Development of Emission Rates for 

Heavy-Duty Vehicles in the Motor 

Vehicle Emissions Simulator 

MOVES2010

Final Report
Assessment and Standards Division

Office of Transportation and Air Quality

U.S. Environmental Protection Agency

NOTICE

This technical report does not necessarily represent final EPA decisions or 

positions. It is intended to present technical analysis of issues using data 

that are currently available. The purpose in the release of such reports is to 

facilitate the exchange of technical information and to inform the public of 

technical developments.

Development of Emission Rates for 

Heavy-Duty Vehicles in the Motor 

Vehicle Emissions Simulator 

MOVES2010

Final Report

EPA-420-B-12-049

August 2012
1

1 Heavy Duty Diesel Emissions ......................................................................................................... 3

1.1 Running Exhaust Emissions ......................................................................................................... 5

1.1.1 Nitrogen Oxides (NOx) ......................................................................................................... 6

1.1.1.1 Data Sources ................................................................................................................... 7

1.1.1.2 Calculate STP from 1-hz data ......................................................................................... 8

1.1.1.3 Calculate emission rates ................................................................................................ 12

1.1.1.4 Sample results ............................................................................................................... 18

1.1.2 Particulate Matter (PM) ....................................................................................................... 22

1.1.2.1 Data Source ................................................................................................................... 22

1.1.2.2 Analysis ........................................................................................................................ 24

1.1.2.3 Sample results ............................................................................................................... 32

1.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO) ............................................................... 36

1.1.3.1 Data Sources ................................................................................................................. 36

1.1.3.2 Analysis ........................................................................................................................ 37

1.1.3.3 Sample results ............................................................................................................... 38

1.1.4 Energy .................................................................................................................................. 42

1.2 Start Exhaust Emissions .............................................................................................................. 44

1.2.1 HC, CO, and NOx ................................................................................................................ 44

1.2.2 Particulate Matter ................................................................................................................. 46

1.2.3 Adjusting Start Rates for Soak Time ................................................................................... 46

1.3 Extended Idling Exhaust Emissions ........................................................................................... 48

1.3.1 Data Sources ........................................................................................................................ 48

1.3.2 Analysis ............................................................................................................................... 49

1.3.3 Results .................................................................................................................................. 51

2 Heavy-Duty Gasoline Truck emissions ......................................................................................... 52

2.1 Running Exhaust Emissions ....................................................................................................... 52

2.1.1 HC, CO, and NOx ................................................................................................................ 52

2.1.1.1 Data and Analysis ......................................................................................................... 52

2.1.1.2 Sample Results .............................................................................................................. 53

2.1.2 PM ........................................................................................................................................ 55

2.1.2.1 Data Source ................................................................................................................... 55
2

2.1.2.2 Analysis ........................................................................................................................ 56

2.1.3 Energy .................................................................................................................................. 57

2.2 Start Emissions ........................................................................................................................... 58

2.2.1 Available Data ..................................................................................................................... 58

2.2.2 Estimation of Mean Rates .................................................................................................... 59

2.2.3 Estimation of Uncertainty .................................................................................................... 61

2.2.4 Projecting Rates beyond the Available Data ....................................................................... 64

2.2.4.1 Regulatory class LHD2b3 ............................................................................................. 65

2.2.4.2 Regulatory classes LHD45 and MHD .......................................................................... 66

2.2.4.3 Particulate Matter .......................................................................................................... 67

A. Appendices ................................................................................................................................... 68

A.1 Calculation of Accessory Power Requirements ......................................................................... 68

A.2 Tampering and Mal-maintenance .............................................................................................. 70

A.2.1 Modeling Tampering and Mal-maintenance ....................................................................... 70

A.2.2 Data Sources ....................................................................................................................... 72

A.2.3 Analysis ............................................................................................................................... 73

A.3 Extended Idle Data Summary .................................................................................................... 81

A.4 Regression to develop PM emission rates for missing operating modes ................................... 86

A.5 Heavy-duty Diesel EC/OC Fraction Calculation ....................................................................... 87

A.5.1 Introduction ......................................................................................................................... 87

A.5.2 PERE for Heavy-duty Vehicles (PERE-HD) and Its Extensions ....................................... 87

A.5.2.1 PERE-HD Fleet-wide Average Emission Rate Estimator ........................................... 87

A.5.2.2 Prediction of Elemental Carbon and Organic Mass based on PERE-HD .................... 93

A.5.2 Comparison of Predicted Emissions with Independent Measurements ............................ 102

A.5.3 Variability in Predicted EC and OC Emission Rates ........................................................ 104

A.5.4 Calculating EC/OC fraction by MOVES operating mode ................................................ 106

A.6 Heavy-duty Gasoline Start Emissions Analysis Figures ......................................................... 108
3

Executive Summary

This report describes the analysis conducted to generate emission rate and energy rate inputs 

representing exhaust emissions and energy consumption for heavy-duty vehicles in MOVES2010. 

Exhaust emission rate inputs were developed for total hydrocarbons (THC), carbon monoxide 

(CO), nitrogen oxides (NOX), and particulate matter (PM). Energy consumption rates were 

developed based on measurements of carbon dioxide (CO2), CO and THC. We developed inputs 

for heavy-duty vehicles powered by both diesel and gasoline fuels, 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. 

Estimation of energy consumption rates for heavy-duty vehicles is covered in this report, but 

emissions of greenhouse gases other than CO2 are not covered. Estimation of the emissions of 

methane and nitrous oxide (N2O) are described in a separate report1. 

Evaporative emissions from heavy-duty gasoline vehicles are not covered in this report. Estimation 

of evaporative hydrocarbon emissions from heavy-duty gasoline vehicles is described in a separate 

document2

. Note that the methods described were developed for light-duty vehicles, but are also 

applied to heavy-duty gasoline vehicles. The model does not estimate evaporative emissions for 

diesel-powered vehicles.

Large volumes of continuous (“second-by-second”) data from various sources were analyzed , 

including onboard emissions measurement systems, chassis dynamometer tests, and engine 

dynamometer tests. Data were collected by a number of entities, including EPA, West Virginia 

University, and private parties under contract to EPA. For running exhaust emissions, data were 

analyzed by model year, regulatory class, and operating mode. 

As with the development of emission rates for light-duty vehicles, operating modes for heavy-duty 

vehicles 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. For 

heavy-duty vehicles, we have continued to related 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). Because heavy-duty 

vehicles are primarily regulated on an engine work basis (g/kW-hr), we conclude that the use of 

STP preserves the emission to power relationship, whereas the use of VSP confounds it, resulting 

in unintended consequences in estimation of emissions in relation to vehicle size or weight. 

Additionally, to address the question of deterioration, 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 data adequate to directly estimate 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 these to estimate overall emissions effects for each 

pollutant. 

Final emission rates in grams per hour were developed for inclusion in the emissionRateByAge 

table in the MOVES database. The rates describe the effects of operating mode as well as model 

year group, which serves as a broad surrogate for changes in technology and emissions standards, 
4

especially for NOX and PM. The MOVES framework and the emissionRateByAge table are 

discussed in the report documenting the rates for light-duty vehicles3. 
5

1 Heavy Duty Diesel Emissions

This section details our analysis of data to develop emission rates for heavy-duty diesel vehicles. 

Three emission processes (running, extended idling, and starts) are discussed. 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 which will be discussed below. 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.

1.1 Running Exhaust Emissions

MOVES running-exhaust emissions analysis requires accurate second-by-second measurements of

emission rates and parameters that can be can used to estimate the tractive power exerted by a 

vehicle. Compared to volumes of data available for light-duty vehicles, the amount of data 

available for heavy-duty vehicles is small. Light-duty emissions were analyzed with respect to 

vehicle-specific power (VSP), which represents vehicles’ tractive power normalized by their 

(individual) weights. The model approach used in MOVES was first developed for light-duty 

vehicles, relying on the VSP concept, and later adapted for use with heavy-duty vehicles. For 

practical reasons, it was thus desirable to retain the same operating mode structure for heavy-duty 

emission rates. 

While VSP is an effective way to characterize emissions from light-duty vehicles, the range of 

running weights, coarseness of the VSP bin structure, and work-based (rather than distance-based) 

emissions standards make VSP-based emissions analysis for heavy-duty diesel vehicles an 

untenable approach. This report describes how we analyzed continuous “second-by-second’ 

heavy-duty emissions data to develop emission rates applied within the predefined set of operating 

modes. As mentioned, the emission rates were using scaled-tractive power (STP), rather than VSP. 

The development of STP is described in greater detail below.

MOVES source bins are groupings of parameters which distinguish differences in emission rates 

according to physical differences in the source type or vehicle classification. The source bins are 

differentiated by fuel type (gasoline or diesel), regulatory class (light heavy duty to heavy-heavy 

duty) and model year group. 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) 

and the model year group. The regulatory groups were determined based on gross vehicle weight 

rating (GVWR) classifications. The model year groupings are designed to represent major changes

in EPA emission standards. 
6

Table 1. Regulatory Classes for Heavy-Duty Vehicles.

Regulatory Class Description regClassName regClassID Gross Vehicle Weight Rating 

(GVWR) [lb]

Light-heavy duty ≤ 14,000 lb LHD<=14k 41 8,501 – 14,000

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

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

Heavy-heavy duty HHD 47 > 33,000

Urban Bus Urban Bus1 48 N/A

1 see CFR § 86.091(2).

Heavy-duty diesel truck emission rates in MOVES are also stratified by age group. Within a 

particular model year group, these age groups are used to account for the effects of deterioration 

over time. The age groups are used in the model are shown in Table 2. 

Table 2. 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 ~ 

1.1.1 Nitrogen Oxides (NOx)

For NOx rates, we stratified heavy-duty vehicles into the model year groups listed in Table 3. 

These groups were defined based on changes in NOx emissions standards and the outcome of the 

Heavy Duty Diesel Consent Decree4

, which required additional control of NOx emissions during 
7

highway driving for model years 1999 and later. This measure is referred to as the “Not-toExceed” (NTE) limit.

Table 3 – 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

1.1.1.1 Data Sources 

For NOx emissions from HHD, MHD, and urban buses, we relied on two data sources: 

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. EPA5

: 

This ongoing program started in October 2000. Due to time constraints and data quality issues, we 

used only data collected from October 2003 through September 2007. The data was compiled and 

reformatted for MOVES analysis by Sierra Research6

. 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)7,8

 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 
8

involving urban, suburban, and rural driving. Several trucks were re-acquired and tested a second 

time after 2-3 years. Data were collected 5-Hz frequency, which we averaged around each second 

to convert the data to a 1.0-Hz basis.

