Exhaust Emission Rates for Heavy-Duty On-road Vehicles in 

MOVES2014

DRAFT REPORT

USEPA Office of Transportation and Air Quality 

Assessment and Standards Division 

2015

Disclaimer

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 of the release of such reports is to facilitate the exchange of technical information and 

to inform the public of technical developments which may form the basis for a final EPA 

decision, position, or regulatory action.
Table	of	Contents

1 Principles of Modeling Heavy-duty Emissions in MOVES.................................................... 5

1.1 Heavy-duty Regulatory Classes....................................................................................... 6

1.2 Emission Pollutants and Processes................................................................................... 7

1.3 Operating Modes.............................................................................................................. 8

1.4 Vehicle Age.................................................................................................................... 12

2 Heavy Duty Diesel Emissions............................................................................................... 14

2.1 Running Exhaust Emissions........................................................................................... 14

2.1.1 Nitrogen Oxides (NOx)........................................................................................... 14

2.1.2 Particulate Matter (PM) .......................................................................................... 42

2.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO) .................................................. 57

2.1.4 Energy..................................................................................................................... 63

2.2 Start Exhaust Emissions................................................................................................. 68

2.2.1 HC, CO, and NOx................................................................................................... 69

2.2.2 Particulate Matter.................................................................................................... 72

2.2.3 Adjusting Start Rates for Soak Time ...................................................................... 72

2.2.4 Start Energy Rates................................................................................................... 75

2.3 Extended Idling Exhaust Emissions............................................................................... 77

2.3.1 Data Sources ........................................................................................................... 77

2.3.2 Analysis................................................................................................................... 78

2.3.3 Results..................................................................................................................... 80

2.3.4 MOVES Extended Idle Emission Rates ................................................................. 80

2.3.5 Auxiliary Power Unit Exhaust................................................................................ 82

3 Heavy-Duty Gasoline Vehicles............................................................................................. 84

3.1 Running Exhaust Emissions........................................................................................... 84

3.1.1 HC, CO, and NOx................................................................................................... 84

3.1.2 Particulate Matter.................................................................................................. 109

3.1.3 Energy Consumption ............................................................................................ 113

3.2 Start Emissions............................................................................................................. 117

3.2.1 Available Data ...................................................................................................... 117

3.2.2 Estimation of Mean Rates..................................................................................... 117
3.2.3 Estimation of Uncertainty..................................................................................... 120

3.2.4 Projecting Rates beyond the Available Data ........................................................ 122

3.2.5 Start Energy Rates................................................................................................. 129

4 Heavy Duty Compressed Natural Gas Transit Bus Emissions............................................ 131

4.1 Transit Bus Driving Cycles and Operating Mode Distributions.................................. 131

4.1.1 Heavy-Duty Transit Bus Driving Cycles.............................................................. 131

4.1.2 Transit Bus Operating Mode Distributions........................................................... 133

4.2 Comparison of Simulated Rates and Real-World Measurements................................ 134

4.2.1 Simulating Cycle Emission Aggregates from MOVES2010b Rates.................... 134

4.2.2 Published Chassis Dynamometer Measurements ................................................. 134

4.2.3 Plots of Simulated Aggregates and Published Measurements.............................. 136

4.3 Development of New Running Exhaust Emission Rates............................................. 140

4.3.1 Determining Model Year Groups ......................................................................... 140

4.3.2 Scaling Model Years After 2007 .......................................................................... 141

4.3.3 Creating CNG Running Rates for Future Model Years........................................ 143

4.4 Start Exhaust Emission Rates for CNG Buses............................................................. 144

4.5 Applications to Other Model Years and Age Groups.................................................. 145

4.6 PM and HC Speciation for CNG Buses....................................................................... 145

4.7 Ammonia and Nitrous Oxide emissions ...................................................................... 147

5 Heavy-Duty Crankcase Emissions...................................................................................... 148

5.1 Background on Heavy-duty Diesel Crankcase Emissions........................................... 148

5.2 Modeling Crankcase Emissions in MOVES................................................................ 149

5.3 Conventional Heavy-Duty Diesel ................................................................................ 150

5.4 2007 + Heavy-Duty Diesel........................................................................................... 151

5.5 Heavy-duty Gasoline and CNG Emissions.................................................................. 152

6 Nitrogen Oxide Composition............................................................................................... 154

6.1 Heavy-duty Diesel........................................................................................................ 155

6.2 Heavy-duty Gasoline.................................................................................................... 155

6.3 Compressed Natural Gas.............................................................................................. 156

Appendix A Calculation of Accessory Power Requirements................................................ 158

Appendix B Tampering and Mal-maintenance...................................................................... 160
Appendix C Extended Idle Data Summary ........................................................................... 171

Appendix D Developing PM emission rates for missing operating modes........................... 176

Appendix E Heavy-duty Diesel EC/PM Fraction Calculation.............................................. 177

Appendix F Heavy-duty Gasoline Start Emissions Analysis Figures................................... 199

Appendix G Heavy-duty CNG Bus Emissions Supplemental Figures.................................. 204

References................................................................................................................................... 208
5

1 Principles of Modeling Heavy-duty Emissions in MOVES

This report describes the analysis 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 by any vehicle with a Gross Vehicle Weight Rating 

(GVWR) above 8,500 lbs. This report discusses the development of emission rate for total 

hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM).

From HC emissions, MOVES produces other measures of organic gases, including volatile organic 

compounds (VOCs), and total organic gases (TOG). From VOC emission rates and fuel property, 

MOVES estimates individual toxic compounds such as formaldehyde and benzene. The derivation 

of the aggregate measures of organic gases and individual toxic emissions are available in the 

Speciation41 and Toxics1 MOVES Reports. MOVES estimate the PM emission rates according to 

18 subspecies as outlined in the Speciation Report41. This report covers the derivation of EC/PM 

fractions used to estimate elemental carbon (EC) emission rates and the remaining non-elemental 

carbon PM (nonEC) emission rates

Energy consumption rates were developed based on measurements of carbon dioxide (CO2), CO

and THC. We developed emission 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.

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. 
6

1.1 Heavy-duty Regulatory Classes

The MOVES heavy-duty regulatory groups are largely determined based on gross vehicle weight 

rating (GVWR) classifications. Each regulatory class groups vehicles together that have similar 

emission standards that may not be solely distinguished by GVWR. For example, Urban Bus 

engines are distinguished from other heavy heavy-duty vehicles (GVWR >33,000 lbs) because they

have tighter EPA PM emission standards between 1994 and 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 40 and 41 are also defined according to additional criteria than GVWR. 

LHD<=10K (RegClassID 40) are defined as trucks with GVWR between 8,500 and 10,000 lbs

(Class 2b trucks), and are trucks with only two axles and four tires. Class 2b trucks between 8,500 

and 10,000 lbs with two axles and six tires are classified in regulatory class 41 (LHD <=14K), as 

well as all trucks between 10,000 and 14,000 lbs (Class 3 trucks). 

Unlike Urban Buses, the distinction between regClassID 40 and 41 in MOVES is not caused by

differences in EPA exhaust emission standards. The reasons for the distinction between regulatory

class 40 and 41 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 into light-duty vehicle categories, which correspond to MOVES sourceTypeID 31 

(Passenger Trucks) and sourceTypeID 32 (Light Commercial Trucks). FHWA reports VMT 

from Class 2b trucks with two axles and six tires, as single-unit vehicles, which corresponds 

to a different set of MOVES source types: Refuse Trucks (51), Single Unit Short-haul (52), 

Single Unit Long-Haul (53), and Motor Homes (54).

(2) Assignment of Operating Modes with MOVES source types. As discussed in the Population 

and Activity Report4, MOVES assigns operating modes within source types. For light-duty 

source types (including passenger trucks and light-commercial) 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 

the operating mode subsection, the emission rates for regulatory class 40 use a different 

scaling factor when computing STP, such that the emission rates are consistent with VSPbased operating modes. The emission rates for regulatory class 41 are based on the standard 

STP scaling factor, to be consistent with the way MOVES assigns operating modes for 

heavy-duty source types. 

Regulatory class 40 is a new regulatory class introduced in MOVES2014. Previous versions of 

MOVES classified all light-heavy duty trucks with GVWR under 14,000 lbs as LHD2b3. The 

emission rates for LHD2b3 were compatible with VSP-based emission rates. As such, previous 

versions of MOVES were unable to model emission rates for LHD2b3 emission rates for singleunit trucks. With the addition of regulatory class 40, and the new definition of regulatory class 41, 

MOVES is able to model light-heavy duty trucks that are classified in both the light-duty and 

heavy-duty source types. 
7

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 40 and 41 trucks 

are often the same, but analyzed with separate scaling factors to derive separate emission rates for 

regulatory class 40, and regulatory class 41. Occasionally, the term LHD2b3 is used in this report, 

to refer to all light-heavy duty trucks with GVWR under 14,000 lbs.

Table 1-1 provides an overview of the regulatory class definitions in MOVES for Heavy-Duty 

vehicles. 

Table 1-1 also provide a distinction if the emission rates are developed to be consistent with VSP or 

STP-based operating modes. 

Table 1-1. Regulatory Classes for Heavy-Duty Vehicles

Regulatory Class 

Description regClassName regClassID

Gross Vehicle 

Weight Rating 

(GVWR) [lb]

Source Types

(SourceTypeID)

Operating 

Mode Basis2

Light-heavy duty < 

10,000 lbs. (Class 2b 

Trucks with 2 Axles 

and 4 Tires.)

LHD<= 10 K 40 8,501 – 10,000

Passenger 

Trucks,(31) Light 

Commercial 

Trucks(32)

VSP

Light-heavy duty ≤ 

14,000 lb. Class 2b 

(Trucks with 2 Axles 

and at least 6 Tires or 

Class 3 Trucks.)

LHD<=14k 41 8,501 – 14,000

Buses (41, 43), 

and Single Unit 

Trucks 

(51,52,53,54)

STP

Light-heavy duty 4-5 LHD45 42 14,001 – 19,500

Buses (41, 42, 43) 

and Single Unit 

Trucks 

(51,52,53,54)

STP

Medium-heavy duty MHD 46 19,501 – 33,000

Buses (41,42,43), 

Single Unit Trucks 

(51,52,53,54), and 

Combination 

Trucks (61,62) 

STP

Heavy-heavy duty HHD 47 > 33,000

Buses (41,42,43), 

Single Unit Trucks 

(51,52,53,54), and 

Combination 

Trucks (61,62)

STP

Urban Bus Urban Bus148 > 33,000 Transit Bus (42) STP

1

see CFR § 86.091(2).

2 MOVES assigns operating modes based on VSP or STP, depending on source type
8

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, auxillary 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 from running exhaust, start exhaust, extended idle 

exhaust, and auxiliary power exhaust. Emissions of greenhouse gases other than CO2 are not 

covered. Estimation of the emissions of methane, nitrous oxide (N2O), and ammonia (NH3) for 

gasoline and diesel heavy-duty vehicles are described in separate reports.

5, 6

The estimation of these 

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

 The model does not estimate evaporative emissions for diesel-powered 

vehicles, but does estimate fuel spillage emissions which are part of the fueling 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 processName Covered in this report?

1 Running Exhaust Y

2 Start Exhaust Y

9 Brakewear N

10 Tirewear N

11 Evap Permeation N

12 Evap Fuel Vapor Venting N

13 Evap Fuel Leaks N

15 Crankcase Running Exhaust Y

16 Crankcase Start Exhaust Y

17 Crankcase Extended Idle Exhaust Y

18 Refueling Displacement Vapor Loss N

19 Refueling Spillage Loss N

90 Extended Idle Exhaust Y

91 Auxiliary Power Exhaust Y
9

1.3 Operating Modes

Operating modes for heavy-duty vehicles 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 

dependent 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 have continued to relate emissions to power output, but in a different 

way. Rather than normalize the tractive power for each vehicle to its own weight, we scale the 

power by a fixed multiple designed to fit the resulting means into the existing operating mode 

framework. We refer to this parameter as “scaled-tractive power” (STP). Heavy-duty vehicles are 

regulated using engine dynamometers, and emissions standards are in units of grams per brakehorsepower-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 

but simply scaled by a constant as described above to bring its numerical values into the same 

range as the VSP values used for light-duty vehicles. 

The equation for STP is located here, with units in scaled kW or skW. :

scale

axle

f

P

STP  Equation 1-1

Where: Paxle is the power demands at the axle for the heavy-duty truck. As discussed later, Paxle 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 in general more accurate at 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 on the 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. 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 to estimate power (Section 2.1.2.2.1). 
10

The values of fscale are located in Table 1-3. As mentioned previously, the operating modes for

regulatory class 40 are VSP-based, because regulatory class 40 are modeled as passenger trucks 

and light commercial trucks, and MOVES assigns operating modes to these source types using 

VSP. Thus, for regulatory class 40, fscale 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. 

Table 1-3. Power scaling factor fscale

Regulatory Class (RegClassID) Power scaling factor (metric tonnes)

LHD<=10K (40) 2.06

LHD<= 14K (41), LHD45 (42), MHD (46), HHD (47), Bus (48) 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:

 =

 + 

 + 

 +  ∙ 

( +  ∙ )



Equation 1-2

where

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],

fscale = fixed mass factor (see Table 1-3),

vt = instantaneous vehicle velocity at time t [m/s],

at = instantaneous vehicle acceleration [m/s2]

 is the acceleration due to gravity 9.8 m⁄s





sin  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, 

this equation is also used in this report to estimate the STP-based emission rates from emission 

tests where a more direct measure of Paxle is not available. 
11

Table 1-4. Definition of the Operating Mode Attribute for Heavy-Duty Vehicles (opModeID)

Operating 

Mode

Operating Mode

Description

Scaled Tractive Power

(STPt, skW)

Vehicle Speed

(vt, mph)

Vehicle Acceleration

(a, mph/sec)

0 Deceleration/Braking

at  -2.0 OR

(at < -1.0 AND

at-1 <-1.0 AND

at-2 <-1.0)

1 Idle -vt < 1.0

11 Coast STPt< 0 0  vt < 25

12 Cruise/Acceleration 0  STPt< 3 0  vt < 25

13 Cruise/Acceleration 3  STPt< 6 0  vt < 25

14 Cruise/Acceleration 6  STPt< 9 0  vt < 25

15 Cruise/Acceleration 9  STPt< 12 0  vt < 25

16 Cruise/Acceleration 12  STPt 0  vt < 25

21 Coast STPt< 0 25  vt < 50

22 Cruise/Acceleration 0  STPt< 3 25  vt < 50

23 Cruise/Acceleration 3  STPt< 6 25  vt < 50

24 Cruise/Acceleration 6  STPt< 9 25  vt < 50

25 Cruise/Acceleration 9  STPt< 12 25  vt < 50

27 Cruise/Acceleration 12  STPt< 18 25  vt < 50

28 Cruise/Acceleration 18  STPt< 24 25  vt < 50

29 Cruise/Acceleration 24  STPt< 30 25  vt < 50

30 Cruise/Acceleration 30  STPt 25  vt < 50

33 Cruise/Acceleration STPt< 6 50  vt

35 Cruise/Acceleration 6  STPt< 12 50  vt

37 Cruise/Acceleration 12  STPt<18 50  vt

38 Cruise/Acceleration 18  STPt< 24 50  vt

39 Cruise/Acceleration 24  STPt< 30 50  vt

40 Cruise/Acceleration 30  STPt 50  vt

Start emission rates are also distinguished according to operating modes in MOVES. MOVES uses 

eight operating modes to classify starts according to different soak times, varying from a hot start 

(opMode 101) where the vehicle has been soaking for less than 6 minutes, to a cold start (opMode 

108) where the vehicle has been soaking for more than 12 hours.
12

Table 1-5. Operating modes for start emissions (as a function of soak time)

Operating Mode Description

101 Soak Time < 6 minutes

102 6 minutes <= Soak Time < 30 minutes

103 30 minutes <= Soak Time < 60 minutes

104 60 minutes <= Soak Time < 90 minutes

105 90 minutes <= Soak Time < 120 minutes

106 120 minutes <= Soak Time < 360 minutes

107 360 minutes <= Soak Time < 720 minutes

108 720 minutes <= Soak Time

Extended idle exhaust and diesel APU exhaust are each modeled in MOVES with a single 

operating mode.

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 derived for 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.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. 

Table 1-6. MOVES Age Group Definitions

ageGroupID

Lower 

bound 

(years)

Upper 

bound 

(years)

3 0 3

405 4 5

607 6 7

809 8 9

1014 10 14

1519 15 19

2099 20 ~
13

Emission processes stored in the “EmissionRate” table do not distinguish emissions by age. Energy

rates are stored in the “EmissionRate table.” This table also includes HC, CO, NOx, PM , and 

ammonia (NH3) emissions from extended idle and auxiliary power units (APU), and nitrous oxide 

(N2O) from start and running emissions, 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 ammonia9and tire and brake wear10 emission rates 

are documented in separate reports.
14

2 Heavy Duty Diesel Emissions

2.1 Running Exhaust 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. Running Exhaust 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 sections 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. 

Stratification of the data sample and generation of the final MOVES emission factors were done 

according to the combination of regulatory class (shown in

Table 1-1) and the model year group. As mentioned, the emission rates were using scaled-tractive 

power (STP), rather than VSP.

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-toExceed” (NTE) limit.

Table 2-1. Model year groups for NOx analysis based on emissions standards

Model year group FTP standard 

(g/bhp-hr)

NTE limit (g/bhp-hr)

Pre-1988 None None

1988-1989 10.7 None

1990 6.0 None

1991-1997 5.0 None

1998 4.0 None

1999-2002 4.0 7.0 HHD; 5.0 other reg. classes

2003-2006 2.4

2007-2009 1.2 1.25 times the family emission level

2010+ 0.2

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 
15

2007. The data was compiled and reformatted for MOVES analysis by Sierra Research13. 

The process of analysis and rate development was performed by EPA. The data we used 

represents approximately 1,400 hours of operation by 124 trucks and buses in model years

1999 through 2007.

The vehicles were driven mainly over two routes:

• “Marathon” from Aberdeen, MD 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

 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 HeavyDuty 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. 

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.

16

 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 2010.17 The trucks studied were diesel-fueled, heavy-heavy-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, 
16

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 be used as 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 datai) and (2) when the comparisons between MOVES2010 and independent data show a 

clear indication of disagreement. 

From each data set, we used only tests we determined to be valid. For ROVER dataset, due to time 

constraints, we eliminated all tests that indicated any reported problems, including GPS 

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. CO2 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 CO2 (from PEMS) and engine power (from ECU) was less than 0.6. 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.

 

i

Specific subsets of rates used in MOVES2010 rates were forecasted by proportioning to emission standards as 

described in Section 2.1.1.3.3.
17

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

Regulatory Class

Data Source MYG HHD MHD LHD BUS

ROVER and 

Consent Decree 

Testing

1991-1997 19 - - 2

1998 12 - - -

1999-2002 78 30 - 25

2003-2006 91 32 - 19

HDIU 2003-2006 40 25 15 -

2007-2009 68 71 24 -

Houston Drayage

1991-1997 8 - - -

1998 1 - - -

1999-2002 10 - - -

2003-2006 8 - - -

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 eng and 

torque eng from the ECU were used to determine engine power Peng, 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.

Peng eng eng   Equation 2-1

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. These power loads are not subtracted in the engine torque values that are output 

from the engine control unit. Heavy-duty trucks use accessories 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. 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 
18

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

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."18 

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 Ploss,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 HDT MHD Urban Bus

Low 8.1 6.6 21.9

Mid 8.8 7.0 22.4

High 10.5 7.8 24.0

 Speed

Load

Low Mid High

Cooling Fan

Low Air cond. Air cond. Air cond.

Engine Access. Engine Access. Engine Access.

Alternator Alternator Alternator

Air Compress Air Compress

Cooling Fan Cooling Fan

Mid Air cond. Air cond. Air cond.

Engine Access. Engine Access. Engine Access.

Alternator Alternator Alternator

Air Compress Air Compress

Cooling Fan Cooling Fan Cooling Fan

High Air cond. Air cond. Air cond.

Engine Access. Engine Access. Engine Access.

Alternator Alternator Alternator

Air Compress Air Compress
19

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 driveline varies with engine speed, vehicle speed, 

and vehicle power requirements. Using sources available in the literature, we estimated an average 

value for driveline efficiency.

19,20,21,22,23,24,25,26,27

 Table 2-5 summarizes our findings.

Table 2-5. Driveline efficiencies found through literature research

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 Paxle.

( ) Paxle driveline Peng  Ploss,acc Equation 2-2

Finally, we scaled the axle power by a multiplicative factor fscale to fit light-duty operating-mode 

ranges. The LHD<= 14K, MHD, HHD, and Bus classes were scaled by 17.1, which is 

approximately the average running weight for all heavy-duty vehicles, and the LHD<= 10K trucks 

were scaled by 2.06, which is equivalent to the fleet-average mass of light commercial trucks in 

MOVES as previously discussed in Section 1.3 to obtain emission rates for LHD<= 10K that are 

consistent with the VSP-based operating modes for passenger trucks and light-commercial trucks. 

shows the conversion of axle power to scaled tractive power using the method explained above.

scale

axle

f

P

STP  Equation 1-1

We then constructed operating mode bins defined by STP and vehicle speed according to the 

methodology outlined earlier in MOVES development28 and described in Table 1-4. The 

General truck:

Barth (2005) 80-85%

Lucic (2001) 75-95%

HDT:

Rakha 75-95%

NREL (1998) 91%

Goodyear Tire Comp. 86%

Ramsay (2003) 91%

21st Century Truck (2000) 94%

SAE J2188 Revised OCT2003:

Single Drive/direct 94%

Single Drive/indirect 92%

Single Drive/double indirect 91%

Tandem Drive/direct 93%

Tandem Drive/indiriect 91%

Tandem Drive/double indirect 89%

Bus:

Pritchard (2004): Transmission Eff. 96%

Hedrick (2004) 96%

MIRA 80%
20

implementation of STP in MOVES for heavy-duty emission rates is the same as that of VSP for 

light-duty emission rates. We will refer to the units of STP as scaled kW or skW. 

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. Then the emission rates were again averaged by 

regulatory class and model year group. Data sets were assumed to be representative and each 

vehicle received the same weighting. However, we averaged rates by vehicles first because we did 

not believe the amount of driving done by each truck was necessarily representative. 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).

veh

1

1

, , veh

n

n

r

r

n

j j

n

i

p j i

p

j







































Equation 2-3

where

nj = the number of 1-Hz data points for each vehicle j,

nveh = the total number of vehicles, 

rp,j,i = the emission rate of pollutant p for vehicle j at second i,

p

r

= the mean emission rate (meanBaseRate) for pollutant p. 

For NOx, 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.

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 

within-vehicle variance 2

with s . 
21

tot j

n

j

j

with

n n

n s

s

j







1

2

2

( 1)

Equation 2-4

where

2

j

s = the variance within each vehicle, and 

ntot = the total number of data points for all the vehicles. 

Then we calculated the between-vehicle variance 2

betw s (by source bin, age group, and operating 

mode) using the mean emission rates for individual vehicles ( pj

r,

) as shown in 

Equation 2-5.

 

1 j

1

2

,

2

betw

j









n

r r

s

n

j

p j p

 Equation 2-5

Then, we estimated the total variance by combining the within-vehicle and between-vehicle 

variances to get the standard error 

p

r

s (Equation 2-6) and dividing the standard error by the mean 

emission rate to get the coefficient-of-variation of the mean 

p

c

(Equation 2-7).

tot

2

with

j

2

betw

n

s

n

s

rp

s   Equation 2-6

 

p

r

p

r

s

c

p

 Equation 2-7

2.1.1.3.3 Heavy-Duty Trucks (HHD, MHD, Bus, and LHD not equipped with 

Lean NOx Traps)

Since the data only covered model years 1994 through 2009, we needed to develop a method to 

forecast emissions for future model years and back-cast emissions for past model years. For future 

model years (2010-and-later), we decreased the emission rates for all operating mode bins by a 
22

ratio proportional to the decrease in the applicable emissions standards. Starting in MY2010, the 

NOx standard for all heavy-duty trucks is 0.2 g/bhp-hr. We projected that almost all of these trucks 

will be using SCR after-treatment technology, which we assume to have a 90 percent NOx 

reduction efficiency from levels for MY2006 levels (2.4 g/bhp-hr), and thus, we estimated the rates 

for model year 2010 and later by decreasing MY2003-2006 rates by 90 percent.

