# Development of Emission Rates for Heavy

- Duty Vehicles in the Motor Vehicle Emissions Simulator MOVES2010

Final Report

# Development of Emission Rates for Heavy

- Duty Vehicles in the Motor Vehicle Emissions Simulator MOVES2010

Final Report

EPA-420-B-12-049 August 20121 1 Heavy Duty Diesel Emissions ......................................................................................................... 3 1.1 Running Exhaust Emissions ......................................................................................................... 5 1.1.1 Nitrogen Oxides (NOx) ......................................................................................................... 6

1.1.1. 1 Data Sources ................................................................................................................... 7

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

1.1.1.3 Calculate emission rates ................................................................................................ 12 1.1.1.4 Sample results ............................................................................................................... 18

1.1.2 Part iculate Matter (PM) ....................................................................................................... 22

1.1.2.1 Data Source ................................................................................................................... 22 1.1.2.2 Analysis ........................................................................................................................ 24 1.1.2.3 Sample results ............................................................................................................... 32 1.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO) ............................................................... 36

1.1.3.1 Data Sources ................................................................................................................. 36 1.1.3.2 Analysis ........................................................................................................................ 37 1.1.3.3 Sample results ............................................................................................................... 38

1.1.4 Ener gy .................................................................................................................................. 42

1.2 Start Exhaust Emissions .............................................................................................................. 44 1.2.1 HC, CO, and NOx ................................................................................................................ 44

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

1.2.3 Adjusting Start Rates for Soak Time ................................................................................... 46 1.3 Extended Idling Exhaust Emissions ........................................................................................... 48

1.3.1 Data Sources ........................................................................................................................ 48 1.3.2 Analysis ............................................................................................................................... 49

1.3.3 Results .................................................................................................................................. 51 2 Heavy

- Duty Gasoline Truck emissions ......................................................................................... 52 2.1 Running Exhaust Emissions ....................................................................................................... 52 2.1.1 HC, CO, and NOx ................................................................................................................ 52

2.1.1.1 Data and Analysis ......................................................................................................... 52 2.1.1.2 Sample Results .............................................................................................................. 53

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

2.1.2.1 Data Source ................................................................................................................... 552 2.1.2.2 Anal ysis ........................................................................................................................ 56 2.1.3 Energy .................................................................................................................................. 57

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

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

2.2.2 Estimation of Mean Rates .................................................................................................... 59 2.2.3 Estimation of Uncertainty .................................................................................................... 61 2.2.4 Projecting Rates beyond the Available Data ....................................................................... 64 2.2.4.1 Regulatory class LHD2b3 ............................................................................................. 65 2.2.4.2 Regulatory classes LHD45 and MHD .......................................................................... 66

2.2.4.3 Particulate Matter .......................................................................................................... 67 A. Appendices ................................................................................................................................... 68 A.1 Calculation of Accessory Power Requirements ......................................................................... 68 A.2 Tampering and Mal- maintenance .............................................................................................. 70 A.2.1 Modeling Tampering and Mal- maintenance ....................................................................... 70

A.2.2 Data Sources ....................................................................................................................... 72 A.2.3 Analysis ............................................................................................................................... 73

A.3 Extended Idle Data Summary .................................................................................................... 81 A.4 Regression to develop PM emission rates for missing operating modes ................................... 86 A.5 Heavy

- duty Diesel EC/OC Fraction Calculation ....................................................................... 87

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

A.5.2 PERE for Heavy

- duty Vehicles (PERE -HD) and Its Extensions ....................................... 87

A.5.2.1 PERE- HD Fleet

- wide Average Emission Rate Estimator ........................................... 87 A.5.2.2 Prediction of Elemental Carbon and Organic Mass based on PERE- HD .................... 93 A.5.2 Comparison of Predicted Emissions with Independent Measurements ............................ 102 A.5.3 Variability in Predicted EC and OC Emission Rates ........................................................ 104 A.5.4 Calculating EC/OC fraction by MOVES operating mode ................................................ 106 A.6 Heavy

- duty Gasoline Start Emissions Analysis Figures ......................................................... 1083 

## Executive Summary
This report describes the analysis conducted to generate emission rate and energy rate inputs representing exhaust em issions and energy consumption for heavy -duty vehicles in MOVES2010. Exhaust emission rate inputs were developed for total hydrocarbons (THC), carbon monoxide (CO), nitrogen oxides (NO X ), and particulate matter (PM)

. Energy consumption rates were developed based on measurements of carbon dioxide (CO 2 ), CO and THC . We developed inputs for heavy -duty vehicles powered by both diesel and gasoline fuels

, al though emissions from the heavy -duty sector predominantly come from diesel vehicles. As a result, the majority of the data analyzed were from diesel vehicles. Estimation of energy consumption rates for heavy-duty vehicles is covered in this report, but emissions of greenhouse gases other than CO 2 are not covered. Estimation of the emissions of methane and nitrous oxide (N 2 O) are described in a separate report 1

. Evaporative emissions from heavy-duty gasoline vehicles are not covered in this report. Estimation of evaporative hydrocarbon emissions from heavy- duty gasoline vehicles is described in a separate document 2 . Note that the methods described were developed for light-duty vehicles, but are also applied to heavy-duty gasoline vehicles. The model does not estimate evaporative emissions for diesel

- powered vehicles. Large volumes of continuous (“second- by

- second”) data from various sources were analyzed , including onboard emissions measurement systems, chassis dynamometer tests, and engine dynamometer tests. Data were collected by a number of entities, including EPA, West Virginia Univ ersity, and private parties under contract to EPA . For running exhaust emissions, data were analyzed by model year, regulatory class, and operating mode. As with the development of emission rates for light-duty vehicles, operating modes for heavy-duty vehicles are defined in terms of power output (with the exception of the idle and braking modes). For light-duty vehicles, the parameter used is known as vehicle- specific power (VSP), which is calculated by normalizing the contin uous power output for each vehicle to its own weight. For heavy -duty vehicles, we have continued to related emissions to power output, but in a different way. Rather than normalize the tractive power for each vehicle to its own weight, we scale the power by a fixed multiple designe d to fit the resulting means into the existing operating mode framework. We refer to this parameter as “scaled

- tractive power” (STP). Because heavy -duty vehicles are primarily regulated on an engine work basis (g/kW

- hr)

, we conclude that the use of STP p reserves the emission to power relationship, whereas the use of VSP confounds it, resulting in unintended consequences in estimation of emissions in relation to vehicle size or weight. Additionally, to address the question of deterioration, we estimated the effect s of tampering and mal

- maintenance on emission rates as a fu nction of age. We adopted this approach due to the lack of data adequate to directly estimate deterioration for heavy -duty vehicles. Based on surveys and studies, we developed estimates of frequencies and emission impacts of specific emission control component malfunctions, and then aggregated the se to estimate overall emissions effects for each pollutant.

Final e mission rates in grams per hour were developed for inclusion in the emissio nRateByAge table in the MOVES database . The rates describe the effects of operating mode as well as model
year group , which serves as a broad surrogate for changes in technology and emissions standards,4 especially for NO X and PM. The MOVES framework and the emissionRateByAge table are discussed in the report documenting the rates for light-duty vehicles 3

.5 

## 1 Heavy Duty Diesel Emissions
This section details our analysis of data to develop emission rates for heavy-duty diesel vehicles

. Three emission processes (running, extended idling, and starts) are discussed. The ‘running’ process occurs as the vehicle is operating on the road either under load or in idle mode. This process is further de lineated by 23 operating modes which will be discussed below. The ‘extended idle’ process occurs during an extended period of idling operation such as when a vehicle is parked for the night and left idling. Extended idle is generally a different mechanism (usually a higher RPM engine idle to pow er truck accessories for operator comfort) than the regular ‘curb’ idle that a vehicle experiences while it is operating on the road.



## 1.1 Running Exhaust Emissions
MOVES running- exhaust emissions analysis requires accurate second

- by -second measurements of emission rates and parameters that can be can used to estimate the tractive power exerted by a vehicle . Compared to volumes of data available for light -duty vehicles, the amount of data available for heavy -duty vehicles is small. Light -duty emissions we re analyzed with respect to vehicle -specific power (VSP), which represents vehicle s’ tractive power normalized by the ir (individual) weight s. The model approach used in MOVES was first developed for light-duty vehicles , relying on the VSP concept, and later adapted for use with heavy-duty vehicles. For practical reasons, it was thus desirable to retain the same operating mode structure for heavy -duty emission rates.

While VSP is a n effective way to characterize emissions from light -duty vehicle s, the range of running weights, coarseness of the VSP bin structure, and work- based (rather than distance- based) emissions standards make VSP

- based emissions analysis for heavy

- duty diesel vehicles a n untenable approach . This report describes how we analyzed co ntinuous “ second

- by

- second ’ heavy -duty emissions data to develop emission rates applied within the predefined set of operating modes. As mentioned, the emission rates were using scaled

- tractive power (STP), rather than VSP. The development of STP is de scribed in greater detail below.

MOVES source bins are groupings of parameters which distinguish differences in emission rates according to physical differences in the source type or vehicle classification. The source bins are differentiated by fuel type (gasoline or diesel ), regulatory class (light heavy duty to heavy- heavy duty) and model year group. Stratification of the data sample and generation of the final MOVES emission factors were done according to the combination of regulatory class (shown in Table 1 ) and the model year group. The regulatory groups were determined based on gross vehicle weight rating (GVWR) classifications . The model year groupings are designed to represent major changes in EPA emission standards.6 Table 1. R egulatory C lasses for Heavy

- Duty Vehicles.

Regulatory Class Description regClassName regClassID Gross Vehicle Weight Rating (GVWR) [lb]

Light

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

Light

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

Medium

- heavy duty MHD 46 19,501 – 33,000

Heavy

- heavy duty HHD 47 > 33,000

Urban Bus Urban Bus 1 48 N/A

1 see CFR § 86.091(2).

Heavy

- duty diesel truck emission rates in MOVES are also stratified by age group. Within a particular model year group, these age groups are used to account for the effects of deterioration over time. The age groups are used in the model are shown in Table 2

.

Table 2. MOVES Age Group Definitions

ageGroupID Lower bound (years) Upper bound (years)

3 0 3

405 4 5

607 6 7

809 8 9

1014 10 14

1519 15 19

2099 20 ~



### 1.1.1 Nitrogen Oxides (NOx)
For NOx rates, we stratified heavy -duty vehicles into the model year groups listed in Table 3

. These groups were defined based on changes in NOx emissions standards and the outcome of the Heavy Duty Diesel Consent Decree 4 , which required additional control of NOx emissions during7 highway driving for model years 1999 and later. This measure 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 li mit ( g/bhp

- hr )

Pre

- 1988 None None

1988

- 1989 10.7 None

1990 6.0 None

1991

- 1997 5.0 None

1998 4.0 None

1999

- 2002 4.0 7.0 HHD; 5.0 other reg. classes

2003

- 2006 2.4

1.25 times the family emission level 2007

- 2009 1.2

2010+ 0.2

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

ROVER

. This dataset includes measurements collected during on -road operation using the ROVER system, a portable e missions m easurement s ystem (PEMS) developed by the EPA. The measurements were conducted by the U.S. Army Aberdeen Test Center on behalf of U.S. EPA 5 : This ongoing program started in October 2000. Due to time constraints and data quality issues, we used only data collected from October 2003 through September 2007. The d ata was compiled and reformatted for M OVES analysis by Sierra Research 6

. The process of analysis and rate development was performed by EPA . The data we used represents approximately 1,400 hours of operation by 124 trucks and buses in model ye ar s 1999 thro ugh 2007.

The vehicles were driven mainly over two routes:

• “Marathon” from Aberdeen, MD to Colorado and back along Interstate 70

• Loop around Aberdeen Proving Grounds in Maryland Consent Decree testing

. These data were conducted by West Virginia University using the Mobile Emissions Measurement System (MEMS) 7
, 8 This program was initiated as a result of the consent decree between the several heavy -duty engine manufacturers and the US government, requiring the manufacturers to test in

- use trucks over the road. D ata was collected from 2001 through 2006. T he data we used represented approximately 1,100 hours of operation by 188 trucks in model year s 1994 through 2003. Trucks were heavily loaded and tested over numerous routes8 involving urban, suburban, and rural driving. Several trucks were re

- acquired and tested a second time after 2

- 3 years. Data were collected 5-Hz frequency, which we averaged around each second to convert the data to a 1 .0 -Hz basis

. From each data set, we used only tests we determined to be valid. For ROVER, due to time constraints, we eliminated all tests that indicated any reported problems, including GPS malfunctions, PEMS malfunctions, etc, whether or not they affected the actual emissions results. As our own high-level check on the quality of PEMS and ECU output, we further eliminated any trip where the pearson correlation coefficient between CO 2 (from PEMS) and engine power (from ECU) was less than 0.6. These filters led to a smaller and more conservative subset of the overall ROVER data, than had we applied more detailed and selective criteria. (i.e. not all eliminated tests produced erroneous results). For the WVU MEMS data, WVU itself reported on test validity under the cons ent decree procedure. No additional detailed quality check s w ere 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 s of vehicles by model year group from the ROVER and WVU MEMS programs used for emission rate analysis

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

HHD 19 12 78 91

MHD 0 0 30 32

BUS 2 0 25 19

1.1.1.2 Calculate STP from 1 -Hz data With on-road testing, using vehicle speed and acceleration to estimate tractive power is not accurate given the effect of road grade and wind speed. As a result, we needed to find a n alternate approach. Therefore, we decided to tractive power from engine data collected during operation. We first identified the seconds in the data that the truck was either idling or braking based on acceleration and s peed criteria shown in Table 9 . For all other operation, engine speed ω eng and torque τ eng from the ECU were used to determine engine power P eng , as shown in Equation 1

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

engeng eng 

## P τω = Equation 1

We then determined the relationship between the power required at the wheels of the vehicle and the power required by the engine. We first had to account for the losses due to accessory loads during operation. These power loads are not subtracted in the engine torque value s that are output from the engine control unit. Heavy -d uty 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 powe r steering. Other accessories are required9 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 compre ssor. We identified where the accessories were predominately used on a vehicle speed versus vehicle load map to properly allocate the loads. For example, the cooling fan will be on at low vehicle speed where the forced vehicle cooling is low and at high vehicle loads where the engine requires additional cooling. The air compressor is used mostly during braking operations; therefore it will have minimal load requirements at highway, or high, vehicle speeds. Table 5 identifies the predominant accessory use within each of the vehicle speed and load areas. At this point, we also translated the vehicle speed and engine load map into engine power levels

. The power levels were aggregated into low (green), medium (yellow) and high (red) as identified in Table 5 . Low power means the lowest third, medium is the middle third, and high is the highest third, of the engine’s rated power. For example, for an engine rated at 450 hp, the low power category would include operation between 0 and 150 hp, medium between 150 and 300 hp, and high between 300 and 450 hp.

Table 5 – Accessory use as a function of speed and load ranges , co d ed by power level

.

We next estimated the power required when the accessory was “on” and percentage of time this occurred. The majority of the load information and usage rates are based on information from " The Technology Roadmap for the 21st Century Truck ." 9
The total accessory load is equal to the power required to operate the accessory multiplied by the percent of time the accessory is in operation. The total accessory load for a STP bin is equal to the sum of each accessory load. T he calculations are incl uded in Appendix A.1 Calculation of Accessory Power Requirements

. Speed

Load Low Mid High

Cooling Fan Low Air cond. Air cond. Air cond. Engine Access. Engine Access. Engine Access. Alternator Alternator Alternator Air Compress Air Compress Cooling Fan Cooling Fan Mid Air cond. Air cond. Air cond. Engine Access. Engine Access. Engine Access. Alternator Alternator Alternator Air Compress Air Compress Cooling Fan Cooling Fan Cooling Fan High Air cond. Air cond. Air cond. Engine Access. Engine Access. Engine Access. Alternator Alternator Alternator Air Compress Air Compress10 The total accessory loads P loss,acc listed below in Table 6 are subtracted from the engine power determine d from Equation 1 to get net engine power available at the engine flywheel. For LHD vehicles, w e assume d negligible accessory losses

.

Table 6 – Estimates of accessory load in kW by power range

. Engine power HDT MHD Urban Bus

Low 8.1 6.6 21.9

Mid 8.8 7.0 22.4

High 10.5 7.8 24.0

We then accounted for the driveline efficiency

. The driveline efficiency accounts for losses in the wheel bearings, differential, driveshaft, and transmission. The efficiency values were determined through literature searches. Driveline efficiency η driveline varies with engine speed, vehicle speed, and vehicle power requirements. Using s ources available in the literature , we estimated an average value for driveline efficiency. 10

, 11

, 12

, 13

, 14

, 15

, 16

, 17

, 18 Table 7 summarizes our findings.

Table 7 – D riveline efficiencies found through literature research.

Based on this research, we used a driveline efficiency of 90% for all HD regulatory classes. Equation 2 shows the translation from engine power P eng to axle power P axle

.



### ) (

, acc loss eng driveline axle 

### PP P

− = η Equation 2 General truck:

Barth (2005) 80-85% Lucic (2001) 75-95%

HDT:

Rakha 75-95% NREL (1998) 91% Goodyear Tire Comp. 86% Ramsay (2003) 91% 21st Century Truck (2000) 94% SAE J2188 Revised OCT2003: Single Drive/direct 94% Single Drive/indirect 92% Single Drive/double indirect 91% Tandem Drive/direct 93% Tandem Drive/indiriect 91% Tandem Drive/double indirect 89%

Bus:

Pritchard (2004): Transmission Eff. 96% Hedrick (2004) 96% MIRA 80%11 Finally, we scaled the axle power by a multiplicative factor f scale to fit light -duty operating-mode
ranges . The MHD, HHD, and Bus classes were scaled by 17.1, which is approximately the average running weight for all heavy- duty vehicles, and the LHD trucks were scaled by 2.06, which is equivalent to the fleet

- average mass of light commercial trucks in MOVES. Table 8 shows the values selected for the scaling factor

.

Table 8 – Power scaling factor f scale . Regulatory Class Power scaling factor

MHD, HHD , Bus 17.1

LHD 2.06

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

scale axle 

### f P STP = Equation 3

We then constructed operating mode bins defined by STP and vehicle speed according to the methodology outlined earlier in MOVES development 19 and described in Table 9

. The implementation of STP in MOVES for heavy- duty emission rates is the same as that of VSP for light -duty emission rates. We will refer to the units of STP as scaled kW or skW.

Table 9 – Definition of the Operating Mode Attribute for Heavy

- Duty Vehicles (opModeID)
Operating Mode Operating Mode Description Scaled Tractive Power ( STP t , skW) Vehicle Speed ( v t , mph ) Vehicle Acceleration ( a, mph/sec )

0 Deceleration/Braking a t ≤ - 2.0 OR ( a t <

- 1.0 AND

a t -1 <- 1.0 AND

a t

- 2 <

- 1.0)
1 Idle

- 1.0 ≤ v t < 1.0

11 Coast ST P t < 0 0 ≤ v t < 25

12 Cruise/Acceleration 0 ≤ STP t < 3 0 ≤ v t < 25
13 Cruise/Acceleration 3 ≤ STP t < 6 0 ≤ v t < 25

14 Cruise/Acceleration 6 ≤ STP t < 9 0 ≤ v t < 25
15 Cruise/Acceleration 9 ≤ STP t < 12 0 ≤ v t < 25
16 Cruise/Acceleration 12 ≤ STP t 0 ≤ v t < 25

21 Coast STP t < 0 25 ≤ v t < 50

22 Cruise/Acceleration 0 ≤ STP t < 3 25 ≤ v t < 50
23 Cruise/Acceleration 3 ≤ STP t < 6 25 ≤ v t < 5012 24 Cruise/Acceleration 6 ≤ STP t < 9 25 ≤ v t < 50
25 Cruise/Acceleration 9 ≤ STP t < 12 25 ≤ v t < 50
27 Cruise/Acceleration 12 ≤ STP t < 18 25 ≤ v t < 50
28 Cruise/Acceleration 18 ≤ STP t < 24 25 ≤ v t < 50
29 Cruise/Acceleration 24 ≤ STP t < 30 25 ≤ v t < 50
30 Cruise/Acceleration 30 ≤ STP t 25 ≤ v t < 50
33 Cruise/Acceleration STP t < 6 50 ≤ v t 35 Cruise/Acceleration 6 ≤ STP t < 12 50 ≤ v t 37 Cruise/Acceleration 12 ≤ STP t <18 50 ≤ v t 38 Cruise/Acceleration 18 ≤ STP t < 24 50 ≤ v t 39 Cruise/Acceleration 24 ≤ STP t < 30 50 ≤ v t 40 Cruise/Acceleration 30 ≤ STP t 50 ≤ v t

1.1.1.3 Calculate emission rates

1.1.1.3.1 Means Emissions in the data set were reported in grams per second. First, w e averaged all the 1 -H z NOx emissions by vehicle and operating mode. Then the emission rates were again averaged by regulatory class and model year group. Data sets were assumed to be representative and each vehicle received the same weighting. However, we averaged rates by vehicles first because we did not believe the amount of driving done by each truck was necessarily representative. Equation 4 summarize s how we calculated the mean emission rate for each stratification group (i.e. model year group, regulatory class, and operating mode bin).

veh 1 1 ,, veh



### n n r

r n

k j n

i ijp

p j 

### ∑ ∑

= = 

###   
 

  
 

= Equation 4

where

n j = the number of 1-H z data points for each vehicle j
,

n veh = the total number of vehicles,

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

p 

### r = the mean emission rate (meanBaseRate) for pollutant p

. For NOx, we calculated a mean emission rate, denoted as the “ meanBaseRate ” in the MOVES emissionRateByAge table , for each combination of regulatory class, model year group, and operating mode bin combination.13 1.1.1.3.2 Statistics Estimates of uncertainty were calculated for all the emission rates. Because the data represent subsets of points “clustered” by vehicle, we calculated and combined two variance components, representing “within

- vehicle” and “between

- vehicle” variances. First, we calculated the ov erall within

- vehicle variance 2 with 

### s

.

veh tot n

j veh

with nn sn

s veh

− −

= 

# ∑

= 1 2

2 )1( Equation 5

where

2 veh 

### s = the variance within each vehicle , and

n tot = the total number of data points for all the vehicles.

Then we calculated the between

- vehicle variance 2 betw 

### s (by source bin, age group, and operating mode) using the mean emission rates for individual vehicle s ( jp 

### r

, ) as shown in Equation 6



# ( )

1 veh 1 2

, 2
betw veh



### − −

= ∑

=


### n rr

s n

j pjp Equation 6

Then, we estimated the total variance by combining the within

- vehicle and between

- vehicle variances to get the standard error
pol r s ( Equation 7 ) and dividing the standard error by the mean emission rate to get the coefficient

- of

- variation of the mean

pol r v c

, ( Equation 8 ).

tot 2 with

veh 2 betw

n s

n s

r pol 

## s += Equation 7

pol r polv 

## r s c pol 

## =

, Equation 814 1.1.1.3.3 Hole Filling and forecasting

Since the data only covered model years 1994 through 2006, we needed to develop a method to forecast emissions for future model years and back-cast emissions for past model years. For future model years (2007- and

- later), we decreased the emission rates for all operating mode bins by a ratio proportional to the decrease in the applicable emissions standards. While the NOx standard going into effect for MY 2007 is 0.2 g/bhp-hr, it was assigned to be phased in over a three year period ending in 2010. Rather than phaseing in the after

- treatment technology needed to meet the new standard , most manufacturers decided to meet a 1.2 g/bhp- hr standard for MY2007-2009, which did not require aftertreatment (down from 2.4 g/bhp-hr in 2006). Therefore, to model this strategy, we es timated rates for MY2007 -2009 by decreas ing the rates for MY 2003-2006 emission rates by 50%. Starting in MY 2010, the NOx standard for all heavy -duty trucks is 0.2 g/bhp -hr. We projected that a lmost all of these trucks will be using SCR aftertreatment tec hnology, which we assume to have a 90% NOx reduction efficiency from levels for MY 2006 levels , and estimated rates accordingly. For model year 1990, we increased the 1991-1997 emission rates by 20% to account for the reduction in NOx standard from 6.0 to 5.0 g/bhp-hr from 1990 to 1991. For 1989 and earlier model
years , we increased the 199 1-1997 model year group emission rates by 40%, which is proportional
to the increase of the certification levels from the 1991 model year to the 1989 model year. We

assu med that emission levels did not change by model year for 1989 and earlier

. For MHD and HHD trucks, the maximum operating mode represents a tractive power greater than 513 kW (STP= 30 skW × 17.1). This value exceeds the capacity of m ost HHD vehicles , and MHD vehicles and buses exert even lower levels. As a result, data are very limited in these modes.

