 5 [Equation 2-4] - s2veh should be s2j, n should be nj in description s2veh should be s2j =
the variance in data for vehicle j
p. 13 Eqs. 7 and 8 and in text above [Equation 2-6 and Equation 2-7]-all subscripts pol should be p
to be consistent with Equations 4 and 6 [Equation 2-3 and Equation 2-5]
Response: We have incorporated these corrections to assure that the notation is consistent
across these equations and within the text.
G. 4 Appropriaten ess 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.
G.4.1 Dr. Mohamadreza Farzaneh
Overall, I found the assumptions reasonable and valid; however, some of the assumptions lack any
supporting information/reference. Citing an appropriate reference would increase the validity of
these assumptions.
RESPONSE: In the revised report, we have made an effort to be more transparent about
our assumptions we have used. For example, we have added Equation 2-10 and Equation
2-11 in Section 2.1.1.4.4 to clearly state how our assumptions on the LNTpenetration and
LNTemission impacts are used to estimate the 2007-2009 LHD<=10K emissions.
G.4.2 Dr. Janet Yanowitz
p. 7 [Section 2.1.1.8, Page 36]-"UpdatingMOVES emission rates ...was considered
when... .MOVES 2010 rates ... were not based on actual data... .and the comparisons between
MOVES 2010 and independent data show a clear indication of disagreement" . I would suggest that
you develop criteria for what is a "clear indication of disagreement" such that it can be consistently
applied for all comparisons between existing rates and new data.
RESPONSE: We clarified criteria used when comparing the emission rates inMOVES2010
to the independent data, in the text, stating: "2. the comparison to independent data shows
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that more than a halfofMOVES2010 emission rates are outside the boundary of the 95
percent confidence intervals of the independent data. "
This reviewer recommends that you replace older values not based on actual data, whenever actual
data becomes available, rather than set some arbitrary level of acceptable disagreement. Actual data
should take precedence over estimated values in virtually all circumstances except when there is
reason to believe the actual data is not representative.
RESPONSE: Although we agree that it is preferable to have all emission rates based on
real-data when possible, due to limited resources, we only updated emission rates that met
both of these criteria. We will consider this in our planning for updating MOVES in the
future.
p. 13 [Page 22, Section 2.1.1.4.3]- please clarify why you use[d] MY 2003-2006 data to estimate
the rates for model year 2010 instead of the 2007-2009 data.
RESPONSE: Due to limited resources, we determined to only update emission rates for
which we had additional data. We plan to update the emission rates for model year 2010
and later heavy-heavy trucks based on actual measurements as they become available in
the near future.
p. 14 [Page 21, Section 2.1.1.4.2] - paragraph beginning "For certain model years..." clarify why
you used a ratio of emission standards for missing regulatory classes instead of a ratio of
certification data. Generally it appears that you used certification data to predict missing values (for
example for all the 1990 and earlier data), as opposed to emission standards. To this reviewer this
appears to be the better approach as it is based on actual emissions measurements as opposed to
standards which are frequently exceeded by a significant safety factor. For the years 2007-2009 you
have data to test which approach works best, certification data or emissions standards for different
regulatory classes - consider running a test, although in the absence of further information you
should use a ratio of certification data in place of a ratio of emissions standards where possible.
RESPONSE: We have revised this paragraph so that it better communicates that we used
the ratio in emissions data rather than ratio of emission standards. Also, we agree that it is
preferable to use ratios of certification data, rather than ratios in emission standards. We
have used ratios in certification data rather than emission standards in all places where we
had available data, or a citable reference. For example, for future model years, the
emission standards were used to predict emissions because certification did not exist yet.
These rates could be updated as those model years enter the fleet and enter into test
programs.
G. 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 exhaust emissions
formation and control? Are the resulting model inputs empirically consistent with the body of data
and literature that has come to your attention?
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G.5.1 Dr. Mohamadreza Farzaneh
Yes, the model inputs are appropriate are consistent with physical and chemical processes involved
in exhaust emissions formation and control. The rates can benefit significantly from more data
collection and assembly.
I found the resulting inputs empirically consistent with the body of data and literature that I have
worked with. In my response to Question 1,1 listed a study by TTI that produced emissions testing
data of heavy duty vehicles at high speeds which could be of use to expand the existing database.
The following are a few examples of areas where additional data will greatly enhance the emission
rates developed for heavy duty vehicles:
D NOx emissions increase due to disabling SCR after the end of warranty period
D More data on crankcase emissions for 2007+ to better characterize the effect of aging
D Emissions of 2010+ heavy duty diesel vehicles
D I suggest using "x" symbol instead on "*" in equations on page 37 [Page 50, Equation
2-17].