For model year groups 1988-1989 and 1990, we increased the 1991-1997 model year group 

emission rates by a 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 MHD and HHD trucks, the maximum operating mode (opModeID = 40) represents a tractive 

power greater than 513 kW (STP= 30 skW × 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. 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.

For model year 1998, data existed for HHD trucks but not buses. In these cases, the ratio of 

emission rates between the 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 2-8.

  =

 

 

×   Equation 2-8

2.1.1.3.4 Pickup trucks equipped with Lean NOx Traps

To meet NOx emissions standards for the 2010 model year, the use of after-treatment will probably 

be needed. For example, Cummins decided to use after-treatment starting in 2007 in engines 

designed to meet the 2010 standard and used in vehicles such as the Dodge Ram. The technology

adopted for this purpose was the “Lean NOx Trap” (LNT). 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. 
23

In 2007, EPA acquired a truck equipped with LNT and 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. We will look for opportunities to 

update this assumption based on any additional information that becomes available.

Emissions from this vehicle were not directly used to calculate emission rates. 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. 

Because we assume that LNT-equipped trucks account for about 25 percent of the LHDDT market, 

we again weighted the rates for the two LHD regulatory classes for model years 2007 and later. 

For MY 2007-09, we assume that the remaining 75 percent of LHD diesel trucks will not have 

after-treatment and will exhibit the 2007-2009 model year emission rates described earlier in this

section. Starting in MY2010, we assume that the remaining 75 percent of LHD diesel trucks are

equipped with SCR, and exhibit 90 percent NOx reductions from 2006 levels, described in Section

2.1.1.3.3. 

2.1.1.3.5 Incorporation of Tier 3 Standards

The Tier-3 standards will affect “medium-duty” (MD) diesel vehicles, i.e., vehicles in regulatory 

classes LHD<=10k and LHD<=14k (regClassID = 40, 41, respectively). However, 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 levels on the FTP cycle 

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 MD diesel vehicles.

By contrast, we estimate that the Tier 3 NOx standard will results in a reduction emissions from 

diesel vehicles in Classes 2b and 3. Data on current NOx emissions are limited, as there is little inuse data on MY 2010 and 2011 vehicles using selective catalytic reduction as a NOx control 

strategy, we used a proportional approach to estimate the reductions related to Tier 3, reducing 

NOX 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. 
24

Table 2-6. Phase-in Assumptions for Tier-3 Standards for medium-duty diesel vehicles.

Model Year Phase-in 

fraction (%)

Reduction in Running 

Emission Rate (%)

Reduction in Start 

Emission Rate (%)

2017 0 0 0

2018 38 23 9

2019 54 33 12

2020 69 42 16

2021 85 52 19

2022 100 61.5 23

In generating the reduced rates for running operation, the starting point was a subset of rates for 

MY2017, extracted from a previous database, and taken to represent the pre-Tier-3 baseline.ii

Then 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 98) 

and Equation 3-2 (page 97). Note that these calculations were applied to running rates for the 

LHD<=10K regulatory class (based on VSP) and to those for the LHD<=14k regulatory class 

(based on STP). 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.

As with light-duty vehicles, reductions in standards are assumed to be associated with 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 onset of the 6-7 year ageGroup. This effect is realized partially

for model years 2018-2020 and fully in 2021.

 

ii The subset of rates representing MY2017 was taken from MOVESDBTier320110331.
25

Figure 2-1. NOx: Emission rates for running-exhaust operation in selected operating modes vs. model year, for 

two light-heavy-duty regulatory classes (LOGARITHMIC SCALE).

Figure 2-2. NOx: : Emission rates for running-exhaust operation in a single operating mode (27) vs. age, for 

two light-heavy-duty regulatory classes (LINEAR SCALE).
26

2.1.1.3.6 Summary

Table 2-7 summarizes the methods used to estimate emission rates for each regulatory–

class/model-year-group combination. The emission rates based on the analysis of ROVER and 

Consent Decree testing data were used to populate the rates in MOVES2010. For MOVES2014, 

we made a decision to update the emission rates, for MYG2007-2009 for HHD and MYG2003-

2006 for LHD, based on the comparison of the emission rates in MOVES2010 to HDIU and 

Houston Drayage data, discussed in Section 2.1.1.5. 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

HHD MHD Urban Bus LHD34 and 

LHD<=14K LHD<=10K

1960-1989

HHD 1991-

1997 rates

proportioned 

to ratio of

certification 

levels

Same rates as HHD

Bus 1991-1997 

rates 

proportioned

using ratio of 

HHD 

certification 

levels

Same rates as 

HHD

LHD <=10K 

1991-1993 rates 

proportioned to 

LHD 

certification 

levels

1990

HHD 1991-

1997 rates

proportioned 

to 

certification 

levels

Same rates as HHD

Bus 1991-1997 

rates 

proportioned 

using ratio of 

HHD 

certification 

levels from 1991-

1997

Same rates as 

HHD

LHD <=10K 

1991-1993 rates 

proportioned to 

LHD 

certification 

levels

1991-1997 Data 

analysis1,3 Same rates as HHD Data analysis1 Same rates as HHD

Proportioned to

1998 FTP

standards per 

Table 2-1

1998 Data 

analysis1,3 Same rates as HHD

Bus 1999-2002 

rates 

proportioned

using ratio of 

HHD 1998 rates 

to HHD 1999-

2002 rates

Same rates as 

HHD

Same rates as 

1999-2002

1999-2002 Data 

analysis1,3 Data analysis1 Data analysis1 Same rates as MHD

MHD engine 

data with 2.06 

mass factor

2003-2006 Data 

analysis1,3 Data analysis1,3 Data analysis1 Data analysis2

Data analysis

with 2.06 mass 

factor2

2007-2009 Data analysis2

Proportioned to 2003-2006 

FTP standards per Table 

2-1

3

Bus 2003-2006 

rates 

proportioned to 

FTP standards

per Table 2-1

Proportioned to

2003-2006 FTP

standards per 

Table 2-1

3

Data (LNT), 

and same rates

as 2003-2006 

(non-LNT)3

2010 -

2016

HHD 2003-

2006 rates 

proportioned 

to FTP 

standards per 

Table 2-1

MHD 2003-2006 rates 

proportioned to FTP 

standards per Table 2-1

Bus 2003-2006 

rates 

proportioned to 

FTP standards

per Table 2-1

LHD34 2003-

2006 

proportioned to 

FTP standards per 

Table 2-1

LHD<10K 

2003-2006 

proportioned to 

FTP standards

per Table 2-1

2017-2050 Same as HHD 

2010-2016 Same as MHD 2010-2016 Same as Bus 2010-2016

Proportioned to

Tier 3 standards

Proportioned to

Tier 3 standards

1Analysis based on ROVER and Consent Decree testing data; 2 Analysis based on HDIU data; 3 Confirmed by

HDIU and Houston Drayage data
28

An important point to note is that we did not project increases in NOx emissions with age for 

vehicles not equipped with NOx after-treatment technology (largely 2009 model year and earlier). 

This is because of 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 life30. 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.

We estimated tampering and mal-maintenance effects on NOx emissions to be small compared to 

other pollutants – around a 10 percent increase in NOx over the useful life of the engine. Our 

tampering and mal-maintenance estimation methods are discussed below and detailed in Appendix

2.1.1.3.7 Tampering and Mal-maintenance

Table 2-8 shows the estimated aggregate NOx emissions increases due to T&M. It also shows the 

values that we actually used for MOVES emission rates. As previously mentioned, we assumed 

that in engines not equipped with aftertreatment, NOx does not increase due to T&M or 

deterioration.

Table 2-8. Fleet-average NOx emissions increases from zero-mile levels over the useful life due to tampering and 

mal-maintenance

Model years NOx increase from T&M 

analysis [%]

NOx increase in MOVES 

[%]

1994-1997 10 0

1999-2002 14 0

2003-2006 9 0

2007-2009 11 0

2010-2012 SCR 77 77

2010-2012 LNT 64 64

2013+ 58 58

As described in Appendix B, these emissions increases are combined with information in Table 

B-2 to estimate the emissions increase for each age group prior to the end of the useful life for each 

regulatory class. With the introduction of aftertreatment systems to meet regulatory requirements 

for MY 2010 and later, EPA expects tampering and mal-maintenance to substantially increase 

emissions over time compared to the zero-mile level. Though 77 percent 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 percent lower than the 2009 model year effective standard (1.2 g/bhp-hr). 

This still yields a substantial reduction of about 71 percent from 2009 zero-mile levels to 2010 fully

deteriorated levels. As more data becomes available for future model years, we hope to update 

these tampering and mal-maintenance and overall aging effects. 
29

2.1.1.3.8 Defeat Device and Low-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 on 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 2-9 and Equation 2-11. To estimated “reflashed” rates in the remaining operating modes, 

we multiplied the reflashed rates by ratios of the remaining operating modes to mode 27 for 

MY1991-98, as shown in 

Equation 2-10 and Equation 2-12.

Operating modes 

(OM) 21-30

















  

1999 ,16

1999 ,27

,91 98 ,27 91 98,16

r

r

r r reflash























 

91 98,27

91 98,OM

,91 98,OM ,91 98,27

r

r

r r

x

reflash x reflash

Equation 

2-9

Equation 2-10

Operating modes 

(OM) 31-40

















  

1999 ,16

1999 ,37

,91 98 ,37 91 98 ,16

r

r

r r reflash























 

91 98,37

91 98,OM

, 1991 1998 , ,91 98,37

r

r

r r

x

reflash MY OMx reflash

Equation 2-11

Equation 2-12
30

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 2008.31 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.

2.1.1.4 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 HHD 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. 

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.

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.

0

500

1000

1500

2000

2500

3000

3500

4000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean N

Ox rate [g/hr]

Operating Mode
31

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.

0

500

1000

1500

2000

2500

3000

3500

4000

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean N

Ox rate (g/hr)

Operating Mode
32

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. 

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 (#24) 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.

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.

0

1000

2000

3000

4000

5000

6000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean N

Ox rate [g/hr]

Operating Mode

MHD

Bus

HHD
33

Age effects were only implemented for after-treatment-equipped trucks (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.

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.

0

500

1000

1500

2000

2500

Mean N

Ox rate (g/hr)

Model year group
34

Figure 2-8 and Figure 2-9 shows the mean emission rates for LHD<= 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 (RegClass 40) trucks age 

0-3. Error bars represent the 95% confidence interval of the mean.

0

20

40

60

80

100

120

140

Mean N

Ox rate [g/hr]

HHD

MHD

Bus

0-3 4-5 6-7 8-9 10-14 15-19 20+

Age group [years]

0

100

200

300

400

500

600

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean N

Ox rate (g/hr)

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.

2.1.1.5 Evaluation of NOx Emission Rates in MOVES2010

This section presents the results from the efforts to verify the NOx emission rates in MOVES2010 

by comparing the rates in the database to the emissions data from the Heavy Duty In-Use and 

Houston Drayage data programs. The HDIU data includes results for HHD, MHD, and LHD 

trucks, whereas the Houston Drayage data only includes HHD trucks. 

As discussed in Section Data Sources, HDIU and Houston Drayage data have become available 

after the MOVES2010 release and have served two purposes – to evaluate the rates in 

MOVES2010 and to be used as a new data source 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 a clear indication of disagreement.

2.1.1.5.1 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. MOVES rate is lower in the high speed operating modes (33 and above) compared to 1998 

model year trucks (Figure 2-11). However, only a single truck is represented in the Houston 

Drayage data. 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.

0

100

200

300

400

500

600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean N

Ox rate [g/hr]

Operating mode
36

Figure 2-10. Comparison of Means: MOVES 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.

Figure 2-11. Comparison of Means: MOVES emission rates vs. Houston Drayage Data (n=1) for model year 

1998 HHD trucks. Error bars represent the 95% confidence interval of the mean.

0

1000

2000

3000

4000

5000

6000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES Drayage

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES Drayage
37

Figure 2-12. Comparison of Means: MOVES 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.

In Figure 2-13 and Figure 2-14, MOVES rates for model years 2003-2006 are compared to results

from the Houston Drayage and HDIU datasets, respectively. Although MOVES’ rates for middle 

and high speed operating modes are lower, it is 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, MOVES is 

generally within the variability of the data except for the low speed operating modes. Although 

both comparisons showed that MOVES rates are slightly lower, since the rates in MOVES2010 for 

model years 2003-2006 were also based on the analysis of the testing data (ROVER and Consent 

Degree Testing), no change was made to the rates in MOVES2014.

Figure 2-13. Comparison of Means: MOVES 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.

0

500

1000

1500

2000

2500

3000

3500

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES Drayage
38

Figure 2-14. Comparison of Means: MOVES rates vs. HDIU (n=40) for model years 2003-2006 HHD trucks. 

Error bars represent the 95% confidence interval of the mean.

In the MOVES2010 database, rates for model years 2007-2009 were forecasted from those for 

MYG 2003-2006 based on the ratio of emissions standards for these two model-year groups, as 

described in Section 2.1.1.3.3. This approach was adopted in view of the fact that neither of the two 

0

500

1000

1500

2000

2500

3000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES Drayage

0

200

400

600

800

1000

1200

1400

1600

1800

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES HDIUD
39

datasets used at the time (ROVER and Consent-Decree) included data for trucks in this model-year 

group. However, 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 MOVES rates are lower than 

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 2.1.1.5, 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.

2.1.1.5.2 Medium-Heavy Duty Trucks

Figure 2-16. and Figure 2-17 show that MOVES 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.

Figure 2-16. Comparison of Means: MOVES rates vs. HDIU (n=25) for model years 2003-2006 MHD trucks. 

Error bars represent the 95% confidence interval of the mean.

0

100

200

300

400

500

600

700

800

900

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES HDIUD
40

Figure 2-17. Comparison of Means: MOVES rates vs. HDIU (n=71) for model years 2007-2009 MHD trucks. 

Error bars represent the 95% confidence interval of the mean.

0

200

400

600

800

1000

1200

1400

1600

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES HDIUD

0

100

200

300

400

500

600

700

800

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES HDIUD
41

2.1.1.5.3 Light-Heavy Duty Trucks

In MOVES2010, the LHD rates were not based on actual measurements – they were scaled from 

MHD rates and forecast based on standards, for model years 2003-2006 and 2007-2009, 

respectively, as described in Sections 2.1.1.3.3 and 2.1.1.3.4. The comparison to HDIU data for 

model years 2003-2006 (Figure 2-18) shows that existing MOVES rates are generally higher than 

the HDIU results. Thus, the LHD rates for model years 2003-2006 were updated based on HDIU 

data in MOVES2014. In contrast, MOVES compares well with the HDIU data for model years 

2007-2009 (Equation 2-18), and thus, MOVES2010 rates for this model-year group were retained 

in MOVES2014.

Figure 2-18. Comparison of Means: MOVES rates vs. HDIU (n=15) for model years 2003-2006 LHD trucks. 

Error bars represent the 95% confidence interval of the mean.

0

200

400

600

800

1000

1200

1400

1600

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES HDIUD
42

Figure 2-19. Comparison of Means: MOVES rates vs. HDIU (n=24) for model years 2007-2009 LHD trucks. 

Error bars represent the 95% confidence interval of the mean.

2.1.2 Particulate Matter (PM)

In this section, particulate matter emissions refers to particles emitted from heavy-duty engines 

which have a mean diameter less than 2.5 microns, known as PM2.5. Conventional diesel 

particulate matter are primarily carbonaceous, measured by elemental carbon (EC) and organic 

carbon (OC). Particles also contain a complex mixture of metals, elements, and other ions, 

including sulfate. The total PM2.5 emission rates are typically filter-based, which measure 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 the 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 range and the 

applicable brake-specific emissions standards. 

0

100

200

300

400

500

600

700

800

 0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

M

e

a

n

N

O

x rate (g

/

hr)

Operating Mode

MOVES HDIUD
43

Table 2-9. Model year groups used for analysis based on the PM emissions standard

Model Year Group Range PM Standard [g/bhp-hr]

1960-1987 No transient cycle standard

1988-1990 0.60

1991-1993 0.25

1994-1997 0.10

1998-2006 0.10

2007+ 0.01

2.1.2.1 Data Sources

All of the data used to develop the MOVES PM2.5 emission rates was generated in the CRC E55/59 research program32. 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. It is reproduced in the following paragraphs immediately below: 

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 

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. 
44

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 particulate 

emissions.

The HHDDTs were tested under unladen, 56,000 lb, and 30,000 lb truck load weights. The 

driving cycles used for the HHDDT testing included:

• AC50/80;

• UDDS;

• 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 of 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. 

Table 2-10. Vehicle and Test Counts by Regulatory Class and Model Year Group

Regulatory Class Model Year Group Number of tests Number of vehicles

MHD

1960 - 1987 82 7

1988 - 1990 39 5

1991 - 1993 22 2

1994 - 1997 39 4

1998 - 2006 43 5

2007 + 0 0

HHD

1960 - 1987 31 6

1988 - 1990 7 2

1991 - 1993 14 2

1994 - 1997 22 5

1998 - 2006 171 18

2007 + 0 0

Counts of tests are provided by test cycle in Table 2-11.
45

Table 2-11. Vehicle Test Counts by Test Cycle

Test Cycle Number of tests

CARB-T 71

CARB-R 66

CARB-I 42

UDDS_W 65

AC5080 42

CARB-C 24

CARBCL 34

MHDTCS 63

MHDTLO 23

MHDTHI 24

MHDTCR 29

2.1.2.2 Analysis

2.1.2.2.1 Calculate STP in 1-hz data

Within source bins, data was further sub-classified on the basis of operating mode. For motor 

vehicles, 23 operating modes are defined in terms of scaled tractive power (STP), vehicle speed and 

vehicle acceleration. These modes are defined above in Table 1-4.

The first step in assigning operating mode is to calculate scaled tractive power (STP) for each 

emissions measurement. At a given time t, the instantaneous STPt represents the vehicle’s tractive 

power scaled by a constant factor. STP is calculated as a third-order polynomial in speed, with 

additional terms describing acceleration and road-grade effects. The coefficients for this 

expression, often called road load coefficients, factor in the tire rolling resistance, aerodynamic 

drag, and friction losses in the drivetrain. We calculated STP using Equation 2-13 (which is the 

same as Equation 1-2 with grade = 0):

scale

t t t t t

t

f

Av Bv Cv mv a

STP

  



2 3

 Equation 2-13

where

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],

fscale = fixed mass factor (see Table 1-3),
46

vt = instantaneous vehicle velocity at time t [m/s],

at = instantaneous vehicle acceleration [m/s2]

The values of coefficients A, B, and C are the road load coefficients pertaining to the heavy-duty 

vehicles33 as determined through previous analyses for EPA’s Physical Emission Rate Estimator 

(PERE). This method of calculating STP calculates tractive power using the same equation used to 

calculate vehicle-specific power (VSP) in the development of emission rates for light-duty vehicles

except that the scaling factor is used in the denominator, instead of the actual test weights of 

individual vehicles

34

. 

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 HDDV6 and 

HDDV7 groups were combined in the table because there were only seven HDDV6 vehicles in the 

study.
47

Table 2-12. Vehicle and Test Counts by Heavy-Duty Class and Model Year

Model Year HDDV6/7 HDDV8

No. Vehicles No. Tests No. Vehicles No. Tests

1969 - - 1 6

1974 1 10 - -

1975 - - 2 10

1978 - - 1 5

1982 1 5 - -

1983 1 10 1 6

1985 1 28 1 10

1986 1 3 1 4

1989 2 11 1 4

1990 1 12 1 3

1992 1 11 1 11

1993 1 11 1 3

1994 1 9 3 15

1995 2 24 3 13

1998 2 20 3 28

1999 - - 3 43

2000 2 18 5 44

2001 1 5 2 21

2004 - - 4 29

2005 - - 1 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.

j j i

i

j

i j PM

PM

PM

PM TEOM , ,

TEOM ,

filter ,

normalized , ,



 Equation 2-14

 Where

i = an individual 1-Hz measurement (g/sec),

j = an individual test on an individual vehicle,

PMTEOM,j,i = an individual TEOM measurement on vehicle j at second i,
48

PMfilter,j = the Total PM2.5 filter mass on j,

PMnormalized,i,j = an estimated continuous emission result (PM2.5) emission result on vehicle j

at second i.

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 PM2.5

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, 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. 

The emission rates of LHD<= 10K (regulatory class 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.28 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 

factors36, which accounts for the lower power requirements per mile (bhp-hr/mile) of light-heavy 

duty trucks versus MHD trucks. The equation used to derive the PM emission rates for regulatory 

class 40 is shown below:

 ≤ 10   = 0.46 ×  (_)  Equation 2-15

Where the MHD VSP-based emission rate is obtained from MOVES2009.

Urban Bus (Regulatory class48) 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 
49

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 the 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 Engines.

Heavy-duty Highway 

Compression-Ignition 

Engines Urban Buses

Ratio in 

standards

1991-1993a0.25 0.1 0.4

1994-1995 0.1 0.07 0.7

1996-2006 0.1 0.05 0.5

aThe 0.1 g/bhp-hr US EPA Urban Bus standard began with model year 1993. 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 1991. 

2.1.2.3.3 Model year 2007 and later trucks (with diesel particulate 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 of ten 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.

Unfortunately, no continuous TEOM data were available for analysis on the 2007 and later modelyear 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 particulate 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, TEOM based, 1998-2006 model 

year PM emission factors to estimate in-use factors 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 particulate 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. 

The reason for the change 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 inhouse 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 
50

demonstrate that the effectiveness of working particulate traps is very high. The interested reader 

can review the ACES report.

37

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

Mean

(g/bhp-hr) St. Dev. n

2003 0.08369 0.01385 91

2004 0.08783 0.01301 59

2005 0.08543 0.01440 60

2006 0.08530 0.01374 60

2007 0.00308 0.00228 21

2.1.2.3.4 Tampering and Mal-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 malmaintenance (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 

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.
51

We followed the same tampering and mal-maintenance methodology and analysis for PM as we did 

for NOx, 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 Percent increase in PM due to 

T&M

Pre-1998 85

1998 - 2002 74

2003 – 2006 48

2007 – 2009 100

2010 – 2012 89

2013+ 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 forcer38. Elemental carbon from vehicle exhaust is routinely 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 its importance in quantifying 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 2-16.

PM 2 .5  EC  NonECPM Equation 2-16

NonECPM is a species used to represent the fraction of PM that is not elemental carbon, computed 

using Equation 2-17. 

2.5 PM2.5

EC 1.0

PM

NonECPM   Equation 2-17
52

The EC fractions used in MOVES for pre-2007 model year trucks (i.e. before diesel particulate 

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 

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 translate the primary PM emission results into MOVES 

parameters (i.e., operating modes). 

Figure 2-20. Elemental Carbon fraction by operating mode for pre-DPF-equipped trucks

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) Report

39

. 

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 

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 Report40. 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 Report41.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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 bin

E

C fraction

HHD

MHD
53

2.1.2.4 Sample results

Figure 2-21 and Figure 2-22 show the trend of increasing PM rates with STP. As with the NOx

plots, 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. Particulate Matter rates by operating mode representing Heavy heavy-duty vehicles (model year 

2002 at age 0-3 years)
54

Figure 2-22. Particulate Matter rates by operating mode for Heavy heavy-duty vehicles (model year 2007 at age 

0-3 years)

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. Particulate Matter rates by age group for Medium heavy-duty vehicles (model year 2002, operating 

mode 24)

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. Particulate Matter rates for Heavy heavy-duty vehicles by model year group (age 0-3 years, 

operating mode 24)
57

2.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO)

Diesel engines account for a substantial portion of the mobile source HC or 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-55/5932: Mentioned earlier, this program represents the largest volume of heavyduty 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)

42: 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 inuse fleet in the Northern Front Range of Colorado were tested over three different transient 

drive cycles.

