-------
200
150
100
2009 Toyota Camry Coolant Temperature During Soak Start Drives
200-
150-
100-
200-
150-
£100-
0)
I—
I200-
0>
g-150-
K 100-
c
ro
o 200
o
0 150
100-
MOVES Operating Mode
101
102
— 103
— 104
— 105
106
106
200
150-
100
200-
150-
100-
200
400
Trip Counter (seconds)
600
800
Figure 3-86 Mean trends in coolant temperature for the Toyota Camry, by soak period
174
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3.9.2.2 Measuring Soak-time Relationships on the Dynamometer
The data collected by EPA using PEMS was supplemented by a dataset collected by the
California Air Resources Board and used to update start emission rates for EMFAC2017.47
These data were measured as cycle aggregates on the California Unified Cycle. We made use of
data from Phase 1 of the cycle for 32 vehicles certified to LEV-II standards. The start phase of
the Unified cycle is approximately 300 sec in duration.
To make use of the CARB data, we assigned the soak periods used in its collection to soak
periods corresponding to MOVES start operating modes.
3.9.2.3 Comparing Dynamometer and PEMS Measurements
To obtain a broad overview of the data from both sources, we first averaged all sets of results by
vehicle and soak period.
Emissions trends by vehicle and method are shown in for THC, CO and NOx in Figure 3-87,
Figure 3-88, and Figure 3-89, respectively.
As is typical with emissions data, the trends in start emissions with soak period are highly
variable across individual vehicles in both datasets. The CARB dataset is much larger and hence
the range of variability is wider, capturing more vehicles with emissions at the low end of the
range, as well as small numbers of vehicles with unusually high emissions.
With these considerations in mind, it appears the CARB and EPA datasets are broadly similar,
both in terms of emissions levels and in the shapes of trends by pollutant. However, we can also
conclude that results derived solely from the smaller PEMS dataset would be biased high. We
also note that the PEMS dataset is limited in that only one vehicle was measured at the nine-hour
soak period (540 min). The CARB data is also valuable in covering this period, which represents
operating mode 107.
175
-------
Dynamometer (CARB)
PEMS (EPA)
600 0 200
Soak Time (minutes)
Vehicle
AUDI 2010 A4
HOND
2004 ACCORD DX
HOND 2009 FIT
TOTA
2007
SCION TC
2009
CAMRY
BMW 2004 325i
HOND
2004 ACCORD LX
HYND 2006 SONATA
TOTA
2007
YARIS
2009
EXPL
BMW 2005 325 i
-#¦
HOND
2004 CRV
HYND 2007 ELANTRA
#-
TOTA
2008
CAMRY XLE
2009
OUTL
CHRY 2007 CALIBER
HOND
2005 ACURA TL
MB 2005 C230
TOTA
2008
ES350
2011
F150
FORD 2004 FOCUS
HOND
2006 CIVIC
MB 2006 S500
TOTA
2009
CAMRY
2017
CAMRY
GM 2007 CTS
HOND
2006 ELEMENT
NISS 2009 SENTRA
TOTA
2009
COROLLA LE
2017
F150
GM 2007 ION
HOND
2007 CIVIC
TOTA 2004 CAMRY LE
VOLK
2007
J ETTA
GM 2008 MALIBU
HOND
2008 ACCORD
TOTA 2005 COROLLA
VOLV
2004
S60
Figure 3-87 THC: Start emissions by soak period and vehicle for dynamometer and PEMS measurement methods
176
-------
Dynamometer (CARB)
15-
U)
V>
C
o
'35
v>
£
LU
r
CO
+->
V)
"co
4-1
o
10-
5-
0-
200
400
PEMS (EPA)
600 0 200
Soak Time (minutes)
400
600
Vehicle
AUDI 2010 A4
HOND
2004
ACCORD DX
HOND 2009 FIT
TOTA
2007
SCION TC
*%-
2009
CAMRY
-#¦
BMW 2004 325i
HOND
2004
ACCORD LX
HYND 2006 SONATA
TOTA
2007
YARIS
2009
EXPL
BMW 2005 325 i
HOND
2004
CRV
HYND 2007 ELANTRA
TOTA
2008
CAMRY XLE
2009
OUTL
CHRY 2007 CALIBER
HOND
2005
ACURA TL
MB 2005 C230
-•-TOTA
2008
ES350
2011
F150
FORD 2004 FOCUS
HOND
2006
CIVIC
MB 2006 S500
TOTA
2009
CAMRY
2017
CAMRY
GM 2007 CTS
HOND
2006
ELEMENT
NISS 2009 SENTRA
TOTA
2009
COROLLA LE
2017
F150
-«h
GM 2007 ION
HOND
2007
CIVIC
TOTA 2004 CAMRY LE
VOLK
2007
J ETTA
GM 2008 MALIBU
HOND
2008
ACCORD
TOTA 2005 COROLLA
-•-VOLV
2004
S60
Figure 3-88 CO: Start emissions by soak period and vehicle for dynamometer and PEMS measurement methods
177
-------
Dynamometer (CARB)
PEMS (EPA)
600 0 200
Soak Time (minutes)
400
600
Vehicle
AUDI 2010 A4
HOND 2004 ACCORD DX HOND 2009 FIT
TOTA
2007
SCION TC
-€h
2009
CAMRY
BMW 2004 325i
HOND 2004 ACCORD LX
HYND 2006 SONATA
TOTA
2007
YARIS
2009
EXPL
BMW 2005 325 i
HOND 2004 CRV
HYND 2007 ELANTRA
TOTA
2008
CAMRY XLE
2009
OUTL
~
CHRY 2007 CALIBER
HOND 2005 ACURA TL
MB 2005 C230
TOTA
2008
ES350
2011
F150
FORD 2004 FOCUS
HOND 2006 CIVIC
MB 2006 S500
TOTA
2009
CAMRY
~
2017
CAMRY
GM 2007 CTS
HOND 2006 ELEMENT
NISS 2009 SENTRA
TOTA
2009
COROLLA LE
2017
F150
GM 2007 ION
HOND 2007 CIVIC
TOTA 2004 CAMRY LE
VOLK
2007
J ETTA
GM 2008 MALIBU
HOND 2008 ACCORD
TOTA 2005 COROLLA
VOLV
2004
S60
Figure 3-89 NO*; Start emissions by soak period and vehicle for dynamometer and PEMS measurement methods
178
-------
After averaging the data by vehicle and soak period, mean soak-time trends were constructed by
following several additional steps.
Step 1: Correct for running-exhaust emissions
In addition to the emissions attributed to the excess fuel injected into cylinders during an engine
start period, we assume that typical "running emissions" and "hot-start emissions" are included
in the total. To isolate the excess emissions attributable to the start condition, we subtracted the
results for the 0-6 minute soak period from the measurements for the remaining soak periods.
This calculation was performed separately for each vehicle. This step is analogous to subtracting
Bag 3 from Bag 1 when estimating FTP start emissions.
Step 2: Average results across vehicles
Next, we averaged the means for individual vehicles across vehicle to obtain average trends. We
performed this step separately for the dynamometer and PEMS datasets.
Step 3: Calculate program-specific soak ratios
As in initial step in developing soak-time relationships, we normalized the mean emissions (in
grams) at all soak periods to those for the 12-hr soak period, i.e., cold start. We called this step
"program-specific" because we performed the normalization separately for the dynamometer and
PEMS datasets.
These intermediate ratios are shown for THC, CO and NOx in Figure 3-90, Figure 3-91 and
Figure 3-92 below, respectively. The ratios for the PEMS and dynamometer datasets are labeled
"EPA" and "CARB," respectively.
Step 4: Calculate final ratios
In this final step, we averaged the program-specific ratios for the two datasets to obtain a single
set of soak-time ratios. For each soak period, the final ratio was calculated as an average of two
intermediate ratios, weighted by numbers of vehicles in each data source for that period. The
final ratios are also shown in the figures, labelled as "EPA + CARB weighted average."
Due to the subtractions performed in step 1, the ratios for the first operating mode, opModelD
101, could not be directly estimated from the means. After correcting for running and hot-start
emissions, operating mode 101 would have had a mass of 0.0 g. To impute the ratios for this
mode, the soak ratios for the opModelD 101 was extrapolated. This fraction was estimated by
multiplying the fraction at operating mode 102 (soak time =18 minutes) by 3/18, the
proportional difference between the midpoints of the soak periods for these two operating modes.
For comparison, the figures also include the "older" soak curves, previously shown in Figure
3-83, page 170. The comparisons show the largest differences in soak curves for THC and NOx,
especially for soak times less than 240 minutes. Both the THC and NOx ratios surpass 1.0 before
the 720-minute soak mark, indicating that THC and NOx emissions from starts after less than 240
minutes soaking are greater than after 720 minutes or more.
179
-------
1.2-
Source
CARB
EPA
MOVES
EPA + CARB Weighted Average
(with respect to number of vehicles)
200 400 600
Soak Time (minutes)
Figure 3-90 THC: Program-specific and final soak-time ratios for Tier-2/LEV-II vehicles. The "MOVES" line
refers to values used in MOVES2014 and retained in M0VES3 for MY 2003 and earlier
180
-------
1.00-
0.75'
CO
0£
X. 0.50'
ro
o
CO
0.25-
0.00-
Source
- EPA
¦ MOVES
. EPA + CARB Weighted Average
(with respect to number of vehicles)
200 400
Soak Time (minutes)
600
Figure 3-91 CO: Program-specific and final soak-time ratios for Tier-2/LEV-II vehicles. The "MOVES" line
refers to values used in MOVES2014 and retained in MOVES3 for MY 2003 and earlier
181
-------
Source
EPA
• MOVES
. EPA + CARB Weighted Average
(with respect to number of vehicles)
200 400
Soak Time (minutes)
Figure 3-92 NO*: Program-specific and final soak-time ratios for Tier-2/LEV-II vehicles. The "MOVES" line
refers to values used in MOVES2014 and retained in MOVES3 for MY 2003 and earlier
The final results for use in MOVES3 are shown in Table 3-53. As mentioned, these fractions will
be applied to model years 2004 and later.
182
-------
Table 3-53 Revised Soak Fractions for Light-duty Start Emissions, for MY 2004 and later
opModelD
Midpoint
Soak time
(min)
S
THC
>oak Fraction
CO
s
NOx
101
3
0.0193
0.0167
0.0509
102
18
0.1159
0.1003
0.3053
103
45
0.4974
0.3649
1.4425
104
75
0.7149
0.5732
2.0743
105
105
0.7646
0.5931
2.2659
106
240
0.8039
0.6303
2.0355
107
540
1.160
0.8719
1.8055
108
720
1.000
1.000
1.000
3.9.3 Applying Deterioration to Starts
3.9.3.1 Assessing Start Deterioration In Relation to Running Deterioration
The large datasets used to develop rates for running emissions provided much information about
deterioration for hot-running emissions, but no direct information on deterioration for start
emissions. Our best data source for start deterioration was data from the IUVP program, used to
develop running rates for NLEV and Tier 2 vehicles (see Section 3.3). However, because the
IUVP data is a relatively small data set, and restricted to vehicles in good repair, we were
concerned that it would not capture the true variation in emissions. We considered whether it
would be better to simply apply the running deterioration rates described in Sections 3.2, 3.6 and
3.7, to start emissions. To investigate this, we compared start and running deterioration in the
IUVP data. As described below, we eventually applied adjusted running deterioration rates that
accounted for the differences in start and running deterioration as seen in the IUVP data.
A valuable aspect of the IUVP data is that they provide FTP results with the measurement phases
separated. As before, we focused on cold-start emissions, calculated as Bagl - Bag3 (g), and hot-
running emissions, represented by Bag2 (g/mi). For this purpose, these data are also valuable
because they provide emissions measured over a wide range of mileage, up to 100,000 mi,
although the corresponding range of vehicle age is relatively narrow (0-5 years). Thus, we
elected to first evaluate trends in emissions vs. mileage and only later convert to the age-based
rates needed for MOVES.
Starting with the National LEV standards in MY 2001, the hydrocarbon species used for
certification is non-methane organic gases (NMOG), rather than total hydrocarbons (THC). At
the outset, we plotted the data for NMOG and NOx vs. odometer reading, on linear and
183
-------
logarithmic scales. Scatterplots of start and running NMOG emissions are shown in Figure 3-93
and Figure 3-94; corresponding plots for InNMOG are shown in Figure 3-95 and Figure 3-96.
Similarly, scatterplots of start and running NO* emissions are shown in Figure 3-97 and Figure
3-98; corresponding plots for InNOx are shown in Figure 3-99 and Figure 3-100.
In viewing the data, some observations are apparent. The data are grouped, with one group
representing vehicles measured at less than 50,000 miles, centered around 10,000-20,000 miles,
and a second group representing vehicles measured at 50,000 to 100,000 miles. Given that the
purpose of the IUVP program is compliance assessment, the two groups are designed to assess
compliance with certification (< 50,000 mi) and useful-life (>50,000 mi) standards, respectively.
As expected, distributions of emissions are skewed, but with running emissions more skewed
than start emissions. On a logarithmic scale, the degree of skew is shown by the variability of
the transformed data, with the ln(start) spanning 3-3.5 factors of e, and the ln(running) spanning
6-7 factors of e. Finally, and of most relevance to this analysis, deterioration trends are visible in
the In plots, with the masses of points at >50,000 miles centered higher than those for < 50,000
miles.
To assess the presence of trends in emissions and mileage more rigorously, we ran linear
statistical models on the ln-transformed data. To illustrate, we will focus on models run on
vehicles certified to LEV standards, as shown in Table 3-56 and Table 3-57. The model structure
includes a grand intercept for all vehicle classes (LDV, LDT1-4), and separate intercepts for each
vehicle class. All parameters are highly significant, both for InNMOG and InNOx. A more
complex model structure was attempted, which included individual mileage slopes for different
vehicle classes. However, this model was not retained, as it did not improve the fit, nor were the
interaction terms themselves significant. The covariance structure applied was simple, in that a
single residual error variance was fit for all vehicle classes.
Models were fit to vehicles certified to other standards, such as ULEV and Tier 2/Bin-5, the
results for which are not shown here. The models for ULEV show very similar patterns to those
for LEV, whereas the models fit to Bin-5 data were not considered useful as the range of mileage
covered for these more recent vehicles was not wide enough to demonstrate deterioration trends
(i.e., < 25,000 mi).
The models confirm the visual impression given by the plots of InNMOG and InNOx. Positive
trends in emissions do appear evident in these data, but the increase in emissions with mileage is
very gradual. The trends in InNOx are steeper than those for InNMOG, and the trends for
running emissions are steeper than those for start emissions. However, the differences between
the slopes for start and running are less pronounced for InNOx than for InNMOG. For InNOx, the
running slope is 1.25 times that for starts, and for InNMOG, the running slope is 1.65 times that
for starts.
184
-------
::Li ;:jjt
n * ~*
~,05 0
*
Y~i—i—ii—i—r
10000 20000
—i—1—1—i-i—r
30000 40000
50000 60300 70000 30000 90000 1COOCO 110000 120000 130000
I 1 1 1 1
140000 150000
odometer
vehclass n D n LDT2 o Q o UNT1 a
Figure 3-93 Cold-start FTP emissions for NMOG (g) vs. odometer (mi), for LEV vehicles, from the IUVP
program
185
-------
-I—I—I—I—I—I—I—I—I—I—I—I—I—r-^1—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—r
90000 100000 110000 120000 130000 140000 150000
' I '
20000
i I i
60000
o o o LDVTl MDV2
*= * MDV3 +-*¦+¦ MDV4
Figure 3-94 Hot-running (Bag 2) FTP emissions for NMOG (g/mi) vs. odometer (mi), for LEV vehicles, from
the IUVP program
186
-------
In cs
10000 20000 30000 40000 50000 60000 70000 30000 90000 100000 110000 130000 130000 14KJC0 150000
odometer
vehcliss °°° LET2 ° o o LDVT! ^ * *= * MDV3 + + +* MDV4
Figure 3-95 Cold-start FTP emissions for ln(NMOG) vs. odometer (mi), for LEV vehicles, from the IUVP
program (LOGARITHMIC SCALE)
187
-------
In hr
^ —i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—|—i—i—i—i—[
10000 20000 30000 40000 50000 60000 70000 3COOO 90000 100000 110000 120000 130000 14TOC0 150000
odometer
vehcliss LET2 o q o LDVT! ^ * & MDV2 * *= * MDV3 + + +* MDV4
Figure 3-96 Hot-running (Bag 2) FTP emissions for ln(NMOG) vs. odometer (mi), for LEV vehicles, from the
IUVP program (LOGARITHMIC SCALE)
188
-------
' I '
50000
' I '
70000
' I '
MOOO
100000 110000 130000 130000
~ ~ ~ LDT2 o o o LDVT1 * A a MDV2 * * * MDV3
+ + + MDV4 + + +¦ MIJV5
Figure 3-97 Cold-start FTP emissions for NO.v (g) vs. odometer (mi), for LEV and ULEV vehicles, from the
IUVP program
189
-------
hot running
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it
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1000Q 20000 30000 40000 50000 60000 70000 80000 90000 100000 110000 120000 130000 140000 150000
odometer
vehdass n D n LDT2 o a o ldvtI ^ a a MDV2 * <= * MEf\r3 + + + MDV4 + + +* MDV5
Figure 3-98 Hot-running (Bag 2) FTP emissions for NO.v (g/mi) vs. odometer (mi), for LEV and ULEV
vehicles, from the IUVP program
190
-------
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Table 3-54 Model fit parameters for InNMOG, for LEV vehicles
Parameter
Predictor
Estimate
Standard error
Denom. D.F.
f-value
Pr > t
Cold-Start i
Bag 1 - Bag 3) i
residual error = 0.1942)
Slope
Odometer (mi)
0.000004982
0.0
2,404
CO
<0.0001
intercept
LDV-T1
-1.9603
0.02224
2,404
-88.14
<0.0001
intercept
LDT2
-1.7353
0.02429
2,404
-71.43
<0.0001
intercept
LDT3 (MDV2)
-1.5735
0.03520
2,404
-44.70
<0.0001
intercept
LDT4 (MDV3)
-1.2937
0.03233
2,404
-40.01
<0.0001
Hot-Running (Bag 2) (residual error = 1.3018)
Slope
Odometer (mi)
0.000008237
0.0
2,225
oo
<0.0001
intercept
LDV-T1
-6.1604
0.05961
2,225
-103.34
<0.0001
intercept
LDT2
-6.2554
0.06577
2,225
-95.11
<0.0001
intercept
LDT3 (MDV2)
-5.9018
0.09239
2,225
-63.88
<0.0001
intercept
LDT4 (MDV3)
-5.5949
0.08766
2,225
-63.83
<0.0001
Table 3-55 Model fit parameters for lnNO,y, LEV+ULEV vehicles
Parameter
Predictor
Estimate
Standard error
Denom. D.F.
f-value
Pr > t
Cold-Start (Bag 1 - Bag 3) (residual error = 0.68)
Slope
Odometer (mi)
0.000009541
0.0
1,657
CO
<0.0001
intercept
LDV-T1
-2.6039
0.05231
1,657
-50.74
<0.0001
intercept
LDT2
-2.4538
0.06056
1,657
-40.52
<0.0001
intercept
LDT3 (MDV2)
-2.0769
0.08173
1,657
-25.41
<0.0001
intercept
LDT4 (MDV3)
-1.645
0.08882
1,657
-18.52
<0.0001
Hot-Running (Bag 2) (residual error = 2.9643)
Slope
Odometer (mi)
0.000012
0.00000165
1,622
7.13
<0.0001
intercept
LDV-T1
-4.7396
0.1092
1,622
-43.40
<0.0001
intercept
LDT2
-4.9527
0.1304
1,622
-37.98
<0.0001
intercept
LDT3 (MDV2)
-4.3144
0.1740
1,622
-24.80
<0.0001
intercept
LDT4 (MDV3)
-4.1214
0.1835
1,622
-22.47
<0.0001
Having drawn these conclusions, we developed an approach to apply them to emission rate
development. To begin, we applied the statistical models by calculating predicted values of
InNMOG and InNOx at mileages from 0 (the intercept) to 155,000 miles. We reverse-
transformed the models using Equation 3-28 (page 40) to obtain predicted geometric and
arithmetic means with increasing mileage, as shown in Table 3-56 for NMOG and Table 3-57 for
NO*.