To e stimate rates in the modes beyond the ranges of available data, we linearly extrapolated the rates from the highest operating mode in each speed range where significant data were collected for each model year group. In most cases, this modes was mode 16 for the lowest speed range, 27 or 28 for the middle speed range, and 37 or 38 for the highest speed range. For each of these operating modes, work- specific emissions factors (g/kW -hr) were calculated using the midpoint STP. Then, these emissions factors were multiplied by the midpoint STP of the higher operating modes (e.g. modes 39 and 40 for speed>50mph) to inpute emission rates for the modes la cking data . For the highest bins in each speed range, a “midpoint” STP of 33 skW (564.3 kW) was used. For certain model years, such as 1998, data existed for HHD trucks, but not MHD or buses. In these cases , the ratio of standards between the missing reg ulatory class and HHD regulatory class from the 1999-2002 model year group was used to determined missing class’s rates by multiplying that ratio by the existing HHD emission rates for the corresponding model year group.

1.1.1.3.3.1 LNT -equipped pickup trucks To meet NOx emissions standards for the 2010 model year , the use of aftertreatment will probably be needed. For example, Cummins decided to use aftertreatment starting in 2007 in engines designed to meet the 2010 standard and used in vehicles such as the Dodge Ram . The technology adopted for this purpose was the “ Lean NOx Trap ” (LNT) . This technology allows for the storage of NOx during fuel

- le an operation and conversion of stored NOx into N 2 and H 2 O during brief periods of fuel -rich operation. In addition, to meet particulate standards in MY 2007 and later,15 heavy -duty vehicles are equipped with diesel particulate filter s (DPF). At regular intervals, the DPF must be regenerated to remove and combust accumulated PM to relieve backpressure and ensure proper engine operation. This step require s high exhaust temperatures. However, these conditions adversely affect the LNT’s NOx storage ability, resulting in elevated NOx emissions.

In 2007, EPA acquired a truck equipped with LNT and DPF and perform ed local on -road measurements, using portable instrumentation . We used the PEMS and ECU output to assign operating modes and calculate emission rates by the same methods used to develop the heavy

heavy -duty truck NOx rates. W hile analyzing the se data, we distinguished regimes of PM regeneration from normal operation based on exhaust temperature , with temperatures exceeding 300°C assumed to indication PM regeneration. We performed the emission rate by operating mode analysis separately for each regim e, and weighted the two regimes together based on an assumed PM regeneration frequency of 10% of VMT. This value is an assumption based on the limited data available . We will look for opportunities to update this assumption based on any additional inform ation that becomes available

. Because we assume that LNT -equipped trucks account for about 25% of the LHDDT market, we again weighted the rates for the two LHD regulatory classes for model years 2007 and later. For MY 2007-09, w e assume that the remaining 75% of LHD diesel trucks will not have aftertreatment and will exhibit the 2007-2009 model year emission rates described earlier in th is s ection. Starting in MY2010, w e assume that the remaining 75% of LHD diesel trucks will be equipped with SCR, and will exhibit 90% NOx reductions from 2006 levels, also described in the hole filling section.

Table 10 summarizes this discussion and previous subsections regarding the methods used to estimate emission rates for each regulatory

– class /model- year -group combination.

Table 10

. Summary of methods for heavy

- duty diesel NOx emission rate development for each regulatory class

a nd model year group

Model year group HHD MHD Bus LHD

Pre

- 1988 Proportioned to certification levels Proportioned to certification levels Proportioned to certification levels Proportioned to certification levels

1988

- 1989 Proportioned to certification levels Proportioned to certification levels Proportioned to certification levels Proportioned to certification levels

1990 Proportioned to certification levels Proportioned to certification levels Proportioned to certification levels Proportioned to certification levels

1991

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

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

1999

- 2002 Data analysis Data analysis Data analysis MHD engine data with LHD scale factor

2003

- 2006 Data analysis Data analysis Data analysis MHD engine data with LHD scale factor

2007

- 2009 Proportioned to standards Proportioned to standards Proportioned to standards Data (LNT), and proportioned to standards (non

- LNT)16 2010 + Proportioned to standards Proportioned to standards Proportioned to standards Proportioned to standards

An important point to note is that we did not make a stratification based on age for vehicles not equipped with NOx aftertreament technology (largely 2009 model year and earlier). This is because of a few reasons:

• The WVU MEMS data did not show an increase in NOx emissions with odometer (and consequently, age) during or following the regulatory usef ul life 20 . Since the trucks in this program were collected from in

- use fleets, we do not believe that these trucks were necessarily biased toward cleaner engines.

• Manufacturers often certify zero or low deterioration factors. We estimated tampering and mal

- maintenance effects on NOx emissions to be small com pared to other pollutants – around a 10% increase in NOx over the useful life of the engine. Our tampering and mal

- maintenance estimation method s are discussed below and detailed in Appendix A.2 Tampering and Mal -maintenance

. 1.1.1.3.4 Tampering and Mal -maintenance

Table 11 shows the estimated aggregate NOx emissions increases due to T&M. It also shows the values that we actually used for MOVES emission rates. As previously mentioned, we assumed that in engines not equipped with aftertreatment, NOx does not increase due to T&M or deterioratio n.

Table 11

. Fleet

- average NOx emissions increases from zero

- mile levels over the useful life due tampering and mal

- maintenance

Model years NOx increase from T&M analysis [%] NOx increase in MOVES [%]

1994

- 1997 10 0

1999

- 2002 14 0

2003

- 2006 9 0

2007

- 2009 11 0

2010

- 2012 SCR 77 77

2010

- 2012 LNT 64 64

2013+ 58 58

As described in Appendix A.2 Tampering and Mal -maintenance , these emissions increases are combined with information in Table 37 to estimate the e missions increase for each age group prior17 to the end of the useful life for each regulatory class. With the introduction of aftertreatment systems to meet regulatory requirements for MY 2010 and later, EPA expects tampering and malmaintenance to s ubstan tially increase emissions over time compared to the zero

- mile level. Though 77 % may appear to be a large increase in fleet

- average emissions over time, i t should be noted that the 2010 model year standard (0.2 g/bhp- hr) is about 83% lower than the 2009 model year effective standard (1.2 g/bhp-hr). This still yields a substantial reduction of about 71 % from 2009 zero- mile levels to 2010 fully deteriorated levels. As more data becomes available for future model years, we hope to update these tampering an d mal

- maintenance and overall aging effects.

1.1.1.3.5 Defeat Device and Low

- NO x Rebuilds The default emission rates in MOVES for model years 1991 through 1998 are intended to include
t he effects of defeat devices as well as the benefits of heavy -duty low- NO x rebuilds (commonly called reflash) that occurred as the result of the heavy

- duty diesel consent decree. Reflashes reduce NOx emissions on these engines by reconfiguring certain engine calibrations, such as fuel injection timing. The MOVES database also includes a set of alternate emission rates for model years 1991 through 1998 assuming a hypothetical fully reflashed fleet. Users with questions about the use of these alternate emission factors should contact EPA at mobile@epa.gov. Since defeat devices were in effect mostly during highway or steady cruising operation, we assume that NO x emissions were elevated for only the top two speed ranges in the running exhaust operating modes (>25mph). To modify the relevant emission rates to represent reflas h programs, we first calculated the ratios emission rates in modes 27 and 37 to that for opMode 16, for model year 1999 (the first model year with not- to

- exceed emission limits). We then multiplied the MY 1999 ratios by the emission rates in mode 16 for model years 1991 through 1998, to get estimated “reflashed” emission rates for operating modes 27 and 37. This step is described in Equation 9 and Equation 11


## . To estimated “reflashed” rates in the remaining operating modes, we multiplied “reflashed rates by ratios of the remaining operating modes to mode 27 for MY1991-98, as shown in Equation 10 and Equ ation 12

.

Operating modes (OM) 21

- 30 

###        

=

− − 16,1999 27,1999 16,989127,9891, 

### r r r r

reflash



###        

=

− −

− − 27,9891 OM ,9891 27,9891, OM ,9891, 

### r r r r x reflash x reflash Equation 9

Equation 10

Operating modes (OM) 31

- 40 

###        

=

− − 16,1999 37,1999 16,989137,9891, 

### r r r r

reflash Equation 11

Equation 1218 

###        

=

− −

− − 37,9891 OM ,9891 37,9891, ,19981991, 

### r r r r x reflash OMx MY reflash

The default emission rates were also slightly adjusted for age for the consent decree model years. An EPA assessment shows that about 20% of all vehicles eligible for reflash had been reflashed by the end of 2008. 21 W e assumed that vehicles were receiving the reflashes after the heavy -duty diesel consent decree (post 1999/2000 calendar year) steadily, such that in 2008, about 20% had been reflashed. We approximated a linear increase in reflash rate from age zero.

1.1.1.4 Sample r esults

The charts in this sub- section show examples of the emission rates that resulted from the analysis. Not all rates are shown; the intention is to illustrate the most common trends and hole- filling results. For brevity, the light-heavy duty regulatory classes are not shown, since the light

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

In Figure 1

, we see that NOx emission rates increase with STP for HHD trucks

. Figure 2 adds the MHD and bus regulatory classes, with the error bars removed for clarity. As expected, the emissions increase with power, with the lowest emissions occurring in the idling/coasting/braking bins.

Figure 1 . Trends in NOx Emissions by operating mode fr om HHD trucks for model year 2002.

0 500 1000 1500 2000 2500 3000 3500 4000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean NOx rate [g/hr]

Operating Mode19 The high est operating modes in each speed range will rarely be attained due to the power limitations of heavy -duty vehicles, but are included in the figures (and in MOVES) for completeness. Nearly all of the activity occurs in modes 0, 1, 11-16, 21-28, and 33-38, with activity for buses and MHD vehicles usually occurring over an even smaller range. In some model year groups, the MHD and HHD classes use the same rates , based on lack of significant differences between those two classes’ emission rates.

Figure 2 . Trends in NOx emissions by operating mode fr om MHD, HHD, and bus regulatory classes for model year 2002.

The effects of mo del year , representing a rough surrogate for technology or standards, can be seen in Figure 3 , which shows decreasing NOx rates by model year group for a sample operating mode (# 24) for HHD trucks . Other regulatory classes show similar trends. Th e rates in this chart were derived with a combination of data analysis (model years 1991 through 2006) and hole filling. The trends in the data are expected, since the model year groups were formed on the basis of NOx standards. 0 1000 2000 3000 4000 5000 6000

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean NOx rate [g/hr]

Operating Mode MHD
Bus
HHD20 Figure 3. Trends in NOx by model year for HHD trucks in operating mode 24. Increasingly stringent emissions standards have caused NOx emissions to decrease significantly

.

Age effects were only implemented for aftertreatment -equipped trucks (mostly model year 2010 and later) based on an analysis of tampering and mal

- maintenance effects. Due to faster mileage accumulation, the heavy-heavy duty trucks reach their maximum emission at the youngest ages , as shown in Figure 4 . Coefficients of variation from previous model year groups were used to estimate uncertainties for MY 2010. 0 500 1000 1500 2000 2500Mean NOx rate [g/hr]

Model year group21 Figure 4

. M odeled NOx trends by age for model year 2010 for operating mode 24.

Figure 5 shows the mean emission rates for LHD trucks for model years 2007-2009. As described previously, this group of vehicles includes vehicles with LNTs (with NO x increases during PM regeneration ) and vehicles without any aftertreatment. The estimated uncertainties are greater than for the other heavy -duty regulatory classes, since the re were fewer vehicles in our test data.

Figure 5 : Mean NOx rates by operating mode for model years 2007

- 2009 LHD trucks age 0 -3 0 20 40 60 80 100 120 140Mean NOx rate [g/hr]

HHD

MHD

Bus

0-3 4- 5 6

- 7 8

- 9 10

- 14 15

- 19 20+ Age group [years]

0 50 100 150 200 250 300

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean NOx rate [g/hr]

Operating mode22 

### 1.1.2 Particulate Matter (PM)

In this section

, particulate matter emissions refers to particles emitted from heavy -duty engines which have a mean diameter less than 2.5 microns, known as PM 2.5 . Such particles consist of three subtypes, including: (1) elemental carbon ( EC ), usually composed of black colored soot emitted from combustion, (2) organic carbon (OC), consist ing of particles of organic matter formed during the combustion process or immediately after in the tailpipe. It does not include particles formed in secondary reactions in the atmosphere, and (3) sulfate particulate , which formed by agglomeration of sulfur-containing compounds formed during combustion. These subtypes are used to for m the inputs to MOVES.

As described above for NOx, t he heavy

- duty diesel PM emission rates in MOVES are a function of: (1) source bin, (2) operating mode, and (3) age group.

We classified the data in to the following model year groups for purposes of emission rate development . These groups are generally based on the introduction of emissions standards for heavy -duty diesel engines. They also serve as a surrogate for continually advancing emission control technology on heavy-duty engines. Table 12 shows the model year group range and the applicable brake- specific emission s standards.

Table 12

. Model year groups used for analysis based o n the PM emissions standard

Model Year Group Range PM Standard [g/bhp

- hr ]

1960-1987 No transient cycle standard

1988-1990 0.60

1991-1993 0.25

1994-1997 0.10

1998-2006 0.10

2007+ 0.01

1.1.2.1 Data Sources All of the data used to develop the MOVES PM2.5 emissio n rates was generated in the CRC E

55/59 research program 22 . The following description by Dr. Ying Hsu and Maureen Mullen of E. H. Pechan, in the“ Compilation of Diesel Emissions Speciation Data – Final Report ” provides a good summary of the program. It is reproduced in the following paragraphs immediately below:

The objective of the CRC E55/59 test program was to improve the understanding of the California heavy -duty vehicle emissions inventory by obtaining emissions from a representativ e vehicle fleet, and to include unregulated emissions measured for a subset of23 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-d uty (HHD) diesel trucks • Phase 1.5: 13 HHD diesel trucks

• Phase 2: 10 HHD diesel trucks , 7 medium heavy -duty (MHD) diesel trucks

, 2 MHD gasoline trucks

• Phase 3: 9 MHD diesel, 8 HHD diesel, and 2 MHD gasoline The vehicles tested in this study were procured in the Los Angeles area, based on model years specified by the sponsors and by engine types determined from a survey. WVU measured regulated emissions data from these vehicles and gathered emissions samples. Emission samples from a subset o f 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 vehi cles. 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 sampl e lines and research grade gas analyzers. PM was measured gravimetrically. Additional sampling ports on the dilution tunnel supplied dilute exhaust for capturing unregulated species and PM size fractions. Background data for gaseous emissions were gathered for each vehicle test and separate tests were performed to capture background samples of PM and unregulated species. In addition, a sample of the vehicles received Tapered Element Oscillating Microbalance (TEOM) measurement of real time particulate emiss ions.

The HHDDTs were tested under unladen, 56,000 lb, and 30,000 lb truck load weights. The driving cycles used for the HHDDT testing included: • AC50/80;
• UDDS; • Five modes of an HHDDT test schedule proposed by CARB: Idle, Creep, Transient,

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

• The U.S. EPA transient test The proposed CARB HHDDT test cycle is based on California truck activity data, and was developed to improve the accuracy of emissions inventories. It should be noted that the transient portion of this proposed CARB test schedule is similar but not the same as the EPA certification transient test.24 The tables below provide a greater detail of the data used in the analysis. Vehicles counts are provided by number of vehicles, number of tests, model year group and regulatory class (46 = MHD, 47=HHD) in Table 13

.

Table 13

. Vehicle and Test Counts by Regulatory Class and Model Year Group Regulatory Class Model Year Group Number of tests Number of vehicles

MHD 1960

- 1987 82 7
1988 - 1990 39 5
1991 - 1993 22 2
1994 - 1997 39 4
1998 - 2006 43 5
2007 + 0 0

HHD 1960 - 1987 31 6
1988 - 1990 7 2
1991 - 1993 14 2
1994

- 1997 22 5
1998

- 2006 171 18
2007 + 0 0

Counts of tests are provided by test cycle in Table 14

. Table 14

. Vehicle Test Counts by Test Cycle Test Cycle Number of tests
CARB

- T 71
CARB

- R 66
CARB

- I 42 UDDS_W 65
AC5080 42
CARB

- C 24
CARBCL 34
MHDTCS 63
MHDTLO 23
MHDTHI 24
MHDTCR 29

1.1.2.2 Analysis

1.1.2.2.1 Calculate STP in 1-hz data Within source bins, data was further sub-classified on the basis of operating mode. For motor vehicles, 23 operating modes are defined in terms of scaled tractive power (STP), vehicle speed and vehicle acceleration. These modes are defined above in Table 9

.25 The first step in assigning operating mode is to calculate scaled tractive power ( STP ) for each emissions measurement. At a given time t , the instantaneous STP t represents the vehicle’s tractive
power scaled by a constant factor

. STP is calculated as a third -order polynomial in speed, with additional terms describing acceleration and road- grade effects. The coefficients for this expression, often called road load coefficients, factor in the tire rolling resistance, aerodynamic drag, and friction losses in the drivetrain . We calculated STP using the equation below:

scale ttttt
t 

### f a mv Cv BvAv STP + ++

= 32 Equation 13

where

A = the rolling resistance coefficient [kW

⋅ sec/m],

B = the rotational resistance coefficient [kW

⋅ sec 2 /m 2 ],

C = the aerodynamic drag coefficient [kW

⋅ sec 3 /m 3 ],

m = mass of individual test vehicle [ metric ton ], f

scale = fixed mass factor ( see Table 8 ),

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

a t = instantaneous vehicle acceleration [m/s 2 ]

The values of coe fficients A

, B , and C are the road load coefficients pertaining to the heavy -duty vehicles 23 as determined through previous analyses for EPA’s Physical Emission Rate Estimator (PERE). This method of calculating STP calculates tractive power using the same equation used to calculate vehicle

- specific power ( VSP ) in the developm ent of emission rates for light -duty vehicles except that the scaling factor is used in the denominator, instead of the actual test weights of individual vehicle s 24

. Note that this approach differs from that described above the NO X emission rate analysis since the particulate data was collected on a chassis dynamometer from vehicles lacking electronic control units (ECU). Grade effects are not explicitly included in either case because grade does not come into play in chassis dynamo meter tests, and it is already accounted for if STP is calculated through engine speed and torque from the engine control unit.

We have not formally compared the results of the two methods of calculating STP. However, on
average, we did find the operati ng

- mode distributions to be similar between the two calculation methods for a given vehicle type. For example, we found that the maximum STP in each speed range was approximately the same.26 1.1.2.2.2 Compute Normalized TEOM Readings The TEOM readings were obtained for a subset of tests in the E-55/59 test program. Only 29 vehicles had a full complement of 1- hz TEOM measurements . However, the continu ous particulate values were modeled for the remaining vehicles b y Nigel Clark of West Virginia University , and results provided to EPA. In the end, a total of 56 vehicles (out of a total of 75) and 470 tests were used in the analysis out of a possible 75 vehicles. Vehicles and tests were excluded if the total TEOM PM2.5 reading was negative or zero, or if co rresponding full

- cycle filter masses were not available. Table 15 provides vehicle and test counts by vehicle class and model year. The HDDV6 and HDDV7 groups were combined in the table because there were only seven HDDV6 vehicles in the study.

Table 15 Vehicle and Test Counts by Heavy

- Duty Class and Model Year

Model Year HDDV6/7 HDDV8
No. Vehicles No. Tests No. Vehicles No. Tests
1969 1 6
1974 1 10
1975 2 10
1978 1 5
1982 1 5
1983 1 10 1 6
1985 1 28 1 10
1986 1 3 1 4
1989 2 11 1 4
1990 1 12 1 3
1992 1 11 1 11
1993 1 11 1 3
1994 1 9 3 15
1995 2 24 3 13
1998 2 20 3 28
1999 3 43
2000 2 18 5 44
2001 1 5 2 21
2004 4 29
2005 1 6

Since the development of MOVES emission rates is cycle independent, all available cycles / tests which met the above requirement s were utilized. As a result, 488,881 seconds of TEOM data were

u sed . The process required that each individual second by second TEOM rate be normalized to its corresponding full- cycle filter mass, available for each combination of vehicle and test . This step was necessary because individual TEOM measurements are highl y uncertain and vary widely in terms of magnitude (extreme positive and negative absolute readings can occur). The e quation below shows the normalization process for a particular one second TEOM measurement.27 ij j

i j ji 

### PM PM PM PM

,, TEOM

, TEOM , filte r

, , normalized ∑ 

### = Equation 14

Where

i = an individual 1- Hz measurement (g/sec),
j = an individual test on an individual vehicle,

PM TEOM, j,i = an individual TEOM measurement on vehicle j at second i
, PM filter,j = the Total PM2.5 filter mass on j
, PM normalized,i,j = an estimated continuous emission result (PM2.5) emission result on vehicle j
at second i

. 1.1.2.2.3 Compute Average Normalized TEOM measures by MOVES Bin After normalization, the data were classified b y regulatory class, model-year group and the 23 operating modes. Mean average results, sample sizes and standard deviation statistics for PM2.5 emission values were computed in terms of g/hour for each mode . In cases where the vehicle and TEOM samples were sufficient for a given mode , these mean values were adopted as the MOVES emission rates for total PM2.5. In cases of insufficient data for particular modes, a regression technique was utilized to impute missing values.

1.1.2.2.4 Hole filling and Forecasting 1.1.2.2.4.1 Missing operating modes

D etailed in Appendix A.4 Developing PM emission rates for missing operating modes , a log- linear regression was performed on the existing PM data agains t STP to fill in emission rates for missing operating mode bins. Similar to the NOx rates , emission rates were extrapolated for the highest STP operating modes.

1.1.2.2.4.2 Other Regulatory Classes The TEOM data was only available in quantity for MHD and HHD classes. There were no data available for the LHD or bus classes

. Thus, rates for these vehicle classes were computed using simple multiplicative factors based either on engine work ratios or PM emission standards (i.e., buses versus heavy trucks). The LHD classes’ emission rates were set as a ratio of t he MHD emission rates, and bus (class 48) emission rates were proportioned to HHD rates

. Because the certification standards in terms of brake horsepower

- hour (bhp

- hr) are the same for all of the he avy

- duty engines, t he emission rate s of LHD2b3 are assumed to be equal to 0.46 * MHD emission rate s. The emission rate of LHD45 is assumed to be 0.60 * MHD emission rate.

