Development of Emission Rates for 

Heavy-Duty Vehicles in the Motor 

Vehicle Emissions Simulator 

(Draft MOVES2009) 

Draft Report 
Development of Emission Rates for 

Heavy-Duty Vehicles in the Motor 

Vehicle Emissions Simulator 

(Draft MOVES2009) 

Draft 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. 

EPA-420-P-09-005 

August 2009 
Table of Contents 

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

1.1 Running Exhaust Emissions ......................................................................................................... 1 

1.1.1 Nitrogen Oxides (NOx) ......................................................................................................... 2 

1.1.1.1 Data Sources ................................................................................................................... 2 

1.1.1.2 Calculating VSP from in 1-hz data ................................................................................. 3 

1.1.1.3 Calculating emission rates .............................................................................................. 7 

1.1.1.4 Sample results............................................................................................................... 12 

1.1.2 Particulate Matter (PM) ....................................................................................................... 14 

1.1.2.1 Data Source................................................................................................................... 15 

1.1.2.2 Analysis ........................................................................................................................ 17 

1.1.2.3 Sample results............................................................................................................... 23 

1.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO) ............................................................... 25 

1.1.3.1 Data Sources ................................................................................................................. 25 

1.1.3.2 Analysis ........................................................................................................................ 26 

1.1.3.3 Sample results............................................................................................................... 27 

1.1.4 Updates for final MOVES release ....................................................................................... 31 

1.2 Start Exhaust Emissions.............................................................................................................. 32 

1.2.1 HC, CO, and NOx................................................................................................................ 32 

1.2.2 Particulate Matter................................................................................................................. 34 

1.2.3 Adjusting Start Rates for Soak Time ................................................................................... 34 

1.3 Extended Idling Emissions ......................................................................................................... 36 

1.3.1 Data Sources ........................................................................................................................ 36 

1.3.2 Analysis ............................................................................................................................... 37 

1.3.3 Results.................................................................................................................................. 38 

2 Heavy-Duty Gasoline Truck emissions ......................................................................................... 39 

2.1 Running Exhaust Emissions ....................................................................................................... 39 

2.1.1 HC, CO, and NOx................................................................................................................ 39 

2.1.1.1 Data and Analysis ......................................................................................................... 39 

2.1.1.2 Sample Results.............................................................................................................. 40 

2.1.2 PM........................................................................................................................................ 42 

2.1.2.1 Data Source................................................................................................................... 42 

2.1.2.2 Analysis ........................................................................................................................ 43 

2.2 Start Emissions ........................................................................................................................... 44 

2.2.1 Available Data ..................................................................................................................... 44 

2.2.2 Estimation of Mean Rates.................................................................................................... 45 

2.2.3 Estimation of Uncertainty.................................................................................................... 47 

2.2.4 Projecting Rates beyond the Available Data ....................................................................... 48 

2.2.4.1 Regulatory class LHD2b............................................................................................... 49 

2.2.4.2 Regulatory classes LHD345 and MHD ........................................................................ 50 

2.2.4.3 Particulate Matter.......................................................................................................... 51 

A. Appendices................................................................................................................................... 52 

A.1 Calculation of Accessory Power Requirements for VSP bins ................................................... 52 

A.2 Tampering and Mal-maintenance .............................................................................................. 54 

A.2.1 Modeling Tampering and Mal-maintenance....................................................................... 54 

i 
A.2.2 Data Sources ....................................................................................................................... 56 

A.2.3 Analysis............................................................................................................................... 56 

A.3 Extended Idle Data Summary .................................................................................................... 63 

A.4 Regression to develop PM emission rates for missing operating mode bins............................. 68 

A.5 Heavy-duty Diesel EC/OC Fraction Calculation....................................................................... 70 

A.5.1 Introduction......................................................................................................................... 70 

A.5.2 PERE for Heavy-duty Vehicles (PERE-HD) and Its Extensions ....................................... 70 

A.5.2.1 PERE-HD Fleet-wide Average Emission Rate Estimator ........................................... 70 

A.5.2.2 Prediction of Elemental Carbon and Organic Mass based on PERE-HD.................... 76 

A.5.2 Comparison of Predicted Emissions with Independent Measurements.............................. 84 

A.5.3 Variability in Predicted EC and OC Emission Rates.......................................................... 85 

A.5.4 Calculating EC/OC fraction by MOVES operating mode bin............................................ 88 

A.6 Heavy-duty Gasoline Start Emissions Analysis Figures ........................................................... 90 

ii 
1 Heavy Duty Diesel Emissions 

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

trucks. 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 defined by 23 operating modes which will be discussed below. The 

‘extended idle’ process occurs after 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 accuracy of second-by-second emission rates 

and parameters that can determine vehicle-specific power, vehicle tractive power normalized by 

weight (VSP). Heavy-duty emission rate analysis is no exception. However, compared the lightduty, the amount of data available is small. The VSP approach was developed with light-duty 

vehicles in mind, and as a result, the same approach needed to be applied for heavy-duty vehicles. 

While VSP is a good way to characterize emissions from light-duty vehicles, the range of running 

weights, coarseness of the VSP bin structure, and work-based (not distance-based) emissions 

standards make VSP-based analysis for heavy-duty diesel vehicles a challenge. Nevertheless, this 

report explains how we analyzed second-by-second heavy duty emission data to fit the VSP 

structure in MOVES. 

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 long standing gross 

vehicle weight rating (GVWR) classifications and the model year groupings are differentiated by 

EPA emission standards. 

Table 1 – MOVES stratifies HD emission rates into five regulatory classes 

Regulatory Class name MOVES Name Gross Vehicle Weight Rating 

(GVWR) [lb] 

2b Light-heavy duty [LHD2b] Class 41 8500 – 14000 

Light-heavy duty [LHD345] Class 42 14000 – 19500 

Medium-heavy duty [MHD] Class 46 19500 – 33000 

Heavy-heavy duty [HHD] Class 47 > 33000 

Urban Bus [BUS] Class 48 N/A 

1 
 

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 which are used in the model are shown in Table 2. 

Table 2 – MOVES Age Group Definitions 

Age Group ID Lower Age Upper Age 

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 model year groups in Table 3. These groups 

were categorized based on changes in NOx emissions standards and the outcome of the Heavy 

Duty Diesel Consent Decree1, which required additional control of NOx emissions during highway 

driving for model years 1999 and later. This is referred to as the Not-to-Exceed (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/bhpr-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: 

1.	 ROVER PEMS (Portable Emissions Measurement System) testing conducted by U.S. 

Army Aberdeen Test Center on behalf of U.S. EPA2

: This ongoing program started in 

October 2000. Due to time constraints and quality of data, we only used data collected 

from October 2003 through September 2007. Data was compiled and reformatted for 

MOVES analysis by Sierra Research3

. EPA performed the data analysis itself. The data we 

used consisted of 124 trucks and buses ranging from 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 

2 
 

2.	 Consent Decree testing conducted by West Virginia University using their Mobile 

Emissions Measurement System (MEMS)4,5: This program came 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 consisted of 188 trucks from model 

years 1994 through 2003. Trucks were heavily loaded and tested over numerous routes 

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

second time after 2-3 years. Data were collected in 5-hz frequency, which we averaged 

around each second to convert the data to 1-hz. 

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, compared to if a more detailed quality check were performed (i.e. not all eliminated 

tests produced erroneous results). For the WVU MEMS data, WVU itself reported which tests were 

valid as part of the consent decree procedure. No additional detailed quality check was 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 – Number 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 Calculating VSP from in 1-hz data 

With on-road testing, using vehicle speed and acceleration to determine VSP is not accurate given 

the effect of road grade and wind speed. As a result, we needed to find a better way to calculate 

VSP. Therefore, we decided to derive VSP from engine data collected during testing. We first 

determined which seconds in the data that the truck was either idling or braking based on 

acceleration and speed criteria shown later 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 then engine 

was performing work. 

P =ω τ	 Equation 1 eng eng eng 

We then determined the relationship between the power required at the wheels of the vehicle and 

the power required by the engine. We first must 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 

3 
 

 

 

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 load map into VSP bins. The VSP bins 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 

Speed 

Load 

Low Mid High 

Low 

Cooling Fan 

Air cond. 

Engine Access. 

Alternator 

Air Compress 

Air cond. 

Engine Access. 

Alternator 

Air Compress 

Air cond. 

Engine Access. 

Alternator 

Mid 

Cooling Fan 

Air cond. 

Engine Access. 

Alternator 

Air Compress 

Cooling Fan 

Air cond. 

Engine Access. 

Alternator 

Air Compress 

Air cond. 

Engine Access. 

Alternator 

High 

Cooling Fan 

Air cond. 

Engine Access. 

Alternator 

Air Compress 

Cooling Fan 

Air cond. 

Engine Access. 

Alternator 

Air Compress 

Cooling Fan 

Air cond. 

Engine Access. 

Alternator 

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 off of information from 

"The Technology Roadmap for the 21st Century Truck."6 

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 VSP bin is equal to the 

sum of each accessory load. The calculations are included in Appendix A.1 Calculation of 

Accessory Power Requirements for VSP bins. 

4 
The total accessory loads Ploss,acc listed below in Table 6 is subtracted from the engine power 

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

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. We performed a literature search7,8,9,10,11,12,13,14,15 to determine an 

average value for driveline efficiency. Table 7 summarizes our findings. 

Table 7 shows driveline efficiencies found through literature research. 

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% 

Based on our 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 (Peng − Ploss,acc ) Equation 2 

Finally, to calculate VSP, we divided the axle power by the average weight for each regulatory 

class. We did not use the test weight because our test weights varied by truck or by test. Also, 

since running weights vary on a much broader range than light-duty vehicles and since we are 

characterizing NOx emissions by engine parameters, using the vehicles actual test weight would 

confound the results. Using an average running weight was the most appropriate. We did this by 

analyzing the Vehicle Inventory and Use Survey16 data to establish a VMT-weighted average 

running weight for each regulatory class mavg.regclass, shown in Table 8. 

Table 8 – Average running weight by regulatory class in metric tons 

Regulatory Class Average running weight 

HHD 27.7 

MHD 11.4 

5 
 

 

 

 

 

 

LHD345 6.3 

LHD2b 5.0 

BUS 16.6 

Equation 3 shows the conversion of axle power to VSP using the method explained above. 

Paxle VSP = Equation 3 

mavg,regclass 

Due to low power-to-weight ratios compared to light-duty vehicles, HHD trucks do not regularly 

reach VSP levels greater than 12 kW/metric ton, and MHD trucks greater than 18 kW/metric ton. 

To calculate VSP and emission rates for LHD trucks, engine and emissions data for MHD trucks 

were used, given the similarity in average engine size, to calculate engine power. We assume 

negligible accessory losses and the same 90% driveline loss. Then we divided the resulting axle 

power by the average weights in the LHD regulatory classes (listed in Table 8). We then 

constructed operating mode bins defined by VSP and vehicle speed according to the methodology 

outlined earlier in MOVES development17

 and described in Table 9. 