We normalized the predicted means at each mileage to the value at 0 miles to obtain a
"deterioration ratio" Rdst, by dividing each predicted value at a given mileage by the predicted
value at 0 miles (i.e., the intercept); Rdst for the intercept =1.0 (Equation 3-48).
193
-------
7j miles
'\ict — ~~3 Equation 3-48
^,o
We took this step to express start and running trends on a comparable relative multiplicative
basis, as trends in absolute running and start emissions are clearly not comparable.
Finally, to relate start and running trends, we calculated the ratio in Rdst for start to that for
running, designated as Rrei
D
n det, start
^rei ~~ ~7} Equation 3-49
det, running
Values or Rdst and Rrel for NMOG and NOx are shown in Table 3-56 and Table 3-57,
respectively, with corresponding results shown graphically in Figure 3-101 and Figure 3-102,
respectively.
Table 3-56 Application of models for NMOG, representing emissions trends for LDV-T1 vehicles certified to
LEV standards
Parameter
Odometer (mi, xl0,000)
0
1.5
2.5
3.5
4.5
5.5
6.5
7.5
8.5
Cold Start
InNMOG
-1.960
-1.886
-1.836
-1.786
-1.736
-1.686
-1.636
-1.587
-1.537
Geometric mean
0.141
0.152
0.159
0.168
0.176
0.185
0.195
0.205
0.215
Arithmetic
mean
0.156
0.168
0.176
0.185
0.195
0.205
0.215
0.226
0.238
Deterioration
ratio
1.000
1.078
1.133
1.190
1.251
1.315
1.382
1.453
1.527
Hot Running
InNMOG
-6.160
-6.037
-5.954
-5.872
-5.790
-5.707
-5.625
-5.543
-5.460
Geometric mean
0.00211
0.00239
0.00259
0.00282
0.00306
0.00332
0.00361
0.00392
0.00425
Arithmetic
mean
0.00404
0.00458
0.00497
0.00540
0.00586
0.00636
0.00691
0.00750
0.00815
Deterioration
ratio (Ilk,)
1.000
1.132
1.229
1.334
1.449
1.573
1.708
1.855
2.014
Relative Ratio
(i?rel)
1.000
0.9952
0.922
0.892
0.864
0.836
0.809
0.783
0.758
194
-------
Table 3-57 Application of models for NO*, representing emissions trends for LDV-T1 vehicles certified to LEV
standards
Parameter
Odometer (mi, xl0,000)
0
1.5
2.5
3.5
-/.5
5.5
6.5
7.5
8.5
Cold Start
InNO,
-2.604
-2.461
-2.365
-2.270
-2.175
-2.079
-1.984
-1.888
-1.793
Geometric mean
0.0740
0.0854
0.0939
0.1033
0.1137
0.1250
0.1376
0.1513
0.1665
Arithmetic mean
0.1039
0.1199
0.1319
0.1452
0.1597
0.1757
0.1933
0.2126
0.2339
Deterioration
ratio (Ilk,)
1.000
1.154
1.269
1.396
1.536
1.690
1.859
2.045
2.250
Hot Running
InNO,
-4.740
-4.560
-4.440
-4.320
-4.200
-4.080
-3.960
-3.840
-3.720
Geometric mean
0.0087
0.0105
0.0118
0.0133
0.0150
0.0169
0.0191
0.0215
0.0242
Arithmetic mean
0.0385
0.0461
0.0520
0.0586
0.0660
0.0745
0.0840
0.0947
0.1067
Deterioration
ratio (Ilk,)
1.000
1.097
1.350
1.522
1.716
1.935
2.181
2.460
2.773
Relative Ratio (R,c\)
1.000
0.964
0.940
0.918
0.895
0.874
0.852
0.832
0.811
2.5
2.0
1.5
1 10
0.5
0.0
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
Mileage (mi, x 10,000)
Figure 3-101 LEV deterioration ratios for cold-start and hot-running NMOG emissions, plus the ratio of the
two ratios (Start: Running)
Hot-running
Start: Running
195
-------
3.0
Cold-start
2.5
Hot-running
2.0
Start:Running
1.5
1.0
0.5
0.0
0
) 4.0 5.0 6.1
Mileage (miles, x 10,000)
7.0
Figure 3-102 LEV deterioration ratios for cold-start and hot-running NOv emissions, plus the ratio of the two
ratios (Start:Running)
For both NMOG and NOv, the difference between running and start deterioration was large
enough that we decided that it was not appropriate to assume that starts deteriorate at the exactly
the same rate as running emissions. Instead we elected to use the IL VP data to estimate distinct
start deterioration assumptions.
3.9.3.2 Translation from Mileage to Age Basis (MY 1989 and earlier)
The question remained, as to how the results derived from the IL VP data and presented above
could be applied during the generation of emission rates. At the outset, a question arises from the
fact that the results shown above were generated on the basis of mileage, whereas MOVES
assigns deteriorati on on the basis of age. It was therefore necessary to translate the RTSl from a
mileage basis to an age basis. We achieved the translation through a series of steps.
First, we assumed a rate of mileage accumulation of about 10,000 miles per year6-48 from which
it follows that the Rrsl at 125,000 miles would occur at about 12.5 years of age, or would be
represented by the 10-14 year ageGroup. Accordingly, we assigned midpoints to the 0-3 and 10-
14 year ageGroups of 2 and 12.5 years, respectively, and assume that l declines linearly with
age. These assumptions allow calculation of a declining trend in the ratio with respect to age.
The slope of the trend is the change in ratio (A/
-------
AR,
m„, = -
Tel
0.675-1.0 -0.325
Atime 12.5-2
10.5
= -0.30952
Equation 3-50
The calculation of the slope lets us estimate a value of Rrsi for each ageGroup.
R
~rel,age
: 1.000 -mRrelage
Equation 3-51
The results, as applied for hydrocarbons and NOx, are shown in Table 3-58 and Figure 3-103.
The net result is a 15-40 percent reduction in multiplicative start deterioration, relative to running
deterioration. The ratios for hydrocarbons were also applied for CO, as the results of analyses
with CO were similar.
Table 3-58 Relative deterioration ratios (/?rei), for THC and NO* assigned to each ageGroup (Note: ratios for
AgeGroup
Age (years)
Relative Ratio (/?,ei)
THC
NOx
0-3
2
1.000
1.000
4-5
5
0.845
0.892
6-7
7
0.783
0.848
8-9
9
0.721
0.805
10-14
12.5
0.613
0.729
15-19
17.5
0.613
0.729
20 +
23
0.613
0.729
197
-------
1.000
0.900
0.800
d 0.700
^ 0.600
^ 0.500
^ 0.400
(1)
£ 0.300
¦f 0.200
02 0.100
0.000
- M/"\»
-*-lML
ana ou
10
Age (years)
15
20
25
Figure 3-103 Relative deterioration ratios (/?rei), for THC and NO, assigned to each ageGroup
3.9.3.3 Translation from Mileage to Age Basis (MY 1990 and later)
3.9.3.3.1 Start Process for NOx
As we have shown in 3.6.8 and 3.7.8, the revised analysis has yielded meaningful reductions in
proportional deterioration compared to the levels in MOVES2014. As in MOVES2014, we
propose to model deterioration for start emissions as less than but proportional to that for running
emissions. Then, we need to develop emission rates.
As in MOVES2014, the relation between start and running emissions is based on regression
analyses of data measured on the FTP cycle through the In-Use Verification Program, described
above in 3.9.3.1. As the regressions were performed on the basis of mileage, and MOVES
assesses deterioration on the basis of age, it was necessary to relate mileage to age, assuming
mileage accumulation of 12,500 mi/year, i.e., at age 1 mileage is 12,500 mi, and at age 2 mileage
= 25,000 mi, etc. (Table 3-59).
Based on the regression results, the deterioration ratio for starts (i?start)is calculated in terms of
the ratio for running (R run) &S
Rstart — 1 "I" ^run^ start,run Equation 3-52
Where -SWartmn is the relative sensitivity of start to running emissions, calculated as the ratio of
fractional differences in predicted emissions E in each ageGroup a to that at age 2, as shown in
Equation 3-53.
198
-------
-'a,start
^2,start
- 1
Equation 3-53
The calculation of the relative sensitivity is illustrated in Table 3-59. Deterioration ratios for
running and start emissions are shown graphically in Figure 3-104.
Table 3-59 NO*: Calculation of relative sensitivity of cold-start to hot-running emissions
Age
Mileage
0
0
2
25.000
5
62.500
7
87.500
9
112.500
12.5
156.250
17.5
218.750
23
287.500
Cold-Start
InNOx
NOx (g/mi)
Norm. 2 yr
frac. diff.
-2.6039
0.1039
-2.3654
0.1319
1.0000
0.0000
-2.0076
0.1887
1.4302
0.4302
-1.7691
0.2395
1.8154
0.8154
-1.5305
0.3041
2.3044
1.3044
-1.1131
0.4616
3.4982
2.4982
-0.5168
0.8379
6.3507
5.3507
0.1391
1.6147
12.2376
11.2376
Hot-Running
InNOx
NOx (g/mi)
Norm. 2 yr
frac. diff.
-4.7396
0.0385
-4.4396
0.0520
1.0000
0.0000
-3.9896
0.0815
1.5683
0.5683
-3.6896
0.1100
2.1170
1.1170
-3.3896
0.1485
2.8577
1.8577
-2.8646
0.2510
4.8307
3.8307
-2.1146
0.5313
10.2267
9.2267
-1.2896
1.2123
23.3361
22.3361
Sensitivity
0.0000
0.7569
0.7300
0.7022
0.6522
0.5799
0.5031
rcj
cc
c
o
2.5
2.0
1.5
o 1.0
-Cars: Starts
¦Trucks: Running—~—Trucks: Starts
Figure 3-104 NO*: Deterioration ratios for running and start emissions
3.9.3.3.2 Start Process for THC
For THC, proportional deterioration for starts was calculated in relation to running emissions as
for NOi, using Equation 3-52 and Equation 3-53.
The calculation of the relative sensitivity is illustrated in Table 3-60. Deterioration ratios for
running and start emissions are shown graphically in Figure 3-105.
199
-------
Table 3-60 THC: Calculation of relative sensitivity of cold-start to hot-running emissions
Age
Mileage
0
0
2
25.000
5
62.500
7
87.500
9
112.500
12.5
156.250
17.5
218.750
23
287.500
Cold-Start
InTHC
THC (g/mi)
Norm. 2 yr
frac. diff.
-1.9603
0.1556
-1.8358
0.1763
1.0000
0.0000
-1.6489
0.2125
1.2054
0.2054
-1.5244
0.2407
1.3653
0.3653
-1.3998
0.2726
1.5464
0.5464
-1.1819
0.3390
1.9230
0.9230
-0.8705
0.4628
2.6255
1.6255
-0.5280
0.6518
3.6979
2.6979
Hot-Running
InTHC
THC (g/mi)
Norm. 2 yr
frac. diff.
-6.1604
0.0093
-5.9545
0.0114
1.0000
0.0000
-5.6456
0.0156
1.3619
0.3619
-5.4397
0.0191
1.6733
0.6733
-5.2337
0.0235
2.0559
1.0559
-4.8734
0.0337
2.9479
1.9479
-4.3586
0.0563
4.9329
3.9329
-3.7923
0.0993
8.6903
7.6903
Sensitivity
0.0000
0.5676
0.5425
0.5174
0.4738
0.4133
0.3508
2.0
ro
cc
c
o
4-»
rcj
o
(U
4-»
(U
Q
1.5
1.0
0.5
0.0
—
10
15
20
25
• Cars: Running -^>-Cars: Starts
• Trucks: Running—~—Trucks: Starts
Figure 3-105 THC: Deterioration ratios for running and start emissions
3.9.3.3.3 Start Process for CO
For CO, proportional deterioration for starts was calculated in relation to running emissions as
for NOi, using Equation 3-52 and Equation 3-53 .
The calculation of the relative sensitivity is illustrated in Table 3-61. Deterioration ratios for
running and start emissions are shown graphically in Figure 3-106.
Table 3-61 CO: Calculating the relative sensitivity of start to running deterioration
Age
Mileage
0
0
2
25.000
5
62.500
7
87.500
9
112.500
12.5
156.250
17.5
218.750
23
287.500
Cold-Start
InCO
CO (g/mi)
Norm. 2 yr
frac. diff.
-0.2186
0.9604
-0.0954
1.0863
1.0000
0.0000
0.0895
1.3068
1.2030
0.2030
0.2127
1.4782
1.3608
0.3608
0.3359
1.6721
1.5392
0.5392
0.5516
2.0745
1.9097
0.9097
0.8596
2.8229
2.5987
1.5987
1.1985
3.9616
3.6468
2.6468
Hot-Running
InCO
CO (g/mi)
Norm. 2 yr
frac. diff.
-2.7594
0.1828
-2.5333
0.2292
1.0000
0.0000
-2.1941
0.3217
1.4038
0.4038
-1.9680
0.4033
1.7600
0.7600
-1.7418
0.5057
2.2066
1.2066
-1.3461
0.7512
3.2777
2.2777
-0.7808
1.3221
5.7688
4.7688
-0.1590
2.4622
10.7436
9.7436
Sensitivity
0.0000
0.5028
0.4747
0.4469
0.3994
0.3352
0.2716
200
-------
ro
cc
c
o
4-»
ro
<_
O
(U
4-»
(U
Q
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
====&===:
33
aifl iii^M
i
i
i
s
N
II
¦
10
15
20
25
¦Cars: Running —~—Cars: Starts
¦Trucks: Running—~—Trucks: Starts
Figure 3-106 CO: Deterioration ratios for running and start emissions
3.10 Constructing Updated Rates (Model Years 1990 and Later)
Having completed the analyses described in 3.6, 3.7, 3.9.2 and 3.9.3.3, we constructed the
updated MOVES3 running and start gaseous exhaust rates for light-duty cars and trucks by
adjusting the MOVES2014 rates in the emissionRateByAge table. Note that these updates apply
only to rates for MY 1990 and later. The rates for MY 1989 and earlier are unchanged.
We did this in several steps, described below.
3.10.1 Step 1: Extract LD gasoline rates from the Input database
We extracted a subset of rates from the emissionRateByAge table in the previous MOVES
database. The scope of rates extracted is described below:
Database: MOVESDB20200123.
Pollutant/Process: Running and start exhaust for HC, CO and NO* (polprocessid = 101,
201, 301 and 102, 202, 302),
Age Group: Ages 0-3 years (ageGroupID = 3),
Operating Modes: 23 Modes for running coast/cruise/acceleration (0, 1, 11-16, 21-30,
33-40), f eight modes for start operation (101-108),
Fuel type: Gasoline (fuelTypelD) = 1,
Regulatory Class: light-duty cars (LDV) and trucks (LDT) (regClassID = 20, 30),
Model year: Model year groups from 1990-93 through 2051-2060.
f Note that operating modes 26 and 36 do not exist.
201
-------
3.10.2 Step 2: Apply Young-vehicle Adjustments to Running Rates
We applied the "young vehicle adjustments" described in 3.6.7 and 3.7.7 to calculate revised
I/M reference rates (meanBaseRatelM) in the first ageGroup (0-3 years). The adjustments were
merged into the emissionRateByAge segment on basis of regulatory class and model year. We
applied these adjustments to running rates but not to start rates. The adjustments for model year
2010 were applied to all future model years through 2060.
3.10.3 Step 3: Apply Deterioration Adjustments
We calculated revised I/M reference rates for the remaining six ageGroups, based on results of
analyses described in 3.6.8 and 3.7.8 for running emissions and 3.9.3.3 for start emissions. We
merged the deterioration adjustments into the rates segment on the basis of pollutant process,
regulatory class and ageGroup. The deterioration adjustments were applied multiplicatively and
uniformly to both running and start rates in all model years 1990-2060.
3.10.4 Step 4: Apply Non-1 \I .Ratios
We calculated the non-I/M reference rates (meanBaseRate) from the I/M reference rates
(meanBaseRatelM) by applying non-I/M ratios. These ratios increase by ageGroup and were
merged into the rates segment on basis of pollutant process and ageGroup. These ratios are the
same values for all model years (see 3.5) and are applied multiplicatively and uniformly to both
running and start rates for both regulatory classes in all model years.
3.10.5 Step 5: Replicate Rates for Additional Fuel Types
After completing Step 4, we replicated the subset of rates for gasoline (fuelTypelD = 1) to
generate corresponding subsets for diesel (fuelTypelD = 2) and E85 (fuelTypelD = 5). Because
data on E-85 and diesel-fueled LD vehicles is lacking and at least since the introduction of Tier-2
standards, they are required to meet the same emission standards as gasoline vehicles, we found
it appropriate to use the same rates in modelling their emissions.
As we do not represent an "I/M difference" for light-duty diesel vehicles, for this fuel only, we
reset the meanBaseRatelM to equal the meanBaseRate.
For E85 and diesel, we assigned the dataSourcelD as 4900 and 4910, respectively.
I rial Results U > I pdate for Y10VES3
Having completed the steps described in 3.10, we have generated a complete set of updated rates
for model years 1990 and later, encompassing the Tier 0, Tier 1, National LEV, Tier 2 and Tier 3
emissions standards.
In this section, we present and review the resulting emission rates, including comparison to rates
developed for MOVES3 in comparison to the rates used for the previous public release,
MOVES2014b. We note trends in the rates from the perspective of key variables in the table
structure. These include vehicle-specific power (for running rates), soak time (for start rates),
age (for both running and start rates) and I/M status.
Because the rates are generated by applying multiplicative factors, the patterns and trends are
generally proportional, so only a few representative examples need be shown.
202
-------
3.11.1 Trends with Vehicle-Specific Power
The operating modes for most of the rates for the running-exhaust process, with the exception of
the idle and deceleration/braking modes, are defined in term of vehicle-specific power (VSP,
kW/Mg).
We present rates for a subset of the operating modes, 21-30, which show a complete VSP trend
at moderate speed (25-50 mph), from < 0 kW/Mg (coasting) to > 30 kW/Mg (hard acceleration).
To give proper scaling, the midpoint values of VSP for each mode are used for plotting, as
shown in Table 3-62.
Table 3-62 Midpoint VSP values assigned to selected operating modes for plotting purposes
Operating Mode
Vehicle Specific Power (VSP, kW/Mg)
21
-2
22
2.5
23
4.5
24
7.5
25
10.5
27
15.0
28
21.0
29
27.0
30
34.0
The plots present the "I/M reference rate" (meanBaseRatelM) for cars (regClassID = 20, on left)
and trucks (regClassID = 30, on right). The plot shows four model years, taken as cross sections
across the long-term trend of improving technology and declining standards. The model years
1998, 2004, 2010 and 2017 represent the "Tier 1", "Onset of Tier 2", "mature Tier 2" and "onset
of Tier 3," respectively.
The appearance of all plots is generally similar, because the scaling in the rates is proportional
throughout, and because each row in the plots is scaled independently of the others. In viewing
the plots, it is important to note the differences in scales by model year.
Plots for THC, CO and NOx are presented in Figure 3-107, Figure 3-108 and Figure 3-109
below. These figures present rates for "young vehicles" in the 0-3 year ageGroup.
In all cases, the updated MOVES3 rates are higher than the previous rates in all cases at VSP <
15 kW/Mg, and in many but not all cases at VSP >15 kW/Mg. This difference is largely due to
the application of the "young-vehicle" adjustments described above, although it is not always
conspicuous at low VSP where the rates are smaller. The difference is the most marked for THC,
as the "young-vehicle" adjustments for this pollutant were often more than twice as large as for
CO and NOx. See for 3.6.7.1 for NOx, 3.6.7.3 for THC, and 3.7.7 for CO.
For CO, the updated rates are higher than the previous rates for both cars and trucks in all model
years. Of the three pollutants, CO shows the most marked increase in the steepness of the trend
at higher VSP, which may reflect the tendency towards increased CO production as the engine
shifts towards rich operation.