RESPONSE: We agree that additional data on 2007+ diesel vehicles are needed,
particularly real-world data on vehicles equipped with SCR technology. We will consider
these suggestions as we set our priorities for future MOVES updates. We have made change
to using "x " symbol for multiplication in Equation 2-17.
G.5.2 Dr. Janet Yanowitz
Yes.
G. 6 CNG Transit Bus Running and Start Exhaust Emission Rate
Methodology
Is the methodology for creating new MOVES2014 running and start exhaust emission rates for
compressed natural gas transit buses sufficiently explained? Can you follow the procedure that
was used to calculate ratios from the MOVES2010b rates to the MOVES2014 rates and how those
ratios were applied? Do you have any suggestions for improving this methodology for CNG
emission development or the documentation itself?
G.6.1 Dr. Mohamadreza Farzaneh
Yes, the methodology and data were adequately explained and I was able to follow the procedures.
G.6.2 Dr. Janet Yanowitz
All references to MOVES2013 should be replaced with MOVES2014.
RESPONSE: All references refer to the released model (MOVES2014).
p. 85 - Figure 42 [Page 134, Figure 4-1] does not show what is discussed in the text referencing this
table. A table which showed the number of natural gas buses relative to the total number of buses
would have been useful.
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RESPONSE: We have replaced the original plot with Figure 4-1 showing the growth of
natural gas transit buses out of the total transit buses and amended the text appropriately.
p. 88 - Equation 28 [Page 137, Equation 4-1] use consistent subscript - either p or pol for pollutant
RESPONSE: Equation 4-1 has been updated to use the subscript p throughout.
p. 90 - [Page 139] text says that in some cases the same vehicle may have been driven over more
than one driving cycle, although the table says that each study included only one driving cycle; it is
possible that one of the NREL vehicles was used in one of the other tests but that seemed unlikely.
RESPONSE: We added text in the paragraph proceeding Table 4-1 specifying which
measurements were made on the same vehicle/aftertreatment configuration. Beside the
exception given in the text, each program tested a unique set of vehicles, and each
measurement represents a unique vehicle.
The studies chosen tested the same vehicles over various driving cycles, but we only
evaluated each vehicle over one driving cycle, as indicated in Table 4-1. The seven CNG
buses in the NREL study (Melendez 2005), three had Cummins-Westport engines and four
had John Deere engines, were only tested on the WMATA cycle. Ideally these vehicles from
the NREL study would have been tested on the CBD cycle, but they were not. Therefore, we
decided to juxtapose data from these two cycles in order to create emission rates for more
recent model years.
p. 101, Table 42 [Page 148, Table 4-5]- This table could use some clarification - for example the
title and the caption should indicate that the table also includes calculated data not just measured
and certification data, and the caption could explain that the last line is the calculated values. The
caption could also include a brief description of how the calculation was made, i.e. Equation 29
[Equation 4-2]. It is unclear what the footnote refers to -1 would think it would be better placed
under the column for THC on the two certification lines with an explanation. A footnote to explain
where the red value for CO comes from would also be useful.
RESPONSE: We have edited the title, caption, and footnote in Table 4-5 to better explain
how the calculated emission rates were derived and how we adjusted the MY 2002-2006
CO rate.
p. 102 and p. 99 seem to conflict [Section 4.3.1 and 4.3.2, Starting Page 144] On p. 102 compare
the sentence which begins: "We replaced it with a value equal...." To what is written at the bottom
of p. 99 " We did, however, throw out the CO rate (0.14 g/mi) for these WMATA vehicles "
p.. 99 appears to say that the anomalous CO data point was discarded, and that there was more than
one vehicle involved in the calculation of the anomalous CO data point, where p. 102 refers to only
a single vehicle with an anomalous CO rate, and appears to say (it is not really clear) that the new
value somehow includes the certification level.
RESPONSE: The measured CO rate for MY 2002-2006 was adjusted rather than discarded,
omitted, or removed. We multiplied it by the ratio between the MY 2004 sales-weighted CO
certification level and the CO certification level for that particular John Deere vehicle. This
way the CO rate should align better to the other 2004 models. The description of this
adjustment has been clarified in the text in Section 4.3.1 and 4.3.2.
p. 102 - typo - developing for developed
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RESPONSE: The verb tense has been changed in that sentence.
p. 102 Table 43- [Table 4-2, Page 145] This table can provide a useful summary, with a little
additional explanation in the caption and explanatory subtitles.