3. New York Department of Environmental Conservation (NYSDEC)

43: 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 heavyheavy 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

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 Regulatory class

Age group

0-3 4-5 6-7 8-9 10-14 15-19 20+

1960-2002

HHD 58 19 16 9 16 6 7

MHD 9 6 5 4 12 15 6

Bus 26 1 3

LHD45 2 1

LHD2b3 6

2003-2006 HHD 6

2.1.3.2 Analysis

As for PM, STP was calculated using an equation similar to the light-duty VSP equation, but 

normalized with average regulatory class weight instead of test weight, as described by Equation 

2-18.

scale

t t t t t

t

f

Av  Bv Cv  mva



2 3

STP Equation 2-18

The track road-load coefficients A, B, and C pertaining to heavy-duty vehicles33 were estimated

through previous analyses for EPA’s Physical Emission Rate Estimator (PERE).

21

Using a method similar to that used in the NOx 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 used in development of the NOx rates. Instead of using our results to 

directly populating 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.

 
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 Model year group Age group

HHD 1960-2002 0-3

HHD 2003-2006 0-3

MHD 1960-2002 15-19

BUS 1960-2002 0-3

LHD41 1960-2002 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

Appenidx 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

Increase in HC and CO 

Emissions (%)

Pre-2003 300

2003 – 2006 150

2007 – 2009 150

2010 - 2012 29

2013+ 22

We multiplied these increases by the T&M adjustment factors in Table B-2 in Appendix B.6 to get 

the emissions by age group.

With the increased use of diesel oxidation catalysts (DOCs) in conjunction with DPFs, we assume 

an 80 percent reduction in zero-mile emission rates for both HC and CO starting with model year 

2007.

2.1.3.3 Sample results

The charts in this sub-section show examples of the emission rates that 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 

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.
60

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.

0

10

20

30

40

50

60

70

80

90

100

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean H

C rate [g/hr]

Operating mode

MHD

HHD/Bus

0

100

200

300

400

500

600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean C

O rate [g/hr]

Operating mode

MHD

HHD/Bus
61

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).

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.

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.

0

10

20

30

40

50

60

70

80

90

100

Mean H

C rate [g/hr]

HHD

MHD

Bus

0-3 4-5 6-7 8-9 10-14 15-19 20+

Age group [years]
62

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 model year group reflect the 

use of diesel oxidation catalysts. Due to the sparseness of the data and the fact that HC and CO 

emission do not correlate as well with STP (or power) as NOx and PM do, uncertainties are much 

greater. Rates from HHD regulatory class were used for buses. All regulatory classes have the 

same rates for model years 2003 and later.

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.

0

100

200

300

400

500

600

Mean C

O rate [g/hr]

HHD

MHD

Bus

0-3 4-5 6-7 8-9 10-14 15-19 20+

Age group [years]
63

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.

2.1.4 Energy

2.1.4.1 LHD<=10k Energy Rates for Model Years 1960-2013

0

5

10

15

20

25

30

1960-2002 2003-2006 2007-2050

Mean H

C rate [g/hr]

Model year group

MHD

HHD/Bus

0

20

40

60

80

100

120

140

160

180

200

1960-2002 2003-2006 2007-2050

Mean C

O rate [g/hr]

Model year group

MHD

HHD/Bus
64

. The energy rates for LHD<=10k for pre-2007 diesel energy rates are unchanged from the 

LHD2b3 regulatory class from 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 energy updates report.

5

2.1.4.2 LHD<=10k Energy Rates for Model Years 2014-2050

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 CO2

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 consumption (across all running 

operating modes) for model years 1970 through 2030. The rates are constant between 1960 to 

1983, and from 2018 to 2050.

Figure 2-31. Average Energy Consumption Rates for LHD<=10K diesel vehicles across all running operating 

modes

0.0e+00

3.0e+06

6.0e+06

9.0e+06

1.2e+07

1970 1980 1990 2000 2010 2020 2030

modelYearID

A

v

era

g

e

e

n

erg

y

e

mis

sio

n rate, k

J/h

o

ur

regClassName

LHD <= 10k
65

2.1.4.3 LHD<=14k, LHD45, MHD, Urban Bus, and HHD 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 CO2.

As for previous versions of MOVES, CO2 emissions were used as the basis for calculating energy 

rates. To calculate energy rates [kJ/hour] from CO2 emissions, we used a heating value (HV) of 

138,451 kJ/gallon and CO2 fuel-specific emission factor (fCO2) of 10,084 g/gallon for diesel fuel, 

using Equation 2-19. 

2

2

CO

energy CO f

HV

r  r Equation 2-19

This analysis updates the running-exhaust energy rates estimated for MOVES2004 for diesel 

LHD<=14k, LHD45, MHD, urban bus, and HHD regulatory classes.28 The energy rates for these 

heavy-duty diesel vehicle classes are shown in Figure 2-32. 

. 

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.

0

1

2

3

4

5

6

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean Energy rate [kJ/hr] Millions

Operating mode
66

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.

44 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) 

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

also sets separate standards for fuel consumption, CO2, N2O, CH4 and HFCs.iii 

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.44 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 CO2 Emission Rates from the HD GHG

Program Phase 1

GVWR Class Fuel Model Years CO2 Reduction From Baseline

HHD (8a-8b) Diesel 2014-2016 3%

2017+ 6%

 

iii HFCs are not modeled in MOVES, and the N2O and CH4 standards are not considered forcing on emissions.
67

LHD(4-5) and

MHD (6-7) 

Diesel 2014-2016 5%

2017+ 9%

Gasoline 2016+ 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. 

Table 2-20 Estimated Total Vehicle CO2 Reductions for HD Diesel and Gasoline Pickup Trucks and Vans

GVWR 

CLASS

FUEL MODEL 

YEARS

CO2 REDUCTION 

FROM BASELINE

LHD 2b-3 Gasoline 2014 1.5%

2015 2%

2016 4%

2017 6%

2018+ 10%

Diesel 2014 2.3%

2015 3%

2016 6%

2017 9%

2018+ 15%

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 LHD<=14k the energy rates are constant 2018 going 

forward, for the other categories (LHD45, MHD, Urban Bus, HHD) the energy rates are constant 

going forward starting in model year 2017. 
68

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.

2.2 Start Exhaust Emissions

The ‘start’ process occurs when the vehicle is started and is operating in some mode in which the 

engine is not fully warmed up. For modeling purposes, we define start emissions as the increase in 

emissions due to an engine start. That is, we use the difference in emissions between a test cycle 

with a cold start and the same test cycle with a hot start. There are also eight intermediate stages 

which are differentiated by soak time length (time duration between engine key off and engine key

on), between a cold start (> 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 
69

found later in this section and in the MOVES light-duty emission rate counterpart document8. The 

impact of ambient temperature on cold starts is discussed in the Emission Adjustments MOVES 

report45.

2.2.1 HC, CO, and NOx

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 starts with a hot start. A similar approach was performed 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 LHD 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.

Table 2-21. The average start emissions increases for light-heavy duty diesel vehicles (g) for regulatory class 40, 

41, and 42. No differentiation by model year or age.

HC CO NOx

0.13 1.38 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 is with CO, 

whereas there is a slight increase in NOx (cold start NOx is lower than hot start NOx), and an 

insignificant change in HC.
70

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

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 start is in Table 2-22. The NOx increment is negative since cold start emissions are lower 

than warm start emissions.

Table 2-22. Cold-start emissions increases in grams on the 2007 Cummins ISB

HC CO NOx

0.0 16.0 -2.3

We also considered data from University of Tennessee46, 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 hot-start 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-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), and several trucks produces 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 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.

0

5

10

15

20

25

30

35

40

45

50

0.00 1.00 2.00 3.00 4.00 5.00

time [hrs]

mg/s

NOx

HC

CO
71

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 CO NOx

0.0 16.0 0.0

As discussed in the Emission Adjustments Report45, MOVES2014 applies an additive adjustment 

to HC cold-start emissions to the diesel start emissions for ambient temperatures below 72F. No 

adjustments are applied to CO or NOx. Therefore, MOVES2014 produces start emissions from 

heavy-duty diesel vehicles for HC and CO at ambient temperatures below 72 F.

2.2.1.1 Incorporation of Tier-3 Standards for Light-Heavy-Duty Diesel

The Tier-3 exhaust emission standards affect medium-duty diesel vehicles in the LHD<=10k and 

LHD<=14k (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 

2.1.1.3.5 (page 23). 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.

Figure 2-35. NOx: Start emission rates in selected operating modes vs. model year for two regulatory classes 

(LOGARITHMIC SCALE).
72

2.2.2 Particulate Matter

Data for particulate 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 from engine dynamometer testing performed on one heavy-heavy-duty 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 = 1.9314 g PM2.5

Warm start FTP average = 1.8215 g PM2.5

Cold start – warm start = 0.1099 g PM2.5

We applied this value to 1960 through 2006 model year vehicles. A corresponding value of 

0.01099 g was used for 2007 and later model year vehicles (90 percent reduction due to DPFs). 

We plan to update this value when more data becomes available. The value is the same for all 

heavy-duty diesel regulatory class vehicles.

2.2.3 Adjusting Start Rates for Soak Time

The discussion to this point has concerned the development of rates for cold-start emissions. In 

addition, it was necessary to derive rates for additional operating modes that account for varying 

(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 Description

101 Soak Time < 6 minutes

102 6 minutes <= Soak Time < 30 minutes

103 30 minutes <= Soak Time < 60 minutes

104 60 minutes <= Soak Time < 90 minutes

105 90 minutes <= Soak Time < 120 minutes

106 120 minutes <= Soak Time < 360 minutes

107 360 minutes <= Soak Time < 720 minutes

108 720 minutes <= Soak Time
73

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. For light-heavy duty vehicles 

(regulatory classes 40, 41, and 42), the soak distributions apply to the cold starts for HC, CO and 

NOx. For heavy-heavy duty vehicles (regulatory classes 46, 47, and 48) only the CO soak 

fractions in Figure 2-36 is applied to the cold-start emissions, because the base cold start HC and 

NOx emission rates for heavy-heavy duty emission rates are zero. 

Figure 2-36. Soak Fractions Applied to Cold-Start Emissions (opModeID = 108) to Estimate Emissions for 

shorter Soak Periods (operating modes 101-107). This Figure is reproduced from Figure 43 in the Light-duty 

emissions Report8

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.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1.10

1.20

0 120 240 360 480 600 720

 Soak Time (minutes) 

Ratio of Start to Cold-Start

NOx

CO

HC
74

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.

HC CO NOx

opModeID LHD1 Other HD2 LHD Other HD LHD Other HD

101 0.0052 0 0.055 0.64 0.275 0

102 0.0273 0 0.276 3.2 0.760 0

103 0.0572 0 0.607 7.04 1.350 0

104 0.0780 0 0.869 10.08 1.481 0

105 0.0832 0 1.007 11.68 1.481 0

106 0.0949 0 1.090 12.64 1.468 0

107 0.1183 0 1.256 14.56 1.376 0

108 0.1300 0 1.380 16 1.298 0

1LHD 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 assuming 

a linear decrease in emissions between a full cold start (>720 minutes) and zero emissions at a short 

soak time (< 6 minutes).

Table 2-26. Particulate Matter Start Emission Rates by Operating Mode (soak fraction) for all HD 

vehicles (regClass ID 40 through 48)

Operating Mode PM2.5 (grams per start)

1960-2006 MY

PM2.5 (grams per start)

2007+ MY

101 0.0000 0.00000

102 0.0009 0.00009

103 0.0046 0.00046

104 0.0092 0.00092

105 0.0138 0.00138

106 0.0183 0.00183

107 0.0549 0.00549

108 0.1099 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 (opModeID 108)45. The ambient temperature effects in MOVES model the 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 
75

the vehicle is the same temperature of the ambient conditions. Accordingly, the impact of ambient 

temperature should be less for vehicles that are still warm from driving. 

However, for HC the temperature effects applied in MOVES are modeled as additive adjustments. 

Accordingly, the additive temperature adjustments need to be reduced for the hot and warm starts. 

Due to lack of data, we applied soak fractions, to obtain cold start temperature adjustments for

opModeID 101 through 107. The additive cold start adjustment for HC emission factors are 

displayed in Table X, along with the soak fractions applied. The soak fractions are taken from the 

non-catalyst soak fractions derived in a CARB report47 and reproduced in a Mobile 6 report48. We 

used the non-catalytic converter soak fractions to adjust the diesel start temperature adjustments for 

HC temperature starts. These additive HC starts are applied to all diesel sources in MOVES, 

including light-duty diesel (regulatory class 20 and 30).

As stated in the emissions adjustments report, there are currently no diesel temperature effects in 

MOVES for PM, CO, and NOx. 

Table 2-27. HC Diesel Start Temperature Adjustment by opModeID.

opModeID Start Temp Adjustment Soak fraction

101 -0.0153×(Temp – 75) 0.38

102 -0.0152×(Temp – 75) 0.37

103 -0.0180×(Temp – 75) 0.44

104 -0.0201×(Temp – 75) 0.50

105 -0.0211×(Temp – 75) 0.52

106 -0.0254×(Temp – 75) 0.62

107 -0.0349×(Temp – 75) 0.86

108 -0.0406×(Temp – 75) 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 MOVES200428, and updated in MOVES2010 as 

documented in the updates report5. As shown, there is more granularity 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 ‘data-driven’ approach used to derive the energy rates in 

MOVES2004. The only updates to the energy rates post-2000 is the impact of the Phase 1 Heavyduty GHG standards, which begin phase-in in 2014 and have the same reductions as the running 

energy rates as presented in Table 2-19.
76

Figure 2-37. Heayv-duty energy cold start energy rates (opMode 108) by model year and regulatory class. 

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

Report28. 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 reduced by the energy consumption 

emission rates at cold start are provided in Table X. 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 Description

Fraction of energy 

consumption compared 

to cold start

101 Soak Time < 6 minutes 0.013

102 6 minutes <= Soak Time < 30 minutes 0.0773

103 30 minutes <= Soak Time < 60 minutes 0.1903

104 60 minutes <= Soak Time < 90 minutes 0.3118

105 90 minutes <= Soak Time < 120 minutes 0.4078

106 120 minutes <= Soak Time < 360 minutes 0.5786

107 360 minutes <= Soak Time < 720 minutes 0.8751

108 720 minutes <= Soak Time 1
77

Table 2-29 displays the relative contribution of total energy consumption estimated from a National 

MOVES 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. 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. Relative contribution of total energy consumption from each pollutant process by regulatory class 

for heavy-duty diesel vehicles in calendar year 2011.

processID processName 40 41 42 46 47 48

1 Running Exhaust 97.4% 99.2% 99.3% 98.1% 95.1% 99.7%

2 Start Exhaust 2.6% 0.8% 0.7% 0.6% 0.1% 0.3%

90 Extended Idle Exhaust 1.3% 4.7%

91

Auxiliary Power 

Exhaust 0.01% 0.04%

2.3 Extended Idling Exhaust Emissions

In the MOVES model, extended idling is "discretionary" 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 will also allow 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 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 sourceType 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 use types modeled in MOVES. 

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.
78

Testing was conducted on twelve heavy-duty diesel trucks and twelve transit buses in Colorado 

(McCormick)49. 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)50. 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)51. 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.

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)52. 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 a large Coordinating Research Council (CRC) study of heavy 

duty diesel trucks with idling times either 900 or 1,800 seconds long (Gautam)53.

The National Cooperative Highway Research Program (NCHRP)54 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)55. 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)56. 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)

57. These are the same trucks used in the EPA study (Lim).

The University of Tennessee 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)46.

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). This analysis used all 

of the data sources referenced above. The MOVES2010b update reflects new data available since 
79

the initial development of extended idle emissions for the MOVES model. The additions include 

the testing at Research Triangle Park (Broderick), the University of Tennessee study (Calcagno), 

and the completed E-55/59 study conducted by WVU and CRC. In addition, the data was 

separated by truck and bus and by idle speed and accessory usage to develop an emission rate more 

representative of extended idle rates.

The important conclusion from the 2003 analysis 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. 

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 expected effects of the 2007 heavy duty diesel vehicle emission standards on extended idling 

emission rates are taken from the EPA guidance analysis (EPA 2003). The 2007 heavy duty diesel 

emission standards are expected to result in the widespread use of PM filters and exhaust gas 

recirculation (EGR) and 2010 standards will result in after-treatment technologies. However, since 

there is no requirement to address extended idling emissions in the emission certification 

procedure, EPA expects that there will be 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 do not 

expect DPFs to lose much effectiveness during extended idling. As a result, we project that idle 

NOx emissions will 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 assume an extended idling emission rate equal to the curb idling rate (operating mode 1 from 

the running exhaust analysis). Detailed equations are included in the appendix. 
80

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, which is reflected in the variation of the mean statistic.

Table 2-30. Mean Extended idle emission rates from data analysis (g/hour)

Model years NOx HC CO PM

Pre-1990 112 108 84 8.4

1990-2006 227 56 91 4.0

2007 and later 201 53 91 0.2

2.3.4 MOVES Extended Idle Emission Rates

Table 2-31 shows the emission rates used in MOVES for extended idle for diesel MHD67 and 

HHD trucks. These are the only regulatory classes in MOVES within diesel combination trucks, 

which are the only types of trucks with extended idle vehicle activity in MOVES. The emission 

rates for regulatory class 47 (HHD) are equal to the mean extended emission rates from Table 2-30

for HC, CO, and NOx. Due to limited data we used the calculated the MHD67 (regClassID 46) 

extended idle emission rates as 1⁄2 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)

HC CO NOx

MHD67 HHD MHD67 HHD MHD67 HHD

1960-1990 54 108 42 84 56 112

1991-2006 28 56 45.5 91 113.5 227

2007+ 26.5 53 45.5 91 100.5 201

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. The PM emission rates in MOVES are similar in magnitude to the PM emission rates 

measured to the mean rates reported in Table 2-30. 
81

Table 2-32. Particulate Matter Emission rates for Extended Idle Emission Rates

Regulatory Class MHD67

EC NonECPM PM EC/PM

1960 - 1993 1.77 2.44 4.21 42.1%

1994 - 1997 3.07 4.21 7.28 42.1%

1998 - 2002 2.91 4.00 6.91 42.1%

2003 - 2006 2.63 3.60 6.23 42.1%

2007+ 0.032 0.288 0.32 9.98%

Regulatory Class HHD

EC NonECPM PM EC/PM

1960 - 1993 1.08 3.13 4.21 25.7%

1994 - 1997 1.66 4.78 6.44 25.7%

1998 - 2002 1.58 4.57 6.16 25.7%

2003 - 2006 1.43 4.13 5.56 25.7%

2007+ 0.03 0.31 0.35 9.98%

The extended idle energy emission rates were originally developed in MOVES200428, 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. As shown in Table 2-29, extended idle is 

estimated to contribute 1.3% and 4.7% of the energy consumption from regulatory class MHD67 

and HHD8 diesel vehicles in the United States in calendar year 2011. 
82

Figure 2-38. Extended idle energy emission rates for regulatory class HHD8 and MHD67 diesel trucks.

2.3.5 Auxiliary Power Unit Exhaust

In MOVES2014, we added a new emission process for auxiliary power unit (APU) exhaust. APU 

usage only applies to the vehicles with extended idling activity, which are the heavy-duty 

regulatory classes (MHD and HHD) within the combination truck source types. The MOVES 

default activity assumes APU’s are used for 30% hotelling activity for model year 2010 and later 

trucksiv, with extended idling occurring for the remaining 70% of hotelling activity. Users can 

update hotelling activity among extended idling, APU usage, and engine off activity as discussed in 

the MOVES2014 User Guide58

The APUs in MOVES are assumed to be Tier 4-compliant, small (<8 kW) nonroad compressionignition engines. We use the THC, CO, NOx, and PM2.5 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 Rule44. The PM2.5 emissions were divided into EC 

 

iv The 2014 Medium and Heavy-duty Greenhouse Gas rulemaking assumed a 30% APU penetration in model years 

2010-2014, and 100% APU penetration for 2015 and later. The assumption for APU penetration for 2015 and later was 

revised in MOVES2014 to continue at 30%.
83

(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 Emission Rate Units

THC 6.72 g/hr

CO 36 g/hr

NOx 26.88 g/hr

EC 0.45 g/hr

NonEC 1.35 g/hr

EC/PM2.5 25% %

Total Energy 27171.336 KJ/hr
84

3 Heavy-Duty Gasoline Vehicles

3.1 Running Exhaust Emissions 

3.1.1 HC, CO, and NOx

3.1.1.1 Data and Analysis for 1960-2007 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 number of vehicles in cumulative 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, if any, HHD gasoline trucks remaining in 

use. 

Table 3-1. Distribution of vehicles in the data sets by model-year group, regulatory class and age group

Model year group Regulatory class

Age group

0-5 6-9

1960-1989

MHD 2

LHD2b3 10

1990-1997

MHD 1

LHD2b3 33 19

1998-2002

MHD 1

LHD2b3 1

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

Figure 3-1. Brake-specific certification emission rates by model year for heavy-duty gasoline engines

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 LHD <=10K

The emission rates are 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 40 (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.

0

5

10

15

20

25

30

1980 1985 1990 1995 2000 2005 2010

Model year

C

O, NOx [g/bhp-hr]

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

HC [g/bhp-hr]

CO

Nox

HC
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

Figure 3-3 shows the emissions trends by age group. Since we did not use the tampering and malmaintenance 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.

0

1000

2000

3000

4000

0

50

100

150

0

100

200

300

400

500

C

O

H

C

N

O

x

0 1 11 12 13 14 1516212223242527282930333537383940

opModeID

E

mis

sio

n rate, g/h

o

ur

Model.Years

1960-1989

1990-1997

1998-2007
87

Figure 3-3. Emission rates by operating mode and age group for MY 1998-2007 vehicles in Regulatory Class 

LHD <=10K

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. 

0

500

1000

1500

2000

2500

0

25

50

75

100

0

50

100

150

200

C

O

H

C

N

O

x

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

opModeID

E

mis

sio

n rate, g/h

o

ur

age

6+

0-5
88

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.

OpModeID HC CO NOx

0 2.85 1.45 1.67

1 2.43 1.79 1.85

11 3.12 1.66 1.88

12 2.85 2.05 1.69

13 3.55 2.68 1.48

14 3.43 2.84 1.46

15 3.37 3.03 1.26

16 3.76 3.88 1.06

21 2.78 1.67 1.42

22 2.64 1.64 1.36

23 2.96 1.67 1.32

24 2.83 1.62 1.21

25 3.23 2.79 1.43

27 3.21 3.20 1.21

28 3.20 4.04 1.11

29 3.00 3.90 1.05

30 2.55 2.56 1.05

33 1.95 2.00 1.77

35 2.67 2.20 1.59

37 2.80 2.24 1.42

38 2.46 2.06 1.34

39 2.46 2.30 1.27

40 2.47 2.59 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 (40) that classifies LHD2b trucks that are 

classified as passenger or light-commercial trucks. Regulatory class 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 (2010b and earlier), regulatory class 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 Class 2b and 3 trucks (LHD2b3). We thus used the term LHD2b3 

trucks to refer to trucks in both regulatory class 40 and 41. However, we only used the data in this 

section to update the emission rates for regulatory class 40. Emission rates for regulatory class 41 

for 2008+ vehicles are discussed in the following section. 
89

3.1.1.3.1 Comparison of LHD2b3 emission rates in MOVES2010 with relevant 

emission standards

Gasoline vehicles in MOVES regulatory class LHD2b3 (RegClassID 40 and 41) are a mixture of 

engine certified HD vehicles, chassis certified HD 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).v

Table 3-3. Useful Life FTP Standardsvi

MDPV

(Tier 2 Bin 5)

59

8.5k – 10k 

(Class 2B)

10k-14k

(Class 3)

Engine

Certified

Units g/mile g/mile g/mile g/bhp-hr

Fully Phased in MY 2009 2009 2009 2010

HC 0.09 NMOG 0.195NMHC 0.230 NMHC 0.14 NMHC

CO 4.2 7.3 8.1 14.4

NOx 0.07 0.2 0.4 0.2

The relative proportions of the vehicles within MOVES LHD2b3 regulatory class vary each year 

depending on demand. Consequently, we estimated proportions based on recent year data and 

engineering judgment. MOBILE6 documentation from 2003 indicates that MDPVs are 

approximately 16% of the gasoline 8,500 to 10,000 truck class.60 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.60 

Extrapolating, 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.61 This is roughly 

consistent with recent model year sales totals.