LHD2b 3 emission rate = 0.46 * MHD emission rate

LHD45 emission rate = 0.60 * MHD emission rate28 The values of 0.46 and 0.60 are the ratios of the MOBILE6.2 heavy-duty conversion factors 25 (bhphr/mile) for the lighter trucks versus the MHD trucks. These are ratios of the relative amount of work performed by a lighter truck versus a heavier truck for a given distance. Urban Bus (Class48) emission rates are assumed to be either the same as the HHD emission rates, or for some selected model year groups, to be a ratio of the EPA certification standards : 1991 – 1993 model years Bus Emiss ions = (0.40 ) * HHD emissions 1994 – 1995 model years Bus Emissions = 0.70 * HHD emissions 1996 – 2006 model years Bus Emissions = 0.50 * HHD emissions

1.1.2.2.4.3 Model year 2 007 and later trucks (with diesel particulate filters) EPA heavy -duty diesel emission regulations were made considerably more stringent for total PM2.5 emissions starting in model year 2007. Ignoring phase-ins and banking and trading issues, the basic emission standard fell from 0.1 g/bhp-hr to 0.01 g/bhp- hr. This increase by a factor of ten in the level of regulatory stringency required the use of particulate trap systems on heavy-duty diesels. As a result, we expect the emission performance of diesel vehicles has changed dramatically. Unfortunately, no continuous TEOM data we re available for analysis on the 2007 and later modelyear vehicles. However, heavy and medium heavy- duty diesel PM2.5 data are available from the EPA engine certification program on model years 2003 through 2007. These data provide a snapshot of new engine emission performance before and after the introduction of particulate trap technology in 2007. The existence of these data makes it possible to determine the relative improvement in PM emissions from model years 2003 through 2006 to model year 2007. This same relative improvement can then be applied to the existing, TEOM based, 1998-2006 model year PM emission factors to estimate in -use factors for 2007 and later vehicles. An analysis of the available certification data is shown in Table 16 below. It suggests that the actual ratio of improvement due to the particulate trap is reduction of a factor of 27.7. This factor is considerably higher than the relative change in the certification standards , i.e., a factor of 10. The reason for the change is that the new trap equipped vehicles certify at emission levels which are much lower than the standard and thus create a much larger ‘margin of safety’ than previous technologies could achieve.As an additional check on the effectiveness of the trap technology EPA conducted some limited inhouse testing of a Dodge Ram truck, and carefully reviewed the test results from the CRC Advanced Collaborative Emission Study (AC ES) phase

- one program, designed to characterize emissions from diesel engines meeting 2007 standards. The limited results from these studies demonstrate that the effectiveness of working particulate traps is very high. The interested reader

can review the ACES report

. 2629 Table 16 shows average certification results for model years 2003

- 2007. Average ratio from MYs 2003

- 2006 to MY 2007 is 27.7.

Certification Model Year Mean (g/bhp

- hr) St. Dev. n
2003 0.08369 0.01385 91

2004 0.08783 0.01301 59

2005 0.08543 0.01440 60

2006 0.08530 0.01374 60

2007 0.00308 0.00228 21

1.1.2.2.5 Tampering and Mal-maintenance The MOVES model contains assumptions for the frequency and emissions effect of tampering and mal -maintenance on heavy -duty diesel trucks and buses. The assumption of tampering and malmaintenance (T&M) of heavy -duty diesel vehicles is a departure from the MOBILE6.2 model which assumed such vehicles operated from build to final scrappage at a design emission level which was lower than the prevailing EPA emission standards. Both long term anecdotal data sources and more comprehensive studies now suggest that the assumption of no natural deterioration and/or no deliberate tampering of emission control components in th e heavy -duty diesel fleet was likely an unrealistic assumption, particularly with the transition to emission aftertreatment devices with the 2007/2010 standards The primary data set was collected during a limited calendar year period, yet MOVES requires data from a complete range of model year/age combinations. As a result, the T&M factors shown below in Table 17 were used to forecast or back

- cast th e basic PM emission rates to predict model year group and age group combinations not covered by the primary data set. For example, for the 1981 through 1983 model year group, the primary dataset contained data which was in either the 15 to 19 or the 20+ age groups. However, for completeness, MOVES must have emission rates for these model years for age Groups 0-3, 4-5, 6-7, etc. As a result, unless we assum e that the higher emission rates which are were measured on the older model year vehicles have always prevailed – even when they were young, a modeling approach such as T&M must be employed. Likewise,
more recent model years could only be tested at young er ages . The T&M methodology used in the MOVES analysis a llows for the filling of age – model year group combinations for which no data is available.

One criticism of the T&M approach is that it may double count the effect of T&M on the fleet because the primary emission measurements, and base emission rates, we re made on in- use vehicles that may have had some maintenance issues during the testing period. This issue would be most acute for the 2007 and later model year vehicles where all of the deterioration is subject to projection. However, for this model year group of vehicles, the base emission rates start at low levels, and represent vehicles that are virtually free from T&M. We followed the same tampering and mal-maintenance methodology and analysis for PM as we did for NOx, as described in Appendix A.2 Tampering and Mal -maintenance

. The overall MOVES tampering and m al -m aintenance e ffects on PM emissions over the fleet’s useful life are shown in30 Table 17 . The value of 89 percent for 2010+ model years reflects the projected e ffect of heavy

duty on

- board diagnostic deterrence/early repair of Tampering and Mal

- maintenance effects. It is an eleven percent improvement from model years which do not have OBD (i.e., 2007-2009).

Table 17 . Estimated increases in HC and CO emissions attributed to Tampering and mal

- maintenance over the

u seful life of Heavy

- Duty Vehicles

.

Model Year Group Percent increase in PM due to T&M

Pre -1998 85

1998 - 2002 74

2003 – 2006 48

2007 – 2009 100

2010 + 89

1.1.2.2.6 Computation of E lemental Carbon and Organic Carbon Emission Factors The MOVES model reports Total PM2.5 emissions according to three s pecies. These includ e Elemental Carbon ( EC ), Organic Carbon (OC) and sulfate . In the results of a MOVES run , Total PM2.5 is the sum of these three constituents. During rate development, the process is reversed, and both EC and OC are computed directly from the total PM2.5 emissions using multiplicative factors. Elemental Carbon is generally the black ‘soot’ that is often visible in engine exhaust. Organic Carbon generally includes organic particles of large molecular compounds and metals. Sulfate is computed using a fuel sulfur balance (see the report “ Development of Gasoline and Diesel Vehicle Sulfate and SO2 Emissions for the MOVES Model ” for details ). Total PM2.5 as computed by MOVES includes EC, OC and sulfate emissions. Gaseous sulfur dioxide is also part of the fuel sulfur balance, and is reported by the MOVES model. It is not considered a particulate in the MOVES model, but can react in the atmosphere to form secondary particulate. Since the fuel sulfur levels in the underlying stud ies were not generally known, but believed to be small (about 1 percent or less), sulfate emissions were ignored in the total PM2.5 emission levels. As a result, total PM2.5 in this analysis was assu med to be comprised of only EC and OC

.



### OCEC PM 5.2 

### += Equation 15

Dividing both sides by PM2.5 and rearranging gives31 5.2 5.2 

### PM EC 0.1 PM

OC

−= Equation 16

Thus, the OC fraction = 1.0 – EC f raction

The final EC fractions used in MOVES for pre-2007 model year trucks (i.e. before diesel particulate filters (DPFs) were standard) are shown in Figure 6 . These vary according to regulatory class and MOVES operating mode. They typically range from 25 percent at low loads (low STP ) to over 90 percent at highly loaded modes. All of the EC fra ctions were developed in a separate analysis and are documented in Appendix A

. 5

. The primary dataset used in the analysis came from Kweon et al. (2004) where particulate composition and mass rate data were collected on a Cummins N14 series test engine over the CARB eight- mode engine test cycle. The EPA PERE model and a Monte Carlo approach were used to simulate and translate the primary PM emission results into MOVES parameters (i.e., operating modes).

Figure 6

. Elemental Carbon fraction by operating mode for pre

- DPF

- equipped trucks.

A different methodology was used to compute EC factors for 2007 and later model year, DPF

equipped vehicles. For these vehicles it is believed that virtually all of the particulate that is emitted from the tailpipe will be OC and that only a modest fraction will be EC . The traps are designed to capture virtually all of the carbon. Potentially, small amounts of OC and sulfate may escape. This is essentially the opposite of the non-trap equipped heavy- duty diesel vehicles where the total PM2.5 is dominated by EC . Unfortunately, only limited particulate data exists on trap equipped vehicles. These data are based on particulate -matter bound ionic species and EC/OC emissions data from a few trap equipped buses and a heavy- duty tractor. The data were extracted and a simple average computed from a published source. 27 Based on the date of the pape r, it is likely that all of the diesel vehicle/trap systems were prototypes. Extraction of data from the paper 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

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

HHD

MHD32 yielded a single factor which will be applied to all regulatory types and operating modes for 2007 and later diesel trucks and buses. This fact or is the elemental carbon fraction. Table 18 summarizes the EC and OC fractions estimated from the paper. These fractions were used for all operati ng mode bins for model years 2007 and later heavy-duty vehicles.

Table 18 shows EC and OC fractions used for DPF

- equipped heavy

- duty diesel vehicles.

EC fraction 0.0861

OC fraction 1 – ECfraction = 0.9139

As additional data beco me available, EPA will probably revise the EC Fraction used in MOVES for these vehicles

.

Temperature Correction Factors

The draft MOVES model released in March 2009 did not contain temperature correction factors for PM2.5 emissions from heavy- duty diesel vehicles. This absence of temperature correction factors does not imply that EPA believes that heavy- duty diesel vehicle PM emissions are insensitive to temperature effec ts. In fact, it is quite likely that the re verse is true. Both running and start PM emissions from at least non

- trap equipped vehicles are sensitive to temperature. However, EPA at this time cannot adequately quantify such emission effects, and is curr ently using a multiplicative placeholder value of 1.0 as the temperature correction factor. EPA will update the MOVES model when sufficient data on diesel temperature correction factors is available for analysis and inclusion in the model.

1.1.2.3 Sample r esults Figure 7 and Figure 8 show how PM rates increase with STP

. As with the NO x plots, the high est operating modes in each speed range will rarely be attained due to the power limitations of heavy

duty vehicles, but are included in the figures for completeness. At high speeds (greater than 50 mph; operating mod es ≥ 30), the overall PM rates are lower than the other speed ranges. For pre

2007 model years the PM rates are dominated by EC. With the introduction of DPFs in model year 2007, we anticipate large reductions in overall PM rates and that the remaining P M will be dominated by OC.33 Figure 7. P articulate M atter rates by operating mode representing Heavy heavy

- duty vehicles ( model year 2002 at age 0 -3 years)

.

Figure 8

. P articulate M atter rates by operating mode for Heavy heavy

- duty vehicles ( model year 200 7 at age 0

- 3 years)

.

Figure 9 shows an example of how tampering and mal- mainten ance estimates increase PM with age. The EC /OC proportion does not change by age, but the overall rate increases and levels off after the end of useful life. This figure shows the age effect for MHD. The rate at which emissions increase toward their max imum depends on regulatory class. 0 20 40 60 80 100 120 140 160

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean PM rate [g/hr]

Operating mode EC

OC

0 1 2 3 4 5 6 7 8

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean PM rate [g/hr]

Operating mode EC

OC34 Figure 9

. P articulate M atter rates by age group for Medium heavy

- duty vehicles ( model year 2002 , operating mode 24 ).

Figure 10 shows the effect of model year on emission rates. Emissions generally decrease with new PM standards. The EC fraction stays constant until model year 2007, when it is reduced to nearly zero due to widespread DPF use. The overall PM level is substantially lower starting in model year 2007. The emission rates shown here for earlier model years are an extrapolation of the T&M analysis since young

- age engines from early model years could not be tested in the E -55
program. 0 5 10 15 20 25 30 35 40

0-3 4-5 6-7 8-9 10

- 14 15

- 19 20+Mean PM rate [g/hr]

Age group [years] EC

OC35 Figure 10 . P articulate M atter rates for Heavy heavy

- duty vehicles by model year group ( age 0 -3 years, operating mode 24 ).

0 10 20 30 40 50 60 70 80 90 100

1960

- 1987 1988

- 1990 1991

- 1993 1994

- 1997 1998

- 2006 2007

- 2009 2010

- 2012 2013

- 2050Mean PM rate [g/hr]

Model year group EC

OC36 

### 1.1.3 Hydrocarbons ( HC ) and Carbon Monoxide (CO)
Diesel engines account for a substantial portion of the mobile source HC or CO emission inventories. Recent regulations on non- methane hydrocarbons (NM HC ) (sometimes in conjunction with NOx) combined with the common use of diesel oxidation catalysts will yield reductio ns 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 certificatio n levels combined with emissions standards to develop appropriate model year groups. Since standards did not change frequently in the past for either HC or CO, we created fewer model year groups than we did from NOx and PM. The HC/CO model year groups are:

• 1960-1989

• 1990-2006

• 2007+

1.1.3.1 Data Sources The heavy -duty diesel HC and CO emission rate development followed a methodology that resembles the light

- duty methodology , where emission rates were calculated from 1

- hz data produced from chassis dynamometer testing. Data sources were all heavy

- duty chassis test
programs:
1. CRC E -55/59 22 : Mentioned earlier, this program represents the largest volume of heavy

duty emissions data collected from chassis dynamometer tests. All tests were used, not just those using the TEOM. Overall, 75 trucks were tested on a variety of drive cycles. Model years ranged from 1969 to 2005, with testing conducted by West Virginia University from 2001 to 2005.

2. Northern Front Range Air Quality Study (NFRAQ S) 28 : This study was performed by the Colorado Institute for Fuels and High- Altitude Engine Research in 1997. Twenty -one HD diesel vehicles from model years 1981 to 1995 selected to be representative of the in

use fleet in the Northern Front Range of Colorado were tested over three different transient drive cycles.

3. New York Department of Environmental Conserv ation ( NYSDEC ) 29 : NYSDEC sponsored this study to investigate the nature and extent of heavy-duty diesel vehicleemissions in the New York Metropolitan Area. West Virginia University tested 25 heavyheavy and 12 medium-heavy duty diesel trucks under transi ent and steady

- state drive cycles.

4. West Virginia University: Additional historical data collected on chassis dynamometers by WVU is available in the EPA Mobile Source Observation Database.

The on- road data used for the NOx analysis was not used since HC and CO were not collected in the MEMS program, and the ROVER program used the less accurate non-dispersive infrared (NDIR) technology instead of flame-ionization detection (FID) to measure HC . To keep HC and CO data sources consistent, we used chassis tes t programs exclusively for the analysis of these two37 pollutants. Time

- series alignment was performed using a method similar to that used for light -duty chassis test data. The numbers of vehicles in the data sets are shown in Table 19

.

Table 19 N umber s of vehicles by model year group, regulatory class, and age group .

Model year group Regulatory class Age group
0-3 4-5 6-7 8-9 10-14 15-19 20+

1960-2002 HHD 58 19 16 9 16 6 7

MHD 9 6 5 4 12 15 6

Bus 26 1 3

LHD45 2 1

LHD2b3 6

2003-2006 HHD 6

1.1.3.2 Analysis

As for PM, STP was calculated using an equation similar to the light -duty VSP equation, but normalized with averag e regulatory class weight instead of test weight, as described by Equation 17

.

scale tt t t t
t 

### f a mv Cv BvAv +++

= 3 2 

### STP Equation 17

The track road

- load c oefficients A

, B , and C pertaining to heavy-duty vehicles 23 were estimated through previous analyses for EPA’s Physical Emission Rate Estimator (PERE)

. 21
Using a method similar to that used in the NOx analysis, we averaged emissions by vehicle and operating mode. We then averaged across all vehicles by model year group, age group, and operating mode. Estimates of uncertainty for each mean rate were calculated using the same equations and methods used in development of the NOx rates

. Instead of using our results to directly populating all the emission rates, we directly populated only the age group that was most prevalent in each regulatory class and model year group combination

. These age groups are shown in Table 20

.

Table 20

. A ge groups used directly in MOVES emission rate inputs for each regulatory class and model year group present in the data

. Regulatory class Model year g roup 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

- 338 We then applied tampering and mal

- maintenance effects through that age point, either lower ing emissions for younger ages or raising them for older ages , using the methodology described in Appendix A.2 Tampering and Mal-maintenance

. The tampering and mal

- maintenance effects for HC and CO are shown in Table 21

. Table 21 T ampering and mal

- maintenance effects for HC and CO over the useful lif e of trucks.

Model year s I ncrease in HC and CO Emissions (%)

Pre -2003 300

2003-2006 150

2007-2009 150

2010 and later 33

We multiplied these increases by the T&M adjustment factors in Table 37 in section A.2.3 Analysis to get the emissions by age group.

With the increased use o f diesel oxidation catalysts (DOCs) in con junction with DPFs, we a ssum e an 80% reduction in zero-mile emission rates for both HC and CO starting with model year 2007.

1.1.3.3 Sample r esults

The charts in this sub- section show examples of the emission rates that derived from the analysis described above . Not all rates are shown ; the intent is to illustrate the most common trends and hole- filling results. For simplicity, t he light-heavy duty regulatory classes are not shown, but since the medium- heavy data were used for much of the light -heavy duty emission rate development, the light -heavy duty rates follow similar trends. Uncertainties were calculated as for NOx.

In Figure 11 and Figure 12 , we see that HC and CO mean emission rates increase with STP , though there is much higher uncertainty than for the NOx rates. This pattern could be due to the smaller data set or may truly reflect a less direct correlation between HC

, CO and STP . In these figures, the data for HHD and bus classes were combined to generate one set of rates for HHD and buses.39 Figure 11 . T HC emission rates [g/hr] by operating mode for model year 2002 and age group 0 -3.

Figure 12

. CO emission rates [g/hr] by operating mode for model year 2002 and age group 0 -3. 0 10 20 30 40 50 60 70 80 90 100

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean HC rate [g/hr]

Operating mode MHD
HHD/Bus

0 100 200 300 400 500 600

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean CO rate [g/hr]

Operating mode MHD
HHD/Bus40 Figure 13 and Figure 14 show HC and CO emission rates by age group. Due to our projections of T&M effect s , there are large increases as a function of age. Additional data collection would be valuable to determine if real -world deterioration effects are consistent with those in the model, especially in model years where diesel oxidation catalysts are most prevalent (2007 and later).

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

.

0 10 20 30 40 50 60 70 80 90 100Mean HC rate [g/hr]

HHD

MHD

Bus

0- 3 4- 5 6

- 7 8

- 9 10

- 14 15

- 19 20+ Age group [years]41 Figure 14

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

Figure 15 and Figure 16 show sample HC and CO emission rates by model year group. The two earlier model year groups are relatively similar. The rates in the model year group reflect the use of diesel oxidation catalysts. Due to the sparseness of the data and the fact that HC and CO emission do not correlate as well with STP (or power) as NOx and PM do , uncertainties are much greater. Rates from HHD regulatory class were used for buses. All regulatory classes have the same rates for model years 2003 and later. 0 100 200 300 400 500 600Mean CO rate [g/hr]

HHD

MHD

Bus

0- 3 4- 5 6

- 7 8

- 9 10

- 14 15

- 19 20+ Age group [years]42 Figure 15

. T HC emission rates by model year group for operating mode 24 and age group 0- 3.

Figure 16

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



### 1.1.4 Energy

The new data used to develop NO x rates also allowed us to develop new running

- exhaust energy rates. These were based on the same data , STP structure and calculation steps as in the NOx analysis; however , unlike NO x , we did not classify the energy rates by model year or by age

, because neither variable had a significant impact on energy rates or CO 2
. 0 5 10 15 20 25 30

1960

- 2002 2003

- 2006 2007

- 2050Mean HC rate [g/hr]

Model year group MHD

HHD/Bus

0 20 40 60 80 100 120 140 160 180 200

1960

- 2002 2003

- 2006 2007

- 2050Mean CO rate [g/hr]

Model year group MHD

HHD/Bus43 As for previous versions of MOVES, CO 2 emissions were used as the basis for calculating energy rates. To calculate energy rates [kJ/hour] from CO 2 emissions, we used a heating value (HV) of 138,451 kJ/gallon and CO 2 fuel

- specific e mission factor (

f

CO2 ) of 10,084 g/gallon for diesel fuel.

2 2 CO CO energy 

### f HV
rr = Equation 18

This analysis updates the running- exhaust energy rates estimated for MOVES2004 for diesel HHD, MHD, and bus regulatory classes. 19 The revised inputs are shown in Figure 17

.

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

Compared to other emissions , the uncertainties in the energy rates are smaller in part because there is no classification by age, model year, or regulatory class. Thus, the number of vehicles used to determine each rate is larger, providing for a greater certainty of the mean energy rate. 0 1 2 3 4 5 6

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40MeanEnergy rate [kJ/hr]Millions

Operating mode44 

## 1.2 Start Exhaust Emissions
The ‘start’ process occurs when the vehicle is started and is operating in some mode in which the engine is not fully warmed up. For modeling purposes , we define start emissions as the increase in emissions due to an engine start. That is , we use the dif ference in emissions between a test cycle with a cold start and the same test cycle with a hot start. There are also eight intermediate stages which are differentiated by soak time length (time duration between engine key off and engine key on), between a cold start (> 720 minutes of soak time) and a hot start FTP (< 6 minutes of soak time). More details on how start emission rates are calculated as a function of soak time can be found later in this section and in the MOVES light -duty emission rate count erpart document Development of Emission Rates for Light-Duty Vehicles in the Motor Vehicle Emissions Simulator

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



### 1.2.1 HC, CO, and NOx
For light-duty vehicles, start emissions are e stimated by subtracting FTP bag 3 emissions from F TP bag 1 emissions. Bag 3 and Bag 1 are the same dynamometer cycle, except that Bag 1 starts with a cold start, and Bag 3 starts with a hot start. A similar approach was performed for LHD vehicles tested on the FTP and ST01 cycles , which also have separate bags containing cold and hot start emissions over identical drive cycles

. Data from 21 vehicles, ranging from model years 1988 to 2000, were analyzed

. No classifications were made for model year or age due to the limited number of vehicles. The results of this analysis for HC, CO, and NOx are shown in Table 22 :

Table 22 shows average start emissions increases for light

- heavy duty vehicles (g).

HC CO NOx

0.13 1.38 1.68

For HHD and MHD trucks, data were unavailable . To provide at least a minimal amount of information, w e measured emissions from a 2007 Cummins ISB on an engine dynamometer at the EPA National Vehicle and Fuel Emissions Laboratory in Ann Arbor, Michigan. Among other idle tests, we performed a cold start idle test at 1 ,100 RPM lasting four hours, long enough for the engine to warm up. Essentially, the “drive cycle” we used to compare cold start and warm emissions was the idle cycle, analogous to the FTP and ST01 cycles used for LHD vehicles. Emissions and temperature stabilized about 25 minutes into the test. The emission rates through time are shown in Figure 18 . The biggest drop in emission rate through the test is with CO, whereas there is a slight increase in NOx (cold start NOx is lower than hot start NOx), and an insignificant change in HC.45 Figure 18

. Trends in the stabilization of idle emissions from a diesel engine following a cold start . Data were collected from a 2007 Cummins ISB measured on an engine dynamometer.

We calculated the area under each trend for the first 25 minutes and divided by 25 minutes to get the average emission rate during the cold start idle portion. Then, we averaged the data for the remaining portion of the test, or the warm idle portion. The difference between cold start and warm start is in Table 23 . The NOx increment is nega tive since cold start emissions are lower than warm start emissions.

Table 23

. Cold

- start emissions increases in grams on the 2007 Cummins ISB

.

HC CO NOx

0.0 16.0 -2.3

We also considered data from University of Tennessee 30 , 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 em issions. We calculated the hot- start idling emissions by multiplying the reported warm idling rate by the stabilization time. We used the stabilization period from our engine dynamometer tests (25 minutes). Then we subtracted the cold

- idle emissions from the warm idle emissions to estimate the cold start increment. We found that several trucks produced lower NOx emissions during cold start ( similar to our own work), and several trucks produces higher NOx emissions during cold start. Due to these co nflicting results, and the recognition that many factors affect NOx emission during start (e.g. air

- fuel ratio, injection timing, etc), we set the cold

- start increment to zero. Table 24 shows our final MOVES inputs for HHD and MHD diesel start emissions increases. The HC and CO estimates are from our 2007 MY in-house testing. 0 5 10 15 20 25 30 35 40 45 50

0.00 1.00 2.00 3.00 4.00 5.00 time [hrs]mg/s

NOx
HC
CO46 Table 24 MOVES inputs for HHD and MHD start emiss ions ( grams /start)

.

HC CO NOx

0.0 16.0 0.0



### 1. 2.2 Particulate Matter
Data for particulate start emissions from h eavy

- duty vehicles are rare. Typically, heavy -duty vehicle emission measurements are performed on fully warmed up vehicles. Th ese procedures bypass the engine crank and early operating periods when the vehicle is not fully warmed up.

