Development of Emission Rates for
   Heavy-Duty Vehicles in the Motor
   Vehicle Emissions Simulator
   (Draft MOVES2009)

   Draft Report
United States
Environmental Protection
Agency

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                 Development  of Emission Rates for
                 Heavy-Duty Vehicles  in  the Motor
                      Vehicle Emissions  Simulator
                            (Draft MOVES2009)

                                  Draft Report
                               Assessment and Standards Division
                              Office of Transportation and Air Quality
                              U.S. Environmental Protection Agency
v>EPA
                 NOTICE

                 This technical report does not necessarily represent final EPA decisions or
                 positions. It is intended to present technical analysis of issues using data
                 that are currently available. The purpose in the release of such reports is to
                 facilitate the exchange of technical information and to inform the public of
                 technical developments.
United States                                      EPA-420-P-09-005
Environmental Protection                                .    ^ „„_
Agency                                         August 2009

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Table of Contents

1 Heavy Duty Diesel Emissions	1
1.1 Running Exhaust Emissions	1
  1.1.1 Nitrogen Oxides (NOx)	2
     1.1.1.1 Data Sources	2
     1.1.1.2 Calculating VSP from in 1-hzdata	3
     1.1.1.3 Calculating emission rates	7
     1.1.1.4 Sample results	12
  1.1.2 Particulate Matter (PM)	14
     1.1.2.1 Data Source	15
     1.1.2.2 Analysis	17
     1.1.2.3 Sample results	23
  1.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO)	25
     1.1.3.1 Data Sources	25
     1.1.3.2 Analysis	26
     1.1.3.3 Sample results	27
  1.1.4 Updates for final MOVES release	31
1.2 Start Exhaust Emissions	32
  1.2.1HC,  CO, and NOx	32
  1.2.2 Particulate Matter	34
  1.2.3 Adjusting Start Rates for Soak Time	34
1.3 Extended Idling Emissions	36
  1.3.1 Data Sources	36
  1.3.2 Analysis	37
  1.3.3 Results	38
2 Heavy-Duty Gasoline Truck emissions	39
2.1 Running Exhaust Emissions	39
  2.1.1HC,  CO, and NOx	39
     2.1.1.1 Data and Analysis	39
     2.1.1.2 Sample Results	40
  2.1.2PM	42
     2.1.2.1 Data Source	42
     2.1.2.2 Analysis	43
2.2 Start Emissions	44
  2.2.1 Available Data	44
  2.2.2 Estimation of Mean Rates	45
  2.2.3 Estimation of Uncertainty	47
  2.2.4 Projecting Rates beyond the Available Data	48
     2.2.4.1 Regulatory class LHD2b	49
     2.2.4.2 Regulatory classes LHD345 and MHD	50
     2.2.4.3 Particulate Matter	51
A. Appendices	52
A.I Calculation of Accessory Power Requirements for VSP bins	52
A.2 Tampering and Mai-maintenance	54
  A.2.1 Modeling Tampering and Mai-maintenance	54

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  A.2.2 Data Sources	56
  A.2.3 Analysis	56
A.3 Extended Idle Data Summary	63
A.4 Regression to develop PM emission rates for missing operating mode bins	68
A.5 Heavy-duty Diesel EC/OC Fraction Calculation	70
  A.5.1 Introduction	70
  A.5.2 PERE for Heavy-duty Vehicles (PERE-HD) and Its Extensions	70
    A.5.2.1 PERE-HD Fleet-wide Average Emission Rate Estimator	70
    A. 5.2.2 Prediction of Elemental Carbon and Organic Mass based on PERE-HD	76
  A.5.2 Comparison of Predicted Emissions with Independent Measurements	84
  A.5.3 Variability in Predicted EC and OC Emission Rates	85
  A.5.4 Calculating EC/OC fraction by MOVES operating mode bin	88
A.6 Heavy-duty Gasoline Start Emissions Analysis Figures	90

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1  Heavy Duty  Diesel Emissions
This main section details our analysis of data to  develop  emission rates for heavy-duty diesel
trucks.  Three  emission processes (running,  extended  idling,  and starts) are discussed.   The
'running' process occurs as the vehicle is operating on the road either under load or in idle mode.
This process is further defined by 23 operating modes which will be discussed below.   The
'extended idle' process occurs after an extended period of idling operation such as when a vehicle
is parked for the night and left idling.  Extended idle is generally a different mechanism (usually a
higher RPM engine idle to power truck accessories for operator comfort) than the regular 'curb'
idle that a vehicle experiences while it is operating on the road.

1.1 Running Exhaust Emissions
MOVES running exhaust emissions analysis requires accuracy of second-by-second emission rates
and parameters that can determine vehicle-specific power, vehicle tractive power normalized by
weight (VSP). Heavy-duty emission rate analysis is no exception. However, compared the light-
duty, the amount of data available is small.  The VSP approach was developed with light-duty
vehicles in mind, and as a result, the same approach needed to be  applied for heavy-duty vehicles.
While VSP is a good way to characterize emissions from light-duty vehicles, the range of running
weights, coarseness of the VSP bin structure, and work-based  (not distance-based) emissions
standards make VSP-based analysis for heavy-duty diesel vehicles a challenge. Nevertheless, this
report explains how we analyzed second-by-second heavy  duty emission data to fit the VSP
structure in MOVES.

MOVES source bins are groupings of parameters which distinguish differences in emission rates
according  to physical differences in the source type or vehicle classification.  The source bins are
differentiated by fuel type (gasoline  or diesel), regulatory class (light heavy duty to heavy-heavy
duty) and model year group.  Stratification of the data sample and generation of the final MOVES
emission factors were done according to the combination of regulatory class (shown in Table 1)
and the model year group.  The regulatory groups were determined based  on long standing gross
vehicle weight rating (GVWR) classifications and the model year groupings are differentiated by
EPA emission standards.
               Table 1 - MOVES stratifies HP emission rates into five regulatory classes
Regulatory Class name
2b Light-heavy duty [LHD2b]
Light-heavy duty [LHD345]
Medium-heavy duty [MHD]
Heavy-heavy duty [HHD]
Urban Bus [BUS]
MOVES Name
Class 41
Class 42
Class 46
Class 47
Class 48
Gross Vehicle Weight Rating
(GVWR) [Ib]
8500 - 14000
14000-19500
19500-33000
> 33000
N/A

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

                           Table 2 - MOVES Age Group Definitions
Age Group ID
3
405
607
809
1014
1519
2099
Lower Age
0
4
6
8
10
15
20
Upper Age
O
5
7
9
14
19
~
1.1.1 Nitrogen Oxides (NOx)
For NOx rates, we stratified heavy-duty vehicles into model year groups in Table 3.  These groups
were categorized based on changes in NOx emissions standards and the outcome of the Heavy
Duty Diesel Consent Decree1, which required additional control of NOx emissions during highway
driving for model years 1999 and later. This is referred to as the Not-to-Exceed (NTE) limit.

              Table 3 - Model year groups for NOx analysis based on emissions standards
Model year group
Pre-1988
1988-1989
1990
1991-1997
1998
1999-2002
2003-2006
2007-2009
2010+
FTP standard
[g/bhp-hr]
None
10.7
6.0
5.0
4.0
4.0
2.4
1.2
0.2
NTE limit [g/bhpr-hr]
None
None
None
None
None
7.0 HHD; 5.0 other reg. classes
1.25 times the family emission level
1.1.1.1  Data Sources
For NOx emissions from HHD, MHD, and urban buses, we relied on two data sources:
   1   ROVER PEMS (Portable Emissions Measurement System) testing  conducted by U.S.
       Army Aberdeen Test Center on behalf of U.S. EPA2:  This ongoing program started in
       October 2000.  Due to time constraints and quality of data, we only  used data collected
       from October 2003 through September 2007.  Data was compiled and reformatted for
       MOVES analysis by Sierra Research3. EPA performed the data analysis itself.  The data we
       used consisted of 124 trucks and buses ranging from model years 1999 through 2007.
       The vehicles were driven mainly over two routes:
          •  "Marathon" from Aberdeen, MD to Colorado and back along Interstate 70
          •  Loop around Aberdeen Proving Grounds in Maryland

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   2.  Consent  Decree testing  conducted by West Virginia University using their Mobile
       Emissions Measurement System (MEMS)4'5:  This program  came as  a result  of the
       consent  decree between  the  several  heavy-duty engine  manufacturers and  the US
       government, requiring the manufacturers to test in-use trucks over the road.  Data was
       collected  from 2001 through 2006.  The data we used consisted of 188 trucks from  model
       years  1994 through 2003.  Trucks were heavily loaded and tested over numerous  routes
       involving urban, suburban, and rural  driving.  Several trucks were re-acquired and tested a
       second time after 2-3 years. Data were collected  in 5-hz frequency, which we averaged
       around each second to convert the data to 1-hz.

From each data  set,  we used only tests we determined to be valid.  For ROVER, due to time
constraints,  we  eliminated all  tests  that  indicated  any  reported  problems,  including GPS
malfunctions, PEMS malfunctions, etc, whether or not they affected the actual emissions results.
As our own high-level check on the quality  of PEMS  and  ECU output, we further eliminated any
trip where the pearson correlation coefficient between CC>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, compared to if a more detailed quality check were performed (i.e. not all eliminated
tests produced erroneous results). For the WVU MEMS data, WVU itself reported which tests were
valid as part of the consent decree procedure. No additional detailed quality check was performed
by EPA. Table 4 shows the total distribution of vehicles by model year group from both  of the
emissions test programs above.

Table 4 - Number of vehicles by model year group from the ROVER and WVU MEMS programs used for
emission rate analysis
Regulatory class
HHD
MHD
BUS
1991-1997 MY
19
0
2
1998 MY
12
0
0
1999-2002 MY
78
30
25
2003-2006 MY
91
32
19
1.1.1.2 Calculating VSP from in 1-hz data
With on-road testing, using vehicle speed and acceleration to determine VSP is not accurate given
the effect of road grade and wind speed. As a result, we needed to find a better way to calculate
VSP.  Therefore, we decided to derive VSP from engine data collected during testing. We first
determined which seconds in  the data that the truck was  either idling  or  braking based on
acceleration and speed criteria shown later in Table 9. For all other operation, engine speed a>eng
and torque  Teng from the ECU were used to determine engine power Peng, as shown in Equation 1.
Only torque values greater than zero were used so as to only include operation where then engine
was performing work.
                                     f>eng=G}engTeng               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 must account for the losses due to accessory  loads
during operation. These power loads are not subtracted in the engine torque values that are output
from  the engine control  unit.   Heavy-duty trucks  use accessories  during  operation.   Some
accessories are  engine-based and are required for operation.  These include the engine  coolant
pump, alternator, fuel pump, engine oil  pump, and power steering.  Other accessories are required

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for vehicle operation, such as cooling fans to keep the powertrain cool and air compressors to
improve braking.  The third type of accessories is discretionary, such as air conditioning, lights,
and other electrical items used in the  cab. The calculation of the accessory load requirements is
derived below.

We grouped the accessories into five categories: cooling fan, air conditioning, engine accessories,
alternator to run electrical accessories, and air compressor. We identified where the accessories
were predominately used on a vehicle speed versus vehicle load map to properly allocate the loads.
For example, the cooling fan will be on at low vehicle speed where the forced vehicle cooling is
low and at high vehicle loads where the engine requires additional cooling.  The air compressor is
used mostly  during braking operations; therefore it will have  minimal  load requirements at
highway, or high, vehicle speeds.  Table 5 identifies the predominant accessory use within each of
the vehicle speed and load areas.

At this point, we also translated the vehicle speed and load map into VSP bins.  The VSP bins were
aggregated into low (green),  medium (yellow) and high (red) as identified in Table 5. Low power
means  the lowest third, medium is the middle third, and high is the highest third, of the  engine's
rated power.  For example, for an engine rated at 450 hp, the low power category would include
operation between 0 and 150 hp, medium between 150 and  300 hp, and high between 300 and 450
hp.

                    Table 5 - Accessory use as a function of speed and load ranges
\^ Speed
Load ^^\
Low
Mid
High
Low
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Mid
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
Cooling Fan
Air cond.
Engine Access.
Alternator
Air Compress
High
Air cond.
Engine Access.
Alternator
Air cond.
Engine Access.
Alternator
Cooling Fan
Air cond.
Engine Access.
Alternator
We next estimated the power required when the accessory was "on" and percentage of time this
occurred.  The majority of the load information and usage rates are based off of information from
"The Technology Roadmap for the 21st Century Truck."6

The total accessory load is  equal to the power  required to operate the accessory multiplied by the
percent of time the accessory is in operation.  The total accessory load for a VSP bin is equal to the
sum  of each accessory load.   The  calculations are  included in Appendix A.I  Calculation of
Accessory Power Requirements for VSP bins.

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The total accessory loads Pioss,acc listed below in Table 6 is subtracted from the engine power
determined from Equation 1 to get net engine power available at the engine flywheel.

                      Table 6 - Estimates of accessory load in kW by power range
Engine power
Low
Mid
High
HDT
8.1
8.8
10.5
MHD
6.6
7.0
7.8
Urban Bus
21.9
22.4
24.0
We then accounted for the driveline efficiency.  The driveline efficiency accounts for losses in the
wheel bearings, differential, driveshaft, and transmission.  The  efficiency values were determined
through literature searches. Driveline efficiency rjdriveiine varies with engine speed, vehicle speed,
and vehicle power requirements.  We performed a literature search7'8'9'10'11'12'13'14'15 to determine an
average value for driveline efficiency. Table 7 summarizes our findings.

                  Table 7 shows driveline efficiencies found through literature research.
                             General truck:
Barth (2005)
Lucic (2001)
80-85%
75-95%
HDT:
Rakha
NREL (1998)
Goodyear Tire Comp.
Ramsay (2003)
21st Century Truck (2000)
SAE J2188 Revised OCT2003:
Single Drive/direct
Single Drive/indirect
Single Drive/double indirect
Tandem Drive/direct
Tandem Drive/indiriect
Tandem Drive/double indirect
75-95%
91%
86%
91%
94%

94%
92%
91%
93%
91%
89%
Bus:
Pritchard (2004): Transmission Eff.
Hedrick (2004)
MIRA
96%
96%
80%
Based on our research, we used a driveline efficiency of 90% for all HD regulatory classes.
Equation 2 shows the translation from engine power Peng to axle power Paxie-
                                "axle  = tf driveline \"eng ~ "loss,acc )           Equation 2
Finally, to calculate VSP, we divided the axle power by the  average weight for each regulatory
class.  We did not use the test weight because our test  weights varied by truck or by test.  Also,
since running weights vary on a much broader range than light-duty vehicles and since we are
characterizing NOx  emissions by engine parameters, using the vehicles actual test weight would
confound the results. Using an average running weight  was the most appropriate. We did this by
analyzing the Vehicle Inventory and Use Survey16 data to establish a VMT-weighted average
running weight for each regulatory class mavg.regciass, shown in Table 8.
Table
3 - Average running weight by regulatory class in metric tons
Regulatory Class
HHD
MHD
Average running weight
27.7
11.4


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LHD345
LHD2b
BUS
6.3
5.0
16.6
Equation 3 shows the conversion of axle power to VSP using the method explained above.

                                              *axle
                                     VSP = -
                                           m
                                                                     Equation 3
                                             avg,regclass
Due to low power-to-weight ratios compared to light-duty vehicles, HHD trucks do not regularly
reach VSP levels greater than 12 kW/metric ton, and MHD trucks greater than 18 kW/metric ton.
To calculate VSP and emission rates for LHD trucks, engine and emissions data for MHD trucks
were used, given the similarity in average engine size, to calculate engine power.  We assume
negligible accessory  losses and the same 90% driveline loss.  Then we divided the resulting axle
power by the average weights in the LHD regulatory  classes (listed  in Table 8).   We then
constructed operating mode bins defined by VSP and vehicle speed according to the methodology
outlined earlier in MOVES development17 and described in Table 9.

Table 9 - Definition of the MOVES Operating Mode Attribute for Motor Vehicles (opModelD)
Operating
Mode Bin
0
1
11
12
13
14
15
16
21
22
23
24
25
27
28
29
30
33
35
37
Operating Mode
Description
Deceleration/Braking
Idle
Coast
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Coast
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
Vehicle-Specific Power
(VSPB kW/metric ton)


VSP,< 0
0 
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38
39
40
Cruise/Acceleration
Cruise/Acceleration
Cruise/Acceleration
18
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(Equation 6) and dividing the standard error by the mean emission rate to get the coefficient of
vari ati on cv _ (Equati on 7).
          'pot
                                     S-  = hL^ + ^HL               Equation 6
                                      rpol    \ "veh   "tot
                                               5- ;
                                        - v pol = -=*-                  Equation 7
                                               r
                                               'po/
1.1.1.3.3 Hole Filling and for coasting
Since the data only covered model years 1994 through 2006, we needed to develop a method to
forecast emissions for future model years and back-cast emissions for past model years. For future
model years (2007 and later), we decreased the emission rates for all operating mode bins by a ratio
proportional to the decrease in the effective emissions  standard.  While the  2007 NOx standard is
0.2 g/bhp-hr, it is phased in through 2010 MY. Instead of phasing in aftertreatment technology,
most  manufacturers decided  to meet  a constant  1.2  g/bhp-hr standard, which did not require
aftertreatment, from 2007 to 2009 MY (down from 2.4 g/bhp-hr in 2006). Therefore, we decreased
the 2003-2006 emission rates by 50%  for 2007-2009  MY.  Starting in the 2010 MY, the NOx
standard for all heavy-duty trucks will be 0.2 g/bhp-hr.  Almost all of these trucks will be using
SCR aftertreatment technology, which we are assuming to have a 90% NOx reduction  efficiency
from 2006 MY levels.

For model  year 1990, we  increased the 1991-1997 emission  rates by 20% to account for  the
reduction in NOx standard from 6.0 to 5.0 g/bhp-hr from 1990 to 1991. For 1989 and earlier model
years, we increased the 1991-1997 model year group emission rates by 40%, which is proportional
the increase of the certification levels from the 1991  model year to the 1989 model year.  The
certification levels  came from MOBILE618. We assumed that emission levels did not change by
model year for 1989 and earlier.

While we do not expect rates for high  VSP for heavy- and medium-heavy duty vehicles, due to
their power-to-weight ratios,  we still need to have rates present in the model for completeness.
Thus, we applied to those operating mode bins the rates calculated from data for the highest VSP
level.

For certain model years, such as 1998, data existed for HHD trucks, but not MHD or buses.   In
these situations, the ratio between the missing  regulatory class and HHD regulatory class from the
1999-2002  model year group was used to determined missing class's rates by multiplying that ratio
by the existing HHD emission rates  for the corresponding model  year group.

1.1.1.3.3.1 LNT-equipped pickup trucks
To meet the 2010 model year NOx emissions standards, the use of aftertreatment will likely be
needed. Cummins decided to use aftertreatment starting in 2007 on their Dodge Ram pickup truck
to meet the 2010 standards in  2007. The technology they used was a Lean NOx Trap (LNT). This

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technology allows for the storage of NOx during fuel-lean operation and conversion of this stored
NOx into N2 and H2O during brief periods of fuel-rich operation.  EPA's NVFEL acquired one of
these trucks and performed local on-road PEMS testing in 2007. We used the PEMS and ECU
output to determine VSP and emission rates in a similar way we did for the heavy-heavy-duty truck
NOx rates. For heavy-duty vehicles in 2007 and later, a diesel paniculate filter (DPF) is required
to meet PM standards.  Every so often, the DPF must be regenerated to remove and combust the
PM from  the  filter to relieve backpressure and ensure proper engine exhaust function.  This
required that exhaust temperatures be high. However, these high temperatures adversely affect the
LNT's NOx storage ability, causing tailpipe emission levels of NOx to increase.  Therefore, while
analyzing  the  2007 Dodge Ram  data,  we  separated regimes of PM regeneration from normal
operation based on exhaust temperature.  We performed the emission rate-VSP analysis separately
for each regime, and weighted the two regimes together based on  an assumed PM regeneration
frequency  of 10% of VMT.  This is only an assumption based on the limited testing conducted. We
will look to update this number based on any further data collection or research.

Since these LNT-equipped trucks  are only about 25% of the LHDDT market, we again weighted
the rates for the two LHD regulatory classes for model years 2007 and later.  We assume that the
remaining 75% of LFtD diesel trucks from model year 2007 to 2009 will not have aftertreatment
and exhibit the emission rates described in the hole filling section. We assume that the remaining
75% of LFID  diesel  trucks in model year 2010 and  later will be equipped with SCR,  and will
exhibit 90% NOx reductions from 2006 levels, also described in the hole filling section.

Table 10 summarizes this and previous subsections regarding the methodology used to determine
emission rates  for each regulatory class-model year group combination.

Table 10 - Summary of methods for heavy-duty diesel NOx emission rate development for each regulatory class
and model year group
Model year group
Pre-1988
1988-1989
1990
1991-1997
1998
1999-2002
2003-2006
2007-2009
2010 +
HHD
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to
certification levels
Data analysis
Data analysis
Data analysis
Data analysis
Proportioned to
standards
Proportioned to
standards
MHD
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to HHD
Proportioned to HHD
Data analysis
Data analysis
Proportioned to
standards
Proportioned to
standards
Bus
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to
certification levels
Data analysis
Proportioned to HHD
Data analysis
Data analysis
Proportioned to
standards
Proportioned to
standards
LHD2b
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to HHD
Proportioned to HHD
MHD engine data
with LHD2b weight
MHD engine data
with LHD2b weight
Data (LNT), and
proportioned to
standards (non-LNT)
Proportioned to
standards
LHD345
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to
certification levels
Proportioned to HHD
Proportioned to HHD
MHD engine data
with LHD345 weight
MHD engine data
with LHD345 weight
Proportioned to
standards
Proportioned to
standards
An important point to note is that we did not make a stratification based on age for vehicles not
equipped with NOx  aftertreament technology (largely 2009 model year and earlier).   This  is
because of a few reasons:
    •  The WVU MEMS data did not show an increase in NOx emissions with odometer (and
consequently age) before and after useful life19.  Since the trucks in this program were collected

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from in-use fleets, we do not believe that these trucks were necessarily biased toward cleaner
engines.
    •  Manufacturers often certify zero or low deterioration factors.
    •  We estimated tampering and mal-maintenance effects on  NOx emissions to be  small
compared to other pollutants - around a 10% increase in NOx over the useful life of the engine.
Our tampering and mal-maintenance estimation methodology is discussed in detail in Appendix
                                                                                       10

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A.2 Tampering and Mai-maintenance.
1.1.1.3.4 Tampering and Mai-maintenance
Table 11 shows the estimated aggregate NOx emissions increases due to T&M. It also shows the
values that we actually used for MOVES analysis.  As previously mentioned, we assumed that in
engines not equipped with aftertreatment, NOx does not increase due to T&M or deterioration.