62 Combining these assumptions, we get the sales 

fractions shown below (Table 3-4).

Table 3-4. Population Percentage of LHD2b3 Trucks

% of Reg Class 

MDPV 15%

Class 2B 60%

Class 3 20%

Engine Certified 5%

 

v This mixture of vehicles was not explicitly considered during the development of MOVES2010.

vi 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 in this sample dilutes the impact.
90

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.

vii

 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).viii

Table 3-5. Aggregate Useful Life FTP for LHD2b3 Trucks

g/mile

NMOG 0.18

CO 7.49

NOX 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),33 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 sourceTypeID

32 (Light Commercial Truck) in MOVES (2.06 Tons). We incorporated emission rates from the 

MOVES DB 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.

Table 3-6. Operating Mode Bin Distribution for a Light-Commercial Truck on the Federal Test Procedure 

(FTP)

OpModeID N % OpModeID N %

0 160 12% 25 41 3%

1 258 19% 27 49 4%

11 94 7% 28 17 1%

12 68 5% 29 13 1%

13 70 5% 30 15 1%

14 36 3% 33 13 1%

15 48 3% 35 12 1%

16 141 10% 37 13 1%

21 68 5% 38 17 1%

22 44 3% 39 15 1%

23 97 7% 40 6 0%

24 77 6%

Total 1372 100%

 

vii The engine standard was converted to a g/mile standard using a factor of 1.2 as described in the MOBILE6 report

viii 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

Using this operating mode distribution, we constructed a simulated FTP out of four components 

(bag 1/3 running,ix 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,xand compared the 2008 and later rates in MOVES to the aggregate 

standard calculated above (Table 3-7). MOVES 2010a estimates at age 0-3 are two to ten times 

larger than the standard, which indicates that the average vehicle HD gas vehicle in MOVES2010a 

is significantly out of compliance with the relevant standard in this timeframe.

Table 3-7. Comparison between MOVES DB FTP and Aggregate FTP for LHD2b3 Trucks

MOVES2010

FTP for 

LHD2b3 

Trucks 

(g/mile)

LHD2b3 

Aggregate

FTP

Standard 

(g/mile)

Ratio – MOVES

to Aggregate

Standard

NMOG 0.36 0.18 1.93

CO 14.54 7.49 1.94

NOx 2.04 0.22 9.28

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.xi We evaluated whether vehicles during these MYS 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, and accounting for the standards applicable to each vehicle, we calculate average ratios of 

test value to standard of 0.42 (NMOG) and 0.23 (NOx) (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

Average Average

Ratio Certification 

FTP/ Standard Headroom

NMOG 0.42 0.58

NOx 0.23 0.77

 

ix Bag 1 and Bag 3 are considered to have the same emission rate.

x

FTP =( (Bag 1 + Bag 2)*0.43+ (Bag 3+ Bag 2)*0.57)/ 7.45

xi While this population of vehicles is not identical, these test weights significantly overlap with these GVWR classes.
92

Figure 3-4. Distribution of IUVP FTP Tests for LHD2b3 Trucks

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.xii 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 ID 40 

vehicles in 2008 and later MYs. 

In conducting this analysis, we lacked any modal data on regulatory class 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.34 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.

34

 

xii Even in the absence of emission equipment deterioration, tampering and malmaintenance will increase the emissions 

from a on-road vehicle.
93

Because we are basing the emission rates from light-duty emission rates (which are also VSPbased), the emission rate update is limited to regulatory class 40 vehicles.

We scaled the modal data from Tier 2 Bin 8 vehicles by the ratio of FTP standardsxiii so that the 

rates would be consistent with the higher emission rates of regulatory class 40 vehicles. 

Table 3-9. Aggregate LHD2b3 Standard Ratios against Bin 8 Modal Rates

Aggregate 

LHD2b3 FTP

Standard

Bin 8

FTP 

Standard

Aggregate/Bin 8

NMOG 0.18 0.1 1.8

CO 7.49 3.4 2.2

NOx 0.22 0.14 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 emissions34.Error! Bookmark not defined. 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 40 emission 

rates for 2008-2017 MY.

HC CO NOx

Running 2.73 2.73 1.95

Start 1.37 1.37 1.00

We also adopted the light-duty deterioration effects and applied them to the 2009 and later 

regulatory class 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. 

 

xiii The aggregate FTP standards used include both Class 2b and 3 trucks. However, the ratio is only applied to develop 

updated regulatory class 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.
94

Table 3-11. Multiplicative Age Effect Used for Running Emissions for RegClass 40 2008+ model years.

ageGroupID HC CO NOx

3 1 1 1

405 1.95 2.31 1.73

607 2.80 3.08 2.21

809 3.71 3.62 2.76

1014 4.94 4.63 3.20

1519 5.97 5.62 3.63

2099 7.20 6.81 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 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 regclass 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 40 emission 

rates are higher than the regulatory 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 40 than 

regulatory class 30, and when used in MOVES, the simulated FTP emission rates are significantly 

higher for regulatory class 40 vehicles. 
95

Figure 3-5. Age Effects for CO, HC, and NOx emission rates for RegClass 40 vehicles in Running Operating 

Modes for MY 2008-2017.

After calculating new regulatory class 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 Bin 8 rates are based on extensive I/M testing, 

and are considered more representative of the entire fleet.

0

500

1000

1500

2000

2500

0

20

40

60

0

50

100

150

C

O

H

C

N

O

x

5 10 15 20

age

A

v

era

g

e

e

misio

n rate, g/h

o

ur

opModeID

0

1

11

12

13

14

15

16

21

22

23

24

25

27

28

29

30

33

35

37

38

39

40
96

Table 3-12. Ratio of Final rates against standards

Simulated 

regulatory class 

class 40 2008+ 

FTP (g/mile)

Aggregate 2010+

LHD2b3

FTP Standard

(g/mile)

Achieved /

Aggregate 

FTP

NMOG 0.06 0.18 33%

CO 3.08 7.49 41%

NOx 0.18 0.22 84%

In terms of the phase-in, we assumed that the regulatory class 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 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, however, 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 2b and 3 vehicles, “medium-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 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 enginecertified vehicles were assumed to be unaffected by the new 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 (page 89). Values of the weighted means by model year are shown in Table 3-15.

weighted  2b 3

 Engine-certified 15FTPMDPV FTP 0.8 0.75FTP 0.25FTP 0.05FTP 0. Equation 3-1
97

Table 3-13. Composite FTP NMOG+NOx Standards for Class 2b and 3 Vehicles (mg/mi).

Vehicle Class LEV ULEV34 ULEV25 SULEV17

2b 395 340 250 170

3 630 570 400 230

Table 3-14. Phase-in fractions by standard level for Class 2b and 3 Vehicles.

Model Year LEV ULEV34 ULEV25 SULEV17

2017 0.10 0.50 0.40 0.0

2018 0.0 0.40 0.50 0.10

2019 0.0 0.30 0.40 0.30

2020 0.0 0.20 0.30 0.50

2021 0.0 0.10 0.20 0.70

2022 0.0 0.0 0.10 0.90

Table 3-15. Projected FTP composite values for four vehicle classes (mg/mi), plus weighted means, by model 

year during the Tier-3 phase-in.

Model Year Vehicle Class Weighted Mean

2b 3 MDPV Engine-Certified

2017 400

2018 278 451 102 408 293

2019 253 400 84 408 265

2020 228 349 66 408 237

2021 203 298 48 408 209

2022 178 247 30 408 181

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 181 400 (1 ) weighted T 3 T 3  f   f Equation 3-2
98

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 fT3 1 − fT3

2017 0.00 1.00

2018 0.49 0.51

2019 0.62 0.38

2020 0.75 0.25

2021 0.87 0.13

202211.00 0.00

1Also 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.xiv 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 <F to <F, respectively.

 

xiv The database version used was MOVESt3DB20110331.
99

Figure 3-6. THC: Running-exhaust emission rates for vehicles in the LHD<=10k regulatory class 

(regClassID=40), during the Tier-3 phase-in.
100

Figure 3-7. CO: Running-exhaust emission rates for vehicles in the LHD<=10k regulatory class 

(regClassID=40), during the Tier-3 phase-in.
101

Figure 3-8. NOx: Running-exhaust emission rates for vehicles in the LHD<=10k regulatory class 

(regClassID=40), during the Tier-3 phase-in.

Figure 3-9 displays the average emission rates (across all operating modes) for CO, HC, and NOx 

across 1980 to 2007 model years for LHD<=10k vehicles. Although not shown, the emission rates 

within the model years 1960-1980 are the same, as well as the emission rates within model years 

2030-2050. Figure 3-9 summarizes the decreasing trend in emissions from the analysis documented 

in this chapter.
102

Figure 3-9. Average emission rate (across all operating modes) for regulatory class 40 trucks for CO, HC, and 

NOx. The1960-2007 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.

3.1.1.5 Running Emission Rates for Regulatory Class LHD <14 K, LHD45, and 

MHD, and HHD for 1960-2007 model years

The emission rates are equivalent across all the heavy-duty gasoline running emission rates for 

these regulatory classes (41, 42, 46, and 47). The same emissions data is used to derive these 

regulatory class emission rates as was used to derive the regulatory class 40 emission rates. As 

discussed earlier, the emissions data comes from a mix of LHD2b/3 and MHD vehicles outlined in 

Table xx. 

The only difference in the analysis with regulatory class 40 emission rates is that these emission 

rates were analyzed using STP operating modes with a fixed mass factor of 17.1. The same model 

year groups are used to classify the emission rates: 1960-1989, 1990-1997, and 1998-2007. Also, 

the same relative increase in emission rates for the age effect as derived for LHD <= 10K is used. 

0

500

1000

1500

0

25

50

75

100

0

50

100

150

200

C

O

H

C

N

O

x

1980 1990 2000 2010 2020 2030

modelYearID

A

v

era

g

e

e

misio

n rate, g/h

o

ur

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
103

Sample emission rates for HC, CO, and NOx for the 1994 MY Group are presented in Figure X for 

these source types. 

Figure 3-10. Emission Rates by STP operating mode for MY 1994 at age 0-3 years for Regulatory Class LHD < 

14K, LHD45, MHD, and HHD

Figure 3-11 and Table 3-17 display the multiplicative age effects by operating mode for 

LHD<14K, LHD45, MHD, and HHD gasoline vehicles. Once again, the age effects were derived 

from the same data as the LHD<=10K vehicles. The 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 (opModeID 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 was binned in the high STP power modes (that typically only a HHD 

0

10000

20000

30000

0

200

400

600

0

500

1000

1500

2000

C

O

H

C

N

O

x

0 1 11121314151621222324 25 27 28 29 30 333537383940

opModeID

E

mis

sio

n rate, g/h

o

ur

Model.Years

1960-1989

1990-1997

1998-2007
104

truck would reach). As such, the higher operating modes use the same values as the closest bin with 

data (opModeID 13-16, 24-30, and 35-40).

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.

0

2500

5000

7500

0

100

200

300

400

500

0

250

500

750

1000

C

O

H

C

N

O

x

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

opModeID

E

mis

sio

n rate, g/h

o

ur

age

6+

0-5
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.

OpModeID HC CO NOx

0 2.85 1.45 1.67

1 2.43 1.79 1.85

11 3.12 1.66 1.88

12 3.36 3.12 1.13

13 3.53 3.16 1.11

14 3.53 3.16 1.11

15 3.53 3.16 1.11

16 3.53 3.16 1.11

21 2.78 1.67 1.42

22 3.08 2.59 1.23

23 2.97 3.31 1.05

24 1.80 1.54 1.03

25 1.80 1.54 1.03

27 1.80 1.54 1.03

28 1.80 1.54 1.03

29 1.80 1.54 1.03

30 1.80 1.54 1.03

33 2.45 2.41 1.33

35 2.16 2.41 1.19

37 2.16 2.41 1.19

38 2.16 2.41 1.19

39 2.16 2.41 1.19

40 2.16 2.41 1.19

3.1.1.6 Running Emission Rates for Regulatory Class LHD <14 K, LHD45, and 

MHD, and HHD for 2008 and later model years

Of the on-road heavy duty vehicles GVW class 4 and above, about 15% are gasoline, as opposed to 

85% diesel.xv The gasoline percentage decreases as the GVW class increases. Consequently, there 

is relatively little data on these vehicles, and we did not update the 2008 and later model year 

emission rates from MOVES201063 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”.

64 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 

 

xv Negligible portions are run on other fuels. The figures are aggregated from data supplied by Polk.
106

emission rates, as shown in Figure 3-14. The analysis of regulatory class 40 emission rates for 2008 

and later model years is based on light-duty truck VSP-based emission rates. We did not have loadbased data on class 2b and 3 trucks to derive STP-based emission rates specific for regulatory class 

41 trucks. As such, we estimate regulatory class 41 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 41 includes Class 2b and 3 trucks, as such the Tier 3 Vehicle 

Emission standards apply to 2b trucks. 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 (page 96). 

However, for these two classes, the rates for running operation differ in that those for regClass 40 

are based on vehicle-specific power (as are rates for light-duty vehicles), whereas those for 

regulatory class 41 are based on “scaled-tractive power” (STP), as are rates for heavy-duty vehicles 

in regClasses 42, 46 and 47.

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 regClasses 40 and 41 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 

VSP-based (regClass 40) and the STP-based (regClass 41) rates. Note also that start rates for the 

two regulatory classes are identical, as they are not defined in terms of either VSP or STP.
107

Figure 3-12. THC emission rates vs. model year for regulatory classes 40 and 41, showing selected operating 

modes for the running-exhaust process (Note the logarithmic scale).
108

Figure 3-13. NOx emission rates vs. model year for regulatory classes 40 and 41, showing selected operating 

modes for the running exhaust process (Note the logarithmic scale).
109

Figure 3-14. Average emission rate (across all operating modes) for regulatory class 41, 42, 46, 47, and 48 for 

CO, HC, and NOx. Emission rates for 1960-1989, and 2022 – 2050 are constant.

3.1.2 Particulate Matter

Unfortunately, the PM2.5 emission data from heavy-duty gasoline trucks are too sparse to develop 

the detailed emission rates for which the MOVES model is designed. 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 PM2.5 emission rates are calculated by 

multiplying the 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 particulate 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 

41 42,46,47

0

5000

10000

15000

0

100

200

300

400

0

250

500

750

C

O

H

C

N

O

x

1980 1990 2000 2010 2020 1980 1990 2000 2010 2020

modelYearID

A

v

era

g

e

e

misio

n rate, g/h

o

ur

pollutant

CO

HC

NOx

age

0-5

6+
110

PM emission rates are made, because the HD 2007 Rule PM standards are not assumed to be 

technology-forcing standards for spark-ignition medium and heavy-duty gasoline vehicles.xvi.

3.1.2.1 Data Sources

This analysis is based on the PM2.5 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 UDDS 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 MY Age Test cycle GVWR 

[lb] PM2.5 mg/mi

1

2001 3 UDDS 12,975 1.81

2001 3 UDDS 19,463 3.61

2

1983 21 UDDS 9,850 43.3

1983 21 UDDS 14,775 54.3

3

1993 12 UDDS 13,000 67.1

1993 12 UDDS 19,500 108.3

4

1987 18 UDDS 10,600 96.7

1987 18 UDDS 15,900 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 12 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 Tier2 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 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 Older Group

Mean for Vehicle 1: 2.71 mg/mi Newer Group

 

xvi As presented in Section XX, the MOVES2014 PM rates for 2008+ vehicles is based on UDDS results of 2 mg/mile, 

while the standard for 2008+ spark-ignition vehicles is 20 mg/mile64
111

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 30). The UDDS cycle represents 

standardized operation for the heavy-duty vehicles. 

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 UDDS emission factors for the older light-duty gas truck group using 

MOVES2010b are 38.84 mg/mi 2.5(Ignoring sulfate emissions which are on the order of 110-4

mg/mile for low sulfur fuels), This value leads to the computation of the ratio: 1.679

38.84

65.22

mile

mg

mile

mg

 .

The simulated PM2.5 UDDS 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 110-5 mg/mile 

for low sulfur fuels),

This value leads to the computation of the ratio: 0.578

4.687

2.71

mile

mg

mile

mg

 .

The newer model year group produces a ratio which is less than one and implies that large trucks 

produce less PM2.5 emissions than smaller trucks. This result is intuitively inconsistent, and is the 

likely result of a very small sample and a large natural variability in emission results.

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:

Ratio Ratio Ratio (1 )

final olderWtFrac newer WtFrac

= 1.6790.75 + 0.5780.25 = 1.40
112

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 40 

(LHD <= 40) because the emission rates for both regulatory class 30 and 40 are normalized to 

vehicle mass (or VSP-based emission rates).

As documented in the light-duty report (cite), 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 

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.

3.1.2.3 Emission Rates for Regulatory Class LHD <14 K, LHD45, MHD, and HHD

For the larger heavy-duty gasoline emission rates, the emission rates are STP-based with a fixed 

mass factor of 17.1. Unlike for 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 41 and 40. We then multiplied this ratio to the PM 

emission rates in regulatory class 40 to obtain STP-based PM emission rates in regulatory class 41, 

42, 46 and 47. The regulatory class 40 PM emission rates, STP/VSP HC ratios, and the calculated 

STP-based PM2.5 emission rates are displayed in Table 3-19.
113

Table 3-19. A demonstration of how STP-based PM emission rates were derived from VSP-based rates using the 

ratio of HC VSP to STP emission rates as a surrogate, using model year 2001 as an example.

opModeID RegClass 40 

(g/hr)

HC STP to 

VSP Ratio RegClass 41, 42, 46, 47 (g/hr)

0 0.59 1.000 0.59

1 0.54 1.000 0.54

11 0.60 1.000 0.60

12 0.79 2.263 1.78

13 1.38 3.677 5.08

14 2.62 5.095 13.37

15 5.55 5.443 30.22

16 64.52 5.427 350.13

21 8.38 1.000 8.38

22 2.92 1.154 3.37

23 2.08 2.173 4.52

24 2.92 2.825 8.24

25 10.94 4.842 52.95

27 20.50 7.906 162.10

28 126.42 8.796 1,112.05

29 523.16 6.471 3,385.32

30 2,366.75 7.102 16,809.50

33 26.59 2.121 56.40

35 10.76 4.780 51.42

37 13.29 4.010 53.28

38 43.61 8.979 391.56

39 75.73 9.522 721.06

40 74.96 5.300 397.26

3.1.3 Energy Consumption

3.1.3.1 LHD<=10k Energy Rates for Model Years 1960-2013

The energy rates for LHD<=10k for pre-2007 gasoline energy rates are unchanged from the 

LHD2b3 regulatory class from 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 energy updates report.

5
114

3.1.3.2 LHD<=10k Energy Rates for Model Years 2014-2050

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 CO2

emission reductions for gasoline 2b 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 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

0.0e+00

5.0e+06

1.0e+07

1.5e+07

1970 1980 1990 2000 2010 2020 2030

modelYearID

A

v

era

g

e

e

n

erg

y

e

mis

sio

n rate, kJ/h

o

ur

regClassName

LHD <= 10k
115

3.1.3.3 LHD<=14k, LHD45, MHD, and HHD Energy Rates for Model Years 1960-

2013

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, HHD, and bus classes. Similar to the diesel running exhaust energy rates, classifications 

were not made based on model year group, 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 fuelspecific emission factor (fCO2) of 8,788 g/gallon for gasoline (see Equation 2-19). 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)

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 2.1.1.3.3).

3.1.3.1 LHD<=14k, LHD45, MHD, Urban Bus, and HHD Energy Rates for Model 

Years 2014-2050

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 Rule44 as 

discussed in Section 2.1.4.4. Figure 3-17 displays the average energy consumption rates for the 

heavy-duty gasoline source types that are modeled using Scaled Tractive Power (STP) with a fixed 

0

1

2

3

4

5

6

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Mean Energy rate [kJ/hr] Millions

Operating mode
116

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 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 LHD<=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 (41), LHD45 (42), MHD (46) and HHD (47) 

gasoline vehicles across all running operating modes.

0e+00

1e+07

2e+07

3e+07

4e+07

2012 2014 2016 2018 2020

modelYearID

A

v

era

g

e

e

n

erg

y

e

mis

sio

n rate, k

J/h

o

ur

RegulatoryClass

41

42,46,47
117

3.2 Start Emissions

3.2.1 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 lb, placing all 

trucks in the LHD2b3 regulatory class.

Table 3-20 shows the model-year by age classification for the data. The model year groups in the 

table were assigned 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 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 account for different scaling factors used to calculate STP-based operating modes, as 

was done for running exhaust rates. As discussed later, start emission rates are modeled differently 

among regulatory classes to account for differences in the emission standards/and or the data 

supported different start emission rates. No differences in start emissions are modeled between 

regulatory class 40 and 41 vehicles, because no differentiation by STP mass scaling factor are 

needed.

Table 3-20. Model-year Group by AgeGroup Structure for the Sample of Heavy-Duty Gasoline Engines

Model-year Group Standards (g/hp-hr) Age Group (Years) Total

CO HC NOx 0-3 4-5 6-7 8-9 10-14

1960-1989 19 22 41

1990 14.4 1.1 6.0 1 29 30

1991-1997 14.4 1.1 5.0 73 59 32 4 168

1998-2004 14.4 1.1 4.0 8 8

Total 81 59 33 52 22 247

3.2.2 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
118

Heavy-duty Gasoline Start Emissions Analysis FiguresSample 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 (xl) as

l

x

g

x

ln

e 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 Appendix F.

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. Assuming that 

emissions distributions should be strongly skewed suggests 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 (sl), is the standard deviation of natural logarithm of emissions (xl). The 

values of sl 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 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 NOx 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 sl. For this calculation we assumed values of 0.9 for CO and HC and 1.2 for NOx. These values 

approximate the maxima seen in these data and are broadly comparable to rates observed for lightduty 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.

2

2

e

l

s

a g

x  x

Equation 3-4
119

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.
120

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 xl and sl)

3.2.3 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 sl, it 

was necessary to re-estimate corresponding standard deviations for the parent distribution s, as 

shown in Equation 3-5.

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12 14

Age (years)

FT

P

ColdStart (g)

co

g_co

a_co

0

2

4

6

8

10

12

14

16

0 2 4 6 8 10 12 14

Age (years)

FTP

C

oldStart (g)

thc

g_thc

a_thc

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10 12 14

Age (years)

FTP

ColdStart (g)

nox

g_nox

a_nox

(a) CO

(b) THC

(c) NOx
121

e (e 1)

2 2

2 s

 

s

g

s x 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 s/ 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 3-21 and in Figure 3-19. Note 

that these results represent only “cold-start” rates (opModeID 108).

Table 3-21. 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 81 101.2 6.39 4.23

 4-5 59 133.0 7.40 5.18

 6-7 33 155.9 11.21 6.12

 8-9 52 190.3 11.21 7.08

 10-14 22 189.1 11.21 7.08

Standard Deviations

 0-3 108.1 6.82 8.55

 4-5 142.0 7.90

 6-7 166.5 11.98 12.39

 8-9 203.2 11.98 14.32

 10-14 202.0 11.98 14.32

Standard Errors

 0-3 12.01 0.758 0.951

 4-5 18.49 1.03 1.18

 6-7 28.98 2.08 2.16

 8-9 28.18 2.08 1.99

 10-14 43.06 2.08 1.99
122

Figure 3-19. Cold-start Emission Rates for Heavy-Duty Gasoline Trucks, with 95% Confidence Intervals

3.2.4 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. We describe the derivation of rates in each group below.