Data from engine dynamometer testing performed on one heavy

- heavy

- duty engine , using the FTP cycle with particulate mass collected on filters

. The engine was manufactured in MY2004. The cycle was repeated six times, under both hot and cold start conditions (two tests for cold start and four replicate tests for hot start). The average difference in PM2.5 emissions (filter measurement - FTP cycle) was 0.10985 grams. The data are shown here:

Cold start FTP average = 1.9314 g PM2.5 Warm start FTP average = 1.8215 g PM2.5

Cold start – warm start = 0.1099 g PM2.5 We applied this value to 1960 through 2006 model year vehicle s . A corresponding val ue of 0.01099 g was used for 2007 and later model year vehicles (90% reduction due to DPFs ) . We plan to update this valuewhen more data becomes available

.


### 1.2. 3 Adjusting Start Rates for Soak Time
The discussion to this point has concerned the development o f 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. A s no data are available for heavy -duty vehicles, we applied the same fractions used for light -duty emissions. Table 25 describes the different start

- related operating modes in MOVES as a function of soak time. The value at 720 min (12 hours) represents cold start. These modes are not related to the operating modes defined in Table 9 , which are for running exhaust emissions.47 Table 25 . O perating mode s for start emissions (as a function of soak time)

Operating Mode Description

101 Soak Time < 6 minutes

102 6 minutes <= Soak Time < 30 minutes

103 30 minutes <= Soak Time < 60 minutes

104 60 minutes <= Soak Time < 90 minutes

105 90 minutes <= Soak Time < 120 minutes

106 120 minutes <= Soak Time < 360 minutes

107 360 minutes <= Soak Time < 720 minutes

108 720 minutes <= Soak Time

The soak fractions we used for HC, CO, and NOx are illustrated in Figure 19 below. (Although, since our current estimate for NOx starts is zero, the NOx fractions are currently irrelevant.)

Figure 19

. Soak Fractions Applied to Cold

- Start Em issions (opModeID = 108) to Estimate Emissions for shorter Soak Periods (operating modes 101

- 107).

The actual PM start rates by operating mode are given in Table 26 below. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20

0 120 240 360 480 600 720 Soak Time (minutes)Ratio of Start to Cold-Start

NOx
CO
HC48 Table 26

. P articulate M atter Start Emission Rate s by Operating Mode (soak fraction)

. Operating Mode PM2.5 (grams per start) 1960-2006 MY PM2.5 (grams per start) 2007+ MY
101 0.0000 0.00000

102 0.0009 0.00009

103 0.0046 0.00046

104 0.0092 0.00092

105 0.0138 0.00138

106 0.0183 0.00183

107 0.0549 0.00549

108 0.1099 0.01099



## 1.3 Extended Idling Exhaust Emissions
In the MOVES model, extended idling is "discretionary" idle operation characterized by idle periods more than an hour in duration, typically overnight, including higher engine speed settings and extensive use of accessories by the vehicle operator. Extend ed idling most often occurs during long layovers between trips by long- haul trucking operators where the truck is used as a residence, and is sometimes referred to as "hotelling." The use of accessories such as air conditioning systems or heating systems will affect emissions emitted by the engine during idling. Extended idling by vehicles will also allow cool-down of the vehicle’s catalytic converter system or other exhaust emission after

- treatments, when these controls are present. Extended idle is tre ated 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 relative ly short stop, such as idle operation during a delivery. Although frequent stops and idling can contribute to overall emissions, these modes are already included in the normal vehicle hours of operation. Extended idling is characterized by idling periods that last hours, rather than minutes. In the MOVES model, diesel long-haul combination trucks are the only sourceType assumed to have any significant extended idling activity. As a result, an estimate for the extended idling emission rate has not been made for any of the other source use types modeled in MOVES.



### 1.3.1 Data Sources
The data used in the analysis of extend ed idling emission rates includes idle emission results from several test programs conducted by a variety of researchers at different times. Not all of the studies included all the pollutants of interest. The references contain more detailed descriptions of the data and how the data was obtained.

• Testing was conducted on twelve heavy

- duty diesel trucks and twelve transit buses in Colorado (McCormick) 31 . Ten of the trucks were Class 8 heavy

- duty axle semi

- tractors, one was a Class 7 truck, and one of the vehicles was a school bus. The model year range d49 from 1990 t hrough 1998. A typical Denver area wintertime diesel fuel (NFRAQS) was used in all tests. Idle measurements were collected during a 20 minute time period. All testing was done at 1,609 meters a bove sea level (high altitude).

• Testing was conducted by EPA on f ive trucks in May 2002 ( Lim) 32 . T he model years ranged from 1985 through 2001. The vehicles were put through a battery of tests including a variety of discretionary and non- discretionary id ling conditions.

• Testing was conducted on 42 diesel trucks in parallel with roadside smoke opacity testing in California (Lambert) 33 . All tests were conducted by the California Air Resources Board (CARB) at a r est area near Tulare, California in April 2002 . D ata collected during this study were included in the data provided by IdleAire Technologies (below) that was used in the analysis.

• A total of 63 trucks (nine in Tennessee, 12 in New York and 42 in California) were tested over a battery of idle test con ditions including with and without air conditioning (Irick) 34

. Not all trucks were tested under all conditions. Only results from the testing in Tennessee and New York are described in the IdleAire report. T he Tulare, California

, data are described in the Clean Air Study cited above. All analytical equipment for all testing at all locations was operated by Clean Air Technologies.

• Fourteen trucks were tested as part of a large Coordinating Research Council (CRC) study of heavy duty diesel trucks with idling times either 900 or 1,800 seconds long (Gautam) 35

.

• The National Cooperative Highway Research Program (NCHRP) 36 obtained the idling portion of continuous sampling during transient testing was used to determine idling emission rates on two trucks.

• A total of 33 heavy

- duty diesel trucks were tested in an internal study by the City of New York (Tang) 37 . The model years ranged from 1984 through 1999. One hundred seconds of idling were added at the end of the WVU five- mile transient test driving cycle.

• A Class 8 F reightliner Century with a 1999 e ngine was test ed using EPA's on

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

. Both short (10 minute) and longer (five hour) measurements were made during idling. Some testing was also done on three older trucks.

• Five heavy

- duty trucks were tested for particulate and NOx emissions under a v ariety of conditions at Oak Ridge Laboratories (Story) 39 . These are the same trucks used in the EPA study (Lim)

.

• The University of Tennessee tested 24 1992 t hrough 2006 m odel year heavy duty diesel trucks using a variety of idling conditions including variations of engine idle speed and load (air conditioning) 30

.


### 1.3.2 Analysis

EPA estimated mean emission rates during extended idling operation for particulate matter (PM), oxides of nitrogen (NOx), hydrocarbons (HC), and carbon monoxide (CO). This analysis used all of the data sources referenced above. This update reflects new data available since the initial development of extended idle emissions for the MOVES model. The additions include the testing50 at Research Triangle Park (Broderick), the University of Tennessee study (Calcagno), and the completed E -55/59 study conducted by WVU and CRC. In addition, the data was separated by truck and bus and by idle speed and accessory usage to develop an emission rate more representative of extended idle rates.

The important conclusion from the 2003 analysis was that factors affect ing 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 e ngine size, altitude, model year within MOVES groups, and test cycle are negligible. This makes the behavior of truck operators very important in estimating the emission rates to assign to periods of extended idling. The use of accessories ( air condition ers , 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 extend ed idle pollutant process assume both accessory use and engine idle speeds set higher than used for "curb" (non-discretionary) idling. The studies focused on three types of idle conditions. The first is considered a curb idle, with low engine speed (<1

, 000 rpm) and no air conditioning. The second is representative of an extended idle condition with higher engine speed (>1

, 000 rpm) and no air conditioning. The third represents an extended idle condition with higher engine speed (>1,000 rpm) and air cond itioning. The idle emission rates for heavy duty diesel trucks prior to the 1990 model year are based on the analysis of the 18 trucks from 1975-1990 model years used in the CRC E-55/59 study and one 1985 truck from the Lim study. The only data available represents a curb idle condition. No data was available to develop the elevated NOx emission rates characteristic of higher engine speed and accessory loading, therefore, the percent increase developed from the 1991-2006 trucks was used.

Extended idle emi ssion rates for 1991

- 2006 model year heavy duty diesel trucks are based on several studies and 184 tests detailed in Appendix A.3 Extended Idle Data Summary . The increase in NOx emissions due to higher idle speed and air conditioning was estimated based on t hree studies that included 26 tests. The average emissions from these trucks using the high idle engine speed and with accessory loading was used for the emission rates for extended idling. The expected effects of the 2007 heavy duty diesel vehicle emission standards on extended idling emission rates are taken from the EPA guidance analysis (EPA 2003). The 2007 heavy duty diesel emission standards are expected to result in the widespread use of PM filters and exhaust gas recirculation (EGR) and 2010 standards will result in after- treatment technologies. However, since there is no requirement to address extended idling emissions in the emission cer tification procedure, EPA expects that there will be little effect on HC, CO , and NOx emissions after hours of idling due to cool-down effects on EGR and most aftertreatment systems. However, we do not expect DPFs to lose much effectiveness during extende d idling. As a result, we project that idle NOx emissions will be reduced 12% and HC and CO emissions will be reduced 9% from the extended idle emission rates used for 1988-2006 model year trucks. The reduction estimates are based on a ratio of the 2007 standard to the previous standard and assuming that the emission control of the new standard will only last for the first hour of an eight hour idle. For PM, we

assum e an extended idling emission rate equal to the curb idling rate (operating mode 1 from the running exhaust analysis). Detailed equations are included in the appendix.51 

### 1.3.3 Results

Table 27 shows the resulting NOx, HC, and CO emission rates estimated for heavy -duty diesel trucks. Extended idling measurements have large variability due to low engine loads, which is reflected in the variation of the mean statistic.

Table 27 . E xtended idle emission rate s ( g/hour ).
Model years NOx HC CO PM

Pre -1990 112 108 84 8.4

1990-2006 227 56 91 4.0

2007 and later 201 53 91 0.252 

## 2 Heavy -Duty Gasoline Vehicles

2.1 Running Exhaust Emissions

2.1.1 HC, CO, and NOx

2.1.1.1 Data and Analysis As gasoline- fueled vehicles are a small percentage of the heavy -duty vehicle fleet, the amount of data available for analysis was small. We relied on four medium-heavy duty gasoline trucks from the CRC E -55 program and historical data from EPA’s Mobile Source Observation Database (MSOD), which has re sults from chassis tests performed by both EPA, contractors and outside parties

. The heavy

- duty gasoline data in the MSOD is mostly from pickup trucks which fall mainly in the LHD2b3 regulatory class. Table 28 shows the number of vehicles in cumulative data sets. In the real world, most heavy- duty gasoline vehicles fall in either the LHD2b3 or LHD45 class, with a smaller percentage in the MHD class. There are very few, if any, HHD gasoline trucks remaining in use.

Table 28

. D istribution of vehicles in the data sets by model

- year group , regulatory class and age group.

Model year group Regulatory c lass Age group
0-5 6-9

1960-1989 MHD 2

LHD2b3 10

1990-1997 MHD 1

LHD2b3 33 19

1998-2002 MHD 1

LHD2b3 1

Similar to the HD diesel PM, HC

, and CO analysis, the chassis vehicle speed and acceleration, coupled with the average weight for each regulatory class, were used to calculate STP ( Equation 13 ). To supplement the meager data available, we examine d certification data as a guide to developing model year groups for analysis. F igure 20 shows averages of certification results by model year.53 F igure 20 . B rake

- specific certification emission rates by model year for heavy

- duty gasoline engines.

Based on these certification results, we decided to classify the data into the coarse model year groups listed below.

• 1960-1989

• 1990-1997

• 1998-2002

• 2003-2006

• 2007 and later

Although there was little data for 2007- and

- later, w e made a split at model year 2007 to account for possible increases in three- way catalyst use and efficiency due to tighter NOx standards. We assumed that these catalysts in gasoline vehicles will yield a reduction in HC and CO also. We estimate that each of th ese three pollutants will decrease 70% from 2003-2006 MY levels.

Unlike the analysis for HD diesel vehicles, we used the age effects present in the data itself. We did not incorporate external tampering and mal

- maintenance assumptions into the HD gasoline
rates. Due to sparseness of data we used only the two age groups listed in Table 28 . We also did
not classify by regulatory class since there was on ly one regulatory class ( LHD2b3 ) predominantly represented in the data.

2.1.1.2 Sample Results

Selected results are shown graphically below . The first ( Figure 21 ) shows all three pollutants vs. operating mode for the LHD2b3 regulatory class. In general, emissions follow the expected trend with STP , though the trend is most pronounced for NO x . As exp ected, NOx emissions for heavy

duty gasoline vehicles are much lower than for heavy- duty diesel vehicles. 0 5 10 15 20 25 30

1980 1985 1990 1995 2000 2005 2010 Model yearCO, NOx [g/bhp-hr]

-0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4HC [g/bhp-hr] CO

Nox
HC54 Figure 21 . E mission Rates by operating mode for MY 1994 at age 0 -3 years .

Figure 22 shows the emissions trends by age group. Since we did not use the tampering and malmaintenance methodology as we did for diesels, the age trends reflect our coarse binning with age. For each pollutant, only two distinct rates exist – one for ages 0-5 and another for age 6 and older.

Figure 22 . E mission rates s by age group for MY 1994 in operating mode bin 24. 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0 50 100 150 200 250 300 350 400 450

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean CO rate [g/hr]Mean HC and NOx rates [g/hr]

Operating mode NOx
HC
CO

0 200 400 600 800 1000 1200 1400

0 20 40 60 80 100 120Mean CO rate [g/hr]Mean HC and NOx rates [g/hr]

NOx

HC

CO

0-3 4- 5 6

- 7 8

- 9 10

- 14 15

- 19 20+ Age group [years]55 Figure 23 shows emissions by model year group. Emissions generally decrease with model year
group. Uncertainties are relatively high but not shown in this plot for clarity.

Figure 23 . E mission rates by model year group for age 0

- 3 in operating mode 24.

Assumptions regarding the increased effectiveness of catalysts substantially reduce emissions estimates for 2007 model year and later.



### 2.1.2 P articulate M atter Unfortunately, the PM2.5 emission data from heavy-duty gasoline trucks are too sparse to develop the detailed emission factors the MOVES model is designed for. As a result, only a very limited analysis could be done. EPA will likely revisit and update the se emission rates when sufficient additional data on PM2.5 emissions from heavy- duty gasoline vehicles become available.

For MOVES2010 , the heavy

- duty gas PM2.5 emission rates will be calculated by multiplying the light -duty gasoline truck PM2.5 emission ra tes by a factor of 1.40, as explained below. Since the MOVES light -duty gasoline PM2.5 emission rates comprise a complete set of factors - classified by particulate sub -type (elemental and organic carbon), operating mode, model year and regulatory class, the heavy -duty PM2.5 emission factors will also be a complete set.

2.1.2.1 Data Sources This analysis is based on the PM2.5 emission test results from the four gas oline trucks tested in the CRC E55 -E59 test program. The specific data used were collected on the UDDS test cycle. Each of the four vehicles in the sample received two UDDS tests, conducted at different test weights. Other emission tests using different cycles were also available on the same vehicles, but were not used in the calculation. Th e use of the UDDS data enabled the analysis to have a consistent driving cycle. The trucks and tests are described in Table 29

. 0 100 200 300 400 500 600

0 20 40 60 80 100 120

1960

- 1989 1990

- 1997 1998

- 2006 2007

- 2050Mean CO rate [g/hr]Mean HC and NOx rates [g/hr] HC

NOx

CO56 Table 29

. Summary of data used in HD gasoline PM emission rate analysis

.

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

1 2001 3 UDDS 12,975 1.81

2001 3 UDDS 19,463 3.61

2 1983 21 UDDS 9,850 43.3

1983 21 UDDS 14,775 54.3

3 1993 12 UDDS 13,000 67.1

1993 12 UDDS 19,500 108.3

4 1987 18 UDDS 10,600 96.7

1987 18 UDDS 15,900 21.5

The table shows only four vehicles, two of which are quite old and certified to fairly lenient standards. A third truck is also fairly old at 12 years and certified to an intermediate standard. The fourth is a relatively new truck at age three and certif ied to a more stringent standard. No trucks in the sample are certified to the Tier2 or equivalent standards.

2.1.2.2 Analysis Examination of the heavy

- duty data shows two distinct levels: vehicle #1 (MY 2001) and the other three vehicles. Because of its lower age ( 3 years old ) and newer model year status, this vehicle has substantially lower PM emission levels than the others, and was separated in the analysis. T he emissions of the other three vehicles were averaged togethe r to produce these mean res ults :

Mean for Vehicles 2 through 4: 65.22 m g/mi Older Group

Mean for Vehicle 1: 2.71 m g/mi Newer Group

To compare these rates with rates from light

- duty gasoline vehicles, we simulated UDDS cycle emission rates based on MOVES light-duty gas PM2. 5 emission rates (with normal deterioration assumptions) for light-duty gasoline trucks. The UDDS cycle represents standardized operation for the heavy-duty vehicles. To make the comparisons appropriate , the simulated light

- duty UDDS results were matched to the results from the four heavy-duty gas trucks in the sample. This comparison meant that the emission rates from the following MOVES model year groups and age groups for light-duty trucks were used:57 • MY group 1983-1984, age 20+

• MY group 1986-1987, age 15-19

• MY group 1991-1993, age 10-14

• MY group 2001 , age 0 -3

The simulated UDDS emission factors for the older light -duty gas truck group are 36.2 m g/mi for MOVES organic carbon PM2.5 emissions and 2. 64 1 mg/mi for elemental carbon. Ignoring sulfate emissions ( on the order of 1 × 10 -4 mg/mile for low sulfur fuels) , these values sum to 38.84 m g/mile

.

This value leads to the computation of the ratio : 

### 679.1 84.38 22.65

mile g mile

g



### =

.

The simulated UDDS emission rates for the newer light -duty gas truck group are 4.368 m g/mi for MOVES organic carbon PM2.5 emissions and 0.3187 mg/mi for elemental carbon. Ignoring sulfate emissions (which are in the order of 1 × 10 -5 m g/mile for low sulfur fuels)

, these values sum to 4.687 m g/mi

.

This value leads to the computation of the ratio : 

### 578.0 687.4

71.2

mile g mile

g



### =

.

The newer model year group produces a ratio which is less than one and implies that large trucks produce less PM2.5 emissions than smaller trucks. This result is intuitively inconsistent, and is the likely result of a very small sample and a large natural variability in emission results. All four data points were retained and averaged together by giving the older model year group a 75 percent weighting and the newer model year group (MY 2001) a 25 percent weighting. This is consistent with the underlying data sample. It produces a final ratio of:



### )1( Ratio Ratio Ratio

newer older final 

### WtFrac WtFrac

− + =

= 1.679 × 0.75 + 0.578 × 0.25 = 1.40

We then multiplied this final ratio of 1.4 0 by the light

- duty gasoline truck PM rates to calculate the input emission rates for heavy- duty gasoline PM rates.



### 2.1.3 Energy Consumption
The data used to develop heavy

- duty running exhaust gasoline rates were the same as those used for HC, CO, and NO x . However, new energy rates were only developed for MHD, HHD, and bus classes. Analyses performed for LHD vehicles were not updated in this analysis. Also, similarly to the diesel running exhaust energy rates, classifications were not made based on mode l year group, age, or regulatory class. To calculate energy rates (kJ/hour) from CO 2 emissions, we used a heating value (HV) of 122,893 kJ/gallon and CO 2 fuel

- specific emission factor ( f

CO2 ) of 8,78858 g/gallon for gasoline (see Equation 18 ). STP was calculated using Equation 13

. Figure 24 summarizes the gasoline running exhaust energy rates stored in MOVES.

Figure 24

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

A linear extrapolation to determine rates at the highest operating modes in each speed range was performed analogous ly to diesel energy and NO x rates (see Section 1.1.1.3.3 Hole Filling and forecasting ).



## 2.2 Start Emissions

2.2.1 Available Data
To develop start emission rates for heavy

- duty gasoline

- fueled vehicles, we extracted data available in the USEPA Mobile- Source Observation Database (MSOD). These data represent aggregate test results for heavy -duty spark- ignition (gasoline powered) engines measured on the Federal Test Procedure (FTP) cycle. The GVWR for all trucks was between 8,500 and 14,000 lb, placing all trucks in the LHD2b3 regulatory class

. Table 30 shows the model- year by age classification for the data. The model year groups in the table were assigned based on the progression in NOx standards between MY 1990 and 2004. Standards for CO and HC are stable over this period, until MY 2004, when a combined NMHC+ NOx standard was introduced. However, no measurements for trucks were available for MY2004 or later. 0 1 2 3 4 5 6

0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40Mean Energy rate [kJ/hr]Millions

Operating mode59 Table 30 . Model

- year Group by Age Gro up Structure for the Sample of Heavy

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

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

1960-1989 19 22 41

1990 14.4 1.1 6.0 1 29 30

1991-1997 14.4 1.1 5.0 73 59 32 4 168
1998

- 2004 14.4 1.1 4.0 8 8

Total 81 59 33 52 22 247



### 2.2.2 Estimation of Mean Rates
As with light

- duty vehicles, we estimated the “cold

- start” as the mass from the cold

- start phase of the FTP (bag 1) less the “hot-start” phase (Bag 3). As a preliminary exploration of the data, we averaged by model year group and a ge group and produced the graphs shown in Appendix A.6 Heavy -duty Gasoline Start Emissions Analysis Figures

. Sample sizes are small overall and very small in some cases (e.g. 1990, age 6-7) and the behavior of the averages is somewhat erratic. In contrast to light-duty vehicle emissions, strong model- year effects are not apparent. This may not be surprising for CO or HC, given the uniformity of standards throughout. This result is more surprising for NOx but model year trends are no more evident for NOx than for the other two. Broadly speaking, it appears that an age trend may be evident. If we assume that the underlying population distributions are approximately log-normal, we can visualize the data in ways that illustrate underlying relationships. As a first step, we calculated geometric mean emissions, for purposes of comparison to the arithmetic means calculated by simply averaging the data. Based on the assumption of log-no rmality, the geometric mean ( x

g ) was calculated in terms of the logarithmic mean ( x l ) as

l x g x ln e = Equation 19

This measure is not appropriate for use as an emission rate, but is useful in that it represents the “center” of the skewed parent distribution. As such, it is less strongly influenced by unusually high or outlying measurements than the arithmetic means in Appendix A.6 Heavy-duty Gasoline Start Emissions Analysis Figures

. In general, the small differences between geometric means and arithmetic means suggest that the distributions represented by the data do not show strong skew in most cases. Assumin g that emissions distributions should be strongly skewed suggests that these data are not representative of “real -world” emissions for these vehicles. This conclusion appears to be reinforced by the values in Figure 30 which represent the “logarithmic standard deviation” calculated by model -year and age groups. This measure ( s l ), is the standard deviation of natural logarithm of emissions ( x l ) in . The values of s l 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 for60 emissions data; e.g., corresponding values for light-duty running emissions are generally 1.0 o r 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 th e log -normal parent distributions allows us to re- estimate the arithmetic mean after assuming reasonable values for s l . For this calculation we assumed values of 0.9 for CO and HC and 1.2 for NOx. These values approximate the maxima seen in these data and are broadly comparable to rates observed for lightduty vehicles.

The re

- estimated arithmetic means are calculated from the geometric means, by adding a term that represents the influence of the “dirtier” or “higher

- emitting” vehicles, or the “upper tail o f the distribution,” as shown in Figure 31 above.

2 2



### e l

s

ga 

### xx = Equation 20

For purposes of rate development using these data, we concluded that a model

- year group effect was not evident and re

- averaged all data by age Group alone. Results of the coarser averaging are presented in Figure 25 with the arithmetic mean (directly calculated and re

- estimated) and geometric means shown separately. We then addressed the question of the projection of age trends. As a general principle, we did not allow emissions to decline with age. We implemented this assumption by stabilizing emissions at the maximum level reached between the 6 -7 and 10-14 age groups.61 Figure 25 . Cold

- start FTP Emissions for Heavy

- Duty Gaso line Trucks, averaged by Age Group only (g = geometric mean, a= arithmetic mean recalculated from x l and s l ).