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

As our own high-level check on the quality of PEMS and ECU output, we further eliminated any 

trip where the pearson correlation coefficient between CO2 (from PEMS) and engine power (from 

ECU) was less than 0.6. These filters led to a smaller and more conservative subset of the overall 

ROVER data, than had we applied more detailed and selective criteria. (i.e. not all eliminated tests 

produced erroneous results). For the WVU MEMS data, WVU itself reported on test validity under 

the consent decree procedure. No additional detailed quality checks were performed by EPA. 

Table 4 shows the total distribution of vehicles by model year group from both of the emissions test 

programs above. 

Table 4. Numbers of vehicles by model year group from the ROVER and WVU MEMS programs used for 

emission rate analysis. 

Regulatory class 1991-1997 MY 1998 MY 1999-2002 MY 2003-2006 MY 

HHD 19 12 78 91 

MHD 0 0 30 32 

BUS 2 0 25 19 

1.1.1.2 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 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 9. For all other operation, engine speed ωeng and 

torque τeng from the ECU were used to determine engine power Peng, as shown in Equation 1. Only 

torque values greater than zero were used so as to only include operation where the engine was 

performing work.

Peng engeng = τω Equation 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 
9

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 

highway, or high, vehicle speeds. Table 5 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 5. 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 5 – 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."9 

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. T he calculations are included in Appendix A.1 Calculation of 

Accessory Power Requirements .

 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
10

The total accessory loads Ploss,acc listed below in Table 6 are subtracted from the engine power 

determined from Equation 1 to get net engine power available at the engine flywheel. For LHD 

vehicles, we assumed negligible accessory losses. 

Table 6 – 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 

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 ηdriveline varies with engine speed, vehicle speed, 

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

value for driveline efficiency.10,11,12,13,14,15,16,17,18 Table 7 summarizes our findings. 

Table 7 – Driveline efficiencies found through literature research.

Based on this research, we used a driveline efficiency of 90% for all HD regulatory classes.

Equation 2 shows the translation from engine power Peng to axle power Paxle. 

( ) Paxle =η driveline eng − PP loss,acc Equation 2 

 

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%
11

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

ranges. The 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 trucks were scaled by 2.06, which is 

equivalent to the fleet-average mass of light commercial trucks in MOVES. Table 8 shows the 

values selected for the scaling factor. 

Table 8 – Power scaling factor fscale.

Regulatory Class Power scaling factor

MHD, HHD, Bus 17.1 

LHD 2.06 

Equation 3 shows the conversion of axle power to scaled tractive power using the method 

explained above. 

scale

axle

f

P STP = Equation 3 

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

methodology outlined earlier in MOVES development19 and described in Table 9. The 

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. 

Table 9 – 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 -1.0 ≤ 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
12

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

1.1.1.3 Calculate emission rates 

1.1.1.3.1 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 4

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

k

j

n

i ijp

p

j

∑

∑

=

=





















=

Equation 4 

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. 
13

1.1.1.3.2 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 . 

tot veh

n

j

veh

with

nn

sn

s

veh

−

−

=

∑=1

2

2

)1(

Equation 5 

where

2

veh 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 ( jp r , ) as shown in Equation 6

( )

veh 1

1

2

, 2

betw

veh

−

−

=

∑

=

n

rr

s

n

j pjp Equation 6 

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

variances to get the standard error pol r s (Equation 7) and dividing the standard error by the mean 

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

pol r v c , (Equation 8). 

tot

2

with

veh

2

betw

n

s

n

s

rpol

s += Equation 7 

 

pol

r

polv

r

s

c pol

, = Equation 8 
14

1.1.1.3.3 Hole Filling and forecasting 

Since the data only covered model years 1994 through 2006, 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 (2007-and-later), we decreased the emission rates for all operating mode bins by a 

ratio proportional to the decrease in the applicable emissions standards. 

While the NOx standard going into effect for MY 2007 is 0.2 g/bhp-hr, it was assigned to be 

phased in over a three year period ending in 2010. Rather than phaseing in the after-treatment 

technology needed to meet the new standard, most manufacturers decided to meet a 1.2 g/bhp-hr 

standard for MY2007-2009, which did not require aftertreatment (down from 2.4 g/bhp-hr in 

2006). Therefore, to model this strategy, we estimated rates for MY2007-2009 by decreasing the 

rates for MY2003-2006 emission rates by 50%. 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 

aftertreatment technology, which we assume to have a 90% NOx reduction efficiency from levels 

for MY2006 levels, and estimated rates accordingly.

For model year 1990, we increased the 1991-1997 emission rates by 20% to account for the 

reduction in NOx standard from 6.0 to 5.0 g/bhp-hr from 1990 to 1991. For 1989 and earlier model 

years, we increased the 1991-1997 model year group emission rates by 40%, which is proportional 

to the increase of the certification levels from the 1991 model year to the 1989 model year. We 

assumed that emission levels did not change by model year for 1989 and earlier. 

For MHD and HHD trucks, the maximum operating mode 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 modes 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 inpute 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 certain model years, such as 1998, data existed for HHD trucks, but not MHD or buses. In 

these cases, the ratio of standards between the missing regulatory class and HHD regulatory class 

from the 1999-2002 model year group was used to determined missing class’s rates by multiplying 

that ratio by the existing HHD emission rates for the corresponding model year group. 

1.1.1.3.3.1 LNT-equipped pickup trucks 

To meet NOx emissions standards for the 2010 model year, the use of aftertreatment will probably 

be needed. For example, Cummins decided to use aftertreatment 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, 
15

heavy-duty vehicles are equipped with diesel particulate filters (DPF). At regular intervals, the 

DPF must be regenerated to remove and combust accumulated PM to relieve backpressure and 

ensure proper engine operation. This step requires high exhaust temperatures. However, these 

conditions adversely affect the LNT’s NOx storage ability, resulting in elevated NOx emissions. 

In 2007, EPA acquired a truck equipped with LNT and DPF and performed local on-road 

measurements, using portable instrumentation. We used the PEMS and ECU output to assign 

operating modes and calculate emission rates by the same methods used to develop the heavyheavy-duty truck NOx rates. While analyzing these data, we distinguished regimes of PM 

regeneration from normal operation based on exhaust temperature, with temperatures exceeding 

300°C assumed to indication PM regeneration. We performed the emission rate by operating mode 

analysis separately for each regime, and weighted the two regimes together based on an assumed 

PM regeneration frequency of 10% of VMT. This value is an assumption based on the limited data 

available. We will look for opportunities to update this assumption based on any additional 

information that becomes available. 

Because we assume that LNT-equipped trucks account for about 25% 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% of LHD diesel trucks will not have aftertreatment 

and will exhibit the 2007-2009 model year emission rates described earlier in this section. Starting 

in MY2010, we assume that the remaining 75% of LHD diesel trucks will be equipped with SCR, 

and will exhibit 90% NOx reductions from 2006 levels, also described in the hole filling section. 

Table 10 summarizes this discussion and previous subsections regarding the methods used to 

estimate emission rates for each regulatory–class/model-year-group combination. 

Table 10. Summary of methods for heavy-duty diesel NOx emission rate development for each regulatory class 

and model year group

Model year group HHD MHD Bus LHD

Pre-1988 Proportioned to 

certification levels

Proportioned to 

certification levels

Proportioned to 

certification levels

Proportioned to 

certification levels

1988-1989 Proportioned to 

certification levels

Proportioned to 

certification levels

Proportioned to 

certification levels

Proportioned to 

certification levels

1990 Proportioned to 

certification levels

Proportioned to 

certification levels

Proportioned to 

certification levels

Proportioned to 

certification levels

1991-1997 Data analysis Proportioned to HHD Data analysis Proportioned to HHD

1998 Data analysis Proportioned to HHD Proportioned to HHD Proportioned to HHD

1999-2002 Data analysis Data analysis Data analysis MHD engine data with 

LHD scale factor

2003-2006 Data analysis Data analysis Data analysis MHD engine data with 

LHD scale factor

2007-2009 Proportioned to 

standards

Proportioned to 

standards

Proportioned to 

standards

Data (LNT), and 

proportioned to 

standards (non-LNT)
16

2010 + Proportioned to 

standards

Proportioned to 

standards

Proportioned to 

standards

Proportioned to 

standards

An important point to note is that we did not make a stratification based on age for vehicles not 

equipped with NOx aftertreament 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 life20. 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% increase in NOx over the useful life of the engine. Our tampering 

and mal-maintenance estimation methods are discussed below and detailed in Appendix A.2 

Tampering and Mal-maintenance. 

1.1.1.3.4 Tampering and Mal-maintenance 

Table 11 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 11. Fleet-average NOx emissions increases from zero-mile levels over the useful life due 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 A.2 Tampering and Mal-maintenance, these emissions increases are 

combined with information in Table 37 to estimate the emissions increase for each age group prior 
17

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 malmaintenance to substantially increase emissions over time compared to the zero-mile level. 

Though 77% may appear to be a large increase in fleet-average emissions over time, it should be 

noted that the 2010 model year standard (0.2 g/bhp-hr) is about 83% lower than the 2009 model 

year effective standard (1.2 g/bhp-hr). This still yields a substantial reduction of about 71% from 

2009 zero-mile levels to 2010 fully deteriorated levels. As more data becomes available for future 

model years, we hope to update these tampering and mal-maintenance and overall aging effects. 

1.1.1.3.5 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. Users with questions about the use of 

these alternate emission factors should contact EPA at mobile@epa.gov. 

Since defeat devices were in effect mostly during highway or steady cruising operation, we assume 

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 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 9 and 

Equation 11. To estimated “reflashed” rates in the remaining operating modes, we multiplied 

“reflashed rates by ratios of the remaining operating modes to mode 27 for MY1991-98, as shown 

in Equation 10 and Equation 12. 

Operating modes 

(OM) 21-30

















− = −

16,1999

27,1999

16,989127,9891,

r

r

r r reflash















 =

−

−

− −

27,9891

,9891 OM

,9891, OM 27,9891,

r

r

r r x

reflash x reflash

Equation 9 

Equation 10

Operating modes 

(OM) 31-40 















− = −

16,1999

37,1999

16,989137,9891,

r

r

r r reflash

Equation 11

Equation 12
18















 =

−

−

− −

37,9891

,9891 OM

,19981991, 37,9891,

r

r

r r x

reflash MY OMx reflash

The default emission rates were also slightly adjusted for age for the consent decree model years. 

An EPA assessment shows that about 20% of all vehicles eligible for reflash had been reflashed by 

the end of 2008.21 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% had 

been reflashed. We approximated a linear increase in reflash rate from age zero.

1.1.1.4 Sample results

The charts in this sub-section show examples of the emission rates that resulted from the analysis. 

Not all rates are shown; the intention is to illustrate the most common trends and hole-filling 

results. For brevity, the light-heavy duty regulatory classes are not shown, since the light-heavy 

duty rates were based on medium-heavy data and follow similar trends. 