For THC and NOx, however, the updated rates are lower than the previous rates at higher power,
with this tendency more pronounced for trucks than cars, and becoming more pronounced for
203
-------
model years after 2004. Note that in the MOVES2014 rates, the trends for THC and NOx have
sharp "elbows" in the trends at 15 kW/Mg. These sharp increases in the trends reflect the
assumption that emissions control systems would be less effective at higher VSP, resulting in
sharper VSP trends for the "high power" modes. In this update, this assumption has been
revised, as review of more recently acquired data did not support it as described in Section
3.3.2.4. Accordingly, the MOVES3 trends in the three more recent model years appear
qualitatively similar to that for 1998, although scaled down to represent the more recent
technologies and emission standards.
204
-------
50
40
30
20
10
0
10.0
7.5-
5.0
2.5
O)
0.0
7.5-
5.0
2.5
0.0
6
4-
2
0
0
10
20
30
0
10
20
30
Vehicle-Specific Power (kW/Mg)
Figure 3-107 THC; Emission rate (meanBaseRatelM in g/hr) vs. VSP for operating modes 21-30, for cars (20)
and trucks (30) in four model years (1998,2004,2010,2017), at ages 0-3 (Note that rows are scaled
independently)
205
-------
2500
2000
1500
1000
500
0
1000
500
0
E 1000
500
0
1000
750
500
250
0
0
10
20
30
0
10
20
30
Vehicle-Specific Power (kW/Mg)
Figure 3-108 CO: Emission rate (meanBaseRatelM in g/hr) vs. VSP for operating modes 21-30, for cars (20)
and trucks (30) in four model years (1998,2004,2010,2017), at ages 0-3 (Note that rows are scaled
independently)
206
-------
Figure 3-109 NO*: Emission rate (meanBaseRatelM in g/hr) vs. VSP for operating modes 21-30, for cars (20)
and trucks (30) in four model years (1998,2004,2010,2017), at ages 0-3 (Note that rows are scaled
independently)
30 0
Vehicle-Specific Power (kW/Mg)
3.11.2 Trends with Soak Time
The operating modes for the rates for the start-exhaust process, are defined in term of soak time,
i.e., the time since the engine was last turned off, as described in 3.8.1.2 on page 167.
We present rates for the eight start operating modes, 101-108, which reflect a range in soak time
from several minutes to 12 hours (720 min), at which point we assume that the engine is
completely "cold". To give proper scaling, the midpoint values of soak time for each mode are
used for plotting, as shown in Table 3-63 below.
207
-------
Table 3-63 Midpoint soak-time values assigned to operating modes for plotting purposes
Operating Mode
Soak time (hr)
101
0.05
102
0.30
103
0.75
104
1.25
105
1.75
106
4.0
107
9.0
108
12.0
The plots present the "I/M reference rate" (meanBaseRatelM) for cars (regClassID = 20, on left)
and trucks (regClassID = 30, on right). The plot shows the same four model years used for the
VSP trends above.
As with the VSP trends, the appearance of all plots is generally similar, because the scaling in
the rates is proportional throughout, and because each row in the plots is scaled independently of
the others.
Plots for THC, CO and NOx are presented in Figure 3-110, Figure 3-111 and Figure 3-112
below, respectively. These figures present rates for "young vehicles" in the 0-3 year ageGroup.
In all three figures, note that the updated and previous trends are identical in MY1998. This
pattern follows from the fact that the "young-vehicle" adjustments were not applied to start
emissions, and also that the "older" soak-time relationships apply to this model year (see Figure
3-83, page 170). In addition, note that the rates for the "cold starts" (soak time = 12 hr,
opModeID=108) are also identical, as the "young-vehicle" adjustments were not applied to start
rates. The differences shown for the remaining seven operating modes, i.e., "warm" and "hot"
starts, reflect the differences between the "older" and "updated" soak-time relationships (see
Figure 3-90 to Figure 3-92, page 180).
For THC, the updated soak-time trends are generally similar to the older trends, but the updated
start rates are higher than before for soak times between 1.25 and 9.0 hours. For times < 1 hr, the
updated rates are lower, as the updated trend shows a less steep curvature for hot starts.
For CO, the updated trends are also generally similar in shape to the older trends, but the updated
rates are lower at all times except 12 hours.
For NOx, the updated trends differ markedly from the older trends. Rather than increasing gently
from the 12-hr soak to a broad peak at the 1.25-hr soak, the updated rates increase more steeply
from the 12-hr soak to a sharper peak at the 1.75-hr soak, then declining steeply to the 0.05-hr
soak. The updated rates for the two shortest soak times are lower than before.
208
-------
0.5
0.0
0.75
0.50
MOVES3
0.4
0.2
0.0
0.4
0.2
0.0
0.0
2.5
5.0
10.0
12.5 0.0
2.5
5.0
10.0
12.5
7.5
7.5
Soak time (hours)
Figure 3-110 THC: Emission rate (meanBaseRatelM, g/start) vs. soak time for operating modes 101-108, for
cars (20) and trucks (30) in four model years (1998,2004,2010,2017), at ages 0-3 (Note that rows are
scaled independently)
209
-------
15
10
5
0
8
6
4
2
S
MOVES3
(D
CO
CO
c
03
(1)
E
4
2
0
4
3
2
1
0
0.0
2.5
5.0
7.5
10.0
12.5 0.0
2.5
5.0
7.5
10.0
12.5
Soak time (hours)
Figure 3-111 CO; Emission rate (meanBaseRatelM, g/start) vs. soak time for operating modes 101-108, for
cars (20) and trucks (30) in four model years (1998,2004,2010,2017), at ages 0-3 (Note that rows are
scaled independently)
210
-------
1.2
0.4
1.00
0.75
0.50
*5 0.00
MOVES3
0.4
0.3
0.2
0.1
0.0
0.3
0.2
0.1
0.0
0.0
2.5
5.0
10.0
12.5 0.0
2.5
5.0
10.0
12.5
7.5
7.5
Soak time (hours)
Figure 3-112. NO*: Emission rate (meanBaseRatelM, g/start) vs. soak time for operating modes 101-108, for
cars (20) and trucks (30) in four model years (1998,2004,2010,2017), at ages 0-3 (Note that rows are
scaled independently).
3.11.3 Trends with Age
Trends with age display the deterioration assumptions projected through the rates, reflecting a
variety of data sources and analysis methods throughout the complete set. Comparing age trends
is of particular interest because the reevaluation and revision of deterioration assumptions was
one of the chief motivations in initiating the current update.
We present subsets of rates for the MOVES ageGroups, which show complete deterioration
trends from 0-3 years through 20+ years. To give proper scaling, the midpoint values of age
ranges for each ageGroup are used for plotting, as shown in Table 3-64.
211
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Table 3-64 Midpoint ages for the MOVES ageGroups used for plotting
ageGroupID
Age range (yr)
Midpoint Age (yr)
3
0-3
2
405
4-5
5
607
6-7
7
809
8-9
9
1014
10-14
12.5
1519
15-19
17.5
2099
20+
23
The plots present the "I/M reference rate" (meanBaseRatelM) for cars (regClassID = 20, on left)
or trucks (regClassID = 30, on right). As with previous plots, these plot shows four model years,
although not always the same in all plots.
Unlike the previous two sets of plots, this set includes both rates for running and start operating
modes. Each plot includes two running and two start modes, but with the specific modes varying
by plot.
As before, each row in the plots is scaled independently of the others. In this set, however, the
model years are arranged in rows, so that the decline in the rates with model year is clearly
evident. In fact, for more recent model years, the age trends are difficult to see due to scaling
effects.
Plots for THC, CO and NO* are presented Figure 3-113, Figure 3-114 and Figure 3-115 below.
The figure for THC presents rates for cars, whereas those for CO and NO* present rates for
trucks.
For the running rates the updated rate at age=2 is consistently higher than the previous rates, due
to application of the "young-vehicle" adjustments. This point is particularly conspicuous for the
THC rates.
For the start rates the updated rate at age 2 is always identical to that in the previous rates in
model year 1998 and for operating mode 108 (cold start). In these cases, the lack of difference
follows from not applying the "young-vehicle" adjustments. In model year 1998, the rates at age
2 are identical because the updated soak-time relationships were not applied. For model years
following 1998, however, and for operating modes other than 108, the rates differ at all ages
because the updated soak-time relationships apply, combined with updated deterioration.
For the updated rates, the shape of the age trends is always qualitatively the same, because these
trends reflect the characteristic trends in the underlying three-piece spline deterioration models
applied in the update. While these similarities always apply, they are not always obvious in the
plots due to scaling effects.
In the MOVES2014 trends, however, the trends for MY1998 differ from those in the later model
years, due to differences in methods applied in the development of the rates for MOVES2010.
That the deterioration in the update is substantially reduced is particularly evident in the start
rates for THC and CO, and also to some degree in the start rates for NOx. While not always as
clear in the running rates, due to vertical offsets between the trends, Figure 3-69 (NO*, page
212
-------
148), Figure 3-72 (THC, page 150) and Figure 3-82 (CO, page 165) show clearly that relative or
proportional deterioration is much lower in the updated rates.
Version
MOVES3
MOVES2014
10 15 20
5 10 15 20 5 10 15 20
Age (years)
10 15 20
Figure 3-113 THC for Cars: Emission rate (meanBaseRatelM) vs. age for two running operating modes (13,
25, g/hr) and two start modes (101,106, g/start), in four model years (1998,2004,2010,2017), (Note that
rows are scaled independently)
213
-------
400
300
200
100
500
400
300
200
B 100
MOVES3
% 10.0
5.0
2.5
10
15
20
10
15
20
10
15
20
10
15
20
5
5
5
5
Age (years)
Figure 3-114 CO for Trucks; Emission rate (meanBaseRatelM) vs. age for two running operating modes (15,
27, g/hr) and two start modes (102,108, g/start), in four model years (1998,2004,2010,2017). (Note that
rows are scaled independently)
214
-------
200
150
100
10
15
20
10
15
20
10
15
20
10
15
20
5
5
5
5
Age (years)
Figure 3-115 NO* for Trucks: Emission rate (meanBaseRatelM) vs. age for two running operating modes (21,
28, g/hr) and two start modes (103,108, g/start), in four model years (1998,2004,2010,2017). (Note that
rows are scaled independently)
3.11.4 Trends with I/M Status
The emissionRateByAge table contains two sets of rates, one representing a default "I/M
reference" condition (meanBaseRatelM), and a second representing a default "non-I/M
reference" condition (meanBaseRate).
In the current update, as well as in MOVES2010 and MOVES2014, the meanBaseRatelM was
estimated first, as the datasets available to estimate deterioration are collected in I/M areas and in
association with I/M programs. These datasets include the Phoenix I/M evaluation sample in
MOVES2010 and MOVES2014. This dataset is still applicable in MOVES3 for model years
215
-------
prior to 1990. For MOVES3, newly available datasets include the Denver Evaluation Sample
and the CDPHE remote-sensing data.
The non-I/M reference rates are estimated from the I/M references by applying ratios that vary
by age (see 3.5, page 95). Thus, in the figures below, the I/M and non-I/M rates are presented as
age trends. It is important to emphasize that the differences between the non-I/M and I/M
defaults assume complete program compliance. This difference is discounted somewhat during
model runs, based on the parameters that estimate compliance effectiveness
(IMcompli anceF actor).
Examples are presented below for Figure 3-116, Figure 3-117 and Figure 3-118 for THC, CO
and NOx, respectively. In the plots, the rates represent cars or trucks in an individual model year,
with panels for MOVES2014 and MOVES3. As with the trends with age, the plots include two
operating modes for running operation, and two for start operation.
In the MOVES2014 trends, the non-I/M trend resembles the I/M trend, as it is derived from it by
application of the ratios. Because the ratios are both multiplicative and increase with age, the
implication is that deterioration emission rates are higher and deterioration somewhat steeper in
non-I/M areas.
In the MOVES3 trends, as with the previous age trends, the characteristic shapes of the
underlying deterioration models are evident in both the I/M and non-I/M rates. In the update, the
two sets of rates are exactly proportional.
In the MOVES2014 rates, a difference between the two sets of rates is that the I/M rates tend to
stabilize in the two oldest age groups whereas the non-I/M rates continue to increase, with the
increase more marked in the start rates. These differences are based on assumptions regarding
behavior of emissions trends in non-I/M areas (see 3.2.2.3.1, page 56).
In the updated rates, by contrast, aside from application of the ratios to estimate the non-I/M
default rates, no additional assumptions were made regarding whether deterioration trends in
non-I/M areas would differ from those in I/M areas. This differs from the approach in previous
versions of MOVES as documented in Section 3.2.2.3.1. Deterioration in non-I/M areas is an
important area of uncertainty, due to the lack of large datasets outside of I/M areas. Thus, this
question remains difficult to evaluate.
216
-------
MOVES2014
M0VES3
l/M status
Age (years)
Figure 3-116 THC for Cars in MY1998: Emission rate (meanBaseRatelM, meanBaseRate) vs. age for two
running operating modes (14,28, g/hr) and two start modes (102,108, g/start) (Note that rows are scaled
independently)
217
-------
MOVES2014
M0VES3
160
120
80
40
600
400
4
3
2
1
20
15
10
5
10
15
20
10
15
20
5
5
Age (years)
Figure 3-117 CO for Trucks in MY2008; Emission rate (meanBaseRatelM, meanBaseRate) vs. age for two
running operating modes (14,28, g/hr) and two start modes (102,108, g/start) (Note that rows are scaled
independently)
218
-------
MOVES2014
M0VES3
l/M status
Age (years)
Figure 3-118 NO*for Trucks in MY2008: Emission rate (meanBaseRatelM, meanBaseRate) vs. age for two
running operating modes (14,28, g/hr) and two start modes (102,108, g/start) (Note that rows are scaled
independently)
3.12 Development of Emission Rates representing California Standards
In general, the principle of pre-emption does not allow the states to promulgate or enact their
own vehicle emission standards. However, due to the unique severity of the air pollution issues
in Southern California, the Clean Air Act allows the state of California to seek waivers of
preemption. When granted by EPA, such waivers allow California to enact and enforce its own
emissions standards, under the condition that such standards are at least as stringent as applicable
Federal standards.
219
-------
California has enacted several such programs, beginning with Tier 0 (c. 1977-1992) and Tier 1 in
1993. These were followed by the "Low Emission Vehicle" programs, beginning with "LEV-I"
in 1994g and continuing with "LEV-IT' and "LEV-III" in 2001 and 2015, respectively. Under
the LEV programs, multiple standard levels were assigned, designated as "Transitional Low
Emission Vehicle" (TLEV), "Low Emission Vehicle" (LEV), "Ultra Low Emission Vehicle"
(ULEV) and "Super Ultra Low Emission Vehicle" (SULEV).
Although assigned the same labels, each standard level can be assigned different numeric values
for each vehicle class, i.e., LDV, LDT1, LDT2, LDT3 and LDT4. Tor simplicity, we have
assumed that the California "Medium-Duty" classes, MDV2 and MDV3, can be treated as
equivalent to Tederal LDT3 and LDT4 classes, despite differences in loaded vehicle weights.
In addition, Section 177 of the Clean Air Act allows other states to adopt California emission
standards, with the proviso that adopted standards are identical to standards for which waivers
have been granted. States do not need approval from EPA to adopt California standards. As of
2019, 13 states had elected to adopt California LEV-II standards for emissions of criteria
pollutants from varying classes of light-duty motor vehicles.49 Collectively, these states will be
called the "CA/S177" states.11 In addition, these states have adopted the LEV-III standards.50
Effectively, then, two sets of emission standards are in place throughout the United States. One
outcome of this situation is that many vehicles coming to market over the past 20 years have
been certified to both CA and Tederal standards. The analysis described in this section
incorporates this reality by applying an assumption that the emissions behavior of vehicles with
multiple certifications would be governed by the "most stringent" certification. Tor example, a
vehicle certified to Tier 2/Bin-5 in the Tederal sales regions but certified to LEV-II/SULEV in
California, is assigned to "Bin-2" or "SULEV" for purposes of developing emission rates, rather
than to Bin 5.
This section describes the process used to develop a set of emission rates representing the LEV
programs, covering model years 1994-2031. The methods used are similar to those used to
develop rates representing vehicles under the Tederal standards (NLEV, Tier 2 and Tier 3) as
described in 3.4 (page 79). In general, as the implementation of LEV standards involved higher
fractions of vehicles at lower standard levels than under the corresponding Tederal standards;
rates for a LEV program in a given model year are equal to or lower than corresponding
'Tederal" rates.
To apply this assumption, we developed the CA/S177 rates by scaling down the Tederal rates by
appropriate margins. The calculations were performed in a series of steps, with the first three
steps identical to those used to develop the Tederal rates. The following discussion assumes that
the reader is familiar with the relevant sections of this report (See 3.4.1 (page 80) to 3.4.3)).
However, the final steps differ from that used to generate the default rates, as described below in
3.12.4 and 3.12.5.
g The "National LEV" (NLEV) program was a voluntary program modeled on the LEV-I program, and applicable to
LDV, LDT1 and LDT2 vehicles.
h These states include Colorado, Connecticut, Delaware, Maryland, Maine, Massachusetts, New Jersey, New York,
Oregon, Pennsylvania, Rhode Island, Washington and Vermont.
220
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3.12.1 Averagir F .Results
The calculation of CA/S177 rates uses the same set of average IUVP results as the default rates.
Equivalencies between Federal and corresponding LEV standards is shown in Table 3-65. Note
that the equivalences listed in the table are not exhaustive; they are limited to the subset that
were applied in developing emission rates.
Table 3-65 Selected equivalencies between Federal and corresponding CA/S177 standards
Pro
gram
Fed.
CA/S177
Tier l1
Tier l1
NLEV
LEV-I
Tier 22
LEV-II2
Vehicle Class
Fed.
CA/S177
LDV-T1
LDV-T1
LDT2
LDT2
LDT3
MDV2
LDT4
MDV3
LDV, LDT1
PC, LDT1
LDT2
LDT2
LDV, LDT1,
LDT2,3,4
PC, LDT1,
LDT2,3,4
Standard Level
Fed.
CA/S177
LDV-T1
LDV-T1
LDT2
LDT2
LDT3
MDV2
LDT4
MDV3
TLEV
TLEV
LEV
LEV
ULEV
ULEV
TLEV
TLEV
LEV
LEV
ULEV
ULEV
Bin 5
LEV
Bin 33
ULEV3
Bin 2
SULEV
1 Under Tier 1, each vehicle class was assigned a specific standard.
2 Under this program, there was no assigned correspondence between vehicle class
and standard level for the FTP standards, however, such an assignment remains in
effect for the SFTP standards.
3 This equivalence is exact for THC and CO only, for NOx, LEV-II/ULEV is
equivalent to Bin 5 (LEV-II/LEV).
3,12,2 Develop Phase-In assumptions
Differences between the CA/S177 and Federal programs are expressed primarily through the
phase-in assumptions. For this step we developed phase-in assumptions representing the phase-in
of California Tier-1, LEV-I and LEV-II programs. These assumptions cover model-years from
1994 through 2016. Starting in model year 2017 for cars, and 2018 for trucks, Federal rates are
harmonized with CA rates during the Tier 3/LEV-III phase-in and thereafter.
The CA/S177 phase-in was based on fractions of sales, grouped by standard level and model
year. The LEV phase-in, however, is simplified in that, as in the LEV-II standards, the three
largest truck classes, LDT2, 3 and 4, were consolidated into a single class, which we will refer to
as LDT234.
221
-------
Phase-in assumptions for passenger cars (PC) and light trucks (LDT1) are shown in Figure
3-119. In model year 2009 and later, the CA/S177 fleet is dominated by ULEV, SULEV and
LEV vehicles, in that order. The phase-in for trucks (LDT234) is shown in Figure 3-120
As a final step, a distinct "simplified" Federal phase-in was also developed. In this version, the
truck classes LDT2, LDT3 and LDT4 were also pooled, to facilitate comparison to the CA/S177
version.
40% --
30% :-
20% :-
10% y
0% :
I I I I I I I I
¦ Tier 1
ILEV-I/TLEV
I LEV-I/LEV
I LEV-I/ULEV
ILEV-II/LEV
I LEV-II/ULEV
ILEV-II/SULEV
Model Year
Figure 3-119 Phase-In assumptions for CA Tier-1, LEV-I and LEV-II standards for passenger cars and light-
trucks (PC, LDV, LDT1)
100% j
90% :-
80% :-
70% :-
60% :-
c
aj
u 50% --
QJ
o.