Table 9 – Definition of the MOVES Operating Mode Attribute for Motor Vehicles (opModeID) 

Operating 

Mode Bin 

Operating Mode 

Description 

Vehicle-Specific Power 

(VSPt, kW/metric ton) 

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 VSPt< 0 0 ≤ vt < 25 

12 Cruise/Acceleration 0 ≤ VSPt < 3 0 ≤ vt < 25 

13 Cruise/Acceleration 3 ≤ VSPt < 6 0 ≤ vt < 25 

14 Cruise/Acceleration 6 ≤ VSPt < 9 0 ≤ vt < 25 

15 Cruise/Acceleration 9 ≤ VSPt < 12 0 ≤ vt < 25 

16 Cruise/Acceleration 12 ≤ VSPt 0 ≤ vt < 25 

21 Coast VSPt< 0 25 ≤ vt < 50 

22 Cruise/Acceleration 0 ≤ VSPt < 3 25 ≤ vt < 50 

23 Cruise/Acceleration 3 ≤ VSPt < 6 25 ≤ vt < 50 

24 Cruise/Acceleration 6 ≤ VSPt < 9 25 ≤ vt < 50 

25 Cruise/Acceleration 9 ≤ VSPt < 12 25 ≤ vt < 50 

27 Cruise/Acceleration 12 ≤ VSPt < 18 25 ≤ vt < 50 

28 Cruise/Acceleration 18 ≤ VSPt < 24 25 ≤ vt < 50 

29 Cruise/Acceleration 24 ≤ VSPt < 30 25 ≤ vt < 50 

30 Cruise/Acceleration 30 ≤ VSPt 25 ≤ vt < 50 

33 Cruise/Acceleration VSPt < 6 50 ≤ vt 

35 Cruise/Acceleration 6 ≤ VSPt < 12 50 ≤ vt 

37 Cruise/Acceleration 12 ≤ VSPt <18 50 ≤ vt 

6 
 

 

 

 

38 Cruise/Acceleration 18 ≤ VSPt < 24 50 ≤ vt 

39 Cruise/Acceleration 24 ≤ VSPt < 30 50 ≤ vt 

40 Cruise/Acceleration 30 ≤ VSPt 50 ≤ vt 

1.1.1.3 Calculating 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 bin. Then the emission rates were again averaged by 

regulatory class, 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). nveh n ⎛ rpol ⎞ ∑ ∑⎜ ⎟ ⎜ ⎟

j=1 ⎝ i=1 n ⎠ Equation 4 

r pol = nveh 

where 

n = the number of 1-hz data points for each vehicle, 

nveh = the total number of vehicles, 

r = the mean emission rate of pollutant pol for each stratification group (MOVES input), pol

r = the emission rate of pollutant pol at second i pol

For NOx, we calculated a mean emission rate, called mean base rate in MOVES, for each 

regulatory class, model year group, and operating mode bin combination. 

1.1.1.3.2 Statistics 

Coefficients of variation (cv) were calculated for all the emission rates. First, we calculated the 

overall within-vehicle variance 2 s . with 

nveh 

2

∑(n −1)sveh 

j=1 Equation 5 2

s with = n − n tot veh 

where 

2 

s = the variance within each vehicle, and veh
 

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

Then we calculated the between-vehicles variance 2 sbetw (by source bin, age group, and operating

mode) using the mean emission rates for each vehicle. Then, we determined the total variance by 

combining the within-vehicle and between-vehicle variances to get the standard error srpol 

7 
 

(Equation 6) and dividing the standard error by the mean emission rate to get the coefficient of 

variation c , (Equation 7). v rpol 

2 2 s s betw with s + Equation 6 r = pol n n veh tot 

sr 

c = pol Equation 7 v, pol rpol 

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 effective emissions standard. While the 2007 NOx standard is 

0.2 g/bhp-hr, it is phased in through 2010 MY. Instead of phasing in aftertreatment technology, 

most manufacturers decided to meet a constant 1.2 g/bhp-hr standard, which did not require 

aftertreatment, from 2007 to 2009 MY (down from 2.4 g/bhp-hr in 2006). Therefore, we decreased 

the 2003-2006 emission rates by 50% for 2007-2009 MY. Starting in the 2010 MY, the NOx 

standard for all heavy-duty trucks will be 0.2 g/bhp-hr. Almost all of these trucks will be using 

SCR aftertreatment technology, which we are assuming to have a 90% NOx reduction efficiency 

from 2006 MY levels. 

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 

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

certification levels came from MOBILE618

. We assumed that emission levels did not change by 

model year for 1989 and earlier. 

While we do not expect rates for high VSP for heavy- and medium-heavy duty vehicles, due to 

their power-to-weight ratios, we still need to have rates present in the model for completeness. 

Thus, we applied to those operating mode bins the rates calculated from data for the highest VSP 

level. 

For certain model years, such as 1998, data existed for HHD trucks, but not MHD or buses. In 

these situations, the ratio 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 the 2010 model year NOx emissions standards, the use of aftertreatment will likely be 

needed. Cummins decided to use aftertreatment starting in 2007 on their Dodge Ram pickup truck 

to meet the 2010 standards in 2007. The technology they used was a Lean NOx Trap (LNT). This 

8 
technology allows for the storage of NOx during fuel-lean operation and conversion of this stored 

NOx into N2 and H2O during brief periods of fuel-rich operation. EPA’s NVFEL acquired one of 

these trucks and performed local on-road PEMS testing in 2007. We used the PEMS and ECU 

output to determine VSP and emission rates in a similar way we did for the heavy-heavy-duty truck 

NOx rates. For heavy-duty vehicles in 2007 and later, a diesel particulate filter (DPF) is required 

to meet PM standards. Every so often, the DPF must be regenerated to remove and combust the 

PM from the filter to relieve backpressure and ensure proper engine exhaust function. This 

required that exhaust temperatures be high. However, these high temperatures adversely affect the 

LNT’s NOx storage ability, causing tailpipe emission levels of NOx to increase. Therefore, while 

analyzing the 2007 Dodge Ram data, we separated regimes of PM regeneration from normal 

operation based on exhaust temperature. We performed the emission rate-VSP analysis separately 

for each regime, and weighted the two regimes together based on an assumed PM regeneration 

frequency of 10% of VMT. This is only an assumption based on the limited testing conducted. We 

will look to update this number based on any further data collection or research. 

Since these LNT-equipped trucks are only about 25% of the LHDDT market, we again weighted 

the rates for the two LHD regulatory classes for model years 2007 and later. We assume that the 

remaining 75% of LHD diesel trucks from model year 2007 to 2009 will not have aftertreatment 

and exhibit the emission rates described in the hole filling section. We assume that the remaining 

75% of LHD diesel trucks in model year 2010 and later 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 and previous subsections regarding the methodology used to determine 

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 LHD2b LHD345 

Pre-1988 Proportioned to 

certification levels 

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 

Proportioned to 

certification levels 

1990 Proportioned to 

certification levels 

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 Proportioned to HHD 

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

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

with LHD2b weight 

MHD engine data 

with LHD345 weight 

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

with LHD2b weight 

MHD engine data 

with LHD345 weight 

2007-2009 Proportioned to 

standards 

Proportioned to 

standards 

Proportioned to 

standards 

Data (LNT), and 

proportioned to 

standards (non-LNT) 

Proportioned to 

standards 

2010 + Proportioned to 

standards 

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) before and after useful life19

. Since the trucks in this program were collected 

9 
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 methodology is discussed in detail in Appendix 

10 
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 analysis. 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 through 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+ SCR 87 87 

2010+ LNT 72 72 

As described in Appendix 

11 
A.2 Tampering and Mal-maintenance, these emissions increases are combined with Table 35 to 

determine the emissions increase for each age group prior to the end of useful life for each 

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

for 2010 model year and later, EPA expects tampering and mal-maintenance to significantly 

increase emissions over time compared to the zero-mile level. Though 87% may appear to be a 

large increase of 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/bhphr). This still yields a substantial reduction of about 69% from 2009 zero-mile levels to 2010 fully 

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

these tampering and mal-maintenance and overall aging effects. 

1.1.1.4 Sample results 

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

analysis. Not all rates are shown, but enough are shown to reveal the most common data trends and 

hole-filling results. The light-heavy duty regulatory classes are not shown for simplicity, 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. 

In Figure 1, we see that NOx emission rates increase with VSP. Also, the leveling of emission 

rates at high VSP/operating modes can be seen. This is strictly a matter of hole filling for 

completeness in the model; the high operating modes will not be reached given the low power-toweight ratio of heavy-duty vehicles. For light-heavy duty trucks, which run lighter but have similar 

engine sizes as medium-heavy duty trucks, leveling off for high VSP in not required since those 

VSP levels can be attained in normal operation (similar to light-duty trucks). 

Figure 1 shows NOx trends by operating mode for MHD, HHD, and Transit bus regulatory classes for model 

year 2002. 

0 

200 

400 

600 

800 

1000 

1200 

1400 

1600 

1800 

2000 

Mean NOx base rate [g/hr] 

MHD 

HHD 

BUS 

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

Operating Mode 

12 
The effects of model year can be seen in Figure 2, which shows decreasing NOx rates by model 

year group for a sample operating mode (# 24). This chart is a combination of data analysis (model 

years 1991 through 2006) and hole filling. The trends in the data are expected, since model year 

groups were formed on the basis of NOx standards. 

Figure 2 shows NOx trends by model year for operating mode 24. Stricter EPA standards have caused NOx 

emissions to decrease. 

0 

500 

1000 

1500 

2000 

2500 

3000 

3500 

Mean NOx base rate [g/hr] 

MHD 

HHD 

BUS 

1989 and 1990 1991-1997 1998 1999-2002 2003-2006 2007-2009 2010 and 

earlier later 

Model year group 

Age effects were only implemented for aftertreatment-equipped trucks (mostly model year 2010 

and later) using our tampering and mal-maintenance analysis. Due to faster accumulation of miles, 

the heavy-heavy duty trucks reach their maximum emission rates the fastest, as shown in Figure 3. 

Coefficients of variation from previous model year groups were used to determine uncertainties for 

MY 2010. 

13 
Figure 3 shows NOx trends by age for model year 2010 for operating mode 24. 

0 

20 

40 

60 

80 

100 

120 

140 

160 

Mean NOx base rate [g/hr] 

MHD 

HHD 

BUS 

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

Age group [years] 

1.1.2 Particulate Matter (PM) 

In this section particulate matter emissions are defined as particles emitted from heavy-duty 

engines which have a mean diameter less than 2.5 microns. Such particles consist of three 

subtypes. These are (1) elemental carbon (EC) which is the usually black colored soot that is 

emitted from combustion, (2) organic carbon (OC) which consists of particles of organic matter 

which is formed during the combustion process or immediately after in the tailpipe. It does not 

include particle formed in secondary reactions in the atmosphere, (3) sulfate particulate which 

consists of particles of sulfur emitted from engines as the result of fuel sulfur. These subtypes are 

the actual inputs in MOVES. 

As mentioned for NOx, the heavy-duty diesel PM emission rates in MOVES also are a function of: 

(1) source bin, (2) operating mode, and (3) age group. 

We stratified the data in the following model year groups in MOVES. These are generally based 

on the introduction of EPA emission 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 emission standards in units of gram per brake 

horsepower-hour (g/bhp-hr). The EPA standard was in terms of engine ‘work’ and was applied to 

all classes of heavy-duty engines. 

Table 12 –Model year groups used for analysis based on the PM emissions standard 

Model Year Group Range EPA PM Engine 

Standard [g/bhp-hr] 

1960-1987 No transient cycle standard 

1988-1990 0.60 

14 
1991-1993 0.25 

1994-1997 0.10 

1998-2006 0.10 

2007+ 0.01 

The MOVES model has the capability of differentiating heavy duty trucks according the engine 

size, vehicle weight, and injection type (direct and indirect) as was done for the total energy rate 

inputs20. However, for PM emissions the data was just too sparse to consider such fine 

stratification, and it was not done. 

1.1.2.1 Data Source 

All of the data used to develop the MOVES PM2.5 emission rates comes from the CRC E-55/59 

test program21. The following description by Dr. Ying Hsu and Maureen Mullen of E. H. Pechan, 

“Compilation of Diesel Emissions Speciation Data – Final Report” provides a good summary of the 

test program. It is reproduced in the following paragraphs immediately below. 

The objective of the CRC E55/59 test program was to improve the understanding of the California 

heavy-duty vehicle emissions inventory by obtaining emissions from a representative vehicle fleet, 

and to include unregulated emissions measured for a subset of the tested fleet. The sponsors of this 

project include CARB, EPA, Engine Manufacturers Association, DOE/NREL, and SCAQMD. The 

project consisted of four segments, designated as Phases 1, 1.5, 2, and 3. Seventy-five vehicles 

were recruited in total for the program, and recruitment covered the model year range of 1974 

through 2004. The number and types of vehicles tested in each phase are as follows: 

• Phase 1: 25 heavy heavy-duty (HHD) diesel trucks 

• Phase 1.5: 13 HHD diesel trucks 

• Phase 2: 10 HHD diesel trucks, seven 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 

15 
samples of PM and unregulated species. In addition, a sample of the vehicles received Tapered 

Element Oscillating Microbalance (TEOM) measurement of real time particulate emissions. 