0

50

100

150

200

250

300

0 5 1 0 15 20 25

Age (years)

FTP ColdStart (g)

0

2

4

6

8

10

12

14

16

18

0 5 10 15 20 25

Age (years)

FTPColdStart (g)

0

2

4

6

8

10

12

0 5 10 15 20 25

Age (years)

FTP ColdStart(g)

(a) CO

(b) THC

(c) NOx
123

3.2.4.1 Regulatory class LHD2b3(RegClassID 40 and 41)

For CO the approach was simple. We applied the values in Table 3-21 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 3-21 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 fHC, where 

 

0.37

1.1 g/hp - hr

1.0 g/hp - hr

(0.14 0.20) g/hp - hr

0.14 g/hp - hr

HC 



















f 

Equation 3-6

This ratio represents the component of the 2005 combined standard attributed to NMHC.

We calculated the corresponding value for NOx as

0.147

4.0 g/hp - hr

1.0 g/hp - hr

(0.14 0.20) g/hp hr

0.20 g/hp hr

NOx 

















 



f 

Equation 3-7

For these rates we neglected the THC/NMHC conversions, to which we gave attention for lightduty.

For the 2008-2017 model years, the approach to projecting rates was modified so as to adopt two 

refinements developed for light-duty rates. First, start emission rates from LHD2b3 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. Accordingly, start emission rates adopted the same 

age effects as the light-duty start emission rates. The multiplicative age effects for start emission 

rates for 2008-2017 vehicles are shown in Table 3-22.

3.2.4.2 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 (page 96). Start rates for HC, CO and 

NOx during the Tier-3 phase-in (2018-2022) are shown below in Figure 3-19 to Figure 3-22. Note 

that start rates are identical for both the LHD<=10k and LHD<=14k regulatory classes (regClassID 

= 40, 41, respectively). 
124

Figure 3-20. THC: Emission rates for the start-exhaust process, for the LHD<=10k (40) and the LHD<=14k (41) 

regulatory classes, by operating mode and age group, during the Tier-3 phase-in.
125

Figure 3-21. CO: Emission rates for the start-exhaust process, for the LHD<=10k (40) and the LHD<=14k (41) 

regulatory classes, by operating mode and age group, during the Tier-3 phase-in.
126

 Figure 3-22. NOx: Emission rates for the start-exhaust process, for the LHD<=10k (40) and the LHD<=14k 

(41) regulatory classes, by operating mode and age group, during the Tier-3 phase-in.

Table 3-22. Multiplicative Age Effect Used for Start Emissions for RegClass 40 and 41 vehicles for 2008-2017 

model years. Adopted from the deterioration effects for RegClass 30 vehicles from the Light-Duty Report. 

ageGroupID HC CO NOx

3 1 1 1

405 1.65 1.93 1.73

607 2.20 2.36 2.21

809 2.68 2.54 2.76

1014 3.30 3.00 3.20

1519 3.66 3.35 3.63

2099 4.42 4.06 4.11
127

3.2.4.3 Regulatory classes LHD45, MHD, and HHD

For LHD45 and MHD, we estimated values relative to the LHD2b3 start emission rates estimated 

in MOVES2010.

For CO and HC, we estimated values for the heavier vehicles by multiplying them by ratios of 

standards for the heavier class to those for the lighter class.

The value for CO based on 1990-2004 model year vehicles is 

2.58

14.4 g/hp - hr

37.1 g/hp - hr fCO   Equation 3-8

and the corresponding value for HC based on 1990-2004 model year vehicles is 1.73.

1.73

1.1 g/hp - hr

1.9 g/hp - hr f HC   Equation 3-9

The ratios derived in Equation 3-8 and Equation 3-9 (2.58 and 1.73) are applied to estimate the 

start emission rates for all three model year groups for the LHD45, MHD, and HHD gasoline 

vehicles (Table 3-23). For NOx, all MOVES2014 start emissions for medium and heavy-duty 

vehicles are equal to for 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 shown in Table 3-23.
128

Table 3-23. Methods used to Calculate and Start Emission Rates for Heavy-Duty Spark-Ignition Engines

Regulatory Class Model-year 

Group Method

CO THC NOx

LHD2b3 (LHD<= 

10K and LHD < 14K)

1960-2004 Values from

Table 3-21

Values from

Table 3-21

Values from

Table 3-21

2005-2007 Values from

Table 3-21

Reduce in

proportion

to standards from 1960-

2004

Reduce in 

proportion

to standards from 

1960-2004

2008 - 2017 Values from

Table 3-21 Section 3.2.4.1 Section 3.2.4.1

2018 + Section 3.2.4.2 Section 3.2.4.2 Section 3.2.4.2

LHD45, MHD, HHD

1960-2004

Increase in proportion

to standards from 

LHD2b3

Increase

in proportion

to standards from 

LHD2b3

Same values as

LHD2b3

2005-2007

Increase in proportion

to standards from 

LHD2b3

Increase in proportion

to standards from 

LHD2b3

Same values as

LHD2b3

2008 +

Increase in proportion

to standards from 

MOVES2010b LHD2b3

Increase in proportion

to standards from 

MOVES2010b LHD2b3

Same values as

MOVES2010b 

LHD2b3

As 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).

3.2.4.4 Particulate 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. For 2018 and later 

model years, the start PM emissions for heavy-duty gasoline are estimated to be the same as the 

rates in 2017.xvii 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. 

 

xvii The light-duty PM start rates are projected to decrease in model year 2018 with the implementation of the Tier3Vehicle Emissions and Fuel Standards Program. 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.
129

3.2.5 Start Energy Rates

The heavy-duty gasoline start energy rates were originally derived in MOVES2004, and updated in 

MOVES2010 as described in the corresponding reports28,5. Post-2000, the only changes to the start 

energy rates, 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. 

Figure 3-23. Heayv-duty gasoline cold start energy rates (opMode 108) by model year and regulatory class.

The start energy rates are reduced for shorter soak times using the same factors for diesel vehicles, as presented 

in Table 2-28. 

Table 3-24 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, the start emissions have not been prioritized to be updated with more recent data. 

Table 3-24. Relative contribution of total energy consumption from each pollutant process by regulatory class 

for heavy-duty gasoline vehicles in calendar year 2011.

processID processName 40 41 42 46 47

1

Running 

Exhaust 96.3% 98.9% 99.0% 98.1% 98.1%
130

2 Start Exhaust 3.7% 1.1% 1.0% 1.9% 1.9%
131

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.

65

Figure 4-1. US natural gas bus population by year and fuel type from 1996-2011 (APTA)

66

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.67

Medium HD gasoline trucks are reasonable proxies in terms of vehicle weight and engine size, but 

as this report shows, there are substantial differences in the MOVES2010b emissions rates and 

real-world measurements of CNG transit buses. This section updates 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

for each drive cycle, and then multiplying the time spent in each mode by the corresponding 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

DIESEL ELECTRICITY & HYBRIDS GASOLINE NATURAL GAS OTHER
132

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, constant deceleration back to rest, and then repeated for 600 seconds 

(see Figure 4-2).68 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).70

Figure 4-2. Driving schedule trace of the Central Business District (CBD) cycle (DieselNet)69

Figure 4-3 Driving schedule trace of the Washington Metropolitan Area Transit Authority (WMATA) cycle 

Melendez et al. 2005)70
133

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 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

Figure 4-5. Operating mode distribution for the WMATA cycle

0

50

100

150

200

250

300

350

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Time (sec)

Operating Mode

0

100

200

300

400

500

600

700

800

900

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40

Time (sec)

Operating Mode
134

4.2 Comparison of Simulated Rates and Real-World Measurements

4.2.1 Simulating Cycle Emission Aggregates from MOVES2010b 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:

 

cycle

OM

OM cycle p OM

sim p cycle d

t r

EF





, ,

, ,

 Equation 4-1

where

tOM,cycle = cycle’s total time spend in operating mode OM,

dcycle = distance of the cycle,

rp,OM = time-specific emission rate of pollutant p in operating mode OM.

We compared the published test measurements to simulations using the MOVES2010b CNG transit 

bus rates from Equation 28. 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

These programs 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. 
135

Note that 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. We then multiplied the THC rate by the corresponding ratio 

from the “methanethcratio” table to calculate the CH4 rate. 

All criteria emission rates are dependent on vehicle age, and thus are stored in the 

emissionRateByAge table and, and 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 

nonmethane 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)71

discussed later in this section. All other conversion factors to energy were taken from Melendez et 

al. (2005).70

On a similar note, MOVES does not report particulate 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.

The number of unique measurements is approximately equal to the number of vehicles tested, 

although not exactly. Some vehicles were tested more than once because a variable in the 

experiment changed, such as the after-treatment technology. All of the vehicles were in service 

with a transit agency at the time of testing. In addition, these measurements were typically reported 

as cycle averages based on multiple runs with the same vehicle and configuration over a specific 

driving cycle. Many of the testing programs also included diesel transit buses, but these vehicles 

were excluded from this analysis. 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.72
136

Table 4-1. Summary of external emissions testing programs by driving cycle and number of unique 

measurements

Paper/Article Lead Research Unit Driving 

Cycle(s)

Number of Unique 

Measurements

Melendez 200570 National Renewable Energy Laboratory (NREL) WMATA 7

Ayala 200273 California Air Resources Board (CARB) CBD 2

Ayala 200371 CARB CBD 6

Lanni 200374 New York Department of Environmental Conservation CBD 3

McCormick 199975 Colorado School of Mines CBD 3

LaTavec 200276 ARCO (a BP Company) CBD 2

McKain 200077 WVU CBD 3

Clark 199778 WVU CBD 10

TOTAL 35

As seen above, the CBD driving cycle was applied in each study except for one. Since it (a) had the 

largest sample size and (b) appeared to be representative of the data from other cycles, we focused

our analysis 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.xviii 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.

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.

 

xviii

 Note that for MY 1994 in Figure 4-6 through Error! Reference source not found., CNG (MHD gasoline) 

MOVES predictions are based off age group 405 instead of group 3.
137

Figure 4-6. NOx emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b 

simulated aggregates on the CBD cycle.

Figure 4-7. CO emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b 

simulated aggregates on the CBD cycle.

0

10

20

30

40

50

60

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

N

Ox (grams/

mile)

Model Year

Measurements CNG

(0-3 Age Group)

Measurements CNG

(4-5 Age Group)

MOVES 2010b CNG

0

10

20

30

40

50

60

70

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

C

O (grams/mile)

Model Year

Measurements CNG

(0-3 Age Group)

Measurements CNG

(4-5 Age Group)

MOVES 2010b CNG
138

Figure 4-8. PM emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b 

simulated aggregates on the CBD cycle.

Figure 4-9. THC emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b 

simulated aggregates on the CBD cycle.

0

0.05

0.1

0.15

0.2

0.25

0.3

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

P

M (grams/

mile)

Model Year

Measurements CNG

(0-3 Age Group)

Measurements CNG

(4-5 Age Group)

MOVES 2010b CNG

0

5

10

15

20

25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

T

H

C (grams/

mile)

Model Year

Measurements CNG

(0-3 Age Group)

Measurements CNG

(4-5 Age Group)

MOVES 2010b CNG
139

Figure 4-10. CH4 emission comparisons of CNG transit bus dynamometer measurements and MOVES2010b 

simulated aggregates on the CBD cycle.

Figure 4-11. Total energy consumption comparisons of CNG transit bus dynamometer measurements and 

MOVES2010b simulated aggregates on the CBD cycle.

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 

0

5

10

15

20

25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

C

H4

(grams/

mile)

Model Year

Measurements CNG (0-3

Age Group)

Measurements CNG (4-5

Age Group)

MOVES 2010b CNG

0

10000

20000

30000

40000

50000

60000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Energy (B

T

U/mile)

Model Year

Measurements CNG (0-3

Age Group)

Measurements CNG (4-5

Age Group)

MOVES 2010b CNG
140

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 CH4 in MOVES2010b. 

These relatively high CH4 emissions from CNG buses compared to gasoline or diesel buses are 

likely from the exhaust of uncombusted natural gas, but further study is warranted. Figure 4-11

shows that MOVES2010b under-predicts the total energy consumption seen in the literature. We 

address these discrepancies by using the published measurements to develop new CNG exhaust 

emission rates MOVES2014 rates across all the pollutants and total energy discussed above.

These comparisons show that updating the MOVES2010b with these additional data from CNG 

buses have significant impact on the emissions inventories from CNG buses. As shown in this 

analysis, the existing CNG rates based on MHD gasoline trucks are not adequate surrogates. As 

discussed in the next section, we developed new rates based on cycle averages from the 

dynamometer measurements. The remainder of this report is devoted to a discussion on the 

development of new time-based emission rates (g/hr) for CNG buses.

4.3 Development of New Running Exhaust Emission Rates

4.3.1 Determining Model Year Groups

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 

is 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 

wholesale (as opposed to individual adjustments by operating mode).

Fundamentally, the first necessary step is assigning a set of appropriate model year groups for the 

data. Using too few model year groups may miss important differences in the emission rates, while 

using too many model year groups can give false prominence to outliers and introduce artificial 

“jumps” in the emission rates. We chose to group the model year groups according to similar 

emission rates in the criteria pollutants (THC, CO, NOx and PM).

We separated CNG buses–equipped with oxidation catalysts and those not equipped, to determine 

if this was a reasonable distinction, and to see if these vehicles’ criteria emission rates varied by 

model year and by age. For some model years, there are both after-treatment equipped and nonequipped vehicles. In these years, there was not a visible difference in the criteria emission levels 

between the vehicles in the testing programs with after-treatment (AT) equipment and those with 

no after-treatment (see the figures in Section Heavy-duty CNG Bus Emissions Supplemental

Figures in the appendix).xix Given the small sample size, the lack of clear trends among the 

individual vehicle models, and our lack of data on the relative distribution of after-treatment 

equipped vehicles versus unequipped vehicles in each of these model years, we chose to group all 

the CBD measurements from the literature into one model year group, spanning from MY 1994 to 

 

xix The CNG studies have a large observed impact on several of the unregulated pollutants (e.g. formaldehyde) between 

after-treatment and non-equipped vehicles. This impact is discussed under the PM and HC speciation subsection.
141

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.xx 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.70 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, 

a second model year group runs from MY 2002 to MY 2006 based on a group of MY 2004 

WMATA buses. This MY group ends before MY 2007 when a new series of stringent emission 

standards went into effect.79 We did, however, apply a sales-weighted adjustment factor to the

unusually low CO rate for these John Deere CNG buses according to certification dataxxi, as 

described below.

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 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 vehicle mileage or deterioration.80, xxii These effects 

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. 

Natural gas transit bus emission certification data by model year is publicly available on the EPA’s 

Office of Transportation and Air Quality website.81 Analysis of these data showed that from MY 

2002 to MY 2012 there have been changes in 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. Table 4-2 below indicates the number of CNG transit bus models certified 

for each model year.

 

xx

 A number of papers have discussed more recent vehicles. Examples include “Clark, N., Wayne, W., Khan, A., 

Lyons, D. et al., "Effects of Average Driving Cycle Speed on Lean-Burn Natural Gas Bus Emissions and Fuel 

Economy," SAE Technical Paper 2007-01-0054, 2007. 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.

xxi

 This rate was a full order of magnitude lower than the other 2004 CNG models, and was not supported by additional 

test results, and therefore, was not incorporated into the model. 

xxii

 As with other MOVES emission rates, the certification rates were not used as a direct estimate of on-road 

emissions. However, in absence of contradictory evidence, for projecting CNG emissions, we assumed that changes in 

on-road emission rates correlate with changes in certification levels. 
142

Table 4-2. A summary of the number of certified CNG transit buses by model year (USEPA OTAQ).

Model Year

Number of 

Vehicle Models

2002 4

2003 4

2004 4

2005 6

2006 4

2007 3

2008 1

2009 1

2010 2

2011 2

2012 2

TOTAL 33

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 aggregated certification levels with these annual sales weighted averages 

for MY group 2002-2006 and MY group 2007-2012 are shown in Table 4-3 below. 

Table 4-3. Model year group 2002-2006 and 2007-2012 certification levels for CNG buses used for scaling of 

measured emission rate data

Model Year Group NOx CO PM NMHC1

Certification

(g/bhp-hr) 

2002-2006 1.208 1.355 0.0078 0.147

Certification

(g/bhp-hr)

2007-2012 0.2902 3.032 0.0033 0.057

1. Certification data has measurements of organic material non-methane hydrocarbon equivalent 

(OMNMHCE). For this analysis they were treated as NMHC values.

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 CH4/THC ratio for every model year fell within one 

standard deviation of the average ratio across all model years. The CH4/THC ratios are calculated

from averaged CH4 and THC measurements on the respective CBD and WMATA cycles, as 

displayed in Table 4-4. We left the CH4/THC ratio constant from MY group 2002-2006 to MY 

group 2007-2012 and estimated the new CH4 rate (given in Table 4-5) off that previous ratio. 
143

Table 4-4 Summary of CH4/THC ratios for MOVES2014

Model Year Age Group CH4/THC Ratio

1994-2001 0-3 0.917

2002-2006 0-3 0.950

2007-2012 0-3 0.950

We scaled the newer model year rates  based off the measurements in the MY group 2002-2006 

in proportion to the ratio of certification levels  from MY group 2007-2012 to MY group 2002-

2006. In this case, 

, = , ∙

,

,

. Equation 4-2

As mentioned before, the measured CO rate for MY group 2002-2006 was not used. We adjusted 

the original rate by the ratio between the sales-weighted average for the MY 2004 certification 

level of all models and the certification level for that particular MY 2004 John Deere bus with the 

anomalous CO rate.

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 buses82.

Note that there was limited data on older vehicles in the literature, so the ratios that were developed

using vehicles in the 0-3 age group have been applied to all other age groups. Therefore, we are 

assuming that CNG buses exhibit deterioration rates in control equipment proportional to medium 

heavy-duty gasoline trucks. 

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. The 

ratios between these rates were applied to the 1997, 2004 and 2009 MOVES2010b CNG bus rates 

in order to calculate the MOVES2014 rates. 
144

Table 4-5 Summary of MOVES2010b distance-dependent emission rates for CNG transit buses and 

the ratios to be applied to the MOVES2010b STP-based rates to compute the MOVES2014 rates. 

1. This 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.

2. Note that the increase in the CO rate from MY group 2002-2006 to MY 2007 and later seems to be an 

outcome of transitioning from lean-burn CNG engines to stoichiometric-burn engines. This upward CO trend 

is supported by certification data as well as testing results from some recent studies comparing these two 

technologies.83

The model year basis for scaling was selected based on temporal similarity. For instance, our 

choice to use MY 1997 for MY group 1994-2001 was 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 vehicles in that 

group were manufactured. As for MY group 2007-2012, MY 2009 was also chosen simply for 

being one of the two model years near the median for the group.

For 2014 and later model year, the CNG energy consumption rates are reduced by the same 

reduction as diesel Urban Bus vehicles, in response to the 2014 Medium and Heavy Duty 

Greenhouse Gas Rule as documented in Table 2-19.

4.4 Start Exhaust Emission Rates for CNG Buses

In the absence of any measured start exhaust emissions from CNG transit buses, their start rates are 

copies of 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, 

MOVES2010b CNG Rates (g/mile)

MY

Age 

Group Cycle NOx CO

PM_NonE

C PM_EC

TOTAL 

ENERGY

(BTU/mi) THC CH4

1997 0-3 CBD 9.63 62.4 0.0024 0.0002 31137 1.84 0.049

2004 and 

2009 0-3 WMATA 5.45 18.9 0.0035 0.0003 35489 1.43 0.032

MOVES2014 CNG Rates (g/mile - measured/estimated from analysis)

MY

Age 

Group Cycle NOx CO

PM_NonE

C PM_EC

TOTAL 

ENERGY

(BTU/mi) THC CH4

1994-2001 0-3 CBD 20.8 9.97 0.037 0.0038 42782 13.2 12.1

2002-2006 0-3 WMATA 9.08 2.1710.0039 0.0005 40900 11.2 10.6

2007 and 

later 0-3 WMATA 2.18 5.9320.0016 0.0002 40900 4.33 4.12

Ratios Applied to the STP-Based MOVES2014 Rates

MY

Age 

Group

Cycle 

ratioed NOx CO

PM_NonE

C PM_EC

TOTAL 

ENERGY THC CH4

1994-2001 all CBD 2.16 0.16 15.5 21.6 1.37 7.17 250

2002-2006 all WMATA 1.67 0.11 1.09 1.87 1.15 7.79 330

2007 and 

later all WMATA 0.40 0.31 0.46 0.78 1.15 3.02 128
145

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 as new data on CNG start rates becomes available.

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 rates as MY group 1994-2001. Rates for buses built after MY 2012 maintain the same 

rate as MY group 2007-2012. As new certification data arrives and more testing programs are run,

these rates will be revisited in future MOVES releases.

4.6 PM and HC Speciation for CNG Buses

MOVES estimate methane and nonmethane hydrocarbons (NMHC) through the use of CH4 /THC 

ratios, as shown in Table 4-4. The MOVES2014 CH4/THC ratios are constant across all age 

groups. We set the start CH4/THC ratios equal to the running ratios for all model years and ages. 

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 buses72, 74,84,, but are

significantly reduced with oxidation catalysts71. Large formaldehyde emissions have a large impact 

on the NMOG and VOC emissions estimated from THC emissions from CNG buses because THCFID measurements have a small response to formaldehyde concentrations41

,

85

.

We used hydrocarbon speciation measurements from the Ayala et al. (2003) using measurements 

from the 2000 MY Transit Bus, with a Detroit Diesel Series 50G engine with and without an 

oxidation catalyst collected on the CBD cycle. We used the speciated measurements made on the 

single transit bus 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 located in Table 4-6. The NMOG values are calculated following EPA’s regulation 

requirements using Equation 9 from the MOVES2014 Speciation Report41.

The VOC emissions are calculated from subtracting the ethane from the NMOG values. MOVES 

definition of VOC emissions from mobile-sources is NMOG minus ethane and acetone xxiii. The 

emissions of hazardous air pollutants, including formaldehyde and acetaldehyde, are also estimated 

from this study as documented in the MOVES2014 Toxics Emissions Report86. 

 

xxiii 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 are impacted by less than 2% whether they are included or not
146

Table 4-6 NMOG and VOC Conversion values for CNG transit emissions with no control and with oxidation 

catalyst from Ayala et al. (2003)71

Measured values (mg/mile) No Control Oxidation Catalyst

THC 8660 6150

Methane 7670 5900

Ethane 217 72.2

Acetone 4.67 5.51

Formaldehyde 860 38.4

Acetaldehyde 50.7 32.6

Calculated values (mg/mile)

NMHC 990 250

NMOG 1881.0 309.0

VOC 1664.0 236.8

Ratios

NMOG/NMHC 1.90 1.24

VOC/NMHC 1.68 0.95

In MOVES2014, we applied the uncontrolled NMOG and VOC factors to the pre-2002 model year 

vehicles. This analysis demonstrated that majority of the 2001 and earlier model year CNG transit 

buses were not equipped with exhaust aftertreament. The study conducted on MY 2004 buses70, 

suggested that 2002-2006 model year vehicles are equipped with oxidation catalysts. Therefore we 

apply the oxidization catalyst profile to CNG emissions for the 2002 to 2006 MY group. At the 

time of the analysis, we did not have information on 2007 and later CNG buses, and 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 measurements

87on 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 pre-2002 MY CNG buses, 

and the oxidation catalyst equipped EC/PM profile for the 2002 and later buses.