### 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 s l , it was necessary to re

- estimate corresponding standard deviations for the parent distribution s , as shown in Equation 21

. 0 20 40 60 80 100 120 140 160 180 200

0 2 4 6 8 10 12 14 Age (years)FTP Cold Start (g)

co
g_co
a_co

0 2 4 6 8 10 12 14 16

0 2 4 6 8 10 12 14 Age (years)FTP Cold Start (g)

thc g_thc a_thc

0 1 2 3 4 5 6 7 8

0 2 4 6 8 10 12 14 Age (years)FTP Cold Start (g) nox
g_nox

a_nox 

### (a) CO

(b) THC

(c) NOx62 )1e(e

22 s2

− = s g xs Equation 21

After recalculating the standard deviations, the calculation of corresponding standard errors was simple. Because each vehicle is represented by only one data point, there was no within

- vehicle variability to consider, and the standard error could be calcu lated as 

### ns / . We divided the standard errors by their respective means to obtain CV -of- the

- mean or “relative standard error.” Means, standard deviations and uncertainties are presented in Table 31 and in Figure 26 . Note that these results represent only “cold -start” rates (opModeID 108).63 Table 31. Cold-Start Emission Rates (g) for Heavy-Duty Gasoline Trucks, by Age Group (it alicized 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 1.9964 Figure 26 . Cold

- start Emission Rates for Heavy

- Duty Gasoline Trucks, with 95% Confidence Intervals



### 2.2.4 Projecting Rates beyond the Available Data
The steps described so far involved reduction and analysis of the available emissions data. In the next step, we describe approaches used to impute rates for model years not represented in these data. For purposes of analysis we delineated three model year groups: 1960

- 2004, 2005

- 2007 and 2008 and later. We describe the derivation of rates in each group below. 0 50 100 150 200 250 300

0 5 10 15 20 25 Age (years)FTP Cold Start (g)

0 2 4 6 8 10 12 14 16 18

0 5 10 15 20 25 Age (years)FTP Cold Start (g)

0 2 4 6 8 10 12

0 5 10 15 20 25 Age (years)FTP Cold Start (g) 

### (a) CO

(b) THC

(c) NOx65 2.2.4.1 Regulatory class LHD2b3 For CO the approach was simple. We applied the values in Table 31 to all model

- year groups. The rationale for this approach is that the CO standards do not change over the full range of model years considered. For HC and NOx we imputed values for the 2005 -0 7 and 2008+ model

- year groups by multiplying the values in Table 31 by ratios expressed in terms of the applicable standards. Starting in 2005, a combined HC+NOx standard was introduced. It was necessary for modeling purposed to partition the standard into HC and NOx components. We assumed that the proportions of NMHC and NOx would be similar to those in the 2008 standards, which separate NMHC and NOx while reducing both. We calculated the HC value by multiplying the 1960 -2004 value by the fraction f HC , where



## ( )

37.0 hr-g/hp 1.1 hr

- g/hp0.1 hr-g/hp 0.20) (0.14 hr-g/hp 14.0

HC =        

+

= f Equation 22

This ratio represents the component of the 2005 combined standard attributed to NMHC. We calculated the corresponding value for NOx as

147.0 hr-g/hp 0.4 hr-g/hp0 .1 hrg/hp 0.20) (0.14 hrg/hp20.0

NOx =        

− + −

= f Equation 23

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

duty. 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 f HC and f NOx where

127.0 hrg/hp 1.1 hrg/hp 14.0

HC =

⋅ ⋅

= f Equation 24

05.0 hrg/hp 0.4 hrg/hp 20.0

NOx =

⋅ ⋅

= f Equation 2566 2.2.4.2 Regulatory class es LHD45 and MHD

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

. For CO and HC, we estimated values for the heavier vehicles by multiplying them by ratios of standards for the heavier class to those for the lighter class.

The value for CO is

58.2 hr-g/hp 4.14 hr-g/hp 1.37

CO = = f Equation 26

and the corresponding value for HC is1.73.

73.1 hr-g/hp 1.1 hr-g/hp 9.1

HC = = f Equation 27

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

. Note that in Draft MOVES2009, the ratios in Equation 26 and Equation 27 were erroneously applied to the 2005-2007 model-year groups for LHD45 and MHD vehicles . In MOVES2010, values for these model- year groups were set equal to those for the LHD2b3 vehicles , with the rationale that the standards converge for both groups. For NOx, all values are equal to those for LHD2b3 , because the same standar ds apply to both classes throughout. The approaches for all three regulatory classes in all three model years are shown in Table 32

. Table 32 . Metho ds used to Calculate and Start Emission Rates for Heavy

- Duty Spark

- Ignition Engines
Regulatory Class Model

- year Group Method CO THC NOx

LHD2b3 1960

- 2004 Values from Table 31 Values from Table 31 Values from Table 31

2005

- 2007 Values from Table 31 Reduce in proportion To standards Reduce in proportion To standards

2008 + Values from Table 31 Reduce in proportion To standards Reduce in proportion To standards

LHD45, MHD 1960

- 2004 Increase in proportion To standards Increase in proportion To standards Same values as LHD2b3

2005

- 2007 Increase in proportion To standards Increase in proportion To standards Same values as LHD2b3

2008 + Increase in proportion To standards Increase in proportion To standards Same values as LHD2b367 As for heavy -duty diesel and light-duty vehicles we applied the curve in Figure 19 to adjust the start emission rates for varying soak times. The rat es described in this section were for cold starts (soak time > 720 minutes).

2.2.4.3 Particulate Matter

D ata on PM start emissions from heavy -duty gasoline vehicles were unavailable. As a result, we used the multiplication factor from the running exhaust emissions analysis of 1.40 to scale up start emission rates for light -duty trucks.68 

## A. Appendices

A.1 Calculation of Accessory Power Requirements

Table 33

. Accessory load estimates for HHD trucks

Table 34

. Accessory load estimates for MHD trucks VSP Cooling Fan Air cond Air comp Alternator Engine Accessories Total Accessory Load (kW)

Low Off = 0.5 kW Power (kw) 19.0 2.3 3.0 1.5 1.5 % time on 10% 50% 60% 100% 100%

Total (kW) 1.9 1.2 2.0 1.5 1.5 8.1 Mid Off = 0.5 kW Power (kw) 19.0 2.3 2.3 1.5 1.5 % time on 20% 50% 20% 100% 100%

Total (kW) 3.8 1.2 0.9 1.5 1.5 8.8 High Off = 0.5 kW Power (kw) 19.0 2.3 2.3 1.5 1.5 % time on 30% 50% 10% 100% 100%

Total (kW) 5.7 1.2 0.7 1.5 1.5 10.5

y ( )

VSP Cooling Fan Air cond Air comp Alternator Engine Accessories Total Accessory Load (kW)

Low Off = 0.5 kW Power (kw) 10.0 2.3 2.0 1.5 1.5 % time on 10% 50% 60% 100% 100%

Total (kW) 1.0 1.2 1.4 1.5 1.5 6.6 Mid Off = 0.5 kW Power (kw) 10.0 2.3 2.0 1.5 1.5 % time on 20% 50% 20% 100% 100%

Total (kW) 2.0 1.2 0.8 1.5 1.5 7.0 High Off = 0.5 kW Power (kw) 10.0 2.3 2.0 1.5 1.5 % time on 30% 50% 10% 100% 100%

Total (kW) 3.0 1.2 0.7 1.5 1.5 7.869 Table 35

. Accessory load estimates for buses

VSP Cooling Fan Air cond Air comp Alternator Engine Accessories Total Accessory Load (kW)

Low Off = 0.5 kW Power (kw) 19.0 18.0 4.0 1.5 1.5 % time on 10% 80% 60% 100% 100%

Total (kW) 1.9 14.4 2.6 1.5 1.5 21.9 Mid Off = 0.5 kW Power (kw) 19.0 18.0 4.0 1.5 1.5 % time on 20% 80% 20% 100% 100%

Total (kW) 3.8 14.4 1.2 1.5 1.5 22.4 High Off = 0.5 kW Power (kw) 19.0 18.0 4.0 1.5 1.5 % time on 30% 80% 10% 100% 100%

Total (kW) 5.7 14.4 0.9 1.5 1.5 24.070 

## A. 2 Tampering and Mal

- maintenance
Tampering and mal

- maintenance (T&M) effects represent the fleet

- wide average increase in emissions ov er the useful life of the engines. In laboratory testing, properly maintained engines often yield very small rates of emissions deterioration through time. However, we assume that in real -world use, tampering and mal- maintenance yield higher rates of emi ssions deterioration over time. As a result, we feel it is important to model the amount of deterioration we expect from this tampering and mal

- maintenance. We estimated these fleet

- wide emissions effects by multiplying the frequencies of engine component failures by the emissions impacts related to those failures for each pollutant . Details of this analysis appear later in this section

.


### A. 2 .1 Modeling Tampering and Mal

- maintenance

As T&M affect s emissions through age, we developed a simple function of emission deterioration with age. We applied the zero

- age rates through the emissions warranty period (5 years/100,000 miles), then increased the rates linearly up to the useful life. Then we assumed that all the rates level off beyond the useful life age. Figure 27 shows this relationship.

Figure 27

. Q ualitative Depiction of the implementation of age effects.

The useful life refers to the length of time that engines are required to meet emissions standards. We incorporated this age relationship by averaging emi ssions rates across the ages in each age group. Mileage was converted to age with VIUS 40 (Vehicle Inventory and Use Survey) data, which contains data on how quickly trucks of different regulatory classes accumulate mileage. Table 36 shows the emissions warranty period and approximate useful life requirement period for each of the regulatory classes. Final emission rate d T&M

Zero

- mile emission

End of warranty i d End of useful life Age Emission rate71 Table 36

. Warranty and useful life requirements by regulatory class

Regulatory class Warranty age (Requirement: 100,000 miles or 5 years) Useful life mileage/age requirement Useful life age

HHD 1 435,000/10 4

MHD 2 185,000/10 5

LHD45 4 110,000/10 4

LHD2b3 4 110,000/10 4

BUS 2 435,000/10 10

While both age mileage metric s are given for these periods, whichever comes first determines the applicability of the warranty. As a result, since MOVES deals with age and not mileage, we need to convert all the mileage values to age equiv alents , as the mileage limit is usually reached before the age limit. The data show that on average, heavy- heavy

- duty trucks accumulate mileage much more quickly than other regulatory classes. Therefore, any deterioration in heavy

- heavy -duty truck emissi ons will presumably happen at at younger ages than for other regulatory classes. Buses, on average, do not accumulate mileage quickly. Therefore, their useful life period is governed by the age requirement, not the mileage requirement.

Since MOVES deals with age groups and not individual ages, the increase in emissions by age must be calculated by age group. We assumed that there is an even age distribution within each age group (e.g. ages 0, 1, 2, and 3 are equally represented in the 0-3 age group). Th is is important since, for example, HHD trucks reach useful life at four years, which means they will increase emissions through the 0-3 age group. As a result, the 0-3 age group emission rate will be higher than the zero

- mile emission rate for HHD trucks

. Table 37 shows the multiplicative T&M adjustment factor by age. We determined this factor using the mileage

- age data from Table 36 and the emissions -age relationship that we described in Figure 27 . We multiplied this factor by the emissions in crease of each pollutant over the useful life of the engine, which we determined from the analysis in the section A.2.3 Analysis below and which is listed in the corresponding running exhaust sections above.72 Table 37 shows the T&M multiplicative adjustment factor by age ( f

TM,age group ).
Age Group LHD MHD HHD Bus

0-3 0 0.083 0.25 0.03125

4-5 1 0.833 1 0.3125

6-7 1 1 1 0.5625

8-9 1 1 1 0.8125

10-14 1 1 1 1

15-19 1 1 1 1

20+ 1 1 1 1

In this table

, a value of 0 i ndicates no de terioration

, or zero

- mile emissions level (ZML) , and a value of 1 indicates a fully deteriorated engine , or maximum emissions level, at or beyond useful life (UL)

. The calculation of emission rate by age group is described in the equation below. TM pol represents the estimated emissions rate increase through the useful life for a given pollutant.



### ) 1(

, , , pol agegroup TM ZML pol agegrppol 

### TM frr += Equation 28



### A. 2 .2 Data Sources
EPA used the following information to develop the tamper and mal

- maintenance occurrence rates used to develop emission rates used in MOVES:

• California’s ARB EMFAC2007 Modeling Change Technical Memo 41 (2006). T he basic EMFAC occurrence rates for tampering and mal

- maintenance were developed from the Radian and EFEE reports and internal CARB engineering judgment.

• Radian Study (1988). The report estimated the malfunction rates based on survey and observ ation. T he data may be questionable for current heavy

- duty trucks due to advancements such as electronic controls, injection systems, and exhaust aftertreatment.

• EFEE report (1998) on PM emission deterioration rates for in

- use vehicles. Their work includ ed heavy

- duty diesel vehicle chassis dynamometer testing at Southwest Research Institute.

• EMFAC2000 (2000) Tampering and Mal

- maintenance Rates

• EMA’s comments on A RB’s Tampering, Malfunction, and Mal

- maintenance Assumptions for EMFAC 2007

• University of Cali fornia –Riverside (UCR) “Incidence of Malfunctions and Tampering in Heavy

- Duty Vehicles”73 • Air Improvement Resources, Inc.’s Comments on H eavy

- Duty Tampering and Mal

maintenance Symposium

• EPA internal engineering judgment



### A. 2 .3 Analysis

A.2.3.1 T &M Cate gories EPA generally adopted the categories developed by CARB, with a few exceptions. The high fuel pressure category was removed. We added a category for misfueling to represent the use of nonroad diesel, not ULSD onroad diesel. We combined the injector categories into a single group. We reorganized the EGR categories into “ Stuck Open ” and “ Disabled/Low Flow .” We included the PM regeneration system, including the igniter, injector, and combustion air system in the PM filter leak category. EPA will g roup 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 Cataly st Reduction (SCR) systems will be the primary NOx aftertreatment system for HHDD. T herefore, the occurrence rates and emission impacts will vary in 2010 and beyond depending on the regulatory class of the vehicles.

A.2.3.2 T&M Model Year Groups EPA developed the model year groups based on regulation and technology changes.

• Pre -1994 represents non-electronic fuel control.

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

• 2003 represents early use of EGR.

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

• EPA issued a rule to require OBD f or heavy duty trucks

, beginning in MY 2010 with complete phase

- in by MY 2013.

A.2.3.3 T &M Occurrence Rates

A.2.3.3.1 EPA T &M Occurrence Rate Differences from EMFAC2007

EPA a dopted the CARB EMFAC2007 occurrence rates, except as noted below. Clogged Air Filter : EPA reduced the frequency rate from EMFAC ’s 15% to 8%. EPA reduced this value based on the UCR results, the Radian study, and EMA’s comments that air filters are a maintenance item. Many trucks contain indicators to notify the driver of dirty air filters and the drivers have incentive to replace the filters for other performance reasons.74 Other Air Problems : EPA reduced the frequency rate from EMFAC ’s 8% to 6% b ased on the UCR results.

Electronics Failed : EPA will continue to use the 3% frequency rate for all model years beyond 2010. CARB increased the rate to 30% in 2010 due to system complexity. EPA does not agree with CARB’s assertion that the complexity of electronic systems will increase enough to justify a
ten -fold increase in malfunction occurrence rates. We believe that the hardware will evolve through 2010, rather than be replaced with completely new systems that would justify a higher rate of failure . EPA asserts that many of the 2010 changes will occur with the aftertreatment systems which are accounted for separately. EGR Stuck Open: EPA believes the failure frequency of this item is rare and therefore set the level at 0.2%. This failure will l ead to drivability issues that will be noticeable to the driver and serve as an incentive to repair.

EGR Disabled/Low Flow: EPA believes the EMFAC 20% EGR failure rate is too high and reduced the rate to 10%. All but one major engine manufacturer had EGR previous to the 2007 model year and all have it after 2007. Therefore, EMFAC ’s frequency rate increase in 2010 due to the increase truck population using EGR does not seem valid. However, the Illinois EPA stated that “EGR flow insufficient” is the top OBD issue found in their LDV I/M program 42 so it cannot be ignored.

NOX Aftertreatment malfunction: EPA developed a NOx aftertreatment malfunction rate that is dependent on the type of system used. We assumed that HHDD will use primarily SCR systems and LHDD will primarily use LNT systems. We estimated the failure rates of the various components within each system to develop a composite malfunction rate. The individual failure rates were developed considering the experience in agriculture and stationary industries of NOx aftertreatment systems and similar component applications. Details are included in the chart below. We assumed that tank heaters had a 5% failure rate, but were only required in one third of the country and one fifth of the year. The i njector failure rate is lower than fuel injectors, even though they have similar technology, because there is only one required in each system and it is operating in less severe environment of pressure and temperature. We believe the compressed air delivery system is very mature based on a similar use in air brakes. We also believe that manufacturers will initiate engine power de

- rate as incentive to keep the urea supply sufficient.75 NOx aftertreatment sensor: EPA believes the 53% occurrence rate in EMFAC2007 is too high and will use 10%. CARB assumed a mix of SCR, which uses one sensor per vehicle, and NOx adsorbers, which use two sensors per vehicle. They justified the failure rate based on the increased number of sensors in the field beginning in 2010. We developed the occurrence rate based on the following assumptions:

• Population: HHDD: vast majority of heavy-duty applications will use SCR technology with a maximum of one NOx sensor. NOx sensors are not required for SCR – manufacturers can use models or run open loop. Several engine manufacturers representing 30% of the market plan to delay the use of NOx aftertreatment devices through the use of improved engine -out emissions and emission credits.

• Durability expectations: S wRI completed 6000 hour s of ESC cycling with NOx sensor. Internal testing supports longer life durability. Discussions with OEMs in 2007 i ndicate longer life expected by 2010.

• Forward looking assumptions: M anufacturers have a strong incentive to improve the reliability and durability of the sensors because of the high cost associated with frequent replacements.

PM Filter Leak: EPA will use 5% PM filter leak and system failure rate. CARB used 14% failure rate. They discounted high failure rates currently seen in the field.

PM Filter Disable: EPA agrees with CARB’s 2% tamper rate of the PM filter. The filter causes a fuel economy penalty so the drivers have an incentive to remove it. Oxidation Catalyst Malfunction/Remove: EP A 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 r obust than PM filters, but have the potential to experience degradation when exposed to high temperatures. Occurrence Rate SCR Urea tank 0.5% Tank heaters 1% In-exhaust injectors 2% Compressed air delivery to injector 1% Urea supply pump 1% Control system 5% Exhaust temperature sensor 1% Urea supply 1% Overall 13%

LNT Adsorber 7% In-exhaust injectors 2% Control system 5% Exhaust temperature sensor 1% Overall 16%76 Misfuel: EPA estimated that operators will use the wrong type of fuel, such as agricultural diesel fuel with higher sulfur levels, approximately 0.1 % of the time.

A.2.3.3.2 Tampering & Mal-maintenance Occurrence Rate Summary

A.2.3.3.2 Emission Effects NOx Emission Effect s EPA developed the emission effect from each tampering and mal

- maintenance incident from CARB’s EMFAC, Radian’s dynamometer te sting with and without the malfunction present, EFEE results, and internal testing experience. EPA estimated that the lean NOx traps (LNT) in LHDD are 80% efficient and the selective catalyst reduction (SCR) systems in HHDD are 90% efficient at reducing NO x. EPA developed the NOx emission factors of the NOx sensors based on SCR systems’ ability to run in open-loop mode and still achieve NOx reductions. The Manufacturers of Emission Controls Association ( MECA ) has stated that 75-90% NOX reduction with open loop control and >95% reduction with closed loop control. 43 Visteon reports 60-80% NOX reduction with open loop control. 44
The failure of the NOx aftertreatment system had a different impact on the NOx emissions depending on the type of aftertreatment. The HHDD vehicles with SCR systems would experience a 1000% increase in NOx during a complete failure, therefore we estimated a 500% increase as a midpoint between normal operation and a complete failure. The LHDD vehicles with LNT systems would experience a 500% increase in NOx during a complete failure. We estimated a 300% increase as a value between a complete failure and normal system operation. Tamper & Malmaintenance Frequency of Occurrence: Average rate over life of vehicle
Frequency Rates

1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010+ LHDT Timing Advanced 5% 2% 2% 2% 2% 2% Timing Retarded 3% 2% 2% 2% 2% 2% Injector Problem (all) 28% 28% 13% 13% 13% 13% Puff Limiter Mis-set 4% 0% 0% 0% 0% 0% Puff Limiter Disabled 4% 0% 0% 0% 0% 0% Max Fuel High 3% 0% 0% 0% 0% 0% Clogged Air Filter - EPA 8% 8% 8% 8% 8% 8% Wrong/Worn Turbo 5% 5% 5% 5% 5% 5% Intercooler Clogged 5% 5% 5% 5% 5% 5% Other Air Problem - EPA 6% 6% 6% 6% 6% 6% Engine Mechanical Failure 2% 2% 2% 2% 2% 2% Excessive Oil Consumption 5% 3% 3% 3% 3% 3% Electronics Failed - EPA 3% 3% 3% 3% 3% 3% Electronics Tampered 10% 15% 5% 5% 5% 5% EGR Stuck Open 0% 0% 0.2% 0.2% 0.2% 0.2% EGR Disabled/Low Flow - EPA 0% 0% 10% 10% 10% 10% Nox Aftertreatment Sensor 0% 0% 0% 0% 10% 10% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 1% 1% Nox Aftertreatment Malfunction - EPA 0% 0% 0% 0% 13% 16% PM Filter Leak 0% 0% 0% 5% 5% 5% PM Filter Disabled 0% 0% 0% 2% 2% 2% Oxidation Catalyst Malfunction/Remove - EPA 0% 0% 0% 5% 5% 5% Mis-fuel - EPA 0.1% 0.1% 0.1% 0.1% 0.1% 0.1%77 The values with 0% effect in shaded cells represent areas which have no occurrence rate.

PM Emission Effects EPA developed the PM emission effects from each tampering and mal- maintenance incident from CARB’s EMFAC, Radian’s dynamometer testing with and without the malfunction present, EFEE results, and internal testing experience.

EPA estimates that the PM filter has 95% effectiveness. Many of the tampering and mal

maintenance items that impact PM also have a fuel efficiency and drivability impact. Therefore, operators will have an incentive to fix the se issue s.
E PA estimated that excessive oil consumpt ion will have the same level of impact on PM as engine mechanical failure. The failure of the oxidation catalyst is expected to cause a PM increase of 30%; however, this value is reduced by 95% due to the PM filter effectiveness. We also considered a DOC failure will cause a secondary failure of PM filter regeneration. We accounted for this PM increase within the PM filter disabled and leak categories. The values with 0% effect in shaded cells represent areas which have no occurrence rate. Tamper & Malmaintenance NOX Emission Effect
1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010 LHDT Federal Emission Standard 5.0 5.0 4.0 2.0 0.2 0.2

Timing Advanced 60% 60% 60% 60% 6% 12% Timing Retarded -20% -20% -20% -20% -20% -20% Injector Problem (all) -5% -1% -1% -1% -1% -1% Puff Limiter Mis-set 0% 0% 0% 0% 0% 0% Puff Limiter Disabled 0% 0% 0% 0% 0% 0% Max Fuel High 10% 0% 0% 0% 0% 0% Clogged Air Filter 0% 0% 0% 0% 0% 0% Wrong/Worn Turbo 0% 0% 0% 0% 0% 0% Intercooler Clogged 25% 25% 25% 25% 3% 5% Other Air Problem 0% 0% 0% 0% 0% 0% Engine Mechanical Failure -10% -10% -10% -10% -10% -10% Excessive Oil Consumption 0% 0% 0% 0% 0% 0% Electronics Failed 0% 0% 0% 0% 0% 0% Electronics Tampered 80% 80% 80% 80% 8% 16% EGR Stuck Open 0% 0% -20% -20% -20% -20% EGR Disabled / Low Flow 0% 0% 30% 50% 5% 10% Nox Aftertreatment Sensor 0% 0% 0% 0% 200% 200% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 200% 200% Nox Aftertreatment Malfunction 0% 0% 0% 0% 500% 300% PM Filter Leak 0% 0% 0% 0% 0% 0% PM Filter Disabled 0% 0% 0% 0% 0% 0% Oxidation Catalyst Malfunction/Remove 0% 0% 0% 0% 0% 0% Mis-fuel78 HC Emissio n Effects EPA estimated oxidation catalysts are 80% effective at reducing hydrocarbons. All manufacturers will utilize oxidation catalysts in 2007, but only a negligible number were installed prior to the PM regulation reduction in 2007.