Table 11 - Fleet-average NOx emissions increases from zero-mile levels through useful life due tampering and
mal-maintenance
Model years
1994-1997
1999-2002
2003-2006
2007-2009
2010+ SCR
2010+LNT
NOx increase from
T&M analysis [%]
10
14
9
11
87
72
NOx increase in
MOVES [%]
0
0
0
0
87
72
As
described
                                                         in
Appendix
                                                                                        11

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A.2 Tampering and Mai-maintenance, these emissions increases are combined with Table 35 to
determine the emissions increase  for each  age group prior  to the  end  of useful life for each
regulatory class.  With the introduction of aftertreatment systems to meet  regulatory requirements
for 2010 model  year  and  later, EPA  expects  tampering and mal-maintenance to significantly
increase emissions over time compared to the zero-mile level. Though 87% may appear to be a
large increase of fleet-average emissions over time, it should be noted that the 2010 model year
standard (0.2 g/bhp-hr) is about 83% lower than the 2009 model year effective standard (1.2 g/bhp-
hr). This still yields a substantial reduction of about 69% from 2009 zero-mile levels to 2010 fully
deteriorated levels. As more data becomes available for future model years, we will look to update
these tampering and mal-maintenance and overall aging effects.
1.1.1.4 Sample results
The charts in this sub-section will  show examples of the emission rates that resulted from the
analysis. Not all rates are shown, but enough are shown to reveal the most common data trends and
hole-filling results.  The light-heavy duty regulatory classes are not shown for simplicity, but since
the medium-heavy data were used for much of the light-heavy duty emission rate development, the
light-heavy duty rates follow similar trends.

In Figure 1, we see that NOx emission rates increase with VSP.  Also, the leveling of emission
rates  at high VSP/operating  modes can be seen.   This is  strictly a matter of hole filling for
completeness in the model; the high operating modes will not be reached given the low power-to-
weight ratio of heavy-duty vehicles.  For light-heavy duty trucks, which run lighter but have similar
engine sizes as medium-heavy duty trucks, leveling off for high VSP  in not required since those
VSP levels can be attained in normal operation (similar to light-duty trucks).

Figure 1 shows NOx trends by operating mode for MHD, HHD, and Transit bus regulatory classes for model
year 2002.
2000 -,
1800 -
„ 1600 -
J3> 1400 -
3
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(S 1000 -
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                  0  1  11 12 13  14 15 16  21 22 23  24 25 27  28  29 30  33  35 37  38  39 40

                                         Operating Mode
                                                                                        12

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The effects of model year can be seen in Figure 2, which shows decreasing NOx rates by model
year group for a sample operating mode (# 24).  This chart is a combination of data analysis (model
years  1991 through 2006) and hole filling.  The trends in the data are expected, since model year
groups were formed on the basis of NOx standards.

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

             3500 n
             3000
          V> 2500
             200ฐ
          I/)
          n
          .a
             1500
          a  1000
          o
             500
  i
                  1989 and
                   earlier
                           1990
                                 1991-1997
                                           1998
1999-2002  2003-2006  2007-2009  2010 and
                          later
                                         Model year group
Age effects were only implemented for aftertreatment-equipped trucks (mostly model year 2010
and later) using our tampering and mal-maintenance analysis. Due to faster accumulation of miles,
the heavy-heavy duty trucks reach their maximum emission rates the fastest, as shown in Figure 3.
Coefficients of variation from previous model year groups were used to determine uncertainties for
MY 20 10.
                                                                                          13

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              Figure 3 shows NOx trends by age for model year 2010 for operating mode 24.
              ฃ 120
              a
              8
              S
                      0-3      4-5      6-7      8-9     10-14     15-19      20+
                                        Age group [years]


1.1.2 Particulate Matter (PM)

In this  section particulate  matter emissions  are  defined  as  particles emitted from heavy-duty
engines which have a mean diameter less than  2.5  microns.   Such particles consist  of three
subtypes.  These are (1) elemental carbon (EC) which is  the usually black colored soot that is
emitted from combustion, (2) organic carbon (OC) which  consists of particles of organic matter
which is formed during the combustion process or immediately after in the tailpipe.  It  does not
include particle  formed in  secondary reactions in the atmosphere, (3) sulfate particulate  which
consists of particles of sulfur emitted from engines as the result of fuel sulfur.  These subtypes are
the actual inputs in MOVES.

As mentioned for NOx, the heavy-duty diesel PM emission  rates in MOVES also are a function of:
(1) source bin, (2) operating mode, and (3) age group.

We stratified the data in the following model year groups in MOVES. These  are generally based
on the introduction of EPA emission standards for  heavy-duty diesel engines. They also serve as a
surrogate for continually advancing emission control technology on heavy-duty engines.  Table 12
shows the model year group range and the applicable emission standards in units of gram per brake
horsepower-hour (g/bhp-hr). The EPA standard was in terms of engine 'work' and was applied to
all classes of heavy-duty engines.
            Table 12 -Model year groups used for analysis based on the PM emissions standard
Model Year Group Range
1960-1987
1988-1990
EPA PM Engine
Standard [g/bhp-hr]
No transient cycle standard
0.60
                                                                                        14

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1991-1993
1994-1997
1998-2006
2007+
0.25
0.10
0.10
0.01
The MOVES model has the capability of differentiating heavy duty trucks according the engine
size, vehicle weight, and injection type (direct and indirect) as was done for the total energy rate
inputs20.   However, for PM  emissions  the  data  was  just too sparse to consider such  fine
stratification, and it was not done.


1.1.2.1  Data Source
All of the data used to develop the MOVES PM2.5 emission rates comes from the CRC E-55/59
test program21.  The following  description by Dr. Ying Hsu and Maureen Mullen of E. H. Pechan,
"Compilation of Diesel Emissions Speciation Data - Final Report" provides a good summary of the
test program. It is reproduced in the following paragraphs immediately below.

The objective of the CRC E55/59 test program was to improve the understanding of the California
heavy-duty vehicle emissions inventory by obtaining emissions from a representative vehicle fleet,
and to include unregulated emissions measured for a subset of the tested fleet.  The sponsors of this
project include CARB, EPA, Engine Manufacturers Association, DOE/NREL, and SCAQMD.  The
project consisted of four segments, designated as Phases 1, 1.5,  2, and 3. Seventy-five vehicles
were  recruited in total for the program,  and recruitment covered the model year range of 1974
through 2004. The number and  types of vehicles tested in each phase are as follows:

       • Phase 1:    25 heavy heavy-duty (HHD) diesel trucks
       • Phase 1.5:   13 HHD  diesel trucks
       • Phase 2:    10 HHD  diesel trucks, seven medium heavy-duty (MHD) diesel trucks
                     2 MHD gasoline trucks
       • Phase 3:    9 MHD diesel, 8 HHD diesel, and 2 MHD gasoline

The vehicles tested in this  study were procured in the Los Angeles area, based on model years
specified by the sponsors and by engine types determined from a survey. WVU measured regulated
emissions data  from these  vehicles and gathered emissions samples. Emission samples from a
subset of the vehicles were  analyzed by Desert Research Institute for chemical species detail. The
California Trucking Association assisted in the selection of vehicles to be included in this study.
Speciation data were obtained  from a total of nine different vehicles.  Emissions were measured
using WVU's Transportable Heavy-Duty Vehicle Emissions  Testing Laboratory. The laboratory
employed a chassis dynamometer, with flywheels and eddy-current  power absorbers, a full-scale
dilution tunnel,  heated probes and sample  lines and  research grade  gas  analyzers. PM  was
measured gravimetrically. Additional sampling ports on the dilution tunnel supplied dilute exhaust
for capturing unregulated species and PM size fractions. Background data for gaseous emissions
were  gathered for each  vehicle test and separate tests were  performed to  capture  background
                                                                                       15

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samples of PM and unregulated species. In addition, a sample of the vehicles received Tapered
Element Oscillating Microbalance (TEOM) measurement of real time particulate emissions.

The HHDDTs were tested under unladen, 56,000 Ib, and 30,000 Ib truck load weights. The driving
cycles used for the HHDDT testing included:

       • AC50/80;
       •UDDS;
       • Five modes of an HHDDT test schedule proposed by CARB:  Idle, Creep,  Transient,
        Cruise, and HHDDT_S (a high speed cruise mode of shortened duration)
       • The U.S. EPA transient test

The  proposed CARB HHDDT test cycle  is based  on California truck  activity data, and was
developed to improve the accuracy of emissions inventories. It should be  noted that the transient
portion of this proposed CARB test schedule is similar but not the same as the EPA certification
transient test.

The tables  below provide a greater detail of the data used in the analysis.  Vehicles counts are
provided  by number of vehicles, number of tests, model year group and regulatory class (46 =
MHD, 47=HHD) in Table 13.
Table 13 - Vehicle and Test Counts by Regulatory Class and Model Year Group
Regulatory Class
MHD
HHD
Model Year Group
1960 - 1987
1988 - 1990
1991 - 1993
1994 - 1997
1998-2006
2007 +
1960 - 1987
1988 - 1990
1991 - 1993
1994 - 1997
1998-2006
2007 +
Number of tests
82
39
22
39
43
0
31
7
14
22
171
0
Number of vehicles
7
5
2
4
5
0
6
2
2
5
18
0
Test counts are provided by test cycle in Table 14.
                           Table 14 - Vehicle Test Counts by Test Cycle
Test Cycle Name
CARB-T
CARB-R
CARB-I
UDDS W
AC5080
CARB-C
Number of tests
71
66
42
65
42
24
                                                                                      16

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CARBCL
MHDTCS
MHDTLO
MHDTHI
MHDTCR
34
63
23
24
29
1.1.2.2 Analysis

1.1.2.2.1 Calculate VSP in 1-hz data
Within source bins, data was further sub-classified on the basis of "operating mode," designated as
the MOVES attribute "opModelD." For motor vehicles, operating mode is defined in terms of 23
bins  defined  in terms  of vehicle-specific power (VSP), vehicle speed and vehicle  acceleration.
These are the same bins as were defined in Table 9 in the NOx discussion.

The first step in assigning operating mode  is to calculate vehicle-specific power (VSP) for each
emissions measurement. At a given time t, the instantaneous VSPf (kW/metric ton), at a frequency
of 1.0 Hz) represents the vehicle's tractive power normalized to its weight. The VSP parameter is
expressed as a third-order polynomial in speed, with additional terms describing acceleration and
road-grade effects. The coefficients for this expression, often called road load coefficients, factor in
the tire rolling resistance,  aerodynamic drag,  and friction losses in the drivetrain.  We calculated
VSP using the equation below:
                               VSP = Av<
                                            m
Equation 8
                                              avg,regclass
where
    A = the rolling resistance coefficient [kW-sec/m],
    B = the rotational resistance coefficient [kW-sec2/m2],
    C = the aerodynamic drag coefficient [kW-sec3/m3],
    m  = mass of individual test vehicle [kg],
    tnavg.regciass = average running mass for regulatory class [metric ton] (see Table 8)
    vt = instantaneous vehicle velocity at time t [m/s], and
    at = instantaneous vehicle acceleration [m/s2].

The values of coefficients A, B, and C are the  road load coefficients pertaining to the heavy-duty
vehicles22 as  determined through previous analyses for EPA's Physical Emission Rate Estimator
(PERE).  This method of calculating VSP is similar to how VSP was calculated for the light-duty
vehicle emission rate analysis for MOVES, except that the average regulatory class weight is used
in the denominator, instead of the actual test weight of the vehicle.

This is different from the way VSP was calculated for the NOx emission rate analysis since the
data PM was collected on a chassis dynamometer instead of an onboard emission measurement
system.   For PM,  the power portion  (numerator) of the  VSP equation was determined through
vehicle speed and acceleration measurements from the chassis testing, whereas for NOx, the power
                                                                                        17

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was  determined through engine control  unit broadcasts.  Grade effects are not included  in any
equations because grade does not  come into play on chassis dynamometer tests, and it is already
accounted for if VSP is calculated through engine speed and torque from the engine control unit.
Operating mode bins were determined using the same  method for NOx and all other pollutants
(Table 9).

1.1.2.2.2 Normalize the TEOM Readings
Only heavy-duty diesel vehicles and tests in the E-55/59 test program that received both real time
one hertz TEOM data and full cycle filter paniculate data were utilized in the development of the
emission rates.  Since the  development of MOVES  emission rates  is cycle  independent, all
available cycles / tests which met the above  requirement were utilized.  As  a result, 488,881
seconds of TEOM data were utilized.  The process required that each individual  second by  second
TEOM rate  be normalized using  its  overall cycle filter PM measurement  (each combination of
vehicle  and test received a filter PM test).    This was  necessary because individual  TEOM
measurements  are highly uncertain and vary widely in terms of magnitude  (extreme positive and
negative absolute readings can occur). Thus, only the relative contribution of a given one  second
measurement was used in the computation  of the MOVES emission rates.  The equation below
shows the normalization process for a particular one second TEOM measurement.


                           PM         ~  PMfilter']   PM
                                                     'M
 Where
       / is the individual one second measurement,
      j is the particular test cycle (resultID - combination of vehicle and test),
       PMTEoM,ij is the TEOM measurement at second / during cycley,
       PMfiiterj  is the Total PM2.5 emissions collected on a filter over cycley,
       PMnormauzedtij is the Total PM2.5 emission result for second / and cycley average, according
       the source bin, operating mode bin, model year group and age group.

1.1.2.2.3 Compute Total PM2.5 Mean Emission Results by MO VES Bin
After normalization, the  data were stratified by  regulatory  class, model year group and the 23
operating mode bins (i.e., collectively called "MOVES  bins").  Mean average results,  count and
standard deviation statistics for PM2.5  emission values were computed in terms of grams of PM2.5
per hour for each MOVES bin.  In cases where the vehicle and  TEOM test counts were sufficient
for a given MOVES bin, these mean  values became the MOVES basic emission rates for total
PM2.5.  In cases of no data or insufficient data for particular MOVES bin, a regression technique
was utilized to fill 'holes'.

1.1.2.2.4 Hole filling and Forecasting
1.1.2.2.4.1 Missing operating mode bins
Detailed in Appendix A. 4 Regression  to develop  PM emission  rates for missing operating mode
bins, a log-linear regression was performed on the existing PM data against VSP to fill in emission
                                                                                       18

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

1.1.2.2.4.2 Other Regulatory Classes
The TEOM data was only available in quantity for MHD and HHD classes.  There were no data
available for the LHD or bus classes.   These rates were computed using simple multiplicative
factors based either on engine work ratios or PM  emission standards (i.e., buses versus  heavy
trucks). The LHD classes' emission rates are a ratio of the MHD emission rates, and bus (class 48)
emission rates are proportioned to HHD rates.  The emission rates of LHD2b are assumed to be
equal to 0.46 * MHD emission rates. The emission rate of LHD345 is assumed to be 0.60 * MHD
emission rate.

       LHD2b emission rate        =      0.46 * MHD emission rate
       LHD345 emission rate       =      0.60 * MHD emission rate

The values of 0.46 and 0.60 are the ratios of the MOBILE6.2 heavy-duty conversion factors23 (bhp-
hr/mile) for the lighter trucks versus the MHD trucks.  These are ratios of the relative amount of
work performed by a lighter truck versus a heavier truck for a given distance.  The basis for this
assumption is that the certification standards in terms of brake horsepower-hour (bhp-hr) are the
same for all of the heavy-duty engines.

Urban Bus (Class48) emission rates are assumed to be either the same as the HHD emission rates,
or for some selected model year groups, to be a ratio of the EPA certification standards:

1991-1993 model years           Bus Emissions = (0.10/0.25) * HHD emissions

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

1996 - 2006 model years           Bus Emissions = 0.50 * HHD emissions
1.1.2.2.4.3 Model year 2007 and later trucks (with diesel particulate filters)
EPA heavy-duty diesel emission regulations were made considerably  more stringent for total
PM2.5 emissions starting in model year 2007. Ignoring phase-ins and banking and trading issues,
the basic emission standard fell from 0.1 g/bhp-hr to 0.01 g/bhp-hr. This increase by a factor ten in
the level of regulatory stringency required the use of particulate trap systems on heavy-duty diesels.
As a result, the emission performance of diesel vehicles has likely changed dramatically.

Unfortunately, no second-by-second TEOM data were available for analysis on the 2007 and later
model  year vehicles.  As a result, the emission factors  for this  most important group of vehicles
was  assumed to be a ratio of the 1998-2006 model year vehicles for each respective regulatory
class and operating  mode.   After adjusting for  age effects, we  decreased the 2006 MY PM
emission rates by 90% for the 2007 MY and later PM emission rates.

Since the vehicles of 2007 and later model year are the most critical group for future inventories,
EPA will likely re-evaluate, and if necessary, update this assumption when sufficient data have
                                                                                        19

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been collected on in-use and fully 'seasoned' vehicles.  EPA will also look at recent certification
data to assess the zero-mile PM emission levels, as DPF effectiveness may be higher than the 90%
stated here.

1.1.2.2.5 Tampering and Mai-maintenance
The MOVES model now contains assumptions for the frequency and emissions effect of tampering
and mal-maintenance on heavy-duty diesel trucks and buses.  The assumption of tampering and
mal-maintenance (T&M) of heavy-duty diesel vehicles is a departure from the MOBILE6.2 model
which  assumed such  vehicles operated from build to final scrappage at a design emission level
which  was lower than  the prevailing EPA  emission standards.   Both long term anecdotal data
sources and  more comprehensive  studies now  suggest that  the assumption  of no natural
deterioration  and/or no deliberate tampering of emission control components  in the heavy-duty
diesel fleet was likely an unrealistic assumption.

The primary data set was collected during a limited calendar year period, yet MOVES requires data
from a complete range  of model year / age combinations. As a result, the T&M  factors  shown
below in Table 15 were used to forecast or back-cast the basic PM emission rates to model model
year group-age group combinations that were not part of the primary data set.  For example, for the
1981 through 1983 model year group, the primary dataset contained data which was in either the 15
to 19 or the 20+ age groups. No historical data is  available on these model years.  However, for
completeness, MOVES  must have emission rates for these model years at age 3, 4 to 5, 6 to 7, etc.
As a result, unless the assumption that the higher emission rates which are currently prevailing on
the older model year vehicles have always prevailed - even  when they were young, a modeling
approach such as the T&M must be employed. Likewise, younger model years were tested only at
lower age levels.  No data has yet been collected  on younger model year vehicles at high ages.
The T&M methodology used in the MOVES analysis allows  for the filling of age - model year
group bins for which no data is available.

One criticism of the MOVES T&M methodology is that it may double count the effect of T&M on
the fleet because the primary emission measurements, and base emission rates, were made on in-
use vehicles which during the testing period may have had some maintenance problems. This issue
would be most acute for the 2007 later model year vehicles where all of the deterioration is subject
to projection. However, for this model year group of vehicles, the base emission rates start at low
levels,  and represent vehicles that are virtually free from T&M.

We followed the same tampering and mal-maintenance methodology and analysis for PM as we did
for            NOx,             as            described            in             Appendix
                                                                                      20

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A.2 Tampering and Mai-maintenance.   The overall  MOVES tampering  and mal-maintenance
effects on PM emissions over the fleet's useful life are shown in Table 15.
Table 15 shows tampering and mal-maintenance emissions increase estimates for HC and CO over the useful life
of trucks.
Model Year Group
Pre-1998
1998-2002
2003 - 2006
2007 - 2009
2010 +
Percent increase in
toT&M
PMdue
85
74
48
50
50
1.1.2.2.6 Compute Elemental Carbon and Organic Carbon Emission Factors
The MOVES model reports PM2.5 emissions according to three sub types.  These are Elemental
Carbon (EC), Organic Carbon (OC) and sulfate. Both EC and OC are computed directly from the
total PM2.5 emissions using multiplicative factors.  Sulfate is computed using a fuel sulfur balance
(see MOVES Sulfate report for details).   Since the fuel sulfur levels in the underlying study were
not generally known, but believed to be small, sulfate emissions were ignored in the total PM2.5
emission levels.  As a result, total PM2.5 was assumed to be comprised of only EC and OCarbon
(In the final version of MOVES a generic sulfate emission factor will be subtracted from the Total
PM and the equation below will not be used).
TotalPM2.5  =
EC + OC
Thus, OCarbon Fraction = 1.0 - EC Fraction

The final EC Fractions used  in  MOVES for pre-2007 model year trucks  (i.e. before diesel
particulate filters (DPFs) were standard) are shown in Figure 4. These vary according to regulatory
class and MOVES operating mode bin. They typically range from 25 percent at low loads (low
VSP) to over 90 percent at highly loaded modes.  All of the EC fractions were developed by Chad
Bailey and are documented in Appendix A.5 Heavy-duty Diesel EC/OC Fraction Calculation.  The
primary dataset used in the analysis came from Kweon et al. (2004) where particulate composition
and mass rate data were collected on a Cummins N14 series test engine over the CARB eight-mode
engine test cycle.   The EPA PERE model and a Monte Carlo approach were used to simulate and
translate the primary PM emission results into MOVES parameters (i.e., operating modes).
                                                                                      21

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


C 06
s
O n4
UJ U-4 "
n ^
n 9

n _
• • *
*
•


•
•

• • J
• • •


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•
* .