In Figure 1, we see that NOx emission rates increase with STP for HHD trucks. Figure 2 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 1. Trends in NOx Emissions by operating mode from HHD trucks for model year 2002.

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 NOx rate [g/hr]

Operating Mode
19

The highest operating modes in each speed range will rarely be attained due to the power 

limitations of heavy-duty vehicles, but are included in the figures (and in MOVES) for 

completeness. Nearly all of the activity occurs in modes 0, 1, 11-16, 21-28, and 33-38, with 

activity for buses and MHD vehicles usually occurring over an even smaller range. In some model 

year groups, the MHD and HHD classes use the same rates, based on lack of significant differences 

between those two classes’ emission rates.

Figure 2. Trends in NOx emissions by operating mode from MHD, HHD, and bus regulatory classes for model 

year 2002.

The effects of model year, representing a rough surrogate for technology or standards, can be seen 

in Figure 3, 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 2006) and hole filling. The 

trends in the data are expected, since the model year groups were formed on the basis of NOx 

standards. 

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 NOx rate [g/hr]

Operating Mode

MHD 

Bus

HHD
20

Figure 3. Trends in NOx by model year for HHD trucks in operating mode 24. Increasingly stringent emissions 

standards have caused NOx emissions to decrease significantly. 

Age effects were only implemented for aftertreatment-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 4. Coefficients of variation from previous model year groups were used to 

estimate uncertainties for MY 2010. 

0 

500

1000

1500

2000

2500

Mean NOx rate [g/hr]

Model year group
21

Figure 4. Modeled NOx trends by age for model year 2010 for operating mode 24.

Figure 5 shows the mean emission rates for LHD trucks for model years 2007-2009. As described 

previously, this group of vehicles includes vehicles with LNTs (with NOx increases during PM 

regeneration) and vehicles without any aftertreatment. The estimated uncertainties are greater than 

for the other heavy-duty regulatory classes, since there were fewer vehicles in our test data. 

Figure 5: Mean NOx rates by operating mode for model years 2007-2009 LHD trucks age 0-3 

0 

20

40

60

80

100

120

140

Mean NOx rate [g/hr]

HHD

MHD 

Bus

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

Age group [years]

0 

50

100

150

200

250

300

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

Mean NOx rate [g/hr]

Operating mode
22

1.1.2 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. Such particles consist of three 

subtypes, including: (1) elemental carbon (EC), usually composed of black colored soot emitted 

from combustion, (2) organic carbon (OC), consisting of particles of organic matter formed during 

the combustion process or immediately after in the tailpipe. It does not include particles formed in 

secondary reactions in the atmosphere, and (3) sulfate particulate, which formed by agglomeration 

of sulfur-containing compounds formed during combustion. These subtypes are used to form the 

inputs to MOVES.

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 12 shows the model year group range and the 

applicable brake-specific emissions standards. 

Table 12. 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 

1.1.2.1 Data Sources 

All of the data used to develop the MOVES PM2.5 emission rates was generated in the CRC E55/59 research program22. 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 
23

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. 

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.
24

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 13. 

Table 13. 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 14. 

Table 14. 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

1.1.2.2 Analysis 

1.1.2.2.1 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 9. 
25

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 the equation below: 

scale

ttttt

t f

BvAv Cv mv a STP++ + =

32

Equation 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 8), 

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 

vehicles23 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

24. 

Note that this approach differs from that described above the NOX emission rate analysis since the 

particulate data was collected on a chassis dynamometer from vehicles lacking electronic control 

units (ECU). Grade effects are not explicitly included in either case because grade does not come 

into play in chassis dynamometer tests, and it is already accounted for if STP is calculated through 

engine speed and torque from the engine control unit. 

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.
26

1.1.2.2.2 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 Nigel Clark of West Virginia University, and 

results 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 15 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. 

Table 15 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. 
27

j ij

i

j

ji PM

PM

PM

PM TEOM ,,

TEOM,

filter,

normalized, ,

∑

= Equation 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, 

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. 

1.1.2.2.3 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. 

1.1.2.2.4 Hole filling and Forecasting 

1.1.2.2.4.1 Missing operating modes 

Detailed in Appendix A.4 Developing PM emission rates for missing operating modes , 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. 

1.1.2.2.4.2 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. Thus, rates for these vehicle classes were computed using 

simple multiplicative factors based either on engine work ratios or PM emission standards (i.e., 

buses versus heavy trucks). The LHD classes’ emission rates were set as a ratio of the MHD 

emission rates, and bus (class 48) emission rates were proportioned to HHD rates. 

Because the certification standards in terms of brake horsepower-hour (bhp-hr) are the same for all 

of the heavy-duty engines, the emission rates of LHD2b3 are assumed to be equal to 0.46 * MHD

emission rates. The emission rate of LHD45 is assumed to be 0.60 * MHD emission rate. 

LHD2b3 emission rate = 0.46 * MHD emission rate

LHD45 emission rate = 0.60 * MHD emission rate
28

The values of 0.46 and 0.60 are the ratios of the MOBILE6.2 heavy-duty conversion factors25 (bhphr/mile) for the lighter trucks versus the MHD trucks. These are ratios of the relative amount of 

work performed by a lighter truck versus a heavier truck for a given distance. 

Urban Bus (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 standards: 

1991 – 1993 model years Bus Emissions = (0.40) * HHD emissions

1994 – 1995 model years Bus Emissions = 0.70 * HHD emissions

1996 – 2006 model years Bus Emissions = 0.50 * HHD emissions

1.1.2.2.4.3 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 16 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 

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

can review the ACES report.

26
29

Table 16 shows 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 

1.1.2.2.5 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 17 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.

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

for NOx, as described in Appendix A.2 Tampering and Mal-maintenance. The overall MOVES 

tampering and mal-maintenance effects on PM emissions over the fleet’s useful life are shown in
30

Table 17. The value of 89 percent for 2010+ model years reflects the projected effect of heavyduty 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). 

Table 17. Estimated increases in HC and CO 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 + 89 

1.1.2.2.6 Computation of Elemental Carbon and Organic Carbon Emission Factors 

The MOVES model reports Total PM2.5 emissions according to three species. These include 

Elemental Carbon (EC), Organic Carbon (OC) and sulfate. In the results of a MOVES run, Total 

PM2.5 is the sum of these three constituents. During rate development, the process is reversed, and 

both EC and OC are computed directly from the total PM2.5 emissions using multiplicative factors. 

Elemental Carbon is generally the black ‘soot’ that is often visible in engine exhaust. Organic 

Carbon generally includes organic particles of large molecular compounds and metals. Sulfate is 

computed using a fuel sulfur balance (see the report “Development of Gasoline and Diesel Vehicle 

Sulfate and SO2 Emissions for the MOVES Model” for details). Total PM2.5 as computed by 

MOVES includes EC, OC and sulfate emissions. Gaseous sulfur dioxide is also part of the fuel 

sulfur balance, and is reported by the MOVES model. It is not considered a particulate in the 

MOVES model, but can react in the atmosphere to form secondary particulate.

Since the fuel sulfur levels in the underlying studies were not generally known, but believed to be 

small (about 1 percent or less), sulfate emissions were ignored in the total PM2.5 emission levels. 

As a result, total PM2.5 in this analysis was assumed to be comprised of only EC and OC. 

PM 5.2 += OCEC Equation 15

Dividing both sides by PM2.5 and rearranging gives 
31

5.2 PM 5.2

EC 0.1

PM

OC −= Equation 16

 

Thus, the OC fraction = 1.0 – EC fraction 

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

particulate filters (DPFs) were standard) are shown in Figure 6. 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 A.5. 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 6. Elemental Carbon fraction by operating mode for pre-DPF-equipped trucks.

A different methodology was used to compute EC factors for 2007 and later model year, DPFequipped vehicles. For these vehicles it is believed that virtually all of the particulate that is 

emitted from the tailpipe will be OC and that only a modest fraction will be EC. The traps are 

designed to capture virtually all of the carbon. Potentially, small amounts of OC and sulfate may 

escape. This is essentially the opposite of the non-trap equipped heavy-duty diesel vehicles where 

the total PM2.5 is dominated by EC. Unfortunately, only limited particulate data exists on trap 

equipped vehicles. These data are based on particulate-matter bound ionic species and EC/OC 

emissions data from a few trap equipped buses and a heavy-duty tractor. The data were extracted 

and a simple average computed from a published source.27 Based on the date of the paper, it is 

likely that all of the diesel vehicle/trap systems were prototypes. Extraction of data from the paper 

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

EC fraction

HHD

MHD
32

yielded a single factor which will be applied to all regulatory types and operating modes for 2007 

and later diesel trucks and buses. This factor is the elemental carbon fraction. Table 18

summarizes the EC and OC fractions estimated from the paper. These fractions were used for all 

operating mode bins for model years 2007 and later heavy-duty vehicles. 

Table 18 shows EC and OC fractions used for DPF-equipped heavy-duty diesel vehicles.

EC fraction 0.0861 

OC fraction 1 – ECfraction = 0.9139 

As additional data become available, EPA will probably revise the EC Fraction used in MOVES 

for these vehicles. 

Temperature Correction Factors

The draft MOVES model released in March 2009 did not contain temperature correction factors for 

PM2.5 emissions from heavy-duty diesel vehicles. This absence of temperature correction factors 

does not imply that EPA believes that heavy-duty diesel vehicle PM emissions are insensitive to 

temperature effects. In fact, it is quite likely that the reverse is true. Both running and start PM 

emissions from at least non-trap equipped vehicles are sensitive to temperature. However, EPA at 

this time cannot adequately quantify such emission effects, and is currently using a multiplicative 

placeholder value of 1.0 as the temperature correction factor. EPA will update the MOVES model 

when sufficient data on diesel temperature correction factors is available for analysis and inclusion 

in the model. 

1.1.2.3 Sample results

Figure 7 and Figure 8 show how PM rates increase 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 heavyduty 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 pre2007 model years the PM rates are dominated by EC. With the introduction of DPFs in model year 

2007, we anticipate large reductions in overall PM rates and that the remaining PM will be 

dominated by OC. 
33

Figure 7. Particulate Matter rates by operating mode representing Heavy heavy-duty vehicles (model year 2002

at age 0-3 years). 

Figure 8. Particulate Matter rates by operating mode for Heavy heavy-duty vehicles (model year 2007 at age 0-

3 years). 

Figure 9 shows an example of how tampering and mal-maintenance estimates increase PM with 

age. The EC/OC 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. 

0 

20

40

60

80

100

120

140

160

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

Mean PM rate [g/hr]

Operating mode

EC

OC

0 

1 

2 

3 

4 

5 

6 

7 

8 

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

Mean PM rate [g/hr]

Operating mode

EC

OC
34

Figure 9. Particulate Matter rates by age group for Medium heavy-duty vehicles (model year 2002, operating 

mode 24). 