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

cycles used for the HHDDT testing included: 

• AC50/80; 

• UDDS; 

• Five modes of an HHDDT test schedule proposed by CARB: Idle, Creep, Transient, 

Cruise, and HHDDT_S (a high speed cruise mode of shortened duration) 

• The U.S. EPA transient test 

The proposed CARB HHDDT test cycle is based on California truck activity data, and was 

developed to improve the accuracy of emissions inventories. It should be noted that the transient 

portion of this proposed CARB test schedule is similar but not the same as the EPA certification 

transient test. 

The tables below provide a greater detail of the data used in the analysis. Vehicles counts are 

provided by number of vehicles, number of tests, model year group and regulatory class (46 = 

MHD, 47=HHD) in Table 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 

Test counts are provided by test cycle in Table 14. 

Table 14 – Vehicle Test Counts by Test Cycle 

Test Cycle Name Number of tests 

CARB-T 71 

CARB-R 66 

CARB-I 42 

UDDS_W 65 

AC5080 42 

CARB-C 24 

16 
CARBCL 34 

MHDTCS 63 

MHDTLO 23 

MHDTHI 24 

MHDTCR 29 

1.1.2.2 Analysis 

1.1.2.2.1 Calculate VSP in 1-hz data 

Within source bins, data was further sub-classified on the basis of “operating mode,” designated as 

the MOVES attribute “opModeID.” For motor vehicles, operating mode is defined in terms of 23 

bins defined in terms of vehicle-specific power (VSP), vehicle speed and vehicle acceleration. 

These are the same bins as were defined in Table 9 in the NOx discussion. 

The first step in assigning operating mode is to calculate vehicle-specific power (VSP) for each 

emissions measurement. At a given time t, the instantaneous VSPt (kW/metric ton), at a frequency 

of 1.0 Hz) represents the vehicle’s tractive power normalized to its weight. The VSP parameter is 

expressed 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 

VSP using the equation below: 

Av + Bv 2 +Cv3 

t t t t t + mv a VSPt = Equation 8 

mavg ,regclass 

where 

A = the rolling resistance coefficient [kW⋅sec/m], 

B = the rotational resistance coefficient [kW⋅sec

2

/m2], 

C = the aerodynamic drag coefficient [kW⋅sec3/m3], 

m = mass of individual test vehicle [kg], 

mavg.regclass = average running mass for regulatory class [metric ton] (see Table 8) 

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

at = instantaneous vehicle acceleration [m/s2]. 

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

vehicles22

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

(PERE). This method of calculating VSP is similar to how VSP was calculated for the light-duty 

vehicle emission rate analysis for MOVES, except that the average regulatory class weight is used 

in the denominator, instead of the actual test weight of the vehicle. 

This is different from the way VSP was calculated for the NOx emission rate analysis since the 

data PM was collected on a chassis dynamometer instead of an onboard emission measurement 

system. For PM, the power portion (numerator) of the VSP equation was determined through 

vehicle speed and acceleration measurements from the chassis testing, whereas for NOx, the power 

17 
was determined through engine control unit broadcasts. Grade effects are not included in any 

equations because grade does not come into play on chassis dynamometer tests, and it is already 

accounted for if VSP is calculated through engine speed and torque from the engine control unit. 

Operating mode bins were determined using the same method for NOx and all other pollutants 

(Table 9). 

1.1.2.2.2 Normalize the TEOM Readings 

Only heavy-duty diesel vehicles and tests in the E-55/59 test program that received both real time 

one hertz TEOM data and full cycle filter particulate data were utilized in the development of the 

emission rates. Since the development of MOVES emission rates is cycle independent, all 

available cycles / tests which met the above requirement were utilized. As a result, 488,881 

seconds of TEOM data were utilized. The process required that each individual second by second 

TEOM rate be normalized using its overall cycle filter PM measurement (each combination of 

vehicle and test received a filter PM test). This was necessary because individual TEOM 

measurements are highly uncertain and vary widely in terms of magnitude (extreme positive and 

negative absolute readings can occur). Thus, only the relative contribution of a given one second 

measurement was used in the computation of the MOVES emission rates. The equation below 

shows the normalization process for a particular one second TEOM measurement. 

PM filter, j PM normalized ,i, j = j PMTEOM ,i, j Equation 9 ∑PMTEOM ,i 

 Where 

i is the individual one second measurement, 

j is the particular test cycle (resultID – combination of vehicle and test), 

PMTEOM,i,j is the TEOM measurement at second i during cycle j, 

PMfilter,j is the Total PM2.5 emissions collected on a filter over cycle j, 

PMnormalized,i,j is the Total PM2.5 emission result for second i and cycle j average, according 

the source bin, operating mode bin, model year group and age group. 

1.1.2.2.3 Compute Total PM2.5 Mean Emission Results by MOVES Bin 

After normalization, the data were stratified by regulatory class, model year group and the 23 

operating mode bins (i.e., collectively called “MOVES bins”). Mean average results, count and 

standard deviation statistics for PM2.5 emission values were computed in terms of grams of PM2.5 

per hour for each MOVES bin. In cases where the vehicle and TEOM test counts were sufficient 

for a given MOVES bin, these mean values became the MOVES basic emission rates for total 

PM2.5. In cases of no data or insufficient data for particular MOVES bin, a regression technique 

was utilized to fill ‘holes’. 

1.1.2.2.4 Hole filling and Forecasting 

1.1.2.2.4.1 Missing operating mode bins 

Detailed in Appendix A.4 Regression to develop PM emission rates for missing operating mode 

bins, a log-linear regression was performed on the existing PM data against VSP to fill in emission 

18 
rates for missing operating mode bins. Similar to the NOx rates, emission rates were leveled off 

for the highest VSP bins. 

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. These rates 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 are a ratio of the MHD emission rates, and bus (class 48) 

emission rates are proportioned to HHD rates. The emission rates of LHD2b are assumed to be 

equal to 0.46 * MHD emission rates. The emission rate of LHD345 is assumed to be 0.60 * MHD 

emission rate. 

LHD2b emission rate = 0.46 * MHD emission rate 

LHD345 emission rate = 0.60 * MHD emission rate 

The values of 0.46 and 0.60 are the ratios of the MOBILE6.2 heavy-duty conversion factors23

 (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. The basis for this 

assumption is that the certification standards in terms of brake horsepower-hour (bhp-hr) are the 

same for all of the heavy-duty engines. 

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 = (0.10/0.25) * 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 ten in 

the level of regulatory stringency required the use of particulate trap systems on heavy-duty diesels. 

As a result, the emission performance of diesel vehicles has likely changed dramatically. 

Unfortunately, no second-by-second TEOM data were available for analysis on the 2007 and later 

model year vehicles. As a result, the emission factors for this most important group of vehicles 

was assumed to be a ratio of the 1998-2006 model year vehicles for each respective regulatory 

class and operating mode. After adjusting for age effects, we decreased the 2006 MY PM 

emission rates by 90% for the 2007 MY and later PM emission rates. 

Since the vehicles of 2007 and later model year are the most critical group for future inventories, 

EPA will likely re-evaluate, and if necessary, update this assumption when sufficient data have 

19 
 

been collected on in-use and fully ‘seasoned’ vehicles. EPA will also look at recent certification 

data to assess the zero-mile PM emission levels, as DPF effectiveness may be higher than the 90% 

stated here. 

1.1.2.2.5 Tampering and Mal-maintenance 

The MOVES model now contains assumptions for the frequency and emissions effect of tampering 

and mal-maintenance on heavy-duty diesel trucks and buses. The assumption of tampering and 

mal-maintenance (T&M) of heavy-duty diesel vehicles is a departure from the MOBILE6.2 model 

which assumed such vehicles operated from build to final scrappage at a design emission level 

which was lower than the prevailing EPA emission standards. Both long term anecdotal data 

sources and more comprehensive studies now suggest that the assumption of no natural 

deterioration and/or no deliberate tampering of emission control components in the heavy-duty 

diesel fleet was likely an unrealistic assumption. 

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 15 were used to forecast or back-cast the basic PM emission rates to model model 

year group-age group combinations that were not part of 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. No historical data is available on these model years. However, for 

completeness, MOVES must have emission rates for these model years at age 3, 4 to 5, 6 to 7, etc. 

As a result, unless the assumption that the higher emission rates which are currently prevailing on 

the older model year vehicles have always prevailed – even when they were young, a modeling 

approach such as the T&M must be employed. Likewise, younger model years were tested only at 

lower age levels. No data has yet been collected on younger model year vehicles at high ages. 

The T&M methodology used in the MOVES analysis allows for the filling of age – model year 

group bins for which no data is available. 

One criticism of the MOVES T&M methodology 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 inuse vehicles which during the testing period may have had some maintenance problems. This issue 

would be most acute for the 2007 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 

20 
 

 

 

 

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 Table 15. 

Table 15 shows tampering and mal-maintenance emissions increase estimates for HC and CO over the useful life 

of trucks. 

Model Year Group Percent increase in PM due 

to T&M 

Pre-1998 85 

1998 - 2002 74 

2003 – 2006 48 

2007 – 2009 50 

2010 + 50 

1.1.2.2.6 Compute Elemental Carbon and Organic Carbon Emission Factors 

The MOVES model reports PM2.5 emissions according to three sub types. These are Elemental 

Carbon (EC), Organic Carbon (OC) and sulfate. Both EC and OC are computed directly from the 

total PM2.5 emissions using multiplicative factors. Sulfate is computed using a fuel sulfur balance 

(see MOVES Sulfate report for details). Since the fuel sulfur levels in the underlying study were 

not generally known, but believed to be small, sulfate emissions were ignored in the total PM2.5 

emission levels. As a result, total PM2.5 was assumed to be comprised of only EC and OCarbon 

(In the final version of MOVES a generic sulfate emission factor will be subtracted from the Total 

PM and the equation below will not be used). 

Total PM2.5 = EC + OC 

Thus, OCarbon 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 4. These vary according to regulatory 

class and MOVES operating mode bin. They typically range from 25 percent at low loads (low 

VSP) to over 90 percent at highly loaded modes. All of the EC fractions were developed by Chad 

Bailey and are documented in Appendix A.5 Heavy-duty Diesel EC/OC Fraction Calculation. 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). 

21 
 

Figure 4 shows the Elemental Carbon fraction by operating mode bin for pre-DPF-equipped trucks. 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1 

EC fraction 

HHD 

MHD 

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

Operating mode bin 

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 the SAE paper 2002-01-0432 “Chemical Speciation of 

Exhaust Emissions from Trucks and Buses Fueled on Ultra-Low Sulfur Diesel and CNG”24

. 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 yielded only 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 16 summarizes the EC and OC fractions determined from the 

paper. These fractions were used for all operating mode bins for model years 2007 and later heavyduty vehicles. 

Table 16 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 becomes available, EPA will likely revise the EC Fraction used in MOVES. 

Temperature Correction Factors 

The MOVES model draft release of March, 2009 will not contain any 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 inverse is true. Both 

running and start PM emissions from at least non-trap equipped vehicles are sensitive to 

22 
temperature. However, EPA at this time cannot adequately quantify such emission effects, and is 

currently using a multiplicative placeholder value of unity 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 5 shows how PM rates increase with VSP. Also, the EC fraction of the PM rate increases 

with VSP as well. The leveling off at high VSP is due to our manual hole filling. At high speeds 

(greater than 50 mph; operating modes ≥ 30), the overall PM rates are lower than the other speed 

ranges. 

Figure 5 shows PM rates by operating mode bin for model year 2002 age 0-3 for HHD trucks. 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

PM rate [g/hr]

EC 

OC 

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 

Figure 6 shows an example of how our tampering and mal-maintenance estimates increase PM with 

age. The EC fraction 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. 

23 
 

Figure 6 shows PM rates by age group for model year 2002 in operating mode 24 for MHD trucks. 

0 

2 

4 

6 

8 

10 

12 

14 

PM rate [g/hr] 

EC 

OC 

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

Age group [years] 

Figure 7 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 nearly zero due to 

widespread DPF use. The overall PM level is substantially lower starting in model year 2007. The 

earlier model years’ emission rates represented here 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. 