40% :-
30% y
20% :-
10% y
0% :
tH
I I I I I I I
¦ Tier 1
I LEV-I/TLEV
I LEV-I/LEV
I LEV-I/ULEV
I LEV-II/LEV
I LEV-II/ULEV
I LEV-II/SULEV
Model Year
Figure 3-120 Phase-In assumptions for CA Tier-1, LEV-I and LEV-II standards for light trucks (LDT2, LDT3,
LDT4)
222
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3,12,3 Merge FTP .Results and Phase-In Assumptions
In this step the FTP results and phase-in assumptions were merged so as to calculate weighted
average results for composites, cold-start and hot-running emissions, as described in 3.3.2.3
(page 68). However, as the truck classes for the CA/S177 phase-in were pooled and assigned a
uniform phase-in, calculating weighted averages by truck class did not play a role in these
calculations as in the default calculations.
This step was repeated for the CA phase-in and for the Federal phase-in.1
Sets of weighted averages by model year are shown for FTP Composite Emissions (Figure 3-1,
Figure 3-121), FTP cold-start emissions (Bag 1 - Bag 3) (Figure 3-122), and FTP hot-running
emissions (Bag 2) (Figure 3-123).
1 Note that the 'Federal' phase-in is identical to that used to develop the default rates.
223
-------
THC, Trucks
THC, Cars
—B— Fed TO, Tl, NLEV, T2
—CA/S177 LEV 1, LEV
II, LEV III
2005
Model Year
0.35
. 0.30
j 0.25
0.20
¦ 0.15
0.10
0.05
0.00
1995 2000 2005 2010 2015
Model Year
CO, Trucks
CO, Cars
l-fciu IU, 1 X, INL HV , 1
—»-CA/Sl77 LEVI, LEV
1, LEVIII
2005
Model Year
8.00
___ 7.00
4. 6.00
QO
5.00
0 4.00
Q.
1 3.00
U
o_ 2.00
"" 1.00
0.00
1995 2000 2005 2010
Model Year
NOx, Trucks
NOx, Cars
\
\
n FpH to ti ni f\/ t?
\
—•— CA/S177 LEV I, LEV
, LEVIII
\
\
x
.rrv
*
*****
imn
|Hf ¦ ¦ ¦
2005
Model Year
1.00
0.90
F
0.80
QO
0.70
a;
0.60
0 SO
Q.
F
0.40
o
u
0.30
Q.
0.20
0.10
0.00
2000 2005
Model Year
Figure 3-121 Weighted average FTP composite emissions for cars and trucks, for Federal and CA/S177
standards
224
-------
THC, Trucks
THC, Cars
5.00
4.50
4.00
-22 3.50
re 3.00
2 2.50
3 2.00
£ 1.50
"" 1.00
0.50
0.00
n PpH t
T1 Ml F\/ T'
—CA/S177 LEVI, LEV
, LEVIN
1990
2005
Model Year
5.00
4.50
< 4.00
- 3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
2000 2005 2010
Model Year
CO, Trucks
CO, Cars
40.00
35.00
3 30.00
I 25X10
!£ 20.00
u 15.00
Q.
£ io.oo
5.00
0.00
\
—Fed TO, Tl, NLEV,T2
\
—^CA/S177 LEV I, LEV
II, LEV III
.
L
~ ~
\
\
iU— n n
2000 2005
Model Year
40.00
35.00
^ 30.00
re 25-00
£ 20.00
u 15.00
Q.
£ io.oo
5.00
0.00
2000 2005 2010
Model Year
NOx, Trucks
NOx, Cars
3.00
2.50
"3
¦JT 2.00
re
2 1.50
o
u
Q_ 1.00
i—
0.50
0.00
—B—FedT0,Tl, NLEV, T2
\
>
>
I
II, LEV III
\
^ h h g
N
3.00
2.50
>
' 2.00
1.50
1.00
0.50
0.00
%
•4
n ~ ~
2005
Model Year
2005
Model Year
Figure 3-122 Weighted average FTP cold-start emissions, for Federal and CA/S177 standards
225
-------
THC, Trucks
THC, Cars
—B—FedTO.Tl. NLEV.T
2
\
—•— CA/S177 LEV 1. LEV II. LEV III
\
\
L
X
1990
2000 2005 2010
Model Year
0.45
0.40
E
0.35
QO
0 -50
0.25
c
3
UL
0.20
0.15
I
Q.
0 10
1—
0.05
0.00
2000 2005 2010
Model Year
CO, Trucks
CO, Cars
¦CA/S177 LEV I, LEV
1995
2000 2005 2010
Model Year
2015
2020
1995
2000 2005 2010
Model Year
2015
NOx, Trucks
NOx, Cars
0.80
_ 0.70
-S. 0.60
w 0.50
c 0.40
5 0.30
o
^ 0.2°
^ 0.10
0.00
—B—FedT0,Tl, NLEV, T
2
—CA/S177 LEV 1, LEV
II, LEV III
IC
* *
2000 2005
Model Year
— 0.70
E
0.60
QO
« 0.50
1 0.40
2 0.30
o
J °-20
^ 0.10
0.00
2000 2005
Model Year
Figure 3-123 Weighted average FTP hot-running emissions (Bag 2), for trucks and cars, under Federal and
CA/S177 standards
3.12.4 Scaling CA/177 Rates to Federal Rates
At this point the next step in the calculation differs from the approach used to generate the
default Federal rates. As in the calculation of the default rates, we normalized hot-running
emissions for both FTP and US06 to Federal T1 levels, represented by MY1998. However, in
this calculation, we also performed this normalization for cold-start rates. The results were sets
of ratios relative to Tier 1 for both running and start emissions.
Next, we calculated ratios of the weighted CA ratio to its Federal counterpart, by model year, as
shown in Equation 3-54,
226
-------
n _ RcA
CA:Fed n
Equation 3-54
where RcA.Fed = the ratio of the CA/S177 weighted average to that for the Federal phase-in, and
Rvsd and Rca are ratios of respective weighted averages to that for MY 1998, in the CA/S177 and
Federal phase-ins, respectively. Note that if raw values of /levi ed were > 1.0, they were
adjusted to 1.0, under than assumption that fleet averages under the LEV program(s) would be <
corresponding averages under the Federal program(s).
Values of /levi ed are presented below. Note that ratios were calculated and applied separately for
each of the three gaseous pollutants (THC,CO,NOx) and for start emissions (opmodeid =101-
108), "FTP Bag-2" running emissions (opmodeid = 0,1, 11-16, 21-27, 33-37) and "US06"
running emissions (opmodeid = 28-30, 38-40).
In MY2017 and later, following the onset of the Tier 3/LEV-III phase-in, all ratios are set to 1.0,
to reflect an assumption that under T3, the Federal program is targeting the same NMOG+NOx
fleet average requirements as LEV-III. See Section 3.4 for more information on these rates.
227
-------
THC, Trucks
THC, Trucks
C 0.50
h 0.40
Rjri'i
t 0.20
?
C Q50
3
CC
O 0.40
X
Q.
t 0-20
O.CO
Start
.... t p ... 1 ....
1 . . . ,
t Running
.... 1 ... .
200© 2005 2010
Model Year
2)00 2005 2010 2015
Model Year
CO, Trucks
CO, Trucks
E
1JS
1.20
" 1_0D
S
' o.so
£
E 0.50
3
cc
0.40
Q.
t 0.20
0.00
!
f\
/
i
i
v
® " Start
¦
i ¦ i .
» 1 1 1
• "Running
—i—i 1 i i
2005 2010
Model Year
- J. 5 J
t 0.20
Running
2000 2005 2010
Model Year
NOx, Trucks
MOx, Cars
~Q.BC
E 0.60
3
C£
O 0.40
X
CL
t 0.20
0.00
A, y
V\
i
U Start
• Running
2000 2005 2010
Model Year
t 020
J
1
O Start
* R
JTHting
20 OD 2005 2010
Model Year
Figure 3-124 Ratios of relative emission levels by model year under CA/S177 and Federal standards, both
individually normalized to "Tier-1" levels (See Equation 3-54)
The LEV rates derived by application of the ratios, as described above, are shown in the plots
below. Each plot shows two panels, for cars and trucks, so that each are present in each
comparison. Note that the rates developed in this step are "I/M reference rates"
(meanBaseRatelM). The "non-I/M reference" rates were subsequently generated in relation to
the reference rates.
For each pollutant, one operating mode is shown for running emissions, and one for start
emissions. Due to the proportional scaling in the rates, single modes are sufficient to illustrate
trends and patterns.
The plots show the default Federal rates (in blue), the initial LEV rates derived by ratio as
previously described (in red). Plots are presented for THC, CO and NO.., in that order, with the
same colors used in all plots.
228
-------
Trends for THC and CO, shown in Figures Figure 3-125 to Figure 3-128, are considered first as
the patterns are very similar for these two pollutants. In addition, the qualitative patterns are
similar for running process, represented by opMode 27, and for the start process, represented by
opMode 108.
The plots show trends in rates vs MY in the first age group (0-3 years). As mentioned, the
default Federal rates are shown in blue and the initial LEV rates in red. Note that the LEV trends
for cars drop to a consistent level between MY -2010 and 2016 but then increase from 2016 to
2017, at the beginning of the LEV-III phase-in. For trucks, this behavior is more pronounced,
showing an actual "spike" between 2016 and 2018.
For NOt, shown in Figure 3-129 and Figure 3-130, the pattern differs. The LEV rates, like the
Federal rates, begin to decline at the onset of the Tier3/LEV-III phase-in, without showing any
short-term increases.
Note that the plots also show an additional green trend, labelled 'extrap.' The derivation and
significance of these trends is explained in 3.12.5 below.
the, options before final assignment
opModelD=27
regclass= Cars regclass = Trucks
2.0
0.1
ro
en
ID
V)
TO
CD
TO
0)
E
0.5 -
0.0
2010
2020
2010
2020
modelYearlD
meanBaseRate mbrjev mbr_extrap
Figure 3-125 THC: Trends in Emissions for Federal and Estimated CA/S.177 rates, for cars and trucks at age
0-3 years, for the running emissions process (opModelD = 27)
229
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the, options before final assignment
opModelD=108
regclass = Cars regc lass = Trucks
v
2010 2020 2010 2020
modelYearlD
meanBaseRate mbrjev mbr_extrap
Figure 3-126 THC: Trends in Emissions for Federal and Estimated CA/S.177 rates, for cars and trucks at age
0-3 years, for the start emissions process (opModelD = 108)
co, options before final assignment
opModelD=27
regclass = Cars regclass = Trucks
\
\
"A.
2010 2020 2010 2020
modelYearlD
meanBaseRate mbrjev mbr_extrap
Figure 3-127 CO: Trends in Emissions for Federal and Estimated CA/S.177 rates, for cars and trucks at age 0-
3 years, for the running emissions process (opModelD = 27)
230
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co, options before final assignment
opModelD=108
regclass = Cars regclass = Trucks
2020
modelYearlD
• meanBaseRate
- mbr_extrap
Figure 3-128 CO: Trends in Emissions for Federal and Estimated CA/S.177 rates, for cars and trucks at age 0-
3 years, for the start emissions process (opModelD = 108)
nox, options before final assignment
opModelD=27
regclass = Cars
regclass = Trucks
- meanBaseRate
2020
modelYearlD
rnbr lev -
mbr_extrap
Figure 3-129 NO r: Trends in Emissions for Federal and Estimated CA/S.177 rates, for cars and trucks at age
0-3 years, for the running emissions process (opModelD = 27)
231
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nox, options before final assignment
opModelD=108
regclass = Cars regclass = Trucks
0.5 -
CD
c
0.2 -
E
2010
2020
2010
2020
meanBaseRate mbrjev mbr_extrap
Figure 3-130 NO* Trends in Emissions for Federal and Estimated CA/S.177 rates, for cars and trucks at age
0-3 years, for the running emissions process (opModelD = 108)
3.12.5 Extrapolating Phase-in Trends
The charts above show that based simply on the phase-ins, disjuncts appear at the beginning of
the Tier-3 phase-in (MY 2017-2018), in which the rates increase briefly before declining again.
This behavior gives the impression that the rates during the phase-in would be higher than during
Tier 2/LEV-II, e.g., 2010-2016.
In any case, the simple application of the ratios, as described above, led to the counterintuitive
results shown in the charts above. We developed an approach to adjust and correct these rates.
In projecting the phase-in of the Tier 3 standards, we made specific assumptions. See 3.4.1, page
80 and 3.4.2, page 82. The foundational assumptions can be restated as follows:
- the Tier 3 rates would meet the same NMOG+NO.T fleet-average requirements projected
for LEV-III,
following the onset of the phase-in, the trends in emission rates in Tier 3 and LEV-III
would follow declining linear trends, and
Tier-3 rates would converge with the LEV-III rates starting in 2017 for cars, and 2018 for
trucks. The LEV-III phase-in begins earlier, in 2015, giving LEV-III a "head start." The
Federal rates start later but immediately 'catch up' at the onset of the Tier-3 phase-in.
As mentioned, the initial estimates assume that the LEV rates are meeting LEV-III fleet averages
prior to the onset of the phase-in (2015), and then actually increase before starting to decline
again.
To rectify the situation, we extrapolated the linear phase-in trends backwards to reconstruct their
behavior between 2015 and 2018. Using subsets of rates at age = 0-3 years for MY 2017, 2018,
2020 and 2021, we calculated slopes in the phase-in trends. These slopes were calculated for
each pollutant on the basis of process (running and start) and operating mode. The calculations
were performed separately for cars and trucks.
232
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For cars, we calculated the slopes from between 2020 and 2017 (mcar), the latter of which is the
year when the Tier-3 phase-in began for cars.
Rim, 2017 — Rim, 2020
mrar =
2020 - 2017
where Riumy is the emission rate (meanBaseRatelM) the given model year.
Similarly for trucks, we calculated the slopes between MY 2021 and 2018 (/Struck), the latter of
which is the year when the Tier-3 phase-in began for trucks.
^truck —
Rim, 2018 Rim, 2021
2021 - 2018
Then for cars, we extrapolated this slope backwards from 2017 to earlier model years
Rim,my = Rim, 2017 + (2017 — MY)mcar
where MY = 2016, 2015 and 2014, to obtain projected rates R*immy lying on the linear phase-in
trend.
And for trucks, we extrapolated the slope backwards from 2018 backwards to earlier model years
Rim,my = Rim, 2018
+ (2018 - MY)mtmcks
where MY = 2017, 2016, 2015 and 2014.
For both cars and trucks, the extrapolated value for 2014 was projected backwards for MY to
MY 2005. As mentioned, the extrapolated trends are shown in green for HC, CO and NOx start
and running emissions in Figure 3-125 to Figure 3-130 in 3.12.4 above.
Having performed the extrapolation, modified rates were assigned by applying the following
logic:
For cars:
IF MY > 2005 AND <2016, THEN
IF the initial rate (Rim,my) < the extrapolated rate (R*im,my), THEN
Reassign the rate to the extrapolated value (R*im,my),
ELSE retain the initial rate.
For trucks, the logic is identical except for the applicable model-year range:
IF MY > 2005 AND <2017, THEN
IF the initial rate (Rim,my) < the extrapolated rate (R*im,my), THEN
Reassign the rate to the extrapolated value (R*im,my),
ELSE retain the initial rate.
The plots with the final results are shown below, for the same set of operating modes, for THC,
CO and NO*. The plots show that the extrapolated trends are selected for THC and CO, both for
start and running. For NOx, the initial trends are retained.
233
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the, final assignments
opModelD=27
regclass = Cars regclass = Trucks
12.5
7.5
2000
2005
2010
2015
2020 2000
2005
2010
2015
2020
modelYearlD
meanBaseRate mbr_lev_adj
Figure 3-131 THC: Final assignments for Federal and Estimated CA/S.177 emission rates, for cars and trucks
at age 0-3 years, for the running emissions process (opModelD = 27)
the, final assignments
opModelD=108
regclass = Cars regclass = Trucks
2.0
TO
CD
c.
E
0.5
0.0
2000
2005
2010
2010
2020
2015
2020 2000
2005
2015
modelYearlD
meanBaseRate mbr_lev_adj
Figure 3-132 THC: Final assignments for Federal and Estimated CA/S.177 emission rates, for cars and trucks
at age 0-3 years, for the start emissions process (opModelD = 108)
234
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co, final assignments
opModelD=27
regclass= Cars regclass = Trucks
200
150
en
100
CD
2000
2005
2010
2015
2020 2000
2005
2010
2015
2020
modelYearlD
meanBaseRate mbr_lev_adj
Figure 3-133 CO: Final assignments for Federal and Estimated CA/S.177 emission rates, for cars and trucks
at age 0-3 years, for the running emissions process (opModelD = 27)
co, final assignments
opModelD=108
regclass = Cars regclass = Trucks
15
10
5
0
2000
2005
2010
2015
2020 2000
2005
2010
2015
2020
modelYearlD
meanBaseRate mbr_lev_adj
Figure 3-134 CO: Final assignments for Federal and Estimated CA/S.177 emission rates, for cars and trucks
at age 0-3 years, for the start emissions process (opModelD = 108)
235
-------
nox, final assignments
opModelD=27
regclass = Cars
regclass = Trucks
60 -
I
3 40
\
A
ai
\
1 A
a>
\
1 \
m
i \
1 1
ra
\ \
1 \
E
\ 1
j 1
20 -
v
o -
^—
1 1 1 1 1
2000 2005 2010 2015 2020
1 1 1 1 1
2000 2005 2010 2015 2020
modelYearlD
meanBaseRate —
mbr_lev_adj
Figure 3-135 NO*: Final assignments for Federal and Estimated CA/S.177 emission rates, for cars and trucks
at age 0-3 years, for the running emissions process (opModelD = 27)
nox, final assignments
opModelD=108
regclass = Cars regclass = Trucks
1.25 -
1.00 -
TO
l 075 ~
TO
CD
c
ro
E 0.50 -
0.25 -
0.00
2000
2005
2010
2015
2020 2000
2005
2010
2015
2020
modelYearlD
meanBaseRate mbr_lev_adj
Figure 3-136 NO r: Final assignments for Federal and Estimated CA/S.177 emission rates, for cars and trucks
at age 0-3 years, for the start emissions process (opModelD = 100)
3.12.6 Additional Steps
As mentioned, the rates developed as described represent "I/M reference rates" at age = 0-3
years. Following completion of the steps described in 3.12.1 to 3.12.5, the following three steps
were completed.
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3.12.6.1 Apply Deterioration Adjustments
To project emission rates for the remaining six ageGroups, deterioration was projected by ratio
as described for the Federal default rates in 3.10.3, page 202.
3.12.6.2 Apply Non-I/M ratios
Having projected deterioration for the "I/M reference rates" (meanBaseRatelM), we projected
the "non-I/M reference rates" (meanBaseRate) representing default emission rates in non-I/M
areas, as described for the Federal default rates in 3.10.4, page 202.
3.12.6.3 Replicate Rates for additional Fuels
Having generated I/M and non-I/M reference rates for gasoline (fuelTypelD = 1), we replicated
the gasoline rates in their entirety to represent diesel (fuelTypelD = 2) and E85 (fuelTypelD = 5),
as described in 3.10.5, page 202.
3.12.7 Availability
The emissionRateByAgeLEV table contain the subsets of CA/S177 rates and is incorporated into the
default MOVES database. Instructions for using it are available in the MOVES graphical user interface.
3.12.8 Earlv Adoption of Natior ¥ Standards
The National Low Emission Vehicle Standards program was adopted in 2001. However, a group of states
in the "Northeast Trading Region" (NTR) adopted the standards early, in 1999. Using an approach
identical to that used to develop the CA/S177 rates, we developed a supplemental table for the
emissionRateByAge values representing the adoption of NLEV rates in model years 1999 and 2000. As
with the national program, "early" NLEV applied only to the LDV, LDT1 and LDT2 vehicle classes.
As with the CA/S 177 rates, we developed phase-in assumptions specific to "early" NLEV. Figure 3-137
shows that fractions of Tier-1 vehicles start declining markedly in MY 1999, whereas in the default phase-
in, the fractions for Tier 1 are 100 percent until MY2001 for LDV-T1 and LDT2. The fractions shown
apply to LDT2, as well as to LDV-T1. Vehicle classes LDT3 and LDT4 remain in Tier 1 until the onset
of Tier 2, in MY2004.