Figure 10 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

nearly zero due to widespread DPF use. 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. 

0 

5 

10

15

20

25

30

35

40

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

Mean PM rate [g/hr]

Age group [years]

EC

OC
35

Figure 10. Particulate Matter rates for Heavy heavy-duty vehicles by model year group (age 0-3 years, 

operating mode 24). 

0 

10

20

30

40

50

60

70

80

90

100

1960-1987 1988-1990 1991-1993 1994-1997 1998-2006 2007-2009 2010-2012 2013-2050

Mean PM rate [g/hr]

Model year group

EC

OC
36

1.1.3 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+ 

1.1.3.1 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-55/5922: 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)28: 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)

29: 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 
37

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 19. 

 

Table 19 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 

1.1.3.2 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 

17. 

scale

t t t tt

t

f

BvAv Cv +++ mv a =

2 3

STP Equation 17

The track road-load coefficients A, B, and C pertaining to heavy-duty vehicles23 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 20. 

 

Table 20. 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
38

We then applied tampering and mal-maintenance effects through that age point, either lowering 

emissions for younger ages or raising them for older ages, using the methodology described in 

Appendix A.2 Tampering and Mal-maintenance. The tampering and mal-maintenance effects for 

HC and CO are shown in Table 21. 

Table 21 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 and later 33 

We multiplied these increases by the T&M adjustment factors in Table 37 in section A.2.3 

 Analysis to get the emissions by age group. 

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

an 80% reduction in zero-mile emission rates for both HC and CO starting with model year 2007. 

1.1.3.3 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 11 and Figure 12, 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. 
39

Figure 11. THC emission rates [g/hr] by operating mode for model year 2002 and age group 0-3. 

Figure 12. CO emission rates [g/hr] by operating mode for model year 2002 and age group 0-3. 

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 HC 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 CO rate [g/hr]

Operating mode

MHD 

HHD/Bus
40

Figure 13 and Figure 14 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 13. THC emission rates [g/hr] by age group for model year 2002 and operating mode 24.

. 

0 

10

20

30

40

50

60

70

80

90

100

Mean HC rate [g/hr]

HHD

MHD 

Bus

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

Age group [years]
41

Figure 14. CO emission rates [g/hr] by age group for model year 2002 and operating mode 24. 

Figure 15 and Figure 16 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. 

0 

100

200

300

400

500

600

Mean CO rate [g/hr]

HHD

MHD 

Bus

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

Age group [years]
42

Figure 15. THC emission rates by model year group for operating mode 24 and age group 0-3.

Figure 16. CO emission rates by model year group for operating mode 24 and age group 0-3.

1.1.4 Energy

The new data used to develop NOx rates also allowed us to develop new running-exhaust energy 

rates. These 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. 

0 

5 

10

15

20

25

30

1960-2002 2003-2006 2007-2050

Mean HC 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 CO rate [g/hr]

Model year group

MHD 

HHD/Bus
43

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. 

2

2

CO

energy CO f

HV = rr Equation 18

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

MHD, and bus regulatory classes.19 The revised inputs are shown in Figure 17. 

Figure 17 – Diesel running exhaust energy rates for MHD, HHD, and buses.

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.

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
44

1.2 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 

found later in this section and in the MOVES light-duty emission rate counterpart document 

Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator. 

Start exhaust energy rates were not updated from previous MOVES analyses. 

1.2.1 HC, CO, and NOx

For light-duty 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 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 22: 

Table 22 shows average start emissions increases for light-heavy duty vehicles (g).

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 18. 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.
45

Figure 18. 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 23. The NOx increment is negative since cold start emissions are lower than 

warm start emissions.

Table 23. 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 Tennessee30, 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 24 shows our final MOVES inputs for HHD and MHD diesel start 

emissions increases. The HC and CO estimates are from our 2007 MY in-house testing. 

 

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
46

Table 24 MOVES inputs for HHD and MHD start emissions(grams/start). 

HC CO NOx

0.0 16.0 0.0 

1.2.2 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% reduction due to DPFs). We plan

to update this valuewhen more data becomes available. 

1.2.3 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 25 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 9, which are for running exhaust emissions. 
47

Table 25. 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

The soak fractions we used for HC, CO, and NOx are illustrated in Figure 19 below. (Although, 

since our current estimate for NOx starts is zero, the NOx fractions are currently irrelevant.)

Figure 19. Soak Fractions Applied to Cold-Start Emissions (opModeID = 108) to Estimate Emissions for 

shorter Soak Periods (operating modes 101-107).

The actual PM start rates by operating mode are given in Table 26 below. 

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
48

Table 26. Particulate Matter Start Emission Rates by Operating Mode (soak fraction). 

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 

1.3 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. 

1.3.1 Data Sources

The data used in the analysis of extended idling emission rates includes idle emission results from 

several test programs conducted by a variety of researchers at different times. Not all of the studies 

included all the pollutants of interest. The references contain more detailed descriptions of the data 

and how the data was obtained. 

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

Colorado (McCormick)31. 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 
49

from 1990 t hrough 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 f ive trucks in May 2002 ( Lim)32. T he 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)33. All tests were conducted by the California Air Resources Board 

(CARB) at a r est area near Tulare, California in April 2002. D ata 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)34. 

Not all trucks were tested under all conditions. Only results from the testing in Tennessee 

and New York are described in the IdleAire report. T he 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)35. 

• The National Cooperative Highway Research Program (NCHRP)36 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)37. 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 F reightliner Century with a 1999 e ngine was tested using EPA's on-road 

emissions testing trailer based in Research Triangle Park, North Carolina (Broderick)38. 

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 v ariety of 

conditions at Oak Ridge Laboratories (Story)39. These are the same trucks used in the EPA 

study (Lim). 

• The University of Tennessee tested 24 1992 t hrough 2006 m odel year heavy duty diesel 

trucks using a variety of idling conditions including variations of engine idle speed and load 

(air conditioning)30. 

1.3.2 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. This update reflects new data available since the initial 

development of extended idle emissions for the MOVES model. The additions include the testing 
50

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 A.3 Extended Idle Data Summary. The increase 

in NOx emissions due to higher idle speed and air conditioning was estimated based on t hree 

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% and HC and CO emissions will be reduced 9% 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. 
51

1.3.3 Results

Table 27 shows the resulting NOx, HC, and CO emission rates estimated for heavy-duty diesel 

trucks. Extended idling measurements have large variability due to low engine loads, which is 

reflected in the variation of the mean statistic.

Table 27. Extended idle emission rates (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 
52

2 Heavy-Duty Gasoline Vehicles 

2.1 Running Exhaust Emissions 

2.1.1 HC, CO, and NOx

2.1.1.1 Data and Analysis 

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 both 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 28 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 28. 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 

13). To supplement the meager data available, we examined certification data as a guide to 

developing model year groups for analysis. Figure 20 shows averages of certification results by 

model year. 
53

Figure 20. 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 

• 1998-2002 

• 2003-2006 

• 2007 and later 

Although there was little data for 2007-and-later, we made a split at model year 2007 to account for 

possible increases in three-way catalyst use and efficiency due to tighter NOx standards. We 

assumed that these catalysts in gasoline vehicles will yield a reduction in HC and CO also. We 

estimate that each of these three pollutants will decrease 70% from 2003-2006 MY levels. 

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 28. We also did 

not classify by regulatory class since there was only one regulatory class (LHD2b3) predominantly 

represented in the data.

2.1.1.2 Sample Results 

Selected results are shown graphically below. The first (Figure 21) shows all three pollutants vs. 

operating mode for the LHD2b3 regulatory class. In general, emissions follow the expected trend 

with STP, though the trend is most pronounced for NOx. As expected, NOx emissions for heavyduty gasoline vehicles are much lower than for heavy-duty diesel vehicles.

0

5

10

15

20

25

30

1980 1985 1990 1995 2000 2005 2010

Model year

CO, 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
54

Figure 21. Emission Rates by operating mode for MY 1994 at age 0-3 years. 

Figure 22 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. 

Figure 22. Emission ratess by age group for MY 1994 in operating mode bin 24.

0 

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 

50

100

150

200

250

300

350

400

450

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

Mean CO rate [g/hr]

Mean HC and NOx rates [g/hr]

Operating mode

NOx

HC

CO

0 

200

400

600

800

1000

1200

1400

0 

20

40

60

80

100

120

Mean CO rate [g/hr]

Mean HC and NOx rates [g/hr]

NOx

HC

CO

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

 Age group [years]
55

Figure 23 shows emissions by model year group. Emissions generally decrease with model year 

group. Uncertainties are relatively high but not shown in this plot for clarity. 

Figure 23. Emission rates by model year group for age 0-3 in operating mode 24.

Assumptions regarding the increased effectiveness of catalysts substantially reduce emissions 

estimates for 2007 model year and later. 

2.1.2 Particulate Matter

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

the detailed emission factors the MOVES model is designed for. 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.

ForMOVES2010, the heavy-duty gas PM2.5 emission rates will be 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 (elemental and organic carbon), operating mode, model year and regulatory 

class, the heavy-duty PM2.5 emission factors will also be a complete set. 

2.1.2.1 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 29. 

0 

100

200

300

400

500

600

0 

20

40

60

80

100

120

1960-1989 1990-1997 1998-2006 2007-2050

Mean CO rate [g/hr]

Mean HC and NOx rates [g/hr]

HC

NOx

CO
56

Table 29. 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. 

2.1.2.2 Analysis 

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. T he 

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

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. 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:
57

• 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 UDDS emission factors for the older light-duty gas truck group are 36.2 mg/mi for 

MOVES organic carbon PM2.5 emissions and 2.641 mg/mi for elemental carbon. Ignoring sulfate 

emissions (on the order of 1×10-4 mg/mile for low sulfur fuels), these values sum to 38.84 mg/mile. 

This value leads to the computation of the ratio: 679.1

84.38

22.65

mile

g

mile

g

= . 

The simulated UDDS emission rates for the newer light-duty gas truck group are 4.368 mg/mi for 

MOVES organic carbon PM2.5 emissions and 0.3187 mg/mi for elemental carbon. Ignoring 

sulfate emissions (which are in the order of 1×10-5 mg/mile for low sulfur fuels), these values sum 

to 4.687 mg/mi. 

This value leads to the computation of the ratio: 578.0

687.4

71.2

mile

g

mile

g

= . 

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

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. 

2.1.3 Energy Consumption

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 MHD, HHD, and bus 

classes. Analyses performed for LHD vehicles were not updated in this analysis. Also, similarly 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 fuel-specific emission factor (fCO2) of 8,788 
58

g/gallon for gasoline (see Equation 18). STP was calculated using Equation 13. Figure 24

summarizes the gasoline running exhaust energy rates stored in MOVES. 