Table 4-7 MOVES2014 EC/PM Fraction for CNG transit bus emissions by model year groups

Pre-2002 2003+

9.25% 11.12%

The CARB measurements are also used to estimate the individual PM2.5 composition, including 

organic carbon, elements, and sulfate as discussed in the TOG and PM2.5 speciation report. 

stoichiometric-burn spark ignition CNG engines with three-way catalysts have been introduced in 

2007 and later buses. Future work should be done to improve the emission rates and speciation 

profiles used in MOVES to represent emissions from recent technology CNG buses.
147

4.7 Ammonia and Nitrous Oxide emissions 

No data were available on ammonia emissions rates. As such, 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. They remain 

unchanged from MOVES2009 and later versions as documented in a separate MOVES report5
148

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.

88

5.1 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.

89 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’.88

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 90 91 92.

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.

93 Most manufacturers have adopted open crankcase filtration systems.88

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.

88 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.

94

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 from the tests with and without the crankcase emissions.
149

Table 5-1 Literature review on the contribution of crankcase emissions to diesel exhaust.

Study

Model 

Year Type

# Engines/ 

Vehicles HC CO NOx PM

Hare and Baines, 197797 1966, 

1973

Conv. 

Diesel 2

0.2%-

3.9%

0.01-

0.4%

0.01%-

0.1%

0.9%-

2.8%

Zielinska et al. 200890, 

Ireson et al. 201191

2000, 

2003

Conv. 

Diesel 2

13.5% 

-

41.4%

Clark et al. 200696, Clark 

et al. 200695 2006

Conv. 

Diesel 1 3.6% 1.3% 0.1% 5.9%

Khalek et al. 2009 2007

DPFequipped 4 95.6% 27.2% -0.2% 38.2%

5.2 Modeling Crankcase Emissions in MOVES

MOVES2014 calculates THC, CO, NOx, and PM2.5 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, 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 PM2.5 species are incorporated using the new crankcase calculator in 

MOVES2014. An overview of the crankcase calculator is provided in the MOVES2014 Speciation 

Report.

41

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 allow the ability to 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.
150

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 reported on the MY2006 diesel engine reported by Clark 

et al. 200696. These values compare well with the previous HC, CO, NOx values reported much 

earlier by Hare and Baines (1977), 

97 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 selected a crankcase/tail-pipe ratio of 20%. Zielinska et al. 2008

90 and 

Ireson et al. 201191 reported crankcase contributions to total PM2.5 emissions as high as 40%. 

Jääskeläinen (2012)88 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 engines98. 

Table 5-2 MOVES2014 Conventional Diesel Crankcase/Tail-pipe Ratios for HC, CO, NOx and PM2.5

Pollutant Crankcase/Tailpipe ratio

Crankcase/(Crankcase + 

Tailpipe) ratio

HC 0.037 0.036

CO 0.013 0.013

NOx 0.001 0.001

PM2.5 0.200 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 crankcase PM2.5/exhaust PM2.5 ratio is 20%, 

and the crankcase emissions EC/PM fraction reflects measurements from in-use crankcase 

emissions. Zielinska et al. 200890 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. 

90,91

. 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 PM10 emission rates are subsequently estimated from the PM2.5 exhaust and crankcase 

emission rates using PM10/PM2.5 emission ratios as documented in the MOVES2014 TOG and PM 

Speciation Report.
151

Table 5-3. MOVES2014 Exhaust and Crankcase Emission Factors for pre-2007 Diesel by Pollutant, Process, 

and Model Year Group for PM2.5 species.

Pollutant Process Start Running Extended Idle

EC

Exhaust

1 1 1

nonECnonSO4PM 1 1 1

SO4 1 1 1

H2O 1 1 1

EC

Crankcase

0.009 0.004 0.012

nonECnonSO4PM 0.295 0.954 0.268

SO4 0.295 0.954 0.268

H2O 0.295 0.954 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, 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 0 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 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 vehiclesl. Users can use the ratios listed in Table 5-4 to 

post-process the exhaust emission rates if the crankcase contributions to THC and CO emissions 

are desired. 

Table 5-4 MOVES2014 2007 and Later Diesel Crankcase/Tailpipe ratio for HC, CO, and NOx.

Pollutant

MOVES2014 

Crankcase/Tailpipe ratio ACES Phase 1 

Crankcase/Tail-pipe ratio

ACES Phase 1 

Crankcase/(Crankcase 

+ Tail-pipe)l ratio

HC 0 21.95 95.6%

CO 0 0.37 27.2%

NOx 0 0.00 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 PM2.5 crankcase emissions calculator. The crankcase emissions 

measured in the ACES Phase 1 test program contributed 38% of the total PM2.5 emissions on the 

hot-FTP driving cycle. Emission results reported from industry, have reported that the crankcase 

emissions can contribute to over 50% of the particulate matter emissions from 2007 and later diesel 

technologies98.
152

For PM2.5 emissions, MOVES applies crankcase ratios to each of the intermediate PM2.5 species 

(EC, nonECnonSO4PM, SO4, and H2O). For 2007+ heavy-duty diesel engines, the same crankcase 

ratio is applied to each of the intermediate species. 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 shown, 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 PM2.5 species.

Pollutant Process All processes

EC

Exhaust

0.62

nonECnonSO4PM 0.62

SO4 0.62

H2O 0.62

EC

Crankcase

0.38

nonECnonSO4PM 0.38

SO4 0.38

H2O 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 ottocycle (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 blow-by particle emissions.

Table 5-6 Crankcase to Tailpipe Exhaust Emission Ratio for Heavy-duty Gasoline and CNG Vehicles for HC, 

CO, NOx and PM2.5

Pollutant pre-1969) 1969 and later

HC 0.33 0.013

CO 0.013 0.00052

NOx 0.001 0.00004

PM (all species) 0.20 0.008
153

The crankcase and exhaust ratios used by the crankcase calculator for PM2.5 emissions from heavyduty 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 Emission Factors by Pollutant, Process, Model Year Group, 

and Fuel Type, and Source Type for PM2.5 Species

1960-1968 

Gasoline 

Vehicles

1969-2050 

Gasoline/ 

CNG

Pollutant Process

All 

processes

All 

processes

EC

Exhaust

1 1

nonECnonSO4PM 1 1

SO4 1 1

H2O 1 1

EC

Crankcase

0.2 0.008

nonECnonSO4PM 0.2 0.008

SO4 0.2 0.008

H2O 0.2 0.008
154

6 Nitrogen Oxide Composition

Nitrogen oxides (NOx) are defined as NO + NO2.

99,100 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.

99 NOz compounds are formed in the 

atmosphere as oxidation products of NOx

100

.

Chemiluminescent analyzers used for exhaust NOx measurements, directly measure NO, as NO is 

oxidized by ozone to form NO2 and produces florescence 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.101

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 road tunnel in Europe.102 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 actual 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 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 (pollutantIDs 32 and 33), rather 

than using the direct NOx output in MOVES (polluantID 3). 

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 
155

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.

103

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. 6The 2010+ NO2 fractions are based on the average of three diesel programs of diesel 

vehicles measured with diesel particulate filters. The 2007-2009 values are an average of the 1960-

2006 and 2010-2050 values, which assumes that the NOx fractions change 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 they 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 NO NO2 HONO

1960-2006* 0.935 0.057 0.008

2007-2009 0.764 0.228 0.008

2010-2050 0.594 0.398 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 6-2for 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 (sourceTypeID 21, 31, 32) and Heavy-duty Gasoline 

Vehicles (sourceTypeID 41, 42, 43, 51, 52, 53, 54, 61, and 62)

Light-Duty Gasoline 

Model Year Groups

Heavy-Duty Gasoline 

Model Year Groups

Running Start

NO NO2 HONO NO NO2 HONO

1960-1980 1960-1987 0.975 0.017 0.008 0.975 0.017 0.008

1981-1990 1988-2004 0.932 0.06 0.008 0.932 0.031 0.008

1991-1995 2005-2007 0.954 0.038 0.008 0.987 0.005 0.008

1996-2050 2008-2050 0.836 0.156 0.008 0.951 0.041 0.008
156

6.3 Compressed Natural Gas 

We used the average of three NO2/NOx fraction reported on three CNG transit buses with DDC 

Series 50 G engines by Lanni et al. (2003)

74, 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 NO NO2 HONO

1960-2050 0.865 0.127 0.008
157
158

Appendix A Calculation of Accessory Power Requirements 

Table A-1. Accessory load estimates for HHD trucks

Table A-2. Accessory load estimates for MHD trucks

VSP Cooling Fan Air cond Air comp Alternator Engine 

Accessories Total Accessory Load (kW)

Low Off = 0.5 kW

Power (kw) 19.0 2.3 3.0 1.5 1.5

% time on 10% 50% 60% 100% 100%

Total (kW) 1.9 1.2 2.0 1.5 1.5 8.1

Mid Off = 0.5 kW

Power (kw) 19.0 2.3 2.3 1.5 1.5

% time on 20% 50% 20% 100% 100%

Total (kW) 3.8 1.2 0.9 1.5 1.5 8.8

High Off = 0.5 kW

Power (kw) 19.0 2.3 2.3 1.5 1.5

% time on 30% 50% 10% 100% 100%

Total (kW) 5.7 1.2 0.7 1.5 1.5 10.5

Accessory Load (kW): MDT

VSP Cooling Fan Air cond Air comp Alternator Engine 

Accessories Total Accessory Load (kW)

Low Off = 0.5 kW

Power (kw) 10.0 2.3 2.0 1.5 1.5

% time on 10% 50% 60% 100% 100%

Total (kW) 1.0 1.2 1.4 1.5 1.5 6.6

Mid Off = 0.5 kW

Power (kw) 10.0 2.3 2.0 1.5 1.5

% time on 20% 50% 20% 100% 100%

Total (kW) 2.0 1.2 0.8 1.5 1.5 7.0

High Off = 0.5 kW

Power (kw) 10.0 2.3 2.0 1.5 1.5

% time on 30% 50% 10% 100% 100%

Total (kW) 3.0 1.2 0.7 1.5 1.5 7.8
159

Table A-3. Accessory load estimates for buses

VSP Cooling Fan Air cond Air comp Alternator Engine 

Accessories Total Accessory Load (kW)

Low Off = 0.5 kW

Power (kw) 19.0 18.0 4.0 1.5 1.5

% time on 10% 80% 60% 100% 100%

Total (kW) 1.9 14.4 2.6 1.5 1.5 21.9

Mid Off = 0.5 kW

Power (kw) 19.0 18.0 4.0 1.5 1.5

% time on 20% 80% 20% 100% 100%

Total (kW) 3.8 14.4 1.2 1.5 1.5 22.4

High Off = 0.5 kW

Power (kw) 19.0 18.0 4.0 1.5 1.5

% time on 30% 80% 10% 100% 100%

Total (kW) 5.7 14.4 0.9 1.5 1.5 24.0
160

Appendix B Tampering and Mal-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.1 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-1 shows this relationship.

Figure B-1. Qualitative Depiction of the implementation of age effects.

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 VIUS104 (Vehicle Inventory and Use Survey) data, 

which contains data on how quickly trucks of different regulatory classes accumulate mileage. 

Table B-1 shows the emissions warranty period and approximate useful life requirement period for 

each of the regulatory classes.

Final emission rate 

due to T&M 

Zero-mile 

emission 

rate 

End of warranty 

period

End of useful life 

Age 

Emission rate 
161

Table B-1. Warranty and useful life requirements by regulatory class

Regulatory class

Warranty age 

(Requirement:

100,000 miles or 5 years)

Useful life 

mileage/age 

requirement

Useful 

life age

HHD 1 435,000/10 4

MHD 2 185,000/10 5

LHD45 4 110,000/10 4

LHD2b3 4 110,000/10 4

BUS 2 435,000/10 10

While both age 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 need 

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, any 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 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-1

and the emissions-age relationship that we described in Figure B-1. 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 this section.
162

Table B-2. T&M multiplicative adjustment factor by age (fTM,age group).

Age Group LHD MHD HHD Bus

0-3 0 0.083 0.25 0.03125

4-5 1 0.833 1 0.3125

6-7 1 1 1 0.5625

8-9 1 1 1 0.8125

10-14 1 1 1 1

15-19 1 1 1 1

20+ 1 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. TMpol

represents the estimated emissions rate increase through the useful life for a given pollutant.

(1 )

pol,agegrp pol,ZML TM,agegroupTMpol r r  f Equation B-1

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 ARB EMFAC2007 Modeling Change Technical Memo105 (2006). The 

basic EMFAC occurrence rates for tampering and mal-maintenance were developed 

from the 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”
163

 Air Improvement Resources, Inc.’s Comments on Heavy-Duty Tampering and Malmaintenance 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, not ULSD onroad diesel. 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 will group the LHDD, MHDD, HHDD, and Diesel bus groups together, except for 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. 

 EPA issued a rule to require OBD for heavy duty trucks, beginning in MY 2010 with 

complete phase-in by MY 2013. 

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 EMFAC’s 8 percent to 6 percent

based on the UCR results.

Electronics Failed: EPA will continue to use the 3 percent frequency rate for all model years 

beyond 2010. CARB increased the rate to 30 percent in 2010 due to system complexity. EPA does 

not agree with CARB’s assertion that the complexity of electronic systems will increase enough to 

justify a ten-fold increase in malfunction occurrence rates. We believe that the hardware will 

evolve through 2010, rather than be replaced with completely new systems that would justify a 
164

higher rate of failure. EPA asserts that many of the 2010 changes will occur with the aftertreatment 

systems which are accounted for separately. 

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 believes the EMFAC 20 percent EGR failure rate is too high and 

reduced the rate to 10 percent. All but one major engine manufacturer had EGR previous to the 

2007 model year and all have it after 2007. Therefore, EMFAC’s frequency rate increase in 2010 

due to the increase truck population using EGR does not seem valid. However, the Illinois EPA 

stated that “EGR flow insufficient” is the top OBD issue found in their LDV I/M program106 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 5 percent failure rate, but were only 

required in one third of the country and 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. 
165

Table B-3. NOx Aftertreatment Failure Rates

NOx aftertreatment sensor: EPA believes the 53 percent occurrence rate in EMFAC2007 is too 

high and will use 10 percent. CARB assumed a mix of SCR, which uses one sensor per vehicle, 

and NOx adsorbers, which use two sensors per vehicle. They justified the failure rate based on the 

increased number of sensors in the field beginning in 2010. 

We developed the occurrence rate based on the following assumptions:

 Population: HHDD: vast majority of heavy-duty applications will use 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 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. CARB used 14

percent 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

Occurrence Rate

SCR

Urea tank 0.5%

Tank heaters 1%

In-exhaust injectors 2%

Compressed air delivery to injector 1%

Urea supply pump 1%

Control system 5%

Exhaust temperature sensor 1%

Urea supply 1%

Overall 13%

LNT

Adsorber 7%

In-exhaust injectors 2%

Control system 5%

Exhaust temperature sensor 1%

Overall 16%
166

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.

B.6 Tampering & Mal-maintenance Occurrence Rate Summary

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, 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 75-90 percent NOX reduction with open loop control and >95

percent reduction with closed loop control.107 Visteon reports 60-80 percent NOX reduction with 

open loop control.108 

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 

Tamper & Malmaintenance

Frequency of Occurrence: Average rate over life of vehicle

Frequency Rates

1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010+ LHDT

Timing Advanced 5% 2% 2% 2% 2% 2%

Timing Retarded 3% 2% 2% 2% 2% 2%

Injector Problem (all) 28% 28% 13% 13% 13% 13%

Puff Limiter Mis-set 4% 0% 0% 0% 0% 0%

Puff Limiter Disabled 4% 0% 0% 0% 0% 0%

Max Fuel High 3% 0% 0% 0% 0% 0%

Clogged Air Filter - EPA 8% 8% 8% 8% 8% 8%

Wrong/Worn Turbo 5% 5% 5% 5% 5% 5%

Intercooler Clogged 5% 5% 5% 5% 5% 5%

Other Air Problem - EPA 6% 6% 6% 6% 6% 6%

Engine Mechanical Failure 2% 2% 2% 2% 2% 2%

Excessive Oil Consumption 5% 3% 3% 3% 3% 3%

Electronics Failed - EPA 3% 3% 3% 3% 3% 3%

Electronics Tampered 10% 15% 5% 5% 5% 5%

EGR Stuck Open 0% 0% 0.2% 0.2% 0.2% 0.2%

EGR Disabled/Low Flow - EPA 0% 0% 10% 10% 10% 10%

Nox Aftertreatment Sensor 0% 0% 0% 0% 10% 10%

Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 1% 1%

Nox Aftertreatment Malfunction - EPA 0% 0% 0% 0% 13% 16%

PM Filter Leak 0% 0% 0% 5% 5% 5%

PM Filter Disabled 0% 0% 0% 2% 2% 2%

Oxidation Catalyst Malfunction/Remove - EPA 0% 0% 0% 5% 5% 5%

Mis-fuel - EPA 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%
167

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.

B.8 PM Emission Effects

EPA developed the PM emission effects from each tampering and mal-maintenance incident from 

CARB’s EMFAC, Radian’s dynamometer testing with and without the malfunction present, EFEE 

results, and internal testing experience.

EPA estimates that the PM filter has 95 percent effectiveness. Many of the tampering and malmaintenance items that impact PM also have a fuel efficiency and drivability impact. Therefore, 

operators will have an incentive to fix these issues.

EPA estimated that excessive oil consumption will have the same level of impact on PM as engine 

mechanical failure. The failure of the oxidation catalyst is expected to cause a PM increase of 30

percent; however, this value is reduced by 95 percent due to the PM filter effectiveness. We also 

considered a DOC failure will cause a secondary failure of PM filter regeneration. We accounted 

for this PM increase within the PM filter disabled and leak categories.

The values with 0 percent effect in shaded cells represent areas which have no occurrence rate.

Tamper & Malmaintenance

NOX Emission Effect

1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010 LHDT

Federal Emission Standard 5.0 5.0 4.0 2.0 0.2 0.2

Timing Advanced 60% 60% 60% 60% 6% 12%

Timing Retarded -20% -20% -20% -20% -20% -20%

Injector Problem (all) -5% -1% -1% -1% -1% -1%

Puff Limiter Mis-set 0% 0% 0% 0% 0% 0%

Puff Limiter Disabled 0% 0% 0% 0% 0% 0%

Max Fuel High 10% 0% 0% 0% 0% 0%

Clogged Air Filter 0% 0% 0% 0% 0% 0%

Wrong/Worn Turbo 0% 0% 0% 0% 0% 0%

Intercooler Clogged 25% 25% 25% 25% 3% 5%

Other Air Problem 0% 0% 0% 0% 0% 0%

Engine Mechanical Failure -10% -10% -10% -10% -10% -10%

Excessive Oil Consumption 0% 0% 0% 0% 0% 0%

Electronics Failed 0% 0% 0% 0% 0% 0%

Electronics Tampered 80% 80% 80% 80% 8% 16%

EGR Stuck Open 0% 0% -20% -20% -20% -20%

EGR Disabled / Low Flow 0% 0% 30% 50% 5% 10%

Nox Aftertreatment Sensor 0% 0% 0% 0% 200% 200%

Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 200% 200%

Nox Aftertreatment Malfunction 0% 0% 0% 0% 500% 300%

PM Filter Leak 0% 0% 0% 0% 0% 0%

PM Filter Disabled 0% 0% 0% 0% 0% 0%

Oxidation Catalyst Malfunction/Remove 0% 0% 0% 0% 0% 0%

Mis-fuel
168

Tamper & Malmaintenance

PM Emission Effect

1994-1997 1998-2002 2003-2006 2007-2009 2010

Federal Emission Standard 0.1 0.1 0.1 0.01 0.01

Timing Advanced -10% -10% -10% 0% 0%

Timing Retarded 25% 25% 25% 1% 1%

Injector Problem 100% 100% 100% 5% 5%

Puff Limiter Mis-set 20% 0% 0% 0% 0%

Puff Limiter Dsabled 50% 0% 0% 0% 0%

Max Fuel High 20% 0% 0% 0% 0%

Clogged Air Filter 50% 50% 30% 2% 2%

Wrong/Worn Turbo 50% 50% 50% 3% 3%

Intercooler Clogged 50% 50% 30% 2% 2%

Other Air Problem 40% 40% 30% 2% 2%

Engine Mechanical Failure 500% 500% 500% 25% 25%

Excessive Oil Consumption 500% 500% 500% 25% 25%

Electronics Failed 60% 60% 60% 3% 3%

Electronics Tampered 50% 50% 50% 3% 3%

EGR Stuck Open 0% 0% 100% 5% 5%

EGR Disabled/Low Flow 0% 0% -30% -30% -30%

Nox Aftertreatment Sensor 0% 0% 0% 0% 0%

Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 0%

Nox Aftertreatment Malfunction 0% 0% 0% 0% 0%

PM Filter Leak 0% 0% 0% 935% 935%

PM Filter Disabled 0% 0% 0% 2670% 2670%

Oxidation Catalyst Malfunction/Remove 0% 0% 0% 0% 0%

Mis-fuel - EPA 30% 30% 30% 100% 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 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 

internal experience. We increased the HC emission effect of high fuel pressure to 10 percent

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 since this 

was the most significant type of tampering that occurred.

The values with 0 percent effect in shaded cells represent areas which have no occurrence rate. 
169

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 due to 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 finalization of the heavy-duty onboard diagnostics (HD OBD) rule, we made adjustments 

to our draft 2010 and later model year to reflect the rule’s implementation.