•





• • • •
^

• • •
+


*HHD
•
• MHD


         0   1  11  12  13  14  15  16  21  22 23  24  25  27  28  29 30 33 35  37  38  39  40
                                   Operating mode bin
A different methodology was used to compute EC factors for 2007 and later model year, DPF-
equipped vehicles.  For these vehicles  it is believed that virtually all of the paniculate that is
emitted from the tailpipe will be OC and that only a modest fraction will  be EC.  The traps  are
designed to capture virtually all of the carbon. Potentially, small amounts of OC  and sulfate may
escape. This is essentially the opposite of the non-trap equipped heavy-duty diesel vehicles where
the total PM2.5  is dominated by EC.  Unfortunately, only limited particulate data exists  on trap
equipped vehicles.  These  data are based on particulate matter bound ionic species and  EC/OC
emissions data from a few trap equipped buses and a heavy-duty tractor.  The data were extracted
and  a  simple average computed from  the SAE paper 2002-01-0432  "Chemical Speciation of
Exhaust Emissions from Trucks and Buses Fueled on Ultra-Low Sulfur Diesel and  CNG"24. Based
on the date of the paper, it is likely that all of the diesel vehicle / trap systems were prototypes.
Extraction  of data from the paper yielded only a single  factor which will be applied to  all
regulatory types  and operating modes for 2007 and later diesel trucks and buses. This factor is  the
elemental carbon fraction.  Table  16 summarizes the EC and OC fractions determined from  the
paper.  These fractions were used for all operating mode bins for model years 2007  and later heavy-
duty vehicles.

          Table 16 shows EC and OC fractions used for DPF-equipped heavy-duty diesel vehicles.
EC fraction
OC fraction
0.0861
1 - ECfraction = 0.9139
As additional data becomes available, EPA will likely revise the EC Fraction used in MOVES.

Temperature Correction Factors

The  MOVES  model draft release of March, 2009 will not contain  any temperature correction
factors  for  PM2.5  emissions from heavy-duty  diesel  vehicles.  This  absence of temperature
correction factors does not imply that EPA believes that heavy-duty diesel vehicle PM emissions
are  insensitive to temperature effects.   In fact, it is quite  likely that the inverse is true.   Both
running  and  start PM emissions  from at least non-trap equipped vehicles  are sensitive to
                                                                                         22

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temperature.  However, EPA at this time cannot adequately quantify such emission effects, and is
currently using a multiplicative placeholder value of unity  as the temperature correction factor.
EPA will update the MOVES model when sufficient data on diesel temperature correction factors
is available for analysis and inclusion in the model.
1.1.2.3 Sample  results
Figure 5 shows how PM rates increase with VSP.  Also, the EC fraction of the PM rate increases
with VSP as well. The leveling off at high VSP is due to our manual hole filling. At high speeds
(greater than 50 mph; operating modes > 30), the overall PM rates are lower than the other speed
ranges.

Figure 5 shows PM rates by operating mode bin for model year 2002 age 0-3 for HHD trucks.
  O)
AC,
40
35



or


m
5

n











n-























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-





















PI




-







































-

























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no






LJ


























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

Figure 6 shows an example of how our tampering and mal-maintenance estimates increase PM with
age.  The EC fraction does not change by age, but the overall rate increases and levels off after the
end of useful life. This figure shows the age effect for MHD. The rate at which emissions increase
toward their maximum depends on regulatory class.
                                                                                       23

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Figure 6 shows PM rates by age group for model year 2002 in operating mode 24 for MHD trucks.
      14 -r
      12 --
      10	
       8 --
   2
   S
   Q.
       4	
       2	
             0-3
                       4-5
                                  6-7
                                            8-9
                                                      10-14
                                                                15-19
                                                                           20+
                                    Age group [years]
Figure 7 shows the effect of model year on emission rates.  Emissions generally decrease with new
PM standards.  The EC fraction stays constant until model year 2007, when it is nearly zero due to
widespread DPF use. The overall PM level is substantially lower starting in model year 2007. The
earlier model years' emission rates represented here are an extrapolation of the T&M analysis since
young-age engines from early model years could not be tested in the E-55 program.

Figure 7 shows PM rates by model year group for age 0-3 in operating mode 24 for HHD trucks.
    120 -r-
    100	
  „  80	
     60	
     40	
     20	
D
           pre-1988      1988-1990     1991-1993      1994-1997
                                       Model year group
                                                              1998-2006
                                                                            2007+
                                                                                            24

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1.1.3 Hydrocarbons (HC) and Carbon Monoxide (CO)
Diesel engines are not known to be a significant portion of the mobile source HC or CO emission
inventories.   Recent regulations  on non-methane HC (sometimes in conjunction with NOx)
combined with the common use of diesel  oxidation catalysts will yield reductions in both HC and
CO emissions from heavy-duty diesel engines.  As a result, data collection efforts do not focus on
HC or CO from heavy-duty engines. In this report, hydrocarbons are sometimes referred to as total
hydrocarbons (THC).

We used certification levels combined with emissions standards to develop appropriate model year
groups.   Since standards did not change frequently in  the past for either HC or CO, we created
fewer model year groups than we did from NOx and PM:

    •  1960-1989
    •  1990-2006
    •  2007+

1.1.3.1  Data Sources
The heavy-duty  diesel  HC and CO emission  rate  development followed a methodology that
resembles the light-duty  methodology, where  emission rates were calculated from  1-hz data
produced from chassis dynamometer testing.   Data  sources were all heavy-duty chassis test
programs:
    1.  CRC E-55/5921:  Mentioned earlier, this program represents the largest amount of 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, and testing  was conducted by West Virginia University
       from 2001 to 2005.
    2.  Northern Front Range Air Quality Study (NFRAQ)25: This study was performed by the
       Colorado Institute for Fuels and High-Altitude Engine Research in 1997.  Twenty-one HD
       diesel vehicles from model years 1981 to 1995  designed to be representative of the in-use
       fleet in the Northern Front Range of Colorado were tested over three different transient
       drive cycles.
    3   New York Department  of Environmental  Conservation (NYSDEC)26:   NYSDEC
       sponsored this study to investigate  the nature and extent of heavy-duty  diesel  vehicle
       emissions in the New York Metropolitan Area.  West Virginia University tested  25 heavy-
       heavy and 12 medium-heavy  duty  diesel trucks under transient  and steady-state  drive
       cycles.
    4.  West Virginia University testing:  We have in our  MSOD other historical HD chassis
       testing performed by WVU.

The on-road data used for the NOx analysis was not used since HC and CO were not collected in
the MEMS program, and  the ROVER program used the less accurate non-dispersive infrared
(NDIR) technology instead of flame-ionization detection (FID) to measure HC.  To keep HC and
CO data sources consistent, we used chassis test programs exclusively for the analysis of these two
pollutants.  Time alignment was performed using the method similar to light-duty chassis test data
time alignment. The quantities of vehicles in the data sets are shown in Table 17.
                                                                                     25

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Table 17 shows the number of vehicles by model year group, regulatory class, and age group that were tested in
the programs above.
Model year group
1960-2002
2003-2006
Regulatory class
HHD
MHD
Bus
LHD345
LHD2b
HHD
Age group
0-3
58
9
26
2
6
6
4-5
19
6




6-7
16
5




8-9
9
4
1
1


10-14
16
12
o
J



15-19
6
15




20+
7
6




1.1.3.2 Analysis
Like for PM, VSP was calculated like in light-duty but normalized with average regulatory class
weight instead of test weight, as described by Equation 10.
VSPt =
                                            m
                                                                     Equation 10
                                              avg,regclass
Coefficients A,  B, and  C  are road  load coefficients  pertaining to the heavy-duty  vehicles22
determined through previous analyses for EPA's Physical Emission Rate Estimator (PERE).

Using a similar method to the NOx analysis, we averaged emissions by vehicle and operating mode
bin.  We then averaged across all vehicles by  model year group, age group, and operating mode.
Statistics calculations followed the same equations and methodology as the NOx analysis.

Instead of populating all the emission rates directly into the model, we directly populated  only the
age group that was most prevalent in  each regulatory class and model year group combination.
These age groups are shown in Table 18.

Table 18 shows base age groups used directly in MOVES emission rate inputs for each regulatory class and
model year group present in the data.
Regulatory class
HHD
HHD
MHD
BUS
LHD41
Model year group
1960-2002
2003-2006
1960-2002
1960-2002
1960-2002
Age group
0-3
0-3
15-19
0-3
0-3
We then applied our tampering and mal-maintenance effects through that age point, either lower
through younger ages or higher through older ages, using the methodology described in Appendix
                                                                                        26

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A.2 Tampering and Mai-maintenance.  The tampering and mal-maintenance effects for HC and CO
are shown in
Table 19.
Table 19 shows tampering and mal-maintenance emissions increase estimates for HC and CO over the useful life
of trucks.
Model years
Pre-2003
2003-2006
2007-2009
20 10 and later
Percent increase in HC
and CO due to T&M
300
150
150
33
We multiplied these increases by the T&M adjustment factors in Table 35 in section^.2.3 Analysis
to get the emissions by age group.

With the increased used of diesel oxidation catalysts (DOCs) in conjunction with DPFs, we are
assuming an 80% reduction in zero-mile emission rates for both HC and CO starting with model
year 2007.

1.1.3.3 Sample results
The charts in  this sub-section will  show examples of the emission rates that resulted from the
analysis. Not all rates are shown, but enough are shown to reveal the most common data trends and
hole-filling results. The light-heavy duty regulatory classes are not shown for simplicity, but since
the medium-heavy data were used for much of the light-heavy duty emission rate development, the
light-heavy duty rates follow similar trends.  Uncertainties were calculated just as for NOx.

In Figure 8 and Figure 9, we see that HC and CO mean emission rates increase with VSP, though
there is much higher uncertainty than for the NOx rates.  This could be due to the smaller data set
or generally less significant with VSP. Also, the leveling  of emission rates (hole filling) at high
VSP/operating modes can be seen.
                                                                                       27

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Figure 8 shows HC emission rates [g/hr] by operating mode bin for model year 2002.
50 n
45
40
35
I30
3t 25
5
ฃ 20
15
10
5
n




,

I _
3
: <




ป
s :

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i
1 i


,

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• MHD
A Bus


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1
1 .



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ป <





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

                                 Operating mode bin


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

     400 -,


     350 -


     300 -


  "H"  250 -
  ,g  200
  2
  O
  o  15ฐ
     100
     50
                                                              M111
         0  1   11  12  13  14 15 16 21  22 23 24 25  27  28  29  30  33  35 37 38 39 40
                                 Operating mode bin

Figure 10 and Figure 11 show HC and  CO emission  rates by  age group.  These trends reflect
entirely the T&M analysis, as the age effect for HC and CO were only modeled.  Due to the T&M
effect, there are large increases as a function of age.  Since the age effect is a model, more in-use
data if collected could help determine any real-world deterioration, especially in model years where
diesel oxidation catalysts are most prevalent (2007 and later).
                                                                                          28

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Figure 10 shows HC emission rates [g/hr] by age group for model year 2002 and operating mode bin 24.
  Si
     70 -,
     60 -
     50 -
     40 -
     30 -
o
X
20 -


10 -
n


I
i
I

I i I I I I
I
I
i



#HHD
• MHD
A Bus

            0-3         4-5        6-7         8-9        10-14

                                     Age group [years]
                                                                 15-19
                                                                            20+
Figure 11 shows CO emission rates [g/hr] by age group for model year 2002 and operating mode bin
24.


     450
     400



     350 -



     300 -
  'Z*
  .C

  ฃ> 250 -

  V)

  2  200
o
0



150 -
100 -
50 -
n
\ I I I I I
I
r


ปmo
• MHD
A Bus




             0-3        4-5         6-7         8-9        10-14

                                     Age group [years]
                                                                 15-19
                                                                            20+
Figure 12 and Figure 13 show sample HC and CO emission rates by model year group.  The two
earlier model year groups are closer together, as we reduced the latest model year group due to the
                                                                                             29

-------
use of diesel oxidation catalysts. Due to the sparseness of the data and that HC and CO emission
do not track as well with VSP (or power) as NOx or PM, uncertainties are much greater.
Figure 12 shows HC emission rates by model year group for operating mode bin 24 and ages 0-3.
      30 n
     25-
     20-
  42 15
      5-
                1960-2002
                                                              2007+
                                      2003-2006
                                Model year group
Figure 13 shows CO emission rates by model year group for operating mode bin 24 and ages 0-3.
  Si
  O
  o
      250 n
      200 -
      150 -
      100
      50 -
                 1960-2002
      2003-2006
Model year group
2007+
                                                                                          30

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1.1.4 Updates for final MOVES release
The methodology and results described in this document reflect the analysis performed for the draft
release of MOVES.  For the final release, we have planned a few changes to the heavy-duty diesel
running exhaust emissions rates.
  i.    We plan to calculate VSP  differently.   Instead  of using the average  weight in each
       regulatory class to  calculate  VSP for a vehicle in that regulatory class, we will use one
       average weight for all heavy-duty vehicles to  calculate VSP for each  vehicle.  Since
       emissions regulations are based on engine power regardless of regulatory class, normalizing
       this power by different masses destroys the relation between emissions and power across
       regulatory classes.   This is  especially true for NOx and  PM, the two most significant
       pollutant emissions from diesel engines. This is also important because several source use
       types in MOVES are in multiple heavy-duty regulatory classes, and their VSP and operating
       mode distribution must be allocated to each regulatory class adequately. Using a uniform
       heavy-duty average mass preserves the relation of NOx and power by way of VSP.  Indeed,
       vehicle-specific power would be a  misnomer for the new  calculation. Rather, it can be
       thought of as power scaled by a constant factor.
  ii.    We plan to incorporate NOx benefits resulting from  reflashing heavy-duty engines from
       mid-1990's model  years.  Reflashing  refers to the reprogramming of heavy-duty diesel
       engines' engine control units  to lower NOx, as required under the consent decree that ruled
       against the so-called "emission defeat devices". The motivation for the change is to ensure
       that MOVES recognizes proactive steps taken by states to reflash to  appropriate  engines
       above and beyond the latent number  of  reflashes  in the  fleet.  Benefits  will likely be
       realized only in operating modes where the emission defeat devices were believed to have
       been most operational.
 iii.    We plan to incorporate emissions impacts  of the recently promulgated  heavy-duty onboard
       diagnostic (OBD) rule.  This  rule takes fully into effect in model year 2013 and is designed
       to lower the number of failure of aftertreament systems in the emerging fleet of heavy-duty
       vehicles.
 iv.    In light of i), our method of filling holes for missing regulatory classes will change. Since
       VSP will be essentially a scaled power, and heavy-duty engine emissions are generally a
       function of engine power, we will not adjust or modify rates for missing regulatory classes.
       The differences in overall emissions between classes can then be seen by the larger classes
       (e.g. HHD or Bus) reaching higher VSP levels compared to the smaller classes.

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1.2 Start Exhaust Emissions
The 'start' process occurs when the vehicle is started and is operating in some mode in which the
engine is not fully warmed up.  For MOVES analysis, we define start emissions as the increase in
emissions due to an engine start.  That is, we use the difference in emissions between a test cycle
with a cold start and the same test cycle with a hot start. There are also eight intermediate stages
which are differentiated by soak time length (time duration between engine key off and engine key
on), between a cold start (> 720 minutes of soak time) and a hot start FTP (< 6  minutes of soak
time).  More details on how  start emission rates are distributed as a function of soak time can be
found in subsection 7.2.3 Adjusting Start Rates for Soak Time and in the MOVES  light-duty
emission rate counterpart document Development of Emission Rates for Light-Duty Vehicles in the
Motor Vehicle Emissions Simulator.

1.2.1 HC, CO,  and NOx
For light-duty vehicles, this  is  determined by subtracting  FTP bag 3 emissions from FTP bag  1
emissions. Bag 3 and Bag 1  are the same dynamometer cycle, except that Bag 1 starts with a cold
start, and Bag 3  starts with a hot start. This similar analysis was performed for LHD vehicles tested
on the FTP and ST01, which also has separate bags containing cold  and hot start emissions over
identical drive cycles.  Data from 21  vehicles, ranging from model years  1988 to 2000, were
analyzed.  No stratifications were made for model year or age due to the limited number of
vehicles. The results of this analysis for HC, CO, and NOx are shown in Table 20:

          Table 20 shows average start emissions increases in grams for light-heavy duty vehicles
HC
0.13
CO
1.38
NOx
1.68
For HHD and MHD trucks, data was much more scant.  We tested a 2007 Cummins ISB on an
engine dynamometer at EPA's National Vehicle and Fuel Emissions Laboratory in Ann Arbor,
Michigan. Among other idle tests, we performed a cold start idle test at 1100 RPM lasting four
hours, long enough for the engine to warm up. Essentially, the "drive cycle" we used to compare
cold start and warm emissions was the idle cycle, analogous to the FTP and ST01 cycles used for
LFID vehicles. Emissions and temperature stabilized about 25 minutes into the test.  The emission
rates through time are shown in
Figure 14. The biggest drop in emission rate through the test is with CO, whereas there is a slight
increase in NOx (cold start NOx is lower than hot start NOx), and an insignificant change in HC.
                                                                                      32

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Figure 14 shows the stabilization of emissions from a cold start idle engine dynamometer test on an in-house
2007 MY Cummins ISB
                   50
                   45
                    0.00
                               1.00
                                                              4.00
                                                                        5.00
                                         2.00        3.00
                                             time [hrs]
We calculated the area under each curve for the first 25 minutes and divided by 25 minutes to get
the average emission rate during the cold start idle portion. Then, we averaged the data for the
remaining portion of the test, or the warm idle portion.  The difference between cold start and
warm start is in Table 21.  The NOx increment is negative since cold start emissions are lower than
warm start emissions.

              Table 21 shows start emissions increases in grams on the 2007 Cummins ISB.
HC
0.0
CO
16.0
NOx
-2.3
                                                     27
We also considered data from University of Tennessee , which tested 24 trucks with PEMS at
different load levels during idling.  Each truck was tested with a cold start going into low-RPM idle
with air-conditioning on. We integrated the emissions over the warm-up period to get the total cold
start idling emissions.  We determined the hot start idling emissions by multiplying the reported
warm idling rate by the stabilization time.  We used the stabilization period from our engine
dynamometer tests  (25 minutes).  Then we subtracted the cold idle emissions from the warm idle
emissions to get the cold start increment to be used in MOVES.  We found  that several trucks
produced lower NOx  emissions during  cold  start (including EPA's test), and  several trucks
produces  higher NOx emissions during  cold  start.   Due  to these  conflicting results,  and the
recognition that many factors affect NOx emission during start (e.g. air-fuel ratio, injection timing,
etc), we set  our cold start increment to zero, pending any future data that may be collected.  Table
22 shows our final  MOVES inputs for HHD and MHD diesel start emissions increases.  The HC
and CO estimates are from our 2007 MY in-house testing.

          Table 22  shows MOVES inputs for HHD and MHD start emissions increases in grams.
                                                                                          33

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HC
0.0
CO
16.0
NOx
0.0
1.2.2 Particulate Matter
Heavy-duty  truck  PM2.5 emissions for the  start  process  have not  been collected  in  any
significance.  No such rates were  available  from the  MOBILE6.2 model for use in MOVES.
Typically, heavy-duty vehicle emission measurements begin on fully warmed up vehicles.  This
test procedure bypasses the engine crank and early operating periods when the vehicle is not fully
warmed up.

For the MOVES Draft version data  one heavy-heavy-duty engine with six FTP tests using a filter
PM measurement was available from EPA engine dynamometer testing.  The engine was a 2004
model year engine.

The engine was tested at both hot and cold start conditions (two tests for  cold  start and four
replicate tests for hot start).  The average difference in PM2.5 emissions (filter measurement - FTP
cycle) was 0.10985 grams. The data are shown here:

      Cold start FTP average =     1.9314 g PM2.5
      Warm start FTP average =   1.8215 g PM2.5
      Cold start- warm start =     1.9314 g - 1.8215 g = 0.1099 g PM2.5

We applied this  value of 0.10985 g of Total PM2.5 per start for 1960 through 2006 model year
vehicles. A corresponding value of 0.01099 g was used for 2007 and later model year vehicles
(90% reduction due to DPFs). We will look to update these numbers if more data is collected. For
now, this one engine is our only data point.

1.2.3 Adjusting Start Rates for Soak Time

The discussion to this point has concerned the  development of rates  for cold-start emissions. In
addition, it was necessary to derive rates for additional  operating modes that  account for varying
(shorter) soak times.  As with light-duty  vehicles, we  accomplished this step by  applying soak
fractions. As no data are available for heavy-duty vehicles, we applied the same fractions used for
light-duty emissions. The value at  720 min (12 hours)  represents cold start, and each successive
value represents a shorter soak time, representing operating modes 107 - 101, respectively.  These
bins are not related to the bins in Table 9, which deals with running exhaust emissions. Table 23
describes the different start-related operating mode bins in MOVES as a function of soak time.
T.
ible 23 - MOVES operating mode bins for start emissions (as a function of soak time)
Operating Mode Bin
101
102
103
104
105
MOVES Operating Mode Bin Description
Soak Time < 6 minutes
6 minutes <= Soak Time < 30 minutes
30 minutes <= Soak Time < 60 minutes
60 minutes <= Soak Time < 90 minutes
90 minutes <= Soak Time < 120 minutes

                                                                                      34

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106
107
108
120
360

minutes <= Soak Time <
minutes <= Soak Time <
720 minutes <= Soak
360 minutes
720 minutes
Time
The soak fractions we used for HC, CO, and NOx are illustrated in Figure 15 below.

Figure 15 Soak Fractions Applied to Cold-Start Emissions (opModelD = 108) to Estimate Emissions for shorter
Soak Periods (operating modes 101-107).
             1.20
                            120         240         360         480

                                            Soak Time (minutes)
600
720
The actual PM start rates by operating mode bin are given in Table 24 below.

            Table 24 - MOVES Start PM Emission Rate by Operating Mode Bin (soak fraction)
Operating Mode Bin
101
102
103
104
105
106
107
108
PM2.5 (grams per start)
1960-2006 MY
0.0000
0.0009
0.0046
0.0092
0.0138
0.0183
0.0549
0.1099
PM2.5 (grams per start)
2007+ MY
0.00000
0.00009
0.00046
0.00092
0.00138
0.00183
0.00549
0.01099
                                                                                             35

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1.3 Extended  Idling Emissions
In the MOVES model, extended idling is "discretionary" idle operation characterized by extending
idle periods that are several hours in duration, including higher engine speed settings and extensive
use of accessories by the vehicle operator. Extended idling most often occurs during long layovers
between  trips  by long haul trucking  operators where the truck is used as  a residence, and is
sometimes  referred to as  "hotelling."  The use of accessories such as air conditioning systems or
heating systems  will affect emissions emitted by the  engine  during idling.  Extended idling by
vehicles  will also  cause cool-down of the vehicle's catalytic converter  system or other exhaust
emission after-treatments, when these  controls are present. Extended idle is treated as a  separate
emission process in MOVES.
Extended idling does not include vehicle idle operation  which occurs during normal road operation,
such as the idle operation which a vehicle experiences  while waiting at a  traffic signal or during a
relatively short stop, such as idle operation during a delivery.  Although frequent stops and idling
can contribute to overall emissions, these modes are already included in the normal vehicle hours
of operation. Extended idling is characterized by idling periods that last hours, rather than minutes.
For MOVES model, only diesel long-haul combination trucks are assumed to have any significant
extended idling activity.  As a result, an estimate for the extended idling emission rate  has not been
made for any of the other source use types modeled in MOVES.