Figure 7 shows PM rates by model year group for age 0-3 in operating mode 24 for HHD trucks. 

120 

100 

pre-1988 1988-1990 1991-1993 1994-1997 1998-2006 2007+ 

Model year group 

PM rate [g/hr]

80 

60 

40 

20 

0 

EC
 

OC
 

24 
1.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO) 

Diesel engines are not known to be a significant portion of the mobile source HC or CO emission 

inventories. Recent regulations on non-methane HC (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: 

• 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-55/5921: Mentioned earlier, this program represents the largest amount 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, and testing was conducted by West Virginia University 

from 2001 to 2005. 

2.	 Northern Front Range Air Quality Study (NFRAQ)25: 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 designed to be representative of the in-use 

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

drive cycles. 

3.	 New York Department of Environmental Conservation (NYSDEC)26: 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 testing: We have in our MSOD other historical HD chassis 

testing performed by WVU. 

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 

pollutants. Time alignment was performed using the method similar to light-duty chassis test data 

time alignment. The quantities of vehicles in the data sets are shown in Table 17. 

25 
 

 

 

 

 

 

Table 17 shows the number of vehicles by model year group, regulatory class, and age group that were tested in 

the programs above. 

Model year group Regulatory class Age group 

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

HHD 58 19 16 9 16 6 7 

MHD 9 6 5 4 12 15 6 

1960-2002 Bus 26 1 3 

LHD345 2 1 

LHD2b 6 

2003-2006 HHD 6 

1.1.3.2 Analysis 

Like for PM, VSP was calculated like in light-duty but normalized with average regulatory class 

weight instead of test weight, as described by Equation 10. 

Av + Bv 2 +Cv 3 

t t t t t + mv a VSPt = Equation 10 

mavg ,regclass 

Coefficients A, B, and C are road load coefficients pertaining to the heavy-duty vehicles22 

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

Using a similar method to the NOx analysis, we averaged emissions by vehicle and operating mode 

bin. We then averaged across all vehicles by model year group, age group, and operating mode. 

Statistics calculations followed the same equations and methodology as the NOx analysis. 

Instead of populating all the emission rates directly into the model, 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 18. 

Table 18 shows base age groups used directly in MOVES emission rate inputs for each regulatory class and 

model year group present in the data. 

Regulatory class Model year group Age group 

HHD 1960-2002 0-3 

HHD 2003-2006 0-3 

MHD 1960-2002 15-19 

BUS 1960-2002 0-3 

LHD41 1960-2002 0-3 

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

through younger ages or higher through older ages, using the methodology described in Appendix 

26 
A.2 Tampering and Mal-maintenance. The tampering and mal-maintenance effects for HC and CO 

are shown in 

Table 19. 

Table 19 shows tampering and mal-maintenance emissions increase estimates for HC and CO over the useful life 

of trucks. 

Model years Percent increase in HC 

and CO due to T&M 

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 35 in section A.2.3 Analysis 

to get the emissions by age group. 

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

assuming 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 will show examples of the emission rates that resulted from the 

analysis. Not all rates are shown, but enough are shown to reveal the most common data trends and 

hole-filling results. The light-heavy duty regulatory classes are not shown for simplicity, 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 just as for NOx. 

In Figure 8 and Figure 9, we see that HC and CO mean emission rates increase with VSP, though 

there is much higher uncertainty than for the NOx rates. This could be due to the smaller data set 

or generally less significant with VSP. Also, the leveling of emission rates (hole filling) at high 

VSP/operating modes can be seen. 

27 
Figure 8 shows HC emission rates [g/hr] by operating mode bin for model year 2002. 

50 

HC rate [g/hr] 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

HHD 

MHD 

Bus 

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

Operating mode bin 

Figure 9 shows CO emission rates [g/hr] by operating mode bin for model year 2002. 

400 

HHD 350 

MHD 

Bus 

300 

CO rate [g/hr] 250 

200 

150 

100 

50 

0 

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 

Figure 10 and Figure 11 show HC and CO emission rates by age group. These trends reflect 

entirely the T&M analysis, as the age effect for HC and CO were only modeled. Due to the T&M 

effect, there are large increases as a function of age. Since the age effect is a model, more in-use 

data if collected could help determine any real-world deterioration, especially in model years where 

diesel oxidation catalysts are most prevalent (2007 and later). 

28 
Figure 10 shows HC emission rates [g/hr] by age group for model year 2002 and operating mode bin 24. 

. 

70 

60 

50 

HC rate [g/hr] 40 

30 

20 

10 

0 

HHD 

MHD 

Bus 

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

Age group [years] 

Figure 11 shows CO emission rates [g/hr] by age group for model year 2002 and operating mode bin 

24. 

450
 

400
 

350
 

300
 

CO rate [g/hr] 250 

200 

150 

100 

50 

0 

HHD 

MHD 

Bus 

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

Age group [years] 

Figure 12 and Figure 13 show sample HC and CO emission rates by model year group. The two 

earlier model year groups are closer together, as we reduced the latest model year group due to the 

29 
 

 

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

do not track as well with VSP (or power) as NOx or PM, uncertainties are much greater. 

Figure 12 shows HC emission rates by model year group for operating mode bin 24 and ages 0-3. 

30
 

25
 

HC rate [g/hr] 20 

15 

10 

5 

0 

HHD 

MHD 

BUS 

1960-2002 2003-2006 2007+ 

Model year group 

Figure 13 shows CO emission rates by model year group for operating mode bin 24 and ages 0-3. 

250 

200 

CO rate [g/hr] 150 

100 

50 

0 

HHD 

MHD 

BUS 

1960-2002 2003-2006 2007+ 

Model year group 

30 
1.1.4 Updates for final MOVES release 

The methodology and results described in this document reflect the analysis performed for the draft 

release of MOVES. For the final release, we have planned a few changes to the heavy-duty diesel 

running exhaust emissions rates. 

i.	 We plan to calculate VSP differently. Instead of using the average weight in each 

regulatory class to calculate VSP for a vehicle in that regulatory class, we will use one 

average weight for all heavy-duty vehicles to calculate VSP for each vehicle. Since 

emissions regulations are based on engine power regardless of regulatory class, normalizing 

this power by different masses destroys the relation between emissions and power across 

regulatory classes. This is especially true for NOx and PM, the two most significant 

pollutant emissions from diesel engines. This is also important because several source use 

types in MOVES are in multiple heavy-duty regulatory classes, and their VSP and operating 

mode distribution must be allocated to each regulatory class adequately. Using a uniform 

heavy-duty average mass preserves the relation of NOx and power by way of VSP. Indeed, 

vehicle-specific power would be a misnomer for the new calculation. Rather, it can be 

thought of as power scaled by a constant factor. 

ii.	 We plan to incorporate NOx benefits resulting from reflashing heavy-duty engines from 

mid-1990’s model years. Reflashing refers to the reprogramming of heavy-duty diesel 

engines’ engine control units to lower NOx, as required under the consent decree that ruled 

against the so-called “emission defeat devices”. The motivation for the change is to ensure 

that MOVES recognizes proactive steps taken by states to reflash to appropriate engines 

above and beyond the latent number of reflashes in the fleet. Benefits will likely be 

realized only in operating modes where the emission defeat devices were believed to have 

been most operational. 

iii.	 We plan to incorporate emissions impacts of the recently promulgated heavy-duty onboard 

diagnostic (OBD) rule. This rule takes fully into effect in model year 2013 and is designed 

to lower the number of failure of aftertreament systems in the emerging fleet of heavy-duty 

vehicles. 

iv.	 In light of i), our method of filling holes for missing regulatory classes will change. Since 

VSP will be essentially a scaled power, and heavy-duty engine emissions are generally a 

function of engine power, we will not adjust or modify rates for missing regulatory classes. 

The differences in overall emissions between classes can then be seen by the larger classes 

(e.g. HHD or Bus) reaching higher VSP levels compared to the smaller classes. 

31 
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 MOVES analysis, 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 distributed as a function of soak time can be 

found in subsection 1.2.3 Adjusting Start Rates for Soak Time and in the MOVES light-duty 

emission rate counterpart document Development of Emission Rates for Light-Duty Vehicles in the 

Motor Vehicle Emissions Simulator. 

1.2.1 HC, CO, and NOx 

For light-duty vehicles, this is determined 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. This similar analysis was performed for LHD vehicles tested 

on the FTP and ST01, which also has 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 stratifications 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 20: 

Table 20 shows average start emissions increases in grams for light-heavy duty vehicles 

HC CO NOx 

0.13 1.38 1.68 

For HHD and MHD trucks, data was much more scant. We tested a 2007 Cummins ISB on an 

engine dynamometer at EPA’s National Vehicle and Fuel Emissions Laboratory in Ann Arbor, 

Michigan. Among other idle tests, we performed a cold start idle test at 1100 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 14. 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. 

32 
Figure 14 shows the stabilization of emissions from a cold start idle engine dynamometer test on an in-house 

2007 MY Cummins ISB 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

mg/s 

NOx 

HC 

CO 

0.00 1.00 2.00 3.00 4.00 5.00 

time [hrs] 

We calculated the area under each curve 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 21. The NOx increment is negative since cold start emissions are lower than 

warm start emissions. 

Table 21 shows 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 Tennessee27

, 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 determined 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 get the cold start increment to be used in MOVES. We found that several trucks 

produced lower NOx emissions during cold start (including EPA’s test), 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 our cold start increment to zero, pending any future data that may be collected. Table 

22 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. 

Table 22 shows MOVES inputs for HHD and MHD start emissions increases in grams. 

33 
HC CO NOx 

0.0 16.0 0.0 

1.2.2 Particulate Matter 

Heavy-duty truck PM2.5 emissions for the start process have not been collected in any 

significance. No such rates were available from the MOBILE6.2 model for use in MOVES. 

Typically, heavy-duty vehicle emission measurements begin on fully warmed up vehicles. This 

test procedure bypasses the engine crank and early operating periods when the vehicle is not fully 

warmed up. 

For the MOVES Draft version data one heavy-heavy-duty engine with six FTP tests using a filter 

PM measurement was available from EPA engine dynamometer testing. The engine was a 2004 

model year engine. 

The engine was tested at 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 = 1.9314 g – 1.8215 g = 0.1099 g PM2.5 

We applied this value of 0.10985 g of Total PM2.5 per start for 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 will look to update these numbers if more data is collected. For 

now, this one engine is our only data point. 

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. The value at 720 min (12 hours) represents cold start, and each successive 

value represents a shorter soak time, representing operating modes 107 – 101, respectively. These 

bins are not related to the bins in Table 9, which deals with running exhaust emissions. Table 23 

describes the different start-related operating mode bins in MOVES as a function of soak time. 

Table 23 – MOVES operating mode bins for start emissions (as a function of soak time) 

Operating Mode Bin MOVES Operating Mode Bin 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 

34 
 

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 15 below. 

Figure 15 Soak Fractions Applied to Cold-Start Emissions (opModeID = 108) to Estimate Emissions for shorter 

Soak Periods (operating modes 101-107). 

0 120 240 360 480 600 720

 Soak Time (minutes) 

The actual PM start rates by operating mode bin are given in Table 24 below. 

Table 24 – MOVES Start PM Emission Rate by Operating Mode Bin (soak fraction) 

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 

Ratio of Start to Cold-Start

NOx 

CO 

HC 

Operating Mode Bin 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 

35 
1.3 Extended Idling Emissions 

In the MOVES model, extended idling is "discretionary" idle operation characterized by extending 

idle periods that are several hours in duration, 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 cause 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. 

For MOVES model, only diesel long-haul combination trucks are 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)28. 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 range was from 1990 through 

1998. A typical Denver area wintertime diesel fuel (NFRAQS) was used in all tests. Idle 

measurements were collected during a 20 minute time period. All testing was done at 1,609 meters 

above sea level (high altitude). 

Testing was conducted by EPA on five trucks in May 2002 (Lim)29. The model years ranged from 

1985 through 2001. The vehicles were put through a battery of tests including a variety of 

discretionary and non-discretionary idling conditions. 