Specifically, we reduced our emissions increases for all pollutants due to tampering and malmaintenance by 33 percent. As data are not yet available for heavy-duty trucks equipped with 

OBD, this number is probably a conservative estimate. Still, 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 phases in OBD 

implementation, that 33 percent of all engines will have OBD in 2010, 2011, and 2012 model 

years, and 100 percent will have OBD by 2013 model year and later. Equation B-2 describes the 

calculation of TMpol, the increase in emission rate through useful life, where fOBD 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 2013 and later). The result from this 

equation can be plugged into Equation B-1 to determine the emission rate for any age group.

  pol polnonOBD OBD polnonOBDOBD TM TM f TM f

, ,  1 0.67 Equation B-2

Tamper & Malmaintenance

HC Emission Effect

1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010 LHDT

Federal Emission Standard 1.3 1.3 1.3 0.2 0.14 0.14

Timing Advanced 0% 0% 0% 0% 0% 0%

Timing Retarded 50% 50% 50% 50% 10% 10%

Injector Problem (all) 1000% 1000% 1000% 1000% 200% 200%

Puff Limiter Mis-set 0% 0% 0% 0% 0% 0%

Puff Limiter Disabled 0% 0% 0% 0% 0% 0%

Max Fuel High 10% 0% 0% 0% 0% 0%

Clogged Air Filter 0% 0% 0% 0% 0% 0%

Wrong/Worn Turbo 0% 0% 0% 0% 0% 0%

Intercooler Clogged 0% 0% 0% 0% 0% 0%

Other Air Problem 0% 0% 0% 0% 0% 0%

Engine Mechanical Failure 500% 500% 500% 500% 100% 100%

Excessive Oil Consumption 300% 300% 300% 300% 60% 60%

Electronics Failed 50% 50% 50% 50% 10% 10%

Electronics Tampered 0% 0% 0% 0% 0% 0%

EGR Stuck Open 0% 0% 100% 100% 20% 20%

EGR Disabled / Low Flow 0% 0% 0% 0% 0% 0%

Nox Aftertreatment Sensor 0% 0% 0% 0% 0% 0%

Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 0% 0%

Nox Aftertreatment Malfunction 0% 0% 0% 0% 0% 0%

PM Filter Leak 0% 0% 0% 0% 0% 0%

PM Filter Disabled 0% 0% 0% 0% 0% 0%

Oxidation Catalyst Malfunction/Remove 0% 0% 0% 50% 50% 50%

Mis-fuel
170

These OBD impacts apply to any truck in Class 4 and above. Any 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.
171

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 - HDT

McCormick, High Altitude, HDT Low Idle, AC Off 12 10.2

WVU - 1991-2004 Low Idle, AC Off 48 9.5

Storey Low Idle, AC Off 4 28

Overall 64 10.8

1991-2006 High Speed Idle, A/C On - HDT

Broderick UC Davis High Idle, AC On 1 86

Storey High Idle, AC On 4 48

Overall 5 55.6

1975-1990 MY Low Speed Idle, A/C Off - HDT

Program Condition Samples Mean

WVU - 1975-1990 Low Idle, AC Off 18 21

Overall 18 21.0

1991-2006 MY Low Speed Idle, A/C Off - Bus

Program Condition Samples Mean

McCormick, High Altitude, Bus Low Idle, AC Off 12 8.2

Overall 12 8.2
172

Idle CO Rates (gram/hour) Summary

Program Condition # Samples Mean CO Emiss Rate

1991-2006 Low Speed Idle, A/C Off - HDT

McCormick, High Altitude, HDT Low Idle, AC Off 12 71

Calcagno Low Idle, AC Off 27 37

WVU - 1991-2004 Low Idle, AC Off 48 23

Storey Low Idle, AC Off 4 25

Overall 91 33.6

1991-2006 High Speed Idle, A/C On - HDT

Calcagno High Idle, AC On 21 99

Broderick UC Davis High Idle, AC On 1 190

Storey High Idle, AC On 4 73

Overall 26 91.2

1975-1990 MY Low Speed Idle, A/C Off - HDT

Program Condition Samples Mean

WVU - 1975-1990 Low Idle, AC Off 18 31

Overall 18 31.0

1991-2006 MY Low Speed Idle, A/C Off - Bus

Program Condition Samples Mean

McCormick, High Altitude, Bus Low Idle, AC Off 12 79.6

Overall 12 79.6
173

Idle PM Rates (gram/hour) Summary

Program Condition # Samples Mean PM Emiss Rate

1991-2006 Low Speed Idle, A/C Off - HDT

McCormick, High Altitude, HDT Low Idle, AC Off 12 1.8

Calcagno Low Idle, AC Off 27 2.55

WVU - 1991-2004 Low Idle, AC Off 48 1.4

Storey Low Idle, AC Off 4 1.3

Overall 91 1.8

1991-2006 High Speed Idle, A/C On - HDT

Calcagno High Idle, AC On 21 4.11

Storey High Idle, AC On 4 3.2

Overall 25 4.0

1975-1990 MY Low Speed Idle, A/C Off - HDT

Program Condition Samples Mean

WVU - 1975-1990 Low Idle, AC Off 18 3.8

Overall 18 3.8

1991-2006 MY Low Speed Idle, A/C Off - Bus

Program Condition Samples Mean

McCormick, High Altitude, Bus Low Idle, AC Off 12 2.88

Overall 12 2.9
174

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

Idle Nox Rates (gram/hour) Summary

Program Condition # Samples Mean NOX Emiss Rate

1991-2006 Low Speed Idle, A/C Off

McCormick, High Altitude, HDT Low RPM, AC Off 12 85

Lim, EPA Low RPM, No access 12 109

Irick, Clean Air Tech & IdleAire 49 87

WVU - 1991-2004 Low RPM, AC Off 48 83

WVU, NCHRP 2 47

Tang, Metro NY, 1984-1999 33 81

Calcagno Low RPM, AC Off 27 120

Broderick UC Davis Low RPM, AC Off 1 104

Storey Low RPM, AC Off 4 126

Overall 188 94

1991-2006 High Speed Idle, A/C Off

Lim, EPA CCD High RPM, No access 5 169

Calcagno High RPM, AC Off 21 164

Overall 26 165

1991-2006 High Speed Idle, A/C On

Lim, EPA CCD High RPM, AC On 5 212

Broderick UC Davis High RPM, AC On 1 240

Calcagno High RPM, AC On 21 223

Storey High RPM, AC On 4 262

Overall 31 227

1975-1990 MY Low Speed Idle, A/C Off

Program Condition Samples Mean

WVU - 1975-1990 Low RPM, AC Off 18 48

Lim, EPA CCD, 1985 MY Low RPM, AC Off 1 20

Overall 19 47

1991-2006 MY Low Speed Idle, A/C Off - Bus

Program Condition Samples Mean

McCormick, High Altitude, Bus Low Idle, AC Off 12 121

Overall 12 121.0
175

o 2010: 0.2 g/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%
176

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-1. Regression Coefficients for PM Emission Factor Model

Model-year 

group

Speed Class (mph) Type Medium 

Heavy-Duty

Heavy HeavyDuty

1960-87 1-25 Intercept (β0

) -5.419 -5.143

25-50 -4.942 -4.564

50+ -4.765 -4.678

1988-90 1-25 -5.366 -5.847

25-50 -4.929 -5.287

50+ -4.785 -5.480

1991-93 1-25 -5.936 -5.494

25-50 -5.504 -5.269

50+ -5.574 -5.133

1994-97 1-25 -5.927 -6.242

25-50 -5.708 -5.923

50+ -5.933 -6.368

1998-2006 1-25 -6.608 -6.067

25-50 -6.369 -5.754

50+ -6.305 -6.154

STP Slope (β1

) 0.02821 0.0968

Transformation 

Coefficient

(0.5σ2)

0.5864 0.84035

2

0 1

ln(PM)   STP0.5

Where :

β0 = an intercept term for a speed class within a model year group, as shown in the table above,

β1 = a slope term for STP, and

σ

2 = 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).
177

Appendix E Heavy-duty Diesel EC/PM Fraction Calculation

E.1 Introduction

This memo describes the development and application of a “rough cut” emission model for 

estimating elemental and organic carbonaceous material (EC and OM) emission rates (or EC/OM 

ratios) from MOVES. The memo describes the following steps involved in predicting EC/OM 

ratios. The memo 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 memo 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.33 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).33 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).
178

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] × [engine displacement] × [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:

2

L

M BMI 

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 lb (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:
179

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):

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” 

Distribution of BMI in Simulated Population

%

of Simulate

d

P

o

pulation

BMI

0.000

0.010

0.020

0.030

0.040

0.050

0.060

10 20 30 40 50 
180

(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).xxiv 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:

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.109 A two-way table was constructed to 

estimate VMT classified by combinations of [weight class] × [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:

 

xxiv VIUS 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.

Probability and Cumulative Probability Distributions of Model Years in PERE-HD

0

0.02

0.04

0.06

0.08

0.1

0.12

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Relative Age (Calendar Year - Model Year)

Probability

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Cum. Probability

Probability by Age

Cumulative Probability by Age
181

Because VIUS reports classes defined as ranges in running weight, any value of weight within each

VIUS-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 lb, 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:

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 in3and 915 in3, 

respectively.

Probability Distribution of Vehicle Running Weight based on VIUS

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

[0,15000)

[15000,30000)[30000,45000)[45000,60000)[60000,75000)[75000,90000)

[90000,105000)

[105000,120000)[120000,135000)[135000,150000)[150000,165000)[165000,180000)[180000,195000)[195000,210000)[210000,225000)[225000,240000)

[240000,)

Weight Range

Probability

Distribution of Displacement (cu. in.) by Running Weight (lb) in TIUS

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

150000 and under

15001 to 3000030001 to 4500045001 to 6000060001 to 7500075001 to 90000

90001 to 105000

105001 to 120000120001 to 135000135001 to 150000150001 to 165000165001 to 180000180001 to 195000195001 to 210000210001 to 225000225001 to 240000

240000 and over

Running Weight

Cumulative Probability

850+

800-849

750-799

700-749

650-699

600-649

550-599

500-549

470-499

350-469

430-449

400-429

370-399

350-369

300-349

250-299

1-249
182

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).

110

 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:

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.

Comparison of High / Medium / Low Accessory

Load Cases

0.000

0.200

0.400

0.600

0.800

1.000

 

P8 P13 P18

0 10 20 30
183

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 fuelsaving 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 C15 

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.
184

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.
185

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 samples111 , 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 intercomparison 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.112 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.113 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 
186

of each datum depends on the level of analytical uncertainty reported. The inverse of the DRIreported uncertainty (1/) 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, R2values are 

not considered valid.

Coefficients from the weighted regression for EC and OC are reported below:

Slope Beta Std. Error t-value Sig.

EC-TOR 1.047 0.011 91.331 <0.0001

OC-TOR 1.014 0.007 153.923 <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).

EC-TOR Predicted

1000500400300200100.1.2.3.4.5123451020304050

E

C-T

O

R

M

e

a

s

ure

d

1000

500 400

300

200

100

5040

30

20

10

5

4

3

2

1

.5.4

.3

.2

.1
187

When measured values are regressed against predicted values, the following statistical 

estimates of fit are obtained:

Prediction Slope Std. Error Intercept Std. Error

EC 1.080 0.009 3.737 3.173

OC 1.092 0.069 -4.417 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 ECTOT 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 (opModeID).

OC TOR-Predicted

1000500400300200100.1.2.3.4.5123451020304050

O

C - T

O

R

M

e

a

s

ure

d

1000

500 400

300

200

100

5040

30

20

10

5

4

3

2

1

.5.4

.3

.2

.1
188

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:

V N

Pn MEP

d

R 

Here, P is the power (in kW or hp), nR 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 N 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:

Mode 1 Mode 2 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8

Speed 1800 1800 1800 1200 1200 1200 1200 700

Load% 100 75 50 25 100 75 50 10 (idle)

Equiv. 

Ratio (φ) 0.69 0.50 0.34 0.21 0.82 0.69 0.41 0.09

IMEP 

(MPa) 1.083 0.922 0.671 0.524 1.491 1.225 0.878 0.150
189

The study reports exhaust mass composition, including PM2.5, 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 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/IMEP curve is S-shaped, similar to a logistic curve or growth curve. OC-TOT workspecific 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 IMEP.

Particle Mass, EC-TOT, and OC-TOT vs. IMEP

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

IMEP

Specific emission (g/IHP-HR)

OC-TOT

EC-TOT

PM2.5
190

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:

e C

A

Y Bx  

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 A B C

EC-TOT 2.12 × 10-5-9.79 4.67× 10-5

OC-TOT 0.155 -2.275 -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 

EC/OC Ratio vs. IMEP from Kweon et al. (2004)

Note Logarithmic Scale

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

IMEP

E

C/O

C
191

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 IMEP. 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.

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.

Comparison of EC/OC Ratio (TOT) by IMEP

With and Without Max/Min Constraints

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

IMEP

EC/OC Ratio (TOT)

EC/OC(TOT-Predicted)

EC/OC(TOT-Predicted) with

Constraints

EC/OC(TOT-Measured)
192

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.114 The composition data is as follows:

Idle 48 km/h

Atomic formula C23H29O4.7N0.21 C24H30O2.6N0.18

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.115 

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. 
193

Cycle Distance (mi) Duration (s) Average 

Speed (mph)

Maximum 

Speed (mph)

Maximum 

Acceleration 

(mph/s)

Cold start/idle 0 600 0 0 0

Creep 0.124 253 1.77 8.24 2.3

Transient 2.85 668 15.4 47.5 3.0

Cruise 23.1 2083 39.9 59.3 2.3

The following table presents the EC-TOT and OC-TOT emission rates reported in Table 6 of the 

study:

Rate Idle Creep Transient Cruise

EC (mg/mi) 340±140 446±115 175±172

OC (mg/mi) 607±329 182.9±51.2 74.7±56.3

EC (mg/min) 4.10±2.38 10.4±4.8 110.7±27.0 93.0±68.3

OC (mg/min) 20.9±11.6 17.0±6.4 45.5±13.2 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 IMEPbased 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.
194

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 IMEP, 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. 

Predicted EC and OC Emission Factors(g/mi) vs. Measured Values in Shah et al. (2004)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

H L M Shah

avg.

H Shah

avg.

H L M Shah

avg.

Creep Cruise Transient

Emission Factor (g/mi)

Average of OC g/mi

Average of EC g/mi
195

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 φ), 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.

For a 12 L engine, 3,000 kg/L would correspond to a running weight of 39600 kg (87,302 lb). 

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 

EC/OM Ratio (TOR-Specific) versus Weight/Displacement Ratio for Individual Truck Samples

Transient Driving Cycle, High Accessory Load

0

5

10

15

20

25

0 1000 2000 3000 4000 5000 6000 7000 8000

Running Weight / Displacement (kg/l)

EC/O

M

Ratio

Distribution of kg/l Ratios in Transient, High Accessory Load Simulation

0

1000

2000

3000

4000

5000

6000

7000

8000

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

kg/l

kg/l
196

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.116 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.

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 operatingmode 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 of A/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.

Comparison of Simulated Weights with Atlanta Area Truck Measurements

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20000 40000 60000 80000 100000 120000

Weight (lb)

Cumulative Distribution

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
197

m

Av Bv Cv mv a

VSP t t t t t

t

  



2 3

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 vehiclespecific 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.

Based on this weight distribution, we considered all vehicles weighing more than 40,000 lb 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. 

5000 15000 25000 35000 45000 55000 65000 75000 85000 95000 105000 115000 125000 135000 145000

0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

22.5

Percent

Weight_lb
198

 







EC OC

EC

EC

r r

r

f , 

NonEC EC f  1  f

The resulting EC fractions by operating mode are shown in Figure 2-20 in the main body of this 

report.
199

Appendix F Heavy-duty Gasoline Start Emissions Analysis 

Figures

Figure F-1. Cold-Start Emissions (FTP, g) for Heavy-Duty Gasoline Vehicles, averaged by Model-year and 

Age Groups

(a) CO

(b) THC

(c) NOx
200

Figure F-2. Cold-Start FTP Emissions for Heavy-Duty Gasoline Vehicles, GEOMETRIC MEANS by Modelyear and Age Groups

(a) CO

(b) THC

(c) NOx
201

Figure F-3. Cold-start FTP Emissions for Heavy-Duty Gasoline Trucks: LOGARITHMIC STANDARD 

DEVIATION by Model-year and Age Groups.

(a) CO

(b) THC

(c) NOx
202

Figure F-4. Cold-Start Emissions for Heavy-Duty Gasoline Trucks: RECALCULATED ARITHMETIC 

MEANS by Model-year and Age Groups.

(a) CO

(b) THC

(c) NOx
203

Table F-1. Emission Standards for Heavy-Duty Spark-Ignition On-road Engines

Regulatory Class Model Year Emissions Standards (g/hp-hr)

CO THC NMHC NOx NMHC + NOx

LHD2b3 1990 14.4 1.1 6.0

1991-1997 14.4 1.1 5.0

1998-2004 14.4 1.1 4.0

2005-2007 14.4 1.0

2008+ 14.4 0.14 0.20

LHD45, MHD 1990 37.1 1.9 6.0

1991-1997 37.1 1.9 5.0

1998-2004 37.1 1.9 4.0

2005-2007 37.1 1.0

2008+ 14.4 0.14 0.20
204

Appendix G Heavy-duty CNG Bus Emissions Supplemental

Figures

In our analysis, we found for model year group 1994-2001 that whether or not the bus was 

equipped with after-treatment technology did not greatly affect its emissions, so we decided to 

group all the buses together regardless, as shown in the figures below.

Figure G-1. NOx emissions of vehicles with after-treatment versus no after-treatment tested on the CBD cycle.

0

10

20

30

40

50

60

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Grams/Mile

Model Year

No AT (0-3 Age Group)

No AT (4-5 Age Group)

AT (0-3 Age Group)
205

Figure G-2. CO emissions of vehicles with after-treatment versus no after-treatment tested on the CBD cycle.

Figure G-3. PM emissions of vehicles with after-treatment versus no after-treatment tested on the CBD cycle.

0

5

10

15

20

25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Grams/Mile

Model Year

No AT (0-3 Age Group)

No AT (4-5 Age Group)

AT (0-3 Age Group)

0

0.05

0.1

0.15

0.2

0.25

0.3

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Grams/Mile

Model Year

No AT (0-3 Age Group)

No AT (4-5 Age Group)

AT (0-3 Age Group)
206

Figure G-4. THC emissions of vehicles with after-treatment versus no after-treatment tested on the CBD cycle.

Figure G-5. CH4 emissions of vehicles with after-treatment versus no after-treatment tested on the CBD cycle.

0

5

10

15

20

25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Grams/Mile

Model Year

No AT (0-3 Age Group)

No AT (4-5 Age Group)

AT (0-3 Age Group)

0

5

10

15

20

25

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Grams/Mile

Model Year

No AT (0-3 Age Group)

No AT (4-5 Age Group)

AT (0-3 Age Group)
207

Figure G-6. Total energy consumption of vehicles with after-treatment versus no after-treatment tested on the 

CBD cycle.

0

10000

20000

30000

40000

50000

60000

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

B

T

U/Mile

Model Year

No AT (0-3 Age Group)

No AT (4-5 Age Group)

AT (0-3 Age Group)
208

References

 

1 USEPA (2014). Air Toxic Emissions from On-road Vehicles in MOVES2014. EPA-420-R-14-021 Assessment and 

Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 

December, 2014. http://www.epa.gov/otaq/models/moves/documents/420r14021.pdf.

2 US EPA. Heavy-Duty Highway Compression-Ignition Engines and Urban Buses—Exhaust Emission Standards. 

http://www.epa.gov/otaq/standards/heavy-duty/hdci-exhaust.htm. Accessed February 12, 2015.

3

CFR § 86.091(2).

4 USEPA (2015). Population and Activity of On-road Vehicles in MOVES2014. EPA-420-D-15-001 Assessment and 

Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 

2015. http://www.epa.gov/otaq/models/moves/moves-reports.htm.

5 USEPA (2015). Greenhouse Gas and Energy Consumption Rates for On-road Vehicles: Updates 

for MOVES2014. EPA-420-R-15-003 Assessment and Standards Division. Office of 

Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 2015. 

http://www.epa.gov/otaq/models/moves/moves-reports.htm.

6 USEPA Office of Transportation and Air Quality Use of Data from “Development of Emission Rates for the MOVES 

Model,” Sierra Research, March 3, 2010 . Assessment and Standards Division, Ann Arbor, MI. April 2012.

7 USEPA (2014). Evaporative Emissions from On-road Vehicles in MOVES2014. EPA-420-R-14-014 Assessment and 

Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 

September, 2014. http://www.epa.gov/otaq/models/moves/documents/420r14014.pdf.

8 USEPA (2015). Exhaust Emission Rates for Light-Duty On-road Vehicles in MOVES2014. EPA-420-R-15-005 

Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. 

Ann Arbor, MI. 2015. http://www.epa.gov/otaq/models/moves/moves-reports.htm.

9 USEPA (2012). Use of Data from “Development of Emission Rates for the MOVES Model,” Sierra Research, March 

3, 2010. EPA-420-R-12-022 Office of Transportation and Air Quality. US Environmental Protection Agency. Ann 

Arbor, MI. August, 2012. http://www.epa.gov/otaq/models/moves/documents/420r12022.pdf.

10 USEPA (2014). Brake and Tire Wear Emissions from On-road Vehicles in MOVES2014. EPA-420-R-14-013 

Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. 

Ann Arbor, MI. 2014. http://www.epa.gov/otaq/models/moves/documents/420r14013.pdf.

11. USEPA (1998), Caterpillar, Inc. , Detroit Diesel Corporation, Mack Trucks, Inc., Navistar International 

Transportation Corporation, Renault Vehicules Industriels, s.a., and Volvo Truck Corporation Diesel Engines 

Settlement. October 22,1998. http://cfpub.epa.gov/enforcement/cases/ . 

12 Jack, Jason A. U.S. Army Aberdeen Test Center Support of Heavy Duty Diesel Engine Emissions Testing. U.S. 

Army Aberdeen Test Center CSTE-DTC-AT-SL-E, Aberdeen Proving Ground, Maryland. 

(http://www.epa.gov/ttn/chief/conference/ei15/session1/jack.pdf ).

13 McClement, Dennis. Reformatting On-Road In-Use Heavy-Duty Emissions Test Data. Sierra Research, Sacramento, 

CA. April 2008.

14 Gautam, Mridul, Nigel N. Clark, Gregory Thompson, Daniel K. Carder, and Donald W. Lyons. Evaluation of 

Mobile Monitoring Technologies for Heavy-duty Diesel-Powered Vehicle Emissions. Dept. Mechanical and Aerospace 

Engineering, College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV. 

Gautam, Mridul, Nigel N. Clark, Gregory Thompson, Daniel K. Carder, and Donald W. Lyons. Development of In-use 

Testing Procedures for Heavy-Duty Diesel-Powered Vehicle Emissions. Dept. Mechanical and Aerospace 

Engineering, College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV.
209

 

Note: this URL is no longer active.

15 Gautam, M., et al. Evaluation of In-Use Heavy-Duty Vehicle Emissions Using the Mobile Emissions Measurement 

System (MEMS) for Engine Model Years 2001 to 2003 : Final Reports. Present to engine manufacturers to fulfill 

testing requirements documented in Phases III and IV of the Heavy Duty Diesel Engine consent decree. Dept. 

Mechanical and Aerospace Engineering, College of Engineering and Mineral Resources, West Virginia University, 

Morgantown, WV. 2002 & 2007.

16 “Control of Emissions of Air Pollution From New Motor Vehicles: In-Use Testing for Heavy-Duty Diesel Engines 

and Vehicles”, 70 FR 34594, June 2005.

17 Data Collection of Drayage Trucks in Houston-Galveston Port Area Draft Report. EP-C-06-080. May 27, 2011

18 Bradley, Ron. “Technology Roadmap for the 21st Century Truck Program.” U.S. Department of Energy: Energy 

Efficiency and Renewable Energy, Washington, D.C., December 2000.

19 Rakha, Hesham and Ivana Lucic. Variable Power Vehicle Dynamics Model for Estimating Truck Accelerations. 

Page 6. 

http://filebox.vt.edu/users/hrakha/Publications/Variable%20Power%20Truck%20Acceleration%20-%20Ver%202.0.pdf

20 National Renewable Energy Laboratory. Development of LNG-Powered heavy-Duty Trucks in Commercial 

Hauling. NREL/SR-540-25154, Golden, CO, December 1998.

21 Goodyear. “Factors Affecting Truck Fuel Economy – Section 9” Page 5.

http://www.goodyear.com/truck/pdf/radialretserv/Retread_S9_V.pdf.

22 Ramsay, Euan and Jonathan Bunker. Acceleration of Multi-Combination Vehicles in Urban Arterial Traffic 

Corridors. PhD dissertation, Queensland University of Technology. August 2003, Page 11. 

http://eprints.qut.edu.au/archive/00002359/01/RS&ETechForum2003_Ramsay&Bunker_2.pdf

23 Society of Automotive Engineers. Commercial Truck and Bus SAE Recommended Procedure for Vehicle 

Performance Prediction and Charting. SAE J2188. Revised October 2003.

24 Bradley, Ron. “Technology Roadmap for the 21st Century Truck Program.” U.S. Department of Energy: Energy 

Efficiency and Renewable Energy, Washington, D.C., December 2000. Page 32.

25 Pritchard, Ewan G. D. and Richard R. Johnson. Hybrid Electric School Bus Preliminary Technical Feasibility 

Report. Advanced Energy Corporation and Department of Mechanical Engineering, North Carolina State University, 

Raleigh, NC, September 14, 2004. Page 25. 

26 Hedrick, J.K. and A. Ni. Vehicle Modeling and Verification of CNG-Powered Transit Buses. California PATH 

Working Paper UCB-ITS-PWP-2004-3. California Partners for Advanced Transit and Highways (PATH), Institute of 

Transportation Studies, University of California, Berkeley. February 2004. Page 21. 

http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1169&context=its/path

27 Motor Industry Research Association (MIRA). News from MIRA – Automotive Engineering Specialists. Hybrid 

Theory: Hybrid Vehicle engineering for economy, the environment, and customer delight. Nuneaton, Warwickshire, 

UK. Issue 2, Spring 2007. http://data.mira.co.uk/Newsletter/MIRA-Newsletter2.pdf. 

28 USEPA (2005). Energy and Emissions Inputs. EPA-420-P-05-003 Office of Transportation and Air Quality. US 

Environmental Protection Agency. Ann Arbor, MI. March, 2005. 

http://www.epa.gov/otaq/models/ngm/420p05003.pdf.