1.3.1 Data Sources
The data used  in the analysis of extended idling emission rates includes idle emission results from
several test programs conducted by a variety of researchers at different times. Not all of the studies
included  all the pollutants of interest.  The references contain more detailed descriptions of the data
and how  the data was obtained.
Testing was conducted on twelve heavy-duty diesel trucks and twelve transit buses  in Colorado
(McCormick)28.  Ten of the trucks were Class 8 heavy-duty axle  semi-tractors, one was a Class 7
truck, and one of the vehicles was a school bus.  The model  year range was from 1990 through
1998.  A typical  Denver area wintertime diesel fuel (NFRAQS) was  used in all  tests.  Idle
measurements  were collected during a 20 minute time period.  All  testing was done at  1,609 meters
above sea level (high altitude).
Testing was conducted by EPA on five trucks in May 2002 (Lim)29.  The model years ranged from
1985  through  2001.  The  vehicles were put  through a battery  of tests including  a variety  of
discretionary and non-discretionary idling conditions.
Testing was conducted on 42 diesel  trucks in parallel with  roadside smoke opacity testing in
California (Lambert)30. All tests were conducted by the California Air Resources Board (CARB) at
a rest area near Tulare, California in April 2002. Data collected during this study were included in
the data provided by IdleAire Technologies (below) that was used in the analysis.
A total of 63 trucks (nine in Tennessee, 12 in New York and 42 in California) were tested over a
battery of idle  test conditions including with and without air conditioning (Irick)31.  Not all trucks
were tested under  all conditions.  Only results from the testing in Tennessee and New York are
described in the IdleAire report. The Tulare, California data are described in the Clean Air Study
                                                                                       36

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cited above.  All analytical equipment for all testing at all locations was operated by Clean Air
Technologies.
Fourteen trucks were tested as part of a large Coordinating Research Council (CRC) study of heavy
duty diesel trucks with idling times either 900 or 1,800 seconds long (Gautam)32.
The National Cooperative Highway Research Program (NCHRP)33 obtained the idling portion of
continuous sampling during transient testing was used to determine  idling emission rates on two
trucks.
A total of 33 heavy-duty diesel trucks were tested in an internal study by the City of New York
(Tang)34.  The model years ranged from 1984 through 1999.  One hundred seconds of idling  were
added at the end of the WVU five mile transient test driving cycle.
A Class  8 Freightliner Century with a 1999  engine was testing using EPA's on-road emissions
testing trailer based in Research Triangle Park, North  Carolina  (Broderick)35.  Both short (10
minute) and longer  (five hour) measurements were  made  during  idling.  Some testing was also
done on three older trucks.
Five heavy-duty trucks were tested for paniculate and NOx  emissions under a variety of conditions
at Oak Ridge Laboratories (Story)36. These are the same trucks used in the EPA study (Lim).
The University of Tennessee tested 24 1992  through 2006 model year heavy  duty diesel trucks
using  a variety of  idling conditions including variations of engine idle  speed and  load (air
conditioning)27.

1.3.2 Analysis
EPA performed an analysis of the emission impacts of extended idling for particulate matter (PM),
oxides of nitrogen  (NOx), hydrocarbons (HC), and carbon monoxide (CO) for populating the
MOVES  model.  This analysis used all of the data sources referenced in this document.  This
update reflects new  data available since the initial development of extended idle emissions for the
MOVES  model.  The  additions include the testing at Research  Triangle Park (Broderick), the
University of Tennessee study (Calcagno), and the completed E-55/59 study  conducted by WVU
and CRC.  In addition, the data was separated by truck and bus and by idle speed and accessory
usage to develop an  emission rate more representative of extended idle rates.
The important conclusion from the  EPA 2003 analysis is that factors that affect engine load,  such
as accessory use, and engine idle speed are the important  parameters in estimating the emission
rates of extended idling.  The impacts of most other factors, such as engine size, altitude, model
years  within MOVES groups, and test cycle are negligible.  This makes the  behavior of truck
operators very important in estimating the emission rates to assign to periods of extended idling.
The use  of accessories (A/C, heaters,  televisions,  etc.) provides recreation  and comfort to the
operator and increases load on the engine.  There is  also a  tendency  to increase idle speed during
long idle periods for engine durability. The emission rates estimated for the extended idle pollutant
process assume both accessory use  and engine idle  speeds set higher than used for "curb" (non-
discretionary) idling.
The studies  focused on three types of idle conditions.  The first is considered a curb idle, or low
engine speed (<1000 rpm) and no air conditioning.  The second is representative of an extended
                                                                                        37

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idle condition with higher engine speed (>1000 rpm) and no air conditioning.  The third represents
an extended idle condition with higher engine speed (>1000 rpm) and air conditioning.
The idle emission rates for heavy duty diesel trucks prior to the 1990 model year are based on the
analysis of the  18 trucks from 1975-1990 model years used in the CRC E-55/59 study and one
1985 truck from the Lim study.  The only data available represents a curb idle condition. No data
was  available  to  develop the increased  NOx emission  rates due to higher engine speed and
accessory loading, therefore, the percent increase developed from the 1991-2006 trucks was used.
Extended idle  emission rates for  1991-2006 model year heavy duty diesel trucks are based  on
several studies and 184 tests detailed in Appendix A. 3 Extended Idle Data Summary. The increase
in NOx emissions due to higher  idle speed and air conditioning was estimated based on three
studies that included 26 tests.  The average emissions from these trucks using the high idle engine
speed and with accessory loading was used for the emission rates for extended idling.
The expected effects of the 2007 heavy duty diesel vehicle emission standards on extended idling
emission rates are taken from the EPA guidance analysis (EPA 2003).  The 2007 heavy duty diesel
emission standards  are expected to result in  the widespread  use  of PM filters and exhaust gas
recirculation (EGR) and 2010  standards will result in after-treatment technologies.  However, since
there  is  no requirement to  address  extended idling emissions in the emission certification
procedure, EPA expects that there will be little effect on HC, PM,  and NOx emissions after hours
of idling due to cool-down  effects on EGR and the aftertreatment systems.  As a result, idle NOx
emissions will be reduced  12%, PM emissions will be reduced 11%, and HC  emissions will  be
reduced 9% from the  extended idle emission rates used for  1988-2006 model  year trucks.  The
reduction estimates are based on a ratio of the 2007 standard to the previous standard and assuming
that the emission control  of the new standard will only last for the  first hour of an eight hour idle.
Detailed equations are included in the Appendix.

1.3.3 Results
Table 25 shows the resulting NOx, HC, and  CO emission rates estimated for heavy  duty diesel
trucks.  Extended idling  measurements have large  variability due to  low engine loads, which is
reflected in the variation of the mean statistic.
Model years
Pre-1990
1990-2006
2007 and later
NOx
112
227
201
HC
108
56
53
CO
84
91
91
The PM rates in the draft version of MOVES do not yet reflect the analysis detailed in the previous
subsection.  For the time being, the regular curb  idle emission rates have been substituted (i.e.,
MOVES opmodeid = 1) for use in MOVES as extended idle rates. This is likely an understatement
of the "true" and currently unknown extended idle rates.  We expect the rates in the final version to
increase at least marginally based on the increased use of accessories and, for current and  future
model years, a reduction of aftertreatment effectiveness during extended idling.
                                                                                        38

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2 Heavy-Duty  Gasoline Truck emissions

2.1  Running Exhaust Emissions

2.1.1 HC, CO, and NOx

2.1.1.1 Data and Analysis
As gasoline-fueled vehicles are a small percentage of the heavy-duty vehicle fleet, the amount of
data available for analysis was small.  We relied on four medium-heavy duty gasoline trucks from
the CRC E-55  program and historical  data from EPA's Mobile  Source Observation Database
(MSOD), which has records  of chassis tests performed by both EPA  and  outside parties or
contractors.  The heavy-duty gasoline data the MSOD contains is mostly of pickup trucks which
fall mainly in the LHD2b regulatory class. Table 26 shows the number of vehicles in cumulative
data sets. In the real world, most heavy-duty gasoline vehicles fall in either the LHD2b or LHD345
class, with a smaller percentage in the MHD class. There are nearly  zero HHD gasoline trucks.

 Table 26 shows the distribution of vehicles in the data sets by model year group regulatory class and age group.
Model year group
1960-1989
1990-1997
1998-2002
Regulatory class
MHD
LHD2b
MHD
LHD2b
MHD
LHD2b
Age group
0-5



33
1
1
6-9
2
10
1
19


Similar to the HD diesel PM, HC, and CO analysis, the chassis vehicle speed and acceleration,
coupled with the average weight for each regulatory class, were used to calculate VSP (Equation
8).  Since heavy-duty emissions regulations are not usually focused on gasoline engines,  we
decided to look at certification data as a guide to developing model year groups for analysis.
Figure 16 shows averages of certification results by model year.
                                                                                 39

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





15
10-







I I
* I





'








! J






'


. i







; '
X
1980 1985






;
i:
1990 1995





{^
, x _

• CO
A NOX
• HC







^
f L
:f{r|i":
2000

i
2005



















- 1.2
- 1
- 0.8

- 0.6
- 04

- 0.2
- 0
2010
Model year
                                                                          o
Based on these certification results, we decided to stratify the data into coarse model year groups,
listed below.
   •   1960-1989
   •   1990-1997
   •   1998-2002
   •   2003-2006
   •   2007 and later

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

Unlike our HD diesel  analysis, we used the age effects present in the data itself.   We did not
incorporate an external tampering and mal-maintenance model into the HD gasoline rates. Due to
sparseness of data we used only the two age groups listed in Table 26.  We also did not stratify by
regulatory class since there was only one regulatory class (LHD2b)  predominantly represented in
the data.

2.1.1.2 Sample Results
As for the heavy-duty diesel  analysis, a few sample results graphs are shown.  The first (Figure 17)
shows all three pollutants against  operating mode bin for the LHD2b regulatory class.  In general,
emissions follow the common trend  with VSP.   As expected, NOx emissions  for heavy-duty
gasoline vehicles are much lower than for heavy-duty diesel vehicles.
                                                                                        40

-------
                 Figure 17 shows emissions by operating mode bin for MY 2002 age 0-3.
200
180

160
5" 140
f 120-
IS
•- 100
8
Z 80

O
X 60
40

20
n














'


*THC
ANOx
• CO









I
| *

• • • •
A A A A


A
A A A
A
A A '
A

• • A • '

^ m
A • • •
* *** ^ ป * * * *
- 1200

"i nnn
~ 1 UUU

- 800 „
1
- 600 &
ฃ
O
o
-400 "


- 200
n
                0   1  11  12 13 14 15  16 21 22 23  24 25 27 28  29  30 33 35 37  38 39 40
                                       Operating mode bin


Figure 18 shows the emissions trends by age group. Since we did not use the tampering and mal-
maintenance methodology as we did for diesels, the age trends reflect our coarse binning with age.
For each pollutant, only two distinct rates exist - one for ages 0-5 and another for 6 and older

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





^
2
2
X
0
z

O
X


120 n

100


80

60



40

20
n

III


A



* * *



B H
. 4


I


*THC
ANOx
• CO


^







I






^






- 1400

- 1200

- 1000

-800

-600



-400
-200
n






•^
^
2

2
O
O




             0-3
4-5
6-7         8-9       10-14
        Age group
15-19
20+
Figure 19 shows emissions by model year group.  Except for CO, emissions consistently decrease
with model year group.  Our assumptions regarding implementation of increased effectiveness of
catalysts substantially reduce emissions estimates for 2007 model year and later.
                                                                                          41

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

              300 -,                                                      -r 600
            ฃ 200 -
            &

            & 15
            5

            o" 1 oo -
            X


               50 -
                                                                       -- 400
-- 200
                                                                       -- 100
                      pre-1990
                                   1990-1997        1998-2006

                                      Model year group
2.1.2PM
Unfortunately, the PM2.5 emission data from heavy-duty gasoline trucks are too sparse to develop
the broad and detailed emission factors expected by the MOVES model.  As a  result, only a very
limited analysis could be done, and 'placeholder' emission rates developed for the model. As such,
EPA will likely revisit and update these emission rates, when sufficient data on PM2.5 emissions
from heavy-duty gasoline vehicles become available.

The principal result from this analysis is that for the Draft MOVES2009 model, the heavy-duty gas
PM2.5 emission  rates will be calculated by multiplying the light-duty gasoline truck PM2.5
emission rates by a factor of 1.40. Since the MOVES light-duty gasoline PM2.5 emission rates are
a complete  set of  factors  - stratified by  particulate  sub-type  (elemental  and organic  carbon),
operating mode, model year and regulatory class, the heavy-duty PM2.5 emission factors will also
be a complete set.
2.1.2.1 Data Source
This analysis is based on the PM2.5 emission test results from the FOUR gasoline trucks tested in
the CRC E55-E59 test program.  The specific data which were used are the UDDS test cycle data.
Each of the four vehicles in  the  sample received  two UDDS tests, conducted at different test
weights. Other emission tests using different cycles were also available on the same vehicles, but
were not used in the calculation.   The use of the UDDS data enabled the analysis to have a
consistent driving cycle.  The trucks and tests are described in Table 27.

               Table 27 - Summary of data used in HP gasoline PM emission rate analysis
Vehicle
1
2
MY
2001
2001
1983
1983
Age
3
3
21
21
Test cycle
UDDS
UDDS
UDDS
UDDS
GVWR
12975
19463
9850
14775
PM2.5 mg/mi
1.8115942
3.6101083
43.3212996
54.3478261
                                                                                         42

-------
3
4
1993
1993
1987
1987
12
12
18
18
UDDS
UDDS
UDDS
UDDS
13000
19500
10600
15900
67.0949972
108.336719
96.7349998
21.5098555
The table shows only four vehicles, two of which  are quite old  and certified to fairly lenient
standards. A third truck is also fairly old at 12 years and certified to an intermediate standard. The
fourth is a relatively new truck at age three and certified to a more stringent standard.  No trucks in
the sample are certified to the Tier2 or equivalent standards.

2.1.2.2 Analysis
Examination of the heavy-duty data shows two distinct strata of data - vehicle #1 (MY 2001) and
the other three vehicles.  Because of its lower age (3  years old) and newer model year status, this
vehicle has  substantially  lower  PM emission levels than the others, and was separated in the
analysis.  The emissions of the other three vehicles were averaged together to produce these mean
results:
Mean for Vehicles 2 through 4:     0.06522 g/mi
Mean for Vehicle 1:                0.00271 g/mi
                 Older Group
                 Newer Group
For comparison with the heavy-duty gas PM rates tested over the UDDS cycle, simulated UDDS
cycle emission rates were  developed based on  MOVES light-duty gas PM2.5  emission  rates
(normal deterioration assumptions) for light-duty gasoline trucks.   The UDDS cycle represents
standardized operation for the heavy-duty vehicles.

To make the comparisons fair, the simulated light-duty UDDS results were matched by model year
and  age to the model year and  age  of the four heavy-duty gas trucks in the  sample.   This
comparison meant that the emission rates from the following MOVES model year groups and age
groups for light-duty trucks were used:
       MY group 1983-1984, age 20+
       MY group 1986-1987, age 15-19
       MY group 1991-1993, age 10-14
       MY group 2001, age 0-3
The  simulated UDDS emission factors for the older model year light duty  gas truck group are
0.0362 g/mi for MOVES organic carbon PM2.5 emissions and 0.002641 g/mi for elemental carbon.
Ignoring sulfate emissions (which are in the order of IxlO"7 g/mile for low  sulfur fuels) sum to
0.03884 g/mile.
This value leads to the computation of the ratio:
0.06522^
- ^^
0.03884^
                                                      = 1 .679 .
The simulated UDDS emission factors for the newer model year light duty gas truck group are
0.004368 g/mi for MOVES organic carbon PM2.5 emissions and 0.0003187 g/mi for elemental
carbon.  Ignoring sulfate emissions (which are in the order of IxlO"8 g/mile for low sulfur fuels)
sum to 0.004687 g/mi
                                                                                       43

-------
This value leads to the computation of the ratio:
                                                   mile
                                           0.004687^,
= 0.578.
The newer model year group produces a ratio which is less than one and implies that small trucks
produce less PM2.5 emissions than larger trucks.  This is intuitively inconsistent, and is the likely
result of a very small sample and a large natural variability in emission test results.

All four data points were retained and averaged together by giving the older model year group a 75
percent weighting and the newer model year group (MY 2001) a 25 percent weighting.  This is
consistent with the underlying data sample.  It produces a final ratio of:

                     Ratio final = Ratio MerWtFrac + Ratio newer (1 - WtFrac)
                             = 1.679x0.75 + 0.578x0.25 = 1.40

We multiplied this final ratio of 1.40 by the light-duty gasoline truck PM rates to calculate the input
emission rates for heavy-duty gasoline PM rates.

2.2 Start  Emissions

2.2.1 Available Data
To develop start emission rates  for heavy-duty gasoline-fueled vehicles, we extracted data available
in the USEPA Mobile-Source Observation Database (MSOD). These  data represent aggregate test
results for heavy-duty spark-ignition (gasoline powered) engines measured on the Federal Test
Procedure (FTP) cycle. The GVWR for all trucks was between 8,500 and  14,000 Ib, placing all
trucks in the LHD2b regulatory class.

Table 28 shows the model-year by age classification for the data.  The model year groups  in the
table  were assigned based  on  the  progression  in NOx  standards between MY 1990 and  2004.
Standards for CO and HC are stable over this period, until MY 2004, when a combined NMHC+
NOx  standard was  introduced.  However, no measurements for trucks were available for MY2004
or later.

    Table 28. Model-year Group by Age Group Structure for the Sample of Heavy-Duty Gasoline Engines
Model-year Group

1960-1989
1990
1991-1997
1998-2004
Total
Standards (g/hp-hr)
CO

14.4
14.4
14.4

HC

1.1
1.1
1.1

NOx

6.0
5.0
4.0

Age Group (Years)
0-3


73
8
81
4-5


59

59
6-7

1
32

33
8-9
19
29
4

52
10-14
22



22
Total

41
30
168
8
247
                                                                                       44

-------
2.2.2 Estimation of Mean Rates
As with light-duty vehicles, we estimated the "cold-start" as the mass from the cold-start phase of
the FTP (bag 1) less the "hot-start" phase (Bag 3). As a preliminary exploration of the data, we
averaged by model year group and age group and produced the graphs shown in A. 6 Heavy-duty
Gasoline Start Emissions Analysis Figures.

Sample sizes are small overall and very small in some cases (e.g. 1990, age 6-7) and the behavior
of the averages is somewhat erratic. In contrast to light-duty vehicle emissions, strong model-year
effects are not apparent. This may not be surprising for CO or HC,  given the uniformity of
standards throughout. This result is more surprising for NOx but model year trends are no more
evident for NOx than for the other two. Broadly speaking, it appears that an age trend may be
evident.

If we assume that the underlying population  distributions are approximately lognormal, we can
visualize the data in ways that illustrate  underlying relationships. As a first step, we calculated
geometric mean emissions, for purposes of comparison to the  arithmetic means  calculated  by
simply averaging the data. Based on the assumption of log-normality, the geometric mean (xg) was
calculated in terms of the logarithmic mean (x/) as

                                         xg = eln*'                               Equation 11

This measure is not appropriate for use as an emission rate, but is useful in that it represents the
"center" of the skewed parent distribution. As such, it is less strongly influenced by unusually high
or outlying measurements than the arithmetic means in Appendix A.6 Heavy-duty Gasoline Start
Emissions Analysis Figures.   In general, the small differences between  geometric means  and
arithmetic means suggest that the distributions represented by the  data do not show strong skew in
most cases.  Assuming that emissions distributions should be strongly skewed suggests that these
data are not representative of "real-world" emissions  for these vehicles. This conclusion appears to
be reinforced by the values in Figure  25 which represent the "logarithmic standard deviation"
calculated by model-year and age groups.   This measure (s/), is the standard deviation of natural
logarithm of emissions (x/) in  . The values of 5; are highly variable,  and generally  less  than 0.8,
showing that the degree of skew in the data is also highly variable as well as generally low  for
emissions data;  e.g., corresponding values  for light-duty running emissions are generally 1.0 or
greater. Overall, review of the geometric  means confirms the impression of age trends in the CO
and HC results, and the general lack of an age trend in the NOx results.

Given the conclusion that the data as such are probably unrepresentative, assuming the lognormal
parent distributions allows us to re-estimate the arithmetic mean after assuming reasonable values
for 5/. 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 light-
duty vehicles.
                                                                                        45

-------
Figure 20. Cold-start FTP Emissions for Heavy-Duty Gasoline Trucks, averaged by Age Group only (g =
geometric mean, a= arithmetic mean recalculated from xt and s/).
                                           Age (years)
                                            Age (years)
The re-estimated arithmetic means are calculated from the geometric means, by adding a term that
represents the influence of the "dirtier" or "higher-emitting" vehicles, or the "upper tail of the
distribution," as shown in Figure 26 above.

                                                 i
                                          X =x e2                                 Equation 12


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
                                                                                          46

-------
presented  in  Figure 20 with  the  arithmetic mean (directly calculated and  re-estimated)  and
geometric means shown separately.

We then addressed the question of the projection of age trends. As a general principle, we did not
allow emissions to decline with age. We implemented this assumption by stabilizing emissions at
the maximum level reached between the 6-7 and 10-14 age groups.
2.2.3 Estimation of Uncertainty
We calculated standard errors for each mean in a manner consistent with the re-calculation of the
arithmetic means. Because the (arithmetic) means were recalculated with assumed values of si, it
was necessary to re-estimate corresponding standard deviations for the parent distribution s, as
shown in Equation 13.
                                    s =
Equation 13
After recalculating the standard deviations, the calculation of corresponding standard errors was
simple. Because each vehicle is represented by only one data point, there was no within-vehicle
variability to consider, and  the  standard error could  be calculated as s/Jn. We  divided the
standard errors  by their respective means to obtain CV-of-the-mean or "relative standard error."
Means, standard deviations and uncertainties are presented in Table 29 and in Figure 21. Note that
these results represent only "cold-start" rates (opModelD 108).