Testing was conducted on 42 diesel trucks in parallel with roadside smoke opacity testing in 

California (Lambert)30

. All tests were conducted by the California Air Resources Board (CARB) at 

a rest area near Tulare, California in April 2002. Data collected during this study were included in 

the data provided by IdleAire Technologies (below) that was used in the analysis. 

A total of 63 trucks (nine in Tennessee, 12 in New York and 42 in California) were tested over a 

battery of idle test conditions including with and without air conditioning (Irick)31

. Not all trucks 

were tested under all conditions. Only results from the testing in Tennessee and New York are 

described in the IdleAire report. The Tulare, California data are described in the Clean Air Study 

36 
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)32

. 

The National Cooperative Highway Research Program (NCHRP)33

 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)34

. The model years ranged from 1984 through 1999. One hundred seconds of idling were 

added at the end of the WVU five mile transient test driving cycle. 

A Class 8 Freightliner Century with a 1999 engine was testing using EPA's on-road emissions 

testing trailer based in Research Triangle Park, North Carolina (Broderick)35

. Both short (10 

minute) and longer (five hour) measurements were made during idling. Some testing was also 

done on three older trucks. 

Five heavy-duty trucks were tested for particulate and NOx emissions under a variety of conditions 

at Oak Ridge Laboratories (Story)36

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

The University of Tennessee tested 24 1992 through 2006 model year heavy duty diesel trucks 

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

conditioning)27. 

1.3.2 Analysis 

EPA performed an analysis of the emission impacts of extended idling for particulate matter (PM), 

oxides of nitrogen (NOx), hydrocarbons (HC), and carbon monoxide (CO) for populating the 

MOVES model. This analysis used all of the data sources referenced in this document. This 

update reflects new data available since the initial development of extended idle emissions for the 

MOVES model. The additions include the testing at Research Triangle Park (Broderick), the 

University of Tennessee study (Calcagno), and the completed E-55/59 study conducted by WVU 

and CRC. In addition, the data was separated by truck and bus and by idle speed and accessory 

usage to develop an emission rate more representative of extended idle rates. 

The important conclusion from the EPA 2003 analysis is that factors that affect 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 

years 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 (A/C, 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" (nondiscretionary) idling. 

The studies focused on three types of idle conditions. The first is considered a curb idle, or low 

engine speed (<1000 rpm) and no air conditioning. The second is representative of an extended 

37 
idle condition with higher engine speed (>1000 rpm) and no air conditioning. The third represents 

an extended idle condition with higher engine speed (>1000 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 increased NOx emission rates due to 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 three 

studies that included 26 tests. The average emissions from these trucks using the high idle engine 

speed and with accessory loading was used for the emission rates for extended idling. 

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

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

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

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

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

procedure, EPA expects that there will be little effect on HC, PM, and NOx emissions after hours 

of idling due to cool-down effects on EGR and the aftertreatment systems. As a result, idle NOx 

emissions will be reduced 12%, PM emissions will be reduced 11%, and HC 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. 

Detailed equations are included in the Appendix. 

1.3.3 Results 

Table 25 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 25 shows extended idle emission rate inputs in g/hour. 

Model years NOx HC CO 

Pre-1990 112 108 84 

1990-2006 227 56 91 

2007 and later 201 53 91 

The PM rates in the draft version of MOVES do not yet reflect the analysis detailed in the previous 

subsection. For the time being, the regular curb idle emission rates have been substituted (i.e., 

MOVES opmodeid = 1) for use in MOVES as extended idle rates. This is likely an understatement 

of the “true” and currently unknown extended idle rates. We expect the rates in the final version to 

increase at least marginally based on the increased use of accessories and, for current and future 

model years, a reduction of aftertreatment effectiveness during extended idling. 

38 
 

2 Heavy-Duty Gasoline Truck emissions 

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 records of chassis tests performed by both EPA and outside parties or 

contractors. The heavy-duty gasoline data the MSOD contains is mostly of pickup trucks which 

fall mainly in the LHD2b regulatory class. Table 26 shows the number of vehicles in cumulative 

data sets. In the real world, most heavy-duty gasoline vehicles fall in either the LHD2b or LHD345 

class, with a smaller percentage in the MHD class. There are nearly zero HHD gasoline trucks. 

Table 26 shows the 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 

LHD2b 10 

1990-1997 MHD 1 

LHD2b 33 19 

1998-2002 MHD 1 

LHD2b 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 VSP (Equation 

8). Since heavy-duty emissions regulations are not usually focused on gasoline engines, we 

decided to look at certification data as a guide to developing model year groups for analysis. 

Figure 16 shows averages of certification results by model year. 

39 
Figure 16 shows brake-specific certification emission rates by model year for heavy-duty gasoline engines. 

30 1.4 

25 

1.2 

1 

CO 

Nox 

HC 

1980 1985 1990 1995 2000 2005 2010 

Model year 

CO, NOx [g/bhp-hr] 20 

0.8 

0.6 

0.4 

HC [g/bhp-hr] 

15 

10 

0.2 

5 

0 

0 -0.2 

Based on these certification results, we decided to stratify the data into coarse model year groups, 

listed below. 

• 1960-1989 

• 1990-1997 

• 1998-2002 

• 2003-2006 

• 2007 and later 

We chose to make an extra 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 our HD diesel analysis, we used the age effects present in the data itself. We did not 

incorporate an external tampering and mal-maintenance model into the HD gasoline rates. Due to 

sparseness of data we used only the two age groups listed in Table 26. We also did not stratify by 

regulatory class since there was only one regulatory class (LHD2b) predominantly represented in 

the data. 

2.1.1.2 Sample Results 

As for the heavy-duty diesel analysis, a few sample results graphs are shown. The first (Figure 17) 

shows all three pollutants against operating mode bin for the LHD2b regulatory class. In general, 

emissions follow the common trend with VSP. As expected, NOx emissions for heavy-duty 

gasoline vehicles are much lower than for heavy-duty diesel vehicles. 

40 
Figure 17 shows emissions by operating mode bin for MY 2002 age 0-3. 

200 1200

180 

HC, NOx rate [g/hr] 

160 

140 

120 

100 

80 

60 

40 

20 

0 

THC 

NOx 

CO 

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 

1000 

800 

600 

400 

200 

0 

CO rate [g/hr] 

Figure 18 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 6 and older 

Figure 18 shows emissions by age group for MY 2002 in operating mode bin 24. 

0 

20 

40 

60 

80 

100 

120 

HC, NOx rate [g/hr] 

0 

200 

400 

600 

800 

1000 

1200 

1400 

CO rate [g/hr] 

THC 

NOx 

CO 

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

Age group 

Figure 19 shows emissions by model year group. Except for CO, emissions consistently decrease 

with model year group. Our assumptions regarding implementation of increased effectiveness of 

catalysts substantially reduce emissions estimates for 2007 model year and later. 

41 
 

Figure 19 shows emissions by model year group for age 0-3 in operating mode 24. 

300 600 

250 500 

THC 

NOx 

CO 

pre-1990 1990-1997 1998-2006 2007+ 

Model year group 

HC, NOx rate [g/hr] 200 400 

CO rate [g/hr] 

150 300 

100 200 

50 100 

0 0 

2.1.2 PM 

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

the broad and detailed emission factors expected by the MOVES model. As a result, only a very 

limited analysis could be done, and ‘placeholder’ emission rates developed for the model. As such, 

EPA will likely revisit and update these emission rates, when sufficient data on PM2.5 emissions 

from heavy-duty gasoline vehicles become available. 

The principal result from this analysis is that for the Draft MOVES2009 model, 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. Since the MOVES light-duty gasoline PM2.5 emission rates are 

a complete set of factors - stratified 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 Source 

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 which were used are the UDDS test cycle data. 

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 27. 

Table 27 – Summary of data used in HD gasoline PM emission rate analysis 

Vehicle MY Age Test cycle GVWR PM2.5 mg/mi 

1 2001 3 UDDS 12975 1.8115942 

2001 3 UDDS 19463 3.6101083 

2 1983 21 UDDS 9850 43.3212996 

1983 21 UDDS 14775 54.3478261 

42 
 

 

 

3 1993 12 UDDS 13000 67.0949972 

1993 12 UDDS 19500 108.336719 

4 1987 18 UDDS 10600 96.7349998 

1987 18 UDDS 15900 21.5098555 

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 strata of data – vehicle #1 (MY 2001) and 

the other three vehicles. Because of its lower age (3 years old) and newer model year status, this 

vehicle has substantially lower PM emission levels than the others, and was separated in the 

analysis. The emissions of the other three vehicles were averaged together to produce these mean 

results: 

Mean for Vehicles 2 through 4: 0.06522 g/mi Older Group 

Mean for Vehicle 1: 0.00271 g/mi Newer Group 

For comparison with the heavy-duty gas PM rates tested over the UDDS cycle, simulated UDDS 

cycle emission rates were developed based on MOVES light-duty gas PM2.5 emission rates 

(normal deterioration assumptions) for light-duty gasoline trucks. The UDDS cycle represents 

standardized operation for the heavy-duty vehicles. 

To make the comparisons fair, the simulated light-duty UDDS results were matched by model year 

and age to the model year and age of the four heavy-duty gas trucks in the sample. This 

comparison meant that the emission rates from the following MOVES model year groups and age 

groups for light-duty trucks were used: 

• MY group 1983-1984, age 20+ 

• MY group 1986-1987, age 15-19 

• MY group 1991-1993, age 10-14 

• MY group 2001, age 0-3 

The simulated UDDS emission factors for the older model year light duty gas truck group are 

0.0362 g/mi for MOVES organic carbon PM2.5 emissions and 0.002641 g/mi for elemental carbon. 

Ignoring sulfate emissions (which are in the order of 1×10-7 g/mile for low sulfur fuels) sum to 

0.03884 g/mile. 

0.06522 g 

This value leads to the computation of the ratio: mile = 1.679 . 0.03884 g 

mile 

The simulated UDDS emission factors for the newer model year light duty gas truck group are 

0.004368 g/mi for MOVES organic carbon PM2.5 emissions and 0.0003187 g/mi for elemental 

carbon. Ignoring sulfate emissions (which are in the order of 1×10-8 g/mile for low sulfur fuels) 

sum to 0.004687 g/mi 

43 
g 0.00271 mile This value leads to the computation of the ratio: = 0.578 . 0.004687 g 

mile 

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

produce less PM2.5 emissions than larger trucks. This is intuitively inconsistent, and is the likely 

result of a very small sample and a large natural variability in emission test 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 WtFrac + Ratio (1−WtFrac) final older newer 

= 1.679×0.75 + 0.578×0.25 = 1.40 

We 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. 

2.2 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 LHD2b regulatory class. 

Table 28 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. 

Table 28. 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 

44 
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 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 lognormal, 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 

ln xl xg = e Equation 11 

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 25 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 

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 lognormal 

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. 

45 
FTP Cold Start (g) 

0

1

2

3

4

5

6

7

8 nox

g_nox

a_nox

( c) NOx 

Figure 20. 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). 

0 2 4 6 8 10 12 14

Age (years) 

16

14 (b) THC 

12

10

8

6

thc 4
 

g_thc
 2 a_thc
 

0
 

0 2 4 6 8 10 1 2 14

Age (years) 

0 2 4 6 8 10 12 14

Age (years) 

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 26 above. 

2 sl 

2 Equation 12 x = x e a g 

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 

0

20

40

60

80

100

120

140

160

180

200

FTP Cold Start (g)

co

g_co

a_co

(a) CO 

FTP Cold Start (g)

46 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

presented in Figure 20 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. 

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 13. 

2 s s s = x e 2

(e 2 −1) Equation 13 g 

After recalculating the standard deviations, the calculation of corresponding standard errors was 

simple. Because each vehicle is represented by only one data point, there was no within-vehicle 

variability to consider, and the standard error could be calculated as s / n . We divided the 

standard errors by their respective means to obtain CV-of-the-mean or “relative standard error.” 