Figure 24 . Gasoline running exhaust energy rates for MHD, HHD, and buses.

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 1.1.1.3.3 Hole Filling and 

forecasting).

2.2 Start Emissions

2.2.1 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 30 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.

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
59

Table 30. Model-year Group by Age Group 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 

2.2.2 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 A.6 

Heavy-duty Gasoline Start Emissions Analysis Figures. 

Sample sizes are small overall and very small in some cases (e.g. 1990, age 6-7) and the behavior 

of the averages is somewhat erratic. In contrast to light-duty vehicle emissions, strong model-year 

effects are not apparent. This may not be surprising for CO or HC, given the uniformity of 

standards throughout. This result is more surprising for NOx but model year trends are no more 

evident for NOx than for the other two. Broadly speaking, it appears that an age trend may be 

evident. 

If we assume that the underlying population distributions are approximately log-normal, we can 

visualize the data in ways that illustrate underlying relationships. As a first step, we calculated 

geometric mean emissions, for purposes of comparison to the arithmetic means calculated by 

simply averaging the data. Based on the assumption of log-normality, the geometric mean (xg) was 

calculated in terms of the logarithmic mean (xl) as

l x

g x

ln = e Equation 19

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 A.6 Heavy-duty Gasoline Start 

Emissions Analysis Figures. 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 30 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) in . 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 
60

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 31 above. 

2

2

e

l s

ga = xx Equation 20

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 25 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. 
61

Figure 25. 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).

2.2.3 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 21. 

0

20

40

60

80

100

120

140

160

180

200

0 2 4 6 8 10 12 14

Age (years)

FTP Cold Start (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 Cold Start (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 Cold Start (g)

nox

g_nox

a_nox

(a) CO

(b) THC

(c) NOx
62

)1e(e 22 s2 = − s

g xs Equation 21

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 / ns . 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 31 and in Figure 26. Note that 

these results represent only “cold-start” rates (opModeID 108). 
63

Table 31. 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
64

Figure 26. Cold-start Emission Rates for Heavy-Duty Gasoline Trucks, with 95% Confidence Intervals

2.2.4 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 three model year groups: 1960-2004, 2005-2007 and 

2008 and later. We describe the derivation of rates in each group below. 

0

50

100

150

200

250

300

0 5 10 15 20 25

Age (years)

FTP Cold Start (g)

0

2

4

6

8

10

12

14

16

18

0 5 10 15 20 25

Age (years)

FTP Cold Start (g)

0

2

4

6

8

10

12

0 5 10 15 20 25

Age (years)

FTP Cold Start (g)

(a) CO

(b) THC

(c) NOx
65

2.2.4.1 Regulatory class LHD2b3

For CO the approach was simple. We applied the values in Table 31 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+ model-year groups by multiplying 

the values in Table 31 by ratios expressed in terms of the applicable standards. Starting in 2005, a 

combined HC+NOx standard was introduced. It was necessary for modeling purposed 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 

( )

37.0

hr-g/hp 1.1

g/hp0.1 - hr

(0.14 hr-g/hp 0.20)

14.0 hr-g/hp

HC =







 







+ f = Equation 22

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

We calculated the corresponding value for NOx as 

147.0

hr-g/hp 0.4

.1 hr-g/hp0 (0.14 hrg/hp 0.20)

hrg/hp20.0

NOx =

















+ −

−

f = Equation 23

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

In 2008, separate HC and NOx standards were introduced. To estimate values for this model-year 

group, we calculated the values by multiplying the 1960-2004 value by the fractions fHC and fNOx

where

127.0

hrg/hp 1.1

hrg/hp 14.0

HC = ⋅

⋅ f = Equation 24

05.0

hrg/hp 0.4

hrg/hp 20.0

NOx = ⋅

⋅ f = Equation 25
66

2.2.4.2 Regulatory classes LHD45 and MHD 

For LHD45 and MHD, we estimated values relative to the values calculated for LHD2b3. 

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 is 

58.2

hr-g/hp 4.14

hr-g/hp 1.37 fCO = = Equation 26

and the corresponding value for HC is1.73. 

73.1

hr-g/hp 1.1

hr-g/hp 9.1 fHC = = Equation 27

We applied this ratio in all three model-year groups, as shown in Table 32. 

Note that in Draft MOVES2009, the ratios in Equation 26 and Equation 27 were erroneously 

applied to the 2005-2007 model-year groups for LHD45 and MHD vehicles. In MOVES2010, 

values for these model-year groups were set equal to those for the LHD2b3 vehicles, with the 

rationale that the standards converge for both groups. 

For NOx, all values are equal to those for LHD2b3, 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 32. 

Table 32. Methods used to Calculate and Start Emission Rates for Heavy-Duty Spark-Ignition Engines

Regulatory Class Model-year Group Method

CO THC NOx

LHD2b3

1960-2004 Values from

Table 31

Values from

Table 31

Values from

Table 31

2005-2007 Values from

Table 31

Reduce in

proportion

To standards

Reduce in proportion

To standards

2008 + Values from

Table 31

Reduce in

proportion

To standards

Reduce in proportion

To standards

LHD45, MHD

1960-2004 Increase in proportion

To standards

Increase

in proportion

To standards

Same values as

LHD2b3

2005-2007 Increase in proportion

To standards

Increase in proportion

To standards

Same values as

LHD2b3

2008 + Increase in proportion

To standards

Increase in proportion

To standards

Same values as

LHD2b3
67

As for heavy-duty diesel and light-duty vehicles we applied the curve in Figure 19 to adjust the 

start emission rates for varying soak times. The rates described in this section were for cold starts 

(soak time > 720 minutes). 

2.2.4.3 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. 
68

A. Appendices 

A.1 Calculation of Accessory Power Requirements 

Table 33. Accessory load estimates for HHD trucks

Table 34. 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

y ( ) 

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
69

Table 35. 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
70

A.2 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. 

A.2.1 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 27 shows this relationship. 

Figure 27. 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 VIUS40 (Vehicle Inventory and Use Survey) data, which 

contains data on how quickly trucks of different regulatory classes accumulate mileage. Table 36

shows the emissions warranty period and approximate useful life requirement period for each of the 

regulatory classes.

Final emission rate 

d T&M 

Zero-mile 

emission 

End of warranty 

i d

End of useful life Age 

Emission rate 
71

Table 36. 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 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 37 shows the multiplicative T&M 

adjustment factor by age. We determined this factor using the mileage-age data from Table 36 and 

the emissions-age relationship that we described in Figure 27. 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 the section A.2.3 Analysis below and which is listed in the corresponding 

running exhaust sections above. 
72

Table 37 shows the 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 i ndicates no de terioration, 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( ) ,agegrppol pol,ZML TM ,agegroupTM pol += frr Equation 28

A.2.2 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 Memo41 (2006). T he 

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. T he 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 A RB’s Tampering, Malfunction, and Mal-maintenance

Assumptions for EMFAC 2007 

• University of California –Riverside (UCR) “Incidence of Malfunctions and Tampering 

in Heavy-Duty Vehicles”
73

• Air Improvement Resources, Inc.’s Comments on H eavy-Duty Tampering and Malmaintenance Symposium 

• EPA internal engineering judgment 

A.2.3 Analysis

A.2.3.1 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. T herefore, the 

occurrence rates and emission impacts will vary in 2010 and beyond depending on the regulatory 

class of the vehicles.

A.2.3.2 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. 

A.2.3.3 T &M Occurrence Rates 

A.2.3.3.1 EPA T &M Occurrence Rate 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% to 8%. 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. 
74

Other Air Problems: EPA reduced the frequency rate from EMFAC’s 8% to 6% based on the 

UCR results.

Electronics Failed: EPA will continue to use the 3% frequency rate for all model years beyond 

2010. CARB increased the rate to 30% 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 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%. 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% EGR failure rate is too high and 

reduced the rate to 10%. 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 program42 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% 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. 
75

NOx aftertreatment sensor: EPA believes the 53% occurrence rate in EMFAC2007 is too high 

and will use 10%. 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% 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: S wRI completed 6000 hour s of ESC cycling with NOx sensor. 

Internal testing supports longer life durability. Discussions with OEMs in 2007 i ndicate 

longer life expected by 2010. 

• Forward looking assumptions: M anufacturers 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% PM filter leak and system failure rate. CARB used 14% failure 

rate. They discounted high failure rates currently seen in the field.

PM Filter Disable: EPA agrees with CARB’s 2% 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% 

failure rate. This rate consists of an approximate 2% tampering rate and 3% malfunction rate. The 

catalysts are more robust than PM filters, but have the potential to experience degradation when 

exposed to high temperatures. 

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%
76

Misfuel: EPA estimated that operators will use the wrong type of fuel, such as agricultural diesel 

fuel with higher sulfur levels, approximately 0.1% of the time.

A.2.3.3.2 Tampering & Mal-maintenance Occurrence Rate Summary

A.2.3.3.2 Emission Effects

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% efficient and the selective catalyst 

reduction (SCR) systems in HHDD are 90% 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% NOX reduction with open loop control and >95% 

reduction with closed loop control.43 Visteon reports 60-80% NOX reduction with open loop 

control.44 

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% increase in NOx during a complete failure, therefore we estimated a 500% increase as a 

midpoint between normal operation and a complete failure. The LHDD vehicles with LNT 

systems would experience a 500% increase in NOx during a complete failure. We estimated a 

300% increase as a value between a complete failure and normal system operation. 

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%
77

The values with 0% effect in shaded cells represent areas which have no occurrence rate.

 

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% 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%; however, this value is reduced by 95% 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% 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
78

HC Emission Effects

EPA estimated oxidation catalysts are 80% 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% based on internal 

experience. We increased the HC emission effect of high fuel pressure to 10% 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% effect in shaded cells represent areas which have no occurrence rate. 

Tamper & Malmaintenance

PM Emission Effect

1994-97 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 Disabled 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/Low Flow 0% 0% 100% 5% 5%

EGR Disabled 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% 600% 600%

PM Filter Disabled 0% 0% 0% 1000% 1000%

Oxidation Catalyst Malfunction/Remove 0% 0% 0% 2% 2%

Mis-Fuel 30% 30% 30% 100% 100%
79

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 11 (NOx), Table 17 (PM), and Table 21 (HC and CO).

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%. 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% of all engines will have OBD in 2010, 2011, and 2012 model years, and 100% will have OBD 

by 2013 model year and later. Equation 29 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% for model years 2009 and earlier, 33% for model years 2010-2012, and 100% for model years 

2013 and later). The result from this equation can be plugged into Equation 28 to determine the 

emission rate for any age group. 