RESPONSE: The caption and subtitles in the revised Table 4-2- have been updated to be
more descriptive of the procedure followed to generate the new MOVES2014 emission
rates. Additionally, the cycle average emission rates from the analysis have been removed
since they are already presented in Table 4-5.
p. 104 and p. 107 [Page 149, Section 4.6 and Section 4.5]- it is not at all clear how you propose
changing the CH4/THC ratio with age of bus. If you are changing the ratio with age, please provide
those values. You say (p. 104) that the CH4/THC ratio changes with deterioration of the after-
treatment equipment, but then later state that you keep the CH4/THC ratio constant at all ages ("we
assume that the change in the THC emission rate is proportional to the changes in the methane
emission rate, and keep this ratio constant at all ages." Then on p. 107 you say something different
again: deterioration assumptions used in the MOVES 201 Ob rates are incorporated into the new
model
RESPONSE: We clarified the text in Section 4.6 that the CH4/THC ratios used in MOVES
do not to vary by age. Deterioration assumptions from MOVES2010b CNG emission rates
are applied to criteria pollutants: THC, CO, NOx, andPM as discussed in the revised
Section 4.5.
p. 104 [Page 149, Section 4.6]- It is not clear why keeping "the THC emission rate .. .proportional
to the changes in the methane emission rate.... is consistent with a decrease in combustion
efficiency." Please explain.
RESPONSE: Section 4.3.2 discusses that the THC emission rates for 2007-2012 were
estimated from the 2003-2006 THC emission rates and the ratio of the THC certification
results
We revised the text in Section 4.6 to clarify that the methane emissions are calculated as a
ratio of THC in MOVES. The CH4/THC ratio is unchanged between 2003-2006 and 2007-
2012 model years. We have removed the text referring to the 'proportional changes in the
methane emission rate' and 'decrease in combustion efficiency'.
p. 104 Table 44 [Page 146, Table 4-4]— this table is not a comparison of different ratios as stated
in the title, as no information is given for the MOVES 201 OB CNG bus rates. The values for 2002-
2006 and 2007-1012 should actually be exactly the same as that is how the ratio for 2007-2012 was
derived (see p. 101) - seems like you are including too many significant digits.
RESPONSE: We have revised the heading in Table 4-4 to only refer to the CH4/THC values
for MOVES2014. Also, we have corrected the table to show the same CH4/THC ratio that
MOVES uses (0.950) for the 2002-206 and 2007-2012 model year groups. We have also
decreased the significant digits to 3.
p. 104 [Section 4.6, Pagel49]- The sentence which begins "Studies have shown...." refers to three
categories of buses: "uncontrolled CNG buses, CNG buses with oxidation catalysts and CNG
buses." Looks like a typo or it needs a better explanation.
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RESPONSE: Yes, we have updated the text to only refer to the two categories ofCNG buses
we analyzed only analyzed: uncontrolled and those with oxidation catalysts.
p. 104 [Section 4.6, Pagel49]- - "Formaldehyde has ... a large impact on the NMOG/NMHC ratio
because formaldehyde has a small response to the THC-FID measurements." This is not clear -
please explain further or revise
RESPONSE: We revised this sentence. The sentence also now references the speciation
report, where the THC-FID response for formaldehyde is presented in Table A-L
p. 104 [Section 4.6, Pagel49]- last partial paragraph references tests made by Ayala et al. on an
engine and then speciated measurements made on a vehicle. If this is not a typo, please clarify
where data for the vehicle test came from.
RESPONSE: We clarified that the measurements were made on a transit bus (chassis
dynamometer), while also providing the engine information of the vehicle.
p. 106 - Please explain how you calculated MOVES2010b emission rates for diesel transit buses by
model year if the rates are applied by engine family. Did you do sales weighting of the various
engine families?
RESPONSE: This section is removed. PresentingMOVES201 Ob emission rates is not
relevant in the MOVES2014 report.
G. 7 Changes in Control Technology and Emission Standards for
CNG Buses
Does this EPA analysis of CNG buses accurately reflect the changes in control technology and
emission standards? If not, how would you recommend to make the CNG emission rates more
reflective of bus emission reduction trends over the past two decades?
G. 7.1 Dr. Mohamadreza Farzaneh
To the best of my knowledge and based on the available data used for this purpose by the EPA, the
described methodology accurately reflects the changes in the control strategies and emissions
standards.
G. 7.2 Dr. Janet Yanowitz
The inclusion of emission factors out to four or more significant digits give the appearance of far
more certainty in these emissions factors than is reasonable based on the available data.