Means, standard deviations and uncertainties are presented in Table 29 and in Figure 21. Note that 

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

Table 29. 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 

47 
18

Figure 21. Cold-start Emission Rates for Heavy-Duty Gasoline Trucks, with 95% Confidence Intervals 

300
 

(a) CO 

0 5 1 0 15 20 25
 

Age (years) 

250
 

200
 

150
 

100
 

50
 

0

FTP Cold Start (g) 

16
 

14
 

12
 

10
 

8
 

6
 

4
 

2
 

0
 

(b) THC 

0 5 10 15 20 25
 

Age ( years) 

FTP Cold Start (g) 

0

2

4

6

8

10

12

FTP Cold Start(g) 

(c) NOx 

0 5 10 15 20 25
 

Age (years) 

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. 

48 
2.2.4.1 Regulatory class LHD2b 

For CO the approach was simple. We applied the values in Table 29 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 29 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 

⎛ 0.14 g/hp − hr ⎞ f = ⎟

⎟ ⎜ HC ⎜ (1.0 g/hp - hr) = 0.412 Equation 14

⎝(0.14 + 0.20) g/hp − hr ⎠

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

ERRATUM: the expression in Equation 14 above is in error, in that it does not relate the future 

standard (numerator) to the past standard (denominator). The following corrected value is to be 

used in revised rates for final MOVES. 

⎛ 0.14 g/hp - hr ⎞

⎟

⎟ ⎜⎜ (1.0 g/hp - hr) ⎝(0.14 + 0.20) g/hp - hr ⎠ fHC = = 0.37 

1.1g/hp - hr 

We calculated the corresponding value for NOx as 

⎛ 0.20 g/hp − hr ⎞ f = ⎟

⎟ ⎜ NOx ⎜ = 0.588 Equation 15

⎝(0.14 + 0.20) g/hp − hr ⎠

ERRATUM: as for HC, the value in Equation 15 is erroneous, for similar reasons. Whereas the 

error was small, for HC, it is more substantial for NOx. The corrected value to be used for final 

MOVES, is 

⎛ 0.20 g/hp − hr ⎞

⎟

⎟ ⎜⎜ 1.0 g/hp - hr 

⎝(0.14 + 0.20) g/hp − hr ⎠ f = = 0.147 NOx 4.0 g/hp - hr 

The error for HC is small but for NOx it is substantial. For these rates we neglected the 

THC/NMHC conversions, which we gave attention to for light-duty 

49 
 

 

 

 

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 

0.14 g/hp ⋅hr fHC = = 0.127 Equation 16 1.1g/hp ⋅hr 

0.20 g/hp ⋅hr fNOx = = 0.05 Equation 17 4.0 g/hp ⋅hr 

2.2.4.2 Regulatory classes LHD345 and MHD 

For LHD345 and MHD, we estimated values in terms of the values calculated for LHD2b. 

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 

37.1g/hp - hr fCO = = 2.58 Equation 18 14.4 g/hp - hr 

and the corresponding value for HC is1.73. 

1.9 g/hp - hr f HC = =1.73 Equation 19 1.1g/hp - hr 

We applied this ratio in all three model-year groups, as shown in Table 30. 

ERRATUM: the ratios in Equation 18 and Equation 19 were erroneously applied to the 2005-2007 

model-year groups for LHD345 and MHD vehicles. In final MOVES, values for these model-year 

groups will set be equal to those for the LHD2b vehicles, with the rationale that the standards 

converge for both groups. 

For NOx, all values are equal to those for LHD2b, because the same standards apply to both classes 

throughout. The approaches for both regulatory classes in all three model years are shown in Table 

30. 

Table 30. Methods used to Calculate and Start Emission Rates for Heavy-Duty Spark-Ignition Engines 

1960-2004 Increase in proportion Increase Same values as 

Regulatory Class Model-year Group Method 

CO THC NOx 

LHD2b 

1960-2004 Values from 

Table 29 

Values from 

Table 29 

Values from 

Table 29 

2005-2007 Values from 

Table 29 

Reduce in 

proportion 

To standards 

Reduce in proportion 

To standards 

2008 + Values from 

Table 29 

Reduce in 

proportion 

To standards 

Reduce in proportion 

To standards 

LHD345, MHD 

50 
 

 

To standards in proportion 

To standards 

LHD2b 

2005-2007 Increase in proportion 

To standards 

Increase in proportion 

To standards 

Same values as 

LHD2b 

2008 + Increase in proportion 

To standards 

Increase in proportion 

To standards 

Same values as 

LHD2b 

As for heavy-duty diesel and light-duty vehicles we applied the curve in Figure 15 to adjust the 

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

starts (soak time > 720 minutes). 

2.2.4.3 Particulate Matter 

We did not have data on PM from heavy-duty gasoline vehicles. As a result, we used the 

multiplication factor from the running exhaust emissions analysis of 1.40 to scale up the light-duty 

truck start emission rates. 

51 
A. Appendices 

A.1 Calculation of Accessory Power Requirements for 

VSP bins 

Table 31 – Accessory load estimates for HHD trucks 

VSP Cooling Fan Air cond Air comp Alternator Engine 

Accessories Total Accessory Load (kW) 

Low 

Power (kw) 

% time on 

Total (kW) 

19.0 

10% 

1.9 

2.3 

50% 

1.2 

Off = 0.5 kW 

3.0 

60% 

2.0 

1.5 

100% 

1.5 

1.5 

100% 

1.5 8.1 

Mid 

Power (kw) 

% time on 

Total (kW) 

19.0 

20% 

3.8 

2.3 

50% 

1.2 

Off = 0.5 kW 

2.3 

20% 

0.9 

1.5 

100% 

1.5 

1.5 

100% 

1.5 8.8 

High 

Power (kw) 

% time on 

Total (kW) 

19.0 

30% 

5.7 

2.3 

50% 

1.2 

Off = 0.5 kW 

2.3 

10% 

0.7 

1.5 

100% 

1.5 

1.5 

100% 

1.5 10.5 

y ( ) Table 32 – 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) 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 

52 
Table 33 – Accessory load estimates for buses 

VSP Cooling Fan Air cond Air comp Alternator Engine 

Accessories Total Accessory Load (kW) 

Low 

Power (kw) 

% time on 

Total (kW) 

19.0 

10% 

1.9 

18.0 

80% 

14.4 

Off = 0.5 kW 

4.0 

60% 

2.6 

1.5 

100% 

1.5 

1.5 

100% 

1.5 21.9 

Mid 

Power (kw) 

% time on 

Total (kW) 

19.0 

20% 

3.8 

18.0 

80% 

14.4 

Off = 0.5 kW 

4.0 

20% 

1.2 

1.5 

100% 

1.5 

1.5 

100% 

1.5 22.4 

High 

Power (kw) 

% time on 

Total (kW) 

19.0 

30% 

5.7 

18.0 

80% 

14.4 

Off = 0.5 kW 

4.0 

10% 

0.9 

1.5 

100% 

1.5 

1.5 

100% 

1.5 24.0 

53 
 

 

 

 

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 

and test cells often yield very small rates of emissions deterioration through time. However, in 

real-world use, tampering and mal-maintenance yield higher rates of emissions deterioration 

through 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 affect emissions through age, we developed a simple function of emission deterioration 

through age. We applied the zero-age rates through emissions warranty period (5 years/100,000 

miles), then increased the rates linearly up to useful life. Then we assumed that all the rates level 

off beyond the useful life age. Figure 22 shows this relationship. 

Figure 22 qualitatively shows the implementation of age effects in MOVES. 

Emission rate 

Final emission rate
 

due to T&M
 

Zero-mile
 

emission
 

rate
 

Age End of warranty
 

period
 

The useful life refers to the length of time that engines are required to meet emissions standards. 

We incorporated this age relationship through the age groups in MOVES by averaging emissions 

rates for all ages in each age group. Mileage was converted to age with VIUS37

 (Vehicle Inventory 

and Use Survey) data, which contains data on how quickly trucks of different regulatory classes 

accumulate mileage. Table 34 shows the emissions warranty period and approximate useful life 

requirement period for each of the regulatory classes. 

End of useful life 

54 
Table 34 – Warranty and useful life requirements by regulatory class 

Regulatory class 

Warranty age 

(Requirement: 

100,000 miles/5 years) 

Useful life 

mileage/age 

requirement 

Useful 

life age 

HHD 1 435,000/10 4 

MHD 2 185,000/10 5 

LHD345 4 110,000/10 4 

LHD2b 4 110,000/10 4 

BUS 2 435,000/10 10 

While both an age and a mileage metric are given for these periods, whichever comes first is what 

determines the applicability of the warranty. As a result, since MOVES deals with age and not 

mileage, we need to convert all the mileage numbers to age numbers, as the mileage limit usually is 

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 their 

emissions will happen more quickly than 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 proper 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 within 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 35 shows the multiplicative 

T&M adjustment factor by age. We determined this factor using the mileage-age data from Table 

34 and the emissions-age relationship that we described in Figure 22. 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. 

Table 35 shows the T&M multiplicative adjustment factor by age. 

Age Group LHD MHD HHD Bus 

0-3 0 0.083 0.25 0.03125 

4-5 1 0.833 1 0.3125 

6-7 1 1 1 0.5625 

8-9 1 1 1 0.8125 

10-14 1 1 1 1 

15-19 1 1 1 1 

20+ 1 1 1 1 

In this table a value of 0 indicates no deterioration (or zero-mile emissions level), and a value of 1 

indicates a fully deteriorated engine (or maximum emissions level). The calculation of emission 

rate by age group is described in the equation below. 

rpol,agegrp = rZML (1+ f pol,agegrpTM pol ) 

55 
 

A.2.2 Data Sources 

EPA used the following information to develop the tamper and mal-maintenance occurrence rates 

for MOVES: 

•	 California’s ARB EMFAC2007 Modeling Change Technical Memo38

 (2006). The 

basic EMFAC occurrence rates for tampering and mal-maintenance were developed 

from the Radian and EFEE reports and internal CARB engineering judgment. 

•	 Radian Study (1988). The report estimated the malfunction rates based on survey and 

observation. The data may be questionable for current heavy-duty trucks due to 

advancements such as electronic controls, injection systems, and exhaust aftertreatment. 

•	 EFEE report (1998) on PM emission deterioration rates for in-use vehicles. Their work 

included heavy-duty diesel vehicle chassis dynamometer testing at Southwest Research 

Institute. 

•	 EMFAC2000 (2000) Tampering and Mal-maintenance Rates 

•	 EMA’s comments on ARB’s Tampering, Malfunction, and Mal-maintenance 

Assumptions for EMAFAC 2007 

•	 University of California –Riverside “Incidence of Malfunctions and Tampering in 

Heavy-Duty Vehicles” 

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

•	 EPA internal engineering judgment 

A.2.3 Analysis 

T & M Categories 

EPA generally agreed with 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 groups into EGR Stuck Open and EGR Disabled/Low Flow. We 

included the PM regeneration system, including the igniter, injector, and combustion air system in 

the PM filter leak category. 

EPA will group the LHDD, MHDD, HHDD, and Diesel bus groups together, except for 2010 and 

beyond. We assumed that the LHDD group will primarily use Lean NOx Traps (LNT) for the NOx 

control in 2010 and beyond. On the other hand, we also assumed that Selective Catalyst Reduction 

(SCR) systems will be the primary NOx aftertreatment system for HHDD. Therefore, the 

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

class of the vehicles. 

T&M Model Year Groups 

EPA developed the model year groups based on regulation and technology changes. 

56 
•	 Pre-1994 represents non-electronic fuel control. 

•	 1998-2002 represents the time period with consent decree issues. 

•	 2003 begins the use of EGR. 

•	 2007 and 2010 contain significant PM and NOx regulation changes. 

•	 EPA issued a NPRM to require OBD phase-in beginning in 2010 model year for heavy 

duty trucks with complete phase-in by 2013. However, we will not be applying OBD to 

the frequency rates at this time because we have not issued a final rulemaking. 

T &M Occurrence Rates 

EPA T &M Occurrence Rate Differences from EMFAC2007 

EPA agreed with the CARB EMFAC2007 occurrence rates, except as noted below. 

Clogged Air Filter: EPA reduced the frequency rate from CARB’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. 