( ) pol pol nonOBD OBD pol nonOBD OBD TMTM f TM f , , = 67.01 ⋅+− Equation 29

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
80

As data for current and future model years become available, we may consider refining these 

estimates and methodology. 
81

A.3 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
82

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
83

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
84

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
85

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% 
86

A.4 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. 

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 ln( )PM ββ 10 ++= 5.0STP σ

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 9, page 11).
87

A.5 Heavy-duty Diesel EC/OC Fraction Calculation

A.5.1 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. 

A.5.2 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.45 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).45 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 and OC rates for any given driving cycle.

A.5.2.1 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). 
88

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. 

A.5.2.1.1 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: 
89

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 does Monte Carlo 

simulation with variable inputs. 

For emission modeling purposes using PERE-HD, several key inputs were modeled as probability 

distributions. 

A.5.2.1.2 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”

(VMT) by model year. Accordingly these data were normalized to total VMT to develop a 

Distribution of BMI in Simulated Population

% of Simulated

Population

BMI

0.000

0.010

0.020

0.030

0.040

0.050

0.060

10 20 30 40 50
90

probability distribution. Model year distributions in 1997 were normalized to the current calendar 

year (2008).1 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: 

A.5.2.1.3 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 microdata obtained from the Census Bureau.46 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: 

 

1 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
91

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 in3

 and 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
92

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).

A.5.2.1.4 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 6).47 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:

A.5.2.1.5 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
93

A.5.2.1.6 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. 

A.5.2.2 Prediction of Elemental Carbon and Organic Mass based on PERE-HD

A.5.2.2.1 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. 
94

A.5.2.2.2 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. 
95

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 samples [cite?], 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.48 That standard operating procedure defines 

thermal/optical reflectance (TOR) as the desired method for analysis of ambient carbon PM. 

A.5.2.2.3 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.49 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 

of each datum depends on the level of analytical uncertainty reported. The inverse of the DRI-
96

reported uncertainty (1/σ) associated with the TOR-based measurement was used to weight each 

point in the weighted regression. 

It should be noted that for each regression, the intercept term was set to zero. Models including 

intercepts did not have intercept terms that reached statistical significance. As such, R2 values are 

not considered valid. 

Coefficients from the weighted regression for EC and OC are reported below: 

Slope 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

.1.2.3.4.512 34510203040501002003004005001000

EC-TOR Measured

1000

500 400

300

200

100

5040

30

20

10

5

4

3

2

1

.5.4

.3

.2

.1
97

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. 

A.5.2.2.4 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

.1.2.3.4.512 34510203040501002003004005001000

OC - TOR Measured

1000

500 400

300

200

100

5040

30

20

10

5

4

3

2

1

.5.4

.3

.2

.1
98

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: 

NV

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
99

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
100

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: 

Ce

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 

artifacts that result solely from the behavior of simplistic logistic curves at the bounds of IMEP in 

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

EC/OC
101

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)
102

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.50 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. 

A.5.2 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.51 

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. 
103

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.
104

A.5.3 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
105

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/OM 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
106

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.52 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. 

A.5.4 Calculating EC/OC fraction by OperatingMode 

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 OC emission rates, as opposed to total PM, 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 OC rates, we must calculate EC and OC fractions by MOVES 

operating mode. Then, the total PM rate can be multiplied by the EC and OC fractions to obtain 

EC and OC input emission rates. 

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. 

m

BvAv Cv mv a VSP ttttt

t

+++ =

32

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, 

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
107

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 OC 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 relation 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 OC rates were also computed for each second during each cycle, we were able to 

average the EC and OC rates by operating mode. Then, we calculated the fractions of EC and OC 

for each operating mode. For the LHD classes, we used the MHD fractions, and for buses, we used 

the HHD fractions. 

∑∑

∑

+ =

EC OC

EC

EC

rr

r

f , EC

EC OC

OC

OC f

rr

r

f −= + =

∑∑

∑ 1

The resulting EC fractions by operating mode are shown in Figure 6 in the main body of this 

report.

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
108

A.6 Heavy-duty Gasoline Start Emissions Analysis Figures

Figure 28. Cold-Start Emissions (FTP, g) for Heavy-Duty Gasoline Vehicles, averaged by Model-year and Age 

Groups
109

Figure 29. Cold-Start FTP Emissions for Heavy-Duty Gasoline Vehicles, GEOMETRIC MEANS by Modelyear and Age Groups
110

Figure 30. Cold-start FTP Emissions for Heavy-Duty Gasoline Trucks: LOGARITHMIC STANDARD 

DEVIATION by Model-year and Age Groups.
111

Figure 31. Cold-Start Emissions for Heavy-Duty Gasoline Trucks: RECALCULATED ARITHMETIC 

MEANS by Model-year and Age Groups.
112

Table 38 - 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
113

A.7 Peer Review Comments and Responses

A.7.1 Comments from Josias Zietsman, Ph.D., P.E. 

Dr. Zietsman is the Head of the Environment and Air Quality Division at the Texas Transportation 

Institute (TTI), as well as a faculty member at Texas A&M University. He has over a decade of 

experience in research in the areas of air quality, vehicle and engine emissions and transportation 

planning. Dr. Zietsman has published widely and frequently addresses audiences in the United 

States and internationally. He serves on the Transportation Research Board as Secretary of the 

Performance Measurement Council and as a member of the Air Quality and Sustainability 

Committees.

GENERAL COMMENTS

• MOVES will be a considerable improvement to MOBILE 6.2. The heavy duty 

vehicle component is a very important part of the overall MOVES program. The 

flexibility provided by the VSP approach will increase the flexibility and accuracy 

of emissions estimation. The EPA should be commended for taking on this 

important and ambitious task.

• The report is well written considering how difficult it is to convey the highly 

technical material. I did not notice any major flaws in the methodologies used and it 

is my opinion that the authors did a fine job in coming up with creative ways to 

produce emissions rates with limited data.

• It is clear that the MOVES model will have to be strengthened with a highly focused 

data collection effort that should involve emissions testing in areas where the data is 

lacking or non-existent. The MOVES team can also benefit from existing studies 

that were not included in this analysis. For example, in my answers below I 

highlight a few studies performed by TTI’s Center for Air Quality Studies that could 

be used in adding to the overall dataset. 

• In my specific comments included in change tracker in the attached report I raise 

some questions and make some suggestions that could improve the report and the 

analysis. 

PEER REVIEW CHARGE QUESTIONS

1. a) Does the presentation give a description of selected data sources sufficient to allow 

the reader to form a general view of the quantity, quality and representativeness of data 

used in the development of emission rates? 

The authors did a fine job in describing what data sources they used and what the 

limitations of the data were. 

b) Are you able to recommend alternate data sources might better allow the model to 

estimate national or regional default values?
114

TTI’s Center for Air Quality studies has performed quite a few studies using mostly 

PEMS equipment that could enhance the database used for this analysis. We will be 

happy to share any information gathered during these studies: 

Identifying and Testing SmartWay Technologies for Drayage Trucks 

Sponsor: Texas Commission on Environmental Quality 

Budget: $220,000 

Description: PEMS testing of before and after application of SmartWay technologies for 5 

HHD Mexican drayage trucks.

Location: Study performed in El Paso Texas. 

Emissions Testing of Carbon Chain Combustion Catalyst

Sponsor: Carbon Chain Technologies 

Budget: $300,000

Description: PEMS testing of before and after application of 2CT Combustion Enhancer of 

4 long haul HHD trucks and 2 LHD2b pickup trucks. 

Location: Study performed at TTI’s High Speed Test Track in Pecos, Texas

Expanding MOBILE6 Rates to Accommodate High Speeds

Sponsor: H ouston Advanced Research Center and Center for International Intelligent 

Transportation Research

Budget: $150,000

Description: PEMS testing of 3 long haul HHD trucks and 3 LHD2b pickup trucks. 

Location: Study performed at TTI’s High Speed Test Track in Pecos, Texas

Emissions of Mexican-domiciled Heavy-Duty Diesel Trucks using Alternate Fuels 

Sponsor: EPA Region 6 through AACOG

$160,000 

Description: PEMS, TEOM and filter testing of before and after application of ULSD and 

biodiesel for 5 HHD Mexican drayage trucks and 5 HHD long haul Mexican trucks 

Location: Study performed in Laredo, Texas 

School Bus Biodiesel (B20) NOx Emissions Testing

Sponsors: CAPCOG and CAMPO 

Budget: $35,000 
115

Description: PEMS testing of 5 school buses 

Study performed at TTI’s Riverside Test Facility in Bryan, Texas 

Mexican Truck Emissions at Major Texas Border Locations 

Sponsors: SWUTC, EPA, and Border Environmental Cooperation Commission 

Budget: $100,000 

Description: PEMS, TEOM and filter testing of 5 HHD Mexican drayage trucks 

Location: Study performed in El Paso, Texas 

RESPONSE:

EPA plans to analyze any new relevant data for future versions of MOVES. We are 

particularly interested in filling holes and also current and future model year data. We 

have also conducted an extensive study at the Port of Houston on drayage trucks and hope 

to finalize the results and data from that study in the near future.

2. a) Is the description of analytic methods and procedures clear and detailed enough to 

allow the reader to develop an adequate understanding of the steps taken and 

assumptions made by EPA to develop the model inputs? 

The descriptions are clear for the most part and the reader is able to develop an adequate 

understanding of the steps taken and assumptions made. In the attached document that is 

marked with track changing the specific places that need more clarity are shown. 

RESPONSE: 

EPA appreciates the comment. We have incorporated many of the recommended edits 

directly into the final report.

b) Are examples selected for tables and figures well chosen and designed to assist the 

reader in understanding approaches and methods?

Yes, the examples for tables and figures seem to be representative to help the reader 

understand the approaches and methods. 

3. a) Are the methods and procedures employed technically appropriate and reasonable, 

with respect to the relevant disciplines, including physics, chemistry, engineering, 

mathematics and statistics? 

Yes, the methods and procedures employed seem technically appropriate and 

reasonable. The analyses are highly constrained by a lack of data and the MOVES team 

was able to use creativity to overcome this burden 
116

b) Are you able to suggest or recommend alternate approaches that might better achieve 

the goal of developing accurate and representative model inputs? In making 

recommendations please distinguish between cases involving reasonable disagreement 

in adoption of methods as opposed to cases where you conclude that current methods 

involve specific technical errors.

. In the attached document marked with change tracking I pointed out certain areas where 

I have some specific questions about the analyses or where I think it can be improved. 

The suggestions I made involve reasonable disagreements and I did not identify gross 

technical errors. Overall I think the methodologies are sound. 

4. In areas where EPA has concluded that applicable data is meager or unavailable, and 

consequently has made assumptions to frame approaches and arrive at solutions, do you 

agree that the assumptions made are appropriate and reasonable? If not, and you are so 

able, please suggest alternative sets of assumptions that might lead to more reasonable 

or accurate model inputs while allowing a reasonable margin of environmental 

protection.