The NTR rates were developed by scaling default rates for start and running emissions down
appropriately as implied by the differences in phase-in assumptions, as performed for the LEV rates and
described in 3.12.1 through 3.12.4.
The supplemental table for early NLEV rates is stored in the MOVES default database. Instructions for
using it are available in the MOVES graphical user interface.
237
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c
0)
u
0)
a.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Tier 1
ITLEV
I LEV
ULEV
1998
1999
2000
Model Year
2001
2002
Figure 3-137 Phase-in assumptions for early NLEV adoption, for LDV, LDT1 and LDT2
3.13 Rates for E-85 Vehicles
The rates developed as described in Section 3 represent gasoline-fueled conventional-technology
engines.
Because data on E-85 LD vehicles is lacking and they are required to meet the same emission
standards as gasoline vehicles, we use the start and running rates developed for gasoline vehicles
in modelling other fuels and technologies.
We replicated the entire set of gasoline rates for high-level ethanol blends, i.e., "E77" through
"E85." However, for lower-level ethanol blends (i.e., 0-20 vol. percent), the effect of ethanol
(and other effects related to blending) is represented through fuel adjustments, rather than
through the base rates, as described in this document. The development and application of fuel
adjustments is described in a separate report.79
4 Particulate-Matter Emissions from Light-Duty Vehicles
The emission rates for particulate matter described in this chapter are developed in two parts.
The first part (Section 4.1) derives modal emission factors and deterioration rates for vehicles
manufactured before 2004. The second part (Section 4.2) presents the updated rates in MOVES3
for vehicles manufactured since 2004, by scaling the base modal emission rates in MOVES2014
according to newer test data, and applies emission rate modifications for the phase-in of future
standards.
4.1 Particulate-Matter Emission Rates for Model Year 2004 and Earlier
Vehicles
The primary study that this chapter relies on is the Kansas City Light-duty Vehicle Emissions
Study (KCVES) conducted in 2004-2005.51 The Environmental Protection Agency and several
238
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research partners conducted this study to quantify tailpipe particulate-matter emissions from
gasoline-fueled light duty vehicles in the Kansas City Metropolitan Area. During the summer
and winter phases, 261 and 278 vehicles were measured, respectively, with some overlap
between the phases. The measurements were conducted on a portable dynamometer using the
LA92 driving cycle under ambient temperature conditions.
Analyses of some of the data from this program are presented in the report: "Analysis of
Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas City,"52 This
"analysis report" (which is the partner to this chapter) presented preliminary emission rates for
PM, elemental carbon fraction (EC) and organic carbon fraction (OC), as well as temperature
adjustment factors for start and hot-running emissions processes. These preliminary results form
the basis for the emission rates developed in this chapter. The rates in the analysis report are
based on aggregate or "bag" emissions measured on the filters, and are thus, presented as
grams/start for start emissions and grams/mile for hot running operation.
The dataset included vehicles manufactured over several decades, measured at various ages
during CY2004-05. Thus, the program taken alone did not enable us to forecast emissions for
current vehicles as they age, or to backcast emissions of older vehicles when they were young.
This chapter describes the development of a deterioration model based on a comparison of
former PM studies with the KCVES. The rates from this deterioration model allow both
forecasting and backcasting as required by MOVES.
In addition, the preliminary analyses52 did not attempt to translate results measured on the LA92
cycle (used in KCVES) into terms of other cycles (such as the FTP) or to "real-world" driving.
As with the gaseous pollutants, MOVES has the capability to represent hot running "modal"
emission rates so that emissions vary depending on the driving pattern represented. The
operating modes defined for PM are the same as for the gaseous emissions (see Table 2-5). This
chapter describes how the continuous PM measurements collected in the study were used to
populate the modal rates for MOVES. Because of the reliance on continuous PM measurement,
it is worth describing the measurement procedures used in this program.
4,1.1 Particulate Measurement in the Kansas City Study
For measurements conducted on the dynamometer, vehicles were operated over the LA92
Unified Driving Cycle (see Figure 4-1). The LA92 cycle consists of three phases or "bags."
Phase 1 ("bag 1") is a "cold start" that lasts the first 310 seconds (1.18 miles). "Cold start" is
technically defined as an engine start after the vehicle has been "soaking" in a temperature
controlled facility (typically ~72°F) with the engine off. In the Kansas City study, the vehicles
were soaked overnight under ambient conditions. Phase 1 is followed by a stabilized Phase 2 or
"hot running" (311 - 1427 seconds or 8.63 miles). At the end of Phase 2, the engine is turned off
and the vehicle is allowed to "soak" in the test facility for ten minutes. At the end of the soak
period, the vehicle is started again, and is driven on the same driving schedule as Phase 1. This
Phase 3 is called a "hot start" because the vehicle is started when the engine and after-treatment
systems are still hot. Criteria pollutants were measured both in continuous and aggregate modes.
Particulate was collected during each of the three phases on 47 mm Teflon filters at 47°C ± 2°C.
239
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70.0
60.0
Bag 1
Bag 2
50.0
40.0
o. 30.0
20.0
10.0
0.0
0
200
400
600
800
1000
1200
1400
time
Figure 4-1 Phases 1 and 2 of the LA92 Cycle, representing "cold-start" and "hot-running" operation,
respectively
In addition to the gaseous pollutants measured via the constant-volume sampler (CVS),
continuous measurements of total PM mass were taken using two instruments. The first was a
Booker Systems Model RPM-101 Quartz-crystal microbalance (QCM) manufactured by Sensors,
Inc.; the second was a Thermo-MIE Inc. DataRam 4000 Nephelometer. In addition to total
mass, estimated black carbon was measured continuously with a DRI photoacoustic instrument.
In addition, integrated samples were collected and analyzed by DRI for PM gravimetric mass,
elements, elemental and organic carbon, ions, particulate and semi-volatile organic compounds,
and volatile organic air toxics. All sampling lines were heated and maintained at 47°C ± 2°C.
The samples were extracted from the dilution tunnel through a low particulate loss 2.5 |j,m
cutpoint pre-classifier. Further details and a schematic of the sampling instrumentation are
shown in Figure 4-2 and Figure 4-3.
240
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Diluted exhaust
at 46 C
from vehicle
tailpipe
T
dilution air
V
dilution air
heater (46 C)
to aldehyde sample aldehyde
flow comtroller A cartridge
to particle sample
flow controller
particle filter-
background
sample line
Backgrd HC analyzer
V
Air Conditioner
water
trap -
pump ¦
filter —
flow
measurement
and control
'f
ile Jk heated f vent
l sample I
^heated
|-^filter
heated ^
Positive
Displacement
Pump (PDP)
pump.
sample Heated
line
flow
measurement
and control
HC analyzer
Dilute exhaust
collection bags
lew CO
analyzer
i
high CO
analyzer
NOx analyzer
C02 analyzer
Figure 4-2 Schematic of the constant-volume sampling system used in the Kansas-City Study
<-Dyno
CVS 10cm, 5
PM2.5 IMFACTORS
PROBE
10cm, 'j lpm
RESTER
CONTROL
HEATER
CONTROL
lm, 4.5 lpm
PHOTO ACOUSTIC
(black carbon)
QCM CART SYSTEM
(m ass)
Data RAM
(light
scattering)
DusTrak
(light
scattering)
Figure 4-3 Continuous PM analyzers and their locations in the sample line
It is worth briefly describing the apparatus used to measure PM on a continuous basis. A more
thorough description may be found in the contractor's report.-1 As of the date of this program.
241
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measuring continuous particulate was a daunting technical challenge. Each technique has
specific advantages and disadvantages. For this study, the cumulative mass as measured on the
Teflon filters was treated as a benchmark. Thus, prior to using the continuous measurements to
estimate modal emissions, the sums of the time series for the continuous measurements were
normalized to their corresponding filter masses to compensate for systematic instrument errors.
The Quartz Crystal Microbalance measures the cumulative mass of the PM deposited on a crystal
face by measuring the change in its oscillating frequency. It is highly sensitive to many artifacts
such as water vapor and desorption of lighter organic constituents. Due to the high degree of
noise in the continuous time series, the measurements were averaged over 10 seconds, thus
damping the temporal effects of transients. The QCM can accurately capture cumulative PM
over time, however, measurement uncertainties increase for successive points in time because the
values depend on a calculated difference between two sequential, and similar, measurements.
Due to the resulting high variability, including large and rapid fluctuations from positive to
negative emissions at any given instant, and vice versa, use of the QCM measurements was not
viewed as a practical option for use in emission rate development for MOVES, except as a check
on the other instruments.
The Dustrak and Dataram both work on light-scattering principles. As such, they have very
rapid response times and can measure larger PM volumes with reasonable accuracy. However,
their accuracy degrades when measuring low PM volumes. Since most PM mass lies within the
larger particles, the instruments should be able to capture most of the continuous mass
concentrations though it may miss a substantial portion of the smaller (nano) particles. To
provide a qualitative check on this supposition, the time-series for the QCM and optical
instruments were aligned and checked to ensure that significant mass was not missed. Based on
this analysis, the Dustrak instrument was observed to be the most reliable of the 3 instruments,
and mass correction at low loads was not judged to be worth the effort given the uncertainties
involved. This time-consuming analysis was done by eye for each test and the results are not
presented in this chapter.
The photoacoustic analyzer (PA) is unique among the continuous instruments in its ability to
capture only the soot or elemental carbon components of PM. The fast analyzer detects the
resonances coming off the carbon-carbon bonds in soot. Unfortunately, there were insufficient
Thermal Optical Reflectance (TOR) elemental carbon (EC) measurements from quartz filters to
normalize the PA data, but some comparisons are shown in the contractor's report.51 In this
study, the PA data were compared qualitatively with the Dustrak and Dataram and found to be
consistent with expected ratios of elemental to total carbon during transient events, leading to the
conclusion that these instruments were largely consistent. These results are also not presented in
this chapter as every single trace was compared by eye. The data is used to determine the modal
relationship of elemental to total PM.
Due to the uncertainty of experimental measurement techniques for continuous PM at the time of
the Kansas City study, these instruments were employed only as a semi-qualitative/quantitative
means of determining modal emission rates, and the use of such data does not qualify them as
EPA recommended or approved devices or processes.
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4.1.2 New Vehicle or Zero Mile Level (ZML) Emission Rates
In this section, we develop an approach to extend the PM results from the KCVES to estimate
average emissions across the fleet. The section also compares the new vehicle results from many
different studies in order to estimate "zero mile" level (ZML) emission rates for all model years.
Before modeling deterioration, it is first necessary to capture ZML emission rates.
In constructing a model of emissions from the Kansas City data (Figure 4-4), the greatest
challenge is distinguishing between model-year and age effects. As with most datasets, this issue
arises because the program was conducted over a two-year period. As a result, it is very difficult
to distinguish the reduction in emissions with model year from the increase in emission with age.
Emissions tend to decrease as technologies are introduced on vehicles (with later model years) in
order to comply with more stringent emissions standards. However, these technologies and
vehicles tend to deteriorate over time, thus for the same model year vehicle, older vehicles
(greater age) will have higher emissions (on average) than newer vehicles.
100
90
80
70
60
1
50
30
20 :
: I J- „ i " " II ¦¦ T T
10 }
1 1 1 1 ¦ "
,1 . . I I , . . II 11 t l , M i_i t ; i .
1975 1980 1985 1990 1995 2000
Model Year
Figure 4-4 Average particulate emission rates from the Kansas City study, by model year, shown as cycle
aggregates on the LA92 The five year averages (e.g. 1988-1991,1993-1997,1998-2002) are also shown
without error bar
In concept, the most accurate means of quantifying emissions from vehicles over time is to
conduct a longitudinal study, where emissions are measured for the same vehicles over several
(or many) years. However, implementing such a study would be costly. Moreover, it is
impossible to obtain recent model year vehicles that have been significantly aged. In the
following sections, we will describe some limited longitudinal studies conducted in the past.
~ KC measured
¦ KC 5 yr measured avg
|
|
II
1
¦
T
1 1 1 1
i 1
<• _L
¦
.. i
—•—i—
H • f } i
243
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Then, we will present our modeling methodology to isolate model year (technology) in this
chapter from age (deterioration) in the next.
4.1.2.1 Longitudinal Studies
There have been a few longitudinal studies conducted in the past that are relevant for PM
emissions. Unfortunately, they are all limited in their ability to conclusively distinguish model-
year effects from age effects.
Gibbs et al. (1979) measured emissions from 56 vehicles with mileage ranging from 0 to 55,000
miles (odometer) on 3 different cycles.53 Hydrocarbon emissions were analyzed, but
unfortunately, PM results were not reported as a function of mileage. The authors state that
"emission rates of measured pollutants were not found to be a consistent function of vehicle
mileage," however, the following figure shows that some increasing trend seems to exist for
THC (Figure 4-5).
3.5
3
2.5
£
2
o>
o
1.5
I
1
0.5
0
0 10 20 30 40 50 60
mileage (*1000)
Figure 4-5 Hydrocarbon emissions as a function of mileage (Gibbs et al., 1979)
Hammerle et al. (1992) measured PM from two vehicles over 100,000 miles.54 However, their
results for PM deterioration are somewhat inconclusive, as the following figure shows, since the
deterioration seems to occur mainly in the beginning of life, with very little occurring after
20,000 miles. Also, the study is limited to two specific vehicle models.
244
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3.5
y=2E-05x +0.8218
3
2.5
y = 8E-0ex + 1.4717
2
1.5
~ Explorer
1
— Linear (Explorer)
—• Linear (Escort)
0.5
0
0
20000
60000
80000
100000
Figure 4-6 Particulate emissions as a function of odometer for two Ford vehicles (Hammerle et al., 1992)
Both of these studies assume that odometer is a surrogate for age. While there are some
deterioration mechanisms that worsen with mileage accumulation, there are others that
deteriorate with effects that occur over time, such as corrosion due to the elements, deposits and
impurities collecting in the gas tank and fuel system, etc. Therefore, we believe that any study
that describes deterioration as a function of odometer (alone) may not account for all causes of
deterioration.
Whitney (2000) re-recruited 5 vehicles that had been measured in previous study 2 years prior
(CRC-E24).55 There are two significant limitations of this follow-up study: (1) the interval
between studies was only 2 years, though the odometers had increased 22,200 miles (on average)
and (2) these vehicles were tested on a different drive cycle, the LA92 compared to the previous
study, which used the FTP. We will explore the potential cycle differences on PM later, but
assuming the cycles give similar PM results, the PM emissions were only 8 percent higher (on
average). This increase is due to a single vehicle, which had significantly increased PM
emissions (the rest were the same or slightly lower). Unfortunately, this is not a large enough
sample and time period on which to resolve age effects, but it may be sufficient to conclude that
the differences between PM from the FTP and LA92 drive cycles are minimal for PM.
The three longitudinal studies described above are inconclusive, though they do hint that
deterioration does occur.
4.1.2.2 New Vehicle, or ZML Emission Rates and Cycle Effects
In order to isolate the effect of model year (technology) from age (deterioration), it is useful to
look at the model-year effect independently. This goal can be achieved by analyzing emissions
from new vehicles from historical studies. New vehicle emission rates tend to have lower
variability than older vehicles (in absolute terms) since they have lower emissions that comply
with more stringent THC standards. These standards, which decrease over time, tend to affect
PM emissions as well since many of the mechanisms for HC formation also form PM.
245
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Several independent studies have measured PM emissions from nearly new vehicles. For our
purposes, we will define "new" as a vehicle less than 3 years old, i.e., vehicles within the 0-3
year age Group. Table 4-1 lists the 15 studies employed for this analysis.
Table 4-1 Historical gasoline PM studies including new vehicles at time of study
Program
Year of study
No.
vehicles
Drive cycle
Gibbs et al.53
1979
27
FTP
Cadle et al56
1979
3
FTP
Urban & Garbe57,58
1979, 1980
8
FTP
Lang et al.59
1981
8
FTP
Volkswagen60
1991
7
FTP
CARB61
1986
5
FTP
Hammerle et al., 199254
1992
2
FTP
CRC E24-1 (Denver)62
1996
11
FTP
CRC E24-2 (Riverside)63
1997
20
FTP
CRC E24-3 (San
Antonio)64
1998
12
FTP
Chase et al.65
2000
19
FTP
Whitney (SwRI)55
1999
LA92
KC (summer)51,52
2004
13
LA92
EPA (MSAT)66
2006
4
FTP
Li et al., 200667
2006
3
FTP, LA92
Before we examine these emissions, we should convince ourselves that the LA92 driving cycle
will not give substantially different PM emissions than the FTP so that we can compare these test
programs directly. As described above, the results from Whitney (2000) seem to indicate little
difference between the two cycles. Even though the tests were conducted 2 years apart, one
would expect that the aging effects in combination with the slightly more aggressive LA92 cycle
(used later) would have given higher PM emissions. However, this was not the case, and only
one of the 5 vehicles showed significantly increased emissions.
Li et al., (2006) measured three vehicles on both cycles at the University of California,
Riverside.67 The PM emissions from the LA92 were 3.5 time larger (on average) than the FTP
results. However, the HC emissions were only 1.2 times higher. These results seem rather
contradictory and inconclusive. The 3.5 factor also seems excessive in relation to other results,
such as the one conducted by Whitney (2000).
Finally, the California Air Resources Board conducted an extensive program over several years
comparing many different drive cycles. Unfortunately, PM was not measured in this program.
However, Figure 4-7 shows the HC emissions compared for the two cycles. The trends indicate
little difference on average between the LA92 and FTP cycles for HC.
246
-------
y = 0.966 8IX
R"= 0.3301
o TH
— Liit- ii tenant t»
FTP
Figure 4-7 Hydrocarbon emissions on the LA92 versus corresponding results on the FTP cycle
Based on these studies, we conclude that there is little difference in PM emissions between the
LA92 and FTP cycles on an aggregate basis (though their bag by bag emissions may differ). We
shall demonstrate that, for the purposes of ZML analysis, the overall results will be nearly
identical even if we omit the LA92 data, thus minimizing the significance of this issue.
Figure 4-8 shows the new-vehicle emission rates from the studies listed in Table 4-1. The data
points represent each individual test, and the points with error bars represent the average for each
source. The plot presents evidence of an exponential trend (fit included) of decreasing emissions
with increasing model year. The fit is also nearly identical if we omit the two programs that
employed the LA92 cycle. We will use this exponential ZML relationship as the baseline on
which to build a deterioration model. However, the measurements from the older programs
primarily measured total particulate matter. These have been converted to PMio (for the plot),
which is nearly identical (about 97 percent of total PM is PMio). We also assume that 90 percent
of PMio is PM2.5 (EPA, 1981).68 For the older studies, we accounted for sulfur and lead directly
if they were reported in the documentation. In those cases where sulfur was not reported, the
levels were approximated using sulfur emission factors from MOBILE6 and subtracted as an
adjustment.
Unfortunately, many of the older studies used a variety of methods for measuring particulate
matter. There were many differences in filter media, sampling temperature, sample length,
dilution, dynamometer load/settings etc. It is beyond the scope of this project to normalize all of
the studies to a common PM metric. It is likely that documentation is not sufficient to even
attempt it. Therefore, no attempts at adjustment or normalization were made except for size
fraction, lead and sulfur, as described above.
247
-------
40
35
30
25
20
15
rM = 15.6exp(
0814 MY)
all
¦
Gibbs et al., 1979
o
Cadleet al., 1979
~
Urban&Garbe, 1980
A
Lang et al., 1981
VW, 1991
CARB, 1986
Hammerleetal., 1992
m
D
E24-1 Denver
A
E24-2UC Riverside
E24-3 San Antonio
O
Chrysler/Ford/GM
SwRi/NREL
X
KC-Summer LA92
+-
M SAT-Tier2
• mean for each program
^—Exponential Fit
10 15 20
Model Year (+1975)
Figure 4-8 Particulate emission rates for new vehicles compiled from 14 independent studies
To estimate the ZML emission rates from these data, the next step was to separate results for cars
and trucks, and to separate cold-start from hot-running emissions. Unfortunately, the historical
data does not present PM results by cycle phase. Therefore, the 2005 hot-running ZMLs for cars
vs. trucks were calculated from the KCVES dataset, and the model-year exponential trend from
the aggregate trendline (-0.08136) is used to extend the ZMLs back to model year 1975. The
base hot running ZML emission rate for cars (LDV) (Ehr,j) is:
= ^HR,2005e Equation 4-1
where
y = model year - 1975, and
-Ehr2005 = hot running ZML rate for MY 2005.