Other Air Problems: EPA reduced the frequency rate from CARB’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 occurrence rates. We believe that the hardware will experience an evolution 

through 2010, not 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 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, CARB’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 program39

 so it cannot be ignored. 

NOX Aftertreatment malfunction: EPA developed the NOx aftertreatment malfunction rate 

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. 

57 
 

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. 

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% 

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: SwRI completed 6000 hours of ESC cycling with NOx sensor. 

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

longer life expected by 2010. 

58 
•	 Forward looking assumptions: Manufacturers have a strong incentive to improve the 

reliability and durability of the sensors because of the high cost associated with frequent 

replacements. 

PM Filter Leak: EPA will use 5% 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 is 

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. 

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. 

MOVES Tamper & Mal-maintenance Occurrence Rate Summary 

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% 

Emission Effects 

NOx Emission Effect of Tampering and Mal-maintenance 

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. 

59 
 

 

 

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. MECA states 75-90% NOX reduction with 

open loop control and >95% reduction with closed loop control.40 Visteon reports 60-80% NOX 

reduction with open loop control.41 

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. 

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 

PM Emission Effect of Tampering and Mal-maintenance 

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 driveability impact. Therefore, 

operators will have an incentive to fix the issue. 

60 
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 affectivity. 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
 

PM Emission Effect
 

Federal Emission Standard 

Timing Advanced 

1994-97 

0.1 

-10% 

1998-2002 

0.1 

-10% 

2003-2006 

0.1 

-10% 

2007-2009 

0.01 

0% 

2010 

0.01 

0% 

Timing Retarded 25% 25% 25% 1% 1% 

Injector Problem 

Puff Limiter Mis-set 

Puff Limiter Disabled 

Max Fuel High 

Clogged Air Filter 

100% 

20% 

50% 

20% 

50% 

100% 

0% 

0% 

0% 

50% 

100% 

0% 

0% 

0% 

30% 

5% 

0% 

0% 

0% 

2% 

5% 

0% 

0% 

0% 

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% 

HC Emission Effect of Tampering and Mal-maintenance 

EPA estimated oxidation catalysts are 80% effective at reducing hydrocarbons. All manufacturers 

will utilize oxidation catalysts in 2007, but only a negligible amount installed one 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. 

61 
 

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 

A separate tampering analysis was not performed for CO. The same effects were assumed 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 15 (PM), and 

Table 19 (HC and CO). 

62 
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 

63 
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 

64 
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 

65 
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 

2007 Extended Idle Emissions calculation: 

•	 Assumed 8 hour idle period where the emissions controls, such as EGR, oxidation 

catalyst, PM filter, and NOx aftertreatment, are still active for the first hour. 

•	 PM emissions standards: 

o	 Pre-2007: 0.1 g/bhp-hr 

o	 2007: 0.1 g/bhp-hr 

•	 HC emissions standards: 

o	 Pre-2007: 0.50 g/bhp-hr 

o	 2007: 0.14 g/bhp-hr 

66 
• NOx emissions standards: 

o Pre-2010: 5.0 g/bhp-hr 

o 2010: 0.2 g/bhp-hr 

Idle PM Rate Reduction = 1 - [(1/8 * 0.01 g/bhp-hr + 7/8 * 0.1 g/bhp-hr) / 0.1 g/bhp-hr] = 11% 

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% 

67 
 

A.4 Regression to develop PM emission rates for missing 

operating mode bins 

The regression technique that was used was a log-linear least squares procedure. Regulatory class, 

model year group and speed bin (0 – 25 mph, 25-50 mph and 50+ mph bins) were represented by 

dummy variables in the regression. Natural log of emissions was regressed versus vehicle specific 

power (VSP) to represent the operating mode bins. The regression assumed a constant slope versus 

VSP for each regulatory class. Log 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 equation Eq-2 shows the form of the resulting regression 

equation. 

Regression Coefficients for PM Emission Factor Model 

Regulatory Class 46 Regulatory Class 47 

Regclass Code Coefficient Regclass Code Coefficient 

464410 -5.419 474410 -5.143 

464420 -4.942 474420 -4.564 

464430 -4.765 474430 -4.678 

464510 -5.366 474510 -5.847 

464520 -4.929 474520 -5.287 

464530 -4.785 474530 -5.480 

464610 -5.936 474610 -5.494 

464620 -5.504 474620 -5.269 

464630 -5.574 474630 -5.133 

464710 -5.927 474710 -6.242 

464720 -5.708 474720 -5.923 

464730 -5.933 474730 -6.368 

464810 -6.608 474810 -6.067 

464820 -6.369 474820 -5.754 

464830 -6.305 474830 -6.154 

VSP 0.02821 VSP 0.0968 

Transformation 

Coefficient 0.5864 

 Transformation 

Coefficient 0.84035 

68 
The Regclass Code shown in the Table has the following explanation. The first two digits from the 

left indicate the regulatory class – either 46 or 47. Class 46 is the medium heavy-duty diesel 

vehicle class, and Class 47 is the heavy heavy-duty diesel vehicle class. The MOVES model also 

contains lighter diesel vehicle classes 41 and 42 and the transit bus class 48, but no data were 

available for these. The second two digits represent the model year group. Where the value of 44 

maps to 1960-1987, 45 maps to 1988-1990, 46 maps to 1991-1993, 47 maps to 1994 - 1997 and 48 

maps to 1998 – 2006. A final code value of 49 was also mapped to the 2007+ model years for use 

in MOVES. However, there were no data associated with this group. The final two digits 

represent the speed divisions of the operating mode bins. The value of 10 represents 0 to 25 mph, 

20 represents 25 to 50 mph and 30 represents 50 mph and higher. The VSP is an independent 

variable within each of the speed divisions. 

Log emissions = Coefficient + VSPCoefficient * Avg VSP + Log Trans Coeff 

Where : 

The variable ‘Coefficient’ are the coefficients found in the above table. VSPCoefficient is the VSP 

Coefficient at the bottom of the table, and Log Trans Coeff is the log transformation coefficient in 

the table. Avg VSP is the average VSP utilized by MOVES for each of the 23 operating mode bins 

in units of kW/metric ton (i.e., bin11 = 0, bin12 = 1.5, bin13 = 4.5, etc.). 

69 
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 University of California Riverside’s “Mobile Emission Laboratory.” 

The subsequent sections of the memo describe the following: 

•	 the extension of PERE for heavy-duty diesel vehicles, developed by Nam and Giannelli, 

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 

PERE (Physical Emission Rate Estimator) is a model employed by EPA in earlier 

developmental phases of MOVES.42

 In particular, the MOVES team employed it in “hole filling” 

greenhouse gas emission rates for unlikely SourceBin/OpModeBin combinations in 

MOVES2004.43 

The underlying theory behind PERE and its comparison with measured fuel consumption 

data is described by Nam and Giannelli (2005).42 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 described by Nam and 

Giannelli (hereafter, “PERE-HD”, accessory loads were described by a single estimate of the power 

demand of accessories. 

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, resistive force coefficients, 

transmission type, class [MDT/HDT/bus]); 

70 
• engine parameters (fuel type, displacement); and 

• driving cycle. 

The specification of these inputs allows a 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 

would using a small number of averages alone. 

For the current application, PERE-HD, a model 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 fixed inputs within spreadsheet models to be substituted with probability distributions, 

sample an input value from each input distribution, and re-run the spreadsheet model many times. 

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, compare the results of the following 

equation when using fixed inputs versus distributions of inputs: 

BMI = M / L2 

This is the equation for body mass index in humans, a simple surrogate for overweight and 

underweight. 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 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 (5000 iterations) using 

@Risk, we would predict a probability distribution of BMI in the population as follows: 

71 
 

Distribution of BMI in Simulated Population 

0.060 

0.050 

0.040 

0.030 

0.020 

0.010 

0.000 

10 20 30 40 50
 

BMI
 

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): % of Simulated Population 

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 “average” inputs provides much less information than does Monte Carlo 

simulation of 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 (FMEP) 

vary by model year, improving with later model years. As such, model year was simulated as a 

probability distribution, using data from the Census Bureau’s 1997 Vehicle Inventory and Use 

Survey (VIUS) VIUS reports VMT by model year, so these data were normalized total VMT to 

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

72 
 

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: 

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 

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.44

 A two-way table was constructed to estimate 

VMT broken out as [weight class] * [displacement class]. Analyses were restricted to dieselpowered trucks only. 

First, @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)]. A and B parameters were determined 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. 

73 
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 

Because TIUS reports ranges of running weight, any value of running within the TIUS-specified 

ranges were considered equally likely and modeled as uniform probability distributions. For the 

upper and lower ends 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, an engine displacement based on a discrete 

distribution assigned to every weight class in VIUS, represented below: 

Distribution of Displacement (cu. in.) by Running Weight (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 

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 bins, the 

minimum and maximum engine displacements were assumed to be 100 in3

 and 915 in3, 

respectively. 

This procedure reflects the range in running weights present among HDV in operation, and 

constrains the combinations of weight and displacement to plausible values based in surveyed truck 

operator responses. This allows plausible variability in weight-engine pairings, which translates 

into differences in engine BMEP. As described later, IMEP is a key variable in predicting EC and 

OC emission rates. 

74 
 

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, Gururaja and Cullen (2008) developed a more 

detailed set of accessory load estimates based on several accessories’ power demand while in use 

and the fraction of time that an accessory is in use.45

 Gururaja and Cullen estimated “high,” 

“medium,” and “low” accessory use categories 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. The 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 prior to the final version of MOVES, though 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 powering 

these accessories is distributed in time as follows: 

Comparison of High / Medium / Low Accessory
 

Load Cases
 

1.000 

P8 P13 P18 

0.800 

0.600 

0.400 

0.200 

0.000 

0 10 20 30 

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. [NEEDS REFERENCE] 

A.5.2.1.6 Other Factors 

75 
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. These technologies are promoted by EPA’s Smartway Transport Partnership. 

A.5.2.2 Prediction of Elemental Carbon and Organic Mass based on PEREHD 

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 a compression stroke. The highpressure and high temperature during the cylinder cause spontaneous ignition of the fuel vaporizing 

from the injected droplets. Because temperature can rise more quickly that 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, lube 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. 

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 memo, it is assumed 

76 
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: 

•	 Thermooptical 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	 Thermooptical 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	 Thermooptical 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. 

A number of other techniques also exist within published literature, but the above techniques have 

been most applied in emissions and routine ambient PM sampling. 

Among the available techniques, it has been a point of controversy among academics as to 

what analysis 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. It should be noted that different emission researchers employ different 

analysis techniques. Desert Research Institute (DRI) employed TOR in analyzing the Kansas City 

gasoline PM emission study samples, while other prominent academics employ TOT, notably the 

77 
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. Research of all of these groups is employed throughout this study, so an intercomparison of the methods of TOT/TOR is necessary to be able to “recalibrate” one set of data to 

another. 

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. 

EC and OM inventories sit between these to “columns.” 

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.46

 That standard operating procedure 

defines thermooptical reflectance (TOR) as the method for analysis of ambient carbon PM. 

A.5.2.2.3 TOR – TOR Calibration Curve 

As part 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. 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.47 

Both EC and OC were analyzed using the same approach. All data from all vehicles were 

compiled together into tabular spreadsheets. 

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) in the 

range [0,1). Those cases with RAND ≥ 0.95 were set aside as a cross-validation data set, and 

excluded from any further regression analyses done. 

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-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. 

The 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 <0.001 

78 
1000 

5

100 

200 

300 

400

500

0

.1 

.2

.3 

.4 

.5 

1 

2

3

4

5 

10 

20

30 

40 

1000 

5

100 

200 

300 

400

500

0

.1 

.2

.3 

.4 

.5 

1 

2

3

4

5 

10 

20

30 

40 

OC-TOR 1.014 0.007 153.923 <0.001 

To evaluate the quality of predictions resulting from these statistically-based adjustment 

factors, they were used to predict EC-TOR and OC-TOR values for the subset of data with RAND 

≥ 0.95. Scatter plots of the statistical fits are illustrated below (note logarithmic scaling). 