Table 29.  Cold-Start Emission Rates (g) for Heavy-Duty Gasoline Trucks, by Age  Group
(italicized values replicated  from previous age Groups).
Age Group

Means
0-3
4-5
6-7
8-9
10-14
Standard Deviations
0-3
4-5
6-7
8-9
10-14
Standard Errors
0-3
4-5
6-7
8-9
10-14
n


81
59
33
52
22












Pollutant
CO

101.2
133.0
155.9
190.3
189.1

108.1
142.0
166.5
203.2
202.0

12.01
18.49
28.98
28.18
43.06
THC

6.39
7.40
11.21
11.21
11.21

6.82
7.90
11.98
11.98
11.98

0.758
1.03
2.08
2.08
2.08
NOx

4.23
5.18
6.12
7.08
7.08

8.55

12.39
14.32
14.32

0.951
1.18
2.16
1.99
1.99
                                                                                       47

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

3 200
•K
S irn
5) 15ฐ
3
O 100
& ,0

(a) CO 1

j.
i^r

S




1











*

0 5 10 15 20 2
Age (yea is)


17 -
10

g

2

/l->\ TLJ^"*
(b) THC




-
•^

L/L


J t



J



3 	 1



3 	 E



D






0 5 10 15 20 2
Age (years)




-)
n
(c

)NOx

^

^^




^













-,




-













0 5 10 15 20 2
Age (years)
2.2.4 Projecting Rates beyond the Available Data
The steps described so far involved reduction and analysis of the available emissions data. In the
next step, we describe approaches used to impute rates for model  years not represented in these
data. For purposes of analysis we delineated three model year groups: 1960-2004, 2005-2007 and
2008 and later. We describe the derivation of rates in each group below.
                                                                                    48

-------
2.2.4.1  Regulatory class LHD2b
For CO the approach was simple. We applied the values in Table 29 to all model-year groups. The
rationale  for this approach is that the CO standards do not change over the full range of model
years considered.

For HC and NOx we imputed values for the 2005-07 and 2008+ model-year groups, by multiplying
the values in Table 29 by ratios expressed in terms of the applicable standards.  Starting in 2005, a
combined HC+NOx standard was introduced. It was necessary for modeling purposed to partition
the standard into HC and NOx components. We assumed that the proportions of NMHC and NOx
would be  similar to those in the 2008 standards, which separate  NMHC and NOx while reducing
both.

We calculated the HC value by multiplying the 1960-2004 value by the fraction/He, where

                    /HC=f    ฐ-14g/hP-hr    Vog/hp-hr) = 0.412            Equations
                     HC  1^(0.14 + 0.20) g/hp-hrjv          '

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

ERRATUM: the expression in Equation 14  above is in error, in that it does not relate the future
standard (numerator) to the past standard (denominator).  The following corrected value is  to be
used in revised rates for final MOVES.

            f     0.14g/hp-hr   \,
             - ^-— - -  (l.Og/hp-hr
                                  V    5
           _
        HC              l.lg/hp-hr

We calculated the corresponding value for NOx as

                          /NOX = f    0.20g/hp-hr    ^ =
                           N0x   ^ (0.14 + 0.20) g/hp -hrj

ERRATUM: as for HC, the value in Equation 15 is erroneous, for similar reasons.  Whereas the
error was small, for HC, it is more substantial for NOx.  The corrected value to be used for final
MOVES, is
                           (    0.20g/hp-hr    \
                            - ^— ^ - 1 .0 g/hp - hr
                     ,    _ 1(0- 14 + 0.20) g/hp -hrj   5        _fti^
                     Aox"            4.0 g/hp -hr

The error for HC is small but  for  NOx it is substantial. For these  rates we  neglected the
THC/NMHC conversions, which we gave attention to for light-duty
                                                                                      49

-------
In 2008, separate HC and NOx standards were introduced.  To estimate values for this model-year
group, we calculated the values by multiplying the 1960-2004 value by the fractions/HC
where
                               /HC
0.14g/hp-hr
 l.lg/hp-hr
= 0.127
                                /NOx
 0.20g/hp-hr
 4.0g/hp-hr
 = 0.05
Equation 16
Equation 17
2.2.4.2 Regulatory classes LHD345 and MHD
For LHD345 and MHD, we estimated values in terms of the values calculated for LHD2b.

For CO and HC, we estimated values for the heavier vehicles by multiplying them by ratios of
standards for the heavier class to those for the lighter class.
The value for CO is
                                /CO
 37.1g/hp-hr
 14.4g/hp-hr
 = 2.58
Equation 18
and the corresponding value for HC isl.73.

                                 /HC ~~
 1.9g/hp-hr
 l.lg/hp-hr
= 1.73
Equation 19
We applied this ratio in all three model-year groups, as shown in Table 30.

ERRATUM: the ratios in Equation 18 and Equation 19 were erroneously applied to the 2005-2007
model-year groups for LHD345 and MHD vehicles. In final MOVES, values for these model-year
groups will set be equal to those for the LHD2b  vehicles, with the rationale that the  standards
converge for both groups.

For NOx, all values are equal to those for LHD2b, because the same standards apply to both classes
throughout. The approaches for both regulatory classes in all three model years are shown in Table
30.
Table 30. Methods used to Calculate and Start Emission Rates for Heavy-Duty Spark-Ignition Engines
Regulatory Class

LHD2b
LHD345, MHD
Model-year Group

1960-2004
2005-2007
2008 +
i o^n.onnA
Method
CO
Values from
Table 29
Values from
Table 29
Values from
Table 29

THC
Values from
Table 29
Reduce in
proportion
To standards
Reduce in
proportion
To standards

NOx
Values from
Table 29
Reduce in proportion
To standards
Reduce in proportion
To standards

                                                                                     50

-------


2005-2007
2008 +
To standards
Increase in proportion
To standards
Increase in proportion
To standards
in proportion
To standards
Increase in proportion
To standards
Increase in proportion
To standards
LHD2b
Same values as
LHD2b
Same values as
LHD2b
As for heavy-duty diesel and light-duty vehicles we applied the curve in Figure  15 to adjust the
start emission rates for varying soak times. The rates described in this section were for fully cold
starts (soak time > 720 minutes).

2.2.4.3 Participate Matter
We did not  have data  on PM from heavy-duty  gasoline vehicles.   As a result, we used the
multiplication factor from the running exhaust emissions analysis of 1.40 to scale up the light-duty
truck start emission rates.
                                                                                         51

-------
A. Appendices
A.1  Calculation  of Accessory Power Requirements  for
VSP bins
                Table 31 - Accessory load estimates for HHD trucks
VSP
Low
Power (kw)
% time on
Total (kW)
Mid
Power (kw)
% time on
Total (kW)
High
Power (kw)
% time on
Total (kW)
Cooling Fan

19.0
10%
1.9

19.0
20%
3.8

19.0
30%
5.7
Air cond

2.3
50%
1.2

2.3
50%
1.2

2.3
50%
1.2
Air comp
Off = 0.5 kW
3.0
60%
2.0
Off = 0.5 kW
2.3
20%
0.9
Off = 0.5 kW
2.3
10%
0.7
Alternator

1.5
100%
1.5

1.5
100%
1.5

1.5
100%
1.5
Engine
Accessories

1.5
100%
1.5

1.5
100%
1.5

1.5
100%
1.5
Total Accessory Load (kW)



8.1



8.8



10.5
                Table 32 - Accessory load estimates for MHD trucks
VSP
Low
Power (kw)
% time on
Total (kW)
Mid
Power (kw)
% time on
Total (kW)
High
Power (kw)
% time on
Total (kW)
Cooling Fan

10.0
10%
1.0

10.0
20%
2.0

10.0
30%
3.0
Air cond

2.3
50%
1.2

2.3
50%
1.2

2.3
50%
1.2
Air comp
Off = 0.5 kW
2.0
60%
1.4
Off = 0.5 kW
2.0
20%
0.8
Off = 0.5 kW
2.0
10%
0.7
Alternator

1.5
100%
1.5

1.5
100%
1.5

1.5
100%
1.5
Engine
Accessories

1.5
100%
1.5

1.5
100%
1.5

1.5
100%
1.5
Total Accessory Load (kW)



6.6



7.0



7.8
                                                          52

-------
Table 33 - Accessory load estimates for buses
VSP
Low
Power (kw)
% time on
Total (kW)
Mid
Power (kw)
% time on
Total (kW)
High
Power (kw)
% time on
Total (kW)
Cooling Fan

19.0
10%
1.9

19.0
20%
3.8

19.0
30%
5.7
Air cond

18.0
80%
14.4

18.0
80%
14.4

18.0
80%
14.4
Aircomp
Off = 0.5 kW
4.0
60%
2.6
Off = 0.5 kW
4.0
20%
1.2
Off = 0.5 kW
4.0
10%
0.9
Alternator

1.5
100%
1.5

1.5
100%
1.5

1.5
100%
1.5
Engine
Accessories

1.5
100%
1.5

1.5
100%
1.5

1.5
100%
1.5
Total Accessory Load (kW)



21.9



22.4



24.0
                                                                    53

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A.2 Tampering and Mai-maintenance
Tampering and mal-maintenance  (T&M) effects represent the fleet-wide  average increase in
emissions over the useful life of the engines. In laboratory testing, properly maintained engines
and test cells often yield very small rates of emissions deterioration through time. However, in
real-world use, tampering and  mal-maintenance  yield higher rates  of  emissions deterioration
through time. As a result, we feel it is important to model the amount of deterioration we expect
from this tampering and mal-maintenance.  We estimated these fleet-wide emissions  effects by
multiplying the frequencies of engine component failures by the emissions impacts related to those
failures for each pollutant. Details of this analysis appear later in this section.
A.2.1 Modeling Tampering and Mal-maintenance
As T&M affect emissions through age, we developed a simple function of emission deterioration
through age.  We applied the zero-age rates through emissions warranty period (5 years/100,000
miles), then increased the rates linearly up to useful life.  Then we assumed that all the rates level
off beyond the useful life age. Figure 22 shows this relationship.
               Figure 22 qualitatively shows the implementation of age effects in MOVES.
        Emission rate
     Final emission rate
     due to T&M
           Zero-mile
           emission
           rate
                   End of warranty
                   period
End of useful life
                                                     Age
The useful life refers to the length of time that engines are required to meet emissions standards.
We incorporated this age relationship through the age groups in MOVES by averaging emissions
rates for all ages in each age group. Mileage was converted to age with VIUS37 (Vehicle Inventory
and Use Survey) data, which contains data on how quickly trucks of different regulatory classes
accumulate mileage.  Table 34 shows the emissions warranty period and approximate useful life
requirement period for each of the regulatory classes.
                                                                                     54

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                  Table 34 - Warranty and useful life requirements by regulatory class
Regulatory class
HHD
MHD
LHD345
LHD2b
BUS
Warranty age
(Requirement:
100,000 miles/5 years)
1
2
4
4
2
Useful life
mileage/age
requirement
435,000/10
185,000/10
110,000/10
110,000/10
435,000/10
Useful
life age
4
5
4
4
10
While both an age and a mileage metric are given for these periods, whichever comes first is what
determines the applicability of the warranty.  As a result,  since MOVES deals  with age and not
mileage, we need to convert all the mileage numbers to age numbers, as the mileage limit usually is
reached before the age limit. The data show that on average, heavy-heavy-duty trucks accumulate
mileage much  more quickly than  other regulatory classes.  Therefore, any deterioration in their
emissions will happen  more quickly than other regulatory classes.  Buses, on average, do  not
accumulate mileage quickly.  Therefore, their useful life period is governed by the age requirement,
not the mileage requirement.

Since MOVES deals with age groups and not individual ages, the proper increase in emissions by
age must be calculated by age group.  We assumed that there is an even age distribution  within
each age group (e.g.  ages 0, 1, 2, and 3  are equally represented in the 0-3  age group).  This is
important since, for example, HHD trucks reach useful life within four years, which means they
will increase emissions through the 0-3 age group. As a result, the 0-3 age group  emission rate will
be higher than the zero-mile emission rate for HHD trucks.  Table 35 shows the multiplicative
T&M adjustment factor by age.  We determined this factor using the mileage-age data from Table
34 and the emissions-age relationship that we described in Figure 22. We multiplied this factor by
the emissions  increase of each pollutant over the useful life of the engine, which we determined
from  the analysis in  the section A.2.3 Analysis below and which  is listed in the  corresponding
running exhaust sections above.

                   Table 35 shows the T&M multiplicative adjustment factor by age.
Age Group
0-3
4-5
6-7
8-9
10-14
15-19
20+
LHD
0
1
1
1
1
1
1
MHD
0.083
0.833





HHD
0.25
1
1
1
1
1
1
Bus
0.03125
0.3125
0.5625
0.8125
1
1
1
In this table a value of 0 indicates no deterioration (or zero-mile emissions level), and a value of 1
indicates a fully deteriorated engine (or maximum emissions level).  The calculation of emission
rate by age group is described in the equation below.
                               pol,agegrp
-  J pol,agegrp   pol
                                                             )
                                                                                         55

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A.2.2 Data Sources

EPA used the following information to develop the tamper and mal-maintenance occurrence rates
for MOVES:
      •   California's ARE EMFAC2007 Modeling Change Technical Memo38 (2006).   The
          basic EMFAC occurrence rates for tampering and mal-maintenance were developed
          from the Radian and EFEE reports and internal CARB engineering judgment.
      •   Radian Study (1988).  The report estimated the malfunction rates based on survey and
          observation.  The data may  be questionable for current heavy-duty  trucks due to
          advancements such as electronic controls, injection systems, and exhaust aftertreatment.
      •   EFEE report (1998) on PM emission deterioration rates for in-use vehicles. Their work
          included heavy-duty diesel vehicle chassis  dynamometer testing at Southwest Research
          Institute.
      •   EMFAC2000 (2000) Tampering and Mal-maintenance Rates
      •   EMA's  comments  on  ARB's  Tampering, Malfunction,  and  Mal-maintenance
          Assumptions for EMAFAC 2007
      •   University of California -Riverside "Incidence  of Malfunctions and  Tampering in
          Heavy-Duty Vehicles"
      •   Air Improvement Resources,  Inc.'s Comments on Heavy-Duty Tampering  and Mal-
          maintenance Symposium
      •   EPA internal engineering judgment


A.2.3 Analysis

   T & M Categories

EPA generally agreed with the categories developed by CARB, with a few exceptions.  The high
fuel pressure category was removed.  We added a category for misfueling to represent the use of
nonroad diesel, not ULSD onroad diesel.  We combined the injector categories into a single group.
We reorganized  the EGR groups  into EGR  Stuck  Open and  EGR Disabled/Low Flow.  We
included the PM regeneration system, including the igniter, injector,  and combustion air system in
the PM filter leak category.

EPA will group the LHDD, MHDD, HHDD, and Diesel bus groups together, except for 2010 and
beyond.  We assumed that the LHDD group will primarily use Lean NOx Traps (LNT) for the NOx
control in 2010 and beyond. On the other hand, we also assumed that Selective Catalyst Reduction
(SCR)  systems will be the primary NOx aftertreatment system for HHDD.   Therefore, the
occurrence rates and emission impacts will vary in 2010 and beyond depending on the regulatory
class of the vehicles.
   T&M Model Year Groups

   EPA developed the model year groups based on regulation and technology changes.
                                                                                    56

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       •   Pre-1994 represents non-electronic fuel control.
       •   1998-2002 represents the time period with consent decree issues.
       •   2003 begins the use of EGR.
       •   2007 and 2010 contain significant PM and NOx regulation changes.
       •   EPA issued a NPRM to require OBD phase-in beginning in 2010 model year for heavy
          duty trucks with complete phase-in by 2013. However, we will not be applying OBD to
          the frequency rates at this time because we have not issued a final rulemaking.

T &M Occurrence Rates

   EPA T &M Occurrence Rate Differences from EMFAC2007

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

Clogged Air Filter: EPA reduced the frequency rate from CARB's 15% to 8%. EPA reduced this
value based on the UCR results, the Radian  study, and EMA's comments that  air filters are a
maintenance item.  Many trucks contain indicators to notify the driver of dirty air filters and the
drivers have incentive to replace the filters for other performance reasons.

Other Air Problems: EPA reduced the frequency rate from CARB's 8% to 6% based on the UCR
results.

Electronics Failed:  EPA will  continue to use the 3% frequency rate  for all model years beyond
2010.  CARB increased the rate to 30% in  2010 due to system complexity. EPA does not agree
with CARB's assertion  that the complexity  of electronic systems will increase enough to justify a
ten-fold increase in occurrence rates.  We believe that the hardware will  experience an evolution
through 2010, not completely new systems that would justify a higher rate of failure. EPA asserts
that many of the 2010 changes will occur with the aftertreatment systems which are accounted for
separately.

EGR Stuck Open:  EPA believes the failure frequency  of this item is rare and therefore set the
level at 0.2%.  This failure will lead to drivability issues that will be noticeable to the driver and
serve as an incentive to repair.

EGR Disabled/Low Flow:  EPA believes the 20% EGR failure rate is too high and reduced the
rate to 10%. All but one major engine manufacturer had EGR previous to the 2007 model year and
all have it after 2007.  Therefore, CARB's frequency rate increase in 2010 due to the increase truck
population  using EGR  does not seem valid.  However, the  Illinois EPA stated that EGR  flow
insufficient is the top OBD issue found in their LDV I/M program39 so it cannot be ignored.

NOX Aftertreatment malfunction:  EPA developed the NOx aftertreatment malfunction rate
dependent on the type of system used. We assumed that HHDD will  use primarily SCR systems
and LHDD will primarily use LNT systems.   We estimated the failure rates of the various
components within each system to develop a composite malfunction rate.
                                                                                     57

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The individual failure rates were developed considering the experience in agriculture and stationary
industries of NOx aftertreatment systems and similar component applications. Details are included
in the chart below. We assumed that tank heaters had a 5% failure rate, but were only required in
one third of the country  and one fifth of the year.  The injector failure rate is lower than fuel
injectors, even though they have similar technology, because there is only one required in each
system and it is operating in less severe environment of pressure and temperature.  We believe the
compressed  air delivery system is very mature based on a similar use in air brakes.  We also
believe that manufacturers will initiate engine power de-rate as incentive to keep the urea supply
sufficient.

Occurrence Rate
SCR
Urea tank
Tank heaters
In-exhaust injectors
Compressed
air delivery to injector
Urea supply pump
Control system
Exhaust temperature sensor
Urea supply
0.5%
1%
2%
1%
1%
5%
1%
1%
                                             Overall
13%
LNT
Adsorber
In-exhaust injectors
Control system
Exhaust temperature sensor
7%
2%
5%
1%
                                             Overall
16%
NOx aftertreatment sensor: EPA believes the 53% occurrence rate in EMFAC2007 is too high
and will use 10%.  CARB assumed a mix of SCR, which uses one sensor per vehicle, and NOx
adsorbers, which use two sensors per vehicle. They justified the failure rate based on the increased
number of sensors in the field beginning in 2010.

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

   •   Durability expectations:  SwRI completed  6000 hours of ESC cycling with NOx sensor.
       Internal testing supports longer life durability.  Discussions with  OEMs in 2007 indicate
       longer life expected by 2010.
                                                                                       58

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

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

PM Filter Disable:  EPA agrees with CARB's 2% tamper rate of the PM filter.  The filter causes is
a fuel economy penalty so the drivers have an incentive to remove it.

Oxidation  Catalyst Malfunction/Remove:   EPA  believes   most manufacturers  will  install
oxidation catalysts initially in the 2007 model  year and agrees with CARB's assessment of 5%
failure rate.  This rate consists of an approximate 2% tampering rate and 3% malfunction rate. The
catalysts are more robust than PM filters, but have the potential to experience degradation when
exposed to high temperatures.

Misfuel: EPA estimated that operators will use the wrong type of fuel, such as agricultural diesel
fuel with higher sulfur levels, approximately 0.1% of the time.
   MOVES Tamper & Mai-maintenance Occurrence Rate Summary
Tamper & Malmaintenance
Frequency of Occurrence: Average rate over life of vehicle

Timing Advanced
Timing Retarded
Injector Problem (all)
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter - EPA
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem - EPA
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed - EPA
Electronics Tampered
EGR Stuck Open
EGR Disabled/Low Flow - EPA
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction - EPA
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove - EPA
Mis-fuel - EPA
Frequency Rates
1994-97
5%
3%
28%
4%
4%
3%
8%
5%
5%
6%
2%
5%
3%
10%
0%
0%
0%
0%
0%
0%
0%
0%
0.1%
1998-2002
2%
2%
28%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
15%
0%
0%
0%
0%
0%
0%
0%
0%
0.1%
2003-2006
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
0%
0%
0%
0%
0%
0%
0.1%
2007-2009
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
0%
0%
0%
5%
2%
5%
0.1%
2010+ HHDT
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
10%
1%
13%
5%
2%
5%
0.1%
2010+ LHDT
2%
2%
13%
0%
0%
0%
8%
5%
5%
6%
2%
3%
3%
5%
0.2%
10%
10%
1%
16%
5%
2%
5%
0.1%
Emission Effects

   NOx Emission Effect of Tampering and Mai-maintenance

EPA developed the emission  effect  from each tampering and  mal-maintenance incident from
CARB's EMFAC, Radian's dynamometer testing with and without the malfunction present, EFEE
results, and internal testing experience.
                                                                                      59

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EPA estimated that the lean NOx traps (LNT) in LHDD are 80% efficient and the selective catalyst
reduction (SCR) systems in HHDD are 90% efficient at reducing NOx.
EPA developed the NOx emission factors of the NOx sensors based on SCR systems' ability to run
in open-loop mode and still achieve NOx reductions.  MECA states 75-90% NOX reduction with
open loop control and >95% reduction with closed loop control.40 Visteon reports 60-80%  NOX
reduction with open loop control.41

The  failure of the NOx  aftertreatment  system had a different impact on the NOx  emissions
depending on the type of aftertreatment. The HHDD vehicles with SCR systems would experience
a 1000% increase in NOx during a complete failure, therefore we estimated a 500% increase as a
midpoint between normal operation and a complete failure.   The LHDD vehicles with  LNT
systems would experience a 500% increase in NOx during a complete failure.  We estimated a
300% increase as a value between a complete failure and normal system operation.