To estimate equivalent rates for trucks, we multiplied this expression by a factor of 1.43. This
value is based on an average of all the studies with new vehicles from 1992 onward (before this
model year, there were no trucks measured). It is also multiplied by 0.898 to give hot running
bag 2 rates and 1.972 to give the cold start emission rate (here defined as bag 1-bag 3 in units of
g/mi). These values were estimated by running a general linear model of bag 2 and bagl-3 with
respect to composite PM, respectively, using SPSS statistical software. The averages of these
248
-------
ratios by model year are shown in Figure 4-9, in which no clear trend is discernible. The
parameters of the model are summarized in Table 4-2.
"35
O
o
u
~ coldstart/comp
¦ bag2/cornp
+ *
-~—
~~ *
++
~ *
1960 1970 1980 1990
model year
2000
2010
Figure 4-9 Ratios of hot-running/composite and cold-start/composite, Bag2 and Bagl-Bag3, respectively,
averaged by model year
Table 4-2 Best-fit parameters for cold-start and hot-running ZML emission rates
Parameter
Value
LDV hot-running ZML (g/mi)
0.01558
Exponential slope
0.08136
Truck/car ratio
1.42600
Bag-2 coefficient
0.89761
Cold-start coefficient
1.97218
Figure 4-10 shows the ZML emission rates. The rates are assumed to level off for model years
before 1975.
249
-------
0.050
0.045
0.040
0.035
¦ truck new bag1_3
¦ car new bag1_3
truck new bag2
car new bag2
0.030
0.025
0.020
0_
0.015
0.010
0.005
0.000
1975
1980
1985
1990
1995
2000
2005
model year
Figure 4-10 Particulate ZML emission rates (g/mi) for cold-start and hot-running emissions, for LDV and
LDT
4.1.2.3 Aging or Deterioration in Emission Rates
In this section, a deterioration model is introduced that captures how new vehicles in all model
years deteriorate over time so that gasoline PM in any given calendar year can be modeled in
MOVES. The purpose of this model is to characterize the PM emissions from the fleet and to
backcast the past as well as forecast the future, as required in MOVES
The ZMLs determined in the previous section represent baseline emissions for new vehicles in
each model-year group. By comparing the emissions from the "aged" Kansas City vehicles in
calendar year 2005, to the new rates determined earlier, we can deduce the "age effect" for each
corresponding age. However, simple an approach as this seems, there are many ways to connect
the two points. This section describes the procedure and the assumptions made to determine the
rate at which vehicle PM emissions age.
We first break the data into ageGroups. We use the MOVES age groups which correspond to the
following age intervals: 0-3 (new), 4-5, 6-7, 8-9, 10-14, 15-19, 20+.
As a first step, the bag measurements from all of the vehicles measured in Kansas City were
adjusted for temperature using the equation derived in the analysis report.52 The equation used is:
Z7 TP -0.03344 (72-T) „ ^
pm 7 * pm t Equation 4-2
where Evuji, is the adjusted rate at 72°F for cold-start or hot-running emissions, Evuj is the
corresponding measured emissions for cold-start or hot-running, respectively, at temperature T,
respectively.
250
-------
The temperature-adjusted measurements are the "aged" rates, i.e., the rates in each model-year
group represent emissions for that group at the age of measurement in 2004-05, at 72°F rather
than at the actual ambient temperature.
The method adopted is to ratio the aged rates with the new rates so that the changes with
deterioration rates are all proportional. This approach will be referred to as the "multiplicative
deterioration model," and is analogous to the approach used with the gaseous emissions (Section
3.6 and 3.7).
It is likely that some of the same mechanisms that cause HC and CO to increase over time would
also result in PM increases. These factors include deterioration in the catalyst, fuel control,
air:fuel-ratio control, failed oxygen sensors, worn engine parts, oil leaks, etc. Figure 4-11 shows
trends in the natural logarithm of THC rates over approximately 10 years, based on random-
evaluation samples in the Phoenix I/M program. On a log-linear scale, the deterioration trends
appear approximately linear over this time period, suggesting that the deterioration rates are
exponential. This observation, combined with the approximate parallelism of the trends for
successive model years, implies that emissions follow a multiplicative pattern across model-year
or technology groups, calling for a multiplicative deterioration model. In such a model, the aged
rates and the new rates are converted to a logarithmic scale, after which the slopes are estimated
by fitting a general linear model. The average slope is estimated, with the ZMLs determined
earlier defining the j'-axis offsets. The result is a series of ladder-like linear trends in log scale as
shown in Figure 4-12. The lines fan out exponentially on a linear scale as shown in Figure 4-13.
The dotted lines and the points with uncertainty bars represent the Kansas City data overlaid onto
the model and indicate that the model is consistent with the data.
LDV WEIGHTED
In (THC) vs. Age (years), LDV
S S 8 S S
(J
E
Vehicle age (years)
1S82 o O Q 1983 ©-©-© 1984 O O O 1985
1338 B-B-B iseg ~ ~ ~ 1990 ~ ~ ~ "091
AAA 1994 AAA 1955 AAA 1996 AAA 1997
2000 1 1 1 2001 1 1 1 2002 1 1 1 2003
Figure 4-11 The natural logarithm of THC emissions vs. Age for LDV in the Phoenix (AZ) Inspection and
Maintenance program over a ten-year period (1995-2005)
251
-------
0.0
0 5 10 15 20 25
Age
Figure 4-12 The multiplicative deterioration model applied to PM results from Kansas City. The y-axis
offsets represent ZML rates. The dotted line represents the Kansas-City Data
100
1975
90
-^1980
1985
80
1990
70
1995
60
2000
2005
50
1980
40
30
20
1990
10
10
15
20
25
30
Age (years)
Figure 4-13 The multiplicative deterioration model shown on a linear scale. The y-axis offsets capture the
new-vehicle ZML rates. The dotted lines and points with error bars represents the Kansas-City results (with
95 percent confidence intervals)
252
-------
We applied the multiplicative deterioration factors directly to both cars and trucks, cold start,
hot-running, EC, and OC emissions, assuming that the deterioration factors are independent of
these effects. The estimation of the elemental carbon fractions, modal emission rates, and modal
start rates are discussed in the next sections.
4.1.3 Estimating Elemental Carbon Fractions
After performing the analyses described above to estimate total particulate (PM2.5), we
partitioned the total into components representing elemental carbon (EC) and non-elemental
carbon (nonECPM), respectively. Following this step, the values for EC and nonECPM were
loaded into the emissionRateByAge table, using the pollutant and process codes shown in Table
2-1 (page 16). Non-elemental carbon particulate matter (NonECPM, or pollutantID 118),
represents particulate species other than elemental carbon. For light-duty exhaust, NonECPM is
primarily composed of organic carbon (pollutantID 112), and small amounts of inorganic ions
and elements. Background and further detail on the speciation of PM2.5 is discussed in greater
detail in the MOVES TOG and PM Speciation Report.19
The initial analysis of the EC composition of the light-duty exhaust is documented in the Kansas
City analysis report.52 In the Kansas City study, EC was measured using two different methods.
The first was the technique of thermal optical reflectance (TOR). This procedure also measured
OC and total PM, but unfortunately, not all the vehicles in the study were measured using this
technique. Elemental carbon was also measured using the photoacoustic analyzer, which
measures EC on a continuous basis. More information can be found on these techniques and their
calibration and comparison results in the contractor's report69 and Fujita et al. (2006).70 The
former reference indicates that the photoacoustic analyzer has good correlation with TOR EC
measurement especially at higher PM levels, however, at lower levels (in bag 3 for example), the
correlation is poorer. This is not surprising since all instruments have limited ability to measure
small signals. To accentuate the full range of operation, Figure 4-14 shows a plot of a
comparison of the two instruments on a natural-log scale. The plot reinforces the excellent
agreement between the two instruments in bag 1 of the test, when emissions levels are at their
highest. The correlation (and slope) is also good for the high values in Bag 2, however, as the
measurements get smaller there is relatively more variability (in log-space) between the two
measurements.
253
-------
~ bag 1
¦ bag 2
a bag 3
— Linear (bag 1)
— Linear (bag 2)
— Linear (bag 3)
y = 0.982J
R2 =
ix- 0.2107
J.9417
4
~
¦
~
~
y = 1 PRfifii
- 1 1R9 ft
¦
R2 = 0.
7295
8
3
4 ^ ^
~ >
~ 4J4
- ~
yw
L (
= 0.6468X
R2 = 0.
- (16774
485
m
~ /
A ¦ ¦ ¦
A
*
ln(TOR EC)
Figure 4-14 Comparison of photoacoustic to TOR EC measurements on a logarithmic scale
We explored the EC/PM fraction for the four measurement techniques employed in the Kansas-
City study: photoacoustic analyzer (PM, continuous EC), Dustrak analyzer (DT, continuous
optical PM), gravimetric filter (PM), and thermal optical reflectance (TOR, which measured both
EC and total carbon, TC). Table 4-3 shows the comparison of the 3 different fractions using
results from these instruments. The values were calculated as fractions of average values in the
numerator and denominator. The TOR fractions have two major limitations: the ratios are
unexpectedly high and, after eliminating bad data points, only 75 valid measurements remain.
Due to the latter condition (primarily), the TOR fractions will not be used in subsequent analysis.
The photoacoustic to Dustrak ratios present a reasonable approach, however, since the Dustrak
and PM are not strongly correlated51, we elected to use the photo-acoustic to gravimetric filter
ratios for EC/PM fraction estimation.
Table 4-3 Elemental to total PM ratio for 4 different measurement techniques
Instruments
All
Start
Running
PA/DT
0.128
0.188
0.105
PA/PM
0.197
0.340
0.164
EC/TC
(TOR)
0.382
0.540
0.339
254
-------
In MOVES, the EC/PM fractions for light-duty gasoline vehicles are consistent with detailed
PM2.5 speciation profiles developed for all the measured PM species in the Kansas City Study.71
The EC/PM fractions are estimated using the photoacoustic analyzer to filter-based PM
emissions. The MOVES speciation analysis confirmed our previous analysis52 that the EC/PM
fraction is relatively consistent across the range temperatures measured in Kansas City study, and
across the ranges of model years in the study. For this reason, no differentiation in the EC/PM
fraction is modeled in relation to temperature or model year of vehicles in MOVES.
In developing speciation profiles for light-duty gasoline vehicles from the KCVES,71 we
discovered high concentrations of silicon in the particulate matter samples. Upon further
investigation, we determined that the silicone rubber couplers used in the sampling system
probably contributed to the filter-measured mass. The resulting contamination of filter masses
with silicon substantially impacted the Bag 2 PM2.5 emission rates, which had the highest
exhaust temperatures. No significant contribution of silicon was found in the PM2.5 start
emissions. The adjustment to the MOVES running PM2.5 emission rates based on the silicon
measurements is discussed in Appendix A. Revisions to the Pre-2004 Model Year PM2.5
Emission Rates between MOVES2010b and MOVES2014.
The silicon contamination in these measurements resulted in a positive bias in the values for OC.
In consequence, the EC and nonECPM emission rates in MOVES were revised to account for the
updated data analyses used to derive the PM2.5 profile (e.g. VMT-weighted means), and to
compensate for the silicon contamination in the PM2.5 emission rates. Upon removal of the
silicon contamination, the EC/PM fractions are not significantly different between light-duty cars
and trucks. The data from cars and trucks were pooled as documented in the speciation
analysis.71 The EC/PM2.5 fractions in MOVES are presented in Table 4-4. The EC/PM2.5 ratio is
constant across all operating modes for start and running processes.
Table 4-4 EC/PM2.5 fractions by start and running emissions processes for pre-2004 light-duty gasoline
vehicles
Emission
Process
EC/PM25
Running
14.0%
Start
44.4%
4,1.4 Modal Running Emission Rates
As mentioned in section 4.1.1, the Dustrak instruments was selected as the most reliable second-
by-second PM time-series data measurement from the Kansas City Study. The Dustrak PM2.5
measurements were used to develop the PM2.5 emission rates by operating mode. The following
two figures show Dustrak PM emissions binned by VSP and classified by model year Groups.
Figure 4-15 shows this relationship on a linear scale and Figure 4-16 shows the relationship on a
logarithmic scale. It is clear from the latter plot that VSP trends for PM tend to be exponential
with VSP load, i.e. they are approximately linear on a log scale, showing similar patterns to the
gaseous emissions, particularly CO. Thus, we assume smooth log-linear relations when
calibrating our VSP based emission rates.
255
-------
1983
J
B— 1989
/
1996 ¦
/
—®—2000
/
-¦-2001
/
~~0 2004 "
/ /
¦
i
0 5 10 15 20 25 30 35 40 45 50
VSP, kw/tonne
Figure 4-15 Particulate emissions, as measured by the Dustrak, averaged by VSP and model-year group
(LINEAR SCALE)
Cars
-51 1 1 1 1 1 1 1 r
10 15 20 25 30 35 40 45 50
VSP, kw/tonne
Figure 4-16 Particulate emissions, as measured by the Dustrak, averaged by VSP and model-year group
(LOGARITHMIC SCALE)
In order to calculate VSP-based modal rates, we followed seven steps:
1. The LA92 equivalent hot-running emission rate (g/mi) is calculated for each age group
within each model-year group, using the deterioration model described in section 4.1.2.3.
2. Continuous emission rates (g/sec) are calculated from the Dustrak measurements for cars
and trucks. These trends are then extrapolated to higher VSP levels where data is
missing.
256
-------
3. The VSP operating-mode distribution is calculated for Bag 2 of the LA92 drive cycle for
cars and trucks separately - this step is equivalent to determining the number of seconds
in each mode.
4. The set of continuous measurements (Step 2) are then classified into the operating-mode
distribution and summed to give an aggregate emission rate representing Bag 2 of the
LA92.
5. The results from Step 4 are divided by those from Step 1 to calculate a ratio for each
combination of the model-year and age groups. The ratios are used to normalize the
modal emission rates to the aggregate filter measurements.
6. The rates from step 5 are then apportioned into EC and nonEC components to give final
rates for the hot-running process. These rates are stored in the emissionRateByAge table
under polProcessID 11201 and 11801, respectively.
The output from step 3 (operating-mode distribution) for cars and light trucks is shown in Figure
4-17. For operating-mode definitions, see Table 2-5.
160
140
120 -¦
- 100 -¦
g 80
^ 60 -¦
40 -¦
20
~ car
~ light truck
rfl. m
III
0 1 11 12 13 14 15 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
VSP Bin
el
Figure 4-17 Operating-mode distribution for cars and light trucks representing the hot-running phase (Bag
2) of the LA92 cycle
The output of step 5 for the ZML (0-3 year age Group) in each model year is shown in Figure
4-18.
257
-------
16
14
12
10
tr
LU
¦ i *
(j * *
¦1960-1980
1981-1982
1983-1984
= 1985
11986-1987
-1988-1989
-1990
1991-1993
1994
1995
1996
1997
1998
1999
2000
-2001
2002
2003
2004
10
15
20 25
VSP bin
30
35
40
45
Figure 4-18 Particulate emissions for passenger cars (LDV) from Kansas City results, by model year Group,
normalized to filter mass measurements
After the rates were calculated, a quality check was performed to ensure that the aged rates in
any particular mode were not too high. A multiplicative model with exponential factors risks
excessively high emission rates under extreme conditions. For example, any rate over 100 g/sec
was considered too high, this would be an extremely high-smoking vehicle. This behavior was
corrected in only two cases for cars and trucks in the 1975 model-year group in operating mode
bin 30. In these cases, the value from operating mode 29 was replicated for operating mode 30.
4.1.5 Modal Start Emission Rates
The development of the cold start emission rates (opMode 108; soak time > 12 hours), is
discussed in Section 4.1.2.2. The cold start emission rates (g/start), as estimated using Bagl -
Bag3 of the LA92, were estimated to be a factor of 1.972 times the reported LA92 composite
g/mile emission rate from the Kansas City study. This factor was then used to estimate cold start
emissions from the zero mile level emission rates. Subsequently, the impact of deterioration on
starts was incorporated as discussed in detail in Section 4.1.2.3.
In MOVES, the start rates by operating mode account for the different soak times preceding the
start as shown in Table 2-6. Section 3.9.1.1 discusses how the start emission rates for hot starts
(opModelD 101-107; soak times < 12 hours) are estimated as a fraction of the cold start emission
rates (opModelD 108). Due to limited data on PM emissions at different soak lengths, we apply
the same ratios between start operating modes for hydrocarbon start emissions as for PM
emissions presented in Table 3-51.
258
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4.2 Particulate-Matter Emission Rates for Moiel Year 2004 and Later
Vehicles
4,2.1 Introduction
This section addresses PM running emission rates for gasoline light-duty vehicles for model
years 2004 through 2060. Previously, MOVES2014 used the same PM emission rates for model
years 2003 through 2016 and then applied phase-in assumptions to account for Tier 3 standards.
This section, therefore, represents an update to the MOVES emission rates for vehicles subject to
Tier 2 and Tier 3 standards. Since 2004, gasoline direct injection (GDI) vehicles have entered the
market. In 2016, GDI vehicles represented roughly half of new vehicles sold in the United
States.72 Additionally, several studies of vehicle emissions have been conducted since the Kansas
City study51 using vehicles newer than MY 2004 vehicles. The emission rates derived in this
section are based on the data from six such studies, including studies of GDI vehicles. The
adoption of GDI engines has been taken into account by separately calculating PM emission
rates for PFI (port fuel injection) and GDI vehicles, and then combining them to form
population-weighted average rates by model year. However, the datasets used in these analyses
do not contain enough information to derive completely new modal emission rates or
deterioration rates for these model years. Therefore, to determine the new modal rates, we
rescaled the existing modal rates used for model year 2003 in MOVES using the new data, and
retained the deterioration behavior described in Section 4.1.2.3. Finally, we applied the phase-in
of Tier 3 standards to the newly derived rates.
4,2.1,1 Dataset Description
Data from six studies was used to develop the 2004 and later PM emission rates. The dataset for
each study includes PM filter weight measurements collected on FTP or LA-92 three-phase or
"bag" test cycles. Phase 1 (bag 1) is a cold start where the vehicle has been "soaking" at a
controlled temperature for 12 or more hours with the engine turned off. Typically, vehicles are
soaked at room temperature (~72°F). Phase 2 follows Phase 1 and is used to characterize
temperature-stabilized or "hot running" conditions. At the end of Phase 2, the engine is shut off,
and the vehicle is allowed to soak for 10 minutes under the ambient test cell conditions. Finally,
the engine is restarted and Phase 3 follows the same driving cycles as Phase 1. For the LA92
cycle, Phases 1 and 3 last for 310 seconds, and Phase 2 lasts for 1,135 seconds. Phases 1 and 3 of
the FTP cycle are longer than for the LA92, taking 505 seconds. Phase 2 of the FTP cycle is
shorter at 867 seconds. PM filters were collected and weighed for each phase of the test cycles
providing a measure of the total PM mass emitted during each phase. The studies selected for
analysis are summarized in Table 4-5.
259
-------
Table 4-5 Summary of PM studies analyzed for model year 2 004 and later vehicles
Study name
Engine
Type
Number of
vehicle models
Number of unique
vehicles
EPA Tier 2 Fuel Sulfur Study73
PFI
17
72
EPAct Phase 1 FTP74
PFI
6
6
EPAct Phase 375
PFI
15
15
EPAct Phase 476
PFI
6
6
CARB LEV III PM Emissions Study77
GDI
6
6
EPA Tier 3 Certification Fuel Impacts Study78
GDI
7
8
Altogether, the dataset for PFI vehicles consists of measurements from 99 vehicles representing
19 different models. Unlike the KCVES, these studies were designed to capture properly
functioning vehicles. We assume that the vehicles in the study represent age zero emission rates
in MOVES, with no effects of emissions deterioration due to age. The dataset for GDI vehicles is
composed of measurements from 14 vehicles, and 13 models. Because of the limited number of
GDI vehicles, there was not enough data for both wall-guided and spray-guided injection
architectures to differentiate between them for this study. Only the tests conducted at room
temperature were included in this analysis in order to eliminate influences from hot or cold
temperature tests. Measurements conducted with greater than 20 percent ethanol fuels were
omitted from analysis because MOVES only handles fuel with ethanol content less than or equal
to 15 percent for gasoline vehicles.