1000
 

500 400

300
 

200
 

100
 

5040

30
 

20
 

10
 

OC - TOR Measured EC-TOR Measured

5

4

3
 

2
 

1
 

.5.4

.3
 

.2
 

.1
 

EC-TOR Predicted 

1000
 

500 400

300
 

200
 

100
 

5040

30
 20
 

10
 

5

4

3
 

2
 

1
 

.5.4

.3
 

.2
 

.1
 

OC TOR-Predicted 

79 
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 

EC-TOT and OC-TOT into their respective TOR-based metrics appear reasonable. 

It remains an unverified assumption that the “calibration” factors derived from the emission 

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 Parameter 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 bin (OpModeBin). 

It should be noted that the “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, might be applicable to the extrapolation of the observed rates to other vehicles or driving 

conditions. As a result, identifying an engine parameter that explains the observed variation in 

driving cycle-based emission rates for EC and OM is desirable. Such a parameter will assist in 

estimating emission associated with short-term variations in driving. 

One parameter that is a good candidate for establishing an engine-based emission model is 

mean effective pressure (MEP). MEP is defined as: 

MEP = P*nR / Vd*N 

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.” 

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 wellpredicted by Nam and Gianelli’s trends within PERE, IMEP should be an appropriate metric for 

80 
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 singlecylinder 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 

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. 

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 

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 

81 
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 paper 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. 

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 

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: 

A Y = −Bx 

e + C 

A least-squared error approach was implemented within Microsoft Excel to derive the coefficients 

for the logistic curves for EC-TOT and OC-TOT. The solutions to the fits are as follows: 

Y 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 

82 
 

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 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. 

Comparison of EC/OC Ratio (TOT) by IMEP
 

With and Without Max/Min Constraints
 

100 

10 

1 

0.1 

0.01 

IMEP 

The approach of constraining predictions to the maximum and minimum values observed in 

the measured data set is not grounded in any theoretical basis, but is a “brute force” approach. 

Future revisions to this analysis will consider alternative approaches more grounded in accepted 

theoretical or statistical methodology. 

The logistic curves described above receive IMEP predictions from PERE to predict ECTOT 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, TOTequivalent 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 EC/OC Ratio (TOT) 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 

EC/OC(TOT-Predicted) 

EC/OC(TOT-Predicted) with 

Constraints 

EC/OC(TOT-Measured) 

83 
 

Carbon is only one component of the organic material found in PM emission samples. 

Hydrogen, oxygen, and nitrogen are also atomically part of organic molecules found in exhaust 

PM. For this study, a simple set of OC/OM conversion ratios were employed. 

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.48

 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 be 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.49 

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 likely 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. 

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 

84 
 

Cruise 23.1 2083 39.9 59.3 2.3 

The following table presents the EC-TOT and OC-TOT emission rates and emission factors 

reported in Table 6 of the study: 

Rate/Factor 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 

IMEP-based emission rates. As shown in the graph, it appears that for transient and cruise 

conditions, PERE-HD predicts the general between-cycle trends in EC-TOT and OC-TOT emission 

factors. It appears for the low-speed “creep cycle,” PERE-HD or the IMEP-based emission rates 

underpredicts total carbon (EC+OC) emission factors, but that the general trend in the EC/OC ratio 

is directionally correct. 

Predicted EC and OC Emission Factors(g/mi) vs. Measured Values in Shah et al. (2004) 

Emission Factor (g/mi)

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

H L M Shah 

avg. 

H Shah 

avg. 

H L M Shah 

avg. 

Creep Cruise Transient 

Average of OC g/mi
 

Average of EC g/mi
 

A.5.3 Variability in Predicted EC and OC Emission Rates 

Through the modeling approach used here the influence of variable vehicle weight and 

engine displacement on HDDV EC and OC emission rates can be assessed. It should be noted that 

85 
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. 

EC/OM Ratio (TOR-Specific) versus Weight/Displacement Ratio for Individual Truck Samples 

Transient Driving Cycle, High Accessory Load 

25 

20 

15 

10 

5 

0
 

0 1000 2000 3000 4000 5000 6000 7000 8000
 

Running Weight / Displacement (kg/l) 

 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. EC/OM Ratio

86
 
Distribution of kg/l Ratios in Transient, High Accessory Load Simulation 

kg/l 8000 

7000 

6000 

5000 

4000	 kg/l

3000 

2000 

1000 

0 

0	 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
 

Percentile
 

For a 12 l engine, 3,000 kg/l would correspond to a running weight of 39600 kg (87,302 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 

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.50

 Note that in the graph, the highest weight category reported by Ahanotu (1999) is 

represented as 100%, although the actual maxima of observed trucks are unknown. 

Comparison of Simulated Weights with Atlanta Area Truck Measurements 

1 

0.9 Simulated Weight 

Cumulative Distribution 0.8 

0.7 

GA Tech Class 9 Monday Midday 

0.6 GA Tech Class 9 Monday 3-7 PM 

0.4 

0.5 

GA Tech Class 8 Trucks M-F Midday 

0.3 GA Tech Class 10-13 Trucks Daytime 

0.2 GA Tech Class 10-13 Trucks 

0.1 Nighttime

0 

0 20000 40000 60000 80000 100000 120000 

Weight (lb) 

 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. 

87
 
A.5.4 Calculating EC/OC fraction by MOVES operating mode bin 

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 MOVES. The next step of analysis 

for MOVES consists of appropriately binning the outputs to fit the MOVES operating mode 

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 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. 

Av + Bv 2 + Cv 3 

t t t t t + mv a VSPt = m 

This equation is implemented slightly differently than the one used for analysis of the chassis 

dynamometer testing for PM, HC, and CO since the road load coefficients (A, B, and C) and 

weight (or mass) m were specific to each individual vehicle, not general to the regulatory class. In 

the PM, HC, and CO equation, the road load coefficients and denominator mass were not specific 

to the vehicle and the numerator mass was specific to the vehicle. We felt confident in using 

vehicle-specific numbers because we performed the analysis using a full representative distribution 

of weights and displacements. Also, since we are interested in the EC and 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. 

88 
Representative distribution of weights used in the EC/OC analysis. 

0 

2.5 

5.0 

7.5 

10.0 

12.5 

15.0 

17.5 

20.0 

22.5 

Percent 

5000 15000 25000 35000 45000 55000 65000 75000 85000 95000 105000 115000 125000 135000 145000 

Weight_lb 

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 bin. Then, we calculated the fractions of EC and 

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

we used the HHD fractions. 

∑rEC ∑r

f = , f = OC = 1 − f EC OC EC ∑rEC + ∑rOC ∑rEC + ∑rOC 

The resulting EC fractions by operating mode bin are shown in Figure 4 in the main body of this 

report. 

89 
 

A.6 Heavy-duty Gasoline Start Emissions Analysis Figures 

Figure 23. Cold-Start Emissions (FTP, g) for Heavy-Duty Gasoline Vehicles, averaged by Model-year and Age 

Groups 

(a) CO 

(b) THC 

(c) NOx 

90 
Figure 24. Cold-Start FTP Emissions for Heavy-Duty Gasoline Vehicles, GEOMETRIC MEANS by Modelyear and Age Groups 

(a) CO 

(b) THC 

(c) NOx 

91 
 

 

Figure 25. Cold-start FTP Emissions for Heavy-Duty Gasoline Trucks: LOGARITHMIC STANDARD 

DEVIATION by Model-year and Age Groups. 

(a) CO 

(b) THC 

(c) NOx 

92 
Figure 26. Cold-Start Emissions for Heavy-Duty Gasoline Trucks: RECALCULATED ARITHMETIC 

MEANS by Model-year and Age Groups. 

(a) CO 

(b) THC 

(c) NOx 

93 
 

 

 

 

 

 

 

Table 36 - 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 

LHD2b 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 

LHD345, 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 

94 
 

 

 

 

 

 

 

 

 

 

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20 Koupal, John et al. MOVES2004 Energy and Emissions Inputs - Draft Report February, 2005 

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Metropolitan Area,” Tang and Munn, New York State Dept of Environmental Conservation, November 9, 2001. 35Broderick, Dwyer, et al (2001), "Potential Benefits of Utilizing Fuel Cell Auxiliary Power Units in Lieu of HeavyDuty Truck Engine Idling," Broderick, Dwyer, et al. Institute of Transportation Studies, University of California - 

Davis. Paper UCD-ITS-REP-01-01 

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Storey, John F. Thomas, Samuel A. Lewis, Sr., Thang Q. Dam, K. Dean Edwards. Oak Ridge National Laboratory. 

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01-0289. 

37 Vehicle Inventory and Use Survey, U.S. Census Bureau http://www.census.gov/svsd/www/vius/products.html
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California Air Resources Board, October 2006. 

39 Illinois Environmental Protection Agency. “Effectiveness of On-Board Diagnostic I/M Testing” September 2003. 

Page 21. www.epa.state.il.us/air/publications/obd-report-final.pdf
 40 Manufacturers of Emission Controls Association. www.meca.org 41 Song, Qingwen. (2002). “Model-based Closed-loop Control of Urea SCR Exhaust Aftertreatment System for Diesel 

Engine.” Song, Q. and Zhu, George. SAE 2002-01-287. Page 1.
 

http://www.visteon.com/utils/whitepapers/2002_01_0287.pdf

42 Nam, E.K.; Giannelli, R. (2005) Fuel Consumption Modeling of Conventional and Advanced
 

Technology Vehicles in the Physical Emission Rate Estimator (PERE). EPA Office of Transportation and Air Quality. 

Draft document EPA420-P-05-001. 

43 http://www.epa.gov/otaq/models/ngm/420s05002.htm#pere 

44 U.S. Census Bureau. (2000) Vehicle Inventory and Use Survey 1997 Microdata File [CD-ROM]. U.S. Department
 

of Commerce Economics and Statistics Administration
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Feb.xls”. Personal communication. 

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47 http://www.nrel.gov/vehiclesandfuels/nfti/feat_split_study.html 

96 
 

 

 

 

48 Heywood, J.B. Internal Combustion Engine Fundamentals. McGraw-Hill: New York, 1988. Data from Table 11.8,
 

page 629. 

49 Shah, S.D.; Cocker, D.R.; Miller, J.W.; Norbeck, J.M. (2004) Emission rates of particulate matter and elemental and
 

organic carbon from in-use diesel engines. Environ Sci Technol 38: 2544-2550.
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temporal and spatial variability. PhD Dissertation, Georgia Institute of Technology. 

Additional Extended Idling References 

EPA (2004), "Guidance for Quantifying and Using Long-Duration Truck Idling Emission Reductions in State Implementation Plans and 

Transportation Conformity." (EPA420-B-04-001, January 2004) (http://www.epa.gov/smartway/idle-guid.htm) 

EPA (2003), “Draft Analysis of Heavy-Duty Diesel Vehicle Idle Emission Rates,” (EPA420-D-03-001, November 2003) 

Additions: 

Clark, Khan, Thompson, Wayne, Gautam, Lyons (2005), “Idle Emissions from Heavy-Duty Diesel Vehicles” Presentation by the Center for 

Alternative Fuels, Engines, and Emissions at West Virginia University. 

CRC (2007), “Heavy-Duty Vehicle Chassis Dynamometer Testing for Emissions Inventory, air Quality Modeling, Source Apportionment, and Air 

Toxics Emissions Inventory” CRC Report No. E55/59. August 2007. 

California Air Resource Board (2006), EMFAC Modeling Change Technical Memo: “Revision of Heavy Heavy-Duty Diesel Truck Emission Factors 

and Speed Correction Factors” Zhou, Lei. October 20, 2006. 

Lutsey et al (2004) “Heavy-Duty Truck Idling Characteristics – Results from a Nationwide Truck Survey” Lutsey, Nicholas, Brodrick, Christie-Joy, 

Sperling, Daniel, Oblesby, Carollyn. Transportation Research Record 1880. Pages 30-38. http://www.its.ucdavis.edu/publications/2004/UCD-ITSRP-04-38.pdf 

Wallace (2003) “Modeling of Line-Haul Truck Auxiliary Power Units in ADVISOR 2002” Wallace, John Paul, Thesis August 2003, University of 

California- Davis. http://www.its.ucdavis.edu/publications/2003/UCD-ITS-RR-03-07.pdf 

97