RESPONSE: We have reduced the significant digits from the summary tables (Table 4-5,
Table 4-4) where our analysis had less certainty than the significant digits previously
implied.
Given the limited data which was available to the authors of this report, the approach used was
defensible, and will show, as is warranted, reduced emissions from more modern CNG buses. It is
an improvement on the emission factors used in MOVES2010b. However, as the authors
themselves point out a number of papers discuss newer CNG vehicles. It is hard to believe that
studies from 2007 (cited on p. 99 [Page 1 xxiv]) or 2011 (see first paper cited below) cannot be
included in the 2014 version of the MOVES model, because they were not available in time.
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Although a review of additional data available was beyond the scope of this reviewer's charge, the
authors of the MOVES2014 should consider additional data available in the following documents,
even if they are only able to do so briefly to roughly evaluate the accuracy of their proposed model:
Gautam, M., Thiruvengadam, A., Carder, D., Besch, M., Shade, B., Thompson, G. & Clark, N.
Testing of volatile and nonvolatile emissions from advanced technology natural gas vehicles. West
Virginia University. 2011; available at http://www.arb.ca.gov/research/apr/past/07-340.pdf
Seungju Yoon, John Collins, Arvind Thiruvengadam, Mridul Gautam, Torn Herner, Alberto Ayala.
Criteria pollutant and greenhouse gas emissions from CNG transit buses equipped with three-way
catalysts compared to lean-burn engines and oxidation catalyst technologies Journal of the Air &
Waste Management Association Vol. 63, Iss. 8, 2013.
RESPONSE: Thank you for pointing us to these recent studies. Unfortunately, we did not
have enough time or resources to analyze any data from Stoichiometric-burn CNG buses
with three-way catalysts for MOVES2014; however, we would like to incorporate
measurements of vehicles equipped with these technologies in future releases of MOVES.
G. 8 General/Catch-All Reviewer Comments
G.8.1 Dr. Mohamadreza Farzaneh
The report is well written, and methodologies and assumptions adequately described. The authors
applied creative methodologies to address the data gaps specifically for the newer vehicles. I did
not notice any major flaws in the methodologies and assumptions used.
The MOVES model has come a long way since its first release. It is clear that it still requires more
data to strengthen the overall emission rates as well as to address current data limitation such as
newer model years.
A recent remote sensing data by TTI showed that some of the newer trucks have high NOx levels.
When the researchers checked with the owners, they mentioned that SCR causes problem and
requires repairs, therefore they sometime disable the SCR unit after the warranty period.
RESPONSE: We will consider this information, as we plan future work on MOVES
emission rates for SCR equipped vehicles.
Section 2.1.1.3.4 - As stated above, some truck owners disable the SCR unit entirely. These are not
captured under this section.
RESPONSE: The Tampering and Mai-maintenance effects on NOx were not updated in
MOVES2014. We will consider providing additional rationale for new tampering and mal-
maintenance effects when we update the values. However, in this section we do mention that
we continue to believe that there is deliberate tampering of emission control components
with trucks that comply with to 2007/2010 standards.
Table 42 [Table 4-2]- since certification testing is an engine dynamometer testing, "duty cycle"
would be a better term for vehicle activity than "drive cycle."
RESPONSE: We have removed the term 'drive cycles from Table 4-2 because it no longer
reports emission results from different drive cycles. However, we refers to second-by-
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second speed traces that can be run on a chassis-dynamometer as drive cycles (2.1.3.1) or
driving schedules4 and have retained the terminology 'drive cycle' in Chapter 4.
There are references to MOVES2013 in the text which I believe should be updated to
MOVES2014; especially in section 4.
RESPONSE: These have been changed.
Table 35 [Table 3-6] - The table uses "VSP bin" and "TSP bin", my understanding is that the
correct term for them is "operating mode bins"
RESPONSE: We clarified the heading of Table 3-6, to clarify that the operating mode is
calculated for a light-commercial truck on the Federal Test Procedure. We used the term
Operating Mode Bin in the heading, and use the short hand term, OpModelD, to label the
operating mode bin IDs in the table.
Section 4 - A statement on the use of these rates for LNG buses, whether it is acceptable or not,
would be helpful for users.
RESPONSE: EPA does not encourage or discourage users from utilizing CNG transit bus
emission rates as surrogates for LNG bus rates. Any use of surrogate emission rates is done
at the users' discretion.
G.8.2 Dr. Janet Yanowitz
No further comments
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