4,2,1,2 Fuel. Considerations
The four studies used to generate PM emission rates for PFI vehicles used a combined total of 27
different fuels with ethanol content less than 20 percent. In order to minimize the effects of these
fuels on the emission rate calculations, the measured rates were corrected to the equivalent rates
for Tier 2 certification fuel. The corrected rates were calculated using the EPAct fuel effects
calculator, which uses the same method used by MOVES to calculate fuel-effect adjustments.79
The EPAct calculator applies the set of statistical models developed using the EPAct Phase 3
dataset, also used for developing the particulate matter emission rates in the current analysis.
Additionally, the EPA Tier 2 sulfur study used Tier 2 based fuels and as such required negligible
correction.73 The corrections were applied to all three phases of the FTP and LA92 PM mass
measurements. The effects on the distribution of measured start and running emissions for each
test program are summarized in Figure 4-19.
260
-------
50-
-= 25-
O)
W 0-
u"
tz
E 301
(a)
•
•
•
|-
<5"
•
(—*¦
D
c
JP
M
Ej3 Fuel Corrected
Ej3 Raw Data
Figure 4-19 Boxplots of start (a) and running (b) emissions measurements with and without fuel corrections
applied
261
-------
4.2.2 Calculating FTP and LA92 Cycle Rates Using MOVES Emission Rates
The six datasets used for this analysis are not adequate to develop revised running modal
emission rates de novo for vehicles with model years 2004 and later. Therefore, the modal rates
for model year 2003 vehicles are rescaled to generate the emission rates for 2004 and later model
years. In order to develop the appropriate rescaling factors, Bag 2 emission rates are calculated
for both the FTP and LA92 drive cycles using MOVES model year 2003 emission rates.
The Bag 2 rates of both the FTP and LA92 cycles for both MOVES light-duty regulatory classes
(light-duty cars, and trucks) are calculated using the MOVES operating mode distribution
calculated for the hot running phase of each test cycle, and multiplying the time in each
operating mode with its associated emission rate. To generate an emission rate, the emission
masses calculated for each operating mode are summed, and the total is divided by the distance
driven. The MOVES operating mode distribution for Bag 2 of both the FTP and the LA92 cycles
are shown in Figure 4-20.
o
150-
100-
50-
[ I.
— 150-
o
E
100-
50-
llll-llllll. .Ill
-i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r
0 1 11 12 13 1415 16 21 22 23 24 25 27 28 29 30 33 35 37 38 39 40
regClassName
1~|ldt
~ ldv
MOVES OpModes
Figure 4-20 MOVES operating mode distributions for the hot-running phase (Bag 2) of the FTP and LA92
drive cycles
As the figure illustrates, Bag 2 of the LA92 cycle is more aggressive than Bag 2 of the FTP
cycle. As a result, different average running emission rates result from each cycle. For cold
starts, based on the analysis presented in Section 4.1.2.2, it is assumed that both the LA92 and
FTP cycles will have the same PM mass emitted for each vehicle type. The cold-start masses and
Bag 2 hot running rates calculated using MOVES model year 2003 emission rates are
summarized in Table 4-6. These calculated cycle rates are used as a basis for comparison to the
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measured rates in the datasets that are analyzed in Sections 4.2.3 and 4.2.4. Additionally, these
calculated cycle rates are used in Section 4.2.5 to determine the rescale factors used to develop
the model year 2004 and later PM emission rates used in MOVES.
Table 4-6 Modeled FTP and LA92 start and bag 2 running rates for model year 2003 light-duty vehicles
Test cycle
regClassID
Cold-start mass
(mg)
Hot-running rate
(mg/mi)
FTP
LDT
8.781
1.444
FTP
LDV
6.158
2.090
LA92
LDT
8.781
2.133
LA92
LDV
6.158
1.924
4.2.3 Estimating Start Emissions for Particulate Matter
Start emissions from three-phase test cycles are calculated by comparing the measured masses of
the Phase 1 and Phase 3 PM filters. For both the LA92 and FTP drive cycles, the speed trace for
Phases 1 and 3 are identical. The difference in measured PM masses between the two phases is
attributed to the change in engine condition from cold start to hot stabilized running. Typically,
this transition results in higher Phase 1 PM mass. If the value of the Phase 1 minus the Phase 3
mass is negative, it suggests that the hot stabilized engine emitted more particulate matter than it
did when it was warming up. We observed this behavior in some of the test results. Because we
found no technical reason to exclude these points, they are included in the averaged rates. For
this analysis, we assume that cold-start PM emissions are independent of the test cycle. The
average rates from the data discussed in this section are used in Section 4.2.5 to develop the
scaling factors for constructing the PM start rates.
4,2,3.1 Start Emissions for Vehicles with Port Fuel Injection (PFI)
Figure 4-21 summarizes the cold-start results from the PFI vehicles used in this analysis, which
are drawn primarily from the EPAct Phase-3 study. The solid horizontal lines show the average
cold-start mass for light-duty cars and trucks, as averaged by vehicle model. The dashed
horizontal line shows the cold start mass for new vehicles with model year 2003 in MOVES. For
PFI light-duty cars, the average cold start mass is 2.06 mg and for PFI light-duty trucks, it is 3.75
mg. On average, the measured PM cold start emission masses for the analyzed data were
substantially lower than modeled for model year 2003 vehicles in MOVES.
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Dataset Average
MOVES
(Model Year 2003)
Model
Figure 4-21 Measured PFI PM start emission masses
4.2.3.2 Start Emissions for Vehicles with Gasoline Direct Injection (GDI)
Figure 4-22 summarizes the cold-start results from all of the GDI vehicles used in this analysis.
The solid horizontal lines show the cold-start mass for light-duty cars and trucks, as averaged by
each unique vehicle. The dashed horizontal line shows the cold start mass for new vehicles with
model year 2003 in MOVES. For GDI light-duty cars, the average cold start mass is 20.92 mg.
While only data from two GDI trucks is available in these studies, the average cold start mass for
these two vehicles is 38.34 mg. Generally, the measured PM start emission masses for GDI
vehicles in the analyzed dataset were significantly higher than modeled for model year 2003
vehicles in MOVES.
264
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Figure 4-22 Measured GDI PM start emissions
4.2.4 Estimating Running Emissions for Particulate Matter (PM)
Running emission rates were calculated for each test in units of milligrams per mile. Because the
FTP and LA92 cycles cover different engine power ranges as shown in Figure 4-20, the average
emission rate for each vehicle model was calculated separately for each test cycle. In general, the
results for both PFI and GDI vehicles show substantially lower running PM rates than modeled
for model year 2003 in MOVES. The average rates from the data discussed in this section are
used in Section 4.2.5 to develop rescale factors for constructing the MOVES PM running rates.
4.2.4.1 Running Emissions for Vehicles with Port Fuel Injection (PFI)
For the four test programs used in the PFI analysis (Table 4-5), the running PM rates are grouped
by vehicle model. Figure 4-23 summarizes the results. The solid horizontal lines show the
average Phase 2 running mass for light-duty cars and trucks, as averaged by vehicle model. The
dashed horizontal line shows the Phase 2 running mass for new vehicles with model year 2003 in
MOVES. As Figure 4-20 demonstrates, the LA92 drive cycle has a more aggressive Phase 2 than
the FTP cycle. This difference results in a higher average emission rate for the LA92 cycle than
for the FTP cycle. This difference is reflected in both the measured datasets and the cycle
average rates calculated by combining model year 2003 emission rates and operating-mode
distributions for the two cycles.
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Figure 4-24 Measured GDI PM running emission rates
4.2.5 Developing Base Emission Rates for Model Year 2004 and Later
As mentioned previously, the six datasets, used to develop light-duty PM rates for 2004 and later
vehicles, do not contain the data necessary to assemble new modal running emission rates.
Therefore, the modal running emission rates for model year 2003 are scaled to represent the
observed emission rates from these studies. The scaling factors are calculated by taking the ratio
of the running emissions rate for each measurement to the rate for the same drive cycle
calculated using the model year 2003 MOVES emission rates (Section 4.2.2). For PFI vehicles,
these ratios are calculated as an average weighted by model. For GDI vehicles, the averages are
weighted by unique test vehicle. Table 4-7 summarizes the average scaling factors for both start
and running emissions.
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Table 4-7 Cold-start and hot-running scaling factors for PFI and GDI vehicles
Engine type
regClassID
Cold-start
scaling factor
Hot-running
scaling factor
PFI
LDT
0.427
0.382
PFI
LDV
0.335
0.260
GDI
LDT
4.367a
0.3123
GDI
LDV
3.398
0.515
Note:a See Section 4.2.5.lfor the final scaling factors for GDI LDT.
4,2.5.1 Additional Assumptions Used to Determine GDI Track Scaling Factors
The data for the two GDI trucks included in the six datasets is not sufficient to form the basis for
revised emission rates in MOVES3. To compensate, we developed an approximation of start and
running emission rates for GDI trucks using the data analyzed for PFI vehicles, and for the GDI
light-duty cars. We assume that the apparent difference in PM emissions between GDI and PFI
vehicles are due to the change in injection technology. Additionally, we assume that the change
in injection technology will have a similar proportional emissions effect on engines in light-duty
trucks as in light-duty cars. To calculate GDI truck start emissions, we use the following
equation:
StartLDV(GDI)
StartLDT(GDI) = StartLDT(PFI) Equation 4-3
LDTy J LDTy J StartLDV(PFI)
where LDV indicates light-duty cars, and LDT indicates light-duty trucks.
For running emissions, we used a slightly different approach. Because the datasets only contain
results for GDI vehicles on the FTP cycle, it was difficult to directly compare them to the PFI
results where a significant proportion were measured on the LA92 test cycle. Therefore, we
made the assumption that the scaling of the 2003 model year MOVES rates for GDI light-duty
trucks would be the same as the scaling for light-duty cars, i.e.:
Runninq, nv(GDI)
RunningLDT(GDO = RunningLDT{MOVES) RunningLm{M0VES) "quado, 4-4
Table 4-8 contains the calculated start and running rescale factors using these assumptions as
well as the average measured values from the two trucks in the studies. For start emissions, the
rates calculated from these assumptions are very similar to the measured rates from the two
trucks. The calculated running rates on the other hand show a more modest reduction relative to
the 2003 model year rate than suggested by the test results from the two trucks. The rescale
factors derived from these assumptions are the ones used to derive the final MOVES3 light-duty
truck emission rates.
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Table 4-8 Scaling factors for light-duty trucks calculated from measured data and from modeling assumptions
Cold-start
Hot-running
Unadjusted scaling factor (Table 4-7)
4.367
0.312
scaling factor calculated from Equation
4-3 and Equation 4-4
4.330
0.515
4,2,5.2 EC/NonECPM Fractions
In the MOVES EmissionRateByAge table, total PM emission rates are partitioned into elemental
carbon (EC) and non-elemental carbon (nonECPM). Section 4.1.3 describes the method for using
photo-acoustic to gravimetric filter mass ratio to determine the fraction of EC to total PM.
Because the datasets used for PFI vehicles did not have additional EC information, we retain the
EC/PM2.5 fractions calculated from the Kansas City study to represent light-duty PFI vehicles
with model years 2004 and later. The CARB LEVIII PM study used as part of the GDI rates
analysis, also included photo-acoustic PM mass measurements. As such, we used the same
method to calculate EC/PM2.5 fractions for light-duty GDI vehicles. The resulting fractions show
a significantly higher EC fraction for both start and running emissions from GDI vehicles as
compared to PFI vehicles. The start and running EC/PM2.5 fractions for both PFI and GDI
vehicles are summarized inTable 4-9.
Table 4-9 Start and running EC/PM2.5 fractions for PFI and GDI vehicles
Engine type
Start EC/PM2.5
Running EC/PM2.5
PFI
0.44
0.14
GDI
0.70
0.67
4.2.6 Calculation of Fleet-Average PM Emission Rates by Model Year, Vehicle
Age, and PM component
This section describes how the cold-start and hot-running rescale factors and the EC/PM2.5
fraction determined in Section 4.2.5 are combined to create the PM emission factors used in
MOVES for model years 2004 and later. Here, the emission rates are derived without accounting
for the implementation of new emission standards. Sections 4.2.7 and 4.2.8, describe how the
Tier 3 and LEV-III standards are applied to the PM emission rates.
Thus far, the discussion of PM rates for light-duty vehicles for model years 2004 and later has
divided these vehicles by fuel injection technology, however, MOVES does not currently
accommodate partitioning emission rates for a given regClass by engine technology. Rather,
fleet-average rates must be entered into the emissionRateByAge table. Therefore, average PM
emission rates were calculated for each model year using weights for the PFI and GDI emission
factors determined from vehicle production volumes.
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4.2.6.1 Vehicle Population Data for Model Years 2004 - 2016
For model years 2004 through 2016, the annual EPA Light-Duty Automotive Technology,
Carbon Dioxide Emissions, and Fuel Economy Trends Report provides data on the relative
production volumes of vehicles with different engine technologies.80 Tables 5.3.2 and 5.3.3 of
the report include the proportions of the light-duty car and truck populations that have PFI and
GDI engines. This data is shown by the symbols plotted Figure 4-25. These proportions were
used directly to weight the fleet-average PM emission rates from PFI and GDI vehicles.
4.2.6.2 Modeling Vehicle Populations for Model Years After 2016
The rapid adoption, and expected continued growth of the GDI portion of the light-duty vehicle
population make it inappropriate to use the 2016 population fractions to represent the light-duty
vehicle population into the future. Therefore, the relative PFI and GDI vehicle populations were
extrapolated for model years 2017 and later. To make this projection, a simple sigmoidal
function was fit to the data for years 2004 through 2016. A sigmoidal function was used because
it reasonably reproduced the trend of GDI adoption, and created a smooth transition from one
technology to the other. The functions used for this fit are:
GDI (MY) =
I + e-K{MY-MY0)
PFI (MY) = 1 - GDI(MY)
Equation 4-5
Equation 4-6
Where GDI(MY) and PFI(MY) are the fractions of the light-duty vehicle population with GDI
and PFI engines respectively, and MY is the vehicle model year. The fitted terms of the functions
are K, which represents the rate of change of the populations, and MYo indicates when the
modeled PFI and GDI populations are equal. The fitted values of K and MYo are given in Table
4-10.
Table 4-10
regClassName
K
MYo
LDT
0.421
2016.27
LDV
0.375
2015.17
The fit values of MYo have the populations of light-duty GDI and PFI vehicles becoming equal
in 2017 for trucks, and in 2016 for cars. The symbols in Figure 4-25 show the population
fractions of GDI and PFI vehicles from the trends report.80 The fitted sigmoidal curves are shown
as dashed lines. The solid lines show the combined curves of the 2004 to 2016 population data,
with the modeled post 2016 population fractions.
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Where EC/PM2.5 is the EC fraction, P is the population fraction. The subscripts indicate the
values associated with the combined fleet, and for GDI and PFI vehicles. The EC/PM2.5 values
are used to estimate emission rates are portioned into two PM components (EC and nonECPM)
as discussed in Section 4.2.5.2. Finally, the scale factors and new EC/PM2.5 fractions were
applied to the start and running modal emission rates from MOVES model year 2003 light-duty
cars and light-duty trucks to generate a complete set of revised EC and nonECPM emission rates
in MOVES3 for model year 2004 through 2060. This method thus preserves the modal rate
structure as well as the deterioration effects modeled for earlier model years. Figure 4-26 through
Figure 4-28 illustrate how these emission rates change with model year. Note that these rates do
not yet account for the phase-in of the Tier 3 standards, which is handled in Section 4.2.7.
Figure 4-26 shows how the PM cold start mass for light-duty cars and trucks changes with model
year, showing increases in both EC and nonECPM as the percentage of GDI vehicles increases.
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2020
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Model Year
2010
2020
2030
Figure 4-26 Modeled cold start PM emissions by model year for age 0 vehicles- not adjusted for phase-in of
Tier 3 standards
Figure 4-27 shows calculated FTP Bag 2 running rates to illustrate how the MOVES rates for
light-duty cars and trucks change with model year. For these rates, the nonECPM portion of the
emissions decrease with GDI phase in while the EC portion increases. Together, the changes in
EC and nonECPM rates result in a net increase in Total PM with increasing model year.
272
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4.2.7 Incorporating Tier 3 Emissions Standards for Particulate Emissions
Under the Tier 3 exhaust emissions standards, finalized in April, 2014, the FTP standard for
particulate emissions was reduced from its level under the Tier 2 standard (10.0 mg/mi) to a new
value of 3.0 mg/mi.81
Developing rates to represent particulate emissions from gasoline-fueled vehicles under the Tier
3 standards involved scaling down rates representing vehicles under the Tier 2 standard to a level
that assumes a reasonable compliance margin with respect to the lower standard. More
specifically, we assumed that average FTP emissions for new light-duty vehicles (age 0-3 years)
would be 1.5 mg/mi in MY 2025, corresponding to a compliance margin of 50 percent, when the
new standard was fully phased in. This assumption is independent of engine and fuel-injection
technology. The reduced rates assume that additional controls are needed to meet the new
standard for vehicles employing gasoline direct-injection technologies, but not for the declining
fraction of vehicles in the market employing port-fuel-injection. The analysis above shows that
new PFI vehicles start at about this level, and thus can virtually meet the new standard without
modification.
Additionally, as with the gaseous emissions, the regulatory useful life was increased from
120,000 to 150,000 miles. The concomitant assumption of increased durability was expressed
through a reduction in the assumed deterioration rate.
We applied these modifications to the MOVES EmissionRateByAge table in a series of three
steps.
4,2,7.1 Apply Phase-in Assumptions
The first step was to apply the phase-in assumptions applicable to PM. The phase-in begins with
model year 2017 and ends with model year 2021 for cars (LDV) and trucks (LDT). Fractions of
new vehicles meeting the new standard during the phase-in are shown in Table 4-11. The table
also shows simulated FTP composites during the phase-in. These projections were simply
calculated as averages of the Tier 2 and Tier 3 baselines, with the phase-in fractions used as
weights. Figure 4-29 shows how the simulated Tier 3 FTP composite rates compare against the
base rates derived in Section 4.2.6.2, and to the rates used in MOVES2014.
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Table 4-11 Phase-in Fractions and simulated FTP composites projected for the introduction of the Tier 3
Model year
Fraction
meeting Tier 3
standard
Simulated FTP
composite (mg/mi)
Cars (LDV)
Trucks (LDT)
2016
0.0
1.56
2.03
2017
0.10
1.78
2.28
2018
0.20
1.86
2.39
2019
0.40
1.84
2.30
2020
0.70
1.70
1.95
2021+
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1.50
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Trucks (LDT)
Cars (LDV)
Base Rate
MOVES2014
MOVES201X
2010
2020
Model Year
2030
Figure 4-29 Simulated FTP composite rates for Tier 2 base line and Tier 3 phase-in. Base Rate represents age
zero emissions prior to adjustment for phase-in of Tier 3 standards (MOVES201X refers to MOVES3).
4.2.7.2 Apply Scaling Fractions
The second step was to apply the fractions to the emission rates for running and start emissions
in the EC and nonECPM pollutant processes (11201, 11202, 11801, 11802). The fractions were
applied uniformly to rates in all operating modes, for both cars and trucks.
Figure 4-30 shows an example of scaling, for a subset of non-elemental-carbon (nonECPM,
11801) rates for three model years, 2016, 2019 and 2021. Model year 2016 represents Tier 2
standards prior to the onset of the phase-in, 2021 shows fully phased-in Tier 3 standards, and
2019 shows an intermediate year during the phase-in period. In (a), the rates are shown on a
linear scale to show the steepness and non-linearity of the trends against power, whereas in (b),
rates are shown on a logarithmic scale to make clear that the multiplicative scaling is uniform
across the power range. Although not pictured, note that rates for elemental-carbon (ECPM,
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11201) show an identical scaling pattern. Note also, that for convenience, emissions in the plot
are presented in mg/hr, whereas rates in the emissionRateByAge table are provided in g/hr.