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

Tamper & Malmaintenance
NOX Emission Effect

Federal Emission Standard

Timing Advanced
Timing Retarded
Injector Problem (all)
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed
Electronics Tampered
EGR Stuck Open
EGR Disabled / Low Flow
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove
Mis-fuel
1994-97
5.0

60%
-20%
-5%
0%
0%
10%
0%
0%
25%
0%
-10%
0%
0%
80%
0%
0%
0%
0%
0%
0%
0%
0%

1998-2002
5.0

60%
-20%
-1%
0%
0%
0%
0%
0%
25%
0%
-10%
0%
0%
80%
0%
0%
0%
0%
0%
0%
0%
0%

2003-2006
4.0

60%
-20%
-1%
0%
0%
0%
0%
0%
25%
0%
-10%
0%
0%
80%
-20%
30%
0%
0%
0%
0%
0%
0%

2007-2009
2.0

60%
-20%
-1%
0%
0%
0%
0%
0%
25%
0%
-10%
0%
0%
80%
-20%
50%
0%
0%
0%
0%
0%
0%

2010+HHDT
0.2

6%
-20%
-1%
0%
0%
0%
0%
0%
3%
0%
-10%
0%
0%
8%
-20%
5%
200%
200%
500%
0%
0%
0%

2010LHDT
0.2

12%
-20%
-1%
0%
0%
0%
0%
0%
5%
0%
-10%
0%
0%
16%
-20%
10%
200%
200%
300%
0%
0%
0%

   PM Emission Effect of Tampering and Mai-maintenance

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

EPA estimates that the PM filter  has  95% effectiveness.   Many of the tampering and mal-
maintenance items that impact PM also have a fuel efficiency and driveability impact. Therefore,
operators will have an incentive to fix the issue.
                                                                                     60

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EPA estimated that excessive oil consumption will have the same level of impact on PM as engine
mechanical failure.  The failure of the oxidation catalyst is expected to cause a PM increase of
30%; however this value is reduced by 95% due to the PM filter affectivity.  We also considered a
DOC failure will cause a secondary failure of PM filter regeneration.  We accounted for this PM
increase within the PM filter disabled and leak categories.

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

    Tamper & Malmaintenance
    PM Emission Effect

Federal Emission Standard

Timing Advanced
Timing Retarded
Injector Problem
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed
Electronics Tampered
EGR Stuck Open/Low Flow
EGR Disabled
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove
Mis-Fuel
1994-97
0.1

-10%
25%
100%
20%
50%
20%
50%
50%
50%
40%
500%
500%
60%
50%
0%
0%
0%
0%
0%
0%
0%
0%
30%
1 998-2002
0.1

-10%
25%
100%
0%
0%
0%
50%
50%
50%
40%
500%
500%
60%
50%
0%
0%
0%
0%
0%
0%
0%
0%
30%
2003-2006
0.1

-10%
25%
100%
0%
0%
0%
30%
50%
30%
30%
500%
500%
60%
50%
100%
-30%
0%
0%
0%
0%
0%
0%
30%
2007-2009
0.01

0%
1%
5%
0%
0%
0%
2%
3%
2%
2%
25%
25%
3%
3%
5%
-30%
0%
0%
0%
600%
1000%
2%
100%
2010
0.01

0%
1%
5%
0%
0%
0%
2%
3%
2%
2%
25%
25%
3%
3%
5%
-30%
0%
0%
0%
600%
1000%
2%
100%
   HC Emission Effect of Tampering and Mai-maintenance

EPA estimated oxidation catalysts are 80% effective at reducing hydrocarbons.  All manufacturers
will utilize oxidation catalysts in 2007, but only a negligible amount installed one prior to the PM
regulation reduction in 2007.

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

The values with 0% effect in shaded cells represent areas which have no occurrence rate.
                                                                                       61

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  Tamper & Malmaintenance
  HC Emission Effect

Federal Emission Standard

Timing Advanced
Timing Retarded
Injector Problem (all)
Puff Limiter Mis-set
Puff Limiter Disabled
Max Fuel High
Clogged Air Filter
Wrong/Worn Turbo
Intercooler Clogged
Other Air Problem
Engine Mechanical Failure
Excessive Oil Consumption
Electronics Failed
Electronics Tampered
EGR Stuck Open
EGR Disabled / Low Flow
Nox Aftertreatment Sensor
Replacement Nox Aftertreatment Sensor
Nox Aftertreatment Malfunction
PM Filter Leak
PM Filter Disabled
Oxidation Catalyst Malfunction/Remove
Mis-fuel
1994-97
1.3

0%
50%
1000%
0%
0%
10%
0%
0%
0%
0%
500%
300%
50%
0%
0%
0%
0%
0%
0%
0%
0%
0%

1998-2002
1.3

0%
50%
1000%
0%
0%
0%
0%
0%
0%
0%
500%
300%
50%
0%
0%
0%
0%
0%
0%
0%
0%
0%

2003-2006
1.3

0%
50%
1000%
0%
0%
0%
0%
0%
0%
0%
500%
300%
50%
0%
100%
0%
0%
0%
0%
0%
0%
0%

2007-2009
0.2

0%
50%
1000%
0%
0%
0%
0%
0%
0%
0%
500%
300%
50%
0%
100%
0%
0%
0%
0%
0%
0%
50%

2010+ HHDT
0.14

0%
10%
200%
0%
0%
0%
0%
0%
0%
0%
100%
60%
10%
0%
20%
0%
0%
0%
0%
0%
0%
50%

2010 LHDT
0.14

0%
10%
200%
0%
0%
0%
0%
0%
0%
0%
100%
60%
10%
0%
20%
0%
0%
0%
0%
0%
0%
50%

A separate tampering analysis was not performed for CO. The same effects were assumed for CO.
Combining all of the emissions effects and  failure frequencies  discussed in this  section, we
summarized the aggregate emissions impacts over the useful life of the fleet due to in the main
body of the document in Table 11 (NOx), Table 15 (PM), and
Table 19 (HC and CO).
                                                                                      62

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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 - HOT
McCormick, High Altitude, HOT
WVU- 1991-2004
Storey

Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
12
48
4
10.2
9.5
28
Overall)) 64 || 10.8
1991-2006 High Speed Idle, A/C On - HOT
Broderick DC Davis
Storey

High Idle, AC On
High Idle, AC On
1
4
86
48
Overall)) 5 II 55.6
1 975-1 990 MY Low Speed Idle, A/C Off - HOT
Program
WVU- 1975-1990

Condition
Low Idle, AC Off
Samples
18
Mean
21
Overall)) 18 || 21.0
1 991 -2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus

Condition
Low Idle, AC Off
Samples
12
Mean
8.2
Overall)) 12 || 8.2
                                                               63

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Idle CO Rates (gram/hour) Summary
Program | Condition
# Samples
Mean CO Emiss Rate
1991-2006 Low Speed Idle, A/C Off - HOT
McCormick, High Altitude, HOT
Calcagno
WVU- 1991 -2004
Storey

Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
12
27
48
4
71
37
23
25
Overall)) 91 || 33.6
1991-2006 High Speed Idle, A/C On - HOT
Calcagno
Broderick DC Davis
Storey

High Idle, AC On
High Idle, AC On
High Idle, AC On
21
1
4
99
190
73
Overall)) 26 || 91.2
1975-1990 MY Low Speed Idle, A/C Off - HOT
Program
WVU- 1975-1990

Condition
Low Idle, AC Off
Samples
18
Mean
31
Overall)) ™ II 31.0
1 991 -2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus

Condition
Low Idle, AC Off
Samples
12
Mean
79.6
Overall)) 12 || 79.6
                                                                                  64

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Idle PM Rates (gram/hour) Summary
Program (Condition
# Samples
Mean PM Emiss Rate
1991-2006 Low Speed Idle, A/C Off - HOT
McCormick, High Altitude, HOT
Calcagno
WVU- 1991 -2004
Storey

Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
Low Idle, AC Off
12
27
48
4
1.8
2.55
1.4
1.3
Overall|| 91 || 1.8
1991-2006 High Speed Idle, A/C On - HOT
Calcagno
Storey

High Idle, AC On
High Idle, AC On
21
4
4.11
3.2
Overall|| 25 || 4.0
1 975-1 990 MY Low Speed Idle, A/C Off - HOT
Program
WVU- 1975-1990

Condition
Low Idle, AC Off
Samples
18
Mean
3.8
Overall)) ™ II 3.8
1 991 -2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus

Condition
Low Idle, AC Off
Samples
12
Mean
2.88
Overall!! 12 II 2.9
                                                                                  65

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 Idle Nox Rates (gram/hour) Summary
Program | Condition
# Samples
Mean NOX Emiss Rate
1991-2006 Low Speed Idle, A/C Off
McCormick, High Altitude, HOT
Lim, EPA
Irick, Clean Air Tech & IdleAire
WVU- 1991 -2004
WVU, NCHRP
Tang, Metro NY, 1984-1999
Calcagno
Broderick DC Davis
Storey

Low RPM, AC Off
Low RPM, No access

Low RPM, AC Off


Low RPM, AC Off
Low RPM, AC Off
Low RPM, AC Off
12
12
49
48
2
33
27
1
4
85
109
87
83
47
81
120
104
126
Overaliy 188 II 94
1991-2006 High Speed Idle, A/C Off
Lim, EPA CCD
Calcagno

High RPM, No access
High RPM, AC Off
5
21
169
164
Overall)) 26 || 165
1991-2006 High Speed Idle, A/C On
Lim, EPA CCD
Broderick DC Davis
Calcagno
Storey

High RPM, AC On
High RPM, AC On
High RPM, AC On
High RPM, AC On
5
1
21
4
212
240
223
262
Overall)) 31 II 227
1975-1990 MY Low Speed Idle, A/C Off
Program
WVU -1975-1 990
Lim, EPA CCD, 1985 MY

Condition
Low RPM, AC Off
Low RPM, AC Off
Samples
18
1
Mean
48
20
Overall)) 19 II 47
1 991 -2006 MY Low Speed Idle, A/C Off - Bus
Program
McCormick, High Altitude, Bus

Condition
Low Idle, AC Off
Samples
12
Mean
121
Overall)) 12 || 121.0
2007 Extended Idle Emissions calculation:
          •  Assumed 8 hour idle period where the emissions controls, such as EGR, oxidation
             catalyst, PM filter, and NOx aftertreatment, are still active for the first hour.
             PM emissions standards:
                 o  Pre-2007: 0.1 g/bhp-hr
                 o  2007: 0.1 g/bhp-hr
             HC emissions standards:
                 o  Pre-2007: 0.50 g/bhp-hr
                 o  2007: 0.14 g/bhp-hr
                                                                                      66

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          •  NOx emissions standards:
                 o   Pre-2010: 5.0 g/bhp-hr
                 o   2010: 0.2 g/bhp-hr

Idle PM Rate Reduction = 1 - [(1/8 * 0.01 g/bhp-hr + 7/8 * 0.1 g/bhp-hr) / 0.1 g/bhp-hr] = 11%
Idle HC Rate Reduction = 1 - [(1/8 * 0.14 g/bhp-hr + 7/8 * 0.5 g/bhp-hr) / 0.5 g/bhp-hr] = 9%
Idle NOx Rate Reduction = 1 - [(1/8 * 0.2 g/bhp-hr + 7/8 * 5.0 g/bhp-hr) / 5.0 g/bhp-hr] = 12%
                                                                                       67

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


The regression technique that was used was a log-linear least squares procedure. Regulatory class,
model year group and speed bin (0 - 25 mph, 25-50 mph and 50+ mph bins) were represented by
dummy variables in the regression.  Natural log of emissions was regressed versus vehicle specific
power (VSP) to represent the operating mode bins. The regression assumed a constant slope versus
VSP for each regulatory class.  Log transformation factors (mean square error of the regression
squared / 2) were used to transform the regression results from a log based form to a linear form.
Due  to  the  huge number  of individual second-by-second  data points, all of the regression
relationships were statistically significant at a high level (99% confident level).  The table below
shows the  regression statistics, and equation Eq-2 shows the form  of the resulting regression
equation.
Regression Coefficients for PM Emission Factor Model
Regulatory Class 46
Regclass Code
464410
464420
464430
464510
464520
464530
464610
464620
464630
464710
464720
464730
464810
464820
464830
VSP
Transformation
Coefficient
Coefficient
-5.419
-4.942
-4.765
-5.366
-4.929
-4.785
-5.936
-5.504
-5.574
-5.927
-5.708
-5.933
-6.608
-6.369
-6.305
0.02821
0.5864



















Regulatory Class 47
Regclass Code
474410
474420
474430
474510
474520
474530
474610
474620
474630
474710
474720
474730
474810
474820
474830
VSP
Transformation
Coefficient
Coefficient
-5.143
-4.564
-4.678
-5.847
-5.287
-5.480
-5.494
-5.269
-5.133
-6.242
-5.923
-6.368
-6.067
-5.754
-6.154
0.0968
0.84035
                                                                                  68

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The Regclass Code shown in the Table has the following explanation. The first two digits from the
left indicate the regulatory  class - either 46 or 47.  Class 46 is the medium heavy-duty diesel
vehicle class, and Class 47 is the heavy heavy-duty diesel vehicle class.  The MOVES model also
contains lighter diesel vehicle classes 41  and 42 and the transit bus class 48, but no data were
available for these.  The second two digits represent the model year group.  Where the value of 44
maps to 1960-1987, 45 maps to 1988-1990, 46 maps to 1991-1993, 47 maps to 1994 -  1997 and 48
maps to 1998 - 2006. A final code value of 49 was also mapped to the 2007+ model years for use
in MOVES.  However, there were no data associated with this group.  The  final two digits
represent the speed divisions of the operating mode bins. The value of 10 represents 0 to 25 mph,
20 represents 25 to 50 mph and 30 represents  50 mph and higher.  The VSP is an  independent
variable within each of the speed divisions.

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

Where :

The variable 'Coefficient' are the coefficients found in the above table. VSPCoefficient is the VSP
Coefficient at the bottom of the table,  and Log Trans Coeff is the log transformation coefficient in
the table. Avg VSP is the average VSP utilized by MOVES for each of the 23 operating mode bins
in units of kW/metric ton (i.e., binll = 0, bin!2 = 1.5, bin!3 = 4.5, etc.).
                                                                                      69

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

A. 5.1 Introduction

      This memo describes the development and application of a "rough cut" emission model for
estimating elemental and organic carbonaceous material (EC and OM) emission rates (or EC/OM
ratios) from MOVES. The memo describes the following steps involved in predicting EC/OM
ratios. The memo also briefly describes comparisons with independent emission data collected
using the University of California Riverside's "Mobile Emission Laboratory."

      The subsequent sections of the memo describe the following:
      •  the extension of PERE for heavy-duty diesel vehicles, developed by Nam and Giannelli,
         to estimate heavy-duty fleet-average emission factors for any  specified driving cycle;
      •  the acquisition of data used in estimating EC/OC rates as a function of engine operating
         mode and the fitting of simple empirical models to them;
      •  the application of PERE to estimate EC and OC emission rates for different test cycles;
         and,
      •  the comparison of PERE-based EC and OC emission rates to those measured by
         independent researchers in HD trucks.

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

      PERE (Physical Emission Rate Estimator) is a model employed by EPA  in earlier
developmental phases of MOVES.42  In particular, the MOVES team employed  it in "hole filling"
greenhouse gas emission rates for unlikely SourceBin/OpModeBin combinations in
MOVES2004.43
      The underlying theory behind PERE and its comparison with measured fuel  consumption
data is described by Nam and Giannelli (2005).42 Briefly, PERE estimates fuel consumption and
emission rates on the basis of fundamental physical and mathematical relationships  describing the
road load that a vehicle meets when driving a particular speed trace. Accessory  loads are handled
by addition of an accessory power term. In the heavy-duty version of PERE described by Nam and
Giannelli (hereafter, "PERE-HD", accessory loads were described by a single estimate of the power
demand of accessories.
      For the current project, PERE was modified to incorporate several "extensions" that
allowed it to estimate fleet-average emission rates, simulate a variety of accessory load conditions,
and predict EC and OC rates for any given driving cycle.
A.5.2.1  PERE-HD Fleet-wide Average Emission Rate Estimator

      PERE-HD requires a number of user-specified inputs, including:
      •  vehicle-level descriptors (model year, running weight, resistive force coefficients,
         transmission type, class [MDT/HDT/bus]);
                                                                                  70

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       •  engine parameters (fuel type, displacement); and
       •  driving cycle.

The specification of these inputs allows a PERE to model the engine operation, fuel consumption,
and GHG emissions for a HDV on a specified driving cycle.
       However, the baseline PERE-HD provides output for only one combination of these
parameters at once. To estimate fleet-wide average a large number of PERE-HD runs would be
required. Furthermore, the specification of only fleet-wide average coefficients is likely to
substantially underestimate variability in fuel consumption and emissions.  Emissions data from a
large number of laboratory and field studies suggest that a very large fraction of total emissions
from all vehicles derives from a small fraction of the study fleet.  Therefore, it is desirable to
develop an approach that comes closer to spanning the range of likely combinations of inputs than
would using a small number of averages alone.
       For the current application, PERE-HD, a model built within Microsoft Excel was expanded
to allow for a representative sample of [running weight] *  [engine displacement] * [model year]
combinations. A third-party add-on package to Excel,  @Risk 4.5 (Palisade Corporation, 2004)
allows fixed inputs within spreadsheet models to be substituted with probability distributions,
sample an input value from each input distribution, and re-run the spreadsheet model many times.
This type of procedure is commonly referred to as "Monte Carlo" simulation.

A.5.2.1.1 Monte Carlo Simulation in PERE-HD

       To illustrate how @Risk performs this process, compare the results of the following
equation when using fixed inputs versus distributions of inputs:
             BMI = M / L2
This is the equation for body mass index in humans, a simple surrogate for overweight and
underweight. According to the Centers for Disease Control and Prevention (CDC), the average
U.S. woman weighed 164.3 Ib (74.5 kg) in 2002 and was 5'4" (1.6 m) tall. This corresponds to a
BMI of 28, suggesting that the average U.S. woman is  overweight. While this is useful information
from a public health perspective, it does not provide any indication as to which individuals are
likely to experience the adverse effects of being overweight and obese.  However, if we were to
assume (arbitrarily) that the range of weight and height within the U.S. population was +/-50% of
the mean, distributed uniformly, and perform a Monte Carlo simulation (5000 iterations) using
@Risk, we would predict a probability distribution of BMI in the population as follows:
                                                                                       71

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         Distribution of BMI in Simulated Population
   •i "5   0.030--
   (/) Q.
   x- O
   ฐ ฐ-   0.020+
                            BIVI
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):
    20 r
                              NHANES 1976-1980
                                          NHANES 2005-2006
                                BMI
 SOURCE: CDC/NCHS, 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 "average" inputs provides much less information than does Monte Carlo
simulation of with variable inputs.
       For emission modeling purposes using PERE-HD, several key inputs were modeled as
probability distributions.

A.5.2.1.2Model Year

       Model year is an important factor in PERE, as the frictional losses in the model (FMEP)
vary by model year, improving with later model years. As such, model year was simulated as a
probability distribution, using data from the Census Bureau's 1997 Vehicle Inventory and Use
Survey (VIUS) VIUS reports VMT by model year, so these data were normalized total VMT to
develop a probability distribution.  Model year distributions in 1997 were normalized to the current
                                                                                         72

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calendar year (2008).l  For instance, the fraction of 1996 vehicles reported in the 1997 VIUS is
treated as the fraction of 2002 vehicles in the 2003 calendar year. Although a 2002 VIUS is
available, previous analyses (unpublished) have shown the "relative" model year distribution of
trucks to have changed little between 1997 and 2002, though this assumption is one limitation  of
this analysis.
       The model year distribution for PERE-HD was represented as a discrete probability
distribution, as shown below:
                   Probability and Cumulative Probability Distributions of Model Years in PERE-HD
                                                                   - Probability by Age
                                                                   -Cumulative Probability by Age
         0 1  2 3 4 5 6 7 8  9 10 11 12 13 14 15 16 17 18 19202122232425
                    Relative Age (Calendar Year - Model Year)
A. 5.2.1.3 Vehicle Weight and Engine Displacement
       Vehicle running weights and engine displacements were modeled as a two-way probability
distribution with engine displacement depending on running weight. These data were derived from
VIUS microdata obtained from the Census Bureau.44 A two-way table was constructed to estimate
VMT broken out as [weight class] * [displacement class]. Analyses were restricted to diesel-
powered trucks only.
       First, @Risk selects a running weight from a probability distribution representing the
fraction of truck VMT occurring at a given running weight:
 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)]. AandB parameters were determined by minimizing least squares of the
residuals.  The sum of probabilities for model years older than 10 years was constrained the fraction of VMT driven by
trucks older than 10 years in VIUS.
                                                                                              73

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                       Probability Distribution of Vehicle Running Weight based on VIUS
     0.4 -,
    0.35
     0.3
   >- 0.25 -
  a. 0.15 -
     0.1 -
    0.05
              ooooooooooooooo
                                                               o    in
                                                                    ฐ>
                                                                        o    in
                                          Weight Range
Because TIUS reports ranges of running weight, any value of running within the TlUS-specified
ranges were considered equally likely and modeled as uniform probability distributions. For the
upper and lower ends of the distribution the minimum and maximum running weights were
assumed to be 7,000 and 240,000 Ib, respectively.
       After @Risk selects a running weight, an engine displacement based on a discrete
distribution assigned to every weight class in VIUS, represented below:
                      Distribution of Displacement (cu. in.) by Running Weight (Ib) in TIUS
   2
  Q_
   0)
   ^
  o
                                       Running Weight
D850+
• 800-849
• 750-799
• 700-749
• 650-699
D 600-649
D 550-599
• 500-549
• 470-499
D 350-469
• 430-449
O 400-429
• 370-399
D 350-369
D 300-349
• 250-299
D 1-249
Again, because VIUS describes ranges of values for displacement, all values within each range
were given uniform weight and assigned a uniform distribution. For the extreme bins, the
minimum and maximum engine displacements were assumed to be 100 in3 and 915 in3,
respectively.
       This procedure reflects the range in running weights present among HDV in operation, and
constrains the combinations of weight and displacement to plausible values based in surveyed truck
operator responses. This allows plausible variability in weight-engine pairings, which translates
into differences in engine BMEP. As described later, EVIEP is a key variable in predicting EC and
OC emission rates.
                                                                                             74

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       For use in PERE-HD, all units were converted to SI units (kg and 1).