29 USEPA (2002). Update of Heavy-Duty Emission Levels (Model Years 1988-2004) for Use in MOBILE6. EPA-420-

R-02-018. July 2002. http://www.epa.gov/otaq/models/mobile6/r02018.pdf. (Table 17)

30 Darlington, T., Dennis Kahlbaum and Gregory Thompson. On-Road NOx Emission Rates from 1994-2003 HeavyDuty Diesel Trucks. SAE 2008-01-1299. Society of Automotive Engineers, April 2008.
210

 

31 USEPA Office of Transportation and Air Quality. Heavy Duty Diesel Engine Consent Decree Low NOx Rebuild 

Program Summary. Washington, D.C. http://www.epa.gov/compliance/resources/cases/civil/caa/diesel/progress.pdf.

32 Clark, Nigel et al. California Heavy Heavy-Duty Diesel Truck Emissions Characterization for Program E-55/59. 

West Virginia University Research Corporation. Morgantown, WV. November 2005.

33 Nam, Ed and Robert Giannelli. Fuel Consumption Modeling of Conventional and Advanced Technology Vehicles in 

the Physical Emission Rate Estimator (PERE). EPA420-P-05-001. USEPA Office of Transportation and Air Quality, 

Assessment and Standards Division, Ann Arbor, MI. (http://www.epa.gov/otaq/models/ngm/420p05001.pdf). February 

2005.

34 USEPA Office of Transportation and Air Quality. Development of Emission Rates for Light-Duty Vehicles in the 

Motor Vehicle Emissions Simulator (MOVES 2010): Final Report. EPA-420-R-11-011. Assessment and Standards 

Division, Office of Transportation and Air Quality, Ann Arbor, MI. August, 2011 

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

35 Kinsey, J. S., W. A. Mitchell, W. C. Squier, K. Linna, F. G. King, R. Logan, Y. Dong, G. J. Thompson and N. N. 

Clark (2006). Evaluation of methods for the determination of diesel-generated fine particulate matter: Physical 

characterization results. Journal of Aerosol Science 37(1): 63-87.

36 USEPA Office of Transportation and Air Quality. Update Heavy-Duty Engine Emission Conversion Factors for 

MOBILE6: Analysis of BSFCs and Calculation of Heavy-Duty Engine Emission Conversion Factors. EPA420-R-02-

005, M6.HDE.004. Assessment and Standards Division, Ann Arbor, MI. 

(http://www.epa.gov/otaq/models/mobile6/r02005.pdf). 

37 Khalek, Imad, Thomas L Bougher and Patrick M. Merritt. Phase 1 of the Advanced Collaborative Emissions Study 

(ACES). SwRI Project No. 03.13062. Southwest Research Institute, San Antonio, TX; Coordinating Research 

Council (CRC), Alpharetta, GA; Health Effects Institute, Boston, MA. June 2009. 

http://www.crcao.org/reports/recentstudies2009/ACES%20Phase%201/ACES%20Phase1%20Final%20Report%2015J

UN2009.pdf

38 US EPA. 2012. Black Carbon Report to Congress. EPA-450/R-12-001. March 2012. 

http://www.epa.gov/blackcarbon/2012report/fullreport.pdf.

39 Khalek, I. A.; Bougher, T. L; Merrit, P. M.; Phase 1 of the Advanced Collaborative Emissions Study. CRC Report: 

ACES Phase 1, June 2009.

40 USEPA (2015). Fuel Effects on Exhaust Emissions from On-road Vehicles in MOVES2014. EPA-420-R-15-001 

Assessment and Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. 

Ann Arbor, MI. 2015. http://www.epa.gov/otaq/models/moves/moves-reports.htm.

41 USEPA (2014). Speciation of Total Organic Gas and Particulate Matter Emissions from On-road Vehicles in 

MOVES2014. EPA-420-R-14-020 Ann Arbor, MI. October, 2014. 

http://www.epa.gov/otaq/models/moves/documents/420r14020.pdf.

42 Graboski, Michael S., Robert L. McCormick, Janet Yanowitz, and Lisa Ryan. Heavy-Duty Diesel Vehicle Testing 

for the Northern Front Range Air Quality Study. Colorado Institute for Fuels and High-Altitude Engine Research, 

Colorado School of Mines, Golden, Colorado. Prepared for Colorado State University. February 1998.

43 Energy and Environmental Analysis, Inc. Documentation and Analysis of Heavy-Duty Diesel Vehicle Emission Test 

Data. Prepared for New York Department of Environmental Conservation, December 2000.

44 Final Regulatory Impact Analysis, Chapter 5 (RIA) (PDF) (553 pp, EPA-420-R-11-901, August 2011)

45 USEPA (2014). Emission Adjustments for Temperature, Humidity, Air Conditioning, and Inspection and 

Maintenance for On-road Vehicles in MOVES2014. EPA-420-R-14-012 Assessment and Standards Division. Office of 

Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 2014. 

http://www.epa.gov/otaq/models/moves/documents/420r14012.pdf.
211

 

46 Calcagno, James A. Evaluation of Heavy-Duty Diesel Vehicle Emissions During Cold-Start and Steady-State Idling 

Conditions and Reduction of Emissions from a Truck-Stop Electrification Program. PhD Dissertation, Department of 

Civil and Environmental Engineering, University of Tennessee, Knoxville, December 2005.

47EMFAC 2000 Section 6.7 START CORRECTION FACTORS. 4/20/2000. On-Road Emissions Model Methodology 

Documentation. http://www.arb.ca.gov/msei/onroad/doctable_test.htm

48 Glover, E.; Carey, P. Determination of Start Emissions as a Function of Mileage and Soak Time for 1981-1993 

Model-year Light-Duty Vehicles. EPA420-R-01-058 (M6.STE.003). USEPA Office of Transportation and Air Quality, 

Ann Arbor, MI. November, 2001.

49McCormick, Robert, M. S. Graboski, T. L. Alleman , J. Yanowitz. Idle Emissions from Heavy-Duty Diesel and 

Natural Gas Vehicles at High Altitude. Journal of the Air and Waste Management Association, 50(11):1992-8. 

Revised May 3, 2000.

50Lim, Han. Study of Exhaust Emissions from Idling Heavy-duty Diesel Trucks and Commercially Available IdleReducing Devices. EPA420-R-02-025. US EPA Office of Transportation and Air Quality, Certification and 

Compliance Division. October 2002.

51Lambert, Douglas, et al. Roadside Emissions Study: Preliminary Results for Stationary and On-Road Testing of 

Diesel Trucks in Tulare, California. California Environmental Protection Agency. Air Resources Board. Mobile 

Source Operations Division, Clean Air Technologies International, Inc May 15, 2002.

52Irick, David K. and Bob Wilson. NOx Emissions and Fuel Consumption of HDDVs during Extended Idle. 

University of Tennessee, IdleAire Technologies Inc. In: Proceedings, Coordinated Research Council 12th Annual OnRoad Vehicle Emission Workshop, San Diego, California, April 15-17, 2002.

53Gautam, Mridual and Nigel N. Clark. Heavy-duty Vehicle Chassis Dynamometer Testing for Emissions Inventory, 

Air Quality Modeling, Source Apportionment and Air Toxics Emissions Inventory. Phase I Interim Report, CRC 

Project No. E-55/E-59, West Virginia University Research Corporation, Morgantown, July 2002.

54National Cooperative Highway Research Program (NCHRP). Heavy-duty Vehicle Emissions. NCHRP Project 25-14, 

Cambridge Systematics, Inc., Battelle Laboratories, Sierra Research and West Virginia University. October 2002. 

55Tang, Shida and John Munn. Internal Report – Idle Emissions from Heavy-Duty Diesel Trucks in the New York 

Metropolitan Area. New York State Dept of Environmental Conservation, November 9, 2001.

56Broderick, Dwyer. Potential Benefits of Utilizing Fuel Cell Auxiliary Power Units in Lieu of Heavy-Duty Truck 

Engine Idling. Paper UCD-ITS-REP-01-01. Institute of Transportation Studies, University of California, Davis, 2001. 

57Storey, John M.E., John F. Thomas, Samuel A. Lewis, Sr., Thang Q. Dam, K. Edwards, Dean, Gerald L. DeVault, 

and Dominic J. Retrossa. Particulate Matter and Aldehyde Emissions from Idling Heavy-Duty Diesel Trucks. SAE 

Paper 2003-01-0289. Society of Automotive Engineers, Warrendale, PA.

58 USEPA (2014). Motor Vehicle Emission Simulator (MOVES): User Guide for MOVES2014. EPA-420-B-14-055 

Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 31-Jul-14. 

http://www.epa.gov/otaq/models/moves/documents/420b14055.pdf.

59 EPA. 1999. Tier 2 Vehicle & Gasoline Sulfur Program Final Rule, http://www.epa.gov/otaq/standards/lightduty/tier2stds.htm

60 EPA 2003. Update to the Accounting for the Tier 2 and Heavy-Duty 2005/2007 Requirements in MOBILE6. 

http://www.epa.gov/otaq/models/mobile6/r03012.pdf

61 Final Rulemaking: Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and HeavyDuty Engines and Vehicles. http://www.epa.gov/otaq/climate/regs-heavy-duty.htm. September, 2011.

62 EPA. 2010. Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles. http://www.epa.gov/otaq/climate/regulations.htm
212

 

63 Development of Emission Rates for Heavy-Duty Vehicles in the Motor Vehicle Emissions Simulator MOVES2010 

(131 pp, EPA-420-B-12-049, August 2012)

64 EPA. 2001. “Control of Air Pollution From New Motor Vehicles: Heavy-Duty Engine and Vehicle Standards and 

Highway Diesel Fuel Sulfur Control Requirements; Final Rule” http://www.epa.gov/otaq/standards/heavy-duty/hdsiexhaust.htm 

65

 On the Alternative Fuel Vehicle Database’s user interface, select “Buses” for Vehicle Type, 

“Compressed Natural Gas” for Fuel Type, and “Transit Agencies” for User Group.US Energy Information 

Administration, Department of Energy. 2003-2010, Accessed: 10 August 2012. 

(http://www.eia.gov/renewable/afv/users.cfm). 

66

 Plot created using CNG and LNG data from the annual Public Transportation Fact Books, American Public 

Transportation Association (APTA), 2008-2013. (http://www.apta.com/resources/statistics/Pages/transitstats.aspx). 

Other category includes biodiesel and propane.

67

 Kremer, J. Modeling Emission Factors for Compressed Natural Gas Vehicles. EPA420-P-99-012. Assessment and 

Modeling Division, USEPA Office of Mobile Sources, Ann Arbor, MI. October 2002. 

(http://www.epa.gov/oms/models/mobile6/m6ful004.pdf).

68

 “Central Business District (CBD),” Emission Test Cycles. DieselNet. Last Updated: September 2000, Accessed: 10 

July 2012. (http://www.dieselnet.com/standards/cycles/cbd.php).

69 DieselNet, (http://www.dieselnet.com/standards/cycles/cbd.php).

70 Melendez, M.; Taylor, J.; Zuboy, J. et al. Emission Testing of Washington Metropolitan Area Transit Authority 

(WMATA) Natural Gas and Diesel Transit Buses. Technical Report NREL/TP-540-36355, National Renewable Energy 

Laboratory, Office of Energy Efficiency and Renewable Energy, Department of Energy. Golden, CO. December 2005. 

(http://www.afdc.energy.gov/pdfs/36355.pdf).

71 Ayala, A., Gebel, M., Okamoto, R., Rieger, P. et al., "Oxidation Catalyst Effect on CNG Transit Bus Emissions," 

Society of Automotive Engineers, SAE Technical Paper 2003-01-1900, 2003. (http://papers.sae.org/2003-01-1900).

72 Hesterberg, T.; Lapin, C.; Bunn, W.; 2008. “A Comparison of Emissions from Vehicles Fueled with Diesel or 

Compressed Natural Gas.” Environ. Sci. Technol. 42(17): 6437-6445. (http://pubs.acs.org/doi/abs/10.1021/es071718i).

73

 Ayala, A., Kado, N., Okamoto, R., Holmén, B. et al., "Diesel and CNG Heavy-duty Transit Bus Emissions over 

Multiple Driving Schedules: Regulated Pollutants and Project Overview," Society of Automotive Engineers, SAE 

Technical Paper 2002-01-1722, 2002. (http://papers.sae.org/2002-01-1722).

74

 Lanni, T., Frank, B., Tang, S., Rosenblatt, D. et al., "Performance and Emissions Evaluation of Compressed Natural 

Gas and Clean Diesel Buses at New York City's Metropolitan Transit Authority," Society of Automotive Engineers., 

SAE Technical Paper 2003-01-0300, 2003. (http://papers.sae.org/2003-01-0300).

75

 McCormick, R., Graboski, M., Alleman, T., Herring, A. et al., "In-Use Emissions from Natural Gas Fueled HeavyDuty Vehicles," Society of Automotive Engineers, SAE Technical Paper 1999-01-1507, 1999. 

(http://papers.sae.org/1999-01-1507).

76

 LeTavec, C., Uihlein, J., Vertin, K., Chatterjee, S. et al., "Year-Long Evaluation of Trucks and Buses Equipped with 

Passive Diesel Particulate Filters," Society of Automotive Engineers. SAE Technical Paper 2002-01-0433, 2002. 

(http://papers.sae.org/2002-01-0433).

77

 McKain, D., Clark, N., Balon, T., Moynihan, P. et al., "Characterization of Emissions from Hybrid-Electric and 

Conventional Transit Buses," Society of Automotive Engineers, SAE Technical Paper 2000-01-2011, 2000. 

(http://papers.sae.org/2000-01-2011).

78

 Clark, N., Gautam, M., Lyons, D., Bata, R. et al., "Natural Gas and Diesel Transit Bus Emissions: Review and 

Recent Data," Society of Automotive Engineers, SAE Technical Paper 973203, 1997. (http://papers.sae.org/973203).
213

 

79 “Heavy-Duty Highway Compression-Ignition Engines and Urban Buses -- Exhaust Emission Standards,” Emission 

Standards Reference Guide, USEPA. Last Updated: 5 July 2012, Accessed: 20 July 2012.

80 Search for compliance documents in the “Heavy-Duty Highway Compression-Ignited Engines” category on EPA's 

Transportation and Air Quality Document Index System (DIS), Last Updated: 10 August 2012, Accessed: 10 August 

2012. (http://iaspub.epa.gov/otaqpub/pubsearch.jsp).

81 US EPA OTAQ, 2012. (http://iaspub.epa.gov/otaqpub/pubsearch.jsp).

82 Hajbabaei, M., G. Karavalakis, K. C. Johnson, L. Lee and T. D. Durbin (2013). Impact of natural gas fuel 

composition on criteria, toxic, and particle emissions from transit buses equipped with lean burn and stoichiometric 

engines. Energy 62(0): 425-434.

83 Yoon, S.; Collins, J.; Thiruvengadam, A.; Gautam, M.; Herner, J.; Ayala, A. Criteria pollutant and greenhouse gas 

emissions from CNG transit buses equipped with three-way catalysts compared to leanburn engines and oxidation 

catalyst technologies, Journal of the Air & Waste Management Association, 2013, 63:8, 926-933, 

http://dx.doi.org/10.1080/10962247.2013.800170.

84

 Kado, N. Y.; Okamoto, R. A.; Kuzmicky,, P. A.; Kobayashi, R.; Ayala,A.; Gebel, M. E.; Rieger, P. L.;Maddox, C.; 

Zafonte, L.; Emissions of Toxic Pollutants from Compressed Natural Gas and Low Sulfur Diesel-Fueled Heavy-Duty 

Transit Buses Tested over Multiple Driving Cycles. Environmental Science & Technology 2005 39 (19), 7638-7649.

85 40 CFR 1065.845 “Vehicle-Testing Procedures. Response Factor Determination” Code of Federal Regulations.

86 USEPA (2014). Air Toxic Emissions from On-road Vehicles in MOVES2014. EPA-420-R-14-021 Assessment and 

Standards Division. Office of Transportation and Air Quality. US Environmental Protection Agency. Ann Arbor, MI. 

December, 2014. http://www.epa.gov/otaq/models/moves/documents/420r14021.pdf.

87 Okamoto, R. A.; Kado, N. Y.; Ayala, A.; Gebel, M.; Rieger, P.; Kuzmicky, P. A.; Kobayashi, R.; Chemical and 

Bioassay Analyses of Emissions from Two CNG Buses with Oxidation Catalyst. http://www.arb.ca.gov/research/vehemissions/cng-diesel/cng-diesel.htm.

88 Jääskeläinen, H. Crankcase Ventilation. DieselNet Technology Guide. www.DieselNet.com. Copyright © Ecopoint 

Inc. Revision 2012.12.

89 40 CFR 86.004-11. “Control of Emissions from New and In-Use Highway Vehicles and Engines.” Code of Federal 

Regulations.

90 Zielinska, B.; Campbell, D.; Lawson, D. R.; Ireson, R. G.; Weaver, C. S.; Hesterberg, T. W.; Larson, T.; Davey, M.; 

Liu, L.-J. S. 2008. Detailed characterization and profiles of crankcase and diesel particulate matter exhaust emissions 

using speciated organics Environ. Sci. Technol. 42(15): 5661-5666.

91 Ireson, R.G., Ondov, J. M., Zielinska, B.,. Weaver, C. S., Easter, M. D., Lawson, D. R., Hesterberg, T. W., Davey, 

M. E., Liu, L.-J. S. Measuring In-Cabin School Bus Tailpipe and Crankcase PM2.5: A New Dual Tracer Method, 

Journal of the Air & Waste Management Association, 2011, 61:5, 494-503

92 Hill, L. B.; Zimmerman, N. J.; Gooch, J.; A Multi-City Investigation of the Effectiveness of Retrofit Emissions 

Controls in Reducing Exposures to Particulate Matter in School Buses. January 2005. Clean Air Task Force.

93 40 CFR 86.007-11. “Control of Emissions from New and In-Use Highway Vehicles and Engines.” Code of Federal 

Regulations.

94 Khalek, I. A.; Bougher, T. L; Merrit, P. M.; Phase 1 of the Advanced Collaborative Emissions Study. CRC Report: 

ACES Phase 1, June 2009.

95 Clark, N. McKain, D., Barnett, R., Wayne, S., Gautam, M., Thompson, G., Lyons, D. “Evaluation of Crankcase 

Emissions Abatement Device,” August 8, 2006. West Virginia University. Available at: 

http://www.worldnci.com/files/Documents/WVA_FinalReport_200600808.pdf
214

 

96 Clark, N., Tatli, E., Barnett, R., Wayne, W. et al., "Characterization and Abatement of Diesel Crankcase Emissions," 

SAE Technical Paper 2006-01-3372, 2006, doi:10.4271/2006-01-3372.

97 Hare, C. T.; Baines, T. M.; Characterization of Diesel Crankcase Emissions. Society of Automotive Engineers, OffHighway Vehicle Meeting and Exhibition. MECA, Milwaukee. 1977.

98 Kalayci, Veli. “SpiracleTM Crankcase Filtration Systems: Technical Article” . Donaldson Company, Inc. January 

2011. Available at : www.donaldson.com/en/engine/support/datalibrary/053490.pdf

99 McClenny, W. A. (2000). Recommended Methods for Ambient Air Monitoring of NO, NO2, NOy, and Individual 

NOz Species. EPA/600/R-01/005 National Exposure Research Laboratory, US EPA. September 2000. 

100 Seinfeld, J. H. and S. N. Pandis (2012). Atmospheric chemistry and physics: from air pollution to climate change, 

John Wiley & Sons.

101 Dunlea, E. J., S. C. Herndon, D. D. Nelson, R. M. Volkamer, F. San Martini, P. M. Sheehy, M. S. Zahniser, J. H. 

Shorter, J. C. Wormhoudt, B. K. Lamb, E. J. Allwine, J. S. Gaffney, N. A. Marley, M. Grutter, C. Marquez, S. Blanco, 

B. Cardenas, A. Retama, C. R. Ramos Villegas, C. E. Kolb, L. T. Molina and M. J. Molina (2007). Evaluation of 

nitrogen dioxide chemiluminescence monitors in a polluted urban environment. Atmos. Chem. Phys. 7(10): 2691-2704.

102 Kurtenbach, R., K. H. Becker, J. A. G. Gomes, J. Kleffmann, J. C. Lörzer, M. Spittler, P. Wiesen, R. Ackermann, A. 

Geyer and U. Platt (2001). Investigations of emissions and heterogeneous formation of HONO in a road traffic tunnel. 

Atmos Environ 35(20): 3385-3394.

103 Wang, Y. J., A. DenBleyker, E. McDonald-Buller, D. Allen and K. M. Zhang (2011). Modeling the chemical 

evolution of nitrogen oxides near roadways. Atmos Environ 45(1): 43-52.

104 U.S. Census Bureau. Vehicle Inventory and Use Survey. Service Sector Statistics Division, Transportation 

Characteristics Branch. http://www.census.gov/svsd/www/vius/products.html.

105 Zhou, Lei. Revision of Heavy Heavy-Duty Diesel Truck Emission Factors and Speed Correction Factors.

California Air Resources Board, Sacramento. October 2006.

106 Illinois Environmental Protection Agency. Effectiveness of On-Board Diagnostic I/M Testing: Report to the 

General Assembly, Response to Public Act 92-0682. Bureau of Air, Springfield, IL. September 2003. Page 21. 

www.epa.state.il.us/air/publications/obd-report-final.pdf

107 Manufacturers of Emission Controls Association. Technology Details - Catalytic Converters - SCR System 

.http://www.meca.org/technology/technology-details?id=5

108 Song, Qingwen, and Zhu, George. Model-based Closed-loop Control of Urea SCR Exhaust Aftertreatment System 

for Diesel Engine. SAE 2002-01-287. Society of Automotive Engineers, Warrendale, PA. 

109

 U.S. Census Bureau. Vehicle Inventory and Use Survey 1997 Microdata File [CD-ROM]. U.S. Department of 

Commerce, Economics and Statistics Administration. 2000.

110 Gururaja, Prashanth, and Angela Cullen. Accessory requirement tab in Microsoft Excel spreadsheet, VSP Weights 

2008-Feb.xls. Personal communication. USEPA Office of Transportation and Air Quality, Assessment and Standards 

Division, Ann Arbor, MI. February, 2008.

111 US EPA. 2008. Kansas City PM Characterization Study. Final Report, EPA420-R-08-009. Assessment and 

Standards Division Office of Transportation and Air Quality U.S. US EPA. Environmental Protection Agency Ann 

Arbor, MI, EPA Contract No. GS 10F-0036K, October 27, 2006, Revised April 2008a by EPA staff. 

http://www.epa.gov/oms/emission-factors-research/420r08009.pdf.

112 Desert Research Institute. DRI Standard Operating Procedure: DRI Model 2001 Thermal/Optical Carbon Analysis 

(TOR/TOT) of Aerosol Filter Samples – Method IMPROVE_A. DRI SOP #2-216r2. Division of Atmospheric 

Sciences, Environmental Analysis Facility, Reno, NV. July 2008. (http://www.epa.gov/ttn/amtic/specsop.html).
215

 

113 National Renewable Energy Laboratory. DOE’s Gasoline/Diesel PM Split Study. Department of Energy, Office of 

Energy Efficiency and Renewable Energy, Golden, CO. October, 2009. 

(http://www.nrel.gov/vehiclesandfuels/nfti/feat_split_study.html).

114 Heywood, J.B. Internal Combustion Engine Fundamentals. McGraw-Hill: New York, 1988. (Data from Table 

11.8, page 629).

115 Shah, S.D. D.R. Cocker, J.W. Miller, and J.M. Norbeck. Emission rates of particulate matter and elemental and 

organic carbon from in-use diesel engines. Environ Sci. Technol. 2004. 38(9): 2544-2550.

116 Ahanotu, D.N. Heavy-duty vehicle weight and horsepower distributions: measurement of class-specific temporal 

and spatial variability. PhD Dissertation, Georgia Institute of Technology, Atlanta. July, 1999.