The uniformity of the multiplicative scaling is also clear if the rates for a single model year are
viewed against age for a set of operating modes, as shown in Figure 4-31. The plot shows rates
for six modes of running operation, including idle (mode 1), with the remaining five modes
spanning a range from low to moderate power. As previously described in 4.1.2.3, the
deterioration trends are exponential (or log-linear).
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2019
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Model Year
2016
2019
-m- 2021
0
30
10 20
Vehicle Specific Power (kW/Mg)
Figure 4-30 Non-elemental-carbon (nonECPM) running rates for cars vs. vehicle-specific power for three
model years on (a) linear, and (b) logarithmic scales (NOTE: rates are presented for operating Modes 21-30,
with each mode represented by VSP at its respective midpoint)
276
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20
Figure 4-32 Elemental-carbon rates for cars vs. Age for cold-start emissions in six model years, presented on
(a) linear, and (b) logarithmic scales
278
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4,2.8 Incorporating the L andard for Particulate Matter
The Tier 3 and LEV-III standards are harmonized with respect to the light-duty standard for
particulate matter through MY 2024, at which point, a 3.0 mg/mi FTP standard will be fully
phased in. However, after MY 2025, the LEV-III program goes further, enacting a further
phased-in reduction to a 1.0 mg/mi FTP standard. This reduction is incorporated into the
emissionRateByAgeLEV table applicable to California and Section 177 states.
The assumptions used to express the transition from rates at the 3.0 mg/mi level to the 1.0 mg/mi
level are shown in Table 4-12. We assume a linear phase-in over the three years. The
calculations assume a 50 percent compliance margin with respect to the 3.0 mg/mi standard in
MY 2024, transitioning to a 25 percent compliance margin in MY 2028.
These assumptions were modeled in MOVES by applying the reduction fractions shown in the
right-most column in Table 4-12 to default MOVES rates for the LEV-III phase-in model years.
These fractions were applied uniformly to start and running emissions of EC and nonECPM, for
cars and trucks, across all operating modes.
The emissionRateByAgeLEV table including these rates is incorporated into the default MOVES
database. Instructions for use of the applicable portions of this table in a MOVES run are
available at https://www.epa.gov/moves/tools-develop-or-convert-moves-inputs. Section 3.12
details how the emission rates representing California standards were developed for criteria
pollutants.
Table 4-12 Phase-in assumptions and reduction fractions used to represent a transition to the 1.0 mg/mi PM
standard under LEV-III
Model year
Phase-in fraction
FTP composite
(mg/mi)
Reduction fraction1
At 3.0 mg/mi
At 1.0 mg/mi
2024
1.00
0.00
1.50
1.000
2025
0.75
0.25
1.31
0.873
2026
0.50
0.50
1.13
0.753
2027
0.25
0.75
0.94
0.627
2028+
0.00
1.00
0.75
0.500
1 Applied to default rates in listed model years.
4,3 Comparing Light-Duty PM' Emission Rates Between Pre-2004 and 2004-
arid-Iater model years
As demonstrated in Figure 4-21 and Figure 4-23, the MOVES PM emission rates developed
from the MY 2004 and later PFI vehicles are significantly lower than those developed for MY
2003 PFI vehicles from the studies and analysis discussed in Section 4.1. There are several
differences in the vehicle samples, measurement methods, and data analysis methods that are
likely contributing to the difference in PM emission rates as described below:
• Vehicle samples: The most recent studies (KCVES, MSAT, and Li et al., 200667)
included in the pre-2004 emission rates included MY vehicles between 2002-2005. The
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studies used in the MY 2004 and later emission rate update included later model year
vehicles between (2007 and 2014). The decrease in PM emissions could be partially
attributed to lower PM emission rates from the newer technology vehicles.
• Measurement methods: Particulate matter emissions measurements were not conducted
with consistent methods across the studies. Uncorrected sampling artifacts could be the
cause of the large differences between the pre-2004 and the 2004+ PM emission rates. As
documented in Appendix A of this report, we corrected for a sampling issue in the
KCVES that would have caused the PM emission rates to be significantly overestimated.
Additionally, several years had passed from the last study used in to derive the pre-2004
rates (2006) and the earlier study conducted for the MY 2004+ rates (2013). In this time
there were significant improvements in particulate sampling methods, including filter
handling and filter weighing techniques. These differences in particulate matter sampling
methods could be the cause for much of the differences observed between the pre-2004
and the 2004+ model year rates.
• Data analysis methods: Different data analysis methods were used to estimate the zero-
mile emission rates for the two model year ranges. For example, we fit an exponential
curve to age 0-3 vehicles from 15 different studies (including both FTP and LA-92
cycles) by model year to estimate the pre-2004 zero-mile emission rates. For the MY
2004+ rate update, we assumed that the measured vehicles did not include deterioration
and simply averaged all the measured data according to sample size to represent the zero-
mile emission rates. In addition, we accounted for differences in the MOVES operating
modes between the LA-92 and FTP cycle for the recent update. These different data
analysis methods could contribute to the observed differences.
In general, we have higher confidence in the more recent PM emission rates because they are
based on more recent studies and updated sampling procedures. Additionally, the data analysis
methods for the most recent rates are more straightforward than the analysis conducted for the
pre-2004 MY rates. Despite our higher confidence in the more recent PM rates, we have decided
to leave the pre-2004 MY PM rates unchanged in MOVES3 for at least these three reasons:
• Some of the differences in the pre-2004 and 2004+ emission rates may be due to the
actual differences in engine and aftertreatment differences in MY vehicles
• In a calendar year 2018 MOVES run using a draft version of MOVES, the pre-2004
model year vehicles contribute just over 50% of PM2.5 emissions-" from all light-duty
vehicles (regulatory class LDV and LDT). In current and future years, the contribution of
these older model year vehicles to the overall inventory will decrease, and no longer be
the majority of emissions from light-duty vehicles.
• Revisiting the pre-2004 model years emission rates would be a substantial effort. As
documented in this report, the pre-2004 were based on an analysis of many different
studies which measured PM emissions. The analysis of these different studies provided
data to estimate light-duty deterioration, which continues to serve the basis of the modal
VSP-trends, EC/PM ratios for PFI vehicles, and the deterioration of light-duty PM
deterioration for all model year vehicles. Additional scientific evidence is likely needed
J From a draft MOVES run conducted at national aggregation, using January and July to represent the entire year,
pre-2004 model years contributed 51.5% of PM2.5 exhaust emissions from regulatory class LDV and LDT vehicles.
280
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for us to revisiting the emission rates of these older model year vehicles, which continue
to be used as a basis for the emission rates for the 2004+ model year emission rates.
5 Gaseous and Particulate Emissions from Light-Duty Diesel and
Electric Vehicles (THC, CO, NO*, PM)
This section explains the gaseous and particulate emissions from light-duty diesel vehicles and
provides some important notes on how MOVES models light-duty electric and hybrid vehicles.
Table 5-1 Fuel types and engine technologies represented for gaseous-pollutant emissions from light-duty
vehicles
Attribute
sourceBin attribute
Value
Description
Fuel type
fuelTypelD
01
Gasoline
02
Diesel
05
Ethanol (Ell, E85, etc.)
Engine Technology
engTechID
01
Conventional internal combustion (CIC)
30
Electric
5.1 Light Duty Diesel
In MOVES, emission rates are calculated for each operating mode. However, for the diesel-
fueled passenger cars (LDV) and light-duty trucks (LDT), we lack the necessary continuous or
"second-by-second" measurements to directly calculate emission rates for running emissions in
relation to vehicle-specific power.
Upon additional review, we concluded that the diesel rates developed for draft MOVES and
retained in MOVES2010 were not plausible in relation to corresponding rates for gasoline
vehicles. We concluded that these rates were not adequate to retain in MOVES2014. However,
we also did not consider it a tenable option to release MOVES2014 without rates representing
diesel vehicles.
Consequently, we decided to allow rates for light-duty gasoline vehicles to represent those for
light-duty diesel vehicles. While not an exact parallel and not desirable from a technical
standpoint, we considered it an acceptable solution, as vehicles running on both fuels would be
certified to similar standards. Also, as there are very few light-duty diesel vehicles in the U.S.
fleet, their contribution to the inventory is very small.
However, in contrast to the gasoline rates, we did not incorporate a difference in the base rates
attributable to Inspection and Maintenance. That is to say, values for meanBaseRate (non-I/M
condition) were substituted for both the meanBaseRate and meanBaseRatelM. Note, however,
281
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that for rates representing diesel emissions, the model does not apply the fuel adjustments
applied to gasoline emissions.79
For MOVES3, we used the same approach as in MOVES2014, taking the light-duty gasoline
values for meanBaseRate and using them to populate both the meanBaseRate and
meanBaseRatelM values for light-duty diesel.
The level of detail for the rate substitution is shown in Table 5-2.
Table 5-2 Level of detail for substitution of light-duty gasoline Rates onto light-duty diesel rates
Parameter
Description
Identifier
Pollutant
THC
1
CO
2
NOx
3
EC-PM
112
NonECPM
118
Process
Running Exhaust
1
Start Exhaust
2
Regulatory Class
Passenger Car (LDV)
20
Light Truck (LDT)
30
Model-year Group
All
1960-2031
Data Source
Replicated from corresponding
4910
Rates for light-duty gasoline
5,2 Light Duty Electric Vehicles
MOVES can also model electric vehicles. While electric vehicles are associated with upstream
and life-cycle emissions not modelled by MOVES, and with energy consumption1 and brake and
tire wear emissions82 as described in other MOVES reports, they do not generate direct exhaust
emissions of HC, CO, NOx or PM, and thus these emissions are modelled as zero in MOVES.
Note, EPA is aware that manufacturers can include electric vehicles and hybrid electric vehicles
in their computation of average emissions for compliance with Tier 3 standards. Thus, if a
manufacturer sells a large number of zero or low-emitting vehicles, the manufacturer would be
allowed to increase the average emissions of other vehicles.
In the case of hybrid vehicles, MOVES accounts for this by not modelling hybrids explicitly—
instead, their emissions are combined with all other fleet average vehicles.
MOVES takes a different approach for electric vehicles. While the MOVES3 default fleet
includes no electric vehicles,83 MOVES3 allows users to input an appropriate fraction of electric
vehicles. However, we must caution that MOVES does not account for potential associated
increases in emissions from conventional light-duty vehicles. If the future fraction of electric
vehicles is large, and manufacturers take advantage of the flexibility allowed by the Tier 3
282
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regulations, this could lead to underestimation of light-duty NMOG and NO* emissions from
conventional (i.e. gasoline, diesel and E85) vehicles.
6 Crankcase Emissions
6.1 Background
In an internal combustion engine, the crankcase is the housing for the crankshaft. The enclosure
forms the largest cavity in the engine and is located below the cylinder block. During normal
operation, a small amount of unburned fuel and exhaust gases escape around the piston rings and
enter the crankcase, and are referred to as "blow-by." These unburned gases are a potential
source of vehicle emissions.
To alleviate this source of emissions, the Positive Crankcase Ventilation (PCV) system was
designed as a calibrated air leak, whereby the engine contains its crankcase combustion gases.
Instead of the gases venting to the atmosphere, they are fed back into the intake manifold where
they reenter the combustion chamber as part of a fresh charge of air and fuel. A working PCV
valve should prevent virtually all crankcase emissions from escaping to the atmosphere.
PCV valve systems have been mandated in all gasoline vehicles, since model year 1969.
6.2 Modeling Crankcase Emissions in MOVES
Crankcase emissions are calculated by chaining a crankcase emissions ratio to the calculators for
start, running, and extended-idle processes. Crankcase emissions are calculated as a fraction of
tailpipe exhaust emissions, which are equivalent to engine-out emissions for pre-1969 vehicles.
Crankcase emissions are calculated for selected pollutants, including THC, CO, and NOx, and the
elemental-carbon and non-elemental-carbon particulate fractions of PM2.5. For each of these
pollutants, ratios are stored in the CrankcaseEmissionRatio table.
For vehicles with working PCV valves, we assume that emissions are zero. Based on EPA
tampering surveys, MOVES assumes a failure rate of 4 percent for PCV valves.84 Consequently,
for fuelType/model-year combinations equipped with PCV valves, we assume a crankcase ratio
of 0.04; i.e., emission fractions for the crankcase process are estimated as 4 percent of the
emission fractions assumed for uncontrolled emissions. While this 4 percent estimate may be
pessimistic for new vehicles, and optimistic for old vehicles, available data does not support a
more detailed estimate. As older vehicles have higher overall emissions due to deterioration
effects, use of the aggregate rates may understate the impacts of crankcase emissions.
6.3 Light-duty Gasoline Crankcase Emissions
Very little information is available on crankcase emissions, especially those for gasoline
vehicles. A literature review was conducted to identify available data sources for emission
fractions for gasoline vehicles (Table 6-1).
Table 6-1 Selected Sources of published data on hydrocarbon crankcase emissions from gasoline vehicles
Authors
Year
Fuel
No.
Vehicles
Estimate
Units
Heinen and Bennett85
1960
Gasoline
5
33
% of exhaust
Bowditch86
1968
Gasoline
70
% of exhaust
US EPA87
1985
Gasoline
9
1.21-1.92
g/mi
283
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Based on these sources, we estimated emission fractions for model years without mandated PCV
valves. In absence of better information, gasoline emission fractions are a reflection of diesel
research, with the exception of the gasoline HC ratio. Given that the diesel vehicles studied are
largely heavy duty, and that most gasoline vehicles are light-duty, there is a potential mismatch
between the data sources, which is unavoidable due to the paucity of data. As noted previously,
model years with PCV valves were assigned emission fractions calculated as 4 percent of the
fractions shown in Table 6-2.
Table 6-2 Emission fractions for vehicles without PCV systems (percent of exhaust emissions)
Pollutant
Gasoline
(uncontrolled,
pre-1969)
Gasoline (1969 and
later)
THC
0.33
0.013
CO
0.013
0.00052
NOx
0.001
0.00004
PM (all species)
0.20
0.008
The crankcase emission fractions for THC, CO and NOxmay underestimate emissions. These
percentages of exhaust emissions are generally based on engine- out, uncontrolled exhaust,
which is not estimated by MOVES. MOVES produces exhaust estimates based on a number of
control technologies (such as catalytic converters). Uncontrolled exhaust in the 1970s was
considerably higher than current tailpipe exhaust.
6,4 Light-duty Diesel Crankcase Emissions
After 2001, all light-duty vehicles, including diesels, are required to avoid venting crankcase
emissions into the atmosphere.88 This requirement differs from turbocharged and supercharged
heavy-duty diesel engines, which are allowed to vent crankcase emissions, as long as the
crankcase emissions are included in the certification tests. As such, we modeled crankcase
emissions from light-duty diesel emissions with two model-year groups, pre-2001, and post-
2001. The values used for the pre-2001 are the same as the pre-2007 heavy-duty diesel fractions.
For 2001 and later, we multiply the pre-2007 by 4 percent (our assumed PCV failure rate). These
crankcase emission ratios are located in Table 6-3.
284
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Table 6-3 Light-duty diesel crankcase emission fractions (percent of exhaust emissions)
Pollutant
Light-duty diesel
1960-2000)
Light-duty diesel
(2001-2060)
THC
0.037
0.00148
CO
0.013
0.00052
NOx
0.001
0.00004
PM2.5 (all species)
0.2
0.008
7 Nitrogen Oxide Composition
Nitrogen oxides (NO,;) are defined as NO + NO2. In MOVES, NO* includes NO, NO2, and a
small amount of HONO. The rationale for including HONO in NO* emissions is discussed in the
heavy-duty report.89 Currently, the HONO/NO* ratio is estimated as 0.8 percent of NOx
emissions based a study that measured concentrations of NO* and HONO from a highway tunnel
in Europe.90 The NO/NOx and NO2/NOX fractions were developed from a report by Sierra
Research.8
.glit-Duty Gasoline Vehicles
The NOx and HONO fractions for light-duty gasoline vehicles are presented in Table 7-1 The
HONO fraction of NOx, was subtracted from the original NO2 fraction, because the HONO likely
interferes with the estimated NO2 fraction when measured with a chemiluminescent analyzer, as
discussed in the heavy-duty report.
Table 7-1 NOx and HONO fractions for light-duty gasoline vehicles
Model Year
Running
Start
NO
NO2
HONO
NO
NO2
HONO
1960-1980
0.975
0.017
0.008
0.975
0.017
0.008
1981-1990
0.932
0.06
0.008
0.961
0.031
0.008
1991-1995
0.954
0.038
0.008
0.987
0.005
0.008
1996-2050
0.836
0.156
0.008
0.951
0.041
0.008
7.2 Motorcycles
The NO/NO2 fractions for motorcycles were also developed by Sierra Research.8 The values are
based on measurements on light-duty gasoline vehicles, but apply to different model year groups,
to correspond to similar exhaust emission control technologies. The NO2 fractions reported by
Sierra Research were adjusted to account for the HONO measurements. Development of the
NOx, CO, THC, and PM. emission rates for motorcycles, is documented in the same report.8
285
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Table 7-2 NO,yand HONO fractions for motorcycles
Model Year
Running
Start
NO
NO2
HONO
NO
NO2
HONO
1960-1980
0.975
0.017
0.008
0.975
0.017
0.008
1981-2000
0.932
0.06
0.008
0.961
0.031
0.008
2001-2005
0.939
0.053
0.008
0.97
0.022
0.008
2006-2009
0.947
0.045
0.008
0.978
0.014
0.008
2010-2060
0.954
0.038
0.008
0.987
0.005
0.008
7.3 Light-duty Diesel Vehicles
The NOx and HONO fractions for light-duty diesel vehicles are the same as those for heavy-duty
diesel. Discussion of the heavy-duty diesel fractions is presented in the corresponding report.89
These values are presented in Table 7-3 for completeness.
Table 7-3 NOx and HONO fractions for Light-duty Vehicles
Model Year
NO
NO2
HONO
1960-2006
0.935
0.057
0.008
2007-2009
0.764
0.228
0.008
2010-2060
0.594
0.398
0.008
286
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8 Appendix A. Revisions to the Pre-2004 Model Year PM2.5
Emission Rates between MOVES2010b and MOVES2014
The PM2.5 exhaust emission rates for pre-2004 model year light-duty vehicles are unchanged
between MOVES2014 and the current version, MOVES3. As noted in Section 4.1.3, we
corrected the PM2.5 light-duty gasoline emission rates between MOVES2014 and MOVES2010
to account for the silicon contamination measured in the Kansas City study, using our best
available estimates. The PM2.5 emission rates in MOVES2010 were based on a meta-analysis of
multiple studies and programs. The Kansas City study was used to estimate deterioration from
the estimated zero-mileage emission rates, to estimate the modal PM2.5 emission rates, and the
PM2.5 temperature dependency. In MOVES2014 we reduced the running PM2.5 emission rates
across all age groups and operating modes by the values shown in Table 8-1.
Table 8-1 contains the estimated contribution of silicon to the start (bag 1-bag 3) and the running
(bag 2) PM2.5 emissions measured in Kansas City. The silicone rubber contains silicon, oxygen,
carbon, and hydrogen which contribute to the measured particulate and organic carbon mass. We
estimated the contribution of the silicon to the PM2.5 emission rates by using the elemental
silicon emission rates from the set of 102 tests analyzed for elements. Additionally, we estimated
that the silicone rubber contributed particulate mass equal to 4.075 times the measured silicon
emission rates, as documented in the speciation profile analysis by Sonntag et al. (2013).71 We
applied these estimates to average silicon emission rates measured for each model year group,
and for trucks and cars. The trucks have a higher silicon contribution which is expected due to
higher exhaust temperatures and larger exhaust tailpipes which expose more silicone rubber to
the hot exhaust. The updated emission rates reflect both the reduction in total PM from the
silicon in Table 8-1 and the revised EC/PM ratios in Table 4-4.
Table 8-1 Reductions to PM2.5 in MQVES2014 compared to MOVES2Q10b due to silicon contamination
Stratum
Vehicle
type
Model group
Start
Running
1
pre-1981
0%
35.3%
2
Truck
1981-1990
0%
25.3%
3
1991-1995
0%
34.5%
4
1996-2005
0%
19.1%
5
pre-1981
0%
14.6%
6
Car
1981-1990
0%
3.5%
7
1991-1995
0%
6.1%
8
1996-2005
0%
8.5%
287
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