Yes, I think the assumptions made to deal with limited data are sound. However, in the 

attached document marked with change tracking I did point out certain areas where the 

assumptions can be improved. The key is to assemble and collect the lacking data to 

overcome the burden of making numerous assumptions and extrapolations. 

RESPONSE: 

 Collecting additional data will certainly improve assumptions, but is usually a longerterm project. 

5. a) Are the resulting model inputs appropriate, and to the best of your knowledge and 

experience, reasonably consistent with physical and chemical processes involved in 

exhaust emissions formation and control? 

Yes, the model inputs are appropriate are consistent with physical and chemical 

processes involved in exhaust emissions formation and control. The rates can be used as 

is but it could perhaps be improved by addressing some of the questions/comments 

highlighted in the attached report. The rates will benefit significantly from more data 

collection and assembly. 

b) Are the resulting model inputs empirically consistent with the body of data and 

literature that has come to your attention?

Yes, the model inputs are empirically consistent with the body of data and literature that 

has come to my attention. In my response to Question 1 above I listed 6 studies 

performed by TTI’s Center for Air Quality Studies that produced emissions testing data 

of heavy duty vehicles and that could be of use to expand the existing database. The 

following are some examples of areas where additional testing will greatly enhance the 

emission rates developed for heavy duty vehicles:

• NOx emissions due to DPF regeneration
117

• NOx during cold starts (varying soak times)

• PM emissions during starts (varying soak times)

• Aftertreatment effectiveness during extended idling

• PM2.5 emissions for heavy duty gasoline vehicles

• Emissions of 2007 and later heavy duty gasoline vehicles

• PM emissions at the higher operating mode bins

RESPONSE: 

EPA certainly is aware of our heavy-duty data needs as well as the value in additional 

testing. We will look to incorporate any existing data that you and others have brought up 

to fill appropriate holes in the model. Also, we are looking to improve our current and 

future model year data set with the manufacturer-run in-use heavy-duty diesel test 

program. 

 
118

A.7.1 Comments from John Storey:

Dr. Storey is a senior researcher in the Fuels, Engines and Emissions Research Center at the Oak 

Ridge National Laboratory. He has 20 years of experience in research in emissions measurement 

and characterization, including analysis of the composition of the hydrocarbon and particulate 

components of vehicle exhaust. As a member of the Transportation Research Board, Dr. Storey 

serves on the Committee on Transportation and Air Quality. 

PEER REVIEW CHARGE QUESTIONS

1. The description of the data sources is adequate. This reviewer was familiar with many of 

the studies cited, and in particular, the ROVER study, the WVU MEMS study and the 

Coordinating Research Council (CRC) E-55/59 study on heavy-duty vehicles. My 

comments are divided into sections corresponding to the report:

NOx data The use of the ROVER and WVU MEMS data for NOx emission factor is 

appropriate and valuable in that it represents real-world operating emissions and can be directly 

related to the VSP from the weight of the vehicle and the engine data. However, the on-board 

sensors used in the studies are typically NO sensors. In looking at the referenced report by 

Jack, no detail on the actual sensor is given, except that it is a four gas analyzer for NOx, HC, 

CO, CO2. These units are most common in compliance inspection, not typically of research 

quality, and likely use an electrochemical sensor for NOx which is mostly sensitive to NO. 

Jack reports that intercomparison exercises were done with the EPA-NVFEL instruments, but 

nothing in the Draft MOVES2009 report explains how these results related to measurements 

with research quality instruments, nor is any assumed NO/NOx ratio reported. For instance, if 

the ROVER primarily measures NO, than the report should explain that an assumption was 

made that 95% of the NOx was NO. This may have been done in the subsequent re-analysis of 

the data by EPA staff, but no mention is made. For the WVU MEMS testing, the referenced 

report shows that a NO2 – NO converter is used before measurement by the ZrO2 sensor. Also, 

a comparison was made with the WVU chassis dynamometer facility which employs research 

grade chemiluminescence analyzers for NOx measurement. No mention of a NO2-NO 

converter prior to the ROVER is made in Jack’s report. Finally, none of the NOx data collected 

by WVU as part of the E55/E59 study was included, even though the study had trucks in the 

age bins which were not represented by the ROVER study and the WVU-MEMS study. The 

chassis dynamometer data would likely be much more accurate because VSP is measured 

directly and the NOx emissions measurements are made with a research quality instrument. 

RESPONSE:

In past testing, EPA has found good correlation in emissions results between ROVER and 

chassis and engine dynamometer testing. Further, we have conducted validation of the 

onboard in-use data (via MOVES outputs) with CRC E-55/59 results. These results are 

summarized in validation. 
119

Because of the sensitivity of tropospheric ozone predictions to NOx, and the relative 

importance of HD vehicles to NOx budgets in non-attainment areas, it is my opinion that EPA 

needs to use the NOx section 1.1.1 to carefully justify their decisions. Furthermore, if there is a 

future plan to include the E55/E59 NOx data, or use the data to validate MOVES, than it is 

appropriate to state that in this section.

A new source of data for older vehicles which may be useful is an extensive study done by the 

Texas Transportation Institute and Oak Ridge National Laboratory for the Alamo Area Council 

of Governments under an EPA Region 6 grant.[Zietsman, J. et al. “Emissions of MexicanDomiciled Heavy-Duty Diesel Trucks Using Alternative Fuels.”, October 2007] In this study, 

emissions from ten trucks that operate on the Mexican border at the port of Laredo were 

characterized with ultralow sulfur diesel, as well as biodiesel blends. A wide range of model 

years were represented. Both idling and on-road drive cycle measurements were made, the 

latter with both a Sensors, Inc. Semtech-D PEMS and a Clean Air Technologies Montana 

system. Although the trucks were Mexican-registered, the engines were all American made 

(CAT, Cummins, and DDC), and the serial numbers recorded. These engines were likely 

certified to U.S. emissions standards at the time of purchase. A complete report is available by 

contacting the author at tti.tamu.edu. 

VSP This section was well-explained and the data and assumptions reasonable. The driveline 

efficiencies do seem to vary considerably, but the assumption of 90% appears in line with the 

data. For future consideration, there is a large set of data available to the public that includes 

actual wheel torque measurements of a heavy-duty truck operating on a cross-country route, 

along with engine bus and GPS data for speed, grade, etc. The report describing the data 

collection is titled “Class-8 Heavy Truck Duty Cycle Project Final Report”, and is available at 

http://cta.ornl.gov/cta/publications.shtml#2008 . 

PM This section does an excellent job of explaining the sources of the PM data and the 

assumptions made. The CRC E55/E59 dataset is large and very appropriate, and the use of 

corrected TEOM data is as close to reality for transient emissions given the measurement 

technology available at the time. Absent another huge study like E55/E59, this set of data 

represents the only available comprehensive record of in-use trucks. Recent advances in 

measurement technologies such as the DMM (Dekati), the Electrical Aerosol Analyzer (TSI), 

and the quartz crystal microbalance (Sensors, Inc.) for real-time PM have been demonstrated, 

but have mostly been applied to DPF-equipped vehicles to demonstrate their sensitivity, or 

represent tiny sample sizes. It is recommended that these data, in particular for non DPFequipped vehicles be used in the future to validate the emissions factors in MOVES. 

RESPONSE: 

EPA agrees that it is important to test and analyze DPF-equipped vehicles for MOVES 

model validation and updates. Accurate determination of the in-use emission performance 

of these vehicles is critical in the development of future PM emission inventories. When 

such in-use data become available in sufficient quantity EPA will analyze it and update 

the emission models as required and appropriate. 
120

HC and CO The emissions data for HC and CO are also quite extensive and represent a wide 

range of in-use vehicles. My major concern is that the altitude effects on the NFRAQ data was 

considered to see if there was a systematic bias due to these effects. Of course, the effect would 

be more pronounced at low load conditions that have little or no turbocharging. The decision to 

exclude the NDIR based ROVER data; even though research grade instruments use NDIR for 

CO, the instrument used in ROVER was meant for use with gasoline vehicles, and thus had 

poor sensitivity at the lower levels of CO found in diesels. 

Start emissions The data for diesel vehicles in this category were extremely limited. In 

personal experience with these measurements, the trends shown by Figure 14 for gaseous 

emissions are correct, with NOx emissions increasing after start, and CO and HC decreasing. 

The difference in cold FTP vs hot FTP emissions is an appropriate measure of PM due to cold 

start; I am surprised there isn’t more data available since all engines must include a cold FTP in 

their certification process. Given the long daily duty-cycles of these vehicles, and their 

relatively high emissions, the importance of cold start emissions for MHD HHD vehicles is 

pretty limited in comparison to modern light-duty gasoline vehicles which have virtually zero 

emissions after cold start and shorter daily duty-cycles. The caveat is that the increased 

emphasis on anti-idling policy may result in more start events occurring, in particular for MHD 

vehicles. Thus more data is required, or at least a sensitivity analysis, to determine if multiple 

starts in daily service will have an impact on a vehicle’s overall contribution to the airshed. 

RESPONSE: 

Manufacturers do not typically separate their cold FTP result from their hot FTP result 

when certifying their engines. Rather, they report a weighted average. Thus, publicly 

available certification data was not a viable source for cold start rates.

Extended idling emissions Extended idling is critical to “hot spots” around truck stops and 

understanding the effects of congestion on mobile source emissions. For this part of the model, 

MOVES makes use of a large amount of available data and results presented in the appendix 

show generally good agreement. This reviewer’s study on extended idling found that ambient 

temperature played an important role due in particular to increased accessory loads such as 

cooling fan and air conditioning. It wasn’t clear if these effects were included. 

RESPONSE: 

EPA appreciates the comment. At this point, we do not attempt to model temperature 

effects on extended idling emissions. 

Heavy-duty gasoline data This is a very small class of vehicles and the data available are 

sparse. The reported certification data are appropriate to use for HC. NOx, and CO. The 

analysis used to calculate the PM emissions factors and the cold start emissions will be 

reviewed in the next section. The only other suggestion is that the LHD2B vehicles may be 

represented in the NCHRP database from CE-CERT.

2. The description of the analytical methods and procedures are clear and detailed, for the 

most part. Detailed comments appear below in the order of the report headings. In general, 
121

it would help the reader to have more detail in the figure captions, particularly for the 

examples. As an example, Figure 6 is typical; assumes a MY 2002 MHD vehicle operating 

in bin 24, then displays its emissions changes with time. It would be helpful if these 

example plots throughout the document were explained briefly; i.e.,to say that emissions 

are projected to increase due to T&M, etc. Figure 10 and 11 are confusing in that no 

explanation is given for the huge departures of the MHD vehicles (Figure 10) and bus 

(Figure 11) from the curves for the other two classes of vehicles. The justification for 

geometric mean usage given in Appendix 6 is hard to understand because the figures are of 

such poor quality. 