A.5.2.1.4 Accessory Load

       The original PERE-HD treats accessory load as a fixed value, which may be varied by the
user. It is set at 0.75, and used in calculating fuel rate and total power demand at each second of
driving.
       Following the development of PERE-HD, Gururaja and Cullen (2008) developed a more
detailed set of accessory load estimates based on several accessories' power demand while in use
and the fraction of time that an accessory is in use.45 Gururaja and Cullen estimated "high,"
"medium," and "low" accessory use categories for three vehicle classes:  HOT, MDT, and buses.
For the current version of the model, only the HDT accessory load estimates were employed,
though a sensitivity analysis indicated that mean EC/OM ratios were most sensitive to accessory
load during idle and creep driving cycles.  The in the "base case," a mean ratio of 0.54 was
predicted, while in  the sensitivity case, a mean ratio of 0.50 was predicted.  This issue may be
revisited prior to the final version of MOVES, though the limited sensitivity of total results limits
the importance of the accessory terms within the current exercise.
       Within @Risk, the variable in PERE-HD, "Pace" for accessory use was substituted with a
variable representing the distribution (in time) of accessory loads as estimated as the sum of a
number of discrete  probability distributions.
       Depending  on the assumption of high, medium or low use, the power  demand for powering
these accessories is distributed in time as follows:
                       Comparison of High /  Medium / Low Accessory
                                        Load Cases
                    1.000-

                    0.800-

                    0.600-

                    0.400-

                    0.200-

                    0.000
                               00
                               51
                               CO
                               E
                               n
                               00
                               CL
                        0
10
20
30
A.5.2.1.5 Driving Cycle

       For purposes of this exercise, the four phases of the California Air Resources Board's
Heavy Heavy-Duty Diesel Truck (HHDDT) chassis dynamometer testing cycle were used to reflect
variability in vehicle operations for PERE-HD. [NEEDS REFERENCE]

A.5.2.1.6 Other Factors
                                                                                       75

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       Some elements of variability were not examined as part of this study. Hybrid-electric
transmissions and fuel cell power plants were excluded from the analysis, due to their low
prevalence within the current truck fleet.
       One important source of variability that was not examined in this analysis is the variation in
resistive forces among vehicles with identical running weights. This exclusion is important, given
the potential role for aerodynamic improvements, low rolling resistance tires, and other
technologies in saving fuel for long-distance trucking firms and drivers.  Such considerations could
be incorporated into PERE-HD in the future as a means of estimating the emission benefits of fuel-
saving technologies.  These technologies are promoted by EPA's Smartway Transport Partnership.
A.5.2.2 Prediction of Elemental Carbon and Organic Mass based on  PERE-
HD

A. 5.2.2.1 Definition of Elemental and Organic Carbon and Organic Mass

       In motor vehicle exhaust, the terms "EC," "elemental carbon," and "black carbon" refer to
the fraction of total carbonaceous mass within a particle sample that consists of light-absorbing
carbon. Alternatively, they refer to the portion of carbonaceous mass that has a graphitic
crystalline structure.  Further, one can define EC as the portion of carbonaceous mass that has been
altered by pyrolysis, that is, the chemical transformation that occurs in high temperature in the
absence of oxygen.
       EC forms in diesel engines as a result of the stratified combustion process within a cylinder.
Fuel injectors spray aerosolized fuel into the cylinder during a 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 that oxygen can diffuse to
the fuel at the center of each droplets, pyrolysis can occur as hydrogen and other atoms are
removed from the carbonaceous fuel, resulting in extensive C-C bond interlinking. As a result,
pyrolyzed carbon is produced in a crystalline form similar to graphite.
       "Organic carbon"  or "organic mass" (OC or OM) is used to denote the portion of
carbonaceous material in exhaust that is not graphitic.  Chemical analysis of this non-graphitic
carbon mass indicates that it is composed of an extensive mixture of different organic molecules,
including CIS to C44 alkanes, poly cyclic aromatic hydrocarbons, lube oil constituents (hopanes,
steranes,  and carpanes), and a sizeable fraction of uncharacterized material.  This component of
exhaust can derive from numerous processes inside the engine involving both fuel and oil. Because
of the complex chemical mixture that comprises this mass, its measurement is highly dependent on
sampling conditions.  The wide range of organics that compose it undergo evaporation and
condensation at different temperatures, and the phase-partitioning behavior of each molecule is
dependent on other factors, such as the sorption of vapor-phase organics to available surface area in
a dilution tunnel or background aerosol.
A.5.2.2.2 EPA Carbon Analysis Techniques in Ambient Air

       The definitions of EC and OM are critical, as different groups use different techniques for
quantifying their concentrations within a given medium.  For purposes of this memo, it is assumed
                                                                                       76

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that EC, OC, and OM are operationally defined quantities, meaning that they are defined by the
measurement technique used to quantify their concentrations on a filter or in air.
       The different types of commonly used approaches for carbon include:
       •  Thermooptical techniques, where the evaporation and oxidation of carbon are used in
          conjunction with a laser to measure  optical properties of a particle sample.  The major
          methods used for this type of analysis include:
              o  Thermooptical reflectance (TOR) - EPA is adopting this technique for the PM2.s
                 speciation monitoring network nationwide. It is also employed by the
                 IMPROVE program (Interagency Monitoring of Protected Visual Environments)
                 in national parks.  This technique heats a punch from a quartz fiber filter
                 according to a certain schedule. A Helium gas atmosphere is first employed
                 within the oven, and the evolved carbon is measured with a FID as temperatures
                 are increased in steps up to 580ฐC. 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 840ฐC.  All carbon evolved after the introduction of oxygen is assumed to be
                 elemental carbon.  The reflection of light from a laser by the filter is employed
                 to account for the pyrolysis of organic carbon that occurs during the warm-up
                 process.
              o  Thermooptical transmission  (TOT) - The National Institute of Occupational
                 Safety and Health (NIOSH) uses this technique for measuring EC concentrations
                 in occupational environments.  It is based on similar principles to TOR, but
                 employs a different heating schedule and transmission of light as opposed to
                 reflectance.
       •  Radiation absorption techniques
              o  Aethalometerฎ - This instrument reports "black carbon" (BC) concentrations
                 based the extent of light absorption by a "filter tape," that allows for a time
                 series of BC concentrations to be estimated. It has a time resolution of several
                 minutes.
              o  Photoacoustic Spectrometer  (PAS) - This instrument irradiates an air sample
                 with a laser.  The resulting heat that occurs from the absorption of the laser light
                 by light-absorbing carbon in the air sample produces a pressure wave that is
                 measured by the device.  The signal from this pressure wave is proportional to
                 the light-absorbing carbon content in exhaust.
       •  Thermogravimetric techniques, where the "volatile organic fraction" (VOF) is separated
          by heat from the non-volatile refractory component of a particle sample.
       •  Chemical extraction, where solvents are used to separate the soluble and insoluble
          components of exhaust.
A number of other techniques also exist within published literature, but the above techniques have
been most applied in emissions and routine ambient PM sampling.
       Among the available techniques,  it has been a point of controversy among academics as to
what analysis method provides the "correct" carbon signal.  Rather than addressing these
arguments in detail, this analysis adopts the technique employed by the EPA ambient speciation
monitoring network, TOR.  It should be noted that different emission researchers employ different
analysis techniques. Desert Research Institute (DRI) employed TOR in analyzing the Kansas City
gasoline PM emission study samples, while other prominent academics employ TOT, notably the
                                                                                       77

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University of California Riverside College of Engineering Center for Environmental Research and
Technology (CE-CERT) and the University of Wisconsin-Madison (UWM) State Hygiene
Laboratory. Research of all of these groups is employed throughout this study, so an inter-
comparison of the methods of TOT/TOR is necessary to be able to "recalibrate" one set of data to
another.
       EPA defines measurement techniques for dynamometer-based sampling and analysis of
particulate matter, in addition to techniques for sampling and analyzing particles in ambient air.
EC and OM inventories sit between these to "columns."
       The user community for MOVES is predominantly concerned with emissions that occur
into ambient air.  EPA regulations for demonstration of attainment of state implementation plans
(SIPs) are based on monitored ambient particulate matter using Federal Reference Methods (FRM)
for ambient air.  FRM monitors for particle speciation in ambient air undergo analysis for EC and
OC according to a defined standard operating procedure.46 That standard operating procedure
defines thermooptical reflectance (TOR) as the method for analysis of ambient carbon PM.
A.5.2.2.3 TOR - TOR Calibration Curve

       As part of the Gasoline / Diesel PM Split Study funded by the Department of Energy
(DOE), researchers from DRI analyzed filter samples using both TOR and TOT methods.  These
data were obtained and analyzed in the SPSS 9.0 statistical package.
       Briefly the DOE study included emissions characterizations of 57 light-duty gasoline
vehicles (LDGV) and 34 FID diesel vehicles (HDDV).  The vehicles were operated on a number of
different test cycles including cold-start and warm-start cycles. The data set employed in this study
was generated by DRI and obtained from the DOE study web site.47
       Both EC and OC were analyzed using the same approach. All data from all vehicles were
compiled together into tabular spreadsheets.
       First, EC and OC measured by TOR (denoted EC-TOR and  OC-TOR) were regressed on
EC-TOT and  OC-TOT.  Studentized residuals from these regressions were noted, and those with
Studentized residuals >3 were excluded from further analysis.
       Second,  each test in the reduced data set was assigned a random number (RAND) in the
range [0,1). Those cases with RAND > 0.95 were set aside as a cross-validation data set, and
excluded from any further regression analyses done.
       Third, those cases with RAND < 0.95 were regressed again, this time using an inverse
uncertainty weighting procedure for each data point. When DRI analyzes a filter sample, it reports
an analytical uncertainty associated with the primary estimate of EC and OC.  Accordingly, the
quality of each datum depends on the level of analytical uncertainly reported.  The inverse of the
DRI-reported uncertainty (I/a) associated with the TOR-based measurement was used to weight
each point in the weighted regression.
       It should be noted that for each regression, the intercept term was set to zero. Models
including intercepts did not have intercept terms that reached statistical significance. As such, R2
values are not considered valid.
       The coefficients from the weighted regression for EC and OC are reported below:
Slope
EC-TOR
Beta
1.047
Std. Error
0.011
t-value
91.331
Sig.
<0.001
                                                                                       78

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       OC-TOR
            1.014
0.007
153.923
<0.001
       To evaluate the quality of predictions resulting from these statistically-based adjustment
factors, they were used to predict EC-TOR and OC-TOR values for the subset of data with RAND
> 0.95. Scatter plots of the statistical fits are illustrated below (note logarithmic scaling).
        1000
    T3
    ง
    W
    CD
    CD
    o:
    O
    6
    LU
200-
100-

 20-
 10-

  2-
  1-
  .2-
  .1
             •7
                                                    &&$>> ^
            EC-TOR Predicted
        1000
    I
    w
    CD
    0
    o:
    O
    8
200-
100-

 20-
 10-

  2-
  1-
  .2-
  .1
             •7
            OC TOR-Predicted
                                                                                       79

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       When measured values are regressed against predicted values, the following statistical
estimates of fit are obtained:
Prediction
EC
OC
Slope
1.080
1.092
Std. Error
0.009
0.069
Intercept
3.737
-4.417
Std. Error
3.173
16.188
As shown, the prediction vs. observed comparison yields a slope near unity for both EC-TOR and
OC-TOR, with Nonsignificant intercepts. On this basis, the "calibration" factors for converting
EC-TOT and OC-TOT into their respective TOR-based metrics appear reasonable.
       It remains an unverified assumption that the "calibration" factors derived from the emission
data derived from DRI as part of the DOE Gasoline / Diesel PM Split Study are general enough to
apply to EC-TOT measurements obtained by other research groups.
A. 5.2.2.4 EC and OC Emission Rates

Selection of Engine Parameter for Predictive Modeling

       PERE-HD produces estimates of engine operating conditions and fuel consumption for a
given driving cycle. Prediction of EC and OM emissions requires information on the composition
of particulate matter as a function of some factor that may be related back to MOVES' activity
basis, the time spent in a particular operating mode bin (OpModeBin).
       It should be noted that the "real time" measurement of EC and OM is an exceptionally
complicated endeavor. While measurement techniques for EC have been developed that produce
apparently good correlation with traditional filter-based methods,
       While numerous publications report the EC and OM (or OC) exhaust emission rates across
an entire driving cycle, it is not clear which parameter of a particular driving cycle, such as average
speed, might be applicable to the extrapolation of the observed rates to other vehicles or driving
conditions. As a result, identifying an engine parameter that explains the observed variation in
driving cycle-based emission rates for EC and OM is desirable. Such a parameter will assist in
estimating emission associated with short-term variations in driving.
       One parameter that is a good candidate for establishing an engine-based emission model is
mean effective pressure (MEP).  MEP is defined as:

                                   MEP = P*nR/Vd*N

Here, P is the power (in kW or hp), nR is the number of crank revolutions per power stroke per
cylinder (2 for four-stroke engines, 1 for two-strokes), Vd is the engine displacement, and N is the
engine speed.  In other words, MEP is the engine torque normalized by volume.
       MEP can be broken into various components.  "Indicated MEP" or IMEP refers to the sum
of BMEP (brake MEP) and FMEP (friction MEP). Heywood (1988) writes that maximum BMEP
is an indicator of good engine design and "essentially constant over a  wide range of engine sizes."
Nam and Giannelli (2004) note that it can be related to fuel MEP multiplied by the indicated or
thermal efficiency of an engine, and have developed trend lines in FMEP by model year. As such,
since maximum BMEP is comparable across well-designed engines and FMEP can be well-
predicted by Nam and Gianelli's trends within PERE,  IMEP should be an appropriate metric for
                                                                                       80

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building an engine emission model that can be applied across vehicles with different loads and
engine displacements.

Emission Data

       Kweon et al. (2004) measured particle composition and mass emission rates from a single-
cylinder research engine based on an in-line 2.333 liter turbo-charged direct-injection six cylinder
Cummins N14-series engine, with a quiescent, shallow dish piston chamber and a quiescent
combustion chamber. Emission data were obtained from all eight modes of the CARB  8-mode
engine test cycle:

Speed
Load%
Equiv.
Ratio
(
-------
IMEP. Total work-specific PM2.5 is not monotonic, but appears to be described by a single global
minimum around IMEP ~ 0.9 and two local maxima around IMEP of 0.2 and 1.2, respectively.
       The oppositely signed slopes of the emission-IMEP curves for EC-TOT and OC-TOT
suggest that there are different underlying physical processes.  It is not the intent of this paper to
explicitly describe the particle formation mechanisms in a diesel engine. However, the use of two
separate functions to predict EC-TOT and OC-TOT separately is warranted. This implies that the
EC/OC ratio will vary by engine operating mode. The following figure depicts the EC/OC ratio as
a function of IMEP.
                       EC/OC Ratio vs. IMEP from Kweon et al. (2004)
                               Note Logarithmic Scale
     100 n
     10 -
  8
  o
  LU
     0.1 J
                0.2
0.4
0.6
 0.8
IMEP
1.2
1.4
1.6
Estimation of IMEP-based Emissions of EC and OC

       To produce a relationship that generalizes the implied relationship between EC-TOT and
OC-TOT work-specific emissions and EVIEP in the data presented by Kweon et al. (2004), it is
necessary to specify some functional form of a relationship between the two.
       A priori, on the basis of visual inspection of the data, a flexible logistic-type curve was fit to
the data by a least-squares minimization procedure using the Microsoft Excel "Solver" tool, which
employs the GRG2 optimization approach.
       The functional form of the logistic-type curves fit to both the EC-TOT and OC-TOT data
from Kweon et al. (2004) is as follows:
                                             A
                                      Y =
                                           ,-Bx
                                          e -+C
A least-squared error approach was implemented within Microsoft Excel to derive the coefficients
for the logistic curves for EC-TOT and OC-TOT.  The solutions to the fits are as follows:
Y
EC-TOT
OC-TOT
A
2.12 x 10'5
0.155
B
-9.79
-2.275
C
4.67x 10'5
-0.859
Graphically, in comparison to observed values of EC-TOT and OC-TOT, the fitted curves result in
predictions reasonably close to the observed values. Furthermore, when compared to the observed
                                                                                      82

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PM2.5 values, the sum of predicted EC-TOT and OC-TOT values predict the lack of monotonicity
and patterns of maxima and minimum seen in the PM2.5 data.
      However, as a result of the values predicted by these sigmoid-type curves at high and low
IMEP values, extreme patterns in the EC-TOT/OC-TOT ratios predicted occur.  These extreme
values are artifacts that result solely from the behavior of simplistic logistic curves at the bounds of
IMEP in the observed data sets.  As a result, for predictive purposes, the maximum and minimum
observed EC-TOT and OC-TOT values observed in the  data set were set as the artificial limits of
predicted EC-TOT and OC-TOT, respectively. While this approach is arbitrary, it does ensure that
extreme predictions resulting from the selection of the logistic functional form do not occur.
      The following graph (log-scale) depicts the behavior of the TOT-based EC/OC ratio as a
function of IMEP. As demonstrated on the graph, without the max/min constraints on predicted
EC-TOT  and OC-TOT, the predicted ratio assumes values with a much broader range than found in
the data.
100

10

1
0
8 '
0
UJ
0 1 -
0 01

Comparison of EC/OC Ratio (TOT) by IMEP
With and Without Max/Min Constraints
-^----"~"

~/
/
) 0.2 0.4 ^f %.(, ' / 0.8 1 1.2 1.4 1.6
* *" J
^ *
/
IMEP




	 EC/CC(TOT-Predicted)
.... EC/OC(TOT-Predicted) with
Constraints
EC/OC(TOT-Measured)



       The approach of constraining predictions to the maximum and minimum values observed in
the measured data set is not grounded in any theoretical basis, but is a "brute force" approach.
Future revisions to this analysis will consider alternative approaches more grounded in accepted
theoretical or statistical methodology.
       The logistic curves described above receive IMEP predictions from PERE to predict EC-
TOT and OC-TOT emission rates (g/bhp-hr) for every second of a driving cycle. Combined with
real-time work estimates from PERE, emissions are expressed in g/s, the same units required for
MOVES.
       EC-TOT and OC-TOT emission rates are converted to TOR-equivalent rates for use in
MOVES, using the TOT-TOR "calibration" relationships described above. Alternatively, TOT-
equivalent rates can be used to compare with data from studies employing TOT for carbon analysis.
       It should be noted that these emission estimates are based on a single engine. Therefore,
predictions of EC and OC emission rates based on these relationships are insensitive to model year,
although PERE-HD does vary frictional MEP as a function of model year.

Organic Carbon to Organic Mass Conversion
                                                                                      83

-------
       Carbon is only one component of the organic material found in PM emission samples.
Hydrogen, oxygen, and nitrogen are also atomically part of organic molecules found in exhaust
PM.  For this study, a simple set of OC/OM conversion ratios were employed.
       Heywood (1988) presents data on the chemical composition of diesel exhaust PM,
presenting characterization of both the "extractable composition" and "dry soot" components of
                                48
PM measured at idle and at 48 km/h.   The composition data is as follows:

Atomic formula
OM/OC Ratio
Idle
C23H29O4.7No.21
1.39
48 km/h
C24H3oO2.eNo.18
1.26
       The data for the "extractable composition" is assumed to be the organic mass of particles.
The total molar weight to carbon molar weight ratio was used to convert OC to OM. The idle data
from Heywood were used when engine IMEP was 0.15 or under, corresponding to the idle mode of
the cycle employed by Kweon et al. (2004). All other engine conditions employed the ratio based
on the 48 km/h sample in Heywood.
A.5.2 Comparison of Predicted Emissions with Independent
Measurements
       To ensure that predicted EC and OC emission rates from this approach are reasonable prior
to any application for MOVES, PERE-HD based EC and OC emission factors were compared with
measured emission factors from an independent study.  Shah et al. (2004) report EC and OC
emission factor and rates for a series of heavy heavy-duty diesel trucks (HHDT) in California.49
Shah et al. report the results of emission testing using the CE-CERT Mobile Emissions Laboratory
(MEL), a 53-foot combination truck trailer containing a full-scale dilution tunnel designed to meet
Code of Federal Register (CFR) requirements.  The primary dilution tunnel is a full-flow constant
volume sampler, with a double-wall insulated stainless  steel snorkel that connects the MEL directly
to the exhaust system of a diesel truck. PM collection systems were designed to meet 2007 CFR
specification, including a secondary dilution system (SDS).
       The 11 trucks sampled in this study were all large HHDDTs with engine model years 1996-
2000, odometers between approximately 9,000  and 547,000 miles,  and rated powers from 360-475
hp.  It should be noted that these trucks, on average, have larger engines and higher rated power
than "typical" trucks on the road.  Furthermore, they were loaded with only the MEL, which
weighs 20,400 kg. As a result, the emissions from these trucks do not reflect the likely variability
in truck running weight described above and used in the PERE-HD runs for this study.
       Shah et al. (2004) report emission data for each  of the four modes of the CARB HHDDT
cycle, including cold start/idle, creep, transient, and cruise. The test cycle represents a wide range
of driving patterns, as suggested in the table below. Note that these test cycles are trip-based, so
each begins and ends with the vehicle at stop.
Cycle
Cold
start/idle
Creep
Transient
Distance
(mi)
0
0.124
2.85
Duration (s)
600
253
668
Average
Speed
(mph)
0
1.77
15.4
Maximum
Speed
(mph)
0
8.24
47.5
Maximum
Acceleration
(mph/s)
0
2.3
3.0
                                                                                     84

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Cruise
23.1
2083
39.9
59.3
2.3
       The following table presents the EC-TOT and OC-TOT emission rates and emission factors
reported in Table 6 of the study:
Rate/Factor
EC (mg/mi)
OC (mg/mi)
EC (mg/min)
OC (mg/min)
Idle