We reduced CARB’s HC emission effect for timing advanced because earlier timing should reduce HC, not increase them. The effect of injector problems was reduced to 1000% based on internal experience. We increased the HC emission effect of high fuel pressure to 10% because the higher pressure will lead to extra fuel in early model years and therefore increased HC. Lastly, we used the HC emission effect of advanced timing for the electronics tampering since this was the most significant type of tampering that occurred.

The values with 0% effect in shaded cells represent areas which have no occurrence rate. Tamper & Malmaintenance PM Emission Effect
1994-97 1998-2002 2003-2006 2007-2009 2010 Federal Emission Standard 0.1 0.1 0.1 0.01 0.01

Timing Advanced -10% -10% -10% 0% 0% Timing Retarded 25% 25% 25% 1% 1% Injector Problem 100% 100% 100% 5% 5% Puff Limiter Mis-set 20% 0% 0% 0% 0% Puff Limiter Disabled 50% 0% 0% 0% 0% Max Fuel High 20% 0% 0% 0% 0% Clogged Air Filter 50% 50% 30% 2% 2% Wrong/Worn Turbo 50% 50% 50% 3% 3% Intercooler Clogged 50% 50% 30% 2% 2% Other Air Problem 40% 40% 30% 2% 2% Engine Mechanical Failure 500% 500% 500% 25% 25% Excessive Oil Consumption 500% 500% 500% 25% 25% Electronics Failed 60% 60% 60% 3% 3% Electronics Tampered 50% 50% 50% 3% 3% EGR Stuck Open/Low Flow 0% 0% 100% 5% 5% EGR Disabled 0% 0% -30% -30% -30% Nox Aftertreatment Sensor 0% 0% 0% 0% 0% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 0% Nox Aftertreatment Malfunction 0% 0% 0% 0% 0% PM Filter Leak 0% 0% 0% 600% 600% PM Filter Disabled 0% 0% 0% 1000% 1000% Oxidation Catalyst Malfunction/Remove 0% 0% 0% 2% 2% Mis-Fuel 30% 30% 30% 100% 100%79 A separate tampering analysis was not performed for CO; rather, t he HC effects were assumed to apply for CO. Combining all of the emissions effects and failure frequencies discussed in this section, we summarized the aggregate emissions impacts over the useful life of the fleet due to in the main body of the document in Ta ble 11 (NOx), Table 17 (PM), and Table 21 (HC and CO).

HD OBD impacts With the finalization of the heavy-duty onboard diagnostics (HD OBD) rule, we made adjustments to our draft 2010 and later model year to reflect the rule’s implementation.Specifically, we reduced our emissions increases for all pollutants due to tampering and malmaintenance by 33%. As data are not yet available for heavy -duty trucks equipped with OBD, this number is probably a conservative estimate. Still, PM and NO x reductions from 2010 and later model year vehicles will be substantial compared to prior model years regardless of the additional incremental benefit from OBD. We assumed, since the rule phases in OBD implementation, that 33% of all engines will have OBD in 2010, 2011, and 2012 model years, and 100% will have OBD by 2013 model year and later. Equation 29 describes the calculation of TM pol , the increase in emission rate through useful life, where f OBD represents the fraction of the fleet equipped with OBD (0% for model years 2009 and earlier, 33% for model years 2010-2012, and 100% for model years 2013 and later). The result from this equation can be plugged into Equation 28 to determine the emission rate for any age group.



## ( ) OBD nonOBD pol OBD nonOBD pol pol 

### f TM f TMTM

, , 

### 67.01 ⋅+− = Equation 29 Tamper & Malmaintenance HC Emission Effect
1994-97 1998-2002 2003-2006 2007-2009 2010+ HHDT 2010 LHDT Federal Emission Standard 1.3 1.3 1.3 0.2 0.14 0.14

Timing Advanced 0% 0% 0% 0% 0% 0% Timing Retarded 50% 50% 50% 50% 10% 10% Injector Problem (all) 1000% 1000% 1000% 1000% 200% 200% Puff Limiter Mis-set 0% 0% 0% 0% 0% 0% Puff Limiter Disabled 0% 0% 0% 0% 0% 0% Max Fuel High 10% 0% 0% 0% 0% 0% Clogged Air Filter 0% 0% 0% 0% 0% 0% Wrong/Worn Turbo 0% 0% 0% 0% 0% 0% Intercooler Clogged 0% 0% 0% 0% 0% 0% Other Air Problem 0% 0% 0% 0% 0% 0% Engine Mechanical Failure 500% 500% 500% 500% 100% 100% Excessive Oil Consumption 300% 300% 300% 300% 60% 60% Electronics Failed 50% 50% 50% 50% 10% 10% Electronics Tampered 0% 0% 0% 0% 0% 0% EGR Stuck Open 0% 0% 100% 100% 20% 20% EGR Disabled / Low Flow 0% 0% 0% 0% 0% 0% Nox Aftertreatment Sensor 0% 0% 0% 0% 0% 0% Replacement Nox Aftertreatment Sensor 0% 0% 0% 0% 0% 0% Nox Aftertreatment Malfunction 0% 0% 0% 0% 0% 0% PM Filter Leak 0% 0% 0% 0% 0% 0% PM Filter Disabled 0% 0% 0% 0% 0% 0% Oxidation Catalyst Malfunction/Remove 0% 0% 0% 50% 50% 50% Mis-fuel80 As data for current and future m odel years become available

, we may consider refin ing these estimate s and methodology.81 

## A. 3 Extended Idle Data Summary

Idle HC Rates (gram/hour) Summary Program Condition # Samples Mean HC Emiss Rate 1991-2006 Low Speed Idle, A/C Off - HDT McCormick, High Altitude, HDT Low Idle, AC Off 12 10.2 WVU - 1991-2004 Low Idle, AC Off 48 9.5 Storey Low Idle, AC Off 4 28 Overall 64 10.8

1991-2006 High Speed Idle, A/C On - HDT Broderick UC Davis High Idle, AC On 1 86 Storey High Idle, AC On 4 48 Overall 5 55.6

1975-1990 MY Low Speed Idle, A/C Off - HDT Program Condition Samples Mean WVU - 1975-1990 Low Idle, AC Off 18 21 Overall 18 21.0

1991-2006 MY Low Speed Idle, A/C Off - Bus Program Condition Samples Mean McCormick, High Altitude, Bus Low Idle, AC Off 12 8.2 Overall 12 8.282 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 79.683 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 2.984 2007 Extended Idle Emissions calculation:

• Assumed 8 hour idle period where the emissions controls, such as EGR, oxidation catalyst, and NOx aftertreatment, are still active for the first hour.

• HC emissions standards:

o Pre -2007: 0.50 g/bhp-hr

o 2007: 0.14 g/bhp-hr

• NOx emissions standards:

o Pre -2010: 5.0 g/bhp-hr Idle Nox Rates (gram/hour) Summary Program Condition # Samples Mean NOX Emiss Rate 1991-2006 Low Speed Idle, A/C Off McCormick, High Altitude, HDT Low RPM, AC Off 12 85 Lim, EPA Low RPM, No access 12 109 Irick, Clean Air Tech & IdleAire 49 87 WVU - 1991-2004 Low RPM, AC Off 48 83 WVU, NCHRP 2 47 Tang, Metro NY, 1984-1999 33 81 Calcagno Low RPM, AC Off 27 120 Broderick UC Davis Low RPM, AC Off 1 104 Storey Low RPM, AC Off 4 126 Overall 188 94

1991-2006 High Speed Idle, A/C Off Lim, EPA CCD High RPM, No access 5 169 Calcagno High RPM, AC Off 21 164 Overall 26 165

1991-2006 High Speed Idle, A/C On Lim, EPA CCD High RPM, AC On 5 212 Broderick UC Davis High RPM, AC On 1 240 Calcagno High RPM, AC On 21 223 Storey High RPM, AC On 4 262 Overall 31 227

1975-1990 MY Low Speed Idle, A/C Off Program Condition Samples Mean WVU - 1975-1990 Low RPM, AC Off 18 48 Lim, EPA CCD, 1985 MY Low RPM, AC Off 1 20 Overall 19 47

1991-2006 MY Low Speed Idle, A/C Off - Bus Program Condition Samples Mean McCormick, High Altitude, Bus Low Idle, AC Off 12 121 Overall 12 121.085 o 2010: 0.2 g/bhp-hr

Idle HC Rate Reduction = 1 - [(1/8 * 0.14 g/bhp-hr + 7/8 * 0.5 g/bhp-hr) / 0.5 g/bhp-hr] = 9% Idle NOx Rate Reduction = 1 - [(1/8 * 0.2 g/bhp-hr + 7/8 * 5.0 g/bhp-hr) / 5.0 g/bhp-hr] = 12%86 

## A. 4 D evelop ing PM emission rates for missing operating mode s
In cases where an estimated rate could not be dire ctly calculated from data, we imputed the missing value using a log- linear least

- squares regression procedure. Regulatory class, model year group and speed class (0 –25 mph, 25-50 mph and 50+ mph ) were represented by dummy variables in the regression. The natural log arithm of emissions was regressed versus scaled tractive power ( STP ) to represent the operating mode bins. The regression assumed a constant slope versus STP for each regulatory class. Log arithmic transformation factors (mean square error of the regression squared / 2) were used to transform the regression results from a log based form to a linear form. Due to the huge number of individual second- by -second data points, all of the regression relationships were statistically significant at a high level (99% confident level). The table below shows the regression statistics, and the equation shows the form of the resulting regression equation. Regression Coefficients for PM Emission Factor Model

Model

- year group Speed Class (mph) Type Medium Heavy

- Duty Heavy Heavy

Duty 1960

- 87 1

- 25 Intercept ( β 0 )

- 5.419

- 5.143 25

- 50

- 4.942

- 4.564 50+

- 4.765

- 4.678 1988

- 90 1

- 25

- 5.366

- 5.847 25

- 50

- 4.929

- 5.287 50+

- 4.785

- 5.480 1991

- 93 1

- 25

- 5.936

- 5.494 25

- 50

- 5.504

- 5.269 50+

- 5.574

- 5.133 1994

- 97 1

- 25

- 5.927

- 6.242 25

- 50

- 5.708

- 5.923 50+

- 5.933

- 6.368 1998

- 2006 1

- 25

- 6.608

- 6.067 25

- 50

- 6.369

- 5.754 50+

- 6.305

- 6.154 STP Slope ( β 1 ) 0.02821 0.0968

Transformation Coefficient (0.5σ 2 ) 0.5864 0.84035

2
10 

### 5.0STP )PM ln( σ ββ ++=

Where : β 0 = an intercept term for a speed class within a model year group, as shown in the table above, β 1 = a slope term for STP , and

σ 2 = the mean

- square error or r esidual error for the model fit,

STP = the midpoint value for each operating mode ( kW/metric ton?, see Table 9 , page 11 ).87 

## A. 5 Heavy

- duty Diesel EC/OC Fraction Calculation

A. 5 .1 Introduction
This memo describes the development and application of a “rough cut” emission model for estimating elemental and organic carbonaceous material (EC and OM) emission rates (or EC/OM ratios) from MOVES. The memo describes the following steps involved in predicting EC/OM ratios. The memo also briefly describes comparisons with independent emission data collected using the “Mobile Emission Laboratory,” Operate d by the University of California Riverside. The subsequent sections of the memo describe the following topics:

• the extension of Physical Emission Rate Simulator ( PERE ) to estimate heavy -duty fleet

- average emission factors for any specified driving cycle;

• the acquisition of data used in estimating EC/OC rates as a function of engine operating mode and the fitting of simple empirical models to them;

• the application of PERE to estimate EC and OC emission rates for different test cycles; and,

• the comparison o f PERE -based EC and OC emission rates to those measured by independent researchers in HD trucks.



## A. 5 .2 PERE for Heavy

- duty Vehicles (PERE

- HD) and Its Extensions

The Physical Emission Rate Estimator (PERE ) is a model employed by EPA in earl y development of MOVES. 45 In particular, the MOVES team employed it in development of MOVES2004 to impute greenhouse gas emission rates for combinations of SourceBin and Op erating Mode for which data was unavailable or of insufficient quality. The underlying theory behind PERE and its comparison with measured fuel consumption data is described by Nam and Giannelli (2005). 45 Briefly, PERE estimates fuel consumption and emission rates on the basis of fundamental physical and mathematical relationships describing the road load that a vehicle meets when driving a particular speed trace. Accessory loads are handled by addition of an accessory power term. In the heavy -duty version of PERE (hereafter, “PERE- HD” ), accessory loads were described by a single value. For the current project, PERE was modified to incorporate several “extensions” that allowed it to estimate fleet

- avera ge 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 -l evel descriptors (model year, running weight, track road

- load coefficients ( A

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

• engine parameters (fuel type, displacement); and

• driving cycle (expressed through a speed trace).88 The specification of these inputs allows PERE to model the engine operation, fuel consumption, and GHG emissions for a HDV on a specified driving cycle. However, the baseline PERE-HD provides output for only one combination of these parameters at once. To estimate fleet

- wide average a l arge number of PERE-HD runs would be required. Furthermore, the specification of only fleet- wide average coefficients is likely to substantially underestimate variability in fuel consumption and emissions. Emissions data from a large number of laboratory and field studies suggest that a very large fraction of total emissions from all vehicles derives from a small fraction of the study fleet. Therefore, it is desirable to develop an approach that comes closer to spanning the range of likely combinations of inputs than using a small selection of “ average” or “typical” values. For the current application, PERE- HD ( built within Microsoft Excel ) was expanded to allow for a representative sample of [running weight] × [engine displacement] × [model year] combinations. A third

- party add -on package to Excel, @Risk 4.5 (Palisade Corporation, 2004), allows users to supplement deterministic inputs within spreadsheet models with selected continuous probability distributions, sample input values from each input distribution, and re-run the spreadsheet model with sets of selected inputs over a specified number of iterations. This type of procedure is commonly referred to as “Monte Carlo” simulation.

A. 5.2.1.1 Monte Carlo Simulation in PERE- HD To illustrate how @Risk performs this process, we illustrate the application of a simple model, employing both deterministic calculations and stochastic Monte Carlo simulation :

2 

### L M BMI =

This equation defines the body mass index for humans, a simple surrogate indicating overweight and underweight conditions. According to the Centers for Disease Control and Prevention (CDC), the average U.S. woman weighed 164.3 lb (74.5 kg) in 2002 and was 5’4” (1.6 m) tall. This result corresponds to a BMI of 28, suggesting that the average U.S. woman is overweight. While this is useful information from a public health perspective, it does not provide any indication as to which individuals are likely to experience the adverse effects of being overweight and obese. However, if

we were to assume (arbitrarily) that the range of weight and height within the U.S. population was +/ -50% of the mean, distributed uniformly, and perform a Monte Carlo simulation (5,000 iterations) using @Risk, we would predict a probability distribution of BMI in the population as follows:89 In contrast, here is the BMI distribution in the entire U.S. population, according to the CDC’s National Health and Nutrition Examination Survey (NHANES):

These graphs illustrate how Monte Carlo simulation can be used to provide meaningful information about the variability in a population. Although the model example is very simple, it illustrates the point that a model with “ typical ” inputs provides much less information than does Monte Carlo simulation with variable inputs

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

A. 5.2.1.2 Model Year Model year is an important factor in PERE, as the frictional losses in the model , expressed as “friction mean effective press ure” (FMEP)

, vary by model year, improving with later model years. As such, model year was simulated as a probability distribution, based on data from the Census Bureau’s 1997 Vehicle Inventory and Use Survey (VIUS), which reports “vehicle miles traveled” ( VMT ) by model year . According ly these data were normalized to total VMT to develop a 

### Distribution of BMI in Simulated Population% of SimulatedPopulation

BMI 0.000 0.010 0.020 0.030 0.040 0.050 0.060

10 20 30 40 5090 probability distribution. Model year distributions in 1997 were normalized to the current calendar year (2008). 1 For instance, the fraction of 1996 vehicles reported in the 1997 VIUS is treated as the fraction of 2002 vehicles in the 2003 calendar year. Although a 2002 VIUS is available, previous analyses (unpublished) have shown the “relative” model year distribution of trucks to have changed little between 1997 and 2002, though this assumption is one limitation of this analysis. The model year distribution for PERE- HD was represented as a discrete probability distribution, as shown below:

A. 5.2.1.3 Vehicle Weight and Engine Displacement Vehicle running weights and engine displacements were modeled as a two -way probability distribution with engine displacement depending on running weight. These data were derived from VIUS microdata obtained from the Census Bureau. 46 A two

- way table was constructed to estimate VMT classified by combinations of [weight class] × [displacement class]. Analyses were restricted to diesel -powered trucks only. As a first step , @Risk selects a running weight from a probability distribution representing the fraction of truck VMT occurring a t a given running weight:

1 VIUS reports model years 11 years old and greater as a single number. For the current analysis, the fraction of vehicles within each model year older than 10 years of age through 25 years was estimated using an exponential decay of the form p(x) = A *exp[

- B *(x

- 10)]. Coefficients representing the A and B parameters were estimated by minimizing least squares of the residuals. The sum of probabilities for model years older than 10 years was constrained the fraction of VMT driven by trucks older than 10 year s in VIUS. Probability and Cumulative Probability Distributions of Model Years in PERE-HD

0 0.02 0.04 0.06 0.08 0.1 0.12

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Relative Age (Calendar Year - Model Year)Probability

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1Cum. Probability

Probability by Age Cumulative Probability by Age91 Because V IUS reports classes defined as ranges in running weight, any value of weight within each V IUS

- specified class was considered equally likely and modeled as a uniform probability distribution within the class . For the up per and lower bounds of the distribution the minimum and maximum running weights were assumed to be 7,000 and 240,000 lb, respectively. After @Risk selects a running weight, it selects an engine displacement based on a discrete distribution assigned to eve ry weight class in VIUS, represented below:

Again, because VIUS describes ranges of values for displacement, all values within each range were given uniform weight and assigned a uniform distribution. For the extreme classes , the minimum and maximum eng ine displacements were assumed to be 100 in 3 and 915 in 3

, respectively. Probability Distribution of Vehicle Running Weight based on VIUS

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4[0,15000)[15000,30000)[30000,45000)[45000,60000)[60000,75000)[75000,90000)[90000,105000)[105000,120000)[120000,135000)[135000,150000)[150000,165000)[165000,180000)[180000,195000)[195000,210000)[210000,225000)[225000,240000)[240000,)

Weight RangeProbability

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

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%150000 and under15001 to 3000030001 to 4500045001 to 6000060001 to 7500075001 to 9000090001 to 105000105001 to 120000120001 to 135000135001 to 150000150001 to 165000165001 to 180000180001 to 195000195001 to 210000210001 to 225000225001 to 240000240000 and over

Running WeightCumulative 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

1-24992 This procedure reflects the range in running weights present among HDV in operation, and constrains the combinations of weight and displacement to plausible pairs of values based on surveyed truck operator responses. Th ese steps allow for plausible variability in weight

- engine pairings, which translates into differences in engine parameters influencing EC and OC emissions. For use in PERE-HD, all units were converted to SI units (kg and L ).

A. 5.2.1.4 Accessory Load The original PERE- HD treats accessory load as a fixed value, which may be varied by the user. It is set at 0.75, and used in calculating fuel rate and total power demand at each second of driving. Following the development of PERE

- HD, a more detailed set of accessory load estimates was developed based on several accessories’ power demand while in use and the fraction of time each
accessory is in use (see Table 6 ). 47 High, medium, and low accessory use categories were estimated for three vehicle classes: HDT, MDT, and buses. For the current version of the model, only the HDT accessory load estimates were employed, though a sensitivity analysis indicated that mean EC/OM ratios were most sensitive to accessory load during idle and creep driving cycles. In the “base case,” a mean ratio of 0.54 was predicted, while in the sensitivity case, a mean ratio of 0.50 was predicted. This issue may be revisited at some point, al 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, P acc for accessory use was substituted with a variable representing the distribution (in time) of accessory loads as estimated as the sum of a number of discrete probability distributions. Depending on the assumption of high, medium or low use, the power demand for these accessories is distributed in time as follows:

A. 5.2.1.5 Driving Cycle For purposes of this exercise, the four phases of the California Air Resources Board’s Heavy Heavy

- Duty Diesel Truck (HHDDT) chassis dynamometer testing cycle were used to reflect

var iability in vehicle operations for PERE- HD. 

### Comparison of High / Medium / Low Accessory Load Cases

0.000 0.200 0.400 0.600 0.800 1.000P8P13P18

0 10 20 3093 A. 5.2.1.6 Other Factors Some elements of variability were not examined as part of this study. Hybrid- electric transmissions and fuel cell power plants were excluded from the analysis, due to their low prevalence within the current truck fleet. One important source of variability that was not examined in this analysis is the variation in resistive forces among vehicles with identical running weights. This exclusion is important, given the potential role for aerod ynamic 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 f uel

saving technologies.



### A. 5 .2.2 Prediction of Elemental Carbon and Organic Mass based on PERE

- HD

A. 5.2.2.1 Definition of Elemental and Organic Carbon and Organic Mass In motor vehicle exhaust, the terms “EC,” “elemental carbon,” and “black carbon” ref er 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. Fu el injectors spray aerosolized fuel into the cylinder during the compression stroke. The high- pressure and high temperature during the cylinder cause spontaneous ignition of the fuel vaporizing from the injected droplets. Because temperature can rise more quickly tha n oxygen can diffuse to the fuel at the center of e ach 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 “o rganic 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, incl uding C15 to C44 alkanes, polycyclic aromatic hydrocarbons, lub ricating oil constituents (hopanes, steranes, and carpanes), and a sizeable fraction of uncharacterized material. This component of exhaust can derive from numerous processes inside the engine involving both fuel and oil. Because of the complex chemical mixture that comprises this mass, its measurement is highly dependent on sampling conditions. The wide range of organics that compose it undergo evaporation and condensation at different temperatures, and the phase-partitioning behavior of each molecule is dependent on other factors, such as the sorption of vapor- phase organics to available surface area in a dilution tunnel or background aerosol.94 A. 5.2.2.2 EPA Carbon Analysis Techniques in Am bient Air The definitions of EC and OM are critical, as different groups use different techniques for quantifying their concentrations within a given medium. For purposes of this document , it is assumed that EC, OC, and OM are operationally defined quanti ties, meaning that they are defined by the measurement technique used to quantify their concentrations on a filter or in air. The different types of commonly used approaches for carbon include:

• Thermal/ optical techniques, where the evaporation and oxidation of carbon are used in conjunction with a laser to measure optical properties of a particle sample. The major methods used for this type of analysis include:

o Thermal/optical reflectance (TOR)

. EPA is adopting this technique for the PM 2.5 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 atmo sphere is first employed within the oven, and the evolved carbon is measured with a FID as temperatures are increased in steps up to 580°C. All carbon evolved in this way is assumed to be volatilized organic material. Next, 2% oxygen gas is added to the atmosphere, and temperatures are stepped up a number of times to a maximum of 840°C. All carbon evolved after the introduction of oxygen is assumed to be elemental carbon. The reflection of light from a laser by the filter is employed to account for the pyrolysis of organic carbon that occurs during the warm-up
process.

o Thermal/optical transmission (TOT). The National Institute of Occupational Safety and Health (NIOSH) uses this technique for measuring EC concentrations in occupational environments. It is based on similar principles to TOR, but employs a different heating schedule and transmission of light as opposed to reflectance.