VSP calculations This section was well-written and communicated a somewhat difficult 

concept clearly. The hole-filling methodology is important and was described adequately. 

RESPONSE: 

EPA appreciates the comment. As described in the draft report and detailed in the final 

report, we modified the calculation of tractive power to normalize the axle power by fixed 

scaling factors for the purpose of fitting rates for heavy-duty vehicles into the same 

numeric ranges originally developed and applied to vehicle-specific power (VSP) for 

light-duty vehicles, which involved normalizing by weights of individual vehicles. Rather 

than keeping the term “vehicle-specific power” for this parameter, we renamed it as 

“scaled tractive power” (STP), to minimize confusion. This change in approaches 

preserves relationship between emissions and power for engines certified to brake-specific 

standards, which better characterizes heavy-duty emissions.

Emission Rate Calculations This section also was detailed and provided a good explanation of 

how the emissions rates were calculated, although in Table 10 it illustrates how little NOx data 

exists for the different classes and ages of vehicles. The data from E55/E59 could be used to 

fill some of these missing table cells.

RESPONSE: 

For a given pollutant, we did not want to mix the data sources, given the differences in 

calculating road load/STP and the data collection process (drive cycles, test equipment, 

etc). Also, keeping CRC E55/59 separate helped use that as an independent validation of 

the onboard results. 

PM data analysis (1.1.2.2) The procedures to carry out the PM analysis were detailed enough 

to understand. It is critical that the TEOM correction be understood, so even including a figure 

to illustrate a snapshot of data and corrected data would be great, but not necessary for the 

report. The appendix A4 does not explain the hole-filling very well. An example in the main 

text or the appendix would help. 
122

RESPONSE: 

The design of the MOVES model was originally intended to be “data driven.” To 

implement this approach, large volumes of data were dis-aggregated and reclassified into 

ranges defined as “operating modes,”for which simple statistics were computed and used 

as emission rates in the model. This approach, however, does not produce complete 

results across all vehicle classes, ages or operating modes due to lack of data in specific 

combinations of these categories. To compensate, a “hole-filling” method was developed 

where the entire dataset was regressed using a least-squared log-linear model. Output 

from this model was used to fill in missing bins in the model matrix of operating mode, 

vehicle class and model year group. 

Tampering and Mal-maintenance T&M calculations are detailed and good examples given. 

OC/EC split An excellent and extensive explanation is given in the memo in the Appendix. My 

only comment is that it seems excessively detailed and long (essentially a research paper is 

reproduced in the appendix), thus giving the appearance of much more weight to this 

calculation and the importance of OC/EC than the others. 

RESPONSE: 

It is not the intent of the authors to over-emphasize the importance of the OC/EC split 

topic in relation to other topics in the report. However, it is a significant and exciting new 

area of future work that the authors felt deserved full treatment. 

3. The methods employed in this report are appropriate and reasonable from the viewpoint of 

the physical and mathematical sciences. The VSP approach is well-suited to heavy-duty 

vehicles and off-road vehicles, which will be incorporated in the future. Although 

alternatives to the VSP approach exist, a significant commitment has been made to this 

approach and it would be extremely costly to change. 

An example of an alternative approach would be to use vehicle emissions as a function of speed 

and acceleration. The advantage of this approach is that it makes micro-scale simulations of 

road projects, i.e. for road/signal changes, much easier to perform. In addition, data is 

relatively easy to collect and exists for the light-duty fleet. The data sources for the heavy-duty 

fleet could be the same as used for the development of the emissions factors, just analyzed in a 

different way. The major disadvantage is the inability of this approach to simulate weight and 

grade changes, especially for HHD vehicles. Also off-road vehicles couldn’t be simulated with 

speed and acceleration maps. The VSP approach offers the future advantage of incorporating 

GIS information about road grade so that adjustments can be made in VSP, and thus emissions, 

as a function of terrain. 

Specific recommendations and comments. Figure 3 shows deterioration rates for vehicles in 

MY2010. This appears unrealistic since emissions control systems are certified for 440,000 

miles, and the deterioration factors shown are very high. With modern vehicles and the 

extensive diagnostic systems, it may be important to revamp the deterioration assumptions 

based on earlier, no-catalyst vehicles. If there are valid reasons for Figure 3, they are not 

clearly explained in the text. 
123

RESPONSE: 

In Figure 3 (now Figure 4 in the final report), the age effect is modeled from the 

tampering and mal-maintenance analysis. This analysis and methodology are explained 

in Appendix A.2. We will certainly consider revision of MY 2010+ age effects as more 

data becomes available. We do not feel it is appropriate to use pre-catalyst assumptions 

for newer vehicles since the deterioration behaviors of the two types of technology are 

different. Further, we reduced estimated T&M effects with the introduction of On-board 

Diagnostics (OBD). Heavy heavy-duty Class 8 trucks accumulate mileage quickly; Table 

35 shows the warranty age and useful life age of the various heavy-duty regulatory 

classes.

In section 1.3.2, the justification for the extended idling PM factor of 11% makes no physical 

sense. The MY 2007+ vehicles are equipped with a [diesel particulate filter ]DPF which 

physically filters the exhaust. This component can’t be “turned off” – it remains 95+% 

effective until it fills up and the engine stalls. No bypass exists for extended idling. In extreme 

cases, the heavy HC might accumulate and break through and be counted as PM in a filter 

measurement, but no data or physical evidence exists for such an occurrence. 

RESPONSE: 

We generally agree with this comment. Accordingly, we have revised the extended idle 

PM rates, making them equal to the curb idling rates. The resulting rates are shown in 

Table 26. 

4. EPA has done an adequate job with hole-filling and data sparseness for the most part. I agree 

with most of the assumptions, although I think speculating on the deterioration of MY 2007+ 

emissions controls is very challenging, and applying “old-style” deterioration factors may be 

problematic. It will be critical to update these factors as more data becomes available. Specific 

comments are covered in the previous sections of this review and below: 

RESPONSE: 

We agree that future updates will be required on 2007+ model year PM emission factors 

and 2010+ NOx emission factors. When such data are available, EPA plans a full review 

and update as feasible and required.

NOx I believe there is a wider breadth of MY data available in the CRC E55/E59 program and 

this should be taken advantage of. This was mentioned previously, and the rationale for 

excluding the NOx data from the CRC study, but including the PM and HC/CO data is not 

explained.

RESPONSE: 
124

We did not want to mix the data sources, given the differences in calculating road 

load/STP and the data collection process (drive cycles, test equipment, etc). Also, keeping 

CRC E55/59 separate helped use that as an independent validation of the onboard results. 

PM In section 1.1.2.2.4.2, the LHD and MHD factors are proportioned by engine work ratios. 

There is little justification for this approach given except that less work is done by the LHD. 

The LHD engines are certified to the same values as the MHD engines, and my understanding 

of VSP and the TEOM data taken from the CRC study is that the PM emissions rates were 

developed for VSP bins, so the LHD vehicles populate the higher VSP bins since their power to 

weight ratios are higher. I may be completely off-base with this comment, but at any rate, the 

explanation is not sufficient for me to understand. 

 The bus data correction makes more sense to me since it is based on certification level and 

buses have similar hard accelerations to HHD vehicles.

HD gasoline vehicles The reality of the newer HDGVs is that they are equipped with the same 

three-way catalyst technology as their LDV counterparts and likely have similar calibrations to 

maximize fuel economy. It seems that the data must exist in the NCHRP or similar efforts for a 

LHD2B like the Ford F-250 and a similar model year F-150 for comparison. The larger heavier 

vehicle may not have different VSP emissions since they are likely similarly equipped. 

RESPONSE: 

We will look into this for future analyses. Different regulations are the driving force for 

our separation of the LHD2b’s from LD trucks, for both criteria emissions and energy 

rates. 

5. The resulting model inputs are consistent with the physics and chemistry of exhaust 

emissions and emissions control. I have pointed out previous inconsistencies in the 

previous sections, the most important of which are the following: 

PM for extended idling The DPF for MY 2007+ will physically continue to work, so a factor of 

11% effectiveness is not realistic.

RESPONSE: 

We generally agree with this comment. Accordingly, we have revised the extended idle 

PM rates, making them equal to the curb idling rates. The resulting rates are shown in 

Table 26. 

Gasoline HDVs Because the vast majority of the gasoline HDVs are LHD2B’s, and these trucks 

use powertrains developed for their much more popular LDV cousins - e.g. Ford F series, 

Chevrolet Silverado - it is reasonable to assume similar performance, on a VSP basis, for these 
125

vehicles to the LDVs, despite there being a different certification standard. I would say this would 

apply at least to the last 8 model years. It is not clear to me how cooperative a manufacturer might 

be with the EPA in confirming this assumption, but it is probably worth a try. 

 RESPONSE: 

We will look into this for future analyses. Different regulations are the driving force for 

our separation of the LHD2b’s from LD trucks, for both criteria emissions and energy 

rates. 

Summary thoughts. 

The MOVES report on heavy-duty diesel emissions demonstrates a breadth and depth of 

knowledge of the pertinent aspects of emissions measurement and emissions control for HHDs. 

The issues this reviewer had were fairly minor and can be resolved easily in the future. The report 

model appears ready for use and none of my comments should hinder its timely release. A separate 

attachment will identify misplaced words and typographical errors.

Future data

Of course, it will be important to keep updating the emissions factors as additional information is 

made available. As an example, Oak Ridge National Laboratory is presently evaluating two 

different classes of medium-duty diesel vehicles (three transit buses, three box vans) for the 

purposes of developing duty-cycle simulations. As a disclaimer, the group doing the study is not in 

the same center as this reviewer, and I have no funding relationship with their center. No 

emissions are being measured in this study, but extensive duty-cycle data is being collected from 

each engine and vehicle in its daily operation. These vehicles represent an opportunity for EPA to 

fill holes in the MHD data. Because the recruitment and instrumentation of the vehicles has been 

done, the incremental costs for obtaining emissions data are relatively small, and the DOE program 

manager funding the project has stated his interest in collaborating with EPA and other agencies on 

obtaining more data from the project. 

It is recommended also that funding for these updates be maintained, perhaps with collaboration 

from the Regions, which will be widely affected by the implementation of MOVES. Regions will 

likely be motivated to have the information in MOVES that is critical for their area in order to help 

their airsheds get out of non-attainment. By keeping MOVES in the forefront when new emissions 

data is collected, the value of each new study is enhanced.

RESPONSE: 

EPA welcomes any new relevant data to incorporate into MOVES, especially in filling 

holes in existing data as well as current and future model years.
126

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