4.10ฑ2.38
20.9ฑ11.6
Creep
340ฑ140
607ฑ329
10.4ฑ4.8
17.0ฑ6.4
Transient
446ฑ115
182.9ฑ51.2
110.7ฑ27.0
45.5ฑ13.2
Cruise
175ฑ172
74.7ฑ56.3
93.0ฑ68.3
42.3ฑ26.8
       The following graph illustrates the comparison between predicted EC-TOT and OC-TOT
emission factors predicted by PERE-HD and those reported by Shah et al. (2004).  The letters "H,"
"M," and "L" refer to high, medium, and low accessory loads employed in the PERE-HD runs with
IMEP-based emission rates. As shown in the graph, it appears that for transient and cruise
conditions, PERE-HD predicts the general between-cycle trends in EC-TOT and OC-TOT emission
factors.  It appears for the low-speed "creep cycle," PERE-HD or the IMEP-based emission rates
underpredicts total carbon (EC+OC) emission factors, but that the general trend in the EC/OC ratio
is directionally correct.
            Predicted EC and OC Emission Factors(g/mi) vs. Measured Values in Shah et al. (2004)
                                                               D Average of OC g/mi
                                                               • Average of EC g/mi
A.5.3 Variability in Predicted EC and OC Emission Rates

      Through the modeling approach used here the influence of variable vehicle weight and
engine displacement on HDDV EC and OC emission rates can be assessed.  It should be noted that
                                                                                    85

-------
these relationships are contingent on the particular algorithms employed in PERE-HD for
estimating power and IMEP, as well as on the functional form of the IMEP-based emission
relationship described above.  As such, the analysis of variability in EC and OC emission rates is
constrained within the functional forms of all models employed.
       The graph below depicts the TOR-specific ratios of the total amount of EC and OM emitted
across the transient driving cycle.  As is apparent, increasing running weight per unit of engine
displacement is associated with an increased EC/OC ratio.  The highest EC/OM ratios, located in
the upper right-hand-quadrant of the graph, correspond to vehicles loaded with extreme weight
relative to the total available engine displacement.
               EC/OM Ratio (TOR-Spacific) versus Weight/Displacement Ratio for Individual Truck Samples
                             Transient Driving Cycle, High Accessory Load
    25 -,
    20 -
    15 -
  O
  D 10 -
  iu
     5 -
               1000       2000      3000      4000       5000
                                Running Weight / Displacement (kg/I)
                                                             6000
                                                                      7000
                                                                               8000
       In general, these results reflect the role that running weight has on EVIEP in a truck. Since
IMEP correlates highly with the air/fuel ratio (or equivalence ratio (p), the data suggest that EC/OC
partitioning is driven by the pyrolysis that occurs in engines under load.
       Very few weight/displacement pairings are greater than 3,300 kg/1.  The following graph
depicts the cumulative frequency distribution (CFD) of simulated weight/displacement ratios in
PERE-HD.
                                                                                           86

-------
                       Distribution of kg/I Ratios in Transient High Accessory Load Simulation
    8000
    5000
  "a 4000
    2000
          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
                                          Percentile
       For a 12 1 engine, 3,000 kg/1 would correspond to a running weight of 39600 kg (87,302 Ib).
Such vehicle loadings are infrequent, as they exceed Federal and state limits for vehicle weights on
highways.  The graph below presents the cumulative distribution of simulated weights, based on
the VIUS microdata.  Furthermore, the graph presents cumulative frequency distributions for
several broad weight  categories reported by Ahanotu (1999) for trucks in the Atlanta metropolitan
area.50 Note that in the graph, the highest weight category reported by Ahanotu (1999) is
represented as 100%, although the actual maxima of observed trucks  are unknown.
              Comparison of Simulated Weights with Atlanta Area Truck Measurements
                                                                Simulated Weight

                                                                GA Tech Class 9 Monday Midday
                                                           	GA Tech Class 9 Monday 3-7 PM

                                                           	GA Tech Class 8 Trucks M-F Midday

                                                           	GA Tech Class 10-13 Trucks Daytime
                                                           	GA Tech Class 10-13 Trucks
                                                                Nighttime
              20000    40000    60000    80000
                           Weight (Ib)
                                             100000
                                                     120000
       In general, the sensitivity of EC/OM ratios to the weight/displacement ratio suggest that
properly capturing the variability in both inputs is key to developing representative inputs for
MOVES.
                                                                                                87

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

The modeling described in the previous sections has been employed to create second-by-second
estimates of EC-TOR and OC-TOR emission factors for use in MOVES.  The next step of analysis
for MOVES consists of appropriately binning the outputs to fit the MOVES operating mode
structure. EC and OC emission rates, as opposed to total PM, are the inputs to the MOVES model
for PM inventory calculations. To convert the total PM rates calculated from heavy-duty emissions
analysis into EC and OC rates, we must calculate EC and OC fractions by MOVES operating
mode.  Then, the total PM rate can be multiplied by the EC and OC fractions to obtain EC and OC
input emission rates.

One  of PERE's outputs for heavy-duty vehicles is road load coefficients.  For each individual
weight in the distribution, PERE outputs a set of A/B/C coefficients similar to the ones used to
calculate VSP in the HC, CO, and PM emission rate analysis.  We used these coefficients and
weights to calculate VSP for each second using the equation below.
                              vsp = Avt + BVt+Cyi+mvtat
                                              m
This equation is implemented slightly differently than the one used for analysis of the chassis
dynamometer testing for PM, HC, and CO since the road load coefficients (A, B, and C) and
weight (or mass) m were specific to each individual vehicle, not general to the regulatory class. In
the PM, HC, and CO equation, the road load coefficients and denominator mass were not specific
to the vehicle and the numerator mass was specific to the vehicle.  We felt confident in using
vehicle-specific numbers because we performed the analysis using a full representative distribution
of weights and displacements.  Also, since we are interested in the EC and OC fractions rather than
the actual rates themselves, normalizing by the actual weight provides a more accurate picture.  For
example, a large engine operating at 90% of rated power (high VSP) would have a similar EC
fraction as a smaller engine operating at 90% of rated power, even though the large engine would
likely be hauling a proportionally greater amount of weight. This is also supported by the previous
research and analysis that relation EC fraction to IMEP and not power itself. The large engine
would, however, emit a larger EC rate than the smaller engine, but this difference in rates is
captured by our PM emission rate analysis.

We separated vehicles into two different regulatory classes based on running weight (we did not
have GVWR information). The weight distribution used in the analysis is shown below.
                                                                                     88

-------
                  Representative distribution of weights used in the EC/OC analysis.
Based on this weight distribution, we considered all vehicles weighing more than 40,000 Ib to be
HHD vehicles and all vehicles less than 40,000 to be MHD vehicles. This was a very simple
approach to stratifying by regulatory class.

As EC and OC rates were also computed for each second during each cycle, we were able to
average the EC and OC rates by operating mode bin.  Then, we calculated the fractions of EC and
OC for each operating mode bin.  For the LHD classes, we used the MHD fractions,  and for buses,
we used the HHD fractions.
                    /ซ: =
—  •> JOC ~ '
                                     ' OC
                                                                   'EC
                                                           OC
The resulting EC fractions by operating mode bin are shown in Figure 4 in the main body of this
report.
                                                                                        89

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A.6 Heavy-duty Gasoline Start Emissions Analysis Figures
Figure 23. Cold-Start Emissions (FTP, g) for Heavy-Duty Gasoline Vehicles, averaged by Model-year and Age
Groups
              FTP Cd-2—SlartB (g>. HD SI (lf< - WKJ
                CO slot Is >: Hflt bj lire
    (a) CO
              FTP Cold— Sartt {<$. HD SI [HD
                                                                             90

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Figure 24. Cold-Start FTP Emissions for Heavy-Duty Gasoline Vehicles, GEOMETRIC MEANS by Model-
year and Age Groups
                      FTP Cold-Starts (g), HD SI (HD<-14K)
                     CO GEO-mean  slar is  vs.  Age by  MYG
        (a)  CO
         node1yeargroup
                                  agenid

                                 i 19901990
                      FTP Cold —Starts (g), HD SI (HD< = 1-4K)
                    THC  GEO-mean starts vs. Age  by MYG
        (b) THC
                                                  10    11     12     13
         node 1 yeargroup  O q O 19601989  B-H-B 19901990  -I—I—1- 19911997  & & A 19982004

                      FTP Cold-Starts (g), HD SI (HD< - 14K)
                    NOx GEO-mean star Is vs. Age by HVG
         (c)NQx
                                                       11     12
                       19B01989  ana 19901990
                                            19911997  ^i & ฑ 19982004
                                                                                                               91

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

DEVIATION by Model-year and Age Groups.
                       CO !n_SD sป, Age by HVG
                                                 * 1998Z004
                    FTP Cdo- Saris (g), I ID SI (t D< = WKI

                      THC  In.SP .s. ป9ซ b, KYC
                    FTP CelO-S!art3 (9), I ID SI (kD< = MK|
                      noi  in.su >"s. kg< bf urc
                                                                                                       92

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Figure 26. Cold-Start Emissions for Heavy-Duty Gasoline Trucks: RECALCULATED ARITHMETIC
MEANS by Model-year and Age Groups.
                   CO  ARITH-menn si or Is  it.  Age by HVG

                     FTP Cold-Starts (g), HD SI (HD< - 141^
                  THC ARITH-meon starts vs. Age by MTG
        (b) THC
        node1yeargroup
                                agemi d

                               > 19901990
                     FTP Cold-Starts (g), HD SI (HD< = 14K)
                  NQx  ARITH-mean starts vs. Age by WYG
        (c)NOx

                      19B01989  a a a 19901990
                                                                                                    93

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Table 36 - Emission Standards for Heavy-Duty Spark-Ignition On-road Engines
Regulatory Class

LHD2b




LHD345, MHD




Model Year

1990
1991-1997
1998-2004
2005-2007
2008+
1990
1991-1997
1998-2004
2005-2007
2008+
Emissions Standards (g/hp-hr)
CO
14.4
14.4
14.4
14.4
14.4
37.1
37.1
37.1
37.1
14.4
THC
1.1
1.1
1.1


1.9
1.9
1.9


NMHC




0.14




0.14
NOx
6.0
5.0
4.0

0.20
6.0
5.0
4.0

0.20
NMHC + NOx



1.0




1.0

94

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1 Heavy Duty Diesel Engine Settlement Information.  EPA website.
http://www.epa.gov/compliance/resources/cases/civil/caa/diesel/
2 Jack, J.  "U.S. Army Aberdeen Test Center Support of Heavy Duty Diesel Engine Emissions Testing."
http://www.epa.gov/ttn/chief/conference/eil5/sessionl/jack.pdf
3 D. McClement.  "Reformatting On-Road In-Use Heavy-Duty Emissions Test Data". Sierra Research, prepared for
US EPA, April 2008.
4 Diesel Engine Consent Decree: HD In-Use Testing. EPA website.
http://www.epa.gov/compliance/resources/cases/civil/caa/diesel/test.html
5 Gautam, M, et al. "Evaluation of In-Use Heavy-Duty Vehicle Emissions Using the Mobile Emissions Measurement
System (MEMS) for Engine Model Years 2001 to 2003."  Final Data Reports present to engine manufacturers to fulfill
testing requirements documented in Phases III and IV of the Heavy Duty Diesel Engine consent decree.  West Virginia
University.  2002 & 2007.
6 Oak Ridge National Lab, "Technology Roadmap for the 21st Century Truck Program" December 2000. Bradley,
Ron. http://roadmap.itap.purdue.edu/ctr/documents/21stcenturytruck.pdf
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Lucic, Ivana. Page 6.
http://filebox.vt.edu/users/hrakha/Publications/Variable%20Power%20Truck%20Acceleration%20-%20Ver%202.0.pdf
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December 1998, NREL/SR-540-25154
9 Goodyear.  "Factors Affecting Truck Fuel Economy - Section 9" Page 5.
http://goodyear.com. ve/truck/pdf/radialretserv/Retread_S9_V.pdf
10 Ramsay, Euan and Bunker, Jonathan. "Acceleration of Multi-Combination Vehicles in Urban Arterial Traffic
Corridors"  August 2003 Queensland University of Technology PhD Project. Page 11
http://eprints.qut.edu.au/archive/00002359/01/RS&ETechForum2003_Ramsay&Bunker_2.pdf
11 SAE J2188 Revised OCT2003:Commerical Truck and Bus SAE Recommended Procedure for Vehicle Performance
Prediction and Charting.
12 "Technology Roadmap for the 21st Century Truck Program" December 2000.  Page 32
http://roadmap.itap.purdue.edu/ctr/documents/21stcenturytruck.pdf
13 Pritchard, Ewan.  "Hybrid Electric School Bus Preliminary Technical Feasibility Report" Advanced Energy
Corporation. September 14, 2004. Page 25. http://www.odyne.com/pdf/Pritchard-report.pdf
14 Hedrick, J.K., Ni, A. "Vehicle Modeling and Verification of CNG-Powered Transit Buses"  University of California,
Berkeley. California Partners for Advanced Transit and Highways.  2004. Page 21.
http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1169&context=its/path
15 "Hybrid Theory:  Hybrid Vehicle engineering for economy, the environment,  and customer delight" MIRA. Page 2.
http://www.mira. co.uk/Case_Studies/documents/ProjectChoiceHybridBus.pdf
16 Vehicle Inventory and Use Survey, http://www.census.gov/svsd/www/vius/products.html
17 Koupal, et al. "MOVES2004 Energy and Emissions Inputs - Draft Report February, 2005." EPA420-P-05-003,
March 2005.
18 Lindhjem, C. & Tracie Jackson.  "Update of Heavy-Duty Emission Levels (Model Years 1988-2004+) for Use in
MOBILE6" (M6.HDE.001). EPA420-R-99-010, April 1999.
19 Darlington, T., Dennis Kahlbaum, Gregory Thompson.  "On-Road NOx Emission Rates from 1994-2003 Heavy -
Duty Diesel Trucks." SAE 2008-01-1299, April 2008.
20 Koupal, John et al. MOVES2004 Energy and Emissions Inputs - Draft Report February, 2005
www.epa.gov/otaq/models/ngm/may04/crc0304a.pdf
21 Clark, et al. "California Heavy Heavy-Duty Diesel Truck Emissions Characterization for Program E-55/59." West
Virginia University Research Corporation. November 2005.
22 Nam, Ed and Robert Giannelli. "Fuel Consumption Modeling of Conventional and Advanced Technology Vehicles
in the Physical Emission Rate Estimator (PERE)," US EPA Office of Transportation and Air Quality, EPA420-P-05-
001. http://www.epa.gov/otaq/models/ngm/420p05001.pdf.  February 2005.
23 "Update Heavy-Duty Engine Emission Conversion Factors for MOBILE6 Analysis of BSFCs and Calculation of
Heavy-Duty Engine Emission Conversion Factors" (EPA420-R-02-005).
http://www.epa.gov/otaq/models/mobile6/r02005.pdf
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24 Lev-On, Miriam, et al. "Chemical Speciation of Exhaust Emissions from Trucks and Buses Fueled on Ultra-Low
Sulfur Diesel and CNG", SAE 2002-01-00432, March 2002.
25 Graboski, et al. "Heavy-Duty Diesel Vehicle Testing for the Northern Front Range Air Quality Study." Colorado
Institute for Fuels and High-Altitude Engine Research, Colorado School of Mines.  Prepared for Colorado State
University, February 1998.
26 Energy and Environmental Analysis, Inc. "Documentation and Analysis of Heavy-Duty Diesel Vehicle Emission
Test Data." Prepared for New York Department of Environmental Conservation, December 2000.
27 Calcagno, James A. "Evaluation of Heavy-Duty Diesel Vehicle Emissions During Cold-Start and Steady-State Idling
Conditions and Reduction of Emissions from a Truck-Stop Electrification Program." Dissertation Presented for the
Doctor of Philosophy Degree, University of Tennessee, Knoxville, December 2005.
28McCormick, et al (2000), "Idle Emissions from Heavy-Duty Diesel and Natural Gas Vehicles at High Altitude,"
Robert McCormick, et al, Colorado Institute for Fuels and Engine Research, Colorado School of Mines, Journal of the
Air and Waste Management Association, Revised May 3, 2000.
29Lim (2002), "Study of Exhaust Emissions from Idling Heavy-duty Diesel Trucks and Commercially Available Idle
Reducing Devices," Han Lim, US EPA Office of Transportation and Air Quality, September 2002.
30Lambert, et al (2002), "Preliminary Results for Stationary and On-Road Testing of Diesel Trucks in Tulare,
California," Douglas Lambert, et al, Clean Air Technologies Inc., May 15, 2002.
31Irick (2002), "NOx Emissions and Fuel Consumption of HDDVs during Extended Idle," David K. Irick,  University of
Tennessee, Bob Wilson, IdleAire Technologies Inc., Coordinated Research Council 12th Annual On-Road Vehicle
Emission Workshop, San Diego, California, April 15-17, 2002.
32Gautam, Clark, et al (2002), "Heavy-duty Vehicle Chassis Dynamometer Testing for Emissions Inventory, Air
Quality Modeling, Source Apportionment and Air Toxics Emissions Inventory," Phase I Interim Report, CRC Project
No. E-55/E-59, Mridul Gautam and Nigel N.  Clark, et al, West Virginia University Research Corporation, July 2002.
33NCHRP (2002), "Heavy-duty Vehicle Emissions," National Cooperative Highway Research Program Project 25-14,
Cambridge Systematics, Inc., with Battelle, Sierra Research and West Virginia University. October 2002.
34Tang and Munn (2001), "Internal Report - Idle Emissions from Heavy-Duty Diesel Trucks in the New Your
Metropolitan Area," Tang and Munn, New York State Dept of Environmental Conservation, November 9, 2001.
35Broderick, Dwyer, et al (2001), "Potential Benefits of Utilizing Fuel Cell Auxiliary Power Units in Lieu  of Heavy-
Duty Truck Engine Idling," Broderick, Dwyer, et al. Institute of Transportation Studies, University of California -
Davis. Paper UCD-ITS-REP-01-01
36Storey, et al (2003), "Paniculate Matter and Aldehyde Emissions from Idling Heavy-Duty Diesel Trucks," John M.E.
Storey, John F. Thomas, Samuel A. Lewis, Sr, Thang Q. Dam, K. Dean Edwards.  Oak Ridge National Laboratory.
Gerald L. DeVault, Y-12 National Security Complex. Dominic J. Retrossa, Aberdeen Test Center. SAE Paper 2003-
01-0289.
  Vehicle Inventory and Use Survey, U.S. Census Bureau http://www.census.gov/svsd/www/vius/products.html
37
38 Zhou, Lei. "Revision of Heavy Heavy-Duty Diesel Truck Emission Factors and Speed Correction Factors."
California Air Resources Board, October 2006.
39 Illinois Environmental Protection Agency. "Effectiveness of On-Board Diagnostic I/M Testing" September 2003.
Page 21.  www.epa.state.il.us/air/publications/obd-report-final.pdf
40 Manufacturers of Emission Controls Association, www.meca.org
41 Song, Qingwen. (2002). "Model-based Closed-loop Control of Urea SCR Exhaust Aftertreatment System for Diesel
Engine." Song, Q. andZhu, George.  SAE 2002-01-287. Page 1.
http://www.visteon.com/utils/whitepapers/2002 01  0287.pdf
42 Nam, E.K.; Giannelli, R. (2005) Fuel Consumption Modeling of Conventional and Advanced
Technology Vehicles in the Physical Emission Rate Estimator (PERE). EPA Office of Transportation and Air Quality.
Draft document EPA420-P-05-001.
43 http://www.epa.gov/otaq/models/ngm/420s05002.htm#pere
44 U.S. Census Bureau. (2000) Vehicle Inventory and Use Survey 1997 Microdata File [CD-ROM]. U.S. Department
of Commerce Economics and Statistics Administration
45 Gururaja, P.; Cullen, A. (2008) "Accessory requirement" tab on Microsoft Excel spreadsheet, "VSP Weights 2008-
Feb.xls". Personal communication.
46 Desert Research Institute, Atmospheric Sciences Division. (2005) DRI STANDARD OPERATING PROCEDURE.
DRI Model 2001 Thermal/Optical Carbon Analysis (TOR/TOT) of Aerosol Filter Samples - Method DVIPROVE_A.
DRI SOP #2-216.1. [Online at http://www.epa.gov/ttn/amtic/specsop.html]
47 http://www.nrel.gov/vehiclesandfuels/nfti/feat_split_study.html
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48 Hevwood. J.B. Internal Combustion Engine Fundamentals.  McGraw-Hill: New York, 1988.  Data from Table 11.8,
page 629.
49 Shah, S.D.; Cocker, D.R.; Miller, J.W.; Norbeck, J.M. (2004) Emission rates of paniculate matter and elemental and
organic carbon from in-use diesel engines.  Environ Sci Technol 38:  2544-2550.
50 Ahanotu, D.N. (1999) Heavy-duty vehicle weight and horsepower distributions: measurement of class-specific
temporal and spatial variability. PhD Dissertation, Georgia Institute of Technology.


      Additional Extended Idling References
EPA (2004), "Guidance for Quantifying and Using Long-Duration Truck Idling Emission Reductions in State Implementation Plans and
Transportation Conformity." (EPA420-B-04-001, January 2004) (http://www.epa.gov/smartway/idle-guid.htm)
EPA (2003), "Draft Analysis of Heavy-Duty Diesel Vehicle Idle Emission Rates," (EPA420-D-03-001, November 2003)
Additions:
Clark, Khan, Thompson, Wayne, Gautam, Lyons (2005), "Idle Emissions from Heavy-Duty Diesel Vehicles" Presentation by the Center for
Alternative Fuels, Engines, and Emissions at West Virginia University.
CRC (2007), "Heavy-Duty Vehicle Chassis Dynamometer Testing for Emissions Inventory, air Quality Modeling, Source Apportionment, and Air
Toxics Emissions Inventory" CRC Report No. E55/59. August 2007.
California Air Resource Board (2006), EMFAC Modeling Change Technical Memo: "Revision of Heavy Heavy-Duty Diesel Truck Emission Factors
and Speed Correction Factors" Zhou, Lei. October 20, 2006.
Lutsey et al (2004) "Heavy-Duty Truck Idling Characteristics - Results from a Nationwide Truck Survey" Lutsey, Nicholas, Brodrick, Christie-Joy,
Sperling, Daniel, Oblesby, Carollyn. Transportation Research Record 1880. Pages 30-38. http://www.its.ucdavis.edu/publications/2004/UCD-ITS-
Rp_04-38.pdf
Wallace (2003) "Modeling of Line-Haul Truck Auxiliary Power Units in ADVISOR 2002" Wallace, John Paul, Thesis August 2003, University of
California-Davis. http://www.its.ucdavis.edu/publications/2003/UCD-ITS-RR-03-07.pdf
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