• Radiation absorption techniques

o Aethalometer ® – This instrument reports “black carbon” (BC) concentrations based the extent of light absorption by a “filter tape,” that allows for a time series of BC concentrations to be estimated. It has a time resolution of several minutes.

o Photoacoustic Spectrometer (PAS) – This instrument irradiates an air sample with a laser. The resulti ng 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 exh aust.

• Thermogravimetric techniques, where the “volatile organic fraction” (VOF) is separated by heat from the non- volatile refractory component of a particle sample.

• Chemical extraction, where solvents are used to separate the soluble and insoluble components of exhaust.95 A number of additional techniques are also described in the published literature, but the above techniques have been most commonly applied in emissions and routine ambient PM measurement

. Among the available techniques, it has been a point of controversy among academics as to wh ich method provides the “correct” carbon signal. Rather than addressing these arguments in detail, this analysis adopts the technique employed by the EPA ambient speciation monitoring network, TOR. Needless to say, different researchers employ different sampling, measurement and analysis techniques. Desert Research Institute (DRI) employed TOR in analyzing the Kansas City gasoline PM emission study samples [cite?] , while other prominent academics employ TOT, notably the University of California Riverside College of Engineering Center for Environmental Research and Technology (CE-CERT) and the University of Wisconsin- Madison (UWM) State Hygiene Laboratory. As r esearch results from these groups is employed throughout this analysis , an intercomparison of the methods of TOT/TOR is necessary to “recalibrate” various datasets with respect to each other

. EPA defines measurement techniques for dynamometer -based sampling and analysis of pa rticulate matter, in addition to techniques for sampling and analyzing particles in ambient air. Inventories estimated for EC and OM can be considered to reflect both broad categories of measurement techniques, depending on context. The user community for MOVES is predominantly concerned with emissions that occur into ambient air. EPA regulations for demonstration of attainment of state implementation plans (SIPs) are based on monitored ambient particulate matter using Federal Reference Methods (FRM) for ambient air. FRM monitors for particle speciation in ambient air undergo analysis for EC and OC according to a defined standard operating procedure. 48 That standard operating procedure defines thermal/optical reflectance (TOR) as the desired method for analysis of ambient carbon PM.

A. 5.2.2.3 TOR – TOR Calibration Curve

In the course of the Gasoline/Diesel PM Split Study funded by the Department of Energy (DOE), researchers from DRI analyzed filter samples using both TOR and TOT methods [cite] . These da ta were obtained and analyzed in the SPSS 9.0 statistical package. Briefly , the DOE study included emissions characterizations of 57 light-duty gasoline vehicles (LDGV) and 34 HD diesel vehicles (HDDV). The vehicles were operated on a number of different test cycles including cold

- start and warm

- start cycles. The data set employed in this study was generated by DRI and obtained from the DOE study web site. 49 Both EC and OC were analyzed using the same approach. All data from all vehicles were compiled.

Fi rst, 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) o n the range [0,1]. Those cases with RAND ≥ 0.95 were set aside as a cross-validation data set, and excluded from additional regression analyses

. Third, those cases with RAND < 0.95 were regressed again, this time using an inverse uncertainty weighting procedure for each data point. When DRI analyzes a filter sample, it reports an analytical uncertainty associated with the primary estimate of EC and OC. Accordingly, the quality of each datum depends on the level of analytical uncertainty reported. The inverse of the DRI

-96 reported uncertainty (1/ σ ) associated with the TOR

- based measurement was used to weight each point in the weighted regression. It should be noted that for each regression, the intercept term was set to zero. Models including intercepts did not have intercept terms that reached statistical significance. As such, R 2 values are not considered valid.

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

Slope Beta St d. Error t- value Sig.

EC

- TOR 1.047 0.011 91.331 <0.0001

OC

- TOR 1.014 0.007 153.923 <0.0001

To evaluate the quality of predictions resulting from these statistically -based adjustment factors, they were used to predict EC- TOR and OC -TOR values for the subset of data with RAND ≥ 0.95. Scatter plots of the statistical fits are illustrated below (note logarithmic scaling).

EC-TOR Predicted 1000 500 400 300 200 100 50 40 30 20 10 5 4 3 2 1 .5 .4 .3 .2 .1EC-TOR Measured 1000

500 400 300 200

100

50 40 30 20

10

5 4 3 2

1

.5 .4 .3 .2 .197 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 s data derived from DRI as part of the DOE Gasolin e / Diesel PM Split Study are general enough to apply to EC -TOT measurements obtained by other research groups.

A. 5.2.2.4 EC and OC Emission Rates

Selection of Engine Parameters for Predictive Modeling

PERE

- HD produces estimates of engine operating conditi ons 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 particu lar operating mode (opMode ID ). OC TOR-Predicted 1000 500 400 300 200 100 50 40 30 20 10 5 4 3 2 1 .5 .4 .3 .2 .1OC - TOR Measured 1000

500 400 300 200

100

50 40 30 20

10

5 4 3 2

1

.5 .4 .3 .2 .198 It should be noted that continuous (“second- by -second, or “real time” ) measurement of EC and OM is an exceptionally complicated endeavor. While measurement techniques for EC have been developed that produce apparently good correlation with traditional filter -based methods, While numerous publications report the EC and OM (or OC) exhaust emission rates across an entire driving cycle, it is not clear which parameter of a particular driving cycle, such as average speed (or p ower) , might be applicable to the extrapolation of the observed rates to other vehicles or driving conditions. As a result, identifying one or more engine parameter s that explain the observed variation in driving cycle-based emission rates for EC and OM is desirable. Such parameter (s) will assist in estimating emission associated with short

- term variations in driving. One good candidate for establishing an engine- based emission model is mean effective pressure (MEP). MEP is defined as:



### NV Pn MEP

d R 

### =

Here, P is the power (in kW or hp), n R is the number of crank revolutions per power stroke per cylinder (2 for four -stroke engines, 1 for two- strokes), V d 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 BME P is an indicator of good engine design and “essentially constant over a wide range of engine sizes. [cite] ” Nam and Giannelli (2004) note that it can be related to fuel MEP multiplied by the indicated or thermal efficiency of an engine, and have developed trend lines in FMEP by model year. As such, since maximum BMEP is comparable across well

- designed engines and FMEP can be well

- predicted by Nam and Gian nelli’s trends within PERE, IMEP should be an appropriate metric for building an engine emission model that can be applied across vehicles with different loads and engine displacements.

Emission Data Kweon et al. (2004) measured particle composition and mass emission rates from a single-cylinder research engine based on an in -line 2.333 liter turbo- charged direct

- injection six cylinder Cummins N14 -series engine, with a quiescent, shallow dish piston chamber and a quiescent combustion chamber. Emission data were obtained from all eight modes of the CARB 8- mode engine test cycle:

Mode 1 Mode 2 Mode 3 Mode 4 Mode 5 Mode 6 Mode 7 Mode 8

Speed 1800 1800 1800 1200 1200 1200 1200 700

Load% 100 75 50 25 100 75 50 10 (idle) Equiv. Ratio (φ) 0.69 0.50 0.34 0.21 0.82 0.69 0.41 0.09

IMEP (MPa) 1.083 0.922 0.671 0.524 1.491 1.225 0.878 0.15099 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 (R 2 = 0.96). As such, it is reasonable to report the data as a function of IMEP, expecting it to have approximately equal explanatory power as has the equivalence ratio variable. The figure below plots the emission data from Kweon et al. (2002) as a function of IMEP.

As shown in the figure, the EC- TOT work

- specific emission rate is relatively insensitive to IMEP except between IMEP of approximately 0.85 and 1.1, where it undergoes a rapid i ncrease. Overall, the EC

- TOR/IMEP curve is S -shaped, similar to a logistic curve or growth curve. OC- TOT work

specific emissions are highest at low IMEP (i.e. idle) and are monotonically lower with higher IMEP. Total work -specific PM2.5 is not monotonic, but appears to be described by a single global minimum around IMEP ~ 0.9 and two local maxima around IMEP of 0.2 and 1.2, respectively. The oppositely signed slopes of the emission- IMEP curves for EC

- TOT and OC

- TOT suggest that there are different underl ying physical processes. It is not the intent of this document to explicitly describe the particle

- formation mechanisms in a diesel engine. However, the use of two separate functions to predict EC- TOT and OC

- TOT separately is warranted. This implies that the EC/OC ratio will vary by engine operating mode. The following figure depicts the EC/OC ratio as a function of IMEP. Particle Mass, EC-TOT, and OC-TOT vs. IMEP

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

IMEPSpecific emission (g/IHP-HR)

OC-TOT
EC-TOT

PM2.5100 Estimation of IMEP -based Emissions of EC and OC To produce a relationship that generalizes the implied relationship between EC- TOT and OC- TOT work -specific emissions and IMEP in the data presented by Kweon et al. (2004), it is necessary to specify some functional form of a relationship between the two. A priori , on the basis of visual inspection of the data, a flexible logistic- type curve was fit to the data by a least -squares minimization procedure using the Microsoft Excel “Solver” tool, which employs the GRG2 optimization approach. The functional form of the logistic -type curves fit to both the EC- TOT and OC

- TOT data from Kweon et al. (2004) is as follows:



### Ce A Y Bx 

### + =

−
A least

- squared error approach was implemented within Microsoft Excel to derive the coefficients for the logistic curves for EC- TOT and OC -TOT. The solutions to the fits are as follows:

Y A B C

EC

- TOT 2.12 × 10 -5 -9.79 4.67× 10 -5

OC

- TOT 0.155 -2.275 -0.859
Graphically, in comparison to observed values of EC- TOT and OC

- TOT, the fitted curves result in predictions reasonably close to the observed values. Furthermore, when compared to the observed PM2.5 values, the sum of predicted EC- TOT and OC -TOT values predict the lack of monotonicity and patterns of maxima and minimum seen in the PM2.5 data. However, as a result of the values predicted by these sigmoid -type curves at high and low IMEP values, extreme patterns in the EC- TOT/OC

- TOT ratios predicted occur. These extreme values are artifacts that result solely from the behavior of simplistic logistic curves at the bounds of IMEP in EC/OC Ratio vs. IMEP from Kweon et al. (2004) Note Logarithmic Scale

0.1

1 10 100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

IMEPEC/OC101 the observed data sets. As a result, for predictive purposes, the maximum and minimum observed EC

- TOT and OC

- TOT values observed in the data set were set as the artificial limits of predicted EC

- TOT and OC

- TOT, respectively. While this approach is arbitrary, it does ensure that extreme predictions resulting from the selection of the logistic functional form do not occur. The following graph (log- scale) depicts the behavior of the TOT

- based EC/OC ratio as a function of IMEP. As demonstrated on the graph, without the max/min constraints on predicted EC- TOT and OC-TOT, the predicted ratio assumes values with a much broader range than found in the data.

The approach of constraining predictions to the maximum and minimum values observed in the measured data set is not grounded in any theoretical basis, but is a “brute force” appr oach. Future revisions to this analysis may consider alternative approaches more grounded in accepted theoretical or statistical methodology. The logistic curves described above receive IMEP predictions from PERE to predict EC

- TOT and OC

- TOT emission rate s (g/bhp-hr) for every second of a driving cycle. Combined with real- time work estimates from PERE, emissions are expressed in g/s, the same units required for MOVES.

EC

- TOT and OC

- TOT emission rates are converted to TOR -equivalent rates for use in MOVES, using the TOT-TOR “calibration” relationships described above. Alternatively, TOT-equivalent rates can be used to compare with data from studies employing TOT for carbon analysis. It should be noted that these emission estimates are based on a single engine. Therefore, predictions of EC and OC emission rates based on these relationships are insensitive to model year, although PERE-HD does vary frictional MEP as a function of model year.

Organic Carbon to Organic Mass Conversion Carbon is only one component of the organic material found in PM emission samples. Hydrogen, oxygen, and nitrogen are also components of organic molecules found in exhaust PM. For this study, a simple set of OC/OM conversion ratios were employed. Comparison of EC/OC Ratio (TOT) by IMEP

With and Without Max/Min Constraints

0.01 0.1

1 10 100

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

IMEPEC/OC Ratio (TOT) EC/OC(TOT-Predicted)

EC/OC(TOT-Predicted) with Constraints EC/OC(TOT-Measured)102 Heywood (1988) presents data on the chemical composition of diesel exhaust PM, presenting characterization of both the “extractable composition” and “dry soot” components of PM measured at idle and at 48 km/h. 50 The composition data is as follows:

Idle 48 km/h

Atomic formula C 23 H 29 O 4.7 N 0.21 C 24 H 30 O 2.6 N 0.18

OM/OC Ratio 1.39 1.26

The data for the “extractable composition” is assumed to represent the organic mass of particles. The total molar weight to carbon molar weight ratio was used to convert OC to OM. The idle data from Heywood were used when engine IMEP was 0.15 or under, corresponding to the idle mode of the cycle employed by Kweon et al. (2004). All other engine conditions employed the ratio based on the 48 km/h sample in Heywood.



## A. 5 .2 Compariso n 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 fac tors from an independent study. Shah et al. (2004) report EC and OC emission factor and rates for a series of heavy heavy -duty diesel trucks (HHDT) in California. 51 Shah et al. report the results of emission testing using the CE -CERT Mobile Emissions Labo ratory (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 stainl ess 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 H HDDTs 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 r oad. Furthermore, they were loaded with only the MEL, which weighs 20,400 kg. As a result, the emissions from these trucks do not reflect the expected variability in truck running weight described above and used in the PERE-HD runs for this study. Shah et al. (2004) report emission data for each of the four modes of the CARB HHDDT cycle, including cold start/idle, creep, transient, and cruise. The test cycle represents a wide range of driving patterns, as suggested in the table below. Note that these te st cycles are trip

- based, so each begins and ends with the vehicle at stop.103 Cycle Distance (mi) Duration (s) Average Speed (mph) Maximum Speed (mph) Maximum Acceleration (mph/s)

Cold start/idle 0 600 0 0 0

Creep 0.124 253 1.77 8.24 2.3

Transient 2.85 668 15.4 47.5 3.0

Cruise 23.1 2083 39.9 59.3 2.3

The following table presents the EC- TOT and OC -TOT emission rates reported in Table 6 of the study:

Rate Idle Creep Transient Cruise

EC (mg/mi) 340±140 446±115 175±172

OC (mg/mi) 607±329 182.9±51.2 74.7±56.3

EC (mg/min) 4.10±2.38 10.4±4.8 110.7±27.0 93.0±68.3

OC (mg/min) 20.9±11.6 17.0±6.4 45.5±13.2 42.3±26.8

The following graph illustrates the comparison between predicted EC- TOT and OC

- TOT emission factors predicted by PERE -HD and those reported by Shah et al. (2004). The letters “H,” “M,” and “L” refer to high, medium, and low accessory loads employed in the PERE

- HD runs with IMEP

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

- HD predicts the general between -cycle trends in EC- TOT and OC -TOT emission factors. It appears that for the low- speed “creep cycle,” PERE

- HD or the IMEP

- based emission rates underpredict total carbon (EC+OC) emission factors, but that the general trend in the EC/OC ratio is directionally correct.104 

## A. 5 .3 Variability in Predicted EC and OC Emission Rates
Through the modeling appro ach used here the influence of variab il ity in vehicle weight and engine displacement on heavy -duty EC and OC emission rates can be assessed. It should be noted that these relationships are contingent on the particular algorithms employed in PERE

- HD for

es timating 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 highes t EC/OM ratios, located in the upper right-hand-quadrant of the graph, correspond to vehicles loaded with extreme weight relative to the total available engine displacement. Predicted EC and OC Emission Factors(g/mi) vs. Measured Values in Shah et al. (2004)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

H L M Shah avg. H Shah avg. H L M Shah avg. Creep Cruise TransientEmission Factor (g/mi)

Average of OC g/mi Average of EC g/mi105 In general, these results reflect the role that running weight has on IMEP in a truck. Since IMEP correlates highly with the air/fuel ratio (or equivalence ratio φ), the data suggest that EC/OC partitioning is driven by the pyrolysis that occurs in engines under load. Very few weight/displacement pairings are greater than 3,300 kg/ L. The following graph depicts the cumulative frequency distribution (CFD) of simulated weight/displacement ratios in PERE

- HD.

For a 12 L engine, 3,000 kg/L would correspond to a running weight of 39600 kg (87,302 lb). Such vehicle loadings are infrequent, as they exceed Federal and state limits for vehicle weights on highways. The graph below presents the cumulative distribution of simulated weights, based on EC/OM Ratio (TOR-Specific) versus Weight/Displacement Ratio for Individual Truck Samples Transient Driving Cycle, High Accessory Load

0 5 10 15 20 25

0 1000 2000 3000 4000 5000 6000 7000 8000 Running Weight / Displacement (kg/l)EC/OM Ratio

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

0 1000 2000 3000 4000 5000 6000 7000 8000

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

Percentilekg/l kg/l106 the VIUS microdata. Furthermore, the graph presents cumulative frequency distributions f or several broad weight categories reported by Ahanotu (1999) for trucks in the Atlanta metropolitan

area. 52 Note that in the graph, the highest weight category reported by Ahanotu (1999) is represented as 100%, although the actual maxima of observed trucks are unknown.

In general, the sensitivity of EC/OM ratios to the weight/displacement ratio suggest that properly capturing the variability in both inputs is key to developing representative inputs for MOVES.



## A. 5 .4 Calculating EC/OC fraction by O perating M ode
The modeling described in the previous sections has been employed to create second

- by -second estimates of EC

- TOR and OC -TOR emission factors for use in the MOVES emissionRateByAge table . The next step of consists of appropriately binning the outputs to fit the MOVES operating

mode structure. EC and OC emission rates, as opposed to total PM, are the inputs to the MOVES model for PM inventory calculations. To convert the total PM rates calculated from heavy-duty emissions analysis into EC and OC rates, we must calculate EC and OC fractions by MOVES operating mode. Then, the total PM rate can be multiplied by the EC and OC fractions to obtain EC and OC input emission rates. One of PERE’s outputs for heavy- duty vehicles is the track road

- load co efficients. For each individual weight in the distribution, PERE outputs a set of A / B / C coefficients similar to the ones used to calculate VSP in the HC, CO, and PM emission rate analysis. We used these coefficients and weights to calculate VSP for each second using the equation below.

m a mv Cv BvAv VSP ttttt
t +++

= 32

This equation is implemented slightly different ly than the one used for analysis of the chassis dynamometer testing for PM, HC, and CO since the road load coefficients ( A

, B , and C ) and weight (or mass) m were specific to each individual vehicle, not general to the regulatory class. In the PM, Comparison of Simulated Weights with Atlanta Area Truck Measurements

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

0 20000 40000 60000 80000 100000 120000 Weight (lb)Cumulative Distribution Simulated Weight

GA Tech Class 9 Monday Midday

GA Tech Class 9 Monday 3-7 PM

GA Tech Class 8 Trucks M-F Midday

GA Tech Class 10-13 Trucks Daytime

GA Tech Class 10-13 Trucks Nighttime107 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 fe lt confident in using vehiclespecific numbers because we performed the analysis using a full representative distribution of weights and displacements. Also, since we are interested in the EC and OC fractions rather than the actual rates themselves, norma lizing 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.

R epresentative distribution of weights used in the EC/OC analysis.

Based on this weight distribution, we considered all vehicles weighing more than 40,000 lb to be HHD vehicles and all vehicles less than 40,000 to be MHD vehicles. This was a ver y simple approach to stratifying by regulatory class. As EC and OC rates were also computed for each second during each cycle, we were able to average the EC and OC rates by operating mode. Then, we calculated the fractions of EC and OC for each operati ng mode. For the LHD classes, we used the MHD fractions, and for buses, we used the HHD fractions.



## ∑∑ ∑

+ =

OC EC EC EC rr r f

, EC OC EC OC OC f

rr r f −= + = 

## ∑∑ ∑ 1

The resulting EC fractions by operating mode are shown in Figure 6 in the main body of this report. 5000 15000 25000 35000 45000 55000 65000 75000 85000 95000 105000 115000 125000 135000 145000 0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5Percent

Weight_lb108 

## A. 6 Heavy

- duty Gasoline Start Emissions Analysis Figures

Figure 28 . Cold

- Start Emissions (FTP, g) for Heavy

- Duty Gasoline Vehicles, averaged by Model

- year and Age Groups109 Figure 29 . Cold

- Start FTP Emissions for Heavy

- Duty Gasoline Vehicles, GEOMETRIC MEANS by Model

year and Age Groups110 Figure 30 . Cold

- start FTP Emissio ns for Heavy

- Duty Gasoline Trucks: LOGARITHMIC STANDARD DEVIATION by Model

- year and Age Groups.111 Figure 31 . Cold

- Start Emissions for Heavy

- Duty Gasoline Trucks: RECALCULATED ARITHMETIC MEANS by Model

- year and Age Groups.112 T able 38 - Emission Standards for Heavy

- Duty Spark

- Ignition On

- road Engines Regulatory Class Model Year Emissions Standards (g/hp

- hr)

CO THC NMHC NOx NMHC + NOx

LHD2b3 1990 14.4 1.1 6.0

1991

- 1997 14.4 1.1 5.0

1998

- 2004 14.4 1.1 4.0

2005

- 2007 14.4 1.0

2008+ 14.4 0.14 0.20

LHD45 , MHD 1990 37.1 1.9 6.0

1991

- 1997 37.1 1.9 5.0

1998

- 2004 37.1 1.9 4.0

2005

- 2007 37.1 1.0

2008+ 14.4 0.14 0.20113 

## A.7 Peer Review Comments and Responses

A.7.1 Comments from Josias Ziet s man , Ph.D., P.E.

Dr. Zietsman is the Head of the Environment and Air Quality Division at the Texas Transportation Institute (TTI), as well as a faculty member at Texas A&M University. He has over a decade of experience in rese arch in the areas of air quality, vehicle and engine emissions and transportation planning. Dr. Zietsman has published widely and frequently addresses audiences in the United States and internationally. He serves on the Transportation Research Board as Secretary of the Performance Measurement Council and as a member of the Air Quality and Sustainability Committees.

GENERAL COMMENTS

• MOVES will be a considerable improvement to MOBILE 6.2. The heavy duty vehicle component is a very important part of the overall MOVES program. The flexibility provided by the VSP approach will increase the flexibility and accuracy of emissions estimation. The EPA should be commended for taking on this im portant and ambitious task.

• The report is well written considering how difficult it is to convey the highly technical material. I did not notice any major flaws in the methodologies used and it is my opinion that the authors did a fine job in coming up wit h creative ways to produce emissions rates with limited data.

• It is clear that the MOVES model will have to be strengthened with a highly focused data collection effort that should involve emissions testing in areas where the data is lacking or non

- existen t. The MOVES team can also benefit from existing studies that were not included in this analysis. For example, in my answers below I highlight a few studies performed by TTI’s Center for Air Quality Studies that could be used in adding to the overall datas et.

• In my specific comments included in change tracker in the attached report I raise some questions and make some suggestions that could improve the report and the analysis.

PEER REVIEW CHARGE QUESTIONS

1. a) Does the presentation give a descript ion of selected data sources sufficient to allow the reader to form a general view of the quantity, quality and representativeness of data used in the development of emission rates?

The authors did a fine job in describing what data sources they used and what the limitations of the data were.

b) Are you able to recommend alternate data sources might better allow the model to estimate national or regional default values?114 TTI’s Center for Air Quality studies has performed quite a few studies using mostly PEMS equipment that could enhance the database used for this analysis. We will be happy to share any information gathered during these studies:

Identifying and Testing SmartWay Technologies for Drayage Trucks Sponsor: Texas Commission on Environmental Quality Budget: $220,000 Description: PEMS testing of before and after application of SmartWay technologies for 5 HHD Mexican drayage trucks. Location: Study performed in El Paso Texas.

Emissions Testing of Carbon Chain Combustion Catalyst Sponsor: Carbon Chain Technologies Budget: $300,000 Description: PEMS testing of before and after application of 2CT Combustion Enhancer of 4 long haul HHD trucks and 2 LHD2b pickup trucks. Location: Study performed at TTI’s High Speed Test Track in Pecos, Texas

Expanding MOBILE6 Rates to Accommodate High Speeds Sponsor: H ouston Advanced Research Center and Center for International Intelligent Transportation Research Budget: $150,000

Descri ption: PEMS testing of 3 long haul HHD trucks and 3 LHD2b pickup trucks. Location: Study performed at TTI’s High Speed Test Track in Pecos, Texas

Emissions of Mexican- domiciled Heavy

- Duty Diesel Trucks using Alternate Fuels Sponsor: EPA Region 6 through AACOG $160,000 Description: PEMS, TEOM and filter testing of before and after application of ULSD and biodiesel for 5 HHD Mexican drayage trucks and 5 HHD long haul Mexican trucks Location: Study performed in Laredo, Texas

School Bus Biodiesel (B20) NOx Emissions Testing Sponsors: CAPCOG and CAMPO Budget: $35,000115 Description: PEMS testing of 5 school buses Study performed at TTI’s Riverside Test Facility in Bryan, Texas

Mexican Truck Emissions at Major Texas Border Locations Sponsors: SWUTC, EPA, and Border Environmental Cooperation Commission Budget: $100,000 Description: PEMS, TEOM and filter testing of 5 HHD Mexican drayage trucks Location: Study performed in El Paso, Texas

RESPONSE: EPA plans to analyze any new relevant data for future versions of MOVES. We are particularly interested in filling holes and also current and future model year data. We have also conducted an extensive study at the Port of Houston on drayage trucks and hope to finalize the results and data from that study in the nea r future.

2. a) Is the description of analytic methods and procedures clear and detailed enough to allow the reader to develop an adequate understanding of the steps taken and assumptions made by EPA to develop the model inputs?

The descriptions are cle ar for the most part and the reader is able to develop an adequate understanding of the steps taken and assumptions made. In the attached document that is marked with track changing the specific places that need more clarity are shown.

RESPONSE : EPA appre ciates the comment. We have incorporated many of the recommended edits directly into the final report.

b) Are examples selected for tables and figures well chosen and designed to assist the reader in understanding approaches and methods?

Yes, the example s for tables and figures seem to be representative to help the reader understand the approaches and methods.

3. a) Are the methods and procedures employed technically appropriate and reasonable, with respect to the relevant disciplines, including physics, chemistry, engineering, mathematics and statistics?

Yes, the methods and procedures employed seem technically appropriate and reasonable. The analyses are highly constrained by a lack of data and the MOVES team was able to use creativity to overcome thi s burden116 b) Are you able to suggest or recommend alternate approaches that might better achieve the goal of developing accurate and representative model inputs? In making recommendations please distinguish between cases involving reasonable disagreement i n adoption of methods as opposed to cases where you conclude that current methods involve specific technical errors.

. In the attached document marked with change tracking I pointed out certain areas where I have some specific questions about the analyses or where I think it can be improved. The suggestions I made involve reasonable disagreements and I did not identify gross technical errors. Overall I think the methodologies are sound.

4. In areas where EPA has concluded that applicable data is meager or unavailable, and consequently has made assumptions to frame approaches and arrive at solutions, do you agree that the assumptions made are appropriate and reasonable? If not, and you are so able, please suggest alternative sets of assumptions that might l ead to more reasonable or accurate model inputs while allowing a reasonable margin of environmental protection. Yes, I think the assumptions made to deal with limited data are sound. However, in the attached document marked with change tracking I did poin t out certain areas where the assumptions can be improved. The key is to assemble and collect the lacking data to overcome the burden of making numerous assumptions and extrapolations.

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

5. a) Are the resulting model inputs appropriate, and to the best of your knowledge and experience, reasonably consistent with physical and chemical processes involved in exhaust emissions formation and control? Yes, the model inputs are appropriate are consistent with physical and chemical processes involved in exhaust emissions formation and control . The rates can be used as is but it could perhaps be improved by addressing some of the questions/comments highlighted in the attached report. The rates will benefit significantly from more data collection and assembly. b) Are the resulting model inputs empirically consistent with the body of data and literature that has come to your attention? Yes, the model inputs are empirically consistent with the body of data and literature that has come to my attention. In my response to Question 1 above I listed 6 studies performed by TTI’s Center for Air Qualit y Studies that produced emissions testing data of heavy duty vehicles and that could be of use to expand the existing database. The following are some examples of areas where additional testing will greatly enhance the emission rates developed for heavy du ty vehicles:

• NOx emissions due to DPF regeneration117 • NOx during cold starts (varying soak times)

• PM emissions during starts (varying soak times)

• Aftertreatment effectiveness during extended idling

• PM2.5 emissions for heavy duty gasoline vehicles

• Emissions o f 2007 and later heavy duty gasoline vehicles

• PM emissions at the higher operating mode bins

RESPONSE : EPA certainly is aware of our heavy -duty data needs as well as the value in additional testing. We will look to incorporate any existing data that you and others have brought up to fill appropriate holes in the model. Also, we are looking to improve our current and future model year data set with the manufacturer

- run in -use heavy- duty diesel test
program.118 

## A.7.1 Comments from John Storey:
Dr. Storey is a senior researcher in the Fuels, Engines and Emissions Research Center at the Oak Ridge National Laboratory. He has 20 years of experience in research in emissions measurement and characterization , including analysis of the composition of the hydrocarbon and particulate components of vehicle exhaust. As a member of the Transportation Research Board, Dr. Storey serves on the Committee on Transportation and Air Quality.

PEER REVIEW CHARGE QUESTIONS

1. The description of the data sources is adequate. This reviewer was familiar with many of the studies cited, and in particular, the ROVER study, the WVU MEMS study and the Coordinating Research Council (CRC) E-55/59 study on heavy-duty vehicles. My comments are divided into sections corresponding to the report:
NOx data The use of the ROVER and WVU MEMS data for NOx emission factor is appropriate and valuable in that it represents real-world operating emissions and can be directly related to the VSP from the weight of the vehicle and the engine data. Ho wever, the on-board sensors used in the studies are typically NO sensors. In looking at the referenced report by Jack, no detail on the actual sensor is given, except that it is a four gas analyzer for NOx, HC, CO, CO 2 . These units are most common in com pliance inspection, not typically of research quality, and likely use an electrochemical sensor for NOx which is mostly sensitive to NO. Jack reports that intercomparison exercises were done with the EPA -NVFEL instruments, but nothing in the Draft MOVES20 09 report explains how these results related to measurements with research quality instruments, nor is any assumed NO/NOx ratio reported. For instance, if the ROVER primarily measures NO, than the report should explain that an assumption was made that 95% of the NOx was NO. This may have been done in the subsequent re- analysis of the data by EPA staff, but no mention is made. For the WVU MEMS testing, the referenced report shows that a NO 2 – NO converter is used before measurement by the ZrO 2 sensor. Also, a comparison was made with the WVU chassis dynamometer facility which employs research grade chemiluminescence analyzers for NOx measurement. No mention of a NO 2 - NO converter prior to the ROVER is made in Jack’s report. Finally, none of the NOx data c ollected by WVU as part of the E55/E59 study was included, even though the study had trucks in the age bins which were not represented by the ROVER study and the WVU- MEMS study. The chassis dynamometer data would likely be much more accurate because VSP is measured directly and the NOx emissions measurements are made with a research quality instrument.

R ESPONSE:
In past testing, EPA has found good correlation in emissions results between ROVER and chassis and engine dynamometer testing. Further, we have conducted validation of the onboard in -use data (via MOVES outputs) with CRC E- 55/59 results. These results are summarized in validation

.119 Because of the sensitivity of tropospheric ozone predictions to NOx, and the relative importance of HD vehicles to NOx budgets in non- attainment areas, it is my opinion that EPA needs to use the NOx section 1.1.1 to carefully justify their decisions. Furthermore, if there is a future plan to include the E55/E59 NOx data, or use the data to validate MOVES, than it is appropriate to state that in this section. A new source of data for older vehicles which may be useful is an extensive study done by the Texas Transportation Institute and Oak Ridge National Laboratory for the Alamo Area Council of Governments under an EPA Region 6 grant.[Zietsman, J. et al. “Emissions of Mexican

Domiciled Heavy -Duty Diesel Trucks Using Alternative Fuels.”, October 2007] In this study, emissions from ten trucks that operate on the Mexican border at the port of Laredo were characterized with ultralow sulfur diesel, as well as biodiesel blends. A wide range of model years were represented. Both idling and on- road drive cycle measurements were made, the latter with both a Sensors, Inc. Semtech

- D PEMS and a Clean Air Technologies Montana

s ystem. Although the trucks were Mexican

- registered, the engines were all American made (CAT, Cummins, and DDC), and the serial numbers recorded. These engines were likely certified to U.S. emissions standards at the time of purchase. A complete report i s available by contacting the author at tti.tamu.edu.

VSP This section was well -explained and the data and assumptions reasonable. The driveline efficiencies do seem to vary considerably, but the assumption of 90% appears in line with the data. For fut ure consideration, there is a large set of data available to the public that includes actual wheel torque measurements of a heavy -duty truck operating on a cross-country route, along with engine bus and GPS data for speed, grade, etc. The report describin g the data collection is titled “ Class

- 8 Heavy Truck Duty Cycle Project Final Report”, and is available at http://cta.ornl.gov/cta/publications.shtml#2008

.

PM This section does an excellent job of explaining the sources of the PM data and the assumptions made. The CRC E55/E59 dataset is large and very appropriate, and the use of corrected TEOM data is as close to reality for transient emissions given the measurement technology availab le at the time. Absent another huge study like E55/E59, this set of data represents the only available comprehensive record of in- use trucks. Recent advances in measurement technologies such as the DMM (Dekati), the Electrical Aerosol Analyzer (TSI), and the quartz crystal microbalance (Sensors, Inc.) for real -time PM have been demonstrated, but have mostly been applied to DPF-equipped vehicles to demonstrate their sensitivity, or represent tiny sample sizes. It is recommended that these data, in partic ular for non DPFequipped vehicles be used in the future to validate the emissions factors in MOVES.

R ESPONSE : EPA agrees that it is important to test and analyze DPF

- equipped vehicles for MOVES model validation and updates. Accurate determination of the in

- use emission performance of these vehicles is critical in the development of future PM emission inventories. When such in -use data become available in sufficient quantity EPA will analyze it and update the emission models as required and appropriate.120 HC and CO The emissions data for HC and CO are also quite extensive and represent a wide range of in

- use vehicles. My major concern is that the altitude effects on the NFRAQ data was considered to see if there was a systematic bias due to these effect s. Of course, the effect would be more pronounced at low load conditions that have little or no turbocharging. The decision to exclude the NDIR based ROVER data; even though research grade instruments use NDIR for CO, the instrument used in ROVER was meant for use with gasoline vehicles, and thus had poor sensitivity at the lower levels of CO found in diesels.

Start emissions The data for diesel vehicles in this category were extremely limited. In personal experience with these measurements, the trends shown by Figure 14 for gaseous emissions are correct, with NOx emissions increasing after start, and CO and HC decreasing. The difference in cold FTP vs hot FTP emissions is an appropriate measure of PM due to cold start; I am surprised there isn’t more d ata available since all engines must include a cold FTP in their certification process. Given the long daily duty- cycles of these vehicles, and their relatively high emissions, the importance of cold start emissions for MHD HHD vehicles is pretty limited i n comparison to modern light-duty gasoline vehicles which have virtually zero emissions after cold start and shorter daily duty- cycles. The caveat is that the increased emphasis on anti- idling policy may result in more start events occurring, in particula r for MHD vehicles. Thus more data is required, or at least a sensitivity analysis, to determine if multiple starts in daily service will have an impact on a vehicle’s overall contribution to the airshed.

R ESPONSE : Manufacturers do not typically separate their cold FTP result from their hot FTP result when certifying their engines. Rather, they report a weighted average. Thus, publicly available certification data was not a viable source for cold start rates.

Extended idling emissions Extended idling is critical to “hot spots” around truck stops and understanding the effects of congestion on mobile source emissions. For this part of the model, MOVES makes use of a large amount of available data and results presented in the appendix show generally good agreement. This reviewer’s study on extended idling found that ambient temperature played an important role due in particular to increased accessory loads such as cooling fan and air conditioning. It wasn’t clear if these effects were included.

R ESP ONSE : EPA appreciates the comment. At this point, we do not attempt to model temperature effects on extended idling emissions. Heavy -duty gasoline data This is a very small class of vehicles and the data available are sparse. The reported certificati on data are appropriate to use for HC. NOx, and CO. The analysis used to calculate the PM emissions factors and the cold start emissions will be reviewed in the next section. The only other suggestion is that the LHD2B vehicles may be represented in the NCHRP database from CE

- CERT.

2. The description of the analytical methods and procedures are clear and detailed, for the most part. Detailed comments appear below in the order of the report headings. In general,121 it would help the reader to have more detail in the figure captions, particularly for the examples. As an example, Figure 6 is typical; assumes a MY 2002 MHD vehicle operating in bin 24, then displays its emissions changes with time. It would be helpful if these example plots throughout the document were explained briefly; i.e.,to say that emissions are projected to increase due to T&M, etc. Figure 10 and 11 are confusing in that no explanation is given for the huge departures of the MHD vehicles (Figure 10) and bus (Figure 11) from the curves for t he other two classes of vehicles. The justification for geometric mean usage given in Appendix 6 is hard to understand because the figures are of such poor quality.

VSP calculations This section was well

- written and communicated a somewhat difficult concept clearly. The hole -filling methodology is important and was described adequately.

R ESPONSE : EPA appreciates the comment. As described in the draft report and detailed in the final report, we modified the calculation of tractive power to normalize the axle power by fixed scaling factor s for the purpose of fitting rates for heavy

- duty vehicles into the same numeric ranges originally develop ed and applied to vehicle-specific power (VSP) for light

- duty vehicles, which involved normalizing by weight s o f individual vehicles . Rather than keeping the term “vehicle

- specific power” for this parameter, we renamed it as “scaled tractive power” (STP) , to minimize confusion . This change in approaches preserves relationship between emissions and power for engine s certified to brake

- specific standards , which better characterizes heavy

- duty emissions.

Emission Rate Calculations This section also was detailed and provided a good explanation of how the emissions rates were calculated, although in Table 10 it illust rates how little NOx data exists for the different classes and ages of vehicles. The data from E55/E59 could be used to fill some of these missing table cells.

R ESPONSE : For a given pollutant, we did not want to mix the data sources, given the differences in calculating road load/STP and the data collection process (drive cycles, test equipment, etc). Also, keeping CRC E55/59 separate helped use that as an independent validation of the onboard results.

PM data analysis (1.1.2.2) The procedures to carry out the PM analysis were detailed enough to understand. It is critical that the TEOM correction be understood, so even including a figure to illustrate a snapshot of data and corrected data would be great, but not necessary for the report. The appendix A4 does not explain the hole- filling very well. An example in the main text or the appendix would help.122 R ESPONSE : The design of the MOVES model was originally intended to be “ data driven .” To implement this approach, large volumes of data were dis -aggregated and reclassified into ranges defined as “operating modes,”for which simple statistics were computed and used as emission rates in the model. This approach, however, does not produce complete results across all vehicle classes, ages or operating m odes due to lack of data in specific combinations of these categories. To compensate , a “ hole

- filling ” method was developed where the entire dataset was regressed using a least

- squared log -linear model. Output from this model was used to fill in missing bins in the model matrix of operating mode, vehicle class and model year group.

Tampering and Mal-maintenance T&M calculations are detailed and good examples given. OC/EC split An excellent and extensive explanation is given in the memo in the Appendix. My only comment is that it seems excessively detailed and long (essentially a research paper is reproduced in the appendix), thus giving the appearance of much more weight to this calculation and the importance of OC/EC than the others.

R ESPON SE: It is not the intent of the authors to over -emphasize the importance of the OC/EC split topic in relation to other topics in the report. However, it is a significant and exciting new area of future work that the authors felt deserved full treatment.

3. The methods employed in this report are appropriate and reasonable from the viewpoint of the physical and mathematical sciences. The VSP approach is well -suited to heavy-duty vehicles and off-road vehicles, which will be incorporated in the future. Although alternatives to the VSP approach exist, a significant commitment has been made to this approach and it would be extremely costly to change. An example of an alternative approach would be to use vehicle emissions as a function of speed and acceleration. The advantage of this approach is that it makes micro

- scale simulations of road projects, i.e. for road/signal changes, much easier to perform. In addition, data is relatively easy to collect and exists for the light -duty fleet. The data sources for th e heavy -duty fleet could be the same as used for the development of the emissions factors, just analyzed in a different way. The major disadvantage is the inability of this approach to simulate weight and grade changes, especially for HHD vehicles. Also off -road vehicles couldn’t be simulated with speed and acceleration maps. The VSP approach offers the future advantage of incorporating GIS information about road grade so that adjustments can be made in VSP, and thus emissions, as a function of terrain.

Specific recommendations and comments. Figure 3 shows deterioration rates for vehicles in MY2010. This appears unrealistic since emissions control systems are certified for 440,000 miles, and the deterioration factors shown are very high. With modern vehicles and the extensive diagnostic systems, it may be important to revamp the deterioration assumptions based on earlier, no- catalyst vehicles. If there are valid reasons for Figure 3, they are not clearly explained in the text.123 RESPONSE : In Figure 3 (now Figure 4 in the final report), the age effect is modeled from the tampering and mal-maintenance analysis. This analysis and methodology are explained in Appendix A.2. We will certainly consider rev is ion of MY 2010+ age effects as more data becomes available. We do not feel it is appropriate to use pre- catalyst assumptions for newer vehicles since the deterioration behaviors of the two types of technology are different. Further, we reduced estimated T&M effect s with the introduction of O n-board Diagnostics (OBD). Heavy heavy-duty Class 8 trucks accumulate mileage quickly; Table 35 shows the warranty age and useful life age of the various heavy- duty regulatory classes.

In section 1.3.2, the justification for the extended idling PM factor of 11% makes no physical sense. The MY 2007+ vehicles are equipped with a [diesel particulate filter ] DPF which physically filters the exhaust. This component can’t be “turned off” – it remains 95+% effective until it fills up and the engine stalls. No bypass exists for extended idling. In extreme cases, the heavy HC might accumulate and break through and be counted as PM in a filter measurement, but no data or physical evidence exists for such an occurrence.

R ESPONSE :
We generally agree with th is comment. Accordingly, w e have revised the extended idle PM rate s , making them equal to the curb idling rates . The resulting rates are shown in Table 26.

4. EPA has done an adequate job with hole-filling and data sparseness for the most part. I agree with m ost of the assumptions, although I think speculating on the deterioration of MY 2007+ emissions controls is very challenging, and applying “old- style” deterioration factors may be problematic. It will be critical to update these factors as more data becomes available. Specific comments are covered in the previous sections of this review and below:

R ESPONSE : We agree that future updates will be required on 2007+ model year PM emission factors and 2010+ NOx emission factors. When such data are available, EPA plans a full review and update as feasible and required.

NOx I believe there is a wider breadth of MY data available in the CRC E55/E59 program and this should be taken advantage of. This was mentioned previously, and the rationale for excluding the NOx data from the CRC study, but including the PM and HC/CO data is not explained.

R ESPONSE :124 We did not want to mix the data sources, given the differences in calculating road load/STP and the data collection process (drive cycles, test equipment, etc). Also, keeping CRC E55/59 separate helped use that as an independent validation of the onboard results.

PM In section 1.1.2.2.4.2, the LHD and MHD factors are proportioned by engine work ratios. There is little justification for this approach given except that less work is done by the LHD. The LHD engines are cer tified to the same values as the MHD engines, and my understanding of VSP and the TEOM data taken from the CRC study is that the PM emissions rates were developed for VSP bins, so the LHD vehicles populate the higher VSP bins since their power to weight ra tios are higher. I may be completely off

- base with this comment, but at any rate, the explanation is not sufficient for me to understand.

The bus data correction makes more sense to me since it is based on certification level and buses have similar hard accelerations to HHD vehicles.

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

- way catalyst technology as their LDV counterparts and likely have similar calibrations to maximize fuel economy. It seems that the data must exist in the NCHRP or similar efforts for a LHD2B like the Ford F- 250 and a similar model year F -150 for comparison. The larger heavier vehicle may not have different VSP emissions since they are likely similarly equipped.

R ESPONSE :
We will look into this for future analyses. Different regulations are the driving force for our separation of the LHD2b’s from LD trucks, for both criteria emissions and energy
rates.

5. The resulting model inputs are consistent with the physics and chemis try of exhaust emissions and emissions control. I have pointed out previous inconsistencies in the previous sections, the most important of which are the following:

PM for extended idling The DPF for MY 2007+ will physically continue to work, so a factor of 11% effectiveness is not realistic.

RESPONSE :
We generally agree with th is comment. Accordingly, w e have revised the extended idle PM rate s , making them equal to the curb idling rates . The resulting rates are shown in Table 26.

Gasoline HDVs Because the vast majority of the gasoline HDVs are LHD2B’s, and these trucks use powertrains developed for their much more popular LDV cousins - e.g. Ford F series, Chevrolet Silverado - it is reasonable to assume similar performance, on a VSP basis, for these125 vehicles to the LDVs, despite there being a different certification standard. I would say this would apply at least to the last 8 model years. It is not clear to me how cooperative a manufacturer might be with the EPA in confirming this assumption, but it is probably worth a try.

R ESPONSE : We will look into this for future analyses. Different regulations are the driving force for our separation of the LHD2b’s from LD trucks, for both criteria emissions and energy
rates.

Summary thoughts

. The MOVES rep ort on heavy-duty diesel emissions demonstrates a breadth and depth of knowledge of the pertinent aspects of emissions measurement and emissions control for HHDs. The issues this reviewer had were fairly minor and can be resolved easily in the future. The report model appears ready for use and none of my comments should hinder its timely release. A separate attachment will identify misplaced words and typographical errors.

Future data Of course, it will be important to keep updating the emissions facto rs as additional information is made available. As an example, Oak Ridge National Laboratory is presently evaluating two different classes of medium -duty diesel vehicles (three transit buses, three box vans) for the purposes of developing duty- cycle simul ations. As a disclaimer, the group doing the study is not in the same center as this reviewer, and I have no funding relationship with their center. No emissions are being measured in this study, but extensive duty- cycle data is being collected from each engine and vehicle in its daily operation. These vehicles represent an opportunity for EPA to fill holes in the MHD data. Because the recruitment and instrumentation of the vehicles has been done, the incremental costs for obtaining emissions data are r elatively small, and the DOE program manager funding the project has stated his interest in collaborating with EPA and other agencies on obtaining more data from the project. It is recommended also that funding for these updates be maintained, perhaps with collaboration from the Regions, which will be widely affected by the implementation of MOVES. Regions will likely be motivated to have the information in MOVES that is critical for their area in order to help their airsheds get out of non- attainment. By keeping MOVES in the forefront when new emissions data is collected, the value of each new study is enhanced.

R ESPONSE : EPA welcomes any new relevant data to incorporate into MOVES, especially in filling holes in existing data as well as current and futu re model years.126 

## References

1 USEPA Office of Transportation and Air Quality. Development of Greenhouse

- gas Emission Rates for all Vehicles, and Updates to Energy Consumption Rates for Light

- Duty Vehicles in the Motor Vehicle Emissions Simulator (MOVES2010a) . Asses sment and Standards Division, Ann Arbor, MI. April 2012.

2 USEPA Office of Transportation and Air Quality

. Development of Evaporative Emissions Calculations for the Motor Vehicle Emissions Simulator (MOVES2010): Final Report

. Assessment and Standards Div ision, Ann Arbor, MI. July 2012

.
3 USEPA Office of Transportation and Air Quality. Development of Emission Rates for Light

- Duty Vehicles in the Motor Vehicle Emissions Simulator (MOVES2010): Final Report . EPA

- 420 -